SALES FORCE AUTOMATION USE AND
SALESPERSON PERFORMANCE
by
MURAT SERDAROGLU
DISSERTATION
submitted to the Faculty of Business Administration and
Economics of
University of Paderborn
in partial fulfillment of the requirements
for the degree of
Ph.D. of Management (Dr. rer. pol.)
June 2009
Sales Force Automation Use and
Salesperson Performance
Murat Serdaroglu
Abstract
Understanding how technology investments create business value is a
research priority in today's technology-intensive world. Drawing on a
literature review as well as a qualitative study in the pharmaceutical
industry, this research suggests that sales technology can support both:
externally focused tasks towards managing customer relationships and
internal administrative tasks. Building on this distinction, our quantitative
analysis reveals that sales technology impacts salesperson performance
directly when used as a customer relationship tool. In contrast, it has a
perfectly mediated impact when used for internal coordination purposes. To
unleash its real potential, sales technology should be designed to enable
customer relationships rather than being perceived as a cost cutting tool. In
addition, the motivational structure for using sales technology differs
between two SFA-use dimensions. While the customer relationship
dimension is driven by factors that trigger voluntary usage, the internal
coordination dimension is predominantly explained by factors imposed from
outside. Management should not impose technology usage. Rather, they
should support self-initiating factors that stimulate technology usage for
improving customer relationships. Combining upstream research focusing
on the drivers of SFA-usage with downstream research shedding light on its
performance impact, the study offers important implications for maximizing
the pay-back from SFA-technology investments.
Sales Force Automation Einsatz und
Außendienstmitarbeiter Leistung
Murat Serdaroglu
Zusammenfassung
In der heutigen Technologie-intensiver Wirtschaft ist es wichtig zu
verstehen, wie Informationstechnologie Unternehmenswert schafft. In einem
ersten Schritt unserer Forschung wurden eine Literaturrecherche sowie eine
qualitativen Studie in der pharmazeutischen Industrie durchgeführt. Diese
zeigen, dass Vertrieb orientierte Informationstechnologie (Sales Force
Automation, SFA) sowohl nach außen fokussierte Aufgaben zum
Management von Kundenbeziehungen als auch interne administrativen
Aufgaben unterstützen kann. In einem zweiten Schritt wurde eine
quantitative Studie, basierend auf diese Unterscheidung zwischen zwei
Dimensionen, durchgeführt. Diese bestätigt, dass SFA Technologie die
Außendienstmitarbeiterleistung direkt beeinflussen kann, wenn es als
Customer-Relationship-Tool verwendet wird. Im Gegensatz hat SFA nur
eine voll vermittelte Auswirkung, wenn es für interne Koordination und
Verwaltung verwendet wird. SFA soll als Kundenbeziehungsmanagement
Tool wahrgenommen werden, um sein eigentliches Potential zu enthüllen.
Darüber hinaus wird die „Customer-Relationship“ Dimension von
innerlichen Faktoren beeinflusst, die die freiwillige Akzeptanz auslösen. Die
zweite Dimension, „Internal Coordination“, wird eher durch externe
Faktoren bestimmt. Unsere Studie kombiniert die Einflussfaktoren des SFA-
Einsatzes mit den Folgen solcher Anwendung und bietet dadurch
signifikante Implikationen für die Maximierung der Rentabilität von SFA-
Technologie-Investitionen.
Acknowledgements
This dissertation is the result of a research effort that started just about four
years ago. In this part, I would like to take the opportunity to thank to those
people who have contributed to its development and refinement in one way
or another.
First of all, I would like to express my greatest appreciation to my advisor,
Prof. Dr. Andreas Eggert, for his continuous support, encouragement, and
valuable ideas during the entire research process. His remarks and
suggestions were always stimulating and constructive. His mentorship has
equally great influence on my thesis and on my future research career.
Thank you again, Andreas.
Secondly, I am very grateful and honored for having Prof. Dr. Bettina
Schiller, Prof. Dr. Hartmut Holzmüller, Prof. Dr. Klaus Rosenthal and Prof.
Dr. Bernd Frick in the dissertation committee. Their valuable comments and
questions on my dissertation have provided me a deeper insight into my
research work.
Furthermore, I greatly appreciate my mentor Nils at the company
sponsoring my research. He believed in me with enthusiasm and stayed
behind me since the beginning of our work. Our constructive discussions
have been an inexhaustible source of inspiration for me. It was my great
opportunity to work together with you. I am also greatly indebted to the
head of marketing department François for his financial support and
recognition. I value Christine equally for her great mentoring at later stages
of the study.
Also, I would like to thank to the head of sales department in Brazil for
permitting me to collect data in Brazil. My special thanks here are for
Antonio, who supervised the pilot and actual data collection in Brazil. I
thank also to the heads of sales departments in Germany, Belgium, Austria,
India and the U.K. for their remarks and suggestions which helped me shape
and position our research problem. The same holds for the head of sales
force effectiveness and his team.
Moreover, I am most grateful to Prof. Dr. Gary Hunter from Illinois State
University for providing me with detailed comments and suggestions aimed
at improving the quality of our study. In addition, I wish to express my
sincere thanks to the members of the marketing chair, Dr. Ina Garnefeld,
Franziska Weis, Sabine Hollmann, Eva Münkhoff and Lena Steinhoff. With
their continuous support I never felt as an external Ph.D. candidate.
Last but not least, I am indebted to Maria Leonor Alvarenga, a best friend
who has also double-checked the translation of our questionnaire into
Portuguese.
Finally, I thank my Mother, my Father and my Brother for making me who I
am. Without you I could never come so far. Thank you.
Murat Serdaroglu
Frankfurt, 1st of February 2010
i
TABLE OF CONTENTS
TABLE OF CONTENTS ................................................................................ i
LIST OF FIGURES ...................................................................................... iv
LIST OF TABLES ......................................................................................... v
1.
INTRODUCTION ................................................................................. 1
1.1.
Research Justification..................................................................... 4
1.1.1.
Importance of SFA Research for Businesses ......................... 4
1.1.2.
Literature Review ................................................................... 7
1.2.
Research Problem and Research Questions ................................. 12
1.3.
Intended Contributions ................................................................. 15
1.4.
Thesis Structure ............................................................................ 16
2.
SALES FORCE AUTOMATION AND SALES MANAGEMENT ... 17
2.1.
Introduction to the Chapter .......................................................... 17
2.2.
Defining Sales Technology .......................................................... 17
2.2.1.
Sales Force Automation ....................................................... 17
2.2.2.
Customer Relationship Management ................................... 20
2.2.3.
Benefits of Sales Force Automation .................................... 23
2.3.
Implications of SFA for Sales Management ................................ 28
2.3.1.
Strategic Issues ..................................................................... 28
2.3.2.
Data Ownership and Management ....................................... 31
2.3.3.
Implementing SFA Technology ........................................... 34
3.
THEORETICAL FOUNDATION ....................................................... 37
3.1.
Introduction to the Chapter .......................................................... 37
3.2.
Theorizing Information Technology Business Value .................. 39
3.2.1.
Microeconomic Theory ........................................................ 40
3.2.2.
Resource Based View .......................................................... 44
3.2.3.
Process-Oriented Models ..................................................... 54
3.3.
Concluding the Theoretical Discussion ....................................... 64
4.
SALES FORCE AUTOMATION AND SALESPERSON
PERFORMANCE ........................................................................................ 66
4.1.
Introduction to the Chapter .......................................................... 66
4.2.
SFA Adoption and Use: One-Dimensional Measurement ........... 67
4.3.
SFA Adoption and Use: Multidimensional Measurement ........... 74
4.4.
Conceptualizing SFA-Use Dimensions........................................ 81
4.4.1.
Qualitative Study .................................................................. 82
4.4.2.
SFA-Use Dimensions ........................................................... 85
4.4.3.
Customer Relationship Dimension ...................................... 87
ii
4.4.4.
Internal Coordination Dimension ......................................... 90
5.
RESEARCH MODEL AND HYPOTHESES ..................................... 93
5.1.
Introduction to the Chapter .......................................................... 93
5.2.
Research Model ............................................................................ 93
5.3.
SFA-Use Dimensions and Salesperson Performance .................. 97
5.3.1.
Customer Relationship and Salesperson Performance ......... 97
5.3.2.
Internal Coordination and Salesperson Performance ......... 100
5.4.
Antecedents of SFA-Use Dimensions ........................................ 104
5.4.1.
Technology Acceptance Model.......................................... 104
5.4.2.
Perceived Usefulness ......................................................... 107
5.4.3.
Perceived Ease-of-Use ....................................................... 109
5.4.4.
Supervisor Support ............................................................. 110
5.4.5.
Facilitating Conditions ....................................................... 113
5.4.6.
Computer Self-Efficacy ..................................................... 114
5.4.7.
Team-Use ........................................................................... 115
5.4.8.
Supervisor SFA-Control .................................................... 117
5.5.
Control Variables ....................................................................... 119
5.6.
Logical Structure ........................................................................ 121
6.
EMPIRICAL STUDY ........................................................................ 123
6.1.
Introduction to the Chapter ........................................................ 123
6.2.
Empirical Design ........................................................................ 123
6.2.1.
Non-Experimental Design .................................................. 123
6.2.2.
Cross-Sectional Design ...................................................... 126
6.2.3.
Data Collection Method ..................................................... 127
6.3.
Sampling .................................................................................... 129
6.4.
Data Collection........................................................................... 133
6.4.1.
Questionnaire ..................................................................... 133
6.4.2.
Data Collection................................................................... 136
6.4.3.
Describing the Sample ....................................................... 139
6.5.
Item Generation and Testing ...................................................... 139
6.5.1.
Reflective Constructs ......................................................... 142
6.5.2.
Formative Constructs ......................................................... 152
7.
DATA ANALYSIS ............................................................................ 162
7.1.
Introduction to the Chapter ........................................................ 162
7.2.
Data Analysis Method ................................................................ 163
7.3.
Data Examination ....................................................................... 167
7.3.1.
Acceptable Sample Size: Power Analysis ......................... 167
7.3.2.
Handling Missing Data ...................................................... 168
7.4.
Measurement Model Evaluation ................................................ 169
7.4.1.
Constructs with Reflective Items ....................................... 169
7.4.2.
Constructs with Formative Items ....................................... 172
iii
7.5.
Structural Model Evaluation ...................................................... 173
7.5.1.
Results of the Structural Model.......................................... 174
7.5.2.
Evaluation of a Rival Model .............................................. 179
8.
DISCUSSION .................................................................................... 182
8.1.
Introduction to the Chapter ........................................................ 182
8.2.
Implications for Theory.............................................................. 183
8.3.
Implications for Management .................................................... 194
8.4.
Limitations and Suggestions for Future Research ..................... 203
8.5.
Conclusion ................................................................................. 205
APPENDIX ................................................................................................ 207
REFERENCES ........................................................................................... 238
iv
LIST OF FIGURES
Figure 3.1: A Process Oriented Model of IT Business Value ..................... 57
Figure 3.2: The Value Chain ....................................................................... 59
Figure 3.3: Typology of Business Processes .............................................. 61
Figure 3.4: Dimensions of IT-Business Value ............................................ 62
Figure 5.1: System-to-Value Chain ............................................................ 94
Figure 5.2: Updated DeLone and McLean IS Success Model .................... 94
Figure 5.3: Research Model ........................................................................ 96
Figure 5.4: Technology Acceptance Model .............................................. 105
Figure 6.1: Questionnaire Layout ............................................................. 135
Figure 6.5: Theoretical and Observable Levels in Empirical Research .... 140
Figure 6.6: Principle Factor Model ........................................................... 142
Figure 6.7: Composite Latent Variable Model ......................................... 153
Figure 7.1: Structural Model Results ........................................................ 175
Figure 7.2: Illustration of the Mediating Effect ........................................ 178
Figure 6.2: Descriptive Statistics: Sales Experience ................................. 207
Figure 6.3: Descriptive Statistics: Age ..................................................... 208
Figure 6.4: Descriptive Statistics: Gender ................................................ 209
v
LIST OF TABLES
Table 6.1: Reflective Construct Definitions .............................................. 145
Table 6.2: Sources of Reflective Measurement Items .............................. 147
Table 6.3: Decision rules for determining whether a construct should be
formative or reflective ............................................................................... 154
Table 6.4: Formative Construct Definitions .............................................. 157
Table 6.5: List of Formative Items ............................................................ 159
Table 6.6: Item Validity and Reliability Based on Pilot Data ................... 210
Table 6.7: List of Reflective Items ............................................................ 212
Table 7.1: Descriptive Analysis of Reflective Items ................................ 214
Table 7.2: Exploratory Factor Anal ysis .................................................... 216
Table 7.3: Exploratory Factor Anal ysis without Facilitating Conditions.. 217
Table 7.4: Reflective Items Validity and Reliability................................. 218
Table 7.5: Discriminant Validity (AVE Analysis) .................................... 220
Table 7.6: Cross Loadings ......................................................................... 221
Table 7.7: Descriptive Analysis of Formative Items ................................ 222
Table 7.8: Multicollinearity Analysis and Item Weights .......................... 223
Table 7.9: Hypothesis Testing Results ...................................................... 224
Table 7.10: Evaluation of a Rival Model .................................................. 226
Chapter 1: Introduction
1
1. INTRODUCTION
Sales forces are caught in the middle. On the one
side, their customers have changed dramatically in
terms of how they purchase and what they expect.
On the other side, their own corporations have
shifted, going through downsizing, restructuring,
and cost cutting. Traditional boundaries such as
those between sales and marketing have crumbled.
Salespeople have to cope with more products,
introduced faster with shorter life cycles, and less
competitive differentiation. (Rackham and De
Vincentis 1999, p. ix)
Sales forces today face many challenges originating from both outside and
inside of their organizations (Jones, Brown, Zoltners, and Weitz 2005). As
the biggest external actor, customers constantly raise their expectations.
Through the Internet they inform themselves about product alternatives
before making a purchase. They expect from salespeople to be equally well
informed about the best solution possibilities and the latest market trends.
Recent advances in communication technologies give the capacity to
communicate quickly and effectively, making customers demand quick
response and accessibility from the salesperson side. Buying procedures are
becoming complex and require salespeople to deal with greater networks
within client organizations. Furthermore, customers are increasingly opting
for customized solutions which place additional burden on salespeople in
terms of information gathering, communication and coordination within
both buyer and seller organizations (Zoltners et al. 2001).
Chapter 1: Introduction
2
In addition to rising customer expectations, intense competition places great
pressure on salespeople by squeezing the profit margins. Globalization
brings down the borders and makes market entry easier for competitors.
Companies and sales forces have to deal with a reduced amount of
differentiation from competition and increased product complexity. As
product life cycles shorten, salespeople must more frequently update their
product knowledge. It gets increasingly difficult to access profitable
customers, and companies need to develop better ways of allocating their
resources to the right customer segments (Reinartz and Kumar 2000).
Emerging ethical and legal environment also constrains sales organizations’
ability to freely pursue certain selling activities. Companies are introducing
codes of conduct which set strict standards that must be upheld when
encountering clients. Salespeople are increasingly asked to document their
activities and be accountable for their actions such as managing expense
accounts, giving gifts, making promises about product performance and
delivery, and selling products that can be perceived as ‘unnecessary.’ In
industries such as pharmaceuticals and medical equipment, salespeople have
to keep a record of the samples and other material they distribute to their
clients.
Companies respond to challenges in their markets with various strategic,
organizational and operational measures which bring additional burden on
salespeople. They move the strategic direction of their sales forces away
from a transaction focus to a relationship focus (Ingram 1996; Weitz and
Bradford 1999). In this setting, salespeople are expected to shift their time
from order taking to creating customized solutions for their customers and
seeking new business (Shoemaker 2001). Besides, companies adopt new
selling models and organization structures such as team selling and key
account management (Jones, Dixon, Chonko, and Cannon 2005). This
Chapter 1: Introduction
3
development makes sales, marketing and other functions merge gradually to
better identify customer needs and offer solutions addressing those needs
(Rouzies et al. 2005). Last but not least, innovative sales channels are being
introduced such as Internet and call-centers (Stone et al. 2002). In this
overall framework, salespeople are required to act like an orchestrator to
manage the value-generating network by communicating in real time with
their companies and coordinating their activities with their team members,
chain partners and clients.
With the anticipation to meet these challenges and improve sales force
effectiveness, companies continue investing in information technologies
(IT) (Shoemaker 2001; Parthasarathy and Sohi 1997). Sales specific IT,
which is often called Sales Force Automation (SFA),
1
enables salespeople to
store, retrieve, and analyze customer data and manage important
information throughout the sales cycle (Morgan and Inks 2001). However, it
has not been straightforward for companies to realize this potential so far.
Lack of SFA adoption and vanishing person-job fit may be the outcomes of
an ambitious SFA project (Speier and Venkatesh 2002). One typical reason
of failure is shown to be the lack of measuring the impact of sales
technologies on sales force (Erffmeyer and Johnson 2001). The impact of
SFA technology on salesperson performance and organizational profitability
has been largely neglected in literature.
2
It is crucial for firms investing in SFA technology to understand how IT
contributes to sales effectiveness. In the end, firms cannot keep investing in
a technology without knowing its return on investment. Our research
objective is to understand how SFA impacts salesperson performance. We
propose that not every salesperson benefits from SFA in the same way. How
1
Refer to Chapter 2 for a detailed definition of the Sales Force Automation technology.
2
Refer to Buttle et al. (2005) and Landry et al. (2005) for reviews of SFA research.
Chapter 1: Introduction
4
salespeople use SFA should be determining the extent to which they benefit
from this technology. We further argue that salespeople have different
motivations when using SFA. We expect to offer substantial insights on
how SFA-use impacts salesperson performance and the factors drive that
usage, which in turn should help firms maximizing their return on SFA-
technology investments.
In this chapter we present our motivation for a research in sales technology
field. We first discuss the importance of SFA technology for businesses and
identify the gaps in relevant literature (section 1.1). Following that, we
define our research problem and formulize research questions based on the
identified gaps (section 1.2). After introducing our research questions, we
elaborate on the potential contributions of our study for both theory and
practice (section 1.3). We present the structure of our thesis in the last
section (section 1.4).
1.1. Research Justification
1.1.1. Importance of SFA Research for Businesses
SFA technology represents a significant research field with important
implications for businesses. This is mostly because, although SFA promises
great benefits to companies, it is often not easy to realize and quantify these
benefits. In this section we will be discussing the benefits, costs and risks of
investing in SFA technology.
Despite the emergence of new direct channels such as the Internet and call-
centers, sales forces still occupy an important position in linking companies
Chapter 1: Introduction
5
to their customers today. This is mainly the result of an increased emphasis
on developing and maintaining strong customer relationships (Cannon and
Perreault 1999; Ingram 1996). Salespeople still carry the primary
responsibility of building, and maintaining relationships with customers
(Homburg and Stock 2004). They are a strong enabler of market orientation
(Brown and Peterson 1993) and market intelligence (Pass et al. 2004; Le
Bon and Merunka 2006). Salespeople have a strong influence in reducing
customer defection (Johnson et al. 2001). As a result, the strategic
importance of the sales force to organization success is at an all-time high.
For businesses and researchers alike, understanding the efficiency and
effectiveness of sales force should be a high research priority.
SFA can improve sales force effectiveness by freeing salespeople from
costly administrative activities in favor of relationship building tasks, which
better suit the skills and abilities of the sales force (Ingram et al. 2002). SFA
can enhance communication and increase the overall quality of the sales
effort through faster access to relevant and timely information (Jelinek et al.
2006). SFA carries significant potential for sales management and
salesperson effectiveness which cannot be ignored by sales organizations.
Therefore, it represents a phenomenon deserving strong research attention.
Consistent with the big potential promised by customer relating
technologies, the Customer Relationship Management (CRM) market
should continue to grow significantly, reaching $18 billion worldwide in
2010, according to a market report from AMR Research (Beal 2006).
3
Strong competition and shareholder pressure for increased profitability force
3
Most CRM solutions stem from SFA functionality and often represent customer
relationship technologies serving organizational functions other than sales, such as
marketing and service. The terms CRM and SFA are used often interchangeably in
literature although they stand for different concepts. We use the term SFA to ensure
consistency throughout the text. More on the association between CRM and SFA is
given in section 2.2.2.
Chapter 1: Introduction
6
companies to spend more on their CRM investments, increasing also the
demand for sales force specific SFA solutions (Buttle et al. 2006). On the
supply side, information technology vendors invest in improving the ability
of their SFA solutions to integrate with back-office applications, add mobile
capability, develop attractive licensing solutions and tailor them to meet the
needs of particular industry verticals. SFA is a significant research topic as
the investment for SFA systems gets bigger shares in corporate budgets.
However, SFA is an expensive investment. A typical SFA system costs
from $5000 to $15000 per salesperson (Erffmeyer and Johnson 2001). The
implementation of a classic CRM solution may last up to 24 months (Rigby
et al. 2002).
4
Moreover, SFA technologies consist of computer-based
equipment, which become rapidly obsolete. Hence, there is a substantial
continuous expense if the SFA system is to be kept up to date over the years
(Parthasarathy and Sohi 1997). The decision to automate the sales force is
made even more difficult because in the short run it is difficult to measure
most of its benefits in dollar terms and to quantify the gain that can be
enjoyed by the adoption of such a system.
What’s more, implementing SFA has turned out to be a difficult task and a
painful experience for many companies. Despite its intuitive appeal and
continual advancements in technology, SFA initiatives regularly fall short of
expectations (Bush et al. 2005). SFA projects suffer failures at high rates;
estimates predict 55 percent to 80 percent of initial efforts to end up with
either losses or no improvement in company performance (Morgan and Inks
4
CRM software is nowadays available in two ways. It can be installed on a client’s own
servers (on-premise) or it can be accessed on a provider’s servers via the Internet in a
manner much similar to an ordinary website (on-demand) (Buttle 2006). The former is
often preferred by many large-scale enterprises and the costly option. The alternative
on-demand deployments, in contrast, require much less investment at the beginning
(due to the low set-up costs and shorter implementation times) and are suitable for
smaller scale deployments of small and medium scaled enterprises.
Chapter 1: Introduction
7
2001; Reinartz et al. 2004; Rivers and Dart 1999). In a recent survey of
business executives, only less than 50 percent of the respondents appeared
to be satisfied with the business value delivered by their CRM and SFA
systems (Beal 2008). SFA deployment is a difficult and complex task which
should be taken seriously. It is important to understand why some
organizations are successful at implementing SFA and why others are not.
To sum up, sales force effectiveness represents a significant opportunity for
organizations and is high in corporate agendas. SFA is promising substantial
benefits for sales force and companies are heavily investing in this
technology. However, SFA is expensive and it is often difficult to quantify
this technology’s benefits, making it in the end difficult to justify the
investment made in SFA. Therefore SFA and its impact on sales
effectiveness represent a significant research field.
1.1.2. Literature Review
Major investments have been made in SFA to enhance the effectiveness and
efficiency of sales personnel, even though it is expensive, difficult to
manage and fast changing. In the light of the potential and risks
simultaneously inherent in sales technology, interest in CRM and SFA is
gaining momentum among academicians.
5
Conflicting reports on the
success rates of SFA implementations have initiated strong calls for
additional research in this domain.
6
For that reason, considerable amount of
conceptual and empirical studies about SFA is coming out in the last years.
5
Ahearne et al. 2008; Boulding et al. 2005; Jayachandran et al., 2005; Payne and Frow
2005, 2006; Rigby and Ledingham 2004; Srivastava et al. 1999; Thakur et al. 2006
6
Engle and Barnes 2000; Ingram et al. 2002; Jones et al. 2002; Landry et al. 2005; Leigh
and Marshall 2001, Marshall et al. 1999; Tanner and Shipp 2005; Tanner et al. 2005
Chapter 1: Introduction
8
As intended outcomes of an IT system can be realized only through system-
use, IT-adoption is suggested to be a key link between IT investment and
performance (Dixon 2000; Devaraj and Kohli 2003). As a matter of fact, a
given technology cannot deliver any benefit if end-users do not use it.
Researchers therefore argue that low adoption of installed systems is a
major reason of the missing returns on organizational investments in IT
(Venkatesh and Davis 2000). Besides, salespeople have been among the
most technophobic employee groups in organizations (Greenberg 2004).
One of the major risks of introducing IT to a sales force is that individual
salespeople resist using the technology (Parthasarathy and Sohi 1997). Early
empirical work and anecdotal evidence also support the argument that the
failure of SFA initiatives is, in part, being prompted by limited user
acceptance of the implemented technology (Speier and Venkatesh 2002).
Consequently, issues associated with the underutilization of technology in
the sales force is a research priority (Jones et al. 2002). Most of the early
research on SFA has been either about explaining the adoption and diffusion
of SFA
7
or retrospectively examined salesperson failure to adopt technology
and the consequences for organizational commitment, job satisfaction, and
fit.
8
While this research stream has explained a great deal of salesperson
intention to adopt SFA and actual adoption of SFA, it has fallen short of
explaining the consequences of that SFA adoption. In both practice and
research, the lack of SFA adoption among salespeople has usually been
equated with SFA project failure (Honeycutt et al. 2005). Motivating use
has often been assumed to be the only critical issue for SFA implementation
success (Hunter and Perreault 2007). Ahearne and his colleagues (2004)
reveal this assumption clearly:
7
Some studies which investigate the antecedents of SFA adoption and use: Jones et al.
2002; Morgan and Inks 2001; Robinson et al. 2005a; Schillewaert et al. 2005
8
Speier and Venkatesh (2002) study the dynamics of SFA implementation in two sales
organizations after the SFA initiatives failed.
Chapter 1: Introduction
9
Each model [to explain technology acceptance] has
the same dependent variable, usage, but uses various
antecedents to understand acceptance of technology.
An implicit assumption in all these models is a
positive and linear relationship between performance
and usage. There is an underlying assumption that
technology utilization is a proxy of its perceived
effectiveness. (p. 297)
However, SFA adoption among salespeople does not automatically translate
into better sales performance (Landry et al. 2005). Ahearne and others
(2004) empirically disprove the positive and linear assumption of SFA use
and performance link and demonstrated a curvilinear relationship between
sales performance and technology use, which hints for the negative effect of
SFA over-use. SFA use may also result in negative perceptions among
salespeople such as role conflict and ambiguity (Rangarajan et al. 2005). It
is not plausible to assume that SFA adoption by itself will bring increased
salesperson effectiveness. End-user adoption alone should not be the
ultimate objective of any SFA effort.
In fact, SFA is gathering interest in academic research as a fundamental
business process with significant impact on organizational results
(Srivastava et al. 1999). Researchers are increasingly calling for additional
research in the area of technology use and its realized impact on salesperson
performance (Good and Stone 2000; Jones et al. 2002; Leigh and Marshall
2001; Marshall et al. 1999; Marshall and Michaels 2001; Raman et al.
2006). The call for additional research on the impact of SFA usage on
salesperson performance is well warranted. As Ahearne and others (2005)
argue, what should really matter in an SFA project is the technology’s actual
contribution to salesperson efficiency, effectiveness, or both:
Chapter 1: Introduction
10
Technology adoption is only important if it truly
leads to performance improvements. (…) The proper
criteria by which to judge if an SFA initiative has
been successful rest not simply in determining
whether or not salespeople adopt technology, but
whether or not adoption (i.e. use) actually improves
performance. (p. 380)
There are actually a number of studies in the literature which investigate the
relationship between technology usage and sales performance.
9
Using multi-
source empirical data, Ko and Dennis (2004) show that salespeople with
higher expertise benefit more from an SFA system. Jelinek and others
(2006) demonstrate in a longitudinal setting that SFA-adoption increases
salesperson performance. In another study, no frequency of use but infusion
(i.e., the degree to which the person maximizes the potential of the
technology) explains salesperson performance (Sundaram et al. 2007). On
the other hand, Rivers and Dart (1999) can report no apparent relationship
between the extent of SFA acquisition and the benefits generated. Similarly,
Avlonitis and Panagopoulos (2005) cannot empirically validate a significant
relationship between SFA acceptance and salesperson performance.
Many of these studies theorize a direct link from SFA adoption to
salesperson performance and do not investigate the facilitating mechanisms
through which this link occurs. SFA research, in general, has focused on
people and technology issues and mostly neglected the business processes
(Buttle et al. 2006). Uncovering the processes through which technology
influences sales force performance should be a research priority (Avlonitis
9
Refer to Collins and Schibrowsky 1990; Moriarty and Swartz 1989; Wedell and
Hempeck 1987; and Zablah et al. 2004 for early conceptual studies on SFA and
performance relationship, and Keillor et al. 1997 and Moncrief et al. 1991 for
exploratory studies applying descriptive data.
Chapter 1: Introduction
11
and Panagopoulos 2005). Mithas and others (2005) conclude in their paper
that additional research is necessary to consider how SFA technology is
used by employees to improve business processes:
CRM applications merely enable firms to collect
customer knowledge. Only when firms act on
customer knowledge by modifying service delivery
or by introducing new services will they truly
benefit from their CRM applications. There is a need
for further research to trace the causal chain linking
CRM applications and customer satisfaction at a
finer level of granularity by specifically accounting
for such complementary actions. (p. 207)
Such a granular view of the relationship between SFA-adoption and
performance may be established by incorporating SFA-specific salesperson
behavior into research models. There are certainly different ways to use an
information technology tool and the way SFA is used should have a decisive
impact on customer satisfaction and the bottom line (Hunter and Perreault
2007). In fact, while some salespeople benefit from the SFA technology,
others do not (Ahearne et al. 2005). However, the differences between
salespeople in terms of their SFA-use behavior are often overlooked in the
literature, where the analysis is limited to answer the question if the
salesperson uses SFA or not. Research in outcomes of sales technology use
needs to examine the circumstances under which such use leads to higher
levels of salesperson effectiveness, efficiency, and customer satisfaction
(Tanner et al. 2005; Parthasarathy and Sohi 1997).
Even when the link between the right way of using SFA and sales
performance were completely illuminated, present research could not
Chapter 1: Introduction
12
answer the question of what motivates salespeople to use SFA technology in
that right way. While there are studies successfully explaining the drivers of
increased SFA-adoption and use (Schillewaert et al. 2005), to best of our
knowledge, there is no study which examines the impact of such driver
factors on a specific direction of SFA-related behavior. In one relevant
study, Ahearne and others (2005) report that the salespeople who received
adequate training also benefited more from SFA technology. In their
literature synthesis on performance impacts of IT, Soh and Markus (1995)
draw attention to ‘appropriate’ use of IT and call for additional research to
study “what constitutes appropriate use, how organizations promote
appropriate use, and how appropriate use translates into IT impacts” (p. 39).
1.2. Research Problem and Research Questions
The literature has developed a rich understanding of SFA technology and its
use in the workplace. Both organizational and individual drivers of SFA
adoption have been widely tested so far and it has been made clear that the
performance impacts of SFA technology must be the focus of future
research. In contrast, empirical analysis is mostly limited to modeling
salesperson performance as a simple linear function of SFA-use. Such a
conceptualization of the relationship between SFA-use and salesperson
performance restricts the value of the theory for both researchers and
practitioners. Researchers lack empirical evidence to evaluate competing
theoretical models. Practitioners lack guidelines to decide on the appropriate
form and extent of SFA-use under different sets of conditions.
Our research objective is to build upon existing literature by understanding
how SFA technology relates to salesperson performance. Specifically, we
Chapter 1: Introduction
13
want to demonstrate empirically that how salespeople use SFA to
accomplish their daily tasks, and not only if they are using SFA or not, has a
direct impact on their performance. We further aim to show that salespeople
have different motivations when using SFA technology and different
antecedent factors drive certain SFA-use behavior. Based on the theoretical
foundations of resource based view of IT business value (Melville et al.
2004) and process-oriented models of IT business value (Barua et al. 1995),
we develop and empirically test a conceptual model to investigate the
following research problem:
The mechanism through which Sales Force Automation technology affects
salesperson performance has not been fully clarified in the literature yet.
Above all, further research is necessary to explain how particular SFA-use
behavior
10
impacts salesperson performance. Furthermore, we do not know
yet for which reasons a salesperson uses SFA in that particular way.
10
We define SFA-use as the application of sales technology by a salesperson to support
sales job relevant tasks and processes. We define ‘particular SFA-use behavior’ as the
specific behavior which distinguishes a salesperson from others in terms of using SFA
technology. Refer to Chapter 4 for a discussion on these issues in greater detail.
Chapter 1: Introduction
14
This research problem is subdivided into the following research questions:
(1) How should the SFA-use construct be conceptualized to better
incorporate the particular SFA-use behavior of a salesperson?
In order to further illuminate the functioning mechanism of SFA
when affecting salesperson performance, a granular view of SFA-use
is necessary. In this way it will be possible to better distinguish
salespeople in terms of their SFA-use and identify the cases where
SFA-use makes a positive contribution. Therefore, our first objective
is to conceptualize an SFA-use construct which will enable to better
incorporate the particular SFA-use behavior of a salesperson.
(2) Does the way SFA is used by salespeople impact their performance?
Our second research objective is to test how SFA-use impacts
salesperson performance. We will link our SFA-use construct to
salesperson performance in a conceptual model and empirically test
their relationship to see how a particular SFA-use behavior affects
sales performance.
(3) Which antecedent factors will explain those particular ways SFA is
used by the salespeople?
Organizational and individual antecedents of SFA-adoption which
already exist in literature should be tested again for their effects on
differentiated SFA-use behavior. Our third research objective is to
test a number of well established antecedents of SFA-adoption to see
how they drive SFA-use in a certain behavioral direction.
Chapter 1: Introduction
15
1.3. Intended Contributions
We expect our study to provide practical guidance to sales and marketing
practitioners on critical areas such as key sales processes supported by IT,
type and quality of IT assets, specification of appropriate SFA-use, and its
outcomes. The intended contributions of our research are threefold:
First, we argue that SFA-use should be conceptualized as a task-based
construct. Tapping the job-related tasks achieved by employing the system
along organizationally relevant dimensions (Doll and Torkzadeh 1998), we
can better distinguish salespeople in terms of their SFA-use behavior.
Then, we insert our SFA-use construct into an operational selling context by
linking it to its antecedent variables and salesperson performance. By the
granular sight provided by our task-based SFA-use construct, we can shed
more light on the process through which SFA impacts the bottom line.
Therefore, our second contribution lies in better explaining the relationship
between SFA-use behavior and salesperson performance.
Our third contribution derives from the antecedents driving our SFA-use
construct. By applying well established antecedents of SFA-adoption to
explain our SFA-use construct, we can make more precise recommendations
to practitioners in order to stimulate SFA-use in the desired manner.
In sum, our research approach can offer substantial insights on how SFA-
use impacts salesperson performance and which factors drive that way of
usage, which in turn helps firms maximizing their return on SFA-technology
investments.
Chapter 1: Introduction
16
1.4. Thesis Structure
In this thesis, we first define SFA technology and present its benefits and
implications for sales management and businesses in general (chapter 2).
Next, we devote a chapter for the theoretical underpinnings of how
information technology creates business value (chapter 3). Following that,
we argue the necessity of a task-based multidimensional measure of SFA-
use to understand sales technology’s impact on salesperson performance
(chapter 4). Against the background of a literature review and a qualitative
study, we further conceptualize task-based dimensions of SFA-use. This
section is then followed by our conceptual model and hypotheses (chapter
5). After, we present our empirical design decisions and data analysis
methodology and results (chapters 6 and 7). We conclude the thesis with a
discussion of our results, limitations and suggestions for future research
(chapter 8).
Chapter 2: Sales Force Automation and Sales Management
17
2. SALES FORCE AUTOMATION AND SALES
MANAGEMENT
2.1. Introduction to the Chapter
The introduction of Information Technology (IT) to the sales profession has
many implications for how salespeople and sales managers do their jobs.
The objective of this chapter is to draw an overall framework for IT
deployment in sales environment and to provide insight regarding the
capabilities of the technology and the potential impact of such applications
on organizations. In the first section of the chapter a definition of Sales
Force Automation (SFA) technology is given. This will be followed by a
brief discussion of Customer Relationship Management and its association
with SFA. Finally, potential benefits of SFA sought by sales management
are presented. In the second part, we discuss the implications these
technologies have for sales profession and the salesperson.
2.2. Defining Sales Technology
2.2.1. Sales Force Automation
SFA can essentially be described as the application of information
technology to support salespeople in their selling and/or administrative
activities (Morgan and Inks 2001). SFA systems utilize computerized
hardware, software, and telecommunications technology to capture, access,
analyze, and exchange high quality information in order to improve sales
Chapter 2: Sales Force Automation and Sales Management
18
force productivity and effectiveness (Jayachandran et al. 2005).
11
This
information generally includes transactional and profiling data about
customers, market data, competitor profiles, product libraries, pricing
schedules and other information (Buttle et al. 2006). Such rich information
can support salespeople when developing long-term mutually beneficial
relationships with customers.
However, there has been no clear and widely accepted definition of SFA
(Rivers and Dart 1999). SFA means different things to different people and
to different firms (Erffmeyer and Johnson 2001). The exact nature of SFA
varies dramatically from one firm to the next (Morgan and Inks 2001), as
“each firm is unique, as are its customers, markets, business objectives,
resources, and perhaps most important, the stakeholders who will be
germane to its specific CRM circumstances” (Plouffe et al. 2004, p. 324).
Some researchers and firms prefer narrow conceptualizations of SFA.
Schillewaert and others (2005) do not include general office tools (e.g. word
processing and presentation) or separate e-mail and Internet applications
into their SFA definition. Parthasarathy and Sohi (1997) define SFA
systems consisting of centralized database systems that can be accessed
through a modem by remote laptop computers. Ko and Dennis (2004) define
SFA as hardware and software applications to provide knowledge that
enhances learning and improves performance. Other authors who make
broader conceptualizations of SFA also include information technology that
salespeople use to perform their roles such as mobile phones, e-mail, word-
processors and web browsers in their definitions and not just the dedicated
software offered by SFA vendors (Erffmeyer and Johnson 2001; Hunter and
Perreault 2007).
11
An overview of common SFA functionality is given in the Appendix.
Chapter 2: Sales Force Automation and Sales Management
19
In general, a definition can be classified according to its specificity, as either
narrow or broad. Narrow definitions help fine-tune our understanding of a
specific application of a phenomenon in a given setting. Such narrow
definitions are useful within the limited scope of a research context;
however it is difficult to generalize the findings to other situations.
Moreover, narrow definitions of IT may suffer from reduced relevance as
technologies, systems, and skills become obsolete over time. In contrast,
broad definitions have the advantage of being easily generalized beyond a
specific research situation. However, broad definitions tend to be overly
abstract and can be therefore difficult to apply at narrow situations.
It is recommended in the literature that not a specific SFA system, but the
functionality, sales processes and tasks supported by technology should be
considered when defining the scope of the SFA definition (Tanner and
Shipp 2005). According to Honeycutt and others (2005):
Rapid technological changes and rate of
technological obsolescence suggest that future
researchers should concentrate on SFA as a process
of automating routine and manual sales tasks rather
than getting bogged down in the details of what
specific technology and equipment constitutes SFA.
(p. 321)
Against the background of this discussion, we define SFA as dedicated
computer systems designed for salespeople to manage customer, market and
product information and perform daily sales activities. The technology pool
provided to a given sales force may vary considerably, whereas sales tasks
are relatively comparable across different organizations. Our definition
focuses on those sales activities supported by SFA, rather than a specific IT
Chapter 2: Sales Force Automation and Sales Management
20
tool. This approach does not allow us to go deep into the characteristics of a
specific SFA system; nonetheless, it helps us establish a certain level of
consistency within the literature and generalizability to other contexts.
In the meantime, Customer Relationship Management (CRM) is gaining
momentum across industries as a dominant approach in managing customer
facing activities. CRM strategies pursued by companies have significant
reflections for sales organizations and the information technology geared
solely towards the salespeople (i.e., classic SFA solutions). There has been a
recent interest in considering SFA as a part of a broader CRM network with
additional capabilities and responsibilities for the salesperson. In the next
section we briefly present CRM as a strategy and technology, and contrast it
with classic SFA deployments. We argue that SFA and CRM are not rival
but relevant concepts complementing each other. Our aim is to present an
actual view on CRM applications and to enrich our definition of the SFA
technology.
2.2.2. Customer Relationship Management
CRM is defined as “a cross-functional process for achieving a continuous
dialogue with customers, across all of their contact and access points, with
personalized treatment of the most valuable customers, to increase customer
retention and the effectiveness of marketing initiatives” (Day and Van den
Bulte 2002, p. 5). In sales force intensive organizations implementation of
the CRM strategy relies heavily on salespeople. The sales force is the main
means of customer contact and it plays a critical role in realizing a
relationship marketing philosophy and maintaining customer relationships
(Cannon and Perreault 1999). In addition, salespeople are increasingly asked
to take greater roles in other important activities of the firm—such as
Chapter 2: Sales Force Automation and Sales Management
21
product design, customer service, production, and research and development
(Pass et al. 2004).
In such companies, SFA tools are frequently implemented to facilitate the
CRM processes (Speier and Venkatesh 2002). In fact, the bulk of CRM
functionality is originally designed to enhance sales and sales management
(Shoemaker 2001). Early functionality was geared towards solely the
improvement of salesperson efficiency (doing same things faster, easier).
“Regardless of the nature of any particular SFA system, its primary purpose
is to reduce the time spent on support activities and thereby free up the sales
force to sell” (Rivers and Dart 1999, p. 60). In such settings, research on
CRM has its roots in understanding SFA. Typical foci of the studies thus
include the use of e-mail to communicate with customers, contact
management software to guide salesperson/customer relationship
development, and sales presentation technologies (Ingram et al. 2002).
Many papers have been more narrowly focused on SFA, in particular the
factors driving acceptance and use of IT by the sales force.
Actually, CRM is rather a business strategy and philosophy, integrating
customer focus, relationships with customers and team-based consultative
selling into a coherent organizational strategy (Brown 1999; Swift 2001).
CRM encompasses different functions such as marketing and service,
production and logistics in addition to sales. Whereas much of the extant
literature on SFA technology has focused narrowly on personal selling,
CRM clearly speaks to the management of organizational processes (Leigh
and Marshall 2001). Ingram and others (2002) make a very clear distinction
between classic SFA and modern CRM thinking:
Chapter 2: Sales Force Automation and Sales Management
22
The basic purpose of SFA is to automate selling and
administrative tasks so that salespeople and sales
managers can perform current activities more
efficiently. CRM technology includes this efficiency
capability, but also addresses effectiveness issues,
such as salespeople doing different things. Thus,
sales organizations can use the technology to address
the effectiveness and efficiency of their customer
relationship processes. (p. 564)
Indeed, “as organizations recognize the enterprise-wide nature of CRM,
SFA is being overtaken by broader, relationship-wide (or enterprise-wide)
technology.” (Tanner et al. 2005, p. 170) Basic SFA tools are further
integrated into enterprise-wide data management systems encompassing
sales, marketing, and customer service (Morgan and Inks 2001). The
fundamental drive is to reduce transaction costs while providing better
service (Donaldson and Wright 2004). Salespeople also favor SFA when the
sales processes are integrated with other functions (customer service and
marketing) and back-office (e.g., billing, logistics, purchasing) systems
(Shoemaker 2001), because such technological advances represent new
capabilities for the salesperson which were not possible before. As the
strategic focus of IT applications move to optimizing resources by serving
the selected customers, a firm progresses beyond the idea of simply
reducing transaction costs
12
to maximizing revenues (Landry et al. 2005).
Leigh and Marshall (2001) explain the changes a company goes through to
be more customer-centric in addition to installing SFA technology:
12
In literature computerizing repetitive tasks which were previously used to be done per
hand is often suggested to increase the efficiency and reduce costs. For instance,
electronic data interchange (EDI) technology is used to transfer data electronically
between buyers and suppliers in an attempt to reduce selling costs (MacDonald and
Smith 2004).
Chapter 2: Sales Force Automation and Sales Management
23
Organizations that are interested in becoming more
customer-oriented are (…) more likely to consider
the sales force to be only one of several channels to
reach customers. In fact, these firms may employ a
full range of sales and channel options to reach
different target markets as well as serve strategic
customers. They are more likely to stress selling as a
core business process, to adopt CRM technology,
and to customize their systems to better select, train
and reward employees who deliver customer value,
profitably. In short, market-driven firms treat
customer relationships as the core of their business
enterprise. (p. 83)
To sum up, a bigger picture of IT is necessary when conceptualizing sales
related technologies in future research. The terms SFA and CRM are
merging and used in the literature interchangeably to mean the same, yet
increasingly broader concept (Hunter and Perreault 2006). Sales related
technology is clearly being developed to include many new uses and
integrated to other organizational functions. While involving this CRM
thinking in our SFA definition, we stay with the term SFA for the sake of
consistency with past research.
2.2.3. Benefits of Sales Force Automation
SFA technology promises many benefits to sales management and
salespeople. By increasing available selling time and enhancing
communication and providing faster access to relevant and timely
information, SFA can increase the overall quality of the sales effort (Rivers
Chapter 2: Sales Force Automation and Sales Management
24
and Dart 1999). The expected end-effect is to facilitate a greater
understanding of the selling situation, to deliver superior customer value and
to forge close mutually beneficial relationships needed to develop market-
relating ability for competitive advantage (Dickie 1999). In this section we
present potential benefits of SFA technology that encourage companies
invest in SFA technology.
Improved Salesperson Efficiency and Productivity
One of the most important reasons companies invest in SFA is to increase
the efficiency and productivity of the sales staff (Erffmeyer and Johnson
2001). SFA can minimize the amount of time salespeople spend on routine,
repetitive, easily automated tasks such as sending sales call reports, expense
reports, and ordering promotional material (Gohmann et al. 2005).
Moreover, SFA improves time management and call planning (Weeks and
Kahle 1990). Automated routers can interface with planners to identify the
downtime in a salesperson’s schedule and direct new leads to the
salesperson during such time (Khandpur and Wevers 1998). Eventually, by
reducing the amount of downtime in a salesperson's workday and
optimizing call schedules, the amount of time devoted to activities more
closely associated with selling can be maximized (Ahearne et al. 2005).
Furthermore, SFA facilitates and improves information processing and
communication, which in return can increase the quantity of work
performed in a given time period (Good and Stone 1995). Technology also
helps reduce errors and thus saves from time consuming corrective action.
Improved Customer Relationships
Many companies are turning to SFA to help them increase customer
acquisition and retention and enhance their customer relationships (Ingram
et al. 2002; Wright and Donaldson 2002). SFA increases the depth, the
breadth, and the mobility of knowledge through increased communication
Chapter 2: Sales Force Automation and Sales Management
25
speed and access to customer relevant information (Campbell 2003;
Jarvenpaa and Ives 1994; Jayachandran et al. 2005). Sales representatives
can in return employ this high quality knowledge to support their customer
relationships (Day 1994; Huber 1991; Sinkula 1994).
SFA applications can help salespeople manage customer relationships more
effectively across the stages of relationship initiation, maintenance, and
termination (Reinartz et al. 2004). Traditionally, salespeople have been best
in capturing information about customers and competitors as boundary-
spanners of an organization (Pass et al. 2004). At initiation stage,
technology assists salespeople in their role as market sensors; salespeople
have the important task of sensing the trends and opportunities in the
marketplace. Salespeople can search databases, pull data from outside
sources, and easily enter new data themselves (Marshall et al. 1999). Search
engines enable salespeople to quickly access vast amounts of information at
a mouse-click. Through SFA systems, information obtained from various
sources such as call-center data, marketing campaigns or other outside
suppliers can be rapidly merged and forwarded to the sales force
(Shoemaker 2001). Thus, technology can reduce the amount of time spent
searching for potential sales prospects (Keillor et al. 1997).
SFA allows salespeople to manage higher quality information about a
greater number of customers (Ahearne et al. 2005). At later stages of the
customer relationship management process, SFA technology can inform
salespeople about the business potential of each prospect to decide which
prospects to target (Ahearne et al. 2007). The complete customer
information from purchase history to account preferences captured across
multiple service encounters is available and accessible for all future
transactions, helping salespeople customize their value proposition and
offerings to suit the individual needs of their clients (Mithas et al. 2005).
Chapter 2: Sales Force Automation and Sales Management
26
Salespeople who possess context relevant information have higher chances
of successfully closing a sales call (Weitz et al. 1986). SFA systems give
sales force quick access to timely information that can be beneficial in
closing a sale (Rivers and Dart 1999). For instance, a salesperson can
convincingly contrast product benefits with the weaknesses of competitive
offerings based on the market and technical knowledge provided by the
system (Ahearne et al. 2007). Salespeople also attribute a key role to
presentation technology in terms of the level and quality of information they
are able to provide during sales calls (Marshall et al. 1999).
Improved Operational Efficiency
SFA technology brings superior internal synergies in serving the customer
and offering better value-adding service through its ability to share
information between departments within a company (Pullig et al. 2002;
Swenson and Parrella 1992). At the organization level, better within
organization communication can facilitate seamless purchase transactions
with improved order accuracy and cost savings (Shoemaker 2001). Through
a well integrated SFA system, order status can be checked in real time for
shipment and delivery dates (Mithas et al. 2005). In the end, the sales force
benefits from an increased speed of response (e.g. shorter sales cycles) and
the management benefits from cost savings (e.g., reduced support costs,
reduced inventory requirements, reduced transactional errors) and faster
revenue generation (e.g., accelerated cash flow) (Erffmeyer and Johnson
2001).
At the individual level, SFA enhances salesperson's ability to communicate
to customers in a precise manner and makes him a reliable business partner
(Hunter and Perreault 2007). Enhanced accessibility of the salesperson
reduces the time it takes to deal with customer concerns even when the
salesperson is away from the customer's site. Strong within organization
Chapter 2: Sales Force Automation and Sales Management
27
communication can also aid the salesperson in timely identifying and
solving the problems that customers face. This gives the salesperson an
increased perception of dependability (Keillor et al. 1997). Last but not
least, technology can facilitate quick access to information about specific
customer needs, product knowledge, industry trends and competing
products and thus boost the perceived competency of the salesperson
(Hunter and Perreault 2007).
Better Within-Team Collaboration
SFA tools can mediate the information flow and consequently improve the
communication within sales teams (Brown and Jones 2005). Improved
within-team communication can in return help salespeople become more
efficient at synchronizing team activities and setting appointments. On the
other hand, technological tools such as collaboration software and
networking portals can link a salesperson to other professionals within and
across organizational boundaries and simplify the process for sharing tacit
information (Shoemaker 2001). The use of tools such as e-mail newsletters
and company intranets can keep salespeople informed about company
policies, procedures, products, and goals (Hanover 2000).
SFA deployments usually bring significant changes in the way salespeople
do their jobs (Speier and Venkatesh 2002). Without the perception of a real
advantage, a sales force is less likely to accept the SFA system and whole-
heartedly use the technology. Consequently, the benefits of SFA (e.g., the
capture and flow of strategic information) will be diminished. To address
this type of resistance, management needs to clearly demonstrate the
advantage(s) (e.g., more selling time, shorter sales cycle, less paperwork) of
using the SFA system over the current system (Morgan and Inks 2001).
Chapter 2: Sales Force Automation and Sales Management
28
2.3. Implications of SFA for Sales Management
SFA technology brings new informational and communicational capabilities
which were not available in the past. Such capabilities have a potential to
change the way salespeople and sales managers do their jobs. In this section
we discuss the implications of SFA implementations for sales organizations
by reflecting on the general framework proposed by Tanner and his
colleagues (2005).
2.3.1. Strategic Issues
Perhaps one of the greatest consequences of SFA deployments is seen in the
way companies make strategic decisions regarding their sales forces. Sales
force objectives, structure, and salesperson empowerment have to be
rethought in the SFA era. In this part we will be discussing the
consequences of SFA technology in strategic issues.
Strategic Account Management
Salespeople generally have the greatest influence in customer retention and
reducing customer defection (Johnson et al. 2001). As companies across
industries move from a transaction focus to a relationship focus, the sales
function is viewed as firms’ means of ‘partnering’ with customers (Ingram
1996; Weitz and Bradford 1999). In this context, SFA is positioned as an
enabler for the sales force’s role of developing market relating capability.
With the introduction of SFA technology, many simple after-sale service
tasks may take less time or no longer be performed at all, leaving the
salesperson only more complex tasks (Shoemaker 2001). The integration
and alignment of internal and external processes through SFA offer
Chapter 2: Sales Force Automation and Sales Management
29
salespeople further empowerment and control over company resources
when meeting customer needs. Furthermore, SFA technology makes a much
greater amount of information transparent to the sales force. This includes
aspects of the firm's strategy that in the past often have been withheld from
salespeople or only provided on a need-to-know basis. In the end, the role of
the salesperson is redefined upwards, where salespeople become more like
relationship managers or strategic account managers, with a partnering
perspective on the customer (Yim et al. 2004). Clearly, the role of the
selling function as informant and decision maker becomes essential (Leigh
and Marshall 2001).
Sales Force Structure
The introduction of relationship management philosophy initiated a growing
emphasis on selling the way customers want to buy. Consequently, today’s
sales organizations are using a variety of methods in their selling strategies.
These methods may include the traditional field sales force, team selling,
cross-selling by sales divisions and other evolving sales structures such as
contact centers, part-time salespeople, sales support personnel, supply-chain
personnel, and organizational partners (Tanner et al. 2005). While these
methods have different elements and organization structures, they all require
a right functioning knowledge management capability. With multiple
employees now responsible for customer relations within an account, an
information system harmonizing account information is crucial to give all
participants access to updated knowledge (Sharma 2002). The introduction
of SFA technology has certainly been a catalyst to this shift towards such
collaborative approaches, into which the salesperson has to adapt.
Another change stimulated by SFA technology is the multichannel selling.
Sales channel alternatives range from relatively inexpensive electronic
channels to extremely expensive multi-functional teams, such as a global
Chapter 2: Sales Force Automation and Sales Management
30
account management program. The specific sales channel mix for each
customer group shall be determined by defining the selling activities to be
performed for each customer group and choosing the optimal sales channel
alternative for each selling activity (Ingram et al. 2002). In such an overall
framework, the sales force may just be one of the channels which an
account interacts with.
Sales Force Objectives
On the one hand, the customer-centric model emphasizes customer and
market responsiveness, consultative selling, integrated customer solutions
and cross-functional linkages (Leigh and Marshall 2001). SFA systems can
provide salespeople with high amounts of customer, product and competitor
information; facilitate relationship selling processes and help salespeople be
more ‘customer oriented’ (Moncrief and Marshall 2005). On the other hand,
SFA technology, by automating repetitive processes and reducing costs, can
also support industries where transaction efficiency and price leadership are
crucial. Such firms may further prefer to implement multichannel strategies
as a method of reducing costs (Tanner et al. 2005).
Salespeople conventionally sell to customers within target segments. Trying
to sell to all of these customers in the same way will not be effective as
some customers are simply less profitable and should be dealt with
differently or dropped altogether (Dwyer et al. 1987). The type of
relationship and the selling model used for each customer segment must
balance customer value and cost (Rackham and De Vincentis 1999).
Therefore, a key goal must be to allocate available resources more
effectively so that customers receive the appropriate attention, at the right
cost (Zeithaml et al. 2001). SFA provides a much more complete view of
customer segments and supports the salesperson in better prioritizing them.
Among the new segmentation techniques made available to the individual
Chapter 2: Sales Force Automation and Sales Management
31
salesperson through SFA are customer portfolio analysis, sales forecasting,
activity-based costing, and customer lifetime value (Buttle 2004; Levin and
Zahavi 2001). Early sales process efforts can thus refocus from identifying
potential new customers to identifying customers with greatest profitability
(Shoemaker 2001). The emphasis is the appropriate relationship, meaning
that the objective is not always a deeper relationship and in some cases no
relationship at all (Landry et al. 2005). However, serving only a limited set
of customers and redesigning how less-profitable accounts will be served
may be distressing to some salespeople.
Cultural and Environmental Issues
SFA has implications for organizational culture through increased
transparency of salesperson activities. Most SFA systems provide sales
management with real-time access to salesperson activity and performance
information. The number of sales calls per day, the amount of attention
given to each customer, the position of customers in the sales cycle, and the
implementation of promotional programs are made instantly available to
management. This increased visibility of salesperson activities may lead to a
feeling of ‘big brother’-style management, eliminating any gain from the
new system (Widmier et al. 2002; Gohmann et al. 2005). To help reduce
concerns about management interference in selling activities, SFA should be
positioned and used as a tool to help improving the productivity of the sales
force, rather than as a monitoring tool for sales management.
2.3.2. Data Ownership and Management
Data Gathering
Businesses are increasingly realizing that a complete record of customer
interactions in a single cross-functional and integrated database (360-degree
Chapter 2: Sales Force Automation and Sales Management
32
view of customer) is a key for enterprise-wide relationship management
success. Such a holistic view of the customer will enable enterprise-level
marketing, sales, and channel decisions that drive customer satisfaction
through more timely, relevant, and personalized product and service offers,
messages, and interactions (Tanner et al. 2005). Yet, success of these efforts
depends on the quality and completeness of the underlying customer data. In
this context, salespeople will play an important role in data collection and
analysis because of their boundary-spanning role (Pass et al. 2004; Le Bon
and Merunka 2006). Salespeople will help initiate new data management
processes and technologies, because they are experts in the types of
information needed to enhance the performance of their sales role (Ingram
et al. 2002). As a result, “the sales role may become increasingly
intertwined with the information system manager and data analyst roles”
(Landry et al. 2005, p. 239). On the down side, capturing detailed customer
information can be a tedious task when badly managed. Salespeople
naturally expect to receive a real benefit out of their input. The history of
SFA implementation has shown that attention must be paid to organizational
issues and incentives if adequate data are to be collected.
Data Ownership
To many salespeople, customer information is a property of the salesperson
but not of the firm. Salespeople often tend to take their customer lists with
them when they change their firms. Compared with transactional marketing,
relationship marketing requires a much greater degree of customer
information sharing (Selnes and Sallis 2003). In an SFA setting, salespeople
could be charged with accumulating and relaying the customer data needed
for the firm to properly analyze and manage overall customer profitability
(Abbott et al. 2001; Anderson and Kerr 2002). This situation could be
perceived by salespeople as a potential loss of control over their own
customer accounts and could easily be viewed as aiding in the elimination
Chapter 2: Sales Force Automation and Sales Management
33
of their role in the organization (Speier and Venkatesh 2002). Salespeople
may hence be reluctant to transfer their customer knowledge base into an
SFA system which is accessible for management. Management must
therefore ensure that its salespeople perceive SFA as a productivity tool, not
as a tool for the management to gain control over sale force’s customers
(Morgan and Inks 2001).
Data Analysis
There are a wide variety of sales-analytics tools available for salespeople.
According to one sales analytics hierarchy, data analysis methods can be
classified in a pyramid. Basic descriptive reporting tools come at the base
level, followed by correlation analyses at the second level to understand the
reasons behind descriptive data, and sophisticated predictive models which
use data mining algorithms come at the third level (Desisto 2004). Such
analytics tools can make salespeople precious sources of market insight for
their clients. For example, a salesperson in retailing business can use data
analysis tools and scanner data to identify current retail market trends in his
territory. Such insight can later be used to optimize shop floor allocations.
Thus, the representative’s capacity to use the SFA system is tested over time
as customer accounts monitor the results attained from the salesperson’s
recommendations. Salespeople whose recommendations are beneficial to
the retailer add incremental value and differentiate the seller’s offering.
However, the availability of such sophisticated technologies demands new
skills to be an effective salesperson (Hunter and Perreault 2007). What's
more, these data analysis techniques often produce tacit knowledge which is
difficult to quantify. For these reasons, it is likely that salespeople will have
difficulty in justifying any effort to learn and apply analytics tools in their
daily jobs. In the end, additional management effort may be necessary to
convince salespeople for the usefulness of these tools.
Chapter 2: Sales Force Automation and Sales Management
34
2.3.3. Implementing SFA Technology
SFA Adoption
No matter how technically advanced a given SFA system is, it is the sales
force in the field who is ultimately responsible for accepting and making use
of that system. Therefore, a critical issue in realizing the intended gains is
the acceptance and use of the system by the sales force (Jones et al. 2002).
However, successful implementation of SFA is a serious challenge for
companies as it involves significant organizational change. Turbulence and
uncertainty are likely outcomes of an SFA implementation due to the
changes in business processes, salesperson tasks and sales priorities, all of
which any typical SFA system brings along with (Morgan and Inks 2001).
Salesperson buy-in to such organizational change initiated by the SFA
system is one of the major determinants of project success (Bush et al.
2005).
Some of the potential reasons to explain the underutilization of SFA are:
natural inertia, low perceived value (costs vs. benefits), lack of support from
the organization, personal and demographic factors, and lack of rewards to
change (Jones et al. 2002; Parthasarathy and Sohi 1997). The organization is
suggested as the major responsible for such negative feelings among
salespeople (Schillewaert et al. 2005; Bush et al. 2005). For instance,
companies which fail to get appropriate salesperson feedback right at the
planning stage will likely face missing salesperson ownership towards the
system (Morgan and Inks 2001). Honeycutt and others (2005) suggest that
lack of clearly defined goals, missing communication strategy and
inadequate compensation metrics are further reasons of SFA project failure.
Indeed, Wright and Donaldson (2002) suggest missing SFA strategy and
poor company-wide and executive-level backing as biggest barriers to SFA
success. Interestingly, organizations participated in their study reported
Chapter 2: Sales Force Automation and Sales Management
35
rather technical barriers such as high development costs and fragmented
data quality as more important.
Pullig and others (2002) suggest, the organization should be responsible for
creating the ‘facilitating conditions’ necessary for successful
implementation. Among the listed important enabling conditions are
training, encouragement, facilitative leadership and organizational support.
While facilitating conditions guarantee a right functioning SFA system, the
fit between organizational members’ shared values and the characteristics of
the SFA innovation is necessary for company-wide commitment to effective
SFA implementation. Five shared values emerged as important correlates of
SFA success: customer orientation, adaptive cultural norms, an information-
sharing culture, entrepreneurial values and high levels of interpersonal trust
(Pullig et al. 2002).
The salespeople who participated in an often cited longitudinal field study
reacted fairly positively to SFA tools immediately after training (Speier and
Venkatesh 2002). However, this initial response turned negative after they
had access to the SFA tool for six months. The result was not only the
rejection of the SFA tools but also increased absenteeism and voluntary
turnover among the salespeople. The primary driver of this reversal was
interpreted by the authors as the growing lack of professional fit between
the SFA tools and the sales force. Salespeople perceived that the SFA tools
had a negative impact on the sales process to the point that the system did
not play to their strengths as salespeople.
To sum up, missing SFA adoption among salespeople seems to be a serious
problem and represents a significant impediment to the SFA project success.
Companies implementing SFA technology have to pay sufficient attention
Chapter 2: Sales Force Automation and Sales Management
36
on the issues put forward in the literature in order to maximize the potential
promised by SFA.
SFA Outcomes
It is crucial for companies investing in SFA technologies to document the
Return on Investment (ROI) numbers to justify their investments. Due to the
nature of SFA investments, which are often made to facilitate customer
relationship management strategy, a new class of ‘soft’ metrics is needed in
addition to the often used quantitative, traditional sales metrics (e.g., sales,
profitability, call-to-sales). In fact, one significant implication of sales
related technologies to the sales force is the introduction of customer-centric
metrics to monitor the sales force. Such metrics may include, among others,
customer satisfaction, customer profitability and lifetime value, share of
customer or wallet, retention or attrition rates, customer satisfaction, loyalty,
up-sell and cross-sell rates, and cost to serve.
However, it has not been easy to quantify the outcomes of SFA adoption
and use so far. Erffmeyer and Johnson (2001) inform in their paper that only
a limited number of their respondents could present formalized goals and
objectives for their SFA projects. Similarly, Wright and Donaldson (2002)
report that their sample failed to measure achievement of their strategic SFA
objectives, opting instead for operational measures such as number of sales
generated, contribution to profits, opportunities identified and revenue per
customer.
Chapter 3: Theoretical Foundation
37
3. THEORETICAL FOUNDATION
3.1. Introduction to the Chapter
To justify the large amounts of financial and human capital invested for
information technology (IT) projects, it is critical for companies to
demonstrate the return of their IT systems. Therefore, there is a strong
research tradition investigating the impact of IT investments on specific
operations and overall firm performance.
13
Early efforts in this stream have
been rather inconclusive, leading to the coining of the ‘IT Productivity
Paradox’— the case in which businesses demonstrated higher levels of
investments in IT even in the absence of measured productivity gains.
14
The
paradox was then followed by an extensive stream of investigation by
various IT researchers and economists.
15
Finally, Brynjolfsson (1993)
concluded the debate on the productivity paradox:
After reviewing and assessing the research to date, it
appears that the shortfall of IT productivity is as
much due to deficiencies in our measurement and
methodological toolkit as to mismanagement by
developers and users of IT. (p. 67)
13
Refer to Brynjolfsson (1993), Brynjolfsson and Yang (1996), Brynjolfsson and Hitt
(2000) and Melville et al. (2004) for literature reviews of return on IT investments.
14
Ahituv and Giladi 1993; Baily and Chakrabarti 1988; Berndt and Morrison 1992;
Loveman 1994; Roach 1987; Strassmann 1985; Weill 1992
15
Brynjolfsson and Hitt (1993; 1996), Lichtenberg (1993; 1995), Lee and Barua (1999)
showed using different secondary data sets that IT contributes to firm productivity,
while acknowledging output and input measurement challenges. For instance, Hitt and
Brynjolfsson (1996) assessed the value of IT in terms of productivity, profitability, and
consumer welfare and found a positive relationship.
Chapter 3: Theoretical Foundation
38
Indeed, “the ‘whether’ of IT value research now lies in the past” (Kohli and
Grover 2008, p. 26). A significant number of recent studies demonstrate a
positive relationship between IT and business value (Brynjolfsson and Hitt
1996, 2000; Devaraj and Kohli 2003). Founding a theory to explain how IT
can affect performance is the significant challenge now (Ray et al. 2005).
In response, IT business value research represents an important stream of
work that examines the organizational performance impacts of information
technology. It deals with economic impacts of IT and its manifestations, at
economy, industry and firm levels (Melville et al. 2004). The main goal is to
understand how and to what extent the application of IT leads to improved
organizational performance.
The conceptual question addressed in this thesis is when, how, and why a
firm’s investments in information technology result in improved
organizational performance. In the end, our argument is that, how IT is used
is different than whether IT is used or not. We believe that the insights of IT
business value research have strong applicability in explaining the SFA
phenomenon and SFA’s impact on salesperson performance. For this
reason, we devote this chapter to a discussion of the conceptual foundations
of IT business value research and its implications for our research model.
We structured this chapter as following: first, we give an overview of the IT
business value research. Then, we present three theoretical lenses applied to
understand how IT increases business value. We place particular emphasis
on the resource based view and process-oriented models of IT business
value. We conclude the chapter by summarizing the implications of these
views for our conceptual model.
Chapter 3: Theoretical Foundation
39
3.2. Theorizing Information Technology Business Value
The scope of IT business value research includes conceptual, analytic, and
empirical studies.
16
Conceptual studies apply theory and grounded
observation to explain IT business value.
17
Analytic studies utilize game
theory and other modeling techniques to develop models of IT business
value whose solutions inform our understanding of the organizational
performance implications of alternative IT investment and ownership
regimes as well as the role of the competitive environment.
18
Finally,
empirical studies include qualitative research—case studies and field
studies
19
—and quantitative studies estimating IT business value at the
process, business unit, firm, industry, and country levels of analysis.
20
IT can create value in the form of productivity similar to other forms of
capital. Mukhopadhyay et al. (1995) refer to the business value of IT as the
“impact of IT on firm performance.” Indeed, the term IT business value is
commonly used to refer to the organizational performance impacts of IT,
including productivity enhancement, profitability improvement (return on
assets), cost reduction, competitive advantage, process improvements (e.g.,
inventory turnover, cycle time), and consumer surplus (Barua and
Mukhopadhyay 2000; Devaraj and Kohli 2003; Hitt and Brynjolfsson 1996;
Kriebel and Kauffman 1988). Value can also be created through
improvements in supply chains or innovation at inter-organizational levels
(Rai et al. 2006).
16
Brynjolfsson 1993; Brynjolfsson and Hitt 1996, 2000; Brynjolfsson and Yang 1996;
Dedrick et al. 2003; Devaraj and Kohli 2003; Jorgenson 2001; Jorgenson and Stiroh
2000; Kohli and Devaraj 2003; Kraemer and Dedrick 2001; Mukhopadhyay et al. 1995;
Oliner and Sichel 2000; Santhanam and Hartono 2003; Wilson 1995
17
Lee 2001; Mata et al. 1995; Porter 2001; Soh and Markus 1995
18
Bakos and Nault 1997; Belleflamme 2001; Clemons and Kleindorfer 1992
19
Clemons and Row 1988; Cooper et al. 2000
20
Alpar and Kim 1990; Dewan and Kraemer 2000; Siegel 1997
Chapter 3: Theoretical Foundation
40
IT business value appears at many levels (e.g., individual, group, process,
firm, or industry) (Kohli and Grover 2008). IT business value at individual
and group levels may comprise personal productivity or group effectiveness
(DeLone and McLean 1992). IT value at process level denotes a range of
measures associated with operational efficiency enhancement within
specific business processes, such as on-time shipping (McAfee 2002),
customer satisfaction (Devaraj and Kohli 2000), and inventory turnover
(Barua et al. 1995). Firm level IT value denotes aggregate performance
impacts across all firm activities, with metrics capturing bottom-line firm
impacts through operations measures (cost reduction, productivity
enhancement, etc.) and market-based measures (e.g., stock market
valuation, Tobin's q) (Brynjolfsson and Hitt 2003; Dehning and Richardson
2002). However, the range of potential measures is not limited to financial
metrics, and may include perceptual measures, usage metrics, and others
(Tallon et al. 2000).
There are a variety of theoretical lenses applied to study IT business value,
among which microeconomic theories, resource-based view and process-
oriented models are discussed more in detail in following sections.
3.2.1. Microeconomic Theory
Microeconomic perspectives provide useful insights into the contribution of
computerization on economic growth. Papers following this stream often
use econometric techniques to estimate the contribution of IT to several
measures of multifactor productivity growth. They provide a rich set of
well-defined constructs such as product/service demand, capital costs, labor
costs, and the total cost of doing business, being interrelated via theoretical
models and mathematical specifications.
Chapter 3: Theoretical Foundation
41
Microeconomic theories such as transaction cost economics and production
theory offer guidance on how information technology can interact with
organizational processes to add value. The production theory has been
particularly useful in conceptualizing the process of production and the
contribution of various inputs to output.
21
This theory posits that each firm
employs a method for transforming various inputs into outputs, which is
generally represented by a production function. For any given set of inputs,
the maximum amount of output that can be produced, according to the
known laws of nature and existing technology, is determined by this
production function. Depending on prices and desired levels of output,
different firms may choose different combinations of inputs and outputs, but
they will all adhere to the set defined by their production function (Berndt
1991). No inputs will be ‘wasted,’ so the only way to increase output for a
given production function is to increase at least one input. In ideal case, the
marginal cost of each input should just equal the marginal benefit produced
by that input. Organizational inputs may include capital and labor.
Organizational outputs include the products and services delivered by the
organization and other monetary returns, such as units produced, revenue,
and market share.
Taken as an investment good and an organizational input, the effect of IT on
economic welfare depends on how successfully it supports the production of
other goods and services. Computer hardware and software can typically be
substituted for labor or other types of capital along a given production
function. With a straightforward thinking, users of ever-cheaper computer
equipment can achieve greater output for a given cost of inputs. However,
IT possesses certain characteristics elevating it to a unique kind of
21
Brynjolfsson and Hitt 1995, 1996; Brynjolfsson and Yang 1996; Dewan and Min 1997;
Hitt and Brynjolfsson 1996; Lehr and Lichtenberg 1999; Lichtenberg 1995; Morrison
and Berndt 1991; Siegel 1997
Chapter 3: Theoretical Foundation
42
organizational input. By capturing, manipulating, storing and disseminating
information, IT can support work systems and influence the combination of
inputs that can be used to generate a certain level of output (Alter 1999; Hitt
and Brynjolfsson 1995). Computers can change the production process itself
and provoke complementary innovations within and among firms in an act
of computerizing a business process or collection of processes. Rather than
merely substituting a cheaper input (e.g., computers) for another input (e.g.,
labor) in the context of a fixed production process, companies can thus
combine computers with other innovations to fundamentally change their
production function. Viewed another way, the complementary innovations
can themselves be thought of as a kind of input, or organizational capital
(Brynjolfsson et al. 2002). This could lead to an output elasticity that is
greater than computers’ input share and the appearance of excess returns on
computer capital stock.
Microeconomic view on IT business value provides empirical specifications
enabling estimation of the relationship between growth in computer
spending and growth in output productivity.
22
The widely used production
function approach relates production inputs such as labor, IT, and other
capital to firm performance (Melville et al. 2004). For the single output
case, one can use a parametric production function which simply returns the
maximum output per unit time, given the amount of inputs used during the
same time period. The strength of these approaches derives from their
reliance on commonly accepted economic theories and the use of existing
accounting data that makes them transparent for review and comparison.
22
Brynjolfsson and Hitt 1995, 1996; Devaraj and Kohli 2000; Dewan and Min 1997; Hitt
and Brynjolfsson 1996; Hitt et al. 2002; Lee and Barua 1999; Lehr and Lichtenberg
1999; Lichtenberg 1995; Siegel 1997; Tam 1998
Chapter 3: Theoretical Foundation
43
Although these economic perspectives offer a high degree of objectivity,
however, they treat the firm as a ‘black box’ and do not “adequately control
for other factors (other than IT) that drive firm profits” (Bharadwaj 2000, p.
170). Many studies measure IT capital spending, but do not study whether
such spending is transformed into actual hardware and software functions or
whether such functions are actually used (Lee 2001). Mooney (1994)
similarly criticizes such analyses that they do not stand up to more detailed
scrutiny, and that the datasets which they are based on are problematic.
They provide limited insight as to how productivity gains can be realized by
individual firms and are limited in capturing intangible impacts such as
improved product and service quality, increased managerial effectiveness,
or enhanced customer relations. IT impacts business performance probably
through a much complex process of transformation, which is difficult to
capture by production function models (Tallon et al. 1999). The lack of
intermediate mapping of IT impacts on processes provides limited insights
into the dynamic process by which business value is created and measured
and makes firm-level approaches problematic for determining whether IT
investments do pay off (Pavlou et al. 2005).
In the case of SFA deployments, such production functions could be applied
by setting SFA investment at firm level as the direct determinant of sales
performance. While this would be an extreme case, a number of studies
apply a similar logic when the degree of SFA-adoption or use is modeled as
the direct determinant of salesperson performance. Higher level of SFA-
adoption and use is expected to increase performance. This way of modeling
represents a ‘black-box’ approach without telling much about the
mechanisms inside the ‘box’ through which business value is created. There
are yet mediating and complementary factors playing a role in determining
the organizational outcomes of SFA investment.
Chapter 3: Theoretical Foundation
44
3.2.2. Resource Based View
While economic theories offer guidance that IT can add value by processing
standard inputs and reducing transaction costs, organizational theories such
as resource based view (RBV) help understand how IT can bring differential
value to firms when compared to their counterparts in the industry.
23
RBV is
taken as a robust theoretical perspective in IT research for anticipating the
conditions under which aspects of a firm’s IT deployments will be sources
of competitive disadvantage, when they will be sources of competitive
parity, and when they will be sources of either temporary or sustained
competitive advantage (Clemons and Kimbrough 1986). Strategy
researchers have applied RBV to analyze the competitive advantage
implications of information technology (Mata et al. 1995) and to empirically
assess the complementarities between IT and other firm resources (Powell
and Dent-Micallef 1997).
RBV focuses on firm resources as sources of economic rents and, therefore,
as fundamental drivers of performance and competitive advantage (Conner
1991). Among those resources, some enable firms to achieve competitive
advantage, and a further subset leads to superior long-term performance
(Barney 1991; Grant 1991; Penrose 1959; Wernerfelt 1984). The resources
possessed, developed, and deployed by an organization and the relationships
of those internal resources with competitiveness characterize the subjects of
RBV (Jarvenpaa and Leidner 1998).
As in industrial organization-related theories, a firm's ultimate objective in a
resource-based approach is generally assumed to be above-normal returns
(Barney 1986, Wernerfelt 1984). However, in contrast to the production-
23
Amit and Shoemaker 1993; Barney 1986, 1991; Bharadwaj 2000; Caldeira and Ward
2003; Powell and Dent-Micallef 1997; Rumelt 1984; Wernerfelt 1984
Chapter 3: Theoretical Foundation
45
function view, the RBV places relatively less emphasis on the size of capital
and focuses instead on the importance of the scope of resources (e.g.,
properties of resources) (Radhakrishnan et al. 2008). Several information
systems researchers have argued in this line that establishing a direct link
between the size of IT investment and firm performance can be problematic
and even misleading (Soh and Markus 1995). Bharadwaj et al. (1999) assert
that IT investment is a necessary but not a sufficient factor that affects
organizational performance.
Firm resources include all financial assets, capabilities, organizational
processes, firm attributes, information, knowledge, etc. controlled by a firm
that enable the firm to conceive of and implement strategies that improve its
efficiency and effectiveness (Barney 1991). Wade and Hulland (2004)
describe resources as a set of assets and capabilities available for a firm to
detect and respond to market opportunities or threats. Assets are defined as
anything tangible (e.g., hardware, network infrastructure) or intangible (e.g.,
software patents, strong vendor relationships) the firm can use in its
processes for creating, producing, and/or offering its products (goods or
services) to a market (Hall 1997; Itami and Roehl 1987). Assets can serve as
inputs to a process, or as the outputs of a process (Srivastava et al. 1998).
Capabilities, in contrast with assets, are firm specific repeatable patterns of
actions in leveraging assets to produce value for the market (Sanchez et al.
1996). They transform inputs into outputs of greater worth. Capabilities can
include skills, such as technical or managerial ability, or processes, such as
systems development or integration. Typically, firms create organizational
capabilities by using standard resources.
24
Capabilities, thus, refer to an
organization’s ability to effectively deploy valued resources, usually in
24
Amit and Schoemaker 1993; Capron and Hulland 1999; Christensen and Overdorf
2000; Sanchez et al. 1996; Schoemaker and Amit 1994, Mata et al. 1995
Chapter 3: Theoretical Foundation
46
combination or co-presence (Amit and Schoemaker 1993). Capabilities in
RBV embrace the notion of organizational competencies and are rooted in
organizational processes (Prahalad and Hamel 1990).
In the same line of reasoning, many studies divide IT resources into two
categories that can be broadly defined as IT assets (technology-based) and
IT capabilities (systems-based) (Kohli and Jaworski 1990, Marchand et al.
2000, Mata et al. 1995, Powell and Dent-Micallef 1997). Research has
suggested that IT assets (e.g., infrastructure) are the easiest resources for
competitors to copy and, therefore, represent the most fragile source of
sustainable competitive advantage for a firm (Leonard-Barton 1992; Teece
et al. 1997). In contrast, there is growing evidence that competitive
advantage often depends on the firm’s superior deployment of capabilities
(Christensen and Overdorf 2000; Day 1994) as well as intangible assets
(Hall 1997; Itami and Roehl 1987; Srivastava et al. 1998).
The RBV of the firm is based on two underlying assertions, as developed in
strategic management theory.
25
The first is that, the resources and
capabilities possessed by competing firms differ (resource heterogeneity).
RBV assumes that the resources needed to conceive, choose, and implement
strategies are heterogeneously distributed across firms (Barney 1991).
Second, the resource heterogeneity across firms remains stable at least in the
short and middle term (resource immobility). Barney (1991) justifies this
assumption by stressing that resource heterogeneity cannot be feasible if
firm resources are perfectly mobile. In such a case, any resource that allows
some firms to implement a strategy can easily be acquired by other firms to
implement the same strategy in question.
25
Barney 1986, 1991; Rumelt 1984; Wernerfelt 1984
Chapter 3: Theoretical Foundation
47
Resource Attributes
Although firms possess many resources, only a few of these have the
potential to lead the firm to a position of sustained competitive advantage.
RBV prescribes specific sets of resource attributes to separate regular
resources from those that confer a sustainable competitive advantage. The
objective is to connect the conditions of resource heterogeneity and resource
immobility to sustained competitive advantage. Only resources exhibiting
all of these attributes should be able to lead to a sustained competitive
advantage for the firm (Jarvenpaa and Leidner 1998).
Barney (1991) argues that advantage-creating resources must possess four
key attributes: value, rareness, inimitability, and non-substitutability.
26
Resources that are valuable and rare and whose benefits can be appropriated
by the owning (or controlling) firm will provide a temporary competitive
advantage. When the firm is able to protect those resources against
imitation, transfer, or substitution; that advantage can be sustained over
longer time periods (Wade and Hulland 2004). In contrast, if a firm
possesses a resource or capability that is possessed by numerous other
competing firms, that resource or capability cannot be a source of
competitive advantage. Such common sources do not meet the resource
heterogeneity requirement and are, at best, sources of competitive parity
(Mata et al. 1995).
Firm resources can only be a source of sustained competitive advantage
when they are valuable. RBV describes a resource as valuable when it
enables a firm to implement strategies that improve efficiency and/or
effectiveness (Barney 1991).
27
In the selling context, an SFA system is a
26
Refer to Amit and Schoemaker (1993), Black and Boal (1994), Collis and Montgomery
(1995), and Grant (1991) for other resource attribute typologies.
27
The studies of Bharadwaj (2000), Feeny and Willcocks (1998), Lopes and Galletta
Chapter 3: Theoretical Foundation
48
valuable resource for a sales force as long as it helps salespeople increase
their efficiency and effectiveness. In addition to IT hardware and software,
‘soft’ aspects of a sales organization such as knowledgeable and
experienced salespeople, sales processes and sales culture represent valuable
resources for the firm.
Resources that are valuable cannot become sources of competitive
advantage if they are in plentiful supply. Rarity refers to the condition where
the resource is not simultaneously available to a large number of firms
(Amit and Schoemaker 1993). IT infrastructure can be acquired or copied
relatively easily once it has been in existence even for a comparatively short
period of time, although it may be very rare initially. This is the case for
SFA and CRM applications. First generation of customer relating IT
systems were welcomed as sources of competitive advantage when first
introduced to the market in early 1990’s. However, the market has been
highly saturated since then, and SFA technology, by itself, cannot be taken
as a source of competitive advantage anymore. In contrast, soft metrics tend
to be socially complex and cannot be easily acquired in factor markets, and
must instead be developed through on-going, firm-specific investments or
through mergers with and/or acquisitions of other companies. Therefore,
such intangible resources are likely to be associated with a higher degree of
rarity than are tangible IT resources.
The appropriability of a resource relates to its rent earning potential (Amit
and Schoemaker 1993; Collis and Montgomery 1995; Grant 1991). The
advantage created by a rare and valuable resource or by a combination of
resources may not be of major benefit if the firm is unable to appropriate the
returns accruing from the advantage. While SFA infrastructure is a valuable
(1997), Marchand et al. (2000), Mata et al. (1995), and Ross et al. (1996) show that IT
resources have value to their firms.
Chapter 3: Theoretical Foundation
49
resource, it is not appropriable unless salespeople use it in the right way. On
the other hand, salespeople capable of using SFA to bring additional profits
are a valuable, rare and appropriable resource for the firm. In general, the
appropriability of the soft resources—salespeople, processes, an innovative
culture, etc.—tends to be lower than that of the hard resources. This stems
from the fact that they tend to be organizationally complex, and thereby
more difficult to deploy successfully.
In order to sustain a competitive advantage, firms must be able to defend
that advantage against imitation. The advantage accruing from newly
developed features of computer hardware, for instance, is typically short-
lived since competitors are able to quickly duplicate the technology (Mata et
al. 1995). According to Barney (1991), there are three factors that can
contribute to low imitability: unique firm history, causal ambiguity, and
social complexity. The role of history recognizes the importance of a firm’s
unique past that other firms are no longer able to duplicate. Causal
ambiguity exists when the link between a resource and the competitive
advantage it confers is poorly understood (Dierickx and Cool 1989; Reed
and DeFillipe 1990). Finally, social complexity refers to the diverse
relationships within the firm and between the firm and key stakeholders
such as shareholders, suppliers, and customers (Hambrick 1987; Klein and
Lefler 1981). Over time, pure IT resources such as SFA technology become
easier to imitate. In fact, existing empirical evidence suggests that IS
infrastructure is particularly easy to imitate over moderate to longer time
periods (Wade and Hulland 2004). ‘Soft’ resources such as the
innovativeness of employees and right modeled business processes are
likely to be more difficult to imitate because these resources will develop
and evolve uniquely in time for each firm. Moreover, these resources are
likely to be socially complex.
Chapter 3: Theoretical Foundation
50
A resource has low substitutability if there are few, if any, strategically
equivalent resources that are rare and inimitable (Amit and Schoemaker
1993; Black and Boal 1994; Collis and Montgomery 1995). Resource
substitutability may involve the use of very different resource sets, but could
also reflect a decision to acquire and deploy resources in-house versus
obtaining them from third parties. In the case of SFA technology, it seems
unlikely that strategic alternatives exist that lead to the same ultimate
competitive position. Paper-based systems of the past to manage customer
information have no place in today’s competitive markets. Thus, the
substitutability of this resource at first glance will be low. However, firms
may still be able to outsource their IT development and other operations to
third parties and thereby compete effectively. For instance, ‘on-demand’
solutions allow companies to hire full SFA functionality installed on
external servers owned and operated by a vendor (Buttle 2006). In contrast,
strategic substitutes for ‘soft’ resources are likely to be rare, although it may
be possible for firms with a subset of these capabilities (e.g., market
responsiveness) to compete on an equal basis with firms possessing a
different subset (e.g., IT-business partnerships).
The second resource-based condition, that the differences in resources and
capabilities may be long lasting (resource immobility), depends on the
transferability of a resource. A resource is mobile if firms without a resource
(or capability) face no cost disadvantage in developing, acquiring, and using
that resource compared to firms that already possess and use it. A primary
source of resources is factor (i.e., open) markets (Grant 1991). If firms are
able to ‘purchase’ a resource necessary to imitate a rival’s competitive
advantage, the resource can only be a source of temporary competitive
advantage. Thus, a requirement for sustained competitive advantage is that
resources be imperfectly mobile or non-tradable (Barney 1991). Some
resources are more easily bought and sold than others. Technological assets,
Chapter 3: Theoretical Foundation
51
for example, such as computer hardware and software, are relatively easy to
acquire. Technical knowledge, managerial experience, and many skills and
abilities are less easy to obtain. External relationship management, market
responsiveness, and IT-business partnership capabilities are generally not
readily available in factor markets. Other resources, such as company
culture, brand assets, and so on, may only be available if the firm itself is
sold (Grant 1991).
Based on our discussion of resource attributes above, we posit that
organizations can create differential value over their competitors by
effectively deploying IT to create unique, hard to copy, non-substitutable
and immobile organizational capabilities. In particular, the key driver of a
longer-term competitive position is more likely to be the result of superior
‘soft’ resources. Firms possessing superior supplier relations, lean business
processes and motivated human resources are likely to initially outperform
competitors that rely more on ‘hard’ resources that are rather internally
focused (e.g., IT infrastructure). Furthermore, because it is harder to imitate,
acquire, or find strategic substitutes for the former set of resources than for
the latter, outside-in and spanning resources are more likely to maintain
their rarity, and thus support a sustainable competitive position for a longer
period of time.
Contingency View
The RBV is criticized for not adequately considering the fact that resources
rarely act alone in creating or sustaining competitive advantage (Wade and
Hulland 2004).
28
In fact, IT resources often act in combination with other
firm resources to provide strategic benefits (Keen 1993; Walton 1989).
These resources together “form part of a complex chain of assets and
28
Refer also to Amit and Schoemaker 1993; Dierickx and Cool 1989; Teece 1986
Chapter 3: Theoretical Foundation
52
capabilities that may lead to sustained performance.” (Wade and Hulland
2004, p. 109) In response, a number of researchers suggest that the strategic
value of IT resources must be understood in conjunction with a firm’s
strategy and stresses the importance of a ‘good fit’ between business
strategy and IT strategy (Chan 2000; Chan et al. 1997; Kohli and Devaraj
2004; Sabherwal and Chan 2001).
Other variables (e.g., IT characteristics, management practices,
organizational structure and culture, competitive and macro environment,
complementary investments) may mediate or moderate the payoff from IT
investments.
29
For example, IT used in an efficient process will be expected
to bring more value to performance than the same IT used in an inefficient
process (Kohli and Grover 2008). The issue of complementarity is important
since it implies a more complex role for IT resources within the firm (Alavi
and Leidner 2001; Henderson and Venkatraman 1993).
A number of studies reveal that IT investments bring indeed greater returns
when IT resources are aligned with complementary resources. Milgrom and
Roberts (1990) show that due to the complementary nature of new
technological advancements such as shorter cycle time, smaller batch size,
and more product improvements, it is optimal for manufacturing firms to
adopt an entire series of new changes instead of isolated one. In another
study, the efficiency of process, the extent of IT used, and users' incentive
systems are identified as major complementary factors in a reengineering
project (Barua et al. 1996). According to Kettinger and others’ (1994) study,
IT-based success rests on the ability to “fit the pieces together”. Powell and
Dent-Micallef (1997) conclude that the complementary use of IT and human
resources lead to superior firm performance. Benjamin and Levinson (1993)
29
Refer to Barua et al. 1996; Brynjolfsson et al. 1998, 2002; Cooper et al. 2000; Dewan
and Kraemer 2000; Doty et al. 1993; Markus and Soh 1993; Weill 1992
Chapter 3: Theoretical Foundation
53
conclude that performance depends on how IT is integrated with
organizational, technical, and business resources. Jarvenpaa and Leidner
(1998) note that IT can generate competitive value only if deployed so that
it leverages preexisting business and human resources in the firm via co-
presence or complementarity.
In CRM literature too, the contingency view has a firm place. Zablah and
others (2004) argue in their conceptual paper that CRM success requires a
fit between employee skills, process definitions and IT capabilities. Mithas
and others (2005) emphasize the risk of relying on CRM technology alone:
CRM applications merely enable firms to collect
customer knowledge. Only when firms act on this
knowledge by modifying service delivery or by
introducing new services will they truly benefit from
their CRM applications. Furthermore, firms may
need to make changes in their incentive systems and
institute complementary business processes to
leverage CRM investments. (p. 207)
Similarly, Campbell (2003) comments on the significance of further
complementary factors for CRM success:
Integrating customer information into an
organization’s marketing and selling efforts requires
more than just the more efficient use of technology.
(...) To reap the rewards of CRM, managers need to
complement new CRM technologies with
organizational processes that integrate customer
information throughout the firm; improve the
Chapter 3: Theoretical Foundation
54
strength of ties between marketing and IT
departments; signal senior management
involvement; and encourage employees to adopt
new customer-focused behaviors both within the
firm and with external customers. (pp. 378-382)
From the preceding discussion, it seems clear that there will be conditions
under which specific IT resources (SFA technology in the sales context)
must interact with non-IT resources if they are to confer competitive
advantage on the firm, both in the immediate and longer terms. It is not the
SFA itself, but how it is used, that should define the end results. The real
difference must be resting on the success of the salespeople in realizing the
strategic objectives of a company by applying the SFA system in question.
3.2.3. Process-Oriented Models
Aforementioned studies on the aggregate impact of IT at the economy,
industry and firm levels measure the relationship between IT spending and
firm performance often directly without examining the possible underlying
mechanisms. Chan (2000) note that such models contribute to the IT
literature by addressing the question “what value do IT investments
provide?” It is yet equally important to address questions such as “why,
where, when, how, and to whom do [IT] investments provide value?” (Chan
2000, p. 245) Furthermore, classical approaches of resource-based view
(RBV) have certain limitations, again inherent in taking aggregate firm
performance as the focal dependent variable. In fact, RBV “assumes that
resources are always applied in their best uses, saying little about how this is
done.” (Melville et al. 2004, p. 291) Perhaps the use of aggregate measures
of firm performance alone has actually supported the productivity paradox,
Chapter 3: Theoretical Foundation
55
suggesting the need for a combination of both aggregate and intermediate
measures of IT impact.
Ray and others (2004) give three reasons to adopt business process
effectiveness as the dependent variable in strategic management research.
First, capabilities typically consist of a combination of firm assets and
business processes. A firm may excel in some of those business processes,
be only average in others, and be below average in still others. A firm’s
overall performance depends on, among other things, the net effect of these
business processes. Second, it is possible for a firm’s stakeholders to
appropriate the economic profits that can be generated by a firm’s business
processes before those profits are reflected in a firm’s overall profitability.
Profits generated by a firm may not always appear as higher levels of
performance for that firm (Coff 1999). Third, the potential of resources and
capabilities for generating competitive advantage can be realized only if
they are used in business processes, as it is through business processes that a
firm’s resources and capabilities get exposed to the market, where their
value can be recognized. Porter (1991, p. 108) argue that “resources are not
valuable in and of themselves, but they are valuable because they allow
firms to perform activities (...) business processes are the source of
competitive advantage.” In all these cases, aggregating the outcomes of
numerous business processes can make it very difficult to examine whether
a particular set of firm resources and capabilities actually creates
competitive advantage for a firm. A more appropriate way is to adopt the
performance of a business process as the dependent variable, and to
examine the kinds of resources and capabilities that can generate
competitive advantages at this level of analysis (Ray et al. 2004).
Ray and others’ (2004) remarks for strategic management research are
applicable for studies of IT business value as well. In fact, there should be a
Chapter 3: Theoretical Foundation
56
significant causal distance between IT systems and firm performance (Lee
2001). IT interacts with many other variables, going through layers of
interactions to finally make an impact on profit (Mooney et al. 1995). By
attempting to relate IT spending directly to output variables at the firm level,
the intermediate processes through which IT impacts are felt are mostly
ignored (Barua, et al. 1995, Radhakrishnan et al. 2008). Moreover, given the
complexity of the technology and the difficulty of implementing it in
organizations, some systems may be effective, while others may bring
negative returns. By aggregating over all systems, the favorable impact of
effective systems may be nullified by poorly designed systems
(Mukhopadhyay et al. 1995).
In response, process-oriented IT success models attempt to answer the
‘how’ question by linking IT success variables to intermediate success
measures and then to higher-level firm performance measures (Byrd et al.
2006).
30
Process-based models posit that IT investments essentially
influence intermediate level activities and processes which are critical to a
firm’s success, such as supply chain management and marketing (Tallon et
al. 1999). The resultant ‘primary’ effects may include improvements in
capacity utilization, inventory turnover, relative quality, relative price, and
new products. These primary effects consecutively relate to higher levels of
performance measures such as revenues, return on assets, and market share.
Process-based models of IT business value have been applied in a number
of studies).
31
Figure 3.1 depicts such a process oriented model of IT value.
30
Refer also to Barua et al. 1995; Byrd et al. 2006; Hitt and Snir 1999; Hu and Quan
2005; Mukhopadhyay et al. 1995, 1997; Ray, Muhanna, and Barney 2005; Tallon et al.
2000
31
Using data from the manufacturing sector for a period of five years, Barua and others
(1995) report that IT has a mostly favorable impact on intermediate variables.
Intermediate variables are found to be significant determinants of high-level economic
variables such as ROA and market share. Francalanci and Gallal (1998) propose that
managerial choices regarding the mix of clerical, managerial, and professional
employees mediate the relationship between IT and firm performance. For other studies
Chapter 3: Theoretical Foundation
57
Figure 3.1: A process oriented model of IT business value
(source: Mooney et al. 1995)
Process-oriented models have certain advantages against other research
models applying aggregate data to measure the outcomes of IT use. As the
distance between a first-order effect and higher levels increases, the ability
to detect and measure an impact decreases. Furthermore, any organization is
expected to have multiple IT applications in each primary or supporting
activity and the effectiveness of these applications is not uniform across all
activities. “To capture these impacts, measurements should be taken in the
organization where the potential for first-order effects exists.” (Barua et al.
1995, p. 6) The principal aim of process-oriented models of IT business
value is therefore to identify and isolate the economic impacts of IT at lower
responsibility units in an organization. By isolating economically and
technologically distinct activities within a business, one may better identify
the value added of IT to individual outputs. In the end, studying business
processes is a way of illuminating the black box of microeconomic
production theory.
refer to Mukhopadhyay, Lerch and Mangal 1997; Davamanirajan, Mukhopadhyay and
Kriebel 1999; Rogawski and Adams 1998 and Weill 1992.
Chapter 3: Theoretical Foundation
58
Another advantage of process-oriented models is the generalizability of
empirical findings. A process focus should enhance the validity of the
business value assessment, since the analysis is conducted at the same level
that the technology is deployed. This is usually the lowest possible level of
analysis where situation specific external effects are kept at minimum.
Therefore, the impact of IT at intermediate business process level is
generalizable to other situations where comparable processes and IT
systems are in question. In contrast, the impact on the ‘bottom line’ depends
on many contingent factors and harms the generalizability of results
(Mooney et al. 1995).
Process-oriented models can be applied in sales and marketing research to
capture SFA’s contribution to individual sales tasks. As we argue in Chapter
4 in greater detail, an isolated measurement of SFA use to facilitate separate
sales tasks should provide with a clearer view of the value creation
mechanism of SFA. Furthermore, such a process-oriented (task-based)
approach should make generalizations to other sales contexts possible.
Business Processes and Business Value of IT
An important concept which highlights the role of IT in a company's
business processes is the “value-chain” framework suggested by Porter
(1985) (figure 3.2). Porter’s (1985) framework divides a corporation's
activities into distinct processes necessary for engaging in business
activities. Products pass through all activities of the chain in order and at
each process the product gains some value. These distinct processes are
classified as primary activities (e.g., inbound logistics, operations, outbound
logistics, marketing and sales, and service) and support activities (e.g., firm
infrastructure, human resource management, technology development, and
procurement).
Chapter 3: Theoretical Foundation
59
Figure 3.2: The Value Chain (source: Porter 1985)
A similar approach to value creation mechanism of organizations is
suggested by Day (1994). Day (1994) argues that the capabilities (as a
subset of the firm’s resources in resource based view) held by a firm can be
sorted into three types of processes: inside-out, outside-in, and spanning.
Inside-out capabilities are deployed from inside the firm in response to
market requirements and opportunities, and tend to be internally focused
(e.g., manufacturing, logistics, human resource management, technology
development, cost controls). In contrast, outside-in capabilities are
externally oriented, placing an emphasis on anticipating market
requirements, creating durable customer relationships, and understanding
competitors (e.g., market responsiveness, managing external relationships).
Finally, spanning capabilities are needed to integrate the firm’s inside-out
and outside-in capabilities (e.g., managing IT/business partnerships, IT
management and planning).
Chapter 3: Theoretical Foundation
60
Davenport (1993), in his discussion of the role of IT in supporting process
innovation, provides a comprehensive analysis of the interaction of IT and
organizations from a process perspective. Davenport (1993) states that
“process improvement and innovation are the best hope we have for getting
greater value out of our vast information technology expenditures, yet
neither researchers nor practitioners have rigorously focused on business
process change as an intermediary between IT initiatives or investments and
economic outcomes.” (p. 45) Davenport develops a typology of business
processes and classifies them into operational processes and management
processes. On the one hand, operational processes are those that embody the
execution of tasks comprising the primary activities of an organization's
value chain. For this reason, operational processes can be argued to
represent the ‘doing of business.’ Management processes, on the other hand,
are those activities associated with the administration, allocation of
resources, communication, coordination and control within organizations.
Management processes are not directly related to the primary (core)
activities of the value chain but they help in efficiently and effectively
carrying out the primary operations of an organization (Radhakrishnan et al.
2008). Figure 3.3 illustrates the typology:
Chapter 3: Theoretical Foundation
61
Figure 3.3: Typology of Business Processes
(source: Mooney et al. 1995)
A typical salesperson has to perform a wide variety of daily tasks in order to
accomplish his or her job (Marshall et al. 1999). While some of these tasks
are more related to the customer and ultimately making a sale, others are
internally oriented that are necessary for a correct functioning sales
organization. In this sense, sales tasks can also be classified into two groups
as prescribed by the business process typologies given above, where one
group consists of core value creating (i.e., operational) processes and the
other stands for support and coordination (i.e., management) processes. We
build on this typology in Chapter 4 as we conceptualize a task-based SFA-
use construct.
IT Impacts on Business Processes
Davenport (1993) defines a business process as the specific ordering of
work activities across time and space, with a beginning, an end, and clearly
identified inputs and outputs. Business processes include, among others,
supplier relations, production, marketing, support, and customer relations.
Chapter 3: Theoretical Foundation
62
How well business processes perform individually and how well they are
linked are important determinants of the added value created by an
organization. As IT continues to permeate and penetrate the organization,
impacting an increasing number of business processes at a deeper level, the
potential value of IT increases (Porter and Millar 1985). This potential is
further enhanced by redesigning business processes and by associated
modifications to the organization structure. Such structural modifications
result in new organizational forms that enhance the productivity and
business value potential of IT (Mooney et al. 1995).
In order to evaluate IT business value, the key processes vital to a business
must be identified and the linkages and contributions of IT to those
processes defined. Mooney and others (1995) propose that IT can have three
separate but complementary effects on business processes; automational,
informational and transformational effects (see figure 3.4).
Figure 3.4: Dimensions of IT business value
(source: Mooney et al. 1995)
Chapter 3: Theoretical Foundation
63
First, automational effects refer to increased efficiency when IT as a capital
asset is substituted for labor and other factors of production. Within this
dimension, value derives primarily from productivity improvements, labor
savings and cost reductions. Such automational effects help organizations
do things more quickly and cheaply (Grover et al. 1998). For instance,
computer aided design (CAD) can automate the product design process.
Similarly, salespeople in many industries can use their SFA tools to
configure products to fit their offers to specific customer needs. In pharma
industry, salespeople can easily order product samples for their clients
through their SFA tools.
Second, IT can have informational effects which emerge from the capacity
of IT to capture, store, process, and distribute information. Business
processes are enhanced by the availability and communication of critical
information. Some expected positive outcomes of informational effects are
improved decision quality, employee empowerment and coordinated utility
of organizational resources. For instance, e-mail, databases, and video-
conferencing can improve the effectiveness of communication inside an
organization. In sales context, SFA helps salespeople quickly enter and later
access customer information. SFA technology can also provide salespeople
with market statistics, product information and competitive intelligence.
Through team management facility of SFA technology, salespeople can
coordinate their activities around the customer.
Third, transformational effects refer to the value deriving from the ability of
IT to facilitate and support process innovation and transformation. Through
such effects new tasks can be executed which were previously not possible
without technology, offering new capabilities and skills to businesses
(Grover et al. 1998). The business value associated with transformational
effects will appear as reduced cycle times, improved responsiveness,
Chapter 3: Theoretical Foundation
64
downsizing, and service and product enhancement as a result of re-
engineered processes and redesigned organizational structures. For instance,
IT can be applied to support interorganizational business processes, such as
the end-to-end linking of value chains of two partner organizations. A
buyer's inbound logistics could thus be linked with the outbound logistics of
a supplier. SFA technology offers significant opportunities to sales
organizations to employ innovative CRM strategies as better connections
between the sales force, call center and online channels. IT can also provide
stronger links between sales and marketing departments in an organization,
with the potential for better developed and customized marketing campaigns
targeted at the individual customer. Last but not least, it is possible to access
inventory levels and even manage the entire supply chain through SFA,
again making salespeople a valuable and reliable partner of the client.
3.3. Concluding the Theoretical Discussion
In Chapter 3 we have presented three complementary theoretical lenses
applied in IT literature to explain the business value of IT investments. In
this last section of the chapter we make a brief overview of these views and
highlight their implications for our research.
Early papers on the business value of IT apply microeconomic theories and
attempt to directly link IT investments to firm performance at aggregate
levels. These studies usually report inconsistent results, resulting in the
‘productivity paradox.’ In the meantime, IT has become a highly replicable,
standardized commodity available to all entrants in an industry. For this
reason, IT in itself cannot provide any sustained advantages to an
organization anymore (Carr 2003). In contrast, RBV focuses on differences
Chapter 3: Theoretical Foundation
65
in performance in terms of the types of resources and capabilities that
different firms possess. According to the RBV, valuable resources explain
variance in performance across competing firms depending on how rare and
costly to imitate these resources are (Ray et al. 2005). Bringing RBV a step
further, contingency theorists suggest that IT resources should be acting in
combination with non-IT resources to provide strategic benefits. Last but
not least, process-oriented models of IT business value suggest that the
greater the extent to which IT impacts individual processes and their
linkages, the greater is the contribution of IT to firm performance (Tallon et
al. 1999).
Our discussion in this chapter reveals firstly that companies derive
differential value from their IT investments. Second, it is necessary to open
the black box of IT investments by disaggregating the input and output
constructs into meaningful subcomponents. In this way, how IT creates
business value can be better understood. Third, ‘hard’ IT resources interact
with ‘soft’ non-IT resources such as employee practices, business processes
and organizational structure when realizing organizational outcomes. Soft
resources seem to have potentially higher value than hard resources to firms
as they are typically complex, intertwined and difficult to replicate. Third,
IT impacts organizational performance via intermediate business processes.
Business critical processes affected by IT should be considered when
modeling IT business value.
Chapter 4: Sales Force Automation and Salesperson Performance
66
4. SALES FORCE AUTOMATION AND SALESPERSON
PERFORMANCE
4.1. Introduction to the Chapter
Improving employee performance in organizations is a primary goal for
organizations to increase competitiveness (Marshall et al. 2000). Sales force
represents an employee group whose performance have a direct impact on
company results. SFA creates business value by increasing salesperson
performance, which should as a consequence bring a positive impact on
organizational performance (DeLone and McLean 2003). Understanding the
“consequence of use, the impacts (direct and indirect, intended and
unintended) of [IT] artifacts on the humans who directly (and indirectly)
interact with them, structures and contexts within which they are embedded,
and associated collectives (groups, work units, organizations)” must be a
research priority (Benbasat and Zmud 2003, p. 186). Therefore, the business
value created by SFA technology is necessary to find out.
The objective of this chapter is to explain how SFA impacts salesperson
performance. Our central argument is that, how SFA is used by salespeople
must have an impact on performance. We will first illustrate the problem
inherent in past studies which often apply reflective SFA-use constructs in
their models. Later, we will suggest that multidimensional conceptualization
and task-based operationalization of the SFA-use construct is better at
understanding how SFA is used and thus theorizing the SFA-performance
relationship. Finally, we will conceptualize a multidimensional SFA-use
construct based on our literature review and a qualitative study.
Chapter 4: Sales Force Automation and Salesperson Performance
67
4.2. SFA Adoption and Use: One-Dimensional
Measurement
Beyond conceptual discussions and anecdotal arguments, papers are being
published in recent years which empirically investigate the relationship
between SFA technology adoption and salesperson performance.
A positive impact of SFA adoption on salesperson performance has been
demonstrated more than once. For instance, Gulati and others (2004)
collected responses from 335 independent sales agents who were members
of a national manufacturer’s agents association in U.S. Their results support
a positive relationship between a sales agent’s Internet utilization and self
reported sales performance. Good and Stone (2000) surveyed 183 industrial-
marketing executives familiar with computers. Their results suggest that
ease of use and encouraged use impact individual performance in a positive
fashion, suggesting easy to use systems improve individual user
performance. Similarly, Jelinek and others (2006) confirmed in a
longitudinal setting that adoption of SFA technology relates positively to
improvements in sales performance. In another study, infusion affected
salesperson performance, where routine or mere frequent use had no direct
impact (Sundaram et al. 2007).
It has also been proposed that the SFA adoption - performance relationship
depends on a number of contingent factors. Ahearne and others (2005)
demonstrated that increased IT use enhances sales efficiency and
effectiveness only under the conditions of sufficient support and training.
However, increased IT use decreased sales efficiency and hurt effectiveness
dramatically when salesperson received low levels of support and training.
In another study based on objective measures of technology usage and
Chapter 4: Sales Force Automation and Salesperson Performance
68
performance collected for 1340 pharmaceuticals salespeople, salesperson
expertise moderated the SFA – performance relationship. Sales
representatives who exceeded their sales quota in the previous year derived
significantly greater benefit from SFA system use than other sales
representatives did (Ko and Dennis 2004).
A number of studies investigated how SFA impacts performance by
assessing some factors expected to mediate the relationship. Robinson and
others (2005b) tested a model based on the data collected from a sample of
118 field salespeople working for an information services company. Their
results provide evidence that behavioral intentions to use technology
positively affect salesperson performance through enhanced propensity to
practice adaptive selling. Mithas and his colleagues (2005) revealed that the
effect of SFA applications on customer satisfaction is mediated by the
improvement in firms’ customer knowledge. Hunter and Perreault (2006)
collected data from the sales force of a major consumer packaged goods
company. Their results indicate that a salesperson’s technology orientation
affects performance with customers through a double-mediated mechanism
involving effective planning and adaptive selling behaviors. Again, two
recent studies show that salespeople who use IT, gain in return improved
customer service, adaptability and targeting and presentation skills, which
help them increase their performance (Ahearne et al. 2007; Ahearne et al.
2008).
On the other hand, a number of papers reached some conflicting findings.
Ahearne and others (2004) obtained objective measures of technology usage
and performance by 131 sales reps in a mid-sized American pharmaceutical
company to discover a curvilinear relationship between SFA usage and
salesperson performance. Technology may have a negative impact on
salesperson beyond a certain level of technology use. Based upon self-
Chapter 4: Sales Force Automation and Salesperson Performance
69
reported perceptional data from 240 salespeople who utilize a CRM system,
Avlonitis and Panagopoulos (2005) report that perceived usefulness of the
system positively impacts salesperson performance, whereas SFA adoption
do not.
From this review of the literature we can conclude that, despite exceptional
cases, SFA has a positive impact on salesperson performance. Research
efforts are slowly refocusing towards understanding how and under which
circumstances SFA technology makes a positive contribution to sales
organizations and salesperson efficiency and effectiveness. We also observe
that most of these papers tend to overlook the drivers of SFA-use behavior
and do not answer the important ‘why’ question.
Another remark of the literature is how the relationship between SFA and
organizational outcomes is modeled. A big majority of the empirical studies
in sales literature measure SFA-adoption and -use behavior with one-
dimensional constructs (e.g., only considering hours of usage). For instance,
SFA-use is operationalized in these studies as the total number of system
hits and number of screens used (Ahearne et al. 2004; Ahearne et al. 2008),
number of knowledge documents displayed (Ko and Dennis 2004), rated
score of general adoption and use (Ahearne et al. 2005, 2007; Avlonitis and
Panagopoulos 2005; Jelinek et al. 2006; Schillewaert et al. 2005; Speier and
Venkatesh 2002), rated score of intention to use (Jones et al. 2002;
Robinson et al. 2005a, 2005b), rated score of routinization and infusion of
technology in daily job (Gulati et al. 2004; Jones et al. 2002; Rangarajan et
al. 2005; Sundaram et al. 2007), and rated score of general attitude towards
technology (Hunter and Perreault 2006; Keillor et al. 2001). As exceptions,
two studies apply multidimensional measures (Hunter and Perreault 2007;
Moutot and Bascoul 2008) and one study applies a task-based
multidimensional measure (Engle and Barnes 2000).
Chapter 4: Sales Force Automation and Salesperson Performance
70
Application of one-dimensional constructs are justified to the extent that
SFA-adoption and -use is thought to be an adequate indicator of SFA
implementation success, either taken as sufficient by itself (as a dependent
variable) or theorized to bring increased performance (as an independent
variable). In such settings, it has often been enough to conceptualize SFA-
usage construct as a continuum, where the salesperson is asked to report the
extent to which he or she uses the system. As Tanner and Shipp (2005, p.
307) point out, another reason why researchers prefer one-dimensional
technology use constructs may lay in the difficulty in distinguishing the
various tasks carried out by salespeople:
Developing a framework of mutually exclusive
functions for technology is difficult, in part because,
while the full range of duties carried out by
salespeople are well defined (e.g., Moncrief, 1986;
Marshall et al. 1999); sales researchers have
concerned themselves with the functions associated
primarily with acquiring customers. Other important
functions such as knowledge management, customer
support and internal relationship building were
somewhat ignored. Non-selling functions that
salespeople might have carried out were not
considered interesting or important.
However, one-dimensional constructs may prove to be problematic. The
outcome of such approaches to measurement is often a ‘yes-no’ dichotomy
where the salesperson reports if he is using the system or not. These
constructs often represent a ‘black-box,’ while they inform whether SFA is
used or not, they do not give details of how SFA is used in a sales setting.
They implicitly overlook the impact of SFA on individual tasks, specific
Chapter 4: Sales Force Automation and Salesperson Performance
71
processes, or intermediate outcomes (such as the quality of services)
(Hunter and Perreault 2006). They do not consider the cost of usage (for
example, the time which could well be spent with clients) and thus
implicitly assume that more usage is always better than less, which is not
sustainable in effect (Ahearne et al. 2004). In fact, “in post implementation
context, hours of use may be a success measure, but less rather than more
hours is desirable.” (Doll and Torkzadeh 1998, p. 173) DeLone and McLean
(2003, p. 16) in the same way criticize approaches where one-dimensional
IT system use constructs are applied:
The problem to date has been a too simplistic
definition of this complex variable (system use).
Simply saying that more use will yield more
benefits, without considering the nature of this use,
is clearly insufficient. Researchers must also
consider the nature, extent, quality, and
appropriateness of the system use. (…) Simply
measuring the amount of time a system is used does
not properly capture the relationship between usage
and the realization of expected results.
On the contrary, there is heterogeneity in SFA-use across salespeople which
is not easy to detect by one-dimensional measurement items. A firm’s
investment on sales technology does not ensure it will be used equally by all
salespeople. It is usually at the salesperson’s discretion to choose how much
to rely on individual technologies (Morgan and Inks 2001; Hunter and
Perreault 2006). While some people welcome new technologies
enthusiastically, others may prefer the old way of doing business. In the
study of Bush and others (2005), the companies implementing SFA had a
target buy-in percentage, ranging from 50% to 70%, which is not a high rate
Chapter 4: Sales Force Automation and Salesperson Performance
72
at all. In other cases, salespeople are forced to use the SFA, soon after the
organization adopts it (Buehrer et al. 2005). Since, in this context, adoption
is against their will, these salespeople will likely use the system at a
minimum level and underutilize its capabilities (Parthasarathy and Sohi
1997). For instance, although the respondents in one study were
predominantly satisfied with their SFA tools, they reported to be using SFA
only as a basic personal efficiency tool (Stoddard et al. 2002). To illustrate,
close to 80% of the respondents use e-mail to communicate with their sales
managers, customers and each other. In contrast, less than 40 percent of the
respondents use SFA for sales forecasting and less than 25 percent use SFA
for order entry and order status, which represent more sophisticated
functionality where the real potential of SFA is (Stoddard et al. 2002). In
another study, Donaldson and Wright (2004) observed that pharmaceutical
salespeople cannot achieve their strategic-level objectives such as enhancing
customer relationship management due to the limited use of their SFA
systems. Jasperson and others (2005, p. 532) comment on the
multifunctional nature of IT systems and its consequences on voluntary use:
Typically, IT applications have many more features
than those mandated for work accomplishment.
After some individuals have gained experience in
using a specific feature (or set of features), they may
discover ways to apply the feature that go beyond
the uses delineated by the application’s designers or
implementers, thereby engaging in feature extension
behaviors. By definition, feature extensions are
always voluntary.
Widmier and others (2002) argue that as a result of this multi-purpose
nature of SFA tools salespeople may benefit from some part of the SFA
Chapter 4: Sales Force Automation and Salesperson Performance
73
functionality instead of completely refusing to adopt the system:
Respondents may be finding some of the technology
introduced by a failed SFA initiative to be helpful
and useful in their jobs. Given the range of activities
supported by sales technology, it is not surprising
that many implementations are failures, although
most salespeople feel that some part of the sales
force technology introduced in an SFA
implementation helped them in their jobs.
Indeed, modern SFA systems come with a bunch of functionalities, from
account management to data analysis and from call planning to sales
forecasting (Buttle et al. 2006). Reflective measurement of SFA-use may be
problematic in such cases where the SFA system consists of various
functions and use is voluntary. For example, one salesperson may be using
the system mainly for administrative purposes, while another may be using
for targeting and analysis. Both of these salespeople might report similar
levels of adoption, but their actual system usage (as well as drivers and
consequences of that usage) might be very different. SFA research should
adopt better developed SFA-use constructs which take the multifunctional
nature of SFA technologies into account. We deal with the problem of one-
dimensional measurement of SFA-use by suggesting a task-based
multidimensional SFA-usage construct in the next section.
Chapter 4: Sales Force Automation and Salesperson Performance
74
4.3. SFA Adoption and Use: Multidimensional
Measurement
IT-use is necessary, but not sufficient, to produce business value (DeLone
and McLean 2003; Seddon 1997). SFA may improve the performance of an
organization and its salespeople but only to the extent that the system is
properly utilized by the sales force (Morgan and Inks 2001). In this section,
we argue that how a salesperson uses an SFA system makes the real
difference, and thus, SFA-use construct should be operationalized in a way
to better capture the SFA-related salesperson behavior. A multidimensional
SFA-use construct, operationalized with task-based items is necessary.
At organizational level, more investment in IT does not always bring
profitability (Brynjolfsson and Hitt 2000). Companies differ in the extent to
which they benefit from their IT investments. Stratopoulos and Dehning
(2000) compared successful users of IT that have successfully integrated IT
into their business processes with less successful users of IT by using a
quasi-experimental design. Their results confirmed that successful users of
IT have superior financial performance relative to less successful users of
IT. As the Resource-Based View
32
puts out, besides IT investment, a bundle
of non-IT assets and capabilities determine the impact of IT on company
performance, such as complimentary investments, new strategies, new
business processes and new organizations (Brynjolfsson and Hitt 1998;
Kohli and Grover 2008).
33
One significant factor defining IT success is the
various ways IT is used within the firm (Barua et al. 1995). In a recent
study, Ray and others (2005) make out that the shared knowledge between
32
Resource-Based View and its implications for IT Business Value are given in Chapter 3
more in detail
33
Markus and Soh (1993) describe “IT assets” as an intermediate outcome between IT
investments and organizational performance. IT assets are outcomes of a conversion
process in which IT spending is necessary, but not a sufficient condition.
Chapter 4: Sales Force Automation and Salesperson Performance
75
IT and business managers moderates the impact of IT investment on firm
processes. They further highlight the manner and context of IT deployment
for positive results:
Superior relative process performance from IT rests
less on the level of IT spending or on the technical
skills of the IT staff and more on how these
resources are deployed in a firm-specific manner in
general, and on creating effective partnerships
between IT and business managers in particular.
This reaffirms the growing consensus that the
context within which IT is applied is as important as
the IT itself. This contingency view of the
relationship between IT investments and
performance suggests that just throwing technology
at a process does not necessarily improve that
process. Indeed, such indiscriminant applications of
technology may actually reduce process
performance. (p. 643)
At individual level, more usage of SFA technology does not guarantee
increased performance. “As how simply accessing information usually does
not lead to an integrative proposal, data alone does not become usable
knowledge without further value-adding activity.” (Hunter and Perreault
2007, p. 21) Therefore, system-usage is a necessary but not sufficient
condition to produce value (Igbaria and Tan 1997). Value to individuals
arises when use of the knowledge in the SFA system (for example, market
trends) changes their behavior and enables them to perform their work in
ways that are more efficient, more effective, or more satisfying (Ko and
Dennis 2004). This improved individual performance, then, may ultimately
Chapter 4: Sales Force Automation and Salesperson Performance
76
lead to improved organizational performance (DeLone and McLean 1992).
IT must be ‘appropriately’ used to create the intended effects (Lucas 1993,
McKeen and Smith 1993). For instance, Sundaram and others (2007)
demonstrate how the technology is used mediated the relationship between
the extent of use and performance. In Gelderman’s (1998) empirical study,
not frequency-of-use but user-satisfaction did explain performance.
Similarly, in the absence of training and user support, increased IT usage
decreased salesperson effectiveness (Ahearne et al. 2005). In another study,
where salespeople with greater expertise benefited four times more than
other salespeople, Ko and Dennis (2004) conclude that that high-performing
salespeople know better how to apply SFA in their jobs. SFA increases sales
effectiveness only when salesperson has better knowledge management,
adaptive selling and relationship building skills (Ahearne et al. 2007;
Ahearne et al. 2008; Hunter and Perreault 2007). Overall, how a salesperson
uses the SFA is much more decisive than whether he uses it or not.
Increased SFA-use alone, particularly in compulsory settings, means rather
compliance than motivated involvement. Empirical studies on SFA-use
should incorporate the quality dimension of SFA-related behavior in their
research models in addition to simple quantity measures.
The limitations of one-dimensional system-use constructs in properly
capturing technology related salesperson behavior necessitate
multidimensional conceptualizations of SFA-use construct. Hunter and
Perreault (2007) highlight the importance of considering various dimensions
of SFA-use:
Although one-dimensional conceptualizations of use
provide enlightening theoretical and empirical
results, a consideration of multiple dimensions of
use may enrich the understanding of [SFA]. In
Chapter 4: Sales Force Automation and Salesperson Performance
77
essence, there is a gap in the literature regarding
how different uses of [SFA] influence behaviors that
can help representatives build stronger relationships
with customers and improve administrative
performance. (p. 18)
The need for multidimensional measures of SFA-adoption and -use has its
place in the literature. Jayachandran and his colleagues (2005) invite further
research to examine the differential influence of aspects of CRM technology
use such as sales support, marketing support, and service support on
customer relationship performance. Ahearne and others (2004) propose that
“research on the specific effect of individual screens or groups of screens
(i.e., call planning versus analysis versus calendaring, etc.) will enhance our
understanding of the differential effect of the various components of the
CRM technology” (p. 308). Young and Benamati (2000) suggest that full
functional use of an e-commerce system should include informational use,
transactional use, and customer service use. As Jelinek and others (2006)
suggest, “the literature would benefit from examining performance
enhancement stemming from adoption and use of presentation tools as
compared to enhancement resulting from adoption and use of prospecting
tools” (p. 19). Good and Stone (2000) ask questions in the same direction:
What computerization tools (hardware, software) are
the most (and least) useful to marketers and their
organization? An investigation into emerging
information technologies would also allow
marketers and IS personnel to develop a stronger
grasp on the impact of specific technologies within
specific frameworks. For instance, are there certain
types of computer technologies (e.g. software) that
Chapter 4: Sales Force Automation and Salesperson Performance
78
are more promising than others within specific
organizational functions and responsibilities? (p. 50)
Multidimensional constructs have been successfully applied in Marketing
and CRM literature (Reinartz et al. 2004; Brady and Cronin 2001). In sales
management research, Engle and Barnes (2000) created an index of
salesperson activities which are supported by technology. Their exploratory
factor analysis based on data from pharmaceuticals salespeople has
identified 5 dimensions of SFA use each having a different impact on
performance. Hunter and Perreault (2007) have developed a 3-dimensional
SFA-use construct. Their results indicate that using SFA to analyze and
communicate information helps salespeople forge relationships with
customers (2007). In another recent study, the proportion of successful sales
calls significantly increased because of the call planning function and
proposal configuration related positively to the number of sales calls and
reports, whereas the use of reporting functionality decreased the number of
sales calls, the ratio of successful calls, and the number of proposals
(Moutot and Bascoul 2008). Except the exploratory study of Engle and
Barnes (2002), most of the studies applying multidimensional constructs did
not attempt to mirror the complete functionality in their SFA-use constructs
and preferred a selected sample of functions instead (for example, call
planning, proposal configuration and call reporting). Moreover, the SFA-use
dimensions were most of the time measured by reflective items, which, in
certain cases, may be unsuitably favored against formative items
(Diamantopoulos and Winklhofer 2001).
DeLone and McLean (2003) suggest that the complex nature of system use
could be better addressed by determining whether the full functionality of a
given system is being used for the intended purposes. Melone (1990)
likewise calls for new system-use measures that consider the context in
Chapter 4: Sales Force Automation and Salesperson Performance
79
which work is actually accomplished and the extent to which information
provided by the system is actually used. Kallman and O'Neill (1993) argue
that success with computer technology must be viewed within the context of
specific users and the results valued to the user, as the functional differences
in an organization vary (e.g., marketing, human resources). For example,
accounting computer systems should be evaluated differently than
marketing and sales systems (Good and Stone 2000). In this line,
operationalizing system-use as a task-based multidimensional construct may
help understand “why different users evolve very differing patterns of
feature use and, as a result, extract differential value from an IT application”
(Jasperson et al. 2005, p. 531).
Doll and Torkzadeh (1998) criticize the literature for devoting “little effort
in developing a multidimensional concept of system-use (a taxonomy of
performance-related behaviors) that recognizes the organizational functions
for which IT is utilized in the post-implementation context” (p. 173). In
response, they develop a task-based multidimensional measure of system-
use. Based on their operationalization, the respondent rates the extent to
which he or she uses the given system to achieve a conclusive index of
business related tasks. How extensively IT is used to perform these job-
critical tasks should define how effectively it is employed in the
organizational context (Doll and Torkzadeh 1998).
Doll and Torkzadeh (1998) argue that a task-based system-use construct
which measures the impact of IT on job-relevant tasks can help better
hypothesize the link between IT-use and organizational outcomes. This
makes conceptual sense as “the extent to which the expected benefits of an
innovation (...) are realized is largely reflected in the success by which an
innovation has been incorporated within the organization’s operational
and/or managerial work system” (Zmud and Apple 1992, p. 148). In the
Chapter 4: Sales Force Automation and Salesperson Performance
80
end, “the set of IT application features recognized and used by an individual
likely change over time, and it is the specific features in use at any point in
time that influence and determine work outcomes” (Jasperson et al. 2005, p.
529).
Doll and Torkzadeh’s (1998) approach, which develops a system-use
construct incorporating the tasks affected by technology, is also in
accordance with the general view that business processes represent a
significant component of an SFA strategy. According to the process-
oriented models of IT business value, IT creates value through increasing
the effectiveness of intermediate business processes (and tasks) (Barua et al.
1995).
34
In fact, SFA implementations basically involve people performing
selling processes with the help of technology (Buttle et al. 2006). An SFA
program should be properly aligned with employees, processes, and
technology (Bush et al. 2005; Zablah et al. 2004a).
35
Firms which alter
processes at the same time as adding SFA generally are more successful
than those that did not (Rivers and Dart 1999).
Marshall et al. (1999) identified 49 new sales activities that emerged since a
previous study in 1986 (Moncrief 1986). Tanner and Shipp (2005) call for
future research to understand the impact of SFA technology on those
activities. Accordingly, adopting Doll and Torkzadeh’s (1998) task-based
system-use construct to the SFA context has significant potential for the
sales management literature. Identifying the salesperson tasks affected by
SFA technology will be equivalent to modeling the intermediate business
processes through which SFA technology creates business value. Since each
34
Process-based models of IT Business Value are given in Chapter 3 more in detail
35
CRM literature maintains that designing or re-engineering key customer facing
processes so that they are both effective and efficient is critical for an organization to be
able to execute its CRM strategy and to fulfill customers’ needs (Hansotia 2002; Lee
2000; Massey et al. 2001; Rigby et al. 2002; Ryals and Knox 2001; Wilson et al. 2002).
Chapter 4: Sales Force Automation and Salesperson Performance
81
task corresponds to an intermediate business process, each task is in effect
an indicator of the SFA-business value construct (Tallon et al. 1999).
Combining these sales tasks along organizationally relevant dimensions in a
single model will effectively disaggregate the measurement of SFA-
business value into distinct components or dimensions.
To sum up, we posit that a task-based multidimensional conceptualization of
SFA-usage is necessary to recognize what salespeople do with the SFA
system and to hypothesize the link between SFA-usage and salesperson
performance. In the next part we present how we conceptualized our SFA-
use dimensions by means of a literature review and a qualitative study.
4.4. Conceptualizing SFA-Use Dimensions
In order to conceptualize a task-based multidimensional SFA-use construct
with the procedure prescribed by Doll and Torkzadeh (1998), we combined
a review of the personal selling and sales literature and the insights gleaned
from our qualitative research. Our first objective was to describe mutually
exclusive definitions of SFA-use dimensions which serve our research
purposes and reflect our research setting. Second, we aimed to develop a
taxonomy of salesperson tasks that are supported by SFA technology based
on these dimensions. One particular challenge at this stage of our research
was to agree on definitions specific enough to make sense in a certain
sample and broad enough to generalize the study findings to other sales
situations.
Chapter 4: Sales Force Automation and Salesperson Performance
82
4.4.1. Qualitative Study
Sales literature has significant implications for a multidimensional SFA-use
construct. We have further undertaken a qualitative study in order to
confirm the literature and fine-tune the construct to fit in our research
setting. For this reason we present our qualitative study methodology before
conferring about the SFA-use dimensions.
The objective of our qualitative study was to identify the tasks materializing
the SFA-use construct and their impact on salesperson performance. We
were also interested in identifying the drivers of SFA-use. We developed an
interviewer’s guide around these three critical questions:
1. What do your salespeople do with the available SFA system?
Which particular sales activities can be carried out by using the
system?
2. How do your salespeople benefit from the SFA? Which specific
organizational and personal outcomes can be accredited to the
system?
3. Which factors may be playing a role in defining the level of
SFA-acceptance among your salespeople?
Sample
These open-ended questions were asked to a sample of seven sales directors
of a mid-sized pharmaceutical company in six countries, namely Brazil,
Belgium, Germany, India, Spain and United Kingdom. Sales forces ranged
in size from 68 to over 1000. These countries were selected based on the
availability of modern SFA systems provided to the sales force and
considerable experience with such technologies since their introduction in
Chapter 4: Sales Force Automation and Salesperson Performance
83
late eighties. The selection of chief sales executives provides a certain level
of comparability between the countries (Reynolds et al. 2003).
Methodology
We have chosen semi-structured interview as the method to collect
qualitative data (Aghamanoukjan et al. 2007). An interview is defined as
encounters between a researcher and a respondent in which the latter is
asked a series of questions relevant to the subject of the research. The
respondent's answers constitute the raw data analyzed at a later point in time
by the researcher (Ackroyd and Hughes 1983). Interviews can thus yield
rich sources of data on people's experiences, opinions, aspirations and
feelings. Among several interview types, the semi-structured interview
follows a predetermined set of questions (a question catalogue) and allows
the respondent to answer these in any manner he or she chooses
(Aghamanoukjan et al. 2007). These types of interviews allow people to
answer more on their own terms than a standardized interview permits, but
still provide a greater structure for comparability over the focused interview
(May 1993). Consequently, we specified standard questions for every
respondent and then looked for both clarification and elaboration on the
answers given. This enabled us to have more latitude to probe beyond the
answers.
There are three necessary conditions for the successful completion of
interviews: (1) accessibility: whether or not the person answering the
questions has access to the information which the interviewer seeks; (2)
cognition, an understanding by the interviewee of what is required of him or
her in the role of interviewee; (3) motivation, where the subjects feel that
their participation and answers are valued and their co-operation is
fundamental to the conduct of the research. All of our respondents had
considerable experience in the company and were all in a position to judge
Chapter 4: Sales Force Automation and Salesperson Performance
84
and inform about the SFA system deployed in their country. To fulfill the
second requirement, we have made our respondents clear in advance about
our research objectives and what we were interested to learn from them. A
critical issue at this point was to keep the underlying hypotheses of our
study obscured to avoid consciously biasing the responses. All of our
respondents were fluent in English language keeping the risk of foreign-
language related misunderstandings at minimum. Finally, all respondents
were motivated to participate in our interviews as they expected to see
interesting findings from our empirical study.
36
We conducted personal interviews in Germany and other interviews were
done via telephone due to geographical distance. On average, they lasted
one hour and within in-advance agreed time limits. We have audio-taped all
interviews and transcribed the recordings immediately after the sessions in
order not to miss any information.
To observe real cases where SFA technology is actually used and thus to
confirm our conclusions, we made two additional field sales trips with
salespeople of the company in Germany. These field sales trips lasted an
entire day and represented a regular day in pharmaceutical selling context.
36
We have respected our respondents’ privacy and kept their answers anonymous,
actively avoided any verbal and nonverbal influence during the interviews – although a
‘zero’ influence would not be natural and therefore not preferable, and prepared
additional questions to ask in case the discussion deviates from the main topic in line
with the recommendations of Aghamanoukjan and others (2007) for conducting
effective interviews.
Chapter 4: Sales Force Automation and Salesperson Performance
85
4.4.2. SFA-Use Dimensions
Recalling Chapter 3, we build on the typologies suggested in process-
oriented frameworks of IT business value to group SFA-enabled tasks into
generic yet meaningful dimensions.
The main idea in such firm-level conceptualizations of business processes
and consequent classifications is that, organizations conduct certain
activities to bring products and services to the market (i.e., operational
processes), and some other certain activities to ensure that the activities in
the former group are deployed effectively and efficiently (i.e., management
processes) (Davenport 1993, Porter 1985). Day (1994) offers a similar
classification in which inside-out capabilities are responsible for creating
product and services whereas outside-in capabilities connect inside-out
processes to the external environment and enable the business to anticipate
market requirements ahead of competitors and create durable relationships
with customers.
37
The same way of thinking can be applied in sales context as well. A sales
organization has its primary objective of meeting its customers’ needs with
the product and services offered by its firm. Operational activities involved
in this objective can be, among others, all types of tasks related to the
customer, managing customer relationships and making most out of the
customer facing time. In contrast, managerial activities could be, in this
setting, all other tasks carried out ‘internally,’ to ensure a properly
functioning sales force and sales organization. Such tasks can be, among
others, reporting daily activities, all controlling activities, training,
communication and team coordination, and so on.
37
These business process typologies are discussed in Section 3.2.3 more in detail
Chapter 4: Sales Force Automation and Salesperson Performance
86
This conceptualization has its place in the sales literature. For instance,
Moncrief (1986, 1999) suggests that customer facing activities of a
salesperson represent the core of the sales job, thus they can be called as
operational processes in a selling context. In the same line of reasoning,
internal coordination and administration activities of a salesperson
constitute the “back-office” management processes, such as information
management and order processing, not in the core of the selling job, but
necessary to support the customer facing tasks (Moncrief 1986, 1999).
Engle and Barnes (2000) conducted an exploratory factor analysis to group
salesperson tasks based on overall application and influence of information
technology. Salesperson tasks for direct sales-related tasks differed
significantly from other task groups such as administration, planning and
communication (Engle and Barnes 2000). Similarly, Hunter and Perreault
(2007) argue that as salespeople are typical boundary-spanners of an
organization, “some sales tasks focus more internally on the sales
organization, whereas others center more externally on market constituents”
(p. 19). They have empirically tested their proposition in same study and
found that an SFA system can support both types of salesperson tasks
(Hunter and Perreault 2007).
What we mean by this bi-dimensional formation is that, SFA systems in our
sample countries can be used to support salespeople in external oriented
customer relationship management activities and internal oriented team
coordination and administration. The distinction between both usage
dimensions is nicely laid out by one sales director:
Chapter 4: Sales Force Automation and Salesperson Performance
87
It is possible to use our [SFA] to prepare for the
call. All the knowledge you may need to prepare for
the call can be in. What kind of doctor am I visiting?
I had a call, what have I done, have I solved his
problem? So, use it as a CRM [tool]. (...) 50-60% of
our reps very regularly use the computer for such
purposes. Everyone is using [SFA] for reporting,
this is something they consider as something they
have to do, but a lot of reps don't see their interest in
reporting. They do it for the boss.
As a consequence, we prefer to develop a two-dimensional SFA-use
construct to apply in our study, where we call the first dimension as
customer relationship and the second as internal coordination. In next two
sections we will give details on the scope and contents of these dimensions.
4.4.3. Customer Relationship Dimension
Based on our conceptualization, our first dimension captures outside
oriented tasks directly related to the customer and the selling job, which
include processes such as managing sales contacts, understanding customer
needs and profitability, organizing activities around the customer,
scheduling sales calls, preparing for the visit, making the sales presentation,
overcoming objections, and serving the customer after sale (Widmier et al.
2002). For instance, one country manager commented on the use of the SFA
system as a customer relationship tool to target the right customers with the
right frequency and the right content:
Chapter 4: Sales Force Automation and Salesperson Performance
88
We are absolutely determined to ensure that
contacts are made with right customers and with
right frequency. We insist that all business planning
is done on our [SFA] system. Our salespeople make
annual, quarterly and daily business plans on the
system. The system tells them which customers they
should visit in a day and it gives them what
happened in previous calls and hints about future
calls. Our [SFA] is the system which they use to
direct their efforts.
Another constituent of the customer relationship dimension, data analysis, is
mentioned by the following sales director:
I cannot make analysis without [SFA]. It is a tool
which tells me where my doctor sits, how many times
I have visited him and which results came out of
these visits. (...) When a salesperson wants to make
good analysis, to see whether what he did was
successful, then he can do it with the [SFA].
In return, we define the Customer Relationship Dimension of SFA-Use as
the use of an SFA system to serve customers, to collect, analyze and manage
customer information, to plan and execute sales calls and to develop sales
skills with the overall objective of better managing customer relationships.
Related SFA technology for this dimension can be, among others, account
and contact management, activity management, lead, opportunity and
pipeline management, product configuration and visualization, sales
forecasting and presentation software (Buttle et al. 2006; Marshall et al.
Chapter 4: Sales Force Automation and Salesperson Performance
89
1999).
The outside-in capabilities given in Day’s (1994) framework or the
operational processes of Davenport’s (1993) conceptualization will be likely
affected by the customer relationship dimension of SFA-use. Outside-in
processes connect the organization to the customer and other external
constituencies and include market-sensing and customer-linking
capabilities. Market-sensing activities involve the acquisition and
distribution of market information including information about competitors,
customers, and channel members. For example, contact management
software captures important information that can later be synthesized and
analyzed into a more complete understanding of customers and markets
(Tanner and Shipp 2005). Such SFA functionality to identify and target
most valuable customers makes it possible to allocate available resources
optimally for salespeople and the sales management. What's more,
customer-linking capabilities in Day’s (1994) framework refer to the
creation and management of close customer relationships. These
relationships are accomplished by close communication between the
customer and the firm requiring high levels of inter-functional coordination
and information sharing. The outside-in processes are enhanced as
information about customers is shared throughout the organization. The
combined result is a more knowledgeable and competent sales force and
support staff (Pullig et al. 2002). Johnson and Bharadwaj (2005) argue that
SFA functionality specifically oriented at developing customer relationships
elevate the salesperson to a more strategic role:
Digitized systems produce precise data and real time
analysis, allowing salespersons to perform complex
analysis and sales planning for each customer. The
potential to generate detailed customer-centric
Chapter 4: Sales Force Automation and Salesperson Performance
90
reports from digitized systems sets the stage for
salespersons to develop a new and perhaps more
advanced set of tacit skills that preserve their ability
to create value within the firm. (p. 6)
Call targeting function specifies priority contacts or accounts and estimates
optimal call-patterns. Call targeting function of SFA can be particularly
valuable in the pharmaceuticals selling context:
Certain call patterns were found to be effective in
gaining trial usage of new products by physicians.
Since market share for new drugs is often
established in the first three to six months in the
marketplace, ultimate profitability is therefore
determined by this introductory period. The obvious
strategy is to execute the desired call pattern starting
with the highest potential prescriber physicians and
working back toward lower prescribers. Since these
call patterns demonstrate diminishing returns after a
certain point, call targeting becomes a preeminent
issue. (Petersen 1997, p. 132)
4.4.4. Internal Coordination Dimension
The second usage dimension captures internal coordination tasks such as
information management, working with orders, team selling activities,
training, call reporting, and sample management. For example, one SFA
manager highlighted the SFA system as an effective tool for coordinating
team-selling activities:
Chapter 4: Sales Force Automation and Salesperson Performance
91
We have introduced team-selling to our sales force.
It would be impossible to conduct team-selling
without [SFA], our salespeople must exchange
information every day. For example, the same
doctor could be visited by three team colleagues in
the same week without our [SFA] system. A good
functioning system and an up-to-date database are
necessary to support the team-selling activities.
As an internal coordination tool, SFA systems can also be used for
administrative purposes, as one sales director pointed out:
In our country we are obliged to make the
bookkeeping of the samples we distribute to doctors,
the batch numbers and so on, there are limits on
how many samples we may distribute, and we have
to know each batch number, in case of an accident
with the sample we must be able to trace it. So this
must be put in the system.
We define Internal Coordination Dimension of SFA-Use is the use of an
SFA system to communicate within organization to manage team-selling, to
communicate with management, to report sales calls, to participate in
professional training, and to manage various administrative tasks. In
Davenport’s (1993) typology, internal coordination dimension corresponds
to management processes responsible from supporting the core value chain.
SFA functionality related to this dimension consists of e-mail systems,
online collaboration tools, web-browsers for Internet access, intranets,
online training, order processing, and reporting and sample management
Chapter 4: Sales Force Automation and Salesperson Performance
92
modules. These technologies support within-team and within-organization
communication and thus improve the ability of salespeople to act in teams
and in concert with customers (Keillor et al. 1997). Information-sharing
technology that allow salespeople to work together to serve specific
customers reduces duplication of effort and ensures adequate customer
coverage (Tanner and Shipp 2005). Salespeople can also benefit from
reporting tools when reporting customer information, their own activities,
and other data to the management.
Interfunctional coordination (Narver and Slater 1990) or a system of
spanning processes (Day 1994) is necessary in order to coordinate the
commitment to utilize market information and to create superior value for
the customer. This process includes the customer order-fulfillment process
and other customer service activities that might involve different functions
of the firm. Spanning capabilities will be positively affected by internal
coordination dimension of SFA-use. SFA innovations improve the ability of
the salesperson and firm to provide accurate information to the customer
and shorter order delivery times (Bondra and Davis, 1996). In addition, the
firm is capable of providing greater dependability in keeping its
commitments to customers as the entire organization becomes more
involved in providing customer service (Keillor et al. 1997). Perhaps the
greatest potential of SFA systems is the sharing of contact information and
increased coordination across the firm’s various customer service functions
(Pullig et al. 2002).
Chapter 5: Research Model and Hypotheses
93
5. RESEARCH MODEL AND HYPOTHESES
5.1. Introduction to the Chapter
In previous chapters we have first developed an understanding of SFA
technology as an enabler of salesperson tasks based on the resource based
view of IT and process-based models of IT business value. Following that,
we conceptualized a two-dimensional SFA-use construct derived from a
review of the literature and a number of qualitative interviews. Our research
objective is to place this construct into a bigger research model where we
can test how SFA-use dimensions relate to salesperson performance as well
as to discover how these dimensions are differentiated by typical
determinants of SFA-adoption. In this chapter we first give an overall view
of our research model and the theoretical foundations maintaining our
model. Following that, we present the hypotheses constituting the research
model. We complete the chapter by a discussion of the logical structure
which advises about how the research model should be interpreted.
5.2. Research Model
Doll and Torkzadeh (1991) propose a ‘System-to-Value Chain’ to explain
how IT systems create value (see figure 5.1). The system-to-value chain
consists of various system success constructs such as beliefs, attitudes,
behavior (system-use) and the social and economic impacts of IT.
According to this conceptualization, system-use is a pivotal construct that
links the antecedents of system quality with the social and economical
Chapter 5: Research Model and Hypotheses
94
impacts of IT. Thus, system-use can be viewed as both a success measure in
upstream research and as a complex causal agent that predicts the
downstream impacts of IT (Doll and Torkzadeh 1998). A comparable
approach to the system-to-value chain is suggested by DeLone and McLean
(2003) in their often cited ‘IS Success Model’ (see figure 5.2). This updated
model offers a comprehensive framework to assess the contribution of an
information system to organizational outcomes. Again in this framework
system-use, together with user satisfaction, plays a major role by fully
mediating the impact of quality variables on user net benefits. Studying the
antecedents and organizational consequences of system-use together in a
single model is an approach rarely applied in SFA research (Avlonitis and
Panagopoulos 2005).
Figure 5.1: System-to-value chain (source: Doll and Torkzadeh 1991)
Figure 5.2: Updated DeLone and McLean IS Success Model
(source: DeLone and McLean 2003)
Chapter 5: Research Model and Hypotheses
95
In fact, system-use as a focal construct makes conceptual sense from both
ends of the value chain. It has long been argued that IT systems can only
add value to an organization when they are fully employed by end-users
(Devaraj and Kohli 2003). Furthermore, missing adoption of IT systems
among intended end-users has been a chronic problem in IT
implementations (Davis 1989). Therefore, there has been considerable
upstream research in the past which takes system-use as a dependent
variable and examines the factors that drive system acceptance and use
(Venkatesh et al. 2003). Upstream research, however, cannot shed much
light on the organizational outcomes of system-use. While relatively
neglected so far, there is also considerable downstream research focusing on
the organizational outcomes of system-use (Heine et al. 2003).
Our conceptual model (see figure 5.3) draws on the ‘System-to-Value
Chain’ and the ‘DeLone and McLean IS Success Model’ and allows us to
simultaneously assess our upstream and downstream hypotheses. We
believe that integrating upstream and downstream perspectives into a single
model represents a major strength of our study. In the following sections,
we first take the downstream perspective and develop a set of hypotheses
linking the two SFA-use dimensions to salesperson performance. Then, we
turn upstream and present our hypotheses relating the dimensions to their
direct and indirect drivers.
Chapter 5: Research Model and Hypotheses
96
Figure 5.3: Research Model
Chapter 5: Research Model and Hypotheses
97
5.3. SFA-Use Dimensions and Salesperson Performance
Sales organizations expect that sales force use of SFA technologies will lead
to increased effectiveness and efficiency in managing various selling tasks
which should in return mean better sales performance (Jones et al. 2002;
Widmier et al. 2002). Consistent with company expectations, managers and
sales representatives believe that sales technology tools will be useful in
their job performance (Buehrer et al. 2005; Engle and Barnes 2000).
However, neither all responsibilities are equally important in a salesperson's
job, nor do they equally impact salesperson performance (Tripoli 1998).
Salespeople need to deploy their efforts wisely in order to achieve high
performance. As given in chapter 3 in greater detail, the impact of SFA on
performance will depend on the success and magnitude of the tasks and
processes it supports (Barua et al. 1995). Therefore, we propose in our
conceptual framework that SFA impacts salesperson performance through a
two-dimensional mechanism. We expect that the SFA-use dimensions will
have distinctive effects on salesperson performance.
5.3.1. Customer Relationship and Salesperson Performance
SFA technologies enable sales activities directly facing the customer and
can help salespeople manage their customer relationships along the sales
cycle, from customer acquisition to maintenance, efficiently and effectively.
First, SFA can be a very helpful tool to understand customer needs and sales
opportunities. Due to its storage, retrieval, and network capacities, IT has
the potential to enable and facilitate information acquisition, dissemination,
Chapter 5: Research Model and Hypotheses
98
and utilization (Huber 1991). Today, salespeople have extensive access to
data (e.g., past shipments to distributors, retail store sales, consumer buying
habits, and product performance characteristics). By the help of SFA
systems, salespeople can convert such available data into high quality
information about a greater number of customers, products and competitors
(Tanner et al. 2005). For instance, a sales representative can search online
databases or the Internet for customer- and business-related information,
thus improving his or her understanding of unmet customer needs. Because
greater market knowledge leads to a better sense of the potential customer
base and segments, salespeople can focus their efforts accordingly and
target customers who are most likely to fit the sales organization's offerings
(Ahearne et al. 2007). Salespeople who can focus their efforts on customers
who are qualified and ready to buy will be more efficient and be more likely
able to achieve quotas (Moutot and Bascoul 2008).
Second, SFA will help salesperson approach the customer with correct
timing. Calendaring and routing tools enable sales representatives to
effectively manage their time, set up appointments accurately, and engage in
weekly planning. Better planning helps salesperson allocate his time across
clients optimally and ensure that every client receives the necessary
salesperson attention (Ahearne et al. 2005).
Third, technology can play a significant role in performing a sales call.
Salespeople are normally recommended to collect information about the
customer to assist adaptation to a specific sales situation (Spiro and Weitz
1990) and to plan for the interactions with the buyer (Sujan et al. 1994).
SFA databases and applications often have capabilities that allow sales
representatives to keep detailed records about clients and past sales calls.
Utilizing customer purchase history and preferences, salespeople can tailor
presentations to adapt to specific buying needs and make better customized
Chapter 5: Research Model and Hypotheses
99
sales calls (Ahearne et al. 2008). Reviewing the account history before the
actual face-to-face sales call enhances a salesperson’s ability to select the
appropriate sales strategy and to determine which products to emphasize
during the sales call based on the customer’s previously stated preferences
(Hunter and Perreault 2006). The information can in return be used toward
developing recommendations and proposals that balance sales objectives
with customer objectives (Hunter and Perreault 2007). Salespeople report
that sales technology helps make sales calls more professional (Marshall et
al. 1999). During a sales interaction, the effective use of information
improves the salesperson’s ability to anticipate and respond to buyer
concerns and objections.
Last but not least, technology should permit salespeople to serve customers
more reliably. Delivering high quality customer service has emerged as a
strategic imperative and a source of competitive advantage, and it is
increasingly tied to a firm’s overall IT resources and capabilities. Using
technology, a salesperson can communicate with customers more easily and
with greater precision across time and geographic location (Ahearne et al.
2007). SFA can make a salesperson a valuable partner for his customers, a
reliable source of market knowledge, and a problem solver (Hunter and
Perreault 2007). IT enables salespeople to more quickly access relevant
databases and organizational units in order to make an order, retrieve
information about inventory levels and shipping dates even during the
customer visit. Such capabilities improve the speed at which salespeople
respond to customers’ needs. IT usage should promote reliability also
through the storage and retrieval of key customer concerns and detailed
notes regarding the customer’s interests. Dependable information allows
customers to make informed decisions about the impact of buying or not
buying the salesperson’s product or service (Ahearne et al. 2008).
Chapter 5: Research Model and Hypotheses
100
As a conclusion, we posit that using SFA technology to support customer
oriented tasks should increase salesperson performance. Consequently, we
posit that:
H1a: Using SFA-technology as a customer relationship tool will have a
direct and positive impact on salesperson performance.
5.3.2. Internal Coordination and Salesperson Performance
In addition to supporting the customer relationship lifecycle, SFA systems
can also increase the efficiency of repetitive administrative tasks and
improve communication within the organization. We expect that using SFA
to perform such internal oriented tasks will have an impact on salesperson
performance, yet in an indirect character.
Sales job involves a considerable amount of repetitive ‘back-office’
activities, such as submitting call reports, ordering promotional material and
reclaiming expenses, which have to be most of the time performed by the
salesperson himself. Such tasks are necessary for properly monitoring and
controlling of salespeople, considering the fact that most salespeople work
on the field and from home-offices. SFA technology can automate most of
these administrative tasks and thus reduce the time salespeople spend on
non-selling activities (Buehrer et al. 2005; Moriarty and Swartz 1989). In
fact, such efficiency has been the explicit purpose of many sales automation
software applications (Hunter and Perreault 2006).
Moreover, SFA can support team-selling by coordinating and synchronizing
team activities (Widmier et al. 2002). SFA tools facilitate information flow
and improve communication within sales teams (Brown and Jones 2005)
Chapter 5: Research Model and Hypotheses
101
and help salespeople become more efficient at synchronizing team activities
and setting appointments. Effective team-selling enabled by technology
should in return increase sales.
SFA also helps salespeople improve their technical knowledge with respect
to their products and their ability to compare and analyze their product's
standing against competitive products (Ahearne et al. 2007). When
salespeople have greater insight into their markets and products, they are
also in a better position to demonstrate higher levels of knowledge and
competence.
On the other hand, training and development constitutes a big part of the
selling job (Cron et al. 2005). A salesperson spends substantial amount of
his time at training courses to improve his sales skills and strategies.
Modern technologies such as Internet, or the SFA itself, make it possible to
participate at online training sessions at one’s own convenience and at
almost no cost.
Together, SFA can ease a salesperson’s administrative burden and facilitate
better functioning internal processes of a sales force. Accordingly:
H1b: Using SFA-technology as an internal coordination tool will have a
positive impact on salesperson performance.
One of the biggest promises of SFA technology is the time spared for
personal selling activities by automating repetitive tasks and mundane
administrative work (Ahearne et al. 2008; Honeycutt et al. 2005). By
reducing the amount of ‘downtime’ in a salesperson's workday and
optimizing call schedules; SFA helps salespeople fit more sales calls into a
given period (Ahearne et al. 2005). Salespeople are aware that the more
Chapter 5: Research Model and Hypotheses
102
sales calls they can make, higher the opportunity to achieve the sales quotas
will be (Ahearne et al. 2007). Indeed, no matter how sophisticated
technological tools get, buyer–seller exchanges still rely heavily on
cumulative face-to-face communication, relationship building and problem-
solving (Goldenberg 1996; Moncrief et al. 1991; Moriarty and Swartz 1989;
Rivers and Dart 1999). Moreover, there is an inherent risk that the
efficiency effects of SFA will suffer additional tasks being assigned to
salespeople in last decades, such as increased market intelligence and
documentation (Marshall et al. 1999). Therefore, while it is true that
technology reduces the time spent on repetitive tasks, the extent to which
the expected impact on sales performance is realized should depend on how
that additional selling time is spent by the salesperson. Hunter and Perreault
(2007) comment on this important issue:
Gains in efficiency will have a net positive effect
only if they free sales representatives from time
spent on non-selling activities and if the
representative redirects that incremental time to
tasks that improve relationship-building
performance with customers (i.e., relationship-
forging tasks). (p. 29)
Sujan (1986) and Sujan et al. (1994) conceptualize the direction chosen to
channel effort as ‘working smart,’ while the overall amount of effort
salespeople devote to their work is conceptualized as ‘working hard.’ For
example, working hard would mean working more hours, making more
calls, and/or putting in more effort with tough customers. In contrast,
‘working smart’ is defined as “behaviors directed toward developing
knowledge about sales situations and utilizing this knowledge in sales
situations” (Sujan et al. 1994, p. 40). Working smart is proposed to be a key
Chapter 5: Research Model and Hypotheses
103
factor for increasing sales force effectiveness (Weitz et al. 1986). In the end,
salesperson performance is more strongly related to what salespeople do
rather than merely how hard they work (Sujan et al. 1988).
Additional selling time available to the salesperson must be complemented
with smart working behavior, such as collecting information about the
customer and the specific selling situation, planning the sales strategy, and
altering selling behavior during customer interaction and across customer
interactions based on the situation all refer to working smart. Only in such a
case the real potential of SFA can be realized. As we argued above, SFA
technology, when used to support customer relationships, provides
salespeople with the tools to manage customer information and to plan
around the customer, which in return make it possible to adapt to the single
customer and selling situation. In a similar logic, the impact of training and
product knowledge on salesperson performance should depend on the
customer relationship dimension. Increased product knowledge can help a
salesperson only when it is used to better serve the customers. Therefore, we
posit that the positive effect of internal coordination dimension on
salesperson performance will be indirect in nature and hypothesize that:
H1c: The effect of using SFA-technology as an internal coordination tool on
salesperson performance will be mediated by the customer relationship
dimension.
Chapter 5: Research Model and Hypotheses
104
5.4. Antecedents of SFA-Use Dimensions
A salesperson’s motivation to act in a certain way is determined by the
interplay between management, organizational, social, personal and
environmental factors. In this part we embrace an upstream perspective and
link a number of well-known antecedents to our two SFA-use dimensions.
5.4.1. Technology Acceptance Model
Various theoretical models have been developed in the IT literature to
explain the adoption and use of technology in the workforce (Leong 2003).
A major stream of this literature has focused on employing intention-based
models that use behavioral intention to predict usage (Lee et al. 2003).
These models focus on identifying the determinants of intention, such as
attitudes, social influences, and facilitating conditions across a broad range
of end-user computing technologies and settings.
38
Most of this research is
grounded in social psychology models such as the Theory of Reasoned
Action (TRA) (Ajzen and Fishbein 1980) and the Theory of Planned
behavior (TPB) (Ajzen 1985, 1991).
The Technology Acceptance Model (TAM) has emerged from this literature
as a powerful and parsimonious way to explain IT users’ intention and
behavior regarding IT usage (Davis 1989). TAM identifies two central
beliefs, perceived usefulness and perceived ease of use, as the primary
predictors of user’s attitude or overall affect toward IT usage (Davis 1989).
Perceived usefulness is the extent to which a person believes that using a
38
Refer to King and He (2006), Legris et al. (2003), Schepers and Metzels (2007),
Venkatesh et al. (2003) and Yi et al. (2006) for Meta analyses of technology adoption
research.
Chapter 5: Research Model and Hypotheses
105
system will enhance her performance, and perceived ease of use is the
extent to which a person believes that using the system will be relatively
free of effort. The core idea of the TAM is that a person’s attitude toward
using a technology is jointly determined by perceived usefulness and
perceived ease of use (see figure 5.4). User attitude influences behavioral
intention to use IT, which in turn, influences actual usage behavior.
In contrast with TRA, the mediating role of attitude played in TAM is often
debated. Within professional settings, “people form intentions toward
behaviors they believe will increase their job performance, over and above
whatever positive or negative feelings may be evoked toward the behavior
per se” (Davis et al. 1989, p. 986). Utilitarian considerations may dominate
users’ decision to use IT, regardless of any negative attitude toward such
usage. Empirical studies demonstrate a consistent and strong perceived
usefulness – intention link whereas attitude tends to have a mixed effect,
especially when perceived usefulness is included as a predictor of intention
(Venkatesh et al. 2003). This has led many recent TAM studies to drop
attitude entirely from their models (Venkatesh and Davis 2000).
Figure 5.4: Technology Acceptance Model (source: Davis 1989)
Chapter 5: Research Model and Hypotheses
106
Empirical tests of TAM have shown that it explains much of the variance in
intention to use and actual usage behavior.
39
For instance, Davis, Bagozzi,
and Warshaw (1989) apply TAM to examine students’ usage of a word
processing software at two points in time – following their initial exposure
to the system and then again 14 weeks after initial acceptance – in order to
demonstrate model’s predictive ability for short-term and long-term (post-
adoptive) usage. More recent longitudinal studies also employ TAM to
examine post-adoption intention and/or behavior.
40
Perceived usefulness has
consistently been the predominant predictor of user intentions to use IT and
actual usage behavior, though ease of use has had a somewhat inconsistent
effect, especially during later stages of usage (Venkatesh et al. 2003).
Effort-oriented constructs are expected to be more salient in the early stages
of a new behavior, then learning-curve effects take place and effort
expectancy becomes overshadowed by instrumentality concerns (Szajna
1996; Venkatesh 1999). TAM has also frequently been applied and
validated in the sales domain.
41
Innovation processes do not take place in vacuum (Burkhardt 1994; Kraut et
al. 1998). In fact, TAM suggests that organizational, social and individual
variables that are not explicit in the TAM could have an impact on IT-usage
(at least partially) mediated by the belief variables (i.e., perceived usefulness
and ease-of-use). In this way, the model provides a source for tracing the
impact of external factors on internal beliefs, attitudes, intentions and actual
behavior (Davis et al. 1989). Several studies indicate that individual
adoption of innovations not only depends upon beliefs but also on
management policies and actions (Ives and Olson 1984; Leonard-Barton and
39
Davis 1993; Davis et al.1989; Doll et al. 1998; Igbaria et al. 1995; Karahanna and
Straub 1999; Karahanna et al. 2006; Mathieson 1991; Venkatesh et al. 2003;
40
Karahanna et al. 1999; Venkatesh and Brown 2001; Venkatesh and Davis 2000
41
Avlonitis and Panagopoulos 2005; Jones et al. 2002; Robinson et al. 2005a, 2005b;
Schillewaert et al. 2005; Sundaram et al. 2007
Chapter 5: Research Model and Hypotheses
107
Deschamps 1988). Organizational efforts to support technology (e.g.,
training, user support) and several social influences (e.g., originating from
peers, supervisors or customers) may trigger learning mechanisms which
influence technology adoption by end-users (Huber 1991; Sinkula 1994;
Slater and Narver 1995).
To sum up, TAM theorizes that salesperson intention-to-use and adoption of
an SFA system is explained by SFA’s perceived usefulness and ease of use.
External factors such as the accuracy of expectations regarding the
implementation, intrapersonal attributes such as innovativeness and
organizational efforts such as availability of training and technical support
may have an indirect impact on usage behavior, mediated by two central
beliefs, perceived usefulness and ease-of-use of the focal system. In the
following part we put forward our hypotheses in which a number of well
studied antecedents of technology adoption and use are proposed to explain
our SFA-use dimensions.
5.4.2. Perceived Usefulness
According to the expectancy theory (Porter and Lawler 1968), within
organizational settings, people evaluate the consequences of their behavior
in terms of potential rewards, and they base their choice of behavior on the
desirability of the rewards. Salespeople usually have a fair amount of
autonomy in performing their jobs and are under constant pressure to
perform as their evaluation and compensation are often directly linked to
their performance. Consequently, “salespeople will choose to use or not use
a technology tool to the extent they believe it will help them accomplish
their job-related goals, enhance their performance, and achieve desired
rewards” (Robinson et al. 2005b, p. 413). One sales director in our
Chapter 5: Research Model and Hypotheses
108
qualitative study has commented that his salespeople adopted SFA as it is
useful for them, more specifically; technology makes daily reporting easier:
Our salesreps are happy just for the part that
benefits them; it makes them easier to report their
job daily. (...) We don't ask them to do so many
things. I am completely convinced that the easier do
it, better. Now it is much easier because they can do
it in the morning when they are working, and
sometime between visits, and when they are home
they just have to plug-in the PDA (personal digital
assistant), it is not hard for them, it is easier.
In sales research, perceived usefulness of SFA technology has been
demonstrated as a driver of SFA-use more than once (Avlonitis and
Panagopoulos 2005; Rangarajan et al. 2005; Robinson et al. 2005a;
Schillewaert et al. 2005). In a case study salespeople reported that they use
SFA-technology because it is useful (Buehrer et al. 2005). In particular, they
mentioned that technology helps them be more efficient and productive,
save time, and improve communication with customers.
We argue in this thesis that using SFA to support customer relationships and
internal coordination tasks should increase salesperson performance. If
salespeople agree with this proposition, they should be inclined to use SFA
in both ways. So we hypothesize that:
H2a: Perceived usefulness will have a positive impact on the customer
relationship and internal coordination dimensions of SFA-usage.
Chapter 5: Research Model and Hypotheses
109
5.4.3. Perceived Ease-of-Use
Employees’ perceptions of a technology's accessibility relate to their
intentions to use that technology (Saga and Zmud 1994). Innovation theory
suggests that the degree that an innovation is perceived as relatively difficult
to understand and use would affect the rate of its adoption (Rogers 1995).
TAM’s departure point is that, the easier a system is to interact with, the
greater should be the user's sense of efficacy (Bandura 1982) and personal
control (Lepper 1985) regarding his or her ability to operate the system
(Davis et al. 1989).
Salespeople are among the most technophobic employee groups (Greenberg
2004). They will assess the amount of effort necessary to utilize an SFA tool
and will likely develop positive attitudes toward those tools where the
performance benefits are not outweighed by the required effort (Robinson et
al. 2005b). There are a few studies testing the impact of perceived ease-of-
use on SFA-adoption and use. Schillewaert and others (2005) have shown
that PEU increases adoption. Rangarajan and others (2005) empirically
demonstrate that the complexity of using SFA-technology increases role
conflict, which has in turn negative consequences on salesperson effort and
SFA-infusion. At least three studies show that PEU positively impacts
attitude, which in turn has a significant impact on intention to use SFA
(Jones et al. 2002; Robinson et al. 2005a, 2005b). Therefore, we expect that
perceived ease-of-use will positively impact both dimensions of SFA-use:
H2b: Perceived ease of use will have a positive impact on the customer
relationship and internal coordination dimensions of SFA-usage.
Chapter 5: Research Model and Hypotheses
110
TAM posits that perceived ease-of-use has an additional instrumental
impact on a salesperson’s attitude toward using a technology through its link
to perceived usefulness (Davis et al. 1989). To the extent that increased ease
of use contributes to improved performance, perceived ease of use will have
a direct effect on perceived usefulness. This logic is given by Robinson and
others (2005b, p. 412):
As a salesperson perceives that a technology will be
free of added effort (or that it reduces effort), he/she
may take the opportunity to redirect the unused
effort toward other tasks. This will allow for
accomplishment of more work for the same effort,
hence greater productivity (and presumably greater
rewards).
Consequently our hypothesis follows:
H2c: Perceived ease of use will have a positive impact on perceived
usefulness.
5.4.4. Supervisor Support
Subjective norms reflect the normative beliefs of important others and allow
the focal individual to adapt his or her own belief structure (Fishbein and
Ajzen 1975). Through social persuasion and interpersonal communication,
recipients learn about innovations, develop attitudes (Burkhardt 1994; Kraut
et al. 1998) and finally adopt them (Barclay et al. 1995; Hartwick and Barki
1994; Rogers 1995). We define supervisor support as the support and
encouragement from the supervisor and his or her acting as a role model in
Chapter 5: Research Model and Hypotheses
111
terms of instrumentality and priority of the SFA technology.
Research into the implementation of IT innovations considers supervisor
support as a critical factor in successful implementation.
42
Supervisor
support is critical as the implementation of IT innovations often requires
substantial material resources to support end-users during implementation
and continued use of the system. Such resources are more likely to be
accessible when management support exists (Atuahene-Gima 1997; Sharma
and Yetton 2003). Beside material considerations, supervisors can also
impact adoption through their own behavior (Igbaria et al. 1996; Karahanna
and Straub 1999) and persuasive communication (Bhattacherjee 1998;
Leonard-Barton and Deschamps 1988). Managers may emphasize the
benefits in terms of usefulness, minimize the drawbacks in terms of ease of
use and use their personal influence to push technology adoption (Anderson
and Robertson 1995).
In a professional selling context, sales manager is often the most influential
person for a salesperson (Deeter-Schmelz et al. 2002). Through a mentor’s
teaching, coaching, and role modeling, salespeople develop competencies
and effectiveness (Hunt and Michael 1983). The mentoring function of
coaching/teaching provides a role model for necessary skills in the sales,
interpersonal, and technical areas and, ultimately, leads to high performance
(Brashear 2006). Support and encouragement should provide incentives that
reward complying behavior (Pullig et al. 2002).
42
Guimaraes and Igbaria 1997; Howard and Mendelow 1991; Igbaria 1990, 1993; Igbaria
and Guimaraes 1994; Igbaria and Iivari 1995; Jarvenpaa and Ives 1991; Kwon and
Zmud 1987; Purvis et al. 2001; Stajkovic and Luthans 2001; Sviokla 1996; Yates et al.
1999
Chapter 5: Research Model and Hypotheses
112
Supervisory support has been argued to be an important driver of SFA
adoption and use (Pullig et al. 2002; Jones et al. 2002). In one study, top
management support was found to have a positive impact on individual
perceptions of SFA technology (Speier and Venkatesh 2002). In
Schillewaert and others’ (2005) study, supervisor support appeared to have a
significant impact on both perceived usefulness and SFA adoption.
Avlonitis and Panagopoulos (2005) have similarly found that supervisory
support has an immediate impact on SFA adoption. One sales director has
drawn attention to managerial support as an important determinant of SFA
adoption during our interviews:
Sometimes, if the regional manager does not push
[SFA] as much as he could, that's relevant. By
large, if you have a manager, an SFA expert in his
region, who uses SFA and checking to make sure
that things are done right, I think the usage in that
region must be better. If the regional manager is not
good on that, the usage in that region is bad as well.
Consequently, we develop the hypotheses below:
H3a: Supervisor support will have a positive impact on perceived
usefulness.
H3b: Supervisor support will have a positive impact on perceived ease-of-
use.
Chapter 5: Research Model and Hypotheses
113
5.4.5. Facilitating Conditions
Marketing researchers have shown that organizational practices affect the
perceptions and behaviors of boundary spanners (Singh et al. 1996). We
define facilitating conditions as the extent to which a salesperson believes
that he or she has been provided with the resources and the external support
to use SFA technology. Investing in facilitating conditions such as tutorials,
help lines, training sessions and technical maintenance signals the
importance an organization places on SFA technology and reassure
salespeople that using sales technology is beneficial (Hunter and Perreault
2006). Such facilitating conditions enable employees to acquire the skills
they need to continue to be productive members of the organization, even
after the innovation has been deployed (Johnson and Bharadwaj 2005;
Zablah et al. 2004b). For these reasons, some form of formalized,
organization-sponsored SFA support would seem to be a necessary
ingredient for the effective implementation of SFA (Morgan and Inks 2001,
Pullig et al. 2002).
In many SFA adoption studies user support has been shown to be a key
element for continual use of SFA-technology (Buehrer et al. 2005; Jones et
al. 2002; Mathieson 1991; Schillewaert et al. 2005). Facilitating conditions
can reduce nonmonetary costs such as the uncertainty and stress associated
with the introduction of the new system by easing the learning process
(Parthasarathy and Sohi 1997, Rangarajan et al. 2005). Salespeople that
receive adequate training and support can apply information technology
more effectively to specific work problems and thus achieve better
performance (Ahearne et al. 2005). This, in turn facilitates increased
expectations of the technology’s usefulness by users (Landry et al. 2005;
Pullig et al. 2002). Consequently we hypothesize:
Chapter 5: Research Model and Hypotheses
114
H3c: Facilitating conditions will have a positive impact on perceived
usefulness.
Furthermore, perceived level of availability of support services is positively
related to perceived ease of use (Robinson et al. 2005a). By asking for help
with the practical use of technology, salespeople from firms with adequate
user assistance will become more proficient users and reduce the required
effort to use the sales technology (Schillewaert et al. 2005). Therefore:
H3d: Facilitating conditions will have a positive impact on perceived ease
of use.
5.4.6. Computer Self-Efficacy
Compeau and Higgins (1995) define computer self-efficacy as “an
individual’s perceptions of his/her ability to use computer (software) in the
accomplishment of a task” (p. 191). Venkatesh and Davis (1996) model
computer self-efficacy as an antecedent of perceived ease of use, with the
argument that a person uses his or her sense of overall computer abilities as
an anchor to judge the usability of a computer system, even if the user has
little or no knowledge about the ease of use of a specific system. Typically,
lower scores on computer self-efficacy lead to more negative individual
perceptions about the technology in question (Venkatesh 2000).
Only a small percentage of salespeople consider themselves as experienced
technology users, and the vast majority has little to no experience (Petersen
1997). Fear of technology is a likely impediment to sales force acceptance
of automation (Buehrer et al. 2005). If a salesperson feels that he or she is
Chapter 5: Research Model and Hypotheses
115
not capable of using the SFA system, his or her motivation to do so will be
greatly reduced (Morgan and Inks 2001). Thus, computer self-efficacy is
proposed to be an important personal characteristic in explaining SFA-use
behavior (Speier and Venkatesh 2002; Schillewaert et al. 2005). One sales
director explained his opinion on the role of computer self-efficacy in
establishing SFA adoption:
There are some people who are more computer
literate than others, there are also people who are
just more interested in doing research, they are more
driven to spend time looking into things, they spend a
lot more time using [SFA] and they get better results
after it. (…) There are always people who are more
able, keen up and spend more time using [SFA] and
using more effectively than other people.
Consequently, we propose the following hypothesis:
H3e: Computer self-efficacy will have a positive impact on perceived ease
of use.
5.4.7. Team-Use
In addition to superiors, there are other important others in a recipient’s
surrounding who can exert their normative beliefs on the recipient regarding
the behavior in question (Fishbein and Ajzen 1975; Triandis 1971).
Salespeople are natural boundary spanners and influenced by a variety of
role partners such as their customers, managers and sales peers (Singh and
Rhoads 1991). In team selling settings in particular, salespeople work in
Chapter 5: Research Model and Hypotheses
116
close collaboration with their colleagues.
We define team-use as the extent to which a focal sales representative's
team colleagues employ SFA and rely on the system in managing their
team-selling activities. Colleagues and peers in an organizational setting can
influence an individual's beliefs and behaviors by supplying information
(Thompson et al. 1991) and also by allowing the individual to observe
others while using the system (Bandura 1977). Greater the number of others
who are experts in using the system, easier it is for a salesperson to ask
other users for help with the commands and other functions of the system
(Parthasarathy and Sohi 1997). There is empirical support in the literature
for a significant relationship between team-use and salesperson’s perceived
ease-of-use of the system (Schillewaert et al. 2005). Therefore:
H3f: Team-use will have a positive impact on perceived ease of use.
Technology is a significant enabler of synchronized teamwork (Dennis et al.
2001). Increased connectivity between team members through information
technology improve group coordination, minimize time between exchanges,
and reduce the risk for communication errors (Shirani et al. 1999). In
addition, chances are higher to discover useful functionalities provided by
the system when colleagues heavily use SFA-technology (Schillewaert et al.
2005). Therefore, we expect that salespeople engaged in team-selling will
benefit from SFA to a great extent and find it useful:
H3g: Team-use will have a positive impact on perceived usefulness.
TAM prescribes the impact of external variables on acceptance to be fully
mediated by perceived usefulness and ease-of-use. However, there are
recent studies demonstrating a direct impact (Burton-Jones and Hubona
Chapter 5: Research Model and Hypotheses
117
2006). Such a direct impact can be relevant for the case of team-use. When
team colleagues rely on the system, SFA becomes a platform to coordinate
team selling activities (Good and Schultz 1997; Powell et al. 2004). Thus,
the social utility of SFA applications which support team-selling (e.g.
shared knowledge databases for sales teams) increases with the number of
users within a focal salesperson’s social environment (Markus 1990;
Schillewaert et al. 2005). Furthermore, when internal-coordination activities
are managed through the SFA system, the opportunity cost for not using the
SFA increases. This may make salespeople feel obliged to use SFA to
facilitate team-selling activities regardless of the extent to which they find
technology useful or easy-to-use. Therefore, team-usage should have a
direct impact on internal-coordination dimension:
H3h: Team-use will have a direct positive impact on internal coordination
dimension of SFA-usage.
5.4.8. Supervisor SFA-Control
While the aforementioned variables have already been validated as drivers
of SFA usage in the extant literature, we identify supervisory SFA-control
(Challagalla and Shervani 1996) as an important and not yet tested
antecedent based on the insights of our qualitative study. The impact of
sales managers’ control orientation on SFA adoption has not been tested yet:
Management places a layer of expectations on
salespeople that are influenced by the available
technology. (...) That research should explicitly and
carefully consider the role of technology in
monitoring performance, providing strategic
Chapter 5: Research Model and Hypotheses
118
direction, (...) essential job functions of the sales
manager. (Tanner and Shipp 2005, p. 308)
In many studies supervisor feedback, behavior and control orientations have
been shown to direct the attitudes, learning and behavior of salespeople.
43
Sales managers evaluate salespeople not only on outputs, but also on
methods, their selling processes and even organizational norms and culture
(Anderson and Oliver 1987; Jaworski 1988; Tyagi 1982). Such behavior-
based control systems allow managers a great deal of control over the
selling operation (Anderson and Oliver 1987). Consequently, we define
supervisor-SFA-control as the extent to which a supervisor (1) specifies the
activities he or she expects salespeople to perform using the SFA system,
(2) monitors to see whether they are performing those activities, and (3)
informs them if they are meeting his or her expectations. Supervisor-SFA-
control behavior is best explained by a sales director who participated in our
qualitative study:
On the regional manager's monthly report there is a
tick box to say whether the salespeople’s [SFA]
administration is lacking or not. If the [SFA]
administration is not good, then they are
disqualified from the bonus payment. There is not an
incentive but a penalty that applies. The second
thing, we have also a grading system to rate
salespeople. Satisfactory [SFA] use is one of the
criteria that they have to achieve in order to be
graded.
43
Jaworski and Kohli 1991; Kohli, Shervani, and Challagalla 1998; Singh 1993; Singh,
Verbeke, and Rhoads 1996; Sujan, Weitz, and Kumar 1994
Chapter 5: Research Model and Hypotheses
119
SFA technology certainly improves the capability of sales managers to
monitor salesperson activities in great detail (Tanner and Shipp 2005).
Robinson and others (2005) suspect that control/reward system utilized by
the firm may influence the technology acceptance process. SFA technology
is often a strategic priority of the firm and provides crucial sales information
for management, rationalizing the sales manager behavior to promote
technology usage as standard sales practice for his or her sales team
(Gohmann et al. 2005b). The obligation to use SFA-technology in
conjunction with managers’ monitoring activities should have a direct
impact on SFA adoption (Buehrer et al. 2005). We posit that control and
monitoring behavior of sales managers will signal a clear incentive to adopt
SFA, regardless of the extent to which salespeople find it useful or easy to
use. Therefore, similar to the team-use variable, we expect a direct impact
not mediated by perceived usefulness and ease of use:
H3i: Supervisor SFA-control will have a positive impact on the customer
relationship and internal coordination dimensions of SFA-usage.
5.5. Control Variables
The likelihood of alternative explanations can be reduced in cross-sectional
surveys through appropriate data collection strategies. For example, many
cross-sectional studies attempt to rule out competing explanations by adding
control variables to the research model which may have separate but
significant impact on the dependent variable (Rindfleisch et al. 2008).
Previous research suggests that other variables need to be considered when
examining IT-adoption and organizational outcome variables. Therefore, we
Chapter 5: Research Model and Hypotheses
120
added control factors to our model to test the impact of SFA-use on
salesperson performance in the presence of other important variables. The
control variables we added into our research model are as follows: (1) the
length of time a sales representative had been with the company, (2) the
length of time a sales representative had been working in his or her territory,
(3) total sales experience, (4) age, and (5) gender.
Meta-analyses of sales literature have found that these effects significantly
explain individual salesperson performance (Churchill et al. 1985). A
number of researchers have investigated the connections between SFA
adoption and the age or experience of the adopters. Two investigations
(Buehrer et al. 2005; Keillor et al. 1997) have found that younger sales reps
were more positively inclined towards technology adoption. Less
experienced salespeople appear to be more receptive to using computer
technology in the sales process, feel less occupationally threatened by such
technology, and generally believe computers make them more productive
(Keillor et al. 1997). Ko and Dennis (2004) argue that highly experienced
sales reps will gain the least performance benefits from SFA system use.
Others have argued that age has a negative effect on usage (Morris and
Venkatesh 2000; Speier and Venkatesh 2002). Finally, studies investigating
the gender and adoption relationship identified significant differences
among men and women in terms of approaching information technology
(Gefen and Straub 1997; Venkatesh and Morris 2000).
Chapter 5: Research Model and Hypotheses
121
5.6. Logical Structure
Before concluding this chapter, we present a brief discussion on the logical
structure of our research model. This discussion is necessary as the
distinction between process and variance theories has important
implications for the interpretation of our hypotheses.
The ‘necessary, but not sufficient’ cause-effect argument essentially
characterizes process theories and differentiates them from variance theories
(Markus and Robey 1988). In variance theories, the antecedent (the cause) is
posited as a necessary and sufficient condition for the outcome. The effect is
expected to happen every time contingent conditions are obtained. In
process theories, in contrast, the antecedent is assumed insufficient to cause
the outcome, but is held to be just a necessary condition for it to occur
(Mohr 1982). The outcome can happen only under such necessary
conditions, but the outcome may also fail to happen.
Variance and process theories also differ in their conceptualization of
outcomes and precursors. In variance theories, these constructs are usually
conceptualized as variables which can take on a full range of values.
Increased levels of antecedent variables are expected to lead to equally
higher levels of the outcome. In process theories, however, outcomes are
conceived as discrete or discontinuous phenomena, which might be called
‘changes of state.’ (Soh and Markus 1995) For this reason, contrary to
variance theories, process theories cannot be extended to predict what
happens when there is more of the precursor variable.
The assumption of an invariant relationship between antecedents and
outcomes posited by variance theories may be too stringent for IT business
value research, where outcomes are not always certain—sometimes
Chapter 5: Research Model and Hypotheses
122
occurring, sometimes not (Soh and Markus 1995). By limiting the
prediction to say only that the outcome is likely (but not certain) under some
conditions and unlikely under others, process theories should better fit in
our research purposes. Therefore we argue that our conceptual framework
should be interpreted according to the assumptions of process theories.
While we hypothesize that certain conditions (perceived usefulness, ease-of-
use, and so on) are ‘necessary’ for increased SFA-use and salesperson
performance; we do not imply that these factors are ‘sufficient’ in
themselves for these dependent variables to occur. In the end, we are
dealing with social phenomena which depend on many different factors,
impossible to capture altogether in an empirical study. We also tend to avoid
the proposition that higher levels of independent variables will necessarily
cause higher levels of dependent variables. Such a generalization, again for
the same reason of dealing with complex social phenomena, would probably
be too bold for a single empirical study.
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123
6. EMPIRICAL STUDY
6.1. Introduction to the Chapter
We have conducted a quantitative study to empirically test our hypotheses.
In this chapter we present our research methodology. This discussion
includes the decisions we made regarding the study design, sample
selection, data collection procedure and developing our measures with
formative and reflective items.
6.2. Empirical Design
The choice of an adequate research method should mainly be based on the
type of research problem investigated (Kerlinger 1986). Therefore, each of
the choices made in this section is evaluated in light of the specific problem
investigated in this study.
6.2.1. Non-Experimental Design
Research strategies in social and behavioral sciences can be divided into two
general types: experiments and surveys
44
(Crano and Brewer 2002). Surveys
include all observations that occur in ‘natural’ (i.e., non-laboratory) settings
and involve a minimum of interference over people’s normal behavior or
choices, whereas experiments include those observational studies in which
44
The terms ‘non-experimental design’ and ‘ex post facto design’ are also used to call
surveys in literature.
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data are collected under conditions where behavioral choices are limited or
in some way constrained by the controlled manipulation of variables and
measures selected by the researcher (Crano and Brewer 2002). In this study,
we apply a non-experimental as opposed to an experimental research
method. First, non-experimental designs are plausible in cases like ours
where the researcher has no direct control over the study’s independent
variable(s) (e.g., managerial support, facilitating conditions), as their
manifestations have already occurred and/or they are not manipulable
(Stone 1978). Second, while experimental research generally allows
obtaining high levels of internal validity as a result of the possibility to
control, randomly assign, and manipulate independent variables, its
artificiality and lower external validity are considered to be weaker elements
(Black 1999). In contrast, surveys have the value of ‘real world’ context and
the availability of mass data in developing information about human actions
(Crano and Brewer 2002). As our study aims at generating generalizable
results, external validity is important. Third, a major advantage of
correlational research is that it permits the free variation of both variables of
interest so that the degree of relationship between them can be determined
without the loss of information inherent in the experimental design (Crano
and Brewer 2002).
However, one potential drawback of non-experimental designs is the
inability to document causality. In most of the non-experimental studies,
both independent and dependent variables are measured concurrently. In
case the two are found to be related to one another, it is concluded that the
independent variable is responsible for changes in the dependent. However,
“since the researcher often knows little or nothing about numerous other
variables that may be impacting upon either or both of the study’s
‘independent’ and ‘dependent’ variables, the conclusion of a causal
relationship between the two is totally unjustified” (Stone 1978, p. 104). For
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125
this reason, a possible conclusion that one variable ‘causes’ the other should
be made with caution. Nevertheless, Buttle and others (2006) argue that
non-experimental design could be valuable in SFA research by comparing
early-adopters with non-adopters:
Researchers have been unable to say with
confidence that salespeople and companies that have
adopted SFA perform better than companies that
have not. None of the research has compared data
from companies that have employed SFA with
comparable companies that have not. No control
groups have been employed. As SFA becomes
commonplace in business-to-business environments,
the opportunity to conduct this research will be lost.
However, there will still be opportunities within
individual companies to measure outcomes at the
level of the salesperson. Do early adopters in a sales
force obtain significantly different sales results from
those who have not adopted? This would be
indicative of experimentally valid effects of SFA on
performance (p. 228).
We suggest that, by comparing salespeople who have successfully adopted
SFA technology in terms of beliefs, attitudes and sales results with others
who have not; we can achieve significant insights regarding the role of SFA
in personal selling and sales management.
Chapter 6: Empirical Study
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6.2.2. Cross-Sectional Design
Survey research can either be cross-sectional (i.e., surveys completed at a
single point in time) or longitudinal (i.e., gathering data over multiple
periods). Longitudinal design is recommended to reduce the threat of
common method variance (CMV) bias inherent in cross-sectional design and
enhance causality inference (CI) (Podsakoff and Organ 1986; Podsakoff et
al. 2003). However, longitudinal surveys may raise several potential
problems, such as confounds due to intervening events and a reduction in
sample size due to respondent attrition (Rindfleisch et al. 2008). On the
other hand, CMV bias is not caused only by cross-sectional design but it is a
byproduct of the research process as a whole, including measurement
procedures, the choice of respondent, and the study context (Ostroff et al.
2002). The risk of these influences can be reduced by appropriate empirical
design strategies, many of which can be employed in a cross-sectional
survey (Podsakoff et al. 2003).
45
Moreover, creating temporal separation
between initial and follow-up data collection (i.e., longitudinal design) may
not necessarily enhance CI in cases where “relational ties appear to have
already passed their start date at the time of the initial survey” (Rindfleisch
et al. 2008, p. 273). Moreover, CI depends on covariation and coherence in
addition to temporal order, both of which can be dealt with by cross-
sectional study designs as well.
Overall, we have decided to implement a cross-sectional design as (1) our
constructs were relatively concrete and verifiable (i.e., based on existing
sales literature), (2) low levels of response bias were expected due to our
informant characteristics (i.e., salespeople are highly educated adults); (3)
we have applied heterogeneous formats and scales to disrupt consistency
45
The particular measures taken at data collection are discussed in Section 6.4.
Chapter 6: Empirical Study
127
biases and increase validity; (4) it was difficult to mark our predictors with a
defined end date; (5) our research model had well-established theoretical
foundations; (6) we expected intervening events to be likely (i.e., high
employee turnover in sales forces in general); (7) the likelihood of
alternative explanations was low; and (8) our study focused on between-
subject arguments (Rindfleisch et al. 2008).
6.2.3. Data Collection Method
Non-experimental research designs can consist of observation as well as
survey methods of data collection. Given our focus on relatively abstract
attitudes and other perceptual data, observational research methods are not
useful in the context of this study. Therefore, we opt for survey research in
our study. A survey may be defined as a method of gathering information
about a number of individuals, in order to measure some characteristics or
opinion of its respondents (May 1993). Surveys involve administering
structured and standardized questions to individuals which reduce bias and
ensure reliability, generalizability and validity. A survey may measure one
or more of the following things: attitudes, opinions, and demographic
characteristics of a subject (Stone 1978).
Survey research consists of personal interviews, telephone interviews and
mail questionnaires (Webb 2003). The mail questionnaire presents a
uniform stimulus to all subjects (i.e., each subject receives an identical
questionnaire) and avoids the biases resulting from researchers’ subjectivity
related to interpreting observed behavior inherent in studies of observation
and interviews (Webb 2003). Among these options we select questionnaire
administered via the Internet (Dillman et al. 1998). In comparison to mail
questionnaires, online questionnaires deliver similar response rates
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128
(Kaplowitz et al. 2004). An online questionnaire enables to cover wide
geographical areas with almost no field staff (Stone 1978). The anonymity
that often accompanies an online questionnaire may lead subjects to be more
open and truthful than they would be in an interview situation (Webb 2003).
Respondents can fill in the online questionnaire in their own time at their
convenience. Online questionnaires are delivered almost instantly, responses
and feedback are quick, they are cheaper than paper-based mail
questionnaires, and the messages are read usually by the respondent (Kumar
1999).
On the other hand, even though an online questionnaire is sent to named
individuals through their e-mail addresses, there is no way of knowing who
exactly fills it (Webb 2003). Furthermore, there is no one to explain
possibly ambiguous questions (Stone 1978). In general, mail questionnaires
suffer low response-rates, especially the ones which are perceived to be long
(Stone 1978). Finally, online questionnaires are not feasible in cases where
respondents lack computer literacy (Webb 2003).
Nevertheless, online questionnaire was particularly appropriate in our case.
Our respondents were geographically spread in an overseas country, they
were used to working with computers, and they had regular access to the
Internet (Zukerberg et al. 2000).
46
With the assistance of an external IT
professional we have created an online questionnaire which could be
answered with any computer connected to the Internet.
According to Podsakoff and his colleagues (2003), the most preferred data
collection strategy for reducing CMV bias and increasing CI is to employ
multiple respondents or obtain multiple sources of data. Although this
46
Refer to Section 6.3 for information on our sample
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129
sounds attractive, it would necessitate identifying respondents personally
which was not desirable by management and the employees. Moreover,
actual sales amount depends on many factors and objective sales data would
not necessarily represent our dependent variable, salesperson performance.
Therefore, we relied on our respondents as the only data source to test our
hypotheses.
6.3. Sampling
We chose the pharmaceutical industry as the setting for this research. This is
a profitable industry in general, enjoying scientific developments, new
treatments and faster drug discovery. However, narrowing product
pipelines, expiring product patents, intense competition and price scrutiny
from governments have led to a decline in margins and mergers and
acquisitions in the industry (Devitt 2003). In this climate, increasing costs to
generate awareness and improve customer-focused service make it vital that
resources, especially personal sales resources, are efficiently and effectively
deployed, both before a product reaches the market and in the early stages
of product launch (Kager et al. 2002).
Pharmaceutical salespeople
47
are responsible for marketing and selling
48
product lines directly to physicians. Salesreps carry information about
existing and newly released products to physicians, encouraging the
physician to accept and prescribe their company’s products (drugs), rather
than their competitors’ products, to their patients. A busy physician often
47
Pharmaceutical salespeople are often referred to in the industry as ‘sales representative’
or shortly ‘salesrep’.
48
Pharmaceutical selling activity is often called as ‘detailing’ in the industry.
Chapter 6: Empirical Study
130
needs to rely on the information provided by the salesrep in addition to
reading scientific journals and joining medical associations in order to keep
up with the newly introduced drugs (Ahearne et al. 1999; DeSarbo et al.
2002).
Each salesrep is normally responsible for a specific geographical area (i.e.,
territory) and a specific specialty for a given set of drugs. Salesreps work to
increase the knowledge of disease states and firm’s products during their
interactions with the physicians in their territories. Salesreps call on doctors
quite often, implying that doctors become very familiar with the salesreps’
behaviors and characteristics. In this context, a salesrep’s role is not to sell;
they cannot take direct orders that immediately translate into a sale (i.e.,
missionary salesperson, Moncrief 1986). Rather, salesreps inform and
educate physicians regarding their products that require multiple rounds of
presentations. A physician’s prescription of a drug that is purchased by a
patient makes the ‘sale’ for a sales representative responsible for that
territory. Thus, a sales representative’s performance is tied to the number of
prescriptions that are filled, and his or her ability to meet or exceed the
predefined quota for sales is tied closely to his or her compensation.
Pharmaceuticals-selling is often selected as a suitable context to investigate
SFA technology.
49
While salesreps maintain face-to-face contact with
physicians, they use technology for retrieval of prior contact information
and for planning purposes (Widmier et al. 2002). Sales representatives can
manipulate and analyze sales and market data through the use of IT
(Morgan and Inks 2001). Communication among colleagues and with the
home office is critical in pharmaceuticals industry, and IT tools such as e-
49
Refer to Ahearne et al. 2007; Ahearne et al. 2008; Donaldson and Wright 2004;
Leonard-Barton and Deschamps 1988 for studies with pharmaceuticals-selling as the
research context.
Chapter 6: Empirical Study
131
mail and groupware can facilitate such communication. (Powell et al. 2004).
We have chosen a middle-sized pharmaceutical firm to collect empirical
data (the same firm from which the qualitative data were obtained). A leader
in the pharmaceutical industry with headquarters in Europe, the firm
develops and markets pharmaceutical products throughout the world
through application of the latest research from their own laboratories with
multiple locations across the world. The firm’s vision is to respond to the
medical needs across the Globe for the purpose of saving and improving
lives while also trying to reduce health-care costs. The firm is organized by
both functional and geographic business units. Research and manufacturing
are organized in separate centralized business locations, but the sales
department is organized by geographic region.
Study participants were salespeople who worked for the Brazilian division
of the firm, which is responsible for all sales within Brazil. The Brazilian
affiliate has marketing and other support functions, but its largest
component is the sales department selling patent-protected prescription-only
drugs to different types of customers (e.g., primary care physicians,
specialist physicians, nurses, pharmacists). The success in managing these
patent-protected products, each of which has only a limited life before the
patents expire, is extremely important to the firm’s success. In the present
company, the sales representatives are responsible for marketing directly to
physicians, rather than to managed-care organizations or hospitals. All sales
representatives receive training for each of these product lines and receive
support from top management. A division sales manager supervises several
sales representatives. A salesrep's compensation consists of a fixed salary
and flexible commissions based on individual performance.
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The company provided salesreps with desktop computers and PDAs which
salesreps regularly synchronize with central system via their desktops. SFA
systems are specially designed to support the pharmaceutical salesperson in
all major tasks such as call planning, post-call reporting, territory
management and analysis, communication with other salespeople and sales
managers. The system also provides updates on product information and
company marketing activities such as customer profiling, product
information and competitive product profiling. These screens deliver
insights about the customer and sales environment through digital
dashboards and consultative reviews which the salespeople can refer to
when preparing for each sales call. Sales representatives and their managers
received training on the technology prior to implementation and follow-up
sessions after the system implementation.
The company provided a good sample frame for testing our empirical model
as it fulfilled certain conditions necessary for our research:
(1) Salespeople conducted typical internal and external B2B sales tasks
(e.g., they did not sell to end-consumers),
(2) There was a broad array of IT applications available to the sales force
since a long time ensuring stable usage patterns among salespeople,
(3) The use of technologies was voluntary such that variance in IT usage
among sales representatives existed,
(4) Technology skills varied across salespeople,
(5) The sales force was large enough to support statistical tests of the
hypothesized relationships, and
(6) Management would encourage participation in the survey.
Choosing the respondents from a single firm (as opposed to a cross-
sectional study across various firms) had certain advantages. In the present
Chapter 6: Empirical Study
133
setting we developed a close cooperation with management and received
their support for our research and thus could ensure a higher response rate
and minimize non-response bias. Furthermore, by collecting data from a
single firm helped us control for confounding external effects due to the
variability in market contexts (e.g., competitive situations) and
organizational factors (e.g., information systems and sales management
practices). However, the limitation of investigating salespeople from any
single firm can lead to a question of representativeness of the firm and the
generalizability of results. What is being tested in our model is the
theoretical relationship between degree of technology usage and its
relationship to performance, and a rather homogeneous sample free from
external effects is more favorable. Therefore, in this study’s context, having
a single firm was an advantage.
6.4. Data Collection
6.4.1. Questionnaire
In a cross-sectional study like ours, measurement procedures should be
handled properly to avoid CMV bias as much as possible. In this part we
present the measures we have taken to minimize CMV bias when
developing our questionnaire, in line with the recommendations of
Podsakoff and others (2003).
50
50
The questionnaire in full version is available in the Appendix.
Chapter 6: Empirical Study
134
Questionnaire length
The total number of items in a set that constitute an operational definition
will influence the reliability. The more questions are available, the higher is
the reliability (Black 1999). However, long questionnaires carry also the
risk of low response rates. As a consequence, we had to adjust the
questionnaire length to optimize measurement reliability and response rate.
Question sequence
Question order is very important in establishing rapport to help ensure the
quality of the interaction and the truthfulness and completeness of the
answers the respondents provide the researcher (Crano and Brewer 2002). It
is generally recommended to use simple, interesting, and non-threatening
questions at the start of a questionnaire (Crano and Brewer 2002). We have
introduced our questionnaire with a welcome screen that is motivational,
emphasizing the ease of responding, and instructing the respondents on the
action needed for proceeding to the next page. Our questionnaire started
with general questions related to respondent's attitude to computers and
technology in general. Second, demographic information should be asked at
the end of the questionnaire, as the basic information should come last in
case respondents discontinue answering questions. Third, scholars agree that
difficult or sensitive questions should be positioned towards the middle in a
questionnaire (Black 1999). Questions related to SFA-use and sales
performance of the respondent, which are generally regarded as more
threatening, were asked in the middle part of our questionnaire. Finally, we
feared that skipping from topic to topic in a random fashion might confuse
respondents and cause errors in the data. Therefore we divided our
questionnaire into several logical parts such as overall IT competence,
company support for SFA and opinions about sales profession.
Chapter 6: Empirical Study
135
Questionnaire layout
Physical characteristics of a questionnaire can affect the accuracy of the
information obtained. We have presented each question in a conventional
format similar to that normally used on paper questionnaires (see figure
6.1). We placed anchors at the top of the scales on the right. We have
limited the line length and the number of questions given in one screen to
avoid scroll bars which might confuse the respondents. We did not require
respondents to provide an answer to each question before being allowed to
answer any subsequent ones (Dillman et al. 1999). We have used a progress
bar to convey a sense of where the respondent in the completion progress is
(Dillman et al. 1999).
Figure 6.1: Questionnaire Layout (from an earlier test version in English)
Chapter 6: Empirical Study
136
Questionnaire instructions
It is very important to provide clear instructions to respondents. We have
provided computer operation instructions as part of each question where the
action is to be taken, not in a separate section prior to the beginning of the
questionnaire. It is a common practice to distinguish instructions from
questions by using distinctive appearance. In our questionnaire, instructions
were located immediately above the corresponding questions in a separate
box.
Questionnaire translation
The original questionnaire was developed in English. As the questionnaire
was administered in Brazil, the original English questionnaire had to be
translated into Portuguese. One professional translator first translated the
original English version of the questionnaire into Portuguese. The translator
was a native speaker of Brazilian Portuguese and fluent in English. In a
second step, the quality of the translation was evaluated by a native
Portuguese and two Brazilian colleagues from the sample on clarity and
comprehensiveness of the translated questionnaire.
6.4.2. Data Collection
A major challenge with surveys is to succeed in getting the subjects to
return the questionnaire (Black 1999). The degree to which sample
estimates truly represent population parameters depends upon how similar
the survey's respondents and non-respondents are. As the response rate of a
survey increases, errors in the estimates due to non-response decrease (Cole,
Palmer and Schwanz 1997). Incentives, multiple contacts, and respondent-
friendly questionnaires are in general the response-enhancing techniques
that have been shown to increase mail response rates across research studies
Chapter 6: Empirical Study
137
and over time (Cole et al. 1997). Moreover, the saliency of the topic for the
respondent is a strong determinant of response rate, as the respondent will
probably be more confident that his personal input will be of some
importance to the study (Heberlein and Baumgartner 1978).
Advance notifications are known to increase response rates (Fox et al. 1988;
Kanuk and Berensen 1975). In July 2007, the sales director of Brazil sent an
e-mail to all salesreps one week in advance introducing our research and
informing them about the upcoming survey. One week after the sales
director has sent another e-mail to all salesreps with a cover letter and a link
to our survey. The cover letter appealed to the subjects as well as assured
them that any information would be kept strictly confidential. This is
essential even when no names are requested (Black 1999). The researchers’
names and contact information were given in the cover letter to emphasize
university sponsorship (Fox et al. 1988; Kanuk and Berensen 1975). A four-
week deadline was also given in the cover letter. Simultaneously the
company has placed a banner on the company Intranet informing
salespeople about our study and placed a link to our survey. Following up
after the initial contact is shown to increase response rates (Heberlein and
Baumgartner 1978; Fox et al. 1988). Before the deadline expired the sales
director has sent a reminder e-mail to every salesperson and informed them
about a one-week extension of the deadline. Our efforts produced 244
usable responses representing an 82% response rate.
Non-Response Bias
One of the most important issues is to ensure that non-response was not due
to some aspect of the questionnaire itself that the instrument did not offend
or for some other reason prevent the person from responding (Baumgartner
and Steenkamp 2001). To examine response bias, late respondents were
compared to early respondents for meaningful differences (Armstrong and
Chapter 6: Empirical Study
138
Overton 1977). The observations were ordered by the questionnaires’ return
dates and divided into upper and lower quartiles to provide the groups of
late and early respondents. A t-test was then performed for a number of
variables across the early and late respondent groups. For all variables and
reported performance, these t-tests displayed no meaningful differences
between late and early respondents.
Common-Method Bias
When dependent and independent variables are collected from the same
source, common method variance, variance that is attributed to the
measurement method rather than the constructs of interest, may represent a
potential problem (Podsakoff et al. 2003). Following Podsakoff and Organ
(1986), Harman’s one-factor test was used to examine the extent of this
bias. All items were entered into an exploratory factor analysis, using
principal component analysis with Varimax rotation, to determine the
number of factors that are necessary to account for the variance in the
variables. The analysis revealed that there are eight factors with eigenvalues
greater than 1.0 which accounted for 68% of the total variance. Common
method variance does not represent a serious problem because several
factors were identified, the first factor did not account for the majority of the
variance, and there was no general factor in the un-rotated structure
(Podsakoff and Organ 1986).
51
Secondly, we added a common method
factor to our structural model to explicitly estimate the amount of common
method variance in our indicator variables (Liang et al. 2007). The largest
method variance was under 4%, and no substantial common method bias is
present in our sample (Podsakoff et al. 2003).
51
Refer to the Appendix for the results of the Harman’s test.
Chapter 6: Empirical Study
139
6.4.3. Describing the Sample
We calculated the descriptive statistics of our sample to make comparisons
against any known population characteristics and to assess its
generalizability. On average, respondents reported 30.13 years of age, 7.01
years of job experience in sales and 4.92 years average tenure within the
company. 31 percent of the sample was female. These statistics reflect an
average sales force in the literature, demonstrating generalizability of our
sample.
52
6.5. Item Generation and Testing
Modern measurement methods distinguish observable variables from
theoretical constructs (Fassott and Eggert 2005), where the latter can be
described as “an abstract entity which represents the ‘true’, nonobservable
state or nature of a phenomenon” (Bagozzi and Fornell 1982, p. 24). As
theoretical constructs, by definition, cannot be directly measured, they are
often called ‘latent variable’ (Homburg and Giering 1996). In contrast,
observable variables can be directly observed and called ‘indicators’ in
empirical research (Fassott and Eggert 2005). The distinction between
observable and latent variables is given in figure 6.2.
52
Further details of the descriptive statistics are given in the Appendix.
Chapter 6: Empirical Study
140
Figure 6.2: Theoretical and Observable Levels in Empirical Research
(source: Bagozzi 1998, p. 50)
The objective of empirical measurement is to specify the relationship
between observable variables and latent constructs and thus to make a
theoretical construct empirically accessible and measureable (Homburg and
Giering 1996). The strength of theoretical conceptualizations rests in their
operationalization through observable indicators. Therefore, measurement
quality of indicators plays an important role in empirical research.
According to the classical test theory, the variation in the scores on
measures of an observable construct (observed score, X
O
) is a function of
the real score of that measure (true score, X
T
), plus error (Jarvis et al. 2003).
The fundamental objective in measurement is to obtain an X
O
which
approximates the X
T
of that variable as closely as possible (Churchill 1979).
In principle, the researcher can only infer the X
T
score through the X
O
. The
quality of this inference can be estimated through indices of construct
reliability and validity. A measure is taken as valid “when the differences in
Chapter 6: Empirical Study
141
observed scores reflect true differences on the characteristic one is
attempting to measure and nothing else (X
O
= X
T
)” (Churchill 1979, p. 65).
Validity thus constitutes the conceptual accuracy of a measure. On the other
hand, the measure will be reliable when the error is kept at minimum (error
= 0). Reliability thus depends on the size of the error term. Peter and
Churchill (1986, p. 4) define reliability as “the degree to which measures are
free from random error and thus reliability coefficients estimate the amount
of systematic variance in a measure.” It is possible to reliably measure a
variable (i.e., with null error) but it is the validity which ensures that it is the
variable of interest. Thus, a measure can be reliable but not valid, while a
valid measure is always reliable (Carmines and Zeller 1979; Peter 1979).
It is generally acknowledged that multi-item measures should be preferred
to single-item measures in order to measure constructs, where multiple
indicators are applied to measure one latent variable (Churchill 1979). A
first advantage of such a multi-item scale is its ability to better capture the
full domain of multifaceted and complex constructs (Homburg and Giering
1996). Second, multi-item scales allow the assessment of reliability and
validity (Dillon et al. 1993). Third, by applying multi-item measures one
can make relatively fine distinctions between people. Last but not least,
reliability tends to increase and measurement error to decrease as the
number of items in a combination increases (Nunnally 1978).
We applied multi-item scales to measure our variables of interest. In the
next section, we discuss the methods used for generating and testing scale
items. We start with reflective constructs, followed by formative indexes of
SFA-use.
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6.5.1. Reflective Constructs
The links between latent variables and their corresponding indicators
(correspondence rules) define theoretical concepts in empirical terms
(Fornell 1989). The direction of the correspondence rules specifies whether
indicators define the latent variable or vice versa (Fassott and Eggert 2005).
In this line of reasoning, Jarvis and others (2003) distinguish two types of
latent variable measurement models, namely principal factor model and
composite latent variable model. In the principal factor model, covariation
among the measures is caused by, and therefore reflects, variation in the
latent factor (Jarvis et al. 2003). In this model, the direction of causality
(correspondence rules) is from the construct to the indicators, and changes
in the underlying construct are hypothesized to cause changes in the
indicators, thus the measures are referred to as reflective (Fornell and
Bookstein 1982) or effects (Bollen and Lennox 1991) indicators (See figure
6.3). Thus, reflective indicators are perceived to be observations
(reflections) of the underlying construct (Homburg and Giering 1996).
Figure 6.3: Principle Factor Model (source: Jarvis et al. 2003)
Chapter 6: Empirical Study
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In the principal factor model with reflective indicators, error terms are
captured at individual indicators level (Homburg and Dobratz 1998). The
portion of the variance shared among the indicators is interpreted as the
variance of the latent variable cleaned from error terms (Fassott and Eggert
2005).
Constructs of attitude can be given as typical examples of reflective
measurement. Attitudes are generally viewed as subjective predispositions
to respond in a consistently favorable or unfavorable manner toward an
object and are usually measured on multi-item scales with end-points such
as good-bad, like-dislike, and favorable-unfavorable (Jarvis et al. 2003).
Reflective constructs are widely applied in marketing and sales literature.
Theoretically, reflective indicators are equally valid indicators of the
underlying construct and therefore expected to be internally consistent and
highly correlated (Bollen and Lennox 1991). Therefore, two highly
correlated reflective measures are assumed to be interchangeable (Jarvis et
al. 2003). Thus, although it would lower the overall reliability, it is not
harmful to remove a single indicator from a set of reflective indicators as all
facets of a unidimensional construct should be adequately represented by
the remaining indicators (Bollen and Lennox 1991). The extent to which
reflective items are correlated will inform about the fit of a reflective
measurement model (Fassott and Eggert 2005). A high correlation between
reflective indicators of a latent variable is interpreted as a reliable and valid
measurement model (Homburg and Giering 1996).
We have applied reflective indicators to measure some of our constructs.
There are a number of measure development guidelines focusing on
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developing reflective measures.
53
In the next part we present the steps we
have taken when developing our reflective constructs.
Content Specification
The first step in the suggested procedure for developing better measures
involves specifying the domain of the construct. Churchill (1979)
recommends setting clear borders of the construct domain to describe what
is included in the definition and what is excluded. Poor construct
conceptualization makes it difficult to develop measures that faithfully
represent its domain, leads to difficulties in correctly specifying how the
construct should relate to its measures, and finally undermines the
credibility of a study’s hypotheses (MacKenzie 2003).
Qualitative techniques (literature review, expert interviews, etc.) can be
applied to observe a construct from various angles and understand meaning
of its underlying dimensions (Homburg and Giering 1996). Existing
construct definitions in the literature should also be considered as “the use
of different definitions makes it difficult to compare and accumulate
findings and thereby develop syntheses of what is known” (Churchill 1979,
p. 67).
Accordingly, we first scanned the available literature for the presence of
constructs that are of interest to our study. Second, sources containing these
constructs were examined for their construct conceptualizations. This
examination of literature resulted in valuable insights related to definitions
of our constructs. We present our construct definitions in table 6.1.
53
Anderson and Gerbing 1982; Bagozzi 1979; Churchill 1979; DeVellis 1991; Homburg
and Giering 1996; Jacoby 1978; Peter 1979; Spector 1992
Chapter 6: Empirical Study
145
Table 6.1: Reflective Construct Definitions
Construct
Definition
Supervisor SFA Control
Supervisor SFA control is the extent to which a
supervisor specifies the activities he expects
salespeople to perform using sales technology,
monitors to see whether they are performing those
activities, and (3) informs them how they are
meeting his or her expectations (Kohli et al. 1998).
Facilitating Conditions
Facilitating conditions is the degree to which a
person believes that he or she has been provided
with the resources and the external support (e.g.,
tutorials, training sessions, help-lines) to use sales
technology (Triandis 1979).
Supervisor Support
Supervisor support refers to the extent to which
salespeople’s immediate supervisors explicitly
encourage their subordinates to use sales technology
(Schillewaert et al. 2005).
Team Use
Team use of sales technology is the extent to which
the members of a sales team rely on sales
technology in conducting their day-to-day activities.
Perceived Usefulness
Perceived usefulness is the degree to which a person
believes that using sales technology enhances his or
her job performance (Davis 1989).
Perceived Ease of Use
Perceived ease-of-use is the degree to which a
salesperson believes that using sales technology is
easy to use (Davis 1989).
Computer Self-Efficacy
Computer self-efficacy is a salesperson’s
Chapter 6: Empirical Study
146
perceptions of his or her ability to use sales
technology in the accomplishment of a task
(Compeau and Higgins 1995).
Salesperson Performance
Salesperson performance is the extent to which a
salesperson finds him or her better than company
average in terms of sales results.
Item Specification
The second step in the procedure for developing measures is to generate
items which capture the domain as specified. When developing measures of
a construct, the goal is to make sure that (a) all key aspects of the conceptual
definition are reflected in the measures, (b) the items are not contaminated
by the inclusion of things that are not part of the conceptual domain, and (c)
the items are properly worded (e.g., unambiguous, specific, no leading
questions, no double-barreled questions) (MacKenzie 2003). Existing
literature again served as a basis for drawing a comprehensive picture of
existing measurement scales for each of the constructs examined.
Measurement scales for all constructs were available, but some of them had
to be adapted in order to suit our sample environment. In table 6.2 we
indicate the sources that were used as input in order to generate items for
measuring the reflective constructs in this study.
Chapter 6: Empirical Study
147
Table 6.2: Sources of Reflective Measurement Items
Construct
Source
Supervisor SFA Control
Challagalla and Shervani 1996;
Cravens et al. 1993; Javorski and
MacInnis 1989; Kohli et al. 1998;
Oliver and Anderson 1994;
Piercy et al. 2003;
Rouziès and Macquin 2003
Facilitating Conditions
Hunter and Perreault 2006; Jelinek et
al. 2006; Robinson et al. 2005a
Supervisor Support
Avlonitis and Panagopoulos 2005;
Leonard-Barton and Deschamps
1988; Schillewaert et al. 2005;
Speier and Venkatesh 2002
Team Use
Jelinek et al. 2006;
Schillewaert et al. 2005
Perceived Usefulness
Davis 1989
Perceived Ease of Use
Davis 1989
Computer Self-Efficacy
Brinkerhoff 2006
Salesperson Performance
Avlonitis and Panagopoulos 2005;
Behrman and Perreault 1982
Measurement Format of Items
We have applied Likert method to construct our scales, where items are
presented in a ‘multiple choice’ format and participants are asked to pick
one of the alternatives that indicate the extent to which they agree with the
position espoused in the item (Crano and Brewer 2002). Surveys that
employ a single-scale format (e.g., a seven-point Likert scale) and common-
scale anchors (e.g., ‘strongly disagree’ versus ‘strongly agree’) are believed
Chapter 6: Empirical Study
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to be especially prone to CMV bias as repeated contact with a single format
and/or anchor will reduce cognitive processing and thus encourage straight-
line responding that has little to do with actual item content. The influence
of measurement procedures can be reduced through measurement separation
in a cross-sectional approach by employing different formats and scales for
predictors versus outcomes (Crampton and Wagner 1994; Lindell and
Whitney 2001). We used different anchors to measure attitudes, opinions
and views (agree – disagree); behavior (never – more than once a day); and
outcome (above average – below average). Our scales have seven-point
spread as opposed to five points to better represent the range of answers and
increase variance across respondents (Black 1999).
Qualitative Item Testing
It is generally recognized that data collection should never begin without an
adequate pre-test of the content and physical appearance of items (Churchill
1995). Item pre-testing is considered as testing items on a small sample for
the purpose of improving these items by identifying and eliminating
potential understandability problems. Pre-tests are also recommended to
check the content relevance of the indicators for the latent construct
(Homburg and Giering 1996). Variables with unclear formulations or
missing relevance to the latent variable of interest should be removed from
the scale.
The items were pre-tested by selected salespeople of the company in
different countries with a sample of 6 (Brazil, UK, and Belgium). Care was
taken that tested salespeople were similar to those included in the final data
collection in terms of age, gender, and familiarity with the topic.
Respondents were asked to complete the questionnaire after which they
Chapter 6: Empirical Study
149
were asked to evaluate item wording, describe the meaning of each
question, to explain their answer, and to state any problems they
encountered while answering questions. Moreover, respondents were asked
to comment upon item sequence and layout. After each session, they
described the major problems encountered. Salesperson pre-tests have lead
to considerable adaptations of item wording, sequence, and layout. Based
upon the literature study and the pre-tests, an initial pool of items was
formulated. In the next section we present the analytical approaches we
applied to further test our items by means of quantitative pilot data.
Quantitative Item Testing
It is necessary to test items’ validity and reliability based on quantitative
data. Four types of criteria for validity and reliability of reflective
measurement items are suggested in literature (Götz and Liehr-Gobbers
2004).
Content validity is the extent to which the variables of a measurement model
belong to the construct (Bohrnstedt 1970). This property of the scale, having
each of its measurement items relate to it better than to any others, is known
as unidimensionality (Gerbing and Anderson 1988). Unidimensionality is an
assumption underlying the calculation of reliability. Unidimensionality
should therefore be assessed for all multiple-indicator constructs before
assessing their reliability (Hair et al. 1998). Exploratory factor analysis is a
suitable method to investigate unidimensionality (Vinzi et al. 2003). When
all indicators are shown to belong to their respective factors in factor
analysis, further investigation of validity and reliability can be made
(Homburg and Giering 1996; Krafft et al. 2005).
Chapter 6: Empirical Study
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Item reliability reports the variance of an item explained by the
corresponding latent variable. Individual item reliability can be assessed by
examining the correlations (loadings) of the measures with their respective
construct (Hulland 1999). An item loading of 0.7 is accepted as necessary
(Hulland 1999). Since loadings are correlations, this implies that more than
50 percent of the variance in the observed variable (i.e., the square of the
loading) is due to the construct rather than error variance (Carmines and
Zeller 1979).
54
Convergent validity
55
demonstrated collectively by the indicators should be
considered when multiple items are used to measure a latent construct
(Bagozzi and Baumgartner 1994; Bagozzi and Phillips 1982; Rodgers and
Pavlou 2003). Convergent validity requires that the indicators which are
assigned to a latent variable strongly correlate with each other. Cronbach’s
alpha is a popular measure of internal consistency (Cronbach 1951).
56
The
square root of Cronbach’s alpha is the estimated correlation of the k-item
test with errorless true scores (Nunnally 1967). Cronbach’s alpha thus
indicates the success of the sample of items in correlating with the true
scores (Churchill 1979). Fornell and Larcker (1981) suggest another
internal consistency measure
57
as an alternative to Cronbach’s alpha and
argue that their measure is superior to the alpha since it uses the item
loadings obtained within the nomological network (or causal model).
Nonetheless, the interpretation of the values obtained is similar, and the
guidelines offered by Nunnally (1978) can be adopted for both. Specifically,
Nunnally suggests 0.7 as a benchmark for ‘modest’ composite reliability,
54
In exploratory settings, items with loadings of 0.5 can still be tolerated (Hulland 1999)
55
Convergent validity is referred also as ‘composite reliability’, ‘internal consistency’, or
‘construct reliability’ in literature
56
Cronbach’s alpha, Į = (k / (k-1)) (1 - (Ȉ ı
i2
/ ı
t2
)), where k is the number of indicators,
ı
i2
is the variance of indicator i, and ı
t2
is the variance of all indicators.
57
Internal consistency = ((Ȉ Ȝ
yi
)
2
) / ((Ȉ Ȝ
yi
)
2
+ Ȉ var (İ
i
)), where Ȝ
yi
is the loading of each
item of the measure and İ
i
is the error of measurement.
Chapter 6: Empirical Study
151
applicable in the early stages of a research.
It is also suggested that convergent validity is shown when each of the
measurement items loads (outer model loadings) with a significant t-value
on its latent construct (Gefen and Straub 2005). Typically, the t-value
should be significant at least at the 0.05 level (t-value being above 1,645 at a
one-sided test) (Hildebrandt 1984).
Discriminant validity complements internal consistency and represents the
extent to which measures of a given construct differ from measures of other
constructs in the same model. One criterion for adequate discriminant
validity is that a construct should share more variance with its measures
than it shares with other constructs in a given model (Bagozzi et al. 1991;
Hulland 1999). To assess discriminant validity, Anderson and Gerbing
(1993) suggest the use of average variance extracted (AVE).
58
For each
specific construct, AVE shows the ratio of the sum of its measurement item
variance as extracted by the construct relative to the measurement error
attributed to its items (Fornell and Larcker 1981). This measure should be
greater than the variance shared between the construct and other constructs
in the model (i.e., the squared correlation between two constructs). This can
be demonstrated in a correlation matrix which includes the correlations
between different constructs in the lower left off-diagonal elements of the
matrix, and the square roots of the average variance extracted values
calculated for each of the constructs along the diagonal. For adequate
discriminant validity, the diagonal elements should be greater than the off-
diagonal elements in the corresponding rows and columns. In addition, an
AVE less than 0.5 will be insufficient, as the majority of the variance in
such a case would depend on the error term (Homburg and Giering 1996;
58
Average Variance Extracted (AVE) = ȈȜ
i2
/ (ȈȜ
i2
+ Ȉ (1- Ȝ
i2
)), where Ȝ
i
is the loading of
each measurement item on its corresponding construct.
Chapter 6: Empirical Study
152
Rodgers and Pavlou 2003). Finally, Gefen and Straub (2005) recommend to
examine cross-loadings, as the correlation of the latent variable scores with
the measurement items needs to show an appropriate pattern of loadings,
one in which the measurement items load highly on their theoretically
assigned factor and not highly on other factors.
Reflective indicators are assumed to be equivalent and interchangeable
reflections of the same construct. Therefore, reflective indicators which
demonstrate weak correlations can, in principle, be eliminated from the
model (Churchill 1979; Anderson and Gerbing 1982; Homburg and Giering
1996). In this way it is possible to ex post increase the fit of a measurement
model (Fassott and Eggert 2005).
We have collected data from another sales force in Belgium (n=39) as part
of a pilot study to further assess the reliability of our measures.59 Overall,
the results demonstrated an appropriate fit of our reflective constructs. After
a number of minor adjustments and eliminating at least one item, we have
finalized the development of our reflective items.60
Table 6.3 Table 6.4
6.5.2. Formative Constructs
The composite latent variable model differs from the principal factor model
in terms of the direction of corresponding rules. Unlike the reflective model,
this model does not assume that the measures are all caused by a single
underlying construct. Rather, it hypothesizes that the measures together
have an impact on (or cause) a single construct. That is, the direction of
causality flows from the indicators to the latent construct, and the indicators
59
We present the results of o ur statistical tests in table 6.6 in the Appendix.
60
Refer to T able 6.7 in Appendix for the last ver sion of the reflective items before they
were translated into Portuguese.
Chapter 6: Empirical Study
153
together determine the conceptual and empirical meaning of the construct
(figure 6.4) (Diamantopoulos and Winklhofer 2001). Thus, this model’s
measures are referred to as causal (Bollen and Lennox 1991) or formative
(Fornell and Bookstein 1982) indicators.
Figure 6.4: Composite Latent Variable Model
(Source: Jarvis et al. 2003)
As can be seen in the figure, the composite latent variable model includes an
error term, as does the principal factor model. However, unlike the principal
factor model, error is represented at the construct level rather than at the
individual item level. Thus, when using this model, one obtains an estimate
of the overall amount of random error in the set of items rather than an
estimate attributable to each individual item. While this information allows
evaluating the reliability of the scale, it is less prescriptive about how the
scale can be improved, because the error is associated with the set of items
rather than the individual items themselves (Diamantopoulos and
Winklhofer 2001).
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154
Formative models do not require correlation between the measures, as it is
assumed that formative measures influence—rather than are influenced
by—the latent construct (Cohen et al. 1990; MacCallum and Browne 1993).
There is no reason that a specific pattern of signs (i.e., positive vs. negative)
or magnitude (i.e., high vs. moderate vs. low) should characterize the
correlations between formative indicators. Indeed, internal consistency is of
minimal importance because two variables that might even be negatively
related or mutually exclusive can both serve as meaningful indicators of a
single construct (Diamantopoulos and Winklhofer 2001). “Observed
correlations among the measures associated with a construct may not be
meaningful, rendering irrelevant traditional assessments of individual item
reliability and convergent validity” (Hulland 1999, p. 201). As a result,
measures of internal consistency should not be used to evaluate the
adequacy of formative indicator models.
In return, the evaluation of formative measurement models should be based
on the weights of the indicators (Helm 2005). “The weights provide
information as to what the make-up and relative importance are for each
indicator in the creation/formation of the component.” (Chin 1998, p. 307)
The weight of a formative construct specifies its contribution to a latent
variable (Sambamurthy and Chin 1994). Furthermore, Bollen and Lennox
(1991) note that “to assess validity we need to examine other variables that
are effects of the latent construct” (p. 312). This can be done by placing the
formative construct into a broader model and observing its behavior in the
presence of other latent variables. Last but not least, formative indicators
should be tested for multicollinearity, as collinear formative indicators
comprise a significant problem for measurement model parameter
estimates.
61
61
More detailed discussion on the multicollinearity issue is given in Section 6.5.2.
Chapter 6: Empirical Study
155
Formative indicators differ from reflective indicators also when handling the
indicators with weak properties. The latter are actually a subset of a
universal item pool and therefore the removal of a reflective item does not
change the essential nature of the underlying construct. In contrast,
formative indicators together form a conclusive index to reflect a latent
construct, thus omitting an indicator is “omitting a part of the construct”
(Bollen and Lennox 1991, p. 308). Therefore, for formative indicator
models, following standard scale development procedures (e.g., dropping
items that possess low item-to-total correlations) “will result in the removal
of precisely those items that would most alter the empirical meaning of the
composite latent construct” (MacKenzie 2003, p. 324). Doing so could
make the measure deficient by restricting the domain of the construct
(Churchill 1979).
To date, established measurement models for SFA-usage have been of
reflective nature (e.g., Jelinek et al. 2006). According to the decision rules
provided by Jarvis and others (2003), however, our task-based usage
dimensions require formative measurement models (see table 6.3). First, the
choice of a formative versus a reflective specification depends on the causal
priority between the indicator and the latent variable (Bollen 1989).
Arguably, causality flows from the items representing technology-enabled
sales tasks to the SFA-use dimensions. Second, the indicators are not
interchangeable; each task represents a unique aspect in its respective
dimension. Third, while the tasks may coincide they do not need to correlate
with one another – neither within a usage dimension nor between
dimensions. For example, the extent to which a salesperson applies SFA to
analyze data does not need to correlate with the extent of using SFA to
report his or her sales activities. Fourth, we expect the tasks along our SFA-
use dimensions to have differing antecedents and consequences.
Consequently, we opt for formative specification to operationalize our SFA-
Chapter 6: Empirical Study
156
use dimensions.
Table 6.3: Decision rules for determining whether a construct should be
formative or reflective (source: Jarvis et al. 2003)
As formative indicators altogether define a corresponding latent construct
and ex post elimination of weakly correlated indicators are not feasible, item
generation takes a significant role in determining the measurement fit of
formative scales (Rossiter 2002). Four issues are suggested to be critical for
successful index construction: content specification, indicator specification,
indicator collinearity and external validity. In the following sections we
present the steps we have taken when developing our two SFA-use
constructs, namely customer relationship and internal coordination.
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Content Specification
The first issue in index construction is the specification of the scope of the
latent variable, that is, the domain of content the index is intended to capture
(Diamantopoulos and Winklhofer 2001). The “breadth of definition is
extremely important to causal indicators” (Nunnally and Bernstein 1994, p.
484), as the failure to consider all facets of a construct will lead to an
exclusion of relevant indicators and thus exclude part of the construct itself.
In our case, we specify the domain of content of the focal constructs
customer relationship and internal coordination dimensions of SFA-use.62
Our objective is to capture in broad terms the range of tasks which are
possible to carr
y out through a t
ypical SFA system. See Table 6.4 for
construct definitions.
Table 6.4: Formative Construct Definitions
Construct
Definition
Customer Relationship
Customer relationship dimension of SFA-use is the use of
an SFA system to serve customers, to collect, analyze
and manage customer information, to plan and execute
sales calls and to develop sales skills with the overall
objective of better managing customer relationships.
Internal Coordination
Internal coordination dimension of SFA-use is the use of
an SFA system to communicate within organization to
manage team-selling, to communicate with management,
to report sales calls and to manage various administrative
tasks and to attend electronic training sessions.
62
Refer to Chapter 4 for a more detailed discussion of SFA-use dimensions
Chapter 6: Empirical Study
158
Item Specification
The items used as indicators must cover the entire scope of the latent
variable as described under the content specification. Cohen et al. (1990)
suggest that when the relationship is formative, researchers must be careful
to employ strong theory (to identify appropriate measures) and multiple
measures (to ensure acceptable content validity). Bollen and Lennox (1991,
p. 307) require that researchers “need a census of indicators, not a sample.
That is, all constructs that form [the underlying construct] should be
included.” Therefore, the indicator specification stage should be sufficiently
inclusive in order to capture fully the construct's domain of content.
Table 6.5 lists the items generated to be used as formative indicators for the
customer relationship and internal coordination dimensions. Reflecting on
the indices of Engle and Barnes (2000) and Doll and Torkzadeh (1998), we
produced a conclusive list of twelve selling tasks potentially enabled by the
SFA systems in our sample countries. Relying on our definitions of SFA-
use dimensions the items were assigned to their respective usage dimension.
In writing up the items, conventional guidelines regarding clarity, length,
directionality, lack of ambiguity, and avoidance of jargon were followed
(e.g., Churchill 1979). A seven-point Likert format was used for scoring
(never vs. several times a day).
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159
Table 6.5: List of Formative Items
Construct
Items
I use my computer…
Customer Relationship
1. To serve customers.
2. To improve the quality of customer service.
3. To identify most important customers from the list of
potential customers.
4. To plan selling activities.
5. To prepare sales calls.
6. To analyze call and sales data.
7. To record and retrieve customer call information.
Internal Coordination
1. To learn about our existing and new products.
2. To report travel expenses to headquarters.
3. To receive information from, or provide information to,
my manager.
4. To coordinate activities with my team members.
5. To develop my sales skills.
Indicator Collinearity
Multicollinearity happens when a particular indicator turns out to be almost
a perfect linear combination of the other indicators. Whereas in reflective
measurement model high correlation between indicators and thus
multicollinearity is assumed, it represents a problematic issue for formative
indicators (Backhaus et al. 2003). The formative measurement model is
based on a multiple regression, where each indicator coefficient (Ȗi) shows
the direct structural relation between indicator and latent variable, and the
magnitudes of Ȗs can be interpreted as validity coefficients (Bollen and
Chapter 6: Empirical Study
160
Lennox 1991). Therefore, excessive collinearity among indicators would
make it difficult to isolate the distinct influence of the individual indicators
on a latent variable, making the assessment of indicator validity problematic
(Diamantopoulos and Winklhofer 2001). In cases of high multicollinearity,
an indicator is likely to contain redundant information and can therefore
become a candidate for exclusion from the index (Bollen and Lennox 1991).
Under reflective measurement, multicollinearity is not an issue because only
simple regressions are involved (in which the indicator serves as the
criterion and the latent variable as the predictor).
A few methods have been suggested in literature to investigate
multicollinearity in a formative measurement model. One method is to
calculate a linear regression where one indicator is explained by other
indicators. This linear regression calculation will give a coefficient of
determination (R
2
), which is the proportion of dependent variable’s variance
explained by the independent variables. This R
2
value should be close to 0
in order to rule out multicollinearity (Hair et al. 1998). This R
2
value is then
used to calculate the Variance Inflation Factor (VIF),
63
which is then based
on the part of the variance explained by other indicators. VIF reaches its
minimum value of 1, when R
2
is minimum, namely 0. While literature gives
no exact answer to the maximum value of VIF to rule out multicollinearity,
a rule of thumb suggests that VIF value should be below 10, and preferably
as small as possible, such as 2 (Kleinbaum et al. 1988).
64
63
Variance Inflation Factor = 1 / (1-R
j2
) where R
j2
is the coefficient of determination for
variable j when explained by other indicators.
64
We conducted a VIF analysis for the formative items based on the pilot data collected in
Belgium (n=39). Only one item received a VIF above 3 (VIF=3,279) and rest of the
items were around 2.
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161
External Validity
The fourth and last step in construct specification is checking external
construct validity. Taken in isolation, the formative indicator measurement
model is statistically under-identified; the model can therefore be estimated
only if it is placed within a larger model that incorporates antecedents
and/or consequences of the latent variable in question (Diamantopoulos and
Winklhofer 2001). Therefore, one approach to ‘qualify’ formative indicators
for the measurement model is to include the entire construct in a wider
nomological context, meaning that other constructs and their relationships to
the construct in question have to be measured (Bagozzi 1994). If the
construct has the theoretically hypothesized impact on the other constructs
in the structural model, this confirms the nomological validity of the
measurement models used (Diamantopoulos and Winklhofer 2001; Eggert
and Fassott 2003).
65
65
Due to the limited sample size in our pilot study (n=39), we report the results of external
validity test based on the actual data in Chapter 7. In our conceptual model the two
formative dimensions significantly relate to other reflective constructs in expected way
and confirm their external validity.
Chapter 7: Data Analysis
162
7. DATA ANALYSIS
7.1. Introduction to the Chapter
Chin (1998) calls for the adequate reporting of an empirical study to assist
the review process and reliable replication and argues that literature could
accumulate only with complete documentation. “Enough information needs
to be provided to understand (a) the population from which the data sample
was obtained, (b) the distribution of the data to determine the adequacy of
the statistical estimation procedure, (c) the conceptual model to determine
the appropriateness of the statistical models analyzed, and (d) statistical
results to corroborate the subsequent interpretation and conclusions.” (Chin
1998, p. viii)
Accordingly, we document our empirical findings in this chapter based on
the data we collected from a pharmaceuticals sales force in Brazil. Section
7.2 describes choices and underlying motivations related to the use of
Partial Least Squares (PLS) for data analysis. Section 7.3 clarifies the steps
that were taken for examining the properties of the raw data set. In sections
7.4 and 7.5, we evaluate the performance of the measurement model and the
structural model respectively.
Chapter 7: Data Analysis
163
7.2. Data Analysis Method
Recent advances in multivariate data analysis techniques have made it
possible to simultaneously examine measurement quality and theoretical
basis. For instance, Causal Modeling
66
is a multivariate technique that
facilitates testing of the psychometric properties of the scales used to
measure a variable, as well as estimating the parameters of a structural
model – that is, the magnitude and direction of the relationships among the
model variables.
Causal modeling techniques can be taken as superior to more traditional
techniques (e.g., regression, factor analysis) that assume error-free
measurement. Causal modeling techniques (1) account for the harmful
effects of measurement error, and (2) apply multiple indicators to
incorporate abstract and unobservable constructs (i.e., latent variables) that
cannot be measured directly (Fornell 1982). Bagozzi (1980) suggests further
that causal models are beneficial as they add a degree of precision to a
theory, since they require clear definitions of constructs, operationalizations,
and functional relationships.
One of the best-known causal modeling techniques applies covariance-
based structural equation modeling, applying maximum likelihood
estimation and using computer software such as LISREL, AMOS and EQS
(Jöreskog and Sörbom 1989; Hagedoorn and Schakenraad 1994). However,
maximum likelihood techniques impose strict assumptions of normally
distributed residuals and interval scaling. Furthermore, such covariance-
based approaches are poorly suited to deal with small data samples (Fornell
1982) and can yield non-unique (factor indeterminancy) or inadmissible
66
Also called as ‘Structural Equation Modeling’ in literature
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164
solutions (solutions outside the admissible parameter space) (Fornell and
Bookstein 1982).
An alternative causal modeling approach applying component-based
estimation procedure and known as Partial Least Squares (PLS) has been
developed to avoid these limitations (Lohmoller 1989; Wold 1974, 1982,
1985). Under this approach, it is assumed that all the measured variance is
useful variance to be explained. PLS estimates the latent variables as exact
linear combinations of the observed measures, thereby avoiding the factor
indeterminancy problem and providing an exact definition of component
scores (Gopal et al. 1992). By using a fixed-point estimation technique, PLS
provides a general model that encompasses, among other techniques,
canonical correlation, redundancy analysis, multiple regression, multivariate
analysis of variance, and principal components.
PLS estimation is sometimes considered superior to covariance based
approaches (Chin 1997). PLS uses an iterative algorithm consisting of a
series of ordinary least squares analyses. For this reason, identification does
not represent a problem for recursive models and no distributional form is
assumed for measured variables in PLS method. Because it makes no
distribution assumptions, PLS is robust to violations of multivariate
normality (Igbaria et al. 1995).
67
For its aforementioned advantages, the
PLS procedure is gaining increasing interest and use among researchers
from personal selling and sales management
68
, marketing
69
and information
systems fields
70
.
67
Refer to Chin and Newsted 1999 and Gefen et al. 2000 for a more detailed comparison
of covariance-based and components-based approaches.
68
Guenzi et al. 2007; Rangarajan et al. 2005; Sundaram et al. 2007
69
Fornell, Tellis, and Zinkhan 1982; Reinartz et al. 2004; Smith and Barclay 1997; Ulaga
and Eggert 2006; Zinkhan, Joachimsthaler, and Kinnear 1987
70
Armstrong and Sambamurthy 1999; Bhattacherjee and Sanford 2006; Burton-Jones and
Hubona 2006; Compeau and Higgins 1995; Igbaria et al. 1995; Real et al. 2006
Chapter 7: Data Analysis
165
The philosophical distinction between covariance and components-based
approaches is whether to use structural equation modeling for theory testing
and development or for predictive applications (Anderson and Gerbing
1988; Chin 1997). In situations where prior theory is strong and further
testing and development is the goal, covariance based full-information
estimation methods are more appropriate. However, due to the
indeterminacy of factor score estimations inherent in this approach,
predictive accuracy will be limited. In contrast, PLS methodology, which
uses the Ordinary Least Squares (OLS) algorithm, is often more suitable for
application and prediction where theory is not as well developed (Chin
1997; Fornell and Bookstein 1982; Igbaria 1990).
In addition to situations with limited theory, PLS is considered as better
suited also for explaining complex relationships (Chin et al. 2003; Fornell et
al. 1990). As stated by Wold (1985), “PLS comes to the fore in larger
models, when the importance shifts from individual variables and
parameters to packages of variables and aggregate parameters. (…) In large,
complex models with latent variables PLS is virtually without competition.”
(pp. 589~590)
Another strength of PLS is its suitability to work with small to medium
sample sizes. Chin, Marcolin and Newsted (2003) demonstrate with a
Monte Carlo study that sample size is not constrained by the number of
product indicators as would be the case in covariance-based estimations,
which require increasingly larger sample sizes as the number of indicators
grows. Nevertheless, Chin (1998) suggests that a researcher should use a
rule of thumb, where the overall sample size is 10 times the largest of two
possibilities: (1) the block with the largest number of indicators (i.e., the
largest measurement equation) or (2) the dependent variable with the largest
number of independent variables impacting it (i.e., the largest structural
Chapter 7: Data Analysis
166
equation).
The presence of identification constraints, due to the formative indicators,
makes it problematic to use a covariance-based approach (MacCallum and
Browne 1993). As a components-based approach, PLS allows for the use of
both formative and reflective measures in the same model, which is not
generally achievable with covariance based techniques (Chin 1998; Chin
and Newsted 1999; Fornell and Bookstein 1982).
Another difference between covariance based modeling approaches and
PLS is that there are no proper overall goodness-of-fit measures for models
using the latter (Hulland 1999). The structural model in a PLS approach is
evaluated instead by examining the R
2
values and the size of the structural
path coefficients.
Since PLS makes no distributional assumptions in its parameter estimation,
traditional parameter-based techniques for significance testing and model
evaluation are considered to be inappropriate (Chin 1998). The stability and
precision of the estimates is examined by using the approximate t-statistics
and standard deviations obtained from the bootstrap test available in PLS
software (e.g., PLSGraph, SmartPLS). In this procedure, the performance of
an estimator of interest is judged by studying its parameter and standard
error bias relative to repeated random samples drawn with replacement from
the original observed sample data (Chin 1998; Wold 1982). This overcomes
non-parametric methods’ disadvantage of having no formal significance
tests for the estimated parameters.
We chose PLS approach against covariance-based techniques to estimate
our research model because of the mixed nature of our model (i.e., SFA-use
dimensions have formative indicators, and the other constructs are
Chapter 7: Data Analysis
167
reflective). We also considered the complex nature of our conceptual model
(i.e., downstream and upstream variables in same model) and the more
restrictive assumptions of covariance-based approaches (i.e., assumptions of
normality) when making our decision. The software we used for execute our
analysis was SmartPLS (Ringle et al. 2005). We proceed with the
examination of our sample data.
7.3. Data Examination
7.3.1. Acceptable Sample Size: Power Analysis
PLS method is particularly suitable to work with small sample sizes.
However, PLS should not be taken as a ‘silver bullet’ to completely ignore
the appropriate sample size (Marcolides and Saunders 2006). “Being a
limited information method, PLS parameter estimates are less than optimal
regarding bias and consistency. The estimates will be asymptotically correct
under the joint conditions of consistency (large sample size) and consistency
at large (the number of indicators per latent variable becomes large).” (Chin
et al. 1997, p. 39)
Despite the common rules of thumb for estimating the appropriate sample
size, Chin (1998) explicitly invites researchers to apply power analysis to
calculate the necessary sample size to certainly reject a poor model. The
statistical power can be plainly defined as the ability to detect and reject a
poor model. Statistical power depends to a large extent on the sample size.
We have used G*Power 3 software
71
to calculate the appropriate sample
71
G*Power Version 3.0.10, Faul et al. 2008
Chapter 7: Data Analysis
168
size for our research model. Based on our input parameters, the power
analysis suggests a satisfactory power of 0.99.
72
7.3.2. Handling Missing Data
Before starting with any analysis of collected data, we first checked whether
coding errors appeared in the raw data sets. As our data collection procedure
was fully automated, we ruled out any coding errors which might originate
from manual data entry. Then, we looked at the missing data. When
handling the missing parts in our data, we preferred to apply two systematic
approaches. To keep the data loss at minimum, first, we have eliminated a
case (i.e., entire data coming from a single respondent) when the dependent
variable or an independent variable was completely missing. Eliminating ten
cases has left us with a sample size of 244. For other cases where no
deliberate data loss (i.e., missing answer for a single question) was detected,
we have applied the mean replacement procedure built in the SmartPLS
software. This procedure replaces missing values with the mean value of the
item and has been executed for 10 single values.
72
We have made a post hoc power analysis to compute the achieved power given Į,
sample size, and effect size with the following parameters: F-tests family/Multiple
Regression: Omnibus (R
2
deviation from zero)/Medium effect size (f
2
=.15)/0.05 error
probability/244 total sample size/7 predictors (i.e., most complex construct in our
model, customer relationship).
Chapter 7: Data Analysis
169
7.4. Measurement Model Evaluation
Although PLS estimates parameters for both the links between measurement
items and latent constructs (i.e., loadings) and the links between different
constructs (i.e., path coefficients) at the same time, a PLS model is usually
analyzed and interpreted sequentially in two stages (Barclay et al. 1995).
The first step requires the assessment of the reliability and validity of the
measurement model. This allows the relationships between the observable
variables and theoretical concepts to be specified. This analysis is performed
in relation to the attributes of individual item reliability, construct reliability,
average variance extracted (AVE), and discriminant validity of the
indicators as measures of latent variables. For the second step, the structural
model is evaluated. The objective of this is to confirm to what extent the
causal relationships specified by the proposed model are consistent with the
available data. This sequence ensures that the researcher has reliable and
valid measures of constructs before attempting to draw conclusions about
the nature of the construct relationships (Hulland 1999). This section starts
with an evaluation of the reflective constructs and continues with the
formative constructs.
7.4.1. Constructs with Reflective Items
We have applied reflective items to operationalize our perceived usefulness
and ease of use, facilitating conditions, computer self-efficacy, managerial
support, team-use and supervisor-SFA-control constructs. In order to
describe our reflective items, we present their mean, median and standard
deviation values.73 The items are rightwards skewed to some extent with
73
Descriptive analysis o f these indicators is given in table 7.1 in the appendix..
Chapter 7: Data Analysis
170
standard deviations between 0.75 and 1.66.
In a PLS setting, the adequacy of the measurement model consisting of
reflective items can be assessed by looking at: (1) unidimensionality of the
constructs, (2) individual item reliabilities, (3) the convergent validity of the
measures associated with individual constructs, and (4) discriminant validity
(Hulland 1999).
Table 7.1
Content Validity (Unidimensionality)
Unidimensionality cannot be measured with PLS but is assumed to be there
a priori (Gefen 2003; Gefen and Straub 2005). To check for
unidimensionality and thus ensure the content validity of our reflective
constructs, we have run an exploratory factor analysis on our reflective
indicators. We have extracted the factors by the principal components
method from the covariance matrix with Varimax rotation. In order to get a
complete exploratory structure, we avoided predetermining the number of
factors and extracted all factors with eigenvalues above 1.74 The exploratory
factor analysis extracted 8 factors with eigenvalues above 1, which together
explain 71% of the variance. It can be seen in the table that there are a few
problematic items which load higher on other constructs than their intended
construct (shown in red color in the table). In particular, the indicators of the
facilitating conditions construct appear to be problematic. Consequently, we
repeated the factor analysis without facilitating conditions items.75 In this
case, all items load uniquely on their intended factors, underlining the
unidimensionality of all constructs. As a result, we conclude that
unidimensionality for all constructs and thus their content validity was
obtained, whereas the facilitating conditions construct requires extra
74
Factor analysis results are given in Table 7.2 in the Appendix.
75
Results of the modified factor analysis are given in Table 7.3 in the Appendix.
Chapter 7: Data Analysis
171
concern in next steps. We proceed with tests of reliability and validity.
Table 7.2 Table 7.3 Table 7.4
Item Reliability
We looked at individual item loadings to investigate item reliability
(Hulland 1999). In our measurement model, all loadings are well above 0.7
with two exceptions which are slightly below 0.7, indicating overall item
reliability.
76
Convergent Validity
The SmartPLS software automatically calculates Cronbach’s alpha and
Fornell and Larcker’s (1981) composite reliability scores. The big majority
has an alpha score above the recommended cut-off level of 0.70 (Nunally
1978). Computer self-efficacy, one exception, has a lower than optimal
alpha score of 0.54. However, all constructs demonstrate high composite
reliability scores above 0.80 with computer self-efficacy having 0.76, above
the recommended 0.70 minimum (Nunally 1978). Therefore, we decided to
retain computer self-efficacy construct along with other successful
constructs in the measurement model. Moreover, all measurement items in
our model load on their latent constructs with significant t-values,
demonstrating further evidence of convergent validity (Gefen and Straub
2005).
Table 7.5 Table 7.6
Discriminant Validity
We establish the criterion of discriminant validity for our constructs in
Table 7.5 in the Appendix. The table consists of a correlation matrix which
includes the correlations between constructs in the lower left off-diagonal
elements of the matrix, and the square roots of the average variance
extracted values calculated for each of the constructs along the diagonal.
76
An overview of the item reliability statistics as well as other validity statistics of our
items is given in Table 7.4 in Appendix.
Chapter 7: Data Analysis
172
The values on the diagonal are greater than the off-diagonal elements in the
corresponding rows and columns, indicating discriminant validity.
Moreover, all constructs have AVE values greater than 0.50, providing
further support for discriminant validity (Homburg and Giering 1996).
Finally, as given in the cross-loadings table computed by the SmartPLS
software (Table 7.6 in the Appendix), the measurement items load higher on
their theoretically assigned factor than on other factors (Gefen and Straub
2005).
Our analysis of reflective items reveals that a number of individual statistics
seem to be problematic. However, they are compensated by strong results in
the remaining tests. In particular, facilitating conditions items failed to load
on the intended factor in the exploratory factor analysis, indicating a
problem in content validity of the construct. However, same items reveal
satisfying discriminant validity statistics. Consequently, we conclude that all
items and scales overall are valid and reliable, and the measurement
properties of our reflective constructs are strong enough to support the
structural model.
7.4.2. Constructs with Formative Items
We have applied formative items to operationalize our SFA-use dimensions,
namely customer relationship and internal coordination. We measured the
customer relationship dimension with 7 formative indicators and the internal
coordination dimension with 5 formative indicators.
Table 7.7
The descriptive statistics of customer relationship and internal coordination
dimensions of SFA-use are presented in Table 7.7. The customer
relationship dimension received higher mean scores in average, indicating
Chapter 7: Data Analysis
173
that salespeople spend more time using these functions of their SFA system
than the functions for internal coordination.
Diamantopoulos and Winklhofer (2001) state that conventional procedures
used to access the validity and reliability of scales of reflective indicators
(e.g., factor analysis and assessment of internal consistency) are not
appropriate for composite variables (i.e., indexes) with formative indicators.
Therefore, Cronbach’s alphas are not reported for our formative constructs.
Nonetheless, it is necessary for formative items to report multicollinearity
analysis results and individual item weights. Our formative items have
indicator weights above .1 and they are all statistically significant.
77
The formative items are tested regarding multicollinearity and external
construct validity. In our case, multicollinearity among the 12 indicators did
not seem to pose a problem as the maximum variance inflation factor came
to 2.86, which is far below the common cut-off threshold of 10 (Kleinbaum
et al. 1988). Therefore, all 12 items were retained.
78
Table 7.8
7.5. Structural Model Evaluation
In this part, we first evaluate the structural paths of our hypothesized model
we presented in Chapter 5. Second, we judge the performance of a rival
model in order to assess whether the hypothesized model is robust against
alternative formulations of structural paths.
77
The results of the multicollinearity analysis are given in table 7.8 in the Appendix.
78
Multicollinearity test results are listed in Table 7.8 in the Appendix
Chapter 7: Data Analysis
174
7.5.1. Results of the Structural Model
For our sample, the estimated structural paths are visualized in figure 7.1.
The model shows the hypothesized relationships between our latent
constructs and their corresponding standardized path coefficients.
Significant path coefficients are marked with (*). Standardized coefficients
are used for comparing the relative strength of path coefficients. Moreover,
the model indicates the coefficient of determination (R
2
) of each
endogenous latent construct, providing a relative measure of fit for each
structural equation.
The multiple coefficient of determination (R2) value may be interpreted in a
manner similar to the way it is in traditional regression analysis, as
indicative of the proportion of variation in a variable that is explained by its
relationship with the variables that are hypothesized to impact it (antecedent
variables). As in traditional regression analysis, the R2 value does not show
causal direction. Rather, causal ordering is specified in the research model,
and is based on theoretical expectations. The path coefficients can also be
interpreted within the context of a regression model (Gopal et al. 1992).
Table 7.9 in the Appendix sets out the proposed hypotheses, the path
coefficients and the t-values observed with the level of significance
achieved from the bootstrap test. We report one-tailed significance levels.
This is appropriate because we exclusively test directional hypotheses.
Table 7.10
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175
Figure 7.1: Structural Model Results
Chapter 7: Data Analysis
176
A first evaluation of the structural model involves checking whether all
significant path coefficients are in the hypothesized direction (i.e., positive
or negative path coefficients). For our sample, all significant relationships
between latent constructs are in the hypothesized direction, providing
support for our conceptual model and its related hypotheses. As
nomological validity is normally assessed by testing the relationships with
other constructs in a nomological framework (Ruekert and Churchill 1984;
Steenkamp and van Trijp 1991), this result provides evidence for the
nomological validity of the constructs integrated in the hypothesized model.
A second evaluation of the structural model is done by checking the R
2
levels of endogenous constructs. Perceived usefulness has an R
2
level of
0.40 whereas perceived ease-of-use has an R
2
at 0.34. While not particularly
high, these levels are not exception in IT acceptance research (Venkatesh et
al. 2003). Furthermore, customer relationship and internal coordination
dimensions of SFA-use report R
2
values of 0.41 and 0.13 respectively. The
internal coordination dimension is strongly linked to the former and the
hypothesized direction of this path explains to some extent the difference
between the R
2
values. These R
2
values may not look very high at first sight.
However, the scales to measure these two constructs are newly developed
for this study (in contrast with the well established and fairly generic
system-use and adoption scales). What's more, applying a two-dimensional
measure of SFA-use construct divides in some sense the explanatory power
of antecedents into two. For these reasons, we argue that the obtained R
2
values for SFA-use dimensions are acceptable. Finally, the salesperson
performance construct reports an R
2
value of 0.12. Again, this value should
be taken as normal in sales research, as there are numerous factors
determining the performance of a salesperson which are not all easy to
incorporate into an empirical study. For example, there are differences in
territory potential unrelated to the efforts of a salesperson (Behrman and
Chapter 7: Data Analysis
177
Perrault 1982). Market position of the firm, company strategy, competitor
activities and macroeconomic developments among others can also play
significant role in determining the end results of a salesperson.
Further evaluation of the structural model is related to testing each of the
hypotheses formulated in Chapter 5. Our parameter estimates reveal several
interesting findings. All hypotheses relating the task-based SFA-usage
dimensions to salesperson performance (H1a-c) could be confirmed. The
customer relationship dimension has a positive and significant direct impact
on salesperson performance (p=.242, t=2.203), while internal coordination
does not (p=-.006, t=.077). As suggested by hypothesis H1b, internal
coordination has a positive and significant impact on the customer
relationship measure (p=.528, t=7.554).
To test the significance of the meditative pattern as suggested in hypotheses
H1a-c, we have applied the approach recommended by Iacobucci and
Duhachek (2003). According to this approach, as given in figure 7.2 below,
a mediating effect can be established when the indirect effect ‘a x b’ is
significant. To test for significance, the z-statistic of Sobel (1982) is
applied.
79
If the z-value exceeds 1.96 (at p < .05) the null hypothesis can be
rejected, i.e., internal coordination has no indirect impact on salesperson
performance via the customer relationship dimension.
79
z = (a * b) / ¥ (b
2
* s
a2
+ a
2
* s
b2
+ s
a2
* s
b2
), where a and b are path coefficients and s
i2
is
the variance.
Chapter 7: Data Analysis
178
Figure 7.2: Illustration of the Mediating Effect
The indirect effect of internal coordination on salesperson performance is
significant according to Sobel’s z-statistic (z=2.64) (Sobel 1982).
80
Together
with the non-significant direct effect of internal coordination on salesperson
performance (i.e., path c=-.006), this result establishes the customer
relationship dimension as a perfect mediator between internal coordination
and salesperson performance.
With respect to PU and PEU as mediators of technology usage (H2a-c), we
find an interesting pattern. PU has a positive and significant impact on using
SFA-technology as a customer relationship tool (p=.229, t=2.513) while
PEU drives its use for internal coordination tasks (p=.148, t=1.857). In line
with TAM, PEU significantly explains PU (p=.427, t=4.891).
80
z = (.242 * .528) / ¥ ((.528)
2
* (.096)
2
+ (.242)
2
* (.078)
2
+ (.096)
2
* (.078)
2
) = 2.64
Chapter 7: Data Analysis
179
Turning further upstream to our exogenous variables (H3a-i), we could
confirm the positive and significant impact of supervisory support on PU
(h3a, p=.152, t=1.912) as well as the positive impact of facilitating
conditions (h3d, p=.259, t=3.017), computer self-efficacy (h3e, p=.213,
t=3.621), and team usage (h3f, p=.310, t=3.820) on PEU. A positive impact
of supervisor support on PEU (h3b) and facilitating conditions on PU (h3c)
could not be confirmed. Team usage has also a significant impact on PU
(h3g, p=.184, t=2.062). As hypothesized, team usage has a direct impact on
using SFA systems for internal coordination (h3h, p=1.401, t=1.704). The
expected direct link between supervisory SFA control and SFA usage was
only significant for internal coordination tasks (h3i, p=.156, t=1.889) but not
for the customer relationship dimension. Thus h3i was only partially
supported. Among the control variables we have inserted into our model to
explain salesperson performance, while sales experience (p=.259, t=3.218)
and gender (p=-.119, t=1.645) had a significant impact, age (p=-.090,
t=1.100) had none.
Concluding, we obtained significant support for most of the hypothesized
main effects. An additional means for assessing the robustness of the
hypothesized model is to compare this model to a rival model. In Section
7.5.2, we present the results of comparing our hypothesized model to a rival
model.
7.5.2. Evaluation of a Rival Model
For any given SEM model, there will often be alternative models that are
equivalent in terms of overall model fit (Chin 1998). For instance,
MacCallum and others (1993) show that such equivalent models exist in
published studies, often in large numbers. Such models may produce
Chapter 7: Data Analysis
180
substantially different explanations of the data. Therefore, it is often
recommended to compare alternate models to test the robustness of the
original proposed model (Bollen and Long 1992; Hair et al. 1998; Morgan
and Hunt 1994).
In order to assess the robustness of our hypothesized model, we formulated
a rival, less parsimonious model positing direct relationships from
antecedents not only to SFA-use, but also to salesperson performance,
Moreover, in the rival model, we estimated direct paths from perceived
usefulness and ease-of-use to performance. Although this rival model has
never been suggested in literature, there is some support for some of the
unmediated paths estimated in the rival model.
81
Table 7.9
We compared the hypothesized model with the rival model on the following
criteria: (1) overall fit of both models as measured by R
2
in PLS setting, (2)
parsimony of both models, and (3) percentage of both models’ hypothesized
parameters that are statistically significant.
82
With respect to the overall fit
of both models, the R
2
of the dependent variables in the rival model are
overall slightly higher than the R
2
of dependent variables in the original
model. However, in order to achieve this slight increase in R
2
, an additional
17 paths were needed to be estimated in the rival model, reducing this
model’s parsimony. Moreover, only 36% (12 of 33) of the paths in the rival
model were significant as opposed to 75% (12 of 16) in the original model.
The robustness of the hypothesized model is further supported as all
significant effects in the original model are equally significant in the rival
81
Karahanna and others (2006) test the direct impact of individual compatibility beliefs on
technology usage. Avlonitis and Panagopoulos (2005) find a positive relationship
between perceived usefulness and salesperson performance. Leong (2003) tests the
unmediated impact of management support and system quality on system use.
Schillewaert and others (2005) insert direct links from external variables to SFA
adoption in their conceptual model.
82
The comparison of the R
2
values is given in Table 7.10 in the Appendix
Chapter 7: Data Analysis
181
model. After all, given the low sacrifice in R
2
and the major gain in
parsimony, we find support for the robustness of the hypothesized model.
.
Chapter 8: Discussion
182
8. DISCUSSION
8.1. Introduction to the Chapter
[Future research] will require reexamination of
previous findings in light of what we encounter
through new or unusual research methods,
relationship to a stronger nomological framework,
better examination of generalizability, and
observational studies. (…) What we do need is
careful consideration of where the results of each
study fit, what the results mean to all of those studies
that were mentioned in the positioning (and what
they might mean now), and, perhaps most
importantly, what is the highest priority for our
research in order to develop understanding of either
theory, practice or both. (Tanner 2002, pp. 570-571)
With these words Tanner (2002) explains what should be expected from a
good discussion of research findings. Indeed, this last chapter is particularly
important as it is where the contribution of our study lies. Our intention is
therefore to meet Tanner’s criteria by portraying what our findings mean for
literature and practice, and to draw a new picture of the literature in light of
our study findings. Equally important, we will later present the limitations
inherent in our study and future research possibilities inspired by our study
to extend our knowledge in this area.
Chapter 8: Discussion
183
8.2. Implications for Theory
Understanding how technology influences organizational efficiency and
effectiveness should be a research priority in today's technology-intensive
world (Raman et al. 2006). Such an understanding can help organizations
gain the competitive advantage they seek through their technology
investments.
Computers are nowadays widely available at increasingly lower prices and
they have become a commodity for most businesses. For this reason, IT, by
itself, does not represent a source of absolute competitive advantage
anymore (Carr 2003). The same amount of investment (this investment can
be made for the same technology) in two different organizations may lead to
success in one organization and failure in the other. According to Grover
and others (1998), “what really matters is the extent to which IT is
effectively utilized in the organization, not the sheer amount of investment
in that technology.” (p. 144)
The same situation applies in the sales field as well. Investing in SFA tools
alone should not be enough to achieve competitive advantage. As Honeycutt
and his colleagues (2005) argue, “such an advantage can only be gained via
adoption of cutting-edge SFA tools that accomplish more than competitive
SFA tools, or by providing a smoother transition for the sales force and,
more importantly, for customers when implementing SFA.” (p. 319) SFA’s
contribution to a sales organization will depend more on why and how it is
deployed than the absolute amount of investment made.
We argue throughout our study that past research approaches relying only
on the ‘extent’ of IT deployment (for example, IT expenditure or adoption),
Chapter 8: Discussion
184
are limited in their capacity to explain how IT generates business value. To
shed light on the underlying mechanism, our research proposes a task-based
multidimensional perspective on SFA-usage. Our literature review yielded
two generic yet meaningful usage dimensions, which are further supported
by a series of qualitative interviews. As confirmed by our quantitative study,
both dimensions have distinctive groups of antecedents and different effects
on salesperson performance, lending support for a multidimensional
perspective on SFA usage. In this section we discuss our research findings
and present their implications for personal selling and sales management
research.
First contribution
The first contribution of our study is the task-based measurement of SFA-
use. Salespeople typically use only a fraction of the available SFA
functionality (Donaldson and Wright 2004) and they differ significantly in
their choice of SFA functionality to adopt. Therefore, assessing usage with
reflective measures may not sufficiently capture the entire scope of SFA
application. Furthermore, SFA-use appears to be an abstract construct which
may mean different things to different people. A measurement approach to
more precisely distinguish SFA users from each other is necessary. Speier
and Venkatesh (2002) recommend studying SFA-adoption at the task-level
to better capture the perceptional differences among salespeople:
A more proactive set of measures that requires the
participant to conceptualize how the technology
could be used for specific activities might capture
inconsistencies in perceptions regarding technology
in use in advance of implementation. (p. 109,
emphasis added)
Chapter 8: Discussion
185
Consistent with Speier and Venkatesh’s (2002) call, we asked salespeople in
our sample to rate the extent to which they use their SFA system when
completing a range of sales tasks. For example, respondents could evaluate
the extent to which they use SFA to plan their selling activities or to analyze
sales data, each task being measured by a separate item. This approach
provides a much granular view of SFA-use than simply asking the
respondent if he or she is using the system or not. By means of this task
based measurement we can examine the way SFA is used in a sales context
at individual task-level.
As we argue in Chapter 3, IT must be generating business value at the
intermediate process level. The main idea is that, IT produces improvements
in business processes, which in return create business value visible at the
bottom line (Mooney et al. 1995, Ray et al. 2004). Research models with
mediating constructs corresponding to the intermediate business processes
are in this sense superior to the microeconomic models, in which IT is taken
as a black box and expected to generate profits by itself. Salesperson
activities at individual level can be assumed to correspond to the
intermediate business processes at firm level. By measuring the extent to
which SFA is used to support individual sales tasks, we can better reflect the
mechanism through which SFA contributes to sales performance. Our task-
based approach thus makes it possible to go beyond the ‘black-box’
approach of reflective measurement of SFA-adoption dominant in literature.
After all, task-based measurement of SFA-use can be instrumental in
proposing more effective models linking SFA-use to organizational
outcomes, where SFA-use pattern has an impact on salesperson
performance. It may also represent a first step towards solving the
ambiguity in conceptualizing and measuring the system-usage construct
which can be a reason for the conflicting results found in existing literature
Chapter 8: Discussion
186
(Buttle et al. 2006).
Second contribution
As a second contribution, our findings demonstrate that SFA-use construct
warrants a multidimensional conceptualization. Multidimensionality of a
construct is normally established when separate dimensions occupy unique
positions in a nomological network as determined by unique sets of
antecedent causes, consequential effects, or both (Iacobucci et al. 1995).
Based on a literature review as well as a qualitative study, we first
conceptualized two generic dimensions of SFA-usage, namely customer
relationship and internal coordination. Then, we demonstrated with a
quantitative study that both dimensions have unique antecedents and
consequences.
The customer relationship dimension covers customer facing tasks such as
identifying the most important customers from a list of prospects and
recording and retrieving customer call information. These tasks support
salesperson in establishing, developing and maintaining customer
relationships. In ideal case, managing customer relationships and realizing a
purchase is the main objective of a sales organization. For this reason, and
recalling Chapters 3 and 4, we argued in our study that the customer
relationship dimension of SFA-use appropriately reflects the operational
processes of Davenport’s (1993) typology.
In contrast, the internal coordination dimension stands for internal
communication and administrative tasks supported by SFA technology such
as coordinating activities with team members and ordering promotional
material from headquarters. These activities are necessary for an efficient
functioning sales department but often not visible to outside the
organization and thus do not necessarily influence customer satisfaction. As
Chapter 8: Discussion
187
the case in our first dimension, the internal coordination dimension is
theoretically justified and corresponds to the management processes of
Davenport (1993).
There is good conceptual and empirical support in recent sales literature for
SFA serving different purposes in a sales organization. Depending on the
study context and research purposes, multidimensional SFA-use constructs
found in literature differ in the number of dimensions and how these
dimensions are defined (Engle and Barnes 2000; Hunter and Perreault 2007;
Moutot and Bascoul 2008). It is becoming clear that SFA technology serves
multiple purposes and future conceptualizations of SFA-use should reflect
this multi-purpose nature of SFA systems. Our findings verify these
previous studies and thus make an incremental contribution to the literature
at first sight.
Nevertheless, we argue that our study makes a noteworthy contribution to
the literature by providing a theoretically sound and applicable distinction to
the uses of SFA technology in sales settings. The difference between
external oriented selling activities and internal oriented administrative tasks
is significant and should not be disregarded. Former group represents
relatively abstract tasks whereas latter stands for well defined, easy to
automate processes. It is arguably unproblematic to document the outcomes
of administrative tasks—explicit knowledge. In contrast, customer oriented
tasks often result in tacit knowledge, which is very difficult to capture
digitally. The well recognized technology acceptance problem seen among
sales forces may be stemming particularly from this abstract nature of the
customer relationship dimension. While our respondents reported a high
acceptance of customer relationship functionality, we speculate that this can
be an exception. In problematic cases we expect salespeople be rather
hesitant to using SFA for the tedious tasks of capturing and reusing
Chapter 8: Discussion
188
customer knowledge, whereas they warmly welcome other ‘basic’
functionality to automate repetitive tasks and save time. In Donaldson and
Wright’s (2004) study this is exactly the case; salespeople prefer using only
the simple functionality for reporting and contact management. Research
efforts in direction of technology acceptance should distinguish these two
different aspects of SFA systems.
Third contribution
Third, our findings contribute to the research stream studying the
organizational consequences of SFA-use. By linking our two-dimensional
SFA-use construct to salesperson performance, we shed more light on the
mechanism through which SFA relates to salesperson performance. We
demonstrate that the customer relationship dimension of our SFA-usage
construct has a direct and significant impact on salesperson performance. In
contrast, our analysis reveals that the impact of internal coordination on
salesperson performance is perfectly mediated by the customer relationship
dimension. This is a robust finding since research efforts to explain how
SFA improves performance are escalating in the literature.
SFA increases salesperson performance when it is deployed in a certain
way. On the one hand, using SFA for customer related tasks, such as
customer analysis, targeting, call planning and preparation, as well as
customer service, helps establish, maintain, and improve customer
relationships, which in turn positively impact the bottom-line. Augmented
customer insight provided by the technology and increased reliability of
salesperson in the eyes of the customer should be the key drivers of positive
outcomes. On the other hand, employing SFA for internal coordination
increases a salesperson’s performance only to the extent that the efficiency
gains are deployed for more effective customer relationship activities.
Chapter 8: Discussion
189
Certainly, one of the greatest promises of SFA technology has been
improved salesperson efficiency, allowing increased customer face time that
eventually results in more effective selling and higher salesperson
performance (Buehrer et al. 2005; Widmier et al. 2002). Furthermore, there
is considerable conceptual argument in literature for using technology to
enable customer relationships to unleash the real potential of selling
technologies (Ingram et al.2002). Hunter and Perreault (2006) maintain that
customer facing applications of SFA will provide bigger benefits:
Case studies in the popular press tend to emphasize
sales automation applications, the focus of which
tends to be on cutting sales force costs or making
more efficient the flow of information needed by the
supplier company. Further, these applications focus
on existing tasks rather than on enabling tasks that
previously were not performed (or performed well).
On the other hand, the ability and effort required of a
sales rep in applying information technology to
come up with integrative, win-win solutions for both
the company and the retailer are less structured and
tend to require more adaptive, custom efforts. Yet it
is this type of application where sales technology
may have a greater impact on the revenue-generating
side of category management efforts. (p. 110)
To the best of our knowledge, our study is the first to reveal a perfect
mediation between SFA-usage dimensions and salesperson performance.
Our study thus provides strong empirical support to the claims in literature
that SFA’s real potential lies in its use for managing customer relationships
effectively.
Chapter 8: Discussion
190
Overall, SFA systems should be considered as part of a wider framework
consisting of strategy, processes and organization with the aim to improve
customer relationships. Indeed, “as organizations recognize the enterprise-
wide nature of CRM, SFA is being overtaken by broader, relationship-wide
(or enterprise-wide) technology” (Tanner et al. 2005, p. 170). We make a
contribution to the literature by empirically justifying the shift from
efficiency focused SFA applications to effectiveness focused CRM
applications in sales forces.
Fourth contribution
Our fourth contribution to the literature is showing that salespeople have
different motivations for using SFA technology in customer facing activities
as opposed to the ‘back-office’ tasks. The customer relationship dimension
is explained by factors that trigger voluntary usage such as perceived
usefulness and indirectly through perceived ease-of-use and supervisor
support. In other words, salespeople use SFA for customer relationship tasks
only when they are convinced that it is instrumental for increased
performance.
Salespeople are concerned most with the benefits offered by new
technology (Gohmann et al. 2005). The salespeople who believe that SFA is
instrumental for better job performance will likely adopt the technology
(Avlonitis and Panagopoulos 2005). Based on our results, perceived
usefulness of SFA is a major driver of the customer relationship dimension
of SFA use. Salespeople who find the SFA system useful to support their
customer relationships are using SFA in that way. Perceived usefulness has
been tested and confirmed as a strong driver of SFA-adoption (Rangarajan
et al. 2005; Schillewaert et al. 2005). However, our study is the first to show
that perceived usefulness of SFA technology drives a certain SFA-use
behavior among salespeople (i.e., customer relationship).
Chapter 8: Discussion
191
Perceived ease-of-use relates strongly to perceived usefulness. This is
consistent with past research (Schepers and Wetzels 2007). Salespeople who
find the SFA easy to administer will have higher chances of applying more
sophisticated and probably more beneficial modules of SFA (these modules
are probably the ones to support customer relationships).
Supervisor support has a significant impact on perceived usefulness of SFA.
Sales manager plays an important role in convincing salespeople for the
value of technology, just as in any aspect of the selling job. This is no big
surprise as sales manager is often the only individual to rate a salesperson’s
job performance and thus have a direct influence on salesperson
compensation. Considering our finding that perceived usefulness relates to
the customer relationship dimension, salespeople must be under influence of
their managers in terms of using SFA to manage their customer
relationships.
Team use has a significant impact on perceived usefulness of SFA.
Salespeople, who work in teams where SFA technology is valued and well
relied on, tend to report higher levels of perceived usefulness for the system.
This effect can have a number of reasons. First, these salespeople may be
readily accepting the team norms regarding the value of the SFA. Second,
they may be enjoying the SFA expertise established in their teams to see
new and helpful uses of the system. Third, they must be benefiting from the
system themselves when managing their team coordination tasks.
In contrast with the customer relationship dimension, the internal
coordination dimension is mostly explained by factors imposed from
outside. Supervisory SFA control, team use and perceived ease-of-use have
a direct impact on internal coordination. However, salespeople's use of SFA
for internal coordination does not depend on perceived usefulness of the
Chapter 8: Discussion
192
technology. Reflecting the state-of-the-art in many companies, reporting had
to be done via the IT system in our sample sales force. Consequently, our
respondents must be using the system for administrative tasks to a certain
extent regardless of their perception of usefulness.
As opposed to perceived usefulness, perceived ease-of-use is significantly
related to the internal coordination dimension. Our study is the first to test
and confirm the link between perceived ease-of-use and back-office related
SFA-use behavior. Salespeople use SFA to coordinate internal activities and
perform administrative tasks when they find the system easy to use. The
usability of an SFA system is an important factor in ensuring that
administrative activities are properly and timely accomplished.
Facilitating conditions provided by the organization such as training and
user support and the confidence of a salesperson with computers in general
together determine the salesperson perception of usability of the system
(perceived ease-of-use). This finding is in line with previous research
emphasizing that IT implementation projects must take user training and
support as priority to establish adequate end-user acceptance of the
implemented technology. Furthermore, team-use appears to be a strong
driver of perceived ease-of-use. This finding is also not a surprise
considering the fact that a salesperson is best supported by his or her
colleagues in the team in case of a usability problem with the SFA system.
In addition to significantly explaining perceived usefulness and ease-of-use,
team-use has a direct impact on internal coordination dimension of SFA-
use. While such unmediated effects of external antecedents on IT-use have
been validated in literature (Burton-Jones and Hubona 2006), our study is
the first to demonstrate that an external factor (team-use) drives SFA-use in
a certain way (internal coordination). It seems that no matter how useful or
Chapter 8: Discussion
193
easy to use the system is, salespeople must be feeling obliged to use the
SFA when their colleagues rely on the system. In such a case SFA becomes
a platform salespeople use to coordinate team selling activities and the
opportunity cost of not adopting the system will probably be very high.
Our supervisor SFA-control construct is also significantly linked to the
internal coordination dimension. This means that salespeople use SFA for
internal coordination when their supervisors closely monitor usage behavior
and penalize its absence. Salespeople comply with their managers’
expectations and use the system where usage is most visible, namely for
internal coordination tasks. On the contrary, customer relationship activities
remain rather opaque for the supervisor and SFA-use for those tasks become
relatively personal for the salesperson. Consequently, the motivational
structure for using SFA-technology differs between identified SFA-use
dimensions.
To sum up, while most of the aforementioned antecedent variables are not
new to the SFA adoption literature (Jones et al. 2002); our study is the first
to investigate how they drive SFA-use behavior in a certain direction. Our
findings illustrate that self-initiating factors primarily drive SFA-usage to
enable customer relationship tasks. In contrast, external factors bring rather
compliance and SFA-use for internal coordination. As a consequence, our
research allows a more fine-grained view of the drivers of SFA usage. In the
next section we discuss the implications of our findings for marketing and
sales management practitioners.
Chapter 8: Discussion
194
8.3. Implications for Management
Against the background of our research findings, we suggest that neither
investing in SFA technology by itself, nor using SFA only to automate
repetitive tasks will bring a company any sustainable competitive
advantage. Our results demonstrate that how salespeople apply SFA
technology in their jobs is decisive for its realized outcomes. It should be of
particular importance that SFA is understood as a strategic initiative
requiring strong awareness in planning and implementation stages. As Leigh
and Marshall (2001) point out, the focus during customer facing technology
deployments
83
should be on strategic issues and not solely on technology:
The strategic issues involved in designing a CRM
system (…) include customer segmentation and
profiling, clearly defined objectives and market
offers, defining critical success factors and
measures, developing customer-driven organization
structures, specifying the role of the sales force and
the Internet, and establishing the means to model
consumer response (Swift 2001). From this
perspective, it is apparent that CRM is a
fundamental business philosophy and process, not
simply an IT application (p. 88).
In our study we empirically distinguish between different dimensions of
SFA technology use, each having differing impacts on salesperson
performance as well as being driven by different sets of antecedents. Our
83
As we argue in Chapter 2, SFA is such a customer facing technology application
oriented towards sales organizations and can be integrated to other business applications
such as e-Commerce and Enterprise Resource Planning (ERP) systems.
Chapter 8: Discussion
195
results reveal a number of implications for management to maximize the
benefit they can obtain from their SFA deployments.
First, SFA is being applied by the salespeople in our sample for two distinct
purposes. This multi-purpose nature of SFA technology has its place in
literature. For instance, all three organizations that participated in a study
reported different objectives for their SFA systems (Bush, et al. 2005). One
was driven more by sales management and emphasized information
exchange for its SFA system. Another was more logistics-driven, adopting
SFA in an attempt to better manage and track its inventory. Finally, the SFA
for the third organization was driven more by corporate goals as it strived
for consolidation of information and efficiency. Overall, as confirmed by
our findings, it becomes clear that SFA technology is a highly flexible tool
and can be applied to serve multiple needs. A company planning to invest in
SFA should therefore be aware that SFA is a multi-purpose technology.
As SFA can serve multiple purposes, the definitions of success and failure
when evaluating an SFA initiative must be contextually defined. Indeed,
Tallon and others (2000) suggest that corporations follow different goals
when implementing IT and studies investigating IT payoffs must control for
these goals:
[Business] executives in corporations have very
different goals for IT, which means that the context
or environment in which IT operates is a key factor
that should be considered by IS researchers
investigating IT payoffs. In that sense, failure to
control for goals for IT is tantamount to assuming
that all corporations are homogeneous with respect
to strategic intent for IT—clearly an erroneous
Chapter 8: Discussion
196
assumption. (p. 166)
In our sample of pharmaceutical salesreps, where customer relationships are
the key for sales performance, the customer relationship dimension
displayed a positive impact on salesperson performance. In this situation,
salesperson’s success in managing relationships and customer satisfaction
levels could be recommended to be good success criteria. However, this
may not be the case in other contexts as the SFA-use dimensions may
operate differently depending on the context. For instance, it may be well
expected that the internal coordination dimension of SFA-use has a direct
impact on performance in industries where organizational efficiency and
cost leadership play a greater role. Efficiency related metrics such as cost-
to-serve or time-to-deliver can be applied in such situations.
Consequently, executive management should set clear objectives for
deploying SFA technology. As Bush and his colleagues (2005, p. 8)
observe: “the first point in managing SFA systems is to know exactly what
the technology is set out to accomplish.” Explicit objectives set in advance
should guide the company in fine tuning the relevant business processes and
selecting the right SFA system to install. Although it may sound obvious at
first, literature reports many companies which fail to have formally defined
the objectives they wish to achieve through the acquisition of SFA
technology (Erffmeyer and Johnson; Rivers and Dart 1999).
After identifying explicit objectives for SFA, the management should timely
communicate them to the sales organization prior to making the investment
decision. Honeycutt and others (2005) at this point recommend receiving
input from the sales force–via focus groups, surveys, and interviews–during
the planning stage. Such a two-way communication will enable salespeople
influence the purchase decision and make them understand what SFA will
Chapter 8: Discussion
197
mean for them in practical terms, what efforts they must undertake, and
finally how they can benefit from the system. These in turn should increase
SFA buy-in among salespeople and match SFA-use with company strategy
(Morgan and Inks 2001). Rangarajan and others (2005) also recommend
management setting clear guidelines and procedures for salespeople using
SFA technology:
The content of the guidelines should explicitly state
(1) the reasons for using the SFA technology, (2) the
possible change in work activities expected from
salespeople due to SFA technology, (3) information
regarding sharing of private customer information
with the rest of the organization, (4) the scope for
monitoring activities of salespeople, and (5)
changing expectations on the job as a result of SFA
technology. (p. 352)
A lack of such communication and missing orientation among salespeople
in terms of the objectives for SFA may have negative consequences.
Gohmann and his colleagues (2005b) report that sales force’s perceptions of
the system may differ substantially from management’s expectations, which
can lead to unrealistic expectations of sales productivity and unmet needs
for the sales force from the standpoint of management. In Stoddard and
others’ (2002) study, sixty percent of non-users reported that their company
did not know what SFA would do for their firm. On the other hand, those
using SFA (who had an average of five years of experience using SFA)
were satisfied with their tools.
Chapter 8: Discussion
198
Our study has further implications for organizations which seek to set
objectives for their SFA investments. Our results suggest that SFA
technology positively impacts sales performance when used to maintain
customer relationships. Our results in that sense supports the argument that
SFA technology helps salespeople free themselves from costly
administrative activities in favor of customer relationship management
tasks, which better suit the skills and abilities of the sales force (Ingram et
al. 2002). Obviously, no technology can replace salespeople in establishing,
maintaining and improving customer relationships. However, technology
can provide salespeople with right information at the right time, target right
customers with right approach, help them hold their promises against their
customers and in the end enable tasks and processes that were not possible
to perform before. It is SFA’s capability to support customer relationships
where the biggest potential for defensible competitive advantage lies.
Therefore, for companies where ongoing customer relationships are
essential, management should set supporting customer relationships as the
major objective to be sought by the SFA deployment. We argue that other
objectives for SFA such as increased speed and efficiency in performing
existing tasks and processes are, in contrast, only a competitive necessity in
today’s markets, and therefore should stay as non-core requirements of an
SFA-implementation project.
To make sure that salespeople use SFA-technology for the effective
management of their customer relationships, supervisors need to rely on
voluntary usage which can be triggered through salesperson’s perception of
usefulness, supervisory support and perception of ease-of-use. Therefore,
sales management has a major role to play in the system acceptance process,
by supporting and encouraging salespeople to use the system and providing
adequate training and technical infrastructure to the sales force.
Chapter 8: Discussion
199
According to our findings, first and foremost determinant of appropriate use
of SFA technology (customer relationship dimension) has turned out to be
salesperson’s perceived usefulness of SFA. Salespeople should believe that
the available SFA tool is capable of supporting customer relationship
processes and thus provides an extra value to the end-user. Bush and his
colleagues (2005) argue in this direction:
The key issue with SFA is to show the value to the
sales organization. The company should try to
achieve early sales rep buy-in to the process if you
expect to be successful. (...) If salespeople do not
understand the changes in organizational processes
(e.g. shift from transactional to relational selling)
there is bound to be resistance to the SFA initiative
and possible SFA failure. (pp. 375-376)
Certainly, implementing SFA almost always brings a noteworthy change in
the sales strategy and organizational processes and it alters the way
salespeople work. According to the new strategy, salespeople may be
provided with SFA technology to support their customer relationships.
However, in the end, the salesperson will be the one who will engage in
relationships with customers. It is therefore at that salesperson’s discretion
to buy-in to the new technology in managing their customer relationships.
For this reason, management should convince the sales force that SFA
technology will be valuable for managing customer relationships. However,
according to Parasuraman and Grewal (2000), this may represent a big
challenge for management:
Chapter 8: Discussion
200
Does giving the employees instant access to detailed
customer information through technology motivate
them to deliver more personalized service and higher
value to customers and thereby foster stronger
loyalty? What employee and organizational
characteristics are likely to determine the degree of
such motivation? (p. 172)
Probably the best way of ensuring that salespeople use the SFA in expected
direction is encouraging them to do so. Our results suggest that management
support for SFA in terms of encouragement is a strong determinant of
perceived usefulness of the system. When salespeople have managers who
themselves believe in the value of the SFA, they will have an additional
incentive to comply. Area manager buy-in for the new system is therefore
crucial for success. Top management should first start at the intermediate
levels of the organization (team managers) when communicating their SFA
strategy.
According to our findings, team-use is another driver of perceived
usefulness. Salespeople who have team-colleagues regularly using SFA tend
to report higher value for their SFA systems. This situation is likely a result
of the interactions between team-colleagues on the usefulness of the
available system. Analogous with word-of-mouth marketing, such messages
must be more convincing and effective when received from equivalent
peers. Furthermore, salespeople can observe their colleagues when using
SFA and discover useful features of the tool. Management can stimulate
such positive interactions by appointing ‘IT-fascinated’ salespeople in sales
teams as SFA champions. These champions can then be asked to promote
the SFA system in their teams and support their ‘less-fascinated’ colleagues
when using the system.
Chapter 8: Discussion
201
An appropriate incentive and organizational scheme to support CRM-
compatible behavior is implied in literature as one of the core requirements
for successful CRM implementation (Reinartz et al. 2004). Campbell (2003,
p. 380) recommends “firms redesign their employee evaluation and reward
structures to promote internal team-based incentives based on concrete
behaviors” during CRM deployments. Correctly selected, communicated,
and monitored metrics provide a clear goal for the sales force (Honeycutt et
al. 2005).
However, our findings tell us that management control and monitoring
behavior of SFA-use has an impact only on internal coordination dimension.
Salespeople may comply but only with respect to their internally-visible
tasks that have been shown not to have a direct impact on salesperson
performance. This is an interesting finding as we argue above that the
customer relationship dimension of SFA-use should be the core requirement
for increased salesperson performance.
In fact, this finding is partly in agreement with previous studies that indicate
increased control relates negatively to SFA adoption (Speier and Venkatesh
2002; Widmier et al. 2002). Moutot and Bascoul (2008) recently report that
SFA reporting functionality has negative effects on the number of proposals
and sales calls. Gohmann and others (2005b) also warn management for the
situation where sales force perceives SFA system as a micromanagement
tool. In such a case, salesperson dissatisfaction and refusal of the SFA
system seem inevitable. Therefore, we advise management that SFA-
monitoring must be aligned with a relationship marketing strategy. The
organizations’ financial reward and incentive policies must reflect the
commitment to improving customer relationships and should be tied directly
to the use of the system (Gohmann et al. 2005b).
Chapter 8: Discussion
202
Our findings support the view that SFA-technology is an effective tool to
facilitate team-selling. What’s more, salespeople working in teams where
SFA is accepted as a means of coordinating team-selling tend to use the
SFA to do so. In such cases salespeople feel rather obliged to use the system
as not doing so would mean falling themselves behind team-selling
activities. We advice management invest in SFA systems which correctly
mirror company-specific within-team communication and coordination
processes. A properly functioning SFA-tool should provide salespeople with
a single view of the customer, help them synchronize their activities around
individual customers and offer these customers consistent experiences with
the company over the relationship lifecycle.
Usability of the system is an important determinant of SFA-use to assist
team-selling and other administrative tasks besides major customer
relationship activities. The time and effort required for learning the SFA
technology is one of the most significant barriers to successful salesperson
adoption of SFA (Honeycutt et al. 2005). Only those sales representatives
with a high level of support show performance gains associated with
technology use (Ahearne et al. 2005). For this reason, management should
provide sufficient SFA-related resources to ensure that the internal
organization runs smoothly and reporting and other communication
channels are open. Buehrer and others (2005) precisely conclude on the
issue of supporting opposed to monitoring salespeople:
Management seems to have great importance when
it comes to reducing barriers that hinder
salespeople’s usage of technology. Thus, it may be
more profitable for managers to allocate their
resources to support activities rather than to allocate
them to control activities such as keeping track of
Chapter 8: Discussion
203
who is using or not using the implemented
technology. (p. 397)
Management is advised to demonstrate commitment to the SFA by
providing salesperson sufficient training time and technical support, rather
than leaving him or her with the computer alone. In this aspect, team
colleagues represent a significant source of user support for the salesperson.
‘Train the trainer’ and other coaching approaches can help increase the
perception of usability among salespeople. Other user-support initiatives
such as practice oriented trainings, 7/24 helpdesks and online support
portals are also recommended. Allocating sufficient financial resources for
future maintenance of the hardware is also crucial to ensure that end-users
do not experience any technical difficulties with outdated technology. Last
but not least, salespeople, who feel stronger in their abilities to use
computers, will find SFA-systems relatively easier to use. We recommend
management to implement recruitment and personal development practices
reflecting the SFA strategy of the organization. For instance, overall
computer skills can be introduced as a new criterion when evaluating new
job-candidates. Management can also target improving the computer skills
of existing employees through regular training seminars.
8.4. Limitations and Suggestions for Future Research
As any research effort, our work has certain limitations. Firstly, our results
are based on the data collected from pharmaceuticals salesreps visiting
generalist physicians. Salespeople selling to generalists differ significantly
from salesreps selling to specialist doctors in terms of relationship to the
customer, product sophistication and the intensity of involved information.
Chapter 8: Discussion
204
Although we expect a similar pattern of findings in a specialist selling
context—since deep customer relationships are central for both—our results
should be interpreted accordingly.
Second, we have chosen the sales force of one company as population for
our data collection effort. Including salespeople from various companies
would contribute to the generalizability of our results. However, it would
come at the cost of substantially lower response rates. Given this trade-off
decision, we felt that minimizing potential non-response bias was of
particular importance in our research context. For future projects, however,
we encourage researchers to set different priorities thus contributing to
robust insights.
Third, we have selected pharmaceutical selling as research context.
Different industries have different needs and sales requirements, which
directly determine the role of the sales force in that industry (Moncrief
1986). For example, in an industry with a stronger emphasis on transactional
selling and operational efficiency, our SFA-usage dimensions could
possibly be expected to impact performance differently. Therefore, future
studies should explore how the SFA-usage dimensions perform in other
sales settings.
Fourth, we relied on self-evaluation when measuring salesperson
performance. While this is a widely accepted practice among researchers in
the sales performance domain
84
, objective performance data would be useful
for validating our findings.
85
We have also applied a self-report SFA-use
84
Refer to Anderson and Robertson 1995; Behrman and Perreault 1982, 1984; Cravens et
al. 1993; Jaramillo, Mulki, and Marshall 2005; Oliver and Anderson 1994; Singh 1998;
Sujan et al. 1994
85
Objective sales data is not without perils. It often includes ‘noise’—external factors—
beyond the control of a salesperson such as cyclical developments, territory potential
Chapter 8: Discussion
205
measure due to the lack of availability of actual usage information.
86
Future
studies should attempt to replicate the results obtained here with actual
usage data, if possible.
Finally, other relevant constructs (e.g., customer expectations, competitor
pressure, customer orientation, performance orientation, adaptive selling)
could be tested as additional antecedents to our SFA-usage dimensions. For
instance, customer orientation at an organizational level is proposed to play
a significant role leading to effectively implementing an SFA innovation
(Pullig et al. 2002). It would be interesting to see how customer orientation
explains customer relationship dimension of SFA-use as opposed to internal
coordination.
8.5. Conclusion
Literature claims that using SFA for better understanding customers and
coming up with integrative win-win solutions has the strongest impact on
sales performance (Ahearne et al. 2008; Hunter and Perreault 2006). Our
findings empirically support this claim. SFA applications make a real
difference when they are used as customer-oriented effectiveness tools.
Using SFA as a cost-cutting efficiency tool is also instrumental, but it does
not have a direct impact on the performance of the sales force. Increased
efficiency improves performance only when salespeople use their time gains
for relationship-building tasks.
and competitor activities. Furthermore, pure objective data do not reflect the long-term
relationship-building ambitions of a company, as customer relationships take long time
to establish.
86
Matching actual user data with self-reported data required personally identifying the
respondents, which was not possible in our case.
Chapter 8: Discussion
206
In sum, SFA technology can mean different things and serve many purposes
at the same time. Management should set clear objectives before investing
into SFA systems. It may still seem sensible to implement SFA as an
efficiency tool in some industries. To materialize the real potential of SFA
in a relationship selling context, however, a focus on improving salesperson
effectiveness is the key.
We hope that this study stimulates further research to deepen our
understanding of the drivers and performance outcome of SFA-technology
use. Shedding more light on the question of how technology investments
translate into business value represents a promising and challenging
research opportunity.
Appendix
207
APPENDIX
Figure 0.1: Descriptive Statistics: Sales Experience
Appendix
208
Figure 0.2: Descriptive Statistics: Age
Appendix
209
Figure 0.3: Descriptive Statistics: Gender
(Absolute Number; Percent)
Appendix
210
Table 6.6: Item Validity and Reliability Based on Pilot Data
(Belgium, n=39)
Composite
Reliability
Cronbach’s
Alpha AVE
t-Value
Item
Loading
Supervisory SFA Control
0.89 0.88 0.62
1. My manager informs me about the way I should use our SFA
system in my job.
2. My manager monitors my SFA usage.
3. My manager informs me on whether I meet his/her expectations
on SFA usage.
4. If my manager feels I need to adjust my SFA usage, she tells me
about it.
5. My manager evaluates my SFA usage.
1.98
1.96
2.23
2.63
2.51
0.654
0.650
0.821
0.843
0.952
Supervisor Support
0.81 0.75 0.63
1. I am continuously encouraged by my immediate supervisor to
use our SFA tool in my job.
2. My immediate supervisor explicitly supports my using of our
SFA system
3. My immediate supervisor truly believes in the benefits of our
SFA system.
38.26
110.02
2.62
0.926
0.969
0.324
Team Use
0.87 0.77 0.69
1. The majority of my sales colleagues in my sales team use our
SFA tool.
2. In my sales team, our SFA system is heavily employed by
everyone.
3. A lot of my sales colleagues rely on our SFA system.
57.60
12.26
19.79
0.907
0.777
0.809
Perceived Usefulness
0.95 0.94 0.85
1. Using our SFA system helps me increase my sales.
2. Using our SFA application enhances my effectiveness in my job.
3. Using our SFA program in my job increased my productivity.
4. I find our SFA system useful in my job.
82.23
123.43
52.98
95.80
0.927
0.945
0.903
0.927
Perceived Ease of Use
0.88 0.80 0.71
1. My interaction with our SFA system is clear and understandable.
2. I find it easy to get the SFA system to do what I want it to do.
3. I find our SFA system easy to use.
87.15
78.64
12.20
0.914
0.871
0.750
Facilitating Conditions 0.65 0.32 0.52
1. In our company we get good technical support for our SFA
system.
2. My company supplies all technologies that I need to perform my
job.
3. My company adequately trains me on the use of sales
technology.
4. I need more help with technology than I get. (negative)
72.37
51.29
12.39
5.26
0.886
0.829
0.640
-0.470
Appendix
211
Composite
Reliability
Cronbach’s
Alpha AVE
t-Value
Item
Loading
Computer Self-Efficacy
0.85 0.74 0.66
1. I am very confident in my abilities to use computers.
2. I can usually deal with most difficulties I encounter when using
computers.
3. Using computers is something I usually enjoy.
6.63
7.29
7.14
0.799
0.851
0.793
Salesperson Performance 0.83 0.73 0.56
1. Generating sales volume.
2. Increasing market share.
3. New account development.
4. Servicing existing customers.
13.77
20.90
6.69
9.11
0.839
0.848
0.640
0.652
Appendix
212
Table 6.7: List of Reflective Items
Construct
Items
Supervisor
SFA Control87
1. My manager discusses with me about the way I should use our
SFA system in my job.
2. My manager monitors my SFA-use.
3. My manager informs me on whether I meet his/her expectations
on SFA-use.
4. If my manager feels I need to adjust my SFA-use, she tells me
about it.
5. My manager evaluates my SFA-use.
Facilitating Conditions
8
7
1. In our company we get good technical support for our SFA
system.
2. My company supplies all technologies that I need to perform my
job.
3. My company adequately trains me on the use of sales technology.
Supervisor Support
8
7
1. I am continuously encouraged by my immediate supervisor to use
our SFA tool in my job.
2. My immediate supervisor explicitly supports my using of our
SFA system.
3. My immediate supervisor truly believes in the benefits of our
SFA system.
Team Use
8
7
4. The majority of my sales colleagues in my sales team use our
SFA tool.
5. In my sales team, our SFA system is heavily employed by
everyone.
6. A lot of my sales colleagues rely on our SFA system.
Perceived Usefulness
8
7
1. Using our SFA system helps me increase my sales.
2. Using our SFA application enhances my effectiveness in my job.
3. Using our SFA program in my job increased my productivity.
4. I find our SFA system useful in my job.
Perceived Ease of Use
8
7
1. My interaction with our SFA system is clear and understandable.
2. I find it easy to get the SFA system to do what I want it to do.
3. I find our SFA system easy to use.
Computer Self-Efficacy
8
7
1. I am very confident in my abilities to use computers.
2. I can usually deal with most difficulties I encounter when using
computers.
3. Using computers is something I usually enjoy.
Construct
Items
87
The seven-point response cues for each item are strongly disagree (1) to strongly agree
(7).
Appendix
213
Salesperson
Performance
88
1. Generating sales volume.
2. Increasing market share.
3. New account development.
4. Servicing existing customers.
88
The seven-point response cues from each item are below average (1) to above average
(7), in response to the statement: “In comparison to my peers in my company …”
Appendix
214
Table 7.1: Descriptive Analysis of Reflective Items
Construct
Item Mean Median St. Dev.
Supervisory
SFA Control
1. My manager discusses with me about the way I
should use our SFA system in my job.
2. My manager monitors my SFA-use.
3. My manager informs me on whether I meet his/her
expectations on SFA-use.
4. If my manager feels I need to adjust my SFA-use,
she tells me about it.
5. My manager evaluates my SFA-use.
6.42
6.64
6.42
6.53
6.61
7
7
7
7
7
1.14
0.85
1.14
0.97
0.91
Supervisor
Support
1. I am continuously encouraged by my immediate
supervisor to use our SFA tool in my job.
2. My immediate supervisor explicitly supports my
using of our SFA system.
3. My immediate supervisor truly believes in the
benefits of our SFA system.
6.60
6.69
6.56
7
7
7
1.00
0.75
0.96
Team Use
1. The majority of my sales colleagues in my sales
team use our SFA tool.
2. In my sales team, our SFA system is heavily
employed by everyone.
3. A lot of my sales colleagues rely on our SFA
system.
5.48
6.20
5.65
6
7
6
1.64
1.23
1.64
Perceived
Usefulness
1. Using our SFA system helps me increase my sales.
2. Using our SFA application enhances my
effectiveness in my job.
3. Using our SFA program in my job increased my
productivity.
4. I find our SFA system useful in my job.
6.36
6.40
6.38
6.71
7
7
7
7
1.21
1.08
1.14
0.69
Perceived Ease of
Use
1. My interaction with our SFA system is clear and
understandable.
2. I find it easy to get the SFA system to do what I
want it to do.
3. I find our SFA system easy to use.
6.27
5.78
6.20
7
6
7
1.19
1.60
1.29
Facilitating
Conditions
1. In our company we get good technical support for
our SFA system.
2. My company supplies all technologies that I need
to perform my job.
3. My company adequately trains me on the use of
sales technology.
5.84
6.00
5.76
6
7
6
1.54
1.55
1.66
Computer
Self-Efficacy
1. I am very confident in my abilities to use
computers.
2. I can usually deal with most difficulties I encounter
when using computers.
3. Using computers is something I usually enjoy.
6.61
6.13
6.62
7
7
7
0.80
1.29
0.87
Appendix
215
Construct
Item Mean Median St. Dev.
Salesperson
Performance
1. Generating sales volume.
2. Increasing market share.
3. New account development.
4. Servicing existing customers.
5.54
5.76
6.01
6.28
6
6
6
7
1.28
1.20
1.11
0.99
Appendix
216
Table 7.2: Exploratory Factor Analysis
(n=244, Varimax Rotation, Ȉ explained variance = 68.36%)
Variable Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8
SE_1 -0,733 0,209 0,009 -0,128 0,049 0,075 0,154 -0,027
SE_2 -0,386 0,579 -0,180 0,137 0,166 -0,058 0,037 -0,018
SE_3 -0,769 -0,028 0,015 -0,004 0,024 -0,149 0,063 -0,009
FC_1 -0,036 0,491 0,273 -0,329 0,25 0,029 0,218 0,099
FC_2 0,132 0,417 0,223 -0,462 0,190 -0,097 0,195 0,134
FC_3 -0,027 0,289 0,336 -0,504 -0,079 -0,109 0,191 0,009
SS_1 0,006 -0,107 0,700 0,020 0,434 0,003 0,219 -0,065
SS_2 -0,066 0,096 0,561 -0,298 0,512 -0,191 0,026 -0,004
SS_3 0,023 0,185 0,648 -0,205 0,113 -0,268 0,012 -0,045
TU_1 -0,117 -0,060 -0,050 -0,796 0,349 -0,136 0,054 -0,038
TU_2 -0,029 0,051 0,069 -0,513 0,621 -0,239 -0,163 -0,031
TU_3 -0,043 0,090 0,188 -0,702 0,208 -0,262 0,170 0,006
SC_1 -0,152 0,145 0,150 -0,128 0,772 -0,219 -0,099 -0,022
SC_2 -0,110 0,124 0,248 -0,100 0,819 -0,193 -0,096 -0,016
SC_3 0,056 0,122 -0,075 -0,095 0,747 -0,198 0,324 -0,091
SC_4 0,109 0,086 0,102 -0,100 0,714 -0,136 0,455 -0,108
SC_5 -0,021 -0,065 0,251 -0,230 0,639 0,025 0,437 0,064
PU_1 0,084 -0,025 0,023 -0,068 0,132 -0,700 0,340 -0,042
PU_2 -0,066 0,071 0,091 -0,200 0,075 -0,831 0,121 -0,024
PU_3 -0,062 0,028 0,213 -0,158 0,290 -0,771 0,153 -0,084
PU_4 -0,065 0,145 0,048 -0,074 0,195 -0,781 -0,007 -0,067
PEU_1 -0,261 0,138 0,082 -0,253 0,131 -0,396 0,607 -0,092
PEU_2 -0,149 0,165 0,046 -0,395 0,111 -0,406 0,470 -0,147
PEU_3 -0,193 0,201 0,119 -0,071 0,082 -0,294 0,675 -0,007
SP_1 0,067 0,102 -0,103 0,014 0,085 -0,012 0,044 -0,810
SP_2 0,129 -0,018 -0,069 0,017 0,077 0,004 0,076 -0,886
SP_3 -0,199 -0,070 0,178 -0,105 -0,084 0,007 0,068 -0,767
SP_4 -0,080 -0,004 0,093 0,058 0,030 -0,209 -0,087 -0,761
Appendix
217
Table 7.3: Exploratory Factor Anal
ysis without Facilitating Conditions
(n=244, Varimax Rotation, Ȉ explained variance = 69.83%)
Variable Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7
SE_1 0,724 0,076 -0,167 0,003 0,110 -0,221 -0,022
SE_2 0,654 -0,186 0,072 0,198 -0,073 -0,068 -0,012
SE_3 0,700 0,077 0,007 -0,034 -0,149 -0,052 -0,024
SS_1 -0,064 0,679 0,102 0,475 -0,001 -0,144 -0,064
SS_2 0,074 0,594 -0,299 0,482 -0,189 -0,054 0,004
SS_3 -0,008 0,688 -0,217 0,085 -0,227 -0,133 -0,025
TU_1 0,027 0,025 -0,817 0,274 -0,103 -0,162 -0,022
TU_2 0,026 0,129 -0,627 0,534 -0,229 0,103 -0,017
TU_3 0,024 0,274 -0,660 0,156 -0,229 -0,300 0,017
SC_1 0,212 0,196 -0,262 0,710 -0,225 0,121 -0,018
SC_2 0,173 0,284 -0,210 0,767 -0,211 0,143 -0,014
SC_3 0,012 -0,070 -0,138 0,776 -0,203 -0,254 -0,087
SC_4 -0,057 0,087 -0,067 0,781 -0,145 -0,365 -0,105
SC_5 -0,018 0,260 -0,162 0,673 0,023 -0,357 0,064
PU_1 -0,077 0,000 0,009 0,176 -0,708 -0,329 -0,047
PU_2 0,077 0,119 -0,199 0,044 -0,819 -0,202 -0,026
PU_3 0,050 0,235 -0,160 0,261 -0,772 -0,183 -0,088
PU_4 0,121 0,053 -0,120 0,173 -0,786 -0,042 -0,066
PEU_1 0,227 0,115 -0,223 0,162 -0,323 -0,700 -0,074
PEU_2 0,134 0,103 -0,370 0,114 -0,342 -0,590 -0,132
PEU_3 0,197 0,111 -0,004 0,154 -0,246 -0,723 0,004
SP_1 0,006 -0,107 -0,004 0,100 -0,018 -0,042 -0,810
SP_2 -0,111 -0,087 0,020 0,108 0,002 -0,055 -0,885
SP_3 0,121 0,223 -0,095 -0,117 0,036 -0,114 -0,767
SP_4 0,067 0,085 0,031 0,011 -0,223 0,087 -0,767
Appendix
218
Table 7.4: Reflective Items Validity and Reliability
(Brazil, n=244)
Composite
Reliability
Cronbach’s
Alpha AVE
t-Value
Item
Loading
Supervisory SFA Control
0.91 0.87 0.67
1. My manager discusses with me about the way I should use our
SFA system in my job.
2. My manager monitors my SFA-use.
3. My manager informs me on whether I meet his/her expectations
on SFA-use.
4. If my manager feels I need to adjust my SFA-use, she tells me
about it.
5. My manager evaluates my SFA-use.
11.61
15.07
16.67
25.02
12.02
0.799
0.834
0.827
0.863
0.767
Supervisor Support
0.84 0.72 0.63
1. I am continuously encouraged by my immediate supervisor to
use our SFA tool in my job.
2. My immediate supervisor explicitly supports my using of our
SFA system.
3. My immediate supervisor truly believes in the benefits of our
SFA system.
6.81
25.77
12.98
0.710
0.884
0.790
Team Use
0.87 0.78 0.69
1. The majority of my sales colleagues in my sales team use our
SFA tool.
2. In my sales team, our SFA system is heavily employed by
everyone.
3. A lot of my sales colleagues rely on our SFA system.
35.00
18.52
27.24
0.866
0.807
0.823
Perceived Usefulness
0.90 0.85 0.70
1. Using our SFA system helps me increase my sales.
2. Using our SFA application enhances my effectiveness in my job.
3. Using our SFA program in my job increased my productivity.
4. I find our SFA system useful in my job.
14.99
25.98
49.32
15.00
0.792
0.864
0.897
0.796
Perceived Ease of Use
0.88 0.80 0.72
1. My interaction with our SFA system is clear and understandable.
2. I find it easy to get the SFA system to do what I want it to do.
3. I find our SFA system easy to use.
46.02
26.55
17.96
0.890
0.846
0.811
Facilitating Conditions 0.81 0.66 0.60
1. In our company we get good technical support for our SFA
system.
2. My company supplies all technologies that I need to perform my
job.
3. My company adequately trains me on the use of sales
technology.
19.32
14.42
13.30
0.807
0.760
0.756
Appendix
219
Composite
Reliability
Cronbach’s
Alpha AVE
t-Value
Item
Loading
Computer Self-Efficacy
0.76 0.54 0.52
1. I am very confident in my abilities to use computers.
2. I can usually deal with most difficulties I encounter when using
computers.
3. Using computers is something I usually enjoy.
10.54
6.49
6. 96
0.815
0.652
0.688
Salesperson Performance 0.88 0.82 0.65
1. Generating sales volume.
2. Increasing market share.
3. New account development.
4. Servicing existing customers.
30.27
52.59
8.41
13.36
0.870
0.901
0.684
0.752
Appendix
220
Table 7.5: Discriminant Validity (AVE Anal
ysis)
(Brazil, n=244)89
1 2 3 4 5 6 7 8
1. Supervisory SFA Control 0.81
2. Supervisor Support 0.60 0.79
3. Team Use 0.59 0.53 0.83
4. Perceived Usefulness 0.45 0.42 0.46 0.83
5. Perceived Ease of Use 0.44 0.39 0.47 0.57 0.84
6. Facilitating Conditions 0.44 0.49 0.55 0.35 0.43 0.77
7. Computer Self-Efficacy 0.17 0.10 0.15 0.16 0.32 0.21 0.72
8. Salesperson Performance 0.10 0.07 0.05 0.14 0.14 -0.04 0.04 0.80
89
Bold numbers on the diagonal show the square rooted AVE. Numbers below the
diagonal represent construct correlations.
Appendix
221
Table 7.6: Cross Loadings
(Brazil, n=244)
Variable CompSE FaciliC SuperS TeamU SuperC PercU PercEU SalesP
SE_1 0,815 0,188 0,108 0,158 0,131 0,077 0,285 0,034
SE_2 0,652 0,180 0,059 0,073 0,151 0,103 0,205 0,043
SE_3 0,688 0,097 0,046 0,087 0,103 0,190 0,207 0,030
FC_1 0,225 0,808 0,420 0,430 0,464 0,240 0,347 -0,046
FC_2 0,130 0,760 0,396 0,472 0,386 0,306 0,315 -0,077
FC_3 0,148 0,757 0,348 0,393 0,192 0,272 0,358 0,004
SS_1 0,011 0,322 0,710 0,290 0,510 0,224 0,233 0,086
SS_2 0,141 0,496 0,884 0,571 0,610 0,404 0,368 0,049
SS_3 0,064 0,352 0,791 0,377 0,330 0,348 0,332 0,055
TU_1 0,144 0,447 0,360 0,867 0,456 0,333 0,382 0,047
TU_2 0,083 0,384 0,508 0,807 0,615 0,391 0,316 0,060
TU_3 0,149 0,542 0,481 0,823 0,434 0,443 0,486 0,025
SC_1 0,181 0,319 0,491 0,517 0,799 0,371 0,328 0,067
SC_2 0,175 0,348 0,571 0,528 0,834 0,376 0,271 0,076
SC_3 0,142 0,315 0,401 0,466 0,827 0,405 0,407 0,133
SC_4 0,101 0,400 0,491 0,453 0,864 0,401 0,438 0,155
SC_5 0,131 0,437 0,520 0,491 0,767 0,283 0,378 -0,006
PU_1 0,089 0,257 0,264 0,309 0,333 0,792 0,436 0,121
PU_2 0,155 0,306 0,337 0,413 0,312 0,864 0,522 0,086
PU_3 0,139 0,356 0,473 0,472 0,496 0,897 0,542 0,160
PU_4 0,160 0,244 0,319 0,365 0,348 0,796 0,421 0,132
PEU_1 0,331 0,364 0,380 0,443 0,403 0,525 0,890 0,131
PEU_2 0,243 0,399 0,358 0,484 0,399 0,515 0,847 0,165
PEU_3 0,257 0,357 0,270 0,270 0,337 0,417 0,812 0,048
SP_1 0,066 -0,035 0,049 0,038 0,089 0,103 0,107 0,870
SP_2 -0,041 -0,079 0,038 0,022 0,101 0,078 0,090 0,901
SP_3 0,118 -0,008 0,111 0,100 0,046 0,082 0,178 0,685
SP_4 0,054 -0,023 0,078 0,042 0,096 0,225 0,125 0,752
Appendix
222
Table 7.7: Descriptive Analysis of Formative Items
(Brazil, n=244)
Items Mean Median St. Dev.
Customer Relationship
1. To more creatively serve customers.
2. To improve the quality of customer service.
3. To identify most important customers from the list of
potential customers.
4. To plan selling activities.
5. To prepare sales calls.
6. To analyze call and sales data.
7. To record and retrieve customer call information.
6,512
6,406
6,402
6,217
6,217
5,947
6,385
7
7
7
7
7
7
7
1,032
1,008
1,090
1,298
1,250
1,406
1,050
Internal Coordination
1. To receive information from, or provide information to,
my manager.
2. To order promotional material from the Headquarters.
3. To learn about our existing and new products.
4. To coordinate activities with my team members.
5. To develop my sales skills
6,020
4,275
4,934
5,115
5,889
6
5
5
6
6
0,962
2,011
1,637
1,753
1,394
Appendix
223
Table 7.8: Multicollinearity Anal
ysis and Item Weights
(Brazil, n=244)
Items R
2
VIF Weight t-test
Customer Relationship
1. To more creatively serve customers.
2. To improve the quality of customer service.
3. To identify most important customers from the list of
potential customers.
4. To plan selling activities.
5. To prepare sales calls.
6. To analyze call and sales data.
7. To record and retrieve customer call information.
0,519
0,572
0,651
0,591
0,506
0,480
0,594
2,079
2,336
2,865
2,445
2,024
1,923
2,463
0,116
0,192
0,181
0,215
0,170
0,205
0,172
5,950
14,082
13,965
11,280
9,134
9,052
11,472
Internal Coordination
1. To receive information from, or provide information to,
my manager.
2. To order promotional material from the Headquarters.
3. To learn about our existing and new products.
4. To coordinate activities with my team members.
5. To develop my sales skills
0,158
0,354
0,474
0,464
0,193
1,188
1,548
1,901
1,866
1,239
0,178
0,252
0,304
0,317
0,326
3,711
6,474
10,605
10,421
8,525
Appendix
224
Table 7.9: Hypothesis Testing Results
Hypotheses Original Model
Beta (t-value)
Rival Model
Beta (t-value)
H1a: Customer Relationship ĺ Salesperson
Performance .238 (2.516)** .185 (1.797)*
H1b: Internal Coordination ĺ Salesperson
Performance -.006 (0.092) -.011 (0.139)
H1c: Internal Coordination ĺ Customer
Relationship .528 (7.843)** .528 (6.280)**
H2a
1
: Perceived Usefulness ĺ Customer
Relationship .229 (1.936)* .234 (1.919)*
H2a
2
: Perceived Usefulness ĺ Internal
Coordination .197 (1.532) .216 (1.657)*
H2b
1
: Perceived Ease of Use ĺ Customer
Relationship .046 (0.526) .071 (0.683)
H2b
2
: Perceived Ease of Use ĺ Internal
Coordination .149 (1.719)* .171 (1.923)*
H2c: Perceived Ease of Use ĺ Perceived
Usefulness .427 (5.228)** .418 (4.421)**
H3a: Supervisor Support ĺ Perceived
Usefulness .152 (1.969)* .154 (1.968)*
H3b: Supervisor Support ĺ Perceived Ease of
Use .086 (1.003) .074 (0.829)
H3c: Facilitating Conditions ĺ Perceived
Usefulness -.022 (0.221) -.019 (0.229)
H3d: Facilitating Conditions ĺ Perceived Ease
of Use .205 (2.336)** .217 (2.431)**
H3e: Computer Self-E
fficacy ĺ Perceived Ease
of Use .232 (4.390)** .203 (3.281)**
H3f: T eam-Use ĺ Perceived Ease of Use .331 (3.635)** .203 (1.990)*
Appendix
225
H3g: T eam-Use ĺ Perceived Usefulness .184 (2.028)** .150 (1.577)
H3h: T eam-Use ĺ Inter nal Coordination .145 (1.823)* .186 (1.866)*
H3i
1
: Supervisor SFA-Control ĺ Customer
Relationship .012 (0.657) .082 (0.846)
H3i
2
: Supervisory SFA-Control ĺ Internal
Coordination .156 (1.872)* .182 (1.749)*
We report one-tailed significance levels.
* p < 0.05
**p < 0.01
Appendix
226
Table 7.10: Evaluation of a Rival Model
R
2
Values of the Dependent Variables
Original Model Rival Model
Perceived Usefulness .397 .404
Perceived Ease-of-Use .325 .362
Customer Relationship .409 .418
Internal Coordination .135 .137
Salesperson Performance .121 .143
Appendix
227
Summary of the SFA Functionality
In this section we present a basic understanding of SFA functionality,
applications and capabilities. These descriptions are intended to be generic
and, certainly, the list will not be exhaustive. The modules are highly
interrelated, share information and synchronize automatically.
Lead Management
Track prospect inquiries and seamlessly route
qualified leads to the right people, ensuring
salespeople get timely access to the prospects. Once
prospects are identified, information about them can
be stored and organized in contact management
software and used to customize many aspects of sales
calls and continuing relationships (Widmier et al.
2002).
Account Management
Gives the entire company a 360-degree view of the
customer, enabling to maintain deep knowledge on
every customer account and facilitate cross-
department collaboration. The module enables
salespeople to record and retrieve detailed
information about customer interactions, account
history and requests. It is always possible to establish
account hierarchies with defined access rights to
sensitive information.
Contact Management
Represents functionality that is specifically oriented
to gathering and organizing information regarding
individuals who are either prospects or customers.
Managing contacts can include information about
current customers as well as potential customers and
influential individuals in a network.
Opportunity
Management
An opportunity in sales context is an event with
revenue-generating potential. The focus of
opportunity management is on managing sales
opportunities. It coordinates customer-facing
activities and events to help salespeople organize and
focus around the customer. It standardizes an
organization's work-flows to automate the sales
process for greater operational efficiency, consistency
and control (Petersen 1997). It adds decision points
and conditional requirements before events are
triggered. It manages priorities making sure sales
processes stay on track. According to Greenberg
(2004), this sales process component is the biggest
Appendix
228
distinction of modern SFA systems from traditional
contact management software. Opportunity
management can integrate team members into the
sales process by specifying all the people involved in
a sales deal along with their respective roles, tasks
and shared calendars and thus facilitates team-selling.
Territory Management
Typically provides “sort and search” mechanisms that
allow to view a sales territory from a number of
perspectives. A salesperson can define, administer,
analyze, and change territories to match the sales
organization. It allows seamless territory alignment
and assignment (Petersen 1997).
Proposal Generation
and Quotation
Management
Particularly in B2B settings, every customer has
different requirements. SFA provides a mechanism
for customizing proposals while retaining a uniform
level of quality and content based on already given
rules and criteria. The proposal generator can provide
editing and configuration capabilities that ensure
accurate quotation and pricing. Salespeople can
quickly and accurately generate proposals while with
customers, helping reduce cycle time between sales
(Widmier et al. 2002).
Product Configuration
and Visualization
By using technology, salespeople can create and
customize multimedia presentations giving the
product demonstration a much greater impact on
customers. In many industries special product
configurators give salespeople the ability to configure
products based on customer specifications and check
the availability and price of any configuration while
with the customer.
Call Reporting
Regularly reporting sales calls and expenses to the
central office is a major task of salespeople and
enables sales managers to manage their sales teams
effectively. SFA allows for the introduction of
standardized forms that can be easily transmitted to a
central office, reducing time spent on repetitive
paperwork and introducing the readability and
analyzability of the data by managers.
Order Processing and
Contract Management
The order entry application provides salesperson with
all the information and capability to successfully
conclude the sales process during the sales call. The
salesperson can quickly perform pricing, control the
inventory, enter the order, and arrange shipping and
also payment issues. SFA also offers salespeople the
ability to satisfy customers by quickly obtaining the
Appendix
229
status of a customer's order.
Product Encyclopedias
and Document
Management
SFA can provide instant access to the sales
documents and materials salespeople need at every
step of the sales process. SFA can also help
organizations manage complex product catalogs to
ensure consistent product and pricing information
(Shoemaker 2001).
Data Analysis
SFA systems include analytical tools to leverage the
data available to understand customers and trends.
Salespeople and managers can create a profile or list
of attributes of their best customers, and then match
that profile against a list of prospects to identify the
best prospects. Profiles can also be used to cross-sell,
up-sell or even offer promotions to customers who
are likely to buy soon. SFA systems feature powerful
yet easy-to-use sales dashboards. Managers can
perform win-loss analyses and forecasting to achieve
clear visibility into their sales pipelines and accurate,
timely forecasts of revenue and demand. Salespeople
can use standard or custom reports to gather business
intelligence. Sales managers can evaluate the
performance of sales team and outline strategic
improvements. Last but not least, SFA can help
maintain data quality and ensure that the customer
database is free of duplicate contacts, accounts, and
leads.
E-mail and
Communication
Support
Today's salespeople enjoy an array of technologies
promising instant and accurate communications.
Mobile phones make salespeople accessible for both
customers and the home office. E-mail tools enable
salespeople to send high-impact; graphically rich e-
mail messages to prospects and to easily track the
response. Fax machines allow for the instant
transmission of information contained on standard-
size sheets of paper.
Training
Technologies such as video conferencing and
interactive multimedia provide a means for
salespeople to be trained at home, thereby reducing
travel time and time out of the field. Using this
technology, a live training presentation can be
presented to a number of remote sites at the same
time. Recorded training sessions allow users to work
themselves and the material can be reviewed as many
times as necessary, at any time (Petersen 1997).
Sample Management
Sample management is a specialized application that
Appendix
230
applies when the organization needs to manage
inventory that is controlled by the sales force. For
instance, the pharmaceutical industry is compelled to
track drug samples due to legal requirements in many
countries. These applications track inventory at the
salesperson level and facilitate documenting
transactions through electronic signature capture,
adjustment of sample levels, and electronic updates to
corporate (Petersen 1997).
Personal Productivity
Tools
Personal productivity tools consist of shrink-wrapped
software products that typically include word
processing, spreadsheets and presentation
applications.
Appendix
231
Introductory E-Mail
Appendix
232
Questionnaire
SALES FORCE INFORMATION TECHNOLOGIES
SATISFACTION SURVEY
Welcome to the Sales Force Information Technologies Satisfaction Survey.
This survey should take only about 10-15 minutes to complete. This survey serves
academic purposes only and it is completely confidential. Your name will never be
associated with your responses. Results will be released only in statistical and
summary form.
To start the survey, please click the “Start” button below.
Start
I. PERSONAL EXPERIENCE WITH INFORMATION TECHNOLOGIES IN
GENERAL
The following statements refer to your disposition towards all “new IT and computer
applications” you may possibly encounter in and outside your job, such as mobile
phones or navigation systems. Please indicate whether you agree or disagree with
the statements by clicking a number from the seven point scale on the right.
Rating: (1) strongly disagree, (4) neutral, (7) strongly agree
1. Among my peers, I am usually the first to try out new information technology.
2. I am very confident in my abilities to use computers.
3. I like to experiment with new information technologies.
4. If I heard about a new information technology, I would look for ways to
experiment with it.
5. I can usually deal with most difficulties I encounter when using computers.
6. In general, I am hesitant to try out new information technologies.
7. Using computers is something I usually enjoy.
Page 1 of 8 Continue
Appendix
233
II. COMPANY SUPPORT FOR [SFA]
In the following, you find a number of statements relating to your perception of your
company’s support for [SFA]. Please indicate whether you agree or disagree with
the statements by clicking a number from the seven point scale on the right.
Rating: (1) strongly disagree, (4) neutral, (7) strongly agree
1. My company adequately trains me on the use of [SFA].
2. I am continuously encouraged by my immediate supervisor (1) to use [SFA] in
my job.
3. In our company we get good technical support for our [SFA] system.
4. My immediate supervisor explicitly supports my using of our [SFA] system.
5. My company supplies all technologies that I need to perform my job.
6. I need more help with [SFA] than I get.
7. My immediate supervisor truly believes in the benefits of our [SFA] system.
(1) Supervisor, in this survey, refers to your regional manager you report to, but
also to your coach, where applicable.
Back Page 2 of 8 Continue
III. WORK COLLEAGUES AND [SFA]
In this part, you find statements referring to your evaluations of your colleagues
and superiors on the use of [SFA]. Please indicate whether you agree or disagree
with the statements by clicking a number from the seven point scale on the right.
Rating: (1) strongly disagree, (4) neutral, (7) strongly agree
1. The majority of my colleagues in my sales team use our [SFA] tool to its highest
potential.
2. My supervisor monitors my [SFA] usage.
3. In my sales team, our [SFA] system is heavily employed by everyone.
4. My supervisor evaluates my [SFA] usage.
5. A lot of my sales colleagues in my sales team rely on our [SFA] system.
6. My supervisor informs me on whether I meet his/her expectations on [SFA]
usage.
7. My supervisor discusses with me about the way I should use our [SFA] system
in my job.
8. If my supervisor feels I need to adjust my [SFA] usage, he/she tells me about it.
Back Page 3 of 8 Continue
Appendix
234
IV. EVALUATION OF [SFA]
The following statements ask you as an end-user of the system to evaluate [SFA].
Please indicate whether you agree or disagree with the statements by clicking a
number from the seven point scale on the right.
Rating: (1) strongly disagree, (4) neutral, (7) strongly agree
1. Using our [SFA] system helps me increase my sales.
2. My interaction with our [SFA] system is clear and understandable.
3. I find it easy to get the [SFA] system to do what I want it to do.
4. Using our [SFA] applications enhances my effectiveness in my job.
5. I find our [SFA] system easy to use.
6. Using our [SFA] program in my job increases my productivity.
7. I find our [SFA] system useful in my job.
Back Page 4 of 8 Continue
V. APPLICATION OF INFORMATION TECHNOLOGIES
The following is a list of tasks you may possibly achieve by using your computer.
Please indicate how often you use your computer for each task by clicking a
number from the seven point scale on the right.
Rating: (1) Less than once a month, (2) Once a month, (3) A few times a month,
(4) Once a week, (5) A few times a week, (6) About once a day, (7) Several times a
day
I use my computer…
1. To receive information from, or provide information to, my manager.
2. To develop my sales skills.
3. To record and retrieve customer call information (2).
4. To plan my selling activities.
5. To identify most important customers from the list of potential customers.
6. To more creatively serve customers.
7. To order promotional material from the Headquarters.
8. To prepare my sales calls.
9. To learn about our existing and new products.
10. To improve the quality of customer service.
11. To analyze call and sales data.
12. To coordinate activities with my team members.
(2) Customer, in this survey, is defined as any person(s) (doctor, nurse,
administrator, and committee), hospital, pharmacy, clinic, or organization that can
use or influence the use of your products.
Back Page 5 of 8 Continue
Appendix
235
VI. SALES PROFESSION
In the following, you will find a number of statements relating to the way you
approach the sales profession. Please indicate whether you agree or disagree with
the statements by clicking a number from the seven point scale on the right.
Rating: (1) strongly disagree, (4) neutral, (7) strongly agree
1. I continually work to improve my selling skills.
2. I am always learning something about my customers.
3. I am very flexible in the selling approach I use.
4. I continually work to improve my product knowledge.
5. I try to understand how one customer differs from another.
6. Learning how to be a better salesperson is of fundamental importance to me.
7. I can easily use a wide variety of selling approaches.
8. I learn something from each selling experience.
9. When I feel that my sales approach is not working, I can easily change to
another approach.
Back Page 6 of 8 Continue
VII. PERFORMANCE
This part of the survey asks you to evaluate your performance in 2006. Please rate
yourself in comparison to the country average, by clicking a number from the seven
point scale on the right.
Rating: (1) below average, (4) average, (7) above average
1. Generating Sales Volume
2. Increasing Market Share
3. New Account Development
4. Servicing Existing Customers
Back Page 7 of 8 Continue
Appendix
236
VIII. DEMOGRAPHICS
1. Since when have you been working...?
As a salesperson ---- Years
At your company ---- Years
In your current territory ---- Years
(2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999-97 | 1996-94 | 1993-
91 | 1990-85 | 1984-80 | 1979-75 | 1974 or before)
2. How old are you? ----- Years
(25 or younger | 26-30 | 31-35 | 36-40 | 41-45 | 46-50 | 51-55 | 56 or above)
3. What is your gender? Male/Female
Back Page 8 of 8 Submit
Thank You! The Sales Force Information Technologies Satisfaction Survey is over.
The results of this survey will be made available to the participating countries in the
summer of 2007.
Should you have questions regarding your rights as a participant in this research,
please contact Murat Serdaroglu at <e-mail address>
Appendix
237
Harman’s One Factor Test
Factor Analysis Results
Factors were extracted by the Principal Component method
from the correlation matrix
All factors with eigenvalues > 1 were extracted
Explained Variance (Eigenvalues)
Value Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8
Eigenvalue 8,495 2,764 2,199 1,692 1,427 1,168 1,055 1,026
% of Var. 29,293 9,532 7,582 5,833 4,920 4,028 3,639 3,537
Cum. % 29,293 38,825 46,407 52,240 57,160 61,188 64,828 68,365
References
238
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Aghamanoukjan, A., R. Buber, and M. Meyer (2007), “Qualitative
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