Dynamics of voluntary knowledge sharing in organizations

Lodhi, Suleman Aziz; Ahmad, Munir Article Dynamics of voluntary knowledge sharing in organizations Pakistan Journal of Commerce and Social Sciences (PJCSS) Provided in Cooperation with: Johar Education Society, Pakistan (JESPK) Suggested Citation: Lodhi, Suleman Aziz; Ahmad, Munir (2010) : Dynamics of voluntary knowledge sharing in organizations, Pakistan Journal of Commerce and Social Sciences (PJCSS), ISSN 2309-8619, Johar Education Society, Pakistan (JESPK), Lahore, Vol. 4, Iss. 2, pp. 120-131 This Version is available at: https://hdl.handle.net/10419/188004 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Sci. 2010 Vol. 4 (2), 120-131 Dynamics of Voluntary Knowledge Sharing in Organizations Suleman Aziz Lodhi (Corresponding author) School of Business Administration National College of Business Administration and Economics, Lahore, Pakistan E-mail: [email protected] Munir Ahmad Professor of Statistics National College of Business Administration and Economics, Lahore, Pakistan E-mail: [email protected] Abstract Knowledge has become prime source of economic production in modern enterprises; conceptualized by many researchers as the only source of sustainable competitive advantage. Therefore many organizations are restructuring themselves to gain maximum value from their knowledge sources. The paper investigates the influence of management policies on voluntary knowledge sharing behaviors at individual and group level and presents a model to understand the dynamics of voluntary knowledge sharing by employees in an organization. The problem is approached with a tacit view of knowledge, considering that true knowledge is located only in the minds of the professionals working in the organization. Keywords: Voluntary Knowledge Sharing, Knowledge Management, Organizational Knowledge, Organizational Learning, Communities of Practice, Knowledge Networks. 1. Introduction Recent developments have revolutionized business paradigm, emergence of knowledge economy, rapid globalization, networking of economies, developments in ICT and rise in social awareness has made the environment far more complex than in the previous decades. Knowledge has earned itself a status of critical resource in the modern enterprise; many consider it as fundamental for gaining competitive advantage in a knowledge-based view of enterprise, [Grant (1996), Spender (1996) and later by Cole (1998)]. Ever since the popularity of knowledge management in business domain, a number of models have been presented for understanding knowledge sharing in organizations with a view that somehow knowledge can be managed objectively. Managers in knowledge centric organizations are especially concerned as the major portion of their organizational wealth is in the form of knowledge assets, which are intangible and therefore difficult to trace or control. A new class of knowledge workers have emerged which is different from the traditional worker class that the managers were accustomed to deal with in the past. Measuring their performance or controlling these Lodhi and Ahmad 121 knowledge workers is very complicated because the product they deliver is basically intangible and their productivity cannot be determined by using traditional methods. Purchasing the best of equipment and hiring the most expensive knowledge workers would show some improvement in organizational performance, but it cannot guarantee efficiency or ensure creating maximum value from the knowledge already present in the firm. The management can gather the brightest brains available and bring them together, but the social interaction needed to create true knowledge cannot be forced. The employees would share knowledge on need-to-knowledge bases but avoid sharing their insights on the subject. Therefore a fundamental research issue for the knowledge-centric organizations is to understand the behaviors that promote voluntary knowledge sharing in firms. The knowledge sharing behaviors can be observed firstly at individual level, where employees working in an organization work collaboratively to attain firms‟ objectives; secondly the collaboration can also be between employees working in the same group or department and lastly between different departments. Studies have shown that technology has been over sold in the pretext to solve the knowledge flows barriers in organizations and in reality the organizations have not been able to improve their performance as significantly as envisioned. Snowden (2002) even stated that Organizational Knowledge Management (OKM) offers a little beyond what Information Managers were already doing. Sveiby (1994, 1997, 2000) have dismissed explicit knowledge arguing that technology can only manage information. Holsapple and Joshi (1999) discuss early models on Knowledge Management in organizations given by Wiig in 1993 to models proposed up till 1997 including Alavi‟s (1997), Sveiby (1997), van der Spek and Spijkervet (1997). It is seen that most of these models give us a rather mechanistic view of KM. Sveiby (1997) and Nonaka (1994) have different observations on knowledge, they believe that knowledge is more closely related to human interactions. Recent models given by Tannembaun et al (2000) advocate that knowledge management in organizations should be studied by examining four aspects which are knowledge sharing, accessibility, assimilation and application. Later Laverde et al (2003) also gives a review of some principal KM models including Rastogi (2000), Probst et al (2000), Heisig (2001) and McElroy (2002). Probst et al (2002) and Heisig (2001) view knowledge management as a dynamic cycle that is going through continuous evolution. The Probst et al. model, also called “The Building Blocks of Knowledge Management”, involves eight components that form two cycles, one inner cycle and other outer cycle. The inner cycle is composed by the building blocks of Identification, Acquisition, Development, Distribution, Utilization and Preservation of knowledge. There are two more processes in the outer cycle, which are Knowledge Goals and Knowledge Assessment. The goals provide the direction to the whole Knowledge Management cycle, and determine which capabilities should be built on which level. While Knowledge Assessment completes the cycle, providing the essential data for strategic control of Knowledge Management. Similarly Heisig (2001) model is composed of four processes among which a different process “Create” is added. McElroy (2002), proposed “The Knowledge Life Cycle”, KLC - framework of Knowledge Management. The model assumes that knowledge exits only after it has been produced, and after this it can be captured, codified and shared. McElroy model divides the Knowledge Creation Process in two big processes, namely, Knowledge Production and Knowledge Dynamics of Voluntary Knowledge Sharing 122 Integration. Knowledge Production is the process in which new organizational knowledge is created. This is formed by Individual Group Learning, Knowledge Claim, Information Acquisition, Codified Knowledge Claim, and Knowledge Claim Evaluation. The Model uses this process as synonymous with “organizational learning”. Knowledge Integration is formed by some activities that allow the knowledge sharing and distribution. It includes knowledge Broad-casting, Searching, Teaching, Sharing and other social activities that communicate. The model also, introduces two new concepts of Demand Side and Supply Side in this aspect. Supply-side is the practice of Knowledge Management in any way that is designed to enhance the supply of existing knowledge to workers in an enterprise. While the Demand-side focuses on the supply of existing knowledge to a workforce and seeks to enhance their capacity to produce. The mission of demand-side Knowledge Management, then, is to enhance an organization‟s capacity to satisfy its demand for new knowledge. The important assumption is the impact on an organization‟s capacity to produce and integrate knowledge by making interventions aimed at supporting, strengthening, and reinforcing related patterns of behavior. Allee (2002) argues for a value network approach for managing knowledge organizations and recently Newell et al (2004) are focused on social networks analysis, as they consider social network much better than databases for transferring knowledge. Community of practice is another popular approach, where a community becomes self-sustaining, which may even be defined as self-defining or autopoietic. (Hall 2003). We observe a development direction for KM models, earlier models are more focused on hard structure of organizations while later models have shifted the focus to soft structured approach, viewing knowledge as a dynamic entity rather than an object. The paper investigates the link between behaviors and voluntary knowledge sharing in an organization, exploring whether certain behaviors from individual level to company culture promote or hinder voluntary knowledge sharing. It is kept in mind that the working group in its self is a collection of individuals. Secondly the study explores the possibility of promoting knowledge sharing behaviors in a firm by adapting appropriate management policies. 2. Conceptual Framework and Hypothesis Constructivists argue that true knowledge can be contained in human cognation exclusively, databases and research documents contain potentials for generating knowledge, but these potential resources are converted to knowledge by human interaction. Maturana and Varela (1987) while maintaining systems approach view behavior as “a description an observer makes of the changes in a system with respect to an environment with which the system interacts”. The most elementary type of behavior is vegetative, which include breathing or metabolism, the next higher behavior is reflex behaviors that are action-response-based such as a reaction to pain. Higher to it in order of complexity of behaviors comes reactive behaviors, these depends strongly on external stimulus, or a set of sequence of external stimuli (McFarland, 1981). These behaviors require an action and selection process in contract to reflex behaviors which are executed when ever the triggering stimulus is present. The motivated behaviors depend not only on the external stimuli, but also on internal motivations. Exploring for food is performed when there is an internal stimulus of hunger (see Beer, 1990; Maes, 1991 ). Reasoned behaviors are those that are determined by Lodhi and Ahmad 123 manipulations of abstract concepts or representations. These manipulations can be considered as use of logic (Clark, 1998). It is believed that behaviors in the higher levels are evolved or developed from the behaviors in the lower behavior levels, as in animals we cannot find higher levels of behaviors without the lower ones. As Piaget, (1968) states that children have to develop from lower stages to reach higher stages. Thus higher types of behaviors in many cases can be seen as complex variants of the lower ones. Reasoned behavior requires abstract representation or concepts of the perceived world, and an accurate manipulation of these concepts to produce a specific behavior. It seems that these behaviors are learned from the regularities in the perceptions of the objects and events, this requires embodiment and situated-ness (Clark, 1997). As humans we live in a society, which is shaped by us and shapes us at the same time; we critically depend on language to externalize our ideas and access the concepts generated by other humans to enlarge our knowledge. Language and human thought are strongly interrelated; making language necessary for complex manipulation of concepts (Clark 1998) and an individual can develop language only in a society (Steels and Kaplan, 2002). Developing this argument further, it can be stated that it is through generations, a culture is formed by accumulation of past experiences (Dunbar 1998). Based on the arguments given by constructivists an investigation is initiated to explore the link of behaviors on voluntary knowledge sharing in a firm. It is hypothesized that if employees at individual level treat each other with respect and equality in their business as well as daily contact in a firm. The employees would voluntarily like to share their knowledge with their office workers and on the other hand discriminating behavior practiced by employees; even individually, would hinder knowledge flow. H1: Higher the level of discrimination at individual level, would lower the voluntary knowledge sharing by individual The group behavior is examined on the same lines; since group leader enjoys maximum authority in a group his/her behavior is critical. The hypothesis is that the group leader must show equality and non-discrimination in his behavior. The group members must be treated with respect and encouraged to share their thoughts. H2: Higher the level of discrimination at group level, would lower the voluntary knowledge sharing by individuals in the group The third behavior actor is the organization itself. The actions of an organization are rooted in its policies and the actions taken by the management in the light of the company policies. It is hypothesized that non-discrimination and fair company policies towards its knowledge workers would promote voluntary knowledge sharing. A company‟s sensitivity towards knowledge workers needs promote knowledge sharing whereas a disparity in policy implementation from written documentation hinders knowledge flow. H3: Higher the perception of management regarding fair play and sensitivity towards knowledge workers needs, would higher the voluntary knowledge sharing by individuals The role played by the communication channels in an organization is that of a facilitator for knowledge sharing. The communication channels include computer networks, software and social occasions like seminars that provide opportunities for social Dynamics of Voluntary Knowledge Sharing 124 interaction. It is hypothesized that existence of communication channels is a must for knowledge sharing. H4 : Availability of Communication Channels, would higher the voluntary knowledge sharing The model in figure (1) presents the conceptual framework, linking behaviors with the voluntary knowledge-flow in a firm and the value produced by the firm. This value product can be in the form of winning a new project, a research report, formula for a pharmaceutical product or any patent. The firm would later convert knowledge product formed by knowledge-flow into dollar terms, but first the firm must create value from knowledge. Figure: 1. A Model linking behaviors with knowledge sharing in an organization 3. Methodology Primary data on behaviors which promote voluntary sharing of knowledge in organizations was gathered with the help of a questionnaire. It is assumed that based on their previous experiences knowledge workers would be able to identify behaviors that promote or hinder them from sharing their knowledge voluntarily with their fellow workers. The definition of “knowledge worker” is difficult as “knowledge”, it is a very abstract concept. Pears (1972) recognizes that there are many categories of knowledge worker, which may some time look even contradictory, but for the purpose of survey, definition of knowledge workers as given by Nomikos (1989) is considered. Nomikos categorizes knowledge workers as a group that includes scientists, engineers, professors, attorneys, physicians, and accountants. H2 H3 H1 Value Individual Behavior (IL) Group Behavior (GL) Communication Channels (CC) Organization Policies (OP) Voluntarily Knowledge Sharing /Flow H4 Lodhi and Ahmad 125 A survey was conducted to test the above four hypothesis. A total of four hundred questionnaires developed on seven point Lickert‟s scale were distributed at random among senior teachers, professors, researchers and postgraduate research students of six postgraduate institutions located in Lahore Pakistan. Services were offered for explaining the question naire and the study background to all participants, requesting for assistance. After a follow-up on telephone and personal visits to participants, 198 fully completed questionnaires were received back, giving a response rate of 49.5%. 4. Results Out of the 198 responses received, 121 respondents were females and 77 respondents were males. A total of 99 respondents had 2 or lesser years of research related experience, 71 respondents had 3 to 5 years of experience, and 28 respondents had over 6 years of research experience. Female respondents were in the age bracket of 36 years or less were 39 in numbers, 62 were between 36 to 45 years and rest 20 were in the age bracket of 45 years and above. Male respondents in the age bracket of 36 years or less were 35 in number. The respondents from the age bracket between 36 to 45 years were only 7, while the last age bracket of 45 years and above were 77. Table: 1. Demographics of Data collected . Research- Years Female Male Grand Total Age Age <36 36-45 <45 Total <36 36-45 <45 Total =< 2 37 35 7 79 14 4 2 20 99 3-5 2 27 12 41 20 1 9 30 71 = > 6 1 1 1 2 24 27 28 Grand Total 39 62 20 121 35 7 35 77 198 Analyzing female knowledge workers sample, it is seen that maximum female respondents (i-e 79) had 2 or less years of experience in research related activities, while 41 are in the bracket of 3 to 5 years of research and only one female had more than 6 or higher research experience. On the male respondents‟ side, 20 respondents had 2 years or less research experience, 30 respondents had 3 to 5 years of experience and 27 were related with research activity for over 6 years. A comparison of education degree achieved by the respondents, with the research experiences is shown in table 2 below: Table: 2. Research experience and Qualifications of Knowledge Workers Research- Years Female Male Grand Total Mas M-Phil Phd Total Mas M-Phil Phd Total =< 2 67 12 79 20 20 99 3-5 31 7 3 41 24 1 5 30 71 = > 6 1 1 14 6 7 27 28 Grand Total 99 19 3 121 58 7 12 77 198 Dynamics of Voluntary Knowledge Sharing 126 Most of the female respondents (i-e 99) held Masters degree, while 19 were M-Phil and 3 held Phd, On the male respondents 58 held Masters degree, 7 held M-Phil and 12 Phd. The descriptive statistics for the responses is given in the table 3: Table: 3. Descriptive Statistics of the respondents Combined Male Female Communication Channels Mean 5.8662 5.6266 6.0186 Std. Dev 1.2670 1.4971 1.0699 Individual Behavior Mean 5.9773 5.7175 6.1426 Std. Dev 1.1865 1.3414 1.0447 Group Behavior Mean 5.7866 5.3084 5.9897 Std. Dev 1.3478 1.5875 1.1804 Organization Policies Mean 5.6477 5.0000 6.0599 Std. Dev 1.3449 1.5526 0.9961 The data showed that the knowledge workers strongly agreed with the hypothesis regarding the factors influencing them on sharing knowledge with their coo-workers. I is seen that the female population is generally more in agreement than the male population, giving a higher mean value in all of categories. The Std. Dev of the females is also lower than the males, showing more consistency in their responses. The Std. Dev for the female population is very consistent; especially with their responses on organizational Policies. One reason for the differences between responses made by the male and female participants could be due to the difference between the research and qualifications of the participants. The t-Test is applied to test the hypothesis according to the response of the knowledge workers. Table 4 shows the results of t-Test at 95% significant level. Table: 4. Descriptive Statistics for t-Test (One-Sample Statistics) N Mean Std. Deviation Std. Error Mean Individual Behavior (IL) 792 2.02 1.19 0.0422 Group Behavior (GL) 792 2.21 1.35 0.0479 Communication Channels (CC) 792 2.13 1.27 0.045 Organization Policies (OP) 792 2.35 1.34 0.0478 The standard deviation for the responses on Individual Behavior (IL) and its influence on knowledge sharing at individual level is 1.19. The standard deviation for the responses at group level (GL) is measured as 1.35, while the standard deviations for Communication Channels (CC) and Organizational Policies (OP) come out to be 1.27 and 1.34 Lodhi and Ahmad 127 respectively. The significance of the results are shown in Table (5). The p value for the test is lower than 0.001 for the behaviors at Individual level (IL), Group level, (GL), influence of Communication Channels (CC) and the Organizational Policies (PO) indicating that the results are of significant value in all of the above cases. Table: 5. t-Test Results (One-Sample Test) t df Mean Diff Sig. (2-tailed) P- values 95% Confidence Interval of the Difference Upper Lower Individual Behavior (IL) 35.039 791 1.48** 0.000 1.56 1.39 Group Behavior (GL) 26.865 791 1.29** 0.000 1.38 1.19 Communication Channels (CC) 30.345 791 1.37** 0.000 1.45 1.28 Organization Policies (OP) 24.017 791 1.15** 0.000 1.24 1.05 Rated on seven point Lickert scale Test Value = 3.5 ** p< 0.001 The results show very interesting insight, when we analyze the correlation between the above actors. Table 6. presents a summary of correlations. The correlation between organizational policies and communication channels is highest, with the correlation coefficient 0.906. Next in rank is the correlation between organizational policies and behavior at group level (0.882) followed by the behavior at individual level, and organizational Policies giving the correlation coefficient of 0.878 and then the correlation of communication channel with group behavior (803) and communication channel and individual behavior is nearly the same (0.800). This could indicate that the knowledge workers aspire for better communication channels at individual as well as group level. Table: 6. Showing correlation between main categories Group Correlations Abbreviation Correlation Coefficient Organization Policies & Communication Channels OP-CC 0.906 Organization Policies & Group Behavior OP-GL 0.882 Organization Policies & Individual Behavior OP-IL 0.878 Communication Channel & Group Behavior CC-GL 0.803 Communication Channel & Individual Behavior CC-IL 0.800 Individual & Group Behavior IL–GL 0.861 Knowledge-workers inspiring for better communication channels also vote for organization policies that are sensitive to employee needs. Similarly workers that were agreeing with the conditions for knowledge sharing at individual and group level also inspired for a corporate policies for promoting knowledge sharing. Table 6 shows that organizational policies play a very important role in controlling voluntary knowledge sharing behaviors at Group level as well as Individual level. The next high relationship lies between the organizational policies and the knowledge sharing at Group level. This