Supply chain integration scales validation and benchmark values

Journal of Industrial Engineering and Management JIEM, 2013 – 6(2): 423-440 – Online ISSN: 2013-0953 – Print ISSN: 2013-8423 http://dx.doi.org/10.3926/jiem.517 Supply chain integration scales validation and benchmark values Juan A. Marin-Garcia,1 Rafaela Alfalla-Luque,2 Carmen Medina-López2 1ROGLE – Dept. Organización de Empresas. Universidad Politécnica de Valencia (Spain) 2Dpto. Economía Financiera y Dirección de Operaciones. F.CC. Económicas y Empresariales de Sevilla. Universidad de Sevilla. (Spain) [email protected].es, alfal[email protected], [email protected] Received: August 2012 Accepted: May 2013 Abstract: Purpose: The clarification of the constructs of the supply chain integration (clients, suppliers, external and internal), the creation of a measurement instrument based on a list of items taken from earlier papers, the validation of these scales and a preliminary benchmark to interpret the scales by percentiles based on a set of control variables (size of the plant, country, sector and degree of vertical integration). Design/methodology/approach: Our empirical analysis is based on the HPM project database (2005- 2007 timeframe). The international sample is made up of 266 plants across ten countries: Austria, Canada, Finland, Germany, Italy, Japan, Korea, Spain, Sweden and the USA. In each country. We analized the descriptive statistics, internal consistency testing to purify the items (inter-item correlations, Cronbach’s alpha, squared multiple correlation, corrected item-total correlation), exploratory factor analysis, and finally, a confirmatory factor analysis to check the convergent and discriminant validity of the scales. The analyses will be done with the SPSS and EQS programme using the maximum likelihood parameter estimation method. Findings: The four proposed scales show excellent psychometric properties. Research limitations/implications: with a clearer and more concise designation of the supply chain integration measurement scales more reliable and accurate data could be taken to analyse the relations between these constructs with other variables of interest to the academic l fields. -423- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.517 Practical implications: providing scales that are valid as a diagnostic tool for best practices, as well as providing a benchmark with which to compare the score for each individual plant against a collection of industrial companies from the machinery, electronics and transportation sectors. Originality/value: supply chain integration may be a major factor in explaining the performance of companies. The results are nevertheless inconclusive, the vast range of results obtained are due, amongst other things, to the fact that there is no exactness to the group of scales used, noone has yet published an analysis of the measurement models nor clear benchmarks as to the variety of the scales used. Keywords: scale validation, questionnaire, reliability, validity, psichometric properties, supply chain integration 1. Introduction The concept of supply chain integration is of great interest for academics working in operational management (Zhao, Huo, Selen & Yeung, 2011; Flynn, Huo & Zhao, 2010). One of the main reasons is that it greatly influences the competitive advantage of companies (Flynn et al., 2010; Chang, Ik-Whan & Dennis, 2007; Alfalla-Luque, Medina-Lopez & Schrage, 2012). But it is also a concept whose definition and whose operationalization are still up for debate. There is no consensus as to which components to include, nor how to measure them (Roth, Schroeder, Huang & Kristal, 2008; Zhu, Sarkis & Lai, 2008; Li, Rao, Ragu-Nathan & Ragu- Nathan, 2005; Flynn et al., 2010, Alfalla-Luque, Medina-Lopez & Dey, 2012). In fact, in research carried out so far, it is common to be confronted with a variety of proposals and this means that demonstrating the effects of supply chain integration on the performance of companies is inconclusive giving contradictory results (Zhao et al., 2011; Chang et al., 2007; Flynn et al., 2010). According to recent research, supply chain integration is comprised of two primary dimensions: internal integration and external integration. External integration can then be further subdivided: integration with clients and integration with suppliers (Chang et al., 2007; Flynn et al., 2010; Flynn, Wu & Melnyk, 2010; Narasimhan & Kim, 2002; Topolsek, 2011; Zhao et al., 2011; Alfalla-Luque & Medina-López, 2009; Carter, Sanders & Dong, 2008; Kaynak & Hartley, 2008; Li, Ragu-Nathan, Ragu-Nathan & Subba Rao, 2006). Nevertheless, there is a slight bias in research, both empirical and conceptual, that has leant towards external rather than internal integration (Zhao et al., 2011). This is why there have been calls so that any future research takes into account the relationships between the different components of the supply chain integration and the effect that each one has on the performance indicators of the company (Chang et al., 2007; Flynn et al., 2010; Narasimhan & Kim, 2002; Zhao et al., 2011). -424- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.517 To help with the development of the proposed future research, in this paper our objectives are the clarification of the constructs, the creation of a measurement scale for the components of the supply chain integration, the validation of these scales and a preliminary study on the effects of a variety of control variables (size of the plant, country, sector and degree of vertical integration) in the values of these scales. 2. Definitions of integration According to Flynn et al. (2010) supply chain integration can be defined as: “the degree to which a manufacturer strategically collaborates with its supply chain partners and collaboratively manages intra- and inter-organization processes. The goal is to achieve effective and efficient flows of products and services, information, money and decisions, to provide maximum value to the customer at low cost and high speed.” This is why it is so important to instil confidence amongst all the agents, building long-term relationships, frequent communication, share both profit and risk, and look for effective ways of sharing information, make joint decisions and resolve conflicts (Flynn et al., 2010). There are two main types of integration: external integration and internal integration (Zhao et al., 2011; Chang et al., 2007; Flynn et al., 2010). Internal integration refers to the degree to which a company can organise its practices, procedures, information, decisions and conduct in a collaborative and synchronised way between its different areas, to be able to comply with client requirements and effectively interact with its suppliers (Zhao et al., 2011; Topolsek, 2011; Flynn et al., 2010; Flynn et al., 2010). External integration refers to the degree to which a company understands the need of its clients and collaborates with clients and/or suppliers to develop inter-organisational strategies and shared practices and processes, so that it manages to satisfy its clients’ needs (Flynn et al., 2010). External integration consists of integration with clients and integration with suppliers (Flynn et al., 2010; Zhao et al., 2011; Escrig Tena & Bou-Llusar, 2005). According to earlier work, there are close ties between the three basic components of integration (internal, clients and suppliers) (Chang et al., 2007; Escrig Tena & Bou-Llusar, 2005). So it could be construed that internal integration is the precursor to achieving external integration (Flynn et al., 2010; Pagell & LePine, 2002; Bessant, Kaplinsky & Morris, 2003; Harrison & Van Hoek, 2005; Cagliano, Caniato & Spina, 2006). -425- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.517 3. Control variables for supply chain integration The use of operational management practices in general, and supply chain integration in particular, are normally affected by national culture, meaning that it is quite common to come across research where the country in which the plant is located explains to a certain extent the degree of use of supply chain integration (Oliver & Delbridge, 2002; Hofstede, 1998; Zhao et al., 2011; Pagell, Katz & Sheu, 2005). Another variable that often comes up is the sector (MacDuffie & Helper, 1997; Bruce, Daly & Towers, 2004; Bayraktar, Jothishankar, Tatoglu & Wu, 2007; Oliver & Delbridge, 2002; Roth et al., 2008; Martinez Jurado & Moyano Fuentes, 2011). There are also references to the fact that integration is associated with the size of the company (Underhill, 2001; Roth et al., 2008; Zhao et al., 2011). And finally, the degree of vertical integration can affect the type and degree of supply chain integration (Roth et al., 2008; Hayes & Wheelwright, 1984). 4. Method The aim of this paper is to test the psychometric properties of a questionnaire to identify four constructs of supply chain integration in industrial companies. We begin looking at a reflective model, where the items of the scales are estimators conditioned by a construct that can not be directly observed. The items therefore reflect this construct and are interchangeable, with the result that any group of these items will provide an estimation equivalent to the phenomenon of interest (Hair, Anderson, Tatham & Black, 1999; Brown, 2006; Byrne, 2006; Baxter, 2009). The test bank of items used to build the survey originate from earlier works (Roth et al., 2008). Of these, 4 items have been selected for each construct, aiming to ensure that they are representative of the theoretical definition, used in recent papers, and that they are not redundant, to avoid the survey being excessively long. The score of the scales is the total of the sum of the items (Table 1). Our empirical analysis is based on the HPM project database, the data for which was collected during the third round of this project (2005-2007 timeframe) by an international team of researchers. As a whole, the international sample is made up of 266 plants across ten countries: Austria, Canada, Finland, Germany, Italy, Japan, Korea, Spain, Sweden and the USA. In each country, the plants were randomly selected from three industries: automotive components, electronics and machinery. A stratified sampling design was used to obtain an approximately equal number of plants for each industry-country combination. The items were targeted at plant accounting managers, direct labour, human resource managers, inventory managers, process engineers, plant managers, quality managers, supervisors and plant superintendents. Items are responded to by at least two different managers/workers in the plant. After that, all the responses for each item in each plant were averaged to obtain plant items scores. -426- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.517 Scale Item Description Used in Customer integration It01 We frequently are in close contact with our customers. (Kim, 2009; Narasimhan & Kim, 2002; Sezen, 2008; Stank, Keller & Daugherty, 2001; Swink & Nair, 2007; Tan, Lyman & Wisner, 2002; Vickery, Jayaram, Droge & Calantone, 2003; Wong & Boon- Itt, 2008; Vachon & Klassen, 2007; Germain & Iyer, 2006; Hsu, Kannan, Tan & Leong, 2008; Flynn et al., 2010; Thun, 2010) It02 Our customers give us feedback on our quality and delivery performance. (Kim, 2009; Narasimhan & Kim, 2002; Sezen, 2008; Stank et al., 2001; Swink & Nair, 2007; Tan et al., 2002; Vachon & Klassen, 2007; Hsu et al., 2008; Flynn et al., 2010; Thun, 2010) It03 We strive to be highly responsive to our customers’ needs. (Kannan & Tan, 2005; Sezen, 2008; Swink & Nair, 2007; Tan et al., 2002; Vachon & Klassen, 2007; Hsu et al., 2008; Kulp, Lee & Ofek, 2004; Thun, 2010) It04 Our customers are actively involved in our product design process. (Tan et al., 2002; Vachon & Klassen, 2007; Hsu et al., 2008; Kulp et al., 2004) External Integration It05 We work as a partner with our customers. (Stank et al., 2001; Tan et al., 2002; Wong & Boon- Itt, 2008; Hsu et al., 2008; Sanders, 2005; Danese, Formentini, Romano & Bortolotti, 2010) It06 We work as a partner with our suppliers, rather than having an adversarial relationship. (Kim, 2009; Narasimhan & Kim, 2002; Wong & Boon-Itt, 2008; Hsu et al., 2008; Flynn et al., 2010; Sanders, 2005) It07 We believe that cooperative relationships will lead to better performance than adversarial relationships. (Swink & Nair, 2007; Hsu et al., 2008; Bagchi, Ha, Skjoett-Larsen & Soerensen, 2005; Giménez & Ventura, 2003; Kannan & Tan, 2005) It08 We believe than an organization should work as a partner with its surrounding community. (Sezen, 2008; Hsu et al., 2008; Kannan & Tan, 2005) Supplier integration It09 We maintain close communication with suppliers about quality considerations and design changes. (Kannan & Tan, 2005; Kim, 2009; Sanders & Premus, 2005; Sezen, 2008; Stank et al., 2001; Swink & Nair, 2007; Tan et al., 2002; Wong & Boon- Itt, 2008; Vachon & Klassen, 2007; Hsu et al., 2008; Flynn et al., 2010) It10 We maintain cooperative relationships with our suppliers. (Sanders & Premus, 2005; Stank et al., 2001; Tan et al., 2002; Vickery et al., 2003; Wong & Boon-Itt, 2008; Vachon & Klassen, 2007; Hsu et al., 2008; Sanders, 2005; Flynn et al., 2010; Thun, 2010) It11 Our customers are actively involved in our product design process. (Kim, 2009; Koufteros, Cheng & Lai, 2007; Swink & Nair, 2007; Vachon & Klassen, 2007; Hsu et al., 2008; Kulp et al., 2004; Flynn et al., 2010) It12 We strive to establish long-term relationships with suppliers. (Stank et al., 2001; Sanders, 2005; Flynn et al., 2010; Bagchi et al., 2005; Briscoe & Dainty, 2005) Internal integration It13 We encourage employees to work together to achieve common goals, rather than encourage competition among individuals. (Stank et al., 2001; Wong & Boon-Itt, 2008; Germain & Iyer, 2006; Flynn et al., 2010; Giménez & Ventura, 2003) It14 Departments in the plant communicate frequently with each other. (Kim, 2009; Sanders & Premus, 2005; Stank et al., 2001; Vickery et al., 2003; Wong & Boon-Itt, 2008; Germain & Iyer, 2006; Flynn et al., 2010; Giménez & Ventura, 2003) It15 Management works together well on all important decisions (Narasimhan & Kim, 2002; Sanders & Premus, 2005; Germain & Iyer, 2006; Giménez & Ventura, 2003) It16 Generally, speaking, everyone in the plant works well together. (Sanders & Premus, 2005; Wong & Boon-Itt, 2008; Giménez & Ventura, 2003) Table 1. Items selected in the survey -427- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.517 We will start by analyzing the descriptive statistics, paying close attention to missing values, skip patterns, range of response values, asymmetry and kurtosis (Viladrich Segués & Doval Dieguez, 2011; Doval Dieguez & Viladrich Segués, 2011). We will then carry out internal consistency testing to purify the items (inter-item correlations, Cronbach’s alpha, squared multiple correlation, corrected item-total correlation). The set of items that pass the internal consistency testing will be analysed using exploratory factor analysis with maximum likelihood and varimax rotation, to verify if each of the items has high loads on the predicted scales, and with a multi-trait/multi-item analysis to see the discriminant validity (Doval Dieguez & Viladrich Segués, 2011). And finally, a confirmatory factor analysis will be carried out using robust estimators, which will allow us to check the convergent and discriminant validity of the scales. This model incorporates the correlations of all the scales amongst themselves, given that certain theoretical evidence would appear to show that there is a certain overlapping between the constructs and that their correlations should therefore be taken into consideration (Flynn et al., 2010). Convergent validity will be tested using four criteria. The first is that statistics of the robust model’s goodness of fit are appropriate (P-value Robust Chi2 > 0.05; normed Chi2 < 5; CFI > 0.90; IFI > 0.90; MFI > 0.90; GFI > 0.85; RMSEA < 0.08) (Hair et al., 1999; Sila, 2007; Spreitzer, 1995; Tari, Molina & Castejón, 2007; Ullman & Bentler, 2004). Secondly, composite reliability will be checked as being over 0.7 (Hair et al., 1999). Thirdly, it will be checked that the Cronbach’s alpha are over 0.70 (Hair et al., 1999; Lin, 2006). The fourth criterion will test whether variance extracted is over 40% (Hair et al., 1999). Discriminant validity will be checked using the test of variance extracted compared to squared correlations (Fornell & Larcker, 1981; Hair et al., 1999; Farrell, 2010) and the confidence interval for correlations (Anderson & Gerbing, 1988; Bagozzi, 1994). The analyses will be done with the SPSS and EQS programme using the maximum likelihood parameter estimation method (Ullman & Bentler, 2004). 5. Results Our sample comprises 266 plants. Of those, 66 companies in Sweden and Germany (24.8%) did not respond to the question on the type of company, 26 (9.8%) did not answer the question on the size of the company (the majority of these in South Korea and the US) and 29 (10.9%) did not respond to the question on the level of vertical integration (once again South Korea and the US are the sub-sample with the highest number of missing values). The sampling distribution across countries is uniform and there are only major differences to a lesser degree amongst World-class companies in Australia and Finland; a greater proportion of transport companies in Germany; larger companies in Japan and South Korea and a greater degree of vertical integration in Germany, and a lesser degree in Sweden (Table 2). -428- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.517 Industry Plant size Vertical integration Count. Tot Electr. Machi. Trans. 50 -250 251 -500 > 500 Not Low Med. High Austria 21 10 7 4 10 3 5 0 2 12 5 Finland 30 14 6 10 15 10 5 1 5 16 6 Germany 41 9 13 19 12 13 16 0 8 14 17 Italy 27 10 10 7 14 6 7 2 4 14 6 Japan 35 10 12 13 5 6 23 0 9 13 11 South Korea 31 10 10 11 2 3 14 1 1 16 4 Spain 28 9 9 10 12 8 7 0 7 9 9 Sweden 24 7 10 7 10 8 5 2 7 13 0 USA 29 9 11 9 6 8 7 1 8 8 6 Total 266 88 88 90 86 65 89 7 51 115 64 Table 2. Composition of the sample of companies Practically all the sample plants answered the 16 items concerning the degree of integration. There were only missing values in 6 items (it05, it07, it13, it14, it15, it16). And these missing values stem, for the most part, from two plants so there is no point carrying out a detailed analysis of the missing values. For the majority of the items, the distribution of responses has a high average, a typically not very high deviation, negative asymmetry and are leptokurtic. In other words, the majority of responses are in the upper part of the scale (of around 5 and 6 on a seven-level scale). More than half of the items have a “grounding” effect and the minimum values do not tend to cover the whole scale, with a range of responses covering between 3 and 5 different levels of response (See Table 3). Item N Range Minimum Maximum Average Typ. Dev. Asymmetry Kurtosis It01 266 5.33 1.67 7.00 5.3429 0.77076 -0.738 1.747 It02 266 4.00 3.00 7.00 5.6873 0.70058 -0.328 0.037 It03 266 3.00 4.00 7.00 6.0724 0.51030 -0.734 0.957 It04 266 4.67 2.00 6.67 4.6142 0.84354 -0.142 -0.288 It05 265 4.00 3.00 7.00 5.5844 0.70905 -0.701 0.680 It06 266 4.00 3.00 7.00 5.6444 0.68590 -0.911 1.352 It07 265 3.17 3.83 7.00 6.0305 0.60557 -0.781 0.835 It08 266 2.89 4.11 7.00 5.7789 0.56771 -0.315 -0.084 It09 266 3.81 2.93 6.73 5.2711 0.66239 -0.634 0.248 It10 266 3.60 3.40 7.00 5.5506 0.56121 -0.564 1.027 It11 266 4.46 1.88 6.33 4.5870 0.80275 -0.408 0.038 It12 266 3.50 3.50 7.00 5.7113 0.60831 -0.497 0.319 It13 265 3.11 3.89 7.00 5.8400 0.56874 -0.690 1.022 It14 265 4.00 3.00 7.00 5.3782 0.72600 -0.452 0.332 It15 265 5.33 1.67 7.00 5.1967 0.77881 -0.852 1.413 It16 265 4.00 3.00 7.00 5.4385 0.76041 -0.626 0.431 Table 3. Descriptive statistics -429- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.517 Following internal consistency testing (Tables 4 and 5), Item 13 was removed from the internal integration scale given that its correlation with other items on the scale was too low. So is its correlation with the scale as a whole as well as its multiple squared correlation. Similarly, there would be a slight improvement to Chronbach’s alpha were it to be eliminated. This would indicate that the item be eliminated. The external integration scale for It05 is not related to the other items and this penalizes the statistics. The item should probably be eliminated if it does not pass the next stages of analysis. Were it be eliminated, the average correlations between items would slightly improve (0.462 with a minimum of 0.447 and a maximum of 0.472), and the Cronbach’s Alpha would remain at 0.72. On the other scales, the items with the poorest results are It04 and It11, which have the squared multiple correlation and would result in a slight increase to Cronbach’s Alpha were they to be eliminated. This is the same case for It05, whereby it remains until subsequent analysis determined if it should be eliminated or not. Average Minimum Maximum Alpha Lim Inf IC Alfa Lim Inf IC Alfa No. elements Customer integration 0.480 0.283 0.574 0.775 0.727 0.816 4 External Integration 0.394 0.299 0.472 0.716 0.656 0.768 4 Supplier integration 0.448 0.285 0.568 0.753 0.700 0.798 4 Internal integration 0.550 0.516 0.582 0.785 0.765 0.826 3 Table 4. Inter-item correlations and Cronbach’s alpha Following this, a multi-trait/multi-item analysis was carried out (Table 5). To pass the test, the difference between the corrected item-total correlation and the item correlation with other scales should be greater than 0.123 -2* standard error (Doval Dieguez & Viladrich Segués, 2011)-. Item05, earmarked following earlier analysis as potentially having problems, has more correlation to an access other than that of the one theoretically assigned to it and its correlation is not sufficiently different in the other two axes. It is therefore an item that could create issues during discriminant validation and will therefore be eliminated from the model. Currently, items it04 and It11 have passed the test. The results of the exploratory factor analysis with factor extraction techniques using the maximum likelihood method and Varimax criterion under orthogonal rotation (Table 6), indicate that the sampling adaptation index (0.821) and Bartlett’s test of sphericity (p < 0.000) are adequate. There are 4 factors with values greater than 1, and which make up for 63.8% of the variance. The items are grouped around the factors proposed by the theory. Factor loadings are all greater than 0.5 in the envisaged factor and have a different of more than 0.3 with regard to the loads in other factors. For this reason, no modifications are made to the scales following analysis. -430- Journal of Industrial Engineering and Management – http://dx.doi.org/10.3926/jiem.517 item Squared multiple correlation Cronbach’s Alpha if the element is eliminated Customer integration External Integration Supplier integration Internal integration It01 0.435 0.676 0.656 0.310** 0.284** 0.334** It02 0.472 0.673 0.668 0.330** 0.389** 0.337** It03 0.370 0.752 0.539 0.309** 0.288** 0.390** It04 0.293 0.770 0.510 0.215** 0.248** 0.241** It05 0.169 0.717 0.494** 0.409 0.308** 0.297** It06 0.331 0.614 0.128*0.564 0.381** 0.243** It07 0.298 0.642 0.232** 0.525 0.249** 0.262** It08 0.301 0.639 0.201** 0.537 0.315** 0.254** It09 0.406 0.652 0.336** 0.294** 0.624 0.209** It10 0.435 0.667 0.290** 0.402** 0.620 0.253** It11 0.310 0.736 0.253** 0.318** 0.507 0.199** It12 0.335 0.726 0.300** 0.304** 0.489 0.229** It14 0.424 0.680 0.328** 0.274** 0.172** 0.651 It15 0.361 0.735 0.346** 0.315** 0.256** 0.599 It16 0.394 0.710 0.332** 0.312** 0.289** 0.622 Table 5. In bold, the corrected item-total correlation (as this is the prescribed scale for the item), the rest of the correlations are routine Pearson correlations Item Factor1 Factor2 Factor3 Factor4 It01 0.725 0.121 0.160 0.106 It02 0.738 0.256 0.146 0.092 It03 0.594 0.149 0.247 0.129 It04 0.560 0.124 0.112 -0.001 It06 -0.029 0.299 0.122 0.659 It07 0.151 0.066 0.131 0.670 It08 0.094 0.213 0.124 0.588 It09 0.198 0.725 0.060 0.074 It10 0.147 0.674 0.109 0.255 It11 0.106 0.585 0.085 0.140 It12 0.183 0.526 0.107 0.163 It14 0.192 0.025 0.768 0.100 It15 0.168 0.188 0.686 0.130 It16 0.218 0.119 0.638 0.181 Table 6. Rotated factor matrix. Extraction method: Maximum likelihood The final step in the process was the carrying out confirmatory factor analysis to complete checking the convergent and discriminant validation of each scale. We start with the joint measurement model, which is the best representation of the theoretical model where the scales are interlinked (Flynn et al., 2010). 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