THE CURRENT STATE OF EMPIRICAL RESEARCH ABOUT SUPPLY CHAIN MANAGEMENT

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1 THE CURRENT STATE OF EMPIRICAL RESEARCH ABOUT SUPPLY CHAIN MANAGEMENT Richard W. Monroe, Wall College of Business, Coastal Carolina University, Conway, SC 29528, (843) , ABSTRACT In recent years, structural equation modeling (SEM) has been used with increasing frequency in the logistics and supply chain management areas. This paper will provide statistical evidence of the growth in usage of SEM in supply chain management research. The articles of top journals will be evaluated for SEM usage compared to other research methods. Decision Sciences, the Journal of Business Logistics, the Journal of Operations Management and the Journal of Supply Chain Management will be the main focus for this research. A secondary thrust to this research will be to provide a synopsis of published SEM research topics about SCM and offer some recommendations for future research. Introduction Structural equation modeling (SEM) has been used in other fields such as psychology [23] and within other management subfields such as strategic management [3][4] over the last twenty to thirty-five years. One such example from strategic management is the publication record by Boyd who has published a number of articles over an extended period 1990 forward [3][4][14]. These early adopters of SEM have apparently caught the attention of supply chain management (SCM) researchers and led to growth in the use of SEM for SCM topics. One piece of evidence that suggests the newness of SEM is the research by Scudder and Hill [49]. The authors reviewed nineteen operations management journals for a ten year period from 1985 through In their classification schema, they identified only eleven articles as using Path analysis as the data analysis method out of the total of 680 article classifications that were recorded [49]. SEM was not used as one of their classification labels. As a point of interest, Descriptive statistics was the leading category with 342 tallies followed distantly by 82 articles which employed Regression/Correlation as the data analysis method [49]. Shah and Goldstein [50] conducted an extensive review of SEM research in the operations management field. They review 93 SEM articles published between the years of 1984 and 2003 in four major Operations Management (OM) journals Management Science, Journal of Operations Management, Decision Sciences, and Journal of Production and Operations Management Society. The authors also emphasize the fundamental assumptions of SEM and offer strong recommendations for improving the quality of SEM research in the OM field. Frankel, Naslund and Bolumole [15] review the research published in the Journal of Business Logistics (JBL) over a six year period from 1999 to SEM was one method among several that were discussed. The number of SEM articles included in their research was limited to those

2 SEM studies published in the Journal of Business Logistics during their study period which amounted to seventeen articles over the six year period. Their observation supports one of the intents of this paper which is to show the growth of SEM in SCM research. JBL published eight SEM articles in 2004 which was up from the four SEM articles published in 2003 [15]. Prior years included only one or two SEM articles [15]. Note that the Frankel, Naslund and Bolumole [15] article evaluated JBL articles exclusively over a six year period to identify gaps in logistics research. So the intent of the research in this paper is very different and broader in nature than their research. Why SEM? In many research situations a number of the variables of interest are not easily observed. SEM is designed to be very effective for analyzing exactly those situations where latent variables are involved and where multiple variables are being studied to evaluate their effect on other observable variables. Structural equation modeling is a technique to specify, estimate, and evaluate models of linear relationships among a set of variables SEM models consist of observed variables (also called manifest or measured, MV for short) and unobserved variables (also called underlying or latent, LV for short)that can be independent (exogenous) or dependent (endogenous) in nature. The SEM model is an a priori hypothesis about a pattern of linear relationships among a set of observed and unobserved variables [50]. There are a few variations within SEM including Path Analysis (PA) and Confirmatory Factor Analysis (CFA) models. In many cases the SEM analysis may be composed of two different approaches within the suite of SEM tools. Several software packages are available to perform SEM including LISREL 8, EQS, and AMOS 5. For more details on the SEM fundamentals we recommend Shah and Goldstein [50], Joreskog [23] or Anderson and Gerbing [1]. The Growth of SEM In the last few years supply chain management research has joined the other disciplines in the use of SEM. In this paper we will focus on the three most recent years 2005, 2006 and We use 1999 as an earlier point in time to compare with the most recent published SEM articles. In order to identify the leading journals where SEM studies about SCM would be published we consulted the purchasing and supply management journal rankings by Zsidisin, Smith, McNally and Kull [59]. Their research received 111 useable surveys from academic respondents and led to the following rankings: 1. Journal of Operations Management (JOM) 2. Journal of Business Logistics (JBL) 3. Journal of Supply Chain Management (JSCM) 4. Harvard Business Review (HBR) 5. International Journal of Logistics Management (IJLM)

3 6. Sloan Management Review (SMR) 7. Strategic Management Journal (SMJ) 8. Journal of Purchasing & Supply Management (JP&SM) 9. Decision Sciences journal (DecSci) 10. International Journal of Physical Distribution & Logistics Management (IJPDLM) [59, p.173]. We selected the top Supply Chain Management journals from the top ten on their list. This led to the selection of the top three journals - JOM, JBL, JSCM. Other journals on the list (i.e. HBR and SMR) were excluded since they do not publish empirical research or because SCM is not the main emphasis (i.e. SMJ) to the same extent as the journals that were selected. Decision Sciences was included as the fourth journal from the top ten list. The other three journals were excluded from this study due to lack of ready access to the journals. All articles were accessed by the author through one of several methods hard copies of the full journal, electronic access through Journal Finder and one of several electronic databases (e.g. Ebscohost), or a printed copy of the PDF file of the article which was also obtained via the electronic database. Articles were reviewed in the major sections where SEM might be found the Abstract, the Keywords, the Research Method section or the Data Analysis section (or similar sections carrying some other appropriate label). The search capability within Adobe Reader was utilized in addition to scanning through the paper to locate the terms of interest. Structural, structural equation, LISREL, EQS, or AMOS are the primary terms that were utilized to identify those articles that met some part of our criteria to be counted as a SEM article. Further review was used as needed. Overall, a total of 459 articles were reviewed from the four journals over this four year time period. From these articles, a total of 89 were identified as using SEM. As the Totals indicate, the SEM articles have increased by 87.5 percent (2007 compared to 1999) while the Total article count has grown by 6.35 percent. Table 1 Summarizes the SEM articles as well as the total number of articles published in each respective journal for the years of interest: Table 1 SEM Article Count by Journal Journal 1999 SEM/TOTAL 2005 SEM/TOTAL 2006 SEM/TOTAL 2007 SEM/TOTAL JOM 10 of 34 6 of of 53* 17 of 79* JBL 2 of 21 3 of 19 3 of 17 3 of 16 JSCM 1 of 24 5 of 16 2 of 16 7 of 16 Dec. Sci.** 3 of 47 2 of 23 6 of 23 3 of 23 TOTALS 16 of of of of 134 * Totals for JOM in 2006 and 2007 include Special Issues which in some instances are entirely non-empirical research. ** All articles in Decision Sciences are counted in Total which includes non-scm topics. Table 1 indicates the increased frequency of the use of SEM over this three year period when compared with If we use Percent of Total as an alternate measure of growth, the following numbers result as shown in Table 2:

4 Table 2 SEM Measured as Percent of Total SEM as % of Total Articles 12.7% 17.8% 24.8% 22.4% There is a small drop in the percentage in 2007 but the increase is appreciable when we compare the three most recent years with the 1999 percentage. Frankel, Naslund and Bolumole [15] gave a more detailed report about JBL for the years 1999 through There were eight articles published in 2004 that utilized SEM, which is double the number of such articles in Prior years exhibited smaller numbers of such articles; there were two in 1999 and only one each in 2000, 2001, and 2002 [15]. As the numbers presented here indicate, the number of articles published in JBL in 2005 that utilized SEM dropped off from eight in 2004 [15] to only three in 2005 and three each in 2006 and Another indicator of the increased interest in SEM is the heightened emphasis by the editors of JSCM on the journal s webpage. Listed among the Criteria for Publication the following statement can be found: Statistical analysis of survey research, including, but not limited to: Structural equation modeling; ANOVA and MANOVA Cluster analysis; Regression analysis [22]. The fact that Structural equation modeling is listed first among the statistical analysis techniques seems to indicate a priority for the type of research that is sought for publication. An initial stage of the broader research agenda was completed by looking at a total of eleven years of articles in JOM (i.e. this will ultimately be expanded to cover all years beyond 1995 which complements the research of Scudder and Hill [49]). Table 3 summarizes the SEM article count compared with the Total articles for this time period: Table 3 SEM Article Count in JOM for Year SEM Articles Total Articles

5 SCM Topics utilizing SEM As a first step towards understanding the SCM topics being addressed utilizing SEM we present some samples of SEM research. The November 2007 issue of JOM is a Special Issue on ebusiness and Supply Chain Management serves two purposes. This issue is an example of the growing interest in SEM and it also gives an indication of the topics being studied. A total of ten articles appear in this issue, six of those are empirical articles with the remaining four articles presenting case studies. Four out of the six empirical articles utilized SEM and one other article discussed SEM in the overview of the empirical methods available. Table 4 briefly summarizes the model and the results for one of those four SEM articles from JOM, November 2007: Table 4 SEM Articles from JOM November 2007 Author(s) Journal Topic Vol(Year) Devaraj, Krajewski, and Wei JOM 25 (2007) Power and Singh Sanders Zhou and Benton JOM 25(2007) JOM 25(2007) JOM 25(2007) ebusiness technology for customer integration and supplier integration. The relationship between internet technologies for SC integration, trading partner relationships and the need for structural change. The relationship between ebusiness technology, organizational collaboration and organizational performance. The impact of information integration on supply chain practice Beyond the Special Issue topics, further evaluation was conducted through an iterative process to identify a framework for SEM research. Through this effort, Supply Chain Integration is a broad topic that encompasses the majority of SEM research. For a more detailed discussion of Integration we refer to Christopher [8]. Supply chain integration implies process integration, both upstream and downstream. By process integration we mean collaborative working between buyers and suppliers, joint product development, common systems and shared information. For some companies such ideas are still unthinkable and yet the signs are clearly pointing to a future where it will be the extent and quality of supply chain integration that will determine marketplace performance [8]. For example, over the last ten years or so there has been a significant change in the way in which many car manufacturers in Western Europe have changed from fragmented, transaction-focused businesses to highly integrated and relationship-based supply chains [8]. Based on the Christopher description of supply chain integration [8], the literature was subdivided into categories for new product development (NPD), information systems (IS) and relationships (REL). The following series of Tables, 5A, 5B and 5C, contain the initial sample of articles identified in these three categories:

6 Table 5A SCM Topics New Product Development (NPD) Utilizing SEM Reference Author(s) (Year) Publication Main Topic (Year) [13] Ettlie (1997) JOM Integrated design and new product success [20] Hong, Vonderembse, Doll & Nahm (2005) JOM Role change of design engineers in product development [28] Koufteros, Vonderembse & Jayaram (2005) (1999) Dec Sci Internal and external integration and product development strategies [37] Petersen, Handfield & Ragatz (2005) JOM New product development & supplier integration [42] Quesada, Syamil & Doll (2006) JSCM OEM automakers and product development [53] Swink & Song (2007) JOM NPD time & marketingmanufacturing integration [54] Tan & Tracey (2007) JSCM Collaboration and new product development [55] Tan & Vonderembse (2006) JOM Computer-aided design and concurrent engineering [57] Zacharia & Mentzer (2007) JBL Role of logistics in new product development Table 5B SCM Topics Information Systems (IS) Utilizing SEM Reference Authors (Year) Publication Main Topic [6] Cao & Dowhatshahi (2005) JOM IT alignment; virtual enterprise and business performance [12] Devaraj, Krajewski & Wei (2007) JOM e-business technology, customer & supplier integration [25] Klein (2007) JOM ebusiness/real time information & customization [35] Paulraj & Chen (2007) JSCM Buyer-supplier relationships, IT and external logistics integration [39] Power & Singh (2007) JOM e-business technology, SC integration, partners & structural change [43] Rabinovich (2007) JOM e-service quality, markups & imperfect information [44] Rabinovich, Knemeyer & Mayer (2007) JOM Internet commerce, transaction costs & network strength [46] Sanders (2005) JSCM Buyer-supplier IT alignment and performance [47] Sanders (2007) JOM ebusiness and collaboration [58] Zhou & Benton (2007) JOM Information integration and supply chain practice

7 Table 5C SCM Topics Relationships (REL) Utilizing SEM Reference Authors (Year) Publication Main Topic [9] Cousins & Menguc (2006) JOM Socialization & Integration in SCM [10] Cousins, Handfield, Lawson & Petersen (2006) JOM SC relational capital formal and informal socialization [17] Germain & Lyer (2006) JBL Internal and downstream integration and performance [18] Golicic & Mentzer (2006) JBL Buyer-supplier relationship magnitude [32] Modi & Mabert (2007) JOM Supplier development, improvement through knowledge transfer [34] Narasimhan & Das (1999) Dec Sci Strategic sourcing contribution to mfg. flexibilities and performance [36] Paulraj & Chen (2007) JSCM Environmental uncertainty/strategic supply mgt.: Resource dependence [38] Petersen, Ragatz & Monczka (2005) JSCM Collaborative planning; supplier performance; and buying firm performance [40] Prahinski & Fan (2007) JSCM Supplier evaluations and communication quality [51] Stading & Altay (2007) JSCM ease of doing business within supplier-customer interface There are many other topics that do not fit within our first three categories listed above. One other category that warrants mentioning at this stage is the group of SEM articles in a category called Supply Chain practices (SCP) as listed in Table 6: Table 6 SCM Topics Supply Chain Practices (SCP) utilizing SEM Reference Authors (Year) Publication Main Topic [7] Christensen, Germain & Birou (2005) JOM Build-to-order and just-in-time SC knowledge and market performance [19] Gowen, McFadden, Hoobler & Tallon (2006) JOM Healthcare quality practices employee commitment and employee control (services SCM) [24] Jun, Cai & Shin (2006) JOM TQM practices in maquiladora employee satisfaction and loyalty [27] Kocabasoglu, Prahinski & Klassen JOM Forward and reverse SC investments business uncertainty [28] Koufteros, Vonderembse & Doll (1998) JOM Time-based manufacturing practices & the 7 sub-dimensions [33] Molina, L-Montes, & R- Moreno (2007) JOM Quality management practices & knowledge transfer [52] Swafford, Ghosh & JOM Procurement, Mfg., Distr./Logistics Murthy (2005) [56] Tu, Vonderembse, Ragu- Nathan & Sharkey (2006) JOM related to Supply Chain agility Time-based manufacturing practices & absorptive capacity

8 A large sample size is one of the requirements that must be met for SEM research. Table 7 lists some of the article authors and the sample sizes associated with their SEM research: Table 7 SEM Sample Size Reference Author(s) Journal Vol(Year) [24 Jun, Cai and Shin JOM 24(2006) [27] Kocabasoglu, Prahinski and Klassen [37] Petersen, Handfield and Ragatz JOM 25(2007) JOM 23(2005) [41] Prater and Ghosh JOM 24(2006) [56] Tu, Vonderembse, JOM Ragu-Nathan and 24(2006) Sharkey Sample Size 407 surveys returned from two Maquiladora locations [states that 200 is minimum desired for SEM (Hulland et al., 1996)] 126 surveys of Canadian plants in NAICS 333 and 335 plus a random sample of nonduplicate plants from Scott s Directory 134 useable from 225 companies responding to initial solicitation sent to 3000 companies 310 from all U.S.-owned manufacturers with operations in Europe 303 from Society of Manufacturing Engineers members We can clearly see that the large sample size is a distinct characteristic of some of the SEM research. Certainly the numbers of 407, 310 and 303 stand out [24][41][56]. Sampling Other Methods To get some idea of the other methods in use, a random sample of fifty empirical articles from all four journals for the primary four years resulted in the methods tallies as shown in Table 8. Three articles reported using two methods and were counted in both methods resulting in a total count of fifty-three. Table 8 Random Sample of Other Empirical Methods in Four Journals Analysis Method Article Count Cumulative Total ANOVA Regression Factor Analysis (FA) 6 28 MANOVA 6 34 Simulation 6 40 Multiple Regression 4 44 Optimization 4 48 Confirmatory Factor Analysis (CFA) 3 51 Principle Components Analysis (PCA) 2 53

9 The main focus of this paper is SEM so more detailed information on these methods is not included here. Table 9 includes a small sample indicating SCM topics studied utilizing all other methods. This will provide a small group to compare with the SEM research. Table 9 Other Research Analysis Methods and Topics Reference Authors (Year) Publication Analysis Method [2] Bendoly, Citurs & Konsynski (2007) [5] Boyson, Corsi, Dresner, & Rabinovich (1999) [11] Das, Narasimhan & Talluri (2006) [31] Krause, Terpend & Petersen (2006) Discussion Dec Sci Hierarchical Regression Topic Area RFID benefits JBL ANOVA Third Party Logistics relationships JOM Regression Supplier integration & optimal configuration JSCM Regression Buyer-seller negotiations There is a definite upswing in SEM research among the empirical research on supply chain topics. Through an iterative process this paper identifies Supply Chain Integration as the broad topic category that describes a majority of the SEM research. We divided Supply Chain Integration further into three sub-categories New product development, Information Systems and Relationships. Overall the SEM research on other topics is somewhat fragmented based on this preliminary study. We do offer another category in the form of Supply Chain Practices. Ongoing research will seek to further delineate these categories and identify other categories to provide further organization for the SEM literature. Other topics that are not seen as frequently include healthcare quality practices [19] and other service supply chain studies. With the importance of services we would expect to see further SEM application in healthcare areas and similar topics in other service areas. Another interesting pattern is the multiple times that the same author(s) appear(s) in connection with SEM publications. Sanders is included here with four separate articles on SEM [45][46][47][48]. Others include Vonderembse also with four articles [20][28][55][56] and Mentzer with three appearances [16][18][57]. These are just a few examples. Several other authors also appear with regular frequency among the SEM published work. It will be interesting to track the progress to see the authors who emerge as the leading SEM researchers.

10 Limitations This research has been largely exploratory and is currently at an early stage. There is much more SEM research to be discovered and documented in an organized manner. We have not entered into a discussion about the good applications of SEM and/or the shortcomings of some SEM research. We refer to Shah and Goldstein [50] and Garver and Mentzer [16] for the cautionary statements that they offer for researchers considering SEM as a method for their research. Following their advice such as adhering to the assumptions and required conditions for SEM is something that we strongly endorse. Future Research The journal selection is limited for this early stage of the research project but will be expanded as the research moves forward. Future research plans include documenting the article counts for the full period from 1996 through 2007 and for a larger group of journals. This will provide a more complete picture of the pattern of SEM research and will complement the prior research by Scudder and Hill [49]. Another long range objective is to identify every supply chain article that utilizes SEM and categorize the topics that have been studied utilizing this method. Subsequent research will also focus on the SEM models used for specific topics and develop comparisons between different studies on the same or similar topics. From the growing body of research using SEM, there will be a need to look for gaps in the research and then design research to address those gaps. The categories in this paper represent a first step towards organizing the SCM research that utilizes SEM. These categories will also serve as a launching point for future research on similar topics. For example, someone interested in conducting SEM analysis on a new product development topic would find the articles listed here to be a useful foundation. Going forward the SEM research opportunities certainly seem to be abundant. REFERENCES [1] Anderson, J.C. and D.W. Gerbing. Structural equation modeling in practice: A review and recommended two step approach. Psychological Bulletin 103, 1988, 3: [2] Bendoly, Elliott, Alex Citurs and Benn Konsynski. Internal Infrastructural Impacts on RFID Perceptions and Commitment: Knowledge, Operational Procedures, and Information-Processing Standards. Decision Sciences, 38:3, 2007, [3] Boyd, Brian K. Corporate linkages and organization environment: A test of the resource dependence model. Strategic Management Journal, 11, 1990, pp [4] Boyd, Brian K. Board control and CEO compensation. Strategic Management Journal, 15, 1994, pp [5] Boyson, Sandor, Thomas Corsi, Martin Dresner and Elliot Rabinovich. Managing effective third part logistics relationships: What does it take? Journal of Business Logistics, 1999, 20:1:

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