دانلود مقاله ISI انگلیسی شماره 7735
عنوان فارسی مقاله

استفاده از مدل سازی معادله ساختاری در پژوهش مدیریت عملیات:با نگاه کردن به عقب و جلو

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
7735 2006 22 صفحه PDF سفارش دهید 11780 کلمه
خرید مقاله
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عنوان انگلیسی
Use of structural equation modeling in operations management research: Looking back and forward
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Operations Management, Volume 24, Issue 2, January 2006, Pages 148–169

کلمات کلیدی
- روش های تحقیق تجربی - مدل سازی معادلات ساختاری - مدیریت عملیات
پیش نمایش مقاله
پیش نمایش مقاله استفاده از مدل سازی معادله ساختاری در پژوهش مدیریت عملیات:با نگاه کردن به عقب و جلو

چکیده انگلیسی

This paper reviews applications of structural equation modeling (SEM) in four major Operations Management journals (Management Science, Journal of Operations Management, Decision Sciences, and Journal of Production and Operations Management Society) and provides guidelines for improving the use of SEM in operations management (OM) research. We review 93 articles from the earliest application of SEM in these journals in 1984 through August 2003. We document and assess these published applications and identify methodological issues gleaned from the SEM literature. The implications of overlooking fundamental assumptions of SEM and ignoring serious methodological issues are presented along with guidelines for improving future applications of SEM in OM research. We find that while SEM is a valuable tool for testing and advancing OM theory, OM researchers need to pay greater attention to these highlighted issues to take full advantage of its potential.

مقدمه انگلیسی

Structural equation modeling as a method for measuring relationships among latent variables has been around since early in the 20th century originating in Sewall Wright's 1916 work (Bollen, 1989). Despite a slow but steady increase in its use, it was not until the monograph by Bagozzi in 1980 that the technique was brought to the attention of a much wider audience of marketing and consumer behavior researchers. While Operations Management (OM) researchers were slow to use this new statistical approach, structural equation modeling (SEM) has more recently become one of the preferred data analysis methods among empirical OM researchers, and articles that employ SEM as the primary data analytic tool now routinely appear in major OM journals. Despite its regular and frequent application in the OM literature, there are few guidelines for the application of SEM and even fewer standards that researchers adhere to in conducting analyses and presenting and interpreting results, resulting in a large variance across articles that use SEM. To the best of our knowledge, there are no reviews of the applications of SEM in the OM literature, while there are regular reviews in other research areas that use this technique. For instance, focused reviews have appeared periodically in psychology (Hershberger, 2003), marketing (Baumgartner and Homburg, 1996), MIS (Chin and Todd, 1995 and Gefen et al., 2000), strategic management (Shook et al., 2004), logistics (Garver and Mentzer, 1999), and organizational research (Medsker et al., 1994). These reviews have revealed vast discrepancies and serious flaws in the use of SEM. Steiger (2001) notes that even SEM textbooks ignore many important issues, suggesting that researchers may not have sufficient guidance to use SEM appropriately. Due to the complexities involved in using SEM and problems uncovered in its use in other fields, a review specific to OM literature seems timely and warranted. Our objectives in conducting this review are three-fold. First, we characterize published OM research in terms of relevant criteria such as software used, sample size, parameters estimated, purpose for using SEM (e.g. measurement model development, structural model evaluation), and fit measures used. In using SEM, researchers have to make subjective choices on complex elements that are highly interdependent in order to align research objectives with analytical requirements. Therefore, our second objective is to highlight these interdependencies, identify problem areas, and discuss their implications. Third, we provide guidelines to improve analysis and reporting of SEM applications. Our goal is to promote improved usage of SEM, standardize terminology, and help prevent some common pitfalls in future OM research.

نتیجه گیری انگلیسی

SEM has rapidly become an important and widely used research tool in the OM literature. Its attractiveness to OM researchers can be attributed to two factors. From CFA, SEM draws upon the notion of unobserved or latent variables, and from PA, SEM adopts the notion of modeling direct and indirect relationships. These advantages, combined with the availability of ever more user-friendly software, make it likely that SEM will enjoy widespread use in the future. We have provided both a review of the OM literature employing SEM as well as discussion and guidelines for improving its future use. Table 4 contains a summary of some of the most important issues discussed here, their implications, and recommendations for resolving these challenges. Below, we briefly discuss these issues. As researchers, we should ensure that SEM is the correct method for examining the research question at hand. When theory development is at a nascent stage and patterns of relationships among LVs are relatively weak, SEM should be used with caution so that model confirmation and theory testing do not degenerate into extensive model respecification. Likewise, it is important that we use appropriate measurement methods and understand the distinction between formative and reflective variables. Determining minimum sample size is, in part, dependent upon the number of parameter estimates in the hypothesized model. But emerging research in this area indicates that the relationship between sample size and number of parameter estimates is complex and dependent upon MV characteristics (MacCallum et al., 2001). Likewise, guidelines on degrees of freedom and model identification are not simple or straightforward. Researchers must be cognizant of these issues and we recommend that all studies discuss them explicitly. As the powerful capabilities of SEM derive partly from its highly restrictive simplifying assumptions, it is important that assumptions such as normality and skewness are carefully assessed prior to generating an input matrix and conducting analysis. With regard to model estimation, researchers should recognize that parameter estimates are not fixed values, but rather depend upon the estimation method. For instance, parameter estimates obtained by using maximum likelihood ratio are different from those obtained using ordinary least squares (Browne and Arminger, 1995). Further, in evaluating model fit, the correspondence between the hypothesized model and the observed data should be assessed using a variety of absolute and incremental fit indices for measurement, structural, and overall models. In addition to path coefficients, confidence intervals and standard errors should be assessed. Rather than hypothesizing a single model, multiple alternate models should be evaluated when possible, and research results should be cross validated using split or multiple samples. Given the very real possibility of alternate, equivalent models, researchers should be cautious in over-interpreting results. Because no model represents the real world exactly, we must be more forthright about the “imperfection” inherent in any model and acknowledge the literal implausibility of the model more explicitly (MacCallum, 2003). One of the most poignant observations in conducting this study was the inconsistency in the published reporting of results and, in numerous instances, our inability to reconstruct the tested model based on the description in the text and the reported degrees of freedom. These issues can be resolved by attention to published guidelines for presenting results of SEM (e.g. Hoyle and Panter, 1995). To assist both during the review process and in building a cumulative tradition in the OM field, sufficient information needs to be provided to understand (1) the population from which the data sample was obtained, (2) the distribution of the data, (3) the hypothesized measurement and structural models, and (4) statistical results to corroborate the subsequent interpretation and conclusions. We recommend that every published application of SEM provide a clear and complete specification of the model(s) and variables, preferably in the form of a graphical figure, including the measurement model linking LVs to MVs, the structural model connecting LVs, and specification of which parameters are being estimated and which are fixed. It is helpful to identify specific research hypotheses on the graphical figure, both to clarify the model and to reduce the text needed to describe them. In addition to including a statement about the type of input data matrix, software and estimation method used, we recommend the input matrix be included in paper for future replications and meta-analytical research studies, but we recognize this is an editorial decision subject to space constraints. In terms of statistical results, we suggest researchers include multiple measures of fit and criteria for evaluating fit along with parameter estimates, and associated confidence intervals and standard errors. Finally, interpretation of results should be guided by an understanding that models are imperfect and cannot be made to be exactly correct. We can enrich our knowledge by reviewing the use of SEM in more mature research fields such as psychology and marketing, including methodological advances. Some advances worthy of mention are validation studies using the multi-trait multi-method (MTMM) matrix method (cf. Cudeck, 1988 and Widaman, 1985), measurement invariance (Widaman and Reise, 1997), and using categorical (Muthen, 1983) or experimental data (Russell et al., 1998). Our review of published SEM applications in the OM literature suggests that while reporting has improved over time, we need to pay attention to methodological issues in using SEM. Like any statistical technique or tool, it is important that SEM be used prudently if researchers want to take full advantage of its potential. SEM is a useful tool to represent multidimensional unobservable constructs and simultaneously examine structural relationships that are not well captured by traditional research methods (Gefen et al., 2000, p. 6). In the future, utilizing the guidelines presented here will improve the use of SEM in OM research, and thus, improve our collective understanding of OM theory and practice.

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