[논문]국내 MIS 연구에서 구조방정식모형 활용에 관한 메타분석

김종기; 전진환

국내 MIS 연구에서 구조방정식모형 활용에 관한 메타분석
A Meta Analysis of Using Structural Equation Model on the Korean MIS Research 원문보기

Asia pacific journal of information systems, v.19 no.4, 2009년, pp.47 - 75

김종기 (Division of Management, College of Business, Pusan National University) , 전진환 (Institute of Management and Economics, Pusan National University)

Abstract ▼ AI-Helper

Recently, researches on Management Information Systems (MIS) have laid out theoretical foundation and academic paradigms by introducing diverse theories, themes, and methodologies. Especially, academic paradigms of MIS encourage a user-friendly approach by developing the technologies from the users' perspectives, which reflects the existence of strong causal relationships between information systems and user's behavior. As in other areas in social science the use of structural equation modeling (SEM) has rapidly increased in recent years especially in the MIS area. The SEM technique is important because it provides powerful ways to address key IS research problems. It also has a unique ability to simultaneously examine a series of casual relationships while analyzing multiple independent and dependent variables all at the same time. In spite of providing many benefits to the MIS researchers, there are some potential pitfalls with the analytical technique. The research objective of this study is to provide some guidelines for an appropriate use of SEM based on the assessment of current practice of using SEM in the MIS research. This study focuses on several statistical issues related to the use of SEM in the MIS research. Selected articles are assessed in three parts through the meta analysis. The first part is related to the initial specification of theoretical model of interest. The second is about data screening prior to model estimation and testing. And the last part concerns estimation and testing of theoretical models based on empirical data. This study reviewed the use of SEM in 164 empirical research articles published in four major MIS journals in Korea (APJIS, ISR, JIS and JITAM) from 1991 to 2007. APJIS, ISR, JIS and JITAM accounted for 73, 17, 58, and 16 of the total number of applications, respectively. The number of published applications has been increased over time. LISREL was the most frequently used SEM software among MIS researchers (97 studies (59.15%)), followed by AMOS (45 studies (27.44%)). In the first part, regarding issues related to the initial specification of theoretical model of interest, all of the studies have used cross-sectional data. The studies that use cross-sectional data may be able to better explain their structural model as a set of relationships. Most of SEM studies, meanwhile, have employed. confirmatory-type analysis (146 articles (89%)). For the model specification issue about model formulation, 159 (96.9%) of the studies were the full structural equation model. For only 5 researches, SEM was used for the measurement model with a set of observed variables. The average sample size for all models was 365.41, with some models retaining a sample as small as 50 and as large as 500. The second part of the issue is related to data screening prior to model estimation and testing. Data screening is important for researchers particularly in defining how they deal with missing values. Overall, discussion of data screening was reported in 118 (71.95%) of the studies while there was no study discussing evidence of multivariate normality for the models. On the third part, issues related to the estimation and testing of theoretical models on empirical data, assessing model fit is one of most important issues because it provides adequate statistical power for research models. There were multiple fit indices used in the SEM applications. The test was reported in the most of studies (146 (89%)), whereas normed-test was reported less frequently (65 studies (39.64%)). It is important that normed- of 3 or lower is required for adequate model fit. The most popular model fit indices were GFI (109 (66.46%)), AGFI (84 (51.22%)), NFI (44 (47.56%)), RMR (42 (25.61%)), CFI (59 (35.98%)), RMSEA (62 (37.80)), and NNFI (48 (29.27%)). Regarding the test of construct validity, convergent validity has been examined in 109 studies (66.46%) and discriminant validity in 98 (59.76%). 81 studies (49.39%) have reported the

주제어

질의응답

핵심어	질문	논문에서 추출한 답변
	SEM은 적용기법에 따라 어떻게 구분되는가?	SEM은 적용기법에 따라 공분산 기반(covariance-based)과 컴포넌트 기반(component-based) SEM으로 구분한다[Gefen et al., 2000].
	SEM이 교육학, 심리학, 사회심리학, 경영학, 경제학 등 광범위한 영역에서 비실험자료들을 활용한 다변량 데이터의 인과관계를 추정하는데 주로 활용되는 이유는 무엇인가?	또한 변수들의 관계를 포괄적으로 측정 및 평가할 수 있어 탐색 및 확인적 요인분석이 가능하며, 일련의 사회현상에 대한 설명, 예측 및 통제에 필요한 정보를 제공한다. SEM은 연구모형의 이론적 개념과 개념간 복잡한 관련성에 대해 측정과 예측이 동시에 가능하기 때문에 교육학, 심리학, 사회심리학, 경영학, 경제학 등 광범위한 영역에서 비실험자료들을 활용한 다변량 데이터의 인과관계를 추정하는데 주로 활용된다[Lee, 1990; Bagozzi and Fornell, 1982; McDonald and Ho, 2002; Shah et al., 2006].
	공분산 기반(covariance-based)과 컴포넌트 기반(component-based) SEM의 차이는 무엇인가?	이 두 기법 사이에는 분명한 차이가 있는데, 먼저 공분산기반 SEM은 측정변수(지표 : indicator)간 예측오차가 아니라 실증적 공분산(empirical covariance)과 이론기반의 구축된 가설적 공분산(hypothetical covariance) 사이의 적합을 그 목적으로 한다. 따라서 경로계수(path coefficient)들은 측정변수들 사이의 예측력을 극대화하는데 있지 않다. 반면 PLS는 공분산을 이용하는 것이 아니라 측정오차와 잠재변수들간 예측오차를 최소화시켜 경로계수의 예측력을 극대화하도록 추정한다[Fornell and Cha, 1994]. 이에 따라 PLS는 측정모형과 구조모형을 동시에 측정할 수 있는 비교적 초기단계의 이론검증에 유용하며[Fornell and Bookstein, 1982], LISREL, AMOS, EQS와 같이 공분산 기반 SEM에 비해 표본크기와 분산에 관한 제약이 적다[Chin and Newsted, 1999].

참고문헌 (70)

Arbuckle, J.L., AMOS Users Guide Version 3.6, Small Waters Co., 1997
Anderson, J.C. and Gerbing, D.W., 'Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach,' Psychological Bulletin, Vol. 103, No. 4, 1988, pp. 411-423

상세보기
Bae, B.R., Structural Equation Modeling-Understanding, Applications, and Programming (2nd Eds.), ChoungRam, 2006
Bagozzi, R.P. and Baumgartner, H., 'The Evaluation of Structural Equation Models and Hypothesis Testing,' Principles of Marketing Research, 1994, pp. 386-422
Bagozzi, R.P. and Fornell, C., 'Theoretical Concepts, Measurement, and Meaning,' A Second Generation of Multivariate Analysis, Vol. 2, 1982, pp. 5-23
Bagozzi, R.P. and Yi, Y.J., 'On the Evaluation of Structural Equation Models,' Journal of the Academy of Marketing Science, Vol. 16, No. 1, 1988, pp. 74-97

상세보기
Banvile, C. and Landry, M., 'Can the Field of MIS be Disciplined?,' Communication of th ACM, Vol. 32, No. 1, pp. 48-60

상세보기
Baron, R.M. and Kenny, D.A., 'The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations,' Journal of Personality and Social Psychology, Vol. 51, No. 6, 1986, pp. 1173-1182

상세보기
Baroudi, J.J. and Orlikowski, W.J., 'The Problems of Statistical Power in MIS Research,' MIS Quarterly, Vol. 13, No. 1, 1989, pp. 87-106

상세보기
Baumgartner, H. and Homburg, C., 'Ap plications of Structural Equation Modeling in Marketing and Consumer Research: A Review,' International Journal of Research in Marketing, Vol. 13, No. 2, 1996, pp. 139-161

상세보기
Benbasat, I. and Weber, R., 'Research Commentary: Rethinking ‘Diversity’ in Information Systems Research,' Information Systems Research, Vol. 7, No. 4, 1996, pp. 389-399

상세보기
Bentler, P.M., 'Comparative Fit Indexes in Structural Models,' Psychological Bulletin, Vol. 107, No. 2, 1990, pp. 238-246

상세보기
Bentler, P.M. and Chou, C.P., 'Practical Issues in Structural Modeling,' Sociological Methods and Research, Vol. 16, No. 1, 1987, pp. 78-117

상세보기
Bentler, P.M. and Bonnet, D.G., 'Significance Tests and Goodness of Fit in the Analysis of Covariance Structure,' Psychological Bulletin, Vol. 88, No. 3, 1980, pp. 588-606

상세보기
Bohrnstedt, G.W. and Knoke, D., Statics of Social Data Analysis, Peacock Publishers, 1994
Bollen, K.A., Structural Equations with latent variables, John Wiley and Sons, 1989
Bollen, K.A. and Long, J.S., Testing Structural Equation Models, Sage, 1993
Browne, M.W., 'Asymptotically Distribution Free Methods for the Analysis for Covariance Structures,' British Journal of Mathematical and Statistical Psychology, Vol. 37, 1984, pp. 62-83

상세보기
Browne, M.W. and Cudeck, R., 'Alternative Ways of Assessing Model Fit,' Sociological Methods and Research, Vol. 21, No. 2, 1992, pp. 230-258

상세보기
Brekler, S.J., 'Applications of Covariance Structure Modeling in Psychology: Cause for Concern?,' Psychological Bulletin, Vol. 107, 1990, pp. 260-273

상세보기
Carmines, E. and McIver, J., Analyzing Models with Unobserved Variables: Analysis of Covariance Structures, Social Management: Current Issues, Sage, 1981
Chin, W.W., 'Issues and Opinion on Structural Equation Modeling,' MIS Quarterly, Vol. 22, No. 1, 1998, pp. 7-16
Chin, W.W., 'The Partial Least Squares Approach for Structural Equation Modeling,' in Modern Methods for Business Research, G.A. Marcoulides(Eds.), Lawrence Erlbaum Associates, 1998, pp. 295-336
Chin, W.W. and Todd, P.A., 'On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution,' MIS Quarterly, Vol. 19, No. 2, 1999, pp. 237-246

상세보기
Chin, W.W. and Newsted, P.R., 'Structural Equation Modeling Analysis with Small Samples Using Partial Least Squares,' in Statistical Strategies for Small Sample Research, R.H. Hoyle(Eds.), Sage, 1999, pp. 307-341
Chou, C.P. and Bentler, P.M., Estimates and Tests in Structural Equation Modeling, Structural Equation Modeling: Concepts, Issues, and Applications, Sage, 1995
Cliff, N., 'Some Cautions Concerning the Application of Causal Modeling Methods,' Multivariate Behavioral Research, Vol. 18, 1983, pp. 115-126
Cudeck, R., 'Analysis of Correlation Matrics Using Covariance Structure Models,' Psychological Bulletin, Vol. 105, No. 2, 1989, pp. 317-327

상세보기
Dearden, J., 'MIS is a Mirage,' Harvard Business Review, Vol. 50, No. 1, 1972, pp. 90-99
Emery, J.C. and Sprague, C.R., 'MIS: Mirage or Misconception,' Harvard Business Review, Vol. 50, No. 3, 1972, pp. 22-23
Fornell, C. and Bookstein, F.L., 'Two Structural Equation Models: Lisrel and PLS Applied to Consumer Exit-Voice Theory,' Journal of Marketing Research, Vol. 19, No. 4, 1982, pp. 440-452

상세보기
Fornell, C. and Cha, J.S., 'Partial Least Squares,' in Advanced Methods of Marketing, R.P. Bagozzi(Eds.), Oxford, Blackwell Publishers, 1994, pp. 52-78
Fornell, C. and Larcker, D.F., 'Evaluating Structural Equation Models with Unobservable Variables and Measurement Error,' Journal of Marketing Research, Vol. 18, No. 1, 1981, pp. 39-50

상세보기
Garver, M.S. and Mentzer, J.T., 'Logistics Research Methods: Employing Structural Equation Modeling to Test for Construct Validity,' Journal of Business Logistics, Vol. 20, No. 1, 1999, pp. 33-57

상세보기
Gefen, D., 'Assessing Unidimensionality through LISREL: An Explanation and Example,' Communications of the Association for Information Systems, Vol. 12, 2003, pp. 23-47
Gefen, D., Straub, D.W., and Boudreau, M.C., 'Structural Equation Modeling and Regression: Guidelines for Research Practice,' Communications of the Association for Information Systems, Vol. 4, No. 7, 2000, pp. 1-75
Hair, J.F., Anderson, R.E., Tatham, R.L.,and Black, W.C., Multivariate Data Analysis(5th Eds.), Prentice Hall, 1998
Hayduck, L.A., Structural Equation Modeling with LISREL: Essentials and Advances, Johns Hopkins University Press, 1987
Hayduck, L.A., Cummings, G.G., Boadu, K., Pazderka-Robinson, H., and Boulianne, S., 'Testing! testing! one, two, three-Testing the theory in structural equation models!,' Personality and Individual Differences, Vol. 42, No. 5, 2007, pp. 841-850

상세보기
Holbert, R.L. and Stephenson, M.T., 'Structural Equation Modeling in the Communication Sciences,' Human Communication Research, Vol. 28, No. 4, 2002, pp. 531-551
Hoogland, J.J. and Boomsma, A., 'Robustness Studies in Covariance Structure Modeling: An Overview and a Meta-Analysis,' Sociological Methods and Research, Vol. 26, No. 3, 1998, pp. 329-367

상세보기
Hoyle, R.H. and Panter, A.T., Writing about Structural Equation Modeling: Concepts, Issues, and Applications, Sage, 1995
Hu, L. and Bentler, P.M., 'Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives,' Structural Equation Modeling, Vol. 6, No. 1, 1999, pp. 1-55

상세보기
Joreskog, K.G. and Sorbom, D., Structural Equation Modeling with the SIMPLIS Command Language, Scientific Software International, 1993
Joreskog, K.G. and Sorbom, D., LISREL8: Structural Equation Modeling with the SIMPLIS command language, Scientific Software International, 1996
Kang, S.C., 'A Critical Evaluation of the Use of Statistical Methods in an MIS Journal,' Asia Pacific Journal of International Systems, Vol. 7, No. 2, 1997, pp. 77-102
Kang, S.C., Lee, Z.K., and Choi, J.I., 'Application of Empirical Research Methods in Information Systems Research: Gaining Lessons Through Evaluation,' Asia Pacific Journal of International Systems, Vol. 16, No. 2, 2006, pp. 1-25

원문보기 상세보기
Kelloway, E.K., Using LISREL for Structural Equation Modeling: A Researcher's Guide, Sage, 1998
Kim, J.H., Hong, S.H., and Choo, B.D., 'Applications of Structural Equation Modeling in Management Studies: A Critical Review,' Korean Management Review, Vol. 36, No. 4, 2007, pp. 897-923
Kim, J.K., Lim, H.S., and Lee, D.H., 'A Meta-Analysis of Reliability and Validity of Research Instruments in Korean MIS Research,' Asia Pacific Journal of International Systems, Vol. 11, No. 4, 2001, pp. 81-98
Lee, S.M., Analysis of Covariance Structural Modeling, Sung Won Sa, 1990
MacCallum, R.C. and Austin, J.T., 'Applications of Structural Equation Modeling in Psychological Research,' Annual Reviews of Psychology, Vol. 51, No.1, 2000, pp. 201-226

상세보기
MacCallum, R.C., Brwone, M.W., and Sugawara, H.M., 'Power Analysis and Determination of Sample Size for Covariance Structural Modeling,' Psychological Methods, Vol. 1, 1996, pp. 130-149

상세보기
MacKinnon, D.P., 'Contrasts in Multiple Mediator Models,' in Multivariate Applications in Substance Use Research: New Methods for New Questions, J.S. Rose and L. Chassin(Eds.), Erlbaum, 2000, pp. 141-160
Marsh H.W. and Hau, K.T., 'Assessing Goodness of Fit: Is Parsimony Always Desirable?' Journal Experimental Education, Vol. 64, 1996, pp. 364-390

상세보기
McDonald, R.P., Path Analysis with Composite Variables, Multivariate Behavioral Research, Vol. 31, No. 2, 1996, pp 239-270

상세보기
McDonald, R.P. and Ho, M.R., 'Principles and Practice in Reporting Structural Equation Analyses,' Psychological Methods, Vol. 7, No. 1, 2002, pp. 64-82

상세보기
Lee, B.T., Baura, A., and Whinston, A.B., 'Discovery and Representation of Causal Relationship in MIS Research: A Methodological Framework,' MIS Quarterly, Vol. 21, No. 1, 1997, pp. 109-136

상세보기
Long, J.S., Covariance Structure Models: An Introduction to LISREL, Sage, 1983
Rigdon, E.E., 'A Necessary and Sufficient Identification Rule for Structural Models Estimated in Practice,' Multivariate Behavioral Research, Vol. 30, No. 3, 1995, pp. 359-383

상세보기
Park H.S., 'Research Trends on International Journal of Tourism Sciences with SEM,' Journal of Tourism Sciences, Vol. 29, No. 1, pp. 267-286
Petter, S., Straub, D.W., and Rai, A., 'Specifying Formative Constructs in Information Systems Research,' MIS Quarterly, Vol. 31, No. 3, 2007, pp. 623-656

상세보기
Segars, A.H., 'Assessing the Unidimensionality of Measurement: A Paradigm and Illustration Within the Context of Information Systems,' Omega, Vol. 25, No. 1, 1997, pp. 107-121

상세보기
Segars, A.H. and Grover, V., 'Re-Examining Perceived Ease of Use and Usefulness: A Confirmatory Factor Analysis,' MIS Quarterly, Vol. 17, No. 4, 1993, pp. 517-525

상세보기
Steenkamp, J.E. and Baumgartner, H., 'On the Use of Structural Equation Models for Marketing Modeling,' International Journal of Research in Marketing, Vol. 17, No. 2, 2000, pp. 195-202

상세보기
Shah, R. and Goldstein, S.M., 'Use of Structural Equation Modeling in Operations Management Research: Looking Back and Forward,' Journal of Operations Management, Vol. 24, No. 2, 2006, pp. 148-169

상세보기
Shook, C.L., David, J.K., Hult, G.T., and Kacmar, K.M., 'An Assessment of the Use of Structural Equation Modeling in Strategic Management Research,' Strategic Management Journal, Vol. 25, No. 4, 2004, pp. 397-404

상세보기
Straub, D.W., 'Validating Instruments in MIS Research,' MIS Quarterly, Vol. 13, No. 2, 1989, pp. 147-169

상세보기
Swanson, E.B. and Ramiller, N.C., 'Information Systems Research Thematics: Submissions to a New Journal, 1987-1992,' Information Systems Research, Vol. 4, No. 4, pp. 299-330

상세보기
Tanaka, J.S., 'How Big is Big Enough? Sample Size and Goodness of Fit in Structural Equation Models with Latent Variables,' Child Development, Vol. 58, No. 1, pp. 134-146

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저자의 다른 논문 :

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