Partial least square structural equation modellingâ' use in information systems
an updated guideline in exploratory settings.
DOI:
https://doi.org/10.58216/kjri.v6i1.125Abstract
The purpose of many studies in the field of Information Systems (IS) research is to analyse causal relationship between variables. Structural Equation Modelling (SEM) is a statistical technique for testing and estimating those causal relationship based on statistical data and qualitative causal assumption. Partial Least Square Structural Equation Modelling (PLS-SEM) is the technique that is mostly used in IS research. It has been subject to many reviews either in confirmatory or exploratory settings. However, it has recently emerged that PLS occupies the middle ground of exploratory and confirmatory settings. Thus, this paper intends to propose an updated guideline for the use of PLS-SEM in Information Systems Research in exploratory settings maintaining interpretability. A systematic literature review of 40 empirical and methodological studies published between 2012 and 2016 in the leading journal of the field guide  future empirical work.
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Aguirre-urreta, M. I., & Marakas, G. M. (2012). REVISITING BIAS DUE TO CONSTRUCT MISSPECIFICATION: DIFFERENT RESULTS FROM CONSIDERING COEFFICIENTS IN STANDARDIZED FORM. MIS Quarterly, 36(1), 123–138.
Baron, R. M., & Kenny, D. a. (1986). The Moderator-Mediator Variable Distinction in Social The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
Bartelt, V. L., & Dennis, A. R. (2014). NATURE AND NURTURE: THE IMPACT OF AUTOMATICITY AND THE STRUCTURATION OF COMMUNICATION ON VIRTUAL TEAM BEHAVIOR AND PERFORMANCE. MIS Quarterly, 38(2), 521–538.
Chin, T. Y. (2006). Mediator and Moderator Variables in Social Science Research. Nebraska, USA. Retrieved from http://www.cyfs.unl.edu/docs/CenterScope/ResearchMethodologySeries/Chin.pdf
Evermann, J., & Tate, M. (2010). Testing Models or Fitting Models? Identifying Model Misspecification in PLS. In I. 2010 (Ed.), Thirty First International Conference on Information Systems (Icis) (p. Paper 21). St. Louis: AIS.
Evermann, J., & Tate, M. (2012). COMPARING THE PREDICTIVE ABILITY OF PLS AND COVARIANCE MODELS. In Proceedings of the 33rd International Conference on Information Systems (ICIS) (Vol. 18, pp. 546–550). Orlando.
Evermann, J., & Tate, M. (2014). Comparing the Predictive Ability of Pls and Covariance Models. In I. 2016 (Ed.), Thirty Fifth International Conference on Information Systems (Icis) (pp. 1–18). Auckland: AISel.
Fang, Y., Qureshi, I., Sun, H., McCole, P., Ramsey, E., & Lim, K. H. (2014). TRUST, SATISFACTION, AND ONLINE REPURCHASE INTENTION: THE MODERATING ROLE OF PERCEIVED EFFECTIVENESS OF E-COMMERCE INSTITUTIONAL MECHANISMS. MIS Quarterly, 38(2), 407–427.
Fung, H. P. (2015). Is there any different between “control variable†and ’moderating variable? Retrieved July 25, 2017, from www.researchgate.net/post/Is_there_any_different_between_control_variable_and_moderating_variable2
Garson, G. D. (2016). Partial Least Squares: Regression & Structural Equation Models (2016 Editi). Asheboro: Statistical Associates Publishing.
Gefen, D., Straub, D. W., & Boudreau, M.-C. (2000). Structural Equation Modeling and Regression : Guidelines for Research Practice. Communications of the Association for Information Systems, 4(October), 7. https://doi.org/10.1.1.25.781
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. The Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Han, W., Ada, S., Sharman, R., & Rao, H. R. (2015a). CAMPUS EMERGENCY NOTIFICATION SYSTEMS: AN EXAMINATION OF FACTORS AFFECTING COMPLIANCE WITH ALERTS. MIS Quarterly, 39(4), 909–929.
Han, W., Ada, S., Sharman, R., & Rao, H. R. (2015b). CAMPUS EMERGENCY NOTIFICATION SYSTEMS: AN EXAMINATION OF FACTORS AFFECTING COMPLIANCE WITH ALERTS. MIS Quarterly, 39(4), 909–929.
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., … Calantone, R. J. (2014). Common Beliefs and Reality About PLS: Comments on Ronkko and Evermann (2013). Organizational Research Methods, 17(2), 182–209. https://doi.org/10.1177/1094428114526928
Henseler, J., Hubona, G., & Ash, P. (2016). Using PLS path modeling in new technology research : updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382
Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hreats, A. V. V. A. T., Becker, J., & Ringle, C. M. (2013). DISCOVERING UNOBSERVED HETEROGENEITY IN STRUCTURAL EQUATION MODELS TO AVERT VALIDITY THREATS. MIS Quarterly, 37(3), 665–694.
Hsieh, J. J. P., & Petter, S. (2012). IMPACT OF USER SATISFACTION WITH MANDATED CRM USE ON EMPLOYEE SERVICE QUALITY. MIS Quarterly, 36(4), 1065–1080.
Ifinedo, P. (2015). The Moderating Effects of Age and Computer Knowledge on Nurses ’ Acceptance of Information Systems : A Canadian Study. International Conference on Information Resources Management (CONF-IRM). Retrieved from http://aisel.aisnet.org/confirm2015/29%0AThis
Jarvis, C. B., Mackenzie, S. B., & Podsakoff, P. M. (2012). THE NEGATIVE CONSEQUENCES OF MEASUREMENT MODEL MISSPECIFICATION: A RESPONSE TO AGUIRRE-URRETA AND MARAKAS. MIS Quarterly, 36(1), 139–146.
Johnston, A. C., Warkentin, M., & Siponen, M. (2015). AN ENHANCED FEAR APPEAL RHETORICAL FRAMEWORK: LEVERAGING THREATS TO THE HUMAN ASSET THROUGH SANCTIONING RHETORIC. MIS Quarterly, 39(1), 113–134.
Kankanhalli, A., Ye, H. (Jonathan), & Teo, H. H. (2015). COMPARING POTENTIAL AND ACTUAL INNOVATORS: AN EMPIRICAL STUDY OF MOBILE DATA SERVICES INNOVATION. MIS Quarterly, 39(3), 667–682.
Kante, M., Oboko, R., & Chepken, C. (2017). ICTs ’ Model for Cereal Farmers in the Access and Use of Agricultural Input Information in Developing Countries : Questionnaire Validation Using SEM. American Journal of Information Systems, 5(1), 1–12. https://doi.org/10.12691/ajis-5-1-1
Kline, R. B. (2013). Principales and practice of Strutural equation modeling. Guilford Publications (third, Vol. 53). London: THE GUILFORD PRESS. https://doi.org/10.1017/CBO9781107415324.004
Majchrzak, A., Wagner, C., & Yates, D. (2013). THE IMPACT OF SHAPING ON KNOWLEDGE REUSE FOR ORGANIZATIONAL IMPROVEMENT WITH WIKIS. MIS Quarterly, 37(2), 455–469.
Marett, K., Otondo, R. F., & Taylor, G. S. (2013). ASSESSING THE EFFECTS OF BENEFITS AND INSTITUTIONAL INFLUENCES ON THE CONTINUED USE OF ENVIRONMENTALLY MUNIFICENT BYPASS SYSTEMS IN LONG-HAUL TRUCKING. MIS Quarterly, 37(4), 1301–1312.
Marsh, H. W., Morin, A. J. S., Parker, P. D., & Kaur, G. (2014). Exploratory structural equation modeling: an integration of the best features of exploratory and confirmatory factor analysis. Annual Review of Clinical Psychology, 10(Mimic), 85–110. https://doi.org/10.1146/annurev-clinpsy-032813-153700
Oodhue, G., Ewis, L., Hompson, T., Marcoulides, G. A., & Chin, W. W. (2012). WHEN IMPRECISE STATISTICAL STATEMENTS BECOME PROBLEMATIC: A RESPONSE TO GOODHUE, LEWIS, AND THOMPSON. MIS Quarterly, 36(3), 717–728.
Park, I., Sharman, R., & Rao, H. R. (2015). DISASTER EXPERIENCE AND HOSPITAL INFORMATION SYSTEMS: AN EXAMINATION OF PERCEIVED INFORMATION ASSURANCE, RISK, RESILIENCE, AND HIS USEFULNESS. MIS Quarterly, 39(2), 317–344.
Rönkkö, M., Parkkila, K., & Ylitalo, J. (2012). Use of Partial Least Squares as a Theory Testing Tool - An Analysis fo Information Systems Papers. In ECIS (Ed.), European Conference on Information Systems (Ecis) (p. Paper 145). AISeL.
Rouse, A. C., & Corbitt, B. (2008). There ’ s SEM and “ SEM â€: A Critique of the Use of PLS Regression in Information Systems Research. In the 19th Australasian Conference on Information Systems (ACIS) (pp. 845–855). Christchurch: ACIS. https://doi.org/10.1177/1094428106296642
Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students fifth edition (fifth). Pearson. Retrieved from https://is.vsfs.cz/el/6410/leto2014/BA_BSeBM/um/Research_Methods_for_Business_Students__5th_Edition.pdf
Schmitz, K. W., Teng, J. T. C., & Webb, K. J. (2016). CAPTURING THE COMPLEXITY OF MALLEABLE IT USE: ADAPTIVE STRUCTURATION THEORY FOR INDIVIDUALS. MIS Quarterly, 40(3), 663–686.
Setia, P., Ventkatesh, V., & Joglekar, S. (2013). LEVERAGING DIGITAL TECHNOLOGIES: HOW INFORMATION QUALITY LEADS TO LOCALIZED CAPABILITIES AND CUSTOMER SERVICE PERFORMANCE. MIS Quarterly, 37(2), 565–590.
Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5–40. https://doi.org/10.1037/0021-9010.90.4.710
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). CONSUMER ACCEPTANCE AND USE OF INFORMATION TECHNOLOGY: EXTENDING THE UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY. MIS Quarterly, 36(1), 157–178.
Wang, E. T. G., Tai, J. C. F., & Grover, V. (2013). EXAMINING THE RELATIONAL BENEFITS OF IMPROVED INTERFIRM INFORMATION PROCESSING CAPABILITY IN BUYER–SUPPLIER DYADS. MIS Quarterly, 37(1), 149–173.
Wong, K. K.-K. (2014). Partial Least Squares Structural Equation Modeling ( Pls-Sem ).
Wu, S. P., Straub, D. W., & Liang, T.-P. (2015). HOW INFORMATION TECHNOLOGY GOVERNANCE MECHANISMS AND STRATEGIC ALIGNMENT INFLUENCE ORGANIZATIONAL PERFORMANCE: INSIGHTS FROM A MATCHED SURVEY OF BUSINESS AND IT MANAGERS. MIS Quarterly, 39(2), 497–518.
Xu, J. D., Benbasat, I., & Cenfetelli, R. T. (2014). THE NATURE AND CONSEQUENCES OF TRADE-OFF TRANSPARENCY IN THE CONTEXT OF RECOMMENDATION AGENTS. MIS Quarterly, 38(2), 379–406.