New Developments in PLS Path Modeling: Advanced Course

The following articles can serve as a preparation for the course:

• Benitez, J., Henseler, J., Castillo, A., and Schuberth, F. (2019). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, in print, [click to download]

• Henseler, J. (2017). Bridging design and behavioral research with variance-based structural equation modeling. Journal of Advertising, 46 (1), 178-192 [click to download]

• Henseler, J., Dijkstra, T.K., Sarstedt, M., Ringle, C.M.; Diamantopoulos, A., Straub, D.W., Ketchen, D.J., Hair, J.F., Hult, G.T.M., Calantone, R.J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö & Evermann (2013). Organizational Research Methods, 17 (2), 182-209 [click to download]

In our seminars, we present and discuss scholarly articles on variance-based structural equation modeling and PLS. Some of them are freely available:

• Ajamieh, A., Benitez, J., Braojos, J., and Gelhard, C. (2016). IT infrastructure and competitive aggressiveness in explaining and predicting performance. Journal of Business Research, 69 (10), 4667-4674.

• Benitez, J., Henseler, J. and Castillo, A. (2017). Development and update of guidelines to perform and report partial least squares path modeling in Information Systems research. Proceedings of the 21st Pacific Asia Conference on Information Systems, Langkawi, Malaysia, 1-15.

• Benitez, J., Henseler, J., and Roldan, J.L. (2016). How to address endogeneity in partial least squares path modeling. Proceedings of the 22nd Americas Conference on Information Systems, San Diego, California, USA, 1-10.

• Benitez, J., Ray, G., and Henseler, J. (forthcoming). Impact of information technology infrastructure flexibility on mergers and acquisitions. MIS Quarterly (in press), 1-59.

• Braojos, J., Benitez, J., and Llorens, J. (2015). How do small firms learn to develop a social media competence? International Journal of Information Management, 35 (4), 443-458.

• Dijkstra, T.K. and Henseler, J. (2015). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81 (1), 10-23, [download].

• Dijkstra, T.K. and Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39 (2), 297-316, [download].

• Henseler, J. (2017). Bridging design and behavioral research with variance-based structural equation modeling. Journal of Advertising, 46 (1), 178-192, [download].

• Henseler, J. (2010). On the convergence of the partial least squares path modeling algorithm. Computational Statistics, 25 (1), 107-120, [download].

• Henseler, J., Dijkstra, T.K., Sarstedt, M., Ringle, C.M., Diamantopoulos, A., Straub, D.W., Ketchen, D.J., Jr., Hair, J.F., Hult, G.T.M., and Calantone, R.J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö & Evermann (2013). Organizational Research Methods, 17 (2), 182-209, [download].

• Henseler, J., Ringle, C.M., and Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43 (1), 115-135, [download].

• Henseler, J. and Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28 (2), 565-580, [download].

• van Riel, A.C.R., Henseler, J., Kemény, I., and Sasovova, Z. (2017). Estimating hierarchical constructs using consistent partial least squares: The case of second-order composites of common factors. Industrial Management & Data Systems, 117 (3), 459-477, [download].