Comprehensive Workshop on PLS Path Modeling Using ADANCO 2.0: Introduction, Extensions, Advances, Simulations

hosted by PLS School

Amsterdam Zuid

Start: 03 Nov 2017

End: 04 Nov 2017

  • Introductory reading

    The following article provides a profound introduction to variance-based structural equation modeling in general and PLS path modeling in particular:

    • Henseler, J., Hubona, G., and Ray, P.A. (2016). Using PLS path modeling in New Technology research: Updated guidelines. Industrial Management & Data Systems, 116 (1), 2-20, [download]

  • Additional articles

    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].

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