Research papers

How artificial intelligence will change the future of marketing

Thomas H. Davenport, Abhijit Guha, Dhruv Grewal, Timna Breßgott

Abstract In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors. Building from not only extant research but also extensive interactions with practice, the authors propose a multidimensional framework for understanding the impact of AI involving intelligence levels, task types, and whether AI is embedded in a robot. Prior research typically addresses a subset of these dimensions; this paper integrates all three into a single framework. Next, the authors propose a research agenda that addresses not only how marketing strategies and customer behaviors will change in the future, but also highlights important policy questions relating to privacy, bias and ethics. Finally, the authors suggest AI will be more effective if it augments (rather than replaces) human managers.

A new criterion for assessing discriminant validity in variance-based structural equation modeling

Jörg Henseler, Christian M. Ringle, Marko Sarstedt

Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based structural equation modeling, such as partial least squares, the Fornell-Larcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations. We demonstrate its superior performance by means of a Monte Carlo simulation study, in which we compare the new approach to the Fornell-Larcker criterion and the assessment of (partial) cross-loadings. Finally, we provide guidelines on how to handle discriminant validity issues in variance-based structural equation modeling.

An assessment of the use of partial least squares structural equation modeling in marketing research

Joe F. Hair, Marko Sarstedt, Christian M. Ringle, Jeannette A. Mena

On the evaluation of structural equation models

Richard P. Bagozzi, Youjae Yi

On the Evaluation of Structural Equation Models

Richard P. Bagozzi, Youjae Yi