This research essay highlights the need to integrate predictive analytics into information systems (IS) research, and shows several concrete ways in which this can be accomplished. Predictive analytics include empirical methods (statistical and other) that generate data predictions as well as methods for assessing predictive power. Predictive analytics not only assist in creating practically useful models, they also play an important role alongside explanatory modeling in theory building and theory testing. We describe six roles for predictive analytics: new theory generation, measurement development, comparison of competing theories, improvement of existing models, relevance assessment, and assessment of the predictability of empirical phenomena. Despite the importance of predictive analytics, we find that they are rare in the empirical IS literature. The latter relies nearly exclusively on explanatory statistical modeling, where statistical inference is used to test and evaluate the explanatory power of underlying causal models. However, explanatory power does not imply predictive power and thus predictive analytics are necessary for assessing predictive power and for building empirical models that predict well. To show the distinction between predictive analytics and explanatory statistical modeling, we present differences that arise in the modeling process of each type. These differences translate into different final models, so that a pure explanatory statistical model is best tuned for testing causal hypotheses and a pure empirical predictive model is best in -known explanatory paper on TAM to a predictive context to illustrate these differences and show how predictive analytics can add theoretical and practical value to IS research.

We show that contacts in formal, informal and especially multiplex networks explain transfer of innovative knowledge in an organization. The contribution of informal contacts has been much acknowledged, while that of formal contacts did not receive much attention in the literature in recent decades. No study thus far has included both these different kinds of contacts in a firm, let alone considered their combined effect. The exact overlap between formal as well as informal contacts between individuals, forming multiplex or what we call rich ties because of their contribution, especially drives the transfer of new, innovative knowledge in a firm. Studying two cases in very different settings suggests these rich ties have a particularly strong effect on knowledge transfer in an organization, even when controlling for the strength of ties. Some of the effects on knowledge transfer in an organization previously ascribed to either the formal network or the informal network may actually be due to their combined effect in a rich tie.We would like to thank participants in seminars at

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The objective of this study is to clarify the theoretical and practical problem of continuance intention (CI) of purchasing airline tickets online. Based on an in-depth literature study factors were identified that could explain the level of continuance intention. The first set of factors relate to the expectation-confirmation theory (ECT) in the consumer behavior literature. Based on ECT, continuance intention can be theoretically explained by satisfaction, confirmation, web site quality, and loyalty incentives. The second set of factors relate to the technology acceptance model (TAM) which states that the actual system use -such as using online web sites to purchase airline tickets -is determined by the behavioral intention and the attitude towards usage, which in turn can be explained by perceived usefulness and perceived ease of use. The third factor is the price sensitivity of the consumer. The research model combines the different factors to explain continuance intention. Empirical research was carried out via an online survey among customers of a web-based airline ticket agency in the Netherlands in 2004. The online survey was pretested and refined and sent to 1770 customers at the time they purchased an online ticket. In total 715 customers answered the questionnaire and 492 of these were returning customers. The results of the empirical research support the following conclusions. Customer are coming back to purchase online tickets primarily because of the satisfaction of the online booking process and a positive attitude towards using the online booking system. Loyalty incentives and price sensitivity only play a marginal role. This by and large confirms the ECT model. The satisfaction with the service is explained by the confirmation of pre-purchase expectations as well as the quality of the website. The attitude toward usage was explained by the perceived usefulness and ease of use, in accordance with the TAM model. Somewhat surprisingly, trust and perceived risk played no significant role. Conclusions are formulated and implications for practice and research are presented.