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While there is an explosion in data analytic activity, certain behavioral aspects of a data analyst’s modeling efforts are not well understood. Through the lens of effort minimization theory, this research considers how data analysts make decisions to engage with more data as a part of their modeling process, and the effects that modeling tools have on these decisions. Previous research found that decision aids have no effect on reducing effort in terms of the amount of information referenced and processed. However, our results indicate that there are indeed significant differences in the amount of data analyzed with less effort-intensive modeling methods, and increasing amounts of available data. These results offer new implications for effort minimization research and organizations engaged in data analytics.

Effort Minimization Theory in the Data Analytics Era