An Experimental Study of Multidimensional Hierarchical Accounting Data: Drill-Down Paths Can Influence Economic Decisions.

• Autores: Jacob Peng, Ralph E. Viator, Steve Buchheit
• Localización: Journal of information systems, ISSN 0888-7985, Vol. 21, Nº. 2, 2007, págs. 69-86
• Idioma: inglés
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• Resumen

  • Although decision support systems utilizing multidimensional hierarchical data have rightfully been praised for their ability to enhance decision making, we find that the drill-down path offered by such systems can influence economic decisions--sometimes in a suboptimal fashion. Our experimental investigation offers profit maximizing monetary incentives to decision makers who navigate a simple multidimensional system. Specifically, decision makers view three possible drill-down paths that each contain three lower-level outcomes of subunit performance (i.e., only nine possible outcomes exist). We manipulate the predictive ability of aggregate data by changing the system-offered drill-down path. In our experiment, we keep all numeric performance outcomes constant; however, half of the time, the optimal outcome lies within the best aggregate level performer and half the time it does not. We find economic decisions are significantly worse when aggregate level performance fails to predict the optimal lower-level performance outcome. We also find that reducing decision effort via proper cognitive fit improves economic decisions.