Graduation Year

2015

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Information Systems and Decision Sciences

Major Professor

Balaji Padmanabhan, Ph.D.

Co-Major Professor

Alan R. Hevner, Ph.D.

Committee Member

Terry L. Sincich, Ph.D.

Committee Member

Wolfgang S. Jank, Ph.D.

Keywords

adaptive toolbox, churn, heuristics, Pareto optimal sets, skylines, somatic markers

Abstract

How can we predict key decisions made by organizations in the presence of big data and on-demand information? In this dissertation we exploit a large repository of B2B real-time transactional data with service quality indicators and present evidence that organizational decision analytics apply both rational and boundedly-rational (i.e. behavioral) economic models. The dissertation’s findings demonstrate that both utility and heuristic models, respectively, play significant roles in predicting organizational decisions on churn, a key decision in this context. In the presence of a large data set the assumed rationality of organizations appears to provide accurate predictions in uncontrolled experiences and selected boundedly-rational decision rules appear to cause somatic states that make organizations more sensitive to past total qualities of service. This dissertation makes significant new contributions to the understanding of how organizations can effectively use big data to make key operational decisions. As a managerial implication, organizations must be alert to heuristics that might exacerbate the impact of total service pain on customer’s decision to churn.

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