A Bayesian approach for incorporating expert opinions into decision support systems: A case study of online consumer-satisfaction detection

a r t i c l e i n f o Interest in the use of (big) company data and data-mining models to guide decisions exploded in recent years. In many domains there are human experts whose knowledge is essential in building, interpreting and applying these models. However, the impact of integrating expert opinions into the decision-making process has not been sufficiently investigated. This research gap deserves attention because the triangulation of information sources is critical for the success of analytical projects. This paper contributes to the decision-making literature by (a) detailing the natural advantages of the Bayesian framework for fusing multiple information sources into one decision support system (DSS), (b) confirming the necessity for adjusted methods in this data-explosion era, and (c) opening the path to future applications of Bayesian DSSs in other organizational research contexts. In concrete, we propose a Bayesian decision support framework that formally fuses subjective human expert opinions with more objective organizational information. We empirically test the proposed Bayesian fusion approach in the context of a customer-satisfaction prediction study and show how it improves the prediction performance of the human experts and a data-mining model ignoring expert information. Organizational decision making often relies on a collection of intangible capabilities, which are invisible, subjective human-driven phenomena that include organizational routines and employee learning, and tangible capabilities in the form of procedural knowledge systems [1]. With the advent of (statistical) data-mining tools and computing power, the tangible capabilities for organizational decision making have become more important. In recent years this process has accelerated as a result of the exponential growth of electronically stored information, which is available to companies, organizations, and individuals. The literature on decision support systems highlights several application domains that have been significantly affected by this trend, including credit risk [2,3], bankruptcy prediction [4], customer relationship management [5,6], and fraud detection [7]. Typically, procedural knowledge systems take the form of statistical techniques that are incorporated into a data-mining system (DMS). In line with [8], we define a DMS as the " complete " system — the database or data warehouse, software for mining and analysis, the knowledge derived from these, and the part of the system that supports managerial decision making in a business setting. Traditional DMSs take information about resolved problems and their solutions as input. They then extract rules from that data and use those rules to predict likely outcomes of other …