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

Autor: M. Antioco, Dries F. Benoit, Kristof Coussement
Přispěvatelé: Lille économie management - UMR 9221 (LEM), Université d'Artois (UA)-Université catholique de Lille (UCL)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)
Rok vydání: 2015
Předmět:
CHURN PREDICTION
Bayes
Decision support system
Information Systems and Management
BIG DATA
INFORMATION
Text mining
IMPACT
Process (engineering)
Computer science
COMPETITIVE ADVANTAGE
MODELS
Bayesian probability
Big data
DOMAIN KNOWLEDGE
Context (language use)
Machine learning
computer.software_genre
Competitive advantage
[SHS]Humanities and Social Sciences
Management Information Systems
Business and Economics
Bayes' theorem
Arts and Humanities (miscellaneous)
0502 economics and business
Developmental and Educational Psychology
Expert system
business.industry
05 social sciences
Knowledge fusion
Classification
Data science
BUSINESS
[SHS.GESTION]Humanities and Social Sciences/Business administration
Domain knowledge
050211 marketing
Artificial intelligence
business
computer
050203 business & management
Information Systems
Zdroj: Decision Support Systems
Decision Support Systems, Elsevier, 2015, 79, pp.24-32. ⟨10.1016/j.dss.2015.07.006⟩
DECISION SUPPORT SYSTEMS
Decision Support Systems, 2015, 79, pp.24-32. ⟨10.1016/j.dss.2015.07.006⟩
ISSN: 0167-9236
DOI: 10.1016/j.dss.2015.07.006
Popis: 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. This paper introduces a decision support framework to fuse information sources.Fusing big data with human opinions ensures higher-quality decisions.The paper demonstrates the advantage of the Bayesian machinery for information fusion.
Databáze: OpenAIRE
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