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A Bayesian Network Estimation of the Service‐Profit Chain for Transport Service Satisfaction
Author(s) -
Anderson Ronald D.,
Mackoy Robert D.,
Thompson Vincent B.,
Harrell Gilbert
Publication year - 2004
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.2004.02575.x
Subject(s) - computer science , probabilistic logic , bayesian network , bayesian probability , inference , profit (economics) , service (business) , operations research , context (archaeology) , metric (unit) , data mining , econometrics , machine learning , artificial intelligence , business , mathematics , economics , marketing , microeconomics , paleontology , biology
Bayesian network methodology is used to model key linkages of the service‐profit chain within the context of transportation service satisfaction. Bayesian networks offer some advantages for implementing managerially focused models over other statistical techniques designed primarily for evaluating theoretical models. These advantages are (1) providing a causal explanation using observable variables within a single multivariate model, (2) analysis of nonlinear relationships contained in ordinal measurements, (3) accommodation of branching patterns that occur in data collection, and (4) the ability to conduct probabilistic inference for prediction and diagnostics with an output metric that can be understood by managers and academics. Sample data from 1,101 recent transport service customers are utilized to select and validate a Bayesian network and conduct probabilistic inference.