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Classifying Revenue Management: A Taxonomy to Assess Business Practice *
Author(s) -
Ng Frederick,
Rouse Paul,
Harrison Julie
Publication year - 2017
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/deci.12230
Subject(s) - taxonomy (biology) , business practice , revenue , knowledge management , best practice , business , computer science , data science , management science , marketing , psychology , management , accounting , economics , business administration , ecology , biology
As revenue management (RM) techniques evolve there is a need to take stock of how organizations practice RM and the interactions among techniques. This would help practitioners and researchers better understand how RM practice is influenced by the business setting, including those not traditionally associated with advanced RM techniques. Also, it would facilitate investigations of which practices lead to better outcomes in different contexts. Research to date has focused on individual techniques within individual business settings, with limited attention to the range of environments in which RM practice occurs. This suggests a need for a common framework to classify and assess differences in practice. In this article, we present a taxonomy which comprises (i) seven indicators of practice and (ii) a decision tree to measure RM across diverse businesses. We test the classification system in a survey of 232 businesses. Results show the taxonomy provides a comprehensive view of RM practice, with meaningful discrimination across settings. Findings also offer insight into how practices vary across different settings. Our taxonomy contributes to future research by facilitating systematic comparisons of RM practices, the settings in which it is adopted, and its impact on performance.