Understanding Customers’ Insights Using Attribution Theory

This study attempts to detect associations between complaint attributions and specific consequences by guests of different star-rated hotels. A multifaceted approach is applied. First, a content analysis is conducted to transform textual complaints into categorically structured data. Then, an association rule technique is applied to discover potential relationships amongst complaint antecedents and consequences. Utilizing an Apriori rule-based machine learning algorithm, optimal priority rules for this study were determined for the respective complaining attributions for both the antecedents and consequences. Based on attribution theory, this study found that Customer Service, Room Space and Miscellaneous Issues received more attention from guests staying at higher star-rated hotels. Conversely, Cleanliness was a consideration more prevalent amongst guests staying at lower star-rated hotels. Practical implications are also discussed.