Understanding Customers’ Insights Using Attribution Theory: A Text Mining and Rule-Based Machine Learning Two-Step Multifaceted Method

Abstract

By looking at complaints made by guests of different star-rated hotels, this study attempts to detect associations between complaint attributions and specific consequences. A multifaceted approach is applied. First, a content analysis is conducted to transform textual complaints into categorically structured data. Furthermore, a web graph analysis and rule-based machine learning method are applied to discover potential relationships among complaint antecedents and consequences. These are validated using a qualitative projective technique. Using 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, we 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. Qualitative research was conducted to corroborate the findings. Other machine learning techniques (i.e., Decision Tree) build rules based on only a single conclusion, while association rules attempt to determine many rules, each of which may lead to a different conclusion. The main contributions of this study lie in the fact that this is one of the first attempts to detect correlations within the online complaining behaviors of guests of different star-rated hotels by utilizing rule-based machine learning.

Keywords

• online complaining behavior (OCB)
• association rules (ARs)
• star-rated hotel
• rule-based machine learning
• antecedents vs. consequences
• attribution theory