Understanding Customers’ Insights Using Attribution Theory

EasyChair Preprint 9926

Abstract

Purpose: By looking at complaints made by guests of different star-rated hotels, this study attempts to detect associations between complaint attributions and specific consequences.

Design/methodology/approach: A multifaceted approach is applied. First, a content analysis is conducted to transform textual complaints into categorically structured data. Then, a rule-based machine learning method are applied to discover potential relationships amongst complaint antecedents and consequences.

Findings: 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.

Practical implications: 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.

Keyphrases: Antecedents vs. Consequences, Association Rules (ARs), Online Complaining Behavior (OCB), Star-Rated Hotel, attribution theory, rule-based machine learning

@booklet{EasyChair:9926,
  author    = {Raksmey Sann and Pei-Chun Lai and Shu-Yi Liaw and Chi-Ting Chen},
  title     = {Understanding Customers’ Insights Using Attribution Theory},
  howpublished = {EasyChair Preprint 9926},
  year      = {EasyChair, 2023}}