Optimizing service offerings using asymmetric impact-sentiment-performance analysis

Feng Hu*, Hongxiu Li, Yong Liu, Thorsten Teichert

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)

Abstract

Researchers refer to various theories to investigate the distinct relationships between importance, performance, and the (a)symmetric impact of service attributes on customer satisfaction (CS). However, a fully integrated model that would allow practitioners to automatically execute analyses to optimize their service offerings in a competitive landscape is missing. Previous studies widely rely on importance/performance ratings of predefined service attributes retrieved from closed-ended questionnaires, which can hardly capture the competitive landscape from the customers’ perspective. This paper introduces a novel asymmetric impact-sentiment-performance analysis (AISPA) to address these gaps by performing automated opinion mining on online reviews. Customers’ evaluations of three hotel chains serve as an example application. The impact-asymmetry of the hotel service attributes on CS, the attribute impact and performance are jointly visualized in a 3D grid. An elaborate understanding of service assessments is gained, leading to attribute prioritization and specific recommendations for optimizing future offerings.

Original languageEnglish
Article number102557
JournalINTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT
Volume89
DOIs
Publication statusPublished - Aug 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Asymmetric impact-sentiment-performance analysis
  • Attribute priority
  • Hotel chains
  • Improvement strategies
  • Sentiment analysis
  • User-generated content

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