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)


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
Publication statusPublished - Aug 2020
MoE publication typeA1 Journal article-refereed


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

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