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Measuring service quality with text analytics: Considering both importance and performance of consumer opinions on social and non-social online platforms

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  • Lu, Lin
  • Xu, Pei
  • Wang, Yen-Yao
  • Wang, Yu

Abstract

Online word-of-mouth (WOM) has attracted considerable attention from researchers due to its abundant information on customer perceptions that drive product and service improvement. This study develops a novel weighted service quality (WSQ) metric derived from online customer opinions, leveraging the importance-performance analysis framework. Data collected from social and non-social online platforms confirms that this WSQ approach outperforms the widely used average sentiment score approach and significantly predicts the industry service quality standard, Airline Quality Rating (AQR). In addition, the WSQ metric derived from social media proves to be a more vital indicator for AQR than that derived from a non-social online platform. A significant difference in topic distributions was also identified between consumer opinions from social media and non-social online platforms. Our study makes several crucial contributions to the service quality literature on employing online WOM using sentiment analysis and topic modeling techniques.

Suggested Citation

  • Lu, Lin & Xu, Pei & Wang, Yen-Yao & Wang, Yu, 2023. "Measuring service quality with text analytics: Considering both importance and performance of consumer opinions on social and non-social online platforms," Journal of Business Research, Elsevier, vol. 169(C).
    • Handle: RePEc:eee:jbrese:v:169:y:2023:i:c:s0148296323006574
    DOI: 10.1016/j.jbusres.2023.114298
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