The extant literature has utilized the SERVQUAL scale to measure service quality dimensions and their importance towards customer-satisfaction using close-ended survey-based questions and not open-ended questions and/or user-generated qualitative responses. On the other hand, while measuring customer-satisfaction drivers from user-generated content (UGC), extant studies have majorly used overall or aspect-wise evaluations and not evaluations specific to SERVQUAL dimensions. In this study, the authors try to bridge the gap.
The authors suggest a methodology consisting of text mining, machine learning and econometric techniques that can measure consumer evaluations of SERVQUAL dimensions. The authors used qualitative and quantitative UGC obtained from 27,052 online reviews on 362 airlines by reviewers of 158 nationalities for our analysis.
The authors established a unique method which combines qualitative and qualitative UGC to measure service quality. The authors have also uncovered the comparative importance of such dimensions in creating customer-satisfaction and recommendation in the context of the airline industry.
The paper is one of the pioneering studies that try to find measures of SERVQUAL dimensions from online consumer reviews and their influence on customer satisfaction.
The paper is funded by “Sponsored Research and Industry Consultancy, Indian Institute of Technology Kharagpur” with the project id UAE.
Chatterjee, S., Ghatak, A., Nikte, R., Gupta, S. and Kumar, A. (2023), "Measuring SERVQUAL dimensions and their importance for customer-satisfaction using online reviews: a text mining approach", Journal of Enterprise Information Management, Vol. 36 No. 1, pp. 22-44. https://doi.org/10.1108/JEIM-06-2021-0252
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