Smart service quality in hospitality – A quantitative assessment using MCDM and clustering methods
International Journal of Hospitality Management, ISSN: 0278-4319, Vol: 123, Page: 103931
2024
- 2Citations
- 95Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Technology is transforming the Hospitality and Tourism (H&T) sector from a “high-touch, face-to-face” to a “high-tech, low-touch” service sector. This changing landscape necessitates a reconfiguration of the traditional service quality dimensions. To make the renowned Service Quality (SERVQUAL) model relevant in today’s dramatically different landscape, this study proposes an extended SERVQUAL framework that incorporates smart service quality as a key dimension. Using the best-worst method (BWM), the relative importance of the extended SERVQUAL dimensions is assessed from the consumers’ perspective. Furthermore, the discrepancies amongst different consumer groups are identified using latent class clustering. The findings identify rather balanced preference ratios across quality dimensions and age groups; yet, reliability is the most preferred service dimension, while smart service quality is the least. The analysis results imply several important insights into the weighted importance ranking of quality dimensions and the nuanced preferences of data-driven customer segments, being valuable both from theoretical and managerial perspectives.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0278431924002433; http://dx.doi.org/10.1016/j.ijhm.2024.103931; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85204631408&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0278431924002433; https://dx.doi.org/10.1016/j.ijhm.2024.103931
Elsevier BV
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