Smart service quality in hospitality – A quantitative assessment using MCDM and clustering methods

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. • Advancements in technology are transforming the H&T sector, making it necessary to reconfigure service quality dimensions. • This study proposes an extended SERVQUAL framework that incorporates smart service quality as a key quality dimension. • The best-worst method (BWM) is used to assess the relative weight of extended SERVQUAL dimensions from the consumers’ angle. • Latent class clustering is used to identify discrepancies in quality perceptions among different consumer groups. • Data-driven customer clusters, their nuanced perception and preferences are analysed from theoretical and managerial aspects.