Description
Providing quality service and creating moments of truth is an important part of the hotel business. At the same time due to the high employee turnover, technology is gaining greater importance in hotel services. Personalization and customization are essential parts of quality service, but both require information, time, and human resources (Wang et al., 2010) to adapt the service offering to the individual guest's preferences (Valenzuela et al., 2009; van Riel et al., 2001). Although moments of truth can result from employees' contributions (Keh et al., 2013), to provide good, individualized service, proper technological support in needed more and more. Technology-mediated personalization (TMP) – using information technology tools, such as customer databases and software applications, to personalize and customize customer interactions and service offerings (Ball et al., 2006), can ensure that employees create individualized offerings during the one-on-one encounters based on the database or software rather than based on their own information’s such as memories and experiences (Shen & Dwayne Ball, 2009). If technological support is lacking, then due to the high employee turnover, the service provider will have difficulty maintaining consistent quality. Even with loyal workforce TMP can help employees to be consistent in service delivery.
Research on guest reaction to hotel technology and technology-mediated personalization has gained popularity, but few measuring tools consider the necessary supporting technology for individualized hotel service through the entirety of the guest journey. Consequently, the main questions of this research are how to measure the required technology used for individualized service and what levels of technology requirements can be distinguished on the case of the Hungarian hotel sector.
Firstly, this study aims to identify the most important technological elements that are needed for personalized and customized service. Rigorous scale development procedures were implemented (Churchill,1979; Parasuraman et al., 1985; Chi et al., 2020; Elgaraihy, 2013) entailing both qualitative and quantitative research. Measurement items from scientific articles were collected and refined, followed by two rounds of expert interviews. Finally, 26 items remained following the verification process to measure the technology requirement level (TRL) of personalization and customization in hotel service.
Secondly, this study not only introduces a tool to measure the technology requirement level for technology-mediated personalization/customization of hotels but also utilizes an infrequently used data-driven approach to simultaneously determine the ranks of Hungarian hotels in TRL-TMP/C and the indicators of TRL-TMP/C that are most frequently used.
Biclustering analysis is a novel technique in social sciences (Kosztyán et al., 2019(a), Kosztyán et al., 2023), this method only focuses on research questions. The answers are revealed simultaneously between indicators and respondents. This approach can provide new insights and can be utilized even if the database is nonrepresentative.
Online survey has been sent out to 458 members of the Hungarian Hotels and Restaurant Association (HHRA) and yielded (N) 105 response which according to Finn et al. (2000) is an acceptable response rate.
For the analysis R software and R Studio were used. The data was seriated (both rows and columns are reordered simultaneously to group similar cells as closely as possible (Kosztyán et al., 2023), and a heat map were used to identify the upper and lower league of technology requirement level of personalization and customization.
Kosztyán et al. (2019) proposes that three types of leagues (A, B, and C) and their unions can be considered. League A include indicators and respondents whose responses are significantly more positive therefore they are more likely to have and use various technology. League C includes indicators and respondents whose responses are significantly more negative; therefore, they are using less technology, or these are the indicators that are not used by many. League B is the most homogenous cluster, where a different biclustering method is used to minimize the variance within each bicluster (Kosztány et al. 2019). The method will identify the leagues of technology requirement level of personalization and customization and the leagues of hotels based on their technology requirement level.
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