Journal of Hospitality Marketing & Management最新文献

ABSTRACT

The purpose of this study is to examine the relationships among customer trust toward voice Artificial Intelligence (AI) in guestrooms, information privacy concerns associated with this technology, intention to use, and intention to stay at a hotel providing this technology. This study further examined whether the research model can be applied to different cultures by collecting data from U.S. and Singaporean customers. The hypotheses were tested via structural equation modeling and multiple-group analysis. The findings indicated that trust toward voice AI had a positive effect on intention to use this technology in both groups, and this effect was stronger for the U.S. group. Concern of information collection via voice AI had a negative effect on intention to use in both groups, and this effect was stronger for the Singaporean group. Intention to use voice AI in guestrooms was positively related to intention to stay at a hotel providing this technology.

ABSTRACT

This study aims to explore the role of cloud computing in the creation of smart hospitality experiences and bridge the existing gap in knowledge. The research adopts a mixed research approach, combining qualitative and quantitative methods, to investigate and prioritize criteria that contribute to smart hospitality through cloud computing. Expert perspectives are utilized to develop a comprehensive two-dimensional cloud-based smart hospitality experience model with 14 sub-dimensions, highlighting the pivotal role of cloud computing in facilitating smart hospitality. The study uncovers the interrelations and relative weights among the criteria, shedding light on the fundamental significance of cloud computing in creating smart hospitality experiences. What sets this study apart is its focus on the untapped potential of cloud computing in the context of smart hospitality. It contributes to the existing body of knowledge by providing theoretical insights for interdisciplinary hospitality operations and practical guidance for hospitality practitioners to achieve sustainable outcomes.

ABSTRACT

In a competitive environment, where hotel demand is lower than market supply, it is of interest to determine what factors explain how excellence differentiates certain hotels from others. Using spatial quantile regression, this research investigates the effect of locational factors as well as those related to the management of human capital investment in Spanish hotels rated on Booking.com. The study shows that human capital investment in hotels can significantly increase guest delight. As regards location, hotels in protected natural areas that are far from large cities obtain higher guest review scores. Additionally, a spatial spillover effect due to reputation transfer among nearby hotels with median scores is found. However, this effect is not observed for hotels with very high scores. These results can serve to rationalize the available resources and inform hotel owners and managers about the factors required to achieve high scores.

在酒店市场竞争激烈的环境下，其需求低于市场供给，确定哪些因素更影响酒店评级尤为重要。本文采用空间分位数回归方法，调查了Booking.com上评级的西班牙酒店的地理因素和人力资本投资管理相关的因素的影响。研究表明，酒店的人力资本投入可以显著增加客人的满意度。在地理位置方面，远离大城市的自然保护区内的酒店获得了更高的顾客评分。此外，由于得分居中的临近酒店之间有声誉转移现象，其空间溢出效应被观察到。然而，这种效应并没有在得分非常高的酒店上观察到。这些研究结果可以帮助可用资源合理化分配，并使酒店管理者知晓获得高评分所需的因素。

ABSTRACT

The study here assesses the systematic variability of service performance assessments in customer reviews placed in alternative online social media platforms in the travel industry as well as responses to the firm’s requests for customer evaluations. The research includes an empirical study conducted by a national (Spanish) hotel chain of nine corporately-owned properties (hotels). Data-analyses were performed of data collected for the nine hotels from TripAdvisor and Booking.com, as well as internal data from the hotel chain’s customer data files. The findings support the general conclusion that hotel reviews on different social media platforms vary in systematic ways. TripAdvisor reviews consistently include the largest share of detractors across all social media platforms in this study, ranging from 11 to 51%. This study contributes useful benchmarks for the relative differences in performances in customer experience assessments across different social media platforms.