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This study aims to identify the knowledge development and thematic evolvement in hospitality and tourism technology research, and to suggest potential directions for studies in hospitality and tourism research.

A total of 440 technology articles published from 1990 to 2022 were retrieved from six top-tier journals. By using bibliometric analysis, 440 technology articles were analyzed to discover the proportion of technology research in the hospitality and tourism discipline, knowledge development, intellectual turning points and thematic evolvement of hospitality and tourism technology research.

The findings indicated that the proportion of technology research has continuously increased over the past three decades. The findings revealed the key intellectual turning points in technology research. The topical trends showed the popular topics of technology research for the 1990s, 2000s, 2010s and from 2020. The thematic map analysis results described how the major themes in technology research have evolved and shifted.

By synthesizing past three decades of hospitality and tourism technology research, this study provides an overview of how technology research has evolved in the context of hospitality and tourism and offers suggestions for future studies on technology.

To the best knowledge of the authors’ knowledge, this study is the first bibliometric analysis focusing on technology research in the hospitality and tourism discipline, thereby providing a broad understanding of how technology research has developed in the discipline.

To explore the impact of luxury hotel brands’ technologies on guests’ perceived value enhancement and its consequences, this study aims to examine how task–technology fit (TTF) and luxury–technology fit (LTF) affect luxury hotel guests’ perceived value enhancement, satisfaction and brand loyalty.

A scenario-based online survey was conducted with existing and potential luxury hotel guests. The relationships among TTF, LTF, value enhancement, satisfaction and loyalty were examined using partial least square structural equation modeling. Moreover, the respondents were asked their preferred service agent (human staff vs technology) in a luxury hotel to further understand luxury hotel guests’ perception toward technological services in a luxury hotel setting.

The results reveal that both TTF and LTF have significant impacts on the luxury hotel’s value enhancement, and the impacts of TTF and LTF on value enhancement are moderated by the guests’ technology optimism. The findings of this study suggest that the luxury hotel brands must consider the fit between the image of their brand and the technology to increase guests’ perceived value enhancement, satisfaction and loyalty.

Applying categorization theory, this study extended the concept of brand extension to technology implementation. The findings advance the current understanding of how technology affects luxury hotel consumers’ perceived value enhancement, and how consumers’ optimism influences the impacts of TTF and LTF on value enhancement.

By examining the effects of TTF and LTF on luxury hotel guests’ perceived value enhancement, this study would shed light on luxury hotel brands’ technology implementation strategies. By understanding how TTF and LTF affect consumers’ perceived value enhancement, luxury hotel brands will be able to better design their technology implementation plans.

Even though luxury hotel brands are well known for offering high-level and personalized services through interpersonal interactions between employees and consumers, they have been actively implementing a variety of technologies to enhance guest experience and satisfaction. This study investigates the role of technological innovations in a luxury hotel setting. Particularly, this study examines how technologies improve guests’ perceived value of a luxury hotel and lead to satisfaction and loyalty.

This study aims to identify travelers’ motivational factors to adopt augmented reality (AR) applications at tourism destinations. Furthermore, this study seeks to investigate the role of personal innovativeness as a moderator in the relationships between motivations and travelers’ behavioral outcomes.

Building upon two theories, cognitive evaluation theory and self-presentation theory, this study developed a conceptual framework to identify travelers’ inclination to use AR applications at tourism destinations. A sample size of 473 was collected through an online self-administered survey. Using partial least square (PLS) analysis, the proposed model was examined. To examine the moderating effect of personal innovativeness, a multi-group analysis was conducted with two groups: high-innovative and low-innovative. PLS method is used to test the study’s theoretical model.

Findings of this study indicate that both hedonic and utilitarian motivations significantly influenced travelers’ attitudes toward AR applications at tourism destinations. Self-presentation motivation appears to have a significantly positive impact on travelers’ attitudes toward AR applications only for the highly innovative group.

This study helps a better understanding of what motivates travelers to adopt AR applications at tourism destinations by integrating preeminent theories and applying them to the tourism context.

本论文旨在指出游客在旅游目的地使用AR的驱动因素。此外, 本论文旨在研究个人创新性在驱动因素与游客行为结果之间的调节作用。

本论文采用认知评价理论和自我表现理论作为理论基础, 建立了一个显示游客在旅游目的地使用AR应用的驱动力理论模型。本论文通过在线问卷共搜集473份数据。样本分析采用PLS分析法来验证理论模型。为了检验个人创新性的调节作用, 多组分析法在高创新性vs低创新性两组中进行。

研究论文结果显示享乐型价值和实用型价值都对游客在旅游目的地使用AR的态度有显著作用。自我表现激励因素只对高创新性游客组对AR态度有着显著作用。

本论文通过整合理论, 将其应用到旅游情境中, 解释了游客为何在旅游目的地使用AR的行为。

Given the importance of performance consistency of chain hotels in customers’ decision-making and service evaluation, this study aims to explore how consistently chain hotel brands offer quality service and carry out their performance from the eyes of customers through online reviews on TripAdvisor of the top five US hotel chains (i.e. Choice, Hilton, InterContinental, Marriott and Wyndham) and their brands.

The research objectives were achieved through methodological triangulation: business intelligence, data visualization analytics and statistical analyses. First, the data collection and pre-processing of consumer-generated media (CGM) (i.e. TripAdvisor online reviews) were performed using business intelligence for further analyses. Using data visualization analytics (i.e. box-and-whisker plot by region and brand), the geographic patterns of performance attributes (i.e. online review ratings, including location, sleep, cleanliness, room and service) were depicted. Using a series of analyses of variance and regression analyses, the results were further assessed for the impacts of brand performance inconsistency on consumers’ perceived value, sentiment and satisfaction.

The empirical results demonstrate that there are significant performance inconsistencies in performance attributes (location, sleep, cleanliness, room and service) by brands throughout the six regions in the US hotel market. More importantly, the findings confirm that brand performance consistency significantly influences consumers’ perceived service quality (i.e. perceived value, satisfaction and sentiment).

This study is one of the first attempts to empirically explore hotel brand performance consistency in the US hotel market from customer reviews on CGM. To measure hotel brand performance in the US hotel market, this study collected and analyzed user-generated big data for the top 5 US hotel chains through business intelligence, visualization analytics and statistical analysis. These integrated and novel research methods would help tourism and hospitality researchers analyze big data in an innovative data analytics approach. The findings of the study contribute to the tourism and hospitality field by confirming hotel brand performance inconsistency and such inconsistent performance affected customers’ service evaluations.

This study demonstrates the significant impact of hotel brand performance consistency on consumers’ perceived value, emotion and satisfaction. Considering that online reviews are perceived as a credible source of information, the findings suggest that the hotel industry pays special attention to brand performance consistency to improve consumers’ perceived value, emotion and satisfaction.

This study aims to advance the understanding of the relationships between employee engagement (EE), satisfaction and turnover intention (TI) beyond their known linear functions by providing a set of significant empirical evidence on nonlinear functions including quadratic, cubic and interactive effects.

This study used four 2 × 2 between-subjects experiments sampling 640 hospitality sales professionals through online data collection methods. EE and employee satisfaction (ES) were examined in disaggregation into personal and organizational dimensions. Residual regression models controlling for age and gender as covariates were the main approaches for analyzing data for nonlinear effects.

Both EE and ES consistently have significant negative quadratic and positive cubic effects on employees’ TI. EE and ES have a negative interaction effect, that is, complementing each other, on TI such that the effect is more pronounced at higher levels than lower levels of EE and satisfaction.

Organizations need to understand some threshold phenomena that may exist in the widely believed linear effects of EE and satisfaction on TI. Doing so may help allocate resources more effectively for EE and satisfaction.

This study examined the nonlinear as well as interactive nature of the relationships between EE and TI and ES and TI to expand our understanding of these relationships beyond the known linearity and add new empirical evidence to the literature.

This study aims to investigate how customers’ perceived risks of sharing economy (SE) affect their self-protective behaviors when using SE, leading to their future behavioral intention. Additionally, this study looks into whether there are any differences between accommodation-sharing and ride-sharing customers in the aforementioned relationships.

An online survey targeting two groups of SE customers (i.e. accommodation sharing and ride sharing) was used. Using partial least squares structural equation modeling, the mechanism of how SE customers’ perceived risks of SE affect their self-protective behaviors, which in turn influence their future behavior intention. A multigroup analysis was performed to assess the difference between the two groups of SE customers. Finally, a multivariate analysis of variance (MANOVA) was conducted to see the potential differences between the five classifications of self-protective behaviors in their perceived risks.

SE customers’ psychological risks positively affected their hygiene protective behaviors and social protective behaviors, influencing their behavior intention and relative intention (compared with traditional services). Social risk had a negative impact on SE customers’ hygiene protective behaviors. There was a significant difference between accommodation sharing and ride sharing customers in their psychological mechanism of how perceived risks influence their self-protective behaviors.

The findings of this study help SE platforms and service providers better understand their customers’ perceived risks of their services and suggest them to promote their customers’ self-protective behaviors so that perceived risks can be mitigated, thereby generating strong behavior intentions. As the results indicated that there is a significant difference between the two major forms of SE (i.e. accommodation sharing and ride sharing) in their customers’ perceived risks and self-protective behavior, SE platforms can further refine their operational and marketing efforts based on the findings.

This study offers a comprehensive understanding of SE customers’ self-protective behaviors by examining the effects of SE customers’ different perceived risks on their self-protective behaviors during the unprecedented pandemic. Furthermore, the comparison of the two most popular forms of SE (i.e. accommodation sharing and ride sharing) provides new perspectives to understand customers’ behavior in the SE context.

This study aims to present a novel examination of actor value formation (AVF) in the sharing economy by uncovering its key dimensions (i.e. information seeking, personal interaction and feedback) at each stage of Airbnb consumption. Using multi-source data, how the key dimensions of AVF affect consumers’ evaluative judgment capturing the key aspects of consumption experiences (i.e. communication quality, value for money and overall evaluations) was empirically tested.

This study adopted a triangulated, multi-study approach to test and validate the hypotheses. In Study 1, the authors analyzed 586,778 online reviews through deductive and inductive approaches to determine how three AVF dimensions could be captured and examine these dimensions’ effects on consumers’ evaluative judgments. In Study 2, an online survey was adopted to improve the reliability and validity of findings.

This study identified positive and negative outcomes of AVF. The results also revealed which AVF dimensions significantly led to communication quality and value for money, which in turn influenced consumers’ overall evaluations. The findings further provided robust support for the mediating role of value for money as a mechanism in the relationships between AVF and overall evaluations across the two studies.

This study contributes to the literature by empirically identifying the three key dimensions of AVF in each consumption stage and subsequently testing a conceptual model using different data, research methods and analytical techniques to cross-validate the results. The authors also extended the scope of AVF by integrating both positive and negative aspects in the context of Airbnb.

The hotel industry has witnessed an increasing number of service automation through service robots such as robot concierges. However, few studies have documented how to identify how hotel guests perceive a robot concierge for their service encounter. Therefore, the purpose of this study is to examine the effects of robot concierges on hotel guests’ attitudes and adoption intentions of robot concierges.

This study investigated the effects of robot concierges’ morphology and their level of interactivity with guests at different levels of hotel service on guests’ attitudes and their intentions to adopt robot concierges. To achieve the study’s objectives, this study conducted a 3 × 2 × 3 between-subjects factorial design experiment. Moreover, the survey asked questions about subjects’ preferences of their service encounters (e.g. human employees, robot concierges and/or no preference) and reasons for their selected preference.

The results demonstrated that the robot’s morphology significantly influenced guests’ attitudes toward robot concierges. In particular, the caricatured robot was the most preferred morphology of robot concierges. The findings showed that even if guests had favorable attitudes toward robot concierges, they preferred human employees to robot concierges because of humans’ sincere and genuine interactions.

This study contributes to the literature by investigating the causal impacts of the morphology of robot concierges, level of interactivity and level of hotel service on guests’ attitudes toward robot concierges. The thematic analysis of service encounter preference provides an overview of the factors that guests expect for their service encounters in a hotel setting.