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This study aims to compare the effect of barista type (human vs robot) on perceived safety and examine the role of two moderators (mask-wearing and coronavirus vaccination) on the effects of barista type on perceived safety and visit intention.

The research design consists of three studies. Three experiments were sequentially designed and conducted to address research questions.

Study 1 found that perceived safety mediates the effect of barista type on customers’ visit intention. Study 2 revealed that the mask-wearing of human and robot baristas differently influences perceived safety. Study 3 showed that customers, especially where robot baristas are used, perceive the effect of mask-wearing differently depending on their coronavirus vaccination status.

Given that the levels of restrictions vary worldwide, together with the extent of countries’ vaccination rollouts, caution is required when generalising the research findings.

The findings have practical implications for the hospitality industry, where the roles of face masks and coronavirus vaccines in shaping consumer psychology and behaviour have been underexplored.

Coronavirus vaccination is considered one of the most important driving forces for the recovery of hospitality businesses. As a heuristic-systematic model postulated, this study identified that vaccination status (fully vaccinated vs not vaccinated) changes the level of involvement when customers assess the level of risk in service environments. By pinpointing the function of service robots in safeguarding customers from the potential spread of the disease, this study broadens the scope of human–robot interaction research in hospitality.

The purpose of this study examined the effect of the eight dimensions of consumer innovativeness on brand satisfaction. In addition, this study investigated how brand satisfaction affects brand preference and brand loyalty. Lastly, how brand preference plays to form brand loyalty was also explored.

A survey was conducted via an online platform with the participants using an online firm in South Korea to evaluate the proposed research model. The data was collected from 326 people who used a coffee shop where robots provide the services.

The data analysis results indicated that the five dimensions of consumer innovativeness, which include novelty seeking, openness, quality experience seeking, hedonic experience seeking and social distinctiveness, aid to enhance brand satisfaction. Furthermore, it was discovered that brand satisfaction plays an important role with the formation of brand preference and brand loyalty.

There are few studies which focus how coffee consumers assess the value of a coffee brand. Thus, it would be meaningful to examine what impacts of consumer innovativeness exist in regards to robotic barista services that are related to brand satisfaction, brand preference and brand loyalty.

本研究考察了消费者创新性的八个维度对品牌满意度的影响。此外, 本研究还调查了品牌满意度如何影响品牌偏好和品牌忠诚度。最后, 还探讨了品牌偏好在形成品牌忠诚度方面的作用。

本研究通过在线平台在韩国进行了一项调查, 参与者使用一家提供机器人服务的咖啡店进行评估。数据收集自326名在该咖啡店使用服务的顾客。

数据分析结果表明, 包括新奇寻求、开放性、品质体验寻求、享乐体验寻求和社交独特性在内的五个消费者创新性维度有助于提高品牌满意度。此外, 品牌满意度在品牌偏好和品牌忠诚度的形成中起着重要作用。

目前很少有研究关注咖啡消费者如何评估咖啡品牌的价值。因此, 对于与品牌满意度、品牌偏好和品牌忠诚度相关的机器人咖啡师服务消费者创新性影响的研究具有重要意义。

This study aims to develop a theoretical model to identify the importance of brand modernity with the moderating role of human baristas and robot baristas.

A total of 333 samples were collected from a coffee shop where robots provide the services, and 354 samples were collected in a coffee shop where people provide the services.

The data analysis showed that brand modernity has a positive effect on brand attitude, which in turn positively affects brand attachment and brand loyalty. In addition, there is a moderating role in regard to the type of employee in the relationship between brand modernity and brand attachment and brand attitude and brand loyalty.

This study is different from the previous studies, because it used two types of employees, which included a human barista and a robot barista, as a moderating variable with the formation of brand attitude, brand attachment and brand loyalty.

本研究开发了一个理论模型, 以通过人类咖啡师和机器人咖啡师的调节来确定品牌现代性的重要性。

本研究从机器人提供服务的咖啡店收集了 333 个样本, 在员工提供服务的咖啡店收集了 354 个样本。

数据分析表明, 品牌现代性对品牌态度有积极影响, 进而对品牌依恋和品牌忠诚度产生积极影响。此外, 员工类型在品牌现代性与品牌依恋、品牌态度和品牌忠诚度的关系中具有调节作用。

本研究不同于以往的研究, 因为它使用了两种类型的员工, 包括人类咖啡师和机器人咖啡师, 作为调节变量参与品牌态度、品牌依恋和品牌忠诚度的形成。

This study aims to examine the differences among memorable brand experience, brand preference and behavioral intentions based on the type of service providers, such as robot servers and human servers. In addition, this study investigated the relationships among the four concepts.

The data was collected from 296 customers who experienced robot servers and from 307 customers who experienced human servers.

The data analysis results indicated that there was a statistical difference with the mean value of the five concepts according to the type of employee. The results also revealed that memorable brand experience has a positive influence on brand preference, which in turn positively influences intentions to use, word-of-mouth and willingness to pay more.

This study attempted to find the difference between robot servers and human servers in memorable brand experience, brand preference and behavioral intentions in the restaurant industry for the first time.

本研究旨在根据服务提供商的类型（例如机器人服务员和人工服务员）检查令人难忘的品牌体验、品牌偏好和行为意图之间的差异。 此外, 本研究调查了四个概念之间的关系。

数据收集自 296 名体验过机器人服务器的客户和 307 名体验过人工服务器的客户。

数据分析结果表明, 根据员工类型, 五个概念的平均值存在统计差异。 结果还表明, 难忘的品牌体验对品牌偏好有积极影响, 进而对使用意向、口碑和支付更多的意愿产生积极影响。

这项研究首次试图找出机器人服务员和人工服务员在餐饮业令人难忘的品牌体验、品牌偏好和行为意图方面的差异。

This study profiles and segments potential tourists on the basis of their various attitudes toward artificial intelligence (AI) services. Furthermore, this study distinguishes descriptors among the different clusters, such as preference for using diverse AI services, overall image of AI services, willingness to use AI services (WUAI), willingness to pay more for AI services (WPAI) in tourism and hospitality, and characteristics of respondents.

An online survey was conducted in South Korea. Data on 758 potential tourists were used for K-means cluster analysis.

This study identified three distinct tourist segments with differentiated attitudes toward AI services: the group aspiring to use or fantasizing about AI services (Cluster 1), the group being knowledgeable and supportive of AI services (Cluster 2), and the group having low interest about AI services (Cluster 3).

Members of Cluster 2 were the most marketable as this segment exhibited the greatest knowledge of and support for AI services, while Cluster 1 would be an ideal segment to launch and test novel AI services.

This study extends the authors’ knowledge of AI scholarship by unpacking the existing market segments, which could be tapped to sustain AI penetration in the tourism industry. Hence, this study contributes to existing debates on AI scholarship, which is predominated by conceptual reflections and issues of AI services in the tourism and hospitality field.

This study aims to simultaneously examine the influence of demographic, psychographic and situational factors on consumers’ willingness to pay a price premium (WTPp) for robotic restaurants and to profile market segments based on consumers’ WTPp levels (positive, neutral and negative).

Using an online survey, the data were gathered from a sample of 897 Thai consumers who had dined at a robotic restaurant in the past 12 months. Structural equation modeling, chi-square tests and the one-way analysis of variance were used for data analysis.

Demographic (gender, age, income and marital status), psychographic (perceived advantages/disadvantages, personal innovativeness and personality traits) and situational factors (perceived health risk and self-protection behavior) significantly influence consumers’ WTPp for robotic restaurants. The positive price premium group differs significantly from the neutral and negative price premium groups in terms of demographic, psychographic and situational profiles.

The findings of this study help restaurateurs target the correct customers and set up appropriate price fences to safeguard profits and maximize return on investment.

This study contributes to the literature on technology-based services and hospitality by heeding the calls made by Ivanov and Webster (2021) and providing much-needed empirical evidence of possible changes in consumers’ WTPp for robot-delivered services in restaurants due to COVID-19.

The purpose of this study is to empirically investigate what aspects of service robot interactions with customers can lead to meaningful outcomes in the view of customers. The study examines functional and emotional elements of AI service robots in terms of meaningful outcomes.

This study highlights AI service robots' meaningful outcomes as a viable research problem and proposes a research model utilizing the Stimulus-Organism-Response (SOR) framework. As an empirical approach, 260 datasets were collected from customers who have experience with AI service restaurants in China.

The study examines the functional and emotional elements of AI-powered service robots on the attitude of and meaningful outcomes for customers. The results showed that the emotional (perceived friendliness and perceived coolness) and functional (perceived safety and robot competence) attributes of human–robot interactions (HRI) significantly affect the attitude toward using service robots. Second, the attitude toward using service robots significantly influences the experiential outcome and instrumental outcome of meaningful engagement.

This study highlights two elements (i.e. functional and emotional) of HRI effectiveness using two metrics: experiential and performance outcomes. Future studies should generalize the research findings of service robots in the current study using a larger quantity of data from various service fields.

As the first empirical study highlighting the customer experience with service robots, this study opens up a feasible research direction for the service industry to pursue in terms of conducting HRI studies from the view of customers. It identifies a research model pursuant to customers' experience with HRI in creating meaningful outcomes and it theoretically extends the SOR model to the hospitality study, focusing on the HRI issue.

The purpose of this paper is to consider three factors, namely, intra-week demand fluctuations, interrelationship between the number of robots and order scheduling and conflicting objectives (i.e. cost minimization and customer satisfaction maximization), to optimize the robot logistics system.

The number of robots and the sequence of delivery orders are first optimized using the heuristic algorithm NSGACoDEM, which is designed using genetic algorithm and composite difference evolution. The superiority of this method is then confirmed by a case study of a four-star grade hotel in South Korea and several comparative experiments.

Two performance metrics reveal the superior performance of the proposed approach compared to other baseline approaches. Results of comparative experiments found that the consideration of three influencing factors in the operation design of a robot logistic system can effectively balance cost and customer satisfaction over the course of a week in hotel operation and optimize robot scheduling flexibility.

The results of this study reveal that numerous factors (e.g. intra-week demand fluctuations) can optimize the performance efficiency of robots. The proposed algorithm can be used by hotels to overcome the influence of intra-week demand fluctuations on robot scheduling flexibility effectively and thereby enhance work efficiency.

The design of a novel algorithm in this study entails enhancing the current robot logistics system. This algorithm can successfully manage cost and customer satisfaction during off-seasons and peak seasons in the hotel industry while offering diversified schemes to various types of hotels.

The purpose of this study is to reveal the research trends in guest experiences of service robots in the hospitality industry.

In this study, a review was carried out on the Web of Science (WoS) database with the assistance of bibliometric analysis techniques. Cluster analysis was also employed for this to group important data to determine the relationships and to visualize the areas in which the studies are concentrated. The thematic content analysis method was used to reveal on which customer experiences and on which methods the focuses were.

On the subject of experiences of service robots, the greatest number of publications was in 2021. In terms of country, China has come to the fore in the distribution of publications. As a result of thematic content analysis, it was determined that the leading factor was the main dimension of emotional experience. In terms of sub-dimensions, social interactions attracted more attention. Most of the studies discussed were not based on any theory. Apart from these, the Technology Acceptance Model (TAM), the Service Quality Model (SERVQUAL) and Perceived Value Theory (PVT) were featured more prominently among other studies.

In this study, only the WoS database was reviewed. In future studies, it would be possible to make contextual comparisons by scanning other databases. In addition to quantitative research designs, social dimensions may be examined in depth following qualitative research methods. Thus, various comparisons can be made on the subject with mixed-method research designs. Experimental research designs can also be applied to where customers have experienced human-robot interactions (HRIs).

In the hospitality industry, it is critical to uncover every dimension of guests' robot acceptance. This study, which presents the current situation on this basis, guides future projections for the development of guest experiences regarding service robots in the hospitality industry.

The purpose of this study is to explore robot-phobia as a source of occupational stress among hospitality employees in the context of increasing robotization in the industry.

The study sampled 321 lodging employees and 308 food service employees in the USA. An online panel company recruited the participants and administered an online survey. The study used various analytical methods, including structural modeling, t-tests and multi-group analyses.

The study results reveal that hospitality workers experience robot-phobia regardless of their sector or position. Robot-phobia causes job insecurity and stress, which increases turnover intention. These negative outcomes are more pronounced for those who interact more frequently with robots.

The study findings suggest that hospitality workers fear being replaced by robots in the near future. Therefore, hospitality organizations should offer adequate training and education on the advantages and drawbacks of robots and establish a supportive and collaborative work environment that values human–robot interaction.

This study offers new insights regarding human–robot interaction from the employee perspective by introducing the concept of robot-phobia in the hospitality workplace. A comprehensive picture of how hospitality employees confront the increasing presence of robots is provided in this study.