Search results

Given little light has been shed on the preference of Generation Z tourists or tourists from different cultural backgrounds toward service robot preference in hospitality contexts, this study aims to explore robot service preferences in the accommodation sector in the Chinese market, with a specific focus of Generation Z customers.

This study followed an exploratory sequential research design including two qualitative methods (i.e. projective techniques and semistructured interviews) to reach its objectives.

This study suggests that service robots are more preferred for routine tasks and gender differences exist in the preference of service robot’s anthropomorphism level. The preferences are driven by four factors based on different levels of hedonic and utilitarian values: experienced-hedonic value, utilitarian-hedonic value, task-relevant value and utilitarian value.

First, because of the exploratory nature of this study, the data interpretation is unavoidably subjective; therefore, the results can be confirmed by using a more rigorous research method. Second, this study only focuses on the preference of Generation Z customers in China in the hotel sector; as the cultural differences exist in different countries, this study’s findings cannot be readily generalized across populations and service contexts. Finally, this study fails to dig into the effect of gender differences on varying levels of anthropomorphic attributes.

As hotels adopt service robots more widely, managers need to identify customers’ service preferences and prioritize tasks between robots and human resources for more efficient service. Particularly important is understanding the benefits and challenges of specific robot implementations rather than looking for a one-size-fits-all mode of operation.

To the best of the authors’ knowledge, this is the first study to understand robot service preference with regard to specific tourist groups in illustrating their preference for service delivery. Drawing on self-determination theory, this study potentially provides a theoretical basis for hotel service process optimization.

本研究从接待业情景出发, 针对中国Z世代游客在住宿业环境下对机器人服务使用偏好进行了探索性研究, 并对导致该偏好所产生的关键因素进行了探索。

本研究遵循了探索性次序研究设计步骤, 通过投射技术和半结构化访谈相结合的定性方法对本文研究目标进行了回答。

研究表明, 顾客对服务机器人日常服务事项表现出明显偏好, 但是, 对于服务机器人的拟人化水平偏好存在性别化差异。根据享乐主义和实用主义价值的不同维度角来看, 游客偏好差异的产生主要受到四种价值取向驱动, 包括：1）享乐—体验价值驱动; 2）实用—享乐价值驱动; 3）任务相关价值驱动; 4）实用价值驱动。

由于本研究是探索性研究, 且对投射技术和半结构化访谈数据的解释具有一定主观性, 建议未来通过更加严格的研究方法来验证分析结果。此外, 由于是在中国酒店行业特殊的语境和背景下进行的研究, 存在一定跨国文化差异, 其结论在跨种族和跨服务背景的适用性仍待进一步验证。最后, 本研究未能进一步深入探讨性别差异对不同层次拟人化属性的影响。

随着服务机器人在酒店业的普及, 管理者需要识别顾客的服务偏好, 在具体任务分配过程中, 需要对服务机器人和员工的使用安排进行优先等级划分, 以此全面提高服务效率。最重要的是, 要充分了解使用机器人的利与弊, 而不是通过一刀切的方式管理酒店服务。

本研究首次对特定游客群体（Z世代）对于服务机器人使用偏好进行了研究, 从自主决策理论出发阐述了他们对服务提供者的使用偏好差异及其驱动因素, 为优化酒店服务流程提供了理论指导和实证证据。

Este estudio se diseñó bajo una investigación secuencial exploratoria que incluyó dos métodos cualitativos (es decir, técnicas proyectivas y entrevistas semiestructuradas) para alcanzar sus objetivos.

Dado que se ha arrojado poca luz sobre la preferencia de los turistas de la Generación Z o de los turistas de diferentes orígenes culturales hacia los servicios de robótica en el sector alojativo, este estudio tiene como objetivo explorar las preferencias de servicios de robots en el sector del alojamiento en China, con un enfoque específico hacia la Generación Z.

Este estudio sugiere que los robots de servicio son más preferidos para tareas rutinarias, existiendo diferencias de género en la preferencia del nivel de antropomorfismo del robot de servicio. Las preferencias están impulsadas por cuatro factores basados en diferentes niveles de valores hedónicos y utilitarios: 1) valor hedónico experimentado, 2) valor hedónico utilitario, 3) valor relevante para la tarea y 4) valor utilitario.

Primero, debido a la naturaleza exploratoria de este estudio, la interpretación de los datos es inevitablemente subjetiva, por lo tanto, los resultados pueden confirmarse utilizando un método de investigación más riguroso. Además, este estudio solo se enfoca en la preferencia de los clientes de la Generación Z para el sector hotelero en China, ya que las diferencias culturales existentes en diferentes países, no pueden generalizarse fácilmente entre poblaciones y contextos de servicio. Por último, este estudio no profundiza en el efecto de las diferencias de género en los distintos niveles de atributos antropomórficos.

A medida que los hoteles vayan adoptando servicios con robots, los gerentes deberían identificar las preferencias de servicio de los clientes y priorizar tareas para robots o para recursos humanos cara a un servicio más eficiente. Particularmente importante es comprender los beneficios y desafíos de las implementaciones de robots específicos, en lugar de buscar un modo de operación único para todos.

Este es el primer estudio que comprende la preferencia de servicios de robots con respecto a grupos de turistas específicos para ilustrar su preferencia por la prestación de servicios. Basándose en la teoría de la autodeterminación, este estudio proporciona potencialmente una base teórica para la optimización del proceso de servicio del hotel.

Automation poses to change how service work is organized. However, there is a lack of understanding of how automation influences specific sectors, including specific hospitality jobs. Addressing this gap, this paper looks at the relative automatability of jobs and tasks which fall within one specific hospitality context: frontline food service.

Study 1 analyzes the UK Office for National Statistics' Standard Occupational Classification (2020) data to determine the degree to which frontline food service jobs consist of tasks requiring mechanical, analytical, intuitive or empathetic intelligence. Study 2 contrasts these findings to current state of intelligent automation technology development through interviews and a focus group with food service technology experts (n = 13).

Of all the tasks listed under food service in the ONS SOC 2020, 58.8% are found to require mechanical, 26.8% analytical, 11.3% intuitive and 3.1% empathetic intelligence. Further, the automatability of these tasks is found to be driven by three streams of technology development in particular: (1) autonomous navigation, (2) object manipulation and (3) natural language processing.

Hospitality management literature has started to conceptualize a move from mechanical and analytical service tasks to tasks centered around intuition and empathy. While previous studies have adopted a general view to what this might mean for hospitality jobs, this paper develops a novel, task-centric framework for Actioning Intelligent Automation in Frontline Food Service.

The rapid growth in volume and value of on-demand restaurant food delivery, accelerated by the COVID-19 pandemic, is causing a paradigm shift in the food service sector. However, there is a lack of hospitality management research into this emerging phenomenon. To address this gap, this paper defines and develops a novel conceptual model and typology and proposes a research agenda for ghost production in the context of food service.

This paper uses the Servuction model to explore, define and model the radical separation between food service production sites, points of sale and consumer interaction from the perspective of on-demand restaurant food delivery. A novel typology is developed and illustrated with eight industry examples from the UK and an accompanying cost benefit analysis. Future research priorities are identified.

In the hospitality literature, little attention has been paid to changes on-demand restaurant food delivery brings to production and business models of food service organisations, resulting in significant gaps between food service practice and theory. The knock-on effects to stakeholders include increased convenience for customers, uncertain employment status of riders and, for restaurants, striking a balance between capturing new markets and losing control of the customer. Additionally, for aggregators, there is a lack of profitability in existing models, despite holding the balance of power (and data).

The concept of “ghost production” and its associated typology is novel and offers a contribution to hospitality management literature by defining the term, scope and scale of this new phenomenon. Practical implications are proposed.

The emergence of artificial intelligence (AI) is leading to a job transformation within the service ecosystem in which issues related to AI governance principles may hinder the social sustainability of the sector. The relevance of AI startups in driving innovation has been recognized; thus, this paper aims to investigate whether and how AI startups may influence the sustainable social development (SSD) of the service sector.

An empirical study based on 24 in-depth interviews was conducted to qualitatively explore the perceptions of service sector facing AI policymakers, AI consultants and academics (n = 12), as well as AI startups (founders, AI developers; n = 12). An inductive coding approach was used to identify and analyze the data.

As part of a complex system, AI startups influence the SSD of the service sector in relation to other stakeholders’ contributions for the ethical deployment of AI. Four key factors influencing AI startups’ ability to contribute to the SSD of the service sector were identified: awareness of socioeconomic issues; fostering decent work; systematically applying ethics; and business model innovation.

This study proposes measures for service sector AI startups to promote collaborative efforts and implement managerial practices that adapt to their available resources.

This study develops original guidelines for startups that seek ethical development of beneficial AI in the service sector, building upon Ethics as a Service approach.

This paper aims to explore the implications of integrating humanoid service robots into hospitality service encounters by evaluating two service prototypes using Softbank Robotics’ popular service robot Pepper™: to provide information (akin to a receptionist) and to facilitate order-taking (akin to a server). Drawing both studies together, the paper puts forward novel, theory-informed yet context-rooted design principles for humanoid robot adoption in hospitality service encounters.

Adopting a multiple method qualitative approach, two service prototypes are evaluated with hospitality and tourism experts (N = 30, Prototype 1) and frontline hospitality employees (N = 18, Prototype 2) using participant observation, in situ feedback, semi-structured interviews and photo-elicitation.

The adoption of humanoid service robots in hospitality is influenced by the following four layers of determinants: contextual, social, interactional and psychological factors, as well as extrinsic and intrinsic drivers of adoption. These empirical findings both confirm and extend previous conceptualizations of human-robot interaction (HRI) in hospitality service.

Despite using photo-elicitation to evoke insight regarding the use of different types of service robots in hospitality, the paper mostly focuses on anthropomorphized service robots such as Pepper™.

Adopting humanoid service robots will transform hospitality operations, whereby the most routine, unpleasant tasks such as taking repeat orders or dealing with complaints may be delegated to service robots or human-robot teams.

Working with and receiving service from Pepper™ changes the service encounter from direct practical, technical considerations to more nuanced social and psychological implications, particularly around feelings of self-esteem, social pressure and social judgment.

This paper presents one of the first empirical studies on HRI in hospitality service encounters using Softbank Robotics’ Pepper™. In doing so, the paper presents a novel framework for service robot adoption rooted in first-hand user interaction as opposed to previous, theory-driven conceptualizations of behavior or empirical studies exploring behavioral intention.

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.