Examining the Effects of Customer Service Robot Interaction Attributes and Emotions on Customer Experience

 Examining the Effects of Customer Service Robot Interaction Attributes and Emotions on Customer Experience and Immersive Engagement to Achieve the Sustainability Development Goals 3, 9, and 11: A study of customers’ perceptions in the context of a Tourism and Hospitality setting in Saudia Arabia

  ABSTRACT

Purpose—This study aims to develop a conceptual model to examine the impact of customer service robots’ interactional attributes on the customer experience among customers to Saudi Arabia (SA).  It will assess the effectiveness of these devices in helping to achieve three key European Union (EU) Sustainability Development Goals (SDGs)—SDG3 (Ensure healthy lives and promote well-being for all at all ages); SDG9 (Build resilient infrastructure, promote inclusive and sustainable industrialisation and foster innovation); and SDG11 (Make cities and human settlements inclusive, safe, resilient and sustainable) (Sustainable Development Goals, 2024).

Design/methodology/approach—This study will employ a two-stage mixed-methods research approach to investigate this topic. Phase I will gather qualitative data using semi-structured interviews with (N=25) customers of different nationalities (i) to find out their perceptions about their interaction with service robots via experiences, (ii) to examine how that impacts on the achievement of the Sustainable Development Goals (SDGs) in the Saudi tourism industry, and (iii) to generate the conceptual framework. Phase II will test the mentioned conceptual framework by gathering survey data with customers (N=750) about their perceptions of their interactions with customer service robots. Next, the data from Phases I and II will be used to assess how well the robot can help Saudi Arabia’s tourism sector to achieve SDGs 3, 9, and 11.

Potential Contribution—This research will make four main potential contributions. First, it will extend the existing literature on how customer service robots’ interactional attributes affect the customer experience in Saudi Arabia. Second, it will offer a novel and comprehensive theoretical framework to enable researchers to better understand the customer experience in relation to using customer service robots’ interactional attributes. Third, it intends to assess the extent to which tourism sector customer service robots used in Saudi Arabia can help the country to achieve SDGs 3, 9, and 11. Fourth, it aims to provide recommendations for leveraging robots to enhance sustainability practices in the tourism industry. The hypotheses and the conceptual framework will present an approach for tourism managers to develop and design the tourism sector by users and help Saudi Arabia achieve SDGs 3, 9, and 11.

Keywords: Artificial intelligence, Customer experience, customer service robot Interaction attributes, Sustainable Development Goals.

 

INTRODUCTION

AI-based technologies such as customer service robotics are transforming how the tourism industry interacts with customers and delivers services on a global scale (García-Madurga & Grilló-Méndez, 2023). Customer service robots’ interactional attributes are becoming increasingly prevalent in hotels, airports (Reis et al., 2020), museums (Webster & Ivanov, 2022), and other tourist attractions (Huang et al., 2021) to perform tasks traditionally handled by humans: greeting guests, providing information, managing check-ins, and even delivering room service. These devices are designed to interact with customers in a friendly, efficient, and often personalised manner, offering a novel experience that can enhance customer satisfaction (Wu & Huo, 2023).

Furthermore, customer service robots’ interactional attributes are not only efficient in terms of handling tasks but also have the potential to help countries achieve the UN SDGs, three of which are the focus of this study—SDGs 3, 9, and 11 (Ivanov et al., 2023). Specifically, SDG3 ensures healthy lives and promotes well-being for all at all ages, SDG9 puts emphasis on building a resilient infrastructure, promoting inclusive and sustainable industrialisation and fostering innovation, and SDG11 stresses turning cities and human settlements into inclusive, safe, resilient, and sustainable abodes (Sustainable Development Goals, 2024). This is achieved by promoting sustainable tourism, which is beneficial for creating jobs, enhancing local culture, and minimising environmental impact, creating smart cities, developing infrastructure, and improving the health and well-being of citizens (Haidegger et al., 2023; Ivanov et al., 2023).

Despite the prevalence of the application of customer service robots’ interactional attributes across different industrial contexts, the following research gaps are yet to be tackled in the context of the Saudi tourism industry. There is a distinct absence in the literature of a practical integrative framework to investigate the relationship between the interactional attributes of customer service robots and the customer experience in tourism contexts (Bialkova, 2024; Park et al., 2024; Yoruk et al., 2024). Furthermore, empirical measurement tools available to quantify the effects of customer service robots’ interactional attributes in the tourism sector in terms of the achievement of the SDGs are lacking (Ivanov et al., 2023).​ Additionally, empirical evidence is scarce on the barriers to adopting AI-enabled systems in designing business strategies in the tourism and hospitality sector (Ivanov et al., 2023).​ Many scholars have emphasised the lack of understanding of value mechanisms in relation to the application of customer service robots in this sector (McKinsey, 2023; Shin et al., 2022; WEF, 2022).​

Most of the literature (Chena & Baob, 2023; Kasinathan et al., 2022; Khamis et al., 2019; Varriale et al., 2024) has been based on conceptual studies rather than studies that use concrete pragmatic integrative frameworks to achieve different SDGs through the application of customer service robots’ interactional attributes in the tourism industry, specifically in the context of Saudi Arabia. Meanwhile, although studies have investigated how specific SDGs can be met using customer service robots in education (Park & Han, 2016), the healthcare sector (Umbrello et al., 2021), and the manufacturing industry (Van der Schoor et al., 2023), to date, to the best of the researcher’s knowledge, investigations on the use of customer service robots in achieving the SDGs in the Saudi Arabian tourism sector are lacking.

A key aspect of the management of tourism going forward requires a keen understanding of how to enhance the customer experience in relation to the use of customer service robot (WEF, 2022). Providing a positive AI-enabled customer experience is for creating value and gaining a competitive advantage, as well as being a useful way to achieve particular SDGs (Foroudi et al., 2023) such as well-being (Chen et al., 2022). Tourism in Saudi Arabia is undergoing a major transformation, driven by the Vision 2030 initiative; by 2025, the Saudi tourism sector is projected to contribute over USD 86 billion to the national economy (Statista, 2024). The SDGs were adopted by Saudi Arabia in 2015 with the aim of ensuring that all its citizens would live in peace, health, and prosperity by 2030 (Demeter et al., 2023; Guermazi & Gharbi, 2024).

A few studies (e.g., Chatterjee & Karmakar, 2023; Soliman et al., 2023) have theorised the long-term effects of customer service robots’ interactions on the customer experience in terms of assisting the Saudi tourism industry to help meet the nation’s SDGs. Therefore, the proposed research project is timely to determine how positive immersive engagements with customer service robots can lead to sustained customer experience over time and help Saudi Arabia to meet its SDGs (Flavian & Barta, 2023). Most studies have focused on the application of customer service robots in the hospitality and retail sectors, with scant attention given to other areas such as cultural heritage sites, particularly in the context of the Saudi tourism sector (Ivanov & Webster, 2019). Therefore, there is a need to better understand the dynamics of how the attributes of customer service robots impact the customer experience and can be harnessed to help meet the SDGs (Kasinathan et al., 2022; Mai et al., 2022).

To date, however, most of the literature has been based on conceptual studies rather than using concrete pragmatic integrative frameworks to achieve different SDGs through the application of customer service robots in the tourism industry, particularly in the context of Saudi Arabia. Therefore, to overcome the above shortcomings of the extant literature, the proposed study offers a pragmatic integrative framework that links AI, the customer experience, and meeting the SDGs. This study also aims to improve the tourism sector in Saudi Arabia by assessing the impact of customer service robots. It provides a methodological framework to determine the effects of such devices on achieving SDGs 3, 9, and 11 in the context of the Saudi tourism sector. It also aims to provide insights into the application of customer service robots in the sector in particular to enhance customer satisfaction while helping to meet the above SDGs from stakeholders’ perspectives. Data from this study will help stakeholders in the Saudi Arabian tourism sector to use customer service robots at tourist sites to help achieve SDGs 3, 9, and 11 as part of their commitment. Therefore, this study aims to fill those gaps in the existing literature by developing and examining an integrative framework linking customer service robots’ interactional attributes to enable immersive engagement with tourists to help the Saudi tourism sector achieve SDGs 3, 9, and 11. To achieve this goal, seven research objectives are: (1) To explore how the interaction attributes of service robots impact customer emotions. (2) To analyse how the acceptance of service robots moderates the relationship between the interaction attributes of customer-service robots and customer emotions. (3) To examine how customer emotions influence the overall customer experience. (4) To evaluate how perceptions of ethical AI and the adaptability of adaptive learning influence the relationship between customer emotions and their experience. (5) To investigate how customer experience influences immersive engagement and contributes to the development of Sustainable Development Goals (SDGs) 3, 9, and 11. (6) To investigate how technical trust influences the relationship between customer experience and immersive engagement. (7) To assess how immersive engagement contributes to achieving Sustainable Development Goals 3, 9, and 11. Based on above research aim and objectives seven research questions will guide this study:

1. How can the customer-service robot interaction attribute influence customer emotions?

1. How can acceptance of the use of a service robot influence the relationship between customer service robot attributes and emotions?

3. How can customer emotions influence the customer experience?

1. How can ethical AI perception and adoptive learning adaptability influence the relationship between emotions and customer experience?

5. How can customer experience influence immersive engagement to develop the SDGs 3, 9, and 11?

1. How can technical trust influence the relationship between customer experience and immersive engagement?

7. How can immersive engagement affect the achievement of the SDGs 3, 9, and 11?

Customer service robot interaction attributes are playing a fundamental role in reshaping the tourism industry including hotels, airports, and tourist attractions. The customer service robots are being employed as a business management and customer management strategy for enhancing customer experiences and engagement with the services provided at tourist sites. They have the potential to contribute to achieving SDGs, such as promoting well-being (SDG3), fostering innovation (SDG9), and creating sustainable cities (SDG11). Despite the global advancements in AI technologies and their applications in service industry (e.g., tourism and hospitality), there is a lack of empirical research on whether the integration of customer service robot attributes is beneficial for the Saudi Arabia’s tourism sector to enhance customer experiences and immersive engagements which, subsequently, may mediate the achievement of these SDGs. The proposed study aims to develop a framework that connects AI, customer experience, and SDG achievement in Saudi tourism.

In the next section, the existing literature is reviewed to gain an understanding of the impact of customer service robot attributes on the customer experience and its potential in achieving the SDGs.

 

Literature Review

This study aims to develop and examine an integrative framework linking customer service robots’ interactional attributes to enable immersive engagement with tourists to help the Saudi tourism sector achieve SDGs 3, 9, and 11. This literature review critically discusses the extant literature related to the study aim. It provides a general background to the topic before discussing the interactional attributes of customer service robots in terms of the effects on customers. It then examines the relationships between AI-based robot-delivered tourism devices and the SDGs.

 

Impact of interactional attributes of customer service robots on customers’ experiences and engagements

There has been a growing literature in the field of adoption of customer service robots within the hospitality and tourism sector for tasks like concierge services, check-in/check-out processes, and information provision (İştin et al., 2022; Ivanov & Webster, 2019; Lee, 2024; Moriuchi & Murdy, 2024; Wang & Papastathopoulos, 2023). For instance, Moriuchi and Murdy (2024) highlighted the increasing use of customer service robots in hotels to enhance the guest experience. Advances in AI and robotics have significantly improved the capabilities of such service robots. Ivanov and Webster (2019) discussed how customer service robots are transforming customer service in tourism. Their research indicates that the robot can enhance service quality by providing highly reliable, efficient, and personalised services. Wang and Papastathopoulos (2023) asserted that such devices improve customer satisfaction through efficient service delivery and personalised interactions. Lee’s (2024) exploration of how customers perceive and accept customer service robots suggests that customers’ acceptance of service robots depends on their attributes such as perceived usefulness, ease of use, and social presence. Huang et al. (2022) found that using customer service robots enhanced the participants’ affective and cognitive experiences. However, the results also highlighted that customer service robots need to be improved in terms of the quality of their social interaction skills with humans.

The strengths of these studies include their exploration of various attributes of service robots such as personalisation reliability and efficiency, However, they ignored the complexity of service robots arising from technological trust and acceptance level based on cultural and technological literacy factors and immersive engagements. Therefore, there is a need to develop integrated models of service robot attributes explaining the interplay of different contextual and environmental factors affecting the relationship between the customer experience and service robots-enabled immersive engagement in the culture-specific tourism sector.

The application of customer service robots in hotel settings and its impact on the service quality perceived by the customers was assessed by several studies (Borghi & Mariani, 2024; Choi et al., 2020; Chiang and Trimi, 2020). These reported that assurance, safety, and reliability were the key attributes that positively impacted the customer experience, but service quality attributes were oversimplified without giving reasonable attention to the complex and multifaceted nature of customer experience. For instance, although reliability and safety are critical, warmth and personalised attention attributes were not fully captured by their study. This necessitates further studies to consider the impact of the complexities of factors (e.g., cultural background, technological trust, acceptance of the service robots) on the customer experience and immersive engagement.

Several studies have investigated the effect of customer service robots in the hospitality and tourism settings on customer experience and service outcomes (Becker et al., 2023; Lu et al., 2020; Wirtz et al., 2022). These studies suggest that customers are generally receptive to service robots, but have concerns around the interpersonal aspects of service, such as social interaction, emotional connection, ethical aspects of service robots, and personalised attention. Customers may prefer service robots for certain service tasks, such as order placement and payment, but still value human interaction for more complex or emotionally-charged service encounters (Lu et al., 2020). Although these studies provide some insights into different service robot attributes and customer emotional impact, however, further research could provide empirical evidence regarding the impact of service robot attributes on the customer immersive engagement and their interplay with the achievement of SDGs.

Link Between Customer Service Robots and Sustainable Development Goals

Several studies (Fang et al., 2023; Hlee et al., 2023; Jagatheesaperumal et al., 2021) linked customer service robots with sustainable development in the hospitality and tourism sector.  The deployment of service robots has made significant contributions towards the development of smart and sustainable cities; for example, carbon emissions are lower in smart cities due to the use of climate technologies (Mendes., 2022; Wu., 2022). However, these studies missed the link between the service robot-mediated immersive engagement and the achievement of SDGs; instead, they focused on the environmental aspects of the service robots.

Some studies (e.g., Buhalis, 2019,2020,2023) have posited that smart tourism is a crucial component of smart cities. This refers to the use of advanced technologies in the tourism industry, including sensors, Cloud data storage, Internet-of-Things (IoT) devices, machine learning (ML) techniques, Big Data processing, service robots, and radio-frequency identification (RFID) devices. These technologies enhance the interaction and exchange of information between humans and machines, improving overall service delivery and efficiency in the sector.

Smart tourism is characterised by its capacity to gather vast quantities of data and effectively store, process, merge, analyse, and use Big Data to bring about innovative changes, operational improvements, and enhanced service methods through the application of AI and Big Data-based approaches (Jagatheesaperumal et al., 2021). One example of smart tourism in the context of sustainable tourism is the use of AI technology such as customer service robots and employee service robots to enhance integrated tourist services in tourist locations around the world (Fang et al., 2023; Hlee et al., 2023).

Similarly, Tandon et al. (2023) found that service robots are pivotal for creating more smart, efficient, accessible, and sustainable tourism destinations environments. They emphasised the enhancement of the customers’ experience by providing personalised services, supporting real-time information delivery, and reducing the environmental footprint of tourism operations through energy-efficient practices. However, they could not link the service robot attributes with the customer experiences and engagement, and the latter with the sustainability of the smart cities. Their focus was mainly on energy-efficient practices.

The existing literature shows the effect of AI in achieving the SDG3, which is related to maintaining a healthy lifestyle for people in the hospitality and tourism sector (Basiouny, 2023; Binesh & Baloğlu, 2023; Chena & Baob, 2023; Gursoy and Cai, 2024; Meidutė‐Kavaliauskienė et al., 2021; Soliman et al., 2023). However, most of these studies were restricted to the development of AI applications and robots helping customers to achieve their satisfaction and an enhanced customer service experience. The main focus of these studies was the measurement of comfort level and healthy lifestyle; they failed to link the service experience with the level of customers’ engagement and the impact of the latter on the achievement of SDGs.

Lukova (2021) and Zeng et al. (2024) found that service robots were instrumental in reducing human contact during the COVID-19 pandemic, which helped to lower the risk of disease transmission and helped the doctors to manage the emergency health conditions. Moreover, they revealed that the use of robots for tasks such as cleaning, delivery, and health checks contributed to a safer environment for both tourists and staff. The drawback of this study is that they do not fully address the psychological impact of services delivered by service robots on tourists who may feel uncomfortable or alienated by increased automation, as human warmth and interaction in the tourism industry are highly valued. Additionally, the authors did not sufficiently address the long-term sustainability and feasibility of maintaining these technologies in tourism sites.

Customer service robots can enhance efficiency and productivity in the tourism industry, leading to improved economic growth (SDG8) (Chiang & Trimi, 2020; Ivanov et al., 2023). The adoption of advanced robotics fosters innovation in the tourism industry and infrastructural modernisation to support the integration of customer service robots (SDG9) (Avula & Sithole, 2024; Costa, 2024; Gursoy & Cai, 2024; While et al., 2021). Customer service robots can provide consistent and reliable service, making tourism more accessible and inclusive; and support the notion of the development of smart cities with advanced, sustainable services (SDG11) (Buhalis et al., 2019; Jain et al., 2023). The issue with these studies mainly stems from the lack of empirical evidence to support their arguments. Furthermore, they did not offer insights into how different cultures with touristic attractions can benefit from the deployment of service robots in terms of creating inclusive, innovative, and reliable smart cities.

The literature on the tourism industry shows the potential of service robots in driving economic growth and innovation in the tourism sector, which helps to fulfil SDG8 and SDG9, respectively (Choi et al., 2020; Ghesh et al., 2024; Wakelin & Theorin, 2021). In this regard, research by Choi et al., (2020) highlighted how robots demonstrate the economic benefits of implementing customer service robots in the tourism sector such as reducing the cost of delivering services to the target customers and reducing inflation through the provision of cheaper services to the end-users. Likewise, Ghesh et al. (2024) found that customer service robots contribute to sustainable tourism by enhancing efficiency and reducing environmental impacts and discussed the role of robots in promoting sustainable tourism practices. Existing studies often focus on the short-term impacts of customer service robot interactions on the customer experience; however, there is scant research into the long-term effects and sustainability issues. Further, there is a lack of research specifically examining the impact of customer service robots in the context of Saudi Arabia’s tourism industry. Few studies explicitly link the use of customer service robots with the achievement of specific SDGs, highlighting the need for more targeted research in this area.

Many studies have proposed the application of AI-based applications for urban planning, innovation, and development with touristic significance (Cai et al., 2023; Del Fiore et al., 2016; Khan et al., 2024; Lisi & Esposito, 2015; Sanchez et al., 2023). In the context of the role of AI in urban planning and development with innovation (SDG9), Lisi and Esposito (2015) developed a WIE-OnTour tool which populated the OnTourism app with data extracted from different social platforms such as Trip Advisor, Google Maps, and various websites. Furthermore, a non-invasive, indoor, location-aware architecture has been developed to enhance the user experience at a museum located in the context of museums in Italy (Del Fiore et al., 2016). These tools are designed to drive service innovation within inclusive knowledge societies for sustainable development (Lisi & Esposito, 2015). An IoT-based system has been developed to provide smart tourist services, namely for navigating around museums, which enhances the cultural experience of visitors (Cai et al., 2023).

Furthermore, some studies have suggested the use of IoT-based systems for innovating tourist sites (Raharja et al., 2024; Suanpang & Pothipassa, 2024; Ulrich et al., 2022). For example, Ulrich et al. (2022) theorised the potential of IoT devices in offering customers choices of different beaches to attain optimal customer satisfaction. IoT systems help to mitigate beach overpopulation and provide information on the sanitary conditions of beaches in order to assist each visitor to choose the most suitable beach to provide an optimal experience. In this way, they play a role in the mobilisation, distribution, and conservation of resources to facilitate the well-being of visitors. Another study by Suanpang and Pothipassa (2024) examined the Generative AI–IoT system which was based on the smart tourism ecosystem model and designed to strengthen the relationship between businesses and smart travel itinerary recommendations to ultimately benefit the entire tourism industry. However, although they used the concept of IoT-based smart tourism to develop a more intelligent tourism experience, they could not benefit from the application of service robots to create a smart tourism ecosystem and its ultimate impact on the customer engagement and achievement of SDGs.

Several researchers have reported on the ethical and social implications of service robots in tourism, and studies highlight that the privacy, data security, and potential dehumanisation of service interactions may be major concern in the deployment of service robots (Chen et al., 2023; Fusté‐Forné & Jamal, 2021; Lopez et al., 2024; Reis et al., 2020; Soliman et al., 2023). Additionally, they noted that the portrayal of service robots often reinforces traditional gender stereotypes which have broader social implications. These studies could have benefitted from empirical data to support their claims, as most of their claims come from the theoretical discussions. Moreover, although they raised important questions about the dehumanisation of service interactions, the potential solutions to mitigate relevant concerns of privacy and ethical issues were not fully explored. Hybrid models, for example, may be combined while designing human and robotic services to address some of the ethical and social challenges. There is a need to better understand how customers perceive and interact with service robots, and how this might impact the overall service experience and customer satisfaction (Chen et al., 2023; Ivkov et al., 2020; Meidutė‐Kavaliauskienė et al., 2021).

Critically speaking, the existing literature links the integration of customer service robots in tourism, which presents opportunities for progress toward several SDGs, including improved health outcomes (SDG3), innovation (SDG9), and the development of sustainable and inclusive cities (SDG11). However, the main focus of this literature was on the sustainability of tourism rather than putting emphasis on the customer experiences and immersive engagement with service robots as the catalyst towards the achievement of SDGs, although they do provide theoretical insights into the customers’ perceptions and experiences and their role in promoting sustainable tourism. The existing literature is culture-specific, which means that findings obtained from one cultural perspective cannot extrapolated to another cultural background due to the varying preferences of people in different cultures towards interaction with the service robotic attributes in the tourism settings.

Therefore, a more interdisciplinary approach with the aim of incorporating insights from social sciences, ethics, AI, and environmental sustainability studies would provide a more comprehensive understanding of the long-term impacts of service robots in tourism. Hence, this study is designed with an interdisciplinary approach involving concepts from the social sciences, ethics, AI, and environmental sustainability domains in order to construct an integrated framework for achieving the SDGs 3, 9 and 11 through the interaction of customers with the service robotic attributes in the Saudi tourism setting.

 

Theoretical Framework: Stimulus–Organism–Response Theory

This study will adopt Mehrabian and Russell’s (1974) stimulus–organism–response (SOR) theoretical framework to investigate the effects of customer service robots’ attributes on the customer experience and in meeting SDGs 3, 9, and 11 in the context of the Saudi Arabian tourism sector. The SOR theory was to explain how external stimuli can affect internal states and motivate behavioural responses and contains three components—stimulus, organism, and response. The SOR framework, which originated from environmental psychology, was chosen as suitable for use in the current proposed research project as it has been previously applied to investigate the impacts of a number of technological attributes. These include surveillance (Pozharliev et al., 2021; Xiao & Kumar, 2021), online shopping experience (Rahman et al., 2023; Ruan & Mezei, 2022), social worlds (Huang et al., 2021; Kim et al., 2022), immersive displays (Goncalves et al., 2024; Zhu et al., 2023), and customer experience in the hospitality sector (Chen & Girish, 2023; Hlee et al., 2023). Furthermore, the utility of the SOR framework lies in gaining insights into people’s responses to external stimuli such as during interactions with customer service robots based on their attributes and understanding the changes in users’ perceptions and attitudes towards dealing with such devices (Asyraff et al., 2023; Kim et al., 2020). Therefore, the SOR model will be employed to interpret the emotional and physical reactions of tourists resulting from their use of customer service robots at the tourist sites in Saudi Arabia, as well as the consequences of these reactions on customers’ experiences, behaviours, and attitudes. The discrete elements of the SOR model are now discussed.

Stimulus refers to external variables, mostly environmental, that can impact on a person’s internal states. These stimuli are characterised as influences that stimulate the individual to experience a particular internal state (e.g., satisfaction, happiness) (Lin et al., 2023). In empirical hospitality and tourism studies, various concepts such as servicescape and perceived quality (e.g., product quality, service quality, atmospherics, and hotel ambience) have been commonly adopted as stimuli (Della Corte et al., 2023; Guan et al., 2022). Accurate information and reliable assistance are reported to be provided by customer service robots installed at tourist sites in terms of promptness and willingness to assist (Abumalloh et al., 2024). Customers tend to be willing to place their confidence and trust in customer service robots’ capability to fulfil their needs, especially those that demonstrate accurate knowledge and competence in handling tasks and providing personalised attention and care (Chen & Girish, 2023; Sann et al., 2023). Customer service robots offer tailored recommendations based on customer preferences. Adaptive learning and perceptions of the robot ethical conduct have led to an enhanced customer experience and positive emotional resonance. This is especially the case for customer service robots with a humanoid physical appearance, good functionality, and user-friendly, appealing interfaces and designs (Kim et al., 2020).

Organism refers to the internal structures and processing mechanisms used by a customer as a result of exposure to a given external stimulus, and which determine an individual’s future actions and reactions (Mehrabian & Russell, 1974). In the current context, the emotional and cognitive states together identify the organisms used by customers to generate the expected responses to customer service robots. In the existing hospitality and tourism literature, various constructs are utilised to represent the organism of the consumer. These include service experience evaluation (Chen et al., 2022), customer value (Wu et al., 2021), emotions (Liu et al., 2021), memorable experience (Ng et al., 2022), customer satisfaction (Chen et al., 2022; Sann et al., 2023), perceived service quality (Liu et al., 2021; Ng et al., 2022), and corporate image (Mohammad et al., 2024). Customers report that customer service robot’s hance the customer experience in terms of the convenience, ease, and quality of their visit (Sann et al., 2023). Customers tend to report that interactions with customer service robots are intuitive and straightforward and produce positive feelings of satisfaction and pleasure. However, understandably, frustration can be reported by those who have less-than-optimal interactions (Abumalloh et al., 2024). Mental assessments of and attitudes towards the effectiveness and efficiency of customer service robots by users tend to evaluate them as efficient and helpful tools (Chen & Girish, 2023; Kim et al., 2020). In the current study, the customer experience, trust in the technology, ethical perceptions of the robot, immersive engagement, and perceptions of well-being and good health will represent the organism-based variables.

These variables have been selected due to their overwhelming influence on how customers interact with and perceive customer service robots. Customer experience and trust in customer service robot are essential for adoption and sustained use; ethical perceptions measure the growing concerns in relation to customer service robot impact on society. Immersive engagement measures the depth of customer interaction with customer service robots. Perceptions of well-being and good health was chosen due to its crucial role in directly affecting customer satisfaction and the perceived benefits of customer service robots in service settings. Hence, these variables collectively provide a comprehensive picture of the customer’s interactions with customer service robots.

Finally, the response component of the SOR framework refers to the choices and outcomes expected by the users of a particular AI system. Mehrabian and Russell (1974), in their original paper, described response as an approach towards obtaining some benefit or avoidance strategy towards some service/social entity. In the context of customer service robots’ attributes and the customer experience, the level of involvement and interaction with robots determines whether customers perceive the interactions as beneficial or not (and thus to be avoided) during their visits to tourist sites (de Kervenoael et al., 2020; Van Pinxteren et al., 2019). Frequent use of customer service robots during visits can determine the interactional benefits gained from the use of robots and, ultimately, determines the overall satisfaction of customers with their service experience (Sann et al., 2023). High satisfaction ratings and positive feedback can be regarded as evidence of higher engagement with and lower avoidance of customer service robots. Repeat visits and recommendations to others are indicative of enhanced satisfaction with customer service robots and trust in the services offered at tourist sites (Chen & Girish, 2023). If tourists report being more likely to adopt eco-friendly practices and participate in environmentally friendly activities due to their interactions with customer service robots, this can indicate that these devices are having a positive impact on both tourists’ experiences and the achievement of the relevant SDGs in the context of a particular tourist site (Abumalloh et al., 2024; Kim et al., 2020).

In the current study, the acceptance of the use of customer service robots, adaptive learning, emotional resonance, trust, and competence will be the responses that will be examined as a result of exposure to customer service robots’ attributes as stimuli at tourist sites in Saudi Arabia. These variables are selected due to their direct impact on the adoption and effectiveness of customer service robots. Acceptance of customer service robots helps evaluate users’ willingness to use these devices in the tourism industry. Adaptive learning is a useful construct in measuring the customer service robots’ ability to personalise and improve over time while enhancing the customer experience. Emotional resonance reflects the emotional connection between customers and customer service robots, which affects the immersive engagement of customers with customer service robots; while trust and competence are fundamental constructs for measuring the customer’s confidence and assessing the perceived capability of the AI. Hence, the measurement of these variables can provide deep insights into the long-term adoption of customer service robots by the tourism sector.

The conceptual framework constructed from the above-mentioned SOR-derived constructs and concepts is discussed in the following sections.

 

Conceptual Framework

Overview

An overview of the conceptual framework is provided in Figure 1. The constructs in the proposed framework are illustrated in this section in order to build the hypotheses for this study.

 

Figure 1: The Conceptual Framework of the Research (Source: designed by the author).

Note: The definitions of all constructs are described in Appendix A.

Customer Service Interaction Attributes and Emotions

In this study, emotions of customers are characterised by three key dimensions, which are (i) trust, (ii) emotional resonance with focus on emotional connection, satisfaction and emotional relief, and (iii) competence. Trust is a psychological condition encompassing the intention to accept vulnerability based upon favourable expectations about the intentions or actions of another (Hulliung, 2017; Rousseau et al., 1998). This quality makes it crucial to human–automation interaction (Maehigashi & Yamada, 2023) as trust represents the psychological presumption that others will honour their word and refrain from acting unethically in anticipation of a favour (Blut et al., 2021). According to Tussyadiah et al. (2020), trust in technology is the attitude or belief that the technology (such as websites, automated online chatbots, recommendation agents, and, in the current context, customer service robots) can assist consumers in meeting their requirements and fulfiling their expectations. In the meantime, the relationship between technological trust and willingness to use customer service robots was mediated by customers’ expectations regarding AI performance.

Trust acts as a mediator in the relationship between a customer’s emotional connectedness and the attributes of an AI-based robot (Xu et al., 2020). Trust appears to be one of the most important determinants of the level and strength of the relationships between customers and customer service robots (Alagarsamy & Mehrolia, 2023). For example, according to Sollner et al. (2016), trust can be defined as the desire of a customer to rely on a service provided by customer service robots despite possible ambiguities and losses. Trust is a crucial factor in enabling relationships between customers and virtual services, but also has a significant impact on the interactions between customers and customer service robots within the service industry (Tussyadiah et al., 2020). Customers’ potential affective orientations toward customer service robots, perceived security and dependability in brand interactions, and the conviction that the brand represents the interests of the customer are determined by customers’ level of confidence in such devices (Chi et al., 2023; Tussyadiah et al., 2020). Tussyadiah et al.’s (2020) results highlighted that customers’ trust-based perceptions of robotic bartenders are a strong predictor of their willingness to be served by such devices. However, it remains unclear how customers in different restaurant and tourism segments feel about using customer service robots in terms of trust, expectations, and assistance.

Emotional resonance refers to the extent to which customers emotionally connect with a brand, service, or experience. In the context of customer service robots, this connection is driven by how well the robots’ interactional attributes align with customers’ emotional needs and expectations (Lomas et al., 2022). A closer alignment between customer service robots’ interactional attributes and the needs of customers may improve the level of emotional resonance in the latter which creates satisfaction in the overall experience of customers during their interactions with these devices at tourist sites. Customer service robots that offer personalised, intelligent, precise, caring, and efficient service tend to resonate effectively with the emotional needs of customers (Liu Thompkins et al., 2022; Pozharliev et al., 2021).

Customer service robots exhibit human-like interactional and anthropomorphic attributes, such as displaying empathy, engaging in humour, and understanding the concerns of customers (Yang et al., 2024). These attributes mean that such devices are considered relatable entities; this relatability engenders a strong emotional connection between the customer and the service robot, thereby resulting in the former reporting more optimal and valued experiences during their interactions with the latter (Fiestas et al., 2024). For instance, a service robot with expressive eyes, a friendly voice, and the ability to engage in small talk while interacting with customers at a hotel seems more like a relatable entity rather than just a machine.

The ability of customer service robots to be judged by a human user as having successfully accomplished a particular job or task is known as competence (Blut et al., 2021). This concept comprises the user’s understanding of and proficiency with using such a device. Competence, according to Liu et al. (2024), is a collection of individual traits, mindsets, abilities, and knowledge that result in high-quality performance. While impressions of warmth are linked to friendliness, kindness, sincerity, and caring, views of competence are linked to intelligence, efficiency, knowledge, and effectiveness. The ability to recognise, comprehend, and reflect on pertinent socio-ecological systems from various areas and disciplines is what constitutes competency in systems thinking. By its very nature, competence is a highly politicised term that expresses opinions and hopes for the future abilities that capable persons are expected to possess (Ilomäki et al., 2023).

The customer service-related interactional attributes of customer service robots affect the perceived competence of customers about the services provided by the device and are critical for improving the customer experience. Perceived competence in the context of this study is defined as the perception of the customer about the ability of customer service robots to perform set tasks as desired by users (Kafy et al., 2022; Soliman et al., 2023; Wirtz & Pitardi, 2023). The clarity and effectiveness with which customer service robots can communicate with users by using simple and straightforward language has a positive impact on users’ perceptions of competence. Similarly, being able to respond quickly signals to customers that the customer service robot is skilled enough to handle their enquiries without delay (Alagarsamy & Mehrolia, 2023; Chena & Baob, 2023; Ivanov et al., 2022).

Another important attribute of customer service robots is anthropomorphism; namely, the assignment of human-like physical appearance and personal characteristics such as empathy and emotional intelligence to the devices (Blut et al., 2021; Liu et al., 2023; Rasouli et al., 2022). Empathy and emotional intelligence have a positive impact on the perceived competence of customer service robots in balancing emotional support with effective problem-solving strategies and managing stressful situations while maintaining professionalism, thereby giving confidence to customers that such devices ‘understand’ the user’s problems and is addressing them with the highest level of competence (Mele et al., 2020; Tojib et al., 2023; Yang, 2022).

The efficient applications of various databases and knowledge hubs by customer service robots to solve issues faced by customers during interactions is another important factor in enhancing perceived competence (Wirtz & Pitardi, 2023; Xiao & Kumar, 2023). It suggests that customer service robots are well-versed in the tools necessary to serve the customer effectively. The use of data and technology to personalise interactions positively affects perceived competence as it highlights the ability of customer service robots to use the available resources to provide services tailored to customers’ needs (Molinillo et al., 2022; Wirtz et al., 2022). Therefore, the following hypothesis is proposed:

H1: Customer service robots’ interaction attributes have a positive impact on emotions—(a) trust, (b) emotional resonance, and (c) competence.

 

Moderating Role of Acceptance of the Use of Customer Service Robots on the Relationship Between Customer Service Robots’ Interactional Attributes and Emotions

The acceptance of the use of customer service robots refers to the readiness and willingness of customers to integrate the service robots into their interactions while visiting the tourist sites (Chena & Baob, 2023; Chen et al., 2023). It is influenced by two key factors—psychological acceptance and the usage intention (Meidutė‐Kavaliauskienė et al., 2021; Mele et al., 2020; Zhong et al., 2020). Psychological acceptance refers to the emotional and attitudinal willingness to adopt service robots; while the usage intention is defined as the deliberate decision and willingness of customers to interact with and utilise service robots (Chena & Baob, 2023; Seo & Lee, 2021). These components of acceptance of the use of service robots are essential for describing the acceptance of service robots and for predicting how likely it is that customers can integrate service robots into their interactions at tourist sites (Park et al., 2021).

The acceptance of the use of customer service robots serves as a moderator between consumers’ emotional connection and customer service robots’ interactional attributes. The effectiveness of customer service robots’ interactional attributes lies in the ability of these devices to deliver high-quality services, creating a robust emotional connection between customers and customer service robots during each interaction (Song et al., 2022). Nonetheless, it appears to be difficult to establish an emotional connection with a customer service robot if the device lacks ‘natural’ empathy and warmth as expected by customers (Xiao & Kumar, 2021). Customers can accept being asked to use a customer service robot (or express the intention to use one) if these devices are equipped with the capabilities to provide effective, reliable, accurate, efficient, and caring services to customers at tourist sites (Chi et al., 2023). Such caring, precise, and efficient service also helps to strengthen the emotional bond between customers and customer service robots, thereby improving the willingness of customers to use them regularly at tourist sites (Chian et al., 2022; Hlee et al., 2023). Hence, the relationships between consumers’ emotional connection with customer service robots and the interactional attributes of such devices are moderated by the customers’ acceptance of the use of such devices.

Customers with high levels of technology acceptance are more open to engagement with customer service robots at tourist sites, which can form the basis of a stronger relationship between customers’ emotional connection (trust, emotional resonance, and competence) with customer service robots and the interactional attributes of such devices, despite the limited emotional capabilities of customer service robots (Kumar et al., 2023; McLean & Wilson, 2019). A high level of acceptance of the use of customer service robots can increase the positive effects of any emotional elements embedded in the interactional attributes of the device, leading to a greater level of interaction with customer service robots (Song et al., 2022). Conversely, a low level of acceptance of the use of customer service robots among consumers may cause reluctance to interact with these robots, regardless of a customer service robot’s interaction capability. This reluctance can drastically harm the development of an emotional bond, resulting in less effective interactions with customer service robots (Kim et al., 2022; Li et al., 2023). Thus, the following hypothesis is proposed:

H2: Acceptance of the use of customer service robots moderates the relationship between customer service robots’ interactional attributes and emotions—(a) trust, (b) emotional resonance, and (c) competence.

 

Emotions and Customer Experience

Emotions in this study is characterised by trust, emotional resonance, and competence. Trust is defined as the confidence and comfort level perceived by a customer towards a service provider based on the extent to which the latter preserves the emotional integrity of the relationship with the former rather than simply basing the relationship on logical reasoning (Thamrin, 2024). It represents the belief of customers that service providers care about the well-being of customers and value their input (Khan & Mehmood., 2024).

Enhancing the level of customer trust towards a given service provider or consumer good strengthens the customer’s affiliation with the service or good. Customers who feel emotionally secure with services and goods are more likely to remain loyal and enjoy a higher level of customer experience. In the context of customer service robots, facilitating a high level of consumer trust is helpful in reducing the perceived risk associated with interacting with such devices, thereby generating a smoother and more satisfying customer experience (Alagarsamy & Mehrolia, 2023; Maehigashi & Yamada, 2023; Rhim et al., 2023; Wirtz & Pitardi, 2023).

Emotional resonance refers to the alignment between the values and messages of a service provider or brand and those of its customers. It provides a measure of the emotional connection between service providers/brands and customers. Emotional resonance generates a deep connection between the service/brand and the customer, leading to a long-lasting impact on the customer’s experience. Customers who report a strong sense of resonance between their values and those of a service provider/brand have a stronger brand affinity and greater emotional satisfaction, which further augments their positive feeling   towards the service/brand and enhances their overall positive perceptions and customer experience (Ankita, 2018; Bashir et al., 2018; Gul et al., 2018; Rosado-Pinto et al., 2020; Saputra et al., 2021).

Competence is defined as the perceived ability of offerings to effectively meet customers’ needs and deliver on the promises of the service/brand. In the current context, this encompasses the expertise, reliability, and efficiency of customer service robots. Customers’ positive perceptions of the competence of customer service robots improves their cognitive trust, thereby reinforcing their trust and comfort level with a brand or service (Choi, 2023; Choi et al., 2020; Frank et al., 2023; Sharma, 2022). Customers who are emotionally invested in a brand’s competence are more likely to place their confidence in its products and services. A competent customer service robot provides consistent and reliable service to customers at tourist sites, resulting in predictable and positive customer experiences. The positive perception of the competence of service robots ensures that issues encountered by customers will be handled swiftly and effectively, which enhances the customer experience while interacting with customer service robots at tourist sites (Chena & Baob, 2023; Ivanov et al., 2022; Soliman et al., 2023). Therefore, the following hypothesis is proposed:

 

H3: Emotions—(a) trust, (b) emotional resonance, and (c) competence—have a positive impact on customer experience.

 

Ethical AI perception moderates the relationship between emotions and customer experience

Perceptions of ethical decision making by customer service robots describe the human-centred factors (such as safety, ethics, accountability, and transparency) that determine users’ willingness to engage with such devices (Chai et al., 2021; Ng et al., 2021; Rodgers et al., 2023). Users’ opinions on the ethical qualities of customer service robots tend to influence trust in the organisation providing such devices, which is generally acknowledged to be essential to both long-term success (Du & Xie, 2021; Hunkenschroer and Leutge., 2022; Lee & Lee, 2020) and organisational performance (Chi et al., 2023; Figueroa et al.,2023). In other words, customers’ perceptions of a service provider’s degree of participation in ethical practices and adherence to socially responsible principles are related to the idea of perceived ethical/societal reputation, which is related to long-term success (Lorenz, 2020; McLeay et al., 2021).

Users’ opinions on the ethical qualities of customer service robots span aspects such as fairness, privacy, data use, bias, the overall impact of AI on society, and the transparency of AI-mediated services at service points (Kumar & Suthar, 2024; Singh, 2021). Customers who perceive their interactions with a customer service robot as ethical are more likely to place their trust in the information provided by such devices that results from their interaction (Ryan, 2020). This not only improves the emotional resonance and competence between customers and customer service robots but also positively influences between the relationship between the emotions and the customer experience (Ameen et al., 2021). Therefore, the perception of the ethical decision making of a customer service robot appears to determine the strength of the relationship between users’ emotional connection and the customer service experience (Liu-Thompkins et al., 2022; Trawnih et al., 2022).

On the other hand, if, while interacting with robots, customers develop the sense that the services offered to them are not based on honesty, transparency, and empathy, this can lead to the emergence of negative emotions and the erosion of trust. This in turn raises doubts about the competency of the service robots to deliver the desired services, thereby negatively impacting the relationship between the emotions and customer experiences (Greiner & Lemoine, 2024; Heyder et al., 2023). Therefore, the following hypothesis is proposed:

H4: Ethical AI moderates the relationship between emotions—(a) trust, (b) emotional resonance, and (c) competence—and customer experience.

Adaptive learning adaptability moderates the relationship between emotions and customer experience

Emotional connection is defined as the special bond felt by customers with a particular service or product; it is usually developed during the interaction between the customer and the service in question (Prentice & Nguyen, 2020). Customers who perceive that they are receiving high value, understanding, and care from the given service or product report an enhanced emotional connection with the given brand or service (Puntoni et al., 2021). In the case of customer service robots, the emotional bond becomes stronger if the devices show empathy, attentiveness, and responsiveness to customer needs (Payne et al., 2021). Emotional connection is directly related to the customer experience—i.e., the customers’ perceptions of the customer service robot during their interaction—and is dictated by perceived quality of service and levels of satisfaction and trust. The stronger the emotional connection, the higher the quality of the customer experience and satisfaction with the customer service robot (Haugeland et al., 2022; Huang et al., 2021).

Adaptive learning refers to the ability of a customer service robot to engage in learning from the accumulated set of interactions that it has with customers over time, which, ultimately, results in improvements to its responses and services based on customer preferences and past experiences (Anindyaputri et al., 2020; Kaiss et al., 2023). In this way, customer service robots can tailor their interactions with individual customers through machine learning algorithms, thereby enabling them to provide a more highly personalised service to each customer (Anindyaputri et al., 2020).

Customer service robots that can effectively utilise adaptive learning are better placed to anticipate customer needs, adjust their behaviour based on the unique needs and desires of particular customers, and ‘remember’ customer preferences to cater to their requirements more efficiently in future interactions (Cerejo & Carvalhais, 2023; Monnot, 2020). This leads to higher levels of emotional connection and subsequently a higher-quality customer experience during each interaction. Hence, the customer service robot retains the capability to enhance the strength of the relationship it establishes between itself and the customer by improving the quality of the emotional connection and the customer experience by making each interaction more personal and meaningful for the user (Batat, 2019; Hoyer et al., 2020). Therefore, the following hypothesis is proposed:

H5: Adaptive learning adaptability moderates the relationship between emotions—(a) trust (b), emotional resonance, and (c) competence—and customer experience.

Customer Experience and Immersive Engagement

Immersive engagement gained momentum in the literature of marketing due to its increasing significance in enhancing the customer experience, which is driven by advancement in technologies such as virtual reality, augmented reality, and AI. These technologies have enabled highly personalised and engaging experiences by transforming the interactions of customers with service robots in the tourism sector. Wang et al. (2024) illustrated how virtual reality has revolutionised the tourism industry by providing virtual tours of tourist spots to customers, in the tour phase, virtual reality technology is integrated into interactive tools (such as digital guides and cave displays) to enhance consumers’ perceived value of experience, thereby helping them make informed decisions and achieve higher levels of satisfaction. The ability to experience a destination virtually plays a pivotal role in generating a stronger emotional connection and making the eventual physical visit more appreciated and anticipated.

AI-driven personalisation is another crucial element of immersive engagement that significantly enhances the customer experience. Chen & Wei, (2024) reported the significant role of AI capabilities in enabling touristic businesses to analyse vast amounts of customer data to deliver tailored experiences based on individual preferences. Some examples of a higher level of personalisation and its relationship with heightened customer experience are evident in platforms like Netflix and Amazon, were AI algorithms curate content and product recommendations in line with user behaviour and preferences. Such tailored interactions with customers not only increase the relevance of the customer experience but also foster a deeper emotional connection between the AI services and the customer, which results in higher levels of customer loyalty and advocacy.

Furthermore, AR has been instrumental in bridging the gap between online and offline shopping, creating interactive and immersive experiences that engage customers in new ways (Eru et al., 2022; Sahli & Lichy, 2024) in the retail sector. Several studies have linked the use of AI technologies such as augmented reality with the enhanced customer experience through the power of immersive engagements with the products on display in online stores. Augmented reality helps customers to engage in virtual try-ons and interactive product displays, thereby allowing them to visualise designs and functions, and assess the suitability of products to their needs, without visiting retail stores physically and handling the products (Lee, 2021; Li et al., 2024; Wang et al., 2023). These data indicate that AI enhances the shopping experience by making it more interactive and engaging, which consequently increases customer satisfaction and leads to a higher likelihood of purchase.

Many scholars emphasised the integration of AI technologies including the customer service robots into the business management and customer satisfaction strategies in the pursuit of enhancing the overall customer experience (Habil et al., 2024; Vaidyanathan & Henningsson, 2023). Businesses successfully integrating immersive engagement strategies into their customer management systems are more likely to deliver superior customer experiences that drive long-term loyalty and growth (Chaudhuri et al., 2024; Hoyer et al., 2020). Therefore, the following hypothesis is proposed:

H6: Customer experience has a positive impact on immersive engagement.

 

Technological trust moderates the relationship between customer experience and immersive engagement

Technological trust, in the context of this proposed study, refers to the belief of customers in the value of the customer service robot technology implemented at tourist sites. This is influenced by the cultural background supporting the use of these technologies, the literacy in the application of this technology, service quality perception, and user empowerment (Brachten et al., 2020; Della Corte et al., 2023; Park, 2020).

It is influenced by a range of factors including the cultural background, technological literacy, service quality perceptions, and the user engagement (Mele et al., 2020; Seo & Lee, 2021; Wirtz et al., 2022). Cultural background usually determines whether the societal norms and values are compatible with the adoption of service robots (Chang et al., 2022; Wirtz et al., 2022), while technological literacy refers to the level of familiarity with the service robots and indicates users’ level of confidence and comfort with the usage of technology (Binesh & Baloğlu, 2023; Korn et al., 2021). The way that customers perceive the reliability, efficiency, and effectiveness of customer service robots defines service quality perceptions (Kharub et al., 2021; Seo & Lee, 2021), and the level of interactions between the service robot and customer define the extent of user engagement with the technology (Chena & Baob, 2023; Wirtz et al., 2022).

The customer experience is related to the satisfaction and perceptions of customers which are derived from their interactions with customer service robots at tourist sites (Huang et al., 2021). Immersive engagement refers to the intensity with which customers engage and interact with such devices and involves a high level of cognitive and emotional engagement with these devices during interactions, which is enhanced through gamification, virtual reality, and augmented reality (Fan et al., 2022; Rohit et al., 2024).

Technological trust affects the strength of the relationship between the customer experience and immersive engagement by altering customers’ perceptions of the utility and reliability of technologies during frequent interactions (Park, 2020). Della Corte et al. (2023) showed that customers reporting a high level of trust in such technologies are more likely to fully engage with them, which enhances the immersive experience of customers. Additionally, trust reduces the customers’ cognitive load in relation to privacy concerns, data security issues, and when handling the complex functionalities of customer service robots, thereby allowing customers to access a highly immersive experience which strengthens their engagement with customer service robots (Brachten et al., 2020; Yoganathan et al., 2021).

The technological trust amplifies the customer experience through a positive feedback loop developed during frequent interactions with trustworthy customer service robots (Kushwaha et al., 2021). The positive experiences resulting from customers’ trust in such devices can lead to sustainable engagement with them (Hlee et al., 2023; Seo & Lee, 2021). Conversely, poor technological trust on the part of consumers towards customer service robots dampens the level of immersive engagement by lowering overall satisfaction with their use of such devices due to a perception of poor service quality, limited user empowerment, and technological literacy (Sun & Botev, 2021). Therefore, the following hypothesis is proposed:

H7: Technological trust moderates the relationship between customer experience and immersive engagement.

Immersive Engagement and SDGs (3, 9, 11)

The components of immersion depend on precise, high-quality presentation, including visual, auditory, 360-degree camera tracking on the X, Y, and Z axes, and distinguishability (Fan et al., 2022). The term engagement is used across a wide range of fields (Bouvier et al., 2014); yet, the immersive literature cannot agree on a precise definition. According to Cummings and Bailenson (2016), immersion refers to the technological quality of media transmission in terms of “the extent to which the system presents a vivid virtual environment while shutting out physical reality” (p. 274). Bowman (2018) described immersion as a fundamental condition of human consciousness that results from one’s willingness to interact with a particular attention-grabbing and engaging experience. The definition of immersion proposed by Attfield et al. (2011) in the context of web applications was adopted for this study because it covers multiple relevant psychological aspects; which, according to Verhulst et al. (2021, p. 3) is “the emotional, cognitive and behavioural connection…between a user and a resource” .Immersion can be defined as “the degree in which the range of sensory channels is engaged by the virtual simulation” (Privitera et al., 2024, p. 96).

Flow state, active participation, and sensorial engagement are the key determinants of immersive engagement. Flow state is where an individual is completely immersed in an activity without reflective self-consciousness but retains a deep sense of control (Barhorst, 2021; Brailovskaia & Teichert, 2020; Engeser et al., 2021). Active participation is defined as an individual’s physical, emotional, or mental engagement with technology (Bertella et al., 2018; Malinen et al., 2024) and measures the strength of their individual and/or collective identity with regard to a device (Balcom Raleigh & Heinonen, 2019; Kamarrudin et al., 2022). Sensory experience, according to Huang et al. (2021), refers to what customers see, hear, smell, and taste when interacting with customer service robots. The use of multisensory experiences enhances users’ immersive engagement with robots (Godovykh et al., 2022; Hofmann et al., 2021). The recruitment of multiple senses enhances engagement and perception and is frequently employed in consumer and educational settings (Kersting et al., 2021).

This proposed study intends to link how immersive engagement affects the customer experience during interactions with customer service robots in relation to SDG3, SDG9, and SDG11. The United Nations defines SDG3 as ensuring healthy lives and promoting well-being for everyone at all ages (Renganathan & Davies, 2023). Stahl et al. (2022) pointed out that the SDGs aim to ensure healthy lives and promote well-being for everyone. According to Hammedi et al. (2024), the goal of SDG3 is to ensure that everyone leads an active and healthy life. SDG9 refers to a return to industrialisation in the context of the Sustainable Development Agendas (SDA) for developing countries which vary in population, per capita income, and economic structure (Gulseven et al., 2020; Schüller & Doubravský, 2023). SDG11 is defined as the promotion of sustainable cities and communities via a secure supply chain (Cunha et al., 2024).

The immersive engagement of customers with customer service robots attempts to ensure the continuous usage of such devices to enable customers to resolve their problems and needs efficiently and effectively, thereby reducing stressful situations and, ultimately, paving the way to a healthy, stress-free life (Fang et al., 2023; Heller et al., 2021). Chaudhry et al. (2023) theorised that the continuous engagement of individuals with technologies such as customer service robots can improve consumers’ self-identity, satisfaction levels, and psychological well-being. In the marketing sector, customers who engage continuously with such technologies may attain better health and well-being due to their satisfaction with the services offered (Moisa & Michopoulou, 2022; Ogle & Roberts, 2019).

Several scholars have investigated the impact of immersive environments and systems offered by customer service robots in the service sector on the development of innovation, infrastructure, communication, and development of sustainable smart cities. For example, Pena-Rios et al. (2018) pointed out that industry innovation is mediated by continuous engagement of customers with immersive AI decision support systems and innovation in service provisions. The need to enhance the customer experience and satisfaction at service points has led to improvements in digital transformations, innovation, smart communication, and sustainable cities in the service sector (Buhalis et al., 2019; Caputo et al., 2023; Hassan et al., 2024). Similarly, in the service marketing industry and tourism sector, several scholars maintain that the application of customer service robots enhances the customer experience and their engagement with such technologies which has spurred industrial innovation, smart communication mechanisms, the development of better infrastructure, and the design of sustainable cities for the convenience and satisfaction of customers and the perception of high-quality services (Buhalis et al., 2019; Caputo et al., 2023; Rane et al., 2023). Therefore, the following hypothesis is proposed:

H8: Immersive engagement has a positive impact on SDGs—(a) SDG3 (b), SDG9, and (c), SDG11.

 

Research Methodology

Selection and Justification of Research Methodology

A mixed-methods research design is proposed for this study. This approach combines the strengths of both qualitative and quantitative research methods to provide a comprehensive analysis of the research problem (Teddlie & Tashakkori, 2011). It provides a more complete picture of the impact of immersive engagement on the achievement of SDGs 3, 9, and 11 (McKim, 2017). Using a mixed-methods approach will ensure the reliability and validity of the data by taking and combining insights gathered from multiple data sources (McKim, 2017; Watkins & Gioia, 2015). Specifically, the qualitative data will provide context and depth to the quantitative data in terms of the findings (McKim, 2017). Overall, this research approach will pave the way to gaining a richer understanding of customer perceptions and experiences of interactions with customer service robots at tourist sites in Saudi Arabia (Plano Clark, 2017).

This study will be divided into two phases. Phase I will feature a qualitative study while Phase II will be a quantitative study. Phase I will be implemented to gain useful data about customers’ interactional patterns with customer service robots in terms of how customer service robots to meet the SDGs 3, 9, and 11 and their experiences with the interactional attributes of the robot. Then, the results of Phase I will lead to the development and refinement of the quantitative study in Phase II.

Data Collection Procedures

This section outlines the data collection procedures that will be adopted in the proposed study. First, it outlines the sampling strategy and offers justifications for its use. Next, it outlines the sample sizes of Phases I and II and justifies their use.

The sampling strategy will aim to secure a representative sample with customers of different nationalities in the Saudi Arabia in both Phase I and Phase II to obtain relevant and sufficient data to address the research objectives. Given the focus of the study on customers’ experiences of interacting with customer service robots at Saudi tourist sites and linking them with SDGs 3, 9, and 11, a stratified purposive sampling approach represents a viable option for both Phase I and Phase II.

The stratified purposive sampling technique is chosen due to its ability to allow researchers to select a sample which accurately reflects the diversity within the customers based on their demographic stratum. Customers may vary widely in demographic characteristics such as age, nationality, language, and travel purpose, which can influence their interactions with customer service robots (Teeroovengadum & Nunko, 2018). Penn et al. (2023) held that the division of a population into strata based on the afore-going key demographic characteristics, followed by random sampling within each stratum, can achieve a more balanced and representative sample. This method helps to reduce sampling bias and enhances the validity and generalisability of the study’s findings.

The sample size for Phase I (the qualitative study) will include 25 semi-structured in-depth interviews with customers of different nationalities, sex, and age groups in order to obtain a rich account of their experiences and interactions with customer service robots (Moser & Korstjens, 2018). This approach will enable the researcher to obtain in-depth insights into the perceptions and experiences of the interviewees about their interactions with customer service robots in terms of the robot interaction attributes (Alshenqeeti, 2014). First, the interviewees will be recruited by being asked to participate via advertising at tourist sites. Next, the consent of suitable interviewees will be secured. After that, the interviews will take place face-to-face with the researcher (Englander, 2019). The convenience of interviews in terms of location and timing will be taken into consideration (Willson & Miller, 2014). All interviews will be recorded, transcribed, and interpreted by following the protocols of the University to ensure that the protocols set out by the University’s Ethics Committee on participant anonymity, privacy, and ethical standards are adhered to.

Furthermore, secondary data will also be collected from the newspapers, customers’ reviews on Saudi tourism websites, and Saudi government statistics. These data will help the researcher of this study to make comparisons with the qualitative data and add the extra layer of credibility and reliability of the findings to be reported.

The interview data collected from Phase I will help the researcher to interpret the cognitive patterns, experiences, opinions, and perceptions of the participants towards their interactions with customer service robots at tourist sites in Saudi Arabia. As there is little prior research on this topic in the literature, the qualitative data extracted from Phase I, especially in the pilot study, will shed some light on the robustness of the methodology using the perceptions of participants about the methodological approach, contents of the questions, and ability of the data collection tools to extract the relevant data.

Based on the outcomes of the qualitative data analysis, Phase II will be designed using the quantitative survey with the inclusion of demographic questions to contextualise participants’ responses in terms of their perceptions of the reliability, efficiency, accuracy, safety, trust, intention to use, adaptive learning, and immersive engagement offered by the customer service robots at tourist sites in Saudi Arabia. It will also assess how these responses relate to meeting SDGs 3, 9, and 11.

In Phase II (the quantitative study), purposive sampling will be used to recruit 750 survey respondents using the online SurveyMonkey platform and face-to-face interactions to ensure the statistical power and robustness of the data (Surucu & Maslakci, 2020). All questions will be designed using a five-point Likert Scale (1=Strongly agree to 5=Strongly disagree) (Curry et al., 2009). The online surveys will be administered through SurveyMonkey for the convenience of the participants and the researcher.

 

 

Data Analysis

The qualitative data from Phase I will be analysed using thematic analysis. Themes will be generated through scanning, reading, re-reading, and coding the texts using the method prescribed by Castleberry and Nolen (2018).

The Statistical Package for Social Sciences (SPSS) will be used to analyse the quantitative data from Phase II. It has a user-friendly interface and various statistical functionalities for assessing the relationships and associations between different variables as shown in the conceptual framework developed for this study.

Potential Conclusion

Potential Theoretical Contributions

It is anticipated that the findings from this research will help to integrate the results on customers perceptions of the interactional attributes of the customer service robots in terms of improving the ways that robots can help the Saudi tourism sector meet SDGs 3, 9, and 11. It will also serve as a useful blueprint for building a customer experience framework for use in smart tourist cities in Saudi Arabia. Although there is extant empirical literature covering customer interaction with customer service robots in relation to customer satisfaction, there is a scarcity of research on attempts to establish how the customer experience is mediated by the interactional attributes of customer service robots in terms of the achievement of SDGs 3, 9, and 11. This study will contribute to the existing literature by clearly highlighting the link between the customer experience of customer service robots’ interaction attributes and the achievement of SDGs 3, 9, and 11 by Saudi Arabia.

Moreover, several customer service attributes such as safety, task efficiency, reliability, empathy, accuracy, anthropomorphism, and likeability will form part of the research framework to gather data on the effects of customer service robots on the customer experience and customer satisfaction. Hence, this novel study endeavours to combine knowledge from three domains—sustainable tourism, customer experience, and human–customer service robot interactions. Its overall aim is to build an integrated theoretical framework to illustrate the relationships between the attributes of these devices and the customer experience in relation to how these relationships impact Saudi Arabia in meeting SDGs 3, 9, and 11. The proposed integrated research framework will serve as an important paradigm in terms of guiding other researchers, practitioners, and academics interested in understanding the impact of the customer experience on customer service robots’ interactional attributes and the hospitality and tourism sector’s long-term societal and environmental impacts.

This proposed study seeks to extend the boundary of traditional customer service research on interactions between customers and customer service robots by placing a particular focus on the interactional attributes of these devices and their impact on helping Saudi Arabia to achieve SDGs 3, 9, and 11. Previously, the bulk of research work has been devoted to examining the relationships between the services provided by customer service robots. The novelty of this proposed study, however, is its focus on the perception of customers towards the interactional attributes of customer service robots and how these experiences can be enhanced to better meet SDGs 3, 9, and 11. This research topic is of particular importance to refining and improving the offering of such customer service robots to enhance the customer experience and meet the challenges of implementing sustainable tourism practices.

Although previous research has focused on achieving sustainability goals through the application of various technologies in service settings, such as data-driven analytics, cloud-based data mining, the internet-of-things in healthcare services (Li et al., 2019; Morita et al., 2024; Ngiam & Khor, 2019), Natural Language Processing (NLP) and machine learning tools in educational services (Alqahtani et al., 2023; Shaik et al., 2022), and GPS devices for adaptive traffic management in urban areas (Khattak et al., 2020), this study will reveal the missing links between the applications of technologies—with a focus on customer service robots in the Saudi tourism sector––and their role in improving the well-being of customers and the development of modern infrastructure and smart cities. This research work will open up new avenues for developing sustainable tourism strategies by creating a link between the interactional attributes of customer service robots and sustainable tourism.

A further novelty of this research work is its attempt to link customers’ health and well-being outcomes and AI-mediated sustainable development and to suggest strategies for improving positive social outcomes, operational efficiency, and service precision via the judicious use of robotic technologies in the tourism sector in the context of Saudi Arabia. Another theoretical contribution of this study will be to highlight the critical functions and interactional attributes associated with customer service robots in terms of achieving sustainable development in the tourism industry, thereby suggesting novel theoretical avenues to encourage future research to investigate additional innovative technologies that may be used to assist in meeting the UN SDGs.

Potential Managerial Implications

This proposed study will contribute to managerial practices at tourist sites in Saudi Arabia by highlighting specific interactional customer service attributes of customer service robots that significantly affect the perceptions and experiences of customers about the reliability, trust, precision, safety, and efficiency of the services provided. The outcomes produced by this study can be employed by managers to gain insights into the design and deployment of customer service robots which fulfil customers’ expectations more effectively. This study will suggest ways to optimise customer service robots’ interaction attributes in order to offer tailored and engaging customer experiences at tourist sites. Hence the data from this study are intended to drive improvements in customer satisfaction through enhancing the customer experience in relation to customer service robots.

The key implications of this proposed study for governmental agencies and firms in the Saudi hospitality and tourism industry may span innovations such as tailoring the interactional attributes of customer service robots to improve the customer experience in line with meeting SDGs 3, 9, and 11. By enhancing the degree of empathy, efficiency, and accuracy provided by customer service robots, managers of tourist sites may be able to significantly improve the perceived quality of services provided by ensuring that they resonate with the emotional and social needs of customers. An important aspect of this study is that it pinpoints the most critical interactional attributes of customer service robots which contribute to the achievement of SDGs 3, 9, and 11. Managers of tourist sites using such technologies will be able to effectively employ the outcomes of this study to integrate enhanced sustainability practices into their sustainable tourism strategies.

The findings generated by this study will provide an integrated SDGs-promoting framework for the tourism industry of Saudi Arabia, which will help the Saudi government, stakeholders, and regulators to improve and deploy customer service robots within tourist locations and smart cities to promote tourism and economic growth. It is hoped that the results will inform the Saudi government to allocate resources more effectively and strategically to areas where such devices are likely to have the greatest impact by understanding which interactional attributes most benefit the customer experience while helping to meet the UN SDGs 3, 9, and 11. The findings of the proposed research project will highlight the trust- and safety-related issues associated with the use of customer service robots in the Saudi tourism sector which is critical to facilitate effective interactions between tourists and these devices. This will help the government to develop mitigating strategies to ensure the smooth and effective integration of customer service robots into smart cities.

 

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