Frontline service robots (FLSRs), a term which refers to “system-based autonomous and adaptable interfaces that interact, communicate, and deliver service to an organization’s customers” [1] (p. 909), are the driving forces behind a paradigm shift in the tourism industry [1]. FLSRs are changing the landscape of service encounters in the tourism industry, affecting employees and customers who are critical actors in the service ecosystem [2]. With the introduction of FLSRs, employees faced a change wherein they had to work in the same workplace as robots, and customers experienced automated services provided by robots rather than human services [3]. Therefore, for successful implementation of FLSRs, theoretical grounds considering the viewpoints of both employees and customers are needed [2,3,4]. The existing literature has focused on exploring the mechanism of customer robot adoption [5,6] and customer perception of the robots’ service quality [7,8,9] to theoretically explain the success of the FLSR introduction. Unsurprisingly, understanding customer perspectives on robot adoption in the tourism industry is critical for the development of automated service environments [10,11,12]. The results of previous studies provide us with valuable knowledge about FLSRs from the customer’s perspective; however, these prior studies overlook the relationship between employees and the FLSRs [2].

Recently, some researchers have acknowledged the importance of the employee–FLSR relationship in the changed work environment, but they have focused mainly on job insecurity as they assumed that FLSRs are a threat to employees’ jobs [13,14,15,16]. Recent literature has emphasized the need to view the employee–FLSR relationship as a collaborative relationship rather than a competitive one in an automated workplace [17,18]. As these studies show, the relationship between an employee and FLSRs is complicated. When there is a change in the workplace due to the introduction of innovative technology such as FLSRs, the perspective of employees and organizations on the change must be considered [3,19,20]. However, previous literature explored employee–FLSR relationships from a fragmentary perspective and limits the scope of comprehensive knowledge about employee behavior in workplaces integrated with FLSRs. 

In the tourism work environment, understanding how employees perceive coworkers is crucial for successful organizational management as collaboration between the two is essential [22]. Moreover, since the integration of FLSRs into the work environment causes many changes to organizational management, managers must understand the changes in the workplace and employees’ perceptions of them [20,23]. Therefore, identifying the antecedent factors that form employee–FLSR collaboration relationships may provide important theoretical and practical knowledge from the perspective of organizational management in tourism companies where FLSR adoption is increasing. An employee’s perceived risk to a coworker’s competence level affects the building of their collaboration [24,25], and the employee perceived the unfamiliar collaboration relationship as a risk in the workplace [16]. 

Robots can be defined as “mechanical objects developed to facilitate daily tasks and help people” [3] (p. 2). Robots can be classified into industrial robots or service robots according to their functions and roles [35]. Industrial robots can increase production efficiency by performing repetitive tasks with high precision and high speed and can reduce production costs and potential risks of dangerous and repetitive tasks [36,37]. Service robots evolved from industrial robots [38] and are “robots that specialize in service tasks useful to humans” [39]. Unlike industrial robots, which are rigid and require human control, service robots interact with employees and customers through verbal and non-verbal communication based on human-centered design [38]. Moreover, the adoption of service robots in the tourism industry is being accelerated as the combination of robotics and artificial intelligence (AI) technology enables service tasks by FLSRs without human instructions [2]. FLSR implementation in the tourism industry is compared to the impact of the 18th-century industrial revolution on the manufacturing industry, changing all sectors in the industry [40].

In the tourism industry, the expansion of FLSR implementation cannot be explained by the development of robotics alone, as the adoption of FLSRs incurs financial costs for purchase, installation, and maintenance [5,41]. The adoption of FLSRs provides tourism companies with financial benefits such as labor cost savings [8,42], operational benefits such as working all year round and improving service efficiency [35], and improvements in marketing competitiveness such as positive word of mouth [6,35]. With these benefits, FLSRs are attracting industry attention as the future labor force that replaces frontline employees (FLEs) in the tourism industry [42]. Despite this, the introduction of FLSRs may bring unanticipated results given that it is a change that has never been experienced before [3,4].

As the role of FLSRs increases in service encounters, the concept of automated social presence (ASP), a customer’s perception of believing in the social entity of the robots, has attracted researchers’ attention. Consequently, they have explored social-level operations and enhancement mechanisms in the automated service encounter. Previous studies have demonstrated that a high level of social presence perceived by customers during face-to-face interactions leads to positive performance [43,44,45]. However, even though FLSRs have a higher ASP, customers prefer the FLE service for its emotional connection [46] and compare FLE and FLSR service levels [47]. It is difficult for customers to be satisfied with the unfamiliar service of FLSRs, as they have long experienced the sophisticated and warm service of FLEs [48]. These findings indicate that the traditional paradigm of the service provider and customer relationship works equally in an automated service workplace, regardless of the ASP level of the FSLRs.

McLeay [49] found that customers could reject the introduction of FLSRs from an ethical and social perspective because of unemployment issues that occur when robots completely replace FLE jobs. Accordingly, the authors emphasized that FLEs and FLSRs should collaborate to reduce customer concerns. FLSR implementation in the tourism industry changes the overall service experience for customers [34,50]. Since customers are habituated to FLE services, they perceive the FLSR service process as inconvenient and complex [34]. This creates a psychological barrier to change for customers, which requires FLE–FLSR collaboration in the service workplace to mitigate customer concerns [34]. The findings of the literature presented above show that FLSRs cannot completely replace FLEs, suggesting that FLEs and FLSRs need to collaborate in automated tourism workplaces to deliver the service experience customers expect and to address the various challenges of FLSRs.

It is important to understand the FLEs’ reactions and attitudes when implementing cutting-edge technology that causes psychological challenges for FLEs owing to unpredictable outcomes in the workplace [20,51]. Recently, some researchers realized the necessity of research from the perspective of FLEs, who are the main actors in the service ecosystem, and began to pay attention to the responses of FLEs to FLSRs and the changes in the workplace [3,20,35]. According to the results of these studies, FLEs have an ambivalent attitude toward working with FLSRs. The results of previous literature showed that FLEs have positive or negative reactions from job and technical perspectives to the changes in the workplace brought by the introduction of FLSRs. FLEs are interested in working with FLSRs in anticipation of reducing physical and psychological workload and improving work efficiency [3,17,52]. Furthermore, when FLSRs have high anthropomorphism and work competencies, FLEs respond in a positive manner psychologically to collaborating with the FLSRs [53]. The results of these studies show that FLEs have a positive perception of working with FLSRs if the implementation of the FLSRs can improve their work [17].

However, the literature reports more negative than positive employee attitudes toward FLSRs. FLEs are concerned about the lack of interaction between them and robots, empathy, and communication gaps [3,17,35]. Moreover, if FLSRs’ competencies are inadequate for the task, FLEs’ psychological resistance to collaborating with FLSRs increases [51], and the FLEs’ trust in the FLSRs as coworkers decreases [17]. Previous research emphasized that FLEs’ anxiety about FLSRs’ functional characteristics and fear of their employment relationships contribute to their negative perceptions of working with FLSRs. In contrast to the manufacturing industry, workplaces in the tourism industry have long had the human being at the center of the work process. Therefore, FLEs may fear that implementing FLSRs will place their positions under FLSR control in the workplace [3]. According to the prior literature, FLEs are reluctant to work with FLSRs because they perceive the automation of the workplace as a threat to their employment [13,14,15,16].

Contrary to these FLE fears, the adoption of FLSRs in the tourism industry is the stream of the tourism 4.0 era [54]. Therefore, FLEs need to recognize and accept FLSRs as coworkers with whom they need to work together. The success of workplace changes brought about by technology implementation depends on the receptiveness of employees to new technologies introduced in the workplace [19,51,55]. Accordingly, it is essential to understand the factors that promote or hinder the collaboration between FLEs and FLSRs, which is critical to the success of robot introduction in the workplace [17,51]. This indicates the need to explore FLEs’ acceptance mechanisms for collaboration with FLSRs.

The TAM proposed by Davis [56] is a theoretical model for empirically explaining the acceptance of new technologies [57]. This model focuses on the mechanism of the user’s intention to use technology and antecedent factors based on the assumption that the actual behavior of users who want to use the technology can be explained through their attitude toward the technology [58,59]. Researchers have theoretically and empirically verified TAM, making it the most powerful and effective model for predicting a user’s intention to accept technology [60]. The user’s perceived usefulness and perceived ease of use for technology are critical factors of TAM to explain the user’s intention to accept technology [56]. Davis [56] (p. 320) defined perceived ease of use (PEU) as “the degree to which a person believes that using a particular system would be free of effort.” Perceived usefulness (PU) refers to “the degree to which a person believes that using a particular system would enhance his or her job performance” [56] (p. 320). Applying these definitions to the context of the current study, PEU refers to the extent to which FLEs believe that collaborating with FLSRs requires no effort, and PU can be defined as the extent to which FLEs believe that collaborating with FLSRs will improve their work performance.

TAM is a widely adopted theory that explains the acceptance intent of cutting-edge technology users, such as self-service technology [61,62,63] and tourism 4.0 technologies [55,64,65,66]. TAM has recently been extended to the context of FLSRs, which is receiving attention from tourism researchers and has been verified as a valid theoretical tool for predicting users’ acceptance of robots [67,68]. These studies found that the intention to accept FLSRs is established when customers perceive the positive usability and usefulness of the robots and emphasized that PEU and PU are leading factors for predicting customers’ acceptance of robots. Accordingly, these results showed that the theoretical mechanism of TAM can be applied in the FLSR context.

In addition to TAM, the Unified Theory of Acceptance and Use of Technology (UTAUT/UTAUT2) is another theoretical model used to explain user acceptance of technology [69,70]. In contrast to these two models, TAM has the theoretical flexibility to incorporate external factors into the basic model when a more in-depth explanation of the user’s acceptance is required [65,71]. Furthermore, TAM is an optimal theoretical framework for empirical user behavior prediction studies because it can be generalized across technologies and users [71]. These results show that TAM is an appropriate model for the explanation of user behavior in the early stages of FLSR adoption. Previous studies used TAM to explain employees’ attitudes toward the acceptance of change in organizations implementing technologies, which suggests that TAM is a suitable theoretical model to explain work paradigm changes due to adoption of technology [19,55,72,73,74]. 

Despite the longstanding acceptance of TAM, there is a limit to providing a theoretical basis for improving users’ technology acceptance only with PEU and PU, which are the antecedent factors of the model [75,76,77,78], because external variables may affect the technology acceptance mechanism and interfere with the acceptance process [61]. Therefore, verifying specific drivers that affect users’ acceptance of technology by integrating external variables into the TAM is crucial [57,77,79]. Moon [72] criticized TAM for not explaining the user’s acceptance intention from a work perspective because it focuses only on the technology characteristics perceived by employees and addressed that it should explain the technology acceptance introduced to the workplace through the integration of TAM with external factors related to work.