Abstract
While AI technology is widely integrated into service creation and delivery processes, the empirical evidence about its impact on employees has been mixed. Based on transactional theory of stress, this study explores how service employees appraise and react to AI technology integration utilizing data collected from employees of service companies that integrated AI technologies into their operations. Findings suggest that AI technology integration is simultaneously appraised by employees as both a positive challenge, leading to employees’ thriving at work and enhanced proactive service behaviors, and as a hindrance, leading to job insecurity perceptions and decreased proactive service behaviors. Furthermore, this study investigates employees’ IT mindfulness as a boundary condition that impacts the effects of AI technology integration on employee appraisals and finds that IT mindfulness can attenuate the relationship between AI technology integration and employees’ hindrance appraisals. Findings provide insights on how service companies can effectively integrate AI technologies into their operations to enhance employees’ proactive service behaviors.
Introduction
Rapid advances in artificial intelligence (AI) technology have been bringing significant changes to all facets of society, including businesses (Liang et al., 2022). Many businesses have been integrating AI technologies into their service delivery processes to improve efficiency while saving costs, and ultimately aiming to improve overall performance (Davenport and Ronanki, 2018). Integration of AI robots, virtual AI assistants, autonomous vehicles and other sophisticated AI algorithms into everyday operations can be observed in a number of service industries (Borges et al., 2021; Kang et al., 2023). AI technologies and devices are also increasingly used in building workflows and other processes due to their learning and autonomous decision-making capabilities (Brougham and Haar, 2018). Since these AI technologies and devices can handle tasks on their own in an orderly manner, workflows can be guided and managed by these intelligent technologies and machines with minimal employee involvement (Agrawal et al., 2017; Tang et al., 2022). Therefore, while the integration of AI technologies into operations can provide great benefits to service organizations, it can also have significant impact on how work and occupations are defined, which can affect the future career development of employees and their work-related outcomes (Liang et al., 2022).
As service companies are likely to continue integrating AI technologies into their everyday operations, it is necessary to investigate the effects of increased uncertainty brought by this continuing integration on human employees’ workplace attitudes and behaviors (Kang et al., 2023; Tang et al., 2022). A small number of studies have reported that employees generally view the integration of digital technologies into service delivery processes positively since that integration can improve their productivity while eliminating some of the repeated boring tasks (Christ-Brendemühl, 2022), leading to increased innovative workplace behaviors (Ding, 2022). However, other studies have reported negative consequences of AI technology integration into service delivery on employees’ attitudes and behaviors, such as increasing job burnout (Kong et al., 2021) and turnover intention rates (Li et al., 2019), and decreasing upward job mobility opportunities (Zhang and Jin, 2023). Furthermore, prior research either focused on the negative effects of AI technology integration on employees or explored the positive effects of AI technology integration on employees. However, the AI technology integration into everyday operations is a double-edged sword that can be a motivational force to bring positive work-related outcomes, as well as a taxing experience that consumes internal resources, resulting in negative consequences for employees.
These contradictory findings reported in previous studies and the “double-edged sword” effect of AI technology integration on employees’ workplace attitudes and behaviors clearly indicate a need for further research to thoroughly understand how service employees view this integration (Im and Kim, 2022) and its effects on employee behaviors. A thorough understanding of the effects of AI technology integration on employees can provide significant insight to service companies in developing and implementing policies and practices that can lower the negative impacts of this integration on human employees’ workplace attitudes and behaviors while increasing the efficiency and productivity.
As AI technology continues to evolve and customer needs and expectations change overtime, survival of service providers in a fiercely competitive service landscape heavily depends on service providers’ ability to address constantly changing customer needs and wants and provide better quality services than competitors (Yan et al., 2023). Delivery of services that are viewed as better than competitors largely depend on whether service employees proactively exhibit service behaviors that exceed established service norms and standardized service procedures (Ye et al., 2019). While proactive service behaviors of employees are crucial for success of service organizations (Raub and Liao, 2012; Chen et al., 2017), it is not clear how the integration of AI technologies and devices into service delivery process can influence employees proactive service behaviors. Since increasing AI technology integration into service creation and delivery process and resulting human-AI technology interaction can significantly alter how services are created, delivered, and consumed, it is necessary to explore the effects of these integrations and interactions on employees’ proactive service behaviors.
Thus, this study aims to provide an in-depth understanding of how AI technology integration can affect employees’ attitudes, which in turn affects their behaviors, more specifically their proactive service behaviors. Guided by the transactional theory of stress (Lazarus and Folkman, 1984), this study proposes that employees’ assessment of the effects of AI technology integration into service delivery process on their jobs will play critical roles in determining their proactive service behaviors. Specifically, the integration of AI technologies for improving service efficiency, that can also stimulate personal learning and growth (He et al., 2023) will be viewed as a positive challenge, and thus, improve employees’ proactive service behaviors. Since employees’ appraisals of and reactions to AI technology integration into operations are likely to be influenced by how they utilize AI technologies in performing their work-related tasks, the negative features of this integration will be assessed as a hindrance, which can result in burnout and work stress (Brougham and Haar, 2018), and thus reduce employees’ proactive service behaviors. This study also proposes that IT mindfulness as a boundary condition since it can enhance the positive challenge effects of AI technology integration while reducing its hindrance effects.
Findings of this study will make some contributions to the literature. First, this study responds to calls from organizational behavior scholars for a thorough investigation of the effects of AI technology integration into operations and employee-robot interactions (von Krogh, 2018) on employees’ attitudes and behaviors. Findings will extend the literature by focusing on the role of employees’ appraisal process and employees’ proactive service behaviors. Even though most previous research on stressors of positive challenge and hindrance has identified specific job needs as stressors of positive challenge or hindrance and reported that individuals appraise the stressor accordingly (He et al., 2023), other studies suggest that stressors can be simultaneously viewed as both positive challenges and obstacles (Lazarus and Folkman, 1984) and, thus play a mediating role (Kraimer et al., 2022). This study proposes that AI technology integration can be viewed as both a positive challenge and an obstacle by employees, thus having competitive impacts on employees’ work outcomes. This study also considers challenge and hindrance appraisals as mediators between AI technology integration and important employee work outcomes, contributing to the transactional theory of stress. Findings will also contribute to the literature that has considered how IT mindfulness affects employee behaviors (Chen et al., 2022). This study highlights that IT mindfulness is a key contingency for service employees’ varying appraisals of and response to the integration of AI technologies in service operations. Finally, findings will provide practical insights as this study highlights that the integration of AI technologies can affect employees’ workplace attitudes and behaviors as well as the human resource practices in a company.
The transactional theory of stress
The transactional theory of stress that was developed by Lazarus and Folkman (1984), includes three important components, namely stressor, the appraisal process of stressor, and the outcome an individual experiences. The transactional theory of stress argues that when people experience different job situations, they appraise the impact of those situations on themselves (Lazarus and Folkman, 1984) through evaluating the stressors based on their importance to their well-being. People assess the
Employees’ challenge appraisals of AI technology integration, and its effects on thriving at work and proactive service behavior
Challenge appraisal refers to “an individual’s subjective interpretation that the demands have a potential for personal gain, growth, development, and well-being” (LePine et al., 2016:1039). AI technology integration can have a significant impact on work-related outcomes for employees (Qiu et al., 2022). AI technology can streamline the operational processes of service organizations such as hotels and restaurants (McCartney and McCartney, 2020), and change organizational resource allocation and
Sample and data collection
Since this study aims to test the effect of AI technology integration into the creation and delivery of services on employee’s proactive service behaviors, data were collected from employees of service companies such as hotels, retail stores, restaurants, airlines, and banks that integrated AI technology and devices into their operations. Participants were recruited from Credamo, an online survey platform similar to Amazon Mechanical Turk. A screening question was used to identify participants
Results
A total of 346 participants completed the survey questionnaire. After excluding 32 individuals who failed the attention checks, the responses from 314 participants were used for data analysis. Table 1 provides the socio-demographic profile of the respondents.
Discussion
While AI technology has been widely integrated into the creation and delivery of services in many service organizations, previous studies reported mixed findings on the impact of AI technology integration into service operations on employees’ workplace attitudes and behaviors (Qiu et al., 2022; Kong et al., 2021). Based on the premises of the transactional theory of stress, this paper delineates two mechanisms that could explain how employees appraise and react to AI technology integration in
CRediT authorship contribution statement
Yingying Huang: Conceptualization, Data curation, Investigation, Methodology, Writing – original draft. Dogan Gursoy: Supervision, Writing – review & editing.
Declaration of competing interest
None.
Yingying Huang, Research interests: Consumer behavior in tourism and hospitality, hospitality service experience and destination management and marketing
References
The impact of high-commitment HR practices on hotel employees’ proactive customer service performance
Emotional intelligence and creativity: the mediating role of generosity and vigor
The mediating role of organizational iden-tification in the relationship between qualitative job insecurity, OCB and job performance
Examining the spillover effects of problems at home on proactive customer service performance in the hospitality industry: the overlooked side of the work-family interface
