Linking employees ’
challenge-hindrance appraisals toward AI to service performance:
the in fl uences of job crafting, job insecurity and AI knowledge
Changqing He and Rongrong Teng
College of Economics and Management,
Nanjing University of Aeronautics and Astronautics, Nanjing, China, and
Jun Song
School of Business Administration,
Nanjing University of Finance and Economics, Nanjing, China
Abstract
Purpose–This study aims to explore the associations linking employees’challenge-hindrance appraisals toward artificial intelligence (AI) to service performance while considering the dual mediating roles of job crafting and job insecurity, as well as the moderating role of AI knowledge.
Design/methodology/approach– A survey was administered to a sample of 297 service industry employees. This study examined all the hypotheses with Mplus 8.0.
Findings–This study confirms that challenge appraisal toward AI has an indirect positive influence on service performance via job crafting (motivation process), whereas hindrance appraisal toward AI has an indirect negative influence on service performance via job insecurity (strain process). Meanwhile, AI knowledge, serving as a key personal resource, could strengthen the positive impacts of challenge appraisal toward AI on job crafting and of hindrance appraisal toward AI on job insecurity.
Practical implications– Organizational decision-makers shouldfirst survey employees’ appraisals toward AI and then adopt targeted managerial strategies. From the perspective of service industry employees, employees should adopt proactive coping strategies and enrich their knowledge of AI to meet the challenges brought by this technology.
Originality/value– The primary contribution of this study is that we enrich the literature on AI by exploring the dual mediators (i.e. job crafting and job insecurity) through which AI awareness affects service performance. Moreover, this study advances our understanding of when appraisals toward AI influence job outcomes by identifying the moderating role of AI knowledge.
Keywords Challenge-hindrance appraisals toward AI, Service performance, Job crafting, Job insecurity, Artificial intelligence knowledge
Paper typeResearch paper
Funding:This research was supported by the Project of Humanities and Social Science of Jiangsu Province (Grant No. 20GLC005), Ministry of Education Project of Humanities and Social Science (Grant No. 20YJC880026) and National Natural Science Foundation of China (Grant No. 72001100).
Conflict of interest:The authors declare that they have no conflict of interest.
Contribution:CH: Conceptualization, methodology, writing–original draft and review and editing and supervision. RT: Formal analysis and writing–original draft. JS: Resources, funding acquisition and writing–review and editing.
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Received 13 July 2022 Revised 1 November 2022 6 February 2023 21 April 2023 Accepted 8 May 2023
International Journal of Contemporary Hospitality Management Vol. 36 No. 3, 2024 pp. 975-994
© Emerald Publishing Limited 0959-6119 DOI10.1108/IJCHM-07-2022-0848
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0959-6119.htm
1. Introduction
Artificial intelligence (AI) has been considered the main driver of the Fourth Industrial Revolution. This technology is on course to reshape the layout of many business sectors (Glikson and Woolley, 2020), especially that of the service industry (Kong et al., 2022;
Mariani and Borghio, 2021). For individual employees who are the most direct stakeholders in the AI-enabled workplace (Frey and Osborne, 2017), AI is a mixed blessing. On the one hand, AI can facilitate work and improve the efficiency of employees. On the other hand, AI might cause psychological damage to workers with a range of negative consequences, such as the fear of potential machine substitution for human labor (Liet al., 2019). When adopting AI in the workplace, employees might develop different reactions or perceptions toward AI.
This new phenomenon is calledAI awareness, which refers to employees’perceptions of the likelihood of AI influencing their future job prospects (Liet al., 2019). Given the enormous influence of AI on organizations and employees, a growing number of research is examining the influence of AI awareness on the job outcomes of employees (Liet al., 2019;Yuet al., 2022). For instance,Liet al.(2019)empirically confirmed a positive association linking AI awareness to turnover intention.
Although this line of exploration is fruitful and meaningful, much less is known about the association linking AI awareness to employees’service performance, which refers to their behaviors of serving and helping customers (Liao and Chuang, 2004). First, although the significant influences of AI awareness on some undesirable job outcomes (Brougham and Haar, 2018;Liet al., 2019) have been identified, the association linking AI awareness to employees’service performance is still unclear. This research gap is unfortunate because it limits our knowledge of the AI awareness–service performance relationship in the current era of service intelligence. Specifically, with the intensification of market competition and the change in consumer demand, employees must take the initiative and respond quickly to customers to meet their increasingly personalized and diverse needs (Lianget al., 2022).
Employees’ service performance, which consists of the service nature that is deemed to exceed or meet customers’expectations, is key to satisfying customers and achieving a competitive advantage for organizations (Prenticeet al., 2020). Unlike other industries, the service sector places more emphasis on interacting with customers directly. Thus, the early stage of AI development in the service sector has not always been successful and has often been accompanied by various service failures (Liang et al., 2022). Also, its impact on employees’service performance has not been fully investigated, except in the study byTang et al.(2022), who focused on different types of AI and investigated the relationship linking AI usage to service performance. Thus, exploring the association linking AI awareness to employees’service performance is not only theoretically relevant but also practically useful.
Second, previous research on the consequences of AI awareness has largely concentrated on its negative effects by assuming it is a hindering stressor. To our knowledge, prior to our research, only Ding (2021) and Liang et al. (2022) have begun to explore the positive consequences of AI awareness by proposing the dual nature (i.e. hindering and challenging) of AI awareness. For example, Liang et al. (2022) proved that AI awareness could be considered both a challenging and a hindering stressor that influences service innovative behavior. Although the positive relationship between AI awareness and job outcomes has been proven (Ding, 2021; Liang et al., 2022), few studies have empirically tested the association linking AI awareness to employees’service performance. Given that it can be considered as both a challenging and a hindering stressor, AI awareness may have a mixed impact on employees’service performance. This implies AI awareness might be a double- edged sword that simultaneously enhances and weakens employees’service performance in various ways. Therefore, a critical question for scholars and practitioners remains: How (i.e.
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mediating mechanisms) and when (i.e. boundary condition) does employees’AI awareness influence service performance?
Tofill the abovementioned research gaps, we develop a research model to explore the mediating mechanisms and boundary conditions involved in the AI awareness–service performance relationship drawn upon the job demand–resource (JD–R) model in service settings. Wefirst propose job crafting and job insecurity as dual pathways through which AI awareness influences service performance. Previous literature has proposed the dual nature of AI awareness and has specified the two dimensions, namely, challenge and hindrance appraisals toward AI (Ding, 2021). Drawing on JD–R theory, hindrance appraisal toward AI is considered as a hindering job demands, and responding to it requires employees to expend extra energy and resources (Bakker and Demerouti, 2017). Over time, employees are likely to exhibit psychological strain, such as job insecurity, which further harms their service performance (Darvishmotevali and Ali, 2020). In contrast, challenge appraisals toward AI is seen as challenging job demands, which might play a role in motivating employees (Bakker and Demerouti, 2017). In this scenario, employees are willing to expend extra effort and resources to meet challenges and demonstrate proactive behavior such as job crafting, which has been proven to be a key factor in promoting service performance (Teng, 2019). Therefore, job insecurity and job crafting, representing these two different pathways, might play dual mediating roles (i.e. motivation-strain process) in connecting challenge-hindrance appraisals toward AI to service performance.
Furthermore, we propose that AI knowledge acts as a key moderator that affects the consequences of challenge-hindrance appraisals toward AI. According to JD–R theory, job resources can strengthen the motivational effects of job demands on employees and weaken the negative influence of the strain caused by such demands (Bakker and Demerouti, 2017).
Specifically, job resources might include employees’ knowledge of AI. Therefore, AI knowledge, which is defined as employees’perception of what they know about AI (Chiu et al., 2021), could serve as a personal resource that buffers or boosts the influence of AI awareness on employees. Workers with a high or adequate level of AI knowledge are more likely to have enough resources to cope with challenging and hindering stressors. Thus, we propose that AI knowledge plays a key role in moderating the abovementioned processes of motivation and strain.
In summary, this study has several noteworthy theoretical implications. First, by providing an integrated theoretical framework that explains the association linking AI awareness to employees’ service performance, the study deepens our knowledge of the consequences of challenge-hindrance appraisals toward AI, especially in the service setting.
This study shows that AI awareness can both increase and decrease service performance indirectly, which indicates that AI has both positive and negative effects on employees’ service performance (i.e. double-edged sword effect). Second, the study provides new insight into the underlying mediating mechanisms through which AI awareness affects service performance. By identifying the motivation-strain dual pathway (i.e. job crafting and job insecurity), our studyfills the research gap related to how AI awareness affects service performance. Third, by confirming that AI knowledge is a key personal resource, we take an important step forward in exploring the boundary conditions under which AI alters employee outcomes.
2. Theory and hypotheses development 2.1 Job demand–resource model
The JD–R model has been extensively used as a framework for explaining how work conditions influence employees’ performance (Menguc et al., 2017). According to JD–R
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theory (Demeroutiet al., 2001), all work conditions can be classified into two components:
job resources and job demands.Job resourcesrefer to the resources, such as organizational support.Job demandsrefer to the psychological, social and organizational characteristics of the job and the work environment (e.g. an excessive workload) (Demeroutiet al., 2001).
The JD–R model (Bakker and Demerouti, 2017) argues that job demands instigate two different processes: motivation and strain. When job demands are considered a hindrance, they can strain employees and put them into a state of resource exhaustion. On the positive side, when job demands are considered as a challenge, they might motivate employees to put effort into their work. In addition, JD–R theory maintains that job resources and job demands can jointly affect the motivation-strain process, which in turn determines employees’performance (Bakker and Demerouti, 2017).
Based on the JD–R model (Bakker and Demerouti, 2017), AI awareness is considered as an important job demand. When AI is considered as a challenge, employees are motivated to engage in proactive behaviors, such as job crafting, which results in a high level of performance. When employees consider AI as a hindrance, they feel strain and are prone to negative emotions and mental states, such as job insecurity, which in turn inhibits their performance. That is to say, the influence of employees’ AI awareness on service performance is a dual pathway (i.e. motivation-strain). Furthermore, job resources, such as AI knowledge, can strengthen the motivational effects of job demands on employees and weaken the negative influence of the strain caused by job demands. Thus, drawing on JD–R theory, we investigate two different mediating pathways through which AI awareness affects service performance as well as the potential moderating role of AI knowledge.
2.2 Artificial intelligence awareness as a job demand
Drawing on the work ofDing (2021), AI awareness consists of the dual nature of both hindering and challenging, and it can therefore be divided it into two dimensions: challenge and hindrance appraisals toward AI. Challenge appraisal toward AI means that employees consider AI adoption as an opportunity to improve work efficiency and adapt to a technology-driven workplace. In contrast, a hindrance appraisal toward AI means that employees may feel stressed about job uncertainty and worry about the potential for future unemployment as a result of AI taking over their job.
In this study, we view challenge appraisal toward AI as a challenging job demand and hindrance appraisal toward AI as a hindering demand. Specifically, with the application of AI in service organizations, employees may be concerned about the potential for future unemployment as a result of AI taking over their job, which might lead to uncertainty and insecurity (Brougham and Haar, 2018). As negative emotions and stress accumulate, employees might realize that the likelihood of their job being replaced by AI is increasing. In this case, hindrance appraisal toward AI can be considered as a hindering job demand.
Conversely, when workers realize that organizations are adopting AI technologies, their concerns about unemployment might motivate them to seek proactive strategies to cope with this challenging demand (Lianget al., 2022). Under this condition, employees consider AI adoption as an opportunity to constantly learn and grow. In this context, challenge appraisal toward AI can be considered as a challenging job demand.
2.3 Challenge-hindrance appraisals toward artificial intelligence and job crafting
Job crafting, a form of proactive behavior, denotes the positive actions taken by individuals to redefine and redesign job content, job style and their relationships with others at work (Wrzesniewski and Dutton, 2001). Job crafting is considered a direct reaction to job demands in the workplace. JD–R theory suggests that employees who are motivated by their work are
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more likely to engage in such proactive behaviors (Bakker and Demerouti, 2017). Therefore, when employees face challenging demands, they are motivated to increase their job crafting.
In contrast, when faced with hindering demands, employees need to expend extra energy and resources to meet such job demands, which decreases proactive behaviors. Accordingly, we propose that AI awareness, as a job demand, will significantly influence job crafting.
First, we suggest that challenge appraisal toward AI, as a challenging job demand, promotes job crafting by increasing the motivational state of employees. Drawing on JD–R theory, challenge appraisal toward AI might play a motivational role in employees’behavior (Bakker and Demerouti, 2017). Specifically, employees who appraise AI as a challenge might view this technology as an opportunity to promote personal goals and growth (Bakker and Demerouti, 2017). As a result, employees are more motivated to seek proactive strategies to cope with these challenging demands. Job crafting, which includes the proactive changes employees make in their job, is such a proactive strategy (Meijerinket al., 2020). Previous research has suggested that job crafting is fostered by an increasing motivational state (Zhang and Parker, 2019). In line with this work, when AI is considered as a challenge, employees will be more motivated to demonstrate job crafting to cope with this challenging demand. Therefore, we propose that:
H1a. Employees’ challenge appraisal toward AI is positively associated with job crafting.
We further propose that employees’hindrance appraisal toward AI, serving as a hindering demand, inhibits their job crafting drawing on JD–R theory. This is because meeting this hindering demand consumes employees’ energy and resources, and the process of job crafting requires extra effort and resources from employees (Bakker and Oerlemans, 2019).
As job crafting is about proactively changing one’s work, it has been argued that engaging in this kind of behavior necessitates significant determination and resources (Bakker and Oerlemans, 2019). According to JD–R theory, including a hindrance appraisal of AI, costs employees both energy and resources (Bakker and Demerouti, 2017). Therefore, employees who evaluate AI as a hindrance may might reduce the amount of job crafting they engage in because worrying that AI will encroach on their job role costs them energy and leaves them with less to devote to job crafting. Therefore, we propose that:
H1b. Employees’hindrance appraisal toward AI is negatively related to job crafting.
2.4 Challenge-hindrance appraisals toward artificial intelligence and job insecurity
Job insecurityis defined as employees’overall concern about the continued availability and existence of their job roles (Witte, 1999). It is considered an important negative outcome that is closely connected with hospitality workplaces (Darvishmotevali and Ali, 2020). Research on the antecedents of job insecurity has generally identified environmental (e.g.
technological change) and individual factors as its causes (Shoss, 2017). Based on JD–R theory, we argue that challenge-hindrance appraisals toward AI might, too, influence job insecurity.
Wefirst argue that challenge appraisal toward AI reduces job insecurity. By liberating employees from low-end and repetitive tasks, AI technologies motivate employees who make a challenge appraisal of AI to create moreflexible and autonomous jobs. With more freedom and autonomy, employees are more likely to learn AI-related skills and cooperate with robots. Employees might also be motivated to learn skills that AI cannot replace, such as social skills (Yuet al., 2022). For service employees, who require many skills and a high
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level offlexibility, having such additional skills might mean gaining confidence in their ability to keep their existing job orfind a new one, without fear of being replaced by AI (Ivanovet al., 2020). Therefore, employees who make hindrance appraisals toward AI are more likely to perceive less job insecurity as they have enough capacity and motivation to cope with this challenging demand. Therefore, we propose that:
H2a. Employees’ challenge appraisal toward AI is negatively associated with job insecurity.
Conversely, employees who make hindrance appraisals toward AI are more prone to job insecurity. According to JD–R theory, meeting such a hindering demand consumes employees’ energy and resources (Bakker and Demerouti, 2017). Under this condition, because of the perception that AI might threaten their job and lead to unemployment (Brougham and Haar, 2018;Vatan and Dogan, 2021), employees might perceive more negative emotions, such as emotional exhaustion (Lianget al., 2022). Then, exhausted employees might perceive threats to the continuity and stability of their current employment to a greater degree (Shoss, 2017), which leads to even higher levels of job insecurity. In addition, employees who make hindrance appraisals of AI might interpret their employers’use of AI as proof that they are actively seeking advanced technologies to replace them, which also leads to higher job insecurity.
Similarly, prior studies have reported a positive association between employees’ negative perceptions of AI and job insecurity (Kooet al., 2021). Therefore, we propose that:
H2b. Employees’hindrance appraisal toward AI is positively related to job insecurity.
2.5 Mediating role of job crafting
Among many factors, job crafting is considered a key factor affecting employees’service performance (Hulshofet al., 2020;Teng, 2019). When employees actively change to adapt to the development of AI technologies, they canfind new ways to better respond to customers and improve their services for customers (Teng, 2019). Hence, job crafting will promote employees’service performance.
Wefirst propose that challenge appraisal toward AI might enhance employees’service performance through job crafting. JD–R theory contends that employees will adopt proactive strategies to cope with job demands (Bakker and Demerouti, 2017). Job crafting is one such coping strategy; and through it, job demand influences service performance.
Employees who make challenge appraisals of AI might proactively alter their original service processes and explore new service methods to cope with job changes. Furthermore, once employees enact more job-crafting behaviors, it becomes easier for them to respond quickly to customers’needs. In other words, challenge appraisal toward AI will increase employees’service performance by stimulating job crafting.
Similarly, we propose that hindrance appraisal toward AI will inhibit employees’service performance via job crafting. When employees consider AI as a hindrance (i.e. hindrance appraisals toward AI), they will feel strain and need to expend extra energy and resources to cope with such hindering demand (Bakker and Demerouti, 2017). In turn, this inhibits their service performance. Thus, we argue that employees who appraise AI as a hindrance are less likely to engage in job crafting, which results in lower service performance. Therefore, we propose that:
H3a. The positive relationship between challenge appraisal toward AI and service performance is mediated by job crafting.
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H3b. The negative relationship between hindrance appraisal toward AI and service performance is mediated by job crafting.
2.6 Mediating role of job insecurity
Previous findings have indicated that job insecurity undermines employees’ service performance (Shinet al., 2021). Insecure employees are likely to perceive a threat to the continuity and stability of their current job (Shoss, 2017). In this condition of uncertainty, employees are generally reluctant to devote more resources and energy to improving their services, which leads to lower service performance. Therefore, job insecurity will decrease service performance.
Wefirst propose that challenge appraisal toward AI might influence employees’service performance by decreasing their job insecurity. JD–R theory (Bakker and Demerouti, 2017) indicates that, in addition to the motivational process (i.e. job crafting), the strain process is also a key mechanism linking job demand to performance. Therefore, we propose that job insecurity might serve as such a strain mechanism. As mentioned above, employees who make challenge appraisals of AI might believe that their employability would be enhanced with the help of AI technology, being less concerned about job insecurity. This lower job insecurity, determined by employees’challenge appraisal toward AI, contributes to high levels of service performance (Shinet al., 2021).
Likewise, we propose that hindrance appraisal toward AI will inhibit employees’service performance via job insecurity. JD–R theory proposes that job demands, especially hindering demands, are likely to cause strain, which further decreases performance (Bakker and Demerouti, 2017). Accordingly, when employees consider AI as a hindrance (i.e. hindrance appraisals toward AI), they will feel strain and will be prone to negative emotions and mental states, such as job insecurity, which in turn inhibit their performance. Therefore, we propose that:
H4a. The positive relationship between challenge appraisal toward AI and service performance is mediated by job insecurity.
H4b. The negative relationship between hindrance appraisal toward AI and service performance is mediated by job insecurity.
2.7 Moderating role of artificial intelligence knowledge
Drawing on the JD–R model (Bakker and Demerouti, 2017), personal resources are expected to boost the desirable impact of challenging demands on employees and to buffer the undesirable influence of hindering demands. Specifically, we propose that AI knowledge could serve as a key personal resource buffering or boosting the influence of AI awareness.
As a personal resource, AI knowledge reflects workers’understanding of AI technology and its development stage (Chiuet al., 2021). Hence, there are significant differences in the moderating role of AI knowledge for employees who make a challenge versus a hindrance appraisal toward AI. Specifically, employees with high levels of AI knowledge will focus more on the challenging attribute of AI awareness because a deep understanding of the technology enables employees to fully understand its strengths and weaknesses and how to use it to improve themselves (Chiuet al., 2021). Consequently, employees with high levels of AI knowledge are more likely to amplify the motivational effects of challenge appraisal toward AI on employees. Conversely, employees with low levels of AI knowledge will pay more attention to the hindering attributes of AI because a lack of knowledge about AI makes it difficult for them to understand the positive aspects of AI (Chiuet al., 2021). As a result,
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employees with lower AI knowledge are more likely to focus on the hindering attributes of AI awareness, exacerbating the straining effects of a hindrance appraisal of AI.
Wefirst propose that AI knowledge amplifies the impact of challenge appraisals toward AI on job crafting and job insecurity because high levels of AI knowledge can allow employees to focus more on the positive aspects of AI and actively meet the challenge posed by spread of the technology. Based on the JD–R model (Bakker and Demerouti, 2017), personal resources are expected to boost the desirable impact of challenging demands on employees. For employees who make challenge appraisals toward AI, AI knowledge is a valuable personal resource for coping with AI (Chiu et al., 2021). When faced with job changes due to AI adoption, employees with more AI knowledge can quickly process various pieces of information and invest resources in proactive actions (e.g. job-crafting behaviors) to adapt to this technology. Therefore, AI knowledge is expected to strengthen the positive association linking challenge appraisal toward AI to job crafting.
Similarly, we suggest that AI knowledge might strengthen the negative implications of challenge appraisals toward AI on job insecurity. Having a good knowledge of AI involves cultivating an understanding of the semi-intelligent developmental state and limitations of AI at its current stage (Chiuet al., 2021). Thus, employees with higher AI knowledge are confident in their current stable employment because they are fully aware that AI cannot completely replace human laborers in the short term. In other words, challenge appraisals toward AI combined with higher AI knowledge are more likely to reduce job insecurity.
Therefore, we propose that:
H5a. AI knowledge can strengthen the positive effect of challenge appraisal toward AI on job crafting.
H5b. AI knowledge can strengthen the negative effect of challenge appraisal toward AI on job insecurity.
Regarding hindrance appraisal toward AI, we propose that AI knowledge buffers its effects on job crafting and job insecurity because AI knowledge presents a valuable personal resource for coping with such hindering demands (Chiuet al., 2021). According to JD–R theory (Bakker and Demerouti, 2017), AI knowledge, as a key personal resource, is particularly useful when job demand is high. Employees with high AI knowledge are more likely to regard AI as a challenge and will devote more resources and energy to proactive behavior, such as job crafting. In this case, the negative relationship between hindrance appraisal toward AI and job crafting will be weakened.
Likewise, we argue that employees with high AI knowledge who evaluate AI as a hindrance might suffer less job insecurity. Employees with low AI knowledge might focus more on the hindering attribute of AI awareness and might be more concerned about the potential of AI to replace human beings in particular job roles (Chiu et al., 2021).
Accordingly, employees with lower AI knowledge are less likely to use positive coping strategies to deal with such hindering demand. Under this condition, employees with low AI knowledge are prone to job insecurity. Drawing on JD–R theory, AI knowledge can weaken the undesirable influence of hindering demands on employees because employees with low personal resources are unable to cope with such hindering demands (Bakker and Demerouti, 2017). Literature related to the“buffering effect of personal resources”(Xanthopoulouet al., 2013) has provided similarfindings. Therefore, we propose that:
H6a. AI knowledge can weaken the negative effect of hindrance appraisal toward AI on job crafting.
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H6b. AI knowledge can weaken the positive effect of hindrance appraisal toward AI on job insecurity.
Figure 1depicts the proposed theoretical model.
3. Methodology
3.1 Sample and data collection
In this study, we tested the proposed model in the service context (e.g. hospitality). The criterion for recruiting respondents was experience working with AI. A screening question (i.e.“Do you have experience working with AI [e.g., robots] to serve customers in your current job?”) was applied to select participants from organizations in the service sector.
Respondents who denied having work experience with AI were not included in the study.
Previous studies (Lianget al., 2022) have suggested that the perception of AI by employees at different companies in different regions varies greatly. Thus, carrying out afield study with a few organizations in one region may cause a large research bias (Lianget al., 2022).
To avoid this problem, we adopted an online survey method to recruit participants. Credamo (www.credamo.com/home.html#/) is an online survey platform in China. This platform is considered to provide valid data (Liet al., 2022) and is therefore widely used by scholars in thefield of service management (Lianget al., 2022).
The participants completed a self-report questionnaire online in return for a small monetary remuneration (e.g. a bonus of ¥5). A total of 300 participants agreed to participate in the study.
Three participants who failed to provide complete information (e.g. current job type and location) were excluded. Finally, the answers from the remaining 297 participants were used for data analysis. The detail demographic information of the respondents was shown inTable 1.
3.2 Measures
The measurement scales of six constructs were adopted or adapted from the existing literature. Except for job crafting, which was rated by frequency (1 =never, 5 =always), all the scales were measured using afive-point Likert scale (1 =strongly disagree, 5 =strongly agree). Given that the initial measurements were in English, the standard back-translation procedures recommended by Brislin (1986) were followed to translate the scales from English into Chinese. Furthermore, an online pretest (n = 60) was used to evaluate the questionnaire in terms of wording and design.
Figure 1.
Theoretical model
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Four items ofchallenge appraisals toward AI(Cronbach’s alpha [a] = 0.76) and three items of hindrance appraisals toward AI(a= 0.81) were adapted fromDing (2021). Four items ofjob crafting(a= 0.77) were derived fromLeanaet al.(2009). Five items ofjob insecurity(a= 0.73) were borrowed fromMaunoet al.(2001). Seven items ofservice performance(a= 0.87) were developed based onLiao and Chuang (2004). Five items ofAI knowledge(a= 0.81) were adapted fromChiuet al.(2021). TheAppendixpresents the construct measurements.
Because the data were self-reported by the employees, the empirical results may suffer from common method bias (i.e. CMB). Thus, Harman’s single-factor method was conducted to assess CMB. The results indicated that thefirst single factor accounts for 15.43% of the total explained variance, which is lower than the 40% cutoff value (Podsakoffet al., 2003).
Therefore, CMB was not a major issue for our results.
4. Results
4.1 Descriptive statistics
Table 2 summarizes the means, standard deviations (SD), correlations and reliabilities coefficients of each variable.
4.2 Confirmatory factor analysis
Confirmatory factor analysis was conducted by using LISREL 8.70 to examine the distinctiveness of our measurement model. The results suggested that the hypothesized six- factor model had good goodness-of-fit indices and provided a better indices over the alternative models (x2= 847.33; df = 335;p<0.01; RMSEA = 0.072; CFI = 0.93; IFI = 0.93;
Table 1.
Respondents’ demographic profiles (N= 297)
Characteristic Group Frequency %
Gender Male 133 44.8
Female 164 55.2
Age 18–20 6 2.0
21–30 144 48.5
31–40 116 39.1
41–50 23 7.7
>50 8 2.7
Education Junior high school and below 16 5.4
Senior high school/technical secondary/technical school 40 13.5
Junior college 60 20.2
Bachelor 158 53.2
Master and above 23 7.7
Position Non-management 159 53.5
Management 138 46.5
Tenure Less than a year 20 6.7
1–2 years 54 18.2
2–5 years 110 37.0
5–10 years 87 29.3
Over 10 years 26 8.8
Ownership State-owned enterprise 38 12.8
Private enterprise 205 69.0
Foreign enterprise/joint venture 34 11.4
Others 20 6.7
Source:Authors’own creation
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NNFI = 0.92). The results indicated that the major variables of our research had good discriminant validity.
4.3 Hypotheses testing
Test of the direct effects.We tested the entire model through path analysis using Mplus 8.0.
Figure 2andTable 3show the results of the structural model path analysis. As is shown in Figure 2andTable 3, job crafting was positively predicted by challenge appraisal toward AI (b= 0.63,p<0.001), whereas the influence of hindrance appraisal toward AI on job crafting was not significant (b = 0.10, p>0.05). Therefore, H1a was supported but H1b not.
Meanwhile, the influence of challenge appraisal toward AI on job insecurity was
Table 2.
Descriptive statistics, reliabilities and correlations among study variables
Variables Cronbach’s alpha (a) 1 2 3 4 5 6
1. CA 0.76 (0.77)
2. HA 0.81 0.20** (0.85)
3. JC 0.77 0.44*** 0.08 (0.77)
4. JIS 0.73 0.15* 0.38*** 0.11 (0.70)
5. SP 0.87 0.38*** 0.07 0.50*** 0.18** (0.87)
6. KNO 0.81 0.33*** 0.26*** 0.26*** 0.22*** 0.19** (0.75)
Mean 3.88 2.80 4.01 2.79 4.21 3.30
SD 0.72 1.03 0.64 0.83 0.60 0.87
Notes:N= 297. Values in parentheses on the diagonal are square roots of AVE; ***p<0.001; **p<0.01;
*p<0.05. CA = challenge appraisal toward AI; HA = hindrance appraisal toward AI; JC = job crafting;
JIS = job insecurity; KNO = AI knowledge, similarly hereinafter Source:Authors’own creation
Figure 2.
Path analysis results
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insignificant (b=0.02,p>0.05), but job insecurity was positively influenced by hindrance appraisal toward AI (b= 0.42,p<0.001), which supportedH2bbut notH2a.
Test of the mediating effects.We followed the method recommended byPreacher and Hayes (2008) and then used bias-corrected bootstrapping to test the mediating effects.
Table 4 summarizes the results of mediation analysis. The results revealed that the mediating effect of job crafting in the challenge appraisal toward AI–service performance was significant (indirect effect = 0.151, 95% CI = [0.091, 0.229]), whereas the indirect impact of hindrance appraisal toward AI on performance through job crafting was not significant (indirect effect = 0.001, 95% CI = [0.026, 0.028]). Thus,H3awas supported butH3bnot.
Likewise, the mediating effect of job insecurity in the challenge appraisal toward AI–service performance was not significant (indirect effect = 0.008, 95% CI = [0.002, 0.026]), whereas the indirect impact of hindrance appraisal toward AI on performance through job insecurity was significant (indirect effect =0.028, 95% CI = [0.055,0.011]). Therefore,H4bwas supported but notH4a.
Test of the moderating effects.Figure 2 andTable 3also summarize the moderating effects of AI knowledge. AI knowledge moderated the linkage between challenge appraisal toward AI and job crafting (b= 0.28,p<0.05), providing support forH5a. Unfortunately, AI knowledge did not moderate the linkage between challenge appraisal toward AI and job insecurity (b=0.03,p>0.05), indicating that H5bwas not supported. Meanwhile, AI knowledge failed to moderate the linkage between hindrance appraisal toward AI and job crafting (b= 0.01,p>0.05). Thus,H6awas not supported. Furthermore,H6bstated that the positive effect of hindrance appraisal toward AI on job insecurity will be weakened. The results showed a positively significant interaction between hindrance appraisal toward AI and AI knowledge (b= 0.18,p<0.05), which is contrary toH6b.
Table 3.
Significance testing results of the direct structural model path coefficients
Paths Path coefficients SE Results
H1a: CA!JC 0.63*** 0.10 Supported
H1b: HA!JC 0.10 0.07 Not supported
H2a: CA!JIS 0.02 0.09 Not supported
H2b: HA!JIS 0.42*** 0.07 Supported
H5a: CA*KNO!JC 0.28* 0.11 Supported
H5b: CA*KNO!JIS 0.03 0.10 Not supported
H6a: HA*KNO!JC 0.01 0.08 Not supported
H6b: HA*KNO!JIS 0.18* 0.08 Opposite
Notes:***p<0.001; *p<0.05 Source:Authors’own creation
Table 4.
Results of mediation analysis
Paths Indirect effects LL 95% CI UL 95% CI Results
H3a: CA!JC!SP 0.151 0.091 0.229 Supported
H3b: HA!JC!SP 0.001 0.026 0.028 Not supported H4a: CA!JIS!SP 0.008 0.002 0.026 Not supported H4b: HA!JIS!SP 0.028 0.055 0.011 Supported Notes:CI = confidence interval; LL = lower limit; UL = upper limit
Source:Authors’own creation
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We then conducted simple slopes test to further interpret moderating effects.Figure 3 revealed that the impact of challenge appraisal toward AI on job crafting was significantly positive for both employees with high (bhigh = 0.487, p < 0.001) and low levels of AI knowledge (blow= 0.227,p<0.001). Thus,H5awas further supported.
Figure 4showed that the impact of hindrance appraisal toward AI on job insecurity was also significantly positive for both employees with low (blow= 0.191, p<0.01) and high levels of AI knowledge (bhigh= 0.371,p<0.001). Thus, these results contradictedH6b.
5. Discussion 5.1 Conclusions
This study explores how and when challenge-hindrance appraisals toward AI affect employees’service performance in the service industry. Ourfindings reveal that challenge appraisal toward AI is positively related to service performance via job crafting, whereas hindrance appraisal toward AI is negatively related to service performance through job insecurity. Moreover, our study shows that the positive impacts of challenge appraisal
Figure 3.
Interaction between CA and KNO on JC
Figure 4.
Interaction between HA and KNO on JIS
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toward AI on job crafting and of hindrance appraisal toward AI on job insecurity are both strengthened by AI knowledge.
Contrary to our expectation (H2a), an insignificant path from challenge appraisal toward AI to job insecurity was identified. This might be because the data were collected during the COVID-19 pandemic. As COVID-19 exacerbated employees’job insecurity (Gauret al., 2021;
Khanet al., 2022), we cannot rule out this effect. Thus, challenge appraisals toward AI did not generate significantly low job insecurity, as proposed. Furthermore, an insignificant path from hindrance appraisal toward AI to job crafting was identified (H1b). One possible explanation for this result is that the current stage of AI development and adoption focuses more on tasks than on jobs (Ding, 2021). Accordingly, at this stage, AI adoption might not draw service employees’ full attention to job crafting. Therefore, although employees appraise AI as a hindrance, their likelihood of engaging in job crafting does not significantly decrease. Given these two nonsignificant paths (H1b and H2a), the relevant mediating and moderating hypotheses proposed in this study (H3b,H4a,H5bandH6a) are correspondingly nonsignificant.
Notably, the positive relationship between hindrance appraisal toward AI and job insecurity is strengthened by AI knowledge, contrary to H6b. Uncertainty and pessimism about the future might explain this deviation. Employees with high levels of AI knowledge will weigh the pros and cons of AI from a long-term perspective (Chiuet al., 2021). The service industry, especially the hospitality sector, has been significantly affected by the pandemic in recent years (Kong et al., 2022), and employees in the service sector have generally taken a more pessimistic view of the uncertain future. Indeed, as shown inTable 2, there is a negative correlation between AI knowledge and job insecurity (r= 0.22,p< 0.001). Thus, for employees who appraise AI as a hindrance, it is plausible that the more they learn about AI, the more they feel at risk of unemployment; thus, they perceive themselves as facing higher job insecurity. Therefore, employees with a high level of AI knowledge who evaluate AI as a hindrance might suffer more job insecurity.
5.2 Theoretical implications
Ourfindings have several important theoretical implications. First, by providing an integrated theoretical framework explaining the association linking challenge- hindrance appraisals toward AI to employees’service performance, this study deepens our knowledge of the consequences of AI awareness, especially in the service setting.
Existing research has begun to explore the positive consequences of AI awareness (Liang et al., 2022). However, the association linking AI awareness to employees’ service performance is still unclear. By identifying service performance as a distal outcome of employees’ challenge-hindrance appraisals toward AI, we enrich the literature on the consequence of AI awareness. Unlike existing related studies, which have largely highlighted the dark side of AI (Liet al., 2019), ourfindings indicate that the positive and negative effects of employees’ appraisals toward AI on service performance are concurrent. Thisfinding echoes the suggestion byLianget al.(2022) that employees’ AI awareness might have a mixed effect on service innovative behavior. Our study also provides an empirical answer to the question raised byTang et al.(2022)as to who is affected–whether positively or negatively–by the use of AI.
As shown in Table 2, challenge appraisal toward AI and service performance are significantly positively correlated (r = 0.38, p< 0.001), and the correlation between hindrance appraisal toward AI and service performance is insignificant (r=0.07,p>0.05). It implies that AI awareness may have more positive impacts on employee outcomes, which is inconsistent with the work of Liang et al. (2022).
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Therefore, this study also extends the literature on the consequences of AI awareness (Lianget al., 2022) by linking AI awareness to service performance. In summary, we confirm both theoretically and empirically that AI is a double-edged sword for employees’service performance.
Second, we contribute to the literature by proposing and confirming the dual mediating roles of job crafting and job insecurity. Specifically, our study proves that challenge appraisals toward AI are positively related to job crafting, which leads to improved service performance. This finding is very important because our research is among thefirst to reveal the potential positive mediating mechanisms through which AI awareness affects employees’service performance (i.e. the motivational process). To our knowledge, prior to our study, only Ding (2021) andLiang et al. (2022) focused on the positive mediating mechanisms through which AI awareness affects employee outcomes. Specifically, Ding (2021) confirmed that work engagement mediates the positive impact of employees’ challenge appraisal toward AI on individual competitive productivity.Lianget al.(2022) suggested that employees’intrinsic motivation could play a key mediating role in explaining the AI awareness – service innovative behavior relationship. Following previous works (Ding, 2021;Lianget al., 2022), we identified job crafting as an effective positive mediator linking challenge appraisals toward AI to service performance. Thus, the present study enriches our understanding of how AI positively affects employee outcomes.
Furthermore, we found that job insecurity can act as a mediator (i.e. the strain process) linking hindrance appraisals toward AI to service performance. Thisfinding complements and extends the work ofLingmont and Alexiou (2020), which demonstrated the positive relationship between employees’AI awareness and job insecurity in nonservice settings.
Our results are also consistent with those of several qualitative studies that have provided evidence of the association linking AI awareness to job insecurity (Vatan and Dogan, 2021).
Going beyond this line of inquiry, our results reveal that job insecurity caused by hindrance appraisal toward AI can further reduce employees’ service performance. Therefore, by examining the mediating effects of job insecurity, our study not only extends the existing literature on job insecurity in service settings but also deepens our understanding of the negative mediating mechanisms through which AI awareness affects employee outcomes.
Third, by verifying AI knowledge as a key moderator, our research takes an important step forward in exploring the boundary conditions under which AI affects employee outcomes. Despite the proven positive impact of AI on job outcomes (Ding, 2021), few studies have explored the contextual factors that influence the AI–job outcomes relationship with one important exception byLianget al.(2022), who examined the moderating effects of employees’ future orientation. Going beyond this work, our study empirically tested the moderating roles of AI knowledge in explaining the influences of challenge-hindrance appraisals toward AI by proposing AI knowledge as a key personal resource. Furthermore, by introducing the concept of AI knowledge into the domain of AI awareness, our research also extends the literature on AI knowledge, which has mainly focused on its role in affecting employees’attitudes (Chiuet al., 2021). Therefore, our work not only answers a recent call to explore more boundary conditions (Konget al., 2021) but also enriches our understanding of the AI knowledge by proposing the moderating roles of AI knowledge.
5.3 Practical implications
Practically, our study also provides useful implications for both managers and employees in the service sector. First, the veryfirst step for managers is to invest time in surveying employees’ appraisals toward AI in the pre-adoption phase of the technology. We also recommend that managers guide employees to make challenge appraisals of AI whenever
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possible because appraisals or attitudes, particularly negative ones, are difficult to correct once they are formed (Chanet al., 2017). On their part, service industry employees need to promote their capacities and resources to meet the challenges brought by AI.
Second, for organizations that have incorporated AI technology into their operations, managers should take prompt action to help employees engage in job crafting and mitigate job insecurity. For example, managers should provide employees with sufficient resources support, which will help them develop new skills related to AI technology. From the perspective of service industry employees, employees need to adopt more proactive behaviors to cope with the stresses and challenges of AI.
Third, to achieve the optimal employee performance, managers should provide AI- related training and education only to those employees who make challenge appraisals of AI, rather than involve all employees uniformly. For instance, managers can hold events where AI-related work experience is shared for employees who appraise AI as a challenge. From the perspective of service industry employees, those who appraise AI as a challenge should improve their knowledge of AI by participating in various AI- related activities.
5.4 Limitations and future research
Despite its strength, the present study also has several limitations. First, all the variables were measured by self-reports. While the results suggested that the problem of CMB was not serious in this study, future research can use an other-reporting method or a multisource data method, such as objective data, to reduce CMB. Second, in line with prior studies (Liang et al., 2022), this research adopted a cross-sectional design to test the model. However, this method might not detect a causal relationship among the major variables. Therefore, a longitudinal research design should be used to further verify ourfindings in the future. In addition, an experimental or afield study could be used to reexamine ourfindings. Third, all the respondents were from the service industry, which might restrict the generalizability of our results. Therefore, we encourage other scholars to examine the impact of AI by using samples from other industries.
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Appendix
Challenge appraisal toward AI
The job uncertainty generated from AI will. . . (1) Help me to learn a lot.
(2) Make the experience educational.
(3) Show me i can do something new.
(4) Keep me focused on doing well.
Hindrance appraisal toward AI
The job uncertainty generated from AI will. . . (1) Hinder any achievements I might have.
(2) Restrict my capability.
(3) Prevent me from mastering difficult aspects of the work.
AI knowledge
(1) I know pretty much about AI.
(2) I do not feel very knowledgeable about AI (Reverse coded).
(3) Among my circle of friends, I’m one of the“experts”on AI.
(4) Compared to most other people, I know less about AI (Reverse coded).
(5) When it comes to AI, I really don’t know a lot (Reverse coded).
Job insecurity
(1) I am worried about the possibility of beingfired.
(2) My job is insecure.
(3) My job is likely to change in the future.
(4) My job is not permanent.
(5) The thought of gettingfired really scares me.
Job crafting I will. . .
(1) Introduce new approaches to improve my work.
(2) Change minor work procedures that i think are not productive.
(3) Change the way i do my job to make it easier to myself.
(4) Rearrange equipment and change my working environment.
Service performance I can. . .
(1) Be friendly and helpful to customers.