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Article

Catalyzing Green Work Engagement in Hotel Businesses: Leveraging Artificial Intelligence

by
Hazem Ahmed Khairy
1,
Mohamed Ahmed
2,
Arwa Asiri
3,
Foziah Gazzawe
4,
Mohamed A. Abdel Fatah
5,
Naim Ahmad
6,
Ayman Qahmash
6 and
Mohamed Fathy Agina
7,*
1
Hotel Management Department, Faculty of Tourism and Hotels, University of Sadat City, Sadat City 32897, Egypt
2
Hotel Management Department, Faculty of Tourism and Hotels, Fayoum University, Fayoum 63514, Egypt
3
Faculty of Economics and Administration, Management Information System Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia
4
College of Computing, Umm Al-Qura University, Makkah 24382, Saudi Arabia
5
Human Resources Department, Al-Alson Higher Institute for Tourism, Hotels and Computer, Cairo 11771, Egypt
6
College of Computer Science, King Khalid University, Abha 62521, Saudi Arabia
7
Hotel Management Department, Higher Institute for Specific Studies, Heliopolis, Cairo 11771, Egypt
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(16), 7102; https://doi.org/10.3390/su16167102
Submission received: 9 July 2024 / Revised: 6 August 2024 / Accepted: 8 August 2024 / Published: 19 August 2024 / Corrected: 11 September 2024
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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Abstract

:
This study explores green work engagement in response to the global demand for sustainability in businesses and the shift toward green-oriented agendas. Specifically, this study aims to examine how green work engagement (GWE) is affected by artificial intelligence awareness (AIA) through job stress (JS) as a mediator. It also explores the moderating roles of technological self-efficacy (TSE) in the AIA→JS relationship and trust in leadership (TIL) in the GWE→JS relationship. A PLS-SEM analysis was conducted on 392 valid replies from full-time employees of five-star hotels in Egypt using WarpPLS 7.0. The findings indicated that artificial intelligence awareness (AIA) negatively affects employees’ green work engagement (GWE) and positively affects job stress (JS). In addition, GWE is negatively affected by JS. Moreover, TSE negatively moderates the AIA→JS relationship, while TIL negatively moderates the JS→GWE relationship. The study also found a significant mediating effect of JS on the AIA→GWE relationship. The study enhances research on AIA’s impact on JS and GWE, addressing a gap in existing empirical studies on the relationship between these elements in hotels. Overall, the study of green work engagement has the potential to be a valuable contribution to the growing field of sustainable business practices.

1. Introduction

Green work engagement is essential for an organization’s environmental sustainability. This involvement stems from developing green training programs and incentives, which encourage employees to make commitments to attain green goals [1]. Green HRM policies that incorporate sustainability goals alongside corporate social responsibility inside companies encourage the dedication of environmentally conscious employees [2]. According to Hughes et al. [3], the use of artificial intelligence is crucial for enhancing green work engagement due to the rapid development of machine learning capabilities. In addition, green work engagement is a real struggle within business organizations [4,5]. Green work engagement helps to create a suitable work environment through the clarity of the roles of each worker [2,6,7]. In addition, the intensity of competition in business environment is high, so there must be new strategies for effective green work engagement, so artificial intelligence is an important development in activating green work engagement [8].
The AI concept appeared in 1956, with the beginning of the nineteenth century, technological developments began to appear [9]. Artificial intelligence was defined as an engineering system that gives outputs based on data specified by humans [10]. He et al. [11] reported that due to the importance of artificial intelligence, it can be integrated into various industries. Employee engagement is significantly impacted by artificial intelligence [12]. Huang [13] mentioned that one of the detrimental effects of AI is that companies that adopt artificial intelligence technology will rely on skilled employees. Furthermore, artificial intelligence raises sentiments of job instability, which in turn causes job burnout and elevated stress levels at work [9,10]. Despite this, some research has indicated the positive effects of artificial intelligence such as increasing the request for trained labor and increasing profits to creating new products and jobs [9].
In fact, the advancement of digital technologies like big data, the Internet of Things, and artificial intelligence is crucial to the hospitality sector’s success since it fosters innovation and sustainable growth [14,15,16]. In addition, AI technology is the most widespread innovation in the world due to its effective role in hospitality [17]. Artificial intelligence and robotics have contributed to many changes in service establishment such as hotels, restaurants and tourism [18,19]. However, concerns regarding losing the human aspect of the hospitality business due to the emergence of technologies’ artificial intelligence have been expressed, because the hotel service and product has an intangible part that depends on the human touch of employees [20].
Consequently, AI has developed very quickly in current years in various sectors, which has led to achieving a competitive advantage for many companies and achieving significant economic growth that has had a positive influence on the work market [21]. Brougham and Haar [22] showed the significance of understanding artificial intelligence and how it affects employees’ future. Xu et al. [9] indicated that awareness of AI has a negative influence on the psychological state of employees, like a lack of job security [23], exhaustion and depression. This has an impact on worker productivity and job happiness [24]. Employee behavior is positively impacted by AI knowledge, as seen by an increase in creative activity [9,25]. Also, artificial intelligence plays a critical role in improving employee performance and helping employees focus on creative tasks. On the other hand, AI technology is considered one of the complementary elements to employee skills [26,27]. Awareness of the concept of AI is crucial in hospitality sectors and specifically hotels [28].
As mentioned earlier, we find that the awareness of artificial intelligence positively affects employees in the hospitality sector, and job stress affects the quality of services delivered. In addition, some research has measured the extent to which awareness of artificial intelligence affects employees in terms of performance or productivity. Still, some research has been conducted in relation to whether AI awareness affects green work engagement. To fill the above-mentioned research gap, the current research paper aims to identify the effect of artificial intelligence awareness on green work engagement, examining the mediating role of job stress and moderating roles of technological self-efficacy and trust in leadership. Figure 1 explains the proposed model for this research.

2. Review of Literature and Hypothesis Development

2.1. Artificial Intelligence Awareness, Green Work Engagement and Job Stress

The COVID-19 pandemic has made the hospitality industry explore the use of AI technologies to support high-performance work and improve the level of service provided to customers [19,29,30]. It also helps to create a competitive advantage for organizations; nevertheless, some workers must make a sacrifice in order for it to happen [23,28]. In addition, due to the increasing reliance of hotel establishments on artificial intelligence technology, employees face negative and pessimistic work situations [31]. Moreover, awareness of artificial intelligence leads to job stress and work depression [32].
Additionally, artificial intelligence systems can be classified into two categories: fully digital and digital–physical hybrids, which are commonly referred to as robots [28]. There are various sectors of the hospitality industry that employ both systems [28]. In addition, there are different types of AI applications used in hospitality. First, virtual reality is common in the hospitality sectors; a few examples include virtual booking interfaces, virtual holiday experiences, and virtual hotel tours [28]. Virtual hotel tours provide 3D video tours of the hotel’s amenities and surroundings [32]. Secondly, a chatbot is a software application designed to facilitate written or spoken communication [28]. Thirdly, robots are another form of AI technology that is becoming more prevalent in hotels [28]. These tech-savvy helpers employ Internet of Things (IoT) technology to do basic duties like turning on lights in the bedroom, turning off the TV [28], managing systems that ensure luggage is automatically checked in, and extending a warm welcome to hotel guests [28,33].
As artificial intelligence systems are being developed to carry out difficult tasks, artificial intelligence currently offers good value in the market [32]. The emergence of new applications of artificial intelligence signals a significant advancement in technology [28]. Conventional software is strong, but in order to offer value, it needs to be extensively configured and set up [28]. Because they pick things up rapidly, artificial intelligence systems are adaptable and take less time to complete a task [28].
Lingmont and Alexiou [33] reported that artificial intelligence represents a major threat to employees, and they do not feel job security. Previous studies indicated that job exhaustion and perceiving lower job security due to artificial intelligence technologies lead to a lack of green work engagement and withdrawal from work [19,32,34]. Ersoy and Ehtiyar [15] argued that AI is considered one of the most crucial causes of job insecurity, which negatively affects green work engagement and thus increases the labor turnover rate. Therefore, the hospitality sector must continuously support employees and provide them with skills that help them deal with artificial intelligence technologies [28]. Rao et al. [35] mentioned that artificial intelligence helps with effective green work engagement by listening to the employees to understand their requirements and their needs.
On the other hand, according to Teng et al. [19], the adaptation of AI is an important process to reduce costs and enhance the competitiveness of the hospitality industry. However, artificial intelligence has posed a direct threat to employees; because AI is anticipated to replace over 95% of the jobs in the hospitality sector, hotel staff will experience increased job stress and job security threats as a result [36,37]. Additionally, some researchers have indicated that artificial intelligence helps to improve employee engagement [3,38,39]. Kazmi et al. [26] pointed out that an awareness of AI affects employees’ performance by their level of green work engagement and encourages employees to interact while working [40]. Ersoy and Ehtiyar [15] explained that there is a negative relationship between employees’ awareness of artificial intelligence technologies and their green work engagement. As well, Kazmi et al. [26] highlighted that technical stress resulting from the use of AI and the lack of feeling of job security negatively affects green work engagement and job stress. Therefore, the following hypotheses are offered:
H1: 
Artificial intelligence awareness decreases green work engagement.
H2: 
Artificial intelligence awareness increases job stress.

2.2. Job Stress and Green Work Engagement

According to Lestari et al. [28], job stress refers to a state of tension that causes an imbalance in the physical and psychological balance of employees and affects their emotions, thinking processes, and physical condition. The workload is considered one of the most important causes of job stress, and workplace stress is a typical issue that is found in the service sectors as it affects the performance of employees [41]. Dinh [4] revealed that job stress has a negative effect on the employees, as it leads to making mistakes at work, deteriorating health, and creates turnover intention. In addition, job stress led to a lower level of performance and an increase in the turnover rate [42,43]. Job stress greatly affects individual performance as well as the employee performance inside the organization [44]. In the hospitality industry, several hotels’ employees are suffering from job stress problems such as anxiety, distress, and tension, which affect green work engagement [28].
Work engagement can be impacted by organizational and personal factors. Stress is one of the personal characteristics linked to work involvement [28]. Prior research has demonstrated a negative correlation between stress and involvement at work [28]. Furthermore, stress might influence unanticipated unfavorable outcomes for the company, like intention to quit, absenteeism, and withdrawal from work [28].
Consequently, green work engagement is considered an important strategy not only for doing business but also because the human element is the critical resource in organizations [45]. Green work engagement is an essential element for improving performance and providing high-quality services [4]. The study conducted by Nasrul and Masdupi [46] reported that there is no statistically significant relationship between job stress and green work engagement, and that excessive stress at work reduces the worker’s engagement with his work. Anthony-McMann et al. [47] showed that there is a negative relationship between job stress and green work engagement. Moreover, the study of [4] declared that job stress positively affects green work engagement at work. Therefore, the following hypothesis is offered:
H3: 
Job stress negatively affects green work engagement.

2.3. Mediating Role of Job Stress

Job stress is an internal state that comes from the physical demands or conditions to which employees are exposed, whether environmental or social [45]. Deng et al. [48] found that job stress is a major problem in the workplace that influences many employees. The term stress is used to describe the extent to which a person answers and adapts to events that cause psychological or physical stress, as well as situations that burden him psychologically or physically. To understand the extent to which job stress moderates the association between artificial intelligence and green work engagement, the study of [8,32] indicated that artificial intelligence causes psychological stress on employees, because it requires working long hours, it leads to exhaustion of employee resources and causes job stress for employees, which leads to a lower level of performance and an increase in the turnover rate. Also, the emergence of artificial intelligence poses threats to the workforce, which has led to the replacement of some human jobs [37]. AI can be creating technology misuse, stress, and even counterproductive behaviors that can not only reduce green work engagement and the quality of service provided to customers but also jeopardize organizational performance and reputation [49].
Previous studies have confirmed that although AI is increasing production and efficiency, it may also result in lower work engagement [40]. Employee outcomes that improve organizational effectiveness and result in higher financial gains are referred to as work engagement [40]. The use of AI has implications for market demands and skill requirements [40]. Since advanced AI technologies are decreasing the need for human labor [40], it is necessary to have a thorough awareness of the talents and capacities of organizational members in order to connect these technologies to the needs of the company [40]. This affects work engagement in green initiatives by creating more strain and employees stress [40].
However, artificial intelligence awareness helps employees provide better service to customers [35]. In addition, Agarwal et al. [50] indicated that green work engagement is the psychological process that is described as a satisfying emotional–motivational state of attention for workers. Fu et al. [51] found that job security helps improve organizational commitment and employee performance because they are more involved in the workplace, and it contributes to increased employee integration and green work engagement [52,53]. Job security can enhance the impact of AI on service quality by instilling their confidence that the technology complements their jobs rather than replaces them [37]. Therefore, the hypothesis is offered as below:
H4: 
Job stress negatively mediates the relationship between artificial intelligence awareness and green work engagement.

2.4. Moderating Role of Technological Self-Efficacy

Self-efficacy is defined as an individual’s belief in individual abilities which help him cope well [6,54]. Therefore, self-efficacy is considered a very influential factor in accomplishing various tasks [55]. Also, self-efficacy plays a mediating role in managing employees’ perceptions of job dissatisfaction or job insecurity [41]. Self-efficacy is an individual factor that can influence a person’s response to the stressors they experience [46].
Technology self-efficacy is defined as individuals’ beliefs about their abilities to learn digital data to reach their goals [6]. According to Teng et al. [19], technological self-efficacy affects workers’ behaviors to innovate in service delivery, so self-efficacy may be a way to increase workers’ ability to successfully respond to modern technological developments and artificial intelligence technologies [56]. Rožman et al. [55] point out that employees with high self-efficacy are focused on carrying out various tasks, thus reducing risks and uncertainty resulting from job exhaustion and job stress and overcoming difficulties [57]. Kim and Lee [58] reported that high self-efficacy is an important indicator for reducing negative attitudes resulting from work stress. On the contrary, low self-efficacy leads to a lack of clarity about desired goals and less responsibility in relation to overcoming obstacles, which leads to a higher level of work stress [6]. Shao et al. [41] mentioned that self-efficacy has an impact on job stress and turnover intentions.
On the other hand, technology self-efficacy already influences the business environment [55], and AI technology increases engagement at work and provides more time for learning and skill development [59]. Therefore, managers must provide appropriate training programs for all employees based on the use of AI and retain the most talented employees [60]. In this sense, the application of AI gives employees more freedom, saves time, and reduces stress at work [61].
Regarding the relationship between employees’ technological self-efficacy, awareness of artificial intelligence, and job stress, the study of [41] reported that employees have technological self-efficacy, they have awareness of artificial intelligence and have high green work engagement, which leads to reducing work pressures. Based on the studies mentioned above, we postulated that there is a positive association between technological self-efficacy and artificial intelligence because it helps increase awareness of artificial intelligence technologies. Therefore, the hypothesis is offered as below:
H5: 
Technological self-efficacy negatively moderates the relationship between artificial intelligence awareness and job stress.

2.5. Moderating Role of Trust in Leadership Efficacy

Some studies focused on the relationship between leader trust, job stress, and green work engagement. Baquero [62] indicated that trust in the leader plays an important role in increasing employees’ competence and effective of green work engagement. One element of psychological security and job security, both of which are essential for fostering employee motivation and engagement, is trust in the leader [63,64]. Jia et al. [65] pointed out that when working with a trustworthy leader, employees feel compelled to act in specific ways. Consequently, a sense of commitment and psychological stability boost employees’ engagement in green employment and lessen job stress [66].
On the other hand, the study of [62] indicated that the relationship between leadership trust and green work engagement depends on the fact that true leadership is a decisive indicator of leadership confidence and green work engagement. Jaskeviciute et al. [67] found that mutual trust between the leader and employees has a positive effect on self-esteem, and this principals increases green work engagement. Trust in the leader depends on the transparency and authenticity between the leader and subordinates, which contributes to positive perceptions among them; it also leads to green work engagement [68]. Therefore, trust in leadership and green work engagement are terms that are interconnected with each other, leading to commitment and better performance at work [69], Jia et al. [65] indicated that building a leader’s trust with staff members is critical to having a good influence on green work engagement, and that there is a substantial correlation between employee trust and green work engagement. Paredes et al. [70] showed that a key factor influencing workplace engagement is a leader’s level of trust. Safwat et al. [71] revealed a beneficial relationship between green work engagement and leader trust in Egypt’s hospitality sectors. Employees that have more psychological empowerment engage in green work better when they trust their leader. It provides intrinsic motivation and fosters self-assurance and commitment to one’s work [62].
Additionally, workplace stress is caused by employees’ growing concerns about their working environment and conditions [62]. According to Alzyoud [72], the ability of a leader to prioritize the needs of his subordinates over his own results in a sense of contentment with the workplace, lowers job stress and increases participation in green work [62]. Therefore, the hypothesis is offered as below:
H6: 
Trust in leadership negatively moderates the relationship between job stress and green work engagement.

3. Methodology

3.1. Questionnaire Design and Study Measures

The data were collected through a structured survey as a quantitative research technique. A survey was utilized to evaluate the effect of artificial intelligence awareness on green work engagement in five-star hotels focusing on the mediating role of job stress and the moderating roles of technological self-efficacy and trust in leadership.
The study is based on a 4-item scale suggested by Brougham and Haar [22] to measure artificial intelligence awareness. For example, “I am personally worried about my future in my industry due to AI replacing employees” and “I think AI could replace my job”. In addition, green work engagement was measured by a 5-item scale adapted from Jung et al. [73]. Sample items include: “I find the green work that I do full of meaning and purpose” and “I am enthusiastic about my green work duties”. Moreover, we used a 4-item scale developed by Crank et al. [74] to measure job stress. For example, “There are a lot of aspects of my job that make me upset” and “When I’m at work I often feel tense or uptight”. Furthermore, the study is based on 10-item scale developed by Shu et al. [75] to assess employees’ technological self-efficacy. For instance, “I could complete [my] job using the new software package if there was no one around to tell me what to do as I go” and “I could complete [my] job using the new software package if I had only the software manuals for reference”. Lastly, we used a 7-item scale adapted from Robinson [76] to assess employee trust in leadership. For instance, “I believe my leader has high integrity” and “In general, I believe my leader’s motives and intentions are good”.

3.2. Sample

At the beginning, the research question and target audience were defined. Then, the survey instrument was designed, and a format that suited the information the study wanted to gather was chosen. The survey was pre-tested on a small group to identify any issues. A method of data collection was chosen: “in premises”. After that, the survey was distributed after obtaining permission from the HR departments to visit and disseminate the survey on their premises. This study uses convenience sampling to collect data from five-star hotels workers in Egypt, as they dominate the country’s hospitality business due to their dispersed locations. Only 22 five-star hotels in Egypt’s Greater Cairo Region were chosen for investigation out of the 30 currently operating. Hair et al. [77] recommend a minimum sample size of 300 respondents for this study, as it involves 30 items to be considered. Between November and December 2023, 500 surveys were distributed, but only 392 valid responses were returned, indicating a response rate of 78.4%. The final analysis is conducted with a sample size of 392 employees. The study involved 392 employees, with 78.57% (n = 308) males and 21.43% (n = 84) females. in addition, 45.41% (n = 178) were younger than 30 years old, and 8.16% were more than 50 years old (n = 32). Moreover, 68.37% (n = 268) held bachelor’s degrees and 46.43% (n = 182) had two to five years of experience.

3.3. Common Method Biases

The study used Harman’s single-factor test and principal component analysis to assess common method variance (CMV), finding no dominant factor contributing to over 50% of the overall variation.

3.4. Non-Response Bias

A t-test was used to assess potential non-response bias by comparing early and late responses, with a p-value greater than 0.05 indicating no significant difference between early and late responses, suggesting no significant issue with non-response bias.

3.5. Data Analysis

The study utilized the PLS-SEM approach with WarpPLS statistical software 7.0 to evaluate the measurement and structural model and testing hypotheses.

4. Results

4.1. Measurement Model

The study tested a five-factor model involving artificial intelligence awareness (AIA), green work engagement (GWE), job stress (JS), technological self-efficacy (TSE), and trust in leadership (TiL) using confirmatory factor analysis. The proposed five-factor model, analyzed using Kock’s [78] ten-fit indices, yielded well-fitted data as depicted in Table 1.
Table 2 shows that the research constructs have composite reliability ratings above the minimal acceptable level (CR > 0.70) with satisfactory significant item loadings (loading > 0.50, p < 0.05). The study’s validity was confirmed by AVE values (>0.50) of artificial intelligence awareness (AIA), green work engagement (GWE), job stress (JS), technological self-efficacy (TSE), and trust in leadership (TiL). In addition, the model is free of common method bias (VIFs ≤ 3.3).
Table 3 confirms the study model’s discriminant validity with higher AVE values, less than unity correlations between variables, and the validity of the constructs through HTMT calculation. Table 4 shows the HTMT for validity.

4.2. Results of Testing Hypotheses

Data in Figure 2 reveal that artificial intelligence awareness (AIA) negatively affects employees’ green work engagement (GWE) (β = −0.31, p < 0.01) and positively affects job stress (JS) (β = 0.36, p < 0.01). As AIA increases, GWE decreases and JS increases, indicating that H1 and H2 are supported. In addition, GWE is negatively affected by JS (β = −0.22, p < 0.01). High JS levels tend to decrease GWE, supporting H3.
Moreover, TSE negatively moderates the AIA→JS relationship (β = −0.27, p < 0.01), while TiL negatively moderates the JS→GWE relationship (β = −0.14, p < 0.01). This means that TSE dampens the AIA→JS relationship and Til dampens the JS→GWE relationship, supporting H5 and H6.

4.3. Mediation Analysis

Preacher and Hayes’ (2008) [79] approach was adopted to examine the mediating role of job stress (JS) on the AIA→GWE relationship (see Table 5). The study found a significant indirect effect of JS {β = −0.079 (0.360 × −0.220), SE = 0.031, p < 0.01, t-value = −2.555, LL = −0.140, UL = −0.018} on the AIA→GWE relationship, supporting the hypotheses of mediation (H4).

5. Discussion and Conclusions

This study investigated the effect of artificial intelligence awareness (AIA) on employees’ green work engagement (GWE) in hotel enterprises. It also investigates the mediating role of job stress (JS) in the AIA-GWE relationship, the moderating roles of technological self-efficacy in the AIA-JS relationship, and trust in leadership in the JS-GWE relationship.
The findings reveal that AIA negatively affects GWE (H1-supported) and positively affects JS (H2-supported). In addition, JS mediates the AIA–GWE relationship (H4-supported). AI, including robotic processes and cognitive automations, can be perceived negatively in organizations, especially when employees lack knowledge about its impact on their jobs and careers. Employees who perceive AI as replacing occupations are more inclined to participate in career development [80], aligning with the career self-management perspective that career choice actions become more significant in uncertain environments and hence decreased level of green work engagement. Furthermore, AI’s potential to replace employees’ roles and impact career development may lead to career dissatisfaction and increased turnover intention in hotels [21]. High turnover intentions can deteriorate mental health and lead to job stress. AI’s uncertainty in career progression and the reshaping of management hierarchies may cause employees to feel dissatisfied, depressed, or cynical, potentially increasing job stress in current jobs [24].
The relationship between AI awareness and green work engagement (GWE) is complex with potential negative impacts including job displacement fears, loss of control, and a focus on efficiency over sustainability. This could lead to decreased GWE, a diminished sense of purpose, and a conflict between employees’ perceived green work roles and AI systems’ goals. The impact of AI awareness on green work environment (GWE) depends on organizational culture, training and communication, and specific AI applications. Organizations emphasizing transparency, employee well-being, and green values can see positive effects, while those focusing on monitoring or micromanagement can have a negative effect [81,82].
The findings also reveal that technological self-efficacy moderates the AIA–JS relationship (H5-supported), and trust in leadership moderates the JS-GWE relationship (H6-supported). Employees with higher technological self-efficacy are more capable of adapting to AI-driven challenges, leading to more positive emotional experiences [58]. They are more certain when picking up new skills and finishing duties at work, transforming these stressors into chances for personal development and worth. On the other hand, workers who have lower technical self-efficacy believe they will be less able to adjust to new AI environments and lack confidence in handling challenges [83]. Higher technical self-efficacy views stresses associated with technology hindrances as surmountable difficulties, reducing negative emotions like AI-output job stress. Moreover, trust is crucial for organizational efficiency and green work engagement, as low trust can hinder change and undermine teamwork. Dissatisfied employees with low trust are less productive and provide less customer service. Trust in leaders and organizations leads to increased employee satisfaction, resulting in better service delivery and efficiency, as motivated employees make better decisions and handle risks [84].
An employee’s technological self-efficacy, or confidence in using technology, can moderate the relationship between AI awareness and job stress [85,86]. High technological self-efficacy can lead to less stress associated with AI awareness, as employees feel capable and equipped to handle it. Conversely, low confidence can cause stress due to fear of AI replacement, complexity, or difficulty learning to use it effectively.
Trust in leadership can moderate the relationship between job stress and green work engagement (GWE). Trust in leaders fosters a sense of security and purpose, leading to higher GWE and increased willingness to support green initiatives, especially when leaders are transparent, competent, and committed to environmental sustainability. On the other hand, low trust in leadership can exacerbate job stress and hinder GWE. If employees do not trust their leaders’ motives or competence, they might feel overwhelmed by stressful situations and less motivated to engage in green initiatives. They might question the effectiveness of their efforts or fear being blamed for problems if leadership is not seen as supportive [81]. We can see that this research contributed to the theory by providing the proposed model to measure how green work engagement (GWE) is affected by artificial intelligence awareness (AIA) through job stress (JS) as a mediator. It also explores the moderating roles of technological self-efficacy (TSE) in the AIA→JS relationship and trust in leadership (TIL) in the GWE→JS relationship.

6. Theoretical Implications

The current research addresses the call for more rigorous theory-driven study to increase the comprehension of the artificial intelligence awareness phenomena by overcoming the perceived “immaturity” brought about by a theoretical approach in the area [87]. Additionally, it is a reaction to appeals that stress how important it is to learn about coping mechanisms and assessment to fully grasp this idea [88]. The problem-based coping technique is used in current research to clarify the linkages in a model that is built from appraisal theory. Also, it presents a new moderator trust in leadership in addition to the moderator that is most utilized but least recognized [89], technological self-efficacy. In doing so, it seeks to fill the “missing moderators’ gap” that artificial intelligence awareness study has not yet addressed [90]. In addition it responds to the demand for conditional effect research models grounded on solid and well-conceptualized theories.
On the other hand, by identifying the antecedent variable of artificial intelligence awareness, the current study contributes to the body of literature on the adoption of AI technologies. There has been conflicting evidence in the past on the benefits or drawbacks of AI awareness for workers. To further comprehend the need for workers to implement AI, the present research explores the dualistic character of individuals’ green work engagement.
By include emotions that are both beneficial and detrimental as significant mediating variables, this research unveils the psychological processes by which technology both challenges and hinders people’s awareness of AI. Through an examination of the moderating impacts of technological self-efficacy and trust in leadership, this research advances our knowledge about individual variations in the link between green work engagement and the use of technology. Technological self-efficacy is essential to workplace stress management because it provides a personal resource for managing job stress.
This research adds significantly to the body of literature on AI technology awareness by demonstrating that technological self-efficacy and leadership trust moderate the impact of AI awareness on green work engagement. Our study further advances the notion of green work engagement by investigating the positive correlation between higher technological self-efficacy and technology reliance as well as by creating a measurement tool for this construct. Consequently, it is possible to view reliance on technology as a fundamental aspect of a job and to enhance the original idea of green work engagement to better represent the computer-enabled workplace. Our results further contribute to the literature by showing that workers with higher technological self-efficacy may better adjust to obstacles brought about by AI, which can result in more pleasant psychological outcomes.

7. Practical Implications

The adoption of AI technologies by workers is essential to facilitating an efficient transition of technology within enterprises. According to Li et al. [21], it is recommended that businesses include intelligent processes, provide technical assistance, and grant employees autonomy and green work engagement. Job skills that are in line with personal development improve comprehension and adaptability to shifting AI technology. Furthermore, it is essential to have a strong support system structure [91]. Presenting AI technological stress as a challenge promotes a deliberate adoption of AI and creates a favorable atmosphere for achieving for professional objectives. Businesses ought to be aware of how AI technology affects their workers emotionally. To elicit favorable feelings in workers, companies should, on the one hand, adopt steps to assist staff in viewing AI technology as a challenge.
For instance, by feedback, explanation, and communication, firms may improve workers’ comprehension and green work engagement with AI technology while also strengthening their sense of identity and belonging [40]. Businesses should recognize and commend workers who successfully navigate AI issues, encourage their passion, initiative, and creativity, and support their adoption of AI [92]. However, to reduce anxiety related to AI, companies should assess, address, and mitigate negative emotional reactions in addition to preventing hazards to mental health. For instance, using assistance and mentorship programs, firms may provide psychological counseling and support to workers, assisting them in adjusting to changes in the workplace and safeguarding their mental health [93] .
Employee self-efficacy and motivation in utilizing AI technology are increased by the support and mentorship programs, which also offer workers emotional support, constructive criticism, and direction [94]. Employees’ desire to accept AI technology is improved by these treatments, which help them overcome their unfavorable feelings against the technology. Businesses should recognize the unique qualities of each employee and create and execute training and support plans that are tailored to their specific requirements and ability levels. Through these initiatives, employees’ desire to use AI technology is encouraged, and the business’s overall technological transformation and inventive capacities are strengthened. Personal technical self-efficacy is also increased. It is possible to implement workable solutions throughout the hiring process. Selecting workers with technical self-efficacy and trust in leadership abilities falls under this category. Realistic job previews (RJPs) might be given out throughout the hiring procedure. The idea behind RJPs is to present a precise and unambiguous image of the position., tasks, work environment, competences, etc.) as well as the expectations for the applicant’s performance [95]. The RJP enables the applicant to assess whether the position is too demanding for them. Additionally, letting applicants know that the work involves using computers and that it involves pressure on the job in addition to a difficult workplace may help them prepare. This will provide the applicant the freedom to depart from the hiring process anytime they want. Research indicates that “individuals who proceed with the application process and secure a job are probably going to exhibit greater levels of role clarity, increased confidence in leadership, increased dedication to the company and work, greater happiness at work, and are probably going to remain in their positions longer” [95].

8. Limitations and Future Research

One of the limitations is that respondents were asked to rate their computer self-efficacy using a fictitious situation. Secondly, there can be a response bias present in the self-reported technological anxiety data. Adding some more factors to this study might be an intriguing avenue for future research, given that the complex and multidimensional nature of technological self-efficacy affects job stress in different contexts. Further research might also examine other models that incorporate other significant variables like industry variances and the distinction between end users and IT specialists. Reexamining and validating the technology-dependence measuring scale through the collection of new data in alternative contexts and with alternative techniques represents another potential avenue for further investigation to additionally increase the measuring scale’s generalizability. For instance, considering how commonplace mobile computing is, it would be particularly fascinating to gather fresh data and research the phenomena of artificial intelligence awareness in the context of mobile business. It would be also advisable for further research to consider the quantitative influence of factors on the sustainability of the hotel business.
Consequently, this research relied on the mediating role of job stress and moderating roles of technological self-efficacy and trust in leadership to identify the effect of awareness of artificial intelligence on green work engagement. However, there other relevant variables influencing the relationships between awareness of artificial intelligence on green work engagement, which are not addressed in this study, providing opportunity for future studies to address and generalize these variables. The one-time surveys adopted in the current study lack trend data and cannot definitively prove causation, as they only provide a snapshot at a specific point in time. Therefore, this study suggests tracking a panel of participants over time through repeated surveys to observe changes in opinions or behaviors. The study also recommends a longitudinal study design that could establish trends and identify causal relationships.
Another limitation is that this study is quantitative. Therefore, it would be significant to incorporate qualitative insights to create a more comprehensive picture of the phenomenon and concepts studied. Further studies can move beyond “what” employees think to understand the “why” behind the quantitative results, leading to a richer and more nuanced interpretation of the data. This study was conducted within the Egyptian cultural context. Therefore, the study acknowledges potential biases in Egyptian responses, such as social desirability. To provide a nuanced interpretation of the findings, further culture-specific research is needed to compare the results with other studies in different cultural contexts to reveal cultural variations and strengthen the generalizability of the findings.

Author Contributions

Conceptualization, H.A.K., M.F.A., M.A.A.F., A.A. and M.A.; Data curation, H.A.K., M.F.A., A.A. and A.Q.; Formal analysis, H.A.K., M.F.A., F.G. and M.A.; Investigation, M.F.A., M.A.A.F., N.A., A.A. and A.Q.; Methodology, H.A.K., M.F.A. and M.A.; Resources, M.F.A., M.A. and F.G.; Software, H.A.K., A.A. and N.A.; Supervision, H.A.K., M.F.A., A.A. and M.A.; Validation, H.A.K., A.A., F.G. and A.Q.; Writing—original draft, M.F.A., M.A., A.A., F.G., M.A.A.F. and M.A.; Writing—review and editing, H.A.K., M.F.A., A.A., M.A.A.F., N.A., F.G. and M.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are available upon request from researchers who meet the eligibility criteria. Kindly contact the corresponding author privately through e-mail.

Acknowledgments

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for Supporting this work through Large Research Project under grant number RGP2/179/45.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The research proposed model (source: created by the authors).
Figure 1. The research proposed model (source: created by the authors).
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Figure 2. Final model of the study.
Figure 2. Final model of the study.
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Table 1. Model fit and quality indices.
Table 1. Model fit and quality indices.
AssessmentCriterionResult
Average path coefficient (APC)0.221, p < 0.001p < 0.05Well-fitted
Average R-squared (ARS)0.179, p < 0.001p < 0.05Well-fitted
Average adjusted R-squared (AARS)0.173, p < 0.001p < 0.05Well-fitted
Average block VIF (AVIF)1.222acceptable if <= 5, ideally <= 3.3Well-fitted
Average full collinearity VIF (AFVIF)1.567acceptable if <= 5, ideally <= 3.3Well-fitted
Tenenhaus GoF (GoF)0.357small >= 0.1, medium >= 0.25, large >= 0.36Well-fitted
Simpson’s paradox ratio (SPR)1.000acceptable if >= 0.7, ideally = 1Well-fitted
R-squared contribution ratio (RSCR)1.000acceptable if >= 0.9, ideally = 1Well-fitted
Statistical suppression ratio (SSR)0.800acceptable if >= 0.7Well-fitted
Nonlinear bivariate causality direction ratio (NLBCDR)0.800acceptable if >= 0.7Well-fitted
Source: Created by authors.
Table 2. Item loadings, Cronbach alpha, CR, AVE, and VIFs.
Table 2. Item loadings, Cronbach alpha, CR, AVE, and VIFs.
ConstructIndicatorsItem LoadingCronbach AlphaCRAVEVIFs
Artificial intelligence awareness (AIA)AIA.10.6290.8660.7910.6202.435
AIA.20.822
AIA.30.830
AIA.40.849
Green work engagement (GWE)GWE.10.7570.8940.8500.6291.095
GWE.20.671
GWE.30.836
GWE.40.838
GWE.50.848
Technological self-efficacy (TSE)TSE.10.6680.9120.8920.5132.876
TSE.20.774
TSE.30.767
TSE.40.826
TSE.50.807
TSE.60.764
TSE.70.700
TSE.80.634
TSE.90.596
TSE.100.576
Job stress (JS)JS.10.8380.8910.8360.6711.400
JS.20.810
JS.30.831
JS.40.795
Trust in leadership(TiL)TiL.10.7900.8980.8660.5621.033
TiL.20.806
TiL.30.829
TiL.40.780
TiL.50.742
TiL.60.752
TiL.70.499
Source: Created by authors.
Table 3. Discriminant validity results.
Table 3. Discriminant validity results.
GWEAIATSEJSTiL
Green work engagement (GWE)0.793−0.280−0.218−0.1140.054
Artificial intelligence awareness (AIA)−0.2800.7870.7160.346−0.137
Technological self-efficacy (TSE)−0.2180.7490.7490.523−0.157
Job stress (JS)−0.1140.3460.5230.819−0.059
Trust in leadership (TiL)0.054−0.137−0.157−0.0590.750
Source: Created by authors.
Table 4. HTMT for validity.
Table 4. HTMT for validity.
HTMT ratios (good if <0.90, best if <0.85)GWEAIaTSEJSTiL
Green work engagement (GWE)
Artificial intelligence awareness (AIA)0.338
Technological self-efficacy (TSE)0.2570.395
Job stress (JS)0.1340.4260.633
Trust in leadership (TiL)0.0870.1700.1770.097
p values (one-tailed) for HTMT ratios (good if <0.05)GWEAIaTSEJSTiL
Green work engagement (GWE)
Artificial intelligence awareness (AIA)<0.001
Technological self-efficacy (TSE)<0.001<0.001
Job stress (JS)<0.001<0.001<0.001
Trust in leadership (TiL)<0.001<0.001<0.001<0.001
Source: Created by authors.
Table 5. Mediation analysis’ bootstrapped confidence interval.
Table 5. Mediation analysis’ bootstrapped confidence interval.
Hypo.RelationshipPath aPath bIndirect EffectSEt-ValueBootstrapped Confidence IntervalDecision
95% LL95% UL
H.4AIA→JS→GWE0.360−0.220−0.0790.031−2.555−0.140−0.018Mediation
Source: Created by authors.
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MDPI and ACS Style

Khairy, H.A.; Ahmed, M.; Asiri, A.; Gazzawe, F.; Abdel Fatah, M.A.; Ahmad, N.; Qahmash, A.; Agina, M.F. Catalyzing Green Work Engagement in Hotel Businesses: Leveraging Artificial Intelligence. Sustainability 2024, 16, 7102. https://doi.org/10.3390/su16167102

AMA Style

Khairy HA, Ahmed M, Asiri A, Gazzawe F, Abdel Fatah MA, Ahmad N, Qahmash A, Agina MF. Catalyzing Green Work Engagement in Hotel Businesses: Leveraging Artificial Intelligence. Sustainability. 2024; 16(16):7102. https://doi.org/10.3390/su16167102

Chicago/Turabian Style

Khairy, Hazem Ahmed, Mohamed Ahmed, Arwa Asiri, Foziah Gazzawe, Mohamed A. Abdel Fatah, Naim Ahmad, Ayman Qahmash, and Mohamed Fathy Agina. 2024. "Catalyzing Green Work Engagement in Hotel Businesses: Leveraging Artificial Intelligence" Sustainability 16, no. 16: 7102. https://doi.org/10.3390/su16167102

APA Style

Khairy, H. A., Ahmed, M., Asiri, A., Gazzawe, F., Abdel Fatah, M. A., Ahmad, N., Qahmash, A., & Agina, M. F. (2024). Catalyzing Green Work Engagement in Hotel Businesses: Leveraging Artificial Intelligence. Sustainability, 16(16), 7102. https://doi.org/10.3390/su16167102

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