Leaders’ STARA Competencies and Green Innovation: The Mediating Roles of Challenge and Hindrance Appraisals

Abstract

The hospitality sector is undergoing a rapid digital change due to smart technology and artificial intelligence. This presents both possibilities and problems for the development of sustainable innovation. Yet, little is known about how leaders’ technological competencies affect employees’ capacity to engage in environmentally responsible innovation. This study addresses this gap by examining how leaders’ competencies in smart technology, artificial intelligence, robotics, and algorithms (STARA) shape employees’ green innovative behavior in hotels. Anchored in person–job fit theory and cognitive appraisal theory, we propose that when employees perceive a strong alignment between their skills and the technological demands introduced by STARA, they are more likely to appraise such technologies as opportunities (challenge appraisals) rather than threats (hindrance appraisals). These appraisals, in turn, mediate the link between leadership and green innovation. Convenience sampling was used to gather data from staff members at five-star, ecologically certified hotels in Sharm El-Sheikh, Egypt. According to structural equation modeling using SmartPLS, employees’ green innovation behaviors are improved by leaders’ STARA abilities. Crucially, staff members who viewed STARA technologies as challenges (i.e., chances for learning and development) converted leadership skills into more robust green innovation results. Conversely, employees who perceived these technologies as obstacles, such as burdens or threats, diminished this beneficial effect and decreased their desire to participate in green innovation. These findings highlight that the way employees cognitively evaluate technological change determines whether leadership efforts foster or obstruct sustainable innovation in hotels.

1. Introduction

The global hospitality sector is navigating an era of rapid change under intensifying sustainability pressures (Khatter, 2023). As a result of increased emissions, resource depletion, and climate change, governments throughout the world are closely monitoring the sector and have tightened laws, established carbon-neutral goals, and enforced more stringent reporting standards. For hotels, compliance with sustainability standards has shifted from a voluntary initiative to a mandatory condition of survival (Dai et al., 2025). Moreover, stakeholders increasingly demand greater corporate responsibility and environmental stewardship to meet evolving sustainability expectations while maintaining competitiveness (Shi & Tsai, 2022). At the same time, consumer preferences are evolving, as travelers, particularly Millennials and Gen Z, actively seek environmentally responsible accommodations (Minazzi & Grechi, 2025). The increasing importance of sustainability indices, eco-certifications, and green ratings has made environmental performance a crucial factor in determining a brand’s reputation and competitive edge (Elshaer et al., 2025a). As a result, hotels are being compelled to include genuine green practices into their long-term plans (Alsheref et al., 2024; Mahar et al., 2025). Within this context, green innovation has emerged as a strategic approach for embedding sustainability into core operations (Zhong, 2021).

To effectively realize the goals of green innovation, many hotels are increasingly turning to STARA technologies comprising smart technologies, artificial intelligence, robotics, and algorithms as powerful tools for enhancing environmental performance through automation, data-driven decision-making, and resource optimization (Hossain et al., 2025). These tools are revolutionizing conventional hotel operations by making automation, customization, and data-driven decision-making possible. Chatbots for customer care, dynamic pricing, and real-time demand forecasting are all made possible by artificial intelligence (Bakir et al., 2025). In front-of-house and back-of-house positions, robotics is also becoming more popular. Examples include automated cleaning systems, delivery bots, and robotic concierges. Predictive analytics, labor scheduling optimization, and operational efficiency are all supported by algorithms (Lukanova & Ilieva, 2019). STARA technologies act as external stimuli that employees cognitively evaluate, and their appraisal either as a challenge or a hindrance directly influences their willingness to engage in innovation-oriented behaviors, including green innovation (Kang et al., 2023).

In the context of green innovation, staff response to the demands of STARA technologies in hotel settings is significantly influenced by person–job fit. Employees are more inclined to use these technologies and actively support sustainability initiatives if they believe that their digital skills and the technology demands of their jobs are aligned (Park & Hai, 2024). On the other hand, a mismatch, such as a lack of digital literacy, can cause unease, opposition, and a decrease in participation in green projects (Stentoft et al., 2021). According to the cognitive appraisal theory, this sense of fit influences how workers cognitively assess technological change, either as a challenge that presents opportunities for development and environmental impact or as a barrier linked to job instability or elevated stress (Ding, 2022; Teng et al., 2025). In consequence, these evaluations have a big impact on their desire to encourage STARA-driven innovation. Crucially, by offering focused assistance, training, and task realignment, leaders with high STARA abilities may significantly contribute to resolving person–job mismatch, enhancing workers’ perceptions of fit, and promoting challenge-based evaluations (Ghosh & Kabra, 2025). By doing this, these leaders support the alignment of worker competencies with environmental and technology requirements, resulting in more efficient and long-lasting innovative results in the hospitality industry.

According to Ogbeibu et al. (2021), leaders who possess strong STARA competency are more likely to foster an innovative and creative atmosphere inside their companies. Employees with this culture are more likely to come up with sustainable concepts and solutions that improve environmental performance and give the company a competitive edge (Shahzad et al., 2024). Thinking outside the box, investigating other angles, and developing creative solutions to sustainability problems are all components of green creativity (Begum et al., 2023). Additionally, by actively promoting and assisting green innovation, leaders with high STARA competency may help their firms generate sustainable concepts and behaviors. As a result, the company is better equipped to get a competitive edge through creative goods, sustainable initiatives, and outstanding customer service (Ogbeibu et al., 2021).

However, despite the growing adoption of smart technologies and automation in the hospitality sector, particularly in hotels e.g., AI-powered service robots, energy-saving systems, and smart room management (Buhalis et al., 2024; Khan & Khan, 2025; Sujood et al., 2024), there is limited research on how hotel leaders’ STARA competencies shape green innovation outcomes. Prior research treats digital transformation and green innovation as distinct phenomena (Feng et al., 2022; Yang & Jiang, 2025). Additionally, existing studies on green innovation in hotels often emphasize operational or policy-level factors (e.g., energy-saving initiatives, green certifications) (Kuo et al., 2022; Singh et al., 2024) but overlook the cognitive and psychological dynamics that occur among employees when exposed to technological change driven by their leaders. Finally, prior studies examine leadership influence on green innovation in broad terms (e.g., transformational or ethical leadership) (Hameed et al., 2023; H. T. Pham et al., 2023) without addressing how specific leader capabilities in technology influence the cognitive appraisals of employees. Specifically, most studies on green innovation focus on either top-down leadership strategies or bottom-up employee behavior (Fu et al., 2024; Zhao et al., 2025), without fully exploring how leaders shape the way employees cognitively appraise technology. Moreover, while digital leadership is often associated with innovation outcomes (Erhan et al., 2022; Zia et al., 2024), its indirect influence through employees’ psychological responses to STARA has been largely overlooked, particularly in service sectors like hospitality, where employee buy-in is essential for implementing technology-driven change.

Overall, this study aims to examine the direct effect of leaders’ STARA competencies on subordinates’ green innovation, hindrance, and challenge effects, in addition to the indirect effect via hindrance and challenge effects.

In line with these goals, the research answers the following queries:

• RQ1: What is the impact of leaders’ STARA abilities on hotel staff members’ green, creative behavior?
• RQ2: How does the link between leaders’ STARA abilities and green innovation become mediated by workers’ challenge appraisals?
• RQ3: How do employees’ hindrance appraisals mediate the effect of leaders’ STARA abilities on green innovation?

This study is expected to add theoretically. This research is anticipated to contribute to the person–job fit theory by proving that workers’ perceived alignment with STARA-related job requirements has a substantial impact on their involvement in green innovation. Additionally, it advances the cognitive appraisal theory by demonstrating how this fit influences whether workers view the adoption of technology as a challenge or a barrier. The study also presents leaders’ STARA abilities as a new antecedent that affects assessment results and person–job fit, providing a comprehensive framework that links sustainable innovation, psychological reaction, and leadership in the hospitality industry.

From a practical standpoint, the results are expected to highlight the necessity for hotel companies to make investments in enhancing the digital and innovation-focused skills of their management. Green innovation can be accelerated, employee resistance can be decreased, and engagement can be increased via training programs that improve leaders’ comprehension of STARA and their capacity to steer digital-sustainability shifts. Additionally, hotels may improve staff flexibility and match technology development with environmental objectives by fostering a challenge-oriented workplace.

2. Underpinned Theories

2.1. Person–Job Fit

The person–job fit theory emphasizes how crucial it is to match work requirements with employee skills in order to get the best results. The emergence of STARA technologies has changed the nature of work in the hotel industry, necessitating the acquisition of new digital skills (Ahmad & Bilal, 2025). Strong fit workers are more inclined to support green innovation, whereas misfit workers, such as those with inadequate digital skills, may be less engaged. By offering training, changing positions, and creating a positive environment, leaders with strong STARA abilities significantly contribute to the decrease in misfits (Bakir et al., 2025). These leaders encourage enthusiasm and creativity by assisting staff members in better meeting the needs of technology. Employee dedication to green objectives is further strengthened by their capacity to explain the environmental benefits of STARA instruments (Yang & Jiang, 2025). Therefore, in the hospitality industry, leaders serve as both facilitators of person–job fit and catalysts for sustainable innovation.

2.2. Cognitive Appraisal Theory

According to cognitive appraisal theory, technology can function as a stressor depending on how it is cognitively evaluated by individuals. Employees may perceive new technologies either as challenges, representing opportunities for development, mastery, and success, or as hindrances, viewed as obstacles that threaten resources and well-being (Hur & Shin, 2024). During the primary appraisal, individuals assess whether the introduction of technology constitutes a challenge (e.g., skill enhancement) or a threat (e.g., potential job loss). The secondary appraisal involves evaluating available coping resources such as training, managerial support, and digital competencies (Aboutaleb et al., 2025). When these resources are deemed sufficient, technology is appraised positively; otherwise, it may provoke resistance and stress (Spătaru et al., 2024). Consequently, leaders’ competencies in STARA (smart technology, ai, robotics, and algorithms) play a critical role in shaping these appraisals by influencing how technology is introduced, supported, and framed within organizational contexts (Yang & Jiang, 2025).

In the hospitality industry, employee responses to STARA technologies can be comprehensively understood through the integration of person–job fit (P-J Fit) theory and cognitive appraisal theory. These frameworks offer a complementary perspective (W. Wang et al., 2020). Based on person–job fit theory and cognitive appraisal theory, we contend that a key factor influencing how people react to technological change is the alignment of their STARA-related abilities with job needs (Teng et al., 2025). Person–job fit clarifies whether employees’ knowledge, skills, and abilities match the digital and analytical requirements imposed by STARA (Ding, 2021). This perceived alignment directly informs the appraisal process outlined by cognitive appraisal theory. Strong fit makes workers feel well-resourced, which encourages challenge evaluations and helps them view STARA as a chance for innovation, progress, and mastery. Conversely, when fit is weak, employees experience a resource deficit, which drives hindrance appraisals, perceiving STARA as stressful, threatening, or obstructive (Ahmed et al., 2025). Thus, P-J fit provides the structural foundation of alignment, while cognitive appraisal theory explains the evaluative mechanism, thereby providing a logical explanation of how workers react to STARA abilities on a cognitive and affective level.

The strength of integrating these theories lies in showing that alignment is not only a static condition (fit versus misfit) but also a dynamic trigger of appraisal processes. P-J fit establishes the structural basis (whether employees have the resources) (Z. Zhang & Yan, 2024), whereas cognitive appraisal theory describes how this basis influences the cognitive–emotional assessments that lead to behavioral results (Yeo & Ong, 2024). When combined, they offer a conceptually sound justification for why workers with strong STARA capabilities flourish in the face of technological pressures, whereas workers with inferior competences could become stressed or disengaged (Maqsood et al., 2024).

3. The Hypotheses Development

3.1. Leader STARA Competence (LSC) and Green Innovative Behavior (GIB)

According to Zhou et al. (2011), person–job fit reflects the alignment of individual skills, knowledge, and abilities with job requirements. When employees’ values match their job descriptions, they exhibit greater citizenship behavior (Farzaneh et al., 2014; Kristof-Brown et al., 2005). Similarly, Huang and Gursoy (2024) argue that fit between employees’ competencies and work demands fosters innovative behavior, which Janssen (2000) and Ramamoorthy et al. (2005) classify as a voluntary activity beyond formal tasks. Moreover, alignment between digital skills and technological requirements enhances technology adoption and sustainability support (Park & Hai, 2024), while misfit leads to resistance and disengagement (Stentoft et al., 2021). Person–organization fit, defined as the congruence between employee values and organizational culture (Mackey et al., 2017), enhances psychological ownership, lowers turnover, and stimulates creativity (Ucar et al., 2021). Leaders’ STARA competence may strengthen this fit, increasing satisfaction, loyalty, and performance (Puspitasari & Kusmaningtyas, 2025).

As service firms adopt STARA (Smart Technologies, AI, Robotics, Algorithms) to enhance productivity (Davenport & Ronanki, 2018; Sarc et al., 2019), their affordability and efficiency (Brougham & Haar, 2018) coexist with risks of job loss, burnout, and turnover (Adeniji & Igarashi, 2022; Başer et al., 2025; Kong et al., 2021; Mahlasela & Chinyamurindi, 2020; Zhu et al., 2023). Employees unable to adapt may quit due to job insecurity (Ivanov & Webster, 2020; Parvez et al., 2022), although human interaction remains central in tourism (Parvez et al., 2022). While some employees associate STARA with dismissal risk (Khaliq et al., 2022), others view it as performance-enhancing when guided by STARA-competent leaders (Oosthuizen, 2019; C. Smith, 2019). Developing leaders’ STARA competence reduces task demands, accelerates green initiatives, and fosters environmental sustainability (Ogbeibu et al., 2020, 2021). By applying their expertise, leaders help teams adopt new technologies, manage complex tasks (Ding, 2021), and pursue cleaner manufacturing (Ogbeibu et al., 2022). Consequently, LSC mitigates turnover intentions (Brougham & Haar, 2018; Vishwanath et al., 2019), supports green creativity (Ogbeibu et al., 2020; Oosthuizen, 2019), simplifies tasks (Mahlasela & Chinyamurindi, 2020; Masood & Egger, 2020), and drives sustainable innovation (Jia et al., 2018; Khallash & Kruse, 2012). Following from the previous logic, we suggest the following:

H1. 

Leader STARA competence (LSC) is positively correlated with Green innovative behavior.

3.2. Leader STARA Competence (LSC) and Challenge Appraisal of STARA Technologies

According to the theory of cognitive appraisal, it is the process by which a person decides whether and how an environmental experience relates to their well-being (Ding, 2021). Based on this theory, stressors are appraised as either challenges or threats (Lazarus & Folkman, 1984). In technological transformation, employees may feel job insecurity when firms adopt STARA technologies, realizing these tools can perform some or all of their tasks (Brougham & Haar, 2018). This may cause anxiety and loss of optimism about career goals (H. Wang et al., 2015). Yet, employees aware of STARA adoption may use proactive coping mechanisms to avoid job loss (Repenning, 2000), viewing the stressor as an opportunity to adapt to a changing workplace (Ding, 2021). Koolhaas et al. (2011) note that STARA-linked uncertainty, role ambiguity, and insecurity align with stress, defined as a response to uncontrollable demands. A stressor may be both challenging and hindering (Searle & Auton, 2015). STARA adoption creates emotional strain and cognitive stress from continuous education (Oosthuizen, 2022). Coping begins with an individual’s cognitive evaluation of whether a stressor is a threat or opportunity (Nasaj et al., 2025), influenced by personal traits that shape future responses (Yang & Jiang, 2025).

The Challenge–Hindrance Stressor Model (CHSM) distinguishes stressors as challenges or hindrances (Cavanaugh et al., 1998). Motivation increases when stressors are challenges and decreases when they are hindrances (Pearsall et al., 2009). Stressors are assessed by importance and coping skills, leading to challenge or hindrance appraisals (Ma et al., 2021). Smart technologies can mitigate operational, financial, and health risks (Masood & Egger, 2020). Hindrance stressors relate to negative outcomes such as job insecurity, while challenge stressors relate to positive outcomes such as job satisfaction (Abdelghani et al., 2025; Başer et al., 2025). It remains debated whether STARA increases or replaces productivity in tourism (Lukanova & Ilieva, 2019). Robots may assist or replace employees (Ivanov & Webster, 2020). Technological stressors may be beneficial if managed effectively (Zhu et al., 2023). When employees perceive techno-stress as a challenge, work involvement rises (Adeniji & Igarashi, 2022), with greater enthusiasm, dedication (Başer et al., 2025), and proactive behaviors like creative problem-solving (Ogbeibu et al., 2021). Responses thus depend on whether STARA adoption is seen as an opportunity or a danger (Ding, 2021; Teng et al., 2025). Employees’ understanding of STARA has more influence on job performance than the technology itself (Hur & Shin, 2024), since stressors are judged as beneficial or harmful to objectives, values, and well-being (Başer et al., 2025; Tan et al., 2023).

In this context, subordinates’ assessment of the challenges posed by STARA technologies is significantly influenced by the Leader STARA Competence (LSC) (Khairy et al., 2025b). When leaders exhibit a high level of proficiency in robots, artificial intelligence, algorithms, and smart technologies, they provide an example of confidence and flexibility in the face of technological change (Bock & von der Oelsnitz, 2025). According to the cognitive appraisal theory, leaders are important information sources and cues for subordinates to understand changes in the workplace. Competent leaders are more likely to present STARA technology as possibilities for learning, development, and innovation rather than as threats (Ding, 2021). Subordinates view the introduction of these technologies as challenges to be overcome rather than burdens to be avoided thanks to this positive framing (Nguyen et al., 2023). After this discussion, the following hypothesis is produced:

H2. 

Leader STARA competence (LSC) is positively correlated with Challenge appraisal of STARA technologies.

3.3. Leader STARA Competence (LSC) and Hindrance Appraisal of STARA Technologies

According to Mahlasela and Chinyamurindi (2020) and Masood and Egger (2020), LSC is crucial in reducing team members’ workload and familiarizing them with cutting-edge technology relevant to green initiatives. Ma et al. (2021) noted that individuals assess stress based on its significance and coping mechanisms, leading to two appraisals: challenge and hindrance. In technological change, STARA may replace human labor (DeCanio, 2016). Employees unable to adapt may quit due to job insecurity (Ivanov & Webster, 2020; Parvez et al., 2022). For instance, hotel employees linked their knowledge of STARA to turnover intentions, fearing dismissal from its use (Khaliq et al., 2022). STARA adoption also exposes frontline staff to uncertainty, making them feel their critical resources are threatened (Tan et al., 2023). Since STARA has fewer service failures than humans, frontline service employees (FSEs) may view their competencies as weaker (Brougham & Haar, 2018; Smids et al., 2020). Moreover, its use of smart monitoring reduces job autonomy (Hur & Shin, 2024). A lack of autonomy discourages proactive participation and erodes motivation (Parker et al., 2017). Additionally, STARA adoption increases workload, as employees must acquire STARA-related skills and knowledge (Demerouti, 2022). Consequently, employees may perceive diminished autonomy as automation replaces routine tasks (Parker & Grote, 2022).

Subordinates’ evaluation of STARA technologies’ obstacles is significantly influenced by leader STARA competence (LSC) (Ghosh & Kabra, 2025). Strong STARA competency, or the ability to clearly comprehend and effectively manage these cutting-edge technologies, tends to lessen team members’ perceptions of technological change as a barrier (Olya et al., 2024). The cognitive appraisal theory states that workers look to their leaders for guidance on how to handle difficult circumstances. Employees may experience sentiments of irritation, overload, or blockage as a result of a leader’s assurance and proactive approach to STARA implementation. These emotions are common elements of hindrance appraisal (Hur & Shin, 2024). The risk that subordinates may perceive STARA technologies as obstacles or dangers to their job is decreased when leaders assist them in feeling more capable of handling technological demands by offering clear communication, sufficient resources, and emotional support (Ravani, 2025). In light of these considerations, the following hypothesis can be established:

H3. 

Leader STARA competence (LSC) is negatively correlated with Hindrance appraisal of STARA technologies.

3.4. Challenge Appraisal of STARA Technologies and Green Innovative Behavior

H. Li et al. (2023) define innovative green behavior as the creation and use of creative ideas for ecologically friendly customer service. Asghar et al. (2023) argue that the hotel and tourism industries increasingly embrace green service innovation to reduce environmental impact, enhance social responsibility, and deliver greater value. Nguyen et al. (2024) state that responsible leaders are the main source of employee creativity, while also preserving sustainable organizational environments (Asghar et al., 2023). Zhu et al. (2023) note that if technology stressors are properly managed, they can be beneficial, and employees’ work involvement may rise if they view techno-stress as a challenge (Adeniji & Igarashi, 2022). Thus, self-motivation is linked to evaluating challenges, as employees who view stress as a challenge approach work with energy, enthusiasm, and dedication (Başer et al., 2025), while also showing proactive attitudes by seeking new activities (Ogbeibu et al., 2021). Consequently, employee responses differ depending on whether they view STARA adoption as an opportunity or a danger (Ding, 2021; Tan et al., 2023), and employees’ understanding of STARA has a greater influence on job performance than the technology itself (Hur & Shin, 2024). According to the person–environment fit theory (Edwards et al., 1998), the mismatch created by adopting STARA motivates employees to cope with stress and work innovatively, not only to adapt to but also to master the changing environment. The extent to which employees perceive collaboration with STARA as an opportunity for personal growth and value creation positively influences their level of innovativeness. Drawing from the previously presented logic, we suggest the following:

H4. 

Challenge appraisal of STARA technologies is positively correlated with Green innovative behavior.

3.5. Hindrance Appraisal of STARA Technologies and Green Innovative Behavior

Based on the transactional theory of stress, primary appraisal is a critical psychological process that connects stressors to outcomes and is a way for an individual to evaluate the significance and meaning of a situation (Kraimer et al., 2022). Additionally, an individual’s primary assessment affects the value of consequences that they will encounter, including performance, motivation, strain, and well-being (Dillard, 2019; Huang & Gursoy, 2024). According to Cavanaugh et al. (2000) and Searle and Auton (2015), hindrance appraisals are characterized by an individual’s subjective perception that certain aspects of their profession may impede or obstruct their efforts to accomplish important objectives. In this regard, negative consequences, including increased turnover and withdrawal behavior, are linked to hindrance needs (J. A. Lepine et al., 2005). P. Li et al. (2021) defined hindrance demands as work-related expectations that hinder or obstruct an individual’s ability to achieve desired goals and that impede advancement, such as role conflict, job ambiguity, and organizational limitations.

As per Brougham and Haar (2018), Brougham and Haar (2018), and Ogbeibu et al. (2021), organizational team leaders must be able to manage and execute STARA technologies in order to carry out specific sustainability actions. To achieve this, businesses will need leaders who are knowledgeable in the development, management, and use of smart technology to support technological procedures that promote green initiatives (X. Li et al., 2021). Nevertheless, the emergence of STARA poses a serious threat to traditional jobs, primarily because these technologies are relatively inexpensive to purchase and maintain (Brougham & Haar, 2018; Lukanova & Ilieva, 2019). According to Frey and Osborne (2017), 47% of American employment may be automated, which could significantly affect the labor force over the next 20 years. However, in labor-intensive sectors such as tourism, human-to-human interaction remains essential, and STARA technology cannot fully replace human labor (Parvez et al., 2022). Even so, employees may get gloomy about their jobs and future career opportunities because of the uncertainty that STARA technology brings, which will reduce innovation in work (Başer et al., 2025; H. Li et al., 2023). With the above-mentioned logic in mind, we suggest the following:

H5. 

Hindrance appraisal of STARA technologies is negatively correlated with Green innovative behavior.

3.6. Challenge and Hindrance Appraisal of STARA Technologies Mediate the Relationship Between Leader STARA Competence and Green Innovative Behavior

According to the challenge–hindrance stressor theory, stressors that are seen as challenges lead to positive effects, engagement, and results (M. A. LePine, 2022). Furthermore, challenge stresses are linked to favorable business results like work satisfaction (Başer et al., 2025). Zhu et al. (2023) support this viewpoint, asserting that, when properly controlled, technology shocks can be beneficial. Therefore, employees’ level of work involvement may rise if they view techno-stress as a challenge (Adeniji & Igarashi, 2022). Based on the ability–motivation–opportunity (AMO) theory, employee performance is believed to be influenced by their opportunity, willingness, and performance capacity (Marin-Garcia & Martinez Tomas, 2016). N. T. Pham et al. (2019) have highlighted the significance of the three AMO theory components—ability, motivation, and opportunity—in shaping employee green behavior. When employees have the right direction, support, and encouragement, they demonstrate proactive innovation (Sibian & Ispas, 2021). Employees are already engaging in creative green behaviors when they apply innovative concepts for environmentally responsible customer service (H. Li et al., 2023). Ogbeibu et al. (2021) claim that employees also exhibit proactive attitudes by actively looking for novel activities. As a result, assessing challenges is associated with self-motivation, and workers who see stress as a challenge approach their work with vigor and excitement and are more committed to their responsibilities (Başer et al., 2025).

However, human labor could be replaced by STARA technologies (Başer et al., 2025). In addition, employees who are unable to adjust to STARA technologies may be compelled to quit because they fear losing their jobs (Ivanov & Webster, 2020; Parvez et al., 2022). Additionally, frontline staff are exposed to risks and uncertainties because of STARA adoption, which makes them feel as though their important resources are in threat (Tan et al., 2023). Furthermore, it is tougher to achieve the autonomy criterion since STARA uses smart technology to monitor work procedures and processes (Hur & Shin, 2024). According to Brougham and Haar (2018) and Lukanova and Ilieva (2019), the rise of STARA presents a significant threat to traditional jobs. Moreover, because STARA technology is unpredictable, workers may become pessimistic about their current positions and prospects for advancement (Başer et al., 2025). For businesses to overcome these obstacles and challenges and support technical procedures that promote green initiatives, leaders with experience in the development, management, and use of smart technology will be required (X. Li et al., 2021). In this context, the job autonomy of frontline service jobs is significantly impacted by the knowledge that STARA technology may replace them, which lowers proactive service performance (Hur & Shin, 2024). Finally, employee technostress evaluation affects results: challenge evaluation increases creativity and engagement, while hindrance evaluation reduces creativity and increases anxiety (S. Zhang et al., 2025). Based on the above reasoning, we propose the following (see Figure 1):

Figure 1. Conceptual framework of the study.

H6. 

Challenge appraisal of STARA technologies mediates the relationship between Leader STARA competence and green innovative behavior.

H7. 

Hindrance appraisal of STARA technologies mediates the relationship between Leader STARA competence and green innovative behavior.

4. Methods

4.1. Measures

The measurement scales for the study variables were borrowed from previous studies. 4 items from Ogbeibu et al. (2020) were used to assess leader STARA competencies (LSC). The Ogbeibu et al. (2020) scale for measuring LSC has been widely applied in previous studies, including research conducted in the hospitality sector, and more specifically in Egypt (Ahmed et al., 2025; Khairy et al., 2025b; Olya et al., 2024). Accordingly, the use of this validated scale provides both reliability and contextual relevance to the present study. Furthermore, the challenge appraisal of STARA technologies (CAS) and hindrance appraisal of STARA technologies (HAS) variables were evaluated utilizing 4 items for each one borrowed from Searle and Auton (2015). Finally, green innovative behavior (GIB) was gauged using four items proposed by Z. Wang et al. (2021). Following the guidelines of Brislin (1980), the original English survey was initially translated into Arabic by two bilingual experts. Subsequently, a separate team conducted a back-translation into English to verify accuracy and conceptual equivalence. Additionally, the questionnaire was reviewed by 18 academics and practitioners to confirm the clarity and consistency of the measurement items with the study’s objectives. Only minor modifications were necessary.

4.2. Data Gathering Methods

As Sharm El-Sheikh, Egypt, advances toward becoming the nation’s first green city, data were collected from employees at 21 five-star, environmentally certified hotels in the area. The study employed a convenience sampling method due to practical considerations of time, cost, and participant accessibility. Although this approach may limit representativeness and generalizability (this limitation was explicitly acknowledged in the study), it is commonly applied in hospitality research (Abdulaziz et al., 2025; Elshaer et al., 2024), where random sampling is often constrained. Prior evidence indicates that sufficiently large samples can mitigate such concerns (Jager et al., 2017). Participants were informed that their participation was voluntary, that they could withdraw at any time without penalty, and that completing the questionnaire implied informed consent. They were also advised that there were no right or wrong answers and assured that all responses would remain confidential and used solely for statistical analysis. Ethical approval was obtained from the authors’ institution, and this will be reported in the Ethical Approval section of the manuscript. Activating the mandatory response option in the online questionnaire ensured complete data and yielded 446 valid responses for analysis. The sample consisted of 300 males (67.3%) and 146 females (32.7%), with participants aged 18 to 59. Table 1 presents the demographic characteristics of the respondents.

Table 1. Respondents’ characteristics.

Category	Group (N = 446)	Frequency	%
Gender
	Male	300	67.3
	Female	146	32.7
Age group
	18–29	204	45.7
	30–39	131	29.4
	40–49	81	18.2
	50–59	30	6.7
	From 60 and more	-	-
Education
	High School	36	8.1
	Middle school	239	53.6
	Bachelor’s Degree	118	26.5
	Postgraduate	10	2.2
	Other	43	9.6
Experience
	Below 2 Years	110	24.7
	Between 2 and 5 Years	110	24.7
	Between 5 and 10 Years	75	16.8
	Over 10 Years	151	33.9

4.3. Data Analysis

To align with the study’s predictive orientation and to address the complexity of the research model, partial least squares (PLS) was selected as the analytical technique. The primary objective was to predict the relationships among the independent variable (STARA leadership competence), the dependent variable (green innovative behavior), and the mediators (challenge appraisal of STARA and hindrance appraisal of STARA), rather than to confirm a pre-existing theoretical model (Hair et al., 2017).

5. Results

5.1. Common Method Bias (CMB) and Data Normality

Harman’s single-factor test showed that the first factor explained 40.674% of the variance, below the 50% threshold (Podsakoff et al., 2003), suggesting no serious CMB issues. Furthermore, skewness and kurtosis were evaluated to verify the normality of the data distribution. The observed values were within the accepted thresholds of +2 for skewness and +7 for kurtosis (see Table 2) (Curran et al., 1996).

Table 2. Confirmatory factors.

Measures	λ (>0.7)	VIF (<5)	M	SD	SK	KU
Leader STARA competence (LSC) (α = 0.885, CR = 0.921, AVE = 0.744)
LSC_1	0.863	2.241	3.845	1.148	−0.688	−0.439
LSC_2	0.854	2.256	3.751	1.184	−0.546	−0.700
LSC_3	0.875	2.530	3.814	1.182	−0.661	−0.536
LSC_4	0.857	2.191	3.814	1.145	−0.614	−0.613
Challenge appraisal of STARA (CAS) (α = 0.921, CR = 0.944, AVE = 0.808)
CAS_1	0.905	3.241	3.590	1.308	−0.605	−0.713
CAS_2	0.915	3.437	3.664	1.279	−0.608	−0.749
CAS_3	0.902	3.072	3.704	1.283	−0.702	−0.531
CAS_4	0.874	2.577	3.839	1.224	−0.761	−0.466
Hindrance appraisal of STARA (HAS) (α = 0.871, CR = 0.912, AVE = 0.722)
HAS_1	0.832	1.980	3.516	0.989	−0.310	−0.323
HAS_2	0.861	2.237	3.491	0.982	−0.225	−0.371
HAS_3	0.849	2.121	3.502	0.980	−0.294	−0.288
HAS_4	0.855	2.172	3.561	0.929	−0.304	−0.049
Green innovative behavior (GIB) (α = 0.864, CR = 0.908, AVE = 0.711)
GIB_1	0.797	1.808	3.305	1.200	−0.009	−0.970
GIB_2	0.857	2.095	3.435	1.252	−0.273	−0.927
GIB_3	0.860	2.217	3.383	1.225	−0.145	−1.024
GIB_4	0.857	2.131	3.352	1.281	−0.197	−1.047

Note: “λ = factor loadings, α = coefficient alpha, CR = construct reliability, AVE = average variance extracted, VIF = variance inflation factor, SK = skewness, KU = kurtosis, M = mean”.

5.2. Outer Model Evaluation

The outer model in PLS-SEM was assessed by examining indicator reliability (factor loadings, λ), internal consistency reliability (Cronbach’s alpha, α), and composite reliability (CR), as well as convergent validity (average variance extracted, AVE). According to Hair et al. (2019), λ and α should be >0.7, while AVE should exceed 0.50. As presented in Table 2, all criteria were met.

Many studies (Elshaer et al., 2024, 2025b; Khairy et al., 2025a) have employed the Fornell–Larcker criterion (Fornell & Larcker, 1981) in conjunction with the Heterotrait–Monotrait ratio (HTMT) (Henseler et al., 2015) to assess discriminant validity, addressing criticisms associated with relying solely on the Fornell–Larcker approach. The HTMT criterion demonstrated superior performance, with specificity and sensitivity rates of 97–99% compared to 20.82% for the Fornell–Larcker criterion (Henseler et al., 2015). (Fornell & Larcker, 1981) suggested that discriminant validity is considered adequate when a construct’s AVE is > the squared correlations it shares with other constructs, as shown in Table 3. Meanwhile, (Gold et al., 2001) stated that the HTMT value should be less than 0.90. As indicated in Table 3 and Table 4, the DV was successfully validated.

Table 3. Fornell–Larcker criteria.

	CAS	GIB	HAS	LSC
Challenge appraisal of STARA (CAS)	0.899
Green innovative behavior (GIB)	0.504	0.843
Hindrance appraisal of STARA (HAS)	−0.274	−0.419	0.850
Leader STARA competence (LSC)	0.593	0.505	−0.527	0.862

Table 4. HTMT matrix.

	CAS	GIB	HAS	LSC
Challenge appraisal of STARA (CAS)
Green innovative behavior (GIB)	0.563
Hindrance appraisal of STARA (HAS)	0.305	0.480
Leader STARA competence (LSC)	0.655	0.572	0.599

5.3. Structural Model Assessment and Hypotheses Testing

Table 2 indicates that the VIF values (1.808–3.437) fall below the recommended threshold of 5.0, confirming the absence of multicollinearity among predictors and demonstrating that each variable contributes uniquely to the explanation of the endogenous constructs (Hair et al., 2019). For the coefficient of determination (R²), the R² value for CAS was 0.351. Similarly, the R² values for GIB and LSC were 0.358 and 0.278, respectively, both exceeding the acceptable threshold of 0.10 (Hair et al., 2019). This demonstrates an accepted explanatory power of the model in line with the theoretical framework that was built on person–job fit theory and cognitive appraisal theory. Similarly, the Q² values presented in Table 5 exceeded 0.0, which confirms the predictive relevance of the model. These results confirm the adequacy of the structural model (Hair et al., 2019).

Table 5. Results of hypothesis testing, R2, and Q2.

Path Coefficients			β	t Value	Sig.	Result
Direct Effects
H1: LSC→ GIB			0.189	2.891	0.004	✔
H2: LSC → CAS			0.593	12.621	0.000	✔
H3: LSC → HAS			−0.527	15.845	0.000	✔
H4: CAS → GIB			0.330	5.147	0.000
H5: HAS → GIB			−0.230	4.763	0.000
Indirect mediating effect
H6: LSC → CAS → GIB			0.195	4.660	0.000	✔
H6: LSC → HAS → GIB			0.121	4.709	0.000	✔
Challenge appraisal of STARA (CAS)	R2	0.351	Q2	0.267
Green innovative behavior (GIB)	R2	0.358	Q2	0.235
Hindrance appraisal of STARA (LSC)	R2	0.278	Q2	0.189

Note: Leader STARA competence = LSC; green innovative behavior = GIB; challenge appraisal of STARA technologies = CAS; hindrance appraisal of STARA technologies = HAS; beta coefficients = β; t-value = t; p value = p; R² = coefficient of determination; Q² = predictive relevance/Stone–Geisser’s Q², Supported = ✔.

Table 5 and Figure 2 illustrate that LSC had a significant positive effect on GIB (β = 0.189, t = 2.891, p < 0.004) and CAS (β = 0.593, t = 12.621, p < 0.004), while exhibiting a significant negative effect on HAS (β = −0.527, t = 15.845, p < 0.000), thereby supporting hypotheses H1, H2, and H3. Furthermore, CAS positively influenced GIB (β = 0.330, t = 5.147, p < 0.000), confirming H4. In contrast, HAS had a negative impact on GIB (β = −0.230, t = 4.763, p < 0.000), supporting H5. Regarding the mediation effects, CAS and HAS were found to mediate the effect of LSC on GIB. Specifically, the indirect effects were significant, with β = 0.195, t = 4.660, p < 0.000, and β = 0.121, t = 4.709, p < 0.000, respectively. Thus, hypotheses H6 and H7 were also supported.

Figure 2. Estimation of the structure model.

Multi-Group Analysis (MGA) Based on Gender Group

To explore potential gender-based differences, the SmartPLS Multi-Group Analysis (MGA) was applied. Under MGA guidelines, a hypothesis is supported if the significance value is <0.05 or the t-value is >1.96 (Cheah et al., 2020). As shown in Table 6, no statistically significant differences emerged between male and female groups across any of the proposed structural relationships. These results indicate that gender does not exert a meaningful influence on the structural paths examined in this study.

Table 6. MGA.

The Path	β-diff (Female - Male)	Sig. (Female vs. Male)
Direct effect
H1: LSC → GIB	0.165	0.882
H2: LSC → CAS	0.116	0.882
H3: LSC → HAS	0.063	0.833
H4: CAS → GIB	0.048	0.361
H5: HAS → GIB	0.040	0.658
Indirect mediating effect
H6 and 7: LSC → GIB	0.026	0.395

6. Discussion and Conclusions

6.1. Discussion

Despite the widespread application of STARA competencies in service-related functions, research examining tourism and hospitality employees’ responses to its adoption remains scarce. While prior research has examined the nexus between STARA leadership competencies and green innovation behavior (Ogbeibu et al., 2020; Oosthuizen, 2019) and has also investigated the challenge and hindrance appraisals associated with STARA (Başer et al., 2025; Hur & Shin, 2024; Tan et al., 2023), such efforts have sometimes overlooked the hospitality sector. This gap is particularly evident in developing countries’ contexts, where empirical evidence remains scarce. To address this research gap, the present study investigated the relationship between leader STARA competencies (LSCs) and green innovation behavior (GIB), with challenge appraisal of STARA (CAS) and hindrance appraisal of STARA (HSA) serving as mediating variables.

The findings support the first hypothesis, indicating that LSCs significantly foster GIB in hotels (β = 0.189, t = 2.891, p < 0.004). These results are consistent with Puvaneswary (2024), who emphasized that leaders equipped with technological (STARA) skills are better positioned to effectively address sustainability requirements within their organizations and promote GIB among employees (Ogbeibu et al., 2023). LSCs are essential for enhancing employees’ GIB and enabling them to work creatively toward achieving organizational sustainability agendas, as leaders are often perceived as role models by their followers (Olya et al., 2024). Thus, LSCs could be crucial in helping team members become more comfortable with innovative technologies relevant to improving green activities, as well as in simplifying their tasks (Mahlasela & Chinyamurindi, 2020; Masood & Egger, 2020). Additionally, LSCs help organizations initiate green activities that encourage innovative green organizational practices (Jia et al., 2018; Khallash & Kruse, 2012).

Similarly, the results revealed that LSCs are positively associated with the challenge appraisal of STARA technologies (CAS) (β = 0.593, t = 12.621, p < 0.000) (H2), while showing a negative association with the hindrance appraisal of STARA technologies (HAS) (β = −0.527, t = 15.845, p < 0.000) (H3). Employees tend to fall into two distinct groups: the first perceives technological pressures as challenges (CAS), whereby their self-motivation is linked to challenge appraisals, leading to greater engagement, enthusiasm, and higher levels of dedication in performing their duties (Başer et al., 2025). In contrast, the second group perceives these pressures as work burdens (HAS), as they are required to acquire new skills and knowledge associated with such technologies. Consequently, these employees may experience reduced job autonomy and feel constrained to perform only demanding tasks, particularly as job automation technologies frequently replace routine tasks (Demerouti, 2022; Parker & Grote, 2022). In this regard, Masood and Egger (2020) noted that LSCs can persuade their subordinates that innovative technologies may mitigate or even prevent potential operational, financial, and health risks. Similarly, Cavanaugh et al. (1998) found that leaders can reduce employees’ negative stress perceptions toward technology (HAS), such as job insecurity, while CAS has been shown to yield positive organizational outcomes, including job satisfaction (Başer et al., 2025).

In the same vein, studies have indicated that the more employees perceive collaboration with STARA as an opportunity and anticipate gaining personal value and achieving accomplishments, the more innovative they become at work. However, employees may also feel pessimistic about their jobs and future career prospects due to the uncertainty brought by STARA technologies, which in turn may diminish workplace innovation (Başer et al., 2025). Consistent with these arguments, the results supported Hypotheses 3 and 4, indicating that CAS is positively associated with GIB (β = 0.330, t = 5.147, p < 0.000), whereas HAS is negatively related to GIB (β = −0.230, t = 4.763, p < 0.000).

With regard to the mediating effects, both CAS (H6 → β = 0.121, t = 4.709, p < 0.000) and HAS (H → β = 0.121, t = 4.709, p < 0.000) were found to mediate the relationship between LSC and GIB. This mediation is further substantiated by the significant direct relationships observed among the study variables. Therefore, for organizations to enhance performance and green initiatives, it is essential to have leaders equipped with STARA competencies who possess expertise in developing, managing, and utilizing smart technologies. Such leaders can motivate and support employees in embracing technology, while simultaneously alleviating hindering perceptions from their minds (X. Li et al., 2021).

6.2. Theoretical Implications

In the context of social sciences, it is often preferable to draw on multiple theoretical perspectives in order to gain deeper insights and achieve a more comprehensive understanding of practice (Abdulaziz et al., 2025; Deegan et al., 2000). Accordingly, this study advances theoretical contributions by uniquely integrating person–job fit theory (Edwards, 1991), cognitive appraisal theory (C. A. Smith & Ellsworth, 1985), and the Challenge–Hindrance Stressor Model (CHSM) (Cavanaugh et al., 1998) into a one framework. This multi-theoretical integration offers a novel lens through which to examine green innovation behavior in the hospitality sector, particularly within developing country contexts.

This study extends the application of person–job fit theory to the context of integration technologies in the workplace by proposing that when leaders possess STARA competencies and provide adequate support to their subordinates, they are more likely to embrace new technologies and perceive their own capabilities as aligned with emerging job requirements. In this sense, leaders play a critical role in shaping employees’ appraisals of STARA-related pressures as manageable challenges, thereby reinforcing the perception of person–job fit. Beyond applying cognitive appraisal theory and the Challenge–Hindrance Stressor Model (CHSM), this study advances the theoretical discourse by highlighting the dynamic mechanism through which employees’ appraisals can shift. While cognitive appraisal theory posits that stressors may be appraised as either challenges (CAS) or hindrances (HAS), and CHSM distinguishes between challenge (CAS) and hindrance (HAS) stressors, the present study suggests that threat and hindrance appraisals are not static. Instead, under supportive STARA leadership, these negative appraisals can be transformed into challenge appraisals, thereby enabling employees to reinterpret technology-induced pressures as opportunities for growth and innovation rather than constraints. This theoretical extension provides a nuanced explanation of how resistance to technology adoption can evolve into proactive engagement.

Overall, this study is expected to contribute to person–job fit theory by demonstrating that leaders play a pivotal role in creating alignment between jobs involving STARA-related tasks and employees, which in turn significantly enhances their engagement in green innovation. The study also advances cognitive appraisal theory by suggesting that leaders’ STARA competencies serve as antecedents that can transform employees’ appraisal of technology adoption from a hindrance to a challenge. This transformation fosters a better fit that provides a conducive environment for green innovation. Accordingly, the study offers a comprehensive theoretical framework that integrates sustainable innovation, psychological processes, and leadership within the hospitality sector.

6.3. Practical Implications

Practically, this study provides several insights for managers and decision-makers. First, the findings highlight a positive relationship between LSCs and GIB. This underscores the importance of developing STARA competencies among hospitality leaders, as such capabilities enable them to design and implement innovative solutions that facilitate a smooth going to green. To ensure the long-term greening of hotels, the use of STARA technologies should be reinforced through the design of specialized training programs to enhance leaders’ understanding of innovative technologies such as artificial intelligence, robotics, and intelligent automation (STARA). These programs should emphasize the application of such technologies across different hotel departments to strengthen leaders’ technological knowledge and equip them to serve as trainers of trainers (TOTs) for their subordinates. This knowledge should be linked to the context of green innovation within the organization to foster sustainable transformation.

The widespread adoption of STARA technologies in hotel operations necessitates increased managerial focus on employee performance and the emerging challenges and hindrances associated with technologically driven work environments. Organizations should adopt a proactive approach to addressing the challenges and negative perceptions employees may hold toward STARA technologies to maximize their benefits. When employees’ resources fail to keep pace with the rapid adoption of technology, a misalignment between capabilities and demands emerges. Therefore, leaders must provide adequate organizational support that enables employees to adapt their skills and competencies to the evolving work environment. This, in turn, enhances their readiness to embrace new technologies, fostering more competitive and innovative work behaviors. In addition, hospitality organizations may organize targeted workshops and seminars serving two strategic purposes. First, to strengthen STARA-competent leaders’ persuasion and communication skills to enable them to convey the benefits of technological transformation to their teams effectively. Second, to foster joint participation of leaders and employees in these sessions to reduce resistance to change and position STARA-related demands as motivating challenges rather than hindrances.

In order to make these insights more broadly applicable outside of the Egyptian hospitality setting, businesses in other industries and locations should modify them to fit their own institutional and cultural circumstances. This may include aligning technology adoption with global sustainability frameworks (e.g., the UN Sustainable Development Goals or national green transformation agendas), customizing training to local workforce capabilities, and fostering partnerships between industry, government, and academia to support eco-digital innovation. By embedding both green and digital dimensions into their strategic vision, organizations create a dual pathway to competitiveness where technological efficiency reinforces environmental responsibility. This strategy maximizes long-term organizational and social value by fortifying market position and boosting legitimacy with consumers, authorities, and global stakeholders.

7. Study Limitations and Future Research

Similar to prior studies, this research is subject to several limitations that should be addressed in future investigations. First, the reliance on a cross-sectional design (i.e., data collected at a single point in time from hotel employees) restricts the ability to draw causal inferences. Future studies are therefore encouraged to adopt longitudinal designs to better capture causal dynamics. Second, the study was conducted in a single geographical setting (Sharm El-Sheikh, Egypt) using self-reported data from hotel employees, which may introduce contextual and common method biases. Third, although convenience sampling is widely used in hospitality and tourism research due to its practicality and cost efficiency, it inherently limits sample representativeness and generalizability. Hence, probability-based sampling strategies are recommended in future research. Fourth, while PLS-SEM is a powerful and widely applied method in hospitality studies, it carries methodological constraints such as potential endogeneity concerns and limited capacity to account for unobserved heterogeneity. Thus, complementary approaches such as CB-SEM, Mplus, or multilevel modeling should be considered. Furthermore, as this study was conducted in a developing country context (Egypt), cross-cultural or multi-country studies are recommended to validate the role of STARA competencies in shaping environmental performance across diverse hospitality contexts and to enhance external validity. Finally, future research may explore alternative mediating and moderating mechanisms—such as psychological safety, employees’ environmental orientation, organizational culture, organizational support, or STARA awareness—which could provide deeper theoretical insights and broaden contributions to the literature on STARA competencies and green transformation in the hotel sector.