INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XIV November 2025| Special Issue on Management  
The Dark Side of AI-Augmented Leadership: Authoritarian  
Leadership as A Boundary Condition  
Zeineb ESSID  
Department of Management, Higher Institute of Management of Sousse; Tunisia  
DOI: https://dx.doi.org/10.47772/IJRISS.2025.914MG00233  
Received: 27 November 2025; Accepted: 02 December 2025; Published: 09 December 2025  
ABSTRACT  
Purpose  
This study aims to investigate the influence of AI-augmented leadership on employee well-being in the Middle  
East and North Africa (MENA). It examine the moderating effect of authoritarian leadership styles.  
Design/methodology/approach  
A quantitative study was conducted using data collected from a survey of 104 professionals in Tunisia, Egypt,  
and Saudi Arabia. This research applied the Partial Least Squares Structural Equation Modeling (PLS-SEM)  
technique to test the proposed conceptual framework.  
Findings  
The results indicate that AI-augmented leadership significantly improves employee well-being. However,  
moderation analysis reveals that authoritarian leadership weakens this positive relationship. Specifically, when  
authoritarian leadership dominates, the stress-reducing benefits of using AI are considerably diminished.  
This highlights the impact of cultural leadership norms on the effectiveness of AI-based management  
approaches.  
Originality/value  
This study stands out as one of the earliest to empirically examine the interplay between AI technologies,  
leadership behavior, and employee well-being within the relatively unexplored MENA region. It introduces a  
human-centric, region-specific model of technology leadership, effectively responding to the critical demand for  
ethical and inclusive frameworks for AI adoption in both public and private sectors navigating digital  
transformation.  
Keywords: AI-augmented leadership, employee well-being, authoritarian leadership, MENA region  
INTRODUCTION  
The rapid development and ubiquity of artificial intelligence (AI) is profoundly transforming organizational  
structures and leadership styles. As AI becomes increasingly integrated into strategic and operational processes,  
it is crucial for leaders to master the complexities of human-machine collaboration (Dwivedi et al., 2023;  
Jussupow et al., 2023). This evolution necessitates a re-evaluation of traditional leadership approaches, citing  
the example of AI-augmented leadership, where human capabilities are enhanced, supported, or partially  
delegated to intelligent technologies (Dellermann et al., 2022).  
Recent research in work and organizational psychology and organizational behavior suggests that AI-driven  
management practices are transforming the social dynamics between leaders and employees (Zhang & Liu, 2024;  
Hauff et al., 2023). These studies demonstrate the extent to which employee well-being is influenced by how  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XIV November 2025| Special Issue on Management  
AI-augmanted leadership is exercised and the contribution of AI tools to reducing workload, clarifying  
expectations, and supporting decision-making.  
The applicability of these new leadership paradigms remains uncertain in non-Western contexts, which are  
characterized by distinct cultural and institutional norms. Leadership practices are not applied uniformly across  
cultural contexts; their effects are conditioned by social expectations related to authority (Harms et al., 2022 ;  
Metcalfe, 2023).  
In the MENA region, governments and organizations are accelerating the adoption of AI as part of their digital  
transformation programs. Initiatives such as Saudi Arabia’s National Strategy for Data and AI (2020), Tunisia’s  
SmartGov program, and Egypt’s Vision 2030 demonstrate strong institutional support for AI-driven  
modernization, often targeting public services and state-owned enterprises (OECD, 2023).  
MENA is a theoretically distinct context where institutional pressures coexist with traditional power structures.  
According to sociotechnical systems theory (Trist and Bamforth, 1951), the success of technology adoption  
depends on the alignment between social systems (such as leadership) and technical systems. In practice, this  
alignment remains fragile in many organizations in the MENA region, potentially reinforcing authoritarian  
leadership dynamics rather than empowering employees (Olan et al., 2022 ; Kshetri, 2023).  
The Job Demands–Resources (JD-R) framework offers valuable insights into these dynamics, highlighting AI  
as either a resource capable of alleviating stressors or as a demand that could heighten pressure, depending on  
how it is managed (Bakker & Demerouti, 2017; Meijerink et al., 2022). Notably, prevalent leadership styles in  
the region—especially authoritarian approaches—play a crucial role in determining whether AI is deployed to  
assist employees or to reinforce monitoring and control (El Sawy et al., 2022).  
This study contributes to the literature on AI and leadership by proposing a contextualized framework that  
examines how AI-augmented leadership influences employee well-being, while explicitly considering  
authoritarian leadership as a boundary condition. This research aims to empirically test a model linking AI-  
augmented leadership to employee well-being within local organizations in Tunisia, Egypt, and Saudi Arabia—  
three countries experiencing rapid AI integration.  
The model further investigates how authoritarian leadership modulates this relationship. Data were collected  
from 104 professionals working in public and private organizations in these three countries.  
LITERATURE REVIEW  
AI-Augmented Leadership  
The emergence of artificial intelligence (AI) is profoundly changing how leadership is exercised in modern  
organizations. AI-augmented leadership refers to the integration of intelligent technologies into the roles and  
responsibilities of leaders. These technologies can support managerial capabilities in areas such as  
communication, control, performance evaluation, and strategic planning (Dellermann et al., 2022). Rather than  
replacing human leadership, AI acts as a lever for optimization; it improves management, strengthens the  
consistency of decisions, and enables the scaling of managerial practices (Jussupow et al., 2023). For example,  
AI can propose strategic scenarios or anticipate trends. A recent study by Zaman (2025) describes a model of  
“agentic leadership,” where AI is a decision-making companion that supports autonomous decision-making and  
strategic vision, while maintaining a human dimension. Other research shows how AI can alter power dynamics  
within organizations by automating certain decision-making tasks (Joshi, 2025).  
Finally, the adoption of AI in leadership raises institutional and cultural issues. In this sense, AI-augmented  
leadership presents a dual challenge: leveraging technical skills while preserving the human and ethical integrity  
of the leadership role.  
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ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XIV November 2025| Special Issue on Management  
Employee Well-Being  
According to Bakker and Oerlemans (2011), well-being at work results from the combination of feelings of  
satisfaction and pleasure (hedonic dimension) and optimal functioning (eudaimonic dimension). It encompasses  
various aspects such as life satisfaction, positive and negative emotional states, autonomy, personal  
development, and the quality of social relationships.  
According to the International Labour Organization, well-being at work refers to a state encompassing the entire  
professional experience, including the physical environment, perceptions of work, and the organizational  
climate. Today, it is increasingly seen as a strategic objective for companies (Brough et al., 2023; Grant, 2022).  
Research by Taris and Schaufeli (2015) and by Page and Vella-Brodrick (2009) identifies four key dimensions  
for measuring this well-being: positive affect, negative affect, life satisfaction, and job satisfaction. Furthermore,  
Bakker and Oerlemans (2012) describe well-being at work as a multidimensional phenomenon based on optimal  
functioning, combining job satisfaction, engagement, burnout, and work addiction. This highlights that this  
experience can include both pleasant and unpleasant aspects. Diener and Ryff differentiate between two main  
axes: subjective well-being, based on life satisfaction and emotional balance, and eudaimonic well-being,  
focused on personal fulfillment, development, and individual growth. Finally, according to Grant et al. (2020),  
well-being results from a combination of hedonic and eudaimonic perspectives, while also integrating social  
dimensions and external influences such as culture, community, nature, and governance on employees. The Job  
Requirements and Resources (JDR) model, proposed by Bakker and Demerouti in 2017, provides a robust and  
relevant theoretical framework for examining the effects of AI on employee well-being. In this model, leadership  
is viewed as a resource capable of reducing negative job demands while strengthening work engagement (Bakker  
et al., 2023). Consequently, it plays a key role as a moderator in managing these dynamics, significantly  
influencing the impact of AI on employee well-being, either amplifying or mitigating it (Meijerink et al., 2022).  
The Moderating Role of Authoritarian Leadership  
Authoritarian leadership is characterized by strong discipline, centralized authority, and strict control over  
subordinates. This type of leadership demands rigid and strict work standards (Karakitapoğlu-Aygün et al.,  
2021). According to Chiang et al. (2020), this style exerts high pressure on subordinates, thus limiting their  
confidence and ability to act independently.  
According to Farh and Cheng (2000), authoritarian leadership is characterized by a style in which the leader  
exercises total control, makes all crucial decisions without consultation, and imposes authority unilaterally. This  
type of leadership relies on the leader's dominance, highly centralized authority, and a notable lack of delegation  
to team members.  
Furthermore, authoritarian leadership can stifle innovation and creativity, restrict autonomy, and harm employee  
well-being (Pan et al., 2023). Indeed, when an authoritarian leader uses AI as a means of control or surveillance,  
they can undermine the benefits that AI-augmented leadership might offer, notably by increasing stress and  
reducing freedom of initiative.  
Recent studies, however, suggest that the impacts of authoritarian leadership are not always uniform. For  
example, Zhao, Su, Lou, and Zhang (2022) identified a form of authoritarian leadership focused on discipline,  
which can, under certain circumstances, foster employee creativity. This occurs through creative self-efficacy,  
particularly when strategic events enhance the critical importance of the leader's role.  
Furthermore, Nawaz, Usman, Ul Mulk, Ahmad, and Shahzad (2022) indicate that this type of leadership can  
have a positive effect on employee performance and role clarity. These effects are particularly noticeable in  
organizations characterized by a high power distance, as is often the case in some companies located in the  
Middle East.  
In short, although authoritarian leadership is often perceived negatively, some research shows that it can be  
mitigated if the leader possesses high competence, as mentioned in a longitudinal study that demonstrated that a  
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ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XIV November 2025| Special Issue on Management  
leader's competence moderates the negative impact of their authoritarianism on employee affective commitment  
and performance.  
The MENA Region: A Unique Context for AI and Leadership  
Many countries, such as Saudi Arabia, Egypt, and Tunisia, have explored AI, but empirical research in the  
MENA region remains scarce. Furthermore, organizational cultures often lean toward hierarchical leadership,  
meaning employees will have limited influence over technological changes (OECD, 2023). This makes the  
region a unique context for studying the practical operation of AI-augmented leadership and its impact on human  
factors such as trust, stress, and well-being.  
This study empirically examines the relationships between AI-augmented leadership, employee well-being, and,  
more specifically, the moderating role of authoritarian leadership in organizations in the MENA region.  
Theoretical Framework and Hypotheses Development  
This study aims to develop a multidimensional model that establishes a link between AI-augmented leadership  
and employee well-being. It is based on the Job Demands and Resources (JD-R) theory proposed by Bakker and  
Demerouti in 2017, as well as the situational and contingency leadership approaches developed by Fiedler in  
2023. However, it goes beyond these classic frameworks by integrating an institutional and cultural perspective  
to better understand the specific interactions between leadership, technology, and well-being in the MENA  
region.  
Drawing on theories of intercultural organizational behavior (Gelfand et al., 2021) and sociotechnical systems  
thinking (Brougham et al., 2024), it is argued that the integration of artificial intelligence interacts with  
leadership culture and the degree of power distance, thus directly influencing employee well-being.  
Theoretical Foundation: Job Demands–Resources and Augmented Leadership  
In rapidly changing digital environments, AI systems can act as both a resource and a requirement, depending  
on how they are deployed and perceived by employees (Meijerink et al., 2022; Dwivedi et al., 2023).  
This model explains how job resources such as benevolent leadership and fair practices and job demands (e.g.,  
AI monitoring, digital overload) influence employee outcomes such as well-being, stress, and engagement.  
The JD-R framework offers a foundational but incomplete explanation when applied globally. As noted by Hauff  
et al. (2023), contextual variables such as national culture, power asymmetry, and institutional expectations  
moderate how resources (e.g., leadership support, AI tools) influence well-being. In high power-distance cultures  
like MENA (Hofstede, 2023), leaders often act as gatekeepers of resources, shaping whether AI is perceived as  
empowering or controlling. Thus, AI-augmented leadership can become either a job resource or a job demand  
depending on cultural alignment and leadership behavior.  
Within the framework of leadership practices, we consider perspectives from augmented leadership (Dellermann  
et al., 2022), which emphasize that AI technologies tend to enhance managerial decision-making rather than  
replace it. By leveraging AI to optimize communication, feedback, and fairness, leaders can appear more  
competent and transparent (Jussupow et al., 2023), contributing to a safer and more collaborative work  
environment.  
To adapt this logic to MENA organizations, we integrate insights from socio-technical systems theory, which  
emphasizes the mutual adaptation between social structures (e.g., leadership, norms) and technological  
infrastructures (Trist & Bamforth, 1951; Brougham et al., 2024). In societies where leadership authority is  
centralized, such adaptation may be asymmetrical: AI serves to reinforce existing hierarchies rather than to  
decentralize control. This makes the MENA context theoretically significant, as it tests the boundary conditions  
of JD-R and leadership theories that were primarily validated in Western, low power-distance environments  
(Harms et al., 2022).  
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This study is primarily based on the Job Demands-Resources (JDR) model developed by Bakker and Demerouti  
in 2017. This model explores how job resources, such as benevolent leadership or fair practices, as well as  
demands such as artificial intelligence surveillance or digital overload, influence key aspects such as employee  
well-being, stress, and engagement. In the context of environments marked by digital transformation, AI systems  
can simultaneously play the role of a resource or constitute a requirement, depending on the modalities of their  
implementation and the perception that employees have of them (Meijerink et al., 2022; Dwivedi et al., 2023).  
AI-Augmented Leadership and Employee Well-Being  
Employee well-being in IO psychology refers to a multidimensional construct encompassing hedonic (affective)  
and eudaimonic (functional) well-being, including autonomy, engagement, and perceived purpose (Grant et al.,  
2020; Brough et al., 2023).  
While AI technologies are often linked to process improvement, their influence on well-being depends heavily  
on how managers integrate and manage these tools (George et al., 2022). By leveraging AI, leaders can limit  
areas of uncertainty, enhance the quality of feedback, and encourage more inclusive communication, particularly  
through increased transparency in algorithms (Ransbotham et al., 2023).  
The concept of AI-augmented leadership refers to practices where AI is used to strengthen decision-making,  
improve communication, optimize monitoring, and foster team development (Dellermann et al., 2022). Far from  
seeking to replace leaders, artificial intelligence enhances their cognitive abilities, enabling a more responsive  
and data-driven management style (Jussupow et al., 2023). However, the impact of AI-augmented leadership on  
employee well-being depends not only on efficiency gains but also on how employees perceive the use of this  
technology, particularly in terms of fairness, transparency, and trust.  
Recent research indicates that the use of technology in leadership can have a positive impact on well-being by  
strengthening perceptions of competence, autonomy, and fairness (Meijerink et al., 2022; Hauff et al., 2023).  
Consistent with JD-R theory, AI-augmented leadership is presented as a professional resource capable of  
promoting psychological health, provided it is implemented inclusively and with an ethical approach.  
H1: AI-augmented leadership is positively associated with employee well-being.  
Moderating Role of Authoritarian Leadership Style  
An authoritarian leadership style, often marked by centralized control, reduced upward communication, and  
unilateral decision-making, risks hampering the benefits of psychological safety and fairness, particularly in  
technological environments.  
Integrating contingency and cultural leadership theories (House et al., 2024), this study conceptualizes  
authoritarianism as a contextual moderator that shapes the translation of AI leadership into psychological  
outcomes. While AI tools can enhance communication and fairness in participatory settings, they may instead  
strengthen control and stress in authoritarian systems. This cultural contingency is central to understanding the  
boundary conditions of AI leadership effectiveness in the MENA region.  
In many companies located in the MENA region, the adapted leadership styles are authoritarian, marked by a  
high degree of control, rigid hierarchy, and limited employee involvement, and risk limiting the benefits that AI  
could offer. Although integrating AI into leadership can generate positive results, its effectiveness remains  
closely linked to the overall leadership conditions within organizations. This type of approach can intensify  
stress within teams and reduce perceptions of fairness (Olan et al., 2022; Kshetri, 2023). In these contexts,  
employees may not view AI as an opportunity for empowerment, but rather as a tool intended to strengthen  
control mechanisms.  
According to Sarma and Braganza (2023); Employees in these regions may perceive AI tools more as  
surveillance than as support mechanisms. This negative perception may thus limit the positive impact that  
psychological safety and fairness could have on employee well-being.  
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In short, leadership culture, particularly in societies with significant power distance, such as those in the MENA  
region, exerts a decisive influence on the adoption and integration of artificial intelligence (AI) (Olan et al., 2022  
; Hofstede Insights, 2023).  
H2: Authoritarian leadership moderates the relationship between AI-augmented leadership and employee well-  
being  
Figure 1: The research model  
METHODOLOGY  
Research Design  
Given the exploratory and predictive nature of the research design, a survey methodology was adopted to collect  
data from employees of several organizations in the MENA region.  
A structured questionnaire was developed and distributed to professionals working in public and private sector  
organizations undergoing digital transformation in three MENA countries: Tunisia, Egypt, and Saudi Arabia.  
Sampling and Data Collection  
The study collected data via an online questionnaire targeting employees and leaders in public and private sector  
organizations across Tunisia, Egypt, and Saudi Arabia. These countries were chosen for their advanced digital  
transformation initiatives and culturally diverse environments. To ensure meaningful results, a purposive  
sampling strategy was employed, specifically focusing on individuals actively engaged in or influenced by AI-  
driven leadership practices. This method aimed to gather nuanced insights into the effects of AI integration in  
professional settings. Following data cleaning, the final sample consisted of 104 valid responses. Data collection  
took place between December 2024 and April 2025, utilizing both online surveys and printed questionnaires  
disseminated through professional networks, academic alumni groups, and organizational human resources  
departments. The purposive sample targeted knowledge workers, mid-level managers, and senior professionals  
familiar with the use of AI tools in their work environments. While a total of 137 responses were initially  
received, incomplete or invalid submissions were excluded, leaving 104 usable responses for analysis.  
Participants represented various industries including finance, healthcare, education, manufacturing, and public  
administration. The gender distribution was approximately 58% male and 42% female, with 63% of respondents  
employed in the private sector and 37% in the public sector. Notably, over 70% of respondents reported frequent  
interaction with AI-enabled systems such as chatbots, data analytics platforms, and AI-enhanced decision-  
making tools. Geographically, 43 respondents were based in Tunisia, 31 in Egypt, and 30 in Saudi Arabia.  
Measurement Instruments  
The questionnaire included validated scales adapted to the study context, all measured on a 5-point Likert scale  
(1 = strongly disagree to 5 = strongly agree):  
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AI-Augmented Leadership: Measured using a 6-item scale adapted from Dellermann et al. (2022) and  
Jussupow et al. (2023), capturing leader reliance on AI tools in decision-making, communication, and  
monitoring.  
Authoritarian Leadership Style: Measured using a 5-item scale adapted from Cheng et al. (2004),  
capturing control and hierarchical leadership traits.  
Employee Well-Being: Measured using a 7-item scale adapted from Danna and Griffin (2022), capturing  
affective and cognitive aspects of well-being at work.  
Data Analysis  
The data analysis followed a two-stage approach consistent with best practices for structural equation modeling:  
1. Measurement Model Assessment: 1. Partial Least Squares Structural Equation Modeling (PLS-SEM)  
was used to test the measurement model and structural paths using SmartPLS 4.0. This method was  
chosen due to its suitability for exploratory models with latent constructs, small-to-medium sample sizes,  
and moderation effects (Hair et al., 2023). Measurement model validity was assessed through indicator  
reliability, composite reliability, average variance extracted (AVE), and discriminant validity (Fornell-  
Larcker and HTMT criteria).  
2. Structural Model Testing: Hypotheses were tested using bootstrapping with 5,000 resamples to assess  
path coefficients, significance, effect sizes (f²), and predictive relevance (Q²).  
Justification for Using SmartPLS  
SmartPLS, a variance-based structural equation modeling tool, was selected for several reasons:  
•
It performs robustly with moderate sample sizes, such as the 104 responses in this study, unlike  
covariance-based SEM, which typically demands larger samples (Hair et al., 2019).  
•
•
It is well-suited for analyzing complex models that incorporate moderation effects.  
It accommodates the expected data distribution in the MENA context by not requiring strict normality  
assumptions (Sarstedt et al., 2020).  
•
It includes bootstrapping procedures that enable reliable significance testing for both indirect and  
interaction effects (Ringle et al., 2020).  
RESULTS  
Descriptive Statistics  
The study involved 104 respondents, comprising employees from public and private sector organizations across  
Tunisia, Egypt, and Saudi Arabia. Participants were between the ages of 25 and 55, with a gender distribution  
of 62% male and 38% female. Around 54% held mid-level managerial roles, while 46% worked in front-line  
positions. Nearly 68% indicated that AI technologies were actively integrated into their workplaces, particularly  
for tasks such as performance tracking, scheduling, and supporting decision-making processes.  
Measurement Model Assessment  
To assess construct validity and reliability, the measurement model was evaluated using SmartPLS 4.0.  
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Table 1. Reliability and Convergent Validity of the Constructs  
Construct  
Cronbach’s Alpha Composite Reliability AVE  
AI-Augmented Leadership 0.87  
0.91  
0.87  
0.93  
0.63  
0.59  
0.68  
Authoritarian Leadership  
Employee Well-Being  
0.83  
0.91  
All loading values exceeded 0.70 and discriminant validity was confirmed using both the Fornell–Larcker  
criterion and HTMT ratio (<0.85).  
Structural Model Results  
The structural model was evaluated using bootstrapping (5,000 resamples). The path coefficients, t-values, and  
p-values are shown below:  
Table 2. Hypotheses Testing Results  
Hypothesis Path  
β (Beta) t-value p-value Result  
AI-Augmented Leadership → Well-Being  
0.32  
3.84  
2.15  
<0.001  
0.032  
H1  
H2  
Supported  
Supported  
Authoritarian Leadership (moderator) × AI- –0.22  
Augmented Leadership → Employee Well-Being  
Moderation Effects  
Moderation analyses revealed that an authoritarian leadership style negatively influenced the following  
relationships: employee well-being - AI-augmented leadership. These results suggest that in environments  
characterized by authoritarian leadership, the beneficial effects of AI-augmented leadership are diminished.  
DISCUSSION  
This research aimed to examine the impact of AI-augmented leadership on employee well-being, highlighting  
the moderating role of authoritarian leadership style in MENA-based organizations. The findings provide robust  
empirical support for a majority of hypotheses and provide valuable insights into the implications of human-  
centered implications of AI-augmented leadership in developing digital economies.  
Main Findings  
First, consistent with previous research (Dellermann et al., 2022; Dwivedi et al., 2023), the results confirm that  
AI-augmented leadership positively influences employee well-being, both directly and indirectly. Leaders who  
integrate AI to improve decision-making, transparency, and communication report higher job satisfaction,  
reduced stress, and a fairer perception of their management. These observations support the idea that AI, when  
deployed ethically and strategically, can serve as a valuable resource in the workplace (Meijerink et al., 2022;  
Bakker and Demerouti, 2017).  
Authoritarian leadership style was shown to moderate key relationships. In highly authoritarian environments,  
the positive impacts of AI-augmented leadership on psychological safety and perceived fairness were attenuated,  
consistent with the findings of Olan et al. (2022) and Kshetri (2023). These findings highlight the need to  
consider contextual leadership culture, particularly in hierarchical or paternalistic systems typical of many  
organizations in the MENA region.  
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Short, this study confirms and expands on the findings established in the Global North on several key points.  
Similar to the work of Jussupow et al. (2023), it highlights that human-AI collaboration in leadership can  
generate positive results, but only if it is based on ethical leadership behavior. Unlike in Western contexts, where  
transformational leadership predominates, this research reveals that authoritarian leadership can reduce or even  
reverse the benefits provided by AI, particularly in cultures marked by high power distance (El Sawy et al.,  
2022).  
Practical and Theoretical Implications  
This study adds valuable insights to the expanding research at the crossroads of artificial intelligence, leadership,  
and employee well-being, offering several significant theoretical implications. It enhances the understanding of  
AI leadership within specific contexts. In the MENA region, where leadership tends to be centralized, access to  
digital technologies remains unequal, and employee participation is often limited, the use of AI tools carries the  
risk of reinforcing existing power dynamics if not implemented with care. The results suggest that unless  
leadership evolves toward greater inclusivity and transparency, the ethical and psychological challenges posed  
by AI could surpass its potential benefits in terms of efficiency.  
The moderating role of authoritarian leadership highlights the importance of contextual and cultural variables in  
leadership and AI research. This observation supports the arguments of researchers such as Metcalfe (2023) and  
Kshetri (2023) for a more localized and culturally adapted understanding of digital transformation in non-  
Western contexts. In particular, the identified negative moderating effects indicate that distance and hierarchical  
rigidity can neutralize the developmental benefits of AI, if not intentionally addressed.  
Managerial Implications  
The findings offer several practical insights for organizational leaders, HR professionals, and policymakers in  
the MENA region and similar emerging economies.  
Organizations should treat AI-augmented leadership not as a technical solution but as a strategic capability that  
requires ethical training, change management, and human-centered implementation. Leaders should be trained  
to use AI tools in ways that promote transparency, reduce ambiguity, and invite employee participation.  
In some industries in the MENA region, authoritarian leadership styles often remain deeply entrenched. It is  
crucial for senior leadership teams to evaluate these practices and move toward more inclusive and participatory  
leadership. This transition will not only increase the effectiveness of AI tools but also strengthen organizational  
resilience and employee well-being.  
Finally, policymakers in nations aspiring to digital transformation must establish regulatory frameworks and  
promote educ literacy initiatives aimed at ethical AI adoption. Partnerships between governments and industry  
sectors can help develop ethical standards for the use of AI in leadership and human resources, ensuring that  
this innovation brings tangible benefits to human development.  
Limitations, Future Research and Conclusion  
This study opens promising avenues for future research. Further analysis of the interactions between AI  
technologies and leadership styles, taking into account diverse cultural contexts and organizational levels, would  
be valuable. Longitudinal and experimental approaches could provide a better understanding of how these  
dynamics evolve over time. Furthermore, it would be worthwhile to investigate the influence of other contextual  
factors such as trust in AI, digital maturity, and organizational transparency.  
From a practical perspective, the results demonstrate that the successful integration of AI is not merely a  
technical challenge but also a leadership imperative. Companies seeking to maximize the benefits of AI will  
need to invest in strengthening managerial skills, fostering employee engagement, and designing systems that  
prioritize fairness. In regions like MENA, where authoritarian leadership sometimes remains dominant, it is  
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essential to promote more participatory and inclusive management practices while firmly committing to digital  
transformation initiatives.  
In conclusion, the human impact of AI in organizations depends as much on the technology itself as on how it  
is implemented, managed, and understood. Human-centered leadership, guided by ethical principles and cultural  
sensitivity, is crucial for transforming innovation into a source of well-being.  
Despite the contributions of this study, certain limitations must be acknowledged, paving the way for future  
investigations.  
First, this research uses a cross-sectional design, limiting the possibility of inferring causal relationships.  
Nevertheless, the objective was exploratory and theoretical rather than confirmatory. The use of PLS-SEM is  
appropriate for testing preliminary models and laying empirical groundwork for future longitudinal validations.  
Second, while the MENA region offers a rich and relatively unexplored context, it is not culturally homogeneous.  
Variations between countries in terms of power distance, institutional trust, and digital maturity likely influence  
leadership-AI interactions. Comparative and multi-level studies could better clarify these effects.  
Third, the quantitative methodology used allows for hypothesis testing and model validation, but may be limited  
in capturing the subjective and emotional dimensions of employees' perceptions of AI. Qualitative approaches,  
such as in-depth interviews or focus groups, could enrich our understanding of the relational and ethical  
challenges of AI integration.  
Finally, future research could examine other psychological mechanisms, such as perceived empowerment,  
algorithmic transparency, and trust in AI, to better understand the impact of AI-augmented leadership on  
employee well-being.  
In summary, this study makes a theoretical contribution by situating AI-augmented leadership within  
institutional and intercultural contexts, and a methodological one by demonstrating the exploratory validity of  
PLS-SEM in emerging regions. Future longitudinal or experimental research will be essential to establish causal  
pathways and refine the conditions for the effectiveness of AI-augmented leadership.  
This study aimed to explore the impact of AI-augmented leadership on employee well-being in the MENA  
region, focusing on the role of authoritative leadership. Based on the Job Demands-Resources (JD-R) framework  
and recent research on human-AI collaboration, a conceptual model was developed and empirically tested using  
data collected from 104 professionals in Tunisia, Egypt, and Saudi Arabia. This research makes several important  
contributions. First, it presents AI-augmented leadership as an innovative and influential concept in the fields of  
organizational behavior and digital transformation. Second, it highlights the importance of authoritative  
leadership in determining the effectiveness of AI adoption, particularly in hierarchical and culturally complex  
contexts such as those observed in the MENA region.  
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