Driving Innovation: Entrepreneurial Leadership in the Jordanian IT Sector, the Role of Artificial Intelligence

Abstract

This study investigates the interplay between entrepreneurial leadership and innovation performance in Jordanian IT firms, with a specific focus on the strategic role of Artificial Intelligence (AI). Grounded in a quantitative methodology, data were collected via a structured questionnaire from 162 professionals within the Jordanian IT sector. The research model positions AI not merely as a tool but as a potential catalytic factor, examining its direct and moderating effects on the leadership–innovation dynamic. Entrepreneurial leadership was assessed through the dimensions of innovative thinking, pro-activeness, and risk-taking, while innovation performance was measured across product, process, and organizational domains. The findings demonstrate that entrepreneurial leadership exerts a significant positive influence on innovation performance. Beyond the primary leadership effect, our data also reveal a significant, direct benefit from AI adoption on innovation outcomes. However, contrary to the proposed hypothesis, the analysis indicates that AI does not function as a statistically significant moderator in the relationship between entrepreneurial leadership and innovation. This suggests that, within this context, AI operates as a parallel driver of innovation rather than an enhancer of the leadership’s effectiveness. The study provides a critical contribution to the literature by challenging the assumed interactive role of AI in leadership models within emerging economies. It offers actionable insights for leaders in technology firms, emphasizing the imperative of developing strong entrepreneurial leadership capabilities and pursuing strategic AI adoption as complementary, yet independent, pathways to achieving superior innovation.

1. Introduction

The contemporary business landscape is characterized by relentless technological disruption and hyper-competition, compelling organizations to prioritize innovation as the primary currency for sustainable competitive advantage (Ercantan et al., 2024). In this volatile environment, leadership has evolved beyond traditional management. Success now demands a synthesis of entrepreneurial vision and the capability to mobilize resources—a fusion embodied in Entrepreneurial Leadership (EL) (Pauceanu et al., 2021). EL fosters resilience, champions innovation, and proactively steers organizations through uncertainty (Lee et al., 2020). Its significance is particularly pronounced in technology-driven sectors, such as IT, where it is fundamental to promoting innovative behavior and driving superior innovation performance (IP) (Al-Sharif et al., 2023).

Concurrently, Artificial Intelligence (AI) has emerged as a transformative force, redefining business execution and strategy (Iansiti & Lakhani, 2020). As a new operational paradigm, AI enhances decision-making, optimizes processes, and unlocks novel pathways for value creation (Enholm et al., 2021). Its potential to reshape business dynamics and accelerate innovation presents a compelling area of study, particularly regarding how it interacts with human-driven strategic elements like leadership (Gama & Magistretti, 2025). The central question is whether AI serves as a passive tool or a strategic catalyst that amplifies the impact of entrepreneurial leaders. This interplay is especially critical in the IT sector, where innovation is a prerequisite for survival (T. H. Nguyen et al., 2021).

This study situates itself within Jordan, a nation actively pursuing digital transformation through initiatives like the Jordan AI Strategy and Implementation Roadmap 2021–2025 (MODE, 2020). The Jordanian IT sector represents a dynamic context for examining these relationships, operating at the intersection of a growing digital economy and global pressure to innovate (Abaddi, 2024). Despite the recognized importance of both EL and AI, a significant gap exists in understanding their combined effect on innovation within this specific regional context (Bani-Melhem et al., 2025; Al-Omar et al., 2024; Alalawneh et al., 2022). Empirical research investigating AI’s moderating role in the EL-IP relationship in an emerging economy setting remains scarce. Our investigation aims to bridge this gap by examining the direct impact of EL on IP within Jordanian IT firms and testing the proposed moderating role of AI. The research is guided by two primary questions:

• To what extent do Entrepreneurial Leadership practices impact Innovation Performance in the Jordanian IT sector?
• Does Artificial Intelligence (in terms of organizational awareness and level of use) moderate the relationship between Entrepreneurial Leadership and Innovation Performance?

The significance of this research is twofold. Academically, it contributes to the literature by empirically testing a model that positions AI as a potential moderator, providing a nuanced understanding of how technological adoption interacts with leadership in a high-tech, non-Western context. Practically, the findings offer actionable insights for leaders and policymakers in Jordan and similar economies on cultivating entrepreneurial leadership and investing in AI capabilities (Bani-Melhem et al., 2025; Al-Omar et al., 2024; Alalawneh et al., 2022). To achieve these objectives, the study employs a quantitative methodology. The subsequent sections detail the literature review, methodology, findings, and implications.

2. Literature Review and Hypotheses Development

2.1. Introduction

This section establishes the theoretical foundation for examining the relationship between Entrepreneurial Leadership (EL) and Innovation Performance (IP) within the Jordanian IT sector, with a specific focus on the moderating role of Artificial Intelligence (AI). It synthesizes contemporary literature, defines core constructs, elucidates their interrelationships, and identifies the research gap. The section concludes with the research model and testable hypotheses.

2.2. The Converging Domains: Entrepreneurial Leadership

Entrepreneurial Leadership (EL) represents a critical fusion of entrepreneurial and leadership competencies, essential for navigating today’s volatile business environment (Ataei et al., 2024). It is defined as a leadership style that inspires and directs employees to explore and exploit opportunities, fostering vision, flexibility, and strategic change (Renko et al., 2015; Widyani et al., 2020). Gupta et al. (2004) provide a foundational framework conceptualizing EL through three key behavioral dimensions:

• Innovative Thinking: Encouraging creativity and novel solutions.
• Proactiveness: Anticipating and aggressively competing in the market.
• Risk-Taking: Willingness to undertake calculated risks.

These dimensions position EL as a potent driver of organizational innovation, particularly in dynamic sectors like IT.

2.3. The Outcome Variable: Innovation Performance

Innovation Performance (IP) is a critical indicator of an organization’s ability to adapt, compete, and achieve long-term viability (Santoro et al., 2020). It encompasses the process from idea generation to successful diffusion (Fontana & Musa, 2016). For this study, IP is measured through three dimensions:

• Product Innovation: New or significantly improved goods/services.
• Process Innovation: New or improved production/delivery methods.
• Organizational Innovation: New business practices or workplace organization.

In the IT sector, sustained IP is a prerequisite for survival and growth (T. H. Nguyen et al., 2021).

2.4. Review of Empirical Studies on Entrepreneurial Leadership and Innovation

A substantial body of empirical research has established a positive relationship between entrepreneurial leadership and various innovation outcomes across diverse contexts.

Table 1. Summary of Key Empirical Studies on Entrepreneurial Leadership and Innovation.

Author(s) & Year	Context	Independent Variables	Dependent Variables	Key Findings Related to El & Innovation
Cai et al. (2020)	Chinese Tech SMEs	Entrepreneurial Leadership	Innovative Work Behavior	EL significantly boosts innovative behavior. The firm’s innovative environment mediates this effect.
T. H. Nguyen et al. (2021)	A meta-analysis integrating 59 studies	IT adoption		Resources and market turbulence were the main predictors of IT adoption in SMEs when testing only the direct relationships (MASEM).
T. M. Nguyen et al. (2021)	Vietnamese IT SMEs	Entrepreneurial Leadership	SME Performance	EL enhances performance through mediators like team creativity and dynamic capabilities, which are core to innovation.
Alalawneh et al. (2022)	Jordanian Fashion SMEs	Social Media Usages	Innovation Performance	Social media usages have a significant positive impact on the innovation performance within the Jordanian fashion SMEs
Alshurideh et al. (2024)	Jordanian Insurance Sector	Entrepreneurial Leadership	Organizational Innovation	EL is a critical driver of innovation and long-term competitiveness within a Jordanian context.
Sawaean and Ali (2019)	Kuwaiti SMEs	Entrepreneurial Leadership, Learning Orientation	Organizational Performance	EL has a significant positive impact, with innovation capacity acting as a key mediator.
Al-Omar et al. (2024)	Jordanian SMEs	Social media usages	Digital Innovation Process and Innovation Performance	SM usage assists SMEs in idea generation, experimentation, and implementation stages to improve revenues from new products and services.
Tukiran et al. (2021)	Literature Review	Entrepreneurial Leadership	Organizational Performance	The review concludes that EL has a positive influence on performance by fostering employee creativity and innovation.
Bani-Melhem et al. (2025)	Food Retail supply chain	SC Innovation	SC Resiliency and Service Performance	SCI exerts both direct and indirect positive effects on SVP.

summarizes key recent studies.

As Table 1 illustrates, the positive influence of EL on innovation is well-documented. However, none of these studies incorporates AI as a contextual or moderating variable. Our investigation addresses this gap by proposing AI as a moderating variable that may amplify the effectiveness of entrepreneurial leaders.

2.5. Entrepreneurial Leadership and Innovation Performance: The Direct Link

Grounded in the Resource-Based View (RBV), EL is itself a valuable, rare, and difficult-to-imitate organizational resource (Al-Sharif et al., 2023). Leaders who exhibit innovative thinking, proactiveness, and risk-taking build a culture that identifies opportunities, mobilizes resources, and navigates uncertainty (Kero & Bogale, 2023). Empirical evidence consistently shows that EL positively influences innovation capacity and outcomes (Cai et al., 2020; T. M. Nguyen et al., 2021; Sharma & Bhat, 2022). Therefore, we hypothesize:

H1: 

Entrepreneurial leadership practices have a significant positive impact on innovation performance in the Jordanian IT sector.

H1a: 

Innovative thinking positively enhances innovation performance.

H1b: 

Proactiveness positively enhances innovation performance.

H1c: 

Risk-taking positively enhances innovation performance.

2.6. The Moderating Role of Artificial Intelligence

Artificial Intelligence (AI) refers to systems that can sense, comprehend, act, and learn (Mikalef & Gupta, 2021). Its significance lies in its dual capacity for automation and augmentation (Enholm et al., 2021). In the context of EL and IP, AI is theorized to act as a strategic catalyst. It can augment an entrepreneurial leader’s capabilities by providing data-driven insights for opportunity recognition, streamlining operations to free resources for innovation, and enabling AI-powered products (Wamba-Taguimdje et al., 2020; Gama & Magistretti, 2025). Consequently, the presence and use of AI should strengthen the positive relationship between EL and IP. We propose:

H2: 

Artificial Intelligence (in terms of organizational awareness and level of use) positively moderates the relationship between entrepreneurial leadership practices and innovation performance in the Jordanian IT sector.

AI Readiness as a Precondition: We acknowledge that AI’s moderating effect may be contingent on organizational readiness, including absorptive capacity, data infrastructure, and integration maturity (Mikalef & Gupta, 2021; Russo, 2024). This study focuses on awareness and use as foundational proxies for AI engagement.

2.7. The Jordanian Context and the Research Gap

Jordan has actively promoted digital transformation and AI adoption through national strategies like the Jordan AI Strategy and Implementation Roadmap 2021–2025 (MODE, 2020). While studies have examined EL in Jordanian sectors like insurance and pharmaceuticals (Alshurideh et al., 2024; Alrifae et al., 2024), the retail food industry (Bani-Melhem et al., 2025), SMEs (Al-Omar et al., 2024; Alalawneh et al., 2022), and others have explored AI adoption factors (Almashawreh et al., 2024), a critical gap remains. The specific interplay between EL, AI, and IP within the Jordanian IT sector is underexplored. This study fills this gap by investigating how AI moderates the EL-IP relationship, providing context-specific insights for both theory and practice.

2.8. Research Model and Hypotheses

Based on the literature, the following research model (Figure 1) and hypotheses are proposed.

3. Research Methodology

3.1. Introduction

This section delineates the methodological framework, including research design, population and sampling, data collection, instrument development, and statistical techniques.

3.2. Research Design

This study adopted a quantitative, deductive, cross-sectional design. A quantitative approach was deemed appropriate for objectively measuring relationships and testing hypotheses (Saunders et al., 2019). The design incorporated descriptive and inferential statistics, including correlation and hierarchical regression analysis. This design is robust for examining complex variable interactions in organizational settings (Hair et al., 2019).

3.3. Population and Sampling

The target population was all registered IT companies in Jordan. The sampling frame was derived from the Jordanian Association for Software and Mobile Applications (Intaj), comprising 288 companies. A census approach was attempted, yielding 162 complete responses (56% response rate). The final sample is considered highly representative of the target population for a business survey, mitigating concerns of non-response bias and supporting the generalizability of the findings within this context (Cycyota & Harrison, 2006). The unit of analysis was the individual employee, with respondents spanning various hierarchical levels (e.g., managers, engineers, developers) to capture diverse perspectives on leadership and innovation practices.

3.4. Data Collection Instrument and Procedure

3.4.1. Instrument Development

A structured online questionnaire served as the primary instrument. Scales were adapted from well-established instruments. All items used a five-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree). The questionnaire had two parts:

• Part A: Demographic and organizational data.
• Part B: Multi-item scales for main constructs (see

  Table 2. Operationalization of Constructs and Measurement Scales.

  Construct	Type	Key Dimensions/Focus	Items	Sample Source For Scale Adaptation
  ENTREPRENEURIAL LEADERSHIP (EL)	Independent	Innovative Thinking, Proactiveness, Risk-Taking	15	Gupta et al. (2004); Renko et al. (2015)
  INNOVATION PERFORMANCE (IP)	Dependent	Product, Process, Organizational Innovation	15	Alegre and Chiva (2013)
  ARTIFICIAL INTELLIGENCE (AI)	Moderator	Awareness and Level of Use	7	Adapted from Enholm et al. (2021)

  ).

3.4.2. Data Collection Procedure

The questionnaire was administered electronically via Google Forms and distributed through professional networks and direct outreach. A cover letter explained the research purpose, ensured anonymity, and guaranteed confidentiality.

3.5. Validity and Reliability

3.5.1. Pilot Study and Reliability

A pilot study (N = 30) assessed reliability. Cronbach’s alpha for all constructs exceeded 0.70, indicating high internal consistency (

Table 3. Reliability Coefficients from the Pilot Study (N = 30).

Construct	Dimension	Items	Cronbach’s Alpha
ENTREPRENEURIAL LEADERSHIP	Innovative Thinking	5	0.91
	Proactiveness	5	0.91
	Risk-Taking	5	0.88
INNOVATION PERFORMANCE	Product Innovation	5	0.78
	Process Innovation	5	0.84
	Organizational Innovation	5	0.81
ARTIFICIAL INTELLIGENCE	---	7	0.92

).

3.5.2. Validity Assessment

• Content Validity: Established through literature grounding and expert review.
• Construct Validity: Assessed via Exploratory Factor Analysis (EFA). The Kaiser-Meyer-Olkin (KMO) measure ranged from 0.77 to 0.87, and Bartlett’s Test was significant (p < 0.001) (Hair et al., 2019).
• Common Method Bias (CMB): Harman’s single-factor test indicated that the first factor explained 38.7% of the variance (<50% threshold), suggesting CMB is not a major concern. EFA results, factor loadings, and variances showing loadings > 0.5 and cumulative variance > 60% for each construct.

3.6. Data Analysis Techniques

Data analysis used SPSS v28. The procedure included:

• Data Screening: Checked for missing values, outliers, multicollinearity (VIF < 5), and normality (Skewness/Kurtosis within ±2).
• Descriptive Statistics: Summarized demographics and variable central tendencies.
• Inferential Analysis:

  -

  Pearson’s Correlation: Examined bivariate relationships.

  -

  Hierarchical Multiple Regression: Tested hypotheses with control variables (firm size, job role, experience) included in Step 1.

The statistical significance level for all hypothesis tests was set at p < 0.05. This comprehensive analytical plan ensures a robust examination of the proposed relationships, aligning with best practices in quantitative social science research.

4. Data Analysis and Results

4.1. Introduction

This section presents the empirical findings, including sample profiling, reliability/validity checks, descriptive statistics, and hypothesis testing.

4.2. Sample Characteristics

The survey yielded 162 usable responses (56% response rate). Respondent demographics are in

Table 4. Respondent Demographic Profile (N = 162).

Characteristic	Category	Frequency	Percentage
GENDER	Male	88	54.3%
	Female	74	45.7%
EDUCATION LEVEL	Bachelor’s Degree	142	87.6%
	Postgraduate	18	11.1%
	Higher Diploma	2	1.2%
JOB ROLE	Programmer	80	49.3%
	Engineer	45	27.7%
	Managerial	24	14.8%
	Customer Relationship	13	8.0%
YEARS OF EXPERIENCE	5 years or less	102	63.0%
	6–9 years	36	22.2%
	10–14 years	15	9.3%
	15 years or more	9	5.6%

.

The organizational profile is in

Table 5. Organizational Profile (N = 162).

Characteristic	Category	Frequency	Percentage
COMPANY SIZE (Employees)	101–250	61	37.7%
	50–100	38	23.5%
	Less than 50	34	21.0%
	More than 250	29	17.9%
PRIMARY SECTOR	Software Development	91	56.2%
	IT Services	36	22.2%
	Other (e.g., Data, Cybersecurity)	22	13.6%
	Telecommunications	13	8.0%

.

4.3. Assessment of Measurement Model

Before hypothesis testing, the reliability and validity of the constructs were assessed to ensure the robustness of the measures. In terms of Reliability Analysis, Cronbach’s alpha for all constructs exceeded 0.70 (

Table 6. Reliability Analysis (Cronbach’s Alpha).

Construct	Dimension	Items	Cronbach’s Alpha
ENTREPRENEURIAL LEADERSHIP (EL)	Innovative Thinking	5	0.857
	Pro-activeness	5	0.884
	Risk-Taking	5	0.853
INNOVATION PERFORMANCE (IP)	Product Innovation	5	0.815
	Process Innovation	5	0.859
	Organizational Innovation	5	0.872
ARTIFICIAL INTELLIGENCE (AI)	---	7	0.895

), demonstrating excellent reliability. Meanwhile, Construct Validity was confirmed via EFA (KMO > 0.77, Bartlett’s p < 0.001). Factor loadings were >0.5, and cumulative variance explained exceeded 60% (Hair et al., 2019).

4.4. Descriptive Statistics and Data Screening

For data screening, there were no missing data; no extreme outliers (Z-scores within ±3); and Skewness and Kurtosis were within ±2. VIF values were below 2, indicating no multicollinearity. Based on the descriptive statistics, all constructs were perceived positively (

Table 7. Descriptive Statistics for Main Constructs.

Construct	Mean	Std. Deviation	Interpretation
ENTREPRENEURIAL LEADERSHIP (EL)	3.64	0.58	Medium-High
INNOVATION PERFORMANCE (IP)	3.56	0.61	Medium-High
ARTIFICIAL INTELLIGENCE (AI)	3.32	0.72	Medium

). EL had the highest mean (M = 3.64), followed by IP (M = 3.56). AI had the lowest mean (M = 3.32), suggesting it is the least developed area.

4.5. Hypothesis Testing

Pearson correlations (

Table 8. Inter-Correlations of Study Variables.

Variable	1. EL	2. IP	3. AI
1. ENTREPRENEURIAL LEADERSHIP (EL)	1
2. INNOVATION PERFORMANCE (IP)	0.720 **	1
3. ARTIFICIAL INTELLIGENCE (AI)	0.431 **	0.553 **	1

Note: ** Correlation is Significant at the 0.01 Level (2-Tailed).

) show significant positive relationships. EL and IP were strongly correlated (r = 0.720, p < 0.001). AI correlated positively with both EL (r = 0.431) and IP (r = 0.553) (Table 8).

A hierarchical multiple regression analysis was employed to test the hypotheses. The direct effect of EL on IP was tested in Model 1. The moderating effect of AI was tested in Model 2 by adding the interaction term (EL × AI) (Hayes, 2018). The results are presented in

Table 9. Results of Hierarchical Regression Analysis for Innovation Performance.

Predictor	Model 1		Model 2
	β	t	β	t
Control Variables
Firm Size	0.08		0.08
Job Role	0.05		0.05
Experience	0.03		0.03
STEP 1: DIRECT EFFECTS
ENTREPRENEURIAL LEADERSHIP (EL)	0.591	10.511 ***	0.590	10.456 ***
ARTIFICIAL INTELLIGENCE (AI)	0.299	5.319 ***	0.299	5.297 ***
STEP 2: INTERACTION EFFECT
EL × AI			−0.004	−0.177
MODEL SUMMARY
R2	0.591		0.591
ADJUSTED R2	0.586		0.584
F-STATISTIC	114.712 ***		76.226 ***
ΔR2			0.000

Note: *** p < 0.001; β = Standardized Coefficient.

.

Test of Hypothesis 1 (H1): EL significantly predicts IP (β = 0.591, p < 0.001), supporting H1. Sub-hypotheses H1a, H1b, and H1c were also supported (all p < 0.001).

Test of Hypothesis 2 (H2): The interaction term (EL × AI) was not significant (β = −0.004, p = 0.594), and ΔR² was zero. Thus, H2 is not supported.

Table 10. Summary of Hypothesis Testing Results.

Hypothesis	Relationship	Result
H1	Entrepreneurial Leadership → Innovation Performance	Supported
H1A	Innovative Thinking → Innovation Performance	Supported
H1B	Pro-activeness → Innovation Performance	Supported
H1C	Risk-Taking → Innovation Performance	Supported
H2	AI moderates the EL → IP relationship	Not Supported

summarizes hypothesis testing results.

5. Discussion and Conclusions

This section synthesizes the key findings of this research, interpreting them through the lenses of established and emerging theoretical frameworks. It outlines the study’s limitations, provides actionable recommendations for theory and practice, and offers a forward-looking conclusion on the interplay between entrepreneurial leadership, artificial intelligence, and innovation performance.

5.1. Discussion of Key Findings

5.1.1. The Direct Impact

The robust, direct relationship between Entrepreneurial Leadership (EL) and Innovation Performance (IP) (β = 0.591, p < 0.001) strongly confirms H1. This finding solidly anchors the study within the Resource-Based View (RBV), positioning EL not merely as a managerial style but as a valuable, rare, and inimitable strategic resource that drives competitive advantage through innovation (Al-Sharif et al., 2023; Kuratko et al., 2023). The significance of all EL dimensions—innovativeness, proactiveness, and risk-taking—resonates with the holistic nature of entrepreneurial leadership, which involves envisioning, enabling, and enacting innovative change (Bagheri et al., 2022). Interestingly, the slightly stronger effects for proactiveness and risk-taking offer a nuanced insight into the context of Jordan’s dynamic IT sector. It suggests that in emerging, fast-paced markets, leadership behaviors geared towards anticipating future trends and committing resources to uncertain opportunities may be particularly catalytic for innovation outcomes (Hoang et al., 2024; Latham & Braun, 2023). This finding extends the work of Ataei et al. (2024) by demonstrating that leaders who empower their teams to act autonomously and take calculated risks are effectively bridging the gap between the firm’s latent intellectual capital and its realized, tangible innovation performance. This alignment between leadership action and market dynamism underscores the relevance of dynamic capabilities theory, where EL acts as the micro-foundational capability that senses and seizes opportunities (Teece, 2023).

5.1.2. The Non-Significant Moderating Role of Artificial Intelligence

The rejection of H2, indicating that AI does not moderate the EL-IP relationship, is a critical and insightful finding. Rather than diminishing the study’s contribution, this null result challenges prevalent techno-optimistic assumptions and invites a more sophisticated understanding of technology’s role in organizational leadership (Raisch & Krakowski, 2023). It suggests that, in the current Jordanian IT context, AI and EL function as parallel, complementary drivers of innovation rather than in an interactive, amplifying relationship. We interpret this through two complementary theoretical frameworks and propose a new conceptual lens.

• Technology–Organization–Environment (TOE) Framework: The non-significant moderating effect can be attributed to significant barriers across TOE dimensions. Technologically, despite global advancements, local AI infrastructure may still be immature, with firms relying on generic, off-the-shelf solutions rather than tailored systems (Enholm et al., 2021; Verma et al., 2024). Organizationally, skill gaps and a lack of “AI literacy” among both leaders and employees may prevent the deep integration necessary for AI to transform leadership processes (Shrestha et al., 2023). Environmentally, the regulatory and ecosystem support for sophisticated AI integration in Jordan may still be evolving, focusing more on adoption than strategic exploitation.
• Diffusion of Innovation (DOI) Theory: The moderate mean score for AI adoption (3.32) strongly suggests that, across sampled firms, AI is likely in the early to mid-stages of the diffusion curve (Rogers, 2010). At this stage, AI is predominantly adopted for task automation and isolated operational efficiency (e.g., data analysis, customer service chatbots) rather than for strategic decision support or innovation enhancement that could synergize with EL (Russo, 2024; Mikalef & Gupta, 2023). AI is used by the organization but is not yet woven into the fabric of its leadership and innovation systems.

Consequently, we propose that AI’s hypothesized moderating or amplifying role is contingent upon a threshold of technological moderation readiness—a novel concept emerging from this study. We define this as the level of organizational maturity (encompassing technological infrastructure, human capital, process adaptation, and strategic alignment) required for a specific technology to potentiate the effects of leadership behaviors on organizational outcomes. This finding echoes recent work by Borges et al. (2024), who argue that the “AI leadership gap” often lies not in the technology itself, but in the organization’s readiness to leverage it strategically.

5.2. Implications

Theoretical Implications:

• Contextual Validation and Extension: This study robustly reinforces EL as a critical antecedent to IP, extending its empirical validation to the under-researched Middle Eastern IT context. It thereby strengthens the cross-cultural applicability of RBV and dynamic capabilities theory, showing that the micro-foundations of sensing and seizing are universally critical, albeit with contextual nuances in which dimensions are most potent (Kuratko et al., 2023).
• Nuancing Technology-Leadership Interaction Models: It challenges and refines prevailing models that often assume straightforward positive interactions between advanced technologies and leadership. By introducing the concept of technological moderation readiness, it provides a more conditional and realistic framework for future research, suggesting that the integration stage and organizational context are critical boundary conditions (Raisch & Krakowski, 2023).
• Shifting the Discourse on AI’s Role: The study moves the discourse from “if” AI matters to “how and when” it matters in the leadership-innovation nexus. It positions AI as a potential independent variable or a contextual condition, rather than a universal moderator, aligning with calls for more contingency-based approaches in innovation research (Mikalef & Gupta, 2023).

Practical Implications:

• For Leaders & Executives: Prioritize the development of EL competencies, especially proactiveness and risk-taking, through targeted training and experiential learning. Simultaneously, cultivate personal and organizational AI literacy to transition from mere adoption to strategic integration.
• For Policymakers: Develop national and sectoral strategies that move beyond promoting AI adoption. Focus on building the foundational pillars of technological moderation readiness: investing in digital infrastructure, fostering university-industry partnerships for skill development (e.g., in AI ethics and strategic deployment), and creating incentives for firms that demonstrate mature, integrative use of AI (Verma et al., 2024).
• For Firms (IT Firms in Particular): Conduct an honest audit of your technological moderation readiness. Align AI initiatives directly with strategic innovation goals, and ensure that investments in technology are matched by investments in change management, process redesign, and human capital development to create a synergistic environment where leadership and technology can co-evolve (Shrestha et al., 2023).

5.3. Limitations and Future Research

While providing valuable insights, this study is subject to limitations. Its cross-sectional design precludes causal claims. The single-country (Jordan) and single-sector (IT) focus limits generalizability, and the use of self-reported data may introduce common method bias. Future research should build upon these findings by:

• Employing longitudinal designs to capture how the EL-AI-IP relationship evolves as AI maturity increases within firms and the national ecosystem.
• Using mixed-methods approaches, particularly qualitative case studies, to deeply explore the organizational barriers and facilitators of AI integration that quantitative surveys cannot fully unveil.
• Replicating the study in other sectors (e.g., manufacturing, healthcare) and cultural contexts to test the boundary conditions of our findings and the universality of the technological moderation readiness concept.
• Investigating alternative models, including other potential moderators (e.g., organizational learning culture, digital transformation strategy) and mediators (e.g., team psychological safety, dynamic knowledge management) that may link EL and IP (Borges et al., 2024).
• Developing and empirically validating a multidimensional scale to measure the proposed construct of technological moderation readiness.

5.4. Conclusions

In conclusion, this study firmly establishes Entrepreneurial Leadership as a cornerstone of Innovation Performance in Jordan’s vibrant IT sector, affirming that human vision, courage, and empowerment remain irreplaceable engines of value creation. However, it also offers a crucial reality check: artificial intelligence, in its current state of adoption within this context, is not a magic multiplier of leadership effectiveness. Its role appears to be that of a parallel, independent enabler rather than an interactive catalyst. This insight is profoundly important. It suggests that the path to harnessing the full potential of the digital age is not a simple matter of purchasing advanced software. Instead, it is a dual-track journey of cultivating visionary, entrepreneurial leaders while deliberately and strategically building the organizational readiness to integrate technology at a deep, systemic level. For Jordanian IT firms—and likely for many others in similar emerging innovation landscapes—true excellence in innovation will be achieved by nurturing the human spirit of entrepreneurship and the intelligent architecture of technology, not by expecting one to automatically awaken the other.