Enhancing Sustainable Innovation Performance in the Banking Sector of Libya: The Impact of Artificial Intelligence Applications and Organizational Learning

Abstract

The recent transformation in Libya’s banking industry, driven largely by the Central Bank of Libya, has led to increased financial inclusion, enhanced banking services, and the adoption of digital banking technologies. While most banks have rapidly transitioned from traditional data analysis methods to using Artificial Intelligence (AI) for daily transaction analysis, the impact of AI on sustainable innovation performance and organizational learning remains underexplored. This study, grounded in dynamic capabilities theory, investigates the mediating role of organizational learning in the relationship between AI adoption in the banking sector and sustainable innovation performance. Data were collected from 401 employees across Libya’s conventional and Islamic banking sectors using a judgmental sampling technique. Partial Least Squares Structural Equation Modeling (PLS–SEM) was used to analyze the data and assess the relationships among the variables. The findings indicate that AI adoption significantly and positively influences sustainable innovation performance and organizational learning. Additionally, organizational learning was found to have a significant positive effect on sustainable innovation performance and to partially mediate the relationship between AI adoption and innovation performance. The study recommends that bank management teams implement training programs to enhance employees’ understanding of AI applications, sustainability objectives, and innovative financial services to improve overall efficiency.

1. Introduction

Sustainable innovation performance is important in the banking sector, enhancing long-term competitiveness, operational efficiency, and stakeholder trust [1]. Banks that integrate sustainability into their strategic frameworks can offer environmentally responsible financial products, strengthen risk management systems, and improve digital banking infrastructure. These efforts not only ensure regulatory compliance but also attract socially responsible investors and customers [2].

Sustainable innovation also reduces costs through energy-efficient practices and digital transformation, minimizing the need for physical branches [3]. By making banking services more accessible, sustainable innovation promotes financial inclusion and supports environmental goals through green financing and technology. These initiatives boost the sector’s resilience and adaptability to evolving economic, environmental, and technological challenges.

Artificial Intelligence (AI) is reshaping the banking sector by enhancing productivity, risk management, and customer experience [4]. AI-driven automation streamlines operations, reduces costs, and increases the accuracy of financial transactions. Machine learning algorithms improve fraud detection and credit risk evaluation [5], and AI-powered chatbots and virtual assistants provide real-time, personalized customer service. AI also strengthens regulatory compliance by identifying unusual patterns, flagging potential violations, and helping banks align with financial regulations more effectively [6]. Banks can anticipate customer needs and market trends through predictive analytics, fostering innovation in financial services. Furthermore, AI strengthens cybersecurity and operational resilience, making it indispensable for sustainable banking [7].

AI contributes to sustainable innovation by optimizing resources, improving decision-making, and increasing operational efficiency [8]. Predictive analytics helps identify patterns in energy consumption, waste reduction, and carbon footprint management. Automation lowers operational costs and eliminates redundancies, supporting sustainability goals [9]. AI also enables the creation of intelligent financial products aligned with green investment regulations, embedding sustainability into core business operations. Its role in responsible lending and financial inclusion positions banks as key players in the broader sustainability ecosystem [10]. Integrating AI into sustainable innovation enhances banks’ competitiveness while reinforcing their social and environmental commitments.

Technological advancements and regulatory demands compel banks to balance sustainability with profitability [11]. However, many still prioritize short-term gains, resulting in inefficient resource allocation and limited financial inclusion. Although AI improves efficiency and risk management, its integration with sustainable banking remains insufficiently addressed [12]. A strategic approach is necessary for AI to advance green finance, ethical lending, and ESG compliance [13]. Traditional risk models that overlook sustainability factors jeopardize institutional stability and reputation. To address this, banks must adopt clear strategies that leverage AI for enhancing sustainable innovation, ensuring financial stability, environmental responsibility, and inclusive economic development.

Despite extensive research on AI and innovation, the intersection between AI and sustainable innovation performance in banking remains underexplored. While Shen [14] and Fethi and Pasiouras [15] highlighted AI’s role in operational efficiency, its potential for driving sustainable innovation was not fully examined. Similarly, studies by Chishti et al. [16] and Peng et al. [17] focused on AI in green finance, but not on innovation performance. Research by Königstorfer and Thalmann [18] and Malali and Gopalakrishnan [19] also addressed AI in banking, yet the sustainability dimension was overlooked.

Moreover, the moderating role of organizational learning in the AI–sustainable innovation relationship is rarely investigated. Limited studies explore how organizational learning influences the effectiveness of AI in fostering innovation. Banks must continuously adapt AI strategies to align with environmental, social, and governance (ESG) goals. Addressing this gap could provide deeper insights into how AI and organizational learning jointly enhance sustainable innovation in the sector.

This study addresses these research gaps by examining how AI and organizational learning influence sustainable innovation performance in banking. It explores three key questions:

• What is the influence of AI on organizational learning and sustainable innovation performance in the banking sector?
• What is the influence of organizational learning on sustainable innovation performance?
• How does organizational learning mediate the relationship between AI application and sustainable innovation performance?

This research contributes to the literature by analyzing the impact of AI on both sustainable innovation performance and organizational learning. It highlights how AI facilitates continuous innovation through enhanced learning processes, offering banks a framework to optimize AI-driven initiatives. The study also informs policymakers and practitioners on how to integrate AI into institutional structures to foster ongoing innovation and sustainability.

Additionally, the study examines the direct role of organizational learning in advancing sustainable innovation. It emphasizes the importance of knowledge sharing, adaptive behaviors, and continuous learning in achieving long-term sustainability. By exploring this intersection, the study provides practical strategies for strengthening banks’ creative capacity and their ability to meet social and environmental goals.

Lastly, the research assesses the mediating role of organizational learning between AI adoption and sustainable innovation. It demonstrates how learning processes enhance the effectiveness of innovation initiatives, helping banks cultivate a more adaptive and forward-looking organizational culture. These insights offer actionable guidance for building innovative, resilient, and sustainable financial institutions.

This study is driven by the growing need to integrate sustainability with innovation in the banking sector. As institutions strive to meet ESG expectations, understanding the synergy between emerging technologies and organizational practices is vital. This research outlines strategies for leveraging AI and organizational learning to support sustainable growth, improve operational efficiency, and enhance competitiveness in the evolving financial landscape.

2. Literature Review

2.1. Theoretical Perspective

The relevance of how organizations leverage internal and external resources to adapt to changing conditions, generate innovative ideas, and sustain a competitive advantage has led scholars to adopt the dynamic capabilities theory for this study. This theory underscores the importance of adaptability, learning, and resource reconfiguration in responding to market and technological changes [20]. Examining the interplay between AI and organizational learning can enhance sustainable innovation performance in the banking sector.

The phrase “dynamic capabilities” refers to an organization’s ability to identify and seize new opportunities, reconfigure resources, and maintain a competitive edge in a constantly evolving business environment [21]. The application of AI is particularly relevant to sensing, as it enables businesses to identify emerging trends, client needs, and market fluctuations through advanced data processing and pattern recognition. Organizational learning enables firms to capitalize on opportunities by facilitating the comprehension of insights derived from AI, modifying their strategies, and swiftly acting to exploit identified opportunities. Sustainable innovation performance resembles reconfiguration, as it demonstrates a company’s ability to realign its resources, processes, and competencies in response to environmental changes, hence maintaining competitiveness and adaptability over the long term.

AI serves as a dynamic tool that enables banks to automate operations, harness large datasets, and make swift, informed decisions. Organizational learning, as a dynamic capability, allows banks to evolve, adapt, and innovate through insights drawn from internal and external experiences [22]. These capabilities empower banks to navigate complex regulatory environments and drive sustainable innovation.

AI enables banks to identify emerging trends through advanced data analysis, allowing them to respond to the increasing demand for sustainable financial solutions [23]. It aids in designing environmentally responsible products, strengthens sustainability-related risk assessments, and facilitates adherence to green finance regulations. Moreover, AI enables operational adjustments aligned with sustainability goals [24], promoting continuous innovation, long-term growth, and sustained competitiveness.

Organizational learning equips banks to acquire knowledge, strengthen capabilities, and respond to evolving circumstances [25]. It facilitates the integration of sustainability trends, technological advancements, and innovative practices into sustainable innovation strategies. By fostering a culture of learning, banks can strengthen their sustainability efforts, generate innovative green finance solutions, and enhance overall performance. This ongoing learning process enables them to adapt effectively to emerging opportunities and challenges.

According to dynamic capabilities theory, the integration and application of AI-generated insights in financial institutions depend on organizational learning [26]. A strong learning culture enables banks to adopt AI technologies effectively for continuous innovation. It also allows for the modification of internal processes and the integration of AI insights, ensuring that sustainability efforts align with long-term strategic objectives [27]. This continuous learning process enhances banks’ ability to identify and capitalize on new opportunities, maintaining competitiveness and responsiveness in a rapidly changing market and environmental context.

2.2. Hypothesis Development

This study presents the conceptual framework in Figure 1 (See below).

2.2.1. The Influence of AI on Sustainable Innovation Performance

AI significantly enhances continuous sustainable innovation performance by enabling banks to develop superior, more efficient financial products and services. Technologies such as predictive analytics and machine learning allow banks to offer eco-friendly financial solutions aligned with sustainability goals while anticipating consumer preferences and emerging market trends [28]. Behera et al. [29] found that AI-driven tools help organizations create sustainable investment solutions, addressing the growing demand for socially responsible investing. These innovations not only improve the institution’s reputation but also support environmental objectives, thereby promoting long-term sustainable performance.

Beyond product innovation, AI boosts operational efficiency by optimizing resource allocation and automating processes. AI systems can analyze vast datasets to detect inefficiencies and recommend strategies for waste reduction, energy conservation, and cost savings [30]. Tkachenko [31] demonstrated that AI implementation in operations significantly reduces energy use, waste generation, and carbon emissions across industries, including banking. These efficiency gains contribute to lower operational costs, greater financial stability, and improved sustainability outcomes.

AI also strengthens risk management—a critical component of sustainable innovation—by enabling banks to assess and mitigate ESG-related risks effectively [7]. Using AI algorithms to analyze non-financial data on environmental and social factors, banks can identify risks linked to climate change, regulatory shifts, and supply chain disruptions. Li et al. [32] found that AI-enhanced risk management systems improved banks’ abilities to detect and respond to ESG risks, increasing their resilience to environmental and market challenges. This proactive approach enhances the overall sustainability of banking institutions.

Furthermore, AI promotes financial inclusion, a key driver of sustainable innovation. It allows banks to deliver customized financial solutions to underserved populations, including small businesses and low-income individuals, thereby supporting inclusive economic development [33]. Omokhoa et al. [34] noted that AI-powered credit scoring systems help assess the creditworthiness of clients overlooked by traditional methods, fostering inclusivity and sustainable economic growth. By broadening their client base and advancing financial accessibility and equity, banks strengthen their contribution to sustainable innovation. Based on these insights, the study proposes the following hypothesis:

H1: 

AI application in banks positively and significantly influences sustainable innovation performance.

2.2.2. The Influence of AI on Organizational Learning

AI significantly influences organizational learning by accelerating the speed and depth of knowledge acquisition in banks. AI-driven tools enable banks to analyze vast datasets, producing actionable insights that support informed decision-making [35]. Selvarajan [36] found that automating data analysis with AI enhances organizational learning by allowing employees to focus on strategic decisions. This rapid learning capability enables banks to respond swiftly to market fluctuations and optimize operations, fostering a culture of continuous innovation and growth.

AI also enhances internal information sharing and collaboration, which are key components of organizational learning. Tools such as chatbots, knowledge management systems, and recommendation engines improve staff communication, promote collaboration, and strengthen collective intelligence [37]. According to Olan et al. [38], AI facilitates learning by automating knowledge dissemination and enhancing collaboration. By creating an interconnected work environment, AI supports adaptable, continuous learning across the organization.

Moreover, AI helps identify growth opportunities and areas where knowledge is lacking. By analyzing employee performance data and internal processes, AI can pinpoint learning gaps and guide banks in implementing targeted training initiatives [39]. Viterouli et al. [40] observed that AI-generated insights help organizations detect knowledge deficiencies and design personalized learning paths to improve employee competencies. This results in a more capable banking workforce that is prepared to meet evolving consumer needs and regulatory demands, thereby advancing sustainable development and innovation.

AI also establishes the continuous feedback loop essential for dynamic learning. Machine learning systems update their models with new data, generating insights that enable staff to build on past experiences [41]. Boppiniti [42] noted that AI supports learning and decision-making by delivering real-time, ongoing feedback. This makes organizational learning in banks more adaptive and responsive, allowing constant refinement of strategies and operations in line with changing market and consumer conditions. Based on these insights, the study proposes the following hypothesis:

H2: 

AI application in banks positively and significantly influences organizational learning.

2.2.3. The Influence of Organizational Learning on Sustainable Innovation Performance

Organizational learning enhances a company’s ability to adapt, generate, and implement innovative ideas aligned with sustainability goals, driving sustainable innovation performance [43]. Siebenhüner and Arnold [44] found that organizations with stronger learning capabilities are better equipped to respond to environmental changes and achieve sustainable growth. In the banking sector, institutions prioritizing organizational learning are more likely to develop and offer eco-friendly financial products and services, advancing both financial and environmental sustainability [45].

Additionally, organizational learning facilitates knowledge exchange and collaboration, which are critical for sustained innovation. Lin [46] observed that organizations promoting information sharing among employees tend to show improved innovative performance. In banks, this culture of collaboration can lead to the joint development of sustainable financial products, such as green loans or socially responsible investment portfolios, that meet customer demands for sustainability. By sharing ideas and best practices, banks increase their capacity for sustainable innovation, thus strengthening their market competitiveness [47].

Organizational learning also supports dynamic capabilities, which are vital for sustainable innovation. Uhlenbruck et al. [48] emphasized that firms with effective learning systems are better at reconfiguring resources and competencies to seize emerging opportunities or address challenges. In banking, institutions that focus on organizational learning are more likely to recognize and capitalize on new sustainable finance opportunities such as investing in renewable energy or supporting socially responsible projects [49]. This ongoing adaptation to changing circumstances helps banks maintain long-term sustainability while nurturing their innovative potential.

Furthermore, organizational learning encourages a culture of experimentation and risk-taking, which is essential to driving sustainable innovation [50]. Cannon and Edmondson [51] noted that learning organizations view failure as an opportunity for improvement, making them more willing to take risks in innovative endeavors. In banking, this implies that institutions fostering organizational learning are more likely to engage in the risks associated with developing new sustainable products or services [25]. Such experimentation can lead to the creation of innovative green financial solutions or the adoption of advanced technologies that reduce environmental impact. By nurturing a learning-focused culture, financial institutions can advance innovations that align with long-term sustainability goals [52]. Based on these insights, the study proposes the following hypothesis:

H3: 

Organizational learning positively and significantly influences sustainable innovation performance.

2.2.4. The Mediating Role of Organizational Learning in the Relationship Between AI Application and Sustainable Innovation Performance

Organizational learning plays a key role in incorporating AI-driven insights into corporate innovation processes, influencing the relationship between AI and sustainable innovation performance. Bhatt and Zaveri [53] emphasize that organizational learning enables the assimilation and utilization of new information, facilitating the adoption of contemporary technologies like AI. This suggests that banks with strong learning capabilities are better positioned to leverage AI to drive sustainable innovation. For example, they can use AI to develop sustainable financial products and services that align with long-term sustainability goals.

Additionally, organizational learning enables banks to tailor AI systems to their specific needs, fostering continuous innovation [53]. Mitrache et al. [54] found that firms promoting continuous learning are more effective at integrating new technologies, including AI, to meet business requirements. In banks, organizational learning ensures that employees can utilize AI to enhance risk management or create green finance opportunities. By combining AI with organizational learning, banks can transform their operations and business models, promoting sustainable development while maintaining competitiveness in a dynamic market [55].

Organizational learning also fosters a collaborative culture, facilitating the exchange of AI-driven ideas across departments, which enhances sustainable innovation performance [56]. Giustiniano et al. [57] demonstrated that disseminating organizational knowledge boosts creativity. In banking, organizational learning promotes the sharing of AI-generated data, encouraging collaborative innovation that aligns with sustainability objectives. This collaboration integrates AI into core operations, accelerating the development of new financial products and services, thereby improving sustainable innovation performance.

The integration of AI into sustainable innovation requires organizational learning to nurture a culture of experimentation and adaptability. Peschl [58] argued that organizations embracing learning are more likely to experiment with novel technologies and concepts. Organizational learning helps banks manage the interaction between AI and sustainable innovation by cultivating a mindset that views AI as a catalyst for transformation. AI-driven innovations such as predictive models for assessing environmental challenges and creating new green investment opportunities stem from this willingness to experiment [59]. By fostering a learning culture, banks can more effectively use AI to advance sustainability goals. Based on these insights, this study proposes the following hypothesis:

H4: 

Organizational learning positively and significantly mediates the relationship between AI application in the banking sector and sustainable innovation performance.

3. Methodology

3.1. Sample and Data

This study focuses on the Libyan banking sector, which presents unique opportunities and challenges related to technological advancements and sustainable practices. The significant transformation in Libya’s banking sector, driven largely by the Central Bank of Libya’s initiatives, has resulted in improved financial inclusion, enhanced banking services, and the adoption of digital banking technologies [60]. The study also highlights the importance of sustainable innovation performance, aligning with the Libyan government’s efforts to promote more sustainable economic practices [61]. Despite these advancements, the impact of AI on sustainability and its integration into banking remains underexplored. This gap underscores the urgency of conducting this research, making Libya an ideal context for the study. With Libyan banks facing rapid sector changes and the need for organizational growth, this research is especially timely.

The study targets employees in both Libyan Islamic and conventional banks. The rationale for selecting these two types of banks lies in their distinct operational, regulatory, and cultural characteristics [62]. Conventional banks follow standard banking regulations, while Islamic banks operate in accordance with Sharia-compliant financial principles. This distinction provides a unique opportunity to examine how these different banking models perceive and implement AI, organizational learning, and sustainable innovation. Understanding the broader implications of AI integration and continuous innovation in the Libyan context requires insights from the employees of both banking sectors.

The lack of an official employee register or accessible statistics for both conventional and Islamic banks in Libya complicates determining an appropriate sample size. To address this, the sample size was calculated using the Cochran formula, a well-established method for determining statistically valid sample sizes, especially when the population size is uncertain or difficult to quantify. This approach ensures that the sample size is sufficient and representative, thereby enhancing the validity and reliability of the study’s findings. The formula is quoted as

$$n={\displaystyle \frac{Z^2\times P\times (1-P)}{d^2}}$$

(1)

where “n” is the required sample size,

• “Z” is the Z-score corresponding to the desired confidence level (1,96),
• “P” is estimated proportion or prevalence of the characteristic being studied in the population (unknown = 0.5), and
• “d” is the desired margin of error.

$$n={\displaystyle \frac{{1.96}^2\times 0.5\times (1-0.5)}{{\left(0.05\right)}^2}}$$

(2)

$$n={\displaystyle \frac{0.9604}{0.0025}}$$

(3)

$$n=384$$

(4)

3.2. Inclusion and Exclusion Criteria and Sampling Strategy

Study participants were required to be employees of conventional and Islamic banks in Libya with access to AI systems within their respective institutions. The study aimed to gather insights from staff directly engaged with AI technology, as they possessed relevant information and experiences regarding the impact of AI on sustainable innovation and organizational learning. Employees without access to AI systems or involvement in AI-related tasks were excluded from the study. Participants were expected to be knowledgeable about their bank’s innovation strategies, particularly those concerning AI and sustainability initiatives.

Data were collected from personnel across various positions within conventional and Islamic banks to ensure inclusivity. The sample primarily included employees in administrative roles, IT departments, innovation and strategy departments, and frontline staff who interact directly with customers and AI-driven operations. This diversity ensured the capture of multiple perspectives on sustainable innovation performance, organizational learning, and AI integration, providing a comprehensive understanding of AI’s application in the Libyan banking sector.

Judgmental sampling, a non-probability technique, was employed to select participants based on their relevance to the research objectives. Initially, 384 employees were estimated for inclusion. However, this number increased to 401 due to higher-than-expected participation. To address potential non-response bias, steps were taken to encourage participation, including follow-up reminders and clear communication of the study’s purpose and confidentiality. Additionally, a larger sample size was initially targeted to accommodate possible non-responses. Data were gathered from 68 branches of conventional and Islamic banks across Libya, ensuring a comprehensive representation of both banking sectors and capturing diverse institutional operations.

While judgmental sampling allows the targeted selection of knowledgeable participants, it may introduce selection bias, as the researcher’s judgment could favor certain perspectives over others. To mitigate this potential bias, efforts were made to ensure a diverse sample encompassing various departments within both conventional and Islamic banks. This approach enhanced data consistency and reliability, minimizing human biases and providing a more comprehensive understanding of the study’s focus.

3.3. Procedures for Data Collection

This study employed Computer-Assisted Web Interviewing (CAWI) to efficiently collect data from eligible employees of conventional and Islamic banks in Libya. CAWI is an online survey method wherein respondents complete questionnaires on their own using devices like computers, tablets, or smartphones. To ensure that only qualified participants accessed the survey, the researchers distributed the survey link exclusively within the banks, restricting access to exclude unqualified employees. Data collection commenced in June 2024 and concluded in January 2025, spanning a period of eight months. This duration facilitated comprehensive data gathering across multiple bank branches. While CAWI offers advantages such as broad reach and cost-effectiveness, it also presents challenges like potential selection bias. To mitigate this, the researchers ensured a diverse sample by including participants from various departments within both conventional and Islamic banks. This approach enhanced data consistency and reliability, minimizing biases and providing a comprehensive understanding of AI’s impact on sustainable innovation and organizational learning in the Libyan banking sector.

3.4. Measurement and Scale

The study adapted 18 AI-related questions, including nine multidimensional items relevant to the banking sector, from Emiliya and Rosaline [63]. These dimensions cover augmented fraud detection and security, effective risk management, operational efficiency, sophisticated data analytics, cost reduction, service innovation, regulatory compliance, and improved customer experience. These questions highlight the crucial role of AI in risk management, compliance, and operational efficiencies, which are particularly important for Libyan banks undergoing transformation. The questions are relevant for both conventional and Islamic banks, emphasizing AI’s necessity in meeting regulatory obligations and enhancing operational performance amidst ongoing changes in Libya’s banking system.

The study also adapted ten questions on sustainable innovation performance from Calik and Bardudeen [64], whose questions were originally designed for the manufacturing sector. These questions were modified to suit the banking sector, demonstrating the adaptability of the research. To ensure the validity of these adaptations, the revised items were reviewed by experts in banking and sustainability to assess their relevance and clarity within the financial services context. Additionally, a pilot test was conducted with a small sample of banking professionals to evaluate the appropriateness and comprehensibility of the items. Feedback from both the expert review and pilot testing informed minor revisions, enhancing the accuracy of the instrument in capturing banking-specific sustainable innovation performance. The modified questions are relevant to Libyan banks, where government policies encourage the adoption of sustainable practices and digital transformation. Libya’s current focus on sustainable economic growth provides an opportunity to explore how innovative financial products, green banking initiatives, and operational efficiency contribute to sustainability within the banking sector.

The study incorporated organizational learning with 23 questions across five dimensions from Tohidi et al. [65]. These dimensions include managerial commitment and empowerment (6 questions), experimentation (3 questions), risk-taking (3 questions), openness and interaction with the external environment (5 questions), and knowledge transfer and integration (5 questions). These questions are pertinent to Libyan banks, which must adapt to evolving technology, regulations, and client expectations. As Libyan banks strive to modernize and enhance performance, these questions assess how banks formulate adaptive strategies and establish competitive advantages in a changing financial landscape.

A 5-point Likert scale was used to assess respondents’ agreement with the statements, ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). This scale provides a quantitative method for assessing responses and is widely used in studies due to its clarity and granularity. The consistency of statistical analysis further ensures the validity of the study’s conclusions.

3.5. Statistical Tools for Data Analysis

Partial Least Squares–Structural Equation Modeling (PLS–SEM) was used to analyze the collected data. PLS–SEM is a robust statistical technique suitable for exploratory research focused on understanding and predicting causal relationships. Its versatility allows the modeling of both reflective and formative constructs. It was chosen for this study due to its ability to handle small sample sizes and its effectiveness in predicting relationships among multiple factors [66].

Using the bootstrapping technique, PLS–SEM provides a comprehensive assessment of the model’s fit by evaluating the significance of its parameters. This approach delivers results on model fitness criteria, standardized loadings, average variance extracted (AVE), composite reliability, and Cronbach’s alpha, ensuring a reliable measurement model. The bootstrapping process, with 5000 interactions at a 95% confidence interval, offers a robust evaluation of the model’s fit [67].

4. Results and Data Interpretation

4.1. Results

Table 1. Demographic Analysis of the Employees in the Libyan Banking Sector.

		n	Percentage (%)
Age	18–24 years	52	12.97
	25–34 years	120	29.93
	35–44 years	133	33.17
	45–54 years	69	17.21
	55+ years	27	6.73
Gender	Male	247	61.60
	Female	154	38.40
Educational level	High School	56	13.97
	Bachelor’s Degree	212	52.87
	Master’s Degree	104	25.94
	Professional	23	5.74
	PhD	6	1.50
Experience in the banking sector	Less than 2 years	47	11.72
	3–5 years	105	26.18
	6–10 years	48	11.97
	11–15 years	125	31.17
	Over 15 years	76	18.95
Role/Position within the Bank	Customer Service Representative	95	23.69
	Relationship Manager	71	17.71
	Risk Management Officer	63	15.71
	IT/AI Specialist	45	11.22
	Branch Manager	59	14.71
	Other Roles	68	16.96
Type of Bank	Conventional	168	41.90
	Islamic	233	58.10
Total		401

presents the demographic analysis of employees from the Libyan banking sector who participated in the study. The age distribution of the employees is diverse, with 63.1% falling between 25 and 44 years old. Specifically, 29.93% are aged 25 to 34, and 33.17% are aged 35 to 44. This indicates that the workforce is predominantly made up of young to middle-aged employees, a group likely more receptive to adopting emerging technologies like AI.

The gender distribution shows a male-dominated workforce, with 61.6% male (247 employees) and 38.4% female (154 employees). This disparity highlights the need for gender-inclusive policies in the banking sector, particularly in Libya, to ensure balanced representation in roles associated with emerging technologies and innovation.

Regarding education, 52.87% (212 employees) hold bachelor’s degrees, while 25.94% (104 employees) hold master’s degrees. This suggests a relatively well-educated workforce that may be more open to AI-driven initiatives and sustainable technologies.

In terms of experience, 31.17% (125 employees) have between 11 and 15 years in the banking sector, and 26.18% (105 employees) have 3 to 5 years. This indicates a mix of experienced and relatively newer employees, which could support the successful implementation of AI and innovation in the sector.

The roles of the employees are varied, with 23.69% (95 employees) working as customer service representatives, 17.71% (71 employees) as relationship managers, and 15.71% (63 employees) as risk management officers. This diversity emphasizes the need for a cross-departmental approach to AI implementation and sustainable innovation to ensure all levels of the bank are prepared for digital transformation. The data also shows that most employees work in Islamic banks (58.1%, 233 employees), compared to 41.9% (168 employees) in conventional banks, each operating under distinct regulatory and operational frameworks.

Table 2. Descriptive statistics.

Variables	N	Min	Maxi	Mean	Std. Deviation
AI in banking	401	1	5	4.653	0.571
Sustainable innovation performance	401	1	5	4.340	0.821
Organizational learning	401	1	5	4.065	0.961

presents the descriptive statistics of the study’s variables. The average score for AI reflects a strong consensus among employees regarding its importance and impact on their roles. This suggests that the banking sector views AI as crucial to enhancing operational efficiency, improving client experiences, and driving internal innovation. Banks are encouraged to continue investing in AI to ensure employees receive proper training, enhancing both performance and market competitiveness.

Employees also acknowledge the need for integrating sustainable practices within the banking sector. The average score for sustainable innovation performance highlights a clear focus on sustainability, indicating that staff are increasingly aware of and committed to their social and environmental responsibilities. Banks should develop strategies that align their operations with sustainability goals, ensuring that AI technologies contribute to these objectives.

The average score for organizational learning indicates a positive attitude toward continuous learning and adaptation among employees. This shows that bank personnel are open to adopting new ideas and methods, which is critical for successfully implementing AI and fostering sustainable innovation. Banks should nurture a culture of organizational learning that supports employees in enhancing their skills, ensuring their adaptability in a rapidly evolving technological landscape.

Before presenting the findings, it is important to assess the model’s fit to ensure that the structural SEM accurately represents the data and illustrates the relationships among the variables. The validity of the study’s conclusions depends on the model’s fit, which indicates how well the model reflects the observed data. A good model fit ensures that the expected correlations are supported by the data, strengthening the results and subsequent recommendations. The model’s fit was assessed using the Normed Fit Index (NFI) and the Standardized Root Mean Square Residual (SRMR), with results shown in

Table 3. Model fitness assessment.

Indices	Obtained Values	Interpretation
SRMR	0.045	Excellent fit
NFI	0.962	Excellent fit

.

An SRMR value of 0.045, below the recommended threshold of 0.08 [32], indicates that the model accurately reflects the relationships among the variables with minimal residuals. The NFI score of 0.962 exceeds the recommended threshold of 0.90 [68], demonstrating an excellent fit, showing that the model captures a significant portion of the data’s variance. These results confirm that the model effectively represents the relationships within the dataset and provides strong support for the hypotheses being tested.

Convergent validity refers to the extent to which multiple indicators of a construct are correlated, confirming their measurement of the same underlying concept [69]. It ensures that the components of a construct accurately reflect the construct itself, making it a key aspect of construct validity. In this study, convergent validity was assessed using standardized loadings and AVE, which are common methods for this type of assessment. The results are shown in

Table 4. Reliability and Convergent Validity.

Construct (Dimension)	Item	Standardized Loadings	Average Variance Extracted	Cronbach Alpha	Composite Reliability
AI in banking	ADAT1	0.940	0.759	0.980	0.982
	ADAT2	0.934
	CTRD1	0.935
	CTRD2	0.945
	CUSPT1	0.967
	CUSPT2	0.970
	ECITA1	0.955
	ECITA2	0.959
	ERMT1	0.967
	ERMT2	0.965
	IFDS1	0.952
	IFDS2	0.955
	INSV1	0.960
	INSV2	0.962
	OPEF1	0.960
	OPEF2	0.963
	RECM1	0.940
	RECM2	0.931
Sustainable innovation performance	SINP1	0.760	0.805	0.973	0.976
	SINP2	0.888
	SINP3	0.916
	SINP4	0.931
	SINP5	0.875
	SINP6	0.919
	SINP7	0.903
	SINP8	0.918
	SINP9	0.918
	SINP10	0.928
Organizational learning	EXPM1	0.944	0.777	0.937	0.938
	EXPM2	0.912
	EXPM3	0.930
	EXPM4	0.917
	KWTF1	0.800
	KWTF2	0.848
	KWTF3	0.862
	KWTF4	0.755
	KWTF5	0.779
	MCAE2	0.926
	MCAE3	0.940
	MCAE4	0.907
	MCAE5	0.946
	MCAE6	0.898
	OIEN1	0.850
	OIEN2	0.879
	OIEN3	0.874
	OIEN4	0.820
	OIEN5	0.788
	RISTG1	0.942
	RISTG2	0.942
	RISTG3	0.914

Advanced Data Analytics (ADAT), Cost Reduction (CTRD), 24/7 Customer Support (CUSPT), Enhanced customer insight (ECITA), Efficient Risk Management (ERMT), Improved Fraud Detection and Security (IFDS), Innovation in service (INSV), Operational Efficiency (OPEF), Regulatory compliance (RECM), Sustainable innovation performance (SINP), Experimentation (EXPM), Knowledge transfer and integration (KWTF), Managerial commitment and empower (MCAE), Openness and interaction with the external environment (OIEN), Risk taking (RISTG).

.

Standardized loadings greater than 0.70 are considered adequate, as they indicate a strong relationship between each variable and its construct [70]. The standardized loadings for the constructs are displayed in Figure 2 (Measurement model). Any item with a standardized loading below 0.70 was excluded due to its insufficient support for the construct. For instance, the MCAE1 for organizational learning had a standardized loading below 0.70 and was removed to improve construct validity. An AVE value above 0.50 indicates that the indicators contribute significantly to the variance of the construct [71]. An AVE of 0.50 or higher ensures robust convergent validity, which is crucial for confirming the validity and reliability of constructs in the research findings.

The AI application in the banking sector shows strong convergent validity, with standardized loadings above 0.90—specifically ADAT1 (0.940) and CUSPT2 (0.967). These high values signal confidence in measuring the underlying concept. The AVE for this construct was 0.759, exceeding the recommended threshold of 0.50. The sustainable innovation performance construct also demonstrates strong convergent validity, with standardized loadings ranging from 0.760 to 0.928 and an AVE of 0.805. For organizational learning, items like EXPM1 (0.944) and MCAE5 (0.946) had high standardized loadings, indicating strong correlations with the construct. The AVE for organizational learning was 0.777, confirming robust convergent validity. Overall, the standardized loadings and AVE values affirm that the constructs in this study exhibit strong convergent validity, ensuring that the items accurately measure the intended attributes.

Reliability is a critical measure of how consistently constructs and their indicators reflect the underlying concepts [72]. Cronbach’s alpha and composite reliability (CR) were used in this study to evaluate reliability. These metrics assess the internal consistency of the components within each construct, ensuring their reliable connection and representation of the same concept.

For AI in banking, Cronbach’s alpha (0.980) and Composite Reliability (0.982) indicate exceptional consistency. For instance, ADAT1 has a Cronbach’s alpha of 0.980, demonstrating high consistency among AI items. Similarly, the items evaluating sustainable innovation performance, such as SINP1 and SINP2, showed a higher Cronbach’s alpha of 0.973. Organizational learning exhibited strong consistency, with a Cronbach’s alpha of 0.937 and a Composite Reliability of 0.938. The high Cronbach’s alpha and Composite Reliability scores for all components demonstrate the reliability of the measurements used in this study, providing a solid foundation for assessing the proposed relationships in the model.

Discriminant validity is an essential measure for evaluating whether the components within a model are distinct and show minimal overlap [73]. This study assessed discriminant validity using the Fornell and Larcker Criterion and the Heterotrait–Monotrait Ratio (HTMT), with the results presented in

Table 5. Discriminant validity.

	Heterotrait–Monotrait Ratio (HTMT)
	(1)	(2)	(3)
(1) AI in banking
(2) Organizational learning	0.492
(3) Sustainable innovation performance	0.294	0.306
	Fornell and Larcker criteria
(1) AI in banking	0.871
(2) Organizational learning	0.559	0.881
(3) Sustainable innovation performance	0.269	0.286	0.897

.

The HTMT results indicate that all constructs in this study meet the criteria for discriminant validity, as all HTMT values are below the 0.85 threshold [71]. Specifically, the HTMT values between AI in Banking and Organizational Learning (0.492), and between AI in Banking and Sustainable Innovation Performance (0.294), are well below the 0.85 threshold, confirming that these constructs are distinct. Additionally, the value of 0.306 between Organizational Learning and Sustainable Innovation Performance is also below the threshold, further supporting the distinctiveness of these constructs. These results confirm that each construct in the model represents a unique concept, ensuring discriminant validity.

The Fornell and Larcker Criterion further confirms discriminant validity. According to this criterion, the square root of the AVE for each construct must exceed the correlations between that construct and the others in the model [74]. The AVE values for AI in Banking, Organizational Learning, and Sustainable Innovation Performance are 0.759, 0.777, and 0.805, respectively. The correlation coefficients between constructs are significantly lower than the square roots of the corresponding AVE values, indicating clear distinctions among the constructs. For example, the correlation between AI in Banking and Organizational Learning (0.559) is lower than the square root of the AVE for Organizational Learning (0.881), confirming discriminant validity. Both the HTMT and Fornell and Larcker criteria demonstrate that the constructs in this study possess strong discriminant validity, ensuring that each construct accurately measures a distinct component of the research model.

The structural model assessment evaluates the interactions among the model components, providing insights into their strength, predictive ability, and the impact of various variables. This study used R², Q²predict, f², and Variance Inflation Factor (VIF) to assess the structural model, with the results presented in

Table 6. Structural model assessment).

	R-Square	Q2Predict
Organizational learning	0.312	0.300
Sustainable innovation performance	0.099	0.068
	f-square	VIF
AI in banking -> Organizational learning	0.454	1.000
AI in banking -> Sustainable innovation performance	0.219	1.454
Organizational learning -> Sustainable innovation performance	0.330	1.454

. These metrics help evaluate explanatory strength, predictive relevance, effect size, and potential multicollinearity issues within the model.

R² values indicate the extent to which independent variables explain variations in dependent variables. For example, the R² value for Organizational Learning (0.312) shows that AI in banking accounts for 31.2% of the variance in organizational learning, suggesting a significant influence on how organizations learn. On the other hand, the R² value for Sustainable Innovation Performance (0.099) indicates that AI in banking and organizational learning explain 9.9% of the variation in sustainable innovation performance, signifying a moderate impact.

The Q²predict values indicate the model’s predictive significance. According to Lin & Huynh [75], a Q²predict score above zero indicates predictive relevance. The score of 0.300 for Organizational Learning and 0.068 for Sustainable Innovation Performance confirm the model’s predictive ability, with a medium predictive relevance for sustainable innovation performance.

The f² values show the effect size of predictor variables on dependent variables. An f² value of 0.454 suggests a substantial effect of AI in banking on organizational learning. The f² value of 0.219 between AI in Banking and Sustainable Innovation Performance reflects a moderate effect size, while the f² value of 0.330 for the correlation between Organizational Learning and Sustainable Innovation Performance highlights a significant effect size.

The VIF assesses multicollinearity among predictor variables. All of the VIF values in this study, ranging from 1.000 to 1.454, were below the common threshold of 5 [76,77,78,79,80] indicating no issues with multicollinearity. Overall, the structural model evaluation reveals that the relationships among AI in banking, Organizational Learning, and Sustainable Innovation Performance are significant, with varying effect sizes and explanatory power.

4.2. Evaluation of the Hypothesis

Table 7. Empirical findings.

Relationship	Hypothesis	β	S. E	t-Test	p Values
Direct relationships
AI in banking -> Sustainable innovation performance	H1	0.159 *	0.086	1.857	0.063
AI in banking -> Organizational learning	H2	0.559 ***	0.051	10.947	0.000
Organizational learning -> Sustainable innovation performance	H3	0.197 ***	0.087	2.259	0.024
Mediation relationship
AI in banking -> Organizational learning -> Sustainable innovation performance	H4	0.110 ***	0.050	2.215	0.027

*** p < 0.01, * p < 0.1.

presents the empirical findings and hypothesis evaluations of this study, with the structural model illustrated in Figure 3. This study evaluated the hypothesis using 1%, 5%, and 10% significance levels, as asserted by Gujarati [81]. The results indicate that AI positively and significantly influences sustainable innovation performance (β = 0.159, t = 1.857, p < 0.10), confirming Hypothesis (H1). Additionally, AI positively and significantly impacts organizational learning (β = 0.559, t = 10.947, p < 0.01), supporting and confirming Hypothesis (H2). The study also found that organizational learning positively and significantly influences sustainable innovation performance (β = 0.917, t = 2.259, p < 0.01), confirming Hypothesis (H3). Furthermore, organizational learning partially mediates the relationship between AI and sustainable innovation performance (β = 0.110, t = 2.215, p < 0.01), leading to the confirmation of Hypothesis (H4).

4.3. Discussion of Findings

The study found that AI positively and significantly influences sustainable innovation performance. According to dynamic capabilities theory, organizations must skillfully integrate, cultivate, and reorganize internal and external resources to adapt to evolving environments [82]. Banks utilizing AI develop capabilities to identify emerging trends, adjust operations, and seize new opportunities in line with sustainability standards. This enables banks to meet sustainability requirements, optimize operations, and introduce environmentally friendly financial services [83]. The results highlight AI’s role in helping banks remain responsive to evolving environmental and technological changes, which is crucial to driving long-term innovation [84].

AI’s transformative ability to automate complex processes, analyze large datasets, and generate actionable insights significantly impacts banking decisions [85]. AI plays a crucial role in driving sustainable innovation by enhancing fraud detection, optimizing customer interactions, strengthening risk management, and ensuring better regulatory compliance. Furthermore, AI fosters a culture of continuous learning and flexibility within banks [85], enabling them to remain competitive, enhance efficiency, and achieve long-term sustainability goals, positioning them as leaders in the rapidly evolving banking sector. Libya’s positive result may be influenced by its push for modernization in response to economic disruptions and political instability. The need to improve efficiency and resilience in a volatile environment likely encouraged banks to adopt AI-driven solutions. Additionally, limited traditional innovation infrastructure may have made AI a more attractive and immediate path to sustainable innovation.

From a financial perspective, these findings have important implications for investors and bank management. Banks integrating AI into their sustainable innovation strategies can enhance competitiveness and financial robustness, boosting long-term profitability. Bank management teams should prioritize investment in AI technology to improve operational efficiency, service offerings, and regulatory compliance, ultimately enhancing their banks’ reputations and ability to attract environmentally conscious investors.

The study also found that AI positively and significantly influences organizational learning. Dynamic capabilities theory highlights an organization’s ability to integrate and modify resources in response to changing circumstances [86]. In this context, AI plays a critical role in facilitating knowledge application and learning. AI-driven data analytics enable banks to continuously learn from large datasets, improve decision-making, and adapt strategies based on new insights [87]. This information assimilation process enhances the bank’s capacity to adapt operational and strategic capabilities over time.

AI accelerates organizational learning by automating routine tasks and improving the rate of knowledge acquisition, enabling easy access to information for employees across the organization. AI-driven systems can identify patterns, analyze trends, and generate insights, thereby enhancing decision-making, fostering creativity, and improving flexibility in the dynamic financial landscape [88]. AI enables banks to remain competitive and agile by fostering a culture of continuous learning and real-time knowledge exchange, which helps them address challenges and capitalize on opportunities for innovation and growth [89,90]. Libya’s unstable economic and political environment may have driven organizations to adopt AI as a tool for adaptability and knowledge management. In such a context, AI can support faster decision-making, knowledge retention, and skill development—key elements of organizational learning—helping banks remain responsive and resilient amid uncertainty.

Economically, these outcomes are significant for investors and bank management. Banks that successfully integrate AI into their learning processes are likely to demonstrate improved efficiency, agility, and market responsiveness, ensuring consistent growth and profitability. By cultivating a culture of organizational learning supported by AI, bank management can gain a competitive advantage, enabling adaptation to market fluctuations, improving employee performance, and refining operational strategies, which ultimately enhances the bank’s competitiveness.

The study found that organizational learning positively and significantly influences sustainable innovation performance. Dynamic capabilities theory emphasizes the need for firms to integrate and reconfigure resources in response to external changes [91]. Organizational learning is vital in this process, enabling banks to acquire knowledge, adapt to environmental shifts, and enhance sustainability efforts. Through continuous learning from both internal operations and external market conditions, banks can meet evolving regulatory mandates and industry expectations while advancing sustainable development [92].

The results indicate that organizational learning helps banks convert knowledge into actionable strategies for sustainable innovation. By fostering continuous education, banks can identify new opportunities for eco-friendly products, services, and practices that align with long-term sustainability goals [93]. This includes initiatives like energy efficiency, waste reduction, and improved social responsibility efforts. Organizational learning propels these advancements, equipping banks with the knowledge, agility, and flexibility needed to implement sustainable practices, meet market demands, and promote environmental and social well-being [94]. In Libya’s challenging economic and political context, organizational learning plays a crucial role in enabling banks to adapt and innovate sustainably. With limited external stability, internal learning mechanisms become crucial to building resilience, fostering problem-solving, and continually improving processes, factors that directly contribute to sustainable innovation performance.

From an economic standpoint, these findings are critical for investors and bank management. Investors tend to favor banks that prioritize organizational learning, as it enhances adaptation, innovation, and sustainable development. Banks that consistently innovate to meet sustainability challenges can better satisfy client demand for eco-friendly products and services, boosting their financial performance. Prioritizing organizational learning ensures management maintains the bank’s agility, continuously improves sustainability initiatives, and retains a competitive edge in an increasingly environmentally conscious industry.

The study also found that organizational learning partially mediates the relationship between AI and sustainable innovation performance. According to dynamic capabilities theory, firms must skillfully combine, reconfigure, and modify their resources in response to environmental changes [82]. Financial institutions can leverage AI to improve operational efficiency, customer satisfaction, and decision-making [95]. Organizational learning plays a crucial role in sustaining the effectiveness of AI-driven insights, allowing banks to absorb, apply, and convert AI knowledge into sustainable practices.

The findings suggest that AI alone cannot drive continuous innovation. Organizational learning is essential to bridge the gap between AI capacity and actual innovation outcomes. Through ongoing education, banks can more effectively use AI to discover sustainability opportunities, develop green products, and improve eco-friendly operations [96]. This ensures that AI not only automates existing tasks but also enables radical transformations aligned with sustainability objectives. Organizational learning enables banks to use AI-driven insights strategically, facilitating socially responsible, efficient, and sustainable innovations [96].

The partial mediation effect indicates that while organizational learning enhances the impact of AI on sustainable innovation, both factors are necessary for optimal outcomes. This highlights the importance of investors supporting both AI capabilities and organizational learning cultures within banks. The results emphasize the need for bank management teams to invest in both educational initiatives and AI technologies. This dual approach ensures banks can adapt to market changes effectively, securing their survival and competitiveness. This indicates that bank management cannot simply allocate funds to AI technologies. Organizations must prioritize learning programs to ensure the proper utilization of AI-generated insights. These may include continuous staff training, mechanisms for knowledge dissemination, and adaptive cultures that evolve in response to the emergence of new information. Banks can achieve superior outcomes by integrating direct investments in AI with efforts to enhance learning.

5. Conclusions and Managerial Implications

This study examined the mediation role of organizational learning in the relationship between AI applications in the banking sector and sustainable innovation performance. Data were collected from 401 employees in both the conventional and Islamic banking sectors in Libya using a judgmental sampling technique. The data were analyzed using PLS–SEM to estimate the relationships between the variables. The study found that AI applications in the banking sector positively and significantly influenced both sustainable innovation performance and organizational learning. Additionally, organizational learning was found to positively and significantly influence sustainable innovation performance. The results also revealed that organizational learning partially mediates the relationship between AI applications and sustainable innovation performance.

This research highlights significant growth potential in both conventional and Islamic banks in Libya through the strategic implementation of AI, organizational learning, and sustained innovation performance. The positive impact of AI on sustainable innovation performance suggests that Libyan banks should prioritize AI integration into their operations. By investing in AI technologies such as predictive analytics and machine learning, bank management can enhance operational efficiency, improve customer service, and create sustainable financial solutions. Collaborations with technology firms will accelerate the adoption of AI solutions tailored to the banking sector, helping banks stay competitive in an evolving market.

The positive influence of organizational learning on the relationship between AI and sustainable innovation performance emphasizes the importance of fostering a culture of continuous learning within banks. Bank management teams should implement training programs to improve employees’ understanding of AI applications, sustainability goals, and innovative financial services to increase efficiency. Encouraging collaboration and information sharing across departments can enhance education and facilitate the integration of AI-driven insights into daily operations. This proactive approach to organizational learning will enable banks to quickly adapt to changes in the financial landscape and foster innovative solutions aligned with sustainability objectives.

The study’s findings also indicate that, without organizational learning, AI alone cannot drive continuous innovation. This underscores the critical need for banks to integrate learning systems into their operations to bridge this gap. A key step is establishing feedback mechanisms that allow employees to suggest improvements for the sustainable use of AI-powered technologies. Management must play a pivotal role in setting up knowledge transfer mechanisms to share successful AI applications and sustainability best practices across regions. Through continuous organizational learning, banks can effectively implement AI to achieve long-term sustainability and drive essential changes in the banking sector.

The findings also highlight the necessity of a strategic framework for AI and sustainable innovation in both conventional and Islamic financial institutions. While AI technology can enhance sustainability, it must align with the broader strategic objectives of the bank. For successful implementation, management must ensure that AI integration is closely aligned with the bank’s sustainability goals and regulatory requirements. This alignment is critical to developing eco-friendly AI-powered products, establishing clear sustainability objectives, and adhering to ethical and environmental standards in AI solutions. By aligning AI capabilities with strategic sustainability initiatives, Libyan banks can strengthen their market position, attract socially responsible investors, and contribute to sustainable economic development.

Limitations and Future Directional Studies

A significant limitation of this study was the use of judgmental sampling, a non-probability sampling method. While judgmental sampling allows for the selection of participants based on their expertise and experience, it may introduce bias, since the researcher determines the selection criteria. This limits the generalizability of the findings to the broader group of banking professionals in Libya. Future research could use random sampling techniques to reduce bias and improve the external validity of the results.

The study’s focus on employees directly using AI in the banking sector is another limitation. This focus may have overlooked the perspectives of employees who do not interact with AI systems but are still affected by AI-driven changes. Future studies should include a broader range of bank employees, such as those in support roles, as well as customers who may experience indirect effects of AI implementation. This would provide a more comprehensive understanding of AI’s role in sustainable innovation.

The geographical focus on Libyan banks introduces contextual limitations, as the findings may not apply to banks in other countries or regions with different levels of AI adoption and regulatory frameworks. Future research could expand to include banks in diverse nations with varying technological advancements, offering a comparative analysis of AI’s impact on sustainable innovation in different banking environments.

Organizational learning and sustainable innovation are dynamic processes that evolve; analyzing them at a singular moment may not adequately reveal their development or underlying reasons. Future research should employ longitudinal designs to track changes and interactions over time. This will provide a clearer understanding of the interrelations among various processes within the banking context.

Additionally, future research should consider other factors such as digital transformation, regulatory compliance, and customer satisfaction, which could shed light on AI’s broader impact on sustainable innovation and organizational success in the banking sector. Examining these factors may reveal new opportunities to improve AI acceptance and optimize its industrial potential.