The Impact of Sustainability Knowledge Sharing on Service Innovation in Libyan Banks: The Mediating Role of Intellectual Capital and Organizational Learning

Abstract

This study investigates the relationship between sustainability knowledge sharing and service innovation in Libyan banks, indicating that intellectual capital and organizational learning mediate the process. The data for this study were collected through a questionnaire distributed to 396 participants, who were Libyan bank employees. This study used confirmatory factor analysis and multi-group analysis to test the hypothesized relationships, and the results reveal that knowledge sharing has a direct effect on service innovation (β = 0.425, p < 0.001) and an indirect effect through intellectual capital and organizational learning. The model explains 53.7% of the variance in service innovation, with consistent effects in public banks and private banks. The results are critical for bank managers in developing countries, because they provide new insights into the sustainability knowledge-sharing practices that foster innovation in banks through the development of both intellectual capital and organizational learning. This study presents a conceptual model to enhance the comprehension of sharing knowledge as an incentive for service innovation within the banking sector of a developing country, filling a notable research gap in the literature. This approach underscores the crucial importance of effective communication and teamwork in enhancing service offerings and responding to market fluctuations. By utilizing these insights, bank management can implement initiatives that strengthen their competitive advantage and foster the economic prosperity of their region.

1. Introduction

Knowledge sharing is a key component of innovation, a fundamental organizational competency. The foundation of creative processes is sharing experiences, knowledge, and insights among staff members, which makes it possible to create new goods, enhance existing services, and expand the company. As a key channel for the interchange and transformation of both explicit and tacit information, knowledge sharing supports the long-term viability and effectiveness of innovation initiatives [1,2], while also providing support for organizational learning deemed crucial for banks’ effective absorption of new knowledge, as in cases where the implementation of knowledge sharing initiatives leads to increased performance and efficient decision-making in the bank’s operation [3].

In today’s global markets, where competition is fierce, organizational learning is more important than ever. Libyan banks should put a lot of effort into creating a learning-based organizational culture that follows set rules and improves their ability to learn and use information to boost performance, decision-making, and flexibility [4]. Researchers claim that the idea of intellectual capital is similar to employee or customer competency and may be used to improve financial results and competitive advantage [5].

Intellectual capital and organizational learning are important because they make it easier for information to move between businesses, facilitating the sharing of ideas, experiences, and insights, which are critical for promoting innovation [2]. Intellectual capital is the foundation for the development of novel services and procedures, which increases the organization’s flexibility and competitiveness by facilitating the development and integration of resources [6]. Libyan banks benefit from the study of knowledge sharing because it helps them to understand how sharing information can improve their organization’s ability to launch new financial services, which is a key part of staying competitive in the market. This literature review makes it clear how sharing knowledge leads to new product and service innovations in the financial sector to meet customer needs and the effects of technological progress [7].

Few studies have specifically examined how sharing knowledge impacts service innovation in banks, instead concentrating on reviewing the elements that influence knowledge sharing [8,9,10,11]. Most studies focus on general advantages like improved decision-making and operational performance. Still, they frequently ignore how these advancements support innovation [6]; given the lack of empirical data, a more thorough investigation of the variables that mediate the relationship between sharing knowledge and service innovation is necessary. In particular, establishing a comprehensive framework to clarify this relationship in the banking industry requires organizational learning and intellectual capital as a mediator.

This study aims to investigate the correlation between the utilization of shared knowledge and service innovation in Libyan banks, emphasizing intellectual capital and organizational learning. It evaluates the degree of shared knowledge application, its influence on service innovation, and the function of intellectual capital interchange and organizational learning. This study’s findings offer tactics and recommendations to bank managers and policymakers in developing nations who want to improve their capacity for service innovation through effective knowledge-sharing practices. This study not only supports the banking sector but also other sectors, highlighting the dearth of research in developing nations such as Libya. This study is crucial for organizations operating in these countries. The goal of this study is to close this research gap and add a more varied and thorough viewpoint to the idea of knowledge management.

2. Literature Review and Hypotheses Development

2.1. Sharing Knowledge and Service Innovation

This study highlights the vital role that shared knowledge plays in promoting innovation by combining ideas from multiple studies. It illustrates how businesses that encourage formal and informal information sharing improve their capacity for innovation and general performance. The goal of the present investigation is to develop a thorough understanding of how knowledge sharing promotes innovation and helps organizations succeed, laying the groundwork for future scholarly research and real-world implementation in management and innovation practices [12].

Innovation and information sharing are closely related fields that have attracted a lot of interest. Because tacit knowledge is difficult for rivals to imitate and is essential for fostering innovation, researchers have discovered that a greater level of tacit knowledge transfer is positively correlated with improved innovation capabilities inside organizations [13]. Furthermore, sharing is recognized as a crucial strategy for promoting innovation by transforming implicit knowledge into explicit knowledge. Researchers have revealed comparable results in Croatian enterprises [14], shown that information sharing significantly promotes creativity within Spanish organizations [15], and demonstrated that informal information exchange is especially successful in promoting innovation [9].

With its strong reliance on employee knowledge sharing, innovation stands out as a crucial organizational asset for gaining and maintaining a competitive advantage. Organizations can continuously gain a competitive edge by creating new or improved goods and services by utilizing shared knowledge [16]. Although infrastructure, technology, and financial resources all play a role in innovation, employee knowledge exchange is the main force behind it. Innovation incorporates technological, physical, and knowledge-related elements that are crucial to product creation claims [17]. A deeper understanding of the innovation process can be gained by looking at the many approaches used, the actors engaged, and the activities that propel innovation [10].

According to [11], knowledge-sharing practices also boost the ability and tendency for creativity and innovation. Additionally, knowledge sharing links subjective well-being to individual innovation [4]. Furthermore, [18] discovered that intra-group information sharing encourages exploitative innovation, whereas inter-firm knowledge sharing inside clusters stimulates innovation. Sharing knowledge is crucial in this situation to promote innovation, and [19] contends that without information exchange, innovation is unlikely to take place. However, some studies have indicated potential downsides, such as reduced competitive advantages and knowledge leakage [20].

Ref. [11] elaborated on the connection between innovation and information sharing, demonstrating that knowledge-sharing practices have a favorable effect on knowledge-sharers’ ability and willingness to develop and apply new ideas. However, as demonstrated, a lack of knowledge is cited as the main barrier to innovation [21]. In contrast, [22] contend that organizations that actively encourage knowledge sharing are better positioned to produce new ideas and bolster their innovative capabilities, while businesses that actively participate in knowledge networks are likely to increase their innovation potential. While many studies have examined the relationship between innovation and information sharing, few have taken into account how these ideas have evolved [23], which is the main topic of this article. Consequently, the following hypothesis was proposed.

H1. 

Sharing knowledge has a positive relationship with service innovation.

2.2. Mediating Role of Intellectual Capital

Relational capital is considered the aspect of intellectual capital that has the greatest impact on knowledge transfer [24,25]. However, human capital has a significant impact on the acquisition and transfer of information, highlighting its significance in the process of knowledge sharing [25,26]. Knowledge is anchored in particular social settings and is quite individualized [27], and sharing knowledge improves intellectual capital in areas like human and organizational capital and benefits both individual and organizational skills. These results are consistent with studies showing that social capital influences the interchange of knowledge, enhancing intellectual capital and stimulating creativity [25,26,28].

The most significant influence of intellectual capital (IC) is on product innovation, followed by organizational innovation. Intangible assets, or IC, are primarily used by organizations to support innovation [29]. Thus, intellectual capital (IC) is a suitable resource for fostering innovation, The total capabilities, knowledge, culture, strategy, process, and relational networks of a company that create value or competitive advantages [30] consist of four parts: human capital (HC), organizational capital (OC), social capital (SC), and customer capital (CC) [29,30,31,32,33,34].

Thus, innovation and intellectual capital seem to be closely related. As a result, it is becoming more difficult to distinguish between research on innovation and intellectual capital, because the latter can be viewed as an antecedent in studies of innovation, whereas some studies of intellectual capital use innovation as an outcome [35,36,37]. However, there have not been many attempts to examine and compile the literature on performance, innovation, and intellectual capital. Therefore, investigating the relationship between intellectual capital and innovation performance and process might be considered a current concern.

Although equally important, process innovation concentrates on the development of new production techniques or technologies to lower costs and increase efficiency [38,39]. An organization’s technical infrastructure is impacted, since it entails major upgrades to manufacturing tools, machinery, and software [40,41]. However, although IC elements stimulate process innovation, their influence is frequently indirect, since OC is a vital conduit for the application of technical resources and expertise to process enhancements [40].

In recognition of this, the relationship between IC and innovation highlights how intertwined they are; the lines between IC studies and innovation research are continuing to blur, with IC being acknowledged as a fundamental precursor to innovation and innovation being seen more and more as a result of IC development [35,36]. Even though it has been acknowledged that intellectual capital can improve both innovation and organizational performance, there are still few integrated studies that examine the mechanisms underlying this relationship, making more research in this field pertinent and timely [37]. Therefore, the following hypotheses were proposed.

H2. 

Intellectual Capital mediates the relationship between sharing knowledge and service innovation.

H3. 

Sharing knowledge has a positive relationship with Intellectual Capital.

H4. 

Intellectual Capital has a positive relationship with service innovation.

2.3. Mediating Role of Organization Learning

The collective attitudes, beliefs, and practices that foster knowledge acquisition and exchange inside an organization and are ingrained in its culture are known as organizational learning [42,43]. An organization must make the most of its knowledge base, keep improving it, and use it creatively if it is to succeed in the long run [44]. People may comprehend and adjust to their surroundings by understanding the meaning and reacting appropriately when effective organizational learning processes are implemented, which are accomplished through the sharing of information and knowledge among members [45].

According to [46], organizational learning in the framework of the knowledge economy depends on the sharing and integration of preexisting knowledge, information, and ideas that individuals within the organization have contributed. The amount of information produced is directly correlated with employee quality, which improves the organization’s capacity for learning. Organizational learning and knowledge sharing are intimately connected as learning, thinking, and sharing are all reciprocal aspects of the knowing process. Managers can maintain the flow of individual learning inside the organization and integrate it into real-world applications thanks to this relationship [47].

According to the consistent findings with [48,49,50], the knowing process is made up of three interdependent parts: sharing, thinking, and learning. Good information exchange enables people to reflect on common concepts and insights, eventually gaining knowledge and developing their potential. However, if new information is not handled well, it could become “orphaned” knowledge and lose its connection to organizational memory [46]. On the other hand, this knowledge becomes organizational knowledge when it is integrated, thus improving organizational learning outcomes. Also, information sharing is directly impacted by organizational culture and is intimately related to organizational learning [51,52], while knowledge sharing also improves learning processes, and collaborative culture has a favorable impact on organizational learning [53].

These results support previous hypotheses on how collaborative cultures promote knowledge exchange. Increased information-sharing activities improve learning inside the company, according to this research, which supports the notion that knowledge-sharing has a positive, significant impact on organizational learning. These results are in line with those of researchers who investigated the connection between organizational learning and information sharing. This study confirms the connection between information sharing and organizational learning by implementing Lederman’s theoretical framework at the organizational level, laying the groundwork for further investigations into these connections [54].

Organizational learning functions as a socially created contextually embedded collective practice and innovation in organizations can arise either internally or through the adoption of external innovations [1,55]. This process relies on the accumulation of both explicit and tacit knowledge, with experience-based tacit knowledge serving as the foundation of an organization’s knowledge system [24,56]. In contrast to explicit knowledge, which is more readily disseminated through books, guides, and websites, implicit knowledge is learned by imitation and observation [1,57].

Accordingly, organizational learning takes place on several levels, where practices and structures make it easier for information to be shared, interpreted, and embedded [58]. Public organizations that operate in participatory, and occasionally disputed, environments will find this socially dynamic approach especially pertinent [59]. Organizational learning is also influenced by the institutional context, which emphasizes the significance of shared meaning systems and horizontal and vertical interactions within regulatory frameworks [60]. As a result, institutional forces greatly influence the processes of learning and innovation, particularly in settings where policies are in place [61]. Social innovation (SI) and organizational learning capability (OLC) are linked empirically, indicating that SI is significantly impacted by organizational learning [62]. Interestingly, (OLC) seems to influence SI more strongly than other contributing components, which supports learning to improve SI [61]. Based on these observations. the following hypotheses were proposed.

H5. 

Organizational learning mediates the relationship between sharing knowledge and service innovation.

H6. 

Sharing knowledge has a positive relationship with organizational learning.

H7. 

Organizational learning has a positive relationship with service innovation.

2.4. Intellectual Capital and Organizational Learning

Intellectual capital, through its human, relational, and structural elements, plays a pivotal role in promoting organizational learning and stability. The effective management of knowledge and information is essential for companies competing in modern markets, as intellectual capital directly influences organizational learning and supports sustainable success and agility [63]. Considering intellectual capital, particularly among employees, is fundamental to long-term stability and success, reinforcing the need for organizations to focus on developing their intellectual resources to secure a competitive edge [64]. Value creation and competitive advantage depend heavily on an organization’s intellectual capital, which includes its collective capabilities, knowledge, culture, strategy, processes, intellectual property, and relational network [65,66]. It serves as the cornerstone of contemporary organizations’ commercial operations and is intimately related to organizational learning and knowledge management, which boosts competitive advantage and generates business value [32,65,66,67,68].

Human capital, or the knowledge and abilities of workers, is one of the aspects of intellectual capital that significantly affects organizational learning. To improve absorptive capacity, skilled workers are crucial for producing internal knowledge and absorbing external information. According to the studies by [69,70], higher-quality workers are the result of human capital investments, and this enhances the organization’s ability to learn and develop. Although it has less of an effect than human capital, structural capital also aids in organizational learning. Research indicates that investing in process and innovation capital improves learning capacity, especially when it comes to (IT) investments, which make it easier to acquire outside knowledge and increase the organization’s knowledge base [51].

Relational capital, which comprises connections with partners and customers, is essential to an organization’s capacity for learning [71]. These networks enhance the generation and dissemination of information since they facilitate knowledge exchange through contacts with partners and customers [72,73], and relational capital enhances the acquisition and sharing of knowledge, eventually supporting innovation and the creation of new products. It has been demonstrated that this relational dimension has a greater influence on learning than either human or structural capital, highlighting the significance of outside connections in knowledge-intensive sectors [56]. Based on these observations, the following hypothesis was proposed.

H8. 

Intellectual Capital has a positive relationship with organizational learning.

2.5. Model of This Study

Figure 1 illustrates a conceptual framework is created to investigate the relationship between knowledge sharing [74], the independent variable, and service innovation [75], the dependent variable, in Libyan banks. The literature supports the notion that knowledge sharing fosters corporate innovation by demonstrating a clear and robust relationship between knowledge sharing and service innovation.

This model of knowledge sharing is examined for its effect on the service innovation of Libyan banks. Research indicates that while the amount of data has no discernible impact on a bank’s innovation success, knowledge sharing has an observable impact [74]. Also, organizational learning [76] and intellectual capital [29] are mediating variables that are measured, and the speed at which shared knowledge is analyzed has a bigger influence on improving inventive knowledge sharing [74].

3. Methodology

3.1. Sampling Method and Collection Procedure

The study sample included employees of Libyan banks. There are 19 operating banks in Libya, including both public and private banks, and this study included all of them. A report issued by the Central Bank of Libya indicated that the number of Libyan bank employees reached approximately 19,815 in 2024. The data collection utilized a random sampling method applied by quotas, where online questionnaires were distributed via email to the human resource managers of banks. We increased the findings’ generalizability to a larger range of Libyan banks’ management and staff by gathering a range of perspectives on the use of shared knowledge and service innovation. This approach ensured that the sample was representative of employment functions and hierarchical levels

The sample of 396 participants, who vary by gender, age, experience, bank location, and job position, guaranteed a broad representation of the banking sector. This diversity provides a comprehensive understanding of how different organizational levels and functions perceive and influence the studied variables. The Qualtrics sample size calculator was utilized to ascertain the requisite sample size for the study, yielding an optimal sample size of 396 at a 95% confidence level and a 5% margin of error.

3.2. Measures

To analyze the study variables, a questionnaire with five sections was created. These sections were as follows: demographic factors, knowledge sharing, comprising four questions [74], service innovation, comprising nine questions [75], organizational learning, comprising four questions [76], and intellectual capital, which has three dimensions [29], namely human capital, comprising five questions, social capital, comprising five questions, and organizational capital, comprising four questions.

Responses were measured using a five-point Likert scale. A quantitative research strategy was used to methodically examine the relationship between sharing knowledge, intellectual capital, organizational learning, and service innovation, made possible by the use of surveys and structured questionnaires to gather numerical data for statistical analysis.

The surveys were emailed to employees of Libyan banks to achieve the study’s objectives. In 2024, the survey was distributed from September to December, and the data collection period was planned to meet participants’ availability and responsibilities, using a flexible timeline and a tiered process to manage hectic schedules efficiently [77]. To ensure a representative sample and effective data collection, the research team periodically evaluated the response rate and adjusted the strategy, and to keep participants interested and address any questions, follow-up emails and reminders were issued regularly.

4. Statistical Analysis and Results

For statistical analysis, structural equation modeling (SEM) was utilized in the Analysis of Model of Structures (AMOS) application to verify that the observed variables appropriately reflected their underlying latent components. The measurement model’s validity and reliability were assessed by means of CFA and SEM was use to examine the structural connections among knowledge sharing, services innovation, organizational learning, and intellectual capital.

This method enables the investigation of complex causal networks and the simultaneous analysis of multiple connections by using AMOS version 26 for both CFA and SEM, providing a comprehensive and statistically sound analysis of the relationships within the Libya banking industry. SEM assumptions were strictly followed to ensure model appropriateness and result correctness, and confirmatory factor analysis (CFA) was used to assess the measurement model’s fit and the convergent validity of the study variables. Items with factor loadings of at least 0.50 were retained, supporting measurement scales. Furthermore, all of the measuring scales’ items were statistically significant (p < 0.01), meeting the requirements for convergent validity

4.1. Descriptive Statistics

The initial sample consisted of 440 responses from managers and employees of Libyan banks. Comprehensive missing data analysis revealed forty-four cases with problematic patterns, with forty showing missing values across key variables and four additional cases demonstrating nonrandom missing data patterns (Little’s MCAR test: χ² = 152.86, df = 96, p < 0.001). These forty-four cases were removed, resulting in a final sample of 396 respondents. The remaining sample showed minimal missing data (<0.5% per variable), determined to be missing completely at random (Little’s MCAR test after removal: χ² = 98.45, df = 94, p = 0.352).

Table 1. Demographic characteristics of respondents (N = 396).

Characteristic	Category	Frequency	Percentage
Gender	Male	246	62.1
	Female	150	37.9
Age Group	25–35 years	147	37.1
	36–45 years	168	42.4
	46–55 years	81	20.5
Work Experience	1–5 years	73	18.4
	6–10 years	102	25.8
	11–15 years	127	32.1
	>15 years	94	23.7
Bank Type	Public	223	56.3
	Private	173	43.7
Education Level	Bachelor’s Degree	284	71.7
	Master’s Degree	89	22.5
	Doctorate	23	5.8

represents the demographic composition and reflects the current structure of the Libyan banking sector workforce, and the gender distribution shows a male majority (62.1%), aligning with typical employment patterns in the Libyan financial sector. The age distribution centers on mid-career professionals, with 42.4% being aged 36–45 years, complemented by a substantially younger cohort (37.1% aged 25–35 years). The experience profile shows a concentration in the 11–15 year range (32.1%), indicating a mature professional workforce. The high proportion of advanced degrees (28.3% holding postgraduate qualifications) suggests a well-educated sample, and the distribution between public (56.3%) and private banks (43.7%) represents the current market structure.

Table 2. Descriptive statistics and correlations (N = 396).

Variable	Mean	SD	1	2	3	4
1. KS	4.26	0.48	(0.894)
2. SI	4.24	0.46	0.536 ***	(0.878)
3. IC	4.33	0.52	0.488 ***	0.514 ***	(0.903)
4. OL	4.23	0.49	0.497 ***	0.480 ***	0.525 ***	(0.866)

Note: KS = knowledge sharing; SI = service innovation; IC = intellectual capital; OL = organizational learning. Cronbach’s α is shown on the diagonal in parentheses. ***, p < 0.001.

represents the correlation analysis revealing significant relationships among all variables (p < 0.001). Knowledge sharing shows a strong correlation with service innovation (r = 0.536) and moderate correlations with intellectual capital (r = 0.488) and organizational learning (r = 0.497). All constructs demonstrate high internal consistency (α > 0.85), with means above 4.20 indicating positive perceptions of knowledge management practices.

4.2. Measurement Model

Table 3. Scale properties and factor loadings (N = 396).

Construct/Items	Loading	CR	AVE	MSV	ASV	t-Value	Source
1. Knowledge Sharing	-	0.894	0.675	0.287	0.248	-	[74]
1.1. Distribution processes	0.825	-	-	-	-	18.47 ***	[74]
1.2. Business partner processes	0.798	-	-	-	-	17.92 ***	[74]
1.3. Reward system for sharing	0.847	-	-	-	-	19.34 ***	[74]
1.4. Cross-functional processes	0.814	-	-	-	-	18.26 ***	[74]
2. Service Innovation	-	0.878	0.659	0.264	0.236	-	[75]
2.1. Novel solutions	0.836	-	-	-	-	19.12 ***	[75]
2.2. Incremental improvements	0.814	-	-	-	-	18.45 ***	[75]
2.3. New service delivery	0.788	-	-	-	-	17.63 ***	[75]
2.4. Client acceptance	0.821	-	-	-	-	18.74 ***	[75]
2.5. Organizational changes (new)	0.798	-	-	-	-	17.89 ***	[75]
2.6. Organizational changes (improve)	0.805	-	-	-	-	18.16 ***	[75]
2.7. IT in new services	0.815	-	-	-	-	18.52 ***	[75]
2.8. IT in incremental improvements	0.808	-	-	-	-	18.27 ***	[75]
2.9. IT for efficiency	0.823	-	-	-	-	18.82 ***	[75]
3. Intellectual Capital	-	0.903	0.687	0.275	0.244	-	[29]
3.1. Human Capital							[29]
3.1.1. Employee skill level	0.859	-	-	-	-	19.87 ***	[29]
3.1.2. Industry-best employees	0.825	-	-	-	-	18.93 ***	[29]
3.1.3. Employee creativity	0.800	-	-	-	-	18.12 ***	[29]
3.1.4. Employee expertise	0.812	-	-	-	-	18.48 ***	[29]
3.1.5. Knowledge development	0.821	-	-	-	-	18.76 ***	[29]
3.2. Social Capital							[29]
3.2.1. Collaboration skills	0.839	-	-	-	-	19.24 ***	[29]
3.2.2. Information sharing	0.847	-	-	-	-	19.52 ***	[29]
3.2.3. Cross-department exchange	0.831	-	-	-	-	19.05 ***	[29]
3.2.4. External partnerships	0.840	-	-	-	-	19.28 ***	[29]
3.2.5. Knowledge application	0.834	-	-	-	-	19.13 ***	[29]
3.3. Organizational Capital							[29]
3.3.1. Patents and licenses	0.812	-	-	-	-	18.48 ***	[29]
3.3.2. Documentation systems	0.823	-	-	-	-	18.84 ***	[29]
3.3.3. Organizational culture	0.817	-	-	-	-	18.67 ***	[29]
3.3.4. Embedded knowledge	0.810	-	-	-	-	18.42 ***	[29]
4. Organizational Learning	-	0.866	0.638	0.247	0.228	-	[76]
4.1. Competitive knowledge	0.791	-	-	-	-	17.82 ***	[76]
4.2. Competitive capabilities	0.814	-	-	-	-	18.54 ***	[76]
4.3. Knowledge improvements	0.836	-	-	-	-	19.15 ***	[76]
4.4. Learning organization	0.758	-	-	-	-	16.93 ***	[76]

Note: CR = composite reliability; AVE = average variance extracted; MSV = maximum shared variance; ASV = shared variance. *** Indicates significance at the 1% level.

represents the measurement model demonstrates strong psychometric properties, as all factor loadings exceed 0.758, well above the recommended threshold of 0.50. Composite reliability values range from 0.866 to 0.903, indicating excellent internal consistency, while the average variance extracted values (0.638–0.687) exceed the 0.50 threshold, supporting convergent validity. The maximum shared variance values remain below the AVE values, confirming discriminant validity.

4.3. Structural Model

Table 4. Structural model results (N = 396).

Hypothesis	Path	β	SE	t-Value	p-Value	Result
H1	KS → SI	0.425	0.048	8.854	<0.001	Supported
H2	KS → OL	0.514	0.054	9.519	<0.001	Supported
H3	KS → IC	0.386	0.048	8.042	<0.001	Supported
H4	IC → OL	0.369	0.048	7.688	<0.001	Supported
H5	IC → SI	0.154	0.036	4.278	<0.001	Supported
H6	OL → SI	0.188	0.038	4.947	<0.001	Supported
H8	IC → OL	0.369	0.048	7.688	<0.001	Supported

represents the structural model results supporting all hypothesized relationships. Knowledge sharing demonstrates strong direct effects on service innovation (β = 0.425, p < 0.001) and organizational learning (β = 0.514, p < 0.001). The relationship between intellectual capital and organizational learning shows consistent significance in both direct paths (β = 0.369, p < 0.001). All path coefficients exceed critical t-values, supporting the theoretical framework.

Table 5. Model fit indices (N = 396).

Fit Index	Value	Threshold	Assessment
χ2/df	1.868	<3.0	Excellent
CFI	0.944	>0.90	Excellent
TLI	0.937	>0.90	Excellent
RMSEA	0.056	<0.08	Good
SRMR	0.051	<0.08	Good
90% CI RMSEA	[0.047, 0.065]	Upper < 0.08	Good

represents the model demonstrates excellent fit across all indices; the χ²/df ratio (1.868) indicates optimal fit, while the CFI (0.944) and TLI (0.937) exceed recommended thresholds. The RMSEA (0.056) and SRMR (0.051) values suggest a good fit, with the RMSEA confidence interval supporting model stability.

Table 6. Effect decomposition analysis (N = 396).

Effect Type	Value	Percentage	95% CI
Direct Effect (KS → SI)	0.425	55.4	[0.329, 0.521]
Indirect via OL	0.188	24.5	[0.112, 0.264]
Indirect via IC	0.154	20.1	[0.083, 0.225]
Total Effect	0.767	100.0	[0.671, 0.863]

represents the decomposition of effects reveals the relative contribution of direct and indirect pathways in the relationship between knowledge sharing and service innovation. The direct effect accounts for the majority of the total effect (55.4%), demonstrating the primary importance of knowledge-sharing practices. The indirect effects through organizational learning (24.5%) and intellectual capital (20.1%) provide substantial additional impact, supporting the significance of these mediating mechanisms. The confidence intervals indicate robust statistical significance for all effects, with no intervals crossing zero.

Table 7. Multi-group analysis results (N = 396).

Path	Public Banks (n = 223)	Private Banks (n = 173)	Δχ2	p-Value
KS → SI	0.422	0.429	1.24	0.265
KS → OL	0.511	0.518	0.87	0.351
KS → IC	0.383	0.390	0.92	0.337
IC → OL	0.366	0.373	1.03	0.310
IC → SI	0.151	0.158	0.89	0.345
OL → SI	0.185	0.192	0.94	0.332

represents the multi-group analysis comparing public and private banks reveals remarkable consistency in the structural relationships across both sectors. The differences in path coefficients between public and private banks are minimal, ranging from 0.007 to 0.015, and none of these differences reach statistical significance (p-values > 0.05), indicating that the theoretical framework operates similarly regardless of bank ownership structure. This consistency supports the generalizability of the findings across the Libyan banking sector.

Table 8. Explained variance by sector (N = 396).

Variable	Overall	Public Banks	Private Banks
Service Innovation	0.537	0.532	0.543
Intellectual Capital	0.485	0.478	0.492
Organizational Learning	0.458	0.452	0.465

represents the model demonstrates strong explanatory power across both sectors. The total variance explained in service innovation (53.7%) indicates that the theoretical framework captures a substantial portion of the factors influencing innovation in banking services. The explained variance in intellectual capital (48.5%) and organizational learning (45.8%) further supports the model’s robustness; the similar R-squared values between public and private banks (differences < 0.015) reinforce the model’s consistency across sectors.

Figure 2 illustrates the structural equation model with standardized path coefficients. The results confirm that all hypothesized relationships are statistically significant (p < 0.001). Knowledge sharing demonstrates a strong direct effect on service innovation (β = 0.425), while also significantly influencing the mediating variables of organizational learning (β = 0.514) and intellectual capital (β = 0.386). Both mediators contribute to service innovation, with organizational learning (β = 0.188) and intellectual capital (β = 0.154) providing additional pathways for knowledge sharing’s influence. The relationship between intellectual capital and organizational learning (β = 0.369) further reveals the interconnected nature of these organizational capabilities in facilitating innovation in Libyan banks.

5. Discussion

The empirical findings from this study provide substantial support for the theoretical relationships proposed in the literature between knowledge sharing and service innovation in the banking sector. The strong direct effect of knowledge sharing on service innovation (β = 0.425, p < 0.001) aligns with and extends previous research [12], which identified knowledge sharing as a fundamental driver of organizational innovation. This relationship proves particularly significant in the Libyan banking context, where a systematic review of 7991 articles between 1973 and 2017 previously identified knowledge sharing as being crucial for innovation success.

Our findings particularly reinforce the assertion that tacit knowledge transfer correlates positively with enhanced innovation capabilities [13]. The strong correlation between knowledge sharing and service innovation (r = 0.536, p < 0.001) supports their argument that tacit knowledge is difficult for competitors to imitate and, thus, essential for fostering sustainable innovation. These results also align with [14]’s findings in Croatian enterprises and [15]’s observations in Spanish organizations regarding the significant role of knowledge sharing in promoting creativity and innovation.

The dual mediation effects exhibited in our investigation provide empirical support for the theoretical frameworks proposed in the earlier literature. The significant mediating role of organizational learning (24.5% of the total effect, β = 0.188, p < 0.001) provides empirical validation of the theoretical position of [46], who claim that organizational learning in the knowledge economy is heavily based on sharing and integrating preexisting knowledge. This outcome is also in line with the statement that effective organizational learning processes are carried out using sharing among members, which in turn enables organizations to comprehend and adapt to the environment [45].

Similarly, the mediating effect of intellectual capital (20.1% of the total effect, β = 0.154, p < 0.001) provides empirical validation for the theoretical work on the relationship between relational capital and knowledge transfer. The strong psychometric properties of our intellectual capital measures (CR = 0.903, AVE = 0.687) lend credibility to these relationships [24,25], supporting findings regarding the significant impact of human capital on knowledge acquisition and transfer [26].

The consistency of relationships across public and private banks (Δχ² < 1.24, p > 0.05) provides new insights into the generalizability of knowledge-sharing effects. This finding extends this work by demonstrating that intellectual capital’s influence on innovation transcends organizational ownership structures [29]. The substantial variance explained in service innovation (53.7%) supports the conceptualization of intellectual capital as a crucial resource for fostering innovation [30].

Our results particularly illuminate the relationship between intellectual capital and organizational learning, addressing a gap identified in the literature. The significant path coefficient between these constructs (β = 0.369, p < 0.001) provides empirical support for the theoretical proposition that intellectual capital plays a pivotal role in promoting organizational learning [63]. This finding also aligns with the emphasis on intellectual capital’s fundamental role in long-term organizational success [64].

The strong explanatory power of our model in the Libyan banking context (R² = 0.537 for service innovation) addresses the research gap identified regarding the limited examination of innovation in banking services [8]. Our findings demonstrate how knowledge-sharing supports the conventional banking industry’s capacity for innovation through fostering an organizational learning culture, extending beyond the general advantages of improved decision-making and operational performance noted by [6].

The relationship between organizational learning and service innovation (β = 0.188, p < 0.001) supports the conceptualization of organizational learning as a socially created, contextually embedded collective practice [1,55]. The significant indirect effect through organizational learning validates findings regarding the strong influence of organizational learning capability on social innovation [61].

These findings extend the current understanding by demonstrating how both formal and informal knowledge-sharing mechanisms contribute to innovation outcomes. These findings strongly underline the point that successful innovation in banking services to enable them to become an agile and innovative sector would essentially entail an integrated approach, to which the direct contribution, as well as the indirect effects through organizational capabilities, of knowledge sharing is integral.

This holistic perspective advances theoretical understanding while providing practical insights for banking sector management. The comprehensive nature of our findings addresses multiple gaps identified in the literature review, particularly regarding the mediating roles of intellectual capital and organizational learning in the relationship between knowledge sharing and service innovation. The results provide empirical validation for numerous theoretical propositions while extending an understanding of how these relationships operate in the context of developing economies’ banking sectors.

6. Conclusions

This study has investigated the relationship between knowledge sharing and service innovation in Libyan banks, indicating that intellectual capital and organizational learning mediate the process. Knowledge sharing has a substantial direct effect on service innovation, having a positive influence (β = 0.425, p < 0.001). The impact contributed by knowledge sharing is 55.4% of the total effect.

Our results indicate sophisticated mechanisms that mediate how knowledge-sharing influences innovation. In the mediation, organizational learning accounts for 24.5% of the total effect, while intellectual capital accounts for 20.1%. The strong explanatory power of the model (R² = 0.537 for service innovation) validates the theoretical framework’s practical relevance.

The consistency of our findings across both public and private banks (Δχ² < 1.24, p > 0.05) demonstrates that the benefits of the knowledge-sharing culture transcend organizational structures in the banking sector. With strong psychometric properties of the measurement model and excellent fit indices of the theoretical framework, strong empirical support was derived for the theoretical model.

This research has addressed the literature gap on innovation in banking services, especially in developing economies. The findings show that effective knowledge sharing, with the help of strong organizational learning capabilities and the development of intellectual capital, significantly enhances the innovative capacity of banks.

For bank managers in developing economies, these results provide valuable insights into knowledge-sharing practices that enhance innovation capacity. Despite limitations related to its cross-sectional design and geographic focus, this research advances our understanding of how financial institutions can leverage knowledge sharing to enhance innovative capabilities, with significant implications for banking innovation strategies in developing economies.