Organizational learning capabilities and performance: The impact of digital leadership,IT support,and knowledge sharing

Abstract

Background

The rapid pace of digital transformation and globalization has compelled organizations to adapt urgently.

Objectives

This study examines the role of digital leadership (DL), information technology support (ITS), and knowledge sharing (KS) in enhancing organizational learning capability (OLC), which subsequently impacts organizational performance (OP).

Methods

The research was conducted in South Korea’s Daegu Industrial Area, focusing on the automobile industry. 359 participants were surveyed to assess their understanding of IT support, digital leadership, and knowledge sharing within their organizations. The data collected were analyzed using partial least squares structural equation modeling (PLS-SEM).

Results

Based on the resource-based view (RBV) theory, the study found that DL and ITS positively influence KS, subsequently boosting OLC. OLC significantly impacts OP, with KS fully mediating the relationships between DL–OLC and ITS–OLC. These findings highlight KS as a key driver of OLC and performance, offering managers valuable guidance on aligning KS with strategic objectives.

Conclusion

By examining the relationships among DL, ITS, KS, OLC, and OP, this study builds on existing research and offers practical strategies for developing OLC in digitally driven business environments.

Keywords

digital leadership information technology support knowledge sharing organizational learning capability organizational performance

Introduction

Digitalization and globalization drive firms to deliver high-quality products and services to sustain long-term success and growth. Acknowledging this, digital leadership (DL) has become a key area of focus in organizational performance, placing substantial pressure on companies worldwide. Businesses face significant challenges in digital capabilities, technology, knowledge sharing (KS), innovation, and overall performance.¹ DL is crucial in enhancing performance, particularly in complex, dynamic, and interconnected environments characterized by uncertainty and constant change. Consequently, DL must offer its staff the required training to equip them with essential skills and knowledge.² This training is facilitated through KS activities, which involve transferring knowledge from one individual or group to another within an organizational context.³ When companies successfully establish robust internal and external knowledge exchange mechanisms, they develop a vital dynamic organizational learning capability (OLC). This capability can directly contribute to sustainable Organizational performance (OP). According to Elkjaer,⁴ there are challenges associated with learning in a digitally connected world, and algorithms and digital technologies will continue to influence and shape online learning in ways that support long-term sustainability. A company that successfully integrates information technology support (ITS) to develop information technology (IT) capability can gain a more potent competitive edge.⁵ Furthermore, IT capability enhances organizational learning by enabling the capture, representation, storage, retrieval, and sharing of information and knowledge from internal and external sources.⁶

However, organizations of all sizes and sectors can undertake digital transformation in today’s competitive landscape. Many are already leveraging new digital technologies and managing technological transitions effectively to transform individual knowledge into organizational knowledge through organizational learning.⁷ To navigate the challenges of digital transformation and organizational changes, leaders must possess diverse digital skills to collaborate efficiently in digital environments, demonstrate initiative, respond quickly to emerging situations, and address complex, time-sensitive issues. Organizational learning engages employees in the exploration, exploitation, and exchange of knowledge.⁸ Information technologies have reshaped the distribution learning opportunities,⁹ is the antecedent of KS, which fosters to achieve competitiveness like OLC.⁶ In response to diversity of learning system, digital literacy of DL,^1,10,11 enables individuals to improve their status through online interactions via the internet,⁹ has become key driver in dynamic business environment to facilitate organizational change and digital transformation to achieve competitiveness, which depend on OLC. However, limited research highlights the importance of DL and ITS as essential tools for managing KS to improve OLC, contributing to OP in the organizational environment. Therefore, to advance the body of knowledge, a more precise and comprehensive understanding of the relationships among DL, ITS, and KS as foundational elements for developing OLC, ultimately leading to OP, is especially needed within South Korea’s automobile industry. In addition, digitalization has resulted in a computer and digital skills-related mismatch between education and the demand of labor markets, irrespective of the nature of work done by science, social science, or business graduates.¹² Current models of organizational learning seem outdated and less relevant, highlighting the need for transformational learning strategies in a digitally interconnected world.⁴ In this study, organizational performance has emerged as a key outcome variable, prompting pertinent research questions: RQ1: How will DL and ITS influence and reshape KS and OLC in the future? RQ2: How does OLC affect OP? Furthermore, RQ3: How are these dynamics interconnected?

Moreover, South Korea ranks fifth among the world’s top exporters of motor vehicle products and holds seventh place in global automobile production. The motor vehicle industry is the country’s most significant manufacturing sector, with an estimated production value of 282.5 trillion South Korean won. The automobile industry substantially contributes to South Korea’s economy, accounting for approximately 12% of total employment and 13% of manufacturing output. It makes up approximately 7% of the national GDP and could account for up to 15% of jobs within the manufacturing sector. Furthermore, with nearly 65% of Korea’s automobile production being exported, the industry is susceptible to changes in global trade conditions.¹³ This study examines the relationship between DL, ITS KS, OLC, and OP within the context of the automobile industry in Daegu, South Korea.

Therefore, grounded in the resource-based view (RBV), the proposed conceptual framework adds to the existing body of knowledge. It offers valuable insights for industry managers on how to effectively manage knowledge and digital capabilities in a digital environment, with the ultimate goal of strengthening OLC and enhancing OP. This study extends the traditional RBV by integrating the DL construct. It is among the first studies to explore and analyze the relationship between DL and ITS, with KS as a mediating factor in a dynamic work environment in South Korea. Some studies have used DL as an independent variable to examine factors affecting organizational performance.^14,15 Additionally, ITS and KS have been used as independent variables in OLC.⁸ KS has also been employed as a mediating variable to boost competitive advantage.¹⁶ This research investigates whether DL, ITS, and KS contribute to improving OLC, influencing OP.

This study is organized into seven sections: the first section presents the introduction, the second outlines the literature review, the third underpins the theoretical foundation, the fourth outlines hypothesis development, the fifth details the research methodology, the sixth presents findings, the seventh discusses findings, and the final section offers the conclusion, implications, limitations, and recommendations.

Literature review

In the digital transformation era, business firms, particularly those in the automobile industry, face internal and external challenges related to geographical, cross-cultural, and linguistic boundaries, which can lead to irreversible organizational change.¹⁷ Externally, they must navigate increasing growth, technological advancements, and intense competition at national and international levels. Internally, they confront growing pressure to introduce new or improved products and services. Digitalization has made it necessary for leaders to work with management composed of individuals from different cultural backgrounds and work values. DL is the strategic use of digital tools and platforms to manage the workforce, improve collaboration, and enhance performance. Leaders utilize practical collaborative tools, virtual management strategies, and digital trust-building techniques, leveraging their digital competency to motivate and inspire teams in cross-cultural work settings.^10,15 Leaders in a digitally connected world can achieve organizational effectiveness through communicativeness, strategic agility, digital transformation, trust and relationship building, and goal alignment in the digital work setting of multinational organizations.^1,2,10 In addition, digital literacy of DL minimizes educational inequality and skills difference among team members to address cross-cultural challenges in the globalized marketplace to remain competitive.^6,18 To improve business efficiency and stay competitive, firms must prioritize various factors, including speed, quality, pricing, innovation, and customer responsiveness. These help them achieve rapid and significant performance improvements and drive innovation.^6,8 In strategic management, performance is a key concern for managers, as it indicates how effectively an organization meets its objectives, vision, and mission. Companies face many risks in sustaining, growing, and establishing their presence in a competitive market. Factors such as manufacturing, marketing, retail operations, and financial goals can positively or negatively impact a firm’s performance.⁵ Business success is often measured by operational performance, customer service quality, revenue, and efficiency. Strategic management literature usually equates OLC with OP. However, these are distinct concepts with a complex relationship influenced by the industries in which firms operate. KS is closely linked to OLC, as many corporate advances stem from interfirm KS.^8,19 OLC significantly influences employee attitudes and organizational performance. By leveraging emerging opportunities to strengthen OLC, firms can enhance competitiveness and sustain superior performance, making OLC and OP central themes in management research.¹⁹ The resource-based view (RBV) suggests that each firm possesses a unique combination of resources and capabilities.²⁰ A firm can enhance its learning capability and production performance by leveraging novel and exclusive resource combinations.²¹ RBV emphasizes that a firm’s internal factors may be tangible, such as its technology, or intangible, such as knowledge and leadership quality, which serve as sources of competitive advantage when they are rare, valuable, difficult to imitate, irreplaceable, and capable of generating economic benefits.²⁰

IT has increasingly played a vital role in gaining strategic advantage and supporting organizational functions, making information systems personnel essential for business success, especially as they strive to become more “digital.” ITS offers easy access to information for individuals and fosters learning.²² IT can remove barriers to communication among different units within the organization, and a well-developed IT system facilitates the quick collection, storage, and flow of knowledge, reducing communication obstacles and enhancing reciprocity.²³ Literature describes “lifelong” and “continuous” knowledge creation within organizations, and the leader’s role is to create opportunities for acquiring the necessary skills for the digital future through learning.^2,10,24 The focus on dyadic relationships and leaders’ influence often distracts from the broader, multilevel impact and how leaders influence programs and systems.²⁵ Leaders who implement KS strategies effectively can strengthen an organization’s core competencies.²⁶ Leaders in knowledge-driven organizations face challenges working collaboratively toward shared goals, including developing personal qualities such as emotional intelligence, initiative, integrity, courage, and stress management skills.⁷ Leadership research is closely tied to RBV and its connection to OLC and OP.¹

The role of KS is highlighted by focusing on the ability of organizations to integrate different national and international business units within their existing frameworks and to enable the exchange of information among multinational or cross-cultural work settings.¹⁸ In a multinational work setting, digitalization and organizational complexity foster the development of a flexible strategy that harnesses and shares knowledge across diverse cultural and geographical contexts through leaders’ psychological ownership and professional commitment.²⁶ KS enables businesses to achieve positive outcomes by promoting the creation of new knowledge through collaboration in cross-cultural work settings, which enhances problem-solving skills and deepens understanding of decision-making processes. Knowledge-based assets are vital to OP, and KS plays a key role. For example, ITS and KS are precursors to OLC, which drives innovative performance.⁸ Merely possessing knowledge resources is not enough to boost overall firm performance; effective knowledge retention and sharing are crucial for managing knowledge efficiently, transforming it into intellectual assets, and increasing productivity. In addition, the level of knowledge and skills of workers in the organizations is different because of their other academic backgrounds. The RBV provides a comprehensive explanation for understanding DL, ITS, and KS, aligning well with a firm’s strategic assets and core competencies to support sustained organizational learning, which arises due to education or limited access to digitalized technology.¹² This theory stresses that when DL and KS, as intangible assets, are effectively applied across various organizational levels, they generate specific resources and capabilities that improve performance by leveraging knowledge.²¹

RBV argues that a firm’s resources (DL and KS) are the key contributors to competitive advantage, leading to enhanced performance.²⁷ In an ever-evolving environment, resource scarcity (such as DL and KS) is the rule rather than the exception within organizations. Achieving an optimal balance in distributing these resources is essential, particularly in organizational learning and performance. Significantly, the Knowledge-Based View (KBV) combined with digital capabilities is vital for firms, as the role of KS in performance creates a foundation for management to enhance the OLC and serve as a competitive advantage for improving performance.

According to the existing literature, most studies have explored the factors influencing OLC and OP within the context of either developed or developing countries. However, to the best of our knowledge, no research has examined the impact of these factors on OLC and OP in South Korea’s automobile industry, particularly through the RBV extended by the concept of DL. Most prior research has focused on competitive advantage, with little attention given to the direct impact of DL and the specific role of OLC as a source of competitive advantage within the RBV framework. Additionally, there remains a gap in the literature concerning the factors affecting OLC and OP in highly digitalized work environments like South Korea’s automobile industry.

Theoretical foundation

The Resource-Based Theory (RBT),²⁷ also known as the RBV of the firm, asserts that valuable, rare, inimitable, non-substitutable, and organized (VRIN-O) resources and capabilities serve as the foundation for sustaining a competitive advantage.²⁰ One such capability is organizational learning, which is based on the premise that strategic resources are distributed unevenly among firms and that these differences remain stable over time. Camisón and Villar-López²¹ describe RBV as a capability involving the deployment and reconfiguration of resources to enhance productivity and achieve strategic objectives. These resources and core competencies, characterized by their uniqueness, improve competitive advantage and propel business excellence. The distinctiveness of a firm’s strategic capability relies on its rarity, value, and inimitability, enabling the organization to restructure and effectively allocate valuable resources to promote digital capabilities and KS, which leads to firm performance.

Organizational success is achieved through effective leadership behavior. This collaborative work environment results from ITS and open KS practices, which are essential in enhancing overall OP.²⁵ Undoubtedly, RBV effectively connects intangible assets, such as learning and knowledge, to generate higher profits than acquiring resources through direct purchase. Knowledge-based assets and dynamic capability are considered the core components of a firm, facilitating the creation, integration, and sharing of knowledge to deliver superior value. The ability to generate value depends not on financial or physical resources but on the development of knowledge-based assets like OLC. The proposed framework, grounded on RBV, helps establish a cohesive KS relationship by strengthening DL and ITS, enhancing OLC and OP, as illustrated in Figure 1.

Figure 1.

Proposed research model.

Hypothesis development

Digital leadership (DL)

Digital leadership is a social process mediated by technology that aims to change individuals, groups, or organizations’ attitudes, thoughts, feelings, behavior, and performance. It can involve one-to-one or one-to-many interactions.²⁴ DL has been developed to address new leadership challenges that emerge from digitalization and digital transformation processes.¹⁰ Digital leaders foster a culture of KS and learning through remote work, digital collaboration, and communication tools.¹¹ In this study, DL is defined as the leader’s ability to use digital technology to enhance OLC and KS among stakeholders. Digital leaders support and guide their organizations through changes brought about by digital transformation.² They utilize digital platforms, tools, and technologies to promote a culture of collaboration, ongoing learning, and innovation.¹⁷ Research shows that leadership support and influence are key factors in motivating employees to share knowledge.²⁸ Digital leaders must use digital tools and platforms effectively to improve KS.¹⁷ Accordingly, Fatima and Masood¹⁴ highlight the impact of DL on KS to achieve open innovation. Thus, the proposed hypotheses;

DL has a positive, significant impact on OLC.

A strong DL has a positive, significant impact on KS.

Information technology support (ITS)

Information technology support is the extent to which it facilitates knowledge management,²³ and ITS is a key facilitator in promoting organizational learning.²² In this study, ITS refers to how information technology enhances the OLC of workers within the organization to generate knowledge and develop skills to achieve OP. Information technology provides a fundamental foundation for knowledge-intensive sectors, where deficiencies in IT systems can hinder effective organizational learning.⁸ Individual learning, combined with personal and collaborative information technologies, positively and significantly impacts organizational learning.²² ITS supports thriving and efficient knowledge transfer, as a firm’s IT orientation can improve knowledge acquisition, sharing, and the use of information to foster service innovation and performance.^23,29 Accordingly, we propose the following hypotheses;

ITS has a positive effect on OLC.

ITS has a positive impact on KS.

Knowledge sharing (KS)

Knowledge sharing (KS) is a company’s ability to utilize essential information from business partners or to identify market opportunities.⁸ In this research, KS refers to the process of collecting necessary information from the market and exchanging knowledge with stakeholders to achieve goals. KS behavior reflects an individual’s attitude, willingness, and capability to share their expertise, skills, and information with colleagues.²⁹ It has been shown that KS is vital in enhancing learning capabilities and increasing employee productivity within organizations. Yang, Nguyen³⁰ stated that KS significantly improves an organization’s ability to manage its knowledge assets and helps individuals accomplish their business goals more efficiently. KS boosts organizational efficiency and strengthens a firm’s competitive advantage.⁶ Curado, Muñoz-Pascual⁸ highlighted the positive impact of KS on OLC, contributing to expanded product innovation performance, as KS can drive OLC. Therefore, incorporating the conceptual arguments discussed above leads to the following hypothesis:

Knowledge sharing positively affects OLC.

Organizational learning capability (OLC) and organizational performance (OP)

Organizational learning capability refers to an organization’s ability to create, transfer, acquire, and integrate knowledge to enhance performance.³¹ There is a significant positive relationship between OLC and organizational effectiveness, as it enhances employees’ ability to perform their duties effectively by developing and utilizing knowledge to pursue strategies for achieving organizational goals.³² In this study, OLC refers to the ability of an organization’s members to acquire knowledge and share it among themselves with the help of DL and ITS. A strong correlation exists between organizational learning and various aspects of workplace success, including overall effectiveness, operational efficiency, employee productivity, and managerial performance, suggesting that organizational learning has a broad impact by improving staff effectiveness and efficiency at all levels.³² Furthermore, the relationship between OLC and performance is closely related to an organization’s capability to take risks and innovate in a constantly changing, fast-paced environment.⁸ Therefore, the following hypothesis is proposed:

OLC has a substantial positive impact on OP.

The mediating effect of knowledge sharing

Knowledge sharing is the distribution of relevant and appropriate information among workers within an organization.²⁹ KBV considers KS a resource for exchanging intellectual capital among organizations and helps create a competitive advantage.²⁸ According to Kazim,¹¹ the flow of information across the organization influences the innovative behavior of mid-level managers. Researchers have found a positive indirect effect of KS on gaining new mechanisms, information, and OLC.^8,14,23 KS also promotes information exchange, social satisfaction, and dependence among firms and enhances innovation capability within a collaborative culture.³⁰ Therefore, the hypotheses are as follows;

Knowledge sharing serves as a mediator in the relationship between ITS and OLC.

Knowledge sharing mediates the relationship between DL and OLC.

Research methodology

This study examines the effects of DL, ITS, and KS on OLC and OP. The study was conducted in Daegu, South Korea, a prominent center for automobile manufacture. This site was selected due to the automobile industry’s high level of digitalization and technological intensity in Daegu, which necessitates personnel to possess advanced technical expertise and digital skills. Furthermore, the automobile sector significantly contributes to South Korea’s economy, particularly in terms of GDP, employment, and exports, making Daegu’s automobile industry a suitable and representative context for this study. A structured survey questionnaire was developed to ascertain how OLC might be attained in a digitalized work environment and its subsequent impact on OP. The statistical analysis concentrated on direct and mediating effects to evaluate the relationship among the variables.

Data collection

This study examines DL, ITS, KS, OLC, and OP relationships. The current research uses a quantitative approach, more reliable than qualitative methods. The survey method is often employed to gather primary data and has proven effective for this type of research.³³ The target group comprised mid-level managers from departments like HR, IT, marketing, accounting, and general management, due to their direct engagement in coordination, problem-solving, and sustaining operational efficiency. The sampling frame consisted of 142 active vehicle companies in Daegu, recognized by the Daegu Vehicle Association and corroborated by the Korea Chamber of Commerce and Industry (KCCI). To qualify, enterprises needed to (1) operate in vehicle or component manufacturing, (2) employ over 50 personnel, and (3) implement digitalized systems for production and management. For determining the sample size, Sekaran and Bougie³⁴ suggested that a sample size of 30 to 500 is generally suitable for most behavioral studies. A simple random sampling technique was employed to collect primary data through a structured questionnaire from the target population of 142 companies, regardless of race, nationality, or organizational responsibility, between February 2025 and April 2025. In addition to the structured questionnaire, the characteristics of the people who completed the questionnaire on behalf of the firm were also recorded. To ensure active industry participation, the researcher made in-person visits to automobile company offices to distribute the surveys directly. Before distribution, the management of these firms was briefed on the study’s objectives and overall importance.

Measurement

This study applies the RBV to examine how DL and ITS influence performance, moving beyond the scope of conventional analyses.²¹ From this standpoint, scholars argue that tangible and intangible assets strengthen KS, OLC, and performance.^{1,8,15,22,23,25} In addition, the framework emphasizes the importance of KS culture, including appropriate tools, structured procedures, adequate time, and effective leadership behavior.^2,19,23 Such an environment encourages employees to learn valuable skills, such as knowledge to combat market competitiveness, which are key drivers of OP.^22,25 Kumar, Mamgain²³ argue that ITS and KS behaviors enhance OP through minimizing employees’ knowledge gap. Kazim¹¹ introduced the DL scale to operationalize this model, which has been validated in multiple studies.^1,10,15 The ITS is measured using Lee and Choi’s scale,³⁵ and the KS is evaluated using items of Chen and others,³⁶ which have been validated in studies.^8,23,26

To measure OLC, we employed a 5-item scale from the previous studies.³² These dimensions align with previous research highlighting their validity with OLC.⁸ Our scales align with measures developed by Jerez-Gomez and others,³¹ ensuring comprehensive coverage of OLC dimensions and enabling robust comparison across studies. OP itself was also evaluated with the scale developed by Jyothibabu, Farooq,³² which captures the operational performance and financial performance, which are recognized as central indicators for assessing OP.^1,5,6

The survey used a multi-item approach, employing a 5-point Likert scale to measure all variables, where “1” represented “strongly disagree” and “5” indicated “strongly agree.” All measurement items used to operationalize the constructs were adapted from previously validated studies. Four items were used to measure each construct; details are provided (see Appendix A).

Data analysis

All 500 questionnaires were distributed, and multiple workplace visits were conducted to maximize responses. Of these, 389 questionnaires were returned, 91 completed in hard copy, and 298 submitted online via platforms like Google Forms. Thirty questionnaires were considered incomplete or improperly filled and were excluded from analysis. As a result, 359 fully completed responses were used for final data analysis, yielding a response rate of 61.8%. IBM SPSS 23 was used for data analysis and to present demographic information. The measurement model assessed the reliability and validity of the constructs. In contrast, the structural model tested the research hypotheses using Smart PLS 4.1.1 software, which is one of the powerful software for hypothesis testing. The PLS algorithm technique confirmed the measurement model’s internal consistency, scale reliability, convergent validity, and discriminant validity.

Findings

Demographic profiles

Table 1 presents the demographic data of the respondents. In this study, 14.5% of participants are female, and 85.5% are male. Additionally, 61.6% are under 35 years old, 35.7% are between 35 and 44 years old, and 2.8% are over 44 years old. Furthermore, 71.87% hold a bachelor’s degree, while 27.86% have a master’s degree. Regarding experience, 55.7% have less than 6 years, 36.5% have between 6 and 15 years, and just 7.8% have more than 15 years of experience.

Table 1.

Demographic profile of the respondents.

Items	Categories	Frequency	Percentage
Gender	Female	52	14.5
Gender	Male	307	85.5
Age	24–34	221	61.6
	35–44	128	35.7
	45–54	10	2.8
Education	Bachelor	258	71.87
	Masters	100	27.86
	PhD or MPhil	1	.27
Experience	1–5 years	200	55.7
	6–15 years	131	36.5
	16–25 years	28	7.8

Measurement model

The measurement model was evaluated to assess the convergent and discriminant validity of the constructs. Factor loadings for each construct exceeded the ideal threshold of 0.70, as Esposito Vinzi and Chin suggested.³⁷ Variance Inflation Factor (VIF) values were below the recommended cutoff of,⁵ as Hair and Sarstedt³⁸ advised, indicating no multicollinearity issues. Furthermore, Cronbach’s alpha values confirmed high reliability and consistency across all constructs, with the standard value of 0.7 recommended by the study. Additionally, each construct’s composite reliability (CR) should be 0.7 or higher.³⁸ The Average Variance Extracted (AVE) values exceeded the 0.50 benchmark set by Bagozzi and Yi.³⁹ In this study, Table 2 displays VIF, factor loadings, Cronbach’s alpha, AVE, and CR.

Table 2.

Measurement model.

Construct	Items	VIF	Loadings	Cronbach’s alpha	AVE	CR (rho_a)	CR (rho_c)
Digital leadership (DL)	DL1	1.635	0.802	0.788	0.612	0.792	0.863
	DL2	1.441	0.753
	DL3	1.920	0.838
	DL4	1.574	0.732
Information technology support (ITS)	ITS1	1.553	0.770	0.770	0.592	0.773	0.853
	ITS2	1.603	0.797
	ITS3	1.494	0.740
	ITS4	1.559	0.770
Knowledge sharing (KS)	KS1	1.526	0.737	0.782	0.605	0.785	0.860
	KS2	1.608	0.786
	KS3	2.030	0.791
	KS4	2.046	0.797
Organizational learning capability (OLC)	OLC1	2.296	0.859	0.855	0.698	0.855	0.902
	OLC2	2.154	0.844
	OLC3	1.709	0.804
	OLC4	1.927	0.832
Organizational performance (OP)	OP1	2.114	0.851	0.852	0.693	0.854	0.900
	OP2	2.073	0.841
	OP3	1.785	0.800
	OP4	2.012	0.838

VIF: Variance Inflation Factor; AVE: Average Variance Extracted; CR: composite reliability.

Table 3 demonstrates Fornell and Larcker,⁴⁰ which compares each construct’s square root of the Average Variance Extracted (AVE) with its correlations to other constructs. The AVE’s square root must be greater than its bivariate correlations, confirming discriminant validity. Additionally, Table 4 highlights the Heterotrait-Monotrait Ratio (HTMT), with threshold values below 0.85 being preferable.⁴¹ All HTMT values fall within the acceptable range, thus confirming adequate discriminant validity in the model.

Table 3.

Fornell-Lacker criteria.

Constructs	DL	ITS	KS	OLC	OP
DL	0.782
ITS	0.497	0.769
KS	0.424	0.466	0.778
OLC	0.290	0.342	0.559	0.835
OP	0.255	0.244	0.501	0.504	0.833

DL: digital leadership; ITS: information technology support; KS: knowledge sharing; OLC: organizational learning capability; OP: organizational performance.

Table 4.

Heterotrait-Monotrait (HTMT) ratio.

Constructs	DL	ITS	KS	OLC	OP
DL	0.782
ITS	0.644	0.769
KS	0.540	0.602	0.779
OLC	0.346	0.421	0.678	0.836
OP	0.308	0.298	0.612	0.591	0.833

DL: digital leadership; ITS: information technology support; KS: knowledge sharing; OLC: organizational learning capability; OP: organizational performance.

Structural model

This study used bootstrapping with 5000 samples to estimate the higher-order constructs model. This bootstrapping process generated path coefficients, t-values, and p-values, which are used to evaluate the strength and significance of the relationships between constructs. The study examines the impact of DL and ITS on KS and OLC. It also investigates the influence of OLC on OP. Additionally, the research explores the mediating role of KS in the relationships between DL, ITS, and OLC. The R-squared values for KS, OLC, and OP were 0.267, 0.322, and 0.255, respectively, exceeding the minimum acceptable threshold of 0.1 suggested by Falk and Miller,⁴² confirming the adequate explanatory power of the predictor variables. Figure 2 illustrates the path diagram, loadings, and P-values.

Figure 2.

Path diagram and loadings with p-values.

The confirmation of hypothesis H2 revealed a significant positive effect of DL on KS (β = 0.255, t = 4.473, p < 0.001). Similarly, hypothesis H4 showed a substantial impact of ITS on KS (β = 0.340, T = 6.431, p = 0.000), and H5 demonstrated that KS has a strong positive influence on OLC (β = 0.503, T = 9.083, p = 0.000). In addition, the results indicated a significant and positive effect of OLC on OP (β = 0.503, t = 10.875, p < 0.001), as presented in Table 5.

Table 5.

Structural model.

Path	B	t-Statistics	p-value	BCI
Path	B	t-Statistics	p-value	2.50%	97.50%
H1: DL → OLC	0.031	0.585	0.558	−0.074	0.128
H2: DL → KS	0.255	4.473	0.000	0.143	0.367
H3: ITS → OLC	0.093	1.492	0.136	−0.032	0.216
H4: ITS → KS	0.340	6.431	0.000	0.241	0.448
H5: KS → OLC	0.503	9.083	0.000	0.394	0.615
H6: OLC → OP	0.503	10.875	0.000	0.418	0.601

Significant at p < 0.05; p < 0.001.

Mediation analysis

The mediating role of KS in the relationship between DL and OLC was assessed through mediation analysis (Table 6). The direct effect of DL on OLC was found to be statistically insignificant (β = 0.031, T = 0.585, p = 0.558), but the indirect impact of DL was statistically significant on OLC (β = 0.128, T = 3.864, p = 0.000). In addition, the direct effect of ITS on OLC was statistically insignificant (β = 0.093, T = 1.492, p = 0.136), and the indirect impact of ITS on OLC was significant (β = 0.171, T = 4.725, p = 0.000). These findings confirm H7 and H8, demonstrating that KS is a full complementary mediator between DL, OLC, ITS, and OLC. Moreover, the total effect of DL and OLC shows (β = 0.631, T = 2.589, p = 0.010), and that of ITS and OLC is found to have (β = 0.674, T = 3.989, p = 0.000).

Table 6.

Mediation analysis.

Path	IV	DV	B	t-statistics	p-value	BCI
Path	IV	DV	B	t-statistics	p-value	2.50%	97.50%
$H_{7} :$ DL → KS→ OLC	DL	OLC	0.128	3.864	0.000	0.068	0.199
$H_{8} :$ ITS→ KS→ OLC	ITS	OLC	0.171	4.725	0.000	0.106	0.251

Significant at p < 0.05; p < 0.001.

Discussion

The study results revealed a significant positive effect of DL on KS, which aligns with the findings of Fatima and Masood.¹⁴ Digital leaders leverage digital platforms, tools, and technologies to cultivate a culture of collaboration and continuous learning, which drives innovation within their organizations.¹⁷ This leadership approach supports South Korean automobile companies in navigating the disruptive phases associated with the transformative change necessary to achieve long-term objectives. Such leaders also empower employees by making them feel appreciated and highlighting the significance of their contributions, thereby encouraging KS. Similarly, the ITS has a direct and positive effect on KS among organizational members, consistent with the outcomes reported and further supported by a recent study conducted in the Uttarakhand region of India.²³ The results suggest that when management of highly digital countries in highly technology-based organizations, such as automobile firms, wants to share practical knowledge among organizational members, DL and ITS can assist them in effectively enhancing their learning, and OP. Moreover, the study reveals a significantly positive and direct relation between KS and OLC, which is validated in the Portuguese SME context by Curado and Muñoz-Pascual.⁸ The study result indicates that KS facilitates first and durable learning among stakeholders to enhance performance and creativity. In addition, the study also shows a positive and significant direct relationship between OLC and OP. This finding is supported by research conducted in a power plant company in India, which confirmed the role of OLC in enhancing OP³² but contradicts the results of Migdadi¹⁹ who found an insignificant effect between OLC and OP in Jordan’s service sector. Furthermore, the findings are confirmed by Curado and Muñoz-Pascual,⁸ highlighting that OLC stimulates product innovation performance. The study outcome indicates that an environment with OLC promotes OP. Suppose OLC is developed among the members of a highly digital organization, such as the automobile industry in South Korea. In that case, the organization will be able to enhance OP.

However, the study reveals that DL does not significantly impact OLC among organizational members, which contrasts with the findings of^1,11,17 but supports the findings of,⁴³ who highlighted that Benevolent leadership has an insignificant effect on OLC. Possible reasons for this disparity include DL capabilities or skills, lack of employee readiness, and resistance to cultural shifts from traditional methods to motivate organizational members in using technology within the automobile industry infrastructure in developed countries like South Korea. Additionally, organizational members may exhibit lower levels of thinking or adaptability, as they struggle with continuous self-learning. Despite this, strategic and tactical integration with digital transformation may hinder direct OLC. Additionally, ITS does not significantly affect OLC despite the availability of a highly technological work environment in South Korea’s automobile industry. This finding contradicts the findings of previous authors^8,22 but supports the findings of Ruiz-Mercader and others²² regarding the adverse effect of collaborative and individual technology on OP. Several factors can contribute to this relationship, such as missing human elements, the misalignment between IT and business objectives, a lack of knowledge sharing, management commitment, and the failure to recognize the intangible advantages of IT. Furthermore, elements like organizational culture and human capital are essential for the effective utilization of IT, but these may be lacking within the organization. Additionally, the issue may arise from insufficient technical skills or the users’ inability to manage and adapt to emerging technologies, hindering OLC’s promotion.

The results of the SEM analysis support the mediating role of KS in the connection between DL and OLC, as well as between ITS and OLC. The study demonstrates that DL positively influences KS, subsequently impacting OLC. Moreover, the findings suggest that digital leaders foster a culture of KS among employees, thereby creating a supportive work environment that facilitates OLC. As a result, employees are more inclined to share and acquire knowledge, skills, experiences, and work-related resources, leading to the generation of new ideas related to processes, methods, innovative product designs, and the implementation of new technologies, ultimately enhancing operational and financial performance. As shown in Table 6, the indirect effect is stronger than the direct effect. This suggests that DL in this sector may contribute to OLC by cultivating a KS culture, rather than through a direct influence. This aligns with the findings of Yang and Nguyen,³⁰ who found that a collaborative culture can enhance innovation capabilities through KS.

In addition, the analysis reveals that ITS positively affects KS, which in turn positively influences OLC. The results indicate that ITS contributes to developing a KS culture among employees, such as DL, thereby fostering a supportive work environment that promotes OLC. As illustrated in Table 6, the indirect effect outweighs the direct impact. These findings are consistent with those of Azeem and Naseem,¹⁸ who found that organizational culture can indirectly strengthen competitive advantage through KS. The research suggests that DL and ITS contribute to OLC and OP by establishing an organizational culture for KS.

Theoretical implications

The present study makes a significant theoretical contribution by developing and empirically validating a revised conceptual framework examining how DL and ITS influence OLC, with KS as a central mediating variable. This framework addresses a critical research gap by integrating these constructs into a single model and statistically demonstrating that KS fully mediates the relationship between ITS, DL, and OLC. Additionally, the study shows that OLC significantly affects OP, thus expanding the understanding of how knowledge-related capabilities drive organizational outcomes. Unlike previous studies that mainly discussed these relationships at a conceptual level or focused on limited aspects such as collective responsibility or functional integration, this research empirically explores the combined effects of ITS, DL, KS, OLC, and OP. Notably, the study emphasizes the underexplored integration of DL and ITS in the automotive sector, offering a pioneering perspective on how these elements jointly enhance KS and learning capabilities. Drawing on RBV, the findings underscore the strategic importance of fostering KS cultures and leveraging human and technological resources to sustain intangible competitive advantages. Therefore, the proposed model provides theoretical insights and practical recommendations for industrial firms aiming to adapt and succeed in dynamic, knowledge-driven environments.

Managerial implications

The research findings offer valuable guidance for business professionals and decision-makers by highlighting the critical role of DL, ITS, and KS in achieving OLC and OP. The study provides empirical evidence that DL and ITS significantly and positively influence OLC through KS, which enhances OP. As a result, managers should focus on developing a strong leadership style to fully utilize the benefits of organizational knowledge. In developed countries like South Korea, DL and ITS present opportunities, often driven by the digitalization of KS-related business processes, which improve OLC. Therefore, leaders are encouraged to shape an OLC that aligns with the organization’s core values and promotes collaboration and understanding between management and staff. The findings indicate that cultivating an adaptive and values-driven knowledge culture can effectively motivate employees to develop technologically advanced solutions, thereby boosting overall business performance in fast-changing environments.²³ Additionally, this organizational environment supports knowledge acquisition and practical use, enhancing innovation and operational efficiency.

Furthermore, leaders must dedicate time to creating a KS environment that encourages the development of unique organizational capabilities, specifically OLC. These capabilities enable knowledge absorption from experts, promoting innovation and productivity in digitalized sectors like IT, banking, telecommunication, tourism, television manufacturing, air conditioning, and elevator and escalator, through KS between leadership and OCL.⁴³ They are driven by leadership and supported by IT, which effectively handles operational challenges in a competitive environment through the KS culture to achieve OLC and enhance OP, as DL and ITS cannot foster OLC. More specifically, leaders should empower their mid-level and entry-level managers to improve managerial skills, manufacturing operations, advanced technologies, and processes, and pass this expertise down to frontline employees to boost operational efficiency.

This study presents a unique business model to help industrial firms manage fierce competition by developing distinctive capabilities to enhance internal operations, implement new customer engagement strategies, and retain clients in competitive markets. Additionally, the findings show evidence of a shift in the automobile industry’s approach through adopting a KS culture, DL capabilities, and ITS, all of which improve OLC. Notably, South Korea has faced longstanding challenges, such as the rapid digitalization of automobile manufacturing and ongoing local human resource crises over the past decade, making it increasingly difficult to meet global trade demands. Therefore, the role of automobile industry policymakers is vital in revising existing policies, addressing these issues, and shaping future strategies that promote a knowledge-driven learning culture to support the sustainable development of the sector through task-specific or situation-specific training or interventions program to handle cross-cultural diversity in the digital market place via exploration and exploitation of knowledge and facilitate OLC to meet multinational challenges of competitiveness. Industry leaders must actively support employees embracing KS while developing a digital environment that drives performance. This study opens new avenues for improving organizational culture, especially in the automobile industry across Asia and beyond. Furthermore, it is expected that the study’s findings and proposed model could be adapted to other sectors within South Korea’s economy.

Limitations and future research directions

This study focused on the automobile industry in Daegu, South Korea, which limits the generalizability of the findings. Future research should expand to other diverse geographies and industries domestically and internationally to increase applicability and validate and extend the proposed conceptual model. The cross-sectional design constrains insights into long-term effects; therefore, longitudinal studies are recommended. Including moderating variables such as job commitment or environmental awareness may provide a deeper understanding. Additionally, using random sampling and a sample size of 359 may restrict representativeness; future studies should consider larger samples and purposive sampling. Lastly, causal relationships were not examined, indicating a need for future research using experimental or longitudinal designs.

Conclusions

The present study examined how DL and ITS influence KS and OLC. It also looked at the mediating roles of KS in the relationships between DL, OLC, and ITS. Additionally, it studied how OLC affects OP. This comprehensive analysis aimed to find effective strategies for boosting OLC and enhancing performance in the automobile industry. The results highlight the critical role of DL, ITS, and KS as valuable strategic resources, supporting findings from earlier research. The study confirmed all proposed hypotheses except H1 and H3, aligning with its research goals. DT and ITS were shown to foster knowledge development, enabling firms to enhance their dynamic capabilities in global markets by creating new and improved products and processes.

This study provides a practical diagnostic tool for industrial firms to enhance managerial skills, optimize manufacturing processes, and achieve better performance. DL and ITS, with their unique qualities, significantly shape how businesses evaluate and develop their product and service offerings, ensuring they stay competitive and improve performance. By confirming the importance of DL, ITS, and KS in strengthening OLC and increasing OP, the study adds to the broader theoretical base of the RBV, highlighting knowledge as a vital strategic asset for creating value and enhancing organizational capabilities. Additionally, the study proposes a new, flexible conceptual framework that future researchers can apply across different industries and regions. For long-term success in competitive markets, senior management should focus on quickly developing knowledge-based organizational capabilities to improve OLC and drive OP.

Footnotes

Acknowledgment

We want to express our sincere gratitude to all the individuals and organizations that supported us throughout this project.

ORCID iDs

Md Habibur Rahman

Md Al Amin

Ethical considerations

Ethical clearance for this study is waived, as it exclusively investigates human technology adoption behavior and does not involve any experimentation with humans or animals. The authors secured full consent from the respondents through the questionnaire during the data collection process. Respondents were thoroughly briefed on their rights and the objectives of this study. They could withdraw from the survey at any point during the research.

Consent to participate

Consent was obtained from the respondents to use their responses for academic research purposes.

Consent for publication

Each author has reviewed and approved the final manuscript, indicating collective agreement on the work and findings presented. In addition, the authors give their full consent to publish the manuscript in the Human Systems Management journal.

Author contributions

Conception: Md Habibur Rahman and Jinkyo Shin. Methodology: Md Habibur Rahman, Jinkyo Shin, and Md Al Amin. Data collection: Md Habibur Rahman and Md Al Amin. Interpretation or analysis of data: Md Habibur Rahman. Preparation of the manuscript: Md Habibur Rahman, Jinkyo Shin, and Md Al Amin. Revision and editing: Md Habibur Rahman, Jinkyo Shin, and Md Al Amin. Supervision: Jinkyo Shin.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The research was conducted without any commercial or financial ties that could be considered a potential conflict of interest.

Data Availability Statement

The data used in this study cannot be publicly shared due to confidentiality concerns. However, the data can be made available upon reasonable request from the corresponding author.*

Appendix

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