Rajesh Mamilla1*
Yuen Yee Yen2- 1VIT Business School, Vellore Institute of Technology, Vellore, Tamil Nadu, India
- 2Multimedia University, Malacca, Malaysia
The study aims to evaluate the impact of intellectual capital on the innovation performance of faculty members in higher education institutions. Additionally, it investigates whether knowledge management and technology usefulness mediate these relationships. The research employs a multiple indicator multiple cause model to analyze the data collected from faculty members across various higher education institutions in South India. The study uses a cross-sectional design and the sampling method used is “snowball sampling,” and the data were analyzed to explore the influence of intellectual capital components on innovation outcomes. The results reveal that structural and human capital efficiency has the most substantial and consistent positive impact, significantly enhancing both knowledge management and technology usefulness, which, in turn, positively influence both process innovation and product innovation. Although relational capital efficiency positively affects knowledge management, technology usefulness, and product innovation, its impact on process innovation is not statistically significant. The study provides theoretical insights into the resource-based view and dynamic capabilities theories, demonstrating how intellectual capital components drive innovation.
JEL classification: D83, O32, O33.
1 Introduction
Higher Educational Institutions (HEIs) in India face challenges in effectively leveraging Intellectual Capital (IC) components—such as Human Capital, Structural Capital, Capital Employed, Relational Capital, and Innovation Capital—to enhance innovation performance (Cricelli et al., 2018; Iqbal et al., 2019; Yin et al., 2023). Issues like inadequate technology adoption and suboptimal knowledge management practices hinder their ability to innovate (Pedro et al., 2019; Guerrero and Menter, 2024). The COVID-19 pandemic has highlighted gaps in digital infrastructure and the need for robust knowledge management systems (McKinsey and Company; Dwivedi et al., 2020; Ammirato et al., 2021; Abdalla et al., 2022). Additionally, the National Education Policy (NEP) 2020 underscores the importance of technology and innovation, yet many institutions are not fully equipped to meet these demands (Ministry of Education; UGC Gov; static.pib.gov). This study investigates the mediating effects of Technology Usefulness and Knowledge Management on the relationship between IC components and innovation performance, aiming to provide actionable insights and policy recommendations to strengthen the innovation capabilities of the HEI's.
IIT Madras effectively leverages its Human, Structural, and Relational Capital to enhance innovation performance (IITM RBCDSAI). The institution invests in faculty development through programs like the HR Summit, fostering continuous professional growth and research excellence, exemplified by the Robert Bosch Centre for Data Science and Artificial Intelligence (IITM RBCDSAI; India Today). Structural Capital is bolstered by advanced research facilities, such as the Structural Engineering division and the IIT Madras Research Park, which supports innovation and entrepreneurship through collaboration between academia and industry (Code IITM; RBDCSAI). Relational Capital is strengthened by robust industry partnerships and global collaborations, facilitating knowledge exchange and expanding the institution's research impact (India Today). These initiatives collectively enhance IIT Madras's position as a leader in innovation and research (IITM RBCDSAI).
The expenditure on education as a percentage of GDP in India has remained below 4.5% since 2006–07, with spending on higher and technical education below 1.5% of GDP during the same period (PRS Legislative Research; World Bank Open Data; PIB). This indicates the substantial investment in the education sector, which includes components of Intellectual Capital (IC) such as human capital development (education.gov). In recent years, the expenditure on education has been as follows: 2.8% in 2019–20, 3.1% in 2020–21 (revised estimate), 3.1% in 2021–22 (budget estimate), and 2.9% in 2022–23 (PRS Legislative Research; PIB; India Today). These figures highlight the consistent investment in education, underscoring the importance of developing human Capital and other IC components to drive economic growth and innovation (PRS Legislative Research; PIB; files.eric.ed.gov).
In the rapidly evolving landscape of HEIs increasingly recognize the pivotal role of Intellectual Capital (IC) in driving innovation and maintaining competitive advantage. Intellectual Capital, encompassing Human Capital, Structural Capital, and Relational Capital, is a critical asset (Sullivan, 1999; Subramaniam and Youndt, 2005; Wei et al., 2023) that can significantly enhance the innovation performance of HEIs (Pedro et al., 2019). In the context of India, where the education sector is undergoing transformative changes driven by policy reforms and technological advancements, understanding IC components' impact on innovation is paramount (Secundo et al., 2018; Pedro et al., 2019; Azamat et al., 2023).
Human Capital refers to the collective skills, knowledge, and expertise of an institution's faculty, researchers, and administrative staff (Aman-Ullah et al., 2022; Li et al., 2023; Ammirato et al., 2023; Serenko, 2024). It is the foundation upon which educational excellence and innovative capabilities are built (Winters, 2011; Iqbal et al., 2019; Pedro et al., 2019). Structural Capital includes the supportive infrastructure, processes, databases, and organizational capabilities that facilitate effective functioning and innovation (Antoniou et al., 2008; Zangoueinezhad and Moshabaki, 2009; Fiorillo et al., 2020; Truong et al., 2023). Relational Capital is the value derived from an institution's relationships with external stakeholders, including industry partners, alums, and other academic institutions (Curhan et al., 2008; Kohtamäki et al., 2012; Zahoor and Gerged, 2021; Zhang et al., 2024).
The mediating effects of Technology Usefulness and Knowledge Management are crucial in this dynamic. Technology Usefulness refers to technology's perceived benefits and practical applications in enhancing educational and administrative processes (Adams et al., 1992; Davis, 1989). Effective Knowledge Management involves systematically handling knowledge resources ensuring that valuable information is captured, shared, and utilized to foster innovation (Mårtensson, 2000; Alavi and Leidner, 2001; Yu et al., 2022).
Despite the potential of IC components to drive innovation, many HEIs in India face challenges in effectively leveraging these assets. Issues such as inadequate technology adoption, suboptimal knowledge management practices, and fragmented stakeholder relationships hinder their ability to innovate and remain competitive (Fernandes and Singh, 2022; Quarchioni et al., 2022; Rana and Kaur, 2023). The COVID-19 pandemic has further exposed these gaps, underscoring the need for robust digital infrastructure and efficient knowledge management systems (Barnes, 2020; Wang and Wu, 2021).
This study aims to analyze the impact of Human Capital, Structural Capital, and Relational Capital on the innovation performance of HEIs in India, focusing on the mediating effects of Technology Usefulness and Knowledge Management. By addressing these critical aspects, the research seeks to provide actionable insights and policy recommendations that can enhance the innovation capabilities of HEIs, ultimately contributing to the broader goals of economic growth and societal development.
The research gap in the study lies in the limited empirical exploration of how Intellectual Capital (IC) components—Human Capital, Structural Capital, Capital Employed, Relational Capital, and Innovation Capital—affect innovation performance in HEIs, with a specific focus on South India. While the potential of these components to drive innovation is recognized, there is a lack of comprehensive studies examining the mediating roles of Technology Usefulness and Knowledge Management in this relationship. Additionally, the impact of digital transformation and the optimization of knowledge management practices, particularly in the wake of challenges such as the COVID-19 pandemic, remains underexplored. This study aims to fill these gaps by providing a nuanced analysis of the interplay between IC components, technology adoption, and knowledge management, offering valuable insights for enhancing innovation in HEIs.
The novelty of the research study lies in applying the Resource-Based View (RBV) and Dynamic Capabilities (DC) theories to HEIs in South India, focusing on intellectual capital components—human, structural, and relational. Targeting faculty members highlights their role as strategic resources and examines how knowledge management and technology usefulness mediate the impact of Intellectual Capital on innovation performance. This approach offers a comprehensive analysis of how HEIs can leverage these assets to foster innovation, particularly in the underexplored context of educational settings. The regional specificity adds valuable insights into the unique characteristics of South Indian HEIs, providing practical implications for policymakers and academic leaders in similar regions.
The Resource-Based View (RBV) theory is chosen for its focus on the strategic value of internal resources, which is essential for analyzing how intellectual capital components—human capital efficiency, structural capital efficiency, and relational capital efficiency (Barney, 1991; Tseng and James Goo, 2005)—can enhance innovation performance in HEIs. This theory underscores the importance of these intangible assets in providing a competitive advantage. The Dynamic Capabilities (DC) theory complements this by highlighting the organization's ability to adapt, integrate, and reconfigure resources in response to changes, such as technological advancements and evolving educational demands (Eisenhardt and Martin, 2000; Buzzao and Rizzi, 2021). This is crucial for understanding the mediating roles of knowledge management and technology usefulness in facilitating process and product innovation in HEIs. These theories provide a comprehensive framework for examining how HEIs can leverage their Intellectual Capital and dynamic capabilities to achieve sustained innovation and competitive advantage.
The practical motivations behind choosing the study topic and research objectives include addressing the need for HEIs in South India to enhance innovation performance amidst increasing competition and technological changes. By focusing on Intellectual Capital and its human, structural, and relational capital components, the study aims to provide actionable insights for HEIs to better manage their internal resources. Including knowledge management and technology usefulness as mediating factors reflects a practical concern with how these institutions can leverage their existing capabilities to improve process and product innovation. Additionally, targeting faculty members as respondents offers a practical perspective on how HEIs can support and enhance the contributions of their essential intellectual resources.
2 Theoritcal background
2.1 Resource-based view theory
The Resource-Based View (RBV) theory emphasizes the significance of internal resources—those that are valuable, rare, inimitable, and non-substitutable—in achieving and sustaining a competitive advantage. Developed by author Jay Barney, the RBV posits that an organization's performance and differentiation are primarily driven by its internal capabilities rather than external factors (Barney, 1991). In the context of this study, the RBV theory directly supports the roles of Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Relational Capital Efficiency (RCE) as key internal resources within HEIs. HCE, encompassing the skills, expertise, and knowledge of faculty members, is crucial for driving research, innovation, and academic excellence. SCE, which includes the supportive infrastructure, organizational processes, and culture, facilitates effective knowledge transfer and utilization, creating a conducive environment for innovation (Lockett et al., 2009). RCE involves the networks and relationships HEIs maintain with external stakeholders, which enhance collaborative opportunities and innovation potential. Furthermore, Knowledge Management (KM) acts as a mediating variable within the RBV framework (Figure 1), representing the processes that capture, store, and disseminate knowledge effectively within the institution. The RBV suggests that when these internal resources—HCE, SCE, RCE, and KM—are managed efficiently, they collectively enhance the innovation performance of HEIs. By linking these constructs to the RBV theory, the study underscores the importance of leveraging internal capabilities to foster a competitive edge in the increasingly knowledge-driven educational sector.
Figure 1. Proposed conceptual model.
2.2 Dynamic capabilities theory
The Dynamic Capabilities (DC) theory, established by authors David Teece, Gary Pisano, and Amy Shuen in 1997, extends the Resource-Based View (RBV) by emphasizing an organization's ability to adapt, integrate, and reconfigure internal and external resources in response to rapidly changing environments (Wheeler, 2002). This theory focuses on the critical role of managerial processes and organizational capabilities in seizing new opportunities and addressing emerging threats, thereby maintaining a competitive edge. Within the context of this study, the DC theory is closely linked to the mediating variable of Technology Usefulness and the dependent variable, Innovation Performance. The theory posits that an institution's ability to effectively adopt and integrate new technologies is a key dynamic capability that significantly enhances its innovation performance (Wheeler, 2002). Technology Usefulness, as conceptualized in this study, reflects the degree to which technological tools and systems are seamlessly integrated into the operations of HEIs, thereby facilitating teaching, research, and administrative processes. This integration not only supports dynamic capabilities but also enables the institution to adapt to new educational paradigms and technological advancements (Cunningham and Link, 2014; Dijkstra and Henseler, 2015a,b; Dornbusch and Neuhäusler, 2015; Dul, 2015; Nonaka, 1994; Roos et al., 1997; Crescenzi et al., 2016). The DC theory also underscores the importance of continuously evolving and reconfiguring resources, which directly influences the Innovation Performance of HEIs—their ability to develop and implement new processes, products, and services. By linking these constructs to the DC theory, the study highlights the necessity for HEIs to cultivate technological agility and effective knowledge management, ensuring that they remain innovative and responsive to the evolving demands of the educational sector.
Within this framework, Technology Usefulness represents an institution's ability to integrate and apply technological tools, thereby acting as a dynamic capability that links intellectual capital to innovation outcomes.
3 Hyphotheses development
3.1 Human capital efficiency
Human Capital Efficiency, defined as employee skills, knowledge, and competencies, is highlighted as a key determinant of an organization's innovative capabilities (Huang et al., 2023; Dabić et al., 2023). The study finds that higher levels of HCE are associated with greater process innovation, as knowledgeable and skilled employees are better equipped to identify opportunities for improvement, implement new methods, and adapt to changing environments. Effective knowledge management facilitates the sharing and applying tacit and explicit knowledge, enhancing innovative output. Similarly, technology use amplifies the benefits of HCE by enabling more efficient communication, data analysis, and implementation of innovative processes (Zhang et al., 2017; Prajogo and Oke, 2016; Subramaniam and Youndt, 2005). Research shows that human Capital directly affects innovation performance, including process innovation, by enhancing problem-solving abilities and fostering creative thinking (Bontis, 1998).
H1: Human capital efficiency positively influences process innovation performance.
Innovation is crucial in entrepreneurship, particularly for groundbreaking products by independent entrepreneurs. General human capital broadens the knowledge corridor, enhancing product innovation, while specific human capital can constrain it. Coachability helps mitigate these constraints, emphasizing the importance of human capital efficiency in fostering innovation (Marvel et al., 2020).
H4: Human capital efficiency positively influences product innovation performance.
Human Capital Efficiency (HCE) significantly enhances Knowledge Management (KM) practices within organizations. Studies indicate that firms with high HCE, characterized by the optimal utilization of employees' skills and competencies, are better positioned to implement effective KM practices. This leads to improved knowledge creation, sharing, and application, fostering innovation and organizational performance (Rastogi, 2000; Donate and Sánchez de Pablo, 2015).
H7: Human capital efficiency positively influences knowledge management.
Human Capital Efficiency (HCE) significantly enhances Technology Usefulness. A well-educated and skilled workforce positively influences the adoption and effective utilization of new technologies. This relationship emphasizes the crucial role of human capital in driving technological progress and innovation, contributing to economic growth (Danquah and Amankwah-Amoah, 2017).
H12: Human capital efficiency positively influences technology usefulness.
3.2 Structural capital efficiency
Structural Capital encompasses the organizational routines and infrastructure that support efficient operations and innovation. According to (Youndt et al. 2004), structural Capital includes the codified knowledge, databases, and procedures that institutionalize organizational knowledge and ensure it is accessible and usable. This infrastructure is crucial for process innovation as it facilitates the standardization of new practices and the dissemination of best practices across the organization. Structural Capital Efficiency (SCE) refers to the supportive infrastructure, organizational routines, databases, processes, and systems that enable an organization to function efficiently and leverage its human and relational Capital. In the context of process innovation, structural Capital provides the necessary foundation for implementing new methods and improving existing processes (Gold et al., 2001; Teece, 1998; Edvinsson and Malone, 1997).
H2: Structural capital efficiency positively influences process innovation performance.
Structural capital efficiency plays a pivotal role in product innovation. Efficient structural capital, which includes the internal processes, systems, and organizational culture, provides a supportive framework that facilitates the development and implementation of new products. This efficiency ensures that knowledge and resources are effectively utilized, leading to enhanced innovation capabilities. By optimizing structural capital, SMEs can create an environment that nurtures creativity and accelerates the innovation process (Costa et al., 2014).
H5: Structural capital efficiency positively influences product innovation performance.
Structural Capital Efficiency (SCE) significantly enhances Knowledge Management (KM) practices within organizations. SCE, encompassing organizational processes, systems, and culture, creates a robust framework that facilitates effective KM. Studies indicate that efficient structural capital enables better knowledge creation, storage, and dissemination, which in turn supports innovation and organizational learning (Hsu and Sabherwal, 2012).
H8: Structural capital efficiency positively influences knowledge management.
Structural capital, which includes environmental incentives, senior environmental responsibilities, and external environmental communication, plays a critical role in guiding firms' environmental responses and supporting the adoption of new technologies. These elements of structural capital help organizations effectively bridge the gap between their current technological capabilities and industry standards, thereby enhancing the overall usefulness and implementation of technologies (Amores-Salvadó et al., 2021).
H13: Structural capital efficiency positively influences technology usefulness.
3.3 Relational capital efficiency
Relational capital, a component of Intellectual Capital, plays a pivotal role in managerial performance, especially in tasks related to innovation. In the context of innovation, relational capital is particularly critical as it facilitates knowledge sharing, collaboration, and the creation of trust, all of which are essential for the successful implementation of new processes and products. Studies have consistently shown that relational embeddedness is more influential in driving innovation. For instance, managers who have strong relational ties are better positioned to leverage these relationships for innovative tasks, leading to higher performance in process innovation (Moran, 2005).
H3: Relational capital efficiency positively influences process innovation performance.
The efficiency of relational capital, which encompasses the relationships between suppliers and customers, significantly influences product innovation. Effective leveraging of these relationships fosters a collaborative environment, enhancing the exchange of knowledge and resources. This collaboration enables companies to better understand market needs and integrate diverse insights into their innovation processes. Consequently, optimizing relational capital leads to improved innovation performance, driving the successful development of new products (Onofrei et al., 2020).
H6: Relational capital efficiency positively influences product innovation performance.
Relational Capital Efficiency (RCE) has been shown to significantly enhance Knowledge Management (KM) practices within organizations. RCE, which involves the strength and quality of relationships between a firm and its external stakeholders such as customers, suppliers, and partners, plays a critical role in facilitating knowledge sharing and transfer. Studies highlight that strong relational capital fosters trust and collaboration, essential for effective KM (Hsu and Sabherwal, 2012; Collins and Hitt, 2006).
H9: Relational capital efficiency positively influences knowledge management.
Relational Capital Efficiency significantly impacts Technology Usefulness. The study by shows that strong intra-unit and inter-unit relational capital fosters knowledge combination capabilities, which in turn enhances the performance and technological efficiency of work units. This relationship underscores the importance of social relationships in leveraging shared knowledge and enhancing the capacity to utilize new technologies effectively in high-tech industries (Carmeli and Azeroual, 2009).
H14: Relational capital efficiency positively influences technology usefulness.
3.4 Knowledge management
Knowledge Management is positioned as a mediator based on RBV theory, which views KM as the organizational mechanism that transforms intellectual resources into actionable capabilities. Without effective knowledge acquisition, sharing, and application, IC components cannot translate into innovation outcomes. Therefore, KM logically mediates the IC → Innovation relationship.
Knowledge Management (KM) significantly impacts product innovation by facilitating the creation, sharing, and application of knowledge within organizations. Effective KM practices enhance an organization's innovation capabilities by ensuring that relevant knowledge is efficiently disseminated and utilized in the innovation process (Sonmez Cakir et al., 2024; Tortorella et al., 2024).
H10: Knowledge management positively influences product innovation.
Knowledge Management (KM) significantly enhances process innovation by facilitating the efficient handling of knowledge creation, sharing, and utilization within organizations. KM practices ensure that valuable knowledge is systematically captured and disseminated, thus fostering an environment conducive to continuous improvement and innovation in processes (Sonmez Cakir et al., 2024; Tortorella et al., 2024). Research indicates that Knowledge Management (KM) significantly enhances Process Innovation. KM practices, such as the systematic capture, dissemination, and application of knowledge, drive innovative processes within organizations. Effective KM fosters knowledge sharing and integration, directly contributing to the development and implementation of new processes, which improves efficiency and competitive advantage. A positive correlation exists between KM and innovation, emphasizing that robust KM practices lead to increased process innovation (Darroch, 2005; Andreeva and Kianto, 2012).
H11: Knowledge management positively influences process innovation.
3.5 Technology usefulness
Technology Usefulness acts as a dynamic capability that enables HEIs to reconfigure and deploy intellectual resources effectively. Consistent with Dynamic Capabilities theory, the perceived usefulness of technology determines an institution's ability to integrate digital tools, thereby shaping how IC components influence both product and process innovation. Hence, TU is theoretically justified as a mediator.
Technology Usefulness significantly impacts Process Innovation. AI-driven technologies enhance process innovation by enabling real-time data analysis, predictive customization, and efficient personalization. AI tools analyze vast datasets to identify trends and customer preferences, facilitating rapid prototyping and iterative development. This data-driven approach ensures that processes are continuously optimized, leading to faster time-to-market and improved alignment with user needs, thus driving substantial innovation in processes and product development (Cooper, 2024).
H15: Technology usefulness positively influences process innovation.
Technology Usefulness significantly impacts Product Innovation. The study found that the application of technological tools enhances knowledge sharing and innovation processes within startups. By effectively utilizing technology, startups can streamline their processes, facilitate real-time data exchange, and adapt swiftly to market changes. This enables them to develop innovative products more efficiently, demonstrating the critical role of technology in driving product innovation amidst technological turbulence (Santos et al., 2023; Mehralian et al., 2024).
H16: Technology usefulness positively influences product innovation.
The model suggests a comprehensive framework where the efficiency of Intellectual Capital components (HCE, SCE, RCE) enhances the management of knowledge and the usefulness of technology, which ultimately drives innovation in process and product within HEIs. This indicates that improving Intellectual Capital components can indirectly boost innovation performance by enhancing both knowledge management and technology application.
4 Methods
4.1 Data collection
Online questionnaires were used as the primary data collection tool. The online questionnaire gauges participants' perceptions based on different literature scales on how Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Relational Capital Efficiency (RCE) influence Innovation Performance (IP) through the mediating effects of Knowledge Management (KM) and Technology Usefulness (TU). Participants were faculty members in HEIs across various disciplines (e.g., Arts, Commerce and Management, Engineering, and Sciences). We required respondents to be full-time employed faculty members aged 18 and older, working in South Indian HEIs with a minimum of 1 year of work experience in their current institution. The study chosed Institutions in South India based on NIRF ranking. This criterion ensured that the faculty members had spent sufficient time in their institutions to evaluate the impact of Intellectual Capital components on Innovation Performance. This study used purposive sampling to obtain responses from university professors. Purposive sampling was chosen to specifically target faculty members from universities ranked by the National Institutional Ranking Framework (NIRF) for several key reasons. This method ensures the selection of respondents with the necessary expertise and experience to provide insightful data on the impact of Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Relational Capital Efficiency (RCE) on Innovation Performance. By focusing on faculty from NIRF-ranked universities, the study enhances the validity and reliability of its findings, as these respondents are more likely to be familiar with innovative practices and intellectual capital management. This targeted approach ensures high-quality, relevant data, aligning with the study's goals of exploring the nuanced relationship between intellectual capital components and innovation performance in HEIs in South India. Additionally, purposive sampling allows for the efficient use of resources, focusing on respondents who are most likely to provide valuable information, thus supporting the research objectives effectively. 969 questionnaires were sent out via the above channels and the final sample size was 470 with a response rate of 48.5%. A response rate of this magnitude is generally considered acceptable for survey-based research.
Although snowball sampling helped access faculty members across diverse HEIs, this method may introduce network-based biases. Respondents who are professionally connected may share similar views on innovation, limiting sample heterogeneity. Therefore, the findings should be interpreted with caution as the sampling approach reduces the study's generalisability beyond the surveyed South Indian HEIs.
4.2 Instrument design
The survey instrument was carefully designed to gather data on the utilization of Intellectual Capital by faculty members in universities and its influence on the innovation performance of HEIs. The survey instrument comprised of a series of inquiries pertaining to the demographic characteristics of the participants, as well as multiple constructs that were identified through an extensive review of the existing literature. The constructs under scrutiny were assessed utilizing a five-point Likert scale, whereby participants were requested to express their degree of concurrence with each statement, ranging from 5 (indicating “Strongly Agree”) to 1 (indicating “Strongly Disagree”). The Likert scale is an essential tool in research due to its ability to simplify data collection, convert qualitative data into quantitative data, and provide reliable and valid measurements, making it indispensable for understanding and analyzing human attitudes and behaviors (Jabbour et al., 2023). Studies have demonstrated that the utilization of the 5-point Likert scale yields consistent and accurate measurements, indicating its reliability and validity (Anjaria, 2022). “The questionnaire's structure includes a dichotomous question to filter out respondents who have prior involvement in Process Innovation or Product Innovation initiatives within their institution and those who do not.” Subsequent to the initial inquiry, a series of five inquiries were posed in relation to the demographic profiles of the participants. The respondents were provided with questions pertaining to the seven constructs, namely Human Capital Efficiency, Structural Capital Efficiency, Relational Capital Efficiency, Knowledge Management, Technology Usefulness, Process Innovation performance, and Product Innovation performance. The potential biases arising from common methods and proximity effects were mitigated through the utilization of reverse coding techniques and variable-based separation strategies in the questionnaire. The establishment of content validity was achieved through the collaboration of three academic experts, who provided their valuable inputs. Additionally, face validity was ensured by conducting a pilot study involving a sample of 70 respondents. The feedback received from the experts and the pilot research focused on the use of intricate language and the need for elucidation.
4.3 Measures
The process of conducting a literature review yielded a total of seven constructs and 46 associated items that are relevant to the examination of the influence of Intellectual Capital components on the Innovation Performance in HEIs. Intellectual capital has three sub-constructs: Human Capital, Relational Capital, and Structural Capital. A five-point Likert-type scale was employed for participants to respond to Human Capital Efficiency (HCE) was measured using a 5-item scale (α = 0.85 ) developed by (Bontis 1998), focusing on faculty attributes such as “The qualifications and expertise of faculty members (human capital) enhance student learning outcomes and innovative teaching practices.” Structural Capital Efficiency (SCE) was assessed with a 5-item scale (α = 0.85 ) developed by (Bontis 1998), including items like “The institution's infrastructure and administrative processes (structural capital) respond flexibly to environmental changes to support innovation.” Relational Capital Efficiency (RCE) was measured using a 5-item scale (α = 0.85 ) developed by (Bontis 1998), with items such as “The institution effectively shares knowledge between faculty, students, and external partners to foster innovation in education and research.” The mediating variables in the study are Knowledge Management and Technology Usefulness. Knowledge Management was measured on a five-point Likert-type scale with six items (α = 0.86) developed by (Lin and Lee 2005) and (Chen and Huang 2007), including items like “The institution effectively manages various sources and types of knowledge (e.g., academic research, administrative data) to support innovation.” Technology Usefulness was assessed using a 9-item scale (α = 0.77) developed by Rafi et al. (2019a,b, 2020a,b); (Gupta and George 2016)„ and (Islam et al. 2021), with sample items like “The institution provides advanced hardware and software options for academic and administrative tasks.” The dependent variables are Process Innovation and Product Innovation. Product Innovation was evaluated on a five-point Likert-type scale, with five times (α = 0.91) developed by (Al-Husseini et al. 2021), items such as “Our university constantly emphasizes development and doing research projects.” Process Innovation was also measured on a similar scale, with eight items (α = 0.88) developed by (Al-Husseini et al. 2021), including items like “Our university often develops new technology (internet, databases) to improve the educational process.” The items were adopted and modified as per the context of the study. For more information about the constructs, see Appendix 1.
4.4 Demographic profile
The demographic profile of the participants was obtained through the use of four demographic questions, namely Age, Gender, Teaching Domain, and Educational Qualification. This data provides useful insights into the characteristics of the individuals involved in the study. The survey obtained 470 replies from faculty members, including 157 participants in the age range of 26–30, 157 participants in the age range of 31–35, and 156 people in the age range of 36–40. Out of the total, 166 individuals were female, while 314 individuals were male. The gender distribution reveals a greater percentage of male faculty members, with 66.8% being male and 35.2% being female. The Commerce and Management category has the highest number of participants, accounting for 60.21% of the total. The Engineering and Technology category follows with 37.02%, while the Science category has 1.91% and the Arts, Languages, and Literature category has 0.85%. The majority of participants own a PG degree, accounting for 75.53% of the total. This is followed by individuals with a PhD degree, comprising 23.82% of the participants. Lastly, a small percentage of participants, specifically 0.65%, hold a PDF degree.
4.5 Data analysis
This study uses CB-SEM and multiple indicator multiple cause models (MIMIC) using AMOS. Covariance-Based Structural Equation Modeling (CB-SEM) is a powerful statistical technique widely used in social sciences, particularly for testing complex relationships between observed and latent variables. CB-SEM allows for the simultaneous testing of multiple relationships among variables, making it particularly suitable for the present study. One of the key strengths of CB-SEM is its ability to account for measurement error. CB-SEM can explicitly model and adjust for measurement errors, leading to more accurate and reliable estimates of the relationships between constructs (Hai-Ming et al., 2020). CB-SEM provides various model fit indices (e.g., CFI, TLI, RMSEA) that help assess how well the proposed model fits the observed data. This feature is crucial in ensuring the validity of the structural relationships being tested, which adds rigor to the findings and enhances the credibility of the research (Hu and Bentler, 1999).
The Multiple Indicator Multiple Cause (MIMIC) model is an extension of CB-SEM that is particularly useful when dealing with multiple dependent variables. Since in the present study we have two dependent variables Product Innovation and Process Innovation, we then applied MIMIC Approach. The MIMIC approach allows for the simultaneous estimation of the effects of independent and mediating variables on multiple dependent variables. This simultaneous estimation avoids potential biases that might occur if the dependent variables were analyzed separately (Posey et al., 2014). The MIMIC model is effective in testing for measurement invariance across groups or conditions, ensuring that the constructs are being measured consistently across different contexts. In the present study, this approach helps confirm that the relationships between Intellectual Capital components, mediators, and innovation outcomes are consistent and not affected by unobserved heterogeneity or measurement differences (Jöreskog and Goldberger, 1975).
5 Results
5.1 Meaurement model assessment
Factor loadings above 0.70 are indicative of a good measurement model, as they imply that over 50% of the variance in the observed variable is explained by the underlying latent construct (Hair et al., 2009, 2012). The Average Variance Extracted (AVE) values above 0.50 indicate sufficient convergent validity, meaning that the construct explains more than half of the variance of its indicators (Fornell and Larcker, 1981). Composite Reliability (CR) values above 0.70 are considered to demonstrate adequate internal consistency, while Cronbach's alpha values above 0.70 are generally accepted as showing good reliability (Hair et al., 2009, 2012).
Table 1 presents the reliability and validity measures for the constructs in the research study, including Technology Usefulness (TU), Knowledge Acquisition (KA), Knowledge Sharing (KS), Knowledge Application (KAA), Process Innovation (PI), Product Innovation (PR), Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Relational Capital Efficiency (RCE). Overall, the constructs demonstrate strong reliability and validity, as indicated by high factor loadings (most above 0.70), satisfactory Average Variance Extracted (AVE) values (generally above 0.50), and high Composite Reliability (CR) and Cronbach's alpha values (both typically above 0.70). These results suggest that the constructs are well-measured by their respective items, with more than half of the variance in the constructs being explained by the items, indicating good convergent validity. The overall proposed model appears robust, with reliable and valid measures across the constructs.
Table 1. Reliability and validity measures.
The Table 2 presents the results of the Fornell and Larcker Criterion for assessing discriminant validity among the constructs, which include Human Capital Efficiency (HCE), Knowledge Acquisition (KA), Knowledge Application (KAA), Knowledge Sharing (KS), Process Innovation (PI), Product Innovation (PR), Relational Capital Efficiency (RCE), Structural Capital Efficiency (SCE), and Technology Usefulness (TU). Discriminant validity is confirmed when the square root of the Average Variance Extracted (AVE) for each construct (shown on the diagonal) is greater than the correlations between that construct and all other constructs (shown off the diagonal; Fornell and Larcker, 1981). In this Table 2, each construct demonstrates good discriminant validity, as the diagonal values (e.g., 0.746 for HCE, 0.811 for KA) are higher than any corresponding off-diagonal correlations with other constructs. This indicates that each construct is distinct and measures a unique concept within the model, supporting the robustness of the measurement model according to the Fornell and Larcker Criterion. The Variance Inflation Factor (VIF) values for the constructs (HCE, KM, PI, PR, RCE, SCE, TU) assess the presence of multicollinearity, which occurs when independent variables are highly correlated, potentially distorting the results of regression analyses. VIF values above 5 indicate significant multicollinearity, while values below 5 suggest it is not a severe issue (O'brien, 2007). In this dataset, the VIF values range from 2.13 to 3.82. The highest VIF values are observed for the relationships involving SCE, particularly SCE → PI (3.82) and SCE → PR (3.801). However, since all VIF values are below 5, the constructs have no significant multicollinearity. This indicates that the independent variables do not strongly influence each other, and the model's estimates are likely reliable and unbiased. Thus, the multicollinearity levels in this study are within acceptable limits, allowing for an accurate interpretation of the relationships between the constructs.
Table 2. Discriminant validity—Fornell and Larcker Criterion.
5.2 Structual model assessment
The Table 3 presents the results of hypotheses testing, focusing on the relationships between different forms of capital efficiency—Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Relational Capital Efficiency (RCE), Knowledge Management (KM), Technology Usefulness (TU) and key outcomes such as Product Innovation (PR), and Process Innovation (PI) within the context of HEIs. The findings indicate that Structural Capital Efficiency (SCE) has the most substantial and consistent positive impact across various outcomes, significantly enhancing both KM and TU. Human Capital Efficiency (HCE) shows significant positive effects on KM and TU, highlighting that the skills, expertise, and intellectual capabilities of faculty members are vital for both the effective management of knowledge and the successful integration and use of technology in teaching, research, and administrative tasks.
Table 3. Hypotheses testing.
Relational Capital Efficiency (RCE) positively influences KM, TU, and PR, indicating that strong relationships and networks with external stakeholders, such as industry partners, other academic institutions, and the community, are essential for managing knowledge and fostering innovation in HEIs. However, RCE's impact on Process Innovation (PI) is not statistically significant. Except the hypothesis H3, all the hypotheses of the study are supported. Moreover, the results clearly indicate that TU has a considerable impact on both PR and PI. KM has a beneficial influence on both PI and PR. The structural model depicted in Figure 2 represents the relationships among various constructs in a Multiple Indicator Multiple Cause (MIMIC) model, with key outcomes measured through a series of indicators. The model fit indices indicate an acceptable fit with the data (Jöreskog and Goldberger, 1975), as evidenced by a chi-square to degrees of freedom ratio (χ2/df) of 2.491, a Comparative Fit Index (CFI) of 0.941, a Normed Fit Index (NFI) of 0.896, a Tucker-Lewis Index (TLI) of 0.937, and a Root Mean Square Error of Approximation (RMSEA) of 0.05182. These values suggest that the model is well-aligned with the observed data, with the CFI and TLI values close to or above the recommended threshold of 0.90, and the RMSEA value below the recommended maximum of 0.06 (Hu and Bentler, 1999).
Figure 2. Structural model [χ2/df = 2.491 (p < 0.001); CFI = 0.941; NFI = 0.896; TLI = 0.937; RMSEA = 0.05182].
Given the large number of hypotheses (16), the structural model contains several interconnected paths. To enhance clarity, we restate the key construct codes used in the model:
• HCE = human capital efficiency
• SCE = structural capital efficiency
• RCE = relational capital efficiency
• KM = knowledge management (comprising KA: knowledge acquisition, KS: knowledge sharing, KAA: knowledge application)
• TU = technology usefulness
• PI = process innovation
• PR = product innovation
The model evaluates how each IC component affects KM and TU, and how these mediators, in turn, influence PI and PR. The MIMIC framework allows multiple indicators and multiple causes to be assessed simultaneously, which justifies the use of 16 hypotheses. The detailed coding enhances the readability and interpretability of Figure 2 and the structural paths reported.
The model is structured in two layers:
1. Causal layer (IC → KM/TU → Innovation):
IC components (HCE, SCE, RCE) act as antecedents that strengthen organizational knowledge processes and technology perceptions.
2. Outcome layer (KM/TU → PI/PR):
KM and TU mediate the influence of IC on innovation outcomes, consistent with RBV and Dynamic Capabilities theories.
The MIMIC model enables simultaneous estimation of both direct and indirect effects, which is essential for studying multiple mediators and dual innovation outputs.
The model illustrates the significant relationships between the independent variables—Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Relational Capital Efficiency (RCE)—and the mediating variables [Knowledge Sharing (KS), Knowledge Acquisition (KA), Knowledge Application (KAA), Knowledge Management (KM), and Technology Usefulness (TU)], as well as the dependent variables [Process Innovation (PI) and Product Innovation (PR)]. The standardized estimates (path coefficients) indicate the strength and direction of these relationships, with thicker lines representing stronger effects. HCE, SCE, and RCE are shown to positively influence KM and TU, with SCE having the strongest impact, as indicated by the larger path coefficients. KM and TU, in turn, have significant positive effects on both PI and PR, highlighting their mediating roles in the model. Additionally, the model shows that HCE, SCE, and RCE have direct positive effects on PR, with SCE again demonstrating the strongest influence. The overall model structure suggests that structural and human capital efficiencies are critical drivers of knowledge management and innovation within HEIs, mediated by technology usefulness and other knowledge processes.
6 Discussion
The study takes a novel approach to examining the impact of Intellectual Capital components such as Human Capital, Structural Capital, and Relational Capital on faculty in HEIs. The results of the hypothesis testing using the MIMIC approach provide significant insights into the influence of Intellectual Capital components on the innovation performance of HEIs through the mediating roles of Knowledge Management and Technology Usefulness.
The significant role of Structural Capital Efficiency (SCE) can be attributed to HEIs' dependence on institutional frameworks, administrative systems, and technology infrastructure. In many Indian HEIs, bureaucratic processes and hierarchical governance structures heavily influence how innovations are implemented, making SCE a central enabler of both knowledge flows and technology adoption. In contrast, the non-significant effect of Relational Capital Efficiency (RCE) on Process Innovation suggests that external partnerships, though beneficial for research and course development, have limited influence on internal administrative processes. These internal processes often require approvals, compliance, and structural coordination, which are insulated from external networks. This institutional reality provides a logical explanation for why RCE influences product-related outcomes but not process-related ones.
The RBV theory asserts that an organization's internal resources, which are valuable, rare, inimitable, and non-substitutable, are crucial for sustaining competitive advantage (Barney, 1991). In this study, HCE, SCE, and RCE are identified as critical internal resources within HEIs. The hypotheses proposed in this research are grounded in the RBV theory, which suggests that these Intellectual Capital components will positively impact innovation performance. The findings indicate that Structural Capital Efficiency (SCE) has the most substantial and consistent positive impact across various outcomes, significantly enhancing both KM and TU. This suggests that well-developed institutional frameworks, including effective processes, robust systems, and supportive infrastructure, are critical in fostering effective knowledge management practices and ensuring the perceived usefulness of technology among faculty. In HEIs, this could translate to the availability of academic resources, administrative support, and collaborative platforms that empower faculty members to manage and utilize knowledge effectively.
The DC theory extends the RBV by focusing on an organization's ability to adapt, integrate, and reconfigure resources in response to changing environments. This theory is closely linked to the mediating variables in the study—Technology Usefulness (TU) and Depedent variables (PI and PR). The study's findings reinforce the DC theory by demonstrating that an institution's ability to effectively adopt and integrate new technologies (TU) is a crucial dynamic capability that enhances its overall innovation performance.
The Human Capital Efficiency (HCE) has a notable positive impact on Knowledge Management (KM) and Technological Usefulness (TU). This emphasizes the importance of faculty members' skills, expertise, and intellectual capabilities in effectively managing knowledge and successfully integrating and utilizing technology in teaching, research, and administrative tasks. HCE significantly contributes to both Product Innovation (PR) and Process Innovation (PI) within the institution, emphasizing that faculty expertise is crucial in driving innovative teaching methods, research outputs, and process improvements in academic settings.
Critical elements include subject matter expertise, which allows faculty to generate and apply knowledge within their fields, and research skills, essential for contributing to the institution's research agenda. Pedagogical skills are also vital, enabling faculty to design engaging curricula and adapt teaching methods. Proficiency in educational technologies enhances both teaching and research. Intellectual capabilities like critical thinking and problem-solving are crucial for navigating academic challenges and fostering innovation. Interdisciplinary collaboration brings diverse perspectives, promoting the creation of new knowledge. Mentorship and leadership skills help faculty and contribute to institutional governance, while strong communication and networking skills are essential for effective teaching, research dissemination, and collaboration. Finally, adaptability and a commitment to lifelong learning ensure that faculty stay current in their fields, driving innovation and academic excellence within the institution.
Relational Capital Efficiency (RCE) positively influences KM, TU, and PR, indicating that strong relationships and networks with external stakeholders, such as industry partners, other academic institutions, and the community, are essential for managing knowledge and fostering innovation in HEIs. However, RCE's impact on Process Innovation (PI) is not statistically significant, suggesting that while external collaborations and networks are beneficial for product-related innovations like new courses or research initiatives, they may be less effective in influencing internal process improvements within the institution.
In HEIs, Structural Capital Efficiency (SCE) plays a crucial role in enhancing knowledge management (KM) and innovation among faculty members by providing robust institutional infrastructure, streamlined processes, and advanced technology platforms. SCE's strong impact suggests that when faculty operate within a well-supported environment, they are more effective in managing and utilizing knowledge, leading to greater innovation in teaching and research. While Human Capital Efficiency (HCE) remains essential, reflecting faculty's expertise, mentorship, and continuous development, its effectiveness is significantly amplified by the institutional support provided by SCE. Together, SCE and HCE drive the academic and research excellence of the institution, fostering an environment where innovation can thrive.
Overall, the results highlight the critical roles of structural, human, and relational capital efficiencies in enhancing knowledge management, technology usefulness, and innovation within HEIs. SCE emerges as the most influential factor, particularly in its impact on KM and TU, while HCE and RCE also play significant roles, especially in driving innovation outcomes. These findings suggest that HEIs seeking to enhance their innovation capabilities should focus on strengthening their institutional frameworks, leveraging faculty expertise, and maintaining strong external relationships to support and drive academic and administrative innovations.
6.1 Theoretical implications
The study aligns with Resource-Based View (RBV) theory, which highlights the significance of valuable, rare, inimitable, and non-substitutable resources for sustaining competitive advantage. Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Relational Capital Efficiency (RCE) are identified as strategic resources in HEIs. The findings reveal that SCE has the most substantial positive impact on both Knowledge Management (KM) and Technology Usefulness (TU), indicating that well-structured institutional frameworks foster innovation by supporting knowledge management and technology adoption. HCE also plays a crucial role by enabling faculty members to manage knowledge and integrate technology effectively, driving Process Innovation (PI) and Product Innovation (PR).
The study also resonates with Dynamic Capabilities (DC) theory, which emphasizes an institution's ability to adapt, integrate, and reconfigure resources to maintain a competitive edge. Technology Usefulness serves as a key dynamic capability, significantly mediating the relationship between Intellectual Capital components and innovation performance. HEIs that effectively adopt and integrate technology experience improvements in both PI and PR, highlighting the role of dynamic capabilities in fostering innovation. These findings suggest that HEIs must strengthen institutional frameworks, leverage faculty expertise, and adopt new technologies to maintain competitive advantage in a rapidly changing educational environment.
6.2 Managerial implications
Managers in Higher Education Institutions (HEIs) should prioritize strengthening institutional frameworks, specifically Structural Capital Efficiency (SCE), by investing in academic resources, administrative support, and collaborative platforms that enhance effective knowledge management and technology adoption. These investments will create a strong foundation for faculty to excel in teaching, research, and innovation. In parallel, fostering Human Capital Efficiency (HCE) is crucial; management should invest in continuous faculty development programs aimed at enhancing skills, expertise, and intellectual capabilities. Encouraging interdisciplinary collaboration, mentorship, and leadership development will further amplify faculty contributions to innovation.
Despite Relational Capital Efficiency (RCE) having no significant impact on Process Innovation (PI), its positive influence on Product Innovation (PR) highlights the importance of maintaining strong external relationships. Managers should engage actively with industry partners, academic institutions, and the community to leverage these networks for collaborative research and new academic offerings. Lastly, promoting the integration of knowledge management practices and technology adoption is key to driving innovation. By fostering a culture of knowledge sharing and technological utilization, HEIs can significantly enhance both Process Innovation and Product Innovation outcomes, ensuring continued academic and research excellence.
6.3 Practical implications
Based on the study's findings, Higher Education Institutions (HEIs) should prioritize resource allocation to enhance Structural Capital Efficiency (SCE) and Human Capital Efficiency (HCE), which have the most significant impact on innovation performance. This includes investing in modern infrastructure, advanced technology platforms, and comprehensive faculty development programs. Such investments will provide the necessary foundation for fostering Process Innovation (PI) and Product Innovation (PR). Additionally, technology adoption is crucial, as the study highlights the mediating role of Technology Usefulness (TU) in driving innovation. Faculty should be encouraged to integrate new technologies into their teaching and research, with institutions offering training and support to enhance technological utilization. This will significantly improve educational delivery and research capabilities.
Furthermore, Relational Capital Efficiency (RCE) plays a critical role in Product Innovation, underlining the importance of strengthening external collaborations. HEIs should implement practical initiatives that foster relationships with industry partners, other academic institutions, and the community. Collaborative projects can lead to new academic programs, research opportunities, and innovative solutions that benefit both the institution and broader society. These practical steps align with the study's findings and can substantially enhance innovation performance in HEIs.
Below recommendations will help HEIs systematically strengthen intellectual capital, optimize knowledge flows, and accelerate innovation in both academic and administrative domains:
• Implementing IC audits to periodically measure human, structural, and relational capital gaps.
• Creating innovation-focused faculty development programs that integrate KM practices and emerging technologies.
• Establishing cross-disciplinary innovation teams to encourage collaborative problem-solving and product development.
• Investing in digital platforms that support knowledge sharing, data integration, and faculty collaboration.
Aligning institutional policies with innovation goals, ensuring reduced bureaucratic delays and increased autonomy for academic innovators.
6.4 Societal implications
The study's findings emphasize the significant role of Higher Education Institutions (HEIs) in contributing to the knowledge economy by fostering innovation. By enhancing Structural Capital Efficiency (SCE) and Human Capital Efficiency (HCE), HEIs can develop new academic programs, research initiatives, and innovative teaching methods that increase intellectual capital and benefit society as a whole. Furthermore, the focus on Process Innovation (PI) and Product Innovation (PR) enables HEIs to address key societal challenges such as technological advancements, economic development, and social inclusion. The integration of Knowledge Management (KM) and Technology Usefulness (TU), as highlighted in the study, ensures that these innovations are both relevant and impactful.
This alignment with societal needs enhances the institution's capacity to contribute meaningfully to pressing issues. Additionally, the improvement of educational quality is another important societal implication of the study. By optimizing both SCE and HCE, HEIs can deliver higher-quality education, equipping graduates with the skills and knowledge necessary to contribute effectively to society. This not only elevates the educational standards but also enhances the overall quality of life by producing informed, capable citizens who can drive positive change in their communities.
6.5 Limitations and scope for further research
A key methodological limitation relates to the use of snowball sampling. While effective for reaching expert respondents working in dispersed HEIs, this approach may lead to homogeneity bias, as participants within similar academic networks may hold comparable perceptions. This potentially limits the generalisability and external validity of the results. Future studies should consider probability sampling or institutional-level sampling frames to strengthen representativeness.
The study is geographically limited to Higher Education Institutions (HEIs) in South India, which may restrict the generalizability of the findings to other regions or countries. Cultural, economic, and institutional differences could lead to varying impacts of Intellectual Capital components on innovation performance. Additionally, the cross-sectional research design captures data at a single point in time, limiting the ability to establish causal relationships or observe how these impacts evolve over time. A longitudinal approach could offer deeper insights into the dynamic relationships between these variables.
The study also focuses primarily on internal factors such as Intellectual Capital, Knowledge Management (KM), and Technology Usefulness (TU), while excluding external factors like government policies, funding availability, and economic conditions that might significantly influence innovation performance. Moreover, the findings indicate that Relational Capital Efficiency (RCE) does not significantly impact Process Innovation (PI), but the study does not explore this in depth, leaving a gap in understanding what other factors might drive PI beyond Intellectual Capital. Addressing these limitations could provide a more comprehensive understanding of the factors influencing innovation performance in HEIs.
Future research could expand the study to HEIs in different regions or countries to compare the impact of Intellectual Capital components on innovation performance across various cultural and institutional contexts, identifying region-specific factors that influence these relationships. Longitudinal studies would allow for an examination of how Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Relational Capital Efficiency (RCE) impact innovation performance over time, providing insights into the long-term effects of Intellectual Capital. Incorporating external factors such as government policies, economic conditions, and technological advancements could also offer a more comprehensive understanding of the factors influencing innovation in HEIs.
Additionally, future research could explore other mediators or moderators, such as organizational culture, leadership styles, or institutional technological advancement, to see how they shape the relationship between Intellectual Capital and innovation. Given the limited impact of RCE on Process Innovation (PI), further investigation into other drivers of PI, including decision-making structures and administrative efficiency, could be valuable. Finally, comparing findings from the higher education sector with other sectors, like corporate R&D or healthcare, may reveal industry-specific differences or commonalities in the role of Intellectual Capital on innovation performance.
7 Conclusion
This study aimed to examine how intellectual capital components influence innovation performance in South Indian HEIs and to assess the mediating roles of Knowledge Management and Technology Usefulness. The problem underlying the research—weak innovation ecosystems within HEIs due to gaps in KM practices, inconsistent digital adoption, and bureaucratic constraints—guided the study's objectives. The findings are consistent with previous literature that identifies HCE and SCE as strong determinants of innovation. However, the results also diverge in specific ways: RCE did not significantly influence process innovation, possibly due to institutional rigidity, hierarchical decision-making, and limited autonomy in implementing internal process changes. These contextual differences, along with the demographic characteristics of the faculty (predominantly young and early-career), explain why certain relationships behaved differently from past studies. HCE also demonstrates a notable positive effect on KM and TU, highlighting the vital role of faculty members' skills, expertise, and intellectual capabilities in managing knowledge and integrating technology for innovation. Overall, the study emphasizes that enhancing intellectual capital, particularly through structural and human capital efficiencies, is essential for HEIs to achieve academic and research excellence, thereby driving innovation and contributing to the broader knowledge economy.
The study contributes a practical framework that HEIs can adopt to prioritize intellectual capital investments and implement structured innovation strategies.
Data availability statement
The datasets presented in this article are not readily available because the dataset generated and analyzed during the current study contains responses from faculty members working in Higher Education Institutions (HEIs) across South India. Due to the inclusion of confidential and institution-specific information and the ethical commitments made to participants regarding anonymity and data privacy, the raw data cannot be made publicly available. However, anonymized or aggregated data that support the findings of this study are available from the corresponding author upon reasonable request, subject to institutional data-sharing policies and ethical approval. All data were collected in accordance with informed consent procedures, and no personally identifiable information is disclosed. Requests to access the datasets should be directed to Dr. Rajesh Mamilla, Professor and HOD MBA Online, VITBS, VIT-Vellore, Tamil Nadu, India.
Ethics statement
Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants' legal guardians/next of kin in accordance with the national legislation and the institutional requirements.
Author contributions
RM: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. YY: Conceptualization, Formal analysis, Validation, Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was supported by Vellore Institute of Technology, Vellore.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2026.1742992/full#supplementary-material
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Keywords: higher education institutions (HEIs), innovation performance, intellectual capital, knowledge management, technology usefulness
Citation: Mamilla R and Yen YY (2026) Intellectual capital and innovation in higher education institutions: the mediating role of knowledge management and technology usefulness. Front. Educ. 11:1742992. doi: 10.3389/feduc.2026.1742992
Received: 10 November 2025; Revised: 09 December 2025;
Accepted: 12 January 2026; Published: 30 January 2026.
Edited by:
Reham Salhab, Palestine Technical University Kadoorie, PalestineReviewed by:
Ziska Fields, University of Johannesburg, South AfricaJayamalathi Jayabalan, Tunku Abdul Rahman University, Malaysia
Copyright © 2026 Mamilla and Yen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Rajesh Mamilla, cmFqZXNoLm1hbWlsbGFAdml0LmFjLmlu