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The purpose of this study is to examine the dynamic relationship between intellectual capital (IC) and its components on financial performance of banks within the selected eight countries of Association of Southeast Asian Nations (ASEAN).

The study utilizes the balanced panel data of 37 publicly listed banks from eight leading ASEAN economies for the period of 2017–2021. In this sense, the authors applied the Ante Pulic's typology, i.e. value-added intellectual coefficient (VAIC™) to evaluate the efficiency of intangible and tangible assets. While, investigating the dynamic nature of relationship, the authors employed the generalized system method of moments because of its power to account for the problem of endogeneity and heteroscedasticity.

The results of the study demonstrate that banks in ASEAN countries shed a varied degree of a spotlight on VAIC™ and its components to create value. The findings revealed that structural capital efficiency is significantly associated with earning per share (EPS), return on assets (ROA) and return on equity (ROE), compared to human capital efficiency (HCE) and capital employed efficiency of ASEAN banks. These results endorse the importance of resource- and knowledge-based views of organizations to leverage the financial performance of banks. However, contrary to theoretical expectations, this study found no positive relationship between HCE with ROA and ROE. Whereas, the relationship of VAIC™ is positive and significant with EPS and ROE but it remains statistically very marginal.

There are some inherent limitations in this study that could be opportunities for future research. The current study uses the VAIC™ typology, but future researchers can use the modified value-added intellectual coefficient (MVAIC) or triangulation approach to enhance the validity and reliability of the study. Additionally, future research can investigate the similarities and differences among countries in terms of their cultural backgrounds and regulatory frameworks regarding the disclosure of intangibles. Furthermore, future research can increase the length and sample size of the study to enhance its generalizability.

The robust empirical findings extend the academic debate on IC by unveiling the dynamic nature of relationship between IC and financial performance in context of ASEAN banking sector. The findings provide plausible recommendations for policy makers (managers, regulators and stakeholders) to understand how to increase the IC efficiently, especially human capital as a source to evaluate the firms’ ability in determining value-added and financial performance. Further, findings of this study also suggest that how can policy makers get the benefit by investing more on structural capital as a valuable strategic source to guarantee the optimal performance returns.

Prior studies on IC have been country- and firm-specific, utilizing cross-sectional research designs. However, this research contributes to the limited literature by investigating the dynamic nature of the relationship between IC and financial performance of banks in the context of ASEAN countries using micro-panel data.

Intellectual capital (IC) plays a crucial role in today’s volatile business landscape, yet its measurement remains complex. To better navigate these challenges, the authors propose the Integrated Intellectual Capital Measurement (IICM) model, an innovative, robust and comprehensive framework designed to capture IC amid business uncertainty. This study focuses on IC measurement models, typically reliant on secondary data, thus distinguishing it from conventional IC studies.

The authors conducted a systematic literature review (SLR) and bibliometric analysis across Web of Science, Scopus and EBSCO Business Source Ultimate in February 2023. This yielded 2,709 IC measurement studies, from which the authors selected 27 quantitative papers published from 1985 to 2023.

The analysis revealed no single, universally accepted approach for measuring IC, with company attributes such as size, industry and location significantly influencing IC measurement methods. A key finding is human capital’s critical yet underrepresented role in firm competitiveness, which the IICM model aims to elevate.

This is the first SLR focused on IC measurement amid business uncertainty, providing insights for better management and navigating turbulence. The authors envisage future research exploring the interplay between IC components, technology, innovation and network-building strategies for business resilience. Additionally, there is a need to understand better the IC’s impact on specific industries (automotive, transportation and hospitality), Social Development Goals and digital transformation performance.

The study aims to assess the relative impact of intellectual capital (IC) as opposed to tangible assets on profitability and employee performance in hotels in Serbia before and during the coronavirus disease 2019 (COVID-19) pandemic.

The current study was undertaken in 2019, the year before COVID-19, and 2020, the year of COVID-19's major impact. This study utilizes the Value-Added Intellectual Coefficient (VAIC) as a measure of efficient use of IC. Financial data were collected from 163 hotels in Serbia. Structural equation modeling (SEM) was used to test the proposed hypotheses.

The results revealed that IC was a relevant factor for both profitability and employee performance before and during the COVID-19. However, the study reveals a negative moderating effect of tangible capital efficiency (TCE), meaning that with the increase of TCE, the relationship between IC and performance becomes weaker.

The main limitation of the study is rooted in VAIC's ability to fully incorporate all elements of IC, leaving the relational capital out.

To achieve better performance, hotel management should direct resources more towards IC and less toward tangible assets, which implies doing more with less.

The results indicate the importance of IC in a period of crisis for the industry and economy that are not recognized as knowledge intensive. To the best of the authors' knowledge, no other study has attempted to assess the relative contribution of tangible assets and IC before and during the COVID-19 pandemic.

Recently, machine learning (ML) methods gained popularity in finance and accounting research as alternatives to econometric analysis. Their success in high-dimensional settings is promising as a cure for the shortcomings of econometric analysis. The purpose of this study is to prove further the relationship between intellectual capital (IC) efficiency and firm performance using ML methods.

This study used the double selection, partialing-out and cross-fit partialing-out LASSO estimators to analyze the IC efficiency’s linear and nonlinear effects on firm performance using a sample of 2,581 North American firms from 1999 to 2021. The value-added intellectual capital (VAIC) and its components are used as indicators of IC efficiency. Firm performance is measured by return on equity, return on assets and market-to-book ratio.

The findings revealed significant connections between IC measures and firm performance. First, the VAIC, as an aggregate measure, significantly impacts both firm profitability and value. When the VAIC is decomposed into its breakdowns, it is revealed that structural capital efficiency substantially affects firm value, and capital employed efficiency has the same function for firm profitability. In contrast to the prevalent belief in the area, human capital efficiency’s impact is found to be less important than the others. Nonlinearities are also detected in the relationships.

As ML tools are most recently introduced to the IC literature, only a few studies have used them to expand the current knowledge. However, none of these studies investigated the role of IC as a determinant of firm performance. The present study fills this gap in the literature by investigating the effect of IC efficiency on firm performance using supervised ML methods. It also provides a novel approach by comparing the estimation results of three LASSO estimators. To the best of the author’s knowledge, this is the first study that has used LASSO in IC research.

This study constructs an efficiency evaluation framework for assessing the human, structural and relational capital in the semiconductor industry of Taiwan. Furthermore, we analyze whether there are significant differences in efficiency across different levels concerning the industry supply chain (upstream, midstream and downstream), employee service tenure, capital scale and company establishment years.

This study focuses on Taiwanese semiconductor companies, utilizing data sourced from the Taiwan Economic Journal (TEJ) Database for the period spanning 2017 to 2021, encompassing a total of five years. Due to the nondisclosure of intangible asset values by all companies, an effort was made to ensure a comparable baseline by excluding companies with incomplete or missing data. Finally, empirical analysis was conducted on a sample of 64 companies using the dynamic network data envelopment analysis method.

(1) Overall efficiency demonstrates structural capital as the most prominent, followed by relational capital, while human capital shows relatively poorer efficiency. (2) To enhance the efficiency of intellectual capital, priority should be given to improving the efficiency of outputs related to intellectual property rights such as patents. (3) The midstream segment exhibits the best efficiency in both structural and relational capital. (4) Companies with longer employee service tenures exhibit superior efficiency in human capital in the long run. (5) Companies with extended establishment years and larger capital scales demonstrate superior efficiency in both human and structural capital.

Reflecting on past literature, scholars have primarily focused on the relationship between intellectual capital and firm efficiency, often emphasizing the overall efficiency of intellectual capital. However, within organizations, human capital, structural capital, and relational capital are interrelated. This study, for the first time, assesses the efficiency of these three components within an organization. The research addresses the challenges in analyzing the efficiency of intellectual capital and introduces a highly contemporary approach – dynamic network data envelopment analysis (DNDEA). Using the semiconductor industry in Taiwan as a case study, this paper conducts empirical analysis in a captivating and worthy industry. Therefore, the ideas presented in this paper are original.

Intellectual capital (IC) plays a pivotal role in determining corporate risk profiles in the contemporary knowledge era. Consequently, this study aims to analyze the impact of IC on firm risk (FR) among the manufacturing companies listed on the Pakistan Stock Exchange (PSX).

The authors have adopted the modified value-added intellectual model which combines human capital efficiency, structural capital efficiency, efficiency of capital employed and relational capital efficiency. FR has been used as the dependent variable, measured as the standard deviation of the daily stock prices. The study has used panel data from a sample of 40 manufacturing companies listed in the KSE-100 Index from 2015 to 2021.

The results suggest that IC has a significant impact on the FR of manufacturing companies listed on the benchmark index of PSX. Moreover, this relationship is direct; thus, an increase in IC would also increase FR measured by the change in stock prices.

The current study has only used linear techniques. Future researchers may consider investigating the impact of IC at varying levels of FR using nonlinear techniques.

This study provides corporate managers and policymakers valuable insight into the need to strike a balance between investment in IC and their FR, particularly in an emerging market context.

IC is frequently associated with firm performance. However, the relationship between IC and FR has generally been underexplored. This study adds to the strand of limited IC literature by investigating the impact of a modified IC model on FR in an emerging economy.

The aims of the current study were twofold: first, to rank the world’s emerging economies based on a novel National Intellectual Capital Index (NICI) and its components; and second, to examine the impact of NICI and its components on economic growth, measured in terms of real GDP per capita.

We employed principal component analysis (PCA) to construct the novel NICI based on five key socio-economic indicators including (1) national human capital, (2) national structural capital, (3) national relational capital, (4) national informational capital and (5) national innovational capital. These indicators are publicly available for many countries. The index was generated by considering the most appropriate socio-economic indicators as precise measures of NIC from the Penn world table (version 10.0), the World Bank’s database of world governance and development indicators and the KOF globalization across the selected emerging economies.

The empirical findings revealed that national human capital is a significant driver of NIC, corresponding to higher economic growth. This is followed by national informational capital, national relational capital, national innovation capital and national structural capital. Furthermore, results indicate that the contribution of national structural capital is marginal compared to other critical strands of NIC.

NIC is generally considered the most valuable strategic resource for driving knowledge economies, especially in the Industry 5.0 revolution. Ranking emerging economies based on the NICI sheds light on the accumulated stock of NIC and how it contributes to and improves the economic growth of these economies. The stock of NIC is considered a critical success factor for measuring both current and future economic prosperity. Therefore, using the socio-economic indicators of KOFGI as accurate measures of NICI will assist policymakers in formulating and implementing relevant policies to enhance the accumulation of knowledge-based capital, which are critical components of NIC.

To the best of the authors' knowledge, this is the first study of its kind, both theoretically and empirically, to measure the National Intellectual Capital Index (NICI) using the most nascent socio-economic indicators of NIC. Moving forward, this study evaluates the impact of NICI and its components on economic growth, which is a relatively sparse area of research in the context of emerging knowledge economies.

This study aims to examine the impact of intellectual capital (IC) on default risk in Indian companies listed on the National Stock Exchange.

This study applies panel data regression analysis to derive a relationship between IC and default risk for the sample period 2013–2022. The value-added intellectual coefficient (VAIC) of Pulic (2000) has been applied to measure IC performance, and default risk is estimated using the revised Z-score model of Altman (2000).

The results revealed a positive association between Z-score and VAIC. It implies that a higher value of VAIC improves financial stability and leads to a lower likelihood of default. The findings further suggest that new default forecasting models can be experimented with IC indicators for better default prediction.

The findings can have implications for investors and banks. This paper provides evidence of IC performance in improving the financial solvency of firms. Investors and financial institutions should invest their resources in a healthy firm that effectively manages and invests in their IC. It will eventually award investors and creditors high returns through efficient value-creation processes.

This study provides evidence of IC performance in improving the financial solvency of Indian high-defaulting firms, which lacks sufficient evidence in this domain of research. Numerous studies exist examining the relationship between firm performance and IC value, but this area is inadequately focused and underresearched. This study, therefore, fills the research gap from an Indian perspective.

This study aims to investigate the association of FinTech innovation (FinTechINN) and firm performance (FP) by considering the role of knowledge assets (KA) as a causal mechanism underlying the FinTechINN – FP association.

In this study, the authors consider panel data of 1,049 Chinese A-listed firm and construct a structural model for corporate FinTech innovation, knowledge assets and firm performance while considering endogeneity issues in analyses over the period of 2014–2022. The modified value added intellectual capital (VAIC) and research and development (R&D) expenses are used as a proxy measure for knowledge assets, considering governance and corporate performance measures.

According to the findings of this study FinTech innovation (FinTechINN) has a positive significant effect on firm performance. Particularly; the findings disclose that FinTech innovations has a link with knowledge assets, FinTech innovations indirectly affects firm performance, and the association between FinTech innovation and firm performance is partially mediated by knowledge assets (MVAIC and R&D expenses).

Rooted in the dynamic capability and resource-based view, this study pioneers an empirical exploration of the association of FinTech innovation with firm performance. Moreover, it introduces the novel dimension of knowledge assets (on firm-level), acting as a mediating factor with in this relationship.