Intellectual capital and its effects on the performance of firms, sectors and nations (Vốn trí tuệ và tác động của nó đến hiệu suất của các công ty, ngành và quốc gia)

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INTRODUCTION

Introduction

In the context of the knowledge-based economy, firms enhance their competitive advantage by shifting from tangible assets into intangible assets (Stewart, 1997; Sveiby,

1997) Castro et al (2019) consider that intellectual capital plays a major role in the knowledge-based economy and is the key driver of firm’s sustained competitive advantages Intellectual capital is defined as unique skills, knowledge, and solutions that can be converted into value in the market, leading to an increase in firm’s competitiveness, productivity, and market value (Pulic and Kolakovic, 2003)

The role of intellectual capital in firm’s performance is increasing, so it is necessary to examine the dynamics of this role and the contributions of intellectual capital to firm’s performance Inkinen (2015) demonstrates that firms can benefit from a variety of intellectual capital profiles This means that some businesses require high levels of overall intellectual capital to achieve impressive performance, while others can still achieve positive results with relatively low structural or relational capital In addition, the impact of intellectual capital on a firm performance primarily stems from its combinations, interactions and mediating effects Furthermore, there is substantial proof highlighting the important connection between intellectual capital and a firm's innovation performance (Song, 2022; Inkinen, 2015) Various studies have been conducted to examine the effects of intellectual capital on firm’s performance with a focus on financial firms (Haris et al., 2019; Firer and Williams, 2003) and manufacturing firms (Xu and Wang, 2019) Xu and Li (2019) find a difference in intellectual capital efficiency between high-tech and non-high-tech small and medium-size firms in China The impacts of intellectual capital on firm’s performance in the emerging markets in the Asian region has been examined in previous studies (Indonesia, Soetanto and Liem, 2019; Thailand, Tran and Vo, 2018; Malaysia, Goh, 2005) In particular, Soetanto and

Liem (2019) argue that intellectual capital affects the market-to-book value of the knowledge-based sectors, which have intensively used technology and/or human capital In a study on Thailand’s banking sector, Tran and Vo (2018) conclude that bank profitability is driven mainly by the efficiency of capital employed Goh (2005) asserts that banks have accumulated a lower level of structural capital efficiency than human capital efficiency

Since joining the Association of Southeast Asian Nations (ASEAN), Vietnam has emerged as a country with remarkable national performance and development In recent years, leaders in the miracle of national performance among ASEAN members have been emerging markets, such as Vietnam (OECD, 2018) Figure 1.1 indicates that the pattern of real growth in the gross domestic product (GDP) in Vietnam is stable and higher than that of other emerging countries, such as Indonesia, Malaysia, the Philippines, and Thailand Since 2000, Vietnam's GDP per capita has grown by 6.4 percent annually—one of the fastest rates in the world (Trieu, 2019) In addition, Vietnam has closely integrated into the regional and world economy, with strong trade commitments, such as the European-Vietnam Free Trade Agreement, the Comprehensive and Progressive Agreement for Trans-Pacific Partnership, the Vietnam- Eurasian Economic Union Free Trade Agreement, and the ASEAN–Hong Kong, China Free Trade Agreement, to name a few

ASEAN 5: Indonesia, Malaysia, the Philippines, Thailand, and Vietnam

Figure 1.1 Real GDP growth trends: Vietnam, ASEAN, and the World 1

1 Singapore is not included because of differences in population size, income, infrastructure and technology levels

In addition, as shown in Figure 1.2, Vietnam has increased its investment in infrastructure to bridge the gap with other ASEAN member countries Vietnam’s spending on infrastructure represents the second fastest among the ASEAN members, 11.5 percent, which is almost doubled the rate of GDP growth for the period 2012-2016 However, the business environment in Vietnam is still maturing The gaps in the institutional quality undermine investors’ confidence Figure 1.2 also indicates that satisfaction with the investment environment is still lower in Vietnam than in other emerging markets in Asia

Figure 1.2 Infrastructure spending, GDP growth rate, and business environment factors 2 Moreover, based on the 2017-2018 global competitiveness index, Vietnam lagged behind that of other ASEAN members, such as Malaysia, Indonesia, and Thailand Vietnam has a competitive advantage from its relatively low labor costs However, its low technology readiness (in the technology readiness ranking, as presented in Figure 1.3) poses a disadvantage for Vietnam in technological innovation and automation

Source: PwC (2018); Economist Intelligence Unit (2018)

Figure 1.3 Global competitiveness and technological readiness ranking

2 Singapore is not included because of differences in population size, income, infrastructure and technology levels

Global Competitiveness Index, institutions pillar ranking, 2017-2018

Economist Intelligence Unit technological readiness ranking, 2018-

On the above observations, The Vietnamese firms are facing great opportunities and challenges For example, the ongoing Covid-19 pandemic worldwide puts firms in a new state, a new environment The Vietnamese firms have faced new challenges that have never occurred in the past Therefore, The Vietnamese firms need to be flexible in their production plans, business strategies and images and branding The diversified knowledge-based economy plays an important role in all areas of life and society and gradually replaces the industrial economy, which only focuses on production and consumption In order to meet the important requirements of the knowledge-based economy, firms need to thoroughly utilize the value from intellectual capital – an important intangible asset The development of the knowledge-based economy enhances the role of intellectual capital in businesses and society and pushes it to a new level This practice requires firms to use and implement appropriate and flexible policies to utilize the value of resources, which are considered intangible, from firms including skills, knowledge, innovation, and relationship with customers.

Research problems

Edvinsson and Malone (1997) consider that intellectual capital includes two main categories: human capital and structural capital Pulic (1998) introduces a value-added intellectual coefficient (VAIC) model to measure intellectual capital efficiency This model separates intellectual capital into three components, including (i) human capital, (ii) structural capital and (iii) capital employed Other studies such as Nimtrakoon (2015); Vishnu and Gupta (2014); and Nazari and Herremans (2007) also propose a modified value-added intellectual coefficient (MVAIC) model The MVAIC model has been widely used in measuring intellectual capital efficiency at firms’ level (Bayraktaroglu et al., 2019; Xu and Wang, 2019; Chen et al., 2015)

In addition, the strategy of spreading knowledge of firms is not only for themselves, but also extends to the sector, region and country (Pedro et al., 2018) Medina et al (2007) argue that policymakers can identify solutions to enhance the intangible resources of the sector or region through the analysis of their intellectual capital in order to achieve a sustainable growth Marcin (2013) presents countries around the world are increasingly interested in measuring intellectual capital across sectors As such, it is important and necessary to develop a new sectoral intellectual capital in accordance with the sectors’ development theories (Pedro et al., 2018) Doing so will

5 promote the management of intangible resources in sectors However, a methodology to measure and evaluate the efficiency of the sectoral intellectual capital has been largely ignored in previous studies

Moreover, at the national level, no widely used or highly recognized methods have been used to measure intellectual capital across nations A limited number of studies (Lin, 2018; Kapyla et al., 2012; Lin and Edvinsson, 2011; Schneider, 2007; Andriessen and Stam, 2005; and Bontis, 2004) with the focus on measuring national intellectual capital have been conducted However, the measurements of national intellectual capital adopted in those studies are very impractical to be widely implemented across nations due to the unavailability of required data and/or an excessive usage of judgements Lin and Edvinsson (2011) paper is a pioneering study in measuring intellectual capital across countries This method is impractical to be implemented for other nations outside the intended samples As such, different approaches in measuring intellectual capital across sectors, particularly across nations, are expected to properly measure intellectual capital across sectors and nations for comparison purposes (Salonius and Lonnqvist, 2012)

In the past three decades, the financial sector has played a crucial role in Vietnam's national performance and development In the context of deepening an integration of the Vietnamese economy into the world economy, the financial sector should effectively utilize both tangible and intangible resources, especially intellectual capital The operations of financial firms are directly related to intellectual capital because they are knowledge-based companies (Buallay et al., 2020) In addition, Firer and Williams

(2003) emphasize that financial firms have higher intellectual capital efficiency than other sectors Employees of financial firms exhibit a higher homogeneity of skills and knowledge (Kubo and Saka, 2002) Financial firms operating in a highly regulated environment tend to be more compliant in meeting regulatory expectations while non- financial firms are not As such, these differences result in a different level of intellectual capital across sectors

To the best of my knowledge, contributions of intellectual capital to firm’s performance with a focus on the differences between financial and non-financial firms have largely been ignored in previous studies, particularly in emerging markets such as Vietnam As such, a study directly targeting an emerging market like Vietnam offers

6 crucial implications for the intellectual capital community including firms’ executives, academics and policymakers This study examines the differences in intellectual capital efficiency between financial and non-financial firms in Vietnam and the contribution of intellectual capital to firm’s performance This dissertation extends the existing literature by developing a new sectoral intellectual capital index (SICI) which can be used to measure a different level of intellectual capital across sectors This study uniquely and strikingly extends the current literature concerning intellectual capital measurement by developing a new index of national intellectual capital (INIC) which is hardly seen in previous studies In addition, the effects of intellectual capital on the performance of firms, sectors and nations are investigated.

Research gap

1.3.1 Intellectual capital and its effects to firm’s performance: financial firms versus non-financial firms

Various studies have also been conducted to examine the effects and contributions of intellectual capital on the financial performance of non-financial firms Different models are used to calculate the level of intellectual capital, such as the VAIC model (Hoang et al., 2020a; Ghosh and Modal, 2009; Kamath, 2008) and the MVAIC model (Soetanto and Liem, 2019; Sardo and Serrasqueiro, 2017) Previous studies have used different econometric techniques, such as OLS techniques (Chan, 2009; Ghosh and Mondal, 2009; Firer and Williams, 2003) and GMM techniques (Soetanto and Liem, 2019; Sardo and Serrasqueiro, 2017) The findings in previous studies confirm a positive relationship between intellectual capital and firm performance (Soetanto and Liem, 2019; Li and Zhao, 2018; Sardo and Serrasqueiro, 2017; Ghosh and Mondal, 2009) However, other studies confirm a negative relationship between intellectual capital and firm performance (Britto et al., 2014; Morariu, 2014) Chan (2009) and Firer and Williams (2003) also reveal an insignificant relationship between intellectual capital and firm performance Maali et al (2021) explore the links between corporate governance and sustainability performance using the corporate social responsibility of 300 UK firms from 2005 to 2017 They state that corporate governance has a positive impact on sustainability performance In addition, corporate social responsibility plays a fully mediate role in the relationship between corporate governance and sustainability performance in UK firms Soetanto and Liem (2019) state that capital employed

7 efficiency and structural capital efficiency contribute to firm wealth In this paper, when the sample is divided into industries based on whether they are high-level knowledge- based (those with the intensive use of technology and human capital) and low-level- knowledge based, the results indicate that capital employed has a positive effect on firm performance in those that are high-level-knowledge based

My literature review indicates that the contributions of intellectual capital to firm performance with a focus on the financial and non-financial sectors have largely overlooked in Vietnam The financial sector plays the role of providing financial services to people and businesses This segment includes banks, securities, financial, real estate and insurance firms The main players in Vietnam's financial sector being banks and financial institutions (Zhang et al., 2021) The financial firm plays an important role in national performance in Vietnam by facilitating financial transactions

In addition, the value of the assets in the banking system is nearly twice that of its GDP (Trieu, 2019) The banking system in Vietnam plays a leading role in Vietnam's national performance, so the focus on banking becomes important in this study Buallay et al

(2020) consider banks knowledge-intensive firms The most important financial firm assets are in the form of intellectual capital A financial firm’s activities are mainly related to intellectual capital, such as brand building and human resources Financial firm employees exhibit greater homogeneity than employees in other sectors (Kubo and Saka, 2002) Moreover, it has been argued that banking has accumulated higher levels of intellectual capital than other sectors (Firer and Williams, 2003) The current literature considers that staff identity is important because intellectual capital is one of the key measures for assessing the competence of employees In addition, financial firms operating in a heavily regulated environment, whereas non-financial firms are not, resulting a different levels of intellectual capital efficiency However, to the best of my knowledge, the impact of intellectual capital on the performance of firm with a focus on the differences between financial and non-financial firms has been overlooked in intellectual capital literature, particularly in emerging markets such as Vietnam

Ali et al (2022) argue that intellectual capital consists of three main components: human capital, structural capital and relational capital Human capital contributes to firm performance through the competence and creativity of employees, allowing them to identify, create new knowledge and solve problems (Xu and Li, 2019) Structural capital

8 includes procedures and processes, human resource policies and guidelines for labor management practices such as recruitment, task management, patents, intellectual property (Sardo and Serrasqueiro, 2017) Human capital utilizes structural capital to increase firm performance (Soetanto and Liem, 2019) Relational capital encompasses the relationships with stakeholders that allow for certain behaviors and sustainable relationships (Tran and Vo, 2018) Relational capital facilitates accessing, processing, synthesizing, and exchanging knowledge within and across corporate influences on firm performance (Maali et al., 2021)

1.3.2 Sectoral intellectual capital and its effects to performance across sectors

Liu et al (2021) state that intellectual capital plays the important role of intangible assets, it helps to exploit important knowledge that affects the innovation ability of firms, sectors and regions Marcin (2013) emphasizes that intellectual capital is a fundamental resource for value creation at the sectoral, regional and national levels

In addition, from being one of the poorest countries in the world in the mid-1980s, Vietnam has achieved rapid national performance and sustainable development goals in the last 10 years (Baum, 2020) These achievements of Vietnam are based on broad- based economic reforms and national development strategies, focusing on five main sectors: education, health, roads, water and electricity infrastructure (Baum, 2020) Nguyen and Gregar (2018) emphasize the role of knowledge management in innovation of Vietnamese firms Besides, Nguyen et al (2021) also affirm that intellectual capital has a positive influence on firm’s performance in Vietnam Dutt (1990) asserts that imbalance between sectors can slow down economic development In particular, the coronavirus pandemic affects the economies of countries around the world in a "K- shaped recovery" The characteristic of this type of recovery is that some sectors will improve, while others will continue to decline (Nikkei, 2021) Hence, it is necessary to measure and evaluate the efficiency of intellectual capital across sectors in Vietnam and other emerging markets

Previous studies have measured intellectual capital in the firm level (Phusavat et al., 2011; Hoang et al., 2020b) and national level (Lin and Edvinsson, 2011; Bontis,

2004) In addition, measuring intellectual capital at the regional level has also been considered in previous studies, such as regional level in France (Edvinsson and

Bounfour, 2004); 29 provinces and cities of China (Xia and Niu, 2010); 8 Russian federal districts (Markhaichuk and Zhuckovskaya, 2019) Besides, various regional intellectual capital measurements have been introduced, such as intellectual capital dynamic value (Edvinsson and Bounfour, 2004), principal components analysis and cluster analysis (Xia and Niu, 2010); data envelopment analysis (Nitkiewicz et al., 2014); multiple-criteria decision-making (Liu et al., 2021)

However, the issue of measuring intellectual capital at the sector level has been largely ignored in previous studies Based on the modified value-added coefficient (MVAIC) model, this study proposes a sectoral intellectual capital index (SICI) by examining the intellectual capital efficiency of each firm in the sector In addition, the author uses total assets as a weight to construct the intellectual capital index of the sector Moreover, this dissertation examines the impact of intellectual capital on sector performance in Vietnam

1.3.3 National intellectual capital and its effects to national performance

The Asia-Pacific region is considered the fastest-growing region globally, contributing two-thirds of the global growth The region includes China and Japan, two of the world's three largest economies (Business Insider, 2020) In addition, the region is also home to some of the fastest-growing countries in the world, such as Vietnam (World Bank, 2020a) The S&P (2020) report stresses that the Covid-19 "shadowed" the economic prospects of the Asia Pacific region, leading to shocks in domestic supply and demand in Japan and South Korea, as well as weakening the demand from external markets such as the US and Europe Based on the report, economies in the region are suffering the double effect of weakening demand and a supply reduction Countries in the Asia Pacific region are gradually changing new production methods and new customer approaches The role of intangible assets, such as automated manufacturing technologies and online sales services, has gradually asserted the importance of creating and maintaining a competitive advantage In particular, Stahle et al (2015) state that conducting more detailed analyzes of the role of national intellectual capital in different economies would also add value to intellectual capital literature

Classifying intellectual capital into three distinct levels — firm, sector, and nation

— ensures a comprehensive and multidimensional approach to research (Svarc et al.,

2021) Rather than solely focusing on intellectual capital at the enterprise level, this dissertation delves deeper into measuring intellectual capital across all three levels to gain a nuanced understanding of its significance and impact on performance This classification and measurement framework offers flexibility and applicability across various contexts, allowing for tailored research and theoretical methods at each level, ranging from the specific context of individual firm to the broader scope of sector and nation

By examining intellectual capital management strategies and measures at the firm, sector, and nation levels, this dissertation aims to provide specific and practical recommendations for businesses and policymakers This approach enables the identification of trends and challenges in intellectual capital management and development at each level, facilitating a deeper understanding of potential issues and opportunities for businesses and the economy as a whole Ultimately, this dissertation contributes to informed decision-making and policy formulation by shedding light on the dynamics of intellectual capital across different levels of analysis

Understanding the impact of national intellectual capital on national performance is essential for businesses to navigate the complexities of the global business environment effectively (Lin, 2018) Research into national intellectual capital yields valuable insights that can inform strategic management decisions and guide business actions in an increasingly competitive landscape The findings of this dissertation can directly inform business management and development practices, providing actionable intelligence for administrators and governments to formulate policies aimed at optimizing the utilization of intangible assets, particularly intellectual capital

Research objectives

This study has the overarching objective of measuring intellectual capital at the firm, sector and nation levels In addition, this dissertation also examines the impacts of intellectual capital on the performance of firms and sectors; and on national performance, which is effectively the economic performance of the countries

The objectives of this study are summarized as follows:

1) To measure intellectual capital for firms, sectors and nations:

• Utilize the modified value-added intellectual coefficient (MVAIC) model to assess the intellectual capital of both financial and non-financial firms in Vietnam, and compare the intellectual capital between these two groups

• Develop a new Sectoral Intellectual Capital Index (SICI) to evaluate the intellectual capital across 12 sectors in Vietnam

• Extend the existing literature by creating a new National Intellectual Capital Index (INIC) to measure intellectual capital at the national level, applying this index to 104 countries globally

2) To examine the effects of intellectual capital on the performance of firms, and sectors, and nations

• Examine the impact of intellectual capital on the performance of firms, sectors, and nations This includes analysing how intellectual capital

14 influences performance at different levels using various econometric methods to ensure the validity and robustness of the findings

• Assess firm and sector performance using return on total assets and return on equity metrics, and measure national performance through GDP per capita

• Employ these metrics and methodologies to provide a comprehensive analysis of the relationship between intellectual capital and performance across multiple levels.

Research questions

In achieving the research objectives, this dissertation attempts to answer the following research questions:

1) How can intellectual capital be measured effectively at the firm, sector, and national levels?

2) What are the effects of intellectual capital on the performance of firms and sectors in Vietnam; and on performance of nations?

Research subject and scope

Measuring intellectual capital and the effects of intellectual capital on the performance of firms, sectors and nations are the subjects of the dissertation

For a research scope for firms and sectors, the research scope covers 150 listed firms on the Vietnam’s stock market in the period 2011-2018 For the nations, the scope of the study covers 104 countries in the 2000-2018 period.

New findings

This study has the following new findings:

- First, this dissertation explores the variance in intellectual capital efficiency between financial and non-financial sectors in Vietnam, followed by an analysis of how intellectual capital impacts firm performance within the country As an emerging market in Southeast Asia and one of the most dynamic economies globally, Vietnam's managerial implications hold significant value for the intellectual capital community, encompassing academics, policymakers, and practitioners This dissertation aims to bridge the existing knowledge gap The findings of the thesis validate the disparity in

15 intellectual capital between financial and non-financial firms Moreover, the findings corroborate that intellectual capital enhances the business performance of both categories

- Second, this research enhances existing literature by introducing a novel measure of intellectual capital at the sectoral level – the sectoral intellectual capital index (SICI) The SICI is designed to explore various facets of sectoral intellectual capital efficiency in Vietnam My research supports the notion that sectoral intellectual capital bolsters sector performance

- Third, this study pioneers the creation of a national intellectual capital index

(INIC), which stands as one of the inaugural indices of its kind, to gauge the varying degrees of intellectual capital among nations worldwide This innovative index is founded on core principles: simplicity, ensuring ease of calculation; quantification, allowing for straightforward numerical assessment without subjective judgments; market relevance, reflecting current market and economic conditions; and international comparison, facilitating practical application for cross-country comparisons irrespective of national performance and developmental stages Additionally, my research findings substantiate the contribution of national intellectual capital to economic growth, indicating a bidirectional relationship between the two.

Research framework and steps

Based on the theoretical foundations and empirical research conducted in relation to the field of study, the analytical framework is proposed Research data will be collected and analysis will be performed The research process is described specifically as follows:

● First, research needs to conduct a rigorous theoretical overview to find (i) the theory of intellectual capital and its measurements; factors affect the performance of firms, sectors and nations (ii) variables commonly used in research models in the world, and (iii) research gaps academic

● After identifying the above factors, the second step of the study is to determine the data set to be used to ensure the feasibility of the project Research to collect annual data on intellectual capital at firm, sector and nation These figures are publicly announced in the annual reports of the firms In addition, the data on the new national intellectual capital index is extracted from the source of the World Bank Development Indicators

● Third, this study uses panel data to conduct the study With the data collected and through the theoretical review, this study intends to use econometric methods suitable for the data set in order to solve potential problems (unit root, autocorrelation…), to obtain the best estimate results

● Fourth, after achieving the experimental results, the research needs to explain, discuss the results

Review of the theoretical foundation and previous studies

● Last but not least, the study concludes on intellectual capital measurements and the effects on performance of firms, sectors and nation Along with that, the study proposes solutions to promote efficiency in the use of intellectual capital, contributing to increase the efficiency of firm, sectors and countries

The core of this research is intellectual capital As shown in Figure 1.4, I consider intellectual capital from two perspectives: measuring intellectual capital and examining the effect of intellectual capital on performance In addition, this dissertation also considers at all 3 levels: firm, sector and nation

● For the measure of intellectual capital: I use the structural model and the MVAIC method to measure intellectual capital at the firm level, and compare the difference in intellectual capital between financial and non-financial firms At the sectoral and national level, I propose new intellectual capital measurement, namely sectoral intellectual capital index (SICI) and index of national intellectual capital (INIC)

● For examining the effects of intellectual capital on performance, this dissertation uses two common measures, return on assets (ROA) and return on equity (ROE), to measure performance at the firm and sector levels As for the country level, I use GDP per capita as a measure of national performance

Return on assets (ROA) and return on equity (ROE) are widely used financial metrics to assess firm and sector performance, as they provide insights into a company's profitability and operational efficiency (Mohapatra et al., 2019) ROA measures how efficiently a company uses its assets to generate profit It is calculated by dividing net income by total assets, indicating how much profit a company earns for each dollar of assets it controls (Tran and Vo, 2018) A higher ROA suggests better asset utilization and operational efficiency, making it an important indicator of performance in asset- heavy industries like manufacturing or real estate In a sectoral context, comparing ROA across firms within a sector can reveal which companies are more effective at managing their resources (Joshi et al., 2010) ROE evaluates how effectively a company uses shareholders' equity to generate profits It is calculated by dividing net income by shareholders’ equity, providing insights into the returns generated for investors A higher ROE indicates better profitability and financial management, which is crucial for

18 assessing firm performance, particularly in capital-intensive sectors like banking or technology (Kamath, 2008) ROE is also useful for sector-wide analysis, helping identify companies that deliver strong returns to their shareholders relative to competitors (Goh, 2005) Both ROA and ROE are important for understanding firm and sector performance, with ROA focusing on asset efficiency and ROE emphasizing profitability relative to equity investment Together, they provide a comprehensive view of financial performance (Mavridis, 2004)

The effects of intellectual capital on performance

Modified value-added intellectual coefficient

Sectoral intellectual capital index (SICI)

Index of national intellectual capital (INIC)

The outline of the dissertation

This dissertation is structured into six chapters to present a comprehensive summary of relevant literature and empirical evidence in response to the above- mentioned research questions Each of the chapters is as follows.

LITERATURE REVIEW

Definitions and classifications

The concept of intellectual capital is first mentioned by Senior (1836) Intellectual capital is described as an important resource in creating a firm wealth, but it is not recorded on the balance sheet (Xu and Liu, 2020) Ali et al (2022) argue that intellectual capital is a value not only in terms of monetary returns but also in terms of environmental, social and economic issues Intellectual capital is the sum total of all employee competencies and skills that create company wealth (Shahwan and Habib,

2020) The definition of intellectual capital covers different levels, including firm, sector and nation St-Pierre and Audet (2011) suggest that there is consensus on the significant contribution of intellectual capital to value creation, but there is no generally accepted definition of intellectual capital Stewart (1997) defines intellectual capital as an intangible value created for people Sullivan (2000) states that intellectual capital, which is considered as knowledge of the company, could be converted into tangible profits Bontis and Fitz-enz (2002) note that intellectual capital consists of knowledge, experience, intellectual literature, intellectual property, and information that can be used to create value and to increase competitiveness In addition, Sardo and Serrasqueiro

(2017) argue that intellectual capital is about the hidden value of a firm However, currently available definitions of intellectual capital refer to an intangible nature of capital based on implicit knowledge, and its ability to create value (Vishnu and Gupta, 2014; Roos et al., 1997; Stewart, 1997; Brooking, 1996; Edvinsson and Sullivan, 1996) The difference between the market value and the book value of a firm has been considered the most obvious indicator of intellectual capital because this difference

23 represents the economic value of intangible capital (Maditinos et al., 2011) In the following section, I will present several models of intellectual capital classification

Westberg and Sullivan (1998) point out that Saint-Onge's model explores the tangible and intangible role of knowledge in different types of intellectual capital and how these factors are valued Saint-Onge's contributions, particularly through his work in the late 1990s and early 2000s, have provided a comprehensive approach to identifying and leveraging intellectual capital to enhance organizational performance This model underscores the importance of knowledge and relationships in driving value creation within organizations (Pulic, 1998) Saint-Onge’s model categorizes intellectual capital into three primary components, each highlighting different dimensions of intangible assets Human capital refers to the collective skills, expertise, and competencies of employees This component emphasizes the value that individuals bring through their knowledge, innovation, and ability to solve problems Structural capital encompasses the supportive infrastructure, processes, databases, and intellectual property that facilitate an organization’s operations It includes organizational culture, routines, and procedures that enhance efficiency and productivity Relational capital involves the relationships an organization maintains with external stakeholders, including customers, suppliers, partners, and communities (Bontis, 1998) This component focuses on the value derived from strong, trust-based relationships and the organization’s reputation and brand The integration of these components is a key aspect of Saint-Onge’s model, advocating for a synergistic effect that enhances overall organizational performance By recognizing the interplay between human, structural, and relational capital, organizations can better understand how to leverage these assets for sustained competitive advantage This integrated approach ensures that investments in one area (e.g., training employees) are supported by improvements in other areas (e.g., enhancing IT systems and strengthening customer relationships) Saint-Onge’s model is particularly influential in the field of knowledge management By emphasizing the importance of human and structural capital, the model provides a framework for capturing, sharing, and utilizing organizational knowledge effectively This approach helps organizations to foster a culture of continuous learning and innovation In strategic management, the model aids in identifying key intellectual capital assets that can be

24 leveraged to achieve strategic goals By focusing on the holistic view of intellectual capital, managers can develop strategies that align with the organization’s strengths and address its weaknesses This alignment ensures that resources are allocated efficiently to support long-term growth and sustainability (Gates and Langevin, 2010) Traditional performance measurement systems often overlook the value of intangible assets Saint- Onge’s model addresses this gap by providing a structured approach to evaluate intellectual capital This includes assessing the impact of human skills, organizational processes, and external relationships on overall performance Such comprehensive measurement allows for better decision-making and enhances transparency in reporting organizational value

While Saint-Onge’s model offers a robust framework for understanding intellectual capital, it has faced some criticisms Quantifying intangible assets like human skills and relational capital can be subjective and complex, making it difficult to implement the model effectively Integrating Saint-Onge’s model with traditional financial metrics can be challenging, potentially leading to inconsistencies in reporting Additionally, the rapidly changing business environment means that the components of intellectual capital can evolve quickly, requiring continuous updates and adaptations to the model (Pulic, 1998)

Sveiby (1997) considers that employees are important actors and their actions will create the assets and structure of the business, whether tangible or intangible Introduced in the late 1990s, Sveiby’s model emphasizes the importance of knowledge and competence in driving organizational performance and innovation This model is instrumental in highlighting the critical role of intangible assets, often overlooked in traditional accounting and management practices As presented in Sveiby’s model categorizes intellectual capital into three main components: individual competence, internal structure, and external structure Individual competence refers to the knowledge, skills, and experience possessed by employees It underscores the value that individuals bring to the organization through their expertise and ability to solve problems and innovate Internal structure encompasses the internal capabilities of the organization, including processes, databases, organizational culture, and intellectual property This component represents the systems and infrastructure that support and enhance the productivity of employees External structure involves the relationships and networks an organization maintains with external stakeholders, such as customers, suppliers, partners, and the broader community This aspect of the model highlights the importance of reputation, brand, and customer loyalty in contributing to organizational value (Li and Zhao, 2018)

One of the key strengths of Sveiby’s model is its emphasis on the dynamic interplay between these components By recognizing that intellectual capital is not static but constantly evolving, Sveiby’s model encourages organizations to continually invest in and develop their intangible assets For example, improving individual competence through training and development can enhance the internal structure by fostering innovation and efficiency Similarly, strong external relationships can lead to increased customer loyalty and better market positioning, which, in turn, supports overall organizational growth (Sardo and Serrasqueiro, 2017)

In practical applications, Sveiby’s model is particularly valuable for strategic management and knowledge management In strategic management, the model helps organizations identify and leverage their intellectual capital to achieve competitive advantages By focusing on the holistic view of intangible assets, managers can develop strategies that align with the organization’s strengths and address its weaknesses This

26 comprehensive approach ensures that resources are allocated effectively to support long- term growth and sustainability In the realm of knowledge management, Sveiby’s model provides a framework for capturing, sharing, and utilizing knowledge within the organization By emphasizing the importance of individual competence and internal structure, the model facilitates the creation of a culture of continuous learning and improvement (Soetanto and Liem, 2019)

However, despite its many strengths, Sveiby’s model also faces some criticisms and limitations One of the primary challenges is the difficulty in measuring and quantifying intangible assets Unlike tangible assets, intellectual capital is often subjective and complex to evaluate, which can lead to inconsistencies and challenges in implementation Additionally, integrating Sveiby’s model with traditional financial metrics can be challenging, as conventional accounting systems are not designed to capture the value of intangible assets effectively (Li and Zhao, 2018; Ghosh and Mondal,

Tangible assets minus visible debt

(brands, customer and supplier relations)

(the organization, management, legal structure, manual systems, R&D, software)

2.1.3 Skandia intellectual capital value scheme

The Skandia Navigator, introduced by Edvinsson and Malone (1997), is a groundbreaking framework for measuring and managing intellectual capital This model marked a significant advancement in acknowledging the importance of intangible assets within organizations Utilizing a five-dimensional approach, the Skandia Navigator provides a comprehensive perspective on organizational performance (Brennan, 2001) The financial dimension covers traditional financial metrics to evaluate the company's economic outcomes The customer dimension assesses customer satisfaction, loyalty,

27 and market share The process dimension looks at internal processes, efficiency, and effectiveness The renewal and development dimension reflects innovation, research and development activities, and organizational learning Lastly, the human dimension focuses on metrics related to employee skills, competencies, and satisfaction (Edvinsson and Malone, 1997)

Source: Roos et al (1997); Edvinsson and Malone, (1997)

Figure 2.3 Skandia intellectual capital value scheme

Sullivan (2000) determines that intellectual capital includes 2 main components: human capital and intellectual assets Human capital includes the organization’s employee intellect, which provide know-how and institutional memory to the firm In addition, intellectual assets are defined as firm's tangible or physical description of specific knowledge It includes the source of innovations and competitive edge, which are generated from the various processes undertaken by the organization Moreover, intellectual assets include intellectual property, namely, trademarks, patents, trade secrets, copyrights

Figure 2.4 Sullivan’s approach to visualize intellectual capital

Relevant theories

The resource-based view states that in order to achieve and maintain a competitive advantage, firm’s resources play a crucial role A firm will be successful if it equips the resources that are best suited to the business and its strategy Originating from Penrose

(1959), resource-based theory is introduced by subsequent studies (Wernerfelt, 1984; Dierickx and Cool, 1989; Prahalad and Hamel, 1990; Barney, 1991) This theory reveals that firms in the market operate inconsistently in terms of resources It explains why there are differences in the performance of firms operating in the same sector (Hoopes et al., 2003) Wernerfelt (1984) states that the difference in firm performance occurs when firms own and exploit competitive advantage differently A company's competitive advantage is due to a collection of firm’s internal resources, which are unique, scarce, hard-to-replace and irreplaceable values (Guthrie et al., 2004; Barney,

1991) Internal resources include company competence, culture, management philosophy (Carmeli and Tishler, 2004) Firm's internal resources such as physical resources or human resources can be used in many different ways, depending on the ideas and business orientation of each firm Hence, there is a close relationship between the firm's resources and the knowledge retained by the employees in the organization

In addition, the combination of internal resources and external resources can also contribute to creating firm’s competitive advantages (Barney, 1991) Resource-based theory stresses that company need to be able to connect internal resources with opportunities from outside markets to enhance firm wealth (Russo and Fouts, 1997) Helfat and Peteraf (2003) emphasize that the principle of resource theory is the existence

29 of heterogeneous capacities and resources in firms It therefore accounts for heterogeneous competition on the premise that close competitors differ significantly and permanently in terms of their resources and capabilities The type, extent, capacity and nature of resources are important determinants of firm's profitability (Amit and Schoemaker, 1993; Barney, 1991; Nelson and Winter, 1982) Firm’s resources are classified into intangible and tangible assets (Grant, 1996; Hall, 1992) In a complex business environment, resources management has become the key factor to maintaining a competitive advantage for firms (Sharkie, 2003; Grover and Davenport, 2001; Teece et al., 1997) Szulanski (2003) argues that firms can increase wealth by accumulating resources with lucrative potential and efficiently exploiting those resources

Resource-based theory posits that firms are heterogeneous entities possessing unique resources and capabilities that are valuable, rare, inimitable, and non- substitutable These valuable, rare, inimitable, and non-substitutable resources enable firms to develop and sustain competitive advantages, leading to superior performance outcomes Intellectual capital, by its very nature, aligns closely with the valuable, rare, inimitable, and non-substitutable framework (Grant, 1996; Hall, 1992) Human capital, encompassing the skills, expertise, and knowledge of employees, is a critical resource that firms leverage to innovate and differentiate themselves in the marketplace Structural capital, which includes organizational processes, patents, databases, and culture, supports the effective utilization of human capital Relational capital, involving the relationships with customers, suppliers, and other stakeholders, further enhances a firm's ability to create and capture value (Teece et al., 1997)

The inclusion of resource-based theory in examining intellectual capital's impact on performance is essential because it provides a theoretical foundation for understanding how firms can strategically manage their intangible assets to achieve sustained competitive advantage (Sharkie, 2003) At the firm level, resource-based theory helps explain how differences in intellectual capital among firms lead to variations in performance For example, a firm with a highly skilled workforce (human capital), efficient organizational processes (structural capital), and strong customer relationships (relational capital) is more likely to outperform its competitors This advantage can be particularly pronounced in knowledge-intensive industries, where

30 intellectual capital plays a central role in driving innovation and value creation (Grover and Davenport, 2001)

At the sectoral level, resource-based theory can be used to analyse how different industries leverage intellectual capital to achieve sectoral growth and development Industries that are rich in intellectual capital, such as technology and pharmaceuticals, often exhibit higher growth rates and innovation outputs compared to those in more traditional sectors (Nitkiewicz et al., 2014) The accumulation and management of intellectual capital within a sector can create barriers to entry, leading to sustained competitive advantage for firms within that sector For example, the pharmaceutical industry's reliance on patents (a form of structural capital) and specialized knowledge (human capital) creates a competitive landscape where only firms with substantial intellectual capital can thrive (Marcin, 2013)

At the national level, resource-based theory provides a framework for understanding how countries can leverage their intellectual capital to enhance national competitiveness and economic growth Nations with strong educational systems, advanced technological infrastructures, and robust innovation ecosystems are better positioned to develop and sustain competitive advantages in the global economy (Lin,

2018) For instance, countries like the United States and Germany, which have invested heavily in their intellectual capital, particularly in education and research and development (R&D), have been able to maintain their positions as global leaders in innovation and economic performance In contrast, countries that have not effectively managed or developed their intellectual capital may struggle to compete on the global stage, leading to lower levels of economic growth and development (Svarc et al., 2021)

Previous studies (Balogun and Jenkins, 2003; Hoskisson et al., 1999; Grant, 1996) show that knowledge-based theory is a recent extension of resource-based theory De Carolis (2002) argues that knowledge-based theory considers knowledge the most important strategic resource of an organization Knowledge-based theory considers firms possessing heterogeneous resources of knowledge (Hoskisson et al., 1999), including knowledge-based assets (Marr, 2004; Roos et al., 1997; Stewart, 1997) Wiklund and Shepherd (2003) and Rouse and Daellenbach (2002) state that knowledge-

31 based assets are especially important because these resources are difficult to copy They are the foundation to create a sustainable competitive advantage for businesses Knowledge-based theory is increasingly concerned due to the changes in the global economy in the accumulation and ownership of knowledge assets over the past two decades There have been structural changes in patterns of production and national performance, from the exploitation of tangible resources to the knowledge economy (Fulk and DeSanctis, 1995) Especially in the context of globalization and a knowledge- based economy, resources that create a competitive advantage for companies have shifted from tangible to intangible assets (Stewart, 1997; Sveiby, 1997) In particular, intellectual capital is considered as an intangible asset, which contributes to creating a competitive advantage for businesses (Bollen et al., 2005; Bontis, 2001) Castro et al

(2019) consider that intellectual capital plays a major role in a knowledge-based economy and is the main driver of a company's sustainable competitive advantage

At the firm level, knowledge-based theory provides a theoretical basis for understanding how intellectual capital, particularly human capital, contributes to the creation and application of knowledge (Hoskisson et al., 1999) Firms that invest in the development of their human capital through training, education, and knowledge-sharing practices are better equipped to innovate and adapt to changing market conditions For example, in the technology sector, firms that encourage a culture of continuous learning and knowledge sharing are more likely to develop breakthrough innovations that can drive competitive advantage and superior performance (Marr, 2004) Moreover, knowledge-based theory highlights the importance of knowledge integration, where firms combine knowledge from different sources (internal and external) to create new products, processes, or services This integration is often facilitated by structural capital, such as advanced IT systems and knowledge management practices, which support the efficient flow of knowledge within the organization

At the sectoral level, knowledge-based theory is useful for analysing how knowledge is created and diffused across industries, leading to sectoral growth and innovation Sectors that are characterized by high levels of knowledge intensity, such as biotechnology and information technology, often exhibit rapid innovation cycles and significant economic contributions (Marcin, 2013) The accumulation of knowledge within a sector can create positive spillover effects, where knowledge generated by one

32 firm or organization benefits others within the sector This phenomenon is particularly evident in regions like Silicon Valley, where a dense concentration of knowledge- intensive firms, research institutions, and universities has led to a virtuous cycle of innovation and economic growth Knowledge-based theory also emphasizes the role of knowledge networks and collaborations, where firms within a sector engage in joint R&D efforts, knowledge sharing, and co-creation of value These networks enhance the overall intellectual capital of the sector, leading to higher levels of performance and competitiveness (Stewart, 1997)

Measuring intellectual capital: traditional methods

Based on the resource-based view (Barney, 1991; Nazari and Herremans, 2007), intellectual capital is calculated as the sum of intangibles assets and capabilities that can create firm's competitive advantage In addition, Mondal and Ghosh (2012) argue that intellectual capital is one of the main assets to enhance the firm performance, thanks to the ability to generate unique characteristics for the company Besides, Ray et al (2004) state that the intangible nature of intellectual capital makes firm’s competitors difficult to imitation

Bejinaru (2017) argues that the development of the theoretical construct of intellectual capital can be separated into four phases of research In the first phase, in the 1980s and 1990s, the concept of intellectual capital is proposed and a preliminary theoretical construct is developed by linking the benefits of intellectual capital to create firm's sustainable competitive advantage (Ricceri, 2011) In the second phase, the aim of researchers is to evaluate the impact of intellectual capital on firm financial performance and firm value creation (Petty and Guthrie, 2000) In the third stage, previous studies examine the managerial implications of intellectual capital management (Edvinsson, 2013; Lopes and Serrasqueiro, 2017) In the fourth stage,

36 authors expan the concept of intellectual capital by including new aspects (e.g environment, sustainability) and new study contexts (e.g emerging markets, universities, etc.; Bejinaru, 2017)

Dean and Kretschmer (2007) consider intellectual capital an intangible resource, which creates value and enhances firm performance Intellectual capital reflects firms’ competitive advantages, which come from knowledge-based assets (Pirozzi and Ferulano, 2016) Bontis and Fitz-enz (2002) define intellectual capital as intellectual literature, knowledge, experience and intellectual property that can be utilized to create competitiveness In addition, intellectual capital is related to the hidden value of a firm Pulic (1998) propose a model that is known as a value-added intellectual coefficient (VAIC) Intellectual capital is divided into customer capital and structural capital McElroy (2002) utilizes social capital as a proxy for customer capital Nazari and Herremans (2007) state that the VAIC has three dimensions: human capital efficiency, structural capital efficiency (including both internal and relational capital efficiency), and capital employed efficiency (including physical and financial capital efficiency) Human capital is the most valuable component of intellectual capital and can be defined as the skills, capabilities, knowledge and experience, which can be used to accomplish goals and to increase the efficiency of companies (Cohen et al., 2014; Henry, 2013; Stewart, 1997) Structural capital is described as the infrastructure required for human capital to create value (Sullivan, 2000) Human capital utilizes knowledge and practice to enhance all procedures and cultural structures, transforming it into firms’ structural capital (Gates and Langevin, 2010; Bontis, 1998)

The balanced scorecard method is introduced by Kaplan and Norton (1992) This approach creates a new framework for measuring a firm's performance by focusing on intangible assets, rather than only financial metrics This method determines the firm's current position and the goals for success in the future, and the actions required to achieve the goals Besides traditional financial measurements, the balanced scorecard approach also includes organizational perspectives of operations such as internal business, innovation and learning, customers (Bose and Thomas, 2007) Financial measurements provide a clear view of the current financial situation by measuring return on investment (ROI) or return on equity (ROE) (Kaplan and Norton, 1996) Internal

37 business perspective defines the processes that are critical to a firm's success These processes allow a firm to maintain relationships with existing customers and acquire new customers in market segments, thereby, meeting shareholder expectations (Kaplan and Norton, 1996) Besides, the perspective of innovation and learning has three indicators: human, system and organ These indicators identify the structures and processes that are critical to building long-term success and growth, and developing firm's knowledge (Kaplan and Norton, 1996) Measures of the innovation and learning perspectives can be employee satisfaction, sick days off and staff turnover (Malmi et al.,

2006) Meanwhile, the customer perspective focuses on defining the market and the customer segments which firms participate in (Bose and Thomas, 2007) The customer perspective includes customer satisfaction and retention, attracting new customers, profitability, market share, and success in building strong customer relationships (Kaplan and Norton, 1992)

Brooking (1996) proposes the method of measuring intellectual capital, determined through the Technology Broker's intellectual capital audit To measure the intellectual capital of an organization, this method starts by answering the 20 questions that make up the intellectual capital indicators The less likely a company is to answer in 20 affirmative questions, the lower its efficiency of intellectual capital will be This method describes intellectual capital as a combination of four components, namely, market assets, intellectual property, human-centric assets, and infrastructure assets Each component of this model is measured with a specific number of indicators to determine the contribution of that asset class Brooking's method includes 178 sub-indicators designed to determine the hidden value in each component of intellectual capital

Figure 2.6 Brooking’s intellectual capital measurement model

Employee education audit (including 5 indicators)

Corporate learning audit (including 10 indicators)

Corporate culture audit (including 4 indicators)

Intangible asset monitor model is developed by Sveiby (1997) to measure intellectual capital in an organization This model measures intangible assets using indicators that are relevant to the firm’s internal and external structures and people’s competence Internal structure is measured by ideas, models, patents, concepts, approaches and computer administrative systems These indicators are belonged to firms and created by employees In addition, external structure is defined as the relationships with suppliers and customer, brand names, reputations or image, trademarks Human capital is considered as individual competency such as skill, ability, expertise or capacity of employees

Figure 2.7 Intangible assets monitor example

The Skandia Navigator, developed by Edvinssion and Malone (1997), is a pioneering framework designed to measure and manage intellectual capital This model represents a significant advancement in recognizing the value of intangible assets in organizations The Skandia Navigator uses a five-dimensional framework to provide a balanced view of the organization’s performance (Brennan, 2001) The financial focus

Profit/customer Growth in market share Satisfied customer index

Years of education Level of education

Value added margin on sales

Client satisfaction index Repeats order

Median age of all employee

40 encompasses traditional financial metrics that assess the company’s economic performance Customer focus measures customer satisfaction, loyalty, and market share Process focus evaluates internal processes, efficiency, and effectiveness Renewal and development focus indicates innovation, research and development efforts, and organizational learning Human focus involves metrics related to employee skills, competencies, and satisfaction (Edvinssion and Malone, 1997)

The Skandia Navigator is instrumental in strategic management by providing a comprehensive view of both tangible and intangible assets It helps organizations identify and leverage their intellectual capital to achieve competitive advantages (Ashton, 2005) By focusing on a broad range of indicators, companies can develop strategies that align with their intellectual capital strengths and weaknesses Traditional financial metrics often fail to capture the true value of a company's intangible assets The Skandia Navigator addresses this gap by including non-financial indicators, offering a more nuanced picture of organizational performance (Soetanto and Liem,

2019) This holistic approach enables better decision-making and fosters a culture of continuous improvement Knowledge management is a critical aspect of leveraging intellectual capital The Skandia Navigator facilitates this by highlighting the importance of human and structural capital Organizations can use the insights gained from this model to implement effective knowledge management practices, ensuring that valuable knowledge is captured, shared, and utilized efficiently (Lin, 2018)

2.3.5 Value Added Intellectual Coefficient™ (VAIC™)

One of the most popular intellectual capital measurements is developed by Pulic

(1998), which is namely value-added intellectual coefficient (VAIC) VAIC is an analytical process that allows firm’s managers, shareholders, and other stakeholders to monitor and evaluate the effectiveness of the value-added and each component of the firm's resources The VAIC model is defined as follows:

● ICE is intellectual capital efficiency

● CEE is capital employed efficiency

● VA is described as firm’s value added, which is calculated as total of operating profit, depreciation, amortization and employee costs

● HC is determined as human capital, which is measured as employee expenses

● SC is structural capital, which is calculated as VA minus HC

● CE is capital employed, which is measured as the book value of a firm’s net assets

Although the value-added intellectual coefficient (VAIC) method has several advantages (Soetanto and Liem, 2019) Chan (2009) and Firer and Williams (2003) state that the data being utilized in VAIC is based on audited information which makes the measurement objective and verifiable Besides, Maditinos et al., (2011) point out that VAIC is simple, reliable and comparable Moreover, Nimtrakoon, (2015); Chen et al.,

(2005) state that VAIC provides a standardized and integrated measure, which allows the analysis and the comparison across organizations or firms in different countries

However, the VAIC model has limitations It cannot be used exclusively for intangible assets (Brennan, 2001) and does not include intellectual property and research and development (R&D) expenditure, which are positively related to firm performance (Chang, 2007) In addition, the level of a firm’s risk is not considered in the model

(Maditinos et al., 2011) The VAIC model cannot measure the level of intellectual capital of companies with a negative book value or negative operating profit (Chu et al.,

2011) In addition, it is argued that the model cannot account for the combined effects of different types of tangible and intangible assets (Dzenopoljac et al., 2017)

To overcome these limitations, Phusavat et al (2011) and Nazari and Herremans

Measuring intellectual capital: extended analysis for sectors and nations

Pedro et al (2018) mention that intellectual capital conceptual level has been extrapolated to include also sector, region and nation They also stress that national intellectual capital is recognised as a source of productivity and competitiveness for a country (Užienė, 2014) Meanwhile, sectoral intellectual capital also contributes to the development not only of the sector but also of the country (Nitkiewicz et al., 2014) In addition, regional intellectual capital includes all intangible regional resources that can generate future benefits through integration (Andriessen and Stam, 2008) Marcin

(2013) states that intellectual capital plays crucial role in determining job creation, national performance and quality of life National intellectual capital consists of variables that help determine a nation's wealth, which serves as a root for nurturing and cultivating future happiness (Bontis, 2004) Besides, Cabrita et al (2015) clearly state that sectoral intellectual capital contributes to the creation of wealth and intangible assets for the sector or region In addition, the growth potential of economic sectors is

43 mainly based on their intangible assets, unique infrastructure, intangible resources and latent capabilities (Nitkiewicz et al., 2014; Marcin, 2013)

The concept of national intellectual capital has been mentioned in previous studies using various definitions Andriessen and Stam (2005) describe national intellectual capital as available intangible resources to the country which provide relative advantage and be able to produce future benefits Bontis (2004) mention that intellectual capital of a nation includes hidden values of individuals, firms, institutions, communities and regions that are current and inherent sources for the national wealth establishment Moreover, Lin and Edvinsson (2011) define national intellectual capital as knowledge, wisdom, capability and expertise, which provides a competitive advantage and determines its potential for the future growth of the country National intellectual capital represents a bundle of assets, which assist a country to gain economic, social and environmental development goals (Salonius and Lonnqvist, 2012) National intellectual capital is also determined as knowledge and knowing capability involved in a nation's value creation processes (Kapyla et al., 2012)

In their study, Edvinsson and Malone (1997) develop a clear conceptual and structural basis for intellectual capital which is separated into two main categories, being human capital and structural capital Structural capital is further divided into market capital and organizational capital in which the later is again classified into process capital and renewal capital Andriessen and Stam (2005) further expand the taxonomy using cross-categorized human capital, structural capital, relational capital and three new categories: assets, investments and effects Meanwhile, Lev (2001) focus explicitly on the economic effects of intangibles which classify the source of economic performance into three groups: physical, financial and intangible Bontis (2004) develops the so-called national intellectual capital index (NICI), which contains human capital, market capital, process capital and renewal capital Corrado et al (2006) suggest three elements of intellectual capital, including computerized information, innovative property and economic competencies In order to compare national competitiveness, IMD (2011) divide intellectual capital into four elements, being economic performance, government efficiency, business efficiency and infrastructure

Nitkiewicz et al (2014) point out that the concept of intellectual capital is mainly applied to firms and organizations However, this concept is gradually being expanded and one of the directions of development is to define and classify knowledge capital and its components at the sector and regional level Pedro et al (2018) shows that strategically innovative organizations spread knowledge not only to their own but also to sector, region and country Thus, through sector and regional intellectual capital analysis, public policies can find solutions to improve sector intellectual capital to achieve sustainable development (Medina et al., 2007) Countries around the world are increasingly interested in sector approaches to intellectual capital (Marcin, 2013) At the same time, issues of effective sector innovation strategy have become important Poyhonen and Smedlund (2004) examine region intellectual capital by differentiating three modes of intellectual capital creation, including: production network, innovation network and development network They state that innovation network functioned best, whereas the production network had insufficient structured information flows In addition, Edvinsson and Bounfour (2004) examine intellectual capital dynamic value (IC-dVAl) approach to measure intellectual capital performance at regional level in France Research results show that Paris area and Toulouse region are the two regions with the highest intellectual capital, while Corsica lags behind Xia and Niu (2010) propose a system of 27 indicators to measure regional intellectual capital of 29 provinces and cities of China They estimate regional intellectual capital level by using principal components analysis (PCA) and cluster analysis The results show that intellectual capital efficiency of eastern China is higher than western China Nitkiewicz et al (2014) utilize data envelopment analysis (DEA) for evaluate regional intellectual capital in across Polish regions The results show significant differences between Polish regions in terms of intellectual capital efficiency Pedro et al (2018) emphasizes the need to develop a new sector approach to intellectual capital in relation to sector development theories Thereby, contributing to promoting the management of intangible resources in sectors and regions Liu et al (2021) utilize a set of multiple‐criteria decision‐making to evaluate the regional intellectual capital of 31 provinces in China They reveal that there are differences in the level of regional intellectual capital in different regions in China

However, previous approaches suffer from certain shortcomings These measurements are challenging to apply because the data used to build the index are not widely available, and there is a lot of qualitative data By design of the approach, the value of each sub-indicator is determined based on the largest degree of all sub- indicators within the entire population of all estimates This approach appears to be unsupported As a result, these approaches are impractically implemented for other countries (Kapyla et al., 2012)

Table 2.1 Summary - Sectoral intellectual capital measurements

No Authors Research focus Technique Limitation

1 Liu et al (2021) 31 provinces in

Multiple‐criteria decision‐making (MCDM) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)

The analysis of detailed components of sectoral intellectual capital appears to be complicated and arbitrary

Regional intellectual capital in Poland

Data is not available and fails to attract replicability for authors in different countries

Data not available and fails to attract replicability for authors in different countries

29 provinces and cities of China

The analysis of detailed components of sectoral intellectual capital appears to be complicated and arbitrary

Mea-hiya Community Cultural Council, Chiang Mai, Thailand

Qualitative approach: content analysis, thematic extraction and ground theory

There is no basis to consider that the weighting scheme remains unchanged across sectors and periods

Wood processing industry in Eastern

Interview and system’s theoretical interpretation of organizations

There is no basis to consider that the weighting scheme remains unchanged across sectors and periods

The analysis of detailed components of sectoral intellectual capital appears to be complicated and arbitrary

Intellectual Capital dynamic Value (IC- dVAl)

There is no basis to consider that the weighting scheme remains unchanged across sectors and periods

Cities Intellectual Capital Benchmarking System

Data is not available and fails to attract replicability for authors in different countries

In term of the national level, no widely utilized methodologies or recognized methods have been used to evaluate intellectual capital across nations A limited number of studies on the national intellectual capital have been conducted However, the proposed approaches are very impractical in applications due to unavailability of required data and/or a high degree of judgment required

Even though Edvinsson and Malone (1997) model propose a clear and structured understanding of the elements of intellectual capital, a combination of these elements into a final measure of intellectual capital has never mentioned and discussed (Stahle,

2008) In addition, this model does not mention at all the impact of intellectual capital on national performance which is crucial attention for policymakers Based on Edvinsson and Malone's (1997) model, the concept of national intellectual capital has discussed and measured in limited studies over the last 25 years, including Kapyla et al (2012); Lin and Edvinsson (2011); Schneider (2007); Andriessen and Stam (2005) and Bontis (2004)

Evaluating and quantifying national intellectual capital, despite its recognized importance, presents significant challenges The concept remains poorly defined and is in the early stages of development (Bontis, 2004) Describing national intellectual capital is also complex (Svarc et al., 2021) and ambiguous (Salonius and Lonnqvist,

2012) Few studies have attempted to measure national intellectual capital Bontis

(2004) introduced the National Intellectual Capital Index (NICI) framework, which includes four main components: (1) national human capital index, (2) national process capital index, (3) national market capital index, and (4) national renewal capital index The NICI framework represents a significant step in understanding the relationship between national intellectual capital and national financial capital However, this framework has inherent limitations The assignment of weights is arbitrary, lacking a consistent basis for uniform weight distribution across countries and time periods Additionally, the complex and subjective analysis of the various elements of national intellectual capital adds to the difficulty As a result, the NICI framework faces challenges in cross-national analysis, requiring substantial evaluative judgments Moreover, the direct averaging of national intellectual capital components—human capital, process capital, market capital, and renewal capital—lacks strong empirical

48 support (Stahle, 2008) Lin and Edvinsson (2011) made further contributions by developing a method to measure the relationship between national intellectual capital and national performance Their methodology includes quantitatively rated data, such as “computers in use per capita,” and qualitatively assessed data, rated on a scale of 1–

10 Quantitative variables are normalized and scaled to a 1–10 range The linear structural relations (LISREL) technique ensures validity The national intellectual capital index created by Lin and Edvinsson (2011) remains relevant Their updated analysis, based on data from 48 countries from 2005–2010, represents one of the latest studies Lin (2018) later used this model to compare national intellectual capital in South Africa, Poland, and Romania However, Lin and Edvinsson’s (2011) approach has its limitations The creation of sub-indices lacks transparency; their model combines quantitative and qualitative assessments into an unweighted composite index, ignoring specific national objectives and strategies Additionally, the sub-indicator values are determined solely by the highest value within the estimate population, a method lacking empirical validation Therefore, the model’s practical application is limited beyond the initial 40-country sample (Kapyla et al., 2012) Kapyla et al (2012) introduced a new structural framework for assessing national intellectual capital, incorporating a social capital dimension They used data from the Finnish government database from 2000–

2007 The authors emphasized that the indicator selection is illustrative and evolving rather than fixed Notably, Kapyla et al (2012) highlighted that visualizing the measurement raises questions about the dynamic interactions between different aspects of national intellectual capital They thus advocate for a multidimensional measurement approach for national intellectual capital assessment Various foundational concerns have arisen during the formulation and execution of the three methodologies, as discussed above, for evaluating national intellectual capital First, the dearth of data or the nonpublic availability of essential data to external stakeholders operating beyond national boundaries has been noted (Kapyla et al., 2012) Second, a substantial reliance on evaluative judgments is indispensable when gauging a nation’s intellectual capital These approaches necessitate incorporating qualitative information, predicated upon subjective assessments, which eludes translation into quantitative parameters (Tran,

2024) Thus, the assessment of a nation’s intellectual capital rests significantly on perceptual underpinnings Furthermore, Kapyla et al (2012) consider national

49 intellectual capital’s intricate and contextual nature, acknowledging that it transcends mere self-evidence A congruity between the concepts of national intellectual capital and the corresponding statistical metrics, coupled with financial data, necessitates validation (Stahle, 2008)

Measuring performance of firm, sector and nation

Bouckaert and Halligan (2007) suggest that there are three levels of performance measurement, including micro, meso and macro The micro level represents the efficiency level of the organization, or firm The meso level involves the network of organizations involved in the implementation of sector policies The macro level is the global level that includes the aggregated social outcomes obtained in a given nation The choice of performance measure depends on the choice of level (Bititci, 2015)

Drawing on cost accounting theories and practices, the Du Pont Group develops a performance measurement system through accounting measures and financial ratios, including return on net assets (RONA), return on investment (ROI) and return on equity (ROE), and various performance metrics (Bititci, 2015) RONA measures the ratio of profit (net income) to asset turnover (average total assets) ROI is utilized to calculate the effectiveness of an investment by comparing the ratio between the return on investment and the cost of the investment ROE is the ratio of net income to shareholders' equity, which indicates how much profit is generated from the money that shareholders invest (Chenhall, 1997; Kaplan, 1983)

Several different perspectives are used to measure performance, such as employment, productivity and profitability (Siepel and Dejardin, 2020)

Siepel and Dejardin (2020) argue that the number of people employed by a company represents a core metric for understanding the size and performance of the firm or sector Employment growth is the preferred metric for economists and policymakers

In contrast, entrepreneurs are less likely to consider job growth as a measure of success, as an increase in the number of employees increases the costs involved and reduces the performance of the business This illustrates the point made above about the different measures and assessments made for a part of the company, while emphasizing the relationship between the metrics Coad et al (2014) emphasizes that employment growth will drive further revenue and profit growth, or increase firm or sector performance

Productivity is another important aspect used to measure the efficiency of the firm's use of factors of production Siepel and Dejardin (2020) argue that productivity includes labor productivity (value added per worker), or capital productivity (value added per unit of fixed capital) In addition, Gal (2013) emphasizes that total factor productivity (TFP) is also an important productivity measurement

Profitability is another important metric to evaluate a company's performance (Kaplan, 1983) Coad et al (2017) argues that determining profitability to drive future growth is important for managers There are several ways of measuring profitability, ranging from direct measures as reported on financial statements to financial ratios commonly used in financial literature (e.g return on assets, return on equity) (Chandler et al., 2009; Coad et al., 2017) Return on assets (ROA) is mainly used by analysts to measure firm performance (Haris et al., 2019; Firer and Williams, 2003) However, previous studies (Soetanto and Liem, 2019; Tran and Vo, 2018; Goh, 2005) also utilize return on equity (ROE) as a measurement of sector performance

The performance of a nation is often measured against the achievement of economic goals (Lewis, 2003; Solow, 1956) These goals can be short-term, such as stabilizing the economy in the face of economic shocks, or long-term, such as sustainable growth and sustainable development Hence, I consider economic performance as a measure of national performance Economists often use a variety of economic indicators to examine the economic performance of a country These indicators allow economists to gauge a country's performance Monitoring these

56 indicators is especially valuable for policymakers to take appropriate actions in the market context (Lewis, 2003) A commonly utilized macro indicator to measure economic performance is GDP per capita (Lin, 2018; Macerinskiene and Aleknaviciute,

2017) Various studies (Borensztein et al., 1998; Parui, 2021) have utilized gross domestic product as a proxy for national performance

Gross domestic product (GDP) is the total value of final production of goods and services as a result of economic activities within the territory of a country in a given period Thus, gross domestic product is the result of all economic activities taking place on the territory of a country Firms use labor and investment capital to produce goods and services Existing production technology determines how much output can be produced from a given amount of capital and labor High production output of firms means that they use investment capital effectively, have abundant and highly qualified labor resources, and apply modern science and technology in production and business Thus, the GDP of an economy either high or low reflects the production and performance in that nation (Lewis, 2003)

GDP is measured through three common ways, including spending, production, and income

The measurement of GDP by spending is made according to the following formula:

● C is consumption, includes purchases of durable and non-durable goods and services

● I is investment, calculated by fixed investment plus inventory investment

● G is government spending, measured as the sum of government purchases of goods and services

● X is net exports (exports minus imports)

In terms of production, GDP is measured by value added The value added is calculated by the difference between the revenue the firm earns by selling its products and the amount it pays for the products of other firms it uses as products of other firms it uses as intermediate goods

The income approach to measuring the gross domestic product (GDP) is based on the accounting reality that all expenditures in an economy should equal the total income generated by the production of all economic goods and services It also assumes that there are four major factors of production in an economy, including total national income, sales taxes, depreciation and net foreign factor income

GDP = total national income + sales taxes + depreciation + net foreign factor income.

The effects of intellectual capital on performance of firms, sectors and

2.6.1 Intellectual capital and firm’s performance

Previous studies find a positive relationship between intellectual capital and financial performance (Phusavat et al., 2011; Kamath, 2008) However, other studies also find a negative relationship between intellectual capital and firm performance (Chan, 2009; Ghosh and Mondal, 2009; Firer and Williams, 2003)

In the literature, various studies have examined the relationship between intellectual capital efficiency and firm performance across different contexts Buallay et al (2020) utilized the MVAIC model to investigate this relationship among 59 listed banks in Gulf countries from 2012 to 2016 Their findings, based on ordinary least squares (OLS) analysis, revealed that intellectual capital efficiency positively impacts both financial performance, measured by return on equity, and market performance, measured by Tobin's Q Similarly, Hoang et al (2020a) explored the effect of intellectual capital on the performance of 13,900 Vietnamese firms over the period of

2012 to 2016 using a VAIC model, and found a positive correlation between intellectual capital and firm performance Expanding on this, Hoang et al (2020b) examined the mediating role of dynamic capabilities in the relationship between intellectual capital

58 and firm performance in 350 Vietnamese firms through structural equation modeling They concluded that components of intellectual capital enhance firm performance, with dynamic capabilities serving as a mediator Further, Hoang et al (2020c) observed that intellectual capital components directly affect firm performance, with human and social capital significantly mediating the relationship between organizational capital and firm performance Bayraktaroglu et al (2019) conducted a study on Turkish manufacturing firms from 2003 to 2013, utilizing the MVAIC model to demonstrate that structural capital efficiency is significantly related to firms' productivity, and capital employed efficiency is significantly related to firms' profitability They also found that innovation capital efficiency moderates the relationship between structural capital efficiency and productivity, as well as between capital employed efficiency and profitability Similarly, Diyanty et al (2019) found that human capital efficiency and capital employed efficiency positively impact firm performance, while structural and relational capital efficiency do not significantly affect firms’ financial performance In the context of Malaysian financial firms, Hapsah and Bujang (2019) analyzed data from 21 firms between 2011 and 2015, concluding that intellectual capital and its components significantly influence financial performance Soetanto and Liem (2019) examined 127 firms from 12 industries in Indonesia from 2010 to 2017 and found that intellectual capital positively affects firm performance, with capital employed efficiency and structural capital efficiency contributing to firms' wealth They also noted that in high- level knowledge industries, capital employed efficiency has a positive relationship with firm performance Besides, Xu and Wang (2019) studied textile firms in China and South Korea from 2012 to 2017, finding that intellectual capital and its components significantly impact earnings, profitability, and productivity Yao et al (2019) examined

111 Pakistani financial institutions from 2007 to 2018 and discovered a U-shaped relationship between intellectual capital and profitability, indicating that increased intellectual capital enhances profitability and productivity up to a certain point, beyond which further increases reduce performance They identified human capital efficiency as having the most significant impact on firm performance Tran and Vo (2018) used fixed-effect and random-effect models, along with the GMM estimator, to investigate the impact of intellectual capital on the financial performance of 16 listed banks in Thailand from 1997 to 2016 Their study found no significant correlation between

59 intellectual capital and bank performance in Thailand, but noted that capital employed efficiency had the largest positive impact on bank profitability, whereas human capital efficiency had a negative impact Nimtrakoon (2015) employed the MVAIC model to assess the impact of intellectual capital on the financial performance of 213 technology firms listed on five ASEAN stock exchanges, finding a significant impact of intellectual capital and its components on financial performance, with no notable difference among the countries Vishnu and Gupta (2014) analyzed 25 Indian hospitals and medical research centers from 2002 to 2013, finding that human capital efficiency positively impacts firm performance, while relational capital efficiency does not have a statistically significant effect on performance in the healthcare sector

Numerous studies have investigated the positive impact of intellectual capital on firm performance across various industries and regions For instance, Buallay et al

(2020) examined 59 listed banks in Gulf countries, while Haris et al (2019) focused on

26 Pakistani banks Similarly, Tran and Vo (2018) studied 16 listed banks in Thailand, Joshi et al (2010) analyzed 11 Australian-owned banks, Kamath (2008) investigated 98 banks in India, Goh (2005) examined 16 banks in Malaysia, and Mavridis (2004) studied

141 banks in Japan These studies employed diverse econometric techniques, including ordinary least squares (OLS) in Buallay et al (2020), fixed-effects and random-effects techniques in Tran and Vo (2018), and GMM techniques in Haris et al (2019) Furthermore, various models were utilized to assess the level of intellectual capital, such as the VAIC model in several studies (Mohapatra et al., 2019; Tran and Vo, 2018; Joshi et al., 2010; Kamath, 2008; Goh, 2005; Mavridis, 2004) and the MVAIC model in others (Buallay et al., 2020; Haris et al., 2019) Notably, Haris et al (2019) identified a U- shaped relationship between intellectual capital and profitability in Pakistan, while Buallay et al (2020) and Tran and Vo (2018) emphasized the significant role of human capital efficiency and capital employed efficiency in creating bank wealth

The significance of intellectual capital in influencing firm performance is increasingly recognized, necessitating a thorough examination of its dynamics and impact on organizational outcomes Several studies have explored the relationship between intellectual capital and firm performance, predominantly focusing on financial institutions (Haris et al., 2019; Yao et al., 2019; Tran and Vo, 2018; Ting and Lean, 2009; Firer and Williams, 2003) and manufacturing enterprises (Xu and Wang, 2019;

2018; Vishnu and Gupta, 2014) Xu and Li (2019) identified variations in intellectual capital efficiency between high-tech and non-high-tech small and medium enterprises in China, while Soetanto and Liem (2019) emphasized the impact of intellectual capital efficiency on the market-to-book value of knowledge-intensive industries However, despite these contributions, the role of intellectual capital in financial firms, particularly within emerging markets like Vietnam, remains largely unexplored in the existing literature

Our review of the literature underscores the scarcity of research investigating the relationship between intellectual capital and firm performance, particularly within the financial and non-financial sectors in Vietnam Notably, even the existing studies in the Vietnamese context have not utilized widely recognized models like the MVAIC model to measure intellectual capital, highlighting a significant gap in the current literature

In summary, intellectual capital is an intangible resource that contributes to creating competitive advantages and improving firm performance (Ali et al., 2022; Maali et al., 2021; Xu and Li, 2019) In line with previous studies, the following hypotheses is proposed:

Hypothesis 1: Intellectual capital has a positive influence on firm performance

Table 2.3 Summary - Intellectual capital and firm performance

Region Research sample Research focus Positive

Vietnam Vietnamese firms Profitability Yes

Vietnam ICT firms Firm performance Yes

5 Xu and Li (2019) China high-tech and non- high-tech SMEs

Indonesia Listed firms Profitability and market value

Thailand Listed banks Profitability Yes

India Listed companies Productivity, profitability, market value and sales growth

Nigeria Listed firms Cash flow from operation and EVA

11 Xu et al (2017) China Listed environmental protection companies

Earnings, profitability, efficiency, and market value

14 Morariu (2014) Roman Listed companies Market value Negative

Australia Listed companies Profitability and productivity

16 Tan et al (2007) Singapore Listed companies Profitability and market performance

USA Multinational firms Net value added and return on total asset

Listed companies Profitability and market value

2.6.2 Intellectual capital and sector performance

Various studies have been conducted to investigate the effects of intellectual capital on performance in different sectors, such as banking (Akkas and Asutay, 2022; Soewarno and Tjahjadi, 2020) manufacturing (Xu and Wang, 2019; Vishnu and Gupta,

2014), technology (Nkambule et al., 2022; Xu and Li, 2019) Soewarno and Tjahjadi

(2020) affirm the critical role of intellectual capital in the performance of the banking sector With the homogenous characteristics of human resources, the effective utilization of human capital (the most critical component of intellectual capital) brings a sustainable competitive advantage to the bank Xu and Wang (2019) emphasize that intellectual capital contributes positively to the wealth of the manufacturing industry The processes, patents, and production technology are the critical factors for the manufacturing industry to develop a competitive advantage These factors are the constituent elements of structural capital (the second component of intellectual capital) Meanwhile, Nkambule et al (2022) argue that the technology industry is highly competitive The industry takes many years to build brands and products However, the life cycle of products will rapidly decline in a few years Sales depend on customer retention rates and customer loyalty Therefore, the technology industry should strive to maintain and develop relational capital (the third component of intellectual capital) to build a sustainable competitive advantage Paoloni et al (2022) state that technology and knowledge help the food sector improve its performance and global competitiveness Thus, a deep understanding of structural capital and effective utilization of the relational capital ensure the food industry's survival, especially in the Covid-19 emergency In short, intellectual capital plays a vital role in creating wealth for firms, sectors and countries Each sector has its characteristics, corresponding to the focus on utilizing each component of intellectual capital to achieve sustainable competitive advantage, contributing to an increase in sector performance

However, the issue of measuring intellectual capital at the sector level has been largely ignored in previous studies Based on the modified value-added coefficient (MVAIC) model, this study proposes a sectoral intellectual capital index (SICI) by examining the intellectual capital efficiency of each firm in the sector In addition, the author uses total assets as a weight to construct the intellectual capital index of the sector Moreover, this dissertation examines the impact of intellectual capital on sector

63 performance in Vietnam In line with the above arguments, the second hypothesis is postulated as follows.

Hypothesis 2 (H2): Sectoral intellectual capital boosts sector performance Table 2.4 Summary - Intellectual capital and sector performance

Region Research sample Research focus

China high-tech and non- high-tech SMEs profitability and operating efficiency

Profitability and sustainable growth rate

Earnings, profitability, efficiency, and market value

Earnings, profitability, efficiency, and market value

ASEAN Technology firms Profitability and market value

USA Biotech firms Profitability and stock return

Malaysia Finance sector Profitability Yes

Listed companies Profitability and market value

Malaysia Service and non- service industries

2.6.3 Intellectual capital and national performance

METHODOLOGY

Data

White et al (2010) highlight the significance of annual reports as a key communication tool between businesses and their audiences Chakroun and Hussainey

(2014) view annual reports as instruments of voluntary disclosure Additionally, Davison (2014) analyzes annual reports to assess specific assets, such as graphs and images Furthermore, Lys et al (2015) investigate disclosures of corporate social responsibility within annual reports Thus, the annual report is a vital resource for research in management, accounting, and finance It also serves as a marketing tool and a channel for communicating corporate strategy, as noted by Stanton and Stanton

(2002) Moreover, Yuthas et al (2002) identified the annual report as the most crucial source for evaluating a company White et al (2010) stress that it is the primary source for information on intellectual capital, corporate governance, and social responsibility While some details on intangible assets might be found on a company's website, they are typically communicated through the annual report Notably, most prior research in intellectual capital (Dumay and Cai 2014; Liao et al., 2013; Phusavat et al., 2011) has depended on data from annual reports Consequently, data derived from annual reports are guaranteed to provide ample information regarding intellectual capital and firm performance

The data for this study were gathered from the published annual reports of each firm and the respective stock exchanges where the companies are listed, consistent with prior research (Soetanto and Liem, 2019; Xu and Li, 2019; Tran and Vo, 2018) Data extraction was performed from the website https://cafef.vn The selected firms are industry leaders in market capitalization and are listed on the Vietnam stock market, including the Ho Chi Minh Stock Exchange (HOSE) and the Hanoi Stock Exchange (HNX) The research commenced in 2019, and due to the incomplete disclosure of data

69 for listed companies prior to 2011, the ideal period for study was determined to be from

2011 to 2018 Only firms that have been operational throughout the research period and have not been closed or merged are included Firms with missing data for four or more years, or those with negative figures, are excluded from the sample After eliminating non-qualifying firms (599 in total), the study comprises a sample of 150 firms, representing a sample to population ratio of 20.03% This ratio ensures representativeness of the entire study population (Blatchford et al., 2021)

Figure 3.1 Number of listed firms

Firms are categorized into two sectors: financial and non-financial This categorization draws on prior research (Ali et al., 2022; Buallay et al., 2020; Soetanto and Liem, 2019; Xu and Li, 2019) According to Ali et al (2022), financial firms comprise financial institutions, banks, leasing companies, insurance companies, credit unions, asset management organizations, and others Utilizing the Global Industry Classification Standards, this dissertation segregates listed Vietnamese firms into two groups: the financial sector, which includes banking, securities, and insurance, and the non-financial sector, which encompasses all other sectors In this dissertation, the author focuses on examining intellectual capital in the banking and technology groups Focusing on the Banking and Technology sectors when examining the impact of intellectual capital on firm performance in Vietnam is essential due to several factors

(Nguyen et al 2021) These sectors are highly knowledge-intensive, where human and structural capital are critical for competitive advantage and innovation Vietnam's rapid digital transformation, particularly in banking through fintech adoption and in technology through innovation, highlights the importance of intellectual capital in driving operational efficiency Additionally, these sectors are among the fastest-growing industries in Vietnam, playing a significant role in economic growth (Hoang et al., 2020a) Both face regulatory and competitive pressures, making intellectual capital vital for maintaining compliance, innovation, and competitiveness Furthermore, attracting and retaining skilled talent is crucial in these sectors, as human capital significantly influences firm performance Given the Vietnamese government’s focus on financial and technological infrastructure development, studying these sectors provides valuable insights into how intellectual capital supports both firm performance and national economic goals (Hoang et al., 2020b)

In addition, this study persists in utilizing data derived from firms' annual reports to suggest a novel sectoral intellectual capital index for the corresponding period Firms lacking data or presenting negative figures are excluded from this sample In accordance with the State Securities Commission's 2020 classification, the aforementioned 150 firms are categorized into 12 sectors: aviation, banking, education, energy, food, insurance, oil and gas, pharmaceuticals, real estate, securities, services, and technology

In term of national level, data are collected from the World Development Indicators (World Bank, 2020b) in the last twenty-year periods This data source includes macro information for more than 200 countries worldwide Countries missing data are excluded from the sample Finally, this study uses data from more than 100 countries to construct a national intellectual capital index.

Research methods

3.2.1 Assess the impact of intellectual capital on firm performance

To examine the first hypothesis (H1), modified value-added intellectual coefficient (MVAIC) method has been utilized to measure intellectual capital efficiency by using panel data from 150 listed firms in Vietnam The generalized method of moments (GMM) is used to ensure the robustness of the findings

GMM is an econometric technique used for estimating parameters in statistical models It is particularly valuable when the model's assumptions may be relaxed compared to traditional methods like ordinary least squares (OLS) (Soetanto and Liem,

2019) GMM is based on the idea that, if the model is correctly specified, the population moment conditions (functions of the data that are expected to equal zero) should hold true (Hansen, 2020) By minimizing the discrepancies between the sample moments (derived from the data) and the population moments (implied by the model), GMM provides consistent and efficient parameter estimates (Ullah et al., 2018)

One of the primary advantages of GMM over OLS, fixed effects, and random effects models is its ability to handle endogeneity—a situation where explanatory variables are correlated with the error term, leading to biased and inconsistent estimates in traditional regression models (Soetanto and Liem, 2019) GMM addresses this by using instrumental variables (IVs) that are uncorrelated with the error term but correlated with the endogenous regressors This method ensures that the parameter estimates are consistent even when endogeneity is present, which is a significant limitation of OLS (Haris et al., 2019 While OLS assumes homoskedasticity (constant variance of errors) and no autocorrelation (independence of errors), these assumptions are often violated in real-world data, leading to inefficient estimates GMM is robust to heteroskedasticity and autocorrelation, making it a more flexible and reliable method in cases where these issues are present (Soetanto and Liem, 2019) By allowing for a more general structure of the error term, GMM can produce more efficient estimates compared to OLS, fixed effects, and random effects models GMM is particularly advantageous in the context of dynamic panel data models, where lagged dependent variables are used as regressors (Ullah et al., 2018) Traditional methods like fixed and random effects models struggle with the “Nickell bias” in short panels, where the inclusion of lagged dependent variables leads to biased estimates (Haris et al., 2019) GMM, especially in its system GMM form, effectively addresses this bias by using appropriate lagged instruments This makes GMM the preferred choice for dynamic panels, where understanding the temporal dynamics is crucial (Soetanto and Liem,

2019) In comparison to fixed and random effects models, which rely on within and between variations, GMM allows for a more flexible selection of instruments This flexibility enables researchers to tailor their models more precisely to the underlying

72 data structure, improving the accuracy of the estimates (Haris et al., 2019) Fixed effects models, while controlling for time-invariant unobserved heterogeneity, do not easily accommodate multiple endogenous regressors and can suffer from a loss of efficiency (Hansen, 2020) Random effects models, on the other hand, assume that individual- specific effects are uncorrelated with the regressors, an assumption that is often unrealistic (Soetanto and Liem, 2019) GMM overcomes these limitations by enabling the use of multiple instruments, thereby improving both the consistency and efficiency of the estimates In addition, GMM is asymptotically efficient, meaning that as the sample size grows, GMM estimates converge to the true parameter values faster than those obtained from OLS or other conventional methods (Lee et al., 2016) This property is particularly valuable in large datasets, which are common in panel data analysis The ability of GMM to provide efficient estimates even in large samples makes it a powerful tool in empirical research, where data availability is not a constraint (Ullah et al., 2018)

GMM is widely used in panel data analysis, especially when examining the effect of intellectual capital on performance, due to several reasons that align with the method's strengths (Lee et al., 2016) Intellectual capital is often measured using proxies such as R&D expenditure, human capital investment, and knowledge assets, which are likely endogenous This endogeneity arises because the factors driving intellectual capital (e.g., management practices, firm size, industry characteristics) may also be influenced by firm performance (Haris et al., 2019) Traditional fixed or random effects models fail to fully address this issue, leading to biased estimates GMM, with its instrumental variable approach, effectively handles this endogeneity, providing more reliable estimates of the impact of intellectual capital on performance Panel data typically contains unobserved individual-specific effects that could be correlated with the explanatory variables Fixed effects models control for these time-invariant effects, but they do not address time-varying endogeneity GMM, particularly the difference and system GMM approaches, accounts for both unobserved heterogeneity and endogeneity, providing a more comprehensive analysis of the intellectual capital - performance relationship The relationship between intellectual capital and performance is inherently dynamic, as investments in intellectual capital today (e.g., in training or R&D) affect future performance outcomes Dynamic panel models, which include lagged dependent variables as regressors, are well-suited to capture these temporal effects (Soetanto and

Liem, 2019) GMM is particularly effective in estimating dynamic panels because it corrects for the biases associated with using lagged dependent variables This capability is crucial in understanding how past investments in intellectual capital translate into future performance gains (Haris et al., 2019) In studies of intellectual capital, the variance of the error term may not be constant due to differences in firm size, industry volatility, or economic conditions GMM’s robustness to heteroskedasticity makes it a suitable choice for such analyses, ensuring that the estimates are not only consistent but also efficient under varying error structures Panel data on intellectual capital and performance can vary in terms of the number of time periods and cross-sectional units (Soetanto and Liem, 2019) GMM is versatile in that it can be applied to both short panels (few time periods, many firms) and long panels (many time periods, few firms), making it adaptable to different data structures This flexibility is particularly useful in empirical research, where data constraints often dictate the choice of econometric methods GMM offers several diagnostic tools, such as the Hansen test for over- identifying restrictions and the Arellano-Bond (1991) test for autocorrelation, which help validate the model specification These tests are essential in empirical research, where ensuring the validity of the instruments and the absence of serial correlation in the errors are critical for obtaining reliable estimates The ability to rigorously test the underlying assumptions further strengthens the case for using GMM in panel data analysis (Roodman, 2009)

In conclusion, GMM is a highly advantageous method for estimating the impact of intellectual capital on performance, particularly in the context of panel data Its ability to address endogeneity, handle dynamic relationships, and provide efficient estimates under heteroskedasticity and autocorrelation makes it superior to traditional regression methods like OLS, fixed effects, and random effects models (Chan and Hameed, 2006) The relevance of GMM in business management research is underscored by its widespread application in studies that require robust, reliable, and efficient estimation techniques As intellectual capital continues to play a critical role in firm performance and competitive advantage, the use of GMM in empirical research will remain indispensable, providing insights that are not only theoretically sound but also practically relevant (Sardo and Serrasqueiro, 2017) This method's robustness and flexibility make it particularly well-suited for exploring complex relationships in the

74 rapidly evolving business environment, where knowledge and intangible assets are key drivers of success As such, GMM is not only a methodological choice but also a strategic tool in the analysis of intellectual capital and its effects on firm performance, offering a deeper and more nuanced understanding of the dynamics at play in modern business contexts

To ensure that the recommendations and policy implications are aligned with the specific context in Vietnam, I conducted an additional step of qualitative research This qualitative method was employed to capture the deeper insights of participants through data collected via observations, expert interviews, or group discussions For this study,

I used in-depth interviews with five experts in the field of intellectual capital (details provided in Annexure 3) These interviews allowed for direct interaction with respondents, enabling the collection of their opinions and perspectives on the research topic Additionally, using expert interviews ensured that responses were systematically and clearly controlled (Silverman, 2016)

The in-depth interviews were carried out from August to September 2022, with each interview lasting between 90 and 150 minutes The interviews took place at the respondents' offices and were arranged through face-to-face appointments scheduled at times convenient for the experts Prior to the interviews, relevant documents and questions were sent to the participants via email

In this dissertation, the qualitative research was conducted through three specific steps:

- The first step involved creating an outline for the in-depth interviews The goal was to establish a clear direction for the content and structure of the interviews, ensuring that all relevant topics related to intellectual capital were covered

- The second step involved conducting the in-depth interviews, which served as the foundation for the recommendations and policy implications in this study After introducing the objectives and the intended content of the interview— focused on intellectual capital and its impact on firm performance—I began asking questions based on the prepared outline The interview process involved

Variables: definitions and measurements

3.3.1 Measuring intellectual capital at firm level

One of the most popular intellectual capital measurements is developed by Pulic

(1998), which is namely value-added intellectual coefficient (VAIC) VAIC is an analytical process that allows firm’s managers, shareholders, and other stakeholders to monitor and evaluate the effectiveness of the value-added and each component of the firm's resources

Although the value-added intellectual coefficient (VAIC) method has several advantages (Soetanto and Liem, 2019) Chan (2009) and Firer and Williams (2003) state that the data being utilized in VAIC is based on audited information which makes the measurement objective and verifiable Besides, Maditinos et al., (2011) point out that VAIC is simple, reliable and comparable Moreover, Nimtrakoon, (2015) and Chen et al., (2005) state that VAIC provides a standardized and integrated measure, which allows the analysis and the comparison across organizations or firms in different countries

However, the VAIC model has limitations It cannot be used exclusively for intangible assets (Brennan, 2001) and does not include intellectual property and research and development (R&D) expenditure, which are positively related to firm performance (Chang, 2007) In addition, the level of a firm’s risk is not considered in the model (Maditinos et al., 2011) The VAIC model cannot measure the level of intellectual capital of companies with a negative book value or negative operating profit (Chu et al.,

2011) In addition, it is argued that the model cannot account for the combined effects of different types of tangible and intangible assets (Dzenopoljac et al., 2017)

To overcome these limitations, Phusavat et al (2011) and Nazari and Herremans

(2007) propose the modified value-added intellectual coefficient (MVAIC) model, which includes other components of intellectual capital, such as innovation capital and relational capital Crema and Verbano (2016) propose a model to measure intellectual capital, including human capital, internal structural capital, and relational capital Phusavat et al (2011) view innovation capital as R&D expenses Henry (2013) and Sullivan (2000) define relational capital as the sum of the available and potential resources that emerge from individual and organizational networks Relational capital also consists of a firm’s relationships with its customers, suppliers, marketing channels, and stakeholders in sales activities (Bozbura, 2004; Bontis, 2001) I note that, since its inception, the MVAIC model has been widely used in empirical analyses for the measurement of intellectual capital at firms

MVAIC is measured as follows:

Where: HCEi (human capital efficiency) is the marginal contribution of each unit of human capital to value added, SCEi (structural capital efficiency) is the contribution of structural capital in creating value, CEEi (capital employed efficiency) is the marginal contribution of each unit of physical and financial capital to value added, and RCEi (relational capital efficiency) is the contribution of relational capital in creating value The four components of MVAIC - HCEi, SCEi, CEEi, and RCEi

Where: VAi is defined as the value added to the firm VAi is calculated as the ratio between the total profit before taxes and employee expenditures This is because pretax profit indicates the residual value after all costs are deducted from sales, excluding employee expenditures (Tran and

Vo, 2020; Tran and Vo, 2018) HC (human capital) means employee expenditures SC (structural capital) is calculated as the difference between the value added (VA) and HC CE (capital employed) refers to both physical and financial capital, measured by the difference between total assets and the value of intangible assets RC (relational capital) is measured by marketing, selling, and advertising expenses

Based on the modified value-added intellectual coefficient (MVAIC) model, this study proposes the sectoral intellectual capital index (SICI) by examining the intellectual capital efficiency of each firm in the sector In addition, author uses total assets as the weight to make up the intellectual capital of that sector SICI is defined as follows:

● number of sample firms in the sector

● wi is the weight assigned to firm i in the sector (𝑤 𝑖 = 𝐾 𝑖

● K and Kiare the total assets of all sample firms in the sector and total assets of each firm, respectively, to which the weight for each firm is calculated

● Yi is the intellectual capital of firm i, measuring by MVAIC

3.3.3 New index of national intellectual capital

Previous studies (Lin, 2018; Stahle et al., 2015; The World Economic Forum,

2010) have utilized a range of indicators to measure a country's intellectual capital However, these methods have limitations, including data are not available to researchers in different countries, qualitative data being subjective to the researcher, and cannot be compared over a period of time In addition, previous studies tend to focus on a specific country, reflecting a local context However, in today's globally interconnected economies, I argue that intellectual capital levels should be comparable across nations This would enable governments to gain valuable insights for designing and executing policies aimed at boosting intellectual capital

From the limitations of the previous studies, as presented in Table 2.2, the author synthesizes relevant studies and proposes a new index of national intellectual capital, with specific implementation steps as follows:

Step 1: Choosing suitable scales for constructing an index of national intellectual capital (INIC) using the following guidelines:

● The scale has been frequently utilized in prior research

● The scale employs quantitative data, which is readily accessible to researchers from various countries

● The scale is straightforward and convenient for calculating and comparing intellectual capital levels across different countries and periods

As mentioned in section 2.4.2, my new index of national intellectual capital includes three key components which are well established in current literature, including (i) human capital; (ii) structural capital and (iii) relational capital

First, human capital is described as knowledge, education and competencies of individuals in realizing national tasks and goals Human capital consists of knowledge about facts, laws and principles in addition to teamwork, and other specialized skills and communication skills (Bontis, 2004; OECD, 2000) Bontis (2004) used a number of tertiary schools per capita and the number of tertiary students per capita as proxies

88 of human capital In addition, Lin and Edvinsson (2011) utilized higher education enrollment to measure human capital Secondary school enrolment and higher education achievement are included in human capital (Kapyla et al., 2012; Oliver and Porta, 2007) Government expenditure on education is also used as a proxy of the national intellectual capital component in previous studies (Seleim and Bontis, 2013; Lin and Edvinsson, 2011; Oliver and Porta, 2007) As such, in this dissertation, I use three indicators as the proxies for human capital – the first component of my index of national intellectual capital First, tertiary school enrollment (per cent of the number of students enrolled in tertiary education regardless of age and the total population of the age group, which officially corresponds to tertiary education) is used Second, secondary school enrollment (per cent of the number of students enrolled in secondary education regardless of age and the total population of the age group, which officially corresponds to secondary education) is recognized Third, total government expenditure on education (per cent of GDP) is included

Second, structural capital is measured as models, computer and administrative systems, brand names, patents, technologies, innovations created by research and development departments (Nazari and Herremans, 2007; Sveiby, 1997) Computers in use per capita are utilized as an indicator for structural capital in a national intellectual capital index in Lin and Edvinsson (2011) study Structural capital is also measured using internet users and computers per capita (Kapyla et al., 2012) Bontis

(2004) also included personal computers per capita, internet hosts per capita and internet users per capita as sub-indicators of the national intellectual capital index Mobile phone subscribers are also used as a proxy of a national intellectual capital component in previous studies (Seleim and Bontis, 2013; Bontis, 2004) I consider that the information and digital revolution have established the way the world learns and communicates New technologies create huge opportunities for progress in all walks of life in all countries which are motivated for national

89 performance, improved health, better service delivery, learning through distance education and social and cultural advances (World Bank, 2020)

MEASURING INTELLECTUAL CAPITAL: THE ANALYTICAL

An intellectual capital level for Vietnamese listed firms

To measure intellectual capital at firm level, this dissertation uses the modified value-added intellectual coefficient (MVAIC) model As mentioned in section 3.4.1., this method is quite commonly used in intellectual capital research in recent times (Buallay et al., 2020; Soetanto and Liem, 2019; Xu and Li, 2019) Using MVAIC, I measure the intellectual capital efficiency of Vietnam listed firms At the same time, this dissertation also examines the difference in intellectual capital efficiency between the financial sector and the non-financial sector in Vietnam

This study utilizes data collected from the annual reports of listed firms in Vietnam from 2011 to 2018 Firms used must be in continuous operation, without mergers and acquisitions during the research period In addition, firms that do not fully disclose information for at least 4 years will be excluded from the sample After removing the unsatisfactory data, the sample including 150 firms is used During this period, some data are missing, so my final unbalanced sample consists of 1,176 firm-year observations

Table 4.1 summarizes the descriptive statistics of the dependent variables (ROA and ROE) and independent variables (intellectual capital and its components) The average values of ROA and ROE are 0.0691 (6.91%) and 0.1442 (14.42%), respectively The average intellectual capital coefficient is 4.1847, indicating that, for every VND 1.00 of intellectual capital utilized, listed firms in Vietnam create VND 4.1847 HCE is the most significant component, with the largest mean value of 3.2927, compared to CEE, SCE, and RCE, with mean values of 0.1581, 0.4917, and 0.4474, respectively

Table 4.1 Descriptive statistics of the full sample

Variables Observations Mean Min Max Std Dev

Notes: ROA is the return on assets; ROE is the return on equity; IC represents intellectual capital;

MVAIC is the modified value-added intellectual coefficient model Components of IC: HCE denotes human capital efficiency; SCE represents structural capital efficiency; CEE is capital employed efficiency; and RCE denotes relational capital efficiency Control variables: SIZE is the natural logarithm of the total assets, and LEV is defined as the ratio between total debt and total assets of firms

In addition, Table 4.2 lists the descriptive statistics for financial and non-financial firms in Vietnam My statistical analyses indicate a difference in firm performance and intellectual capital between financial firms and non-financial firms in Vietnam at a 99% level of confidence On average, non-financial firms have higher ROA and ROE than financial firms There is also a significant difference between financial and non-financial firms in relation to the level of intellectual capital, as measured by the MVAIC model Additionally, differences are observed between financial and non-financial firms in the components of intellectual capital Financial firms generally have higher intellectual capital, HCE, SCE, and total assets (SIZE) and a higher ratio of total debt to total assets of firms (LEV) Non-financial firms achieved higher CEE and RCE These results suggest that financial firms have a higher intellectual capital efficiency than another sector These findings are in line with those in previous studies about other countries (Firer and Williams, 2003; Kubo and Saka, 2002) Employees in financial firms are well

96 selected and trained Besides, with the regulations of the Vietnamese government, financial firms, especially banks, are increasingly interested in investing more in the application of modern technology in their business activities That is the reason why the human capital and structural capital of financial firms are higher

Table 4.2 Descriptive statistics for financial firms and non-financial firms

Variables (Mean) Financial firms Non-financial firms Difference t-statistic

Notes: **, *** significant at 5 per cent and 1 per cent, respectively

ROA is the return on assets; ROE is the return on equity; IC represents intellectual capital; MVAIC is the modified value-added intellectual coefficient model Components of IC: HCE denotes human capital efficiency; SCE represents structural capital efficiency; CEE is capital employed efficiency; and RCE denotes relational capital efficiency Control variables: SIZE is the natural logarithm of the total assets, and LEV is defined as the ratio between total debt and total assets of firms

The differences in intellectual capital components between financial and non- financial companies stem from how each sector utilizes and prioritizes human, structural, and relational capital based on their operational needs (Soetanto and Liem,

2019) In financial companies, human capital focuses on specialized skills in financial analysis, risk management, and regulatory compliance, with employees requiring continuous training to stay updated on industry standards (Tran and Vo, 2018) Non- financial companies, on the other hand, emphasize diverse skills such as creativity and innovation, depending on the industry Structural capital in financial companies centers

97 on risk management, compliance, and secure IT systems, while in non-financial companies it focuses on optimizing production processes, innovation management, and operational efficiency Relational capital in financial companies relies heavily on trust and long-term relationships with clients and regulators, whereas in non-financial companies, it emphasizes brand loyalty, customer relationships, and partnerships (Soetanto and Liem, 2019) Both sectors value intellectual capital but prioritize its components differently based on their specific business and regulatory contexts (Goh,

An intellectual capital across sectors in Vietnam

Research data is further classified into 12 sectors, as described in section 3.2 Based on MVAIC model, this study proposes the sectoral intellectual capital index (SICI) by examining the intellectual capital efficiency of each firm in the sector In addition, author uses total assets as the weight to make up the intellectual capital of that sector, as presented in section 3.4.2

This study examines the fluctuations of the sectoral intellectual capital index (SICI) over the years in the period 2011-2018 The results in Figure 4.1 show that SICI has been relatively stable over the last 4 years Securities, Energy, Food and Real Estate have higher SICI than the rest Specifically, the securities sector had the highest SICI and had a strong growth since 2016 Meanwhile, Energy had a strong increase in SICI in the period 2011-2014 but declined in the following years Especially, Technology has the lowest SICI among 12 sectors in Vietnam The results indicate that there is a difference in the efficiency of using intellectual capital of sectors in Vietnam High intellectual capital-intensive sectors such as Banking, Technology (Firer and Williams,

2003) have not yet exploited intellectual capital commensurately Securities firms in Vietnam have a more equal degree of human capital than other sectors (Nguyen et al.,

2021) Employees in this sector are more well-trained and more evenly qualified The application of information technology to the operations of Securities firms also contributes to promoting the effective exploitation of intangible resources, namely intellectual capital Meanwhile, Technology firms need to pay more attention to investing in intellectual capital In addition, the exploitation and efficiency improvement of intellectual capital should also be considered

Figure 4.1 Sectoral intellectual capital index in 2011-2018 period

Measuring national intellectual capital: a tale of two indices

This section examines the implementation of a recently developed index of national intellectual capital (INIC) The estimates obtained from this application are then contrasted with those presented in Lin et al.'s (2014) study, which is currently the most relevant and sophisticated index available The period from 2005 to 2010 is chosen for comparison purposes, despite it being outdated The objective of comparing the INIC index with Lin et al.'s (2014) national intellectual capital findings is to demonstrate the similarity between the two measurement approaches

A national intellectual capital index in Lin and Edvinsson (2011), and then updated in Lin et al (2014), enables the researchers and policymakers to measure a degree of national intellectual capital Additionally, previous studies propose to use Lin and Edvinsson’s (2011) estimates of national intellectual capital to examine its contribution to national performance (Stahle and Stahle, 2012) However, I note that Lin and Edvinsson (2011) index of national intellectual capital is only updated in Lin et al.,

(2014) This limited update and availability indicate that the index is impractical to be frequently updated Besides, their approach appears to be impractical to be used for other countries outside their initial sample In their 2014 paper, Lin et al., (2014) construct a national intellectual capital index for 48 countries 3 These countries are generally advanced economies, using data from 2005 to 2010

In order to compare my INIC with indices in Lin et al (2014) study, I collect data from World Development Indicators reported by the World Bank (2020b) for 48 countries, which are used in Lin et al (2014), for the same period from 2005-2010 4 Data for Taiwan has not been reported by the World Bank (2020b) As such, my final sample includes 47 countries

Figure 4.2 presents the national intellectual capital index for 47 countries in Lin et al (2014)’s study on the left-hand-side axis and our INIC on the right-hand-side axis It

3 The sample includes Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia,

Czech Republic, Denmark, Finland, France, Germany, Greece, Hong Kong, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Korea, Malaysia, Mexico, Netherlands, New Zealand, Norway, The Philippines, Poland, Portugal, Romania, Russia, Singapore, South Africa, Spain, Sweden, Switzerland, Taiwan, Thailand, Turkey, United Kingdom, USA, Venezuela

4 It is noted that the 2005-2010 period is used for comparison purpose with findings from Lin et al (2014) Our data is generally available for the 1990-2018 period as at October 2020 and is updated yearly by the World Bank

100 is noted that the magnitude of my INIC is much lower than that of the index measured in Lin et al (2014) study For example, an index of national intellectual capital in Lin et al (2014) study vary from 20.092 to 39.575, whereas my INIC varies within a range of 0.14 and 0.73 However, it is noted that the magnitude of the index is not relevant because the two approaches are entirely different The most important message from the two indices is that a country with a higher estimated index does higher level of national intellectual capital For example, in my estimates for the period from 2005 to 2010, the INIC for China is 0.24, whereas the INIC for Russia is 0.47 On the basis of these estimates, it can only be inferred that Russia has achieved a higher level of national intellectual capital in comparison with China for the 2005-2010 period

It is important to note that the correlation between these two indices, my new INIC and an index in Lin et al (2014) study, is 0.80 Moore et al (2013) concluded that the relationship between two quantitative variables is generally considered strong when their correlation value is larger than 0.7 As a result, the correlation of 0.80 strongly indicates that our new INIC has a strong relationship with an index estimated in Lin et al (2014) study A closer investigation of relative comparison of a level of national intellectual capital indicates that a ranking across countries in the sample appears to be consistent between my INIC in comparison with an index reported in Lin et al (2014) study, as presented in Figure 4.2 below

Figure 4.2 An index of national intellectual capital: Lin, Edvinsson, Chen and Beding

Ar ge nti na Aus tr ali a Aus tr ia B elgi u m B ra zil B ulgar ia C ana da C hil e C hina C olom bia C ze ch R epub li c De nm ar k F inland F ra nc e Ge rm any Gr ee ce Hong Ko ng Hung ar y Ic eland In dia In done sia Ir eland Is ra el It aly Ja pa n Jor da n Kor ea M alays ia M exico Ne ther lands Ne w Z ea land Nor wa y P h il ipp ines P o land P o rtug al R oman ia R us sia S inga por e S o uth Af rica S p ain S w ed en S w it ze rland T ha il and T ur ke y Uni ted Ki ngdom US A Ve ne zue la

Lin, Edvinsson, Chen and Beding (2014)’s Index (LHS) INIC (RHS)

As mentioned earlier, the national intellectual capital measurement method proposed by Lin, Edvinsson, Chen, and Beding (2014) is regarded as one of the most scientifically and practically significant measurement methods This method has been subsequently adopted by several later studies, such as those by Stồhle and colleagues

(2015) and Lin (2018) The author compares INIC to the national intellectual capital index developed by Lin, Edvinsson, Chen, and Beding (2014) with the purpose of demonstrating the congruity of measurement outcomes between the two methods The author seeks to establish that the measurement of national intellectual capital using the INIC approach yields values equivalent to those obtained through the method devised by Lin, Edvinsson, Chen, and Beding (2014).

A national intellectual capital across nations

As described in section 3.3.3, I use data from available sources in combination with the PCA method to build the new index of national intellectual capital (INIC) I utilize data from the World Development Indicators database from the World Bank (2020b) The previous study also used these data sources when determining national indicators as valuable data to be applied (Dutta and Sobel, 2018; Lin and Edvinsson, 2011; Vo,

2008) World Development Indicators database is available for more than 200 countries and territories from 2000 to the present Hence, I note that the World Development Indicators database is an optimal data source that can be used to construct an index of national intellectual capital across countries at different periods In this dissertation, data from 104 countries around the world from 2000 to 2018 are utilized

4.4.1 National intellectual capital by region

Data is divided into eight regions, and descriptive statistics results are presented in Table 4.3 Africa consists of 21 countries; the Asia-Pacific region contains 23 countries The European region includes 38 countries, divided into Eastern Europe with 13 countries and the rest of Europe with 25 countries The Middle East, North America and South America regions have six countries, eight countries and eight countries, respectively The European region has the highest index of national intellectual capital (INIC) and national intellectual capital components, except relational capital Meanwhile, Africa is the lowest region in human capital, structural capital and INIC In contrast, South America has the lowest relational capital

Table 4.3 National intellectual capital by region

As denoted in Figure 4.3, all seven regions have increased accumulating national intellectual capital from 2000 to 2018 Europe and Eastern Europe are the two regions with the highest index of national intellectual capital (INIC) The Asia & Pacific region, meanwhile, has an INIC equal to that of North America In addition, Africa has the lowest INIC of the seven regions At the same time, the most impressive INIC growth rate is in the South America region, with an increase from 0.3 in 2000 to nearly 0.7 in

2018 Meanwhile, the Middle East region has INIC growing strongly in 2000-2015, and stable until now

Figure 4.3 National intellectual capital across years by region

4.4.2 National intellectual capital by income

The countries are divided into four groups based on World Bank classification The high-income group consists of 42 countries The middle-income countries group contains 50 countries divided into upper-middle-income groups with 28 countries and lower-middle-income groups with 22 countries The low-income group includes 12 countries The results of national intellectual capital accumulation by income are presented in Table 4.4 The average value of national intellectual capital and its components is quite similar to income High-income countries achieve the highest national intellectual capital accumulation, upper-middle-income countries rank second, lower-middle-income countries and low-income countries perform the lowest national intellectual capital

Table 4.4 National intellectual capital by income

I continue to examine the change in the national intellectual capital of four groups of countries from 2000 to 2018 As shown in Figure 4.4, high-income countries accumulated intellectual capital from nearly 0.5 in 2000 increased to 0.8 in 2018 Meanwhile, low-income countries' national intellectual capital accumulation has fluctuated very little, just around the 0.1 mark for more than 20 years At the same time, the other two groups of countries have a significant rise in national intellectual capital accumulation during the period In particular, upper-middle-income countries have the highest growth rate in national intellectual capital accumulation, from 0.3 to more than 0.6

Figure 4.4 National intellectual capital across years by income

For a more detailed evaluation, I pick up the two countries, which are the highest and lowest national intellectual capital accumulation in each group of countries The results are shown in Figure 4.5 Finland achieves the highest national intellectual capital accumulation, while Uganda is the lowest among 104 countries during 2000-2018 Belarus is the representative for the upper-middle-income countries, and Ukraine is the agent for the lower-middle-income countries The two countries have similar levels of national intellectual capital accumulation and growth rates over the period, but Belarus is still higher In addition, both countries have higher national intellectual capital than an emissary of the high-income group, Oman

Figure 4.5 Accumulation of national intellectual capital across years in some countries

I also consider the level of national intellectual capital accumulation of Group of Seven, including Canada, France, Germany, Italy, Japan, the United Kingdom and the United States As shown in Figure 4.6, in the 2000-2018 period, Italy achieves the lowest national intellectual capital, while the United States gets the highest level in this group Besides, the results also reveal that the highest growth rate of accumulating national intellectual capital occurs in Germany

Figure 4.6 Accumulation of national intellectual capital across years in Group of Seven

In terms of the top 10 largest countries by GDP in 2018, the results are presented in Figure 4.7 Besides Group of Seven as mentioned above, Top 10 biggest countries also contain Brazil, China and India The United States is still the highest national intellectual capital accumulation, while India is the lowest At the same time, Brazil and Japan have enjoyed the same growth rate over the whole period

Figure 4.7 Accumulation of national intellectual capital across years in Top 10

In addition, Figure 4.8 presents the accumulation of national intellectual capital across years Overall, Pakistan achieved the lowest national intellectual capital, while Korea got the highest level in 2000-2018 In addition, Australia has become the largest national intellectual capital in the last four years.

Figure 4.8 The accumulation of national intellectual capital for the Asia-Pacific countries for almost two decades, from 2000 to 2018

The average level of national intellectual capital over the period 2000-2018 is shown in Figure 4.9 The light colour indicates the countries with low national intellectual capital accumulation, while the bold colour denotes the countries with high national intellectual capital accumulation Overall, as shown in Figure 4.9, developed countries such as Australia, the United States, and the United Kingdom accumulate a higher index of national intellectual capital than developing countries in Asia and Africa

Figure 4.9 National intellectual capital around the globe

EMPIRICAL RESULTS ON THE EFFECTS OF INTELLECTUAL

Intellectual capital and firm performance

At the organizational level, the knowledge-based theory offers a framework for understanding the role of intellectual capital, especially human capital, in knowledge creation and application (Hoskisson et al., 1999) Companies that invest in their human capital through training, education, and knowledge-sharing are more capable of innovating and adapting to market changes For instance, technology firms promoting continuous learning and knowledge exchange are often at the forefront of pioneering innovations that yield competitive advantages and enhanced performance (Marr, 2004) Additionally, the knowledge-based theory underscores the significance of integrating knowledge from diverse sources, both internal and external, to innovate products, processes, or services Structural capital, including sophisticated IT systems and knowledge management practices, often facilitates this integration by ensuring efficient knowledge flow within the firm The resource-based theory posits that an organization must possess ample resources to achieve its objectives and enhance long-term performance (Wernerfelt, 1984) Building on this, the knowledge-based theory (Grant,

1996) aptly describes the exploration of intellectual capital, particularly its connection to organizational performance Concurrently, performance-based theory delineates organizational performance The amalgamation of resource-based, knowledge-based, and performance-based theories presents a holistic and nuanced method for examining the impact of intellectual capital on firm performance

To increase the validity of the research results, this dissertation also examines the effects of intellectual capital, and each of its four components, on firm performance at both financial and non-financial firms I use the four models outlined in Table 5.1 Table 5.1 Regression models

1 ROA it = β 0 + β 1 MVAIC it + β 2 INIC it + β 3 SIZE it + β 4 LEV it + Ɛ it

2 ROE it = β 0 + β 1 MVAIC it + β 2 INIC it + β 3 SIZE it + β 4 LEV it + Ɛ it

3 ROA it = β 0 + β 1 INIC it + β 2 HCE it + β 3 SCE it + β 4 CEE it + β 5 RCE it + β 6 SIZE it + β 7 LEV it + Ɛ it

4 ROE it = β 0 + β 1 INIC it + β 2 HCE it + β 3 SCE it + β 4 CEE it + β 5 RCE it + β 6 SIZE it + β 7 LEV it + Ɛ it

Notes: ROA is the return on assets; ROE is the return on equity; MVAIC is the modified value- added intellectual coefficient model Components of IC: HCE denotes human capital efficiency; SCE represents structural capital efficiency; CEE is capital employed efficiency; and RCE denotes relational capital efficiency Control variables: INIC: national intellectual capital; SIZE is the natural logarithm of the total assets, and LEV is defined as the ratio between total debt and total assets of firms

Table 5.2 presents a Pearson’s correlation coefficient matrix Intellectual capital has a significant and positive correlation with ROA and ROE, whereas CEE has the strongest correlation with financial performance at both financial and non-financial firms At financial firms, all independent variables are correlated with ROA and ROE (except SIZE with ROE) At non-financial firms, CEE, HCE, and SCE are significantly and positively correlated with ROA and ROE In addition, as shown in Table 5.2, my analyses indicate that no correlation exists in firm performance, whether proxied by ROA or ROE Previous studies (Buallay et al., 2020; Haris et al., 2019; Smriti and Das, 2018; Tran and Vo, 2018) find that the multicollinearity problem between independent variables is weak or nonexistent when the VIF is lower than 10 As shown in Table 5.2, my results indicate that the multicollinearity problem does not occur in this study because the VIF is below 2.

Table 5.2 The pairwise correlation coefficients and the variance inflation factor (VIF) among variables

ROA ROE MVAIC CEE HCE SCE RCE SIZE LEV VIF

Notes: *, **, and *** significant at 10 per cent, 5 per cent, and 1 per cent, respectively

ROA is the return on assets; ROE is the return on equity; IC represents intellectual capital; MVAIC is the modified value-added intellectual coefficient model Components of IC: HCE denotes human capital efficiency; SCE represents structural capital efficiency; CEE is capital employed efficiency; and RCE denotes relational capital efficiency Control variables: SIZE is the natural logarithm of the total assets, and LEV is defined as the ratio between total debt and total assets of firms

I use a modified Wald test to examine the group-wise heteroskedasticity in the four models in this study The results in Table A1.1 (Annexure 1) suggest that heteroskedasticity does exist For an autocorrelation analysis, I perform the Wooldridge test The p-values from this test indicate that all four models exhibit autocorrelation

5.1.3 The effects of intellectual capital on firm’s performance using panel data estimation: generalized method of moments (GMM)

Previous studies have used different techniques to examine the impact of intellectual capital on firm performance, such as OLS, fixed effects (FE), and random effects (RE) These regression techniques have fundamental limitations (Ullah et al.,

2018) One of the most popular estimation methods in applied econometrics involves instrument variables (IVs), including two-stage least squares (2SLS), three-stage least squares (3SLS), and GMM In general, when the error is conditional heteroskedasticity, GMM estimation is more efficient than 2SLS or 3SLS (Hansen, 2020; Lee et al., 2016)

In addition, Ullah et al (2018), Roodman (2009), and Chan and Hameed (2006) argue that GMM can be used to deal with three sources of endogeneity, namely, unobserved, simultaneous, and dynamic endogeneity Besides, previous studies also show that GMM can address heteroskedasticity and autocorrelation issues (Haris et al., 2019; Sardo and Serrasqueiro, 2017) GMM uses the Arellano-Bond (1991) test to examine the first- order and second-order correlation through AR (1) and AR (2) tests In addition, Hansen statistics examine the validity of my IVs In this dissertation, the author uses the number of lags of instruments is 1 for all 4 research models

My GMM results are listed in Table 5.3 Results from the AR (2) test indicate that the second-order autocorrelation is not present at both financial and non-financial firms, as I fail to reject the null hypothesis of “no autocorrelation.” In addition, based on the results from the Hansen test, I conclude that the IVs are not endogenous in the four models for both financial and non-financial firms, which confirms the validity of the GMM estimation technique used in this study

My empirical results show that financial performance in the current year is positively and significantly affected by previous year performance in non-financial firms Overall, national intellectual capital also boosts the performance of financial and

116 non-financial firms In addition, intellectual capital significantly impacts firm performance ROA and ROE, supported H1 There are similar results to previous studies (Haris et al., 2019; Sardo and Serrasqueiro, 2017)

I now shift my attention to the effect of various components of intellectual capital on firm performance at both financial and non-financial firms In financial firms, my empirical findings from models 3 and 4 suggest that three intellectual capital components, including HCE, CEE, and SCE boost firm performance The result is similar to those reported by Ali et al (2022) In addition, RCE has a negative impact on ROA and no relationship with ROE This result is consistent with the observations of previous studies (Ali et al., 2022)

The results of this study confirm the importance of HCE for firms in Vietnam in achieving their main goals in terms of ROA and ROE As mentioned in the previous sections, financial firms are knowledge-intensive, so my results imply that financial firms in Vietnam need to focus more on staff training, skills and education Human capital is an important factor for a business because it has the potential to deliver significant returns This is because it is a source of innovation, creativity and knowledge Investing in employees and their skills can help to create a competitive edge in the market and increase profitability A well-trained and skilled workforce can help a business to improve its operations and achieve greater efficiency in production In addition, the results of this dissertation also reveal that SCE has a significant contribution to firm performance The ability of structural capital to impact firm performance is evident in the way businesses are organized and structured Firms that have strong organizational structures, such as well-defined roles, clear communication channels, and effective decision-making processes, can operate more efficiently and effectively This can result in improved employee morale, higher customer satisfaction, and better overall performance Hence, it is imperative for firms in Vietnam to increase their structural capital to enhance their performance Moreover, CEE also affects firm performance Therefore, physical and financial assets also need to be effectively exploited so that the firm performance can be improved

Table 5.3 Empirical results using GMM estimations

Variables Model 1 Model 2 Model 3 Model 4

Notes: *, **, and *** significant at 10 per cent, 5 per cent, and 1 per cent, respectively

ROA is the return on assets; ROE is the return on equity; MVAIC is the modified value- added intellectual coefficient model Components of IC: HCE denotes human capital efficiency; SCE represents structural capital efficiency; CEE is capital employed efficiency; and RCE denotes relational capital efficiency Control variables: INIC: national intellectual capital; SIZE is the natural logarithm of the total assets, and LEV is defined as the ratio between total debt and total assets of firms

Intellectual capital and financial performance across sectors

At the industry level, the resource-based theory can be applied to examine how various sectors utilize intellectual capital to foster growth and development Industries endowed with intellectual capital, such as the technology and pharmaceutical sectors, typically demonstrate higher growth rates and innovation outputs than more traditional industries (Nitkiewicz et al., 2014) The accumulation and strategic management of intellectual capital within an industry can establish entry barriers, thus providing a sustained competitive edge to companies within that industry For instance, the pharmaceutical sector's dependence on patents (a type of structural capital) and specialized expertise (human capital) results in a competitive environment where only entities with significant intellectual capital can succeed (Marcin, 2013) Furthermore, the knowledge-based theory highlights the significance of knowledge networks and partnerships, wherein industry firms participate in collective R&D, knowledge exchange, and value co-creation Such networks amplify the sector's overall intellectual capital, culminating in enhanced performance and competitiveness (Stewart, 1997) Integrating these theories allows for a comprehensive analysis of intellectual capital, encompassing both the mechanisms of its development and utilization and the resultant performance outcomes To measure sector performance, I utilize return on assets (ROA) and return on equity (ROE) to compute sector financial performance, in line with previous studies (Dalwai and Salehi, 2021; Smriti and Das, 2018)

In addtion, this study also utilizes SIZE and LEV as control variable SIZE is computed as the natural logarithm of total assets LEV is calculated as the ratio between total debt and total assets The regression models are calculated as present in Table 5.4

1 ROA it = β 0 + β 1 SICI it +β 2 SIZE it + β 3 LEV it + Ɛ it

2 ROE it = β 0 + β 1 SICI it +β 2 SIZE it + β 3 LEV it + Ɛ it

Table 5.5 presents the descriptive statistics of all variables The average ROA and ROE of all sector in Vietnam in 2011-2018 are 0.103 and 0.161, respectively Food and Pharmaceuticals have higher returns on total assets and equity, while Banking and Service have lower performance The average SICI is 4.341, in which Energy, Securities, Food and Real estate are higher than average In addition, the results state that banking has the lowest return on assets, while this sector uses the highest total assets of all sectors

Sector ROA ROE SICI SIZE LEV

Notes: ROA denotes the return on assets; ROE denotes the return on equity; SICI denotes sectoral intellectual capital index; SIZE denotes the natural logarithm of the total assets of the sector; LEV is calculated as the ratio between total debt and total assets

Table 5.6 shows Pearson’s correlation coefficient matrix The results indicate that correlation coefficient between ROE and SICI is statistically significant at 5 per cent Besides, I test multicollinearity through variance inflation factor (VIF) The results show

120 that all variables are below 2, which imply that multicollinearity is not a problem in this study

Table 5.6 Correlation matrix and the variance inflation factor among variables

Variables ROA ROE SICI SIZE LEV VIF

Notes: **, *** significant at 5 per cent and 1 per cent level, respectively

ROA denotes the return on assets; ROE denotes the return on equity; SICI denotes sectoral intellectual capital index; SIZE denotes the natural logarithm of the total assets of the sector; LEV is calculated as the ratio between total debt and total assets

5.2.2 The cross-sectional dependence test

Next, this study examines the cross-sectional dependence by employing Pesaran

(2004) and Pesaran (2015) tests The results in Table A1.2 (Annexure 1) indicate that the null hypothesis of cross-section independence cannot be rejected, except SIZE In other words, the level generation’s panel unit root tests should present more reliable inference In addition, these findings reveal that not disturbance in one sector will not significantly affect the other sectors in Vietnam

Besides, I also explore the slope homogeneity by using Pesaran and Yamagata

(2008) technique As presented in Table A1.3 (Annexure 1), I can reject the null hypothesis of slope homogeneity This mean that I should consider to deal with slope homogeneity issues

5.2.4 The panel unit root test

In the next step, the study also utilizes unit root tests as proposed by Pesaran

(2003) This test explores the stationarity and to detect the integration order of concerned variables The results in Table A1.4 (Annexure 1) suggest that all variables are stationary

121 at the first-difference generation The results imply that long-run co-integrating relationship among the variables is possible utilized in this study

In addtion, this study explore the nature of the long-run relationship among the variables by using the Kao (1999); Pedroni (1999; 2004); and Westerlund (2005) cointegration test The results in Table A1.5 (Annexure 1) support a view that long-run relationship between sectoral intellectual capital index and sector performance should be considered in the study

5.2.6 The effects of intellectual capital on financial performance across sectors using Dynamic common correlated effects technique

Table 5.7 presents dynamic common correlated effects results The results in both models show that sectoral intellectual capital index has a positive impact on sector performance In other words, research hypothesis H2 is supported In particular, an increase in the sectoral intellectual capital will increase the level of return on assets and return on equity in these sectors In addition, total assets has a negative impact on sector performance Meanwhile, I consider that the ratio between total debt and total assets is a strong and significant driver of the performance of sectors in Vietnam

Table 5.7 Empirical results - The effects of intellectual capital on financial performance across sectors

Notes: *, *** significant at 10 per cent and 1 per cent level, respectively

ROA denotes the return on assets; ROE denotes the return on equity; SICI denotes sectoral intellectual capital index; SIZE denotes the natural logarithm of the total assets; LEV denotes the ratio between total debt and total assets of firms

5.2.7 The causality relationship flows between sectoral intellectual capital, sector performance and other variables

Finally, the study explores the causality between sectoral intellectual capital index and sector performance by using a panel Granger causality method (Engle and Granger,

1987) As presented in Table 5.8, the results confirm bidirectional causality relationship between SICI and ROE In addition, the causality relationship between SICI and ROA is not statistically significant The results of these causal relationships between sectoral intellectual capital index and sector performance are summarized in Figure 5.1

Table 5.8 The causality relationship flows between sectoral intellectual capital, sector performance and other variables

ROA → SICI 0.156 There is no causal relationship between sectoral intellectual capital index and return on assets

ROA → SIZE 2.444* Unidirectional causality from return on assets to total assets

ROA → LEV 2.488* Unidirectional causality from return on assets to financial leverage

ROE → SICI 2.593* Bidirectional causality between sectoral intellectual capital index and return on equity

There is no causal relationship between total assets and return on equity

There is no causal relationship between financial leverage and return on equity

There is no causal relationship between sectoral intellectual capital index and total assets

There is no causal relationship between sectoral intellectual capital index and financial leverage

There is no causal relationship between total assets and financial leverage

Notes: * significant at 10 per cent level

A → B indicates unidirectional Granger causality running from A to B

ROA denotes the return on assets; ROE denotes the return on equity; SICI denotes sectoral intellectual capital index; SIZE denotes the natural logarithm of the total assets;

LEV denotes the ratio between total debt and total assets of firms

Figure 5.1 The causality relationship flows between sectoral intellectual capital, sector performance and other variables

Intellectual capital and national performance

At the national level, the resource-based theory provides insights into how countries can boost their economic performance by promoting the creation and diffusion of knowledge Nations that give priority to education, research and development, and innovation policies are likely to build a strong knowledge base that propels economic growth (Bollen et al., 2005) The knowledge-based theory also underscores the significance of national innovation systems, where collaborations between government policies, institutions, and the private sector foster an environment that encourages knowledge creation and innovation (Bontis, 2001) By nurturing a robust national innovation system, countries can augment their intellectual capital, which leads to sustained economic growth and development Integrating these theories helps identify crucial factors that affect the relationship between intellectual capital and performance Considering these moderating factors enables the author to formulate more detailed and context-specific intellectual capital theories, enhancing our understanding of its role in driving performance across various contexts

ROA denotes the return on assets; ROE denotes the return on equity; SICI denotes sectoral intellectual capital index; SIZE denotes the natural logarithm of the total assets; LEV denotes the ratio between total debt and total assets of firms.

Data from 2000 to 2018 for 23 economies in the Asia-Pacific region are used These countries include Australia, Bangladesh, Brunei Darussalam, Cambodia, China, Hong Kong, India, Indonesia, Japan, Kazakhstan, Korea, Laos, Macao, Malaysia, Mongolia, Nepal, New Zealand, Pakistan, the Philippines, Singapore, Sri Lanka, Thailand and Vietnam Data are collected from the World Development Indicators (World Bank, 2020b) The variables used in this study are selected based on previous studies and theoretical considerations The effects of national intellectual capital on national performance are confirmed in various studies (Lin and Edvinsson, 2011; Andriessen and Stam, 2005; Bontis, 2004) The contributions of trade openness to national performance are also considered and accepted in Ali et al (2021) Previous studies (Zungu and Greyling., 2022; Ali et al., 2021) have also examined the role of government expenditure in national performance A broad money supply is also incorporated to capture its effect on national performance (Sehrawat and Giri, 2017) The effects of domestic credit on banks' private sector on national performance are also discussed and confirmed in Sethi et al (2019) On the theoretical aspects, I consider that selected variables in this study affect the national intellectual capital – national performance nexus In particular, economic theories indicate that fiscal policy (proxied by government expenditure) and monetary policy (proxied by money supply of interest rate) are implemented to support national performance Trade openness involves international trade activities that support a country's economic activities Table 5.9 summarizes measurements of the variables utilized in this dissertation, together with relevant data sources

Table 5.9 Measurements of variables and data sources

Variables Abbreviation Measurement Data sources

National performance PGDP GDP per capita (constant 2010, 1000 US$) WDI

National intellectual capital INIC Index of national intellectual capital WDI

Trade openness TO Sum of exports and imports normalized by

GDP (per cent of GDP) WDI

Government expenditure GE General government final consumption expenditure (per cent of GDP)

Broad money supply BM Broad money (per cent of GDP) WDI

Domestic credit to the private sector by banks

CE Domestic credit to the private sector by banks (per cent of GDP) WDI

This dissertation utilizes the following models to examine the relationship between national intellectual capital and national performance

𝑃𝐺𝐷𝑃 𝑖𝑡 = 𝛼 𝑖𝑡 + 𝛽 1 𝐼𝑁𝐼𝐶 𝑖𝑡 + 𝛽 2 𝑇𝑂 𝑖𝑡 + 𝛽 3 𝐺𝐸 𝑖𝑡 + 𝛽 4 𝐵𝑀 𝑖𝑡 + 𝛽 5 𝐶𝐸 𝑖𝑡 + 𝜀 𝑖𝑡 (1) 𝑃𝐺𝐷𝑃 𝑖𝑡 = 𝛼 𝑖𝑡 + 𝛽 1 𝑃𝐺𝐷𝑃 𝑖𝑡−1 + 𝛽 2 𝐼𝑁𝐼𝐶 𝑖𝑡 + 𝛽 3 𝑇𝑂 𝑖𝑡 + 𝛽 4 𝐺𝐸 𝑖𝑡 + 𝛽 5 𝐵𝑀 𝑖𝑡 + 𝛽 6 𝐶𝐸 𝑖𝑡 + 𝜀 𝑖𝑡 (2) where: i = 1, 2, …, N refers to each of the economies in the Asia-Pacific region t = 1, 2, …, T represents the time period The parameters β1, β2, β3, β4, and β5 refer to the long-run elasticity estimates of GDP per capita (PGDP) concerning national intellectual capital (INIC), trade openness (TO), government expenditure (GE), broad money supply (BM) and domestic credit to the private sector by banks (CE), respectively and εt denotes the white noise error term

Various common issues of panel data analysis using macroeconomic variables have been recognized in previous studies (Turkay, 2017; Arain et al., 2019) The key issues include cross-sectional dependence, slope homogeneity, stationarity, and panel cointegration This study uses various tests to ensure my selected method is appropriate and robust to be used

5.3.1 The cross-sectional dependence test

I utilize Pesaran (2004) and Pesaran (2015) tests for examining the cross-sectional dependence The results are shown in Table A1.6 (Annexure 1) My results reveal that the null hypothesis of cross-section independence can be rejected, indicating that panel unit root tests of the first-difference generation should provide more reliable inference These findings mean that disturbance in one country will affect the other economies in the region

This dissertation also examines the slope homogeneity test developed by Pesaran and Yamagata (2008) The empirical results in Table A1.7 (Annexure 1) suggest the null hypothesis of slope homogeneity is rejected, implying the presence of slope heterogeneity In other words, this study should consider dealing with cross-section dependence and slope homogeneity issues

5.3.3 The panel unit root test

I examine unit root tests as suggested by Pesaran (2003) to check the stationarity and find the concerned variables' integration order This test manages cross-section dependence As presented in Table A1.8 (Annexure 1), all variables are stationary at the first difference My results reveal that it is possible to have a long-run co-integrating relationship among the variables used in my analysis

In the next step, I examine the nature of the long-run relationship among the variables of interest by employing Pedroni (1999; 2004), Kao (1999), and Westerlund's

(2005) cointegration techniques As shown in Table A1.9 (Annexure 1), the results support a view of rejecting the null of no cointegration at the 5 per cent significance level It can be inferred that there is a long-run relationship between national intellectual capital and national performance in the Asia-Pacific region

5.3.5 The effects of national intellectual capital on national performance using the dynamic common correlated effects

Table 5.10 shows the results of the dynamic common correlated effects (DCCE) The DCCE can solve cross-sectional dependence and slope heterogeneity problems in panel data analysis (Ditzen, 2018) These tests are used to ensure the validity of the

127 regression results in this study The results in both models indicate that national intellectual capital drives national performance (PGDP) In other words, research hypothesis H3 is supported In particular, an increase in the national intellectual capital will increase national performance in these countries In addition, I consider that trade openness does provide a strong and significant impact on national performance in the Asia Pacific region Meanwhile, government expenditure harms national performance in these countries

Table 5.10 Empirical results - The effects of national intellectual capital on national performance

Notes: *, *** significant at 10 per cent and 1 per cent level, respectively

PGDP: GDP per capita; INIC: index of national intellectual capital; TO: trade openness; GE: government expenditure; BM: broad money supply; CE: Domestic credit to the private sector by banks

5.3.6 The causality relationship flows between national intellectual capital, national performance and other macroeconomic variables

My empirical analysis indicates that national intellectual capital contributes positively and significantly to national performance in the Asia-Pacific countries I now consider the causality effect from national intellectual capital to national performance and vice versa The above results show that a co-integration relationship between the variables does exist I consider that it is important to identify the channels through which variables can affect each other (Sethi et al., 2019) I use the panel causality approach to

128 address this issue Besides, a panel causality method by Dumitrescu and Hurlin (2012) is used to determine the directions of the causality I note that the Granger causality analysis has been performed extensively in previous economics-related studies (Qin et al., 2023) The objective of employing the Granger causality analysis in this study is to examine the directional relationship between national intellectual capital and national performance As presented in Table 5.11, national intellectual capital (INIC) does Granger-cause national performance (PGDP) with the Z-bar tilde value of 9.605, which is statistically significant at a 1 per cent level On the other hand, national performance (PGDP) does also Granger-cause national intellectual capital (INIC) with the Z-bar tilde value of 3.148, which is also statistically significant at a 1 per cent level

The findings imply that a bi-directional causality relationship does exist between national intellectual capital and national performance An improved intellectual capital has Granger caused national performance In the opposite direction, increased national performance of the Asia-Pacific countries has also Granger caused an improvement in national intellectual capital over the years For convenience, Figure 5.2 presents empirical results with detailed conclusions

Table 5.11 The causality relationship flows between national intellectual capital, national performance and other macroeconomic variables

Hypothesis Z-bar Z-bar tilde Conclusion

PGDP → INIC 12.876*** 3.148*** Bidirectional causality between national intellectual capital and national performance INIC → PGDP 13.025*** 9.605***

PGDP → TO 4.946*** 3.399*** Bidirectional causality between national performance and trade openness

PGDP → GE 8.667*** 6.257*** Bidirectional causality between national performance and government expenditure

PGDP → BM 16.775*** 4.461*** Bidirectional causality between national performance and broad money supply

PGDP → CE 10.310*** 2.306** Bidirectional causality between national performance and domestic credit to the private sector by banks

Bidirectional causality between national intellectual capital and trade openness

Bidirectional causality between national intellectual capital and government expenditure

Bidirectional causality between national intellectual capital and broad money supply

Bidirectional causality between national intellectual capital and domestic credit to the private sector by banks

Bidirectional causality between trade openness and government expenditure

Bidirectional causality between trade openness and broad money supply

Bidirectional causality between trade openness and domestic credit to the private sector by banks

Bidirectional causality between government expenditure and broad money supply

Bidirectional causality between government expenditure and domestic credit to the private sector by banks

Bidirectional causality between domestic credit to the private sector by banks and broad money supply

Notes: *, **, *** significant at 10 per cent, 5 per cent and 1 per cent level, respectively

A → B denotes unidirectional Granger causality running from A to B

PGDP: GDP per capita; INIC: index of national intellectual capital; TO: trade openness; GE: government expenditure; BM: broad money supply; CE: Domestic credit to the private sector by banks

Figure 5.2 The causality relationship flows between national intellectual capital, national performance and other macroeconomic variables

My empirical results indicate that enhancing national intellectual capital drives national performance I note that countries in the Asia-Pacific region have invested significantly to support human capital development Improving national human capital contributes to the creation and direct distribution of knowledge structures in the form of national structural capital As such, forming systems capable of distilling knowledge concerning science and technology is generally an important factor of national performance in the knowledge-based economy (Seleim and Bontis, 2013) This result also supports findings from Lin (2018), which shows that national intellectual capital development is positively associated with national performance On balance, my empirical results from this study using the Asia-Pacific countries provide similar conclusions concerning the important role of national intellectual capital in national performance with previous studies which mainly focus on developed countries (Andriessen and Stam, 2005; Lin and Edvinsson, 2011)

Notes: PGDP: GDP per capita; INIC: index of national intellectual capital; TO: trade openness;

GE: government expenditure; BM: broad money supply; CE: Domestic credit to the private sector by banks.

CONCLUSIONS AND IMPLICATIONS

Research findings

The importance of intellectual capital in firm performance has been widely recognized in previous studies on many countries (Buallay et al., 2020; Soetanto and Liem, 2019; Xu and Li, 2019; Tran and Vo, 2018) However, this issue has been largely ignored in the context of an emerging market, such as Vietnam This study aims to determine the difference in intellectual capital efficiency between financial and non- financial firms in Vietnam The sample used in this study consists of 75 financial and

75 non-financial firms in Vietnam from 2011 to 2018, where the required data are available The MVAIC is used to measure the intellectual capital at Vietnamese firms

My results indicate that intellectual capital is higher in financial firms than in non- financial firms This result is consistent with the study of Tran and Vo (2018); Firer and Williams (2003); Kubo and Saka (2002) Financial firms in Vietnam are increasingly interested in investing and exploiting intangible assets Intellectual capital contributes to increasing the competitive advantage and operational efficiency of financial firms For the intellectual capital components, capital employed efficiency and relational capital efficiency are higher in non-financial firms than in financial firms Meanwhile, financial firms have higher human capital efficiency and structural capital efficiency

The important role of intellectual capital as the long-term competitive advantage has been confirmed in previous studies (Tiwari, 2022; Tian and Liu, 2019) Measuring intellectual capital at various levels including at firms, regions, and nations have also been conducted (Liu et al., 2021; Bontis, 2004) However, previous studies appear to have overlooked to measure intellectual capital efficiency at the sector level This study

134 proposes sectoral intellectual capital index (SICI) to measure the level of sector intellectual capital Findings from this study indicate that Securities, Energy, and Food sectors have accumulated the relatively higher level of the intellectual capital in Vietnam in comparison with other sectors during the research period On the other hand, Banking and Technology have lower intellectual capital efficiency than other sectors The findings imply that these two sectors have not yet fully recognized the important role of intellectual capital

In the era of globalization, competitive resources are shifting from tangible to intangible Intellectual capital is considered a source that increases the competitive advantage and wealth for firms and nations In this study, I developed new index of national intellectual capital (INIC) to measure the degree of intellectual capital for 104 countries worldwide This new index is satisfied the following vital attributes, including simplicity, quantification, market relevance and international comparison In this study,

I use macroeconomic indicators from the World Development Indicators This data source has a variety of indicators and is widely used in previous studies on economics

I also analyze the national intellectual capital in 104 countries based on different regions and different income levels Key findings of my analysis can be summarized as follows First, the national intellectual capital accumulation level has gradually increased between 2000 and 2018 across 104 countries The South American region has achieved the highest increase in the level of national intellectual capital during this period Second, the national intellectual capital level varies across continents Specifically, Europe has attained the highest level of national intellectual capital, while Africa has achieved the lowest level Third, my findings indicate that the national intellectual capital level is also closely related to income Countries in the high-income groups have achieved the highest national intellectual capital accumulation level, followed by the upper-middle-income, low-middle-income and low-income groups Fourth, among the top 10 largest countries globally, the US has achieved the highest level of national intellectual capital accumulation Finally, China had started at a deficient level in 2020 However, Chinese has been accumulating a significant increase in the level of national capital intellectual in the last 20 years Last but not least, among these 104 countries,

135 my results indicate that Finland has achieved the highest level of national intellectual capital accumulation in the world

6.1.2 The effects of intellectual capital on the performance of firm, sector and nation

Modified value-added intellectual coefficient (MVAIC) method has been utilized to measure intellectual capital efficiency by using data from 150 listed firms in Vietnam The generalized method of moments (GMM) is used to ensure the robustness of the findings This dissertation delves deeper into the differences in the impact of intellectual capital on the performance of financial and non-financial firms My empirical findings indicate that intellectual capital in the current year plays a crucial role in boosting firm performance at both types of firms, when firm performance is proxied by both ROA and ROE Therefore, H1 is supported in this dissertation In addition, I find that components of intellectual capital – human capital, structural capital and capital employed - significantly and positively contribute to firm performance at financial firms, using both proxies for firm performance My results of the estimations confirm for the financial firms one of the central arguments of the capital measurement model through MVAIC, which posits that intellectual capital components can be seen as a set of knowledge that can bring long-lasting benefits in the future and allows to treat expenses as investment (Nguyen et al., 2021) Garcia Castro et al (2021) state that human capital a key component of intellectual capital turns out to be very relevant in explaining the performance of financial firms Furthermore, my results also indicate that national intellectual capital has a positive effect on firm performance Lin (2018) argues that national intellectual capital promotes national competitiveness When national competitiveness is improved, along with stable macroeconomic policies, it will attract foreign investment resources In addition, new production techniques and technologies will also be applied by enterprises Hence, the firm will improve production capacity and efficiency (Songling et al., 2018) Therefore, national intellectual capital also contributes to improving firm performance

In addition, I also investigate the impact of intellectual capital on the performance of 12 sectors in Vietnam using the dynamic common correlated effects (DCCE) My empirical findings indicate that sectoral intellectual capital does contribute positively and significantly to sector performance in Vietnam from 2011 to 2018 This finding is in line with previous studies, which have so far found evidence on a direct and positive impact of intellectual capital on performance of various sectors (Soetanto and Liem, 2019; Smriti and Das, 2018) In addition, my findings also confirm the causal relationship between sectoral intellectual capital and return on equity It sheds light on the view that while sectoral intellectual capital contributes to return on equity, return on equity will also enhance sectoral intellectual capital in Vietnam

National intellectual capital has been widely recognized as an important source in maintaining and improving competitive advantages for the economies However, its assessment and measurement are still challenging Limited previous studies (Macerinskiene and Aleknaviciute, 2017; Seleim and Bontis, 2013) find a strong correlation between national intellectual capital and national performance with a focus on developed countries However, the existing literature has not examined the long-term impact of national intellectual capital on national performance Besides, the causal relationship between national intellectual capital and national performance has not been investigated As such, this study is the first of its kind to examine the contributions of national intellectual capital to national performance for the Asia-Pacific region using a newly developed index of national intellectual capital (INIC) for the 2000 – 2018 period

Empirical findings from this dissertation indicate that national intellectual capital does contribute positively and significantly to national performance for countries in the Asia-Pacific region in the past two decades This finding is in line with previous studies, which have so far found evidence on a direct and positive impact of national intellectual capital on national performance (Macerinskiene and Aleknaviciute, 2017; Lin and Edvinsson, 2011; Bontis, 2004) In addition, my findings also confirm the causal relationship between national intellectual capital and national performance It sheds light on the view that while national intellectual capital contributes to national

137 performance, national performance will also enhance an accumulation of national intellectual capital in the Asia-Pacific region.

New findings and implications

Most previous studies have neglected to measure and compare the intellectual capital efficiency between financial firms and non-financial firms in emerging markets, like Vietnam This study is conducted to fill this gap My results indicate that financial firms have higher levels of intellectual capital in comparison with non-financial firms in Vietnam This observation is encouraging and important From the results of this study, I shed the light on the differences in the use and exploitation of intellectual capital efficiency between financial and non-financial firms In particular, the difference also comes from intellectual capital components Financial firms have a contribution from human capital and structural capital, while relational capital and capital employed contribute significantly to intellectual capital efficiency in non-financial firms This evokes recommendations for managers on the balanced and effective investment and exploitation of intellectual capital components, to improve the efficiency of intellectual capital in each type of firm

As mentioned in Chapter 2, there have been few attempts in the literature to measure sectoral intellecual capital For example, Yodmongkon and Chakpitak (2009) use qualitative approach, including content analysis, thematic extraction and ground theory, to estimate sectoral intellectual capital index in Thailand In addition, Poyhonen and Smedlund (2004) utilize interview and system’s theoretical interpretation of organizations technique to estimate wood processing industry in Eastern Finland Both these approaches provide valuable information on particular aspects of sectoral intellectual capital However, both the approaches suffer from certain shortcomings These measurements are challenging to apply because the data used to build the index are not widely available, and there is a lot of qualitative data

In this dissertation, I propose sectoral intellectual capital index (SICI) as an alternative measure of sectoral intellectual capital The proposed SICI is a

138 multidimensional index based on firm annual report This is an attempt to meaningfully combine various intellectual capital components, including human capital, structural capital, relational capital and capital employed Certain methodological improvements make SICI free from some of the widely criticized shortcomings of the current sectoral intellectual capital measurements Because it captures information on multiple aspects of intellectual capital in a single number, the SICI provides a more comprehensive measure of sectoral intellectual capital than individual indicators such as those used in Yodmongkon and Chakpitak (2009) and the measure estimated by Poyhonen and Smedlund (2004) Further, it can be easily computed on a periodic basis by using secondary data from the firm annual report The SICI proposed here also satisfies important mathematical properties

This research was triggered by a cognitive gap in intellectual capital literature, as described previously Empirical evidence in this study indicates that significant differences in the level of intellectual capital across sectors are confirmed Banking and Technology sectors, which are generally considered the intellectual-capital-intensive sectors However, it appears that these two sectors have not effectively utilized intellectual capital properly Firms in these two sectors should focus on investing in intellectual capital The measurement of sectoral intellectual capital using my SICI approach in this study can be used to compare intellectual capital performance across sectors in different countries over the years This important advance opens up a new direction of research using the concepts of sectoral intellectual capital It is important that these types of data are updated annually As such, the new sectoral intellectual capital index has the potential to become an indicator that reflects the role of intangible assets in the sector performance On the basis of findings from this study, policy implications have emerged for policymakers to consider, develop and implement relevant policies to enhance sectoral intellectual capital, especially in the context that the Vietnamese government is paying more and more attention to the role of intangible assets in creating competitive advantages and balanced development of economic sectors

While the importance of intellectual capital is widely recognized, the literature on national intellectual capital still lacks a comprehensive measure that can be used to measure the extent of intellectual capital in an economy Such a comprehensive measure of national intellectual capital is important in order to take stock of the state of affairs with respect to knowledge management in an economy and to monitor the progress of the policy initiatives undertaken to promote national intellectual capital A robust and comprehensive measure of national intellectual capital is also of importance for the research community to investigate hypotheses relating to national intellectual capital that have been raised in the academic literature This study takes a novel and innovative approach to develop an index which can be used to measure a level of national intellectual capital across countries for many years National intellectual capital is generally considered as an essential concept in the studies of business, economics and organizations The topic of measuring national intellectual capital has, unfortunately, been under-examined in the current literature I agree with Kapyal et al (2012) that the perspectives of intellectual capital, knowledge management, strategic management, macroeconomics and social sciences should be merged to acquire a complete image of the national intellectual capital (Kapyal et al., 2012) In response to the lack of a comprehensive approach which can be used to measure a level of national intellectual capital, I conduct this analysis This study is the first of its type to initiate and implement a novel and innovative approach in which a level of national intellectual capital can be properly measured and compared across countries and years My approach is simple, quantifiable, relevant to the markets/economies, and comparable across nations

My index of national intellectual capital (INIC) incorporates the most important and fundamental aspects from the current literature on intellectual capital, including (i) human capital, (ii) structural capital and (iii) relational capital as the means of forming and articulating the intellectual wealth of a nation These aspects are then proxied by fundamental economic and social indicators which are reliable, publicly available, and quantifiable Besides, the estimates of national intellectual capital from my approach are replicative My careful and intensive literature review indicates that the following social and economic indicators, which are publicly available from the World Bank Indicators, can be used as the reliable proxies for the three fundamental aspects of intellectual

140 capital First, human capital can be proxied by the following three indicators: (i) tertiary school enrollment (per cent of gross enrollment); secondary school enrollment (per cent of gross enrollment); and government expenditure on education (per cent of GDP) Second, structural capital can be proxied by the following two indicators, including (i) a ratio between individuals using the internet and total population; and (ii) a ratio between mobile cellular subscriptions and total population Third, another three indicators, including (i) a ratio between exports of goods and services in total national GDP; (ii) a ratio between information and communication technology goods exports in total goods exports; and (iii) foreign direct investment (per cent of GDP) are used as the proxies for relational capital – the last component of my national intellectual capital index

In total, I use eight economic and social indicators as the proxies for three key components of my INIC index As such, I consider that the multifaceted concept of national intellectual capital is widely and extensively covered by my careful and well- selected indicators As a result, I consider that the estimated level of national intellectual capital using my new INIC approach is highly likely reflective of a level of national intellectual capital

In particular, the estimated level of national intellectual capital using my INIC approach in this study can be used for international comparison across nations and years This important advancement opens a new strand of research using the concepts of national intellectual capital The core strength of my new INIC, which is envy to other indices, is that the index is developed based on publicly available data which are reported for a long period of time for a majority of countries around the globe Importantly, these types of data are frequently updated by the World Bank As such, the new index of national intellectual capital (INIC) is capable of being a reflective indicator of the prevailing market conditions for countries around the world On the ground of the findings from this study, policy implications have emerged for the policymakers to consider, formulate and implement relevant policies with the aim of enhancing and improving national intellectual capital in the current environment of technology transformation

6.2.2 The effects of intellectual capital on the performance of firm, sector and nation

I contribute to the literature on intellectual capital in three ways First, studies on intellectual capital have gained impetus as a consequence of the technology revolution However, the ambiguity in measuring and differentiating various components of intellectual capital may provide misleading empirical evidence In this dissertation, I shed light on the definition of intellectual capital efficiency using the modified value- added intellectual coefficient (MVAIC) model Second, I examine the potential impact of intellectual capital on both financial and non-financial firms Third, I draw implications for both types of firms and the Vietnamese governments based on the findings from this dissertation

The research findings highlight the critical role of intellectual capital in enhancing operational efficiency within both financial and non-financial enterprises The findings from this study also offer several managerial implications for both financial and non- financial firms in emerging market, like Vietnam This understanding has significant managerial implications, as it provides business leaders with strategic insights into how they can effectively leverage intellectual capital to optimize performance and gain a competitive advantage The components of intellectual capital—human capital, structural capital, and capital employed—each contribute uniquely to operational efficiency, and their effective management can lead to substantial improvements in productivity, cost-effectiveness, and overall organizational success This section explores the managerial implications of these findings, offering practical recommendations for business managers on how to harness the power of intellectual capital Human capital, which encompasses the skills, knowledge, and expertise of employees, is a crucial driver of operational efficiency The research indicates that organizations with a strong focus on developing and leveraging human capital are better positioned to improve their operational processes, reduce inefficiencies, and enhance overall productivity For business managers, this implies a need to prioritize the recruitment, development, and retention of skilled employees as a strategic initiative Managers should implement rigorous recruitment processes that focus on attracting top talent with the necessary skills and experience to contribute to the organization’s

142 operational goals This involves not only assessing technical competencies but also evaluating candidates’ ability to innovate, adapt, and work collaboratively By selecting employees who align with the organization’s strategic objectives, managers can ensure that their workforce is capable of driving operational efficiency Once recruited, continuous training and development programs are essential to maintaining a competitive workforce Managers should invest in regular training sessions that enhance employees’ technical skills, problem-solving abilities, and adaptability to new technologies and processes This not only improves individual performance but also ensures that the organization as a whole is agile and capable of responding to changing market conditions Engaged employees are more likely to contribute to operational efficiency by being proactive in identifying inefficiencies and suggesting improvements Managers should foster a positive work environment that encourages employee participation, recognizes achievements, and provides opportunities for career growth

By creating a culture of engagement, organizations can reduce turnover, retain valuable human capital, and maintain consistent operational performance Effective leadership is key to maximizing the potential of human capital Managers should focus on developing leadership skills within their teams, encouraging leaders to mentor and guide employees in ways that align with the organization’s operational objectives Leadership development programs can help cultivate a culture of continuous improvement, where leaders are equipped to drive initiatives that enhance operational efficiency

Structural capital encompasses the systems, processes, and organizational structures essential for a business's efficient operation Research indicates that robust structural capital significantly enhances operational efficiency by standardizing processes, minimizing waste, and facilitating better information flow For business managers, this underscores the necessity of investing in and refining the organizational infrastructure to achieve operational objectives Managers ought to consistently evaluate and improve organizational processes to pinpoint and address inefficiencies This process involves simplifying workflows, removing unnecessary steps, and adopting best practices that curtail waste and boost productivity Initiatives aimed at process optimization, like lean management or Six Sigma, are particularly beneficial in detecting inefficiencies and deploying solutions that bolster operational performance The integration of advanced technologies into organizational processes is a critical

143 component of structural capital Managers should invest in technologies that automate routine tasks, improve communication, and enhance data management For example, implementing enterprise resource planning (ERP) systems can centralize information, streamline operations, and reduce the time and resources required to manage business processes By leveraging technology, managers can significantly enhance operational efficiency and drive organizational success Effective knowledge management is essential for capturing and sharing organizational knowledge, which is a key aspect of structural capital Managers should implement systems that facilitate the documentation, storage, and retrieval of knowledge, ensuring that employees have access to the information they need to perform their tasks efficiently Knowledge management systems can also support innovation by enabling the sharing of best practices and lessons learned across the organization The structure of an organization can have a profound impact on its operational efficiency Managers should consider whether their organizational design supports or hinders efficient operations This may involve restructuring teams to align more closely with operational goals, reducing hierarchical barriers that slow decision-making, and creating cross-functional teams that can address complex operational challenges more effectively By aligning organizational design with operational needs, managers can create a more agile and responsive business

Limitations and suggestions for future research

The challenge of quantifying intangible assets such as intellectual capital can lead to difficulties in implementation, potentially resulting in inconsistencies in how intellectual capital is measured and evaluated across companies, industries, and countries This study focuses on the impact of intellectual capital on firm performance Future research should consider examining and exploring the relationship between intellectual capital and other fundamental aspects of firms, such as corporate value, capital structure, and corporate social responsibility In addition, this study is limited to financial and non-financial firms in Vietnam Empirical studies in the future should incorporate the efficiency aspects of these types of firms because different sectors might have different characteristics, in particular, their current efficiency level, which might cause differences in relation to intellectual capital efficiency In addition, further studies could examine regression models with firm and year fixed effects and errors calculated using double clustered standard errors Additionally, other control variables can be considered to enhance the validity of the research results

The drawback of my sectoral intellectual capital arises when attempting to amalgamate the intellectual capital of individual firms operating within the identical industry The act of merging firms from the same industry to ascertain the intellectual capital of that industry can potentially negate the differences that exist among these firms Consequently, this approach might diminish the meaningfulness of statistical examinations This methodology has a tendency to blur the differentiation in intellectual capital across various companies, consequently undermining the significance of statistical analyses When constructing sectoral intellectual capital index, the previous year's total assets can be used as a weight In addition, this study focuses on the construction of sectoral intellectual capital index and examines the impact on sector performance Future empirical studies may examine the relationship between sectoral intellectual capital and other fundamental aspects such as capital structure or sector risk

In addition, the emphasis on Vietnam offers valuable perspectives on an emerging economy; however, the results may not be universally applicable, especially to developed countries This could limit the wider relevance of the research findings Future studies should prolong the research duration and include comparisons with other nations to gain a more detailed understanding of intellectual capital's impact on sector performance

The methodology for assessing national intellectual capital may be constrained by the availability of necessary data in different nations, which could impede the broad application and comparison of the new indices developed in the research I consider that the novel approach adopted in this study represents a major step in the right direction to measure a level of national intellectual capital However, studies in the future should take into account additional tests of robustness and various scenario analysis to ensure that a level of national intellectual capital is accurately measured When constructing a national index, future studies should consider the weighting of each component contributing to the overall index In addition, this study only examines the direct impact of national intellectual capital on national performance Future studies should consider the mediating/moderating role of macroeconomic variables, such as institutional quality, which may affect the national intellectual capital - national performance nexus

Moreover, I note that the components of national intellectual capital such as human capital, structural capital and relational capital have also been found to support national performance They contribute to improving national competitiveness (Barkhordari et al.,

2019), enhancing innovation (Song, 2023) and reducing environmental pollution (He et al., 2022) As such, future studies can examine the role of the national intellectual capital and its components on national competitiveness, innovation capacity and green energy consumption

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Table A1.1 Wooldridge and modified Wald tests

Wooldridge test Modified Wald test

F-test p-value Presence of autocorrelation χ 2 p-value Presence of heteroskedasticity

Table A1.2 Cross-section dependence test results

Variables ROA ROE SICI SIZE LEV

Notes: *** significant at 1 per cent level, respectively

ROA denotes the return on assets; ROE denotes the return on equity; SICI denotes sectoral intellectual capital index; SIZE denotes the natural logarithm of the total assets of the sector; LEV is calculated as the ratio between total debt and total assets

Table A1.3 Slope homogeneity test results

Notes: *, *** significant at 10 per cent and 1 per cent level, respectively

Table A1.4 Panel unit root test results

Notes: *, **, *** significant at 10 per cent, 5 per cent and 1 per cent level, respectively

ROA denotes the return on assets; ROE denotes the return on equity; SICI denotes sectoral intellectual capital index; SIZE denotes the natural logarithm of the total assets; LEV denotes the ratio between total debt and total assets of firms

Table A1.5 Results of the cointegration test

Notes: **, *** significant at 5 per cent and 1 per cent level, respectively

Table A1.6 Cross-section dependence test results

Variables PGDP INIC TO GE BM CE

Notes: *** significant at 1 per cent level

PGDP: GDP per capita; INIC: index of national intellectual capital; TO: trade openness; GE: government expenditure; BM: broad money supply; CE: Domestic credit to the private sector by banks

Table A1.7 Slope homogeneity test results

Notes: *** significant at 1 per cent level

Table A1.8 Panel unit root test results

Notes: *, **, *** significant at 10 per cent, 5 per cent and 1 per cent level, respectively

The p-values are shown in parentheses The Z[t-bar] is reported

PGDP: GDP per capita; INIC: index of national intellectual capital; TO: trade openness; GE: government expenditure; BM: broad money supply; CE: Domestic credit to the private sector by banks

Table A1.9 Results of the cointegration test

Notes: **, *** significant at 5 per cent and 1 per cent level, respectively

No Sector Name of Firm

1 Aviation Airports Corporation Of VietNam

3 Aviation Danang Airports Services Joint Stock Company

4 Aviation General Aviation Import Export JSC

5 Aviation Noi Bai Cargo Terminal Service Joint Stock Company

6 Aviation Noi Bai Catering Services JSC

7 Aviation Noibai Airport Services Joint Stock Company

8 Aviation Saigon Ground Services JSC

9 Aviation Southern Airports Services JSC

10 Aviation Vietjet Aviation Joint Stock Company

12 Banking Asia Commercial Joint Stock Bank

13 Banking Bac A Commercial Joint Stock Bank

14 Banking Bank for Foreign Trade of Vietnam

15 Banking Bank for Investment and Development of Vietnam JSC

16 Banking Ho Chi Minh City Development Joint Stock Commercial Bank

17 Banking Military Commercial Joint Stock Bank

18 Banking Sai Gon Thuong Tin Commercial Joint Stock Bank

19 Banking Saigon Hanoi Commercial Joint Stock Bank

20 Banking Tien Phong Commercial Joint Stock Bank

21 Banking Vietnam Commercial Joint Stock Export Import Bank

22 Banking Vietnam International Commercial Joint Stock Bank

23 Banking Vietnam Joint Stock Commercial Bank For Industry And Trade

24 Banking Vietnam Prosperity Joint Stock Commercial Bank

25 Banking Vietnam Technological and Commercial Joint Stock Bank

26 Education Binh Thuan Book And Equiptment JSC

27 Education Book & Education Equipment JSC Of HCMC

28 Education Da Nang Education Development & Investment JSC

29 Education Danang Books & School Equipment JSC

30 Education Education Cartography And Illustration JSC

31 Education Educational Book JSC In Da Nang City

32 Education Educational Book JSC In Hanoi City

33 Education Educational Book JSC In Ho Chi Minh City

34 Education Ha Tinh Book And Equipment Education JSC

35 Education Hanoi Education Development & Investment JSC

36 Education Long An School Book & Equipment JSC

37 Education Phuong Nam Education Investment & Development JSC

38 Education Quang Ninh Book & Educational Equipment JSC

39 Education South books and Educational Equipment JSC

40 Education Tan Binh Culture Joint Stock Company

41 Energy BaRia Thermal Power Joint Stock Company

42 Energy Candon HydroPower Joint Stock Company

43 Energy Gia Lai Hydropower JSC

44 Energy Hai Phong Thermal Power SJC

45 Energy Mien Trung Power Investment & Development JSC

46 Energy Petrolimex Gas Corporation - JSC

No Sector Name of Firm

47 Energy PetroVietNam Low Pressure Gas Distribution JSC

48 Energy PetroVietnam Power Nhon Trach 2 JSC

49 Energy Pha Lai Thermal Power Joint Stock Company

51 Energy Southern Gas Trading Joint Stock Company

52 Energy Vinh Son - Song Hinh Hydropower Joint Stock Company

56 Food Hanoi Beer Alcohol And Beverage Joint Stock Corporation

58 Food Quang Ngai Sugar Joint Stock Company

59 Food Saigon Beer - Alcohol - Beverage Corporation

60 Food Sao Ta Foods Joint Stock Company

61 Food Thanh Thanh Cong - Bien Hoa Joint Stock Company

62 Food Viet Nam Dairy Products Joint Stock Company

63 Food Vietnam Vegetable Oils Industry Corporation

64 Food Vinacafé Bienhoa Joint Stock Company

65 Food Vissan Joint Stock Company

66 Insurance Bao Minh Insurance Corporation

70 Insurance Post - Telecommunication Joint - Stock Insurance Corporation

72 Insurance Vietnam National Reinsurance Corporation

73 Oil and gas Binh Son Refining and Petrochemical Company Limited

74 Oil and gas Petroleum Trading Joint Stock Company

75 Oil and gas Petrolimex Equipment Joint Stock Company

76 Oil and gas PetroViet Nam Coating JSC

77 Oil and gas PetroVietNam Chemical And Services Joint Stock Corporation

78 Oil and gas PetroVietnam Drilling & Well Services Corporation

79 Oil and gas PetroVietnam Engineering Consultancy JSC

80 Oil and gas PetroVietnam Oil Corporation

81 Oil and gas PetroVietnam Technical Services Corporation

82 Oil and gas PTSC Offshore Services JSC

83 Oil and gas Viet Nam National Petroleum Group

84 Pharmaceutical Ben Tre Pharmaceutical JSC

85 Pharmaceutical Cai Lay Veterinary Pharmaceutical Joint Stock Company

86 Pharmaceutical Cuu Long Pharmaceutical JSC

87 Pharmaceutical DHG Pharmaceutical Joint Stock Company

88 Pharmaceutical Domesco Medical Import Export Joint Stock Corporation

90 Pharmaceutical OPC Pharmaceutical Joint Stock Company

91 Pharmaceutical Pharmedic Pharmaceutical Medicinal JSC

92 Pharmaceutical Phong Phu Pharmaceutial JSC

93 Pharmaceutical SPM Joint Stock Company

94 Pharmaceutical Traphaco Joint Stock Company

95 Real Estate Ba Ria - Vung Tau House Development JSC

96 Real Estate Becamex Infrastructure Development JSC

No Sector Name of Firm

98 Real Estate Development Investment Construction JSC

99 Real Estate Ha Do Group JSC

100 Real Estate I.D.I International Development & Investment Corporation

101 Real Estate Industrial Urban Development JSC No 2

102 Real Estate Kinh Bac City Development Share Holding Corporation

103 Real Estate Nam Long Investment Corporation

104 Real Estate Phat Dat Real Estate Development JSC

105 Real Estate Refrigeration Electrical Engineering Corporation

106 Real Estate Sai Dong Urban Development And Investment JSC

107 Real Estate Sonadezi Long Thanh Shareholding Company

108 Real Estate Song Da Urban & Industrial Zone Investment & Development JSC

109 Real Estate Vingroup Joint Stock Company

111 Securities Bank for Invesment & Development of Vietnam Securities Company

115 Securities Ho Chi Minh City Securities Corporation

116 Securities Hoa Binh Securities JSC

118 Securities Saigon - Hanoi Securities JSC

120 Securities Viet Dragon Securities Corporation

121 Securities Viet Nam Bank For Industry & Trade Securities JSC

122 Securities VIX Securities Joint Stock Company

124 Service Alpha Seven Group JSC

125 Service Ben Thanh Service Joint Stock Company

126 Service Dam Sen Water Park Corporation

127 Service DIC Tourist & Trade Joint Stock Company

128 Service Hang Xanh Motors Service Joint Stock Company

129 Service Hoi An Torurist Service Joint Stock Company

130 Service Sai Gon Hotel Corporation

131 Service Superdong Fast Ferry Kieng Giang JSC

132 Service Thanh Thanh Cong Tourist Joint Stock Company

133 Service The Pan Group Joint Stock Company

134 Service Vietnam Exhibition Fair Centre JSC

135 Service Vinaconex Trading & Manpower JSC

136 Service Vung Tau Intourco Resort JSC

140 Technology HIPT Group Joint Stock Company

141 Technology HPT Viet Nam Corporation

142 Technology Kasati Joint Stock Company

143 Technology One Communication Technology Corporation

No Sector Name of Firm

147 Technology Sieu Thanh Joint Stock Company

148 Technology Vien Lien Joint Stock Company

149 Technology Viet Tri Chemical Joint Stock Company

II List of experts in Intellectual capital

The experts include 2 lecturers (Expert 1 and Expert 2) with over 15 years of research experience, especially with published studies on intellectual capital in prestigious journals In addition, the experts include 3 people (Expert 3, Expert 4 and Expert 5) who are CEO, manager and have at least 20 years of holding managerial positions in enterprises In addition, their firms are also very interested in the role of intangible assets and especially intellectual capital

No Code Academic title Work place

1 Expert 1 Doctor of Philosophy Lecturer at Australian

Dean of the School of Advanced Study;

Head of Department - Vietnam Chamber of Commerce and Industry (VCCI); CEO high-tech firm

Formerly Head of Strategy Department - State Bank of Vietnam; CEO manufacturing firm

The PhD student greeted the guests and experts, then introduced the objective of the exchange Then, the PhD student presented an overview of the research topic and the interview agenda

Q1: What drives you to be interested in intellectual capital?

Expert 1: Recent studies are paying more and more attention to the role of intangible assets in business performance

Expert 2: Intellectual capital is an important resource to create a competitive advantage for businesses, especially in emerging countries like Vietnam

Expert 3: In the context of increasingly developing information technology, small and medium enterprises are more and more interested in investing and exploiting intangible assets

Therefore, the effective exploitation of intellectual capital is what we care about

Expert 4: The 4.0 technology revolution requires businesses to pay more attention to the exploitation of intangible assets, especially intellectual capital

Expert 5: Exploiting intangible assets is an inevitable need in the current business context

Enterprises that own technological know-how, exclusive patents will have better efficiency While most

Intellectual capital plays a major role in the knowledge-based economy and is the key driver of firm’s sustained competitive advantages

In Vietnam, the role of intangible assets, especially intellectual capital, is receiving more and more attention As tangible resources are increasingly scarce, it is inevitable to invest in and exploit intangible assets Therefore, intellectual capital plays a particularly important role for businesses in Vietnam

Question Answer Implications businesses in Vietnam are currently interested in intellectual property, we consider the exploitation of intellectual capital as the main activity direction

Q2: In your opinion, what components does intellectual capital include?

Which component of intellectual capital is the most important?

Expert 1: Human capital, structural capital and relational capital are considered the three main components of intellectual capital.

Human capital is the most important component of intellectual capital

Expert 2: Previous studies have classified intellectual capital into three components, including human capital, structural capital and relational capital In particular, human capital is considered the most important component of intellectual capital

Expert 3: Reality has proven that machines cannot completely replace the role of humans Enterprises with financial conditions can invest in many modern machines However, people still play an important role in all activities of enterprises Thus, human capital remains the most important component of intellectual capital

Human capital is the most important component of intellectual capital Investing in human capital needs to be taken care of by businesses

Specifically, when the knowledge and skills of employees are improved, combined with the investment in modern processes and technology (structural capital) will contribute to improving firm performance.