Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations

Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.Intellectual Capital and Its Effects on The Performance of Firms, Sectors and Nations.

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-

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.

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

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.

Country Global Competitiveness Index, institutions pillar ranking, 2017-2018

Economist Intelligence Unit technological readiness ranking, 2018-

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

Figure 1.3 Global competitiveness and technological readiness ranking

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 intellectual capital in accordance with the sectors’ development theories (Pedro et al., 2018) Doing so will 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 inVietnam'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. 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 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 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 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 inVietnam'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- 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 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). 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 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.

As the knowledge economy continues to evolve, research on national intellectual capital occupies a crucial position within the realm of modern business administration and economics (Svarc et al., 2021) This dissertation not only informs current business performance and innovation, thereby facilitating informed decision-making at both organizational and governmental levels.

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.

Specifically, I use the modified value-added intellectual coefficient (MVAIC) model to measure intellectual capital of financial and non-financial firms in Vietnam I also examine the difference in intellectual capital of these two groups of firms: financial versus non-financial firms Furthermore, I develop a new sectoral intellectual capital index (SICI) to measure the intellectual capital of 12 sectors in Vietnam In addition, I extend the existing literature by developing a new index of national intellectual capital (INIC) to measure intellectual capital at the nation’s level I then use this newly developed INIC index to measure a degree of intellectual capital for 104 countries globally.

2) To examine the effects of intellectual capital on the performance of firms, and sectors, and nations This dissertation employs a measured level of intellectual capital at firms, sectors, and nations’ levels to investigate the effects of intellectual capital on the performance of firms, sectors and nations Various econometric methods are utilized to ensure the validity and robustness of the findings when examining these effects Drawing on a comprehensive review of previous research, performance at the firm and sector levels is assessed using return on total assets and return on equity metrics In addition, national performance is measured using GDP per capita.

By employing these metrics and methodologies, this dissertation aims to provide a thorough analysis of the relationship between intellectual capital and performance across multiple levels of analysis.

Research questions

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

1) What are the differences in intellectual capital level between financial and non- financial firms in Vietnam? And what are potential advancements in measuring intellectual capital at the sectors and nations’ 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.

Contributions of the study

This study contributes to the existing literature on intellectual capital in the following respects.

- First, this dissertation investigates the differences in intellectual capital efficiency between the financial and non-financial sectors in Vietnam The effects of intellectual capital on firm’s performance in Vietnam are then examined Vietnam is an emerging market in the Southeast Asia, one of the most dynamic economies in the region and the world Managerial implications are important for the intellectual capital community, including academics, policymakers, and practitioners This dissertation provides the bridge to fill the current gap.

- Second, this study extends the current literature by developing a new measure of intellectual capital at the sector level - a new sectoral intellectual capital index (SICI) The SICI can now be used to investigate various aspects of the sectors with intellectual capital efficiency in Vietnam.

- Third, a new index of the national intellectual capital (INIC), one of the first of its kind, is developed to measure the different levels of intellectual capital across nations globally This new index includes the following fundamental attributes: (i) simplicity - a new index should be simple to calculate; (ii) quantification – a new index should be easily quantifiable without using judgments; (iii) market relevance – a new index should be able to reflect the prevailing market and economy conditions; and (iv) international comparison

– a new index should be practically implemented for comparison purposes across countries regardless of the level of national performance and development.

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:

Conclusions and implications Analyze and interpret data

Review of the theoretical foundation and previous studiesResearch problems

 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.

 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 theMVAIC 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

 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.

The effects of intellectual capital on performance

Sectoral intellectual capital index (SICI)

Modified value-added intellectual coefficient

Firm level - Return on assets (ROA)

Economic growth 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.

Chapter 1 introduces the background, main issues and identify specific research gaps Specific research questions are also raised in this chapter.

In the second chapter, intellectual capital definitions, measurements and the research framework are discussed The main objective of this chapter is to identify the gap in the existing literature which will be addressed in this dissertation.

Related research methods are presented and discussed in this chapter Various econometric methods are used to address the research questions.

Chapter 4 presents the differences in intellectual capital between the financial sector and non-financial sectors in Vietnam The newly constructed sectoral intellectual capital index (SICI) and the new index of national intellectual capital (INIC) are also developed in this chapter.

Empirical results relating to the effects of intellectual capital on the performance of firms, sectors and nations are presented and discussed in this chapter.

Chapter 6 presents a summary of key findings and conclusions of this study This chapter presents the main findings and the contributions of this study The potential areas for further research are also discussed.

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 andSerrasqueiro (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 andSullivan, 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 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 management, the model aids in identifying key intellectual capital assets that can be 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 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, 2009).

Tangible assets minus visible debt External structure

(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, theSkandia 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, 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).

Intellectual property Patents, trademarks, copyrights, etc.

Helfat and Peteraf (2003) emphasize that the principle of resource theory is the existence 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.

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- 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 competitive advantage.

Performance measurement has been an integral part of management since its inception However, based on modern business theory, performance measurement is traced back to the planning and control methods of the American railroad industry in the 1860s and 1870s (Chenhall, 1997; Kaplan, 1983) During the first quarter of the 20th century, the DuPont Company introduced a return-on-investment method and a pyramid of financial ratios By 1925, many methods and techniques of measuring the performance continued to be developed such as discounted cash flow method, residual income method, economic value added, or cash flow to invested capital (Chenhall, 1997; Kaplan, 1983).

Chenhall (1997) defines performance as a set of information about the achievement of varying significance to different stakeholders Performance is a concept used to measure the quality of individual and collective efforts (Corvellec,1997) In management research, March and Sutton (1997) argue that performance is often seen as encapsulating the unitary purpose of the organization Specifically,organizations are required to 'implement' and communicate their achievements to key stakeholders As a result, organizational functions and processes are increasingly required to demonstrate their contribution to operational performance Kamensky andMorales (2005) propose a more detailed definition of differentiating performance and results In addition, Gutner and Thompson (2010) stress that assessing organizational performance means analyzing the outcome produced and the process leading to the outcome in terms of efficiency (Gutner and Thompson, 2010) The need to establish the link between planning, decisions, actions, and results has generated considerable interest in measuring organizational performance (Micheli and Mari, 2014) Scholars from management accounting and other areas of management study have examined a wide range of issues related to the design, implementation, use, and review of performance measurement systems (Goold and Quinn, 1990; Neely, 1999) In management practice, organizations have invested considerable resources to measure and demonstrate their performance (Micheli and Manzoni, 2010). performance is based on criteria such as product quality, new product development, ability to attract workers, customer satisfaction and the relationship between management and employees Perception-based measurement has a positive effect on organizational performance (Chenhall, 1997) If the concern is long-term profitability, then performance is often measured by various types of profit ratios, such as return on sales (although different ratios may be calculated depending on whether profitability is measured profit before or after interest and taxes are paid); value-added ratio (sales revenue minus cost of purchased materials); return on total assets or return on equity (Neely, 2002) A general rule of thumb is that each part of the ratio should be relevant to the audience being addressed, and the overall ratio should reflect the interests of the particular audience of the information it provides (Neely et al., 2002) These values vary, depending on each firm and sector (Neely, 2002).

Performance is one of the important dependent variables for researchers and managers Market competition for customers, inputs, and capital makes performance essential to an organization's survival and success Indeed, firm activities such as marketing, human resource management, strategy formulation are ultimately evaluated through the results of business operations It shows that measuring performance is a necessity to evaluate the activities of firm, sector and nation.

The resource-based theory emphasizes that an organization has enough resources to achieve its goals and improve its performance over the long term (Wernerfelt,1984) Inheriting the resource-based theory, knowledge-based theory (Grant, 1996) is appropriate to describe the study of intellectual capital, especially on the link between intellectual capital and organizational performance Meanwhile, the performance- based theory is used to define organizational performance, specifically in this dissertation, the performance of firms, sectors and nations The combination of these three fundamental theories creates a solid research framework to conduct the dissertation Managing intangible resources can help an organization achieve goals,improve performance and increase market value.

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 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, 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 andFerulano, 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 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 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.

Market assets Intellectual property Human-centered Infrastructure assets assets assets

Employee education audit (including 5 indicators)

Corporate learning audit (including 10 indicators)

Human centered assets management audit (including 3 indicators)

Corporate culture audit (including 4 indicators)

Figure 2.6 Brooking’s intellectual capital measurement model

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

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

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 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 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,

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:

Human capital focus Process focus

 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(2007) propose the MVAIC, 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.

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 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 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 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

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

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 Finland

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

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.

Moreover, the direct averaging of national intellectual capital components—human capital, process capital, market capital, and renewal capital—lacks strong empirical 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 inSouth 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 theFinnish 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 the assessment of a nation’s intellectual capital rests significantly on perceptual underpinnings Furthermore, Kapyla et al (2012) consider national 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).

The significance of national intellectual capital in driving economic performance has been extensively debated in academic literature Nahapiet and Ghoshal (1998) posit that national intellectual capital serves as a catalyst for economic production, while Dahlman et al (2006) assert it as the primary engine of national performance and social development Lin and Edvinsson (2008) further emphasize its role as a critical source of national wealth and progress Seleim and Bontis (2013) go as far as to suggest that national intellectual capital can explain up to 70 percent of the variance in economic performance Various methodological approaches have been employed to investigate the impact of national intellectual capital on national performance, including correlation analysis (Lin, 2018; Lin and Edvinsson, 2011), partial least squares (PLS) (Seleim and Bontis, 2013; Bontis, 2004), and analysis of variance (ANOVA) (Macerinskiene and Aleknaviciute, 2017) Bontis (2004) found a relationship between National Intellectual Capital Index (NICI) scores and the GDP per capita of Arab countries Stahle et al (2015) estimate that intangible capital contributes to 45 percent of the GDP in 48 listed countries Moreover, Macerinskiene and Aleknaviciute (2017) support the notion that national intellectual capital significantly influences national performance.

Based on a comprehensive review of the literature, it is evident that there is currently no universally accepted or widely acknowledged method for evaluating the level of intellectual capital at the national level While several studies have attempted to estimate national intellectual capital, the methodologies employed in these endeavors are often impractical for broader application due to data limitations or a subjective nature Notable works in this area include studies by Kapyla et al (2012),Lin and Edvinsson (2011), Schneider (2007), Andriessen and Stam (2005), and Bontis offers a methodological framework for assessing intellectual capital levels across different nations Previous methodologies for measuring national intellectual capital have encountered significant challenges One notable issue is the arbitrary nature of weighting schemes employed in these approaches It cannot be assumed that these weights remain consistent across different countries and over time, as highlighted by Stahle (2008) Additionally, the analysis of individual components of national intellectual capital has proven to be complex and arbitrary Assigning the maximum value to each sub-indicator without a solid scientific foundation raises questions about the validity of such assessments Consequently, these methodologies are not only technically challenging to implement but also impractical for application in diverse country contexts, as underscored by Kapyla et al (2012) Table 2.2 summarizes the previous methods of measuring intellectual capital and the limitations of these methods.

Salonius and Lonnqvist (2012) have emphasized the difficulties associated with employing the above-mentioned indicators of national intellectual capital. Consequently, this study aims to present a new index for national intellectual capital (INIC) that possesses several essential advantages:

(i) simplicity - a new index is simple to be calculated;

(ii) quantification – a new index is easily quantifiable without using judgments;

(iii) market relevance – a new index is able to reflect the prevailing conditions for the economy under investigation; and

(iv) international comparison – a new index is practically implemented for comparison purposes across countries regardless of their level of national performance and development.

Importantly, the estimated level of national intellectual capital derived from theINIC approach in this study can be employed for international comparison across both countries and time periods This crucial development paves the way for future research employing the concept of national intellectual capital. assessments being conducted on a per-country basis In the current globalized business environment, characterized by interconnected economies, there is a growing need for a standardized approach to assess intellectual capital that allows for meaningful cross- country comparisons Therefore, I propose a novel framework to construct an index of national intellectual capital (INIC) that can be universally applied.

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).

Administrative Selling expenses Production cost

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 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: Y = C + I + G + X

 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

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 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 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 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 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

(2020) Vietnam Vietnamese commercial banks Profitability Yes

(2020a) 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

(2018) India Listed companies Productivity, profitability, market value and sales growth

(2018) Nigeria Listed firms Cash flow from operation and EVA Yes

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

Earnings, profitability, efficiency, and market value

Garanina (2016) Russian Manufacturing companies Profitability Yes

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

Africa Listed companies Profitability and market value No

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 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

Various studies have been conducted to explore various aspects of measuring intellectual capital at the firm's level However, measuring intellectual capital at the national level or for the entire nation appears underexamined As a pioneering author, Bontis (2004) introduce the national intellectual capital index (NICI) method, which divides national intellectual capital into four groups: human capital, process capital, market capital and renewal capital In addition, Lin and Edvinsson (2011) propose a national intellectual capital (NIC) model Kapyla et al (2012) measure national intellectual capital, including human capital, structural capital, relational capital, and social capital Based on Lin and Edvinsson's (2011) study, Stahle et al (2015) introduce a new measurement called Edvinsson, Lin, Stahle and Stahle - ELSS, to calculate national intellectual capital.

The above models have been used to measure national intellectual capital for selected countries where qualitative and quantitative data are available However, I note that these models exhibit fundamental weaknesses Required data are limited, particularly for qualitative data Some models apply an arbitrary weighting scheme of intellectual capital components The weighting scheme does not appear to be used across countries and times.

Nonetheless, these models have been used to measure national intellectual capital for international comparison The national intellectual capital is then used to examine its effect on national performance Different techniques are utilized to examine the impact of national intellectual capital on national performance, such as correlation analysis (Lin, 2018; Lin and Edvinsson, 2011); partial least squares - PLS (Seleim and Bontis, 2013; Bontis, 2004); analysis of variance - ANOVA (Macerinskiene and Aleknaviciute, 2017) For example, Bontis (2004) reveals that the NICI scores are related to the GDP per capita of Arab countries In addition, Stahle et al (2015) consider that intangible capital accounts for 45 per cent of the GDP in 48 quoted countries Moreover, Macerinskiene and Aleknaviciute (2017) support the view for sustainable development competitiveness Lin (2018) asserts that national intellectual capital contributes to a nation’s wealth Carayannis and Grigoroudis (2016) believe that effective management and exploitation of knowledge resources contribute to improving national competitiveness However, no widely used methodologies or recognized methods have been employed to evaluate intellectual capital across countries to the best of my knowledge 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 Hence, I propose the third hypothesis:

Hypothesis 3 (H3): National intellectual capital contributes to national performance.

Table 2.5 Summary - Intellectual capital and national performance

No Authors Elements of measurement Research focus

Renewable/Development capital the association of NIC with the national digital transformation in

28 member states of the EU

Effect of NIC on GDP and growth

Utilize 20 indicators to propose the strategic, dialogic and societal measurement of NIC

Structural NIC model developers with 24 indicators to calculate NIC for 48 countries

Government efficiency; Business efficiency; Infrastructure

International comparisons of national competitiveness

Organisation capital (management and marketing) R&D

To explain the impact of IC investments on national performance

Goods market Efficiency; Labour market efficiency; Financial market development; Technological readiness; Market size; Business sophistication; Innovation

International comparisons of national competitiveness

Human capital (investments, assets, effects)

Structural capital (investments, assets, effects)

Relational capital (investments, assets, effects)

Use 38 indicators to measure NIC for 19 European countries

To explain the impact of IC investments on national performance

10 Bontis (2004) Human capital; Process capital;

Use 25 indicators to measureNIC for 10 Arab countries

METHODOLOGY

Data

White et al (2010) emphasize that annual reports are very important communication of businesses and their audiences Chakroun and Hussainey (2014) consider annual reports as a voluntary disclosure tool In addition, Davison (2014) reviews the annual report to evaluate certain assets, e.g graphs and images Besides, Lys et al (2015) explore corporate social responsibility disclosures in annual reports. Hence, annual report is an important resource for research in management, accounting and financial contexts The annual report is also a marketing tool, a communication channel on corporate strategy (Stanton and Stanton, 2002) In addition, Yuthas et al. (2002) pointed out that the annual report is the most important source of information to evaluate the company White et al (2010) emphasizes that the annual report is the main source of information on intellectual capital, corporate governance and corporate social responsibility Although some information about intangible assets may be displayed on a company's website, they typically provide information about their intangible assets through an annual report In particular, most of the previous research in the field of intellectual capital (Dumay and Cai 2014; Liao et al., 2013; Phusavat et al., 2011) all relied on data from the annual report Therefore, the data source extracted from the annual report ensures to provide sufficient information related to intellectual capital, corporate governance and corporate social responsibility.

Therefore, the data utilized in this study are hand-collected from published annual reports of firm and respective stock exchange at which the company is listed, in line with previous studies (Soetanto and Liem, 2019; Xu and Li, 2019; Tran and Vo, 2018).

Stock Exchange (HNX)) This dissertation utilizes data from the year 2011 to 2018. Firms used in this study have been operating continuously for the entire research period, not being closed or merged with other companies Firms with missing data for

4 years or more and negative data are not included in this sample After removing nonconforming samples (599 firms), this study uses a sample of 150 firms The sample to population ratio is 20,03%.

Figure 3.1 Number of listed firms

These firms are classified into two sectors: financial and non-financial This classification is based on previous studies (Ali et al., 2022; Buallay et al., 2020; Soetanto and Liem, 2019; Xu and Li, 2019) Ali et al (2022) argue that the financial firms consist of: financial institutions, banking, leasing, insurance, credit unions, asset management organizations, etc Based on Global Industry Classification Standards, this dissertation divides listed firms in Vietnam into the following two groups The financial sector includes: banking, securities, insurance Meanwhile, the remaining sectors belong to the non-financial sector.

In addition, this study continues to use data collected from annual reports of firms to propose new sectoral intellectual capital index in the same period Firms with missing data and negative data are not included in this sample Based on the classification of the State Securities Commission (2020), the 150 firms mentioned above are classified into

12 sectors, including: aviation, banking, education, energy, food, insurance, oil and gas, pharmaceutical, real estate, securities, services and technology.

In term of national level, data are collected from the World DevelopmentIndicators (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.

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 In addition, 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

In order to have recommendations and policy implications suitable to the actual situation in Vietnam, I have carried out an additional step of qualitative research The qualitative method is used to exploit the inner thoughts of survey subjects through information collected through observations, expert interviews or group interviews For this topic, I utilized the in-depth interview method of 05 experts in the field of intellectual capital (as shown in Annexure 3) In-depth interviews help the author interact with respondents through face-to-face interviews, thereby collecting opinions and views of experts on the research issue In addition, when applying expert interviews, the author also controlled the responses systematically and clearly (Silverman, 2016) The in-depth interviews were conducted from August 2022 to September 2022, duration from 90 to 150 minutes, and the location was conducted at the respondent's office The procedure was conducted through face-to-face appointments at convenient times for experts, documents and interview questions were sent in advance to the subjects via email.

In this dissertation, qualitative research is carried out through 3 specific steps as follows:

- First, the author prepared an outline for an in-depth interview The purpose of this step is to get a clear direction for the content of the interview.

- Second, I conducted in-depth interviews to serve as the basis for the author's recommendations and policy implications After introducing the objective and expected content of the interview about intellectual capital and its impact on firm performance, the author started asking questions based on the prepared interview The interview process started with asking questions to the respondents, listening to the respondents' views, discussing with the respondents the views of the research, the contents of the research, drawing conclusions and agreeing on the same point of view on the issue In this study,research data was obtained through interviews with respondents who are experts in the intellectual capital field The data were then grouped according to the research objective and analyzed in sequence The content of the in-depth interview revolved around the following 4 main questions:

1 What drives you to be interested in intellectual capital?

2 In your opinion, what components does intellectual capital include? Which component of intellectual capital is the most important?

3 According to you, is there a difference in intellectual capital in different types of firms (financial firms versus non-financial firms)?

4 In your opinion, what should be done to improve the intellectual capital and firm performance in Vietnam?

- Third, I synthesized data and wrote reports of qualitative research The author grouped the data according to the research objective Then, I summarized the ideas that are outstanding, have many interested respondents, and are important to the research topic After that, the author compiled and edited the content that was of interest and comments by the respondents.

The results of the expert interviews are summarized as follows: All five experts stressed that the 4.0 technology revolution requires businesses to pay more attention to the exploitation of intangible assets, especially intellectual capital In fact, although firms have invested in current software and technology to serve their operations, all activities require human control and supervision Therefore, human capital is still the most important component of intellectual capital In addition, experts also stated that there is a difference in intellectual capital between financial firms and non-financial firms in Vietnam Non-financial firms, especially manufacturing firms in Vietnam, use mostly unskilled workers, the rate of professional training is still low, so human capital is lower than in other firms In addition, due to limited financial resources, or management perspective, most manufacturing firms in Vietnam have not yet paid attention to investing in modern production technology Moreover, most manufacturing firms in Vietnam have not paid much attention to improving their relationship capital (through promotion and product brand image building) Therefore,all three components of intellectual capital in non-financial firms, including human capital, structural capital and relational capital, are lower than in financial firms In order to improve the impact of intellectual capital on firm performance, experts agree efficiency.

The results of in-depth interviews give opinions and views on the importance of intellectual capital, components of intellectual capital and solutions to improve intellectual capital for firms in Vietnam In addition, from these results, the author has also drawn recommendations and policy implications related to intellectual capital and its influence on firm performance, as follow: 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 firms in Vietnam 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 There is a difference in intellectual capital between financial firms and non-financial firms. Employees in financial firms are more well-trained and homogeneous In addition, financial firms are also pioneers in applying modern technology to business activities. Therefore, financial firms are considered to have higher intellectual capital than non- financial firms Training and professional development for employees are essential for Vietnamese firms to improve human capital, a very important component of intellectual capital In addition, Vietnamese firms also need to improve production and management processes in the direction of modernization Firms need to consider investing in modern production technology and applying information technology to production and business activities Thereby, contributing to the improvement of structural capital, one of the components of intellectual capital.

The questions, answers and implications are detailed in Annexure 3.

3.2.2 Assess the impact of intellectual capital on the performance of sector and nation

First, this study uses Pearson's correlation test to examine the correlation between variables Pearson correlation coefficient is used to test the relationship between variables in the research model It provides information about the degree and direction of the relationship In addition, the phenomenon of multicollinearity between the independent variables can also be detected through the pearson correlation coefficient. Pearson correlation coefficient (r) fluctuates in the continuous range from -1 to +1:

 r = 0 Two variables have no linear correlation.

 r = 1; r = -1 Two variables have an absolute linear relationship.

 r < 0 Negative correlation coefficient That is, the value of independent variable increases, the value of dependent variable decreases and vice versa.

 r > 0 Positive correlation coefficient That is, the value of variable x increases, the value of variable y increases and vice versa.

In addition, the degree of correlation is determined as follows:

 If 0.50 < |r| then it is called to be strong correlation.

 If 0.30 < |r| < 0.49 then it is called the medium correlation.

3.2.2.2 Variance inflation fact then it is called a weak correlation. or (VIF) test

Second, author tests the phenomenon of multicollinearity through the variance inflation factor (VIF) The problem of multicollinearity occurs when the independent variables in the model are highly correlated Multicollinearity can negatively affect the weight estimation and the level of statistical significance VIF values less than 10 show no multicollinearity between the two research variables (Gujarati, 2003).

One disadvantage of panel data with a large number of observations in short time series often arises autocorrelation This is the phenomenon where the error at t is related to the error at t-1 or at any other time in the past Traditional regression methods such as ordinary least squares (OLS), fixed effects (FE), and random effects (RE) will be ineffective when the model exists autocorrelation (Ullah et al., 2018) This study uses Wooldridge (2002) test to check for autocorrelation, with the assumption that H0 is the model that does not exist autocorrelation.

Heteroskedasticity is the phenomenon where the variances of the estimated errors are not equal The main reason for the occurrence of this phenomenon is probably due to the existence of observations of the variable whose values are too different from the rest of the observations Besides, the observations of the same variable but measured with different scales are also the cause heteroskedasticity This study uses the Modified Wald test (Baum, 2001) to explore heteroskedasticity.

Cross-sectional dependence is a common issue in panel data Hence, this dissertation utilizes Pesaran (2004) and Pesaran (2015) CD test to explore the presence of cross-sectional dependence The Pesaran CD test is calculated as follows:

Where: 𝜌̂ 2 denotes the correlation coefficients of residuals Besides, N and T indicate the cross-section dimension and time dimension.

This dissertation uses the slope homogeneity test suggested by Pesaran and Yamagata (2008) The test statistics are presented below.

 𝛽 𝑖 presents the estimate obtained from pooled ordinary least squares;

 𝛽 𝑊𝐹𝐸 shows the estimate obtained from the weighted fixed effect pooled estimator;

 𝜒 𝑖 denotes the matrix of independent variables in deviations from the mean;

 𝑀 𝑇 demonstrates the identity matrix, 𝜎̃ 2 is the estimate of 𝜎 2 , k indicates the

In addition, the bias-adjusted form ∆ is estimated as follows:

In order to avoid spurious regression, I employ the panel unit-root test to identify the stationary properties of the relevant variables The LLC (Levin et al., 2002) test is based on the ADF test with the proposition of the homogeneity in the dynamics of the auto-regressive coefficients across countries The model in a panel framework is expressed as follows: Δyy it = α i d it + ρ i y it−1 + 𝛴 𝑛 𝑖 ∅ i,j Δyy it-j + ε it (5)

Where: d indicates the deterministic component; ∅ denotes the individual deterministic component; ρ signifies the auto-regressive coefficient; i 1, 2, 3,… N and t = 2000, 2001,…T represent countries and time spam, respectively; and ε is the error term.

The IPS (Im et al., 2003) test is emplored which allows for heterogeneous autoregressive coefficients It proposes averaging the ADF unit-root tests while allowing for different orders of serial correlation This dissertation utilized the average of the t_i statistics from Equation (5) in order to present the following Z statistic:

Where: 𝑡̅ = 1 ∕ 𝑁(𝛴 𝑁 𝑡 𝛽 ); 𝐸(𝑡̅) indicates mean and 𝑉(𝑡̅) signifies variance of

ⅈ=1 𝑖 each t_i statistic, and they are generated by simulations Z concentrates to a standard normal distribution This test is also based on the averaging individual unit-root test, denoted by 𝑡̅ = 1 ∕ 𝑁(𝛴 𝑁 𝑡 𝛽 ) The Fisher’s

(1932) unit-root test combines the p-values from individual unit-root tests To test the null hypothesis (H0) is that variable has a unit root, I use non-parametric test statistic based on Fisher test as given below(Fisher, 1932; Maddala and Wu, 1999):

In addition, this dissertation also utilizes second-generation panel unit root test, proposed by Pesaran (2003) to consider the stationarity of the considered variables. The test statistic is as follows:

Where: N denotes the number of panels 𝐶𝐴𝐷𝐹 𝑖 statistics are t-statistics of the following equation:

Where: ∆𝑦̅ 𝑡−𝑗 and 𝑦̅ 𝑡−𝑗 indicate cross-sectional averages of first differences and lagged levels of 𝑦, respectively.

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 toCEE, 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 a99% 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 theMVAIC 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 andRCE 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 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.

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 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 inVietnam 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.

Securities Energy Food Real estate

Edu Pharma Oil & gas Tech

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 WorldBank. 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

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

A rg en tin a A us tr al ia A us tr ia B el gi um B ra zi l B ul ga ri a C an ad a C hi le C hi na C ol om bi a C ze ch R ep ub li c D en m ar k Fi nl an d Fr an ce G er m an y G re ec e H on g K on g H un ga ry Ic el an d In di a In do ne si a Ir el an d Is ra el It al y Ja pa n Jo rd an K or ea M al ay si a M ex ic o N et he rl an ds N ew Z ea la nd N or w ay Ph ili pp in es Po la nd Po rt ug al R om an ia R us si a Si ng ap or e S ou th A fr ic a Sp ai n Sw ed en Sw itz er la nd T ha ila nd T ur ke y U ni te d K in gd om U SA V en ez ue la

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 are 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, NorthAmerica and South America regions have six countries, eight countries and eight 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-

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.

Finland Oman Belarus Sri Lanka

INIC Lower middle income INIC Low income

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 ofSeven, including Canada, France, Germany, Italy, Japan, the United Kingdom and theUnited 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.

Germany United States United Kingdom

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

INIC India Indonesia INIC Kazakhstan Korea, Rep.

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

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

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

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

4 ROE it = _ 0 + _ 1 INIC it + _ 2 HCE it + _ 3 SCE it + _ 4 CEE it + _ 5 RCE it + _ 6 SIZE it + _ 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

(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

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 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

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 = _ 0 + _ 1 SICI it +_ 2 SIZE it + _ 3 LEV it + Ɛ it

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

Table 5.5 presents the descriptive statistics of all variables The average ROA andROE 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 andService 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 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 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 andGranger, 1987) As presented in Table 5.8, the results confirm bidirectional causality relationship between SICI and ROE In addition, the causality relationship betweenSICI and ROA is not statistically significant The results of these causal relationships between sectoral intellectual capital index and sector performance are summarized inFigure 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.

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

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 –

LEV SICI SIZE 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.

(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 _1, _2, _3, _4, and _5 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.

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 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;

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 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

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

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).

In addition, I also find that trade openness positively contributes to national performance in the Asia-Pacific economies This finding can be justified in that the Asia-Pacific economies have pushed forward trade liberalization policies and relaxed restrictions on capital ownership of foreign firms (Ahmed, 2017) These policies have provided significant and positive effects on national performance in these economies.

My empirical findings confirm a negative and significant effect of government expenditure on national performance A standard economic theory considers that increasing government spending may boost national performance (Tahir et al., 2023). However, corruption and poor management of public investment and expenditure exist at a large scale in emerging markets in the Asia-Pacific countries (Azam andEmirullah, 2014) As such, the costs of these social and economic issues may outweigh the benefits concerning the public expenditure Therefore, the benefit of government spending to national performance is not as clear as expected.

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 have overlooked to measure intellectual capital efficiency at the sector level This study 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 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 theAsia-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 sheds light on the view that while national intellectual capital contributes to national performance, national performance will also enhance an accumulation of national intellectual capital in the Asia-Pacific region.

Contributions 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 inEastern 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 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

Indicators, can be used as the reliable proxies for the three fundamental aspects of intellectual 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

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 findings from this study also offer several managerial implications for both financial and non-financial firms in emerging market, like Vietnam As mentioned, financial firms are knowledge-intensive; therefore, owning a firm's knowledge-based assets is very important (Mathew et al., 2020) Knowledge-based assets such as intellectual capital are specific, and not easily imitated by other firms (Tran and Vo, 2018) Hence, having a high level of intellectual capital will give financial firms in emerging markets, such as Vietnam, a competitive advantage in the market Resource- based theory suggests that resources are heterogeneously distributed and that firms have a competitive advantage in the market based on the resources that firms possess (Lahiri, 2013) Financial firms in Vietnam need to pay more attention to the role of intellectual capital and its components Specifically, Mathew et al (2020) argue that a firm with high levels of human capital, innovation, service capabilities, and relationship value possesses a pool of high-quality resources capable of helping the company gain an advantage in the market In particular, superior human resources with knowledgeable and skilled employees can increase competitive advantages for financial firms (Zaheer, 1995).

Managers can use intellectual capital and its components to develop strategy,shape firm culture, and achieve financial goals (Mathew et al., 2020) The dissertation's findings indicate that managers can develop training programs for their employees in certain functional areas of the business to help them develop the critical skill sets needed In addition, the firm manager should encourage employees to practice innovation, improve operational processes, and apply new techniques and initiatives to operations to contribute to the creation of new patents and product brands, meet changing market needs and ensure long-term success (Radulovich et al., 2018) In addition, taking care of the interests of stakeholders also has implications for the company's performance Business development strategies must not only focus on human capital but also pay attention to social welfare In the trend of increasingly paying attention to social and environmental issues, firms need to harmonize the interests of businesses and stakeholders to attract investors and customers.

Moreover, the results of this dissertation also reveal that structural capital has a significant contribution to non-financial firm performance Meanwhile, the contribution of human capital is unclear Hence, it is imperative for non-financial firms in Vietnam to increase their intellectual capital to enhance their performance, especially human capital.

A framework for the role of intangible assets (via sectoral intellectual capital index) in sector performance is developed to address this gap and provide a more systematic way of thinking on the effect of intellectual capital on sector performance.This approach explicitly acknowledges that intellectual capital has a significant contribution to boosting sector performance Developing a more detailed understanding of the intellectual capital-performance relationship can provide insight into the role of intangible assets (and in particular intellectual capital) across sector inVietnam This shows the important role of intellectual capital in enhancing sector performance, which is largely ignored in previous studies Policy implications have emerged based on the above findings for regulators and policymakers Improving human capital efficiency and training, and improving the professionalism for employees Besides, firms also need to better utilize a structural capital Specifically,processes, facilities, and intellectual property should be invested and utilized more effectively to improve the efficiency of intellectual capital My results confirm the positive effect of financial leverage on sector performance Hence, firm managers need to consider an optimal debt structure to add value to the business.

The contributions of this study to the existing literature on intellectual capital are twofold First, on the theoretical level, I extend the current literature by dealing with the explanatory factors for national performance This study analyzes the impacts of national intellectual capital on national performance based on the knowledge-based theory The results largely support the knowledge-based theory regarding the role of the national intellectual capital in enhancing national wealth On the methodological level, I apply a newly developed index of national intellectual capital (INIC) In addition, previous studies have examined the direct impact of national intellectual capital on national performance using conventional regression methods, such as a correlation analysis (Lin, 2018; Lin and Edvinsson, 2011); partial least squares - PLS (Seleim and Bontis, 2013; Bontis, 2004); analysis of variance - ANOVA (Macerinskiene and Aleknaviciute, 2017) My research uses the dynamic common correlated effects (DCCE) method to overcome fundamental problems in panel data analysis concerning the cross-sectional dependence and slope heterogeneity.

Policy implications have emerged on the ground of these findings from this analysis Managers and policymakers are increasingly interested in the critical role of intangible assets as the potential sources for creating national wealth Based on an innovative index of national intellectual capital, this study provides a comprehensive picture of the level of national intellectual capital across 104 countries around the globe My results show that low-income countries in developing regions have achieved the lowest level of intellectual capital accumulation in the last 20 years compared to other regions As such, to improve the competitive advantage for these poor countries,policymakers in these countries and international organizations should focus on improving the accumulation of the national intellectual capital In addition, developed countries also need to support and share intangible resources with developing

Limitations and suggestions for future research

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.

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 In addition, this study focuses on the construction