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

MINISTRY OF EDUCATION AND TRAINING HO CHI MINH CITY OPEN UNIVERSITY

TRAN PHU NGOC

INTELLECTUAL CAPITAL AND ITS EFFECTS ON THE PERFORMANCE OF FIRMS, SECTORS AND NATIONS

SUMMARY OF DOCTORAL DISSERTATION IN BUSINESS ADMINISTRATION

HO CHI MINH CITY – 2024

MINISTRY OF EDUCATION AND TRAINING HO CHI MINH CITY OPEN UNIVERSITY

TRAN PHU NGOC

INTELLECTUAL CAPITAL AND ITS EFFECTS ON THE PERFORMANCE OF FIRMS, SECTORS AND NATIONS

HO CHI MINH CITY – 2024

CHAPTER 1 INTRODUCTION

Previous studies have confirmed the role of creating competitive advantages and improving firm's performance (Bayraktaroglu et al., 2019; Xu and Wang, 2019; Chen et al., 2015) However, previous studies have neglected to measure intellectual capital at the sector and country level, and examine its role in the performance of sector and nation Hence, this study is conducted to measure intellectual capital and examines its effect on the performance of firms, sectors and nations

1.2 Research objectives

1.2.1 The main objective

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

1.2.2 Specific objectives

The objectives of this study are summarized as follows: 1) To measure intellectual capital for firms, sectors and

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

performance of firms and sectors in Vietnam; and on performance of nations?

1.4 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

1.5 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;

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

1.6 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:

Source: Author's synthetic

Research problems

Review of the theoretical foundation and previous studies

Collect data

Analyze and interpret data

Conclusions and implications

• 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

firms, sectors and nation Along with that, the study proposes solutions to promote efficiency in the use of intellectual capital, contributing to increase the efficiency of firm, sectors and countries

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

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

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

Source: Author's synthetic

Intellectual capital

Intellectual capital measurements

The effects of intellectual capital on performance

Firm level

Sector level

National level Modified value-added

intellectual coefficient (MVAIC) model

Sectoral intellectual capital index (SICI)

Index of national intellectual capital (INIC)

- Return on assets (ROA) - Return on equity (ROE)

- Return on assets (ROA) - Return on equity (ROE)

Economic growth

CHAPTER 2 LITERATURE REVIEW

2.1 Definitions and classifications

Various models have been used to classify intellectual capital, including:

2.1.1 Saint-Onge’s model: proposed by Westberg and Sullivan

(1998)

2.1.2 Sveiby’s model: introduced by Sveiby (1997)

2.1.3 Skandia intellectual capital value scheme: proposed by Edvinsson and Malone (1997)

2.2 Relevant theories

2.2.1 Resource-based theory

The resource-based view (Wernerfelt, 1984; Barney, 1991) 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

2.2.2 The knowledge-based theory

Knowledge-based theory is a recent extension of resource-based theory 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)

2.2.3 Performance-based theory

Performance measurement has been an integral part of management since its inception By 1925, many methods and techniques of measuring financial 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)

2.3 Measuring intellectual capital: traditional methods

Various methods have been used to measure intellectual capital, including:

2.3.1 Balanced scorecard: introduced by Kaplan and Norton (1992)

2.3.2 Technology Broker: proposed by Brooking (1996) 2.3.3 Intangible assets monitor: developed by Sveiby (1997) 2.3.4 Skandia navigator: introduced by Edvinssion and Malone

Poyhonen and Smedlund (2004) examine region intellectual capital by differentiating three modes of intellectual capital creation, including: production network, innovation network and development network In addition, Edvinsson and Bounfour (2004) examine intellectual capital dynamic value (IC-dVAl) approach to measure intellectual capital performance at regional level in France Xia and Niu (2010) propose a system of 27 indicators to measure regional intellectual capital of 29 provinces and cities of China Pedro et al (2018) emphasizes the need to develop a new sector approach to intellectual capital in relation to sector development theories Liu et al (2021) utilize a set of multiple‐criteria decision‐making to evaluate the regional intellectual capital of 31 provinces in China

2.4.2 National intellectual capital measurements

Bontis (2004) suggests one of the most common indicators designed to evaluate the national intellectual capital, which is named the National Intellectual Capital Index (NICI) using data of 10 Arab countries

Lin and Edvinsson (2011) measure a national intellectual capital as a composite index which includes human capital, market capital, process capital, renewal capital and financial capital

Kapyla et al (2012) propose a new structural model of national intellectual capital This model expands previous models by adding social capital component

2.5 Measuring performance of firm, sector and nation

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

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

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)

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

2.6.3 Intellectual capital and national performance

Dal Mas (2019) affirms the link between intellectual capital and sustainable competitiveness Suciu and Nasulea (2019) also state that intellectual capital is crucial 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

CHAPTER 3 METHODOLOGY

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) The author extracts data from the website https://cafef.vn The firms selected in this study are leaders in their respective businesses in terms of market capitalization and are listed in the Vietnam stock market (Ho Chi Minh Stock Exchange (HOSE) and Hanoi 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%

3.2.1.2 Qualitative method

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

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

3.2.2.1 Pearson correlation coefficient test: examine the

correlation between variables

3.2.2.2 Variance inflation factor (VIF) test: tests the phenomenon

of multicollinearity

3.2.2.3 Autocorrelation test: check for autocorrelation, with the

assumption that H0 is the model that does not exist

autocorrelation

3.2.2.4 Heteroskedasticity test: this study uses the Modified Wald

test (Baum, 2001) to explore heteroskedasticity

3.2.2.5 Generalized method of moments (GMM): can be used to

deal with three sources of endogeneity, namely,

unobserved, simultaneous, and dynamic endogeneity

3.2.2.6 Cross-sectional dependence test: explore the presence of

cross-sectional dependence

3.2.2.7 Slope homogeneity test: check for slope homogeneity 3.2.2.8 Unit root test: identifies the stationary properties of the

relevant variables

3.2.2.9 Panel cointegration test: determine if a long-run

relationship exists among the variables

3.2.2.10 Dynamic common correlated effects (DCCE): This study

utilizes the dynamic common correlated effects (DCCE), proposed by Chudik and Pesaran (2015a) to explore the

relationship between sectoral intellectual capital index and

sector performance

3.2.2.11 Panel Granger causality test: This study uses the

panel-based VECM (Engle and Granger, 1987) to examine the

causality relationship of employed variables

3.3 Variables: definitions and measurements

3.3.1 Measuring intellectual capital at firm level

To measure intellectual capital at firm level, this study uses the MVAIC model

3.3.2 Sectoral intellectual capital index

Based on the modified value-added intellectual coefficient (MVAIC) model, this study proposes the sectoral intellectual capital index (SICI) by examining the intellectual capital efficiency of each firm in the sector

3.3.3 New index of national intellectual capital

I decide to take a different approach with the aim of developing an index of national intellectual capital (INIC) which can be used and applied for comparison across countries

3.3.4 Other variables

This study uses return on assets (ROA) and return on equity (ROE) as two proxies for firm performance ROA reflects profitability relative to total assets ROE represents the profit available for ordinary

shareholders ROE is calculated as the ratio between net profit and total equity

Two variables are used as proxies for firm characteristics: SIZE, which is estimated as the natural logarithm of total assets, and LEV, the ratio between total debt and total assets

CHAPTER 4

MEASURING INTELLECTUAL CAPITAL: THE ANALYTICAL ANALYSIS

4.1 An intellectual capital level for Vietnamese listed firms

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

Source: Author's calculation

Table 4.2 Descriptive statistics for financial firms and financial firms

non-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

Source: Author's calculation

4.2 An intellectual capital across sectors in Vietnam

Source: Author's calculation

2011 2013 2015 2017Pharma0

2011 2013 2015 2017Edu

2011 2013 2015 2017Oil & gas

2011 2013 2015 2017TechSICI

SICI

4.3 Measuring national intellectual capital: a tale of two indices

Source: Author's calculation

Edvinsson, Chen and Beding (2014) index versus the INIC

4.4 A national intellectual capital across nations

4.4.1 National intellectual capital by region

5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0

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

Table 4.3 National intellectual capital by region

Region

No of Countr

y

Average of Human

capital

Average of Structural

capital

Average of Relational

capital

Average of INIC

Source: Author's calculation

As denoted in Figure 4.3, all seven regions have increased

Source: Author's calculation

4.4.2 National intellectual capital by income

0.20 0.40 0.60 0.80 1.00

South AmericaINIC

Table 4.4 National intellectual capital by income

By income No of Country

Average of Human

capital

Average of Structural

capital

Average of Relational

capital

Average of INIC

Source: Author's calculation

Source: Author's calculation

0.20 0.40 0.60 0.80 1.00

INIC

Source: Author's calculation

years in some countries -

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

High income

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

-Upper middle income

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

-Lower middle income

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

-Low income

Source: Author's calculation

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

Source: Author's calculation

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

0.40 0.50 0.60 0.70 0.80 0.90

Group of Seven

United States

INIC

0.20 0.40 0.60 0.80 1.00

Top 10 biggest countries

INIC

Source: Author's calculation

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

0.50 1.00

0.50 1.00

-CambodiaChinaHong Kong

0.50 1.00

0.50 1.00

-KazakhstanKorea, Rep.Lao PDR

0.50 1.00

0.50 1.00

0.50 1.00

0.50 1.00

Source: Author's calculation

CHAPTER 5

EMPIRICAL RESULTS ON THE EFFECTS OF

INTELLECTUAL CAPITAL ON PERFORMANCE OF FIRM, SECTOR AND NATION

5.1 Intellectual capital and firm performance

Model Regression

1 ROAit = β0 + β1MVAICit + β2INICit + β3SIZEit + β4LEVit + Ɛit

2 ROEit = β0 + β1MVAICit + β2INICit + β3SIZEit + β4LEVit + Ɛit

3 ROA+ β7LEVit = βit + Ɛ0 + βit1INICit + β2HCEit + β3SCEit + β4CEEit + β5RCEit + β6SIZEit

4 ROEit = β0 + β+ β17LEVINICitit + Ɛ + βit2HCEit + β3SCEit + β4CEEit + β5RCEit + β6SIZEit

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

Source: Author's recommendation

5.1.1 Correlation analysis

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

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

ROA is the return on assets; ROE is the return on equity; IC represents intellectual capital; MVAIC is the modified value-added intellectual coefficient model Components of IC: HCE denotes human capital efficiency; SCE represents structural capital efficiency; CEE is capital employed efficiency; and RCE denotes relational