Relationship between intellectual capital and firm performance an empirical study of tokyo stock exchange in japan

VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY LA THU THUY RELATIONSHIP BETWEEN INTELLECTUAL CAPITAL AND FIRM PERFORMANCE: AN EMPIRICAL STUDY OF TOKYO STOCK EXCHANGE IN JAPAN MASTER'S THESIS BUSINESS ADMINISTRATION Hanoi, 2018 VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY LA THU THUY RELATIONSHIP BETWEEN INTELLECTUAL CAPITAL AND FIRM PERFORMANCE: AN EMPIRICAL STUDY OF TOKYO STOCK EXCHANGE IN JAPAN MAJOR: BUSINESS ADMINISTRATION CODE: 60340102 RESEARCH SUPERVISORS: DR DO XUAN TRUONG PROF DR DANIEL ARTURO HELLER Hanoi, 2018 ACKNOWLEDGEMENT I would first like to thank my thesis advisors Prof Dr Daniel Arturo Heller of the Graduate School of International Social Sciences - Department of Business Administration at Yokohama National University and Dr Do Xuan Truong of the Department of Business Administration at University of Economics and Business, Vietnam National University Without their assistance and dedicated involvement in every step throughout the process, this paper would have never been accomplished I would like to thank you very much for your support and understanding during the time I did my research I would also like to thank all the professors and faculty members in Vietnam Japan University and Yokohama National University for the past two years and especially the four-month internship in Japan Without their helps and supports, this thesis could not have been successfully conducted Finally, I must express my very profound gratitude to my parents and to my husband for providing me with unfailing support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis This accomplishment would not have been possible without them Thank you Author La Thu Thuy TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1.1 Background of the study 1.2 Objective and Research Question 1.3 Scope of the study 1.4 Structure of the study CHAPTER 2: LITERATURE REVIEW 2.1 Intangible Assets 2.2 Intellectual Capital 2.2.1 The Origins and Definition of Intellectual Capital 2.2.2 Intellectual Capital Management Models 12 2.2.3 Intellectual Capital Valuation Methods 17 2.3 Firm Performance 25 2.3.1 Definition of firm performance 25 2.3.2 Measurement of firm performance 27 2.4 The relationship between Intellectual Capital and Firm Performance 29 2.4.1 Empirical research on the relationship between intellectual capital and firm performance 29 2.4.2 Empirical research in Japan 34 2.4.3 Hypotheses Development 34 CHAPTER 3: RESEARCH METHODOLOGY 36 3.1 Research methodology 36 3.1.1 Variables 36 3.1.2 Regression Equations 37 3.2 Data and Sample 38 CHAPTER 4: FINDINGS AND DISCUSSIONS 39 4.1 Empirical results 39 4.1.1 Unit root test 39 4.1.2 Pearson correlation analysis and Variance Inflation Factor (VIF) 39 4.1.3 Breusch-Pagan test 41 4.1.4 Wooldridge test 41 4.1.5 Hausman test 42 4.1.6 Impacts of Intellectual Capital on Firm Performance 42 4.1.7 Impacts of Firm Performance on Intellectual Capital 45 4.2 Findings and Discussion 47 CONCLUSION 50 APPENDIX 52 BIBLIOGRAPHY 60 LIST OF ABBREVIATIONS IC Intellectual Capital FP Firm Performance RBV Resource-based view M&A Merges and Acquisitions SMAC The Society of Management Accountants of Canada ICM Intellectual Capital Management MCM Market Capitalization Method ROA Return on Assets SC Scorecard DIC Direct Intellectual Capital Method VAIC Value-Added Intellectual Coefficient EVA Economic Value Added HC Human Capital SC Structural Capital CE Capital Employed ATO Assets Turnover PB Price to Book Ratio LIST OF FIGURES Figure 2.1: The Value Platform 10 Figure 2.2: Pulic´s conceptualization of a company´s total resources in VAIC model 23 Figure 2.3: The domain of business performance 27 LIST OF TABLES Table 2.1: Intellectual Capital Management Models 13 Table 2.2: Intellectual Capital Valuation Models 17 Table 2.3: Some selected empirical research on IC and firm performance 32 Table 2.4: Location of IC research 34 Table 3.1: Description of variables 37 Table 4.1: Unit root test results 39 Table 4.2: Correlation analysis of independent and dependent variables 40 Table 4.3: VIF results 40 Table 4.4: Multiple regression results of Model 42 Table 4.5: Multiple regression results of Model 43 Table 4.6: Multiple regression results of Model 45 Table 4.7: Summary of hypotheses testing results 46 CHAPTER 1: INTRODUCTION 1.1 Background of the study The transition from a traditional retail and manufacturing-based economies to knowledge-based, technologically intensive economy has turned intellectual capital (IC) into one of the primary factors for building sustainable competitive advantage (Menor, Kristal, & Rosenzweig, 2007; Zéghal & Maaloul, 2010) The emergence of knowledge economy has not only opened up brand new markets but also unveiled an innovative means of doing business which does not require huge physical assets New rapidly growing start-ups with higher Return on assets (ROA) and lower marginal costs are much more profitable than many old-style companies, for example: Uber, Snapchat or Airbnb Literature has shown a similar factor in their business models, which is the focus on intangible assets (Libert, Wind, & Fenley, 2014) The very first appearance of Intellectual Capital was published in 1969 by Galbraith in the book “The New Industrial State” He depicted intellectual capital as an important part of company’s value creation process along with a bundle of assets all at once (Galbraith, 1969) Intellectual capital can be defined simply as a set of intangible assets which contribute to the value creation process of the companies During the last two decades, within a highly competitive and ever-changing market, investment in intangible assets have increased rapidly (Menor, Kristal, & Rosenzweig, 2007; Zéghal & Maaloul, 2010) Despite the clear evidence of contribution to firm performance (FP), IC is not disclosed fully in balance sheets (Wyatt & Abernethy, 2008; Dumay, 2016; Wang, Sharma, & Davey, 2016) Thus, firms’ value is not illustrated fully by financial numbers This disclosure is partly due to some conventional accounting standards which restrict the disclosure of intangible assets, a lack of systematic framework for IC reporting or the inconclusiveness results of existing literature Some studies found a positive relationship between IC and performance (Chen, Cheng, & Hwang, 2005; Zéghal & Maaloul, 2010; Clarke, Seng, & Whiting, 2011; Gigante, 2013); while other found no significance (Firer & Williams, 2003; Maditinos, Chatzoudes, Tsairidis, & Theriou, 2011; Joshi, Cahill, Sidhu, & Kansal, 2013) Recently, the relationship between IC and financial performance is suggested as bidirectional (Nadeem, Gan, & Nguyen, 2017), then it is the case of dynamic relationship The application of static estimators which have been done in literature so far will lead to biased results (Baltagi, 2008; Gujarati, 2012) In case of Japan, the relationship between IC and FP have not been widely studied by empirical research, which is surprising given the long history of intangible assets being reviewed as a key resource of competitive advantage of Japanese firms Study has been conducted only in Japanese banking sector (Mavridis, 2004) Therefore, it can be said that there is a lack of empirical study on intellectual capital in Japan, within a long period of time and wider scope 1.2 Objective and Research Question In light of the beforementioned discussion, the aim of this study is to investigate the two-way relationship between firms’ performance and intellectual capital through system generalized methods of moments, in order to contribute to the missing link in IC literature Research Question: How does intellectual capital and its component (human capital, structural capital, capital employed) affect firms’ performances? Inversely, does firm performance affect intellectual capital? 1.3 Scope of the study This study explores the relationship between intellectual capital and firm performance in publicly Japanese companies in Tokyo Stock Exchange 575 Japanese firms with available data have been chosen for sample 1.4 Structure of the study The structure of the study is as follows: chapter discusses intangible assets and intellectual capital as a part of them; why intellectual capital matters and its link with financial performance in literature Thus, hypotheses can be developed to test Chapter describes the methodology used in this study and sample of Japan Chapter analyses the regression results and main findings Lastly, chapter provides some discussions as well as limitations of this study CHAPTER 2: LITERATURE REVIEW 2.1 Intangible Assets Competitive advantage has been developed parallel in both the technique management theory and practice In the realm of practice, several perspectives and trends can be single out such as: knowledge as a sustainable competitive advantage, intangible components of a product (e.g customer satisfaction), concentration on intangible assets and investment in R&D In the realm of theory, two competing perspectives for explaining competitive advantage have dominated the technique literature over the last two decades: the position-based framework (also known as environmental models) as well as the resource-based view (Spanos & Lioukas, 2001) Developed by Porter, the position-based framework explains competitive advantages by the way a business adapts and also uses important attributes in an industry (e.g competitive rivalry, threat of substitute or new entry) Positioning considers even external factors, which places the business within an industry and looks at how the organization enhances its strategic positioning within that industry (Mintzberg, 2013) The different, but complementary framework, the resource-based view (RBV) emerged in 1980s and 1990s, after the major works such as “The Resource-Based View of the Firm” (Wernerfelt, 1984), “The Core Competence of The Corporation” (Prahalad & Hamel, 1990), “Firm resources and sustained competitive advantage” (Barney, 1991) Instead of looking at external environment as the position-based framework, the supporters of this particular perspective argue that organizations need to look for internal sources within the company In RBV model, resources are provided the major role in achieving high performance There are two types of resources: tangible and intangible Panel means: Included Time trend: Included Cross-sectional means removed ADF regressions: lag LR variance: Bartlett kernel, 6.00 lags average (chosen by LLC) -Statistic p-value -Unadjusted t -94.3004 Adjusted t* -64.4375 0.0000 Fixed effects Regression model Fixed-effects (within) regression Group variable: n1 Number of obs = Number of groups = R-sq: 7,670 767 Obs per group: within = 0.589 = between = 0.539 10 avg = overall = 0.507 10.0 max = F(3,766) corr(u_i, Xb) = -0.2458 = Prob > F 10 10.55 = 0.0000 (Std Err adjusted for 767 clusters in n1) -| ROA | Robust Coef Std Err t P>|t| [95% Conf Interval] -+ -VAIC | 2731688 090576 ATO | 0256609 054555 3.02 0.000 045094 095376 4.70 0.000 0149514 0363704 56 PB | 2567907 2.43e-07 _cons | 0246641 0128697 1.05 0.093 -2.22e-07 7.33e-07 1.92 0.056 -.0006001 0499282 -+ -sigma_u | 06397972 sigma_e | 04527915 rho | 66628692 (fraction of variance due to u_i) Fixed effects Regression model Fixed-effects (within) regression Group variable: n1 Number of obs = Number of groups = R-sq: 7,670 767 Obs per group: within = 0.738 = between = 0.794 10 avg = overall = 0.701 10.0 max = F(5,766) corr(u_i, Xb) = -0.1708 = 10 1829.01 Prob > F = 0.0000 (Std Err adjusted for 767 clusters in n1) -| ROA | Robust Coef Std Err t P>|t| [95% Conf Interval] -+ -HCE | 1473978 0048022 3.07 0.000 0241667 0053127 SCE | 1785389 294.1812 2.68 0.007 1367.008 212.0136 CEE | 6110985 22.02692 0.28 0.007 ATO | 0266582 0058619 4.55 0.000 PB | 24188605 2.41e-07 1.00 0.003 37.12931 49.35127 015151 0381654 2.32e-07 7.13e-07 57 _cons | 789.5417 294.1858 2.68 0.007 212.0355 1367.048 -+ -sigma_u | 06116978 sigma_e | 04501534 rho | 64869323 (fraction of variance due to u_i) Fixed effects Regression model Fixed-effects (within) regression Number of obs Group variable: n1 = Number of groups = R-sq: 7,670 767 Obs per group: within = 0.597 = between = 0.574 10 avg = overall = 0.529 10.0 max = F(3,766) corr(u_i, Xb) = 0.1432 = 10 3.80 Prob > F = 0.0102 (Std Err adjusted for 767 clusters in n1) -| VAIC | Robust Coef Std Err t P>|t| [95% Conf Interval] -+ -ROA | 2825075 1.771685 1.59 0.000 ATO | 2278644 1306753 PB | 2597559 6.44e-07 _cons | 1.81576 1725301 6.303008 6528588 1.74 0.082 0.92 0.036 -.0286598 4843886 -1.86e-06 10.52 0.000 6.74e-07 1.477072 2.154448 -+ -sigma_u | 1.0260958 sigma_e | 1.4561573 58 rho | 33179454 (fraction of variance due to u_i) 59 BIBLIOGRAPHY Ahmadi, S A (2012) Intellectual capital and 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UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY LA THU THUY RELATIONSHIP BETWEEN INTELLECTUAL CAPITAL AND FIRM PERFORMANCE: AN EMPIRICAL STUDY OF TOKYO STOCK EXCHANGE IN JAPAN MAJOR: BUSINESS ADMINISTRATION... firm performance affect intellectual capital? 1.3 Scope of the study This study explores the relationship between intellectual capital and firm performance in publicly Japanese companies in Tokyo. .. between Intellectual Capital and Firm Performance 29 2.4.1 Empirical research on the relationship between intellectual capital and firm performance 29 2.4.2 Empirical research in Japan