智慧資本對企業績效之影響研究，以台灣設計業為例

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(1)The Impact of Intellectual Capital on Business Performance A Study of Taiwanese Design Industry. by Wen-Chih Chen. A Thesis Draft Submitted to the Graduate Faculty in Partial Fulfillment of the Requirements for the Degree of MASTER OF EDUCATION Major: International Workforce Education and Development. Advisor: Tse-Ping Dong, Ph. D. National Taiwan Normal University Taipei, Taiwan July, 2009.

(2) ACKNOWLEDGEMENT This thesis could not be accomplished without the assistance and encouragement from my advisor, professors, IWED faculty and classmates, and my family. I would like to express my huge thank you to my dear advisor, Dr. Tse-Ping Dong, for continuously pushing me forward to pursue for high quality of my thesis; Dr. Cheng-Ping Shih, for helping me overcome many dilemmas I was facing; Dr. Chih-Lung. Chou,. for. providing. plenty. of. important. and. constructive. recommendations; Dr. Mong-Yuan Chang, for sparing time for coming to my thesis defense and provide me with many suggestions; Dr. Wei-Wen Chang, for giving me opinions about my survey. Also, I would like to thank my classmate Jeffrey, Jessica, Laura, Shelley, and Sophie for their company whenever I encountered frustrations in the process of work; Melton, for revising my poor English in the thesis; IWED office assistants, Abby and Lynn, who were always there whenever I had administrative problems. Special thanks to my former executives, Lisa and Jessica, for helping me with survey distribution; Ant, for giving me a big hand whenever I have problems; Oscar, for his great support within the years in my graduate school. Finally, I would like to show the greatest appreciation to my family, who supports me completely in pursuing for my master’s degree, without which I could hardly finish writing my thesis..

(3) Abstract Transforming into a knowledge-based economy, there is an increasing need for Taiwan to explore how intellectual capital creates value for companies. Thus, this empirical study aims to understand how intellectual capital influences the business performance, specifically in the context of Taiwanese design companies. An Intellectual capital questionnaire was adopted to measure the intellectual capital components, including human capital, structural capital, and relational capital. The population is managers of Taiwan’s design companies. 87 samples are collected and the data are analyzed by Partial Least Squares (PLS) and multiple regression through backward elimination method. The conclusions are listed below: 1.. PLS analysis pointed out that intellectual capital does have significant influence. on Taiwanese design companies’ performance. The positive influence of human capital to structural capital, structural capital to relational capital, and relational capital to business performance are proved to be significant. However, the model has higher explanatory power on younger companies or companies with fewer employees, which left room for future research improvement. 2.. Multiple regression results proved intellectual capital’s significant impact on. business performance. The outcomes discovered the characteristics of Taiwanese design industry and pointed out room for the industry’s improvement. Also, it improves the problem of PLS analysis. All the models have high explanatory power over Taiwanese design companies performance. Also, some recommendations for the government, Taiwanese deign company managers, and future researches were provided.. I.

(4) TABLE OF CONTENTS Abstract ..................................................................................................................... I Table of Contents ...................................................................................................... II List of Figures ........................................................................................................... IV List of Tables ............................................................................................................. IV. CHAPTER I. INTRODUCTION............................................................ 1 Background of the Study................................................................................... 1 Questions of the Study ...................................................................................... 2 Purposes of the Study ........................................................................................ 3 Significance of the Study .................................................................................. 3 Delimitations and Limitations........................................................................... 4 Definition of Terms ........................................................................................... 5. CHAPTER II. LITERATURE REVIEW ................................................ 7 Literature of Intellectual Capital and Business Performance ........................... 7 Literature of Taiwanese Design Industry .......................................................... 16. CHAPTER III. Research Methods.......................................................... 19 Research Hypotheses ........................................................................................ 19 Conceptual Framework ..................................................................................... 20 Research Procedure ........................................................................................... 21 Measurement Instrument .................................................................................. 23 Data Analysis Methods ..................................................................................... 24. CHAPTER IV. DESCRIPTIVE STATISTICS, PLS FINDINGS AND DISCUSSION ......................................................................................... 30 Descriptive Statistics ......................................................................................... 30 Descriptive Statistics Discussion ...................................................................... 37 Validity and Reliability of the Measurement Instrument .................................. 37 PLS Findings ..................................................................................................... 39 PLS Findings Discussion .................................................................................. 45. CHAPTER V. MULTIPLE REGRESSION FINDINGS AND DISCUSSION ......................................................................................... 48 Multiple Regression Findings ........................................................................... 48 Multiple Regression Findings Discussion ........................................................ 83. II.

(5) CHAPTER VI. CONCLUSIONS AND RECOMMENDATIONS ........ 84 Conclusions ....................................................................................................... 84 Recommendations ............................................................................................. 88. REFERENCES ....................................................................................... 92 APPENDIX A: INTELLECTUAL CAPITAL QUESTIONNAIRE (Bontis, 1997) ....................................................................................................... 96 APPENDIX B. SURVEY OF THIS STUDY ....................................... 104 APPENDIX C. LIST OF EXPERTS .................................................... 110 APPENDIX D. MATRIX OF LOADINGS AND CROSS-LOADINGS ............................................................................................................... 111 APPENDIX E. MULTIPLE REGRESSION RESULTS ...................... 114. III.

(6) LIST OF FIGURES Figure 3.1 Conceptual framework of this study.......................................................... 20 Figure 3.2 Research process ....................................................................................... 22 Figure 3.3 Methodological approach to test research hypotheses using PLS ............. 26 Figure 4.1 Major Structural Model ............................................................................. 43 Figure 4.2 Structural Models of Different Samples Groups ....................................... 46. LIST OF TABLES Table 2.1 Definition of Intellectual Capital ................................................................ 9 Table 2.2 Connotations of Intellectual Capital............................................................ 10 Table 2.3 Intellectual Capital Components ................................................................. 12 Table 2.4 Measurement Indicators of Intellectual Capital .......................................... 14 Table 2.5 Taiwan Design Center Classification of the Design Industry ..................... 18 Table 3.1 Coding System Used in SPSS Data Analysis.............................................. 27 Table 4.1 Data of Variables by Entries and Values ..................................................... 30 Table 4.2 Human Capital by Likert Scale, Mean, and Standard Deviation ................ 33 Table 4.3 Structural Capital by Likert Scale, Mean, and Standard Deviation ............ 34 Table 4.4 Relational Capital by Likert Scale, Mean, and Standard Deviation ........... 35 Table 4.5 Top Five Intellectual Capital Responses ..................................................... 36 Table 4.6 Bottom Five Intellectual Capital Responses ............................................... 36 Table 4.7 Cronbach’s α Value of Survey Instrument .................................................. 38 Table 4.8 PLS Loadings .............................................................................................. 39 Table 4.9 Reliable Items－Comparing Studies in Canada, Malaysia, Portugal and Taiwan ......................................................................................................................... 40 Table 4.10 Factor Loadings ......................................................................................... 42 Table 4.11 Measurement Model Results ..................................................................... 42 Table 4.12 PLS Path Analysis Results ........................................................................ 43 Table 4.13 Summary of PLS Direct and Indirect Effects ........................................... 44 Table 5.1 Multiple Regression Results of Market Leadership Indicators in the Final Equations..................................................................................................................... 49 Table 5.2 Multiple Regression Results of Financial Performance Indicators in the Final Equations ........................................................................................................... 66 Table 5.3 Percentage of Counts of Significant Independent Variables Appearing in Each Constructs .......................................................................................................... 83 IV.

(7) CHAPTER I. INTRODUCTION Chapter Overview This introductory chapter gives the audience an insight of the study which includes the background of the study, the purposes of the research, the research questions, the delimitations and limitations, the significance of the study, and the definition of the terms, in order to introduce a comprehensive logic by the researcher.. Background of the Study In the era of knowledge economy, intangible assets of a company has taken the place of tangible assets and have become the most important resources that create value for enterprises nowadays. “Intellectual capital”, namely the knowledge assets, has become one of the most-discussed business management topics; it also determines success or failure of modern enterprises (Thomas, 2003). Additionally, many researchers regard intellectual capital as an asset that generate a company’s competitive advantage and value (Edvinsson & Malone, 1997; Roos & Roos, 1997; Stewart, 1997; Bontis, 1999, 2001). It is especially so for design industries, as its intangible assets are far more important than its tangible assets. This year Taiwan has so far obtained at least 165 international design awards, which is an improvement from the 133 awards in 2007, and 148 in 2006 (Yang, 2008). This is the evidence that Taiwanese design industry has the potential to contribute to the nation’s economy. It was the first time for design industry to be officially considered in “Challenge 2008 – National Development Plan”, in which design industry development is included as a sub-plan. Also, Taiwan Design Center (TDC), a national design center, was founded to foster the development of Taiwanese design industry. 1.

(8) Theoretically, many researchers have emphasized the influence of intellectual capital on business performance. However, empirical studies are still developing. And even though some researchers has contributed to intellectual capital studies in the scope of Taiwanese high-tech and financial industry (Wang & Chang, 2005; Chen, Cheng, & Huang, 2006; Lin & Huang, 2006; Huang & Liu, 2006; Tsan & Chang, 2006), none of them have conducted empirical researches in design-related industries. As a result, the researcher is interested in investigating the impact of intellectual capital on the performance of Taiwanese design industries. The study thus examines the interrelationships among intellectual capital components and their influence on business performance respectively. Also, recommendations are provided to assist design company managers in managing the intellectual capital of their company.. Questions of the Study Based on the intentions to enrich Taiwan’s intellectual capital studies, specifically in the design industry, as defined in the study, this research aims to answer the following questions: 1.. What are the characteristics of Taiwanese design industry’s intellectual capital?. 2.. How does intellectual capital influence Taiwanese design companies’. performance? 3.. What can the government and the managers of Taiwanese design companies do. to build up the intellectual capital?. 2.

(9) Purposes of the Study The design industry in Taiwan has not been seen as important until recent years. It is hoped to bring Taiwan to a brand new knowledge economy phase. Behind the high value-added industry performance of design industry, it is the intellectual capital of these companies that plays a major role in creating values. Despite the fact that the importance of intellectual capital has been noticed, it is just beginning to be unveiled by Taiwan’s academic and practitioners’ fields. To understand more about intellectual capital of design industry in Taiwan, the following purposes were devised. The purposes of this study are: 1.. To understand the characteristics of Taiwanese design industry intellectual. capital. 2.. To investigate and analyze how the components (i.e., the variables of this study). for intellectual capital (Human Capital, Structural Capital, and Relational Capital as defined in the study) may influence the performance of design industry in Taiwan. 3.. To provide recommendations to the Taiwanese government and managers of. design industry on how to utilize and manage the intellectual capital of their companies.. Significance of the Study The cultural and creative industry features its variety, dispersion and its small-scale staff, but the number of employment and the industry value of it have kept on growing, which enriches the life of quality. It is also an industry that all developed countries, such as north European countries, the UK, and Japan, have been progressively promoting (Council for Economic Planning and Development [CEPD], 2005). With the development of cultural and creative industry, Taiwan could be saved from the dilemma of micro-profit competition, increase the employment, as well as 3.

(10) improve its life quality. Within all the cultural and creative industries, the design sector is one of the industries that rely mostly on its knowledge to create value. Nonetheless, little is known about the intellectual capital of the design industry in Taiwan. The quantitative survey provided statistical data that is significant to the government and the executives of Taiwanese design companies. Accordingly, the findings of the research may: pinpoint problems Taiwanese design companies are facing and provide directions for future improvement; assist the government with meaningful literature to make future design industry development policies; and provide the managers with valuable recommendations on what measures could be taken to enhance the firm’s intellectual capital and performance. Moreover, of great significance is the fact that this research is the first empirical intellectual capital study conducted in the scope of design industry in Taiwan and hopefully this study becomes a pioneer that will encourage more empirical studies on this topic.. Delimitations and Limitations Delimitations The researcher placed several delimitations on this study. For the research participants, it is delimited to Taiwan and not generalized to other regions. It is also delimited to the design companies included in the industry catalog of Taiwan Design Center (TDC). Also, the study is delimited to the definition, classification, and questionnaire items used in the research, which is adopted from the study of Cabrita and Bontis (2008), because the researcher is interested in using their questionnaire to conduct the empirical study in Taiwan. Moreover, the research is delimited to the managers of the companies; researchers recommend the collection of data from 4.

(11) respondents who are at least managers or directors within the organization (Bontis, 1998; Bukh, Larsen, & Mouritsen, 1999). Limitations This study reviewed the related literature, construct the research framework and research hypotheses based on the research purpose. However, there are still some inevitable limitations: first, due to the constraints of difficulty to reach desired respondents, the researcher used only ten people to conduct the pilot test. Second, according to the latest statistics available, there are 2,239 design companies in 2006 (2003-2006 Taiwan Cultural and Creative Industry Relevant Statistics, n.d.). However, only 500 companies are included in TDC’s industry catalog, which could produce sample bias. Third, the responses of variables in the questionnaires were based on the subjective recognition of respondents and might be influenced by their age, seniority, personality, and educational background.. Definition of Terms The following is the definitions of variables in this study: ¾. Human capital Human capital represents the individual knowledge stock of an organization as. represented by its employees. (Bontis, Crossan, & Hulland, 2002) ¾. Structural capital Structural capital is a valuable strategic asset, which is comprised of non-human. assets such as information systems, routines, procedures and databases. (Cabrita & Bontis, 2008). 5.

(12) ¾. Relational capital Relational capital is the knowledge embedded in relationships with customers,. suppliers, industry associations or any other stakeholder that influence the organization’s life. (Cabrita & Bontis, 2008) ¾. Business performance In this study, Cabrita and Bontis (2008) used the following ten indicators to. measure business performance: Industry Leadership, Future Outlook, Profit, Profit Growth, Sales Growth, After-tax Return on Assets, After-tax Return on Sales, Overall Response to Competition, and Success Rate in New Product Launches, Overall Business Performance and Success. We adopted the above ten performance indicators to assess business performance. In this study, we categorize them into two major indicators, including market leadership indicators and financial performance indicators, in order to evaluate a company’s competence in the market. Market leadership indicators include industry leadership, future outlook, overall response to competition, success rate in new product launches, and overall business performance and success. Financial indicators include profit, profit growth, sales growth, after-tax return on assets, and after-tax return on sales. ¾. Taiwanese Design Industry Taiwan Design Center (TDC) is the national design organization of Taiwan. (CEPD, 2005). This study adopted the design industry classification of TDC, which includes following four major categories: product design, service design, activity design, and space design.. 6.

(13) CHAPTER II. LITERATURE REVIEW. Chapter Overview This chapter provides a deep and rich review of relevant literature that assists the researcher to address the effect of the intellectual capital (IC) on business performance. At first it provides an insight about the origin and definition of IC. Then it explores the elements that compose intellectual capital, which are, respectively, Human Capital (HC), Structural Capital (SC), and Relational Capital (RC). The chapter then continues by discussing relevant literature about how IC impact business performance and introduces the measurement indicators of IC and business performance. Finally the chapter addresses the current situation of design industry in Taiwan.. Literature of Intellectual Capital and Business Performance Concept and definition of intellectual capital The concept of “intellectual capital” (IC) was first proposed by an economic scholar named John Kenneth Galbraith (Edvinsson & Sullivan, 1996, p. 358; Edvinsson, 1998, p.279; Roos, Roos, Edvinsson, & Dragonetti, 1998, p. 4). He used it to explain the difference between a company’s market value and book value and further advocated IC an intellectual action, instead of mere knowledge and intelligence (Taiwan Intellectual Capital Research Center [TICRC], & Market Intelligence Center [MIC], 2006). With the approach of “innovation era,” many scholars begin to discuss the issue of IC. It is seen as the most valuable economic resource (Drucker, 1993; Stewart, 1997; Sveiby, 1997; Bontis, 1999) and is considered to be a potential source of sustainable competitive advantage (Nonaka & 7.

(14) Takeuchi, 1995; Edvinsson & Malone, 1997; Bontis, 2002; Choo & Bontis, 2002). Edvinsson and Sullivan (1996) define IC as the knowledge assets that can be converted into value. Whereas Stewart (1997) argues IC is the sum of all the knowledge and abilities of the members that forms the company’s competitive advantage, including intellectual material like knowledge, information, intellectual property and experience that makes profit. Still yet, Ulrich (1998) considers intellectual capital originates from employees’ competence and commitment. Among the many studies, the definition of IC remains inconsistent (Table 2.1 and Table 2.2). However, we can still see the common features of IC: its intangibility, the fact that it creates value, and the growth effect of collective practice (Cabrita, & Bontis, 2008).. 8.

(15) Table 2.1 Definitions of Intellectual Capital Author. Year. Definition. Edvinsson & Sullivan Stewart. 1996. IC is the knowledge asset that can be converted into value.. 1997. Masoulas. 1998. Molyneux. 1998. IC is the intellectual material, such as knowledge, information, intellectual property, and experience, which creates wealth IC can be defined as the combination of intangible assets that add value to the organizational effort in reaching its transcendental goal, understanding as intangible assets the employees skills, experience, attitudes and information that allow them do their job adding value for themselves and for the organization IC resides in human, structural and relational (or external) capital. It can be used to raise revenue and to provide better services for members. IC is the knowledge and knowing capability of a social collectivity, such as an organization, intellectual community, or professional practice. IC = competence x commitment. This equation suggests that within a unit, employees’ overall competence should rise but that competence alone does not secure intellectual capital. A system (of elements and processes) that utilizes human intellect and innovation to create wealth. The brains of its employees, their know-how, the processes and customer knowledge that they create, which has always been a source of competitive advantage. IC can be thought of as the total stock of capital or knowledge-based equity that the company possesses. Besides human capital and structural capital, “social innovation capital (SIC)” should be included in IC and used to not only measure and value a firm’s capacity to innovate, but also enhance performance and output. Nahapiet & 1998 Ghoshal Ulrich. 1998. Johnson. 1999. Bassi & Van Buren. 1999. Dzinkowski. 2000. McElroy. 2002. Source: This study. 9.

(16) Table 2.2 Connotations of Intellectual Capital Author. Year. Content. Bontis. 1996. Human capital: the firm’s collective capability to extract the best solutions from the knowledge of its people. Structural capital: the firm’s organizational capabilities to meet market requirements Relational capital: the organization’s relationships or network of associates and their satisfaction with and loyalty to the company. Grantham , Nichols, & Schonberner. 1997. Human capital: a firm’s capability to apply its employees’ knowledge to business problems Structural capital: a firm’s capacity to respond to environmental changes Customer capital: loyalty of customers. Brooking, Board & Jones. 1998. Market assets: those which belong to the company and give it power in the marketplace. Intellectual property assets: property of the mind which belongs to the company and is protectable in law. Infrastructure assets: those assets which belong to the company and provide the infrastructure without which it could not effectively function. Human-centered assets: assets which belong to the employees and contractors to the company, but which are used by the company in return for salaries and fees. Molyneux. 1998. Human capital: the members’ and the staffs’ collective knowledge, skills, expertise and networks – their competence. Structural capital: the systems (IT and others), policies, culture and distribution channels that the American Society for Prevention of Cruelty to Animals (ASCPA) has at its disposal. Relational capital: the external business relationships with suppliers of goods and services and with those business that have common interests with the ASCPA. 10.

(17) Table 2.2 (continued) Author. Year. Content. Johnson. 1999. Van Buren. 1999. Dzinkowski. 2000. Human capital: the force behind the human intellect and innovation of the firm Structural capital: the structural ability of the firm to utilize human intellect and innovation to create wealth Relational capital: the ability of the firm to interact positively with business community members to stimulate the potential for wealth creation by enhancing Human and Structural Capital Human capital: the knowledge, skills, and competencies of people in an organization Innovation capital: the capability of an organization to innovate and to create new products and services Process capital: An organization’s processes, techniques, systems, and tools Customer capital: the value of an organization’s relationship with its customers Human capital: know-how, capabilities, skills and expertise of the human members of the organization. Organizational (Structural) capital: organizational capabilities developed to meet market requirements such as patents. Customer (Relational) capital: connections outside the organization Human capital: the individual tacit knowledge embedded in the mind of the employees Structural capital: the system and structure of an enterprise Innovation capital: the introduction of a new combination of the essential factors of production into the production system. Customer capital: the value embedded in the marketing channels and relationships that an enterprise develops by conducting business. Chen, Zhu, & 2004 Xie. Source: This study. 11.

(18) Intellectual capital components The above summary shows that the definition and classification varies due to research directions and the background of the researchers. However, as Cabrita and Bontis (2008) have pointed out, a common taxonomy has emerged in which intellectual capital adopts a tripartite dimension which includes: human capital, structural capital and relational capital. This statement can be observed from Table 2.3 produced in this study.. Table 2.3 Intellectual Capital Components Author. Year. HC SC. RC. Bontis Grantham et al. Roos & Roos. 1996 1997. V V. V V. V V. 1997. V. V. V. Brooking et al.. 1998. V. V. V. Molyneux Johnson Lynn Van Buren Dzinkowski. 1998 1999 1999 1999 2000. V V V V V. V V V V. V V V V V. Guthrie & Petty Chen et al.. 2000. V. V. V. 2004. V. V. V. INC. PC. Explanation Customer capital is put to RC Organizational capital is put to SC Market asset is put to RC; intellectual property asset and infrastructure asset are put to SC. V. V. Customer capital is put to RC Organizational capital, and RC is customer capital. V. Note: HC represents human capital; SC represents structural capital; RC represents relational capital; INC represents innovation capital; PC represents process capital Source: This study. 12.

(19) As a result, this study adopts the classification of Cabrita and Bontis’ (2008) study and defines these three major components of IC: i) human capital represents the individual knowledge stock of an organization as represented by its employees (Bontis et al., 2002); ii) structural capital is a valuable strategic asset, which is comprised of non-human assets such as information systems, routines, procedures and databases; iii) relational capital is the knowledge embedded in relationships with customers, suppliers, industry associations or any other stakeholder that influence the organization’s life.. Measurement indicators of intellectual capital and business performance Indicators used to measure IC varies from scholar to scholar, but many of the indicators falls into the three major categories. Table 2.4 gives us a brief view of some IC indicators. However, Bontis has developed a comprehensive Intellectual Capital Questionnaire in 2007, which was administered in Canada (Bontis, 1998), Malaysia (Bontis et al., 2000), and Portugal (Cabrita and Bontis, 2008). Within the questionnaire, fifty-three measurement indicators are used to measure IC. In 2008, Cabrita and Bontis (2008) further extended customer capital to relational capital by adding eight items to relational capital, which comprise of sixty-one IC measurement indicators (twenty HC indicators, sixteen SC indicators, and twenty-five RC indicators). With respect to business performance, ten measurement indicators are used to assess business performance, including industry leadership, future outlook, profit, profit growth, sales growth, after-tax return on assets, after-tax return on sales, overall response to competition, success rate in new product launch, and overall business performance. For the original questionnaire, please refer to the Appendix A. 13.

(20) Table 2.4 Measurement Indicators of Intellectual Capital Author. Human Capital Indicators. Structural Capital Indicators. Relational Capital Indicators. Van Buren (1999). Core IC Measures Retention of key personnel Ability to attract talented people IT literacy Training Expenditures as a percent of payroll Replacement costs of key personnel Employee satisfaction Employee commitment Know-how Education Vocational qualification Work-related knowledge Work-related competencies Entrepreneurial spirit. Core IC Measures Innovation Capital R&D expenditures Percentage of Workforce involved in innovation Product freshness Process Capital Processes documented and mapped Use of documented processes. Core IC Measures Customer Capital Customer satisfaction Customer retention Product and service quality Average duration of customer relationship Repeat orders. Intellectual property Patents Copyrights Trademarks Infrastructure assets Management philosophy Corporate culture Management processes Information systems Networking systems Financial relations Corporate culture Organizational structure Organizational learning Operation process Information system Innovation achievements Innovation mechanism Innovation culture. Brands Customers Customer loyalty Company names Distribution channels Business collaborations Licensing agreements Favorable contracts Franchising agreements. Guthrie & Petty (2000). Chen et al. (2004). Employees’ competence Employees’ attitude Employees’ creativity. Source: This study 14. Customer Capital Basic marketing capability Market intensity Customer loyalty indices.

(21) Review of intellectual capital studies Previous studies (Bontis, 1998; Bontis et al., 2000; Cabrita and Bontis, 2008) identified the positive relationship between IC and business performance. These are three empirical studies conducted respectively in Canada, Malaysia, and Portugal. All of the research results indicated that human capital (HC) significantly influences structural capital (SC) and relational capital (RC), and also impact business performance indirectly through SC and RC. Also, SC and RC showed significant influence on business performance (except in the study of Malaysia). Interestingly, Chen (2001) conducted an IC empirical study in Taiwan investigating the effect on information technology investment and intellectual capital on business performance, and the results support the studies of these three aforementioned studies.. 15.

(22) Literature of Taiwanese Design Industry Background of Taiwanese design industry According to the statement of “Challenge 2008 – National Development Plan” proposed by the CEPD (2005), Executive Yuan, Taiwan is faced with the highly-industrialized economy which used to be manufacturing-oriented; it has lost its advantage under the challenge of China. As a matter of fact, the highest value-added industry is the one that is creativity or design-based, especially the design which originates from aesthetics. This kind of industry, named cultural and creative industry, features its variety, dispersion, small-scale staff but the number of employment and the industry value of it have kept on growing, which enriches the quality of life. It is also an industry that all developed countries, such as north European countries, the UK, and Japan, have been progressively promoting. However, this industry has relatively been ignored in Taiwan’s past economical policies. Within the cultural and creative industry, the design industry shows great potential to contribute to the nation’s economy. According to the latest statistics (2003-2006 Taiwan Cultural and Creative Industry Relevant Statistics, n. d.), the sales growth of the design industry contributed 55.69 billion NTD to the economy in 2006, which accounted for 9.5% of the entire cultural and creative industry. It also ranked the second highest sales growth among all Taiwan’s cultural and creative industry. This showed the great potential of the design industry with regard to its contribution to Taiwan’s economy. Potential to increase employment and, hopefully, Taiwan could be saved from the dilemma of micro-profit competition. Realizing the importance and emergence to foster the development of design industry, the government founded Taiwan Design Center (TDC) to function as a foundation to construct an environment for developing domestic design and art (CEPD, 2005). Consequently, TDC become one of the most important organizations 16.

(23) that integrate industry resources and enhance the high value-added from Taiwanese design industry. Characteristics of design industry According to Oakley (1990), design projects are usually more irrational, unpredictable, and changing. Also, it requires much creativity from individuals. Design companies are usually more like organic organizations; this idea was proposed by Burns and Stalker (1961), which is suitable for companies situated in an unpredictable and changing environment. This kind of organizational structure provides the company with more flexibility and adaptability, and encourages creativity and innovation. On the other hand, it requires higher cost and more complicated administration to maintain the structure, which could be an obstacle to business performance. Definition and scope of Taiwanese design industry Based on Taiwan Ministry of Economic Affairs[MOEA] (2004) definition, the design industry refers to business that are involved in product design and planning, product exterior design, mechanism design, prototype and model production, fashion design, patent logo design, brand visual design, graphic design, packaging design, webpage/multimedia design, and design consultancy. Additionally, TDC represents the key organizations of Taiwanese design industry, however, this research decided to take the companies in the TDC sector catalog as research samples. In the classification of TDC, the design industry falls into the following four categories: product design, service design, activity design, and space design. TDC included space design as its business scope, which is the slight difference from MOEA’s definition. For the TDC classification of the deign industry, please see Table 2.5.. 17.

(24) Table 2.5 Taiwan Design Center Classification of the Design Industry Category. Content. Major Product design domain. Industrial design, computer aided design (CAD), package design, fashion design, crafts design. Service design. Corporate identification system (CIS) design, brand design, graphic design, ad design, web and multimedia design, product planning. Minor Product design domain Service design. Mechanism design, mold design Game software design, animation design. Activity design. Exhibition planning, public relations (PR) planning, trade show planning. Space design. Landscape design, architectural design, interior design, show window design, lighting design, exhibition design, stage design. Source: Taiwan Design Center Web site (http://www.tdc.org.tw) Summary From the above review, it can be seen that the knowledge of design companies plays a vital role in enhancing business performance. Instead of the tangible assets, it is the intangible assets of the firm that make it high value-added. This indicates the importance of studying the effects of IC on Taiwanese design industry performance. Through assessing the IC and investigating how it influences the performance in design companies, the managers can understand how to utilize or manage IC to create value, and the government can make more favorable policies that promote industry development. Hopefully, the entire economy can benefit from knowing more about making the most of this valuable resource.. 18.

(25) CHAPTER III. RESEARCH METHODS. Chapter Overview This chapter contains the research hypothesis, conceptual framework, research procedure, measurement instrument, and data analysis methods. The chapter explains the research framework by showing the hypothesized interrelation among variables. Also, the chapter explains the sampling procedure and the necessity for the researcher to follow this procedure to obtain the required sample. Details of the measurement instrument are given with the reliability and validity explained. Finally, data collection, and data analysis methods are introduced.. Research Hypotheses Previous studies have indicated that human capital is positively associated with structural capital and relational capital (Bontis, 1998; Bontis et al., 2000; Chen, 2001; Cabrita and Bontis, 2008); also, structural and relational capital respectively mediate the impact of human capital on business performance. Therefore, the following hypotheses are developed. H1. Human capital is positively associated with structural capital. H2. Human capital is positively associated with relational capital. H3. Structural capital is positively associated with relational capital. H4. Structural capital is positively associated with business performance. H5. Relational capital is positively associated with business performance.. 19.

(26) Conceptual Framework This research framework was developed in accordance with the literature review. From the review, it was noticed that intellectual capital is related to business performance. The Intellectual Capital Variables defined in the study are in relation to Cabrita and Bontis’ (2008) classification of intellectual capital: Human Capital, Structural Capital, and Relational Capital. Their interrelation and their impact on Business Performance will be tested.. Human Capital (HC) H1. H2. H3. Structural Capital (SC). H4. Relational Capital (RC). H5. Business Performance (P). Figure 3.1. Conceptual framework of this study Source: Revised from Cabrita and Bontis’ (2008) study. 20.

(27) Research Procedure A pilot test was administered in December 2008 and the data were collected by paper questionnaire. Before the distribution of questionnaire, it was reviewed by four experts in this field. For the pilot test sample, four executives of Taiwanese design companies and six students from the extended education division of Department of Fine Arts, National Taiwan Normal University were chosen using convenience sampling method. All participants are managers or directors who come from ten different design companies in Taiwan and their permissions to participate in the pilot study were obtained. The questionnaire items come from the empirical study of Cabrita and Bontis (2008), which are 71 items in total. All items are translated into Chinese by a bilingual translator and are revised by experts to suit the study. Also, the items are placed categorically as Cabrita and Bontis’ (2008) classification of intellectual capital. For the main study, the researcher contacted Taiwan Design Center (TDC) requesting permission to mail surveys electronically using their design industry catalog. The researcher explained by telephone and mails the research background, research purpose, along with a note of confidentiality detailing that the data collected will be used solely for the researcher’s thesis and all names of companies will be excluded. Meanwhile, the researcher made phone calls by using the public catalog provided by the website of TDC (http://www.boco.com.tw). For every phone call, the researcher explained the purpose of the study and the contributions it may have to Taiwanese design industry. The participants are assured their anonymity and that the results will be sent to them if requested. Moreover, the researcher also reminded that the survey should be answered by managers or directors of the company as recommended by Bontis (1998) and Bukh et al. (1999). Electric surveys are mailed to these respondents so as to reduce the trouble of replying to paper questionnaires and 21.

(28) increase respondents’ willingness of reply. After all the phone calls are made, the researcher waited and collected all the data. The data was coded and the information was keyed into the Statistical Package for Social Sciences (SPSS) PC 12.0 statistical software program. Figure 3.2 shows the research process of this study. Identification of the Research Subject. Discussion of the Literature Review. Establishment of Research Questions and Hypothesis. Development of the Framework of the Study. Design of Research Methods. Implementation of the Survey. Analysis of the Data. Conclusions and Suggestions. Design of Final Report Figure 3.2. Research process. 22.

(29) Measurement Instrument To collect the data the survey is conducted by questionnaires. The researcher used the questionnaire developed by Cabrita and Bontis (2008) to assess the intellectual capital and business performance of Taiwanese design industry. All the items in the survey are translated from English to Chinese by one English major and then revised by four experts for face validity. It was six pages in length, containing a total of 71 items and a cover letter explaining the academic purpose of the study, the concept of intellectual capital, and the assurance of their confidentiality. Some of the items were reworded from the original in order the suit the characteristics of the sample. (Please see Appendix B for the survey of the study) The survey consists of five parts. Respondents were asked to refer to their experience of working in their company and to fill out the questionnaire that has a range of items with regard to Human Capital, Structural Capital, Relational Capital, as well as Business Performance. The first three parts assess the intellectual capital of the sample company using a 7-point Likert Scale. (1 = strongly disagree, 7 = strongly agree). They are respectively Human Capital (20 items), coded as H1 to H20; Structural Capital (16 items), coded as S1 to S16; Relational Capital (25 items), coded as R1 to R25. The fourth part measures business performance (10 items), coded as P1 to P10. The respondents are asked to state how their companies’ performance compared to their key competitors in the sector. The answer ranges from 1 to 10. (1 = my company performs the worst in the sector; 10 = my company performs the worst in the sector). The last part requires respondents to provide the information of them (seniority and job title) and their company (location, company age, industry, type of property right, employee number and sales of year 2007).. 23.

(30) Data Analysis Methods There are two major data analysis methods in this study: Partial Least Squares (PLS) and multiple regression analysis. PLS is used to analyze simultaneously the interrelationships among all the constructs, however, one disadvantage of it is that PLS is not able to evaluate each individual item’s impact on business performance. Therefore, this study also used multiple regression analysis to assess each item’s influence on the dependent variables. These two methods are complimentary to each other. Partial least squares Partial least squares (PLS) is a kind of structural equation modeling (SEM) technique. It is based on regression and originates from path analysis. As stated by Cabrita and Bontis (2008), it is a powerful tool in social and behavioral sciences where theories are formulated in terms of hypothetical construct, which are theoretical and cannot be observed or measured directly. Besides, PLS estimation does not require assumptions of normality or independence of observations. Moreover, it works well with small samples and is better suited for exploratory work. These are also the reasons that make PLS a more suitable analyzing method for this study. Therefore, in this study, PLS is used to analyze intellectual capital data and business performance data. Through the use of PLS, the researcher can conduct confirmatory factor analysis and path analysis. Bontis (1998) reports the benefits of using PLS for such studies: The objective in PLS is to maximize the explanation variance. Thus, R² (r-squared) and the significance of relationships among constructs are measures indicative of how well a model is performing. The conceptual core of PLS is an iterative combination of principal components analysis relating measures to constructs, and path analysis permitting the construction of a system of constructs. The hypothesizing of relationships between 24.

(31) measures and constructs, and constructs and other constructs is guided by theory. The estimation of the parameters representing the measurement and path relationships is accomplished using ordinary least squares (OLS) techniques. The first step in PLS is for the researcher to explicitly specify both the structural model and the construct-to-measures relationships in the measurement model. The exogenous constructs are consistent with the idea of independent variables (antecedents). Similarly, the endogenous constructs are consistent with the idea of dependent variables (consequents). The constructs can be specified as “formative” indicators or “reflective” indicators. Formative indicators imply a construct that is expressed as a function of the items (the items form or cause the construct). Reflective indicators imply a construct where the observable items are expressed as a function of the construct (the items reflect or are manifestations of the construct). One looks to theory to decide on which type of epistemic or construct-to-measure relationship to specify. In this case, all constructs were “reflective” indicators. Once specified, the measurement and structural parameters are estimated using an iterative process of OLS, simple and multiple regressions. The process continues until the differences in the component scores converge within certain criteria (p. 69).. Due to the exploratory feature and small samples of this study, the researcher decided to adopt Visual PLS 1.04b1 as one of the major tools to investigate causal relationship between intellectual capital and business performance. Figure 3.3 demonstrates the methodological approach to test research hypotheses using PLS. To examine the measurement model, items with loadings greater than 0.5 should be retained to reach adequate individual item reliability; internal consistency and Cronbach’s α ought to exceed 0.7 to achieve adequate convergent validity; the square root of average variance extracted of a construct should exceed its correlation coefficients with other constructs to get adequate discriminant validity and no items should load in wrong constructs when examining the cross-loading matrix. To analyze 25.

(32) the structural model, the model explanatory power is showed by the R² value and the significance of path coefficients is examined by t-test with degree of freedom N-1. Finally, the “rule of thumb” for sample size requirements suggests that it will be equal to the larger of the following (Cabrita & Bontis, 2008): 1. 10 times the scale with the largest number of formative indicators (scales with reflective indicators can be ignored) or 2. 10 times the largest number of antecedent constructs leading to an endogenous construct. In our study we applied the second requirement as all indicators are reflective. The final full test would have 2 constructs. Therefore, a minimum of 20 (2 x 10) was required. Our sample size (87 samples) met the criterion.. z. z z. Individual item reliabilities Loadings (λ’s) – retained items with factor loadings >0.5. Internal consistency Convergent validity (Cronbach’s α and internal consistency) Discriminant validity (average variance extracted, examination of loadings and cross-loadings). z. Explanatory power R-squares (R²) for each dependent variable. z. Testing of hypotheses Estimation of path coefficients (Standardized β’s). z. Measurement model (outer relations). Structural model (inner relations). Significance of hypotheses Significance testing path estimates (jack-knifing procedure to examine the stability of estimates) Figure 3.3 Methodological approach to test research hypotheses using PLS Source: Cabrita and Bontis’ (2008) study 26.

(33) Multiple regression The multiple regression is conducted by SPSS. Before analysis, the data were coded using number sequences. The 61 intellectual capital questions were coded using a 7-point Likert scale, and the 10 performance questions were coded from 1 to 10 as previously mentioned, including seniority, job title, company location, company age, industry, type of property right, number of employees, and sales of year 2007.. Table 3.1 Coding System Used in SPSS Data Analysis (N=87) Variables Seniority (Unit: years). Job Title. Company Location. 1 = Less than 3 2 = Above 3, less than 5 3 = Above 5, less than 10 4 = Above 10, less than 15 5 = Above 15, less than 20 6 = Above 20 1 = Chairman 2 = President 3 = Vice President 4 = Associate President 5 = Manager 6 = Deptu Manager 7 = Director 8 = Senior Designer 9 = Others 1= Northern Taiwan 2 = Central Taiwan 3 = Southern Taiwan 4 = Eastern Taiwan. 27.

(34) Table 3.1(continued) Variables Company age (Unit: years). Industry. Type of Property Right. Number of Employees (Unit: people). Sales of Year 2007 (Unit: NTD). 1 = Less than 1 2 = Above 1, less than 3 3 = Above 3, less than 5 4 = Above 5, less than 10 5 = Above 10, less than 15 6 = Above 15, less than 20 7 = Above 20 1 = Service design 2 = Activity design 3 = Product design 4 = Space design 5 = Others 1 = Corporation limited 2 = Limited 3 = Partnership 4 = Sole proprietorship 5 = Foreign enterprise Taiwan branch 6 = Others 1 = Less than 3 2 = Above 3, less than 5 3 = Above 5, less than 10 4 = Above 10, less than 15 5 = Above 15, less than20 6 = Above 20, less than 30 7 = Above 30, less than 40 8 = Above 40, less than 50 9 = Above 50, less than 100 10 = Above 100 1 = Less than 500 thousand 2 = Above 500 thousand, less than 1 million 3 = Above 1 million, less than 2 million 4 = Above 2 million, less than 3 million 5 = Above 3 million, less than 5 million 6 = Above 5 million, less than 10 million 7 = Above 10 million, less than 20 million. 28.

(35) The researcher used from the SPSS software, descriptive and inferential statistics to analyze and interpret the data collected from the sample population. These statistical procedures allowed the researcher to understand the impact of intellectual capital on business performance. The descriptive statistics helped the researcher to arrange the data into a more interpretable form by calculating numerical indices such as maximum value, minimum value, means, and standard deviation. All this data can be summarized easily or can be examined on their interrelation. The use of inferential statistics helped the researcher to examine relationships, differences and trends, a process also known as hypothesis testing or significance testing. The researcher analyzed the data to investigate the relationship between intellectual capital and business performance. The inferential statistics provided the researcher with the means to test whether two variables are associated and to assess the strength between the independent and dependent variable; it also assisted the researcher to predict the value of dependent to independent variable, and compare relative changes in trends.. 29.

(36) CHAPTER IV: DESCRIPTIVE STATISTICS, PLS FINDINGS AND DISCUSSION Chapter Overview This chapter contains two major sections. The first sector gives an overview of the descriptive statistics of the research data, from which the features of Taiwanese design industries are discovered. The second section demonstrates PLS findings, including the analysis of the measurement model and the structural model that are used to test the hypotheses. Finally, a discussion part is given to further investigate the implications of PLS findings.. Descriptive Statistics All the companies in the sector catalog were contacted. Of the 500 companies approached 78 responded which is a 15.6% response rate. An additional 15 questionnaires were personally delivered and collected, reaching a total of 93 questionnaires. Of the 93 questionnaires the researcher picked out 87 effective questionnaires, which is a rate of 93.5%. The Table 4.1 below provides a view of the sample population and their demographic information. Table 4.1 Data of Variables by Entries and Values (N=87) Variables Seniority (Unit: years). Job Title. Entries Less than 3 Above 3, less than 5 Above 5, less than 10 Above 10, less than 15 Above 15, less than 20 Above 20 Chairman President Vice President Associate President Manager Deptu Manager Director Senior Designer Others 30. 5 6 28 18 13 17 5 9 4 1 14 2 32 13 7. Percentage 5.7 6.9 32.2 20.7 14.9 19.5 5.7 10.3 4.6 1.1 16.1 2.3 36.8 14.9 8.0.

(37) Table 4.1 (continued) Variables Company Location. Company Age (Unit: years). Industry. Type of Property Right. Number of Employees (Unit: people). Entries Northern Taiwan Central Taiwan Southern Taiwan Eastern Taiwan Less than 1 Above 1, less than 3 Above 3, less than 5 Above 5, less than 10 Above 10, less than 15 Above 15, less than 20 Above 20 Service design Activity design Product design Space design Others Corporation limited Limited Partnership Sole proprietorship Foreign enterprise Taiwan branch Others Less than 3 Above 3, less than 5 Above 5, less than 10 Above 10, less than 15 Above 15, less than20 Above 20, less than 30 Above 30, less than 40 Above 40, less than 50 Above 50, less than 100 Above 100. 31. 58 16 11 1 4 13 19 19 11 10 11 36 5 30 11 5 23 38 4 12 4 6 10 24 26 8 4 4 2 2 2 5. Percentage 66.7 18.4 12.6 1.1 4.6 14.9 21.8 21.8 12.6 11.5 12.6 41.4 5.7 34.5 12.6 5.7 26.4 43.7 4.6 13.8 4.6 6.9 11.5 27.6 29.9 9.2 4.6 4.6 2.3 2.3 2.3 5.7.

(38) Table 4.1 (continued) Variables Sales of Year 2007 (Unit: NTD). Capital (Unit: NTD). Less than 500 thousand Above 500 thousand, less than 1 million Above 1 million, less than 2 million Above 2 million, less than 3 million Above 3 million, less than 5 million Above 5 million, less than 10 million Above 10 million, less than 20 million Above 20 million, less than 30 million Above 30 million, less than 40 million Above 40 million, less than 50 million Above 50 million 20,000 200,000 300,000 500,000 550,000 1,000,000 1,200,000 1,300,000 2,500,000 3,000,000 5,000,000 6,000,000 7,000,000 7,500,000 8,000,000 10,000,000 12,000,000 19,000,000 29,000,000 30,000,000 40,000,000 50,000,000 60,000,000 100,000,000 120,000,000 150,000,000 550,000,000 1,500,000,000 3,000,000,000 6,000,000,000. 32. Entries Percentage 2 2.3 6 6.9 11 12.6 9 10.3 10 11.5 11 12.6 13 14.9 6 6.9 1 1.1 1 1.1 17 19.5 1 1.1 3 3.4 1 1.1 7 8.0 1 1.1 19 21.8 1 1.1 1 1.1 3 3.4 6 6.9 1 1.1 2 2.3 1 1.1 1 1.1 1 1.1 9 10.3 2 2.3 1 1.1 1 1.1 3 3.4 1 1.1 1 1.1 1 1.1 1 1.1 1 1.1 1 1.1 1 1.1 1 1.1 1 1.1 1 1.1.

(39) Human capital Concerning human capital, the executives showed high agreement to H4, which shows that many managers agree that their employees cooperate in teams. H20 pointed out that the employees gave it their all which makes the company different from the others in the industry. The lowest score of H13R indicated that if certain individuals in the firm unexpectedly left, they would be in big trouble. However this is not too significant to notice (See Table 4.2). Table 4.2 Human Capital by Likert Scale, Mean, and Standard Deviation (N=87) Min. Max. Mean 1 7 4.82 1 7 4.92 1 7 4.56 2 7 5.92 1 7 5.29 1 7 5.41 1 7 5.46 2 7 5.44 2 7 5.22 1 7 5.18 2 7 5.36. H1 competence ideal level H2R no succession training program H3 planners on schedule H4 employees cooperate in teams H5R no internal relationships H6 come up with new ideas H7 upgrade employees' skills H8 employees are bright H9 employees are best in industry H10 employees are satisfied H11 employees perform their best. Std. Deviation 1.317 1.894 1.412 1.183 1.670 1.369 1.429 1.158 1.125 1.225 1.131. H12 recruitment program comprehensive. 2. 7. 4.98. 1.312. H13R big trouble if individuals left. 1. 7. 4.43. 1.821. H14R rarely think actions through. 1. 7. 4.54. 1.546. H15R do things without energy. 1. 7. 5.37. 1.390. H16 individuals learn from others. 1. 7. 5.45. 1.265. H17 employees voice opinions. 2. 7. 5.07. 1.246. H18 get the most out of employees. 2. 7. 5.30. 1.221. H19R bring down to others' level. 2. 7. 5.29. 1.405. H20 employees give it their all. 2. 7. 5.56. 1.208. Note: The 7-Point Likert scale is used; R represents reverse coded items, but are positively coded before analysis. 33.

(40) Structural capital In relation to structural capital, item S8, S13R, and S15 pinpointed that the culture and the atmosphere of most companies are supportive and comfortable and that they support the development of new ideas and products. Also, the organization is not a “bureaucratic nightmare,” which means the organizational structure is quite flexible. However, the lowest score of S1 showed the managers’ disagreement and that their companies have the lowest cost per transaction in the industry (See Table 4.3). Table 4.3 Structural Capital by Likert Scale, Mean, and Standard Deviation (N=87) Min. Max. Mean Std. Deviation S1 lowest cost per transaction 1 7 3.80 1.598 S2 improving cost per revenue $ 1 7 4.22 1.458 S3 increase revenue per employee. 2. 7. 4.94. 1.124. S4 revenue per employee is best. 1. 7. 4.76. 1.320. S5 transaction time decreasing S6 transaction time is best S7 implement new ideas S8 supports development of ideas. 1 1 2 1. 7 7 7 7. 4.55 4.25 5.06 5.80. 1.292 1.323 1.297 1.199. S9 develops most ideas in industry S10 firm is efficient S11 systems allow easy info access. 1 1 1. 7 7 7. 5.26 4.95 5.01. 1.316 1.266 1.451. S12 procedures support innovation. 1. 7. 4.90. 1.347. S13R firm is bureaucratic nightmare. 1. 7. 5.63. 1.356. S14 not too far removed from each other 1 7 5.41 1.394 S15 atmosphere is supportive 1 7 5.51 1.380 S16R do not share knowledge 1 7 5.17 1.740 Note: The 7-Point Likert scale is used. R represents reverse coded items, but are positively coded before analysis Relational capital In the dimension of relational capital, five variables showed the managers’ agreement concerning the aspects of customers. Item R13R, R14, R15R, R16, R17 showed that design companies generally care about what customer thinks or wants from them. They capitalize on customers’ wants and needs by: continually striving to make 34.

(41) them satisfied, getting as much feedback out of customers as they possibly can, and launching services or products that fits customers’ needs. Also, they feel confident that their customers will continue to do business with them. Nevertheless, R4 pointed out the market share of design companies are not usually high (See Table 4.4).. Table 4.4 Relational Capital by Likert Scale, Mean and Standard Deviation (N=87) Min. Max. Mean. Std. Deviation. R1 customers generally satisfied 3 7 5.59 1.018 R2 reduce time to resolve problem 1 7 5.00 1.347 R3 market share improving 2 7 4.79 1.374 R4 market share is highest 1 7 3.52 1.477 R5 longevity of relationships 1 7 4.87 1.265 R6 value added service 1 7 5.20 1.310 R7 customers are loyal 2 7 5.30 1.259 R8 customers increasingly select us 1 7 4.90 1.239 R9 firm is market-oriented 1 7 4.72 1.300 R10 meet with customers 2 7 5.56 1.198 R11 customer info disseminated 3 7 5.26 1.289 R12 understand target markets 1 7 5.20 1.284 R13R do not care what customer wants 1 7 6.00 1.248 R14 capitalize on customers’ wants 1 7 5.62 1.287 R15R launch what customers don't want 2 7 5.70 1.202 R16 confident of future with customer 1 7 5.71 1.238 R17 feedback with customer 1 7 5.64 1.161 R18 react to competition 2 7 5.07 1.283 R19 discuss competitors' strength and weakness 1 7 5.00 1.525 R20 contact with sector 1 7 4.44 1.568 R21 consider info from sector 1 7 4.54 1.328 R22 decisions based on info from sector 1 7 4.51 1.311 R23 supports share of info from sector 1 7 4.74 1.316 R24 share competitor info 1 7 5.37 1.192 R25 competitors are sources of innovation 1 7 4.78 1.603 Note: The 7-Point Likert scale is used. R represents reverse coded items, but are positively coded before analysis 35.

(42) From all the tables above, the researcher has decided to show the top 5 and the bottom 5 intellectual variables as indicated by the respondents. In Table 4.5 we can see that Taiwanese design companies do care about customers’ opinions and needs, they have confidence in repeat customers, and they launch new products or services that fits customers’ needs. Also, the employees cooperate in teams and the company supports the development of new ideas and products. Table 4.5 Top Five Intellectual Capital Responses (N=87) Items. Score Descriptions. R13R. 6.00. H4. 5.92. S8. 5.80. R16. 5.71. R15R. 5.70. We generally do not care about what the customer thinks or wants from us The firm gets the most of out of its employees when they cooperate with each other in team tasks Our company supports the development of new ideas and products We feel confident that our customers will continue to do business with us We often launch something new only to find out that our customers do not want it. In Table 4.6 we can see that Taiwanese design companies generally don’t have a high market share, they don’t focus much on improving cost per transaction and cost per revenue dollar, neither on time to complete a whole transaction. In addition, if certain individuals in the firm unexpectedly left, the company would be in big trouble. Table 4.6 Bottom Five Intellectual Capital Responses (N=87) Items. Score Descriptions. R4. 3.52. S1. 3.80. S2. 4.22. S6. 4.25. H13R. 4.43. Our market share is the highest in the industry Our organization has the lowest costs per transaction of any in the industry We have continually been improving our costs per revenue dollar The time it takes to complete one whole transaction is the best in the industry If certain individuals in the firm unexpectedly left, we would be in big trouble. 36.

(43) Descriptive Statistics Discussion From the descriptive statistics, we have found out some characteristics of intellectual capital in Taiwanese design industry. The results showed that employees work in teams in design companies (H4) to complete tasks, and they give it their all when they work (H20). Also, if certain individuals unexpectedly left, the firm would be in big trouble (H13R). This might be due to the fact that design companies are usually smallscaled and teamwork plays a crucial role in contributing to company’s performance. Moreover, the organizational structure of design companies is not bureaucratic (S13) and supports the development of new ideas and products. Also, the culture of the design companies is usually supportive (S15). Additionally, the managers don’t seem to focus on reducing costs (S1). It can be inferred that design companies needs a supportive culture and flexible organizational structure to support creation and innovation. However, to maintain a working environment like this, some efficiency might be sacrificed in replace of more flexibility. Furthermore, customers’ needs (R14 to R17) are considered crucial in the design industry. Another fact is that design companies don’t seem to have high market share (R4). There are few design companies that possesses high market share in Taiwan’s market.. Validity and Reliability of the Measurement Instrument Validity of the instrument was determined by content validity. Content validity is basically the extent to which the measurement questions provide adequate coverage of the investigative questions. Before conducting the pilot test, face validity is reached through revising the items and its meaning by four experts, including three professors from National Taiwan Normal University (NTNU), and one design director of China Productivity Center (CPC). These experts provided assessments of each item in the questionnaire by determining if they are appropriate or not. (Please see Appendix C for the list of experts) To test reliability, the researcher revised the reverse coded questions and conducted a pilot test using 10 samples with Statistical Package for the Social Sciences (SPSS) PC 12.0, which indicated a high internal consistency based on the alpha reliability value: 37.

(44) Human Capital 0.816 (20 questions); Structural Capital 0.894 (16 questions); Relational Capital 0.935 (25 questions); Performance 0.856 (10 questions). For the final test: Human Capital 0.928 (20 questions); Structural Capital 0.868 (16 questions); Relational Capital 0.925 (25 questions); Performance 0.958 (10 questions). Table 4.7 Cronbach’s α Value (Based on Standardized Items) of Survey Instrument Tests Pilot (n=10) Final (n=87) Human Capital .816 .928 Structural Capital .894 .868 Relational Capital .935 .925 Performance .856 .958. 38.

(45) PLS Findings Cronbach’s alpha and individual item reliabilities The reliability of the final test was inspected using Cronbach’s alpha. The reliabilities for each of the four constructs were greater than 0.86, which exceeds the criterion of 0.7, considered good for exploratory research (Nunnally, 1978). Then, PLS is used to assess individual item reliabilities so as to confirm factor findings. At early stages of scale development, loadings of 0.5 or greater maybe acceptable if there exists additional indicators for describing the latent construct (Chin, 1998). Therefore, items with loadings of 0.5 or greater are retained. There are other authors (Birkinshaw, Morrison, & Hulland, 1995) who have also followed this criterion in their exploratory studies. Table 4.8 shows the results of PLS loadings on all the items. Table 4.8 PLS Loadings Items H1 H2R H3 H4 H5R H6 H7 H8 H9 H10 H11 H12 H13R H14R H15R H16 H17 H18 H19R H20. Loading 0.7116 0.2901 0.7169 0.7166 0.2611 0.7339 0.6619 0.8087 0.6665 0.8365 0.8920 0.7353 0.1934 0.4964 0.5569 0.7550 0.6453 0.8123 0.5958 0.8128. Items S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13R S14 S15 S16R. Loading. Items. -0.0429 0.0522 0.7699 0.6938 0.6162 0.3335 0.7976 0.8126 0.7866 0.6127 0.6117 0.6829 0.6052 0.7767 0.8279 -0.0241. 39. R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13R R14 R15R R16 R17 R18 R19 R20 R21 R22 R23 R24 R25. Loading 0.7935 0.5848 0.7495 0.3555 0.6736 0.7546 0.8066 0.7590 0.5124 0.6102 0.7459 0.7647 0.6469 0.7450 0.6802 0.8782 0.5328 0.3527 0.3338 0.3107 0.4583 0.3402 0.4751 0.5666 0.2064. Items P1 P2 P3 P4 P5 P6 P7 P8 P9 P10. Loading 0.7550 0.8353 0.8061 0.9023 0.8617 0.8206 0.8563 0.9116 0.8489 0.8996.

(46) Item R17 (We get as much feedback out of our customers as we possibly can under the circumstances) was dropped because it was loaded incorrectly at 0.5449 for the human capital construct when we used PLS techniques (Please see Appendix D for Matrix of Loadings and Cross-Loadings). This left us with 16 indicators for the human capital construct; 12 indicators for structural construct; 16 indicators for relational capital and; 10 items to measure performance. The researcher compared the results with the studies administered in Canada, Malaysia, and Portugal and confirmed that 15 items were reliable in all four researches and 17 were reliable in at least three contexts (See Table 4.9). Table 4.9 Reliable Items – Comparing Studies in Canada, Malaysia, Portugal and Taiwan Canada H6 H8 H9 H11 H15R H18 H20. Malaysia Portugal Human capital H3 H1 H8 H3 H10 H5R H11 H6 H20 H7 H8 H9 H10 H11 H12 H15R H17 H18 H20. Taiwan. Canada. H1* H3** H4 H6** H7* H8*** H9** H10** H11*** H12* H15R** H16 H17* H18** H19R H20***. S1 S2 S3 S4 S5 S6 S10. 40. Malaysia Portugal Structural capital S7 S2 S9 S3 S10 S6 S11 S7 S12 S8 S9 S10 S11 S12 S15. Taiwan S3** S4* S5* S7** S8* S9** S10*** S11** S12** S13R S14 S15*.

(47) Table 4.9 (continued) Canada R1 R5 R6 R8 R9 R14 R15. Malaysia Portugal Relational capital R5 R6 R6 R8 R7 R9 R10 R10 R14 R11 R16 R14 R17 R16 R17 R18 R19 R20 R21 R22 R23. Taiwan. Canada. Malaysia Portugal Performance P2 P1 P3 P2 P4 P3 P5 P4 P6 P5 P7 P6 P8 P7 P9 P8 P10 P9 P10. Taiwan. R1* P2 P1 R2 P3 P2*** R3 P4 P3*** R5** P5 P4*** R6** P6 P5*** R7* P7 P6*** R8** P8 P7*** R9** P9 P8*** R10** P10 P9*** R11* P10*** R12 R13R R14*** R15R* R16** R24 *reliable measures in the Taiwan context and one other country **reliable measures in the Taiwan context and two other country *** reliable measures in all four studies Note: The relational capital was originally coded with “C” in the study of Canada and Malaysia. This study revised them in to “R” in order to avoid misunderstanding. Source: Revised from Cabrita and Bontis’ (2008) study According to Cabrita and Bontis (2008), in spite of that the measurement and structural parameters are estimated together, a PLS model is analyzed and interpreted in two stages: the assessment of the reliability and validity of the measurement model, and the assessment of the structural model. The sequence ensures reliable and valid measures of constructs before we try to draw conclusions with regard to the relationships among the constructs.. Testing the Measurement Model This study uses Cronbach’s alpha in SPSS and PLS approach to assess the measurement model (outer model). All the Cronbach’s alpha values of the four constructs exceeded 0.91 (0.942 for human capital; 0.914 for relational capital; 0.935 for relational capital; 0.958 for business performance).. 41.

(48) Individual item reliabilities were evaluated by examining the loadings of the measures with their corresponding construct. All loadings were greater than 0.522 except the loading of R9, which is 0.4857; however, it is not too low to be deleted (Table 4.10). Convergent validity was assessed using the internal consistency measure (Table 4.11), developed by Fornell and Larcker (1981). All values for the four constructs exceeded 0.7, as recommended by Nunnally (1978). Concerning discriminant validity, R17 (We get as much feedback out of our customers as we possibly can under the circumstances) was deleted after examining the cross-loading matrix (Please see Appendix D). Concerning model explanatory power, the R² value of this model (35.5%) is not quite different from those in the Canadian study (56.02% and 56.9%), the Malaysian study (32.1% and 37.3%), and the Portugal study (44.5%). Table 4.10 Factor loadings Constructs Loadings H. H1(0.7154), H3(0.7140), H4(0.7156), H6(0.7337), H7(0.6656), H8(0.8157), H9(0.6781), H10(0.8392), H11(0.8938), H12(0.7325), H15R(0.5482), H16(0.7487), H17(0.6493), H18(0.81829), H19R(0.5858), H20(0.8174). S. S3(0.7704), S4 (0.6929), S5(0.6055), S7(0.7977), S8(0.8164), S9(0.7870), S10(0.6038), S11(0.6163), S12(0.6859), S13R(0.6079), S14(0.7800), S15(0.8314). R. P. R1(0.8287), R2(0.6128), R3(0.7605), R5(0.6934), R6(0.7561), R7(0.8230), R8(0.7700), R9(0.4857), R10(0.6177), R11(0.7514), R12(0.7718), R13R(0.6554), R14(0.7605), R15R(0.6964), R16(0.8873), R17(0.4857), R24(0.5222) P1(0.7531), P2(0.8353), P3 (0.8057), P4(0.9025), P5(0.8617), P6(0.8203), P7(0.8562), P8(0.9120), P9(0.8497), P10(0.9000). Table 4.11 Measurement Model Results Constructs. Number of items. Cronbach’s Alpha. Internal consistency. Human Structural Relational Performance. 16 12 16 10. 0.939 0.913 0.935 0.957. 0.949 0.928 0.944 0.963. 42. R² (%). 75.6 70.1 35.5.

(49) In order to evaluate the statistical significance of the loadings and the path coefficients (standardized betas), a jackknife analysis was performed. In this case 86 subsamples were created by removing one case from the total data set. By applying the jackknife formula, PLS estimates the parameters for each sub-sample and compute the “pseudovalues” (Table 4.12). Three paths (human capital to structural capital, structural capital to relational capital, and relational capital to performance) are proved to be significant at the p-value < 0.1. Results showed that the explanatory power (R²) for the model is 35.5 %. Nevertheless, the path between human capital to relational capital, and structural capital to business performance was not significant and thus didn’t support the hypothesis. Table 4.12 PLS Path Analysis Results (Standardized Beta Coefficients and Adjusted Tvalues) Path Hypotheses β-path Adj. t-value Sig. Support Direction H→S H1 0.870 22.261 *** V + H→R H2 0.244 1.136 not sig. X + S→R H3 0.616 3.295 *** V + S→P H4 0.087 0.280 not sig. X + R→P H5 0.521 1.747 ** V + * p < 0.1. **p <0.05. *** p <0.01. 0.870*** (22.261). Structural Capital (SC) R² = 75.6%. 0.087 (0.280). 0.616*** (3.295). Human Capital (HC). 0.244 (1.136). Relational Capital (RC) R² = 70.1%. Performance (P) R² = 35.5%. 0.521** (1.747). Figure 4.1 Major Structural Model * p < 0.1. **p <0.05. *** p <0.01. Figure 4.1 demonstrates the results for the structural model. The results pinpoint that the three constructs that forms intellectual capital really affect one another. One important 43.