Introduction

The introductory chapter begins with a description of the context of the present study and a presentation of the fundamental issue addressed in this empirical investigation. The significance of intangible assets in knowledge era, objectives, conceptual framework and contribution value of this study is also addressed in this chapter.

1.1 Research Context

This section presents the broad context within which this empirical investigation is undertaken. The current problems and significance of intangible assets in knowledge era are explained.

Traditionally, profit and loss figures in the balance sheet and annual financial reports are used as the main financial performance indicators for the action previously taken monitoring and crafting short term strategies. Accounting for intangible assets starts with documenting the various categories of expenses. Profit (or loss) is derived from the financial difference between sales revenue and operating cost. The costs include the expenses in brand building, customer database, training, product development, information technology, etc. These are usually treated as part of the operating cost and marketing expenses. The investment of tangible assets such as equipment, machinery, building, etc. is also recorded in balance sheet. This simple accounting record mechanism is no longer sufficient in the knowledge based economy. There is no linkage with long term strategies to compete with global competitors and survive in dynamic economic. Since an increasing share of market value in this era is not represented by inventory or physical assets. Investments in intangible assets are usually not documented in a proper systematic manner because of data non-availability. Consequently, reasonable estimates of the future performance potential of an organization could not be provided to the management. It is intriguing to note that the cause-effect relationships between marketing, production and human resource and financial performance have not so far been made operational.

Prior to the knowledge era, business lived in the world of tangibles, which worked well with the traditional accounting practices. However, things are different in today's world of intangibles. Modern management style and strategic crafting have adapted in response to global competition and volatile economic environment. The industrial age management has been replaced by the knowledge age leadership, with corresponding transformational effects on the economy and workplace (Figure 1.1). The focus on tangible assets in the industrial age has shifted to intangible assets in the knowledge age. This paradigm shift encourages organizational employees to utilize their knowledge in line with organizational goals. Globalization is the main driver of knowledge economy. Toffler (1990) proposed knowledge as the key success factor in the present competition. Knowledge can be transferred by information flow from manufacturers to customers. Organization knowledge could be frequently managed by well-organized people in organization. Knowledge and information technology form an important part of intangible assets. With the realization of this paradigm shift, issues concerning intangible assets are now more widely researched and practiced.

Industrial Age

Knowledge Age

Production Driven

Customer Driven

Functional

Process (Integrated)

Tangible Assets

Intangible Assets

Top Down

Bottom Up

Management

Leadership

Short-term strategies

Long-Term Strategies

Figure 1.1 The shift in management style from industrial age to the knowledge age

Intangible assets are of increasing importance for the corporate value creation

processes of all kinds of organizations. In 1978, intangible assets were determined to constitute only 5% of all assets, while they become 78% of all assets today. Some 50 to 90 percent of the value created by a firm in today's economy is estimated to come from the management of the firm's intellectual capital rather than from the use and production of material goods (Guthrie and Yongvanich, 2004). Some public and private sector organizations do not attempt to incorporate the value of intangible assets. Sonnier et al. (2007) examined 150 high technology companies and found that management may want to reduce the level of disclosure to conceal sensitive strategic information in order to maintain a competitive advantage. As such, management reporting and financial statements will become increasingly irrelevant as a tool supporting meaningful decision making. Forward-thinking management has to ensure that intangible assets are identified, monitored, built and leveraged. Financial profit alone could not guarantee the long term survival of companies. To be sustainable, companies need to understand and be able to manage intangible factors, including organizational learning and growth, internal process and external structure. Management that aspires for sustainable business growth and industrial leadership in the twenty-first century has to focus on superior management skills and knowledge under limited resources.

Augier and Teece (2005) and Johanson (2005) reported that human capital, knowledge and other intangible assets have emerged as key to business performance in the economic systems. The intangible assets are the competitive edge over competitors. Srivastava et al. (1998) suggested the framework linking market-based assets to shareholder value which could be considered as the subset of present study. The market investment in brand and customer-profile databases leads to cash flows via a combination of price and share premiums, faster market penetration, reduced distribution, sales and service costs, and increased loyalty and retention. Brands are economic assets which are to create value shareholders and develop competitive advantage (Doyle, 2001). During the last three decades, brand is widely recognized as playing the key role in business. Brands influence customer choice, but the influence varies depending on the market in which the brand operates. Ittner (2008) suggested several previous studies that provided at least some evidence that intangible asset measurement is associated with higher performance. Several previous studies are limited by over-reliance on perceptual satisfaction or outcome variables, inadequate controls for contingency factors, simple variables for capturing complex measurement practices, and the lack of data implementation practice.

In this study, the Balanced Scorecard strategy map (Kaplan and Norton, 2004) is chosen to provide a framework to illustrate how strategy links intangible assets to value creating processes. The reasons for choosing Balanced Scorecard as the stage to build the framework for the present research are as follows: First, Balanced Scorecard is a practical approach to measure the intangible assets that has been widely used in a variety of organizations over the past two decades. Second, through the strategy map concept, Balanced Scorecard provides the linkage the relationship between intangible assets and business performance including the interrelationship between intangible assets elements: 1) Learning and growth affect internal process 2) Internal process affects external structure 3) External structure affects business performance. The measures in the four perspectives are linked together by cause-effect relationships. The company builds the core competence and training to support the internal process. The internal process creates and delivers the customer value proposition. When the customers are satisfied, the sales and profit are delivered in terms of financial performance which is the key measure of business performance.

1.2 Research Objectives

Since developed economies have become knowledge-based and technology intensive, view of the firm has significantly changed and intangible assets have become fundamental determinants of value and control. There are three fundamental elements of intangible assets which are learning and growth, internal process and external structure (Sveiby, 1997; Kaplan and Norton, 2004). The ultimate goal of firm is to maximize the business performance (financial performance, sales performance and customer fulfillment).

This study aims to establish empirically the cause-effect relationship between learning and growth, internal process, external structure and business performance, including the interrelationships between the elements leading to business performance.

1.3 Expected Contributions of the Study

There are two key areas of expected outcomes of the study. First, the impact of intangible assets on business performance is expected to be empirically established. In particular, the cause-effect relationship between learning and growth, internal process and external structure would be identified and analyzed. This is so that the detail underlying the relationships can be implemented in practice.

Second, it is expected that the effect of business size, business sector and establishment age on the causal links between intangible assets and business performance would be established. As there are various types of firm's business (service and non-service), sizes of business (large and SME), establishment age in the industry, this study would provide the pattern of cause-effect relationships between intangible assets and business performance in each business characteristic.

Given the expected outcomes, the expected academic contributions of the present study would be to encourage similar studies to establish the causal links between intangible assets and business performance in other types of economies. The study would also provide the foundation for the field of intangible asset management

For business practitioners, top management will benefit from the understanding of cause-effect relationship and the realization of the importance of intangible assets (learning and growth, internal business process and external structure) and business performance. With the clearer understanding, proper budget allocation and intangible assets management will be more properly focused and controlled to increase sustainable competitive advantage. The intangible assets are the strategic key to a sustainable competitive advantage and future economic profit.

1.4 Conceptual Framework

During last decade years, intangible assets are widely expanded and researched. The value of intangible assets is likely to grow over time if the firm undertakes successful intangible assets management. The intangible assets in each fundamental element (learning and growth, internal process and external structure) are selected and classified as shown in Table 1.1. More detail explanation is given in Chapter 2.

Table 1.1 Framework of intangible assets indicators

Intangible assets element

Intangible assets indicators

External structure

Customer satisfaction

Customer loyalty

Brand

Internal process

Process improvement

Innovation

Information technology

Learning and growth

Knowledge

Know-how

Employee competence

Engagement

The cause-effect relationship is covered in strategic mapping (Kaplan and Norton, 2004). There have also been several studies, e.g. Huselid and Becker (1997), Hitt et al. (2001), Liu and Tsai (2007), that examined the relationship between learning and growth and business performance as explain in more detail in Chapter 2. The main hypotheses in the present study are shown in Figure 1.2.

Figure 1.2 Research hypotheses testing model

H1: Learning and Growth is positively related to Internal Process

H2: Internal Process is positively related to External Structure

H3: External Structure is positively related to Business Performance

H4: Learning and Growth is positively related to Business Performance

1.5 Outline of Methodology

The research hypotheses formulated in this study were tested in the mail survey or questionnaire of registered company at the Thai Chamber of Commerce. The initial step in the analysis of the data collected focuses on examining the frequency distribution and the mean and standard deviation for each item or variable considered in this research. The next step in data analysis is to assess the validity of measures. Here the study uses item-total correlation, confirmatory factor analysis and the Cronbach alpha coefficient. The initial data analysis, and reliability and correlation analyses are performed using the SPSS statistical package. Furthermore, the structural equation modeling (SEM) EQS program (Bentler, 1995) is used to perform the confirmatory factor analysis, discriminant validity tests and testing of the structural model. The entire step-by-step model fit process from data collection by field survey questionnaires is shown in Figure 1.3. More details of research methodology are provided in Chapter 3.

1.6 Structure of the Thesis

The thesis is structured on the basis of five chapters, which represent the different stages that are involved in the overall research process. Chapter 1 has covered the research context, current problems, purpose and expected contribution of the studies.

Chapter 2 provides an extensive review of definition of intangible assets, intangible assets value and the Balanced Scorecard strategic mapping. This detail provide support to conceptual model of the study and the set of research hypotheses of the study which links learning and growth, internal process and external structure to business performance through cause-effect relationship.

Chapter 3 presents the step-by-step research methodology used to conduct the study. It illustrates a range of important methodological issues including the research design, sampling, questionnaire development process, data collection and measurement of model variables. The Structural Equation Modeling (SEM) technique is briefly explained.

Chapter 4 provides results of validity testing of the constructs and hypotheses of the present study by using EQS program for SEM technique and Statistical Package for Social Science (SPSS) program. Not only the results of the main research hypotheses testing model, but also other possible models are explored.

Chapter 5 presents a summary of the major findings and conclusions of the study. It also suggests the long-term strategic implications of the study finding for top management. Finally, consideration is given to the limitations of this empirical investigation and suggestions are made for potential directions and strategies for future research.

Literature Review

This chapter reviews the definition of intangible assets and its value. The previous correlation empirical research between intangible assets and performance are reviewed.

2.1 Introduction

There have been a large number of studies in intangible assets during the last two decades (see Figure 2.1). Intangible assets are involved in the customers, external structure, human resources, and internal process. The intangible assets are defined as non-financial assets without physical substance that are held for use in the production or supply of goods or services or for rental to others, or for administrative purpose (Epstein and Mirza, 2005). Intangible asset is an accounting term, but intellectual capital is a noun used in the management field. They both refer to the same thing. Therefore, Edvinsson and Malone (1997) and Tseng and Goo (2005) pointed out that intangible assets and intellectual capital are synonyms. Intangible assets are identifiable and controlled by the enterprise as a result of past events, and from which future economic benefits are expected to flow.

Figure 2.1 Research development on intangible assets

2.2 Intangible Asset Element Classification

Several studies have variously attempted to categorize intangible assets as summarized in Table 2.1. Some categorizations are in more common use than others.

Table 2.1 Approaches for the categorization of intangible assets

Kaplan & Norton

(1992)

Sveiby

(1997)

Edvinsson & Malone

(1997)

Roos et al.

(1997)

Bontis et al.

(2000)

Wingren

(2004)

Balanced Scorecard

(BSC)

Intangible Assets Monitor

Skandia

Value Scheme

Intellectual Capital

Balanced Scorecard with Intellectual Capital

Financial

Financial & expectation

Customer

External structure

Customer capital

Structural capital

Relational

Capital

Customer

Internal processes

Internal structure

Organizational capital

Structural

capital

Internal process

Learning and growth

Competence structure

Human

capital

Human Capital

Human

Capital

Learning and growth

The purpose model of the above intangible assets researchers is summarized by Bontis (2000) in Table 2.2.

Table 2.2 Purpose of intangible model

Studies

Purpose

Kaplan and Norton (1992)

A multi-dimensional, intangible asset accounting system designed to guide management decisions.

Sveiby

(1997)

An intellectual capital performance measurement reporting system that uses an human resources, and information systems rather than financial perspective.

Edvinsson and Malone (1997)

To provide management with a taxonomy for classifying an organization's knowledge assets and a series of metrics to measure them.

Roos et al.

(1997)

To develop and apply a summary index of consolidated measures of intellectual capital.

In Table 2.1 and Table 2.2, there are the intangible elements correspond in each study. Wingren (2004) proposed that framework the correspond to intangible assets framework presented by Sveiby (1997) and Kaplan and Norton (1992) in Figure 2.2.

Wingren (2004) mentioned that the Balanced Scorecard is primarily tool for internal development and evaluating the market value of the company for long run. Bose and Thomas (2007) implemented the concept of Balanced Scorecard to a company and they claimed that the formulating of Balanced Scorecard fits the strategic interest of the organization to achieve sustainable competitive advantage. The Balanced Scorecard encapsulates the short and long-term strategies. The motivation and evaluation of employee to achieve goal in BSC is rather than using it just as a measuring tool.

When intangible assets are addressed and defined, there are four practical approaches to measure the intangible assets (Luthy, 1998):

1. Direct Intellectual Capital Method (DIC)

Estimate the value of intangible assets by identifying its various components. Once these components are identified, they can be directly evaluated, either individually or as an aggregated coefficient.

2. Market Capitalization Method (MCM)

Calculate the difference between a company's market capitalization and its stockholders' equity as the value of the intellectual capital or intangible assets.

3. Return on Asset Method (ROA)

Average pre-tax earnings of a company for a period of time are divided by the average tangible assets of the company. The result is a company ROA that is then compared with its industry average. The difference is multiplied by the company's average tangible assets to calculate an average annual earning from the intangibles. Dividing the above value of average earnings by the company's average cost of capital or an interest rate once can provide an estimate of the value of its intangible assets or intellectual capital.

4. Balanced Scorecard Method (BSC)

The various components of intangible assets or intellectual capitals are identified and indicated. Indices are generated and reported in scorecards or graphs. Wingren (2004) has chosen to use the BSC concept because BSC contains outcome measures and the performance driver of outcomes, linked together in cause-effect relationships. There are linkages between customer, internal process and learning/growth with financial performance. The financial performance is the outcome and visible to the observers.

2.3 Intangible Assets in Balanced Scorecard

Among the above four approaches, the Balanced Scorecard is by far the most well-known, although its original intent was not meant to be the measure for intangible assets, as discussed by Marr and Adams (2004) and Mouritsen et al. (2005). The Balanced Scorecard may be used to measure all the intangible assets in Table 2.1. Bose and Thomas (2007) recently applied the Balanced Scorecard in an empirical study of the Foster Brewing Group. The formulating of a scorecard that best fits the strategic interest of the organization is considered vital. In their view, the Balanced Scorecard is never really complete because the business environment (new competitors, changing customer demand, etc.) is dynamic and constantly evolving.

As is already well-known, the Balanced Scorecard was introduced by Kaplan and Norton (1992) as a tool to link financial performance with non-financial performance dimensions: learning and growth, internal process and customer perspectives. Linkages and relationships between customers, internal process and learning/growth with financial performance are shown in Figure 2.3. The Balanced Scorecard acts as a measurement system, a strategic management system, and a communication tool. Seggie et al. (2007) made an argument for the Balanced Scorecard to be the measurement tool in marketing to measure non-financial assets and provide the organization with a long-term perspective. The Balanced Scorecard is at least partially forward-looking and partially geared toward the long-term performance of the firm. The Balanced Scorecard concept has been examined the performance measurement of bonus plan in major financial services firm. Ittner et al. (2003) recommended that the future research on Balanced Scorecard adoption and performance consequences must move to encompass the entire implementation process.

.

The concept of cause-effect relationship separates the Balanced Scorecard from other performance management systems. The measures appearing on the scorecard should be linked together in a series of cause-effect relationships to tell the organization's strategic story. Increasing promotional expenses will lead to the increase in brand value. Increased brand value will lead to higher sales revenue

The investment of human capital will create the continuous learning and growth in the organization. When the employees have more experience and knowledge, they can create the internal process which serves and fulfills customer satisfaction. The profit and revenue are the final outcomes of this causal chain.

Heskett et al. (1994) explained that the linkage of the above model that investment in employee training leads to improvement in service quality. Better service quality lead to higher customer satisfaction. Higher customer satisfaction leads to increased customer loyalty. Increased customer loyalty generates increased revenues and margins.

The following are five principles of successful Balanced Scorecard users (Kaplan and Norton, 2004):

1. Mobilize change through executive leadership

2. Translate the strategy into operational term

3. Align the organization to the strategy

4. Make strategy everyone's job

5. Make strategy a continual process

Intangible assets can be considered very much part of the Balanced Scorecard. Intangible assets are linked mainly to the marketing and human resources. Following is the review of intangible assets in Balanced Scorecard by Kaplan and Norton (1992) and intangible asset monitored by Sveiby (1997) are reviewed. By using the categories developed by Hall (1993), Sveiby (1997), Shaikh (2004) and Roos et al. (1997) reviewed and classified the intangible assets into a framework of internal structure, external structure, and employee competence as shown in Table 2.3.

Table 2.3 Framework of intellectual capital/ intangible assets indicators

External structure

Brands

Customers/ Customer loyalty

Company name/ Distribution channel/ Business collaborations/ Market information used to capture and retain customers

Customer satisfaction

Internal Process

or

Internal Structure

Intellectual property

* Patents

* Copyrights

* Trademarks

Infrastructure assets

* Management philosophy

* Corporate culture

* Management processes

* Information systems

* Networking systems

* Financial relations

Learning and Growth

or

Employee Competence

Know-how

Knowledge

Competencies

Engagement

From the above table, the intangible assets are reviewed as follows.

1. Learning and Growth

The learning and growth is the capacity of employee to act in a wide variety of situations. Employee is the most valuable asset of the company in the highly competitive market. It is the one asset that creates uniqueness to the company and differentiates the company from the competitors. Sveiby (1997) emphasized employee capability as a key asset for organization growth. Employee satisfaction refers primarily to job and what employees perceive as offerings. Employee satisfaction is positively related to organizational commitment. There are several studies mentioned that human resource is effect to business performance. Huselid (1999) and Hand (1998) have reported the existence of a positive and significant relationship between investments in human resources and the market value of companies. Huselid and Becker (1997) found that there is a strongly positive relationship between a high performance human resource systems and firm performance. Bontis et al. (2000) found that human capital had positive effect on customer retention and loyalty regardless of industry type. Hitt et al. (2001) and Hurwitz et al. (2002) found that human capital has a positive effect on performance. Also, human capital is shown to have moderate cause-effect relationships with strategy and firm performance. Moon and Kym (2006) confirmed that human capital, structural capital and relational capital have direct impact on intellectual capital. Liu and Tsai (2007) surveyed 560 managers from major Taiwanese hi-tech companies and found that knowledge management has a positive effect on operating performance. Lin and Kuo(2007) also investigated that human resource management influences operational performance indirectly through organizational learning and knowledge management capability.

Knowledge is one of learning and growth perspective. In knowledge era, the knowledge management has been widely studies. The knowledge is lost by the organization when the employees leave the firm (Ordonez de Pablos, 2004). McKeen et al.(2006) founded that knowledge management was positive significant to overall organization performance (product leadership, customer intimacy and operational excellence) which is part of internal and customer perspectives in Balanced Scorecard. Organization performance was significant to financial performance. There was no significant direct relationship between knowledge management and financial performance. The knowledge sharing is a key issue in order to enhance the innovation capability that is one of internal process (Saenz et al., 2009). There is also the linkage of learning and growth and internal process. Forcadell and Guadamillas (2002) studies a firm used knowledge management to develop a process of continuous innovation which is in the internal business process perspective.

2. Internal Process

The internal process includes patents, concepts, models, information technology systems, administrative systems and organizational culture (Aaker, 1991). Such leading companies as GE, Sony, IBM, or Ford used to cover a wide variety of products, but after finding that they could not sustain all product lines, they switched to selective products, while improving the intangible factors, quality and innovation. Deng et al. (1999) suggested that patent attributes are statistically associated with stock return and market to book ratio. Research and Development is one of intangible assets which is the most importance performance. Chu et al. (2008) founded that the valuation of assets and long-term focused in operation of US IC's firms are higher than the firms in Taiwan.

3. External Structure

The external structure includes relationship with customers and suppliers. The Balanced Scorecard is concerned only customer value proposition, but the external structure covers supplier. The external structure also encompasses brand-names, customer loyalty, customer satisfaction and the company's reputation or goodwill.

In the brand valuation terminology, brand is a large bundle of trademarks and associated intellectual property rights. Cravens and Guilding (1999) reported that brand valuation is one of the most effective means for business to bring accounting and marketing closer for the purpose of strategic brand management and effective means of communication between marketing and accounting. A branded business valuation is based on a discounted cash flow analysis of future earnings for that business discounted at the appropriate cost of capital. The value of the brand business is made up of a number of tangible and intangible assets. There are 2 brand evaluation models 1) research-based approaches measure consumer behavior and attitudes that have an impact on the economic performance of brands. No financial value on brands is in this model 2) purely financially driven approaches. Market-based approaches are one of the financially driven approaches. It is based on fundamental marketing and financial principles. The marketing principle is related to commercial function that brands perform within businesses. The financial principle is related to the net present value of future expected earnings.

Barth and Clinch (1998) and Seethamraju (2003) reported that brands have significant correlations with the firm's market values. Not only is brand the key intangible assets in external structure elements, but also are customer satisfaction and customer loyalty. Customers are the valuable and key success assets in business. Doyle (2000) offered nine steps of the growth ladder to generating sales growth. Trust and loyalty of existing target customers are key success factors. The businesses have to maximize returns to shareholders by developing and implementing strategies to build relationships of trust with high-value customers and to create a sustainable advantage. Yeung et al. (2002) and Ittner and Larcker (2000) found a positive relationship between measures of customer satisfaction and financial performance. Moreover, customer satisfaction has a significant relationship with positive stock return. Nagar and Rajan (2005) pointed out that today intangibles, such as customer relationships, account for more than half of total assets of the firms in the United States. Cheng et al. (2008) urged that customer capital means the establishment, maintenance and development of corporate external relationship with customers, suppliers and strategic partners. Edvinsson and Malone (1997) pointed out that the value of customer capital lies in the maintenance of customer relationship.

Whereas all organizations attempt to develop their people, technology, and culture, they do not always align these intangible assets with their strategies. The Balanced Scorecard strategy map enables executives to pinpoint the specific human, information, and organization capital required by the strategy (see Figure 2.4). The cause-effect relationships between intangible assets (learning and growth, internal process, customer perspective) and financial performance are shown in the Balanced Scorecard strategy map. Each intangible assets element in the strategy map contains intangible assets sub-elements. When the strategies in each sub-element are identified, they are linked to intangible assets elements and eventually to financial performance.

of Balanced Scorecard, (Kaplan & Norton, 2004)

There are interrelationship linkages among of learning and growth, internal process, customer and financial performance. Olve et al. (1999) reported that Halifax applied the Balanced Scorecard in business and found the relationship that if we have the right staff, we will get doing the right thing and customers will be delighted. When the customers are delighted, we will in turn get more business. There are many functions involved in aligning the intangible assets with strategies. Each function has to build its own intangible assets in order to deliver and convert them to financial performance. Chareonsuk and Chansa-ngavej (2006) found that most of the companies in the Stock-Exchange of Thailand have functional organization structure. The intangible assets are linked to performance. Cheng et al. (2008) verified the accounting rationality of intangible assets classification from the management perspective (customer capital, human capital, process capital, innovation capital and contract capital) using the Tobit censoring method to analyze the composition of related intangible assets. The study provides some references for the control of intangible assets so managers can realize what kind of intangible assets to manage and decide how to do so and add value to the intangible assets.

The Balanced Scorecard has been widely used in formulating business strategy. Green and Ryan (2005) proposed the framework of intangible valuation areas for facilitating the systematic and repeatable identification of intangible assets Balanced Scorecard. It is designed to align the value drivers of intangible assets with its business strategy. Understanding the value of its intangible assets helps a business to develop, sustain and enhance its mission effectiveness and competitive advantage. Canibano et al. (2000) reviewed the importance and evidence value of intangible assets on business performance. Empirical studies of R&D, advertising, patents, brand, trademark, customer satisfaction, human resources were reviewed. Moreover, Green and Ryan (2005) presented the framework of intangible valuation areas (FIVA), which is a framework for facilitating the systematic and repeatable identification of intangible assets. FIVA provides a view of intangible assets within business enterprise. FIVA links all intangible assets driver to business performance and subsequently captures measures to monitor and evaluate performance. The FIVA is constructed based on the analytic hierarchy process (AHP) model to facilitate a ranking of value components in relative order of importance based on defined strategic knowledge management objectives. Intrapairot and Srivihok (2004) studies measurement of the intellectual capital in 10 small and medium size enterprises in Thailand by using multiple criteria decision-making (MCDM).

Chareonsuk and Chansa-ngavej (2008) proposed the framework for intangible assets management in business and industrial organization. The framework refines the strategy map concept in the Balanced Scorecard approach for use in intangible assets management. Intangible assets belong to different functional departments. They must be carefully monitored and properly nurtured by the organization. Intangible assets depend not only on the type of functional departments but also the type of industries.

The comparison between major aspects of Balanced Scorecard and intellectual capital are illustrated in Table 2.4 (based on Mouritsen et al., 2005).

Table 2.4 Conceptual comparison between Balanced Scorecard and intellectual capital

Factor

Balanced Scorecard

Intellectual Capital

Idea of strategy

Positioning theory

Competency-based strategy

The strategic process

1. Management sets financial targets and market segments to be aimed at

2. Targets are reached through customers

3. Customer satisfaction is achieved through the right generic value chain model

4. To maintain the right value chain model in the future, objectives for learning and growth are set up

1. Management determines the metaphoric narrative of the firm's identity and ambition

2. Desired characteristics, capabilities, competencies and relationships are determined

3. Goal and action plans are set up to reach

Strategic aim

The story of future profitability and market position

The narrative of desired future identity

Balanced scorecard focuses on describing the firm as value chain to increase the competitive strategy which closes the gap between a customer's want and firm's products, while intellectual capital delivers a certain value to the user. Balanced scorecard can comprise all activities needed to implement the strategy.

2.4 Types of Data for Analyzing and Investigating Intangible Assets

Two main types of primary data for analyzing and investigating the intangible assets issues could be obtained, as shown in Table 2.5.

Table 2.5 Type of intangible assets primary data

Type

Advantages

Disadvantages

Financial and accounting quantitative data

(monetary values)

Ready-made financial data

Data on intangible assets are not readily available

Difficult to obtain the financial data in some countries

Subjective opinion survey questionnaire

(non-monetary values)

All kinds of subjective opinion data could be widely covered and obtained according to the research needs in all industries in any conditions, without restrictions

The results are often susceptible to the questions' design and the respondent's attitude (Cheng et al., 2008)

Table 2.6 summarized the methodology and finding results of previous empirical studies during the last decade. Most of the previous empirical studies use the subjective opinion survey questionnaire to find the cause-effect relationship while there are only two previous studies, namely Chin et al. (2005) and Cheng et al. (2008), that gather the available financial data.

Bontis et al. (2000) applied the subjective opinion survey questionnaire and used Likert-type scale. They surveyed 107 respondents in both service and non-service sectors in Malaysia for finding the cause-effect relationship between intellectual capital and business performance. They found that the intangible assets elements have positive effect on business performance. Moon and Kym (2006) confirmed the causal linkage of interrelationship of intangible assets proposed by Bontis et al. (2000). The Likert-type scale is used throughout the opinion survey questionnaire.

Table 2.6 Previous empirical studies

Studies

Methodology

Empirical Finding/ Result

Bontis et al.,

2000

Questionnaire survey with 107 respondents both service and non-service industries. Analyzed by structural equation modeling technique, Partial Least Squares (PLS).

Intellectual capital has a significant and substantive relationship with business performance regardless of industry sector.

Carmeli and Tishler,

2005

Questionnaire survey with 99 respondents of general management of local authority in Israel. Use multivariate analysis approach, robust canonical analysis (RCA)

Intangible organization elements (managerial capabilities, human capital, perceived organizational reputation, internal auditing, labor relations and organizational culture) have a significant positive effect on the performance of the organization.

Nagar and Rajan,

2005

Marketing and customer satisfaction survey 87 retail banks. Analyzed by linear regression.

Customer relationship activities link to business performance.

Chin et al.,

2005

Gather financial data of 1,386 samples companies listed. Trademark value estimation by Seethamraju (2003).

Trademark/Brand is positively associated with firm's performance.

Wang and Chang, 2005

Gather data from 131 IT firm in Taiwan and analyzed the model by partial least square (PLS) method

Human capital has no direct effect on performance.

Innovation, process and customer capital have direct effect on performance.

Human capital directly affects innovation and process capital.

Innovation and process capital further influence performance.

Chen et al.,

2005

Gather data from 4,254 registered companies and analyzed the hypotheses by statistic testing

Intellectual capital has a positive impact on financial performance.

Cabrita and Vaz,

2006

53 banks (253 samples) in Portuguese banking were tested using PLS testing model

Intellectual capital has a significantly related to the organizational performance.

Moon and Kym,

2006

200 questionnaires analyzed and using SEM for model testing

Human capital has a positive effect on relational capital and that both human and relational capital affect structural capital.

McKeen et al.,

2006

Questionnaire survey with 90 respondents in US, and analyzed the hypotheses by PLS method

Knowledge management has direct relation to organizational performance.

Organizational performance has direct relation to financial performance.

No direct relation between knowledge management and financial performance.

Liu and Tsai,

2007

Questionnaire survey with 560 managers from Hi-Tech companies in Taiwan. Student's t-test and ANOVA are used.

Knowledge management has a positive effect on business performance.

Cheng et al.,

2008

Directly obtain financial data of 56 companies and analyzed 23 indicators by Tobit's model

Innovation, customer, human and contract capital have some impact on intangible assets.

The primary data can be gathered from not only subjective opinion survey questionnaire, but also financial data. Chin et al. (2005) gathered the data from 1,386 companies in Taiwan and analyzed the relationship between intangible assets and business performance by using Seethamraju model. Data are available and accessed from Taiwan Economic Journal Data Bank and Trademark database of Taiwan. Chen et al. (2008) collected primary data annual report with proxy statement of US S&P500 companies.

It is often difficult to obtain data of intangible assets in financial reports that exhibit positive relationship between intangible assets and business performance. Vergauwen et al. (2007) reported that there is a strong significant positive relationship between the level of structural capital possession of a firm and the firm's intellectual capital disclosure.

2.5 Measuring Intangible Assets Using Balanced Scorecard Strategy Map Model

Section 2.2 reviewed the measurement of intangible assets. Balanced Scorecard model is the practical approach to measure intangible assets. The Balanced Scorecard has no estimation of intangible assets in financial value. The Balanced Scorecard is not a useful tool for evaluating the market value of the company. However, it is useful for internal development for companies. The Balanced Scorecard is defined intangible assets indicators in three perspectives: 1) learning and growth 2) internal process and 3) customer. These three intangible perspectives are linked together in cause-effect relationship with business performance that is tangible. The primary data of intangible assets are gathered by subjective opinion survey questionnaires to find the cause-effect relationship between intangible assets elements and business performance. Previous empirical studies such as Bontis et al. (2000), Carmeli and Tishler (2005), Wang and Chang (2005), and Chen et al. (2005) intended to find not only the cause-effect relationship between intangible assets elements and business performance, but also interrelationship among of intangible assets element. Table 2.3 classified the sub-intangible assets element in each intangible assets perspective. Nagar and Rajar (2005), Chin et al. (2005), Moon and Kym (2006), Liu and Tsai (2007), Cheng et al. (2008) studies the cause-effect relationship between sub-intangible assets element i.e. brand, customer relationship, knowledge etc. and business performance.

This study applies the Balanced Scorecard strategy map because of 1) each intangible assets element contains sub-elements of intangible assets. 2) the interrelationship among intangible assets elements 3) the cause-effect relationship between intangible assets and business performance/tangibles.

2.6 Summary

Intangible assets are currently widely studies and researched in knowledge management era. Balanced scorecard is one practical method to explain the component of intangible assets in firms and cause-effect relationships between intangible assets and business performance. The intangible assets in Balanced Scorecard can be classified in three elements which are learning and growth, internal process and customer which are close to the concept of the intangible assets monitor accept the component of external structure. The external structure is covered not only customer but also external partners while only customer prospective is shown in Balanced Scorecard. There is interrelationship between the intangible assets elements in Balanced Scorecard strategy map. In this study, the intangible assets elements are learning and growth, internal process and external structure. There are some research studies listed in Table 2.6 establishing cause-effect relationships among intangible assets or intellectual capital and performance. However, they are partial studies, involving some elements of the intangible assets. The literature review points out the need for a more comprehensive study exploring the interrelationship between the intangible assets element and business performance. The review does leads naturally to the set of research hypotheses in Figure 1.2.

Research Design and Methodology of Study

This chapter explained the research design and methodology employed for testing the research model and hypotheses. The research design covers questionnaire design, data collection method, study construct and analytical procedures for assessing the reliability and validity of the measurement. Lastly, the hypotheses testing and model fit are explained.

3.1 Research Design

Selecting an adequate research design is important to strengthen the empirical research quality. There are three types of research designs referring to exploratory, descriptive and causal (Chisnall, 2001; Churchill, 1995; Zikmund, 2000). The summary is in Table 3.1.

Table 3.1 Type of research characteristic

Type of research

Characteristic

Exploratory

To investigate and identify the real nature of research problems by finding out what is happening, seeking new insights and ideas, asking questions and assessing phenomena in a new light. It is useful when a research problem is ambiguous or limited knowledge. Ability to observe, gathering information and construct explanation are required skill.

Descriptive

Some previous understanding about the topic under investigation whereas problem is structured and well understood. It aims to describe characteristic of a specific group. Accuracy is most important.

Causal

To examine the cause-and-effect relationships between variables by finding out, which variables are causes and which variables are effects of a phenomenon. It is concerned with learning why (how one variable produce change in another) while descriptive research attempts to find out who, what, where, when or how much.

The casual research is most appropriate for this study because the objectives of the study are highly structured, research problem is well understood, and there are sufficient previous empirical studies to support the formulation of hypotheses testing. Chapter 2 reviewed the concept of Balanced Scorecard strategy map as the practical method for finding the cause-effect relationships between intangible assets and business performance. The intangible assets primary data are gathered from a subjective opinion survey questionnaire. There are several previous empirical studies listed in Table 2.6 using the opinion survey questionnaire to find the cause-effect relationship between intangible assets elements and financial performance or business performance.

3.2 Data Collection Methods

Good planning of the data collection process leads to quality data and in turn delivers quality research. There are three traditional types of communication data sources: personal interview, telephone interviews and mail survey (questionnaire) (Churchill, 1995). The advantages and disadvantages are summarized in Table 3.2.

Table 3.2 Advantage and disadvantage of data collection method (Zikmund, 2000)

Personal Interviews

(Face-to-Face)

Telephone

Interviews

Mail Surveys (Questionnaires)

Speed of data collection

Moderate

Very fast

Slow; researcher has no control over return of questionnaire

Geographic flexibility

Limited to moderate

High

High

Respondent cooperation

Excellent

Good

Moderate; poorly designed questionnaire will have low response rate

Versatility of questioning

Quite versatile

Moderate

Not versatile; requires highly standardized format

Questionnaire length

Long

Moderate

Varies depending on incentive

Item non-response rate

Low

Medium

High

Possibility for respondent misunderstanding

Low

Average

High; no interviewer present for clarification

Degree of interviewer influence on answers

High

Moderate

None, interviewer absent

Supervision of interviewers

Moderate

High, especially with central location WATS interviewing

Not applicable

Anonymity of respondent

Low

Moderate

High

Ease of call back

or follow-up

Difficult

Easy

Easy, but takes time

Cost

Highest

Low to moderate

Lowest

Special features

Visual materials may be shown or demonstrated; extended probing possible

Simplified fieldwork and supervision of data collection; quite adaptable to computer technology

Respondent may answer questions at own convenience; has time to reflect on answers

The questionnaire is most appropriate for gathering the information due to its advantages in terms of cost-effectiveness, low level of administration requirement, allowance for top management respondent to complete upon their convenience, obtaining large amount of information from a large sample and elimination of potential bias as no affluent from the interviewer. For this reasons, the questionnaire survey is widely used in academic and commercial researches.

3.3 Questionnaire Development

Questionnaire is the primary source of data for this study. An appropriate design of the questionnaire is crucial to ensure the success of the study and the achievement of the research objectives (Churchill, 1995; Malhotra, 2004). Questionnaire design has attracted noteworthy attention in general business literature. In the marketing field, Churchill (1995) provides a clear and concise step-by-step procedure to assist researchers in developing and constructing the questionnaire as illustrated in Figure 3.1.

Step 1 Specify what information will be sought

The purpose of this study is to identify the interrelationship of three elements of intangible assets which are learning and growth, internal business process and external structure that can facilitate the achievement of business performance. The required information is linked to those elements. The descriptive research is appropriate type of research selected.

Step 2 Determine type of questionnaire and method of administration

The questionnaire of intangible assets is constructed and distributed to the top management of company. It has to have clear objectives and reliable questions.

Step 3 Determine content of individual question

Kaplan and Norton (1992) illustrated the Balanced Scorecard model which has the internal linkage of learning and growth, internal process and customer to financial performance. In 2004, Kaplan and Norton proposed the strategic map for converting intangible assets to tangible outcomes which is widely used in the business strategies crafting. As mentioned in Chapter 2, there are several studies that reviewed and classified the intangible assets into a framework of learning and growth, internal process and external structure.. In this study, the intangible assets in each element have been explained in Table 1.1.

The designs of the questionnaire in the present study follows the three elements of intangible assets namely, learning and growth, internal process and external structure. The other important group of questions that must be mentioned is business performance.

The number of questions in each of the intangible assets elements is shown in Table 3.3. Simplicity and friendliness to the respondent are the design concept of this questionnaire.

Table 3.3 Number of questions in each element of the questionnaire

Questionnaire elements

Sub-elements

Related survey questions

Learning and Growth

Know-how

5

Knowledge

5

Competency

5

Engagement

5

Internal Process

Process Improvement

6

Innovation

5

Information Technology

5

External Structure

Customer Satisfaction

7

Customer Loyalty

4

Brand

6

Business Performance

Financial Performance

3

Sales Performance

4

Customer Fulfillment

8

Step 4 Determine the from of response to each question

The intangible assets indicators of each intangible assets element in questionnaire are adopted from the literature on the measures of intangible assets to business performance which are reviewed in Chapter 2. The questions in each intangible assets elements are mainly constructed from Bontis (2000), Roos et al. (1997), Olve et al. (1999) and Hinshaw (2005). Each question is classified and gathered in the same group of intangible assets which are learning and growth, internal process and external structure. A five-point Likert-type scale is used through out the questionnaires, ranging from (1)”low/disagree” to (5)”high/strongly agree”. The five-point Likert-type scale has been used by McKeen et al. (2006) and Moon and Kym (2006) for gathering the primary data before analyzing the cause-effect relationship of intangible assets.

Step 5 Determine the wording of each question

Clear and simple words were used to ensure that questions could be easily friendly and understood by the top management. Top management has to spend few minute to complete questions without any unclear message. Questionnaire is developed in both English and Thai, as exhibited in the Appendix C.

Step 6 Determine sequence of question

The simple, general and interesting questions were placed at the beginning to attract and stimulate participants' interests. Thus, the sequence of questionnaire is learning and growth, internal process, external structure. The business performance question is a difficulty and sensitive. They are placed at the end of the questionnaire. The business information, i.e. established year, type of business and respondent's information are placed at the last pages. Moreover, the confident level of respondent in the question is also shown at the last page.

Step 7 Determine physical characteristics of questionnaire

The total length of questionnaire is ten pages. It is organized in two-sided pages to make it compact and practical for this distribution. The front cover consisted of a clear title and objective of this research. The length of the topics covering learning and growth, internal process, external structure, business performance and general information is two pages each.

Step 8 Re-examine step 1-7 and revise if necessary

The draft version of the questionnaires was distributed to two knowledgeable researchers and two practitioners with long experience in the industry. A number of modifications were made according to their suggestions.

Step 9 Pre-test questionnaire and revise if necessary

The pre-survey questionnaire has been given to the top management of twenty companies in different type of business and size of business. There were only a few changes in wordings and sequence of questions. As a result of these valuable suggestions, the questionnaire was modified and improved to ensure that the introduction, sequence, wording in questionnaire are appropriate and ready for mass distribution to the industry.

3.4 Data Collection

The mail survey/questionnaire is appropriate and applicable for this study. The registered companies at the Ministry of Commerce in Thailand were 562,386 companies (from http://knowledgebase.dbd.go.th/dbd/bra/brasummaries.aspx as of January 31, 2009) and 873 registered companies in Thai stock market (Stock Exchange of Thailand, SET, and Market Alternative Investment, MAI). They are two widely recognizes trade associations in Thailand, the Federation of Thai Industries and Thai Chamber of Commerce. The Federation of Thai Industries covers only manufacturing industry, not service sectors. Thus, the Thai Chamber of Commerce is targeted as a channel for distributing the mail survey questionnaires. The members of the Thai Chamber of Commerce are in various business sectors and of business sizes. It has 3,084 member companies. Several of the public-listed companies in Thai stock market are also members of the Thai Chamber of Commerce. The members of the Thai Chamber of Commerce are known to have reliable financial and general information.

To gain access to members of Thai Chamber of Commerce, an introductory letter from the Thai Chamber of Commerce is considered necessary. A letter introducing the research project and signed by a director of the Thai Chamber of Commerce was therefore attached to the cover page of the questionnaire. The questionnaires were distributed to all the members of Thai Chamber of commerce regardless of business sizes, business sectors and establishment age. About a week later, a series of follow up telephone calls were made to check the status of questionnaire responses. There were 26 return-mails because of wrong address information. The correct addresses were sourced by telephone and questionnaires resent. After the repeated follow up attempts, 361 completed questionnaires were received.

It is very important to ensure the reliability of questionnaire respondents and important question was included on the last page of questionnaire. This is the question “How confident are you in answering the aforementioned questions?” Base on the responses to this question 57 responses (16%) were dropped from further analysis because the answer to this question was lower than 3 and no contact person and address were given. The remaining 304 respondents are used in further analysis in this study. The mean response to this question was 4.13 out of 5.

3.5 Statistical Analysis Procedure

As mention in Section 3.4, the screening was explained. This study followed the two-step approach recommended by Anderson and Gerbing (1988). First is to conduct measurement model to assess the convergent and discriminant validity (undertaking confirmatory factor analysis and reliability measurement) which is mentioned in Section 3.5.1. Second is to conduct the structural model to test the goodness of fit of the proposed hypotheses relationships.

As described in Figure 1.3, the preliminary stage of data analysis involves an examination of descriptive statistics and frequency distribution of each variable included in this study in order to initially explore the survey findings and to inspect whether there is a variation in the responses. The following stage is to assess the reliability and validity of the measures through item-total correlation, Cronbach's coefficient alpha and confirmatory factor analysis. Additionally, correlations coefficients between the investigated constructs are examined to preliminary explore the strength and direction of the relationships between the two constructs. The final stage is to test the research hypotheses through structural equation modeling. This method enables researcher to examine all relationships included in the conceptual model at one time. The preliminary data analysis, reliability and correlation analyses were performed by using SPSS 14.0. The confirmatory factor analyses and structural equation modeling were tested by using EQS 6.0 (Bentler, 1995).

The Structural equation modeling has been increasing popular among social sciences studies in testing conceptual model during the past decades (Hair et al., 2006). This is because it enables the researcher to determine the observed measures of the study are adequate; that the technique allows the measurement errors to be controlled; and, that it allows the estimation of all parameters in a conceptual model while multiple regression analysis can estimate only one dependent variable (Anderson and Gerbing, 1988; Fornell and Larcker, 1981; Hair et al., 2006; Kim and Frazier, 1997). Structural equation Modeling in this study was performed using the EQS 6.0 computer program. EQS is a user friendly program that is capable of handling non-normal data compared to other packages.

3.5.1 Reliability and Validity Assessment.

Before testing the hypotheses relationships among the constructs in the conceptual model, it is vital to ensure that all the selected empirical indicators are represented the concepts in which they are supposed to measure in an accurate and consistent manner (Hair et al., 2006). With this regard, two critical issues to be concerned are reliability and validity. Reliability is the extent to which empirical indicators provide consistent results in repeated used, while validity is the extent to which empirical indicators are truly reflected the phenomena being studies (Zikmund, 2000).

a) Reliability Assessment

As described in Figure 1.3, reliability assesses whether the repeated measurements of the same construct in the questionnaire are consistent, in a highly correlated manner (Hair et al., 2006). Prior to reliability assessment, item-total correlations were initially calculated to purify the measures. The principle of this procedure is to indicate which items that do not belong to the content domain of the constructs. The items which exhibited item-total correlations below 0.40 were then eliminated from further analysis (Nunnally and Bernstein, 1994). Reliability assessment of a measure such as the “Test-retest”, “Alternative forms”, “Split-halves”, and “Cronbach's coefficient alpha” (Hair et al., 2006; Malhotra, 2004; Saunders et al., 2007). Among these methods, the most commonly used method in estimating internal consistency of the scales employed in marketing literature is Cronbach's coefficient alpha. Cronbach's coefficient alpha provides an overall indicator of the correlations among a set of items used to measure a construct. It ranges from 0 to 1 (Hair et al., 2006). In general marketing and management fields, the Cronbach's coefficient alpha are accepted at minimum of 0.70 (Nunnally and Bernstein, 1994). Therefore, in the present study, Cronbach's coefficient alpha was employed to assess the reliability of all the scales. The next step of reliability assessment is validity assessment which is explained in Section 3.5.1(b).

b) Validity Assessment

Validity assesses whether a construct measures what it is supposed to measure. The assessment of validity is to associate the relationship between the observed items and the theoretical (abstract) construct in which it is supposed to represent (Hair et al., 2006; Nunnally and Bernstein, 1994). The two types of construct validity that were assessed in this study are convergent and discriminant validity.

Convergent validity is the degree to which the scale correlates positively with other measures of the same construct. According to Byrne (2006), with regard to Goodness-of-fit statistics, a good model fit is generally accepted to be one in which the comparative fit index (CFI) and non-normed fit index (NNFI) exceed 0.9. Another important diagnostic of a model's goodness-of-fit is the root mean square error of approximation (RMSEA) with value lower than 0.05 to 0.08. In addition, standardized residual should be examined for overall model fit assessment. The average off-diagonal absolute standardized residual measure (AOSR) is also used in this study; a low AOSR value indicate good model fit (Byrne, 2006; Ferguson et al., 2005; Steenkamp and Trijp, 1991). Anderson and Gerbing (1988) criterion of convergent validity t-value exceed 2.00 and standard loading exceed 0.6 was satisfied. Convergent validity basically exists when all items load heavily on their a priori construct or construct dimensions. All study constructs are described and measured using theoretically specified indicators. Two-construct measurement models are used in this study. The first model comprises ten indicators measuring three first-order constructs, namely learning and growth, internal process and external structure. The second model assesses the existence of a multi-dimensional business performance construct which is conceptualized and operationalized as a higher-order construct composed of three first order dimensions. A total of 14 items were used to tap the three first-order factors (financial performance, sales performance and customer performance).

Discriminant validity is the degree to which a construct is different from the other constructs (Nunnally and Bernstein, 1994). Structural equation modeling was utilized in the present study to assess the presence of discriminant validity among the study measures. A series of two-factor confirmatory factor analyses were conducted. Each model was run twice, one constraining the correlation between the latent constructs to unity and one freeing the parameter. Thereafter, a chi-square was employed to test whether the chi-square value of the unconstrained model is significantly lower than the constrained one. Discriminability was evident through the statistically significant Chi-square difference between the constrained and unconstrained models (Chi-square at one degree of freedom greater than 3.841, p ≤ 0.05). Moreover, for each pair of constructs a confidence interval was calculated around the estimated correlation by using twice the standard error (i.e., +2 standard errors) (Shoham, 1999).

3.5.2 Correlation Analysis

The purpose of correlation analysis is to observe the sign and strength of the relationship between two investigated constructs. There are different approaches that can be used to assess these associations, such as Spearman's correlation and Pearson's correlation coefficient. These approaches are suitable for assessing different types of scales. Specifically, Spearman's correlation is appropriate for measuring the ordinal scale, whereas Pearson's correlation coefficient is more suitable for the interval/ratio scales. The constructs investigated in this study are interval scales in nature, hence Pearson's correlation coefficient approach is considered as the most appropriate choice for the concurrent data analysis. This approach ranks the value between -1 and +1, where -1 indicates a perfect negative correlation, +1 indicates a perfect positive correlation, and 0 indicates the absence of any correlation between two constructs. In this study, correlation analysis was adopted to provide only a preliminary picture of the interrelations among the constructs to be further examined in the structural equation modeling, not for testing the research hypotheses.

3.5.3 Model Fit Testing by Structural Equation Modeling

Based on the validity testing in Section 3.5.1, the study elements, namely learning and growth, internal process, external structure, business performance, are found to have adequate psychometric properties. The next step is to conduct the goodness of fit testing of the proposed models.

Various goodness-of-fit indices could be used in the Structural Equation Modeling technique. A multiple fit indices is preferred to a single one (Bentler, 1995). Most commonly used goodness-of-fit indices are χ2 (Chi-square) statistic, NNFI (Non-Normed Fit Index), CFI (Comparative Fit Index), RMSEA (Root Mean Square Error of Approximation) and AOSR (Average Off-Diagonal Absolute Standardized Residuals).

Theoretically, an insignificant χ2 (Chi-square) score and lower value indicate a better model fit. Nevertheless, this statistic score is sensitive to sample size. This statistic is commonly significant with large samples and/or with models that include many variables. Practically, a χ2/df (degree of freedom) ratio ranging from 1.0 to 3.0 is regarded as a good model fit indicator (Hair et al., 2006). The comparative fit indices of NNFI, CFI lies between 0 and 1.0 in which larger values indicate a better fit of model to the data. Commonly, these indices are recommended with value exceeding 0.90. (Hair et al., 2006). RMSEA measures the model's discrepancy per degree of freedom. Values of RMSEA lower than .05 to .08 indicate an acceptable model fit (Hair et al., 2006). Finally, AOSR indicates the magnitude of the standardized residuals, whereas relatively large residuals indicate model misspecification and should be dropped and re-estimated the model (Joreskog and Sorborn, 1989). A low value of AOSR indicates a good model fit (Byrne, 2006).

In addition to goodness-of-fit indices, an evaluation of the statistical significance of individual parameters in the model is needed in order to assess the overall fit of the model. The t-test statistic is utilized to assess the statistical significance of each estimated parameter concerning the causal links between constructs and the relationships between constructs and their measured indicators. Generally, t-values ± 2.58 are considered statistically significant at (p ≤ 0.01), ± 1.96 are considered statistically significant at (p ≤ 0.05), and ± 1.65 are considered statistically significant at (p ≤ 0.10) (Hair et al., 2006).

3.6 Summary

This chapter documented the choices made regarding the research design, data collection method, operationalization of the study constructs, unit of analysis, development of the questionnaire, procedure for data collection, and, analytical procedures to assess reliability and validity of the measurement data as well as to test the previously formulated research hypotheses. The method and purpose of each statistic testing are summarized in Table 3.4.

Table 3.4 Generally accepted criteria in each statistical testing

Testing

Purpose

Accepted criteria

Reliability assessment

Questionnaire consistency

Item-total exceeding 0.4

Cronbach's alpha exceeding 0.7

Convergent

validity

Scale correlation with other measures of the same construct

CFI exceeding 0.9

NNFI exceeding 0.9

RMSEA lower than 0.05-0.08

ASOR lower value to zero, better fit

t-value exceeding 2.0 at p<0.05

Standard loading exceeding 0.6

Discriminant validity

Construct different from the other construct

χ2 constrained - χ2 unconstrained > 3.841, p<0.05

correlation+2 (std. error) < 1

Correlation analysis

Observe the strength of the relationship between two investigated constructs

Values closer to 1.0, indicate a stronger correlation.

Hypotheses

Model fit

χ2/ df Ratio between 1-3

CFI exceeding 0.9

NNFI exceeding 0.9

RMSEA lower than 0.05-0.08

ASOR lower value to zero, better fit

t-value > ± 2.58, p<0.01

t-value >± 1.96, p<0.05

t-value > ± 1.65, p<0.1

Results and Discussion

4.1 Introduction

This chapter is to explain the analytical procedures that have been employed for the development of valid and reliable measures for the construct in this research. The explanation of methodology is discussed in Chapter 3.

4.2 Profile of Respondents

According to research design in this study, the purpose is to explore the cause-effect-relationship of intangible assets to business performance. Therefore, the appropriate persons to complete the questionnaires should ideally be the senior manager or at least highly-experienced officers. Among the 304 qualified questionnaires that passed the screening procedure as described in Section 3.4, it is found that all of the respondents are satisfied the above criterion, as shown in Table 4.1.

Table 4.1 Position of respondents in the present study

Position level

No. of respondents

% of total

General manager/ Director/ CEO

197

65%

Marketing/ Financial manager

85

28%

Officer

22

7%

Total

304

100%

The qualified questionnaires are found to represent very wide range of industries as shown in Table 4.2. Note that the industrial classification used in Table 4.2 follows the convention of industrial cluster classification in the Stock Exchange of Thailand. As shown in Table 4.2, the number of respondents from industry/non-service business sector is 207 and that of service sector is 97. The business classification as service and non-service sectors have been used by Bontis et al. (2000) to study the cause-effect relationship between intellectual capital and business performance. The numbers of respondents are considered sufficient to explore the result of cause-effect relationship in either sector.

Table 4.2 Number of respondents in each business sector

Type

Industry

No. of respondents

% of total

Industry/

non-service

68.1%

(207 respondents)

Agribusiness

19

6.3

Food and beverage

30

9.9

Fashion

44

14.5

Home and office products

16

5.3

Personal products and pharmaceuticals

7

2.3

Automotive

13

4.3

Industrial material and machinery

25

8.2

Packaging

9

3.0

Paper and printing material

4

1.3

Petrochemicals and chemicals

12

3.9

Construction material

14

4.6

Energy and utilities

2

0.7

Mining

0

0

Electronic component

0

0

Electrical products and computer

12

3.9

Service

31.9%

(97 respondents)

Banking

5

1.6

Finance and securities

1

0.3

Insurance

7

2.3

Property development

6

2.0

Commerce

25

8.2

Health care service

3

1.0

Professional service

23

7.6

Tourism and leisure

4

1.3

Transportation and logistics

15

4.9

Media and publishing

4

1.3

Information and communication technology

4

1.3

4.3 Overall Assessment of Results

The distribution of the rating score and mean score for each question in questionnaire of intangible assets elements and business performance are shown in Appendix D. The questions receiving the top scores in each item of intangible assets elements are marked by asterisks. The preliminary stage of data analysis involves an examination of frequency distribution of each variable in order to initially explore the survey finding and to inspect whether there is a variation in the response. A five-point Likert-type scale is used through out the questionnaires, ranging from (1) “low/disagree” to (5)”high/strongly agree”. The higher score means the higher degree of agreement. The results of mean and standard deviation in each questionnaire elements are shown in Table 4.3.

Table 4.3 Mean and standard deviation of questionnaire elements

Questionnaire elements

Mean

Standard deviation

Learning & Growth

3.77

0.61

Internal Process

3.65

0.67

External Structure

3.82

0.63

Business Performance

3.42

0.66

In Table 4.3, it is found that the means of intangible asset elements namely, learning and growth, internal process and external structure are in the same direction with those of business performance. There appears to be no significant variation from element to element either in the means or the standard deviations.

Following the steps of analysis described in Table 3.4. The results of the first step of reliability and validity assessment, namely reliability assessment, are given in Section 4.3.1

4.3.1 Reliability Assessment Results

A preliminary step, involving item-total correlation analysis and Cronbach's alpha testing, is performed to identify items that do not belong to the domain of a specific construct or dimension. The questionnaire elements consisting of business performance and intangible assets indicators and the number of questionnaire items in each element are shown in Table 1.1 and Table 3.3. The detail of testing results of the item-total correlation and Cronbach's alpha for each elements are given in Appendix A. All of the questions in each intangible assets element are found to be consistent and reliable. Among the items in the business performance section, only one question is found to be not reliable (item-total consistency equal 0.3 which is less than the set criterion of 0.4). This question “rate of goods return” was therefore removed. The final result of Cronbach's alpha for each questionnaire elements is summarized in the Table 4.4.

Table 4.4 Overall reliability assessment results

Questionnaire element

Sub-element

Cronbach's Alpha

Learning and Growth

Know-how

0.80

Knowledge

0.72

Competency

0.83

Engagement

0.79

Internal Process

Process improvement

0.79

Innovation

0.85

Information

0.81

External Structure

Customer satisfaction

0.88

Customer loyalty

0.73

Brand

0.88

Business Performance

Financial performance

0.92

Sales performance

0.93

Customer fulfillment

0.87

The item-total correlation and Cronbach's alpha testing do not reveal particular problems with regard to any of the items used to measure the constructs in this research. All Cronbach's alpha are higher than 0.7. Therefore, on the basis of these preliminary analyses, each item seems to make a well contribution and sufficiently reliable. The result of confirmatory factor analyses, convergent validity testing and discriminant validity testing, are conducted in this research to assess the dimensionality of the scales employed.

4.3.2 Validity Assessment Result

There are two types of validity assessment, convergent and discriminant validity as explained in Section 3.5.1.

a) Convergent Validity Results

The convergent validity is to analyze the degree to which the scale correlate positive with other measures of the same construct. Two convergent validity testing, convergent validity testing for intangible assets and convergence validity testing for business performance, are conducted as shown in Table 4.5 and Table 4.6. Table 4.5 shows the convergent validity test result of intangible assets. As shown in Section 3.3 regarding the questionnaire development, there are four sub-elements in learning and growth, three sub-elements in internal process, three sub-elements in external structure. The convergent validity testing for intangible assets comprises ten sub-elements that belong to the three intangible assets elements: external structure, internal process, and learning and growth. The results are shown in Table 4.5.

Table 4.5 Convergent validity results for intangible assets

Intangible assets indicator Standardized loading a

Learning and Growth (LG)

Know-how (KH) .82 b

Knowledge (KL) .90 (17.56)

Competency (CP) .90 (17.33)

Engagement (EG) .89 (17.03)

Internal Process (IP)

Process improvement (IM) .89 b

Innovation (IN) .87 (19.20)

Information (IT) .91 (20.87)

External Structure (ES)

Customer satisfaction (CS) .92 b

Customer loyalty (CL) .88 (20.73)

Brand (BR) .86 (19.33)

Goodness-of-Fit Statistics

χ2 32= 107.4 , p <.001

CFI=. 99

NNFI = .98

RMSEA =.08

AOSR = .03

a t-values from the unstandardised solution are in parentheses.

b Fixed parameter

As shown in Table 4.5, the lowest standardized loading was 0.82 and lowest t-value was 17.03. These values are higher than the minimum acceptable levels. All 10 intangible assets indicators are found to satisfy convergent validity test. Specifically, this is evident in the goodness of fit statistics: CFI, NNFI, RMSEA and AOSR as specify in Table 3.4. The results show that know-how, knowledge, competency, and engagement belong to learning and growth elements. Process improvement, innovation and information technology belong to internal process. Customer satisfaction, customer loyalty and brand belong to external structure.

Table 4.6 shows the convergent validity result for business performance. The convergent validity testing for business performance consists of first-order factors and second-order factors. There are three financial performance indicators, four sales performance indicators and seven customer performance indicators. “Rate of goods return” had been rejected from customer performance indicators in Section 4.3.1. Totally 14 indicators of business performance are used which combined into three second-order factors, namely financial performance, sales performance and customer fulfillment performance. The results are shown in Table 4.6.

Table 4.6 Convergent validity results for business performance

First-Order Factor

Standardized loading a

Financial Performance (FP)

Profitability (FP1) .89 b

Profit growth (FP2) .91 (20.05)

Profit margin (FP3) .90 (19.72)

Sales Performance (SP)

Sales volume (SP1) .81b

Sales growth (SP2) .87 (15.62)

Market share (SP3) .89 (16.45)

Market share growth (SP4) .91 (16.68)

Customer Performance (CP)

Customer satisfaction (CP1) .78b

Customer retention (CP2) .71 (10.96)

New customer (CP3) .63 (9.43) New product (CP4) .66 (9.95) Brand recognition (CP5) .69 (10.54) Customer service (CP6) .69 (10.53) Overall response (CP8) .78 (12.09)

Second-order factor

Financial performance (FP) .77b

Sales performance (SP) .91 (8.89)

Customer performance (CP) .77 (8.57)

Goodness-of-Fit Statistics

χ2 74= 266.97 , p <.001

CFI= .96

NNFI = .95

RMSEA = .09

AOSR = .05

a t-values from the unstandardised solution are in parentheses.

b Fixed parameter

As shown in Table 4.6, the lowest standardized loading was 0.63 and lowest t-value was 9.43; these are higher than the minimum acceptable levels. Again, these statistics are found to be higher than acceptable level.

Thus, the intangible assets indicators in each intangible assets element are positively correlated with other indicators in the same element. The know-how, knowledge, competency and engagement are positively correlated within learning and growth. Similarly, process improvement, innovation and IT are positively correlated with internal process. Customer satisfaction, customer loyalty and brand are positively correlated within external structure. Lastly, financial, sales and customer fulfillment performance are positive correlate in business performance.

b) Discriminant Validity Results

The other validity assessment analysis is discriminant validity analysis. As described above, in the present study there are 4 construct variables: learning and growth, internal process, external structure, and business performance. The discriminant validity analysis is to analyze the degree that each construct variable is different from other construct variables. Learning and growth is different from internal structure, external structure and business performance. The analysis is done for each pair of construct variables. The details of the discriminant validity test and the generally accepted criteria have been explained in Section 3.5.1 (b) and Table 3.4. The results of the discriminant validity analysis by using SEM are shown in Table 4.7.

Table 4.7 Results of the discriminant validity analysis

Element

Learning & Growth

Internal

Process

External

Structure

Business Performance

Learning & Growth

Internal

Process

0.88 (0.02)a

141.51b

63.21c

External

Structure

0.79 (0.03)

105.71

54.72

0.88 (0.02)

85.12

30.23

Business Performance

0.55 (0.06)

198.99

71.90

0.55 (0.05)

139.26

37.43

0.67 (0.05)

158.86

44.64

Note: Entries below the diagonal show (a) • coefficients, reflecting correlations among constructs and standard error in the parentheses (b) difference in chi-square from fixed (• =1.00) model and free (• estimated) model (c) chi-square for free model

As shown in Table 4.7, in all cases the pairs of elements were not collinear, which provide the evidence of discriminant validity. It was found that for each pair of element, the difference of chi-square values between constrained and unconstrained models, is consistently greater than the minimum acceptable criterion of 3.841 at p <0.05. In addition, for each pair of elements the value of the correlation plus twice the standard error is found to be less than one. Thus, both chi-square difference tests and confidence intervals assessment provide evidence of discriminant validity among the study element. That is to say, each of the four study elements: learning and growth, internal process, external structure and business performance are found to belong to different grouping from other elements.

4.4 Correlation Analysis Result

The correlation analysis provides an overall relation among study elements. Table 4.8 shows the Pearson's correlations among the study elements.

Table 4.8 Correlation analysis results of the study elements

Elements

Learning & Growth

Internal Process

External

Structure

Business Performance

Learning & Growth

1.0

Internal Process

0.814*

1.0

External

Structure

0.739*

0.817*

1.0

Business Performance

0.479*

0.417*

0.509*

1.0

*Correlation is significant at the 0.01 level (2-tailed)

As shown in Table 4.8, there is statistical significance among the study elements. The correlation between internal process and external structure is found to be strongest. On the other hand, as should be expected, the correlations between the three intangible asset elements and the business performance are not as strong as the correlations among each pair of the intangible asset elements.

4.5 Summary of Reliability and Validity Assessment Results

All the reliability and validity test results are found to be positive. All questions in the questionnaire passed the consistency test based on item-total analysis and Cronbach's coefficient alpha. The results of convergent validity testing indicate that the intangible assets indicator in each element is positively correlated with other indicators in the same study element. Further, each of the four study elements: learning and growth, internal growth, external structure and business performance are found to belong to different grouping from the other elements. Finally, there are statistically significant correlations among the study elements. Thus, all questions in each study element are ready for hypotheses model testing in Section 4.6.

4.6 Hypotheses Testing Results

The purpose of this section is to present the results of the research hypotheses testing undertaken in this study. After some measure purification by eliminating one question that fails the reliability testing as described in Section 4.3.1 and ensuring that the study elements have adequate psychometric properties. Section 4.3.2 analyzes the degree to which the scale correlates positively with other measures of the same construct. The intangible assets indicators in each intangible assets element are positively correlated with other indicators in the same construct. Further, each of four study elements: learning and growth, external structure and business performance are found to belong to different grouping from other elements. This section presents the results of the hypothesis testing. The tests are conducted using the structural equation model to investigate the relationships among the study elements. The research hypotheses model testing has been illustrated in Figure 1.2. For convenience, the hypotheses are repeated here as follows:

H1: Learning and Growth is positively related to Internal Process

H2: Internal Process is positively related to External Structure

H3: External Structure is positively related to Business Performance

H4: Learning and Growth is positively related to Business Performance

4.6.1 Overall Hypotheses Testing Results

This section presents the results of the overall analysis for all the 304 qualified respondents regardless business sectors, establishment age and size of business. The hypotheses testing is conducted based on the diagram as drawn by using EQS program, and shown in Appendix B. Appendix B also shows the overall statistical results for the hypotheses testing taken from EQS program printout.

All goodness-of-fit statistic results are found to meet the generally accepted criteria, CFI=0.982 exceed 0.9, NNFI=0.978 exceed 0.9. The RMSEA is equal to 0.08 and a small AOSR. With the large sample size, 304 respondents, the ratio of chi-square and degree of freedom is 3.0 which is again acceptable criterion. Thus, the goodness-of-fit statistics confirmed the proposed hypotheses model.. It is found that all the hypotheses are supported at p ≤.0.05 with the exception of the relationship between learning and growth and business performance (H4). It is noted that the H4 hypothesis is rejected because of the low t-value 1.86.

This structural model solution produced an R2 value of 0.45 which suggests that the propose model structure explains 45 percent of the variance in business performance. It has very strong explanatory power for this model compared with similar studies along this line of research for example Carbrita and Vaz (2006) 0.44, Wang and Chang (2005) 0.43 and Bontis et al. (2000) 0.37.

4.6.2 Hypotheses Testing Results in Service and Non-Service Business

In Section 4.6.1, the hypothesis testing was conducted for all the 304 respondents. It is desirable to examine the cause-effect relationship in the non-service and service business sector to see if the relationship follows the same pattern. Considering that there are 97 respondents for the service business sectors and 207 respondents for the non-service business sectors, there should be enough samples for this type of analysis. The hypotheses testing results for the non-service business sectors are shown in Figure 4.2.

From Figure 4.2, it is found that all the goodness-of-fit statistics satisfy the generally accepted criteria. Again, the hypotheses testing results show that H1, H2 and H3 are supported. Also, the test results do not support H4, that is, there is no relationship between learning and growth and business performance. These results confirm the pattern establish in the case of overall business sectors.

Similarly, Figure 4.3 shows the hypotheses testing results for the case of service business sectors. It is found that the hypotheses testing results follow the same pattern as non-service business sector and overall business sectors. Therefore, it could be concluded that no relationship was found between learning and growth and business performance no matter of what type of business sectors.

4.6.3 Hypotheses Testing Results for Different Business Sizes

The size of business is another factor of concerns in describing the relationship of intangible assets indicators and business performance. The small-business sector is very important for the overall economic growth. Simpson et al. (2004) and Alasadi and Abdelrahim (2008) studied the key success factors, training, education and knowledge of small business. Bhaskaran (2006) mentioned that the incremental innovation of small and medium enterprises can increase the competitiveness against large businesses. Thus, Small and Medium Enterprise is the important sector in world economy. The analyses in this section follow the classification of the Small and Medium Enterprise Development Bank of Thailand or SME Bank. Business size is classified by registered capital among of registered capital as shown in Table 4.9.

Table 4.9 Business size classification by registered capital

Small Business

Medium Business

Large Business

Registered capital, million baht (MB)

Less than 50 MB (1.4 million USD)

50-200 MB

(1.4-5.7 Million USD)

More than 200 MB

(5.7 million USD)

Note : 1 USD equivalent to 34.7 Baht (February 5, 2009)

Following SME bank classification, the 304 respondents are distributed the business sizes as shown in Table 4.10.

Table 4.10 Number of companies in each business size

Size of business

No. of company

%

Type

Small

185

60.9 %

SME

Medium

42

13.8 %

Large

77*

25.3 %

Large

Total

304

100%

* inclusive of 42 companies in SET

In this study classifies business in two sizes, namely SME and large. The small and medium size companies are grouped as SME, while rests are defined as large. The hypotheses testing results for large business are shown in Figure 4.4.

It is found that the model satisfy all goodness-of-fit criteria. All the hypotheses test results for the large business size are found to follow the same patterns of overall analysis.

For the case of SME business size, the goodness-of-fit statistic again meet the specify criteria. However, all the four hypotheses are supported in this case including the cause-effect relationship between learning and growth and business performance. The support found in the case of SME business size could perhaps be explained in terms of more direct chain of command in SME business relative to the more complex organization in large businesses. That is to say, when an SME business improves its human resources by way of training, employee engagement, knowledge management and so on, the effects are more directly felt in its business performance. This result confirms the work of Alasadi and Abdelrahim (2008). They found that learning and growth is positively related to successful business performance.

4.6.4 Hypotheses Testing Results for Different Establishment Age

Section 4.6.3 explained cause-effect relationships between intangible assets and business performance of SME and large business. Huergo and Jaumandreu (2004) investigated impact of establishment age on productivity growth and business performance. In Thailand around two decades ago, there were many companies established during the period of vast economic expansion especially in the petrochemical business and automotive business. Also, during last 10 years, there were many SME business established as a result of government policy. Therefore, it is logical to classify companies in Thailand into three different age groups. These are 1) younger companies that began at the turn of the millennium (less than or equal to 10 years old) 2) those that were established during the economic boom in the 1990s (11-20 years old) and 3) older companies (more than 20 years old). Accordingly, the respondents in this study are classified by establishment age as shown in Table 4.11.

Table 4.11 Number of companies by establishment age

Establishment age

No. of companies

%

Less than or equal 10 years

69

22.7%

11-20 years

128

42.1%

More than 20 years

107

35.2%

Total

304

100%

Figure 4.6 shows the hypotheses test results for the young business. Again, the goodness-of-fit are found to satisfy generally agree criteria, although it should be noted R2 of 0.29 in this case is relatively low. As for the hypotheses test results only H3 is not supported by data. It should be noted that t-value for casual link between external structure and business performance in the case of young business has negative factor loading, which could be attributed to the lack of firm customer base among younger companies.

4.6.5 Summary of Structural Model Results

The summary of structural model results in all cases is given in Figure 4.9.

Means of Classification

No. of respondents

Categories

Hypotheses test result

H1

H2

H3

H4

Overall

304

all

Support

Support

Support

No Support

Business Sector

97

Service

Support

Support

Support

No Support

207

Non-service

Support

Support

Support

No Support

Business size

227

SME

Support

Support

Support

Support

77

Large

Support

Support

Support

No Support

Establishment age

69

Less than 10yrs

Support

Support

No Support

Support

128

11-20yrs

Support

Support

Support

No Support

107

More than 20yrs

Support

Support

Support

No Support

Several interesting observations may be made based on the summary result in Figure 4.9:

1) Empirical data from Thai companies in this study confirm the cause-effect relationship of intangible assets and business performance.

2) There is no direct positive relationship between learning and growth and business performance in all analysis scenarios except the cases of SME and young companies. This could be because of relatively simple command line in SME and younger companies leading to the positive relationship between learning and growth and business performance.

3) There is positive relationship between learning and growth and internal process in all the analysis scenarios.

4) There is positive relationship between internal process and external structure in all the analysis scenarios.

5) There is positive relationship between external process and business performance in all analysis scenarios except the case of young companies. The reason for this could be they might not have large enough customer base and customer loyalty.

The overall picture of the resulting structural model of this study is shown in Figure 4.10. Note the absence of the direct causal link between learning and growth and business performance which is different from the original proposal hypotheses testing model in Figure 1.2. More detail conclusions and recommendations together with managerial implications are discussed in Chapter 5.

4.7 Relevant Intangible Assets Models

The previous section explores the cause-effect relationship of three intangible assets elements and business performance by using the concept of Balanced Scorecard strategy map. The main research hypotheses testing model in Section 4.6 is illustrated by several studies which have been explained in Chapter 2. These past studies models that explored the relationships between intangible assets and business performance, are as shown in Figure 4.11.

As part of the present studies, there might be other cause-effect relationship models among of intangible assets and business performance, as shown in the above past studies. Four speculative models of cause-effect relationships, adapted from the past studies, are therefore tested for possible fits as shown below:

a) Model I: Conceptual framework of the direct impact of intangible assets to business performance

The first model assumes that learning and growth, internal process and external are positively related to business performance as shown in Figure 4.12.

Figure 4.12 Structural model results of the direct impact of intangible assets to business performance

From the goodness-of-fit statistic result, the model is not properly fit because the ratio of chi-square and degree of freedom is higher than 3 which is more than the general accepted criteria. Thus, this is not a good model of intangible assets and business performance. Moreover, the R2 is rather lower than the overall hypotheses testing model in Section 4.6.1. Thus, this model is rejected and no further discussion.

b) Model II: Conceptual framework of direct and indirect impact of learning and growth, internal process and external structure to business performance

Not only is there the possibility of sequential impact learning and growth, internal process, external structure to business performance, but also there is the possibility direct impact of learning and growth, internal process to business performance as shown in Figure 4.13

Figure 4.13 Structural model results of direct and indirect impact of learning and growth, internal process and external structure to business performance

From Figure 4.13, it is found that all the goodness-of-ft statistics satisfy the generally accepted criteria. It is found that 1) there is no direct relationship between learning and growth and business performance, 2) no direct relationship between internal process and business performance. The result is confirmed that the cause-effect relationship between intangible assets elements and business performance in Section 4.6.

c) Model III: Conceptual framework of the indirect impact of learning and growth to business performance

There is a possibility to have indirect cause-effect relationship of learning and growth to business performance through internal process and external structure as shown in Figure 4.14.

Figure 4.14: Structural model result of the indirect impact of learning and growth to business performance

It is found that the model does not satisfy the goodness-of-fit statistic criteria. The ratio of chi-square and degree of freedom is relative higher than generally accepted criteria and RMSEA is 0.11 is out of range accepted criteria 0.05-0.08. This model is rejected and no further discussion.

d) Model IV: Conceptual framework of indirect impact of learning and growth and external structure to business performance

This model is very close to the model which has been studies by Bontis et al. (2000). There is indirect impact of learning and growth to business performance as shown in Figure 4.15.

Figure 4.15 Structural model result of indirect impact of learning and growth

to business performance with impact of external structure to internal process

Figure 4.15 shows the hypotheses testing result. The goodness-of-fit statistics are found to satisfy criteria. The R2 is 0.35 lower than the R2 of research model in Section 4.6 which is 0.45. This is implied that the model in Section 4.6 is better and stronger explanatory power than this model.

e) Model V: Conceptual framework of indirect impact of learning and growth and interrelation of internal process and external structure

This model is closely to the model which was studied by Cabrita and Vaz (2006). The model is shown in Figure 4.16.

Figure 4.16 Structural model of indirect impact of learning and growth and interrelation of internal process and external structure with direct impact of

external structure, internal process to business performance

It is found that all the goodness-of-fit statistics satisfy the general accepted criteria. There is no direct relation of learning and growth to external structure in this empirical study while there was positive relationship in the study of Cabrita and Vaz (2006). This model also confirms the interrelationship of intangible assets elements and business performance.

4.8 Empirical Results Summary

Section 4.7 on other possible intangible assets models, finds that there are no models that perform better in terms of goodness-of-fit statistics than the main research hypothesis model in Section 4.6. In particular, Model I and Model III are rejected because they do not satisfy goodness-of-fit criteria. Model II and Model V confirm the same result as the present proposed model in Section 4.6, while Model IV has weaker explanatory power than the present proposed model.

Thus, both the main research hypothesis model and other possible intangible assets models in Section 4.7 confirm the cause-effect relationship between learning and growth, internal process, external structure and business performance, including the interrelationships between these three intangible assets elements and business performance.

Conclusion and Recommendations

In Chapter 4, the step-by-step validation procedure and testing have been reported. The hypotheses testing and result is reported in Section 4.6 and Section 4.7. In this chapter, the discussion of the empirical findings and their implications for business practitioners and strategists are presented. Section 5.2 provides information on some limitations of the study and recommendations for future researches in this field.

5.1 Conclusion of the Study

Intangible assets are ubiquitous and could be found in all functions and at all levels of organizations. Not only that, they play important roles in any value chains in business. There are three fundamental intangible assets elements, namely learning and growth, internal process and external structure. These intangible assets elements were initially introduced by Kaplan and Norton (1992) and Sveiby (1997). Kaplan and Norton described the concept of intangible assets in the context of Balanced Scorecard (1992) and strategic mapping (2004). This empirical study confirms the cause-effect relationship between learning and growth, internal process, external structure and business performance, including the interrelationships between the intangible assets elements and business performance.

This present empirical study designs questionnaires to answer research questions regarding to the relationship between intangible assets and business performance. The wording, sequence and format in the questionnaire were carefully designed to make sure it is easy to read and comprehend and friendly to answer by top management and senior officers. The questionnaire is reviewed by knowledgeable researchers and business practitioners prior to the distribution to 3,084 member companies of the Thai Chamber of Commerce. Among the 361 returned questionnaires, 304 responses (9.9% of total no. of distributed questionnaires) are found to be technically qualified for use in the statistical and hypotheses testing stage.

The first step of data analysis is reliability assessment testing. It is found that only one question fails the reliability test. The next step is validity assessment testing. There are two types of validity assessment testing, convergent and discriminant. All the intangible assets indicators in each intangible assets element, learning and growth, internal process and external structure, are positively correlated with other intangible assets indicators in the same element. Moreover, each of the four study elements: learning and growth, internal process, external structure and business performance are found to belong to different grouping from other elements. Thus, all the questions for each study element in the questionnaire are reliable and valid for hypotheses testing.

Based on the 304 respondents, all hypotheses are supported except the relationship between learning and growth and business performance (H4). It is also found that the hypotheses testing results of both non-service and service business sectors confirm the pattern established in the case of overall business.

Grouping the responses by business size, SME and large, the following results are found:

1) All the four hypotheses in the case of SME are supported, including the relationship between learning and growth and business performance. An explanation for this result could be that SME organizations tend to be lean, efficient and have relatively simple command line.

2) The hypotheses testing results of large businesses also confirm the pattern of hypotheses testing results for the case of overall businesses.

When the responses are analyzed by establishment age, it is found that there is a positive relationship between learning and growth and business performance for the case of young businesses. However, there is no relationship between external structure and business performance. The reason might be because there is simple command line in such organizations and they are still in process of establishing the customer relations and developing customer loyalty as well as brand awareness. Again, the hypotheses testing results of the remaining cases, middle-age and old businesses, also confirm the pattern of hypotheses test result for the overall businesses.

Both the main research hypothesis model and other possible intangible assets models testing result confirm the cause-effect relationship between learning and growth, internal process, external structure and business performance, as well as the interrelationships between the intangible assets elements: 1) learning and growth has a positive relationship with internal process 2) internal process has a positive relationship with external structure, and 3) external structure has a positive relationship with business performance.

The investment in human capital i.e. knowledge, know-how, employee competence and engagement, creates the continuous learning and growth in the organization. When employees have more experience and knowledge, they can create efficient internal processes, (process improvement, innovation and information technology). This confirms the finding of Wang and Chang (2005) study. The internal process serves the customers in the end and leads to customer satisfaction. The profit and revenue are naturally the final outcomes of this causal chain.

5.2 Recommendations and Managerial Implications

Results of the present study help explain the relationship between intangible assets and business performance. Section 5.1 concludes that there is the cause-effect relationship between intangible assets and business performance. The questions that receive top scores from the respondents in each intangible assets element (as shown in Appendix D) would therefore tend to indicate the kind of good long-term strategies which can be adopted by business practitioners. These intangible assets sub elements derived from the questionnaire outcomes, as marked asterisks in Appendix D, are incorporated into the appropriate intangible assets sub-elements in Figure 5.1. For example, the item of intangible assets sub element “know-how” that receives the top score in that sub-element is “Training is required for the new staff before starting job” the item of intangible assets sub element “brand” that receives the top score in that sub-element are “The brand accurately reflects the relationship an organization's customer feel they have with it” and “The organization knows how its customers perceive its brand and the brands of its primary competitors”. Thus, this particular item could be used as the guideline for establishing long-term business strategies which would lead to high business performance in the end.

The cause-effect relationships between intangible assets and business performance in this study confirms the framework of intangible assets of the various functional department in the organization and its financial performance (Chareonsuk and Chansa-ngavej, 2008) as shown in Appendix E. When the company is well managed, intangible assets in each functional department would be efficiently and effectively converted into financial performance. Understanding the strategies of intangible assets management help a business develop sustainability and enhance its mission effectiveness and competitive advantage.

Intangible assets are the strategic key for long term profit prospect. The setting of key performance index and investment should be allocated and monitored by the above guideline strategies. When the top management has crystallized its strategies, good management practice and monitoring process would follow. Creating a culture of measurement-driven intangible assets and business performance help top management understand how to drive the greater return from intangible assets investment.

5.3 Limitations and Directions for Future Researches

The present empirical study has several limitations that should be mentioned for the sake of future researchers. First, this research was conducted in the business firms in Thailand. Similar researches should be conducted in developed economies e.g. Japan, Hong Kong, Singapore, etc. which have similar culture and value as those of Thailand. Benchmarking of results among different countries and economies is highly recommended for future researches. In addition, similar research should be conducted in other developing countries, e.g. the Philippines, Vietnam etc. for result comparison.

Second, the survey questionnaire in this research uses subjective rating scale (1-5) for analyzing the cause-effect relationships between intangible assets and business performance. The study results might be subjected to the wordings used in the questions and the respondents' attitude. The financial accounting and other quantitative data might be an alternative measure in establishing the cause-effect relationship between intangible assets and business performance at a specific period or timing.

Third, the intangible assets elements in this study are conducted in internal intangible assets elements i.e. learning and growth, internal process and customer, within an organization. There might be other intangible assets elements which have not been covered such as social and environmental responsibility.. There are also intangible assets elements external to the organization that could be considered, e.g. those related to government relationship and supply network. When all intangible assets elements are properly identified and managed, the company will then to achieve long-term sustainable growth under dynamic environment.

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Appendix A

Cronbach's alpha and item-total correlation testing results

The results of the Cronbach's alpha and item-total correlation testing by SPSS are illustrated below

a. Learning and Growth: Know-how

Know-how: Item-total correlation and Cronbach's alpha

Know-how question

Corrected item-total correlation

Cronbach's alpha

if item deleted

Question # 1

.538

.782

Question # 2

.528

.785

Question # 3

.645

.749

Question # 4

.666

.741

Question # 5

.573

.772

Know-how: Cronbach's alpha = 0.804

All questions are valid and construct in know-how. There is no any deleting question.

b. Learning and Growth: Knowledge

Knowledge: Item-total correlation and Cronbach's alpha

Knowledge question

Corrected item-total correlation

Cronbach's alpha

if item deleted

Question # 1

.351

.722

Question # 2

.473

.682

Question # 3

.460

.686

Question # 4

.555

.647

Question # 5

.583

.634

Knowledge: Cronbach's alpha = 0.724

If the question#1 is deleted, the Cronbach's Alpha is no improved. Thus, the question is still remained.

c. Learning and Growth: Competency

Competency: Item-total correlation and Cronbach's alpha

Competency question

Corrected item-total correlation

Cronbach's alpha

if item deleted

Question # 1

.371

.857

Question # 2

.644

.790

Question # 3

.674

.781

Question # 4

.731

.764

Question # 5

.720

.767

Competency: Cronbach's alpha = 0.829

If the question#1 is deleted, the Cronbach's Alpha is no significant improve. Thus, this question is still remained.

d. Learning and Growth: Engagement

Engagement: Item-total correlation and Cronbach's alpha

Engagement question

Corrected item-total correlation

Cronbach's alpha

if item deleted

Question # 1

.461

.788

Question # 2

.647

.729

Question # 3

.606

.743

Question # 4

.547

.762

Question # 5

.606

.744

Engagement: Cronbach's alpha = 0.793

All questions are valid and construct in engagement. There is no any deleting question.

e. Internal Process: Process Improvement

Process Improvement: Item-total correlation and Cronbach's alpha

Process improvement question

Corrected item-total correlation

Cronbach's alpha

if item deleted

Question # 1

.535

.766

Question # 2

.594

.755

Question # 3

.539

.767

Question # 4

.643

.740

Question # 5

.329

.811

Question # 6

.679

.731

Process Improvement: Cronbach's Alpha 0.794

If the question#5 is deleted, the Cronbach's Alpha is no significant improved. Thus, the question is still remained.

f. Internal Process: Innovation

Innovation: Item-total correlation and Cronbach's alpha

Innovation question

Corrected item-total correlation

Cronbach's alpha

if item deleted

Question # 1

.538

.847

Question # 2

.671

.815

Question # 3

.659

.817

Question # 4

.770

.787

Question # 5

.662

.818

Innovation: Cronbach's Alpha 0.848

All questions are valid and construct in innovation. There is no any deleting question.

g. Internal Process: Information Technology

Information Technology: Item-total correlation and Cronbach's alpha

Information technology question

Corrected item-total correlation

Cronbach's alpha

if item deleted

Question # 1

.679

.749

Question # 2

.533

.788

Question # 3

.597

.769

Question # 4

.547

.797

Question # 5

.665

.749

Information Technology: Cronbach's Alpha 0.807

All questions are valid and construct in information. There is no any deleting question.

h. External Structure: Customer Satisfaction

Customer Satisfaction: Item-total correlation and Cronbach's alpha

Customer satisfaction question

Corrected item-total correlation

Cronbach's alpha

if item deleted

Question # 1

.663

.868

Question # 2

.594

.879

Question # 3

.691

.865

Question # 4

.704

.864

Question # 5

.688

.865

Question # 6

.647

.870

Question # 7

.756

.858

Customer Satisfaction: Cronbach's Alpha 0.884

All questions are valid and construct in information. There is no any deleting question.

i. External Structure: Customer Loyalty

Customer Loyalty: Item-total correlation and Cronbach's alpha

Customer loyalty question

Corrected item-total correlation

Cronbach's alpha

if item deleted

Question # 1

.545

.658

Question # 2

.516

.676

Question # 3

.542

.658

Question # 4

.491

.688

Customer Loyalty: Cronbach's Alpha 0.731

All questions are valid and construct in information. There is no any deleting question.

j. External Structure: Brand

Brand: Item-total correlation and Cronbach's alpha

Brand question

Corrected item-total correlation

Cronbach's alpha

if item deleted

Question # 1

.670

.855

Question # 2

.652

.858

Question # 3

.676

.855

Question # 4

.757

.840

Question # 5

.704

.850

Question # 6

.625

.863

Brand: Cronbach's alpha 0.875

All questions are valid and construct in information. There is no any deleting question.

k. Business Performance: Financial Performance

Financial Performance: Item-total correlation and Cronbach's alpha

Financial performance question

Corrected item-total correlation

Cronbach's alpha

if item deleted

Profitability

.838

.894

Profit growth

.839

.891

Profit margin

.855

.877

Financial Performance: Cronbach's alpha 0.922

All questions are valid and construct in information. There is no any deleting question

l. Business Performance: Sales Performance

Sales Performance: Item-total correlation and Cronbach's alpha

Sales performance question

Corrected item-total correlation

Cronbach's alpha

if item deleted

Sales volume

.783

.917

Sales growth

.838

.899

Market share

.842

.898

Market share growth

.844

.897

Sales Performance: Cronbach's alpha 0.926

All questions are valid and construct in information. There is no any deleting question

m) Business Performance: Customer Fulfillment Performance

Customer Fulfillment Performance: Item-total correlation and Cronbach's alpha

Customer fulfillment performance question

Corrected item-total correlation

Cronbach's alpha

if item deleted

Customer satisfaction

.686

.812

Customer retention

.627

.817

New customer generation

.582

.822

Success rate in new products

.630

.815

Brand recognition

.614

.818

Customer service

.606

.819

Rate of goods return

.300

.867

Overall response to customer

.713

.810

Customer Fulfillment Performance: Cronbach's alpha 0.841

The item-total of rate of goods return is lowest and lower than 0.4. If the question “the rate of goods return” is deleted, the Cronbach's alpha is significant increasing. It has been deleted. The Cronbach's alpha of customer fulfillment is 0.867.

Appendix B

Structural model result by EQS 6.0

Structural model diagram from EQS 6.0

Model result from EQS 6.0

AVERAGE ABSOLUTE RESIDUAL = .0286

AVERAGE OFF-DIAGONAL ABSOLUTE RESIDUAL = .0289

GOODNESS OF FIT SUMMARY FOR METHOD = ERLS (Elliptically Re-weighted least square)

INDEPENDENCE MODEL CHI-SQUARE = 7076.206 ON 78 DEGREES OF FREEDOM

INDEPENDENCE AIC = 6920.206 INDEPENDENCE CAIC = 6552.278

MODEL AIC = 61.508 MODEL CAIC = -226.231

CHI-SQUARE = 183.508 BASED ON 61 DEGREES OF FREEDOM

PROBABILITY VALUE FOR THE CHI-SQUARE STATISTIC IS .00000

FIT INDICES

-----------

BENTLER-BONETT NORMED FIT INDEX = .974

BENTLER-BONETT NON-NORMED FIT INDEX = .978

COMPARATIVE FIT INDEX (CFI) = .982

BOLLEN'S (IFI) FIT INDEX = .983

MCDONALD'S (MFI) FIT INDEX = .818

JORESKOG-SORBOM'S GFI FIT INDEX = .889

JORESKOG-SORBOM'S AGFI FIT INDEX = .834

ROOT MEAN-SQUARE RESIDUAL (RMR) = .034

STANDARDIZED RMR = .064

ROOT MEAN-SQUARE ERROR OF APPROXIMATION (RMSEA) = .081

90% CONFIDENCE INTERVAL OF RMSEA (.068, .095)

RELIABILITY COEFFICIENTS

------------------------

CRONBACH'S ALPHA = .943

RELIABILITY COEFFICIENT RHO = .965

STANDARDIZED FACTOR LOADINGS FOR THE FACTOR THAT GENERATES

MAXIMAL RELIABILITY FOR THE UNIT-WEIGHT COMPOSITE

BASED ON THE MODEL (RHO):

MEAN_KH MEAN_KL MEAN_CP MEAN_EG MEAN_IM MEAN_IN

.756 .830 .827 .825 .845 .828

MEAN_IT MEAN_CS MEAN_CL MEAN_BR MEAN_FP MEAN_SP

.858 .847 .818 .786 .533 .633

MEAN_PC

.671

CONSTRUCT EQUATIONS WITH STANDARD ERRORS AND TEST STATISTICS

STATISTICS SIGNIFICANT AT THE 5% LEVEL ARE MARKED WITH @.

F2 = F2 = .915*F1 + 1.000 D2

.062

14.766@

F3 = F3 = .871*F2 + 1.000 D3

.049

17.617@

F4 = F4 = .474*F3 + .183*F1 + 1.000 D4

.103 .098

4.612@ 1.864

STANDARDIZED SOLUTION: R-SQUARED

MEAN_KH = V82 = .820 F1 + .572 E82 .672

MEAN_KL = V83 = .900*F1 + .436 E83 .810

MEAN_CP = V84 = .897*F1 + .442 E84 .805

MEAN_EG = V85 = .894*F1 + .448 E85 .799

MEAN_IM = V86 = .889 F2 + .458 E86 .790

MEAN_IN = V87 = .871*F2 + .491 E87 .759

MEAN_IT = V88 = .903*F2 + .430 E88 .815

MEAN_CS = V89 = .914 F3 + .405 E89 .836

MEAN_CL = V90 = .883*F3 + .470 E90 .779

MEAN_BR = V91 = .848*F3 + .529 E91 .720

MEAN_FP = V92 = .677 F4 + .736 E92 .459

MEAN_SP = V93 = .806*F4 + .593 E93 .649

MEAN_PC = V94 = .853*F4 + .522 E94 .728

F2 = F2 = .879*F1 + .476 D2 .773

F3 = F3 = .895*F2 + .447 D3 .800

F4 = F4 = .508*F3 + .194*F1 + .741 D4 .451