the influences of balanced scorecard upon intellectual ... yung-chieh chien.pdf · moderator of...

The Journal of International Management Studies, Volume 7, Number 2, October, 201240

The Influences of Balanced Scorecard upon Intellectual Capital Accumulation Implemented by Listed LED Companies in Taiwan: A

moderator of organizational commitment

Yung-Chieh Chien, Department of Recreation Sports Management, Kao-Yuan University, Taiwan

ABSTRACT

This study aims to verify the influence of balanced scorecard implemented by Taiwan LED-listed companies on

the accumulation of the intellectual capital with organizational commitment as an interference variable, management of

human resource, finance, production and marketing departments and staff of production departments as research

subjects. This study uses simple random sampling to select samples from the targeted population, and uses structural

equation modeling (SEM) to validate goodness-of-fit between overall, structural, and measurement model. It is found

that the balanced scorecard implemented by Taiwan LED-listed companies influences the intellectual capital, and the

"organizational commitment" variable has a significantly positive interference / interaction effect, which also implies

that organizational commitment, facilitates the balanced scorecard to influence the accumulation of intellectual capital.

Keywords: balanced scorecard; intellectual capital; organizational commitment.

INTRODUCTION

From the perspective of management accounting, strategic integration has become increasingly important, and is

changing from a traditional single performance evaluation standard to composite performance measurement which takes

into account critical success factors at every level of the organization (Kaplan, 1984; Johnson, 1990; Hall, 1990). In

addition to the traditional financial performance indicator, the four perspectives of the balanced scorecard include three

non-financial performance indicators: viz., the customer, internal procedures and innovation and learning. These three

factors plus one financial factor turn the organization's vision and strategy into a new measurement system through

objectives and measures. All these are key elements in future competition. Chow and Haddad (1997) pointed out the

main feature of the balanced scorecard was that it could integrate the organization’s strategy, structure and vision and

helped enterprises to turn long-term strategies and targets like creating customer value into concrete actions.( Liu, 2002).

Balanced scorecard is a measure to drive future performance and make up for the deficiencies of past financial

measures. It is a tool for strategic management that integrates companies’ strategic vision; intellectual capital, as core

competitiveness, can create high enterprise value and provide competitive advantages for enterprises (Jiang, 2006).

However, in the era of the knowledge economy, intellectual capital has only gradually developed. It emphasizes

long-term profitability and vital factors that contribute to enterprise success. In the knowledge-based economy era,

tangible assets of enterprise are gradually replaced by intangible assets. Considering the reliability of information

quality and in order to objectively measure and faithfully present the transaction data, traditional financial accounting

can only measure part of the existing transaction costs, ignoring many important intangible assets (Chan, 2005);

therefore, more and more company value indicators like patents, customer base, and brand fail to be listed on the

balance sheet. However, recent years have witnessed the gradual development of intellectual capital, which emphasizes

long-term profitability and vital factors that contribute to enterprise success. This is the first motive of this study. The

second motive is that the information and electronic industry has been playing a vital role in the Taiwan economy. In

the past two decades significant output value has been generated by Taiwan LED-listed companies, which undoubtedly

are the main driving force for the creation and cultivation of Taiwan’s economy. Especially in recent years, Taiwan

LED companies have faced increasing competition pressures from the environment; therefore, besides creating new

value and room for growth actively, enterprises must also transform and upgrade themselves, thinking about how to

stabilize the centripetal force of employees and motivate employees to make commitments to the organization so as to

The Journal of International Management Studies, Volume 7 Number 2, October, 2012 41

increase organizational performance, and accumulate intellectual capital.

In order to possess advantages in the rapidly changing environment, companies must implement balanced

scorecard and accumulate intellectual capital through organizational commitment. Only in this way, can companies

achieve sustainable and rapid development. And whether balanced scorecard and organizational commitment will have

a synergy of interaction on the accumulation of companies’ intellectual capital or not, is the main motive of cost study.

Therefore, taking Taiwan LED-listed companies as the research object, this study will establish a new research model

based on past literature, and verify it so as to understand the goodness-of-fit of models. In other words, the specific

purposes of this study are as follows:

(1) To verify and understand whether balanced scoreboard implemented by Taiwan LED-listed companies has a

significant positive effect on the accumulation of intellectual capital.

(2) To verify and understand whether the organizational commitment of Taiwan LED-listed companies has a

significantly positive effect on the accumulation of intellectual capital.

(3) To verify and understand whether the balanced scorecard and organizational commitment have significant and

positive interaction effect on the accumulation of intellectual capital.

LITERATURE REVIEW

This section is to understand the relevance between past research results and the theme of this study, derive research

hypotheses from the literature review, and establish the framework of this study. Elaborations on the theory and research

are given below respectively.

Balanced Scorecard

Chow and Haddad (1997) pointed out that the main features of the balanced scorecard was that it could

incorporate the organization's strategy, structure and vision, combine traditional and modern enterprise performance

measurement indexes, and help enterprises to transform long-term strategies and goals like creating customer value into

the specific activities.

Kaplan and Norton (1996) raised the issue that the balanced scorecard had four prospective, namely financial,

customer, internal procedure, and learning and growth prospective.

Balanced scorecard centers on strategies rather than control. Insightful managers use it to clarify and

communicate on strategy issues, and thus manage strategies properly, which proves that the balanced scorecard has

evolved from an improved measurement system into a core management system (Guo, 2002).

Wu (1999) held that balanced scorecard involves all the functions of the organization, for example, the financial

perspective is about finance and accounting; customer perspective deals with marketing; internal procedure perspective

is in regards to the overall value chain; and employee learning and growth perspective is relation to human resources.

Lu (2000) carried out studies among information and electronic companies listed between 1998 and 1999 in

Taiwan, using the concept of balanced scorecard to explore the relevance between capital structure and performance of

information and electronics industry. Performance indicators for the four dimensions of balanced scorecard are as

follows:

For the financial perspective; cash flow adequacy ratio, growth rate of sales, profitability, return on equity; for

customer perspective: market share, customer return rate; for internal procedure perspective: R & D efficiency

indicators, capital turnover, maintenance expense ratio; for learning and growth perspective: average income of

employees and unit salary.

Ye (2001) used case study method to explore the correlation between organizational learning patterns and

performance of product development department of Taiwan electronic industry. Performance evaluation was carried out

according to the three non-financial performance factors of the balanced scorecard, namely customer, internal procedure

and learning and growth.

It can be seen from the above that regardless of industry, the implementation of the balanced scorecard takes into

account the financial and non-financial dimensions. This study defines the balanced scorecard as a performance


measurement indicator that contains four perspectives—financial, customer, internal procedure and learning and growth

perspectives. This study mainly focuses on the impact of the implementation of the balanced scorecard on financial

performance of small and medium-sized electronics companies in Taiwan, and adopts the four perspectives of balanced

scorecard proposed by Kaplan and Norton (1996) as the measurement index of this study.

The Accumulation of Intellectual Capital The New Wealth of Organizations: Intellectual Capital, written by Stewart in 1997, lists many cases to explain the

three elements of intellectual capital: human resource capital, structure capital and customer capital. Stewart (1997)

believed that intellectual capital included human resource capital, structure capital and customer capital, in which

human resource capital referred to innovation, mentality of employees, annual salary, turnover, experience and learning;

structure capital consisted in highly efficient methods that collected, tested, organized and integrated current knowledge,

removed the out-dated part and then spread the best; customer capital meant the relationship between a certain

organization and its customers such as customer satisfaction, customer retention rates and customer loyalty.

Intellectual Capital: Discover hidden resources and Restore Companies’ true value, written by Edvinsson &

Malone (1997), elaborates the implementation process and measurement indexes of the intellectual capital of

Scandinavian companies. In addition, Edvinsson & Malone (1997) also held that the intellectual capital included human

resource capital, structure capital and customer capital, in which the human resource capital referred to individual

abilities, knowledge, skills, and experience of all employees and managers, including organizational creativity and

innovation; structure capital was a kind of supporting structure that could specify and empower human resource capital ,

including a tangible system that conveyed and stored intellectual materials; customer capital referred to customer

satisfaction and loyalty, price sensitivity and financial condition of long-term customer.

Sveiby (1998) believed that intellectual capital included personal competence, internal structure and external

structure, in which personal competence referred to employees’ ability to act under various circumstances, including

explicit knowledge, skills, experience, value judgments, social networks; internal structure meant patents, concepts,

models, computer and management systems; external structure referred to enterprises’ relationship with customers and

with suppliers, including brand, reputation and trademarks.

Johnson (1999) considered that intellectual capital contained human resource capital, structure capital and

relationship capital. Human resource capital referred to conception capital (knowledge-based workforce and

competence and attitudes of employees) and leadership capital (characteristics of specialists and managers); structure

capital meant innovation capital (patents, trademarks, copyrights, knowledge database) and procedure capital (working

procedures, confidential trade information); relationship capital referred to companies’ relationship with customers,

suppliers and network members.

Knight (1999) held that intellectual capital included human resource capital, structure capital, external capital and

financial performance. Human resource capital referred to staff turnover, employee satisfaction, the number of new

ideas about new products and the number of suggestions that were to submitted and accepted; structural capital referred

to working capital turnover ratio, the ratio between sales staff and general staff, the time when new products would be

launched; external capital referred to customer loyalty, customer satisfaction, list of the most profitable customers,

supplier quality / reliability indicators; financial performance makes Economic Value-added (EVA) as the indicator for

the measurement of financial performance.

Dzinkowski (2000) believed that intellectual capital possessed complex meanings, and was often regarded as

synonymous with intellectual property, intellectual assets and knowledge assets. It can be accumulated through capital

or equivalent to knowledge-based corporate process.

Chen (2001) pointed out that the intangible intellectual capital was an important reference index when evaluating

enterprise value, which comprised human resource capital, structure capital and relationship capital. She defined that

intellectual capital was what covered the skills, knowledge of information, experience, problem-solving ability and

wisdom that the company as a whole possessed and what integrated itself with human resource capital, structure capital

and relationship capital. The so-called “human resource capital” referred to the knowledge, skills and experience of all

staff and managers while structure capital referred to the overall system and procedure that solved problems and created


value for the company and relationship capital meant the establishment, maintenance and development of companies’

external relationship, including that with customers, suppliers and partners.

Edvinsson (2003) came up with a simple explanation for intellectual capital: intellectual capital was the pillar of

any company in the future and an index that determined whether a company could operate effectively. Without

investment in intangible assets, no enterprise could produce the driving force for innovation. (Chen and Hu, 2010).

This study adopts Chen Mei-Chun’s definition for intellectual capital as its conceptual definition and then further

gives its manipulation definition as follows:

A. Human resource capital: the knowledge, skills and experience of all staff and managers.

B. Structure capital: the overall system and procedure that solve problems and create value for the company.

C. Relational capital: the establishment, maintenance and development of Company’s public relationship, including that

with customers, suppliers and partners.

Organizational Commitment

The concept of organizational commitment came from The Organization Man written by Whyte (1965), which

maintained that the organization man not only worked for the organization, but also belonged to it (Chai, 2001).

Organizational commitment is an element that helps employees’ job behaviors be understood within the organization.

Organizational commitment can be regarded as individuals’ recognition for and contribution towards the

organization's objectives and mission (Porter, Steers, Mowday & Boulian, 1974). It can not only enhance organizational

centripetal force but also create a strong sense of belonging within the organization (Jaworski, & Kohli, 1993). In

addition, Mowday, Porter and Steers (1982) held that the outstanding individuals interacted and got along with each

other well through organizational commitment and organizational link.

Price and Mueller (1986) regarded organizational commitment as loyalty and emotional attachment to the

organization. Taking into account the associated costs of leaving the organization, employees thought it an obligation to

stay in the organization (Meyer & Allen, 1991). The definition for organizational commitment varies from scholar to

scholar because of different theories and research fields.

Based on a questionnaire study conducted among state-own enterprises, Scholars like Mowday (1982) further

divided organizational commitment into three types, namely value commitment, retention commitment and effort

commitment. Porter, Steers, Mowday and Boulian (1974) carried out a study among staff of a psychological technician

training center and concluded that organizational commitment referred to an individual’s recognition for and

contribution towards a certain organization. Organization members usually exhibited three tendencies—the tendency to

accept and believe in the organization's goals and values, the tendency to desire to continue to be a member of the

organization, and the tendency of willingness to work for the interests of the organization.

According to Meyer and Allen (1991a), the definition of organizational commitment could be explained from

three aspects: affective attachment arising from psychological factors like feelings, attachment caused by perceived

costs and attachment arising from ethical factors like obligation norms. The above three kinds of attachment were

named by Mayer and Allen (1991b) respectively as affective commitment, continuance commitment and normative

commitment.

Robbins (2001) counted organizational commitment as a kind of work attitude, a degree to which employees

identified themselves with the organization and its goals and wanted to maintain their membership in the organization.

Wu (1993) pointed out that organizational commitment was the degree to which an individual identified with and

stayed loyal to the organization. The greater their commitment, the more they saw themselves as part of the

organization.

Xie (1999) maintained that organizational commitment was the degree of subjective feelings and emotional

response of organization members towards various situations.

Chen and Yu (2000) agreed that organizational commitment meant that members recognized the organization's goals

and were proud of being part of the organization, showed loyalty to the organization, and willing to make their greatest

efforts to achieve the organization's objectives.

In conclusion, although scholars have different definitions for organizational commitment due to the differences


in their research methods, subjects and purpose, they all agree that recognition and contribution are important concepts

for organizational commitment. And many scholars adopt value commitment, retention commitment and effort

commitment proposed by Mowdady as the three main aspects, which proves highly effective in measuring

organizational commitment. Therefore this study also adopts Mowday’s definition and defines organizational

commitment as: the degree to which organization members identify with the organization’s goals and values, and are

willing to pay the extra effort to help organizations achieve goals. (Xie, 2006).

In accordance with Mowdady (1982), this study takes retention commitment, value commitment, and effort

commitment as the three main aspects of organizational commitment and establishes its operational definition as

follows: (1) value commitment means that organization members accept and believe in organizational goals and values;

(2) efforts commitment refers to members’ willingness to strive for organization’s benefits, and (3) retention

commitment refers to members’ strong will to maintain membership in the organization.

The Impact of Balanced Scorecard on the Accumulation of Intellectual Capital

Allee (1999) compared the similarities and differences between intellectual capital and the balanced scorecard,

and noted that both of them were the foundation of the enterprise. The difference between them was that the balanced

scorecard was based on the balance model instead of the dynamic value creation model while intellectual capital used

dynamic traffic patterns to accumulate knowledge capital. Their similarities between lay in the fact that they both

improved our acknowledgement of value creation and organizational performance indicators and made people learn

more about corporate development and non-financial measurement.

Wu (2002) believed that the relationship between balanced scorecard and intellectual capital shows that the

balanced scorecard could guide the formation of intellectual capital, and the use of its framework can reinforce the

management of intellectual capital. In addition, Wu (2002) also considered that the strategic issues and strategic

objectives of learning-growth perspective of the balanced scorecard guided the innovation capital and human resource

capital of intellectual capital; the strategic issues and strategic objectives of the internal procedure perspective of the

balanced scorecard guided capital process; the strategic issues and strategic objectives of customer perspective of

balanced scorecard guided the customer capital of intellectual capital(ie, relationship capital as in this study).

Bukh, Johansen and Mouritsen (2002) pointed out that intellectual capital could explain a company's knowledge

management activities while the balanced scorecard could monitor the process. The integration of the two could

produce a complementary effect.

In On Relevance Between Balanced Scorecard and Intellectual Capital—Take Taiwan IC Industry for Example,

Zeng (2006) pointed out that Taiwan's IC industry’s reinforcing perspectives of balanced scorecards like customer,

internal procedure and learning and growth at the expense of short-term financial performance could contribute to the

accumulation of intellectual capital and the maintenance of its long-term competitiveness.

Although the subjects explored in the above literature belongs to different industries or research fields, they do

share something in common, therefore this study can still deduce the following hypothesis:

Hypothesis one (H1): the implementation of the balanced scorecard has a significantly positive impact on the

accumulation of intellectual capital.

The Influence of Organizational Commitment on the Accumulation of Intellectual Capital In On Organizational Intellectual Capital’s Impact on Employees’ Organizational Commitment – Take the

Culture and Creativity Industries in Taiwan For Example, Sun (2008) points out that organizational intellectual capital

has partial significant impact, negative partial impact and insignificant impact on employees' organizational

commitment respectively in terms of human resources, organizational capital and social capital. Therefore,

organizational intellectual capital can respectively generate significant impact, and insignificant impact.

In Research on Correlation between Internal Marketing and Intellectual Capital – Take Organization

Commitment and Personality Traits as Moderating Variables, Fong (2004) exemplified that internal marketing

mechanism affected both intellectual capital and organizational commitment significantly, and employees'

organizational commitment had remarkable interference effect.


From the above analysis, this study can deduced the following hypothesis:

Hypothesis: Two (H2): organizational commitment has a significantly positive impact on the accumulation of

intellectual capital.

The Influence of the Implementation of Balanced Scorecard and Organizational Commitment on the

Accumulation of Intellectual Capital As the above mentioned, this study shows that whether the implementation of the balanced scorecard and

organizational commitment will have a synergistic effect or synergy on the accumulation of intellectual capital is an

important topic that worth exploring; therefore, this study infers the third hypothesis:

Hypothesis Three (H3): the implementation of the balanced scorecard and organizational commitment has a

significantly positive interaction effect on the accumulation of intellectual capital.

RESEARCH METHOD

Based on the above research motivation, purpose, and literature review, three research hypothesis are given and a

conceptual research framework of the research model is established as shown in Figure 1.

Research Framework

Figure 1: The framework of this study

Balanced

scorecard

Intellectual

capital

Organizational

commitment

Human

resource

Structure

capital

Relationship

capital

Financial perspective

Internal procedure

perspective

Customer perspective

Learning and growth

perspective

Retention

commitment

Value

commitment

Effort

commitment


Questionnaire Design The questionnaire of this study is measured by dual parcels in accordance with every observable perspective. The

measurement of the questionnaire adopted the seven-point Likert-type scale, giving points varying from 7 to 1 according to the extent of agreement. 7 points represents total agreement while 1 point is total disagreement. In other words, the greater the agreement, the higher the point is, and vice versa. Then, the data that had been collected got decentralized, which means that the score that every question of the questionnaire got turned into zero after the average number was deducted from it, which was aimed at eliminating the linear overlap between the independent variables and interference variables and facilitate the test for interaction between them. The mathematic formula for the decentralization is as follows (Leea, 2012): Σ (Yi-) = ΣXi = 0

The design for balanced scorecard questionnaire is based on Kaplan and Norton’s viewpoint on balanced scorecard, including financial, customer, internal procedure, and learning and growth perspectives. Totally there are 16 questions.

The design for intellectual capital questionnaire takes into account the three aspects—human resource capital, structure capital and relationship capital— proposed by Chen (2001), Chen and Hu (2010) and contains 12 questions in total.

The organizational commitment questionnaire includes aspects like retention commitment, value commitment and effort commitment put forward by Mowday, Porter and Steers (1982) and contains 12 questions. Besides, the questionnaire do not exist common method variance (CMV) problems by CFA comparison method to test as shown in the Table 1 (Lindell, and Whitney, 2001).

Table 1: The results of CMV test Model χ2 DF Δχ2 ΔDF P

Single Factor 1356.6 152 1116.3 9 0.000

Multi-Factor 240.3 143

The Sampling Method

Adopting simple random sampling, this study took managers of human resources, finance, production and marketing departments and general staff of production department in Taiwan LED-listed companies as research subjects and issued 30 copies of questionnaire for a pilot-test, following which a post-test was carried out after the questionnaire was amended according to experts’ suggestions. 360 formal questionnaires were distributed, of which 152 valid samples were recovered with a response rate of 42.2%.

Questionnaire Data and Measurement System In order to validate the research framework proposed in this study, a linear structure equation modeling (SEM) was

used to do confirmatory factor analysis (CFA) on the framework. This study divided the questionnaire into three latent variables—balanced scorecard, intellectual capital and organizational commitment, and subdivided each latent variable into several observable variables (observable / Explicit variables), each having several corresponding questions. The data collected from the investigation was addressed, after which an original questionnaire data file was established. Although the questionnaire adopted itemized measurement, the measurement system of this study applied dual measurement in order to facilitate the latter processing with computer software (Chen, 2010). The number of questions and reference sources of implicit variables and explicit variables in this study are as shown in Table 2 (Leea, 2012).

Table2: The number of questions of the implicit-variable questionnaire and explicit-variable questionnaire Implicit Variables Explicit Variables Number of questions Reference of the questionnaire

Balanced scorecard(X)

Financial prospective 4

Kaplan and Norton (1996) Customer prospective 4 Internal procedure prospective 4 Learning and growth prospective 4

Organizational commitment(Mo)

Retention commitment 4 Mowday, Porter & Steers (1982) Value commitment 4

Effort commitment 4


Intellectual commitment(Y)

Human resource capital 4 Chen,（2001）、Chen and Hu (2010)Structure capital 4

Relationship capital 4

RESULTS AND ANALYSIS

Linear Structure Model Analysis Confirmatory factor analysis (CFA) is an analytical method in contrast with exploratory factor analysis (EFA).

This study conducted confirmatory factor analysis among the three implicit variables—balanced scorecard, intellectual

capital and organizational commitment. Linear structural equation modeling (SEM) includes structure model and

measurement mode—these two can effectively elaborate the causal relationship between implicit variables or latent

Variables. As for the validation of the model, this study verifies fit of measurement model and structural model. It also

validates the fit of the overall model to see whether it reaches the required standard, which means using relevant

indicators to determine the goodness-of-fit of the overall SEM model. (Diamantopoulos & Siguaw, 2000; Leea, 2012).

Analyzing Fit of Measurement Model Factor loadings of various latent / implicit variables and manifest / explicit variables mainly measure the linear

correlation between explicit and implicit variables. The closer to 1 the factor loading gets, the more manifest variables

can measure latent variables. In this study, factor loading of each manifest variable ranged between 0.8 and 0.9, which

means they had high reliability. Therefore, all the manifest /explicit variables of the measurement system of this model

could respectively measure latent/implicit variables to moderate degree. Furthermore, average variance expected (AVE)

represents latent/implicit variables’ ability to explain variance of manifest /explicit variables. The higher the value of

AVE is, the higher the reliability and convergent validity of latent/implicit variable. Usually, the value of AVE must be

greater than 0.5, which means the explanatory variance of manifest /explicit variables is greater than measurement

errors (Fornell and Larcker, 1981). In this study, all the AVE are greater than 0.5, suggesting that the explicit variables

had high reliability and convergent validity (as shown in Table 3 and Figure 2).

Table 3: The judgment index of measurement system in the model

Latent variable (implicit variable) Manifest variable －centralized

dual measurement Factor loading Variance Extracted, VE

Balanced scorecard(X) X1C 0.87 0.68 X2C 0.84 0.66

Organizational commitment(Mo) M1C 0.83 0.64 M2C 0.81 0.63

X*Mo X1M1C 0.88 0.70 X2M2C 0.86 0.69

Intellectual capital(Y) Y1C 0.83 0.67 Y2C 0.81 0.66

ANALYZING FIT OF STRUCTURE MODEL

Path Analysis Results of Structure Model

After the whole model passed the goodness-of-fit test, this study shows the estimated parameter value, standard

error (S.E.) and critical ratio (C.R.) between latent variables in Table 4. It can be also seen from Table 4 that the

balanced scorecard and organizational commitment (X * Mo) have significant interaction effect on the accumulation of

intellectual capital (Y) (c = 0.683), that is, if companies can take into account organizational commitment when they are

realizing balanced scorecards’ impact on the accumulation of their intellectual capital, then synergy will be achieved.


Table 4: Path analysis results of structure model Path coefficient of latent variables Estimate S.E. C.R. P LabelBalanced scorecard(X) → Intellectual capital(Y) .463 .046 10.065 *** a Organizational commitment(Mo) → Intellectual capital (Y) .385 .036 10.694 *** b X*Mo → Intellectual capital (Y) .683 .038 17.974 *** c X → X1C .863 .042 20.548 *** X → X2C .872 .039 22.359 *** Mo → M1C .843 .034 24.794 *** Mo → M2C .822 .032 25.688 *** X*Mo → X1M1C .744 .073 10.192 *** X*Mo → X2M2C .793 .071 11.169 *** Y → Y1C .832 .038 21.895 *** Y → Y2C .841 .037 22.730 ***

Note: * means P<0.05; **means P<0.01 ; *** means P<0.001

The Coefficient of Determination The so-called coefficient of determination, also known as the Squared Multiple Correlation (SMC), represents the

explanatory ability of the independent variables on the dependent variables of all latent variables. In other words, the R2

value shown in Table 6 indicates that the implicit independent variable has adequate explanatory ability on the implicit

dependent variable respectively.

Table 5: Path Coefficient of Determination Coefficientsa (Hierarchical Regression)

Model R R Square Adjusted R

Square Std. Error of the

Estimate Change Statistics

R Square Change F Change df1 df2 Sig. F Change1 .863a .745 .763 .315 .018 14.218 2 97 0.000 2 .865b .748 .755 .513 .007 7.024 1 96 0.002

a. Predictors: (Constant), Mo and X b. Predictors: (Constant), Mo, X and Mo*X

Besides, table 6 can be extracted from Table 5 as follows:

Table 6: Coefficientsa Coefficients of Determination R2

Balanced scorecard (X), Organizational commitment (Mo) on Intellectual capital (Y) 0.745 Balanced scorecard (X), Organizational commitment (Mo) and X*Mo on Intellectual capital (Y) 0.748

The Indices of Fit of the Overall Model The purpose of using linear structural equation modeling (SEM) as the modeling of this study is to explore the

relationship between implicit variables of structural model, determine whether the measurement model has reliability,

and measure the goodness-of-fit of this study as a whole. Indexes that measure the overall fit of this study include χ2,

d.f, GFI, AGFI, NFI, CFI, RMR and RMSEA. Usually χ2/df <5; 1> GFI> 0.9; 1> NFI> 0.9; 1> CFI> 0.9; RMR <0.05;

RMSEA <0.05 (Bagozzi & Yi, 1988). In this study, χ2/df < 5; GFI, AGFI, and NFI are greater than 0.90, while RMR is

less than 0.05, indicating that the goodness-of-fit of the model in this study is sound, as shown in Table 5.

Standardized Results of SEM Analysis The overall framework of the standardized results is as shown in Figure 2 (Leeb, 2011)。


Figure 2: Standardized results of SEM analysis

Analysis and Verification of the Path Effect of Structural Model

The verification of interference variables of this study starts from hierarchical regression analysis (as shown in

Table 5) followed by the centralization of Y on X, Mo, X * Mo regression analysis and t-test in order to examine

whether the significance of partial regression coefficient c is established (ie, whether c is equal to zero), as shown in

Table7.

Table 7: Coefficientsa

Model Unstandardized Coefficients Standardized Coefficients

t Sig. B Std. Error Beta

1 (Constant) X Mo

3.816 9.753 6.884

4.171 .912 .412

.461

.473

.311

3.923 10.931 12.334

.000

.000

.000 2 (Constant) X Mo X*Mo

5.041 9.186 6.374 15.411

5.531 .633 .131 .523

.452

.471

.283

.673

4.813 10.742 13.243 26.973

.000

.000

.000

.000 a. Dependent Variable: Organizational Effectiveness (Y)

Intellectual

Capital (Y)

Balanced

Scorecard (X)

X*Mo

Organizational

Commitment (Mo)

X2C X1C

M2C M1C

X2M2C X1M1C

Y1C .00

.43

.41

.46

.39

.68

.87 .84

.83 .81

.86 .88

.83

Y2C

.81

e1

d2 d1

d4 d3

d6 d5

d7 d8


As shown in Table 7, the 0.683 Path Coefficient of Mo*X versus Y suggests a moderating effect of Mo*X on Y.

From the afore-mentioned analyses, some research results were derived:

1. BSC implementation affects a company’s intellectual capital accumulation in a significantly positive way, with an

estimated 0.463 standardized path coefficient (H1 substantiated);

2. Organizational commitment affects a company’s intellectual capital accumulation in a significantly positive way,

with an estimated 0.285 standardized path coefficient (H2 substantiated);

3. The interaction between BSC implementation and organizational commitment affects a company’s intellectual

capital accumulation in a significantly positive way, with an estimated 0.683 standardized path coefficient (H3

substantiated).

CONCLUSION AND SUGGESTIONS

Research Conclusions

Conclusions can be drawn from the above data analysis and findings.

1. In regards to the validation of SEM model, its measurement model, structure model and the overall model have

favorable goodness-of-fit, which suggests that this model fits well.

2. Practical Validation

Balanced scorecard and organizational commitment implemented by Taiwan LED listed companies have

remarkable positive effect on the accumulation of their intellectual capital. In other words, the organizational

commitment variable has a positive interference effect. According to Chen (2010), when interference variables and

independent variables interact on the dependent variables simultaneously and significantly, the effect of the independent

variables or interference variables on the dependent variables becomes less meaningful. And when organizational

commitment has a moderating effect, the implementation of the balanced scorecard (independent variable) and

organizational commitment (interference variable) do not relate to each other. (i.e., the two variables are independent, as

shown in Figure 2).

Contribution of this Study 1. Innovative Application of Research Method

Most of the past literature has used multi-regression analysis to do exploratory research, rarely taking into

account the confirmatory factor analysis of the interference effect of implicit variables. This study mainly focuses on

implicit variables, therefore it is more suitable to use confirmatory factor analysis (CFA) and the linear structure

equation modeling (SEM) respectively as the measurement tool and framework, which manifests the innovation of the

research method of this study.

2. The Practical Applicability of this Study

Studies done by scholars in the past tend to center on exploratory factor analysis (EFA). This study bases its

modeling on past relevant research findings and verifies the fit of the model in order to understand whether this model

has satisfactory goodness-of-fit effect, therefore the theme of this study belongs to important practical subjects of

confirmatory factor analysis (CFA). Besides providing a reference for later scholars to do further research in relevant

fields, results of this study also have managerial reference value for Taiwan LED-listed companies.

Research Limitations and Recommendations This study only applies to confirmatory factor analysis (CFA) of Taiwan LED-listed companies; later scholars can

consider verifying this model in different industries or groups so as to find out whether there will be any differences in

fit.

This study belongs to confirmatory factor analysis, and simple validation pattern is preferable in order to avoid

undesirable model fit due to complex pattern (Chen, 2010). Therefore this study only considers the effect of the

implementation of balanced scorecard on the accumulation of intellectual capital accumulation effects with

organizational commitment being the interference variable.


REFERENCES

Allee, V. (1999). “The art and practice of being a revolutionary”, Journal of Knowledge Management, Vol.3, No.2, pp.121-131.

Bukh, P. N, Johansen, M.R. and Mouristen, J. (2002). “Multiple Integrated Performance Management Systems: IC and BSC in a Software Company”,

Singapore Management Review, Vol.24, No.3, pp.21-33.

Chai, Shuo-chuan (2001). On Acknowledgement of Organizational Revolution, Organizational Commitment, and Attitudes toward Revolution of the

Credit Department of Farmers ‘Association. Master thesis, I-Shou University, Taiwan,

Chen, Hai-ming, Yu and Jing-wen (2000). On Correlation between Development of Enterprise Culture and Organizational Commitment—From the

Perspective of Employees Whose Original Companies are Merged. Taiwan Management and System, 7(2), 249-270.

Chen, Jin-xiu (2005). On the Relevance between Intellectual Capital of Information and Electronic industry and Enterprises’ value. Master thesis,

Soochow University, Taiwan.

Chen, Mei-chun (2001). On the Influence of Informational and Technological Investment and Intellectual Capital on Enterprises’ Performance.

Doctoral dissertation. National central university, Taiwan.

Chen, Shun-yu (2010). Structural Equation Model. Taiwan: psychology press.

Chen, Shu-xiao and Hhu, Xiang-ling (2010). On the Organizational Competitiveness and Intellectual Capital Index of International Tourist Hotel.

Human Resource Management, 10(1).

Chow, W.C. and M. Haddad (1997). “Applying the balance scorecard to small companies”, Management Accounting, pp.21-27.

Diamantopoulos, Adamantios and Judy A. Siguaw (2000). “Introducing LISREL: A Guide for the-uninitiated”, London: Sage Publications.

Ding, Shu-hua (2000). On the Relationship between Acknowledgement of Revolution of Privatization and Turnover Intention—Take Employees in

Chunghwa Telecom for Example. Unpublished master thesis, National Sun Yat-sen University, Taiwan.

Dzinkowski, R. (2000). “The Measurement and Management of Intellectual Capital: An introduction”, Management Accounting, Vol.2, pp.32-36.

Edvinsson, L. & Malone, M. S. (1997). Intellectual Capital: Realizing Your Company’s True Value by finding its Hidden Roots. New York:

HarperCollins Publishers Inc.

Edvinsson, L. (2003). “Corporate Longitude: What You Need To Know To Navigate The Knowledge Economy, Financial Times Prentice Hall”,

Pearson Education, Inc., Upper.

Fong, Shyh-Rong (2004). Research on Correlation between Internal Marketing and Intellectual Capital – Take Organization Commitment and

Personality Traits as Moderating Variables. Master thesis, National Chung Hsing University, Taiwan.

Fornell. C., & Larcker, D. F. (1981). “Evaluating structural equation models with un-observables and measurement error”, Journal of Marketing

Research, 18: 39-50.

Hall, R. W. (1990). “World Class Manufacturing: Performance Measurement, In Peter B. B. Turney, ed. Performance Excellence in Manufacturing

and Service Organizations, Proceedings of the Third Annual Management Accounting Symposium, San Diego”, American Accounting

Association, 103-110.

Jaworski, Bernard J. and Ajay K. Kohli. (1993). “Market Orientation: Antecedents and Consequences”, Journal of Marketing, 57 (July): 53-70.

Jiang, Han-yi (2006). Application of Intellectual Capital and Balanced Scorecard in Periodical Performance Evaluation of Shipping Business.

National Taiwan Ocean University, Taiwan.

Johnson, K. (1999). “Making loyalty programs more rewarding”, Direct Mark, 61 (11), 24-27.

Kaplan (1984). “The Evolutions of Management Accounting”, The Accounting review, Vol.6, No.3, P390-418.

Kaplan and Norton (1996). “The Balanced Scorecard: Translating Strategy into Action”, Boston: Harvard Business School Press.

Knight, G. A. (1999). “Consumer preferences for foreign and domestic products”, Journal of Consumer Marketing, 16 (2), 151 – 162.

Kuo, Jung-Hsing (2002). Assessing the Performance of ROC Female Military Officers：Using the Balanced Scorecard. Master thesis, Graduate school

of Resource Management, National Defense Management College, Taiwan.

Leea, Y. J. (2012). “A study on the influence of organizational change on organizational effectiveness of schools: Using investment for cloud

computing technologies as a moderator”, African Journal of Business Management, Vol.6, No.7, pp.5710-5719.

Leeb, Y. J. (2011). “A Case Study on the Effect of Teaching Innovation on Learning Effectiveness: Using a Moderator of ‘Integrating Information

Technology into Teaching’”, The Journal of Human Resource and Adult Learning, Vol.7, No.1, pp.33-46.

Lindell, M. K., and Whitney, D. J. (2001). Accounting for Common Method Variance in Cross-Sectional Research Designs, Journal of Applied

Psychology, 86 (1), 114-121.

Liu, Chang-jun (2002). Explore the Core Resource and Operational Effectiveness of Enterprises from the Perspective of Balanced Scorecard—Take


Some Electronic Company in Taiwan for Example. Master thesis, National Defense College, Taiwan.

Lv, Jia-rong (2000). On the Relevance between Capital Structure of Information and Electronic Industry and Operational Performance. Master thesis,

Soochow University, Taiwan.

Meyer, J., Bobocel, D., & Allen, N. (1991) “Development of organizational commitment during the first year of employment: A longitudinal study of

pre- and post-entry influences”, Journal of Management, 17, 717–733.

Meyer, J. P. & Allen, N. J. (1991a). “Commitment to Organizational and Occupations：Extension and Test of a Three-component Conceptualization.

＂ Journal of Applied Psychology, Vol.78, pp. 538-551.

Meyer, J. P. & Allen, N. J. (1991b). “A Three-Component Conceptualization of Organizational Commitment.＂Human Resource Management Review,

Vol. 1(1), pp. 61-89.

Mowday, Richard T., Porter, Lyman W. & Steers, Richard M. (1982). “Employee-organization linkages: The psychology of commitment, absenteeism,

and turnover”, New York: Academic Press, p.253-258.

Porter, L. W., Steers, R. M., Mowday, R. T., & Boulian, P. V. (1974). Organizational commitment, job satisfaction, and turnover among psychiatric

technicians. Journal of Applied Psychology, 59 (5), 603- 609.

Price, J. L. and C. W. Mueller (1986). Absenteeism and Turnover of Hospital Employee, Greenwich: JAI.

Robbins, S. P. (2001). “Organizational Behavior”, Upper Saddle River, NJ: prentice-Hall.

Stewart, Thomas A. (1997). Intellectual capital: the new wealth of organizations, Doubleday New York, NY, USA.

Sun, Yu-Chi (2008). On Organizational Intellectual Capital’s Impact on Employees’ Organizational Commitment – Take the Culture and Creativity

Industries in Taiwan For Example. Master thesis, University of Science and Technology of China, Taiwan

Sveiby, Karl-Erik (1998). Measuring Intangibles and Intellectual Capital–An Emerging First Standard, Internet version.

Whyte, Lancelot Law (1965). “Internal factors in evolution” New York: G. Braziller, p.128.

Wu, An-ni (1999). Turn Strategies into Actions. Promotion Thesis on Balanced Scorecard. Taipei: Facebook bookstore ltd.

Wu, An-ni (2002). Analysis of Intellectual Capital. Journal of Accounting Research, 204, 57-66.

Wu, Bing-en (1993). Organizational Behavior. Taipei: Huatai Cultural Enterprise Co. Ltd, 433-457.

Xie, An-tian (1999). Human Resource Management (revision version). Taipei: Issued by the writer.

Xie, Xu-zhong (2006). On the Correlation between Acknowledgement of Organizational Revolution, Organizational Commitment and Job

Efficiency—Take Xingzhong Paper Company as Example. Master thesis, Fo Guang University, Taiwan.

Ye, Xiu-jin (2001). On Correlation between the Organizational Learning Model of Product Development Department of Taiwan Electronic and

Technology Industry and Performance. Master thesis, Chang Gung University, Taiwan.

Zeng, Yu-xin (2006). On Relevance Between Balanced Scorecard and Intellectual Capital—Take Taiwan IC Industry for Example. Master thesis,

Tunghai University, Taiwan.

the influences of balanced scorecard upon intellectual ... yung-chieh chien.pdf · moderator of...

Documents