assessing the equilibrium relationships … 3... · s.chakraborty, b.b.sarkar, raveesh...

Volume 3 Issue 1 2014 Journal of Business Management ISSN 1985-8698

ASSESSING THE EQUILIBRIUM RELATIONSHIPS BETWEEN

MACROECONOMIC VARIABLES AND THE MALAYSIAN STOCK

MARKET: BOUNDS STATISTICS METHODOLOGY

Hussain Ali Bekhet and Mohamed Ibrahim Mugableh

1

LIQUIDITY ASPECTS OF LARGE CORPORATE BUSINESS: A

STUDY WITH REFERENCE TO LISTED COMPANIES IN INDIA

S.Chakraborty, B.B.Sarkar, Raveesh Krishnankutty and Bhushan Chandra

Das

15

AN EMPIRICAL STUDY ON DIRECTORS’ REMUNERATION IN

RELATION TO CORPORATE PERFORMANCE: A COMPARISON

BETWEEN GLCs AND NON-GLCs IN MALAYSIA

Lee Seng Fatt, Mohammad Izzat Amir Abdul Ghani, Adrian A/L Alphonsus

and Prasanth Nair A/L Sasedharan

28

THE EFFECTS OF TEACHING QUALITY ON STUDENT

SATISFACTION AND BEHAVIOURAL INTENTIONS FROM THE

VIEW POINT OF UNIVERSITY STUDENTS

Bahari Mohamed, Saripah bt Basar, Hasmah bt Safiei and Pritam Singh

A/L Santa Singh

40

THE EFFECTS OF SELF-EFFICACY ON THE DEVELOPMENT OF

ENTREPRENEURIAL INTENTION

Tan Kwe Lu

57

THE RELATIONSHIP BETWEEN FIRM CHARACTERISTIC AND

CORPORATE GOVERNANCE MECHANISM WITH FIRM’S

INTELLECTUAL CAPITAL DISCLOSURE IN MALAYSIAN

INITIAL PUBLIC OFFERINGS

Zaifudin Zainol, Rashidah Abdul Rahman, Shahrul Suhaimi Ab. Shokor and

Afdzal Aizat Ramli

66

LEADERSHIP BEHAVIOR AND ORGANIZATIONAL

PERFORMANCE: A CASE OF 100 BEST CO-OPERATIVES IN

MALAYSIA

Mohd Zainal Munshid bin Harun, Othman B Chin, Sharul Nizam B

Salahuddin and Mohd Yunus B Majid

85

HOW CORPORATE SOCIAL RESPONSIBILITY (CSR)

CONTRIBUTES TO CUSTOMER - BASED BRAND EQUITY

AMONG MALAYSIAN MOBILE TELCOS’

Abdul Rahman Zahari, Elinda Esa and Inaliah Mohd. Ali

97

Assessing the equilibrium relationship between macroeconomic variables and the Malaysian stock market:

Bounds statistics Methodology

Journal of Business Management Volume 3 Issue 1 2014 1

ASSESSING THE EQUILIBRIUM RELATIONSHIPS BETWEEN

MACROECONOMIC VARIABLES AND THE MALAYSIAN STOCK MARKET:

BOUNDS STATISTICS METHODOLOGY

Hussain Ali Bekhet

[email protected]

[email protected]

Mohamed Ibrahim Mugableh

[email protected]

University Tenaga Nasional

ABSTRACT

The paper aims at estimating six macroeconomic variables that are influencing the Malaysian

stock market index. Specifically, it assesses the long-run and short-run equilibrium

relationships between the industrial production index (IP), the producer price index (PPI), the

consumer price index (CPI), exchange rates (ER), narrow money supply (M1), broad money

supply (M2) and the Malaysian Stock Market Index (SMI) using annual time-series data for

the 1977-2011 period. To accomplish these goals, the paper utilizes the Augmented-Dickey

Fuller (ADF) and the Phillips-Perron (PP) stationarity bounds statistics tests. The paper then,

uses Pesaran bounds statistics for testing the co-integrating relationships among variables.

Eventually, the results of the stationarity and co-integration tests are used to analyze the long-

run and short-run equilibrium relationships among the variables. The results of the ADF and

the PP tests show that the null hypothesis of non-stationary cannot be rejected even at the 10%

significance level in all cases except for one variable. More specifically, the variables IP, PPI,

CPI, ER, M1, and M2 are stationary at the upper bound, while the variable SMI is stationary at

both lower and upper bounds. However, the results of the pesaran bounds statistics reveal that

all variables are co-integrated with SMI except ER, and CPI. The results of the stationarity

tests and co-integration show the presence of long-run and short-run equilibrium relationships

between four macroeconomic variables and SMI. In particular, IP and M1 are positively

associated with SMI in the long-run, while PPI and M2 are negatively associated. Additionally,

IP and M2 are negatively associated with SMI in the short-run, while PPI and M1 are

positively associated. The study‟s findings are of particular interest and importance to policy

makers, financial economists, and investors dealing with the Malaysian economy and its stock

market.

Keywords: stock prices, macroeconomic variables, economic equilibrium, bounds statistics,

stationarity tests, co-integration test

INTRODUCTION

During the last decades, the equilibrium relationships between macroeconomic variables and

stock prices have been widely studied by academic researchers and practitioners. In fact, the

literature is very rich for the matured stock markets of Canada, France, Germany, Italy, Japan,

the UK, and the US. However, the latest studies in this area support the argument that stock

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prices are influenced by macroeconomic variables such as the industrial production index (IP),

the consumer price index (CPI), the producer price index (PPI), the federal funds rate (FFR), a

narrow money supply (M1), a broad money supply (M2), interest rates (INT), real gross

domestic product (RGDP), and exchange rates (ER) in mature stock markets (Beltratti and

Morano, 2006; Hatemi-J and Morgan, 2009; Humpe and Macmillan, 2009; Kizys and

Pierdzioch, 2009).

Since the early 1980s, there has been an increasing attention to study the relationships between

macroeconomic variables and stock prices in emerging stock markets. However, in the early

1990s, many emerging countries liberalized their stock markets and decided to open their

domestic stock markets to foreign investors. As a result, this lead to rapid growth in their stock

markets and economies as well as increasing their positions in the international economic and

financial environment (Ghosh and Ariff, 2004). In light of these matters, it is reasonable to

conclude that emerging stock markets have features which attract investors and researchers to

recognize and policy makers to evaluate and study these matters.

Notable studies have been conducted to examine the relationships between macroeconomic

variables and stock prices in emerging stock markets. Hanousek and Kocenda (2011) used the

generalized autoregressive conditional heteroscedasticity (GARCH) Model and found

significant evidence that emerging European stock market indices, i.e., the Czech Republic,

Hungary, and Poland were strongly influenced by mature European and the US stock market

indices as well as by their macroeconomic variables. Nguyen (2011) used the moving average

exponential (MAE)-GARCH Model and found that the US macroeconomic variables had

positive effects on the conditional mean and negative effects on the conditional variance of the

Vietnam stock market index (GSE- share index). Using arbitrage pricing theory (APT), Rjoub

et al. (2009) documented significant pricing relationships between stock returns and

macroeconomic variables in the case of Turkey. Tsoukalas (2003) used the vector

autoregressive (VAR) Model and found that macroeconomic variables (CPI, ER, IP, and M2)

were strongly related to stock prices in the case of Cyprus. Similarly, Verma and Ozuna (2005)

showed that the changes in the macroeconomic variables of one Latin American country did

not affect the stock markets of other Latin American countries. Moreover, they found that the

Mexican stock market significantly affected other Latin American stock markets and the

reverse did not hold.

In the Malaysian context, few notable studies have been made in our area of interest. Ibrahim

(1999) studied the relationships between seven macroeconomic variables (ER, foreign reserves

(FR), credit aggregates (CG), consumer prices (CP), IP, M1, and M2) and the Malaysian stock

market index using the vector error correction model (VECM) and monthly time series data for

the 1977-1996 period. The results suggested that the Malaysian stock market index was

efficient with respect to the CP, CG, and FR, while inefficient, with respect to M1, M2, ER,

and IP. Similarly, Ibrahim and Aziz (2003) examined the short-run and long-run relationships

between four macroeconomic variables (IP, CPI, M2, and ER) and the Malaysian stock market

index using monthly time series data for the 1977-1998 period. They found positive short-run

and long-run relationships between the Malaysian stock market index and both CPI and IP.

Additionally, they found negative short-run and long-run relationships between the Malaysian

stock market index and both of M2 and ER.

The current paper provides further evidence to the literature on the equilibrium relationships

between macroeconomic variables and stock prices. In particular, it examines the equilibrium

long-run and short-run relationships between six macroeconomic variables (IP, PPI, CPI, ER,

M1, and M2) and SMI in Malaysia for the 1977-2011 period.




The rest of the paper is divided into six sections. Section 2 presents an overview of the

Malaysian stock market; section 3 discusses the review of previous empirical studies; section 4

provides the bounds statistics methodology; section 5 reports empirical results and analysis;

while section 6 provides policy implications. Conclusions, suggestions, and further studies are

discussed in section 7.

OVERVIEW OF THE MALAYSIAN STIOCK MARKET

The Malaysian stock market is considered second among the largest South East Asian stock

markets according to its domestic market capitalization (See Figure 1).

Source: WFE, http://www.world-exchanges.org/statistics/time-series/value-share-trading.

Figure 1: Domestic Market Capitalization for 1990-2011 Period

Figure 1 shows that the Singapore stock market achieved the highest domestic market

capitalization of U.S.$ 598 million at the end of 2011 followed by the Malaysian, Indonesian,

Thailand, and Philippine stock markets which recorded U.S.$396, 390, 268, and 165 million

respectively. The Malaysian stock market index (SMI) is the weighted average of stock prices

which is used to reflect the market capitalization of its components (Bursa Malaysia, 2012).

SMI started its operations officially in 1977 with a value of 113.40 points as shown in Figure

2.

Source: Bursa Malaysia, available online at: www.bursamalaysia.com.

Figure 2: Stock Market Index for the 1977-2011 Period

0.0

200.0

400.0

600.0

800.0

199

01

99

11

99

21

99

31

99

4

199

5

199

6

199

7

199

8

199

9

200

0

200

1

200

2

200

3

200

4

200

5

200

6

200

7

200

8

200

9

201

0

201

1

Mil

lio

ns

($)

Singapore

Malaysia

Indonesia

Thailand

Philippine

SMI = 203.2e0.057t

R² = 0.761

0

200

400

600

800

1000

1200

1400

1600

1800

Poin

ts

SMI

Expon. (SMI)

http://www.world-exchanges.org/statistics/time-series/value-share-trading

http://www.bursamalaysia.com/




Figure 2 shows that the SMI recorded an annual growth rate of 5.7% from 1977 till 2011.

Before the onslaught of AFC in 1997-98, the performance of SMI rose sharply to reach its first

peak in 1993 and its second peak in 1996 with 1275 points and 1238 points respectively.

However the SMI achieved more than 1400 points at the end of 2011. Besides that, figure 3

shows that the trading volume of shares in the Malaysian stock market was vivid.

Source: Bursa Malaysia, available online at: www.bursamalaysia.com.

Figure 3: Trading Volume for the 1993-2011 Period

However, the trading volume of shares recorded an annual growth rate of 6.7% for the 1993-

2011 period. The trading volume started at RM 20.6 billion and fell gradually to reach the

first sharp decline in 1995 with a value of RM 8.24 billion, then, the trading volume increased

slowly to reach the first peak in 2007 with a value of RM 55.8 billion. The trading volume

remained stable from 2007 till 2009. However, it declined from RM 28.6 billion in 2010 to

RM 27 billion in 2011.

REVIEW OF PREVIOUS EMPIRICAL STUDIES

The equilibrium relationships between macroeconomic variables and stock market indices

received a lot of attention from academics whose studies employed different macroeconomic

variables and data from both mature and emerging stock markets. However, in this section the

researchers review a selected number of previous empirical studies from the vast literature

which was conducted on mature stock markets followed by studies conducted in emerging

stock markets.

Previous empirical studies in matured stock markets

Beltratti and Morano (2006) applied the Markov switching (MS)-GARCH Model and daily

time-series data to examine the relationship between macroeconomic variables (monthly IP,

monthly CPI, FFR, and weekly M1) and the US stock market index (S&P500). They found a

causality direction from S&P 500 volatility to macroeconomic volatility. However, the

causality direction was stronger from macroeconomic to S&P500 volatility.

Trading volume = 10.41e0.067t

R² = 0.479

0

10

20

30

40

50

60

Bil

lio

ns

(RM

)

Trading volume

Expon. (Trading

volume)





Hatemi-J and Morgan (2009) explored whether the Australian stock market was information-

ally efficient in the semi-strong form in relation to the ER and INT using the Auto-Regressive

Conditional Heteroscedasticity (ARCH) Model and daily time-series data for the 1994-2006

period. They found that the Australian stock market was not information-ally efficient with

respect to the INT and ER.

Humpe and Macmillan (2009) investigated the impact of macroeconomic variables (IP, CPI,

M2, and long-term INT) on the S&P 500 and the Japanese stock market index (Nikkei 225)

using VECM. For the US market, they found that S&P 500 was positively related to IP and

negatively related to both CPI and long-term INT. They also found a positive relationship

between S&P500 and M2. However, for the Japanese data, they found that the Nikkei 225 was

influenced positively by IP and negatively by CPI and long-term INT.

Kizys and Pierdzioch (2009) examined the relationships between macroeconomic variables

(short-term INT, inflation (INF), ER, CPI, and PPI) and the mature stock market indices of

Canada, France, Germany, Italy, Japan, the UK, and the US, using the VAR Model and

monthly time-series data for the 1975-2004 period. They found that the stock market indices

were not systematically linked to the macroeconomic variables in both the long-run and the

short-run.

Previous empirical studies in emerging stock markets

Aburgi (2008) examined the impact of macroeconomic variables (ER, INT, IP, and M1) on

stock market indices of four Latin American countries (Argentina, Brazil, Chile, and Mexico)

using the VAR Model and monthly time-series data for the 1986-2001 period. He found that

macroeconomic variables influenced Latin American stock markets indices significantly.

Adjasi (2009) employed the Exponential (E)-GARCH Model and monthly time-series data to

investigate the effects of macroeconomic variables (CPI as a proxy of INF, M2, INT, gold

prices (GP), oil prices (OP), and ER,) on the volatility of the Ghanaian stock market index. He

found that the volatility of INT increased the volatility of the Ghanaian stock market index,

while the volatility of GP, OP, and M2 reduced the volatility of this market.

Liu and Shrestha (2008) examined the long-run relationship between macroeconomic variables

(time deposit INT, INF, M2, IP, and ER) and the two indices of the Chinese stock market,

namely, Shanghai Stock Exchange, and Shenzhen Stock Exchange using the GARCH Model

and monthly time-series data for the 1992-2001 period with a total of 120 observations. The

results showed that a co-integration relationship existed between the stock market indices and

macroeconomic variables in the long-run.

Pal and Mittal (2011) applied VECM and quarterly time-series data for the 1995-2008 period

to examine the equilibrium long-run and short- run relationships between macroeconomic

variables (INT, INF, gross domestic savings (GDS), and ER) and stock market indices in India.

The results indicated co-integration relationships between macroeconomic variables and the

Indian stock market indices in both the short-run and the long-run.

Based on the previous empirical studies, the following hypotheses could be formulated for the

current study:

: There are significant long-run equilibrium relationships between macroeconomic

variables (IP, PPI, CPI, ER, M1, and M2) and SMI.




: There are significant short-run equilibrium relationships between macroeconomic

variables (IP, PPI, CPI, ER, M1, and M2) and SMI.

BOUNDS STATISTICS METHODOLOGY

In fact, the research methodology needs to be carefully designed to obtain results that are quite

robust, objective, and realistic. For the current paper, several steps of the research

methodology have been adopted.

Variables sources

The present study uses annual time series data covering the 1977-2011 period. However, data

on the SMI was obtained from Bursa Malaysia (www.bursamalaysia.com); data on M1, M2,

and ER was obtained from BNM (www.bnm.gov.my); data on IP, PPI, and CPI was obtained

from the department of statistics, Malaysia (DOSM) (www.statistics.gov.my).

Model specification and variables descriptions

The researchers examine the long-run and short-run equilibrium relationships between six

macroeconomic variables (IP, PPI, CPI, ER, M1, and M2) and SMI, by relying on the

following model:

+ + (1)

Where denotes the intercept; represent the coefficients of the explanatory

variables; denotes the error term. represents the logarithms of yearly figures of the

Malaysian stock market index which are obtained by taking the weighted average of daily

closing stock prices; representing the logarithms of yearly weights of the Malaysian

industrial production index, and covering manufacturing, mining, and electricity sectors using

2005 as the base year; denotes the logarithms of yearly weights of the Malaysian

producer price index, and cover agriculture, fishing, mining, manufacturing, electricity, gas,

and water supply sectors using 2000 as the base year; denotes the logarithms of yearly

weights that have been taken to measure the Malaysian aggregate price level of the main

groups of goods and services using 2000 as the base year; represents the yearly values of

bilateral Malaysian Ringgit (RM) exchange rate vis-à-vis the US dollar ($); and

denoting the logarithms of yearly figures of the total amount of money available in the

Malaysian economy, expressed in RM (millions). However, all variables were transformed into

natural logarithmic forms expect ER to make this variable simultaneous with other variables or

series (Chen et al., 1986).

The error-corrections representations for ARDL Approach for the variables in equation 1 can

be written as the following models:


http://www.statistics.gov.my/




t 1 11 t i 12 t i 13 t i 14 t i 15 t i 16 t i 17 t i

1 0 0 0 0 0 0

牋 1 2K K K K K K K

i i i i i i i

LSMI LSMI LIP LPPI LCPI ER LM LM

11 t 1牋 12 t 1牋 13 t 1牋 14 t 1牋 15 t 1牋 16 t 1牋 17 t 1牋 t 1 t1 2LSMI LIP LPPI LCPI ER LM LM ECM

(2)


1 0 0 0 0 0 0


i i i i i i i

LIP LIP LSMI LPPI LCPI ER LM LM

21 t 1牋 22 t 1牋 23 t 1牋 24 t 1牋 25 t 1牋 26 t 1牋 27 t 1牋 t 1 t1 2LIP LSMI LPPI LCPI ER LM LM ECM

(3)


1 0 0 0 0 0 0


i i i i i i i

LPPI LPPI LSMI LIP LCPI ER LM LM

31 t 1牋 32 t 1牋 33 t 1牋 34 t 1牋 35 t 1牋 36 t 1牋 37 t 1牋 t 1 t1 2LPPI LSMI LIP LCPI ER LM LM ECM

(4)


1 0 0 0 0 0 0


i i i i i i i

LCPI LCPI LSMI LIP LPPI ER LM LM

41 t 1牋 42 t 1牋 43 t 1牋 44 t 1牋 45 t 1牋 46 t 1牋 47 t 1牋 t 1 t1 2LCPI LSMI LIP LPPI ER LM LM ECM

(5)


1 0 0 0 0 0 0


i i i i i i i

ER ER LSMI LIP LPPI LCPI LM LM

51 t 1牋 52 t 1牋 53 t 1牋 54 t 1牋 55 t 1牋 56 t 1牋 57 t 1牋 t 1 t1 2ER LSMI LIP LPPI LCPI LM LM ECM

(6)


1 0 0 0 0 0 0

1 牋 1 2K K K K K K K

i i i i i i i

LM LM LSMI LIP LPPI LCPI ER LM

61 t 1牋 62 t 1牋 63 t 1牋 64 t 1牋 65 t 1牋 66 t 1牋 67 t 1牋 t 1 t1 2LM LSMI LIP LPPI LCPI ER LM ECM

(7)


1 0 0 0 0 0 0

2 牋 2 1K K K K K K K

i i i i i i i

LM LM LSMI LIP LPPI LCPI ER LM

71 t 1牋 72 t 1牋 73 t 1牋 74 t 1牋 75 t 1牋 76 t 1牋 77 t 1牋 t 1 t2 1LM LSMI LIP LPPI LCPI ER LM ECM

(8)

Where represent the first difference operators; denotes the short-run

coefficients of the variables; represents the long-run coefficients of the one

lagged variable; ( )‟s denote the error correction terms which are used to link the long-

run equilibrium relationships of the variables with their short-run eqilibria.




The current paper uses the bounds statistics methodology to examine the long-run and short-

run equilibrium relationships among variables in the above 7 models (Equations, 2-8).

Specifically, the researchers started by testing whether the variables achieve their stationarity

at the upper or the lower bounds using both the augmented Dickey-fuller (ADF) and Phillips-

Peron (PP) stationarity bounds statistics tests. Then, the researchers proceeded by testing the

number of co-integrating relationships among variables using Pesaran et al. (2001) bounds

statistics tests. Finally, we use the results of stationarity and co-integration to analyze the long-

run and short-run equilibrium relationships among the variables.

RESULTS AND ANALYSIS

ADF and PP stationarity bounds statistics tests

This study uses both of ADF and PP stationarity bounds statistics tests. However, table 1

reports the stationarity results of ADF and PP tests.

Table 1: Stationarity Results of ADFand PP Bound Statistics Tests

Stage Variables

ADF Critical values PP Critical values

Trend and

intercept 1% 5% 10%

Trend and

intercept 1% 5% 10%

At Lower

Bound

LSMI -3.47(0)***

-4.25 -3.54 -3.22 -3.59[4]**

-4.25 -3.56 -3.21

LIP -2.36(0) -4.26 -3.53 -3.21 -2.36[0] -4.26 -3.55 -3.23

LPPI -1.62(0) -4.25 -3.55 -3.23 -1.78[3] -4.24 -3.54 -3.21

LCPI -1.89(0) -4.28 -3.56 -3.21 -1.91[3] -4.25 -3.55 -3.22

ER -1.86(0) -4.25 -3.55 -3.22 -2.04[5] -4.24 -3.51 -3.25

LM1 2.87(0) -4.26 -3.54 -3.23 4.55[2] -4.25 -3.56 -3.26

LM2 3.13(0) -4.28 -3.53 -3.24 2.55[2] -4.24 -3.55 -3.27

At Upper

Bound

(LSMI) - 7.86(0)* -4.26 -3.55 -3.21 -8.38[4]

* -4.26 -3.55 -3.21

LIP) - 5.81(0)* -4.27 -3.56 -3.22 -5.81[0]

* -4.28 -3.52 -3.22

LPPI) -5.71(0)* -4.24 -3.53 -3.20 - 5.73[3]

* -4.26 -3.55 -3.21

LCPI) - 6.26(0)* -4.26 -3.55 -3.21 - 6.32[3]

* -4.29 -3.52 -3.25

ER) -6.20(0)* -4.25 -3.58 -3.22 -6.30[5]

* -4.27 -3.56 -3.21

(LM1) - 4.97(0)* -4.27 -3.56 -3.23 - 4.94[2]

* -4.28 -3.55 -3.22

LM2) - 3.34(0)***

-4.34 -3.59 -3.25 - 3.33[2]***

-4.26 -3.57 -3.23

Notes: (1), *, **, ***, describe the stationarity at 1%, 5 % and 10% significance level respectively. (2), Source:

Output of EViews 7.2 Software.

Table 1 shows that all variables in both the ADF and PP tests are non-stationary at the lower

bound except LSMI. When the first differences are executed, all the variables are stationary.

Specifically, at the lower bound, LSMI is stationary at the 10% significance level in the ADF

test; while it is stationary at the 5% significance level in the PP test. At the upper bound, all the

variables are stationary at the 1% significance level in both the ADF and PP tests except LM2

which is stationary at the 10% significance level.

Pesaran bounds statistics tests for co-integration

The present study uses the F-statistics as suggested by Pesaran et al. (2001) to test the null

hypotheses of no co-integration among variables by setting the long-run coefficients of the one

lagged variable in the above 7 models (Equations, 2-8) equal to zero i.e., : = 0, against




the alternative hypotheses of co-integration among variables where, the long-run coefficients

of one lagged variables are not equal to zero i.e., : 0.

The calculated F-statistics are compared with the critical values tabulated as statistical tables in

Pesaran et al. (2001). If the calculated F-statistics are greater than the upper bounds, then the

null hypotheses of no co-integration are definitely rejected, which means that the variables

included in the models are shared long-run relationships among themselves (Pesaran et al.,

2001). If the calculated F-statistics are smaller than the lower bounds, then the null hypotheses

of no co-integration are accepted, which means that the variables included in the models are

not shared long-run relationships among themselves (Pesaran et al., 2001). However, if the

calculated F-statistics fall between the upper and the lower bounds, then, the decisions are

inconclusive to either accept or reject the null hypotheses of no co-integration among variables

(Pesaran et al., 2001).

Table 2 presents the computed and the critical values of F-statistics to test the null hypotheses

of no co-integration among variables in the considered models.

Table 2: Bounds Statistics Tests for the Existence of Co-Integration among Variables

Source: Output of Micro-fit 4.1 package.

Models

Computed

F-statistics

Significance

levels

Critical values of F-

statistics

Lower

Bound

Upper

Bound

( / , , , , , ) 2.2258

10% 1.92 2.89

5% 2.17 3.21

2.5% 2.43 3.51

1% 2.73 3.90

( / , , , , , ) 3.7460

10% 1.92 2.89

5% 2.17 3.21

2.5% 2.43 3.51

1% 2.73 3.90

( / , , , , , ) 4.3514

10% 1.92 2.89

5% 2.17 3.21

2.5% 2.43 3.51

1% 2.73 3.90

( / , , , , , ) 2.0262

10% 1.92 2.89

5% 2.17 3.21

2.5% 2.43 3.51

1% 2.73 3.90

( / , , , , , ) 1.8562

10% 1.92 2.89

5% 2.17 3.21

2.5% 2.43 3.51

1% 2.73 3.90

( / , , , , ,

) 3.2295

10% 1.92 2.89

5% 2.17 3.21

2.5% 2.43 3.51

1% 2.73 3.90

( / , , , , ,

) 4.7322

10% 1.92 2.89

5% 2.17 3.21

2.5% 2.43 3.51

1% 2.73 3.90




Table 2 shows that the null hypotheses of no co-integration among variables in , and

models are rejected at all significance levels. We reject the null hypotheses of no co-

integration among variables in , model at the 10%, 5%, and 2.5% significance levels; also

we reject the null hypothesis of no co-integration among variables in model at the10%

and 5% significance levels. However, the researchers accept the null hypothesis of no co-

integration among variables in model.

The decisions are inconclusive to either accept or reject the null hypotheses of no co-

integration among variables in , and models. Specifically, we accept the null

hypothesis of no co-integration among variables in and models.

Finally, the researchers conclude that the variables in , , and models are

co-integrated among themselves, while the variables in and models are not

co-integrated among themselves. In fact, the results of co-integration are confirmed with the

results of Pan et al. (2007) who found a no co-integration between and , and

Ibrahim and Aziz (2003) who found a co-integration between ( , ) and .

Analyzing the long-run and short-run equilibrium relationships

The main objective of the current study is to analyze the long-run and short-run equilibrium

relationships between macroeconomic variables and SMI. However, after conducting the

bounds statistics tests for co-integration, we conclude that all variables are co-integrated with

expect and , therefore we need to examine the long-run and short-run

equilibrium relationships among these variables. Table 3 shows the estimations of long-run

coefficients.

Table 3: Long-Run Coefficients Estimations

model

= -3.89 + 0.12 - 0.17 + 0.60 + 0.13 – 0.01 + 0.39

S.E = (0.86) (0.06) (0.28) (0.32) (0.02) (0.18) (0.18)

S.S = (0.004)a (0.07)

c (0.56) (0.11) (0.002)

a (0.97) (0.07)

c

model

= 5.33 – 0.14 + 0.66 – .80 - 0.12 - 0.08 + 0.19

S.E =(1.53) (0.06) (0.26) (0.47) (0.03) (0.20) (0.18)

S.S =(0.01)a (0.03)

b (0.03)

b (0.11) (0.004)

a (0.71) (0.31)

model

=5.62 + 0.09 + 0.82 – 0.85 - 0.54 - 0.10 + 0.67

S.E =(2.01) (0.06) (0.39) (0.42) (0.47) (0.06) (0.25)

S.S =(0.02)b (0.16) (0.06)

c (0.07)

c (0.28) (0.13) (0.02)

b

model

=0.91 – 0.14 + 0.81 + 0.72 + 0.04 - 0.13 + 0.56

S.E =(0.05) (0.08) (0.18) (0.21) (0.31) (0.04) (0.12)

S.S =(0.43) (0.01)a (0.00)

a (0.00)

a (0.91) (0.01)

a (0.00)

a

Notes: (i) S.E denotes the standard errors of long-run coefficients. (ii) S.S defines the statistical significance of

long-run coefficients. (iii) The notations a, b, and c denote the statistical significance at 1%, 5%, and 10% levels

respectively.

Table 3 shows that at 1% significance level, the variable is positively associated with

model, while it is negatively associated with and models. On the other hand,

the variables , , and are positively associated with the model, while the

variable is negatively associated. At the 10% significance level, the variables




are positively associated with the model. Furthermore, the variable

is positively associated with the model, while is negatively associated. At

the 5% significance level, the variables , and are negatively and positively

associated with the model respectively, while the variable is positively associated

with the model. Table 4 presents short-run coefficients, error-corrections

representations, and estimation methods based on least squares and relevant diagnostic tests.

Table 4: Short-run Coefficients and Error-corrections Representations

model

= -3.69 + + - 0.07 +

(0.01)a (0.13) (0.14) (0.76) (0.06)

c (0.14)

0.53 - 0.46 +

(0.06)c (0.47) (0.03)

b (0.60)

(0.10)c

0.05 – + 0.39 –

(0.07)c (0.012)

b (0.20) (0.03)

b (0.32)

(0.01)a

0.24 – 0.43 – 0.94

(0.12) (0.02)b (0.00)

a

Estimated methods: Least squares

R2 = 0.98 S.E of regression = 0.02 F-statistics = 17.62[0.00]

Diagnostic tests

J-B normality test = 3.23 [0.20] Lagrange Multiplier test of residual serial correlation = 1.13[0.34]

ARCH test = 0.67[0.41] Ramsey RESET test of model specification = 4.11[0.10]

model

= 3.35 + 0.22 + 0.08 + 0.17 + 0.08 + 0.41 –

(0.00)a (0.15) (0.02)

b (0.00)

a (0.01)

a (

0.002)

a

0.31 + 0.59 - 0.08 - 0.60 - 0.27 - 0.24 +

(0.01)a (0.00)

a (0.00)

a (0.00)

a (0.07)

c (0.02)

b

0.04 - 0.16 + 0.37 - 0.63

(0.80) (0.14) (0.00)a (0.00)

a



Diagnostic tests

J-B normality test = 1.16[0.56] Lagrange Multiplier test of residual serial correlation = 1.19[0.30]


model

=3.10 – 0.45 + 0.20 + 0.20 + 0.11 + 0.29 –

(0.00)a (0.00)

a (0.00)

a (0.00)

a (0.00)

a (0.11)

0.47 - 0.40 - 0.73 + 0.34 + 0.47 - 0.06 -

(0.00)a (0.01)

a (0.00)

a (0.10) (0.03)

b (0.002)

a

0.05 - 0.02 + 0.37 - 0.55

(0.08)c (0.17) (0.02)

b (0.00)

a



Diagnostic tests

J-B normality test = 0.98[0.61] Lagrange Multiplier test of residual serial correlation = .45[0.50]


model

= 0.53 + 0.26 + 0.16 - 0.08 + 0.47 + 0.41 +

(0.41) (0.03)b (0.09)

c (0.01)

a (0.00)

a (0.01)

a

0.02 + 0.02 + 0.07 + 0.52 - 0.21 - 0.58

(0.91) (0.15) (0.00)a

(0.00)a (0.01)

a (0.00)

a






Diagnostic tests

J-B normality test = 1.39[0.50] Lagrange Multiplier test of residual serial correlation = 1.74[0.19]


Notes: (i) Figures in parentheses ( ) identify the statistical significance levels of short-run and (

coefficients. (ii) J-B represents jarque-bera for testing normality. (iii) Figures in brackets [ ] identify the p-values.

(iii) ARCH represents the auto-regressive conditional heteroscedasticity for testing heteroscedasticity.

Table 4 shows that the speed of adjustments to obtain the equilibrium, the ( , are

highly significant at the 1% level which suggest a high speed of arriving at the long-run

equilibrium. Specifically, the model records the highest in absolute value

among other models suggesting that 94% of the disequilibria in of the previous year‟s

shock adjust back to the long-run equilibrium in the current year. While, model

records the lowest in absolute value, suggesting a very low speed of converge toward

its long-run equilibrium. It is worth noting that the significances of ( imply the

causality directions among the considered variables in the models. However, we find causality

directions from , , , , and variables to model,

from , , , , and variables to model, from

, , , , , variables to model, and from

, , , , , variables to model in both short and

long-run.

The validity of estimated models by relying on both chi-square and F-version is confirmed

using diagnostic tests such as the J-B test, the Lagrange Multiplier test, the ARCH test, and the

Ramsey RESET test. However, the J-B test approved the normality assumption of the

estimated residual series in all models, the Lagrange Multiplier test confirmed the assumption

of no residual autocorrelation in all the models, the ARCH test confirmed the homoscedasticity

assumption in all models, and the Ramsey RESET test confirmed the correct specifications of

all models.

POLICY IMPLICATIONS

The findings of this study suggest that Malaysian policy makers should pay the most attention

to the effects of monetary policies on the stock market. Specifically, the results of the current

study are confirmed with the results of Ibrahim (1999) that found, both that M1 and M2 are

positively and negatively associated with SMI in both the long-run and short-run. The

contraction of the money supply leads to lower interest rate, lower firm investment and then,

decreases the attractiveness of investors to invest in the stock market. In sharp contrast, the

expansion of the money supply leads to hyper inflation, but increases share prices in the stock

market. Furthermore, the results of the current study are confirmed with the results of Ibrahim

(1999) who found that IP is positively and negatively associated with SMI in both the long-run

and short-run. A high IP in a particular industry, let us say the Malaysian manufacturing

industry, is a sign that the firms in that industry are performing well, thereby leading to

increasing their share prices in the stock market. In sharp contrast, a low IP is a sign that these

firms are not performing well, which leads to a decrease in their share prices in the stock

market.

CONCLUSIONS AND FURTHER STUDIES




The present paper examines the long-run and short-run equilibrium relationships between

macroeconomic variables (IP, PPI, CPI, ER, M1, and M2) and SMI using annual time-series

data for the 1977-2011 period. However, it employs the Augmented-Dickey Fuller (ADF) and

Phillips-Perron (PP) stationarity bounds statistics tests. Then, it uses Pesaran bounds statistics

for testing the co-integrating relationships among variables, and eventually, the results of

stationarity and co-integration tests are used to analyze the long-run and short-run equilibrium

relationships among the variables. Results of ADF and PP tests show that the null hypothesis

of non-stationary cannot be rejected even at the 10% significance level in all cases except one

variable. More specifically, the variables IP, PPI, CPI, ER, M1, and M2 are stationary at the

upper bound, while the variable SMI is stationary at both the lower and upper bounds.

However, the results of pesaran bounds statistics reveal that all variables are co-integrated with

SMI except ER, and CPI. The results of stationarity tests and co-integration show the presence

of long-run and short-run equilibrium relationships between four macroeconomic variables and

SMI. In particular, IP and M1 are positively associated with SMI in the long-run, while PPI

and M2 are negatively associated. Additionally, IP and M2 are negatively associated with SMI

in the short-run, while PPI and M1 are positively associated.

The present study adds to the existing literature and focuses on the long-run and short-run

equilibrium relationships between macroeconomic variables and stock prices in the case of an

emerging stock market, Malaysia, rather than a mature stock market, such as the US or the UK,

which have been frequently studied in the past. Finally, the results of this paper are of

particular interest and importance to policy makers, financial economists, and investors dealing

with the Malaysian economy and the Malaysian stock market.

In fact, our results could lead to research questions that need to be answered. For instance,

further research could broaden this study by adding more variables that have significant

influences on stock prices such as oil prices. Furthermore, further research could broaden this

study by including more than one country to draw robust results, since the main limitation of

this study is the use of one country.

REFERENCES

Abugri, B.A. (2008). Empirical relationship between macroeconomic volatility and stock

returns: Evidence from Latin American markets. International Review of Financial

Analysis, 17, 396-410

Adjasi, K.D.C. (2009). Macroeconomic uncertainty and conditional stock-price volatility in

frontier African markets: Evidence from Ghana. The Journal of Risk Finance, 10(4),

333-49

Beltratti, A.& Morano, C. (2006). Breaks and Persistency: macroeconomic causes of stock

market volatility. Journal of Econometrics, 131, 151-77

Chen, N., Richard R. & Stephen, A.R. (1986). Economic forces and the stock market. Journal

of Business, 59(3), 383-403

Dickey, D. A. & Fuller, W.A. (1979). Distribution of the estimators for auto-regression time

series with a unit root. Journal of the American Statistical Association, 10, 291-321

Ghosh, D.K. & Ariff, M. (2004). Global financial markets, 1st edition, Greenwood Publishing

group, Inc, USA

Hanousek, J.& Kocenda, E. (2011). Foreign news and spillovers in emerging European stock

markets. Review of International Economics, 19(1), 170-88




Hatemi-J, A. &Morgan, B. (2009). An empirical analysis of the informational efficiency of

Australian equity markets. Journal of Economic Studies, 36(5), 437-45

Humpe, A. & Macmillan, P. (2009). Can macroeconomics variables explain long-term stock

market movements? A comparison of the US and Japan. Applied Financial Economics,

19(2), 111-19

Ibrahim, M.H. (1999). Macroeconomic variables and stock prices in Malaysia: an empirical

analysis. Asian Economic Journal, 13(2), 219-31

Ibrahim, M.H. & Aziz, H. (2003). Macroeconomic variables and the Malaysian equity market

a view through rolling subsamples. Journal of Economic Studies, 30, 6-27

Kizys, R. & Pierdzioch, C. (2009). Changes in the international co-movement of stock returns

and asymmetric macroeconomic shocks. Journal of International Financial Markets,

Institutions and Money, 19, 289-305

Liu, M. & Shrestha, K.M. (2008). Analysis of the long-term relationship between macro-

economy variables and the Chinese stock market using heteroscedastic co-integration.

Managerial Finance, 34(11), 744-55

Nguyen, T. (2011). US macroeconomic news spillover effects on Vietnamese stock market.

Journal of Risk Finance, 12(5), 1-16

Pal, K. & Mittal, R. (2011). Impact of macroeconomic indicators on Indian capital markets.

Journal of Risk Finance, 12(2), 84-97

Pan, M.H., Fok, R.C.W & Liu, Y.A. (2007). Dynamic linkages between exchange rates and

stock prices: Evidence from East Asian markets. International Review of Economics

and Finance, 16, 503-520

Pesaran, M.H.,Shin, Y. & Smith, R.J. (2001). Bounds testing approach to the analysis of level

relationships. Journal of Applied Econometrics. 16, 289-326

Phillips, P.C.B. & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika,

75, 335-46

Rjoub, H., Tursoy, T. & Gunsel, N. (2009). The effects of macroeconomic factors on stock

returrns: Istanbul stock market. Studies in Economics and Finance, 26(1), 36-45

Tsoukalas, D. (2003). Macroeconomic factors and stock prices in the emerging Cypriot equity

market. Managerial Finance, 29(4), 87-92

Verma, R. & Ozuna, T. (2005). Are emerging equity markets responsive to cross-country

macroeconomic movements? Evidence from Latin America. Journal of International

Finance Markets, Institutions and Money, 15, 73-87

Liquidity aspects of large corporate business: A study with reference to listed companies in India


LIQUIDITY ASPECTS OF LARGE CORPORATE BUSINESS: A STUDY WITH

REFERENCE TO LISTED COMPANIES IN INDIA

S.Chakraborty,

[email protected]

B.B.Sarkar

[email protected]

Indira Gandhi National Open University

Raveesh Krishnankutty

[email protected]

ICFAI University

Bhushan Chandra Das

[email protected]

M.B.B. College, Agartala, Tripura

ABSTRACT

The present study examines the determinants of liquidity of listed companies in India. The

analysis is based on data collected from 219 large companies of Bombay Stock Exchange 500

index. The study evaluates the determinants of liquidity by using current ratio as well as

quick ratio as the dependent variables. We found current assets to total assets, operating

profit margin and receivable days positively determine the level of liquidity. Payable days,

trade debtors to current assets, current liability to total assets and size of the firm negatively

determine the liquidity.

Keywords: liquidity, panel data, current ratio

INTRODUCTION

Liquidity is a prerequisite for the survival of any firm (Khan and Jain 2011). The major ratios

which indicate liquidity are current ratio (current assets divided by current liability) and quick

ratio or acid-test ratio (current assets minus inventories divided by current liability). There are

also some other ratios such as cash ratio and net working capital. Current ratio is a measure of

short term solvency. Current ratio of 2:1 is considered as an ideal ratio (Pandey, 2011; Khan

and Jain, 2011; Chandra, 2008). Current ratio of 2: 1 indicates that for every one rupee of

current liability the firm is having 2 rupees of current assets. However, in the case of the quick

ratio inventory is omitted from the current assets, because an asset is considered liquid only

when it can be converted into cash immediately without a loss of value (Pandey, 2011). A

ratio of 1:1 is considered as the ideal quick ratio.







Under the present economic condition, after the 2008 economic recession, liquidity has

become the major concern for all investors. Stiff competition in the market pushes the

companies to keep the current ratio and quick ratio as low as possible (Krishnankutty and

Chakraborty, 2011). Table 1 shows the average current ratios over the past 10 years from

2001-2010 based on the Bombay Stock Exchange sectoral classification. From the table it is

evident that except for agriculture and healthcare all the selected large corporate sectors as

well as the sample taken as a whole, for most of the stated periods they are below the ideal

ratio of 2:1. Even for sectors such as FMCG , Metal & metal products & mining, Oil & gas

and Transport equipment, all have current ratios that are much lower than the sample taken as

a whole.

Table 1: Sectoral Average of Current Ratio

Sectors 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Total Sample as a whole 1.50 1.44 1.37 1.26 1.29 1.34 1.39 1.45 1.38 1.39

Agriculture 2.27 2.19 2.00 2.14 1.83 1.96 2.26 1.90 1.53 1.98

Capital Goods 1.68 1.60 1.53 1.47 1.51 1.49 1.42 1.39 1.38 1.33

Chemical & petro

chemical 1.65 1.54 1.54 1.43 1.49 1.46 1.62 1.45 1.30 1.24

FMCG 1.15 1.15 1.10 1.01 1.09 1.13 1.24 1.25 1.41 1.24

Healthcare 2.54 2.54 2.33 2.23 2.31 2.64 2.49 2.22 1.91 1.96

Housing related 1.60 1.55 1.36 1.49 1.32 1.52 1.75 2.15 2.14 2.18

Metal & metal products

& mining 1.15 1.10 0.97 0.99 1.29 1.49 1.81 1.65 1.60 1.74

Miscellaneous 1.55 1.51 1.44 0.63 0.60 1.42 1.79 1.56 1.71 1.74

Oil & gas 1.51 1.37 1.24 1.29 1.21 1.20 1.18 1.38 1.20 1.23

Power 1.96 2.32 2.83 1.72 1.94 1.73 1.61 1.52 1.51 1.56

Transport equipment 1.63 1.44 1.30 1.09 1.18 1.22 1.21 1.08 1.14 0.96

RATIONALE OF THE STUDY

Ratio analysis is a powerful tool of financial analysis (Pandey, 2011, Khan and Jain, 2011).

Financial ratios are the major tool used for evaluating a firm‟s financial condition and

performance (Van Horne et.al, 2008). According to the various users of financial ratios they

are mainly classified into four categories namely liquidity ratios, leverage ratios, activity

ratios and profitability ratios. Liquidity measures a firm‟s ability to meet the current

obligations, leverage ratio shows the liquidity equity proportion in capital structure, activity

ratios shows the efficiency in utilizing the assets and profitability measures the overall

performance and effectiveness of a firm (Pandey, 2011).

Opler et al. (1999) examine the determinants and implication of cash and marketable

securities of publically traded U.S firms. The study found that firms with strong growth

opportunity and riskier cash flows hold relatively high cash to total non-cash assets. And

firms that are having high credit ratings hold lower ratios of cash to total non-cash assets.

Banerjee (2010) said that the liquidity position of a firm is largely affected by the composition

of current assets inasmuch as any considerable shift from relatively more current assets to



relatively less current assets or vice versa. Therefore it is desirable to study the distribution of

current assets to determine the exact liquidity position. Krishnankutty and Chakraborty (2011)

examine the trend and determinants of current ratios of listed companies in India using panel

data with fixed and random effect. The study found that current ratio is showing a negative

trend in the last decade. Receivable days, payable days, inventory days and size of the firm

are the major determinant of current ratio. Chakraborty (2003) says that liquidity considers

two aspects namely the level of investment in current assets and the sources of financing of

the current assets.

There are quite a number of studies on liquidity and profitability trade-off, liquidity in the

aspect of investment and etc. Based on this background, the present study is designed. The

objective of the study is to understand the determinants of liquidity and to study the level of

liquidity in large corporate businesses of India.

DATA SOURCE AND METHODOLOGY

Source of data

This study focuses on the public limited companies listed in the Bombay Stock Exchange

(BSE) 500 index. The period considered for the study is ten years i.e., 2001 – 2010. Banking,

finance and IT companies are kept out of the scope of the study as the current assets and

liabilities structure of these companies are different from others. Moreover companies with

non-availability of data for the entire study period are also kept out of the scope of the study

for a more meaningful interpretation and comparison. Thus the final total numbers of

companies considered in the present study is 219. The CMIE (Centre for Monitoring Indian

Economy) database is used for collecting the financial data.

Variables used for the study

The study used two dependent variables as proxy for measuring liquidity in order to check the

sensitivity of the result- Current ratio (CR) and Quick ratio (LQ).

Current ratio (CR) = Current assets/ Current liability

Liquidity ratio (LQ) = (current assets- inventories)/ current liability

Independent variables used for the study are as follows:

Receivable days (ARDAYS) = (Accounts receivable X 365)/ Sales.

Payable days (APDAYS) = (Accounts payable X 365)/ Sales

Inventory turnover (INVTURN) = Sales/ Inventory

Size of the firm (SIZE) = Natural logarithm of sales

Asset turnover (ASSTRN) = Sales/ Total assets

Current assets to total assets (CATA) = Current assets/ Total assets

Current liability to total assets (CLTA) = Current liability/ Total assets

Operating profit margin (OPEM) = PBIT/Sales

Trade debtors to current assets (SDCA) = Trade debtors/ Current assets



Panel least square with fixed and random effect

The study used balanced panel data for the analysis. A data set contains observations on

different objects studied over a period of time and this is called panel data. It is a combination

of cross-sectional data and time series data. In balanced panel data same time period must be

available for all cross-sections.

To analyse the liquidity, the study proposes the panel least square with fixed and random

effects. For assessing the relationship between liquidity and its determinants static panel data

models are used. There are three types of panel data models: a pooled Ordinary Least Squire

(OLS) regression, panel model with random effects and the panel model with fixed effects.

The evaluation of a pooled OLS regression can be presented in the following way:

)1......(,.........)()()()(

)()()()()(

987

543210

ititititit

itititititit

CLTASDCAOPEMCATA

SATAINVTURNSIZEAPDAYSARDAYCR

)2......(,.........)()()()(

)()()()()(

987

543210

ititititit

itititititit

CLTASDCAOPEMCATA

SATAINVTURNSIZEAPDAYSARDAYLQ

Where i indexes firms, t indexes time,β1, β2, β3……….. β9 are the coefficients of

independent variables. CRit is the current ratio while LQit is quick ratio. Both are measures for

liquidity. ARDAYSit is receivable days, APDAYS it is payable days, INVTURNit is inventory

turnover, SIZEit is size of the firm, SATAit is asset turnover, CATAit is the current assets to

total assets, CLTAit is current liability to total assets, OPEMit is operating profit margin,

SDCA is trade debtors to current assets and it is the error term which is assumed to have a

normal distribution and varies over both company and time. However, by using a pooled OLS

regression, firms‟ unobservable individual effects are not controlled, and so, as Bevan and

Danbolt (2004) conclude, heterogeneity, a consequence of not considering those effects, can

influence measurements of the estimated parameters. By using panel models of random or

fixed effects, it is possible to control the implications of firms‟ non-observable individual

effects on the estimated parameters. Therefore, by considering the existence of non-

observable individual effects, we have:

)3......(,.........,)()()()(

)()()()()(

987

543210

ititititit

itititititit

uCLTASDCAOPEMCATA

SATAINVTURNSIZEAPDAYSARDAYCR

)4......(,.........,)()()()(

)()()()()(

987

543210

ititititit

itititititit

uCLTASDCAOPEMCATA

SATAINVTURNSIZEAPDAYSARDAYLQ

where ,itiitu with i being firms‟ unobservable individual effects. The difference

between a polled OLS regression and a model considering unobservable individual effects lies

precisely in i .



To test the relevance of unobservable individual effects, the study used the LM (Lagrange

Multiplier) test. This tests the null hypothesis of irrelevance of unobservable individual

effects against the alternative hypothesis of relevance of unobservable individual effects. Not

rejecting the null hypothesis, we conclude that unobservable individual effects are not

relevant, and so a pooled OLS regression would be an appropriate way of carrying out

evaluation of liquidity determinants. On the contrary, if we reject the null hypothesis that

unobservable individual effects are not relevant, we can conclude that a pooled OLS

regression is not the most appropriate way of carrying out analysis of the relationship between

liquidity and its determinants.

However, there may be correlation between firms‟ unobservable individual effects and

liquidity determinants. If there is no correlation between firms‟ unobservable individual

effects and liquidity determinants, the most appropriate way of carrying out evaluation is by

using a panel model of random effects. If there is correlation between firms‟ individual effects

and liquidity determinants, the most appropriate way of carrying out evaluation is using a

panel model admitting the existence of fixed effects. For testing the possible existence of

correlation, we use the Hausman test. This tests the null hypothesis of non-existence of

correlation between unobservable individual effects and the explanatory variables, in this

study, liquidity determinants, against the null hypothesis of existence of correlation. By not

rejecting the null hypothesis, we can conclude that correlation is not relevant, and a panel

model of random effects is the correct way of carrying out evaluation of the relationship

between liquidity and its determinants. On the other hand, by rejecting the null hypothesis, we

conclude that correlation is relevant, and so the most appropriate way to carry out evaluation

of the relationship between liquidity and its determinants is by using a panel model of fixed

effects. In this study, we also present the evaluation of the most appropriate panel model,

according to the results of the LM and Hausman tests which is consistent with the existence of

first order autocorrelation.

Quantile regression analysis

Quantile regression (Koenker and Bassett 1978; Koenker and Hallock 2001) is a method for

fitting a regression line through the conditional quantiles of a distribution. It allows the

examination of the relationship between a set of independent variables and the different

parts of the distribution of the dependent variable. Quantile regression overcomes some of

the disadvantages of the conditional mean framework built upon central tendencies, which

tend to lose information on phenomena whose tendencies are toward the tails of a given

distribution (Hao and Naiman 2007). The use of quantile regression approach is chosen also

because of skewed distribution of CR, LQ, ARDAYS, APDAYS, SIZE, INVTURN, SATA,

CATA, CLTA, OPEM, and SDCA (see the evidence in Table 2). Since in such case the

usual assumption of normally distributed error terms is not warranted and could lead to

unreliable estimates. Furthermore, companies analyzed are fundamentally heterogeneous

and it may make little sense to use regression estimators that implicitly focus on the

„average effect for the average company‟ by giving summary point estimates for

coefficients. Instead, quantile regression techniques are robust to outliers and are able to

describe the influence of the regressors over the entire conditional distribution of CR, LQ,

ARDAYS, APDAYS, SIZE, INVTURN, SATA, CATA, CLTA, OPEM, and SDCA.



Standard least squares regression techniques provide summary point estimates that calculate

the average effect of the independent variables on the „average company‟. However, this

focus on the average company may hide important features of the underlying relationship.

As Mosteller and Tukey (1977, pp.266) correctly argued, “What the regression curve does is

give a grand summary for the averages of the distributions corresponding to the set of x‟s.

We could go further and compute several regression curves corresponding to the various

percentage points of the distributions and thus get a more complete picture of the set.

Ordinarily this is not done, and so regression often gives a rather incomplete picture. Just as

the mean gives an incomplete picture of a single distribution, so the regression curve gives a

correspondingly incomplete picture for a set of distributions. Quantile regression techniques

can therefore help us obtain a more complete picture of the underlying relationship between

liquidity (CR, LQ) and its determinants. In our case, estimation of linear models by quantile

regression may be preferable to the usual regression methods for a number of reasons.

While the optimal properties of standard regression estimators are not robust to modest

departures from normality, quantile regression results are characteristically robust to

outliers and heavy tailed distributions. In fact, the quantile regression solution 0̂ is

invariant to outliers of the dependent variable that tend to (Buchinsky, 1994). Another

advantage is that, while conventional regressions focus on the mean, quantile regressions

are able to describe the entire conditional distribution of the dependent variable. In the

context of this study, all determinants of liquidity (CR, LQ) are of interest in their own right,

we do not want to dismiss them as outliers, but on the contrary we believe it would be

worthwhile to study them in detail. This can be done by calculating coefficient estimates at

various quantiles of the conditional distribution. Finally, a quantile regression approach

avoids the restrictive assumption that the error terms are identically distributed at all points

of the conditional distribution. Relaxing this assumption allows us to acknowledge company

heterogeneity and consider the possibility that estimated slope parameters vary at different

quantiles of the conditional distribution of all determents of liquidity.

The quantile regression model, first introduced by Koenker and Bassett (1978), can be

written as:

ititit xy 0

'

with 0

'| ititit xxyQuant (5)

where i denotes company, t denotes time, ity is the dependent variable, itx is a vector of

regressors, is the vector of parameters to be estimated, and is a vector of residuals. itit xyQuant | denotes the

th conditional quantile of ity given itx . The th regression

quantile ,10 solves the following problem:

n

i

it

xyti

itit

xyti

ititn

xyxyn

itititit1:,

'

:,

' 1min||)1(||

1min

''

(6)

where )( , which is known as the „check function‟, is defined as”:

0)1(

0)(

itit

itit

itif

if

(7)



Equation (6) is then solved by linear programming methods. As one increases

continuously from 0 to 1, one traces the entire conditional distribution of ity , conditional on

itx (Buchinsky 1998).

Here we assume that CR and LQ is the function of ARDAYS, APDAYS, SIZE, INVTURN,

SATA, CATA, CLTA, OPEM, and SDCA. Due to the advantages (as stated above) of

quantile regression estimation technique over OLS, fixed and random effect models in the

study, we examined at the 5th

, 25th

, 50th

, 75th

and 95th

quantiles as shown here for first and

second specifications respectively:

it

it

CLTASDCAOPEMCATASATA

INVTURNSIZEAPDAYSARDAYSCRQ

05.5,05.5,05.5,05.5,05.5,05.

4,05.3,05.2,05.1,05.05.05. )(

(8)

it

it



25.5,25.5,25.5,25.5,25.5,25.

4,25.3,25.2,25.1,25.25.25.. )(

(9)

it

it



5.5,5.5,5.5,5.5,5.5,5.

4,5.3,5.2,5.1,5.5.5. )(

(10)

it

it



75.5,75.5,75.5,75.5,75.5,75.

4,75.3,75.2,75.1,75.75.75. )(

(11)

it

it



95.5,95.5,95.5,95.5,95.5,95.

4,95.3,95.2,95.1,95.95.95. )(

(12)

it

it


INVTURNSIZEAPDAYSARDAYSLQQ

05.5,05.5,05.5,05.5,05.5,05.

4,05.3,05.2,05.1,05.05.05. )(

(13)

it

it



25.5,25.5,25.5,25.5,25.5,25.

4,25.3,25.2,25.1,25.25.25.. )(

(14)

it

it



5.5,5.5,5.5,5.5,5.5,5.

4,5.3,5.2,5.1,5.5.5. )(

(15)

it

it



75.5,75.5,75.5,75.5,75.5,75.

4,75.3,75.2,75.1,75.75.75. )(

(16)

it

it



95.5,95.5,95.5,95.5,95.5,95.

4,95.3,95.2,95.1,95.95.95. )(

(17)



We used sqreg module of STATA 11 for simultaneous quantile regression estimation and

obtain an estimate of the entire variance-covariance of the estimators by bootstrapping with

100 bootstrap replications. Simultaneous quantile regression is a robust regression technique

that accounts for the non-normal distribution of error terms and heteroskedasticity (Koenker

and Bassett 1978; Koenker and Hallock 2001). Unlike traditional linear models, such as

OLS regression, that assume that estimates have a constant effect, simultaneous quantile

regression can illustrate if independent variables have non-constant or variable effects

across the full distribution of the dependent variable.

RESULTS

Panel least square with fixed and random effect

Before conducting regression analysis, correlation analysis was carried out in order to find out

whether there is any evidence of severe multicollinearity among the test variables. Since we

do not find evidence of multicollinearity, regression analysis has been carried out with

incorporation of all variables simultaneously. First, we present the results of the static panel

model analysis. Results of panel data models with random and fixed effects have been

presented in Table 2.

Table 2: The Result of Panel Least Square with Fixed and Random Effects

Independent

variable

Model 3 Model 4

FE RE FE RE

ARDAYS .001359***

(0001807)

.001340***

(.0001701)

.0007274***

(.0001623)

.0007693***

(.0001509)

APDAYS -.0025031***

(0004036)

-.0028219***

(.0003958)

-.0008163**

(.0003625)

-.0009949***

(.0003529)

SIZE -.0275186

(.0232609)

-.0424685**

(.0195117)

.0358867*

(.0208895)

.0268993

(.0170912)

INVTURN .0006654

(.0004277)

.0008435**

(.0004218)

.0013438***

(.0003841)

.0014444***

(.0003763)

SATA -.1002509**

(.0463167)

-.1113813***

(.0403838)

-.1999969***

(.0415948)

-.1789142***

(.0355039)

CATA 2.483092***

(.1596137)

2.009579***

(.139276)

1.540021***

(.1433415)

1.161519***

(.1225136)

OPEM .0746823

(.0582761)

.0713454

(.0536048)

.1482225***

(.052335)

.1451047***

(.0474001)

SDCA -.8604573***

(1493588)

-.8202655***

(.1396742)

-.5290588***

(.134132)

-.4320049***

(.1237661)

CLTA -.2818231***

(.0729365 )

-.3506192***

(.065621)

-.013249

(.0655008)

-.0932999

(.0578927)

Constant 1.191029***

(.2016914)

1.556322***

(.1838392)

.2842534

(.1811294)

.4926345***

(.1612464)

Model Summary

R2 with in 0.2178 0.2137 0.1420 0.1378

R2 between 0.0664 0.0975 0.0241 0.0470

R2 overall 0.1101 0.1342 0.0576 0.0776

F- test 60.70*** 36.07***



Fixed effect, F-test 11.97*** 10.24***

Wald test 527.91*** 305.08***

Hausman test 79.49*** 59.87***

No.of firms 219 219 219 219

Total panel

observation

2190 2190 2190 2190

Dependent variable Current ratio Current ratio Quick ratio Quick ratio

Notes:

1. The Hausman test has χ2 distribution and tests the null hypothesis that unobservable individual effects are not

correlated with the explanatory variables, against the null hypothesis of correlation between unobservable

individual effects and the explanatory variables.

2. The Wald test has χ2 distribution and tests the null hypothesis of insignificance as a whole of the parameters of

the explanatory variables, against the alternative hypothesis of significance as a whole of the parameters of the

explanatory variables.

3. The F test has normal distribution N(0,1) and tests the null hypothesis of insignificance as a whole of the

estimated parameters, against the alternative hypothesis of significance as a whole of the estimated parameters.

4. ***, **, and *denote significance at 1, 5 and 10 % level of significance respectively.

5. FE and RE denote fixed effect and random effect respectively.

From the analysis of the results of the Wald and F tests, we can conclude that we cannot reject

the null hypothesis that the explanatory variables do not explain. Taken as a whole, the

explained variable and so the determinants selected in this study can be considered

explanatory for both the model.

The results of the Hausman test show that we cannot reject the null hypothesis in absence of

correlation between firms‟ unobservable individual effects and debt determinants. Therefore,

we can conclude that the most appropriate way to carry out evaluation of the relationship

between debt and its determinants is evaluation of a fixed effects panel model. So the study

will interpret the result based on the fixed effect model in both models.

Receivable days (ARDAYS) and current assets to total assets (CATA) are positively

significant at 1 percent in both models. However payable days (APDAYS), trade debtors to

current assets (SDCA) and sales to total assets (SATA) are negatively significant at 1 percent,

5 percent and 1 percent respectively for model 3, and 5 percent, 1 percent, and 1 percent

respectively in the case of model 4. Size (SIZE), inventory turnover (INVTURN), and

operating profit margin (OPEM) are not showing any kind of significance for model 3. But in

the case of model 4 all variables are positively determined the liquidity at 10 percent, 1

percent and 1 percent respectively. Current liability to total assets (CLTA) is negatively

significant at 1 percent in the case of model 3 and is not showing any kind of significance for

model 4.

Quantile regression

First, we present descriptive statistics of all our variables as shown in Table 3.



Table 3: The Result of Descriptive Statistics

Mean

Media

n

Std.

Dev.

Skewnes

s

Kurtosi

s

Jarque-

Bera

Probabilit

y

Observation

s

CR 1.66 1.40 1.09 4.35 37.26 114016.50 0.00 2190

QR 0.91 0.71 0.89 5.28 53.86 246242.40 0.00 2190

ARDAYS

119.6

3 81.25 189.79 11.36 203.02 3697717.00 0.00 2190

APDAYS 54.06 44.55 50.36 11.86 311.11 8714126.00 0.00 2190

SIZE 6.95 6.88 1.55 0.26 4.24 164.33 0.00 2190

INVTUR

N 13.05 7.30 45.00 20.76 572.23 29724637.00 0.00 2190

SATA 1.01 0.87 0.72 2.41 14.97 15200.36 0.00 2190

CATA 0.49 0.48 0.22 0.18 2.17 75.12 0.00 2190

CLTA 0.28 0.24 0.17 0.92 3.44 326.97 0.00 2190

OPEM 0.24 0.15 0.62 14.20 255.57 5894525.00 0.00 2190

SDCA 0.46 0.46 0.19 0.10 2.63 16.83 0.00 2190

Table 4 shows one measures of tails i.e. the kurtosis among other descriptive statistics. It is

well known that whenever this quantity exceeds 3, we can conclude that the data feature

excess kurtosis, or that their distribution is leptokurtic i.e. it has heavy tails. It is evident

from Table 1 that except for CATA, CLTA and SDCA distribution of all variables is

leptokurtic. This shows that data is not normal which is also proved with the JB test statistic.

JB test statistics shows, in particular, that no variables have the feature of normality.

Therefore, estimation technique (like OLS) based linear Gaussian models will be biased and

the use of quantile regression estimation is more appropriate. Therefore, the study applied

quantile regression estimation technique and reported result of quantiles }95.0,75.0,50.0,25.0,05.0{ is available in Table 4 below.

Table 4: The Result of Quantile Regression

Variable/Quantile 0.05 0.25 0.50 0.75 0.95

ARDAYS -.0000148

(.0001336)

.0000458

(.000141)

.0006652*

(.0004027)

.0024982***

(.0009622)

.0064441***

(.0009615)

APDAYS -.0003102

(.0002405)

-.0008817***

(.000244)

-.0009839*

(.000502)

-.0055724 ***

(.0017402)

-.0106295**

(.0042633)

SIZE -.0116334*

(.0065733)

-.0100971*

(.0055473)

-.014754*

(.0083752)

-.1070995***

(.0266563)

-.2496088***

(.0331077)

INVTURN .0004743

(.0009316)

.0006844

(.0005639)

.0015268 **

(.0006534)

.0010218

(.0011177)

.0000451

(.0017497)

SATA -.0200322

(.0227599)

-.0067823

(.0153479)

.0046304

(.0220382)

-.052079

(.0571485)

-.1080383

(.0694277)

CATA 1.284888***

(.0750841)

1.602161***

(.0804146)

1.709484***

(.137283)

1.104016***

(.4100967)

-.1620684

(.2606661)

OPEM -.02974

(.0345951)

.0267738

(.059165 )

.0665217

(.1426374)

.7173953

(.4456597)

1.343607***

(.4033663)

SDCA -.1734254***

(.0659588)

-.2134072***

(.0608423)

-.2768251***

(.0980112)

-.8202248***

(.2841327)

-2.801899***

(.332155)

CLTA -.7836924***

(.0911939)

-1.494018***

(.1094826)

-2.037849***

(.209865 )

-1.064997

(.776718 )

-.1649004**

(.0709893)

constant .7441981***

(.0775229)

1.07581***

(.0776037)

1.375898***

(.0880631)

2.686525***

(.3909906)

5.817202***

(.4222078)

Model summary

Pseudo R2 0.2463 0.1963 0.1605 0.1463 0.2447



Dependent variable : Current ratio(CR)

Note: ***, **, and *denote significance at 1, 5 and 10 % level of significance respectively.

It is evident from Table 4 that receivable days (ARDAYS) is not showing a significant level

for 0.05th

and 0.25th

quantile but in the case of 0.50th

, 0.75th

and 0.95th

quantiles, they are

positively significance at 10 percent, 1 percent and 1 percent respectively. Except for the

lowest quantile of 0.05th

, payable days (APDAYS) is showing a negative significant in all

other quantiles with 1 percent, 10 percent, 1 percent and 5 percent respectively. Size of the

firm (SIZE) is showing a negative significance irrespective of the quantile with 10 percent,

10 percent, 10 percent, 1 percent and 1 percent respectively. Inventory turnover (INVTURN)

is showing a positive significance at 5 percent only in the case of median quantile (0.50)

and all other cases are not showing any significance. Sales to total assets (SATA) is not

showing any kind of significance irrespective of the quantile. Except for the highest quantile

i.e. 0.95th

, all other cases of current assets to total assets (CATA) is positively significant at

1 percent. Operating profit margin (OPEM) is positively significant at 1 percent only in the

highest quantile 0.95th

while all other cases are not showing significance result. Trade

debtors to current assets (SDCA) are negatively significant at 1 percent irrespective of the

quantiles. Constant is positively significant at 1 percent irrespective of the quantiles.

Table 5: The Result of Quantile Regression

Variable/Quantile 0.05 0.25 0.50 0.75 0.95

ARDAYS .0001704**

(.000082 )

.0000507

(.0001502)

.0002138

(.0001957)

.001535***

(.0005653)

.0050529***

(.0008062)

APDAYS -.0001728

(.0001936)

-.0006685***

(.0002424)

-.0009442**

(.0004246)

-.003045***

(.0008896)

-.001586

(.0024815)

SIZE -.0082464**

(.0038967)

.0019985

(.0047509)

-.009198

(.0077416)

-.0125959

(.0155781)

.0506542

(.0575565)

INVTURN -.0002293

(.0003509)

-.0000676

(.0007831)

.0019822

(.0014558)

.0032103**

(.0015054)

.002015

(.0029973)

SATA .0486478***

(.0108099)

.0227531*

(.0124971)

-.0061727

(.0186605)

-.0776478***

(.029639)

-.2121861***

(.0576296)

CATA .270684***

(.0517543)

.5389883***

(.0660343)

.5342399***

(.1249824)

.449313**

(.1766196)

.2226931

(.2812334)

OPEM .0649691*

(.0366213)

.1630878***

(.0405958)

.2647922***

(.0667938)

.1387364***

(.1749885)

1.296822*

(.6808432)

SDCA .3295429***

(.0533951)

.6128637***

(.044715)

.4985588***

(.0661216)

-.2017263

(.1540333)

-1.510364***

(.3855738)

CLTA -.0894916

(.0565402)

-.2570917***

(.0872533)

-.3542804*

(.1959924)

-.0890996

(238902)

-.2188621***

(.0735277)

constant .0023406**

(.0437993)

-.0165878

(.0475904)

.3377901***

(.0888365)

1.055545***

(.1523291)

1.925666***

(.5318802)

Model summary

Pseudo R2 0.0564 0.0836 0.0622 0.0671 0.1707

Dependent variable : Quick ratio (LQ)

Note: ***, **, and *denote significance at 1, 5 and 10 % level of significance respectively.

Table 5 shows the result of quantile regression for the Quick ratio as the dependent variable.

Receivable days (ARDAYS) are positively significant at 5 percent for the lowest quantile of

0.05th

while 0.25th

and 0.50 are not showing any significance. In the case of 0.75th

and 0.95th,

both are positively significant at 1 percent. In the case of payable days (APDAYS), the lowest

quantile 0.05th

and the highest quantile 0.95th

are not significant. All other cases i.e., 0.25th

,



0.50th

and 0.75th

, they are negatively significant at 1 percent, 5 percent and 10 percent level

respectively. However size of the firm (SIZE) is negatively significant at 5 percent only for

the lowest quantile. All other cases are not showing any kind of significance. Inventory

turnover (INVTURN) is not showing any significance except for the 0.75th

quantile. For this

quantile, it is positively significant at 5 percent. Sales to total assets (SATA) are positively

significant at 1 percent and 10 percent for 0.05th and 0.25th quantiles respectively. 0.50th

quantile is not showing any significance and in the case of 0.75th

and 0.95th

quantiles, they are

negatively significant at 1 percent. Except for the highest quantile 0.95th

, current assets to

total assets (CATA) is positively significant at 1 percent, 1 percent, 1 percent and 5 percent

respectively. Operating profit margin (OPEM) is the only one variable showing positive

significance irrespective of the quantiles at 10 percent, 1 percent, 1 percent, 1 percent and 10

percent respectively. Trade debtors to current assets (SDCA) are positively significant at 1

percent for 0.05th

, 0.25th

and 0.50th

quantile. 0.75th

quantile is also not showing significance

value. For the highest quantile i.e. 0.95th

, it is negatively significant at 1 percent. Current

assets to total assets (CLTA) is negatively significant for 0.25th

, 0.50 and 0.95th

quantile at 1

percent, 10 percent and 1 percent respectively. In the case of constant, except for 0.25th

quantile, all other cases are positively significant at 1 percent.

CONCLUSION

The study intends to identify the determinants of liquidity for Indian firms using a panel

framework. The study has taken current ratio as well as quick ratio as dependent variable in

checking the sensitivity of the ratios. For the analysis, we have taken 219 firms (from the

BSE 500 firms based on the availability of data) during the period 2001-2010, comprising a

panel model with fixed and random effects. However, most of the variables show skewed

distribution and therefore, we relied upon quantile regression analysis as an appropriate tool

and quantiles used for our case are }95.0,75.0,50.0,25.0,05.0{ .

We found that the fixed and random effect model are not performing well. The overall

study find that Receivable days are positively determined the liquidity in the case of upper

quantiles (0.75th

, 0.95th

). However the payable days are negatively determining the liquidity

of the lower quarter to upper quarter quantiles (0.25th

- 0.75th

). Size of the firm is

negatively determining the liquidity only for the lowest quantile (0.05th

). Inventory turnover

does not have any impact on determining the liquidity. In case of sales to total assets we are

unable to draw any kind conclusion because of the un-common result in both models.

Current assets to total assets are positively determining the liquidity except for the upper

quantile (0.95th)

. Operating profit margin is positively determining the liquidity in upper

quantile (0.95th

). Trade debtors to current assets negatively determine the liquidity in upper

quantiles (0.75th

, 0.75th

). In the case of lower quantile, it is showing a significance value in

opposite signs. Current liability to total assets negatively determines the liquidity in the

case of 0.25th

, 0.50th

and 0.95th

quantile.

REFERENCES

Bevan, A.A. and Daubolt, J. (2001) „Testing for inconsistencies in the estimation of UK

capital structure determinants‟, Working Paper, No. 2001/4, Department of

Accounting and Finance, University of Glasgow, Glasgow G 12 *LE



Buchinsky, M. (1998). Recent advances in quantile regression models: a practical guide for

empirical research. Journal of Human Resources, 33, 88-126

Banerjee, B. (2010) Financial policy and management accounting. 7th

Edition. New Delhi:

PHI Learning Pvt.Ltd

Chandra, P. (2008) Financial management theory and practice. 7th

Edition. New Delhi: Tata

McGraw Hill Publish Company Limited. 7th

Edition

Hao, L. and Naiman, D.Q. (2007). Quantile regression. Thousand Oaks, CA: Sage

Khan, M.Y. and Jain, P.K. (2011) Financial management text problems and cases. 6th

Edition. New Delhi: Tata McGraw Hill Publish Company Limited

Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46, 33–50

Koenker R, Hallock K.F. (2001). Quantile regression. J Econ Perspect, 15, 143–156

Krishnankutty, R & chakaraborty, K. S (2011). Determinants of current ratio. MPRA working

paper

Chakraborty, K.S.(2003). Anatomy of overtrading. New Delhi: Mittal publications

Mosteller, F., & Tukey, J. (1977). Data Analysis and Regression Addison-Wesley, Reading,

MA

Pandey, I.M. (2010). Financial management. 10th

Edition. New Delhi: Vikas publishing

house

Opler. T, Pinkowitz. L, Stulz. R and Williamson. R. (1999). The determinants and

implications of corporate cash holdings, Journal of financial economics, 52, 3-46

An empirical study on directors’ remuneration in relation to corporate performance: A comparison between GLCs and Non-GLCs in Malaysia


AN EMPIRICAL STUDY ON DIRECTORS’ REMUNERATION IN RELATION TO

CORPORATE PERFORMANCE: A COMPARISON BETWEEN GLCs AND NON-

GLCs IN MALAYSIA

Lee Seng Fatt

[email protected]

Mohammad Izzat Amir Abdul Ghani

Adrian A/L Alphonsus

Prasanth Nair A/L Sasedharan

Universiti Tenaga Nasional

ABSTRACT

The capital of the Government-linked Companies (GLCs) and Non-GLCs is structured

differently whereby GLCs are companies with government having a direct controlling stake,

which gives the government the right to appoint a board of directors, top management and to

make major decisions. This study examines the relationship between directors‟ remuneration

and corporate performance in both GLCs, and Non-GLCs in Malaysia. It aims to assess how

differently directors in GLCs, and Non-GLCs are rewarded. This study finds a significant

relationship between profit after tax and directors‟ remuneration for both GLCs and Non-

GLCs. The results also provide evidence that there is a significant difference between GLCs

and Non-GLCs‟ directors‟ remuneration.

Keywords: government-linked companies, non-government-linked companies, directors’

remuneration, corporate performance, correlation and relationship

INTRODUCTION

Considerable attention on issues of directors‟ remuneration has been given by local and

foreign researchers. The focus was generally directed on the overall level of directors‟

remuneration, suitability of performance measures linking directors‟ remuneration with

performance and also the role played by the remuneration committee in the setting of

directors‟ remuneration. The perception that directors often rewards themselves with a huge

remuneration package in relation to their effort to boost the corporate performance has fuelled

further interest.

In Malaysia, under its Code of Corporate Governance 2000, rewards should be structured to

link to corporate and individual performance. In addition, directors in Malaysia are to be

guided by the Company Directors‟ Code of Ethics to achieve the objective of ensuring proper

behavior and ethical conduct of directors. The Code of Ethics is concerned with transparency,

integrity, accountability and corporate social responsibilities of company directors. Both of

these Codes are in place for creating and sustaining a healthy investment climate.

Company in Malaysia can generally be classified either as a Government-Linked Company

(GLC) or a Non Government-Linked Company (Non-GLC). The capital of GLCs and Non-


An empirical study on directors‟ remuneration in relation to corporate performance: A comparison between

GLCs and Non-GLCs in Malaysia


GLCs is structured differently whereby GLCs are companies with government having their

direct controlling stake, which gives the government the right to appoint the board of directors,

top management and to make major decisions such as restructuring, financing and

acquisitions. Generally, a GLC is defined as a company in which the government owns a

majority ownership in it. Rahman and Najid (2011) defined a GLC as a company that has a

primary commercial objective and the government has a direct controlling stake through

institutions such as Khazanah, the Ministry of Finance (MOF), Kumpulan Wang Amanah

Pencen (KWAP), and Bank Negara Malaysia (BNM). This definition also extends to

companies in which the GLCs themselves have a controlling stake. A Non-GLC would be

defined as a company that has a majority ownership by individuals or private companies

whereby the government has only a minority ownership or none at all and is not related to any

of the GLCs.

This study aims to examine and compare the relationship between these two types of

companies based on information disclosed in their annual audited financial statements.

LITERATURE REVIEW

Jensen and Meckling (1976) described an agency relationship, as a contract under which one

party (the principal) engages another party (the agent) to perform some services on the

principal‟s behalf. Under the contract, the principal delegates some decision-making authority

to the agent. This would, however, trigger the agent‟s self-interests (e.g., in job protection or

career advancement) to influence their management decisions. Many researchers dealing with

directors‟ remuneration and corporate performance have their results revolved around the

Agency Theory. As stated by Murphy (1999), directors‟ remuneration is designed to align

with the interests of executives and shareholders of the company in order to minimize agency

cost. Therefore, one way to do so is to make managers‟ remuneration a function of firm

performance.

Many research studies have been done in the US and Europe in the past to assess the

relationship between directors‟ remuneration and corporate performance. Their findings

indicated a significant relationship does exist between the two variables in the USA (Mehran,

1995; Murphy, 1985) and Europe (Duffhues and Kabir, 2008; Fernandes, 2008). In Asia, most

of the research done (Unite, 2008; Firth, 2006; Kato, 2007; Mitsudome, 2008) also found a

significant relationship exists between the two variables. Previous studies by Merhebi et al.

(2006) and Firth & Cheng (2005) also reported a significant relationship between directors‟

remuneration and return on equity.

Murphy (1999) mentioned four basic components of directors‟ remuneration. They are base

salary, an annual bonus tied to accounting performance, stock options, and long-term

incentive plans, including restricted stock option plans and multi-year accounting-based

performance plans. Abugu (2012) emphasized the need to hold directors accountable to

shareholders for remuneration received. He drew the attention of scholars, law reformers and

law enforcement agencies to the inadequacies of the rules regulating directors‟ remuneration

packages.

Executives have incentives to make major corporate decisions and report income which

will affect ROA and also their remuneration. Mehran (1995) and Kato and Kubo (2006)




found that there is a relationship between ROA with directors‟ remuneration. The analysis

also found that directors‟ pay is significantly related to shareholder returns but the estimated

elasticity is small (Conyon & Gregg (1994).

Murphy (1985) conducted a research to re-examine the relationship between firms‟

performance and managerial pay, using data that focused on individual directors over

time and found that directors‟ remuneration is related to firms‟ performance. Mehran (1995)

found that remuneration structures of firms with more non-executive directors have a

higher percentage of their executive remuneration in an equity-based form. Mehran

(1994) findings also suggested that directors‟ remuneration is significantly related to the firms‟

performance.

Meanwhile, Brunello, Graziano and Parigi (2001) found that managerial pay is significantly

affected by firms‟ performance. In particular, they estimated that an increase of real profit per

firm will increase the pay of upper and middle rank directors more than the increase found for

lower management. Importantly, they also found that the sensitivity of pay to performance is

higher when the firm belongs to a multinational group, which is owned by foreign capital and

listed on a stock exchange. The interests of the shareholders and the managers can be aligned

by making managerial pay dependent on the measurement of firms‟ performance.

Furthermore, Conyon and Schwalbach (2000) stated that the link between remuneration and

company performance is an important issue for CEOs, directors and their advisors. The

incentives created from the design of remuneration packages can be a very powerful

mechanism to align the behavior of corporate executives with overall business strategy. This

incentive exists notwithstanding the fact that the extent of empirical literature on directors‟

remuneration has often demonstrated only a small quantitative association between the

rewards received by management and company performance.

Mitsudome, Weintrop and Hwang (2008) examined the relationship between changes in

remuneration and various measures of short and long-term firms‟ performance. For the

Japanese firms, they found a significant relationship between remuneration changes and

changes in the same period of accounting earnings and stock returns. The relationship

between remuneration and performance measures of Japanese firms is significant over a

longer time period. This finding is consistent with a popular belief that Japanese executives

are more concerned with, and appropriately rewarded for current as well as long-term firms‟

performance.

Feng (2007) analyzed the directors‟ compensation for Real Estate Investment Trusts (REITs)

and investigated the reactions between directors‟ compensation and other measures of the

board independence and board monitoring. They found that REITS that pay higher equity-

based compensation for their board members is associated with higher financial performance.

The analysis of Le, Trien Vinh et al. (2011) of more than 1,000 Chinese listed firms revealed

a positive association between state ownership and firm performance. They found that when

there is a higher degree of state ownership in a firm, the higher would be the firm value. Their

finding supported the claim that government ownership plays a vital role in creating firm

value in China.




THEORETICAL FRAMEWORK

Agency Theory (Jensen and Meckling, 1976) supports the framework for this study.

Managers are viewed as agents of shareholders, who hire them to run the firms. Firm

managers often have goals that conflict with the interests of shareholders. Furthermore,

managers are typically better informed about a firms‟ conditions compared to shareholders.

Agency theory suggests that it is good for shareholders to align incentives of managers with

their own incentives (Fernandes, 2008). The interests of both the shareholders and managers

can be partly aligned by making managerial pay dependent on observable measures of a

firm‟s performance.

Managers are known to be risk-averse individuals. Managers would want their remuneration

structured so that they bear the less personal risk. In order to reduce their remuneration

risk, managers may engage in activities which could reduce the firm‟s risk.

The Malaysian Code on Corporate Governance (MCCG 2000, revised 2007) emphasizes the

following principles on directors‟ remuneration. Firstly, in the case of executive directors,

remuneration should be structured so as to link rewards to corporate and individual

performance. Secondly, companies should establish a formal and transparent procedure for

developing policy on executive remuneration and for fixing the remuneration packages of

individual directors. Thirdly, company‟s annual reports should contain details of the

remuneration of each director.

METHODOLOGY

Secondary data from published financial statements of companies in Malaysia for the period

2008 to 2010 were used in this study. A total of 66 companies was selected which consist of

33 Government-Linked companies (GLCs) and 33 Non Government-Linked Companies

(Non-GLCs). The 33 GLCs selected in the sample were based on the list provided by “The

Putrajaya Committee on GLC High Performance” as at 13 March 2009. As for the Non-GLCs,

the sample selection was based on a random basis but with their corresponding industry.

All data extracted was processed using SPSS statistical software as the analysis tool.

Normality test of data was conducted to decide whether parametric or non-parametric tests

were to be used. Correlation Coefficients were generated to assess the strength of correlation

between directors‟ remuneration and corporate performance. The directors‟ remuneration

would be the total amount of remuneration received by the directors from the group as

disclosed in the notes to the financial statements. The performance measurements include

Profit after Tax (PAT), Net Profit Margin (NPM) and Return on Assets (ROA).

NPM is calculated as PAT divided by revenue. A high NPM implies that a company is

profitable and has better control over its costs relative to its business competitors. ROA

indicates how profitable the company is in relation to the total assets. It reflects how efficient

the management team is at utilizing its assets in generating its earnings.

Hypothesis testing was performed to test the existence of any relationship between the two

parameters under review.




OBSERVATIONS AND ANALYSIS

Reliability testing

The reliability of data collected was first tested for its consistency and stability by generating

the Cronbach‟s alpha coefficient. This coefficient reflects how well the items in a set are

positively correlated to one another. Cronbach‟s alpha can be measured as a correlation

coefficient range in value from 0 to 1. The closer the alpha is to 1, the higher is the internal

consistency and hence is considered as more reliable (Coakes, Steed and Ong, 2010).

Table 1: Table of Reliability Statistics

Cronbach's Alpha Number of parameters

0.751 16

The Cronbach‟s alpha value of 0.751 shows that the reliability of the sets of data collected is

acceptable.

Normality test

Normality test was performed on the various series of financial data collected to assess

whether they follow a normal distribution or Gaussian distribution. Based on the results of the

normality test, we would then decide on the type of statistical test for analysis.

Generally, statistical procedures can be grouped into parametric statistics and nonparametric

statistic. The major distinction between them lies in the underlying assumptions about the data

to be analyzed. Parametric statistics involve numbers with known, continuous distributions.

(Zikmund et al., 2006) characterized with an interval or ratio scale data from a large sample

size. Nonparametric tests are used for the data measured which do not conform to a known

normal distribution.

To determine whether the normality of the data is violated, we looked at the significance level

of the Kolmogorov-Smirnov (KS) statistic and the Shapiro-Wilk (WS) statistic. If the

significance level is greater than 0.05, then normality is assumed and parametric techniques

are used. When the data does not meet the underlying assumption of a parametric test, non-

parametric techniques would be used instead.

The normality of data collected can be assessed by referring to their respective significance

(Sig.) level columns in Table 2. As the sample size is less than one hundred, we shall refer to

the outcome of the Shapiro-Wilk test (Coakes, Steed and Ong, 2010). Based on a 5 percent

significance level, it can be inferred that most of the data series are not normally distributed,

except for a few that are marked with an asterisk.




Table 2: Test of Normality for All Variables

Variable

Kolmogorov-Smirnov Shapiro-Wilk

GLC Non-GLC GLC Non-GLC

Sig. Sig. Sig. Sig.

PAT2008 0.000 0.000 0.000 0.000

PAT2009 0.001 0.000 0.000 0.000

PAT2010 0.000 0.000 0.000 0.000

Overall 0.000 0.000 0.000 0.000

NPM2008 0.000 0.005 0.000 0.002

NPM2009 0.000 0.127* 0.000 0.013

NPM2010 0.020 0.000 0.011 0.000

Overall 0.000 0.005 0.000 0.002

ROA2008 0.001 0.006 0.000 0.003

ROA2009 0.000 0.145* 0.000 0.233*

ROA2010 0.152* 0.200* 0.106* 0.249*

Overall 0.001 0.006 0.000 0.003

DirectorRem2008 0.000 0.000 0.000 0.000

DirectorRem2009 0.000 0.000 0.000 0.000

DirectorRem2010 0.000 0.000 0.000 0.000

Overall 0.000 0.000 0.000 0.000

Table 2 shows that the directors‟ remuneration of both the GLCs and Non-GLCs for all the

periods under study does not exhibit a normal distribution. Hence, the Spearman

nonparametric coefficient would be used for testing hypothesis H1 to H6 while the Mann-

Whitney U Test was used to assess whether there is any significant difference between the

means of the two sets of sample data representing the GLCs and non-GLCs as stated in H7.

The factor causing the non-Gaussian data could be due to the limited sample size and that the

GLCs selection was based on the list of the Putrajaya Committee. This inevitably introduced a

certain degree of „biasness‟ in the sample selection.

Correlation measurement using spearman correlation coefficient and tests of hypothesis

As stated above, we would use the non-parametric measurement, Spearman correlation

coefficient, to assess the strength of the relationship between the parameters. The Spearman

Correlation Coefficient value ranges between +1 and -1. A positive coefficient indicates that

the variables are positively correlated and vice versa. A zero coefficient would be the result if

there is no relationship between the two variables.

The hypotheses of testing the relationship between the parameters are stated as follows:

H1 : There is a significant relationship between directors‟ remuneration and PAT for GLCs

in Malaysia.

H2 : There is a significant relationship between directors‟ remuneration and PAT for Non-

GLCs in Malaysia.




H3 : There is a significant relationship between directors‟ remuneration and NPM for GLCs

in Malaysia.

H4 : There is a significant relationship between directors‟ remuneration and NPM for Non-

GLCs in Malaysia.

H5 : There is a significant relationship between directors‟ remuneration and ROA for GLCs

in Malaysia.

H6 : There is a significant relationship between directors‟ remuneration and ROA for GLCs

in Malaysia.

H7 : There is a significant difference in the mean [Directors‟ remuneration / PAT]

between GLCs and Non-GLCs in Malaysia.

Based on the 5 percent significance level, if the resulted significance level or ρ-value is less

than 0.05, it would be statistically significant to reject the null hypothesis and accept the

above hypotheses. This would suggest that directors‟ remuneration and the respective

corporate performance parameter are significantly related. On the contrary, with a ρ-value of

larger than 0.05, we would accept the null hypothesis and reject the above hypothesis.

Table 3: Results on Spearman Correlation Coefficient and Significance Level between

Directors‟ Remuneration and Corporate Performance for Government Linked Companies

Variable/Year

Spearman Coefficient Significant Level (2 Tailed)

2008 2009 2010 Overall 2008 2009 2010 Overall

PAT 0.332 0.337 0.334 0.348 0.059 0.055 0.061 0.000

NPM -0.004 -0.199 -0.127 -0.097 0.982 0.267 0.495 0.346

ROA -0.105 -0.264 -0.275 -0.189 0.562 0.138 0.134 0.063

Table 4: Result on Spearman Correlation Coefficient between Directors‟ Remuneration and

Corporate Performance for Non-Government Linked Companies

Variable/Year

Spearman Coefficient Significant Level (2 Tailed)

2008 2009 2010 Overall 2008 2009 2010 Overall

PAT 0.316 0.525 0.574 0.493 0.078 0.002 0.001 0.000

NPM -0.007 -0.126 0.118 0.032 0.971 0.493 0.527 0.756

ROA 0.023 -0.104 -0.054 -0.016 0.902 0.576 0.768 0.879

Correlation analysis: degree of correlation

The results in Table 3 indicate a positive correlation coefficient between PAT and directors‟

remuneration for each of the three years and with an overall correlation coefficient of 0.348

for GLCs. Positive correlation coefficients are also found in Non-GLCs as shown in Table 4

with an overall stronger correlation coefficient of 0.493. The readings are consistent over the




years within their respective group. The results above indicate the directors‟ remuneration of

Non-GLCs has a stronger degree of correlation with Profit after tax than GLCs and their

strength of the correlation is generally moderate and positive.

The results from the tables above indicate that correlation coefficients are weak throughout

the three years between directors‟ remuneration and NPM. Table 3 indicates an unusual

negative coefficient correlation between NPM and directors‟ remuneration in GLCs with an

overall weak correlation coefficient of -0.097. The Non-GLCs, however, posses an overall

positive, but equally weak correlation coefficient of 0.032.

Both GLCs and Non-GLCs show a negative correlation coefficient between directors‟

remuneration and ROA (except for Non-GLCs in 2008). The overall coefficients are generally

weak at -0.189 and -0.016 for GLCs and Non-GLCs respectively. The GLCs‟s figures trigger

a concern as they show a gradual increase but negatively!

Significant relationship between variables

The first and second hypotheses concern whether there is any significant relationship between

directors‟ remuneration and Profit after Tax (PAT) in GLCs and Non-GLCs in Malaysia

respectively. Based on Table 3 and 4, the overall ρ-values for GLCs and Non-GLCs are less

than 0.05. Hence, there is sufficient statistical evidence to reject the null hypothesis and

support the hypothesis H1 and H2 inferring a significant relationship does exist between

directors‟ remuneration and corporate performance measured in terms of profit after tax for

both GLCs and Non-GLCs. This finding is also consistent with research outcome done by

Brunello et al. (2001) on companies in Italy.

The third and fourth hypotheses concern if there is any significant relationship between

directors‟ remuneration and net profit margin (NPM) in GLC and Non-GLC. Both the ρ-

values in Table 3 and Table 4 are more than 0.05, hence, there is insufficient statistical

evidence to reject the null hypothesis and it can be inferred that there is no significant

relationship between directors‟ remuneration and corporate performance measured in terms of

net profit margin for both GLCs and Non-GLCs in Malaysia. The weak correlation

coefficients lend further support to this finding.

The fifth and sixth hypotheses concern whether a significant relationship exists between

directors‟ remuneration and return on assets (ROA) in GLC and Non-GLC. Table 3 and 4

show all the three years 2008, 2009, 2010 and the overall ρ-values for both GLCs and Non-

GLCs are higher than 0.05 inferring no significant relationship exist between the two

parameters. The findings differ from the research findings of Mehran (1995) and Kato and

Kubo (2006). Hence, it can be inferred that irrespective of GLCs or Non-GLCs, directors‟

remuneration of companies in Malaysia do not relate to corporate performance measured in

term of ROA. This observation is consistent with the overall weak correlation coefficient

between them, especially for the Non-GLCs of -0.016.




Mann-Whitney U test

Mann-Whitney U test is used to test the seventh hypothesis stated above. The Mann-Whitney

U test is a nonparametric test specifically used to compare between means of two independent

groups of sampled data. This test is equivalent to the independent T-test. By contrast to the

parametric t-test, this non-parametric test makes no assumptions about the distribution of the

data when the assumption of normality or equality of variance is not met.

A two-tailed ρ-value is used to interpret the output from the Mann-Whitney U test. If the

result of the Mann-Whitney test has a probability greater than 0.05 (ρ value > 0.05), the null

hypothesis is accepted and equal variance estimates are assumed. This infers no significant

difference between the variances of the two groups.

Table 5: Results on Mann Whitney U Test

Co. Type N Mean Rank Sum of Ranks

DRPAT2008 Government Link

Companies

33 28.24 932.00

Non Government Link

Companies

32 37.91 1213.00

Asymp. Sig. (2-tailed) 0.039


Companies

33 28.48 940.00

Non Government Link

Companies

33 38.52 1271.00



Companies

32 29.00 928.00

Non Government Link

Companies

32 36.00 1152.00



Companies

98 84.51 8282.00

Non Government Link

Companies

97 111.63 10828.00


The significance level is 0.05.

DRPAT: Directors‟ Remuneration / Profit after Tax

In this study, the Mann-Whitney U test was used to test the significant difference in the mean

DRPAT (directors‟ remuneration / profit after tax) between GLCs and Non-GLCs in Malaysia.

Based on the ρ-value, the overall value of DRPAT080910 (ρ=0. 001), DRPAT2008, (ρ=0.

039) and DRPAT2009 (ρ=0. 034) are all less than 0.05, the null hypothesis is to be rejected

and H7 hypothesis is accepted for all the periods under study except for 2010 (DRPAT2010,

p= 0.133). This result infers that there is a significant difference between the distribution of

the means of GLCs and Non-GLCs in Malaysia and the Non-GLCs is having a higher mean

rank than the GLCs in all the four series.




CONCLUSIONS

From the observations above, we can conclude that the directors‟ remuneration for both the

GLCs and Non-GLCs does exhibit a significant relationship with PAT and the degree of

correlation is stronger in the case of Non-GLCs. This finding supports the good practice of the

Malaysian Code on Corporate Governance‟s underlying principle on directors‟ remuneration

to be structured so as to link rewards to corporate and individual performance.

Net Profit Margin (NPM) which measures how much a company keeps in its earnings out of

every dollar of sales reflects further the quality of directors‟ ability in cost control. The

finding indicates that director‟s remuneration has no significant relationship with NPM

irrespective of GLCs and Non-GLCs. This implies that changes in profit margin are not

significantly relevant to the determining of the amount of directors‟ remuneration. Though the

degree of correlation is relatively weak for GLCs, the results show a negative correlation to

performance and this observation may deserve further attention.

Findings from this study also reveal that the directors‟ remuneration for both the GLCs and

Non-GLCs in Malaysia does not have a significant relationship with ROA and the degree of

correlation is generally negatively weak. The weak coefficient could be due to the diffusion of

the impact by the denominator. We can conclude that irrespective of GLCs or Non-GLCs, the

directors‟ remuneration does not take into consideration on how efficiently the directors or

management utilize the resources in generating the company‟s earnings. A larger, though not

strong overall negative correlation coefficient of -0.189 in the case of ROA shown for the

GLCs suggests a need to improve on the efficiency of resource utilization.

LIMITATIONS AND RECOMMENDATIONS

The findings of this study are interpreted with certain limitations. The data used for this study

was extracted from the financial statements for the years ended 2008 and 2009 and 2010.

Observations of data for a longer time period would be more revealing. Furthermore, the

directors‟ remuneration and corporate performance are based on what have been presented

and disclosed in the financial statements. In other words, the observations made would also

depend on the accuracy of the recognition and measurement used by the companies for

financial reporting.

This study includes only companies that are listed on Bursa Malaysia and the findings may

not be applicable to non-listed companies. A similar study on non-listed companies might

provide a different perspective.

The accretion or reduction in fair value of a company‟s assets does reflect the quality of

directors‟ decisions and their performance. ROA used as corporate performance parameter in

this study is based on an extracted carrying value from the financial statements. The use of

ROA based on fair or market value instead of carrying value presented in the financial

statements could shed light on a more in-depth perspective.

Apart from structuring directors‟ remuneration to corporate performance measured in absolute

amounts, such as PAT, efficiency in resource utilization and cost control measured in ROA

and NPM respectively should not be ignored.




Unlike companies in US, Malaysian companies are currently not required to disclose

remuneration information of individual directors. The additional disclosure of individual

director‟s remuneration would certainly help explain any inconsistency or deviation from the

good practices of corporate governance.

REFERENCES

Abdul Rahman, R. and Afzan Najid N. (2011), Government Ownership and Performance of

Malaysian Government-Linked Companies, International Research Journal of

Finance and Economics, 61, 1450 – 2887

Brunello, Graziano and Parigi (2001), Executive compensation and firm performance in Italy,

Italy, International Journal of Industrial Organization, 19, 133-161

Cheng, S. and Firth, M. (2005), Ownership, corporate governance and top management pay in

Hong Kong, Corporate Governance, Vol. 13 (2), 291-302

Coakes, S.; Steed, L & Ong C (2010), SPSS Version 17.0 for Windows- Analysis without

Anguish Version, Australia, John Wiley & Sons Australia, Ltd

Conyon, M.J. and Schwalbach, J. (2000). Executive compensation: Evidence from UK and

Germany, Long Range Planning, 33, 504 – 526

Conyon, Martin J; Gregg, Paul (1994). A study of the sensitivity of top director remuneration

to company specific shocks, National Institute Economic Review, Sage Publication

Ltd., issue 149, 83-92

Duffhues, P. and Kabir, R. (2008). Is the pay–performance relationship always positive?

Evidence from the Netherlands, Journal of Multi- Financial Management, 18, 45- 60

Feng, Zhilan; Chinmoy Ghosh; C.F Sirmans (2007), Director compensation and CEO

Bargaining Power in REITs, Journal of Real Estate Finance and Economics 35 (3),

225

Fernandes, N. (2008), EC: Board compensation and firm performance: The role of

“independent” board members, Journal of Multi. Financial Management, 18, 30 - 44

Firth, M (2006), Corporate Performance and CEO Compensation in China, Journal of

Corporate Finance, 12, 693- 714

Jensen, Michael C. and William H. Meckling (1976), Theory of the firm: Managerial

Behavior, agency costs and ownership structure, Journal of Financial Economics, 3

(4), 305 – 360

Joseph E.O. Abugu (2012), Monitoring directors‟ remuneration, fat cat packages and perks of

office, Journal of Financial Crime, 19.1: 6-19

Kato T. and Kubo K. (2006), CEO compensation and firm performance in Japan: Evidence

from new panel data on individual CEO pay, Japanese International Economies, 20, 1

-19

Le, Trien Vinh; Buck, Trevor (2011), State ownership and listed firm performance: a

universally negative governance relationship?. Journal of Management & Governance,

15.2: 227-248

Marija Norusis, (2008), SPSS 16.0 Guide to Data Analysis, 2nd edition, New Jersey, Prentice

Hall

Merhebi, R., Pattenden, K., Swan, P.L. and Xianming, Z. (2006), Australian chief executive

officer remuneration: Pay and performance, Accounting & Finance, 46, 481-497

Mehran H (1995), Executive compensation structure, ownership, and firm performance,

Journal of Financial Economics, 38, 163-184




Mitsudome, Weintrop, and Hwang (2008). The relation between changes in CEO

compensation and firm performance: A Japanese/American comparison, Japanese

International. Economies, 22, 605-619

Murphy K.J (1985). Corporate Performance and Managerial Remuneration: An Empirical

Analysis, Journal of Accounting and Economics, 7, 11- 42

Murphy, K.J, (1999), Executive compensation. In: Ashenfelter, Orley, Card, David (Eds.),

Handbook of Labor Economics, vol. 3B, Elsevier Science, North-Holland, 2485 –2563

Putrajaya High Committee Group website, GLC Transformation Manual - Section I – IV

Retrieved on 24th June 2011: http://www.pcg.gov.my

Sekaran, U. and Roger Bougie (2010), Research Methods for Business: A Skill Building

Approach, 5th Edition, New York, John Wiley & Sons, Inc. New York

Unite, Sullivan, Brookman, Majadillas and Taningco (2008), Executive pay and firm

performance in the Philippines, Pacific-Basin Finance Journal, 16, 606 – 623

Zikmund, W.G., Babin, B.J., Carr, J.C. & Griffin, M. (2010), Business Research Methods,

Eighth Edition. Canada, South Western Cengage Learning

http://www.pcg.gov.my/

The effects of teaching quality on student satisfaction and behavioural intentions from the view point of

university students


THE EFFECTS OF TEACHING QUALITY ON STUDENT SATISFACTION AND

BEHAVIOURAL INTENTIONS FROM THE VIEW POINT OF UNIVERSITY

STUDENTS

Bahari Mohamed

[email protected]

Saripah bt Basar

[email protected]

Hasmah bt Safiei

[email protected]

Pritam Singh A/L Santa Singh

[email protected]

University College Shahputra

ABSTRACT

This study focuses on the effects of teaching quality on student satisfaction and behavioural

intentions, with emphasis on students‟ experiences at a university in the East Coast. Data was

collected from 168 students at the university. Using a PLS-SEM tool, the hypothesised

effects among the constructs were tested empirically. The results indicate that the path

coefficients from tangible, empathy and outcome constructs are the key factors that influence

the students‟ perception of service quality; the path coefficient from service quality of student

experience to student satisfaction is significant and satisfied students are more positive in

their behavioural intentions toward the university.

Keywords: higher education institution, service quality, student satisfaction, Behavioural

intention

INTRODUCTION

Customer satisfaction is a very important marketing concept. Strong competition in today‟s

competitive educational markets forces higher educational institutions in Malaysia to adopt a

market orientation strategy to differentiate their services from those of their competitors (Sirat,

2005). Thus, the administrators of higher education institutions need to understand the target

customer needs in order to boost customer satisfaction. In higher education, the term

“customer” is different from that in other industries since groups such as students, employers,

academic staff, government and families are all customers of the education system with a

variety of needs. However, students are the direct recipients of the service provided by higher

education institutions. Continuous improvement in quality has become an extremely

important issue for higher educational institutions to enhance educational value and to

increase satisfaction among the students and stakeholders. As the core service provided by

higher educational institutions is teaching, the instructors or academic staff have a crucial role

as a service provider for the students who are their customers. Thus, higher educational





university students


institutions should give the highest priority to effective teaching provided by their instructors

and engage students in the learning process because student satisfaction is often used to assess

the quality of service provided by the institution (Standifird, Pons, and Moshavi, 2008; and

Qureshi, Shaukat, and Hijazi, 2010).

Many studies had been conducted on the perception of teaching service quality at the level of

higher education. However, the studies were mostly conducted in the developed countries

(Nasser and Abouchedid, 2005) and there was no consensus on the factors that contribute

significantly to service quality, student satisfaction or how behavioural intentions are

determined. Studies on student evaluations of service encounters, technical and functional

service quality, service satisfaction, and the effects on behavioural intentions are not well

documented. Only a few such studies have been conducted in developing countries, including

Malaysia. Therefore, the present study was conducted to determine the effects of teaching

quality provided by lecturers on student satisfaction and behavioural intentions, from the

view-point of university students.

LITERATURE REVIEW

Customer satisfaction with service quality is a major goal in service organisations. Service

providers cannot detach themselves from this general concern; thus, managers and

practitioners must give priority to addressing issues concerning quality and customer

satisfaction. Service quality is basically difficult to define and measure and has been subject

of much debate (Legčević, 2009). Thus, the concepts of perceived quality and related

customer satisfaction are coined. Service quality in general differs from product quality due to

special characteristics including intangibility, simultaneity and heterogeneity (Parasuraman,

Zeithaml, and Berry, 1985). Service quality is more difficult for the consumer to evaluate than

goods quality. Moreover, quality evaluations are not made exclusively on the outcome of

service but rather they involve evaluations of the process of service provided. (Parasuraman et

al., 1985). Intangible - this is the principal feature of higher education since most quality

attributes cannot be seen, felt, or touched in advance and the production and consumption of

the service are performed simultaneously. That is, personal contact between students and

instructors plays an important role in the service action. Consequently, the student contributes

directly to the quality of service delivered, and to his or her satisfaction or dissatisfaction.

Service quality in higher education especially the lecturers‟ teaching ability varies depending

on many factors. The most important service quality is context specific. Driscoll and Cadden

(2010) in their research of teaching effectiveness found that the lecturer‟s teaching ability is

influenced by the department that offers the course, the course‟s requirement- core or elective,

and the students‟ anticipated grade.

The development of quality management in the education sector is still considered new

compared with the other sectors (Ramseook-Munhurrun, Naidoo, and Nundlall, 2010).

Attention on service quality in the education setting is increasing due to the demand for

excellence in education (Sahney, Banwet, and Karunes, 2004). In recent years, numerous

empirical studies on higher education have shown several examples of the successful use of

systematic quality management in education (Lagrosen, Sayyed-Hashemi, and Leitner, 2004;

Stodnick and Rogers, 2008). Ling, Piew, and Chai, (2010) conducted a study involving 458

undergraduate business students from a private university to evaluate the determinants of

perceived service quality of higher education in that institution and found that contact

personnel, access to facilities, cost of courses offered, physical facilities of the tertiary


university students


institution and resource input model of education quality were positively related to the overall

students‟ perceived service quality. However, according to Douglas, Douglas, and Barnes

(2006) the most important aspects of service quality in HEI are those associated with teaching

and learning.

Dimension of service quality

The quality dimensions can be classified into technical quality and functional quality

(Gronross, 1998). The technical quality or outcome quality of the process can be measured

objectively; it is the practical result of service. The functional quality or process quality

dimension is often perceived in a subjective manner and is related to the interaction between

the service provider and customer (Gronross 2001). Functional quality can be divided into

tangible and intangible; the intangible aspects of quality include reliability, responsiveness,

assurance, and empathy (Banwet and Datta, 2003). The functional quality very much relates

to higher education service where the influence of interaction between instructors and

students is very significant. Due to the abstract nature of the concept of service quality and the

characteristics of the service, measuring service quality appears to be a complicated and

difficult to evaluate (Sultan and Wong, 2010; and Parasuraman et al., 1985).

Service quality is generally defined as a consumer‟s perceived ... superiority of an entity

(Cronin and Taylor, 1992; Parasuraman et al., 1985, 1988; and Bitner and Hubbert, 1994).

Moreover, service quality is context-specific (Dagger, Sweeney, and Johnson, 2007; and

Sultan and Wong, 2010) such as it depends on the nature of work, environment, and culture.

Thus, it is attached to different meanings and inferences depending on contexts. In other

words, there is no conclusive definition of service quality. In order to define service quality in

the right perspective, it is vital to study the context of the service being investigated. In

addition, to comprehend the service quality in the educational sector, we must have a strong

understanding of service quality attributes in other sectors and to do some adaptation if

necessary (Lagrosen et al., 2004).

Approach in measuring service quality

A number of researchers have provided lists of service quality dimensions, but the best known

service quality dimensions is SERVQUAL developed by Parasuraman et al. (1985, 1988).

The SERVQUAL is based on the assumption that customers are able to express their

expectation of service quality and could distinguish these from their perception of the actual

service quality being provided; the instrument is based on the difference between perception

and expectation (Parasuraman et al., 1985, 1988). Although the SERVQUAL instrument has

been widely used, it has not been free of certain criticisms. SERVQUAL has been much

criticised over the years (Cronin and Taylor, 1992; and Asubonteng et al., 1996). Cronin and

Taylor (1992) disagreed with the concept of perception minus expectation and proposed an

alternative measurement, SERVPERF which utilises the perception only in the service quality

model. Both SERVQUAL and SERVPERF are based on the dimensional approach to service

quality (Sultan and Wong, 2010); service dimensions are conceptualised as components of the

service quality construct. On the other hand, Dabholkar, Shepherd, and Thorpe (2000)

consider service dimensions as antecedents to the overall service quality construct. However,

the antecedent concept is more acceptable and has been in use in recent years (Dagger and

Sweeney, 2006; and Dagger et al., 2007). Hence, there seems to be consensus among


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researchers that satisfaction and service quality are two distinctive constructs; however,

dissimilarities in their definitions are not always clear (Choi et al., 2004; Chen and Ting,

2002; and Spreng and Mackoy, 1996). Parasuraman et al. (1988) and Bitner (1990) argued

that customer satisfaction is an antecedent of service quality. On the other hand, many

researchers (such as Cronin and Taylor, 1992; Dabholkar et al., 2000; and Dagger and

Sweeney, 2006) believed that it is service quality that leads to customer satisfaction. As such,

selecting a reliable method to assess service quality is very important. Many models and

instruments have been developed and used in research to determine service quality in higher

educational institutions. Many researchers have adapted SERVQUAL scale (Parasuraman et

al., 1985, 1988), SERVPERF (Cronin and Taylor, 1992), and HEdPERF (Abdullah, 2005) are

among the service quality models that have been used to measure higher educational service

quality by incorporating student satisfaction into their survey instrument.

The present study, as mentioned earlier, sets out to diagnose the effects of service quality

dimensions on perceived service quality (teaching), student satisfaction and behavioural

intentions of the students in the classroom environment by using an integrated scale. The

study takes the view that perceived service quality leads to satisfaction and is in agreement

with the empirical research conducted by Cronin and Taylor (1992); and Dagger and

Sweeney, (2006). The following section discusses briefly the literature of the integrated

model dimensions or constructs as developed by previous researchers.

Behavioural intentions

Studies showed that perceived service quality and service satisfaction have a mixed impact on

behavioural intentions. Many researchers (such as Cronin and Taylor 1992; and Dabholkar et

al., 2000) have found that service quality is indirectly related to behavioural intentions with

service satisfaction as a mediating variable. However, Cronin, Brady, and Hult (2000) in their

study found that service quality has a direct impact on behavioural intentions. Hence,

students‟ intention to re-attend or recommend lectures is dependent on their perceptions of

quality and the satisfaction they received from attending previous lectures (Banwet and Datta,

2003).

Student satisfaction

Kim et al. (2008) describe customer satisfaction as results from customers having good

experiences. Ott and van Dijk (2005) assert that customer satisfaction is an important

indicator of the performance of an organization. According to Storbacka, Strandvik, and

Gronroos (1994), a satisfied customer creates a strong relationship with the provider and this

leads to customer retention or customer loyalty and generates steady revenues and profit for

the firm. When service quality increases, correspondingly, satisfaction with the service will

increase and intentions to reuse the service will also increase (Dagger et al., 2007). A number

of studies have confirmed that service quality is an antecedent to customer satisfaction

(Cronin and Taylor, 1992; Dabholkar et al., 2000; and Dagger and Sweeney, 2006).

Satisfaction is affective, feeling-based, and subjective, and, therefore, satisfaction is hard to

measure accurately (Dabholkar et al., 2000).


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Thus, customer satisfaction as mentioned above is a critical factor that determines the quality

of the product or service. In higher education sectors, student satisfaction is considered to be

an indicator of service quality delivered (Wiers-jenssen, Stensakeri, and Grogaard, 2002).

They conducted a study in Norway to determine student satisfaction in relation to learning

experience. They found that the academic and pedagogic qualities of teaching are important

factors in determining student satisfaction. Banwet and Datta (2003) in their study found

satisfied students are likely to attend another lecture delivered by the same instructor or opt

for another module or course taught by the instructor. However, social climate, aesthetic

aspects of the physical infrastructure and the quality of services from the administrative staff

also influence overall student satisfaction (Rapert et al., 2004; and Diamantis and Benos

2007).

Perceived service quality

Customers usually have some expectations of service given by providers before receiving the

actual service and these expectations will be compared to the actual perception of the service

provided. The degree and gap between service perception and customer expectations is

defined as service quality (Parasuraman et al., 1985, 1988). Thus, perceived service quality in

classroom teaching is fulfilled when a lecturer meets or exceeds students‟ expectation.

Accordingly, Thai students consider teaching and the ability of lecturers to communicate

skilfully as very important attributes in selecting international universities (Srikatanyoo and

Gnoth, 2005). Therefore, student satisfaction with the services offered at a university is

influenced by students‟ perceived service quality (Gruber et al., 2010).

Service quality antecedents

Reliability

The reliability dimension is one the strongest effects on perceived lecture quality (Banwet and

Datta, 2003). Reliability in teaching refers to the lecturers‟ ability to deliver the lecture

dependably, accurately, and consistently (Stodnick and Roger, 2008). Accordingly, the ability

of a lecturer to deliver a lecture clearly, emphasis on the relevance and practicality of the

subject, the punctuality of the lecturer, and the lecturer‟s sincerity and problem solving ability

are rated as very important factors contributing to a superior teaching quality (Banwet and

Datta, 2003).

Responsiveness

Lecturer responsiveness is an important dimension of student perception toward teaching

quality. Among the indicators of responsiveness that students expect from the lecturers are:

responding promptly when needed; willing to go out of his or her way to help students;

always welcoming student questions and comments; and being available and approachable

outside class hours (Stodnick and Roger, 2008; Banwet and Datta, 2003).


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Empathy

The lecturers or faculty members‟ empathy and understanding of students‟ problems and

needs can greatly influence perceived service quality. Faculty members desire students to be

attentive and understanding towards them. According to Stodnick and Roger (2008) and

Banwet and Datta (2003), faculty members must be genuinely concerned about the students,

understand the individual needs of students, have the student‟s best long-term interests in

mind and encourage and motivate students to do their best. These reflect the faculty

members‟ empathy toward the students.

Tangibles

Physical evidence of the college or university will provide the first impression of service

quality and this is very important to the students‟ perceived service quality judgments.

Generally, good appearance of the physical facilities, equipment, personnel and written

materials create positive impressions. A clean and organized appearance of a college or

university, its staff, its premises, restrooms, equipment, classrooms, workshops, laboratories,

library, computer and information systems can influence students‟ impressions about the

college or university. Jones, Jamal, and Babu (1996) study involving international business

students attending colleges and universities in the United States found that tangibles is one of

the most important factors in their assessment of educational service quality. Tangibles are

aspects such as classroom environment, quality of presentations and the lecturer‟s appearance

which have an influence on students‟ perception of teaching quality (Banwet and Datta, 2003;

Markovic, 2006; and Hill and Epps, 2010). In a number of studies students considered

tangibles a very important factor in determining their satisfaction of educational service

quality (Arambewela and Hall, 2006; Markovic, 2006; and Banwet and Datta, 2003).

Outcome

Outcome or technical quality is also a vital dimension that affects perception of service

quality by students. The technical dimension is rated the most important factor contributing to

perception of service quality by students in Banwet and Datta (2003) study. In their survey of

168 students who attended four lectures delivered by the same lecturers, they found that

students placed more importance on the outcome of the lecture than on any other dimension.

The outcome or technical quality in this study refers to knowledge and skills gained during a

lecture, the availability of class notes and reading materials, the lecture‟s feedback on

assessed work, and coverage and depth of the lecture (Banwet and Datta, 2003).

Theoretical framework

Based on the literature review and discussions presented above, the following theoretical

framework for teaching service quality is developed. Figure 1 shows the service quality

dimensions, namely, tangibles, responsiveness, reliability, empathy, and outcome; perceived

service quality construct; student satisfaction construct; and behavioural intentions construct.

All the constructs/dimensions have been explained in the above section.


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Source: adapted from Dabholkar et al., (2000).

Figure 1: Teaching Service Quality Framework

Hypotheses

Prior discussion has led to a brief examination of the existing literature and the resultant

research gaps led to the development of the hypotheses in this research. The eleven

hypotheses are:

H1: Tangibles dimension is positively related to the students‟ perceived service quality.

H2: Responsiveness dimension is positively related to the students‟ perceived service quality.

H3: Reliability dimension is positively related to the students‟ perceived service quality.

H4: Empathy dimension is positively related to the students‟ perceived service quality.

H5: Outcome dimension is positively related to the students‟ perceived service quality.

H6: Students‟ perceived service quality is positively related to student satisfaction.

H7: Student satisfaction is positively related to behavioural intentions.

H8: Student satisfaction mediates the relationship between perceived service quality and

behavioural intentions.


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METHODOLOGY

Instrument

Basically the instrument was adapted from Dabholkar et al. (2000). The instrument has been

adapted to suit the context of this study. However, the service quality dimensions, that is, the

functional quality aspects of service for this study, have been adopted from SERVQUAL

(Parasuraman, 1988); and a technical dimension, that is, outcome has been adopted from

Banwet and Datta (2003). The perceived service quality, student satisfaction, and behavioural

intentions constructs have been adopted from Dabholkar et al. (2000). All the items in this

study have been adapted or adopted from previous studies, namely, Stodnick and Roger

(2008), Banwet and Datta (2003), Markovic (2006), and Dabholkar et al. (2000). To establish

support for content validity a panel of lecturers reviewed the constructs and the initial set of

measure items. Based on their suggestions a few of the items were rephrased but no item was

deleted. This study adapted a 7-point Likert-type scale to assess the model. All constructs

were reflective since the items reflect the meaning of the construct. Reflective indicators

mean they measure the same underlying phenomenon (Chin, 1998).

Sample

The population for this study comprised students enrolled in one of the universities in the East

Coast with an estimated population of 3,000 students pursuing 20 programmes in five

faculties. The sampling unit included all the current full-time students at the university who

had completed at least one semester of their study because they are familiar with the teaching

style and services provided at the university as compared to the first semester students. The

general rule for the minimum number of respondents or sample size is five-to-one ratio of the

number of independent variables to be tested. Hair et al. (2010) suggested that the acceptable

ratio is ten-to-one. Since there are 7 independent variables in this study, a minimum sample

size of 70 respondents would be appropriate.

The self-administer survey questionnaire was randomly distributed to the students during

class hours by the research team. The time allocated for the students to answer the

questionnaire was 15 minutes. The students‟ verbal consent was obtained before they

answered the survey questionnaire. Confidentiality was ensured as the subjects were not

required to state their names or other particulars on the survey form. A total of 168 samples

were collected. Therefore, the response rate achieved was considered adequate for the study.

Results and data analysis

This study used partial least square structural equation modelling (PLS-SEM) tool to evaluate

the manner in which the constructs presented in Figure 1 might relate to each other. The PLS-

SEM technique is a statistical method that has been developed for the analysis of latent

variable structural models involving multiple constructs with multiple indicators. PLS-SEMs

have a number of potential strengths, including ability for the testing of the psychometric

properties of the scales used to measure a variable, as well as the strength and the direction of

relationships among the variables (Akter et al., 2011).


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The PLS-SEM consisted of two sets of testing equations: first, the assessment of measurement

model, which is the process of calculating the item reliability, validity; the second, the

assessment of the structural model, which is the method of determining the appropriate nature

of the relationships (paths) between the measures and constructs. The estimated path

coefficients indicate the sign and the power of the relationships while the item‟s weights and

loadings indicate the strength of the measures (Hair, Ringle and Sarstedt, 2011). The

confirmatory factor analysis was first conducted to assess the measurement model; then, the

structural relationships were examined (Anderson and Gerbing 1988; Hair et al. 2010).

Measurement Model

The two main criteria used for testing the measurement model are validity and reliability. The

reliability of a research instrument concerns the extent to which the instrument produces

consistency results in repeated measurements, whereas validity is the degree to which a test of

how well an instrument that is developed measures and what is supposed to measure (Sekaran

and Bougie 2010). To validate our measurement model, two basic approaches to validity were

assessed: convergent validity, and discriminant validity.

Reliability analysis

To analyse the reliability/internal consistency of the items, we used the Cronbach‟s alpha

coefficient. Table 1 shows all alpha values are above 0.6 as suggested by Nunnally and

Berstein (1994). Another way to determine internal consistency is by looking at composite

reliability values. The composite reliability (CR) values also ranged from 0.805 to 0.941

(Table 1). A composite reliability of 0.70 or greater is considered acceptable (Fornell and

Larcker 1981). As such we can conclude that the measurements are reliable.

Convergent validity

When multiple items are used for an individual construct, the researcher should be concerned

with the extent to which the items demonstrate convergent validity. The measurement model

was tested for convergent validity which is the degree to which multiple items to measure the

same concept are in agreement. Anderson and Gerbing (1988) stated that convergent validity

is established if all factor loadings for the items measuring the same construct are statistically

significant. According to Hair et al. (2010) convergence validity should be accessed through

factor loadings, composite reliability and average variance extracted. The loadings for all

items exceeded the recommended value of 0.5 (Hair et al. 2010). Composite reliability (CR)

values (see Table 1), which is a measure of internal consistency, the value ranged from 0.805

to 0.941 which exceeded the recommended value of 0.7 (Hair et al. 2010). The average

variance extracted (AVE) measures the variance captured by the indicators relative to

measurement error, and it should be greater than 0.50 to indicate acceptability of the construct

(Fornell and Larcker, 1981; Henseler, Ringle, and Sinkovics, 2009). Table 1 shows that the

average variances extracted range from 0.582 to 0.813, which are above the acceptability

value.


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Table 1: Results of Measurement Model

Construct Items Loading Cronbachs

Alpha

CR1 AVE

2

Tangible T1 0.762 0.638 0.805 0.582

T3 0.857

T4 0.655

Empathy E1 0.729 0.838 0.885 0.607

E2 0.817

E3 0.812

E4 0.780

E5 0.754

Reliability REL1 0.864 0.908 0.932 0.732

REL2 0.876

REL3 0.908

REL4 0.825

REL5 0.799

Outcome OC1 0.758 0.853 0.900 0.693

OC2 0.845

OC3 0.898

OC4 0.824

Responsiveness RES1 0.856 0.874 0.913 0.724

RES2 0.866

RES3 0.830

RES4 0.849

Service Quality SQ1 0.888 0.885 0.929 0.813

SQ3 0.930

SQ4 0.887

Satisfaction S1 0.835 0.862 0.906 0.709

S2 0.899

S3 0.889

S5 0.734

Behavioural Intention BI1 0.862 0.921 0.941 0.761

BI2 0.915

BI3 0.867

BI4 0.854

BI5 0.862

Note:

1. Composite reliability (CR) = (square of the summation of the factor loading)/

{(square of the summation of the factor loading) + (square of the summation of the error variances)}

2. Average variance extracted (AVE) = (summation of the square of the factor loadings)/

{(summation of the square of the factor loadings) + (summation of the error variances)}


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Table 2: Summary Results of the Model Construct

Construct Items Standardized

Estimate

t-value

Tangible T1 0.762 13.05

T3 0.857 29.28

T4 0.655 8.24

Empathy E1 0.729 14.02

E2 0.817 26.62

E3 0.812 22.86

E4 0.780 17.28

E5 0.754 17.70

Reliability REL1 0.864 35.82

REL2 0.876 33.26

REL3 0.908 37.37

REL4 0.825 21.93

REL5 0.799 19.17

Outcome OC1 0.758 14.27

OC2 0.845 22.80

OC3 0.898 47.93

OC4 0.824 29.01

Responsiveness RES1 0.856 26.12

RES2 0.866 26.28

RES3 0.830 18.25

RES4 0.849 37.60

Service Quality SQ1 0.888 38.55

SQ3 0.930 78.00

SQ4 0.887 33.14

Satisfaction S1 0.835 18.98

S2 0.899 59.56

S3 0.889 50.54

S5 0.734 14.83

Behavioural Intention BI1 0.862 41.10

BI2 0.915 77.76

BI3 0.867 31.40

BI4 0.854 29.86

BI5 0.862 30.41

Table 2 summarizes the results of the measures in our research model. The results show that

all the constructs, i.e., tangible, empathy, reliability, outcome, responsiveness, service quality,

satisfaction, and behavioural intention are all valid measures of their respective constructs

based on their parameter estimates and statistical significance (Chow and Chan 2008). All

measures are significant on their path loadings at the level of 0.001


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Discriminant validity

Next we validated the discriminant validity of our instrument. The discriminant validity

represents the extent to which measures of a given construct differ from measures of other

constructs in the same model. In a PLS context, the most important criteria for adequate

discriminant validity is that a construct should share more variance with its items than it is

should share with other constructs in a given model (Hulland, 1999). It was assessed by

examining the correlations between the measures of potentially overlapping constructs. Items

should load more strongly on their own constructs in the model, and the square root of the

average variance extracted for each construct is greater than the levels of correlations

involving the construct (Fornell and Larcker, 1981). As shown in Table 3, the square root of

the average variance extracted for each construct is greater than the items on off-diagonal in

their corresponding row and column, thus, indicating adequate discriminant validity. The

inter-construct correlations also show that each construct shares larger variance with its own

measures than with other measures. In sum, the measurement model demonstrated adequate

convergent validity and discriminant validity.

Table 3: Discriminant Validity of Constructs

Construct 1 2 3 4 5 6 7 8

1. Tangible 0.763

2. Empathy 0.378 0.779

3. Reliability 0.224 0.575 0.855

4. Outcome 0.233 0.401 0.639 0.833

5. Respons 0.277 0.642 0.747 0.522 0.851

6. S. Quality 0.503 0.531 0.492 0.613 0.474 0.902

7. Satisfaction 0.449 0.466 0.475 0.506 0.427 0.685 0.842

8. B. Intention 0.455 0.362 0.244 0.309 0.250 0.519 0.730 0.872

Diagonals (in bold) represent the square root of the average variance extracted while the other entries represent

correlations.

Hypotheses testing

Table 4 presents the results and hypothesis testing. The findings support the hypotheses H1

and H4 to H8; hypotheses H2 and H3 were not supported (Table 4). Responsiveness and

reliability were not significant predictors of service quality (H2 and H3). The 2R value of

service quality construct was 0.568 suggesting that 56.8% of the variance in service quality

can be explained by tangible, empathy, reliability, outcome and responsiveness. H1 and H4 to

H8 were supported with t-value range from 2. 956 to 18.419. The 2R value of satisfaction

construct was 0.470 suggesting that 47% of the variance in satisfaction can be explained by

service quality and 2R value of behavioural intention construct was 0.533 suggesting that 53.3%

of the variance in satisfaction can be explained by satisfaction. H8, student satisfaction

mediates the relationship between perceived service quality and behavioural intentions was

supported because the relationship between service quality and satisfaction, and satisfaction


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with behavioural intention were significant. In order to assess if there is full or partial

mediation, we also used the method suggested by Baron and Kenny (1986). We found that

student satisfaction is fully mediated with the service quality and behavioural intention.

Table 4: Path Coefficients and Hypothesis Testing

Hyp Relationship Beta t-value Supported

H1 Tangible-Service Quality 0.310 4.675, 01.0 Yes

H2 Responsiveness Service Quality 0.009 0.094, 05.0 No

H3 Reliability Service Quality 0.001 0.013, 05.0 No

H4 Empathy Service Quality 0.229 2.956, 01.0 Yes

H5 Outcome Service Quality 0.443 5.106, 01.0 Yes

H6 Service Quality Satisfaction 0.685 16.176, 01.0 Yes

H7 Satisfaction Behavioural Intention 0.730 18.419, 01.0 Yes

H8 Service QualitySatisfactionB.

Intention

Yes

DISCUSSION AND CONCLUSION

Higher education institutions (HEI) in Malaysia are facing competitive market challenges, and

customers or students evaluate the services provided by the HEI. In this competitive

environments, student perceptions of service quality and their satisfaction level of the

teaching process are considered of paramount importance in order to attract the customers

(students) and retain them. Thus, the main purpose of this paper is to look at the relationship

between behavioural intention and related constructs; more precisely the relationships

between antecedents of service quality, perceived service quality, student satisfaction and

behavioural intention. Behavioural intention is perceived as being the ultimate dependent

variable of the research model.

The empathy dimension indicates the lecturers‟ willingness to help and motivate the students.

It is also reflects the sensibility and cautions to students' needs. The smallest beta value ( =

0.229, t = 2.866) shows that students are still not satisfied with the quality of this dimension.

The results of analysis of this dimension indicate that the students are not satisfied with their

lecturer‟s supportive behaviour toward fulfilling their needs. That is, the students perceived

the lecturers as not showing interest in their students‟ development and did not encourage

them to do their best in the study. However, the empathy dimension was found to have a

positive impact on student perception of teaching/service quality and satisfaction with the

lecturer (Standifird et al., 2008; Hancock, 2000). The tangible dimension focuses on lecturer

appearance and physical facilities available in the classroom. From the analysis the dimension

generated the value equal to 0.310 and t value equal to 4.639 that show the dimension is

still not adequate to fullfil student needs. In other words, the students learning process was

affected by inadequate classroom comfort. However, we did not find that the lecturers„ attire

and appearance affected the student learning process. The tangible dimension is very

important in the teaching and learning process, that is modern, fully equipped and clean

classrooms would give a positive perception of teaching/service quality provided by the

lecturer (Hill and Epps, 2010). The outcome dimension highlighted the technical part of

service quality. Students evaluate the lecturer in terms of knowledge and skills they gained,


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the availability of class notes and reading materials, the lecturer‟s feedback on assessed work,

and the coverage and depth of the lecture. From the analysis, we found that the dimension was

paramount in determining service quality (with = 0.443 and t = 4.959). In other words, in

an educational context, the lecturer‟s performance during the teaching or service transactions

is very important.

Our final conclusion was that the reliability and responsiveness constructs were not very

important in determining perceived service quality from the students‟ perspective. The

values of the two dimensions were not significant. It was also apparent that the students

perceived that the two constructs were being delivered effectively. The results demonstrate

that student satisfaction was the most influential factor, directly and strongly related to

behavioural intention. The service quality construct has only an indirect relationship to

service quality via student satisfaction. This study confirmed that service quality, satisfaction

and behavioural intention are distinct concepts. Taking into consideration the significance

levels of the path coefficients, satisfaction and behavioural intention dimensions have the

highest degree of association with service quality either directly or indirectly. This shows that

the service quality and satisfaction dimensions are very important in determining student

behavioural intention. Therefore, academic leaders should place more emphasis on these

dimensions. Such insights can help the leaders when making decisions concerning the

allocation of scarce resources.

We suggest that students should be viewed as customers because higher education institutions

are currently facing great competition in attracting students and thus, they are adopting a

marketing approach to attract more students to their institutions (Sultan and Wong, 2010). It is

important to have students who are satisfied for important positive word of mouth and referral

decisions. From the analysis techniques presented here, tangible, empathy and outcome

(technical quality) constructs can be a source of help to lecturers. They should identify the

components and subcomponents that are important in increasing teaching quality which

would eventually lead to student satisfaction. If the constructs (tangible, empathy, and

outcome) are improved, this improvement will have a positive impact on other constructs as

well.

Due to the diversity of courses offered in other higher education institutions that also have

different facilities, equipment, staff and faculty members the results of this study may only be

generalised in a limited way to other institutions. Hence, it is recommended that every

institution carries out a similar study so that a model with a greater conformity can be

produced for purposes of planning and further improving teaching services quality.

REFERENCES

Abdullah, F. (2005). “HEdPERF versus SERVPERF: the quest for ideal measuring instrument

of service quality in higher education sector”, Quality Assurance in Education, 13(4),

305-328

Akter, S., D'Ambra, J. and Ray, P. (2011). An evaluation of PLS based complex models: the

roles of power analysis, predictive relevance and GoF index. AMCIS 2011

Proceedings - All Submissions. Paper 151 (online). Available from:

http://aisel.aisnet.org/amcis2011_submissions/151

http://aisel.aisnet.org/amcis2011_submissions/151


university students


Anderson, J. C., and Gerbing, D. W. (1988). “Structural Equation Modeling in Practice: A

Review and Recommended Two-Step Approach”, Psychological Bulletin, 103(3), 41-

423

Arambewela, R. and Hall, J. (2006). “A comparative analysis of international education

satisfaction using SERVQUAL”, Journal of Services Research, 6(July), 140-163

Asubonteng, A., McCleary, K.J. and Swan, J.E. (1996). “SERVQUAL revisited: a critical

review of service quality”, The Journal of Services Marketing, 10(6), 62-81

Banwet, D. K. and Datta, B. (2003). “A study of the effect of perceived lecture quality on

post-lecture intentions”, Work Study, 52(4/5), 234-243

Baron, R.M. and Kenny, D.A. (1986). “The moderator-mediator variable distinction in social

psychological research: conceptual, strategic and statistical considerations”, Journal

Pers Soc Psychol, 51, 1173–1182

Bitner, M.J. (1990). “Evaluating service encounters: the effects of physical surroundings and

employee responses”, Journal of Marketing, 54(April), 69-82

Bitner, M. J. and Hubbert, A. R. (1992). “Servicescape: the impact of physical surroundings

on customer and employees”, Journal of Marketing, 56(April), 57-71

Chow, W.S. and Chan, L.S. (2008). “Social network, social trust and shared goals in

organizational knowledge sharing”, Information & Management, 45(7), 458–465

Chen, C. N. and Ting, S. C. (2002). “A study using the grey system theory to evaluate the

importance of various service quality factors”, The International Journal of Quality &

Reliability Management, 19(6/7), 838-851

Chin, W.W. (1998). “Issues and opinion on structural equation modelling”, MIS Quarterly,

22(1), 7-16

Choi, K., Cho, W., Lee, S., Lee, H. and Kim, C. (2004). “The relationship among quality,

value, satisfaction and behavioural intention in health care provider choice: a South

Korea study”, Journal of Business Research, 57, 913-921

Cronin, J.J. Jr and Taylor, S.A. (1992). “Measuring service quality: a re-examination and

extension”, Journal of Marketing, 56(July), 55-68

Cronin,Jr. J.J., Brady, M.K., and Hult, G.T.M. (2000). “Assessing the effects of quality, value

and customer satisfaction on consumer behavioural intension in service environments”,

Journal of Retailing, 76(2), 193-218

Dabholkar, P. A., Shepherd, C. D. and Thorpe, D. I. (2000). “A comprehensive framework for

service quality: an investigation of critical conceptual and measurement issues through

a longitudinal study”, Journal of Retailing, 76(2), 139-173

Dagger, T. S. and Sweeney, J. C. (2006). “The effect of service evaluations on behavioural

intentions and quality of life”. Journal of Service Research, 9(1), 3-18

Dagger, T. S., Sweeney, J. C. and Johnson, L. W. (2007). “A hierarchical model of health

service quality: scale development and investigation of integrated model”, Journal of

Service Research, 10(2), 123-142

Diamantis, G. V. and Benos, V. K. (2007). “Measuring student satisfaction with their studies

in an International and European Studies Department”, Operation research. An

International Journal, 7(1), 47-59

Douglas, J., Douglas, A., and Barnes, B. (2006). “Measuring student satisfaction at a UK

university”, Quality Assurance in Education, 14(3), 251-267

Driscoll, J. and Cadden, D. (2010). “Student Evaluation Instruments: The interactive impact

of course requirement, student level, department and anticipated grade”, American

Journal of Business Education, 3(5), 21-30

Fornell, C., and Larcker, D. F. (1981). “Structural Equation Models with Unobservable

Variables and Measurement Errors”, Journal of Marketing Research, 18(2), 39-50


university students


Gronroos, C. (1998). “Marketing services: the case of a missing product”, The Journal of

Business & Industrial Marketing, 13(4/5), 322-338

Gronroos, C. (2001). “The perceive service quality concept – a mistake?” Managing Service

Quality, 11(3), 150 -152

Gruber, T., Fuß, S. Voss, R. and Glaser-Zikuda, M. (2010). “Examining student satisfaction

with higher education services: Using a new measurement tool”. International Journal

of Public Sector Management, 23(2), 105-123

Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. (2010). Multivariate data analysis. Upper

Saddle River: Prentice-Hall

Hair, J. F., Ringle, C. M. and Sarstedt, M. 2011. “PLS-SEM: Indeed a Silver Bullet”, Journal

of Marketing Theory and Practice. 19(2): 139–151

Hancock, D.R. (2000). “Impact of verbal praise on college students' time spent on homework”,

The Journal of Educational Research, 93(6), 384-389

Henseler, J., Ringle, C. M. and Sinkovics, R.R. (2009). “The use of partial least squares path

modelling in International Marketing: new challenges to International Marketing”.

Advances in International Marketing, 20, 277–319

Hill, M.C. and Epps, K.K. (2010). “The impact of physical classroom environment on student

satisfaction and student evaluation of teaching in the university environment”,

Academy of Educational Leadership Journal, 4(4), 65-79

Hulland, J. (1999). “Use of Partial Least Squares (PLS) in strategic management research: a

view of four recent studies”, Strategic Management Journal, 20, 195-204

Jones, T. C., Jamal, S. A. and Babu, G. B. (1996). “An assessment of the service quality

provided to foreign students at U.S. business schools”, Journal of Education for

Business, 71(3), 130-133

Kim, K. H., Kim, K. S., Kim, D. Y., Kim, J. H. and Kang, S.H. (2008). “Brand equity in

hospital marketing”, Journal of Business Research, 61, 75-82

Lagrosen, S., Sayyed-Hashemi, R. and Leitner, M. (2004). “Examination of the dimensions of

quality in higher education”, Quality Assurances in Education, 12(2), 61-69

Legčević, J. (2009). “Quality gap of educational services in viewpoints of students”, Ekon.

Misao Praksa dbk, 18(2), 279-298

Ling, K.C., Piew, T.H. and Chai, L.C. (2010). “The impact of resource input model of

education quality on the overall students‟ perceived service quality”, Canadian Social

Science, 6(2), 125-144

Markovic, S. (2000). “Expected service quality measurement in tourism higher education”,

Nase Gospodarstvo, 52(1/2), 89-95

Nasser, R. and Abouchedid, K. (2005). ”Graduates' perception of university training in light

of occupational attainment and university type”, Education & Training, 47(2/3), 124-

133

Parasuraman, A., Zeithaml, E.V. and Berry, L.L. (1985). “A conceptual model of service

quality and its implication for future researches”, Journal of Marketing. 49(4), 41-50

Parasuraman, A., Zeithaml, E.V. and Berry, L.L. (1988). “SERVQUAL: a multiple items

scale for measuring customers perception of service quality”, Journal of Retailing,.

64, 12-23

Qureshi, T.M., Shaukat, M.Z. and Hijazi, S.T. (2010). “Service quality SERVQUAL model in

higher education institutions, what factors are to be considered?” Interdisciplinary

Journal of Contemporary in Business, 2(5), 281-290

Ramseook-Munhurrun, P., Naidoo, P., and Nundlall, P. (2010). “A proposed model for

measuring service quality in secondary education”, International Journal of Quality

and Service Sciences, 2(3), 335-345

http://proquest.umi.com.eserv.uum.edu.my/pqdweb?RQT=318&pmid=14657&TS=1295831139&clientId=28929&VInst=PROD&VName=PQD&VType=PQD





university students


Rapert, M. I., Smith, S., Velliquette, A. and Garretson, J. A. (2004). “The meaning of quality:

expectations of students in pursuit of an MBA”, Journal of Education for Business,

80(1), 17-24

Sahney, S., Banwet, D.K. and Karunes, S. (2004). “Customer requirement constructs: the

premise for TQM in education: A comparative study of select engineering and

management institutions in the Indian context”, International Journal of Productivity

and Performance Management, 53(6), 499-520

Sekaran, U. and Bougie, R. (2010). Research methods for business: a skill building approach,

Wiley, UK

Sirat, M. (2005). Transnational higher education in Malaysia: balancing benefits and concerns

through regulations, National Higher Education Research Institute. (online).

Available from: http://www.usm.my/ipptn (20 January, 2011)

Spreng, R. A. and Mackoy, R. D. (1996). “An empirical examination of a model of perceived

service quality and satisfaction”, Journal of Retailing, 72(2), 201-214

Srikatanyoo, N. and Gnoth, J. (2005). “Quality dimensions in International Tertiary

Education: a Thai prospective student‟s perspective”, The Quality Management

Journal, 12(1), 30-40

Standifird, S.S., Pons, F. and Moshavi, D. (2008). “Influence Tactics in the Classroom and

Their Relationship to Student Satisfaction”, Decision Sciences Journal of Innovative

Education, 6(1), 135-152

Stodnick, M. and Roger, P. (2008). “Using SERVQUAL to measure the quality of the

classroom experience”, Decision Sciences Journal of Innovative Education, 6(1), 115–

133

Storbacka, K., Strandvik, T. and Gronroos, C. (1994). “Managing customer relationship for

profit: the dynamics of relationship quality”, International Journal of Service Industry

Management, 5(5), 21-38

Sultan, P. and Wong, H. Y. (2010). “Service quality in higher education - a review and

research agenda”, International Journal of Quality and Service Science. 2(2), 259-272

Wiers-jenssen, J., Stensakeri, B. and Grogaard, J.B. (2002). “Student Satisfaction: towards an

empirical deconstruction of the concept”, Quality in Higher Education, 8(2), 183–195

http://www.usm.my/ipptn

The effects of self-efficacy on the development of entrepreneurial intention


THE EFFECTS OF SELF-EFFICACY ON THE DEVELOPMENT OF

ENTREPRENEURIAL INTENTION

Tan Kwe Lu

[email protected]


ABSTRACT

The objective of this study is to investigate empirically the extent to which self-efficacy

contributes to the development of entrepreneurial intention. Structured questionnaires were

deployed to various strategic locations in the UNITEN campus where 246 sample subjects

were conveniently selected. The measurement constructs of self-efficacy were classified into

opportunity, environment, relationship, purpose, challenges and human resources. The

coefficient of determination, R2 shows that 60.5 percent of the variance of intention to

entrepreneurship is explained by the combined variance of the six independent variables. It

was found that the model fits for prediction.

Keywords: self-efficacy, entrepreneurial intention, opportunity, relationship

INTRODUCTION

There is a renewed global interest in entrepreneurship. The evidence of this resurgent interest

is by the sheer amount of concrete efforts and initiatives taken by various governments. Many

policy measures and programs have been provided to increase the supply of entrepreneurship.

These are increasing market incentives, improving the availability of credit and capital,

developing entrepreneurship programs, and reforming the market regulations to facilitate

easier entry into the market, and to increase entrepreneurial activities and opportunities. This

trend of placing greater importance on entrepreneurship was first noted in 1984 by the late

Professor Albert Shapero (Shapero, 1985).

Over the last decades, there has been a growing emphasis on the importance of new

enterprises and SMEs in an economic development process. This growing awareness has led

many public administrations from all political ideologies and all administrative levels to

establish agencies to stimulate and to favor the creation of new enterprises. All agree that

entrepreneurship has a vital role to play in catalyzing and speeding economic activities. It is

also this awareness that has attracted the interest of many academic researchers to join in the

fray in seeking ways to increase the level of entrepreneurial activity so as to achieve a

sustainable rate of economic development. Entrepreneurial activities are the end result of

entrepreneurial behavior. This has a strong relationship with entrepreneurial intention as

reported by several studies such as Boyd and Vozikis (1994) and Krueger (2000).

Past researches have often focused on personal traits and characteristics as antecedent to

entrepreneurship, but they have not been found to be reliable predictors of future

entrepreneurial behavior. There must be some other factors which might possibly and




significantly influence the intention to entrepreneurship. One of these factors is self-efficacy.

As such, this study attempts to look at how the factor affects the intentionality of

entrepreneurship.

LITERATURE REVIEW

Entrepreneurship has been defined in various ways. One of the most cited definition is it is

“the creation of new enterprise” (Low & MacMillan, 1988 pg. 141). Past research has often

focused on personal traits and characteristics that distinguish an entrepreneur from the

general population rather than adapting a “process oriented” approach. Personal

characteristics such as need for achievement, locus of control, risk taking behavior and

tolerance of ambiguity have been identified and investigated as possible contributors to the

development of entrepreneurial intention. However, the findings of these researches revealed

that these personality traits have not been found to be reliable predictors of future

entrepreneurial behavior (Ajen, 1991; Ganner, 1989). In fact, these personality traits are

found to be commonly associated with successful individuals such as managers (Brockhaus

& Horwitz, 1986).

Previous literature has also attempted to identify social, cultural, political and economic

factors that contribute to the business set-up. For example, job displacement (Shapero &

Sokol, 1982), previous work experience (Mokry, 1988), quality of life (Pennings, 1982) and

ethnicity (Greenfield & Strickon, 1981) have all been identified as contributing factors for the

development of entrepreneurial intention.

Entrepreneurial intention is a state of mind that directs and guides the actions of the

entrepreneur towards the forming of an entrepreneurial activity. It is a process oriented that

focuses on a complex relationship between entrepreneurial ideas and theirs resulting outcome.

Kim and Hunter (1993) posit that intentions predict entrepreneurial behavior and attitudes

successfully influence intentions. In studying the entrepreneurial process, the act of initiating

a new entrepreneurial venture is the typical result of a planned behavior (Krueger, 2000).

Krueger (2000) demonstrated further that the constructs of entrepreneurial intention are likely

predictor of individual‟s entrepreneurial actions.

Self-efficacy is defined as a person‟s belief in his or her capability to carry out a task (Gist,

1987) and has a significant influence on the process of development of entrepreneurial

intention. Bird (1988) proposed a framework called the Entrepreneurial Intentionality Model

that suggested that the self-efficacy influences this process of new venture creation. Bandura

(1982) argued that self-efficacy influences one‟s thought which can enhance and undermine

one‟s capability to perform. In other words, if one has a high level of self-efficacy, one is

likely to set a high goal and raise the determination and achieve higher performance. Gist

(1987) concurred that a higher level of self-efficacy can help an individual maintain his/her

effort until a targeted goal is met. Stajkovic and Luthans (1998) in their meta-analysis on 114

previous studies on self-efficacy found a significant weighted average correlation between

self-efficacy and work-related performance. Such correlation raised higher levels of

motivation to initiate new ventures. Drnovšek, and Wincent (2010) found that self-efficacy

helps in bolstering positive thoughts and controlling negative thoughts that are relevant to

business start-ups.



Chen (1998) conducted a study of entrepreneurial self-efficacy (ESE) by developing

constructs such as marketing, innovation, management, risk-taking, and financial control

skills. Using a sample of students he found that ESE had a significant and positive effect on

the likelihood of becoming an entrepreneur. One of the most significant findings was that

innovation and risk-taking appeared to be important cognitive capabilities in the ESE study.

He found that the entrepreneurial self-efficacy formed a reliable measure to differentiate

between business founders and non-founders. Krueger (2000) found that individuals with

high self-efficacy are more willing to make an effort to overcome obstacles in business

ventures and he concluded that self-efficacy is an important antecedent of entrepreneurial

intention. Similarly, Neupert (2004) found that self-efficacy is a significant contributing

factor to start one‟s business. Chandler and Jensen (1992) found that abilities such as

recognizing opportunities and driving the business venture are critical in the entrepreneurial

process. These entrepreneurial skills can be used to create an expanded measure of ESE

developed by De Noble (1999). He contended that self-efficacy to undertake new venture

initiatives will positively influence entrepreneurial intentions. He further developed and

classified a self-efficacy measurement construct into developing new products or market

opportunities, building an innovative environment, initiating investor relationships, defining

core purpose, coping with unexpected challenges, and developing critical human resources.

From the above discussion, it is seen that ESE is important in the process of developing

entrepreneurial intention. The current study empirically analyzes the impact of these ESE

measurement constructs on entrepreneurial intentions among university students. The

following hypotheses were therefore formulated for the study.

H1: There is a significant relationship between the awareness of opportunity and

entrepreneurial intention.

H2: There is a significant relationship between the awareness of the environment and


H3: There is a significant relationship between the awareness of relationship and


H4: There is a significant relationship between the awareness of purpose and entrepreneurial

intention.

H5: There is a significant relationship between the awareness of the challenges and


H6: There is a significant relationship between the awareness of human resources and


METHODOGY

Sample design

The data collectors were deployed to various strategic locations in the College of Business

Management and Accounting, Universiti Tenaga Nasional, Muadzam Shah Campus.

Structured questionnaires were then administered to the student respondents who were



conveniently selected. When the questionnaires were returned, we had a total of 246 usable

sample subjects. These sample subjects were final year students of various degree programs.

Research instrument

This self-administered and structured questionnaire consists of two sections--sections A and

B. Section A extracts respondents‟ demographic characteristics such as gender, age, ethnicity

and degree program. Section B consists of measurement constructs on entrepreneurial

intention and entrepreneurial self-efficacy. To measure entrepreneurial intention, the study

used the instrument developed by Linan (2005), which consists of a set of six items that

asked the respondent to self-assess his/her intention to initiate a business set-up. The

measurement construct for entrepreneurial self-efficacy were adapted from instruments

developed by De Noble (1999), which is divided into six constructs that include the following:

1) Developing new products and market opportunities; 2) building an innovative environment;

3) Initiating investor relationships; 4) Defining core purpose; 5) Coping with unexpected

challenges; and 6) Developing critical human resources. Overall this study consists of a total

of 29 items. However, some modifications were purposely made by the researcher for reason

of clarity. Questionnaires were closed-ended questions with closed alternatives in the form of

a seven-point Likert type scale, being 1 “strongly disagrees” and 7 “strongly agree”.

The model

The model of this study is as follow:

Y = β0 + β1 X1 + β2 X2 + β3 X3 + β3 X3 + β4 X4 + β5 X5 + β6 X6 + εt (1)

where Y is the Entrepreneurial Intention as dependent variable and the independent

components are

X1 = Opportunity

X2 = Environment

X3 = Relationship

X4 = Purpose

X5 = Challenges

X6 = Human Resources

β0 is the constant term of the model. βi, i=1 to 6 as coefficients of the respective variables

for the model and εt is the error term.

Dependent variable

The dependent variable is entrepreneurial intention. It measures the degree of readiness,

determination and seriousness to be an entrepreneur.

Independent variable



The six independent variables were opportunity, environment, relationship, purpose,

challenges and human resources. „Opportunity‟ measures the ability of respondents to

identify, to discover, to create and design product in meeting market needs. „Environment‟

measures the capability to seize working environment to create, to encourage and to take

responsibility to initiate something new. „Relationship‟ measures ability to identify and to

develop favorable networks with potential business partners. „Purpose‟ measures how the

respondent articulates and embraces the vision and value of a business initiative. „Challenge‟

measures ability to persist, tolerate and to work out the continuous stress, pressure and

conflict. „Human resource‟ measures the ability to identify, to recruit and to develop key

management and technical staffs.

OUTPUT ANALYSIS

Table 1: Demographic Profiling

Profile Description Frequency percent

Gender Male 56 22.8

Female 190 77.2

Race Malay 181 73.6

Chinese 22 8.9

Indian 43 17.5

Age 16-20 67 27.2

21-25 175 71.1

26-30 3 1.2

Above 30 1 0.4

Program B. of Accounting 103 41.9

B. of Finance 80 32.5

BBA (Human resource) 48 19.5

BBA (Marketing) 9 3.7

BBA (ED) 4 1.6

B. of International Business 2 0.8

Table 1 show that female respondents contributed a larger percentage which is 77.2% as

compared with male respondents (22.8%). In terms of ethnicity group, Malays constituted

73.6, followed by 17.5% of Indian respondents. Chinese respondents stood at 8.9%. Such

ethnicity distribution truly reflects the actual composition of the student population. There are

71.5% respondents who were aged between 21 to 25 years old. Respondents aged between

16-20 constituted 27.2%. The respondent with ages above 26 was 1.6%. This age pattern is

expected as target respondents who were mainly final year students and the majority intended

to graduate before age 26. Also from the table, most (41.9%) respondents were from the

Bachelor Accounting program, followed by 32.5% Bachelor of Finance respondents. The rest

of the respondents were from BBA programs in various disciplines.



Reliability test

Table 2: Reliability of Scales

Dimension No. of Items Cronbach Alpha

Intention 6 0.933

Opportunity 7 0.929

Environment 4 0.865

Relationship 3 0.903

Purpose 3 0.918

Challenge 3 0.897

Human Resource 3 0.926

Table 2 shows the reliability coefficients of the main variables of the current study. All the 7

variables were higher than 0.7 and they all indicated that the reliability among the items was

consistent (Hair, 2006). Such a result is consistent with the study conducted by De Noble

(1999) which found internal consistency with reliability coefficients for the measurement

constructs were higher than the cutoff point of 0.7. It therefore serves as a reliable foundation

for further testing and subsequent analysis.

Exploratory Factor Analysis was conducted previously and had shown to have valid

measurement constructs. However, with a different current sample size and setting for the

sample subjects, validity, test using Kaiser-Meyer-Olkin was conducted again. The KMO test

value was found to be 0.945 which exceeds 0.5. It is therefore implied that the measurement

constructs are also valid in the current sample with no problem of multi-collinearity.

Regression result

Table 3: Standardized (Simultaneous) Regression between Entrepreneurial Intention and the

Measurement Variables

Dependent variable Entrepreneurial Intention

Independent variables β t Sig. VIF

Opportunities 0.569*** 7.856 0.000 2.987

Environment 0.028 0.187 0.852 3.904

Relationships 0.432*** 3.037 0.003 2.512

Purpose 0.200 1.119 0.264 3.793

Challenges 0.390*** 2.958 0.003 2.224

Human Resources 0.389** 2.589 0.010 2.593

Notes: The regression coefficients shown in the table are standardized regression coefficients (beta coefficients),

***, ** indicate that the estimated coefficient is significantly different from zero at 1 percent and 5 percent

respectively.

R= 0.778 R2 = 0.605 F-Value = 61.105 (P<0.01)

The general results of the linear multiple regression analysis of entrepreneurial intention

arising from opportunity, environment, relationship, purpose; challenges and human

resources are reported in Table 3. Multicollinearity test of the six independent variables

(opportunity, environment, relationship, purpose, challenges and human resources) has been

done. Using a cut-off value of VIF less than 5 (VIF for opportunity = 2.987, VIF for



environment = 3.904, VIF for relationships = 2.512, VIF for purpose = 3.793, VIF for

challenges = 2.224, and VIF for human resource = 2.593 respectively); no multicollinearity

among the variables is found. The coefficient of determination, R2 shows that 60.5% of the

variance of entrepreneurial intention is explained by the variance of opportunity, environment,

relationship, purpose, challenges and human resources. The F-value is statistically significant

at the 1% level, implying that the regression model is reliable for prediction.

The estimated coefficient of correlation (R = 0.778) shows a relatively strong linear

correlation between the independent and dependent variables. The regression result shows

that opportunity has a positive effect on entrepreneurial intention as the estimated coefficient

is 0.569 with the confidence level of 99%. In other words, an increase in exposure to

opportunity by 1%, the entrepreneurial intention would increase by 0.569%. The relationship

also has a positive association with the entrepreneurial intention as the estimated coefficient

is 0.432 and significant at 1% level inferring that when the degree of relationship improve by

1%, the entrepreneurial intention would increase 0.432%. Variable challenge has also shown

as significant with 99% confidence and it is positively related to entrepreneurial intention

with the estimated coefficient 0.390. Another variable that has association with the

entrepreneurial intention is human resource. It is positively and statistically significant with

entrepreneurial intention at 95% confidence level as the estimated coefficient is 0.389. This

can be explained as a 1 % increase in the human resources, the entrepreneurial intention will

increase by 0.389%.

CONCLUSION AND RECOMMENDATIONS

The general linear regression result indicates that the dimensions „opportunity‟ and

„relationship‟ were by far the strongest and positive predictors of entrepreneurial

intentionality. Other variables that have shown association with intention to entrepreneurship

are „challenges‟ and „human resource‟. They are statistically significant. Based on this

regression result, H1, H3,H5 ,H6 are well supported.

This study concerned measurement constructs of self-efficacy and their impact on

entrepreneurial intention. The fact that the intentionality examined here is dependent only on

self-efficacy may simplify the phenomenon excessively. There is likely that other factors that

exist apart from self-efficacy could be deemed as significant. A second limitation in this

study is that it only involves one particular sample from UNITEN campus and the

generalizability of the findings is limited. Future research needs to include a wider and more

representative spectrum of respondents for greater representation.

The results of this study suggest two implications. First, it can help companies implementing

entrepreneurial programs, thus retaining employees particularly those who could help

maintain the organization‟s competitive advantage. Universities may find these results

valuable in order to develop business classes on entrepreneurship. The result would also be

helpful for career counselors and coaches to utilize these findings in helping those who are

deciding if entrepreneurship is good for them.

In terms of future research, it is suggested that the research be extended by treating personal

attitude as a mediating effect to find out why nowadays people may prefer self-employment

rather than seeking employment. There are a whole lot of factors that are related to

entrepreneurial intention at various levels of analysis (e.g., family size, education level,



ethnicity, age ,etc.). We could not incorporate all such factors in this analysis; future

researchers may wish to broaden their inclusion of such phenomena at their respective levels

of analyses in their studies.

REFERENCES

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human

Decision Processes, 50, 179-211

Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37,

122-147

Bird, B. (1988). Implementing entrepreneurial ideas: The case of intentions, Academy of

Management Review, Vol. 13. p. 442-453

Boyd, Nancy., and Vozikis, George S. (1994). The Influence of self-efficacy on the

development of entrepreneurial intentions and actions, Entrepreneurship Theory and

Practice, Summer. pp. 63-77

Brockhaus, R.H. (1986). The Psychology of the Entrepreneur, Encyclopedia of

Entrepreneurship. Ed. C.A. Kent, D.L. Sexton, and K.H. Vesper. Englewood Cliffs,

N.J.: Prentice Hall

Chandler G. N., and Jansen E. (1992). The Founder‟s Self-assessed Competence and Venture

Performance, Journal of Business venturing, 7, 223-236

Chen, Ch. C., Greene, P. G., and Crick, A. (1998). Does entrepreneurial self-efficacy

distinguish entrepreneurs from managers? Journal of Business Venturing, 13, 295-316

De Noble, A. F., Jung, D., and Ehrlich, S. B. (1999). Entrepreneurial self-efficacy: The

development of a measure and its relationship to entrepreneurial action. Frontiers

Entrepreneurship Research, Boston: Babson College

Drnovšek, Mateja; Joakim, Wincent and Melissa S. Cardon, (2010). Entrepreneurial self-

efficacy and business start-up: developing a multi-dimensional definition,

International Journal of Entrepreneurial Behaviour & Research, Vol. 16 Iss: 4,

pp.329 – 348

Gartner, W.B. (1989). Some suggestions for research on entrepreneurial traits and

characteristics, Entrepreneurship Theory & Practice. Fall, p. 33

Gist, M. E. (1987). Self-efficacy: Implications for organizational behavior and human

resource management. Academy of Management Review, 12(3), 472-485

Greenfield, S.M. and Strickon, A. (1981) A new paradigm for the study of entrepreneuruship

and social change. Economic Development and social change, p 467-499 Hair, J.F., Black, W.C. Babin, Anderson, R.E. and Tathan, R. L (2006). Multivaraite Data Analysis.

6th Edition, New Jersey. Pearson International Edition

Kim, Min-Sun., and Hunter, John E. (1993). Relationships Among attitudes, Behavioral

Intentions, and behavior. A meta-analysis of Past Reserach. Part 2. Communication

Reserach . Vol. 20 No. 3. Pp. 331- 364

Krueger, N.F., Reily, M.D., and Carsrud, A.L. (2000). Competing models of entrepreneurial

intentions, Journal of Business Venturing 15(5/6): 411-432

Linan, F. (2005). Development and Validation of an Entrepreneurial Intention Questionnaire

(EIQ). IntEnt2005 Conference, Guildford (United Kingdom), 10-13 July

Low, M.B., MacMillan, I.C. (1988). Entrepreneurship: past research and future challenges,

Journal of management, Vol. 14 No2, p.139-161

Mokry, B. W. (1988). Entrepreneurship and public policy. New York: Quorum Books

Nuepert, K.E., Krueger, N.F., Chua, B.L (2004). Entrepreneurial self-efficacy and its

relationship to business plan competitions: an international examination. In



International Entrepreneurship: The Globalization of SME’s Orientation,

Environment and Strategy (Hamid Etemad, Ed), Edward Elgar Publishing Ltd, p: 126-

141

Pennings, J. M. (1982). The urban quality of life and entrepreneurship, Academy of

Management Journal, Vol. 25 p. 63-79

Shapero, A. (1982). Social dimensions of entrepreneurship. In C. A. kent et al. (Eds.),The

encyclopedia of entrepreneurship (pp. 72-89). Englewood Cilffs, NJ: Prentice-Hall

Shapero, A. (1985). Managing Professional People: Understanding Creative Performance,

Free Press, New York

Shapero, A., Sokol, L. (1985). The social dimensions of entrepreneurship. In Kent, C., Sexton,

D., Vesper, K. (eds) The Encyclopedia of Entrepreneurship. Englewood Cliffs, NJ.

Prentice-Hall, Inc. 72-90

Stajkovic, A. D., & Luthans, F. (1998). Self-efficacy and work-related performance: A meta-

analysis. Psychological Bulletin, 124, 240-261

The relationship between firm characteristic and corporate governance mechanism with firm‟s intellectual

capital disclosure in Malaysian initial public offerings


THE RELATIONSHIP BETWEEN FIRM CHARACTERISTIC AND CORPORATE

GOVERNANCE MECHANISM WITH FIRM’S INTELLECTUAL CAPITAL

DISCLOSURE IN MALAYSIAN INITIAL PUBLIC OFFERINGS

Zaifudin Zainol

[email protected]


Rashidah Abdul Rahman

[email protected]

Universiti Teknologi MARA

Shahrul Suhaimi Ab. Shokor

[email protected]

Afdzal Aizat Ramli

[email protected]


ABSTRACT

The development of value creation in organizations has transformed the functions of

intangible resources. Unfortunately, there has been agreement that information deficiencies

arise from the failure of the traditional financial accounting framework to reflect the value of

intangible resources of today‟s business. This study investigates the relationship between firm

characteristics, corporate governance mechanisms and firm‟s intellectual capital disclosure in

Malaysian IPOs. Correlation and multiple regression analysis are used to examine the

relationship between variables. This study found that firm age, board size, chairman duality

and audit committee size play a significant role in determining the firm‟s level of intellectual

capital disclosure.

Keywords: firm characteristic, corporate governance mechanism, intellectual capital

disclosure, initial public offerings

INTRODUCTION

The development of value creation in firms has been significantly customized through the

globalization of the economy, the advent of the internet and information technology, and also

to boost innovative developments and knowledge skills within industries. These

developments have transformed the functions of intellectual capital resources, which have

become the main factors in growing competitiveness and the strength of a firm (Cordazzo,

2007).








Lately, a lot of companies in Malaysia have shown dedicated interest in reporting intellectual

capital in order to establish an understanding and provide disclosure of the central growth

factors of the companies. The reason for the demand of intellectual capital disclosure is to

achieve more efficient management as well as to increase the companies‟ value. It is also due

to developments in information technology, globalization, new dominating industries, greater

mobility of monetary and actual goods, tougher competition, and the integration of capital

markets (Abdul Rashid et al., 2009; Bukh et al., 2005). Consequently, the particular interest

in external communication applies to both traditional accounting and contemporary types of

reporting such as intellectual capital and supplementary business reporting as well as

prospectuses (Rimmel et al., 2009). Bontis et al. (2004) highlighted that even though the

current Malaysian business are facing a rapid rate of technology growth and increasing

industrialization, most Malaysian businesses are still using and relying on the traditional

financial accounting system and performance measurement approaches; these are suitable for

arm‟s length transactions and may not be suitable in the new competitive environment

(Mustapha & Abdullah, 2004).

Stakeholders will face difficulty to determine the company performance when looking at the

traditional financial statements. The stakeholder will also rely on narrative reporting, thus

increase the relevance of narrative reporting. On the other hand, there has been decreasing

relevance of financial statements (Lev & Zarowin, 1999). Cordazzo (2007) argues that the

insufficient level of publicly available information about these resources made available to

investors in financial markets is due to incomplete treatment of intellectual capital

information by the traditional accounting systems. This situation could have caused higher

cost of capital and information asymmetry especially for companies that have a high level of

intellectual capital information. Less information on intellectual capital will lead to

uncertainty in the marketplace. This negative relation results in higher risk and a larger

compensation required by investors who bear the risk based on low disclosure levels. Hence,

it can be implied that by providing more information, companies will pay a lower cost of

capital (Cordazzo, 2007).

The level of disclosure of intellectual capital has been debated that it is driven by a number of

firm specific variables and CG mechanism. Cordazzo (2007), Rimmel et al. (2009) and Bukh

et al. (2005) argued that firm size, firm age and industry differences play an important role in

determining the firm‟s level of intellectual capital disclosure in IPOs which is can influence

the firm cost of capital and information asymmetry whereas Williams (2000), Haniffa and

Cooke (2000), Abdul Rahman and Mohd Haniffa (2002) and Abeysekera (2010) argued that

CG mechanism is also important in examining the firm‟s level of intellectual capital

disclosure. It is argued that corporate governance has been strengthened with the introduction

of 2007 Revised MCCG in Malaysia, thus increase the level of voluntary disclosure

especially the information on intellectual capital.

The objective of this study is to examine whether the level of intellectual capital disclosure is

related with firm characteristics and corporate governance (CG) mechanisms. Specifically,

the study examines whether the level of intellectual capital disclosure is related to several

firm characteristics (firm age, firm size, industry differences) and CG mechanisms (board

structure, ethnic diversity of the boards, chairman duality, and audit committee size).

The aim of this paper is to give an indication of the relationship between firm characteristics

and CG mechanisms with intellectual capital information in the Malaysia initial public

offering (IPO) prospectuses from Bursa Malaysia in the period of 2006-2010. The rationale




for undertaking this study is due to limited research in intellectual capital disclosure in

Malaysian IPO prospectuses. These provide the opportunity for this study to further enrich

the existing literature and have a better understanding of the current status of intellectual

capital disclosure practices in developing countries, particularly in Malaysia.

LITERATURE REVIEW

Stakeholder-agency theory

In agency theory, principals hire agents to perform some service on their behalf. Thus, the

assumption of a typical agency theory framework is that there is a conflict between the

interest of the shareholders, who are the owners, and the management, who run the

corporation, on behalf of the shareholders (Zahra & Pearce, 1989). In order to monitor the

managers more effectively, shareholders appoint board of directors who act as a link between

shareholders and managers. Thus, the board bears the responsibility to oversee the

management. As such, the agency theory assumes that the corporate board is an essential

internal governance mechanism. However, the board‟s capacity to monitor is jeopardized if

internal members (executives of the corporation or others affiliated with management) make

up the majority of the board. Thus, agency theory argues that an agency relationship has

become the principal focus in analyzing and studying corporate governance. Zahra and

Pearce (1989) explain that the boards are said to perform the critical function of monitoring

and rewarding top executives to ensure shareholders‟ wealth is maximized.

Hill and Jones (1992) argued that other contracts should be considered within an agency

framework, which includes those between managers and the different primary interest groups

of the firm or stakeholder. Stakeholders refer to the groups of constituents who possess a

legitimate claim on the firm. They consist of stockholders, creditors, managers, employees,

customers, suppliers, local communities and the general public. Hill and Jones (1992) also

explained that the managers are the only group of stakeholders who enter into a contractual

relationship with all other stakeholders and have direct control over the decision-making

device of the firm. Therefore, the unique role of managers suggests that they can be

considered to play the role as the agents of other stakeholders; hence the term stakeholder-

agency theory. Other stakeholder groups also place claims on the firm that is, if satisfied,

reduce the amount of resources that management can direct towards the pursuit of growth

through diversification (Hill & Jones, 1992). For instance, meeting employees‟ claims for

higher wages and customers‟ demands for greater quality/lower prices both involve the use of

resources that might otherwise be invested by managers to maximize the growth rate of the

firm. Hence, with higher wages, better knowledge and conducive working conditions, the

employees‟ productivity may improve and thus provide management with more resources i.e.

voluntary disclosures.

Intellectual capital disclosure in prospectuses

The Malaysian Securities Commission (2005) explained that it is mandatory for the

prospectus to include all such information that investors and their professional advisers would

reasonably require and expect to find in the prospectus. This information enables all the

interested parties to make an informed assessment of the assets, liabilities, financial position,




profits and losses, prospects, and rights attached to the securities; it also gives them an

opportunity to evaluate the merits of investing in the securities and to assess the extent of risk

involved in doing so. Some examples of the information include a company‟s pre-listing

performances (earning per share), forecast earnings, potential and risk of the respective

markets. The study done by Abdul Rashid et al. (2009) shows that more intellectual capital

information is disclosed in prospectuses than annual reports.

This is because prospectuses offer additional information on the companies‟ long term

strategy, company risk and future profitability; they are generally more forward looking than

annual reports. Annual reports focus on historical performance while prospectuses provide

information which pertains to companies‟ future prospects (Abdul Rashid et al., 2009).

Previous literature provides examples where researchers (e.g. Bukh et al., 2005; Cordazzo,

2007; Singh and Van der Zahn, 2007; Rimmel et al., 2009) have looked at some initial public

offering (IPO) prospectuses, which were employed by management to describe the shares the

companies are offering to potential investors. It is pertinent to note that prospectuses and

annual reports are tailored to the specific needs of different users. Unlike annual reports that

focus on historical performance, prospectuses provide information that focus on companies‟

future prospects. Cordazzo (2007) asserts that prospectuses offer additional information on

companies‟ long term strategy, business risk and future profitability; it is generally more

future oriented than annual reports. These differences are likely to be reflected in the

disclosure practices of the two documents.

Intellectual capital disclosure and firm characteristics

The extent of intellectual capital disclosure in Malaysia IPO prospectuses can be explained

by three firm characteristics – Company age, company size and industry differences.

Firm age

Firm age has often been seen as a proxy for risk, in the sense that the more established

companies are generally less risky. From this perspective, the extent of a company‟s

disclosure is expected to be related to how long it has been in business. For example, a study

done by Jaggi (1997) and Rimmel et al. (2009) concluded that there were some positive

associations between intellectual capital disclosure and company age, i.e. the number of years

the company has been in business. The mature companies, i.e. companies which have been in

existence for comparatively a longer period of time, are likely to disclose more information

(Jaggi, 1997). This is because the older firms naturally have more resources such as the

number of employees compared to the younger firms to ensure better disclosure in

intellectual capital information.

Firm size

Firm sizes are also likely to influence disclosure. The study of Robb et al. (2001) found that

large firms tend to disclose more information on intellectual capital than smaller firms. This

shows that company size is related to the amount of voluntary disclosure. Another example,

Anton (1955) and Cordazzo (2007) concluded that large companies are more likely to have a

larger ownership base, and that the costs of providing information are more prohibitive for




small companies. The latter problem tends to grow with increased disclosure. However, not

all studies conclude that the size of the company is a significant factor in explaining the

voluntary publication of information. For instance, Wallace (1988), Stanga (1976) and Bukh

et al. (2005) concluded that size is not a significant factor in explaining differences in

companies‟ reporting between Nigeria and the USA. The reason is the cost of disclosure

theory does not have significant importance in the present era of more advanced accounting

systems and instant reporting. Hence, firm size is not an important influence over a firm‟s

level of intellectual capital disclosure.

Industry differences

Industry differences has previously been used to explain differences in disclosure in annual

reports by Bukh et al (2005), Cordazzo (2007) and Rimmel et al. (2009) because there are

differences in industry disclosure norms. Bukh et al. (2005) explained that as intellectual

capital is regarded as being especially important in high-tech industries, it is anticipated that

IT and biotechnology companies will disclose more information than traditional

manufacturing and commercial companies. Furthermore, since the market-to-book values of

IT and biotechnology companies are generally higher, the disclosure of intellectual capital is

relatively important. These results are similar to the findings of Cooke (1989) which stated

that industry differences have significant effects on intellectual capital disclosure; companies

which are categorized as 'trading', disclosed less intellectual capital information than other

industry types.

Intellectual capital disclosure and CG mechanisms

The relationship between intellectual capital disclosures in Malaysia IPO prospectuses with

CG mechanism can be explained by four variables – Ethnic diversity on the board, chairman

duality role, board size and audit committee size.

Board size

Jensen (1993) pointed out that in cases where the number of board members exceeds seven or

eight, it weakens the function of the board and allows the CEO to easily gain control of the

board. When the number of board members is small, the board‟s communication improves,

and the board members are more likely to reach consensus. On the subject of IC disclosure,

Abeysekera (2010) mentioned that Board size can be a “resource” to companies that inform

investors about future earnings through intellectual capital. Such disclosures can help firms to

improve their share price by informing investors about resources not disclosed in financial

statements.

Ethnic diversity on board of directors

A study by Coffey and Wang (1998) on 98 Fortune 500 companies touched on the subjects of

the present dynamic nature of the world‟s business environment and the emergence of greater

power to a wider set of stakeholder groups. It was observed that increased diversity on boards

of directors would improve decision making. Overall, empirical findings support the general




tenants of resource dependence theory that view human resources as the most vital firm level

resource required in establishing competitive advantage. In Malaysia, studies which explore

the connection between board diversity and intellectual capital disclosure is scarce. The study

by Haniffa and Cooke (2000) covers 167 non-unit trust and non-financial companies. The

study shows there is a significant relationship between the ratios of bumiputra directors on

the board and voluntary disclosure. The result of their study seems to be in tandem the belief

that Islamic values encourage transparency in business. Malays, who are generally Muslims,

are expected to be less secretive in terms of disclosure as compared to Chinese.

Chairman duality

Dimma (2002) explains that a widely held company will perform excellently for its

shareholders if the roles of chairman and CEO are separated. The two reasons are: 1)

separation allows the chairman to focus on the board and its members and on sound corporate

governance; 2) The corporation will perform best if power is distributed between the board

and the management. Williams (2000) also supports the separation of the roles of chairman

and CEO because it will enable the board of directors to implement their decisions related to

intellectual capital disclosure in a more effective manner. Lack of duality will also allow the

board to develop a greater affinity with a more diverse set of stakeholders, such as employees

and customers who will increase the firm‟s overall IC disclosure (Williams, 2000). A study

done by Liang and Li (1999) on 228 China private firms and Abdul Rahman and Mohd

Haniffa (2002), found that duality roles of chairman and CEO did not seem to perform as

well as their counterparts with separate board leadership based on accounting performance

measurement, Return on Total Equity (ROE) and Return on Total Asset (ROA).

Audit committee size

In a study done in South Africa by Williams (2000), they found no association between the

size of Audit Committee and firm‟s intellectual capital disclosure. In Malaysia, a study done

by Abdullah (2001) showed that the Malaysian companies which complied with the KLSE

requirement of Audit Committee have at least three members. Similarly, in another study

done by Muhammad Sori et al. (2001) on the compliance of Audit Committee requirement of

the main and second board firms, it was found that most of the Malaysian Audit Committee

fulfilled the minimum number of members required, which is three people.

Theoretical framework

The theoretical framework of this study is to examine the association between intellectual

capital disclosure and firm characteristics. This study also examines the relationship between

intellectual capital disclosure and CG mechanisms. The control variables in this study are

physical, financial performance and auditor‟s reputation.




Independent Variable

Figure 1: The Theoretical Framework on the Relationship between Intellectual Capital

Disclosures, Firm Characteristics and CG Mechanisms in Malaysian IPO

RESEARCH METHODOLOGY

Sample firm

The main study materials are sourced from IPO prospectuses, which will be collected from

the Bursa Malaysia website. This study examines the relationship of firm characteristics and

board structures with intellectual capital disclosure. The sample comprises 130 IPOs applying

for Bursa Malaysia listing on the Main Market and ACE Market for the period from 2006 to

2010. As shown in the table below, the number of companies that were listed during the

period from 2006 to 2010 is 130 which represent the total initial sample during the

observation window.

Out of this total, 35 companies were excluded due to unavailability of prospectuses; 5

companies from Real Estate Investment Trusts (REITS) were also excluded due to lack of

information on independent variables. Hence, the final sample consists of 90 IPOs which

represent 69.23% of the total number of IPOs listed during the review period of 2006-2010.

Dependent Variable

Intellectual capital

level of disclosure

Board Size

Industry Differences

Firm Size

Firm Age

Control Variable

Physical financial

performance and

auditor‟s reputation

Chairman Duality Role

Audit Committee Size

Board Ethnic Diversity




Table 1: Construction of Sampled Companies

Year 2010 2009 2008 2007 2006

Total 34 11 17 28 40

REITS 2 0 0 0 3

Prospectus not

available

0 0 0 6 29

Final sample 32 11 17 22 8

Hypotheses formulation

Firm age

A study done by Bukh et al. (2005) and Cordazzo (2007) found that firm age does not

influence the firm‟s level of intellectual capital disclosure. Meanwhile, Youndt et al. (2004)

argues that new firms will tend to disclose more intellectual capital than the older firms.

However, a study done by Jaggi (1997) and Rimmel et al. (2009) shows that the level of

accuracy of forecasted information disclosed in IPO prospectuses is influenced by the number

of years that a company is in business; the older companies have more accurate forecasts than

younger firms. Therefore, the hypothesis is developed:

H1: Firm age has a positive association with level of disclosure.

Firm size

Premised on the previous literature dealing with voluntary disclosure, there is an indication of

a positive association between firm size and voluntary disclosure. Bukh et al. (2005) and

Rimmel et al. (2009) found no significant relationship of firm size with level of intellectual

capital disclosure. They found that cost of disclosure theory does not have significant

importance in the present era of more advanced accounting systems and instant reporting.

Thus, firm size is not an important influence over a firm‟s level of intellectual capital

disclosure. However, Cordazzo (2007), Anton (1954), Stanga (1976), and Ahmed & Courtis

(1999) and Williams (2000) demonstrate this positive relationship in their studies. Their

results highlight that small companies show less benefits than larger companies by providing

information to their stakeholders. This is because the costs of providing information are more

than the benefits of an increased disclosure. Therefore the hypothesis is developed:

H2: Firm size has a positive association with level of disclosure


Amir and Lev (1996) expect the high-technology companies to disclose more information

than the low-technology firms because their assets include higher levels of intangibles such

as research and development, patents, etc. Meanwhile, Cordazzo (2007) indicates that high

technology companies have only a small effect or no effect at all on intellectual capital level




of disclosure. Similar to a study done by Pulic (1998), the Austrian companies such as

electronics companies, the media and wholesale trade tend to disclose more intellectual

capital compared to the other industries. Bukh et al. (2005) and Rimmel et al. (2009) also

found a significant positive relationship between industry differences and firm‟s level of

intellectual capital disclosure. These bring to another hypothesis;

H3: Industry differences have a positive relationship with the level of disclosure.

Board size

There are contrasting views in respect to the size of the board on voluntary disclosure.

Empirically, Cheng and Courtenay (2006) found that board size is not associated with the

level of voluntary disclosure. Yermack (1996) presents evidence consistent with the theories

that small boards of directors are more effective; he found an inverse association between

board size and firm value. John and Senbet (1998), Williams (2000) and Abeysekera (2010),

Zainal Abidin et al. (2009) advocate that having more members on the board can help in

enhancing monitoring capacities of the board. The study done by Abeysekera (2010) in

Kenyan listed firms also found that firms disclosing more internal structure and strategic

employee competence have larger boards. Therefore, the hypothesis is developed:

H4: Board size has a positive relationship with the level of disclosure.


A study conducted by Haniffa and Cooke (2000) show that there is a close relationship

between the ratios of bumiputra directors on the board and voluntary disclosure. The result of

their study seems to support the idea that Islamic values encourage transparency in business.

Malays who are all Muslims, are expected to be less secretive in terms of disclosure than the

Chinese. A study done by Williams (2000) and Coffey and Wang (1998) shows there is

positive relationship between intellectual capital disclosure and the ethnic diversity on the

board of directors. Empirical findings from the study support the proposition of a significant

positive link between greater racial diversity across a board of directors and an organization‟s

intellectual capital disclosure. Therefore the hypothesis is developed:

H5: Board ethnic diversity has a positive relationship with the level of disclosure.

Chairman duality

Liang and Li (1999) found there is no connection between the duality of titles and firm‟s

performance among the small private firms in China. Williams, on the other hand, found that

there is a significant negative link between the separation of the roles of CEO and

chairperson on intellectual capital performance. A study by Haniffa and Cooke (2000) and

Abdul Rahman and Mohd Haniffa (2002) found a significant negative relationship between a

non-executive chair and voluntary disclosure. As such, there is a need to call for good

practice of separation of the roles of chairman and CEO. Based on Haniffa and Cooke‟s

findings on Malaysian corporations, it is hypothesized that an independent chairman will be

inclined to voluntarily disclose more IC. Therefore, the hypothesis is developed:




H6: Chairman Duality role has a negative relationship with the level of disclosure.


Williams (2000) and Kajola and Sunday (2008) found no association between audit

committee size and firm‟s intellectual capital disclosure. However, Ho and Wong (2001)

found that an effective audit committee should improve internal control. It also acts as a

means of overcoming agency costs. They found the presence of audit committee leads to

improved and increased disclosure. Based on this finding, it is hypothesized that a bigger

audit committee size and more frequent audit committee meetings will encourage more

voluntary disclosure of intellectual capital. Therefore, the hypothesis is developed:

H7: Audit committee size has a positive relationship with the level of disclosure.

Measurement

Disclosure score index (DSI)

The study by Guthrie and Petty (2000) adopted the 24 voluntary disclosure items which are

classified into three most common categories, namely: (i) employee competence, (ii) internal

structure, and (iii) external structure (Sveiby, 1997). Other studies that replicated or extended

the framework include studies by Abeysekera and Guthrie (2005) in Sri Lanka, Bozzolan et

al. (2003) and Cordazzo (2007) in Italy, Brennan (2001) in Ireland, Goh and Lim (2004) and

Abdul Rashid et al. (2009) in Malaysia, Guthrie and Petty (2000) in Australia, Rimmel et al.

(2009) in Japan and Vandamaele et al. (2005) in Netherlands, Sweden and the UK.

Table 2: Intellectual Capital Disclosure Items

Employee Competence Internal Structure External Structure

Know-how Patent Brands

Education Copyright Customers

Vocational qualification Trademarks Customers loyalty

Work-related knowledge Management philosophy Companies' name

Work-related competencies Corporate culture Distribution channel

Entrepreneur spirit Management processes Business collaboration

Information system Licensing agreement

Networking system Favourable contract

Financial relation Franchising agreement

This can be seen in the following formula which is used to calculate the disclosure score

index of the prospectus:

Disclosure Score Index= (

m

i

id1 /M) x 100%

Where di= 1 if the item di is disclosed in the prospectus, and otherwise 0.

M = total number of disclosure items i.e. 24 items.




Table 3: Measurement of Independent Variables

Independent Variable Measurement

Firm Age Date of incorporation until the date of publishing the

IPO prospectuses in the current year

Firm Size Total sales

Industry Differences Dummy variables (1=Trading Service, 0=Other

Industry)

Board size Number of board of directors in a company

Ethnic Diversity on Board of Director Percentage of non-bumiputra directors on the board

Chairman Duality Dummy variables (1=Duality. 0=No Duality)

Audit committee size Number of board of directors serving on the audit

committee

Control variables

The inclusion of the control variables is to avoid intellectual capital performance from being

influenced by other factors. Based on prior research, all the regression models tested were

controlled for the following factors:

a. Physical financial performance

The physical financial performance is referred to as the return on a firm‟s physical assets. It is

anticipated that the return on a firm‟s physical assets may have a positive bearing on a

director‟s decision related to intellectual capital disclosure (Williams, 2000, Rimmel et al.,

2009). If a firm‟s physical performance is deemed satisfactory, then pressure may not be

placed on directors to undertake more immediate short-term goals to churn out financial

returns. As such, greater energy and time may therefore be allocated to the maintenance and

generation of intellectual capital assets such as research and development of patents that may

need time to produce a financial return. Physical financial performance is measured by an

entity‟s return on asset (Abdul Rahman & Haniffa, 2002; Williams, 2000).

b. Auditors reputation

Auditors that are well known and reputable may signal that information disclosed in the

financial statements is of higher quality. This goes to show that an auditor‟s reputation, to

some degree, exerts an influence on the reliability of intellectual capital disclosure in IPOs.

Hence, the choice of auditors becomes one of the controlling factors in this study. The

company will be given one if it is audited by one of the Big 4 firms and zero if it is audited by

other auditing firms, similar to the study done by Zahn et al. (2007).

Model:

DSI = α+ β1AGE+ β2FSIZE + β3INDDIFF + β4BSIZE + β5PERNONBUMI+ β6BDUAL +

β7ACSIZE + β8CONTROL VARIABLES (1)

Where:

DSI = Disclosure score index for intellectual capital disclosures in IPOs

FSIZE = Firm size measured by company's total assets




AGE = Firm age measured by years of incorporation

INDDIF = Industry differences classified into 11 categories

BSIZE = Total number of individuals elected as directors on the firm‟s board at the

time of listing

PERNONBUMI = Percentage of Non Bumiputra directors held on the board

BDUAL = Dummy variable if the position of chairman of the board and chief

executive officer is occupied by the same individual at the time of listing is

scored as one (1); otherwise a zero (0)

ACSIZE = Number of individual elected as audit committee at the time of listing

DATA ANALYSIS AND RESEARCH FINDINGS

Spearman correlation coefficients

The results below show the correlation of disclosure score index (DSI) is significant with

firm age (AGE), board size (BSIZE), chairman duality (BDUAL) and audit committee size

(ACSIZE) but insignificant with firm size (FSIZE), industry differences (INDIFF) and

percentage of non bumiputra (PERNONBUMI). The result also shows DSI is insignificant

with control variables, namely physical financial performance (ROA) and auditor reputation

(AUREP).

For the correlation between independent variables, the study found that industry differences

(INDIFF) are negatively significant with the percentage of non bumiputra (PERNONBUMI)

and board size (BSIZE) negatively significant with chairman duality (BDUAL). For the

correlation between independent variables and control variable, the study found that firm size

(FSIZE) is positively significant with the two control variables, namely physical financial

performance (ROA) and auditor reputation (AUREP).

Table 4: Spearman Correlation Matrix (N=90)

DSI AGE FSIZE INDIFF BSIZE PERNONBUMI BDUAL ACSIZE ROA AUREP

DSI 1.000 .289** .070 -.007 .354** -.139 -.249* .270** -.058 .152

AGE .289** 1.000 -.026 .074 .147 .037 -.038 -.013 .075 .073

FSIZE .070 -.026 1.000 .051 .118 -.126 -.010 .109 .364** .397**

INDIFF -.007 .074 .051 1.000 .065 -.219* .032 -.096 .074 -.054

BSIZE .354** .147 .118 .065 1.000 -.139 -.225* .179 .057 .108

PERNONBUMI -.139 .037 -.126 -.219* -.139 1.000 .115 -.180 .145 -.151

BDUAL -.249* -.038 -.010 .032 -.225* .115 1.000 -.134 -.007 -.054

ACSIZE .270** -.013 .109 -.096 .179 -.180 -.134 1.000 -.003 .064

ROA -.058 .075 .364** .074 .057 .145 -.007 -.003 1.000 .007

AUREP .152 .073 .397** -.054 .108 -.151 -.054 .064 .007 1.000

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

* Correlation is significant at the 0.05 level (2-tailed)




Regression analysis

Table 5: Multiple Regression Analysis

All years (2006-2010) Beta t Sig.

(Constant)

-2.787 0.007

Firm Characteristics

Firm age (AGE) .317 3.271 0.002*

Total Sales (FSIZE) -.127 -1.282 0.204

% type of industry (INDIFF) .026 0.265 0.792

CG Mechanisms

No. of Board of Directors (BSIZE) .250 2.449 0.017**

% of Non Bumiputra Directors (PERNONBUMI) -.049 -0.480 0.633

Chairman and Executive Director (BDUAL) -.158 -1.678 0.097***

No. of Audit Committee (ACSIZE) .172 1.789 0.077***

Control Variables

Physical Financial Performance (ROA) -.006 -0.065 0.949

Auditor Reputation (AUREP) .125 1.312 0.193

Model

Model Summary

R-Squared 0.337

Adjusted R-Squared 0.262

F-Statistics 4.517

p-value 0.00

*Significant at 0.01 level;**Significant at 0.05 level;***Significant at 0.10 level

Firm age

The results above show positively significant relationship between firm age and intellectual

capital level of disclosure at 1% level. Thus, Hypothesis 1 which states that there is a

significant positive relationship between firm age and intellectual capital disclosure is

accepted. This result is similar to the study done by Rimmel et al. (2009) in Japan and Jaggi

(1997) who found that there is positive significant relationship between firm age and

intellectual capital disclosure in IPO prospectuses. The results indicate that the history of the

company does matter to the capital market, and the track record of companies is continually

emphasized by capital market actors. The results also indicate the mature companies, i.e.

companies which have been in existence for comparatively longer periods of time, are likely

to disclose more intellectual capital. This is probably because of their experience in the

business (Jaggi, 1997).

However, the study is contrary to Bukh et al. (2005) who found that firm age does not have a

significant relationship with firm intellectual capital disclosure. Cordazzo (2007) also found

there is no significant relationship between firm age and intellectual capital disclosure in

Italian IPO prospectuses. Thus, this study indicates that the firm age has the greater impact on




the decision making process that would lead to improvement in the level of intellectual

capital disclosure in Malaysian IPO prospectuses.

Firm size

The results of this study show negative, but no significant relationship between firm size and

intellectual capital disclosure in Malaysian IPOs. Thus, Hypothesis 2 which states that there

is a significant relationship between firm size and intellectual capital disclosure in

prospectuses is rejected. This study supports the findings from Rimmel et al. (2009) and

Stanga (1976) who found company size had no significant influence on the extent of

disclosure by Japanese companies on intellectual capital disclosure. Bukh et al. (2005) also

did not find a significant relationship between firm size and firm‟s level of intellectual capital

disclosure. This study indicates that large companies did not disclose more information and

details as compared to small companies. On the contrary, Cordazzo (2007) found that there

is evidence that firm sizes are determinants of the level of intellectual capital disclosure in

Italian IPOs. This study indicates that the cost of disclosure theory does not have a significant

importance in the present era of more advanced accounting systems and instant reporting.

Thus firm size is not an important influence over the firm‟s level of intellectual capital

disclosure.


The third objective of this study is to examine the relationship between industry difference of

public listed companies in Malaysia and the firms‟ level of intellectual capital disclosure in

prospectuses. The results show that there is no significant relationship between industry

difference and a firm‟s intellectual capital disclosure. As such, Hypothesis 3 is rejected.

These studies indicate that industry differences have not played a significantly important

factor on the level of intellectual capital disclosure in Malaysian IPOs.

This study is contradictory to the study done by Bukh et al. (2005), Cordazzo (2007) and

Rimmel et al. (2009) who found that industry difference has a positively significant impact

between industry difference and firm‟s intellectual capital disclosure. The result also

contradicts the findings of Cooke (1989) which stated that industry differences has significant

effects on intellectual capital disclosure and that companies categorized as trading/services

disclosed less intellectual capital information than other industry types.

Board size

The fourth objective of this study is to examine the relationship between board size and

intellectual capital level of disclosure in Malaysian IPOs. The results show that there is

positively significant relationship between board size and a firm‟s level of intellectual capital

disclosure at 5% level. Thus, Hypothesis 4 which indicates that there is positive significant

relationship between board size and firm intellectual capital disclosure is accepted.

The result is also similar to the study done by Abeysekera (2010) in Kenyan listed firms and

Zainal Abidin et al. (2009) in Malaysian public listed firms. They examine the influence of

board size on each intellectual capital disclosure outcome and find that firms disclosing more




tactical internal capital and more strategic human capital have larger boards. The result

indicates that having more members on the board can help in enhancing monitoring

capacities of the board and there seems to be no communication and coordination problem

among the board members. These studies indicate that board size in Malaysian public listed

companies is important in influencing the firms‟ level of intellectual capital disclosure.


The fifth objective of this study is to examine the relationship between racial diversity of

public listed companies in Malaysia and the firms‟ intellectual capital disclosure. The results

indicate that the percentage of non-bumiputra (Malaysia non-bumiputra and foreigners)

individuals on the boards of directors of Malaysian public listed companies has positive but

no significant relationship to the firms‟ intellectual capital disclosure. As such, Hypothesis 5

which states that there is a significant relationship between the percentage of non-bumiputra

individuals on the boards of directors of Malaysian public listed companies and the firm‟s

level of intellectual capital disclosure is rejected.

These results found in this study are in contrast to the studies by Coffey and Wang (1998)

and Williams & Ho (2001) who found a significant positive relationship between diversity

across the board of directors in terms of racial diversity with the firms‟ performances. Thus,

the results in this study indicate that the existence of non-bumiputra on board have no impact

on the board of directors‟ decision making process that would lead to improvement in

intellectual capital disclosures.

Chairman duality

The results of this study also found a negatively significant relationship with regard to

separation between chairman and chief executive officer and intellectual capital disclosures at

10% level. Thus, Hypothesis 6 which states that there is a significant relationship between

role duality (same person occupying the post of Chairman and CEO) in Malaysian public

listed companies and the firms‟ intellectual capital disclosure is accepted. The results of this

study are similar to the study by Abdul Rahman and Mohd Haniffa (2002). They found the

companies with role duality seemed not to perform as well as their counterparts with separate

board leadership in disclosing intellectual capital information. However, Williams (2000) on

the other hand, found a positive significant relationship when the roles of CEO and chairman

are combined. The result of this study is also contrary to Liang and Li (1999) who also found

no relationship between role duality and a firm‟s disclosure. Thus, the results in this study

suggest that a board with role duality may be biased in its decision making that leads to

increase in the intellectual capital level of disclosure. As such, the results of the current study

imply that a clear separation of duties between the board chairman and CEO is an important

factor in determining the intellectual capital level of disclosure. The board cannot be seen as

effective and independent if the board chairman is also the CEO of the company as a conflict

of interests will inevitably arise and the risk of one person dominating the decision making

and the running of the company is high.





The results show a positive significant relationship between audit committee size and

intellectual capital level of disclosure at 10% level. Thus, Hypothesis 7 which states that there

is a significant positive relationship between the audit committee size of Malaysian public

listed companies and the firm‟s level of intellectual capital disclosure is accepted.

The results are similar to Ho and Wong (2001) who found that an effective audit committee

should improve internal control. It also acts as a means of overcoming agency costs. They

found the presence of audit committee leads to improved and increased disclosure. However,

this study is in contrast to the findings by Williams (2000); this result suggests that the audit

committee size does not have any impact on the level of intellectual capital disclosure. Thus,

these results indicate that audit committee size plays an important role in determining the

level of intellectual capital disclosure in firms. The larger the audit committee size, the higher

is the level of intellectual capital disclosure.

Control variables

Table above also shows that physical financial performance (ROA) and Auditor Reputation

(AUREP) for all years (2006-2010) has no significant relationship with the firms‟ level of

intellectual capital disclosure in Malaysian IPOs. These indicate that ROA and AUREP do

not play an important role in determining the level of intellectual capital disclosure.

CONCLUSION

The overall findings in this study do not appear to provide complete support for a stakeholder

or agency theoretical perspective to explain the association between CG mechanisms and

intellectual capital disclosure in Malaysian IPOs. The findings also do not provide complete

support of the information asymmetry theory in explaining the firm characteristics and

intellectual capital disclosure. Rather, the findings show that the Malaysian boards should

support the stakeholder-agency theory and information asymmetry theory where the directors

should be taking care of the stakeholders and not only the shareholders. The implication of

this conclusion is that the boards of directors of public listed companies in Malaysia must

adjust their decision-making processes to meet rapid changes and the importance of

disclosing intellectual capital.

Directors must address the needs of stakeholder groups such as employees, suppliers,

customers and creditors in order to disclose the firms‟ intellectual capital more efficiently. At

the same time, however, strong managerial control has to be maintained to ensure that the

firm remains focused on the dynamic changing business environment when managing and

developing their intellectual capital assets. The company also needs to disclose more

information in the intellectual capital in order to reduce the firm‟s cost of capital and thus

increase the efficiency. Overall, the statistical findings from this study have demonstrated

similar results in most areas of study as compared with previous studies. These results did not

provide any significant relationship between all the control variables with IC disclosure using

disclosure score index (DSI).




REFERENCES

Abdul Rahman, R. & MohdHaniffa, R. (2002). CEO duality and corporate performance:

evidence in Malaysia. Paper presented at International Conference on Corporate

Governance-Trends and Challenges in the millennium, Kuala Lumpur

Abdul Rashid, A., Wan Abdullah, W. M. T., Tarmidi, M. & Zainol, Z. (2009). Voluntary

disclosure of intangible information: a comparative study between prospectuses and

annual reports. Paper presented at International Symposium on Finance and

Accounting 2009, Kuala Lumpur

Abdullah, S.N. (2001). Characteristics board directors and audit committees: among

Malaysian listed companies in period leading to 1997 financial crisis. Akauntan

Nasional, 18-21

Abeysekera, I. (2010). The influence of board size on intellectual capital disclosure by

Kenyan listed firms. Journal of Intellectual Capital, 11(4), 504-518

Abeysekera, I. and Guthrie, J. (2005).An empirical investigation of annual reporting trends of

intellectual capital in Sri Lanka.Critical Perspectives on Accounting, 16(3), 151-163

Ahmed, K. &Courtis, J.K. (1999). Associations between Corporate Characteristics and

Disclosure Levels in Annual Reports: A Meta-Analysis. British Accounting Review,

31, 35–61

Amir, E., & Lev, B. (1996). Value-relevance of nonfinancial information: The wireless

communications industry. Journal of Accounting and Economics, 22, 1-3

Anton, H.R. (1955). Funds statement practices in the United States and Canada. The

Accounting Review, 620-627

Bontis, N. (2004). National intellectual capital index. A United Nations initiative for the Arab

region. Journal of Intellectual Capital. 5(1), 13-39

Bozzolan, S., Favotto, F. &Ricceri, F. (2003). Italian annual intellectual capital disclosure:

An empirical analysis. Journal of Intellectual Capital, 4(4), 543-558

Brennan, N. (2001). Reporting intellectual capital in annual reports: Evidence from Ireland.

Accounting, Auditing and Accountability Journal, 14(4), 423-436

Bukh, P. N., Nielsen, C., Gormsen, P. &Mouritsen, J. (2005). Disclosure of information on

intellectual capital in Danish IPO prospectuses. Accounting, Auditing and

Accountability Journal, 18(6), 713-732

Bursa Malaysia Listing Announcement, Retrieved January 20, 2010 from

http://announcement.bursamalaysia.com

Cheng, E. C. M. & Courtenay, S. M. (2006). Board composition, regulatory regime and

voluntary disclosure. The International Journal of Accounting, 41, 262–289

Coffey, B. S., & Wang, J. (1998), board diversity and managerial control as predictors of

corporate social performance. Journal of Business Ethics, 17(14), 1595-1603

Cooke, T. E. (1989), Voluntary corporate disclosure by Swedish companies, Journal of

International Financial Management and Accounting, 1, 171-95

Cordazzo, M. (2007). Intangibles and Italian IPO prospectuses: a disclosure analysis. Journal

of Intellectual Capital, 8(2), 288-305

Dimma, W.A. (2002). Excellence in the boardroom: Best practices in corporate directorship,

institute of corporate directors, Ontario, John Wiley & Sons Canada Ltd, 69-94

Goh, P. C. & Lim, K. P. (2004).Disclosing intellectual capital in company annual reports:

Evidence from Malaysia, Journal of Intellectual Capital, 5(3), 500-510

Guthrie, J. & Petty, R. (2000). Intellectual capital: Australian annual reporting practices.

Journal of Intellectual Capital, 1(3), 241-250

Haniffa, R. & Cooke, T. (2000). Culture, corporate governance and disclosure in Malaysian

corporations. Paper presented at the Asian AAA World Conference, Singapore




Hill, C.W. & Jones, T.M. (1992). Stakeholder-Agency Theory. Journal of Management

Studies, 29(2), 131-154

Ho, S. S. M. & Wong, K. S. (2001). A study of the relationship between corporate

governance structures and the extent of voluntary disclosure. Journal of International

Accounting, Auditing and Taxation, 10, 139-156

Jaggi, B. (1997). Accuracy of forecast information disclosed in the IPO prospectuses of Hong

Kong companies. The International Journal of Accounting, 32 (3), 301–319

Jensen, M.C. (1993). The Modern Industrial Revolution, Exit, and the Failure of Internal

Control Systems. Journal of Finance. 48(3), 831-880

John, K., Senbet, L. (1998). Corporate governance and board effectiveness, Journal of

Banking and Finance, 22(4), 371-403

Kajola & Sunday O, (2008). Corporate Governance and Firm Performance: The Case of

Nigerian Listed Firms, European Journal of Economics, Finance and Administrative

Sciences, 14, 16-28

Lev, B. and Zarowin, P. (1999). The boundaries of financial reporting and how to extend

them. Journal of Accounting Research, 37(2), 353-385

Liang, N. & Li, J. (1999). Board structure and firm performance: new evidence from china‟s

private firms. Paper presented at the Academy of Management Annual Conference,

Chicago

Malaysia Code of Corporate Governance (Revised 2007). Retrieved December 12, 2010 from

http://www.sc..com.my

Muhammad Sori, Z., Mohamad, S. & Abdul Hamid, M.A. (2001). Compliance of audit

committee requirements: a brief review of the practice. Akauntan Nasional, May, 4-7.

Mustapha, R. & Abdullah, A. (2004). Malaysia Transitions Toward a knowledge-based

economy. The Journal of Technology Studies, 30(3), 51-61

Pulic, A. (1998). Measuring the performance of intellectual potential in knowledge Economy.

Retrieved 1 January 2011 from http://www.measuring-ip.at

Rimmel, G., Nielsen, C. & Yosano, T. (2009). Intellectual capital disclosures in Japanese IPO

prospectuses. Journal of Human Resource Costing & Accounting. 13(4), 316-337

Robb, S. W. G., Single, L. E. & Zarzeski, M. T. (2001).Nonfinancial disclosures across

Anglo-American countries. Journal of International Accounting, Auditing & Taxation,

10 (1), 71-83

Singh, I. and Van der Zahn, J.L. W. M. (2007). Does intellectual capital disclosure reduce an

IPO‟s cost of capital? The case of underpricing. Journal of Intellectual Capital, 8(3),

494-516

Stanga, K. (1976). Disclosure in published annual reports, Financial Management, 5(4), 42-

52

Sveiby, K. E. 1997, The New Organisational Wealth: Managing and Measuring Knowledge

Based Assets, San Francisco, CA, Berrett-Koehler

Vandemaele, S. N., Vergauwen, P. G. M. C. and Smits, A. J. (2005). Intellectual capital

disclosure in The Netherlands, Sweden and the UK: A longitudinal and comparative

study. Journal of Intellectual Capital, 6(3), 417-426

Vergauwen, P., Bollen, L. and Oirbans, E. (2007). Intellectual capital disclosure and

intangible value drivers: An empirical study. Management Decision, 45(7), 1163-

1180

Wallace, R.S. (1998). Corporate Financial Reporting in Nigeria. Accounting & Business

Research, 18(72), 352-362

Williams, S.M. (2000). The association between gender and ethnic diversity of board

structure on the intellectual capital performance of publicly listed companies from an




emerging economy: evidence from South Africa. Retrieved January 10, 2011 from

http://www.vaic-on.net

Williams, S. M. & Ho, C. A. (2001). Testing the association between gender and race

diversity on the boards of directors of publicly listed companies in South Africa and

intellectual capital performance. Area of Accounting, Faculty of Management,

University of Calgary, Canada, 1-37

Yermack, D. (1996). Higher market valuation of companies a small board of directors,

Journal of Financial Economics, 40, 185-202

Youndt M A, Subramaniam, M. & Snell, S.A. (2004). Intellectual Capital Profiles: An

Examination of Investments and Returns, Journal of Management Studies, 41(1), 335-

36

Zahn, J. L. W. M. V.d., Singh, H., & Singh, I. (2007). Association between independent audit

committee members‟ human resource features and underpricing: The case of

Singapore IPOs from 1997-2004. Paper presented at Intellectual Capital Congress

2007, Harlem

Zahra, S.A. & Pearce, J.A. (1989). Boards of directors and corporate financial performance:

A review and integrative model. Journal of Management, 15(2), 291-334

Zainal Abidin, Z., Kamal, N.M. & Jusoff, K. (2009). Board Structure and Corporate

Performance in Malaysia. International Journal of Economics and Finance, 1(1),

150-164

Leadership behavior and organizational performance: A case of 100 best co-operatives in Malaysia


LEADERSHIP BEHAVIOR AND ORGANIZATIONAL PERFORMANCE: A CASE

OF 100 BEST CO-OPERATIVES IN MALAYSIA

Mohd Zainal Munshid bin Harun

[email protected]

Othman B Chin

[email protected]

Sharul Nizam B Salahuddin

[email protected]

Mohd Yunus B Majid

[email protected]


ABSTRACT

The purpose of this study was to investigate the effect of transformational leadership

behavior on organizational performance in the case of 100 best co-operatives in Malaysia.

Data were gathered from 100 best co-operatives‟ top management. Data on the respondent‟s

transformational leadership behavior were collected using MLQ-LF. In this study, it was

found that transformational leadership was significantly correlated with organizational

performance. Specifically, the transformational leadership dimensions were found

significantly correlated with organizational performance. Ultimately, this study contributes to

the existing body of knowledge on the effect of leadership and organizational performance

within the co-operatives movement which was considered limited. As such, the results also

offer more comprehensive understanding of the transformational leadership dimensions that

effects performance in the co-operatives movement.

Keywords: leadership behavior, organizational performance, co-operative movement

INTRODUCTION

Theoretical and empirical studies conducted on organizational performance have been a focus

of many scholars over the past thirty years and had been recognized as one of the main

constructs in many business management researches and also as a tool that measures business

success (Tai & Huang, 2007). Furthermore, interdisciplinary researchers and practitioners

have devoted considerable attention to identifying determinants of organizational

performance. The organizational culture (Banton, 2002), the organizational factors (Ortega,

Azorin & Cortes 2010), the organizational learning (Lopez, Peon & Ordas, 2005) and

leadership (Rowe, Cannella, Rankin & Gorman, 2005) have all been reported to have a

connection to organizational performance. But the most prominent determinant of this

interest is the belief and importantly, the leadership behavior that had the most important

effect on the performance of the organization. The effects of leadership on performance are

important because leadership was viewed as one of the key driving forces for improving







organizational performance (Zhu, Chew & Spangler, 2005; Rowe et al., 2005). Bass (1985)

identified three leadership behaviors, namely; transformational, transactional, and passive

avoidance, that have great influence on performance. Bennis (1997) described those leaders

who are adaptive transformational leadership style, the ability to work with many different

types of people, a skill that makes them more effective at creating solutions to difficult

problems while molding their followers to respond to a wide range of responsibilities much

more other leadership styles. Although there are numerous studies reporting that engaging in

transformational leadership style results in higher levels of organizational performance

(Elenkov, 2002; Dvir, Eden, Avolio, & Shamir, 2002; Avolio, 1999; Bass & Avolio, 1994)

few studies have gone so far as to detail out how transformational leadership dimensions

predict performance (Verdigets, 2008; Bass, Avolio, Jung, & Berson, 2003). Moreover,

limited empirical studies have been conducted in explaining the relationship between

transformational leadership style and performance (Jing & Avery, 2008; Elenkov, 2002).

Thus, there is a need to explore this relationship further. The purpose of this study was to

investigate the relationship between transformational leadership and performance.

Specifically, the objective of the study was to investigate the relationship between

transformational leadership dimensions and performance in the context of co-operatives

movement in Malaysia.

Co-operative movement in Malaysia

A cooperative can be described as an autonomous organization where members come

together voluntarily in order to achieve joint interests and joint aspirations in the field of

economics, social aspects and culture, regardless of gender, socio-cultural and religious

beliefs which are owned jointly and democratically controlled (Salleh, Arshad, Shaarani, &

Kasmuri, 2008). Cooperation has been based on the concept of mutual assistance and concern

for the community around it. In addition, there is also such determination of the existence of a

group of people to work together to solve problems faced without external assistance. Thus

the whole structure of the cooperatives is based on the concept of self-reliance and

cooperation in which members have the same rights, duties and responsibilities and agree to

manage it together (Tan & Selvarani, 2008). In Malaysia, co-operative movements have

played a significant economic and social role and demonstrate their relevancy to economic

and social development. Hence, it is a government aspiration to recognize co-operatives

movement to become a Malaysian fourth growth engine after manufacturing, services and

agriculture that can contribute to national economic growth (Tan & Selvarani, 2008). Even

though the cooperative movement has been experiencing a very significant performance,

especially in contributing to economic growth, but it is still unable to achieve a competitive

position in industry (Thuraisingham, 2008). A study by Sapran (2010) on co-operatives

movement from 2002-2010 highlighted that lack of competent leaders and management

practices remained unsolved toward the movement‟s performance. Yusof (2008) found that

it is an advantage to any cooperatives if they have leaders who are very creative and

innovative especially in efficiently and effectively managing organizational resources. It is

important that, the competitive and ever-changing environment in today‟s co-operatives

movement calls for the leadership style to be more flexible and adaptive to meet the ever

increasing challenges of running the co-operative to a high level of performance (Sapran,

2010).



Transformational leadership dimensions and organizational performance

The study of transformational leadership has confirmed that transformational leaders are one

of the paradigms of leadership and has been the focus of various researches since the early

1980‟s (Northouse, 2004; Podsakoff, MacKenzie, & Bommer, 1996). Transformational

leadership styles have been validated against numerous variables that measure organizational

performance (Podsakoff et al., 1996). Patiar and Mia (2009) explained the relationship

between transformational leadership dimensions and performance in the hotel sector.

Performance was measured in terms of financial and non-financial aspect. The results of the

study supported that manager that exhibited transformational leadership styles played

important roles in contributing to the performance. It is noteworthy to study the effect of the

transformational leadership style of performance over a long period of time in a longitudinal

study because the improvements and deterioration in performance could be meaningfully

assessed and it is essential to replicate in other sectors. Idealized influenced leadership was

found as a predictor of financial performance in 48 Fortune 500 firms (Waldman, Ramirez,

House, & Puranam, 2001). In addition, this study found that idealized influence predicted

performance under conditions of uncertainty, but not under conditions of certainty. Another

study conducted by Purvanova and Bono (2009) also highlighted the connection between

transformational leadership dimensions and performance that involved undergraduate

students at a public university. Transformational leadership was examined in the context of

traditional team using face-to-face communication and virtual teams. The results suggested

that the leaders who enhance their transformational leadership behavior achieved higher level

of performance. However, it is possible that the study using a sample of mature respondents

i.e top managers or leaders who are familiar with the teamwork concept, will lead to a

broader explanation of transformational leadership behavior and its performance relationship.

In a longitudinal study, Elenkov (2002) investigated the effects of transformational

leadership behaviors on organizational performance of Russian companies. The sample

consisted of 253 senior managers and 498 immediate subordinates who reported directly to

the senior managers. Transformational leadership behaviors were measured by the MLQ and

the results of the correlation tests showed that the correlation between transformational

leadership factors and organizational performance were high. Overall, the results showed that

that transformational leadership directly and positively predicted organizational performance

of Russian companies over and above the effects of other leadership styles. With regard to the

transformational leadership dimensions, hypothesis from the study stated that there will be a

positive relationship between idealized influence, individualized consideration and

intellectual stimulation and organizational performance. The results indicated that the

idealized influence-organizational performance was positive; individualized consideration-

organizational performance was positive; intellectual stimulation-organizational performance

was also positive. Nemanich and Keller (2006) conducted a study that involved employees in

a major acquisition integration. The researchers addressed the relationships that

transformational leadership and climate had with performance and job satisfaction in an

uncertain environment. Generally transformational leadership was positively related to

acquisition acceptance, performance and job satisfaction. However, transformational

leadership was analyzed separately to explain more accurate findings for the study. The four

dimensions of transformational leadership: idealized influence, inspirational motivation,

intellectual stimulation and individual consideration were added to the analysis. The results

showed that all transformational leadership dimensions were positively related with

performance. In addition it showed that idealized influence had the strongest relationship

with performance. Idealized influence behavior leader has a strong moral conviction about



values and beliefs in any situation and also persistent and effective in influencing positive

consequences for performance. The findings of the study provided insights into a specific

process by which transformational leadership effects subordinates by encouraging

subordinates to demonstrate new ideas, support for creative thinking and new ways to

perform tasks. Li and Hung (2009) employed the role of leader-member relationships in

explaining the relationship between transformational leadership and job performance since

these relationships remain unclear in contemporary organizational literature. Data were

collected from 1,040 teachers in 52 elementary schools in Taiwan. One of the constructs that

was used in the study to measure job performance was task performance. Results of the

hypothesis tested regarding transformational leadership dimensions and job performance

found a positive relationship between these variables. The results showed that

transformational leadership dimensions individualized consideration, inspirational motivation,

idealized influence, and intellectual stimulation all positively and significantly influenced job

performance, thus supporting the hypotheses. However, since the data was collected from

educational institutions which are different from a private organization, it will limit the

generalizability of the results to other industries. Therefore, based on the above discussion,

the following hypotheses were formulated:

H1: There is a relationship between idealized influence and organizational performance.

H2: There is a relationship between inspirational motivation and organizational performance.

H3: There is a relationship between intellectual stimulation and organizational performance.

H4: There is a relationship between individual consideration and organizational performance

METHODOLOGY

Data collection

Before distributing the questionnaire, the researchers asked for a recommendation and

support in the form of a letter from the Malaysia Co-operative Commission to conduct the

study. The justification of conducting the study was highlighted as the main content of the

letter. As such, it highlighted the benefits of this study to the Malaysia Co-operative

Commission directly and indirectly. The Commission also provides the latest directory of

100 best cooperatives in Malaysia. After permission was received from the respective sectors

to conduct the study, a total of 100 questionnaires (comprising a supporting letter from the

Malaysia Co-operative Commission, an introduction letter from the researchers and a full set

of questionnaires) were mailed to the respondents together with a completed self-addressed

envelope. The letter attached with the questionnaire also stressed that the information

provided would be treated with strictest confidence and would be used only for academic

purposes. A soft reminder letter was mailed after one week to all the respondents reminding

them to complete and return the questionnaires. The respondents in this study were

cooperative managers. In the case of 100 best cooperatives, a manager was appointed to run

the overall operational activities. They were considered as the most likely key person that can

furnish information, since they are directly involved in daily activities of the co-operative. In

addition, the influence of their decision making attributes over organizational performance

therefore their feedback is expected to be more substantial.



Measures

Transformational leadership

Multifactor Leadership Questionnaire – Leader Form, a 45 item questionnaire is commonly

used to measure multiple aspects of transactional, transformational, and laissez faire

leadership styles. The validity of this Multifactor Leadership Questionnaire had been

demonstrated in various ways, including factor inter-correlations consistent with theory,

correlations of self rating of outcomes, and correlations of supervisee ratings of leader with

external criteria (Avolio & Bass, 2004). Further, the Multifactor Leadership Questionnaire

had been included in over 75 research studies with findings published in journals,

dissertations, book chapters, conference papers, and technical reports (Lowe, Kroeck, &

Sivasubramaniam, 1996). According to Lowe et al., (1996), the Multifactor Leadership

Questionnaire has been used to study leaders at all levels of both public and private

organizations in business and industry, the military, and educational and religious institutions,

as well as to study organizational measures of performance. The Multifactor Leadership

Questionnaire is the most widely used measure of transformational leadership (Northouse,

2004). For this study, the researcher adopted the questionnaire and used it to measure the

self-perceptions of manager of cooperatives on transformational leadership style. As this

study focused on evaluating transformational leadership, only 20 items were used in

evaluating transformational leadership dimensions. Furthermore, many empirical studies have

shown consistently that these dimensions are highly correlated and that they reflect a higher-

order construct of transformational leadership (Bass & Avolio, 1999). This is consistent with

theoretical developments (Bass, 1998) and empirical studies on transformational leadership

theory (Walumbwa, Wang, Lawler, & Shi 2004; Bono & Judge, 2003; Kark, Shamir, & Chen,

2003; Walumbwa & Lawler, 2003). To complete the questionnaire, each of the 20 descriptive

statement was measured on a five-point scale (1= not at all, 2 = once in a while, 3 =

sometimes, 4 = fairly often, 5 = always) regarding how frequently each statement applies to

the respondent being described.

Organizational performance

Organizational performance was measured by adopting Murphy, Trailer, & Hill (1996)

measures of efficiency, growth, profit, and size, liquidity as it is an advantage when adapting

multiple indicators that incorporates financial and non-financial performance in the

assessment (Mia & Clarke, 1999). The instrument comprised of 6 items. All items were rated

on a five-point Likert-type scale, and were coded on a scale of 5 (significantly higher) to 1

(significantly lower).

Reliability Test

The reliability tests shown in Table 1 indicated an excellent reliability for all its components with a

coefficient alpha of above 0.7 exceeding the minimum acceptable level as suggested by Nunnally

(1978).



Table 1: Overall Internal Reliability

No Variables Reliability

(Cronbach‟s Alpha)

1 Idealized Influence 0.83

2 Inspirational Motivation 0.75

3 Intellectual Stimulation 0.72

4 Individual Consideration 0.72

5 Organizational Performance 0.75

RESULTS

Demographic profile of respondents

Table 1 indicates that most of the respondents were male (96%). The highest percentage of

the respondents were in the age group of 45 and above (75%), followed by the age group

range 41-45 (24%). The responses from the respondents also indicated that 95% had a degree

level of education; 3% had Master/PhD and 2% had a Diploma level of education. Majority

of the respondents have served the cooperatives 11 to 15 year (71%). Furthermore, it showed

that 77 of the cooperatives were intensively involved in credit/finance (77%) as their main

activities. With reference to the cooperative sales, the sample showed that the majority of the

cooperatives were able to generate RM301, 000- RM450, 000 (50%) annually. Finally, it

showed that those cooperatives that has 501- 1,000 members (53%) were already established

for more than 15 years (64%)

Table 2: Demographic Profile

Demographic variable Frequency Percent

Gender

Male 96 96.0

Female 4 4.0

Age

35-40 1 1.0

41-45 24 24.0

Above 45 75 75.0

Education Level

Diploma 2 2.0

Degree 95 95.0

Master /PhD 3 3.0

Services

6 – 10 year 16 16.0

11-15 year 71 71.0

Above 15 years 13 13.0

Main Activity

Credit/Finance 77 77.0

Plantation 11 11.0

Consumer 12 12.0

Yearly Sales

RM150,000 – RM300,000 19 19.0

RM301,000- RM450,000 50 50.0

More than RM451,000 31 31.0

Total Members

251-500 members 15 15.0



501- 1,000 members 53 53.0

More than 1,001 members 32 32.0

Operations

6-10 years 5 5.0

11-15 years 31 31.0

More than 15 years 64 64.0

Correlations

Generally, the findings (Table 2) indicate that all transformational leadership dimensions

were significantly correlated with organizational performance with correlation coefficients

between r = 0.48 and r = 0.62. Specifically, Table 2 displays the results of testing H1 – H4. It

shows that individualized consideration (r = 0.51, p < 0.01), inspirational motivation (r = 0.62,

p < 0.01), intellectual stimulation (r = 0.48, p < 0.01), and individual consideration (r = 0.59,

p < 0.05), were positive and significantly correlated with organizational performance. These

results imply that in the cooperative movement, leaders that exhibited transformational

leadership behavior dimensions were significantly correlated with the organizational

performance.

Table 3: Descriptive Statistics and Correlation Coefficients of the Variables (N=100)

Variable Mean Std

Deviation II IM IS IC OP

II 4.06 .40 1

IM 3.63 .78 .43**

1

IS 4.11 .43 .52**

.72**

1

IC 4.15 .53 .27**

.28**

.24* 1

OP 3.54 .60 .51**

.62**

.48**

.59* 1

Notes:

**. Correlation is significant at the 0.01 level.

*. Correlation is significant at the 0.05 level.

Discussion

This study has been considered as the earliest study that tests whether transformational

leadership styles correlated with organizational performance within the cooperative

movement. Overall, the results demonstrate that cooperative leaders who exhibit

transformational leadership styles will strongly strive for performance and confirmed the

findings of previous research that transformational leadership styles will affect organizational

performance (Li & Hung, 2009; Ling, Simsek, Lubatkin, & Veiga, 2008; Elenkov, 2002).

Specifically, the current results have shown a significant relationship between

transformational leadership dimensions and organizational performance. Cooperatives leaders

who exhibit idealized influence behavior, motivate others to achieve the organization‟s

mission and vision. He/she communicates organization values, purpose and the importance of

the organization's mission to others. The ability of cooperative leaders to effectively

communicate these behaviors to others serves to internalize in the minds and hearts of others

and eventually will enhance the performance. Moreover, in cooperative movement leaders

displayed inspirational motivation by exhibiting behaviors focused on providing motivation

and inspiration to others correlated with performance. This result was also in line with Avolio,



Waldman, & Yammarino (1991) findings that through inspirational motivation, the leader

portrayed commitment, presentation of innovative ideas and was likely to produce

organizational performance beyond expectations. Cooperatives leaders who intellectually

stimulated the creativity of others by encouraging them to question their own, the

organizations, and the leader's beliefs and assumptions ultimately affect group members'

well-being, and overall organizational performance. These results were similar to others

reported in the literature such as Howell and Avolio (1993) whereby they found that

intellectual stimulation were significantly correlated with consolidated performance.

Cooperatives leaders exhibited individual consideration behavior such as giving

individualized attention to others and focused on the developmental needs of the others will

strive for better performance. This implies that leaders should create a more supportive,

warmer, and friendlier atmosphere in the workplace in order to ensure others reach their

highest potential and subsequently organizational performance. Ristow, Amos, and Staude

(1999) found that individual consideration does indeed have an impact and is significantly

correlated with organizational performance.

Implications

Transforming the cooperative movement to a new paradigm in order to sustain its outstanding

performance is in line with the Malaysian government‟s aspiration and commitment to

recognize cooperative movement to be a major contributor, posed a new challenge to

leadership. Engaging in transformational leadership behavior is an effective strategy to help

managers meet these challenges. Transformational leadership behavior such as idealized

influence, inspirational motivation, intellectual stimulation, and individualized consideration

behaviors were significantly connected to better performance. In the cooperative sector,

leaders should engage in transformational behavior, such as providing individualized

attention to support others during the transformation period and should use their skills at

intellectual stimulation to encourage others to think in a new, creative and innovative ways.

This study also benefits to cooperative leaders in understanding the transformational

leadership dimensions and the necessity of adopting all the dimensions in transforming and

sustaining their performance. Following this, perhaps cooperatives can develop strategic roles

and/or make necessary changes to the current practices with regard to leadership. In addition,

this study is also meaningful to cooperative training institutions to develop such training

programs that cover transformational leadership dimensions since limited attention has been

focused on this important area. With respect to management and leadership, the study has

made an important contribution because it provided a new leadership model and possibly

validated the direct and strong correlations between leadership behaviors and organizational

performance. Possibly the study has also validated the anticipated effects of leadership styles

on the member‟s level of cohesiveness. Understanding if certain leadership styles can relate

to the cohesiveness of members is not only necessary, but also important (Senge, 2006).

LIMITATIONS OF THE STUDY

As the respondents were within the cooperative sector, the results of the study may not be

applicable in other sectors. Further, the accuracy of the findings depends upon respondents‟

full understanding of the survey questions and their complete honesty in answering the

survey questions. In addition, beyond the researcher‟s control in determining or identifying



the targeted respondents really answer all the questions due to not thoroughly comprehend

the intent of the questions and therefore, provide inaccurate responses. All variables were

collected using self-reported measures; therefore the results may be inflated by same-source

bias and may have been affected by common method bias. This study does not attempt to

investigate other leadership styles i.e transactional leadership and laissez-faire, but instead

concentrated only on transformational leadership styles, whereas both leadership styles were

much needed especially in explaining the MLQ.

RECOMMENDATIONS FOR FURTHER RESEAERCH

It would also be useful to extend this study to other variables of the MLQ that predict

organizational performance in the same organization. Replication of this study with other

variables i.e transactional leadership styles might show different results. Completing a similar

study using additional measures for organizational performance might yield additional

insights into the importance of transformational leadership and organizational performance in

the context of cooperative movement in the Malaysian setting. As was evidenced in the

literature review in this study, there are numerous other organizational performance measures

that have been documented in other studies and more are being developed. One of the

problems with the current literature is that there is no standard definition or accepted

objective measure of organizational performance. It would be very interesting to repeat this

research with different clusters of cooperatives movement i.e upper, middle and lower

clusters and compare the results to this research to determine if there are differences between

them. To accomplish this research, however, future researchers should identify appropriate

clusters from the above mention sample from the population. This therefore should consider

additional knowledge for the body of literature and the researchers may find a higher

significance level or a stronger relationship between transformational leadership and

organizational performance. Future studies should consider alternative modes of enquires

such as employing the longitudinal method of data collection design (e.g. experiments,

observations or interviews) to better understand the cause and effect relationships at different

phases of time (Sekaran, 2005). It would help in gaining a better understanding of how the

relationship between the transformational leadership styles and organizational performance

within cooperative movement operate over time. Despite some positive findings, it is

important that future research extend the work to different types of organizations settings,

industries and culture in order to assess the generalizabilty of the effect transformational

leadership styles and organizational performance. This research might offer a better

perspective of the conditions that ease or hinder the effectiveness of transformational

leadership styles and organizational performance. Koh, Steers, and Terborg (1995) reported

that transformational leadership styles had a more direct effect on process variables such as

group cohesiveness, which then, in turn, predicted organization performance in a causal

model sense.

CONCLUSION

This study represents one of the first steps toward understanding the connection between

transformational leadership and organizational performance in the context of the cooperative

movement in Malaysia. This study has helped to fill the gap in an effort to improve our

understanding of the role of leadership and organizational performance in the cooperative



movement in Malaysia. In summary this study shows that the relationship between

transformational leadership dimensions and organizational performance were significant.

Therefore, it provides a piece of relevant contribution to the literature with regard to these

relationships, where idealized influence, inspirational motivation, intellectual stimulation and

individual consideration are found to have significantly enhanced organizational performance

in the context of cooperative movement. Today, organizations must excel to meet the various

stakeholder expectations, and it is imperative that leaders adopt transformational leadership

styles consistently where the endurance of the organization depends largely on it.

REFERENCES

Avolio, B. J., & Bass, B. M. (2004). Multifactor leadership questionnaire, Mutual and

Sampler Set, (3rd Ed.). Redwood City, CA: Mind Gard

Avolio, B. J., Waldman, D. A., & Yammarino, F. J. (1991). Leading in 1990s: The four I‟s of

transformational leadership. Journal of European Industrial Training, 15 (4), 9-16

Avolio, B.J. (1999) Full leadership development: Building the vital forces in organizations.

Thousand Oaks, CA: Sage

Banton, M. (2002). Corporate culture and financial performance: A study of corporate culture

and its relationship to the financial performance of an organization. Digital

Dissertation, 63 (12). (UMI No. 3074870)

Bass, B. M., & Avolio, B. J. (1994). Improving organizational effectiveness through

transformational leadership. California: Sage

Bass, B. M., & Avolio, B.J. (1999). Full- range of leadership development: Manual for the

leadership questionnaire. Palo Alto, CA: Mid Garden

Bass, B. M., Avolio, B. J., Jung, D.I., & Berson, V. (2003). Predicting unit performance by

assessing transformational and transactional leadership. Journal of Applied

Psychology, 88, 207-218

Bass, B.M. (1985). Leadership and performance beyond expectations. New York: Free Press

Bass, B.M. (1998). Transformational leadership: Industry, military & educational impact.

Mahwah, NJ: Erlbaum

Bennis, W. G., & Nanus, B. (1997). Leaders: The strategies for taking charge (2nd

ed.). New

York: Harper and Row

Bono, J. E., & Judge, T. A. (2003). Self-concordance at work: Toward understanding the

motivational effects of transformational leaders. Academy of Management Journal, 46

(5), 554–571

Dvir, T., Eden, D., Avolio, B. J., & Shamir, B. (2002). Impact of transformational leadership

on follower development and performance: A field experiment. Academy of

Management Journal, 45(4), 735–745

Elenkov, D.S. (2002). Effects of leadership on organizational performance in Russian

companies. Journal of Business Research, 55, 467– 480

Howell, J.M., & Avolio, B.J. (1993). Transformational leadership, transactional leadership,

locus of control, and support for innovation: Key predictors of consolidated business

unit performance. Journal of Applied Psychology, 78, 891–902

Jing, F.F., & Avery, G.C. (2008). Missing link in understanding the relationship between

leadership and organizational performance. International Business & Economic

Research Journal, 7, 67- 78

Kark, R., Shamir, B., & Chen, G. (2003). The two faces of transformational leadership:

Empowerment and dependency. Journal of Applied Psychology, 88 (2), 246–255



Koh, W. L., Steers, R. M., & Terborg, J. R. (1995). The effects of transformational leadership

on teacher attitudes and student performance in Singapore. Journal of Organizational

Behavior, 16, 319–333

Ling, Y., Simsek, Z., Lubatkin, M.H., & Veiga, J.F. (2008). The impact of

transformational CEOs on the performance of small- to medium-sized firms:

Does organizational context matter? Journal of Applied Psychology, 93 (4), 923–

934

Lopez, S.P., Peon, J.M.M., & Ordas, C.J.V. (2005). Organizational learning as a determining

factor in business performance. The Learning Organization, 12 (3), 227- 245

Lowe, K. B., Kroeck, K. G., & Sivasubramaniam, N. (1996). Effectiveness correlates of

transformational and transactional leadership: A met analytic review. Leadership

Quarterly, 7, 385–425

Mia, L., & Clarke, B. (1999). Market competition, management accounting systems and

business unit performance. Management Accounting Research, 10, 137-158

Murphy, G.B., Trailer, J.W., & Hill, R.D. (1996). Measuring performance in

entrepreneurship research. Journal of Business Research, 36, 15-23

Nemanich, L.A., & Keller, R.T. (2006). Transformational leadership in an acquisation: A

field study of employees. The Leadership Quarterly , 18, 49-68

Northouse, P. G. (2004). Leadership: Theory and Practice (3rd ed.). Thousand Oaks,

California: Sage Publications, Inc

Nunnally, J. (1978). Psychometric theory. New York: McGraw-Hill

Ortega, E.M.P., Azorin, J.F.M., & Cortes, E.C. (2010). Competitive strategy, structure and

firm performance: A comparison of the resource-based view and the contingency

approach. Management Decision, 48(8), 1282 – 1303

Patiar, A., & Mia, L. (2009). Transformational leadership styles, market competition and

departmental performance: Evidence from luxury hotels in Australia. International

Journal of Hospitality Management, 28, 254 -262

Podsakoff, P.M., MacKenzie, S.B., & Bommer, W.H. (1996). Transformational leadership

behaviors and substitutes for leadership as determinants of employee satisfaction,

commitment, trust, organizational citizenship behaviors. Journal of Management, 22,

259–298

Purvanova, R.K., & Bono, E.J. (2009). Transformational leadership in context: Face –to-face

and virtual teams. The Leadership Quarterly, 20, 343 -357

Ristow, A. M., Amos, T. L., & Staude, G. E. (1999). Transformational leadership and

organizational effectiveness in the administration of cricket in South Africa. South

African Journal of Business Management, 30(1), 1-5 Rowe, W.G., Cannella A.A., Jr., Rankin, D., & Gorman, D. (2005) Leader succession &

organizational performance: Integrating the common-sense, ritual scapegoating &

vicious-circle succession theories. The leadership Quarterly, 16, 197-219

Salleh, H.M., Arshad, A., Shaarani, A.F., & Kasmuri, N. (2008). Cooperative movement in

Malaysia. Kuala Lumpur. Gempita Maju Publishing

Sapran, A.S. (2010). Exploiting cooperative movement strengths. Pelancar, 37, 10-11

Sekaran., U. (2005). Research methods for business. India: John Wiley & Sons, Inc

Senge, P. M. (2006). The fifth discipline: The art & practice of the learning organization.

New York: Doubleday

Tai, D.W.S., & Huang, C.E. (2007). The relationship between electronic business process

reengineering and organizational performance in Taiwan. Journal of American

Academy of Business, 10 (2), 296-301

Tan, C.C., & Selvarani, P. (2008, October 15). Coping with cooperatives. New Sunday Times,

pp. 5



Thuraisingham, S. (2008). Cooperatives in Limbo, Malaysian Business

Verdigets, F.L. (2008). Insights into leadership: The role of political skill, social skill and

self-monitoring in mediating the relationship between transformational leadership and

job performance. Unpublished PhD dissertation, Auburn University, Alabama, United

States

Waldman, D.A., Ramirez, G.G., House, R.J., & Puranam, P. (2001). Does leadership matter?

CEO leadership attributes and profitability under conditions of perceived

environmental uncertainty. Academy of Management Journal, 44 (3), 134-143

Walumbwa, F. O., & Lawler, J. J. (2003). Building effective organizations: Transformational

leadership, collectivist orientation, work-related attitudes, and withdrawal behaviors

in three emerging economies. International Journal of Human Resource Management,

14 (7), 1083–1101

Walumbwa, F. O., Wang, P., Lawler, J. J., & Shi, K. (2004). The role of collective

efficacy in the relations between transformational leadership and work

outcomes. Journal of Organizational and Occupational Psychology, 77(4), 515–530

Yusof, R.M. R. (2008). Leadership and challenge in cooperative management. Coop

Dimension, 1, 5-7

Zhu, W., Chew, I.K.H., & Spangler, W.D. (2005). CEO transformational leadership and

organizational outcomes: The mediating role of human-capital-enhancing human

resource management. The Leadership Quarterly, 16, 39-52

How corporate social responsibility (CSR) contributes to customer - based brand equity among Malaysian

mobile telcos‟


HOW CORPORATE SOCIAL RESPONSIBILITY (CSR) CONTRIBUTES TO

CUSTOMER - BASED BRAND EQUITY AMONG MALAYSIAN MOBILE TELCOS’

Abdul Rahman Zahari,

[email protected]

Elinda Esa

[email protected]

Inaliah Mohd. Ali

[email protected]


ABSTRACT

The purpose of this study is to analyze the role of corporate social responsibility on customer-

based brand equity. In the proposed model, corporate social responsibility is analyzed as a

source of brand equity and its dimensions. This paper studies the dimensions of customer-

based brand which had been suggested by Aaker (1991). An empirical study was conducted

among the young, adults (18 to 33 years old) in the Malaysian market. Brand equity is

analyzed through Malaysian Mobile Telcos‟ namely Maxis, Celcom, Digi, and U Mobile.

Data were collected from the subscribers of Malaysian Mobile Telcos‟ using mall intercept

sampling method. The data collected was analyzed using exploratory factor analysis,

reliability test, descriptive statistics and regression analyses. According to the findings,

corporate social responsibility shows a positive influence on all dimensions of brand equity as

well as brand equity.

Keywords: corporate social responsibility (CSR), brand equity, gen Y

INTRODUCTION

How best did global brands in 2011 i.e. Exxon Mobil, General Motors, Hawlett-Packard,

Citigroup, and General Electric, maintain their strong brands globally? They succeeded in

doing so due to understanding on the importance of brand equity. Since its appearance in

academic literature in the 1980s, brand equity has been one of the main priorities in marketing

research (Marketing Science Institute, 2002). Since then the marketers and firms have realized

that brand equity is the important stem for the firms to create a strong brand in order to

achieve product or service differentiation and competitive advantage. Thus, the use of

corporate social responsibility as a new platform to be adopted by marketers in developing

brand equity has occurred because Maignan and Ferrell (2001) explored the role of what they

then described as “corporate citizenship” as a marketing instrument. Due to this, the role of

corporate social responsibility is required to be tested in determining the development of

customer-based brand equity.

The previous Prime Minister of Malaysia, Tun Abdullah Badawi developed a „silver book‟

during his tenure to promote more transparency in the ways that companies address and





mobile telcos‟


manage environmental, economic and social issues and which can help to improve

relationships with employees, customers and other stakeholders. Ernst and Young (2002)

suggest that five key drivers have influenced the increasing business focus on CSR, namely,

greater stakeholder awareness of corporate ethical, social and environmental behavior; direct

stakeholder pressures; investor pressure; peer pressure and an increased sense of social

responsibility. The Commission for the European Communities (2001) defined CSR as a

concept whereby companies unite social and environmental concerns in the business activity

and in their interactions with their stakeholders on a voluntary basis. Moreover, Wood (1991)

indicates that the basic idea of CSR is that business and society are interwoven rather than

distinct entities. Besides, Mallenbaker (2005) said that the CSR is about how companies

manage the business process to produce an overall positive impact on society. CSR is seen to

focus on a wide range of potential benefits (Bevan et al., 2004). These include improved

financial performance and profitability; reduced operating costs; long-term sustainability for

companies and their employees; increased staff commitment and involvement; enhanced

capacity to innovate; good relations with government and communities; better risk and crisis

management; enhanced reputation and brand value; and the development of closer links with

customers and greater awareness of their needs.

Resulting from the business benefits and stakeholders‟ concern, therefore the main purpose of

this current study is to analyze the role of corporate social responsibility (CSR) in developing

customer-based brand equity among Malaysian Mobile Telcos‟. The remainder of the paper is

organized as follows: The next section reviews relevant literature. The research method is

then explained. Results and discussions are then provided before concluding the paper.

LITERATURE REVIEWS

Brand equity from the financial perspective, is the total value of a brand which is a separable

asset such as when it is sold, or included in a balance sheet (Feldwick, 1996). Aaker (1991)

defined brand equity as a set of brand assets and liabilities linked to a brand, its name and

symbol that add to or subtract from the value provided by a product or service to a firm and or

to that firm‟s customers. Brand equity has many positive effects to the firms and the

stakeholders in terms of customer loyalty and firm‟s performance. Aaker (1991) proposes that

brand equity creates value for the firm as well as for the customer. This proposition has been

well supported. For example, brand equity affects merger and acquisition decision making

(Mahajan et al., 1994) and stock market responses (Lane & Jacobson 1995; Simon & Sullivan

1993) and determines the extendibility of a brand name (Rangaswamy et al., 1993).

What is more, the customer-based brand equity definitions approach the subject from the

perspective of the consumer; whether it is an individual or an organization and the power of a

brand lies in what customers have learned, felt, seen, and heard about the brand as a result of

their experiences over time (Keller, 2003). If the brand has no meaning to the consumer, none

of the other definitions are meaningful (Keller, 1993; Cobb-Walgren & Ruble, 1995; Rio et

al., 2001a).

The operationalizations of brand equity can be grouped into three different categories for

consideration: the financial aspects of the brand equity measurement, the customer-based

measurement issues, and the combined perspective. After examining the past studies, this

research tries to evaluate the brand equity from a customer‟s view due to two reasons; first,

customer-based brand equity is the driving force for incremental financial gains to the firm.


mobile telcos‟


Second, managers do not have a customer-based measure to evaluate brand equity. In addition,

this study borrows the concept of brand equity initiated by Aaker in 1991.

Aaker built his model of brand equity inclusive of five dimensions; however this study only

chose four domains of brand equity and briefly reviewed below, together with the related

hypotheses which have been separately tested in the succeeding sections of this study. One

domain of brand equity called other proprietary brand assets was dropped out because

previous studies conducted by Yoo et al., (2000), Norjaya et al., (2007), and Gil et al., (2007)

only used four domains. Besides, that element also can be used as the independent variable to

test with the dimensions of brand equity. Aaker (1991) defines brand loyalty as a situation

which reflects how likely is it that a customer will switch to another brand, especially when

that brand makes a change, either in price or in product features. On the contrary, Keller

(2003) examines brand loyalty under the term “brand resonance” which refers to the nature of

the customer-brand relationship and the extent to which customers feel that they are “in sync”

with the brand. Amine (1998) in her literature distinguishes two main approaches to define the

loyalty construct: the behavioral one suggests that the repeat purchasing or uses of a brand

over the times by a consumer expresses their loyalty, and; the attitudinal perspective which

assumes that consistent buying is a necessary but not sufficient condition of „true‟ loyalty and

it must be complemented with a positive attitude towards this brand to ensure that this

behavior will be pursued further. In addition, Chaudhuri and Holbrook (2001) had proposed a

model of loyalty that suggests that purchase loyalty tends to lead to greater market share,

while attitudinal loyalty leads to higher relative services pricing.

Zeithaml (1988) defined perceived quality as the customer‟s perception of the overall quality

or superiority of a product or service with respect to its intended purpose, relative to

alternatives. It is a competitive necessity and many companies today have turned customer-

driven quality into a potent strategic weapon. They create customer satisfaction and value by

consistently and profitably meeting customer‟s needs and preferences for quality. In addition

to the above definition, Kotler (2000) mentioned that perceived quality draws attention to the

intimate connection among product and service quality, customer satisfaction, and company

profitability. Moreover, Parasuraman et al., (1985) mentioned that there are several factors to

be considered in order to analyze and measure perceived quality, such as reliability,

serviceability, appearance, performance, durability and etc. From the definitions given above,

perceived quality relates to the ability of the products or services meet the customer

satisfaction in terms of durability, performance, color, and multiple functions. In other words

the perceived quality can be explained as meeting the satisfying level of customers. From a

manufacturer‟s view, the perceived quality can be achieved through the conformance of the

design with the actual products.

Aaker (1991, p. 61) defines brand awareness as “the ability of the potential buyer to recognize

and recall that a brand is a member of a certain product category”. Moreover, brand awareness

plays an important role in consumer decision making by bringing three advantages; these are

learning advantages, consideration advantages, and choice advantages. Customer-based brand

equity occurs when the consumer has a high level of awareness and familiarity with the brand

and holds some strong, favorable, and unique brand associations in memory (Keller, 2003).

According to Chebat and Hedhli (2009), awareness is the informational node associated with

the name of the brand in the shopper's memory, representing the extent to which a customer is

able to recognize and easily recall the brand or company characteristics.


mobile telcos‟


A brand association is “anything linked in memory to a brand” (Aaker, 1991, p. 109). Besides

that, brand associations may be seen in all forms and reflect characteristics of the product or

aspects independent of the product itself (Chen, 2001). In addition, Chen (2001) states that

product associations and organizational associations are taken as the two most referred to as

brand association typology. Krishnan (1996) argued that the associations could be used as a

general term to represent a link between any two nodes, which suggests an association in the

consumer‟s mind. Campbell (2002) mentions that the brand associations that practitioners

should focus on are those that are “meaningful and relevant to customers” (p. 213) and Hart

and Murphy (1998) state the successful branding is founded on creating distinctiveness in a

consumer relevant way.

Gen Y

In their study, Schiffman and Kanuk (2010) define Generation Y (Gen Y) as an age cohort of

individuals born over a relatively short and continuous period of time includes somewhere

between 80 and 100 million American ages 30 and under in 2008. They are the children of

baby boomers and depending on the source, were born between 1977 and 1994, or between

1982 and 2000. In addition, this age of group have significant buying power. Gen Y is often

typified as being highly consumption oriented and sophisticated in terms of tastes and

shopping preferences (Wolburg & Pokrywczynski, 2001). This group has had a profound

impact on retail businesses because Gen Y members love to shop. Research showed that for

members of Gen Y, social motivation predicts perceptions of atmospheric qualities of a

shopping environment, perceptions of excitement at mall and intention to return to a mall in

the future (Martin and Turley, 2004). According to Foot and Stoffman, (2000), Gen Y is the

most important demographic cohort after the baby-boomer generation. They were born

between 1980 and 1995; these young consumers are today between 13 and 28 years old, half

of which are teenagers (13–19 years old). This group was representing about 60 million

consumers in America (Neuborne & Kerwin, 1999).

Sources of brand equity

Most of the companies are fully utilizing the marketing mix variables such as product, price,

place and promotion as sources of developing brand equity (Pappu & Quester, 2008; Keller,

1993; Berry, 2000; Yoo et al., 2000; Ailawadi et al., 2003; Herrmann et al., 2007; Buil I, et al.,

2011). Furthermore, Keller (2008) mentioned that the brand equity also can be build up

through the integrated marketing communication i.e. media advertising, direct response

advertising, online advertising, place advertising, point-of-purchase advertising, trade

promotions, consumer promotions, event marketing and sponsorship, publicity and public

relation and personal selling.

According to Norjaya et al., (2007), brand equity cannot be fully understood without carefully

examining its sources, that is, the contributing factors to the formation of brand equity in the

consumer‟s mind. For this reason, the marketers need to identify new sources to facilitate the

marketing mix variables in forming the brand equity and understanding the sources makes the

marketers succeed in generating the financial gain and developing brand equity. The studies

conducted by Gil et al., (2007) and Abdul Rahman and Norjaya (2011) indicated that the

family is the source of customer-based brand equity. They had mentioned that the family can

give the useful information before the purchase decision takes place. Besides that, viral

http://www.sciencedirect.com/science/article/pii/S0969698910000780#bib56

http://www.sciencedirect.com/science/article/pii/S0969698910001062#bib37


mobile telcos‟


marketing is also considered as a source of customer-based brand equity (Abdul Rahman &

Norjaya, 2011). Due to many sources of customer-based brand equity focusing on marketing

mix variables and non marketing mix variables, this study looks closely at the role of

corporate citizenship as a new source of customer-based brand equity.

Figure 1:Conceptual Framework

From the above Figure 1, the current study tests on the effects of corporate social

responsibility (CSR) on dimensions of brand equity and brand equity. In addition, the

hypotheses are as follows:

H1a : The higher allocation of corporate social responsibility among Malaysian Mobile

Telcos‟, the higher the brand loyalty linked to Malaysian Mobile Telcos‟.

H1b : The higher allocation of corporate social responsibility among Malaysian Mobile

Telcos‟, the higher the brand awareness linked to Malaysian Mobile Telcos‟.

H1c : The higher allocation of corporate social responsibility among Malaysian Mobile

Telcos‟, the higher the brand associations linked to Malaysian Mobile Telcos‟.

H1d : The higher allocation of corporate social responsibility among Malaysian Mobile

Telcos‟, the higher the perceived quality linked to Malaysian Mobile Telcos‟.

H2a : The higher the brand loyalty of Malaysian Mobile Telcos‟, the higher the brand equity.

H2b : The higher the brand awareness of Malaysian Mobile Telcos‟, the higher the brand

equity.

H2c : The higher the brand associations of Malaysian Mobile Telcos‟, the higher the brand

equity.

H2d : The higher the perceived quality of Malaysian Mobile Telcos‟, the higher the brand

equity.

H3 : Corporate social responsibility will have a positive relationship with brand equity

through the mediating effects of brand loyalty, brand awareness, brand associations,

and perceived quality.

Corporate

Social

Responsibility

Brand Equity

Brand Associations

Brand Awareness

Perceived Quality

Brand Loyalty


mobile telcos‟


METHODOLOGY

Using convenience sampling (mall intercept), respondents in this study were Gen Y people

from locations of Klang Valley, Malaysia. Klang Valley was chosen because it is the largest

metropolitan areas that support the largest heterogeneous population as well as a homogenous

group of people. This research was able to obtain a sample size of 300 respondents. Their

completed questionnaires yielded a response rate of 85.71 percent. The respondents were

captured at shopping malls i.e. Mid Valley Megamall, The Mines Shopping Centre, and

Sunway Pyramid Shopping Mall. The participants were real consumers who reported their

usage experiences of Malaysian Mobile Telcos‟ namely Celcom, Digi, Maxis, and U Mobile.

This type of services was chosen because of the commonality and familiarity of these services

among targeted respondents. Furthermore, the present study uses a personally administered

questionnaire survey and the questionnaire consists of five parts: the first part is qualifying

questions about the Telco usage. The second part is dimensions of customer-based brand

equity; the third part refers to the source of customer-based brand equity, which is corporate

social responsibility (CSR). The fourth part represents questions about brand equity and the

last part is pertaining to the respondent‟s profile. Six items have been used to measure

corporate social responsibility and these were adapted from Lichtenstein et al. (2004), Dean

(2003), Berens et al. (2005) and Ricks (2005). The measurement of brand equity dimensions

and overall brand equity were adapted from Yoo, Donthu, and Lee (2000). Perceived brand

quality was adapted from Erdem et al. (2006). Thus, the resulting initial pool contained 25

items. The rating scales of these items were seven points for each. The completed instrument

was pre-tested by 30 respondents in UNITEN. Based on the feedback obtained from these

respondents, the questionnaire was subsequently refined. Data obtained from the personally

administered questionnaire was analyzed using some statistical tools contained in the

statistical software. i.e., „Statistical Package of Social Science‟ (SPSS) 19.0 for Windows.

Besides descriptive analysis, three different statistical analyses were used in this study which

includes factor analysis, a reliability test, and the regression analyses.

RESULT AND DISCUSSIONS

The study sample comprises of 300 respondents who were similar in demographic

characteristics such as gender, age, ethnic, education level, job position level, income level

and marital status. In spite of various demographic characteristics, all respondents are

generation Y. Table 1 below shows the respondents‟ profiles. The study sample constitutes

respondents who depart on such attributes as gender, age, marital status, education level, job

position, income level and ethnicity. From the total of 300 respondents, 44.7 per cent were

male respondents and the remainder 55.3 per cent were female respondents. Also, there were

about 36.0 per cent respondents in the age range between 22 to 25 years old and only 14.7 per

cent of them were in the age range from 30 to 33 years old. The majority of respondents were

not married with 70.0 per cent. Furthermore, with respect to ethnic groups, majority (62 per

cent) are Malays, the remainder of 23.3 per cent were Chinese, followed by Indian, Sabahan,

and Kadazan. Most of the respondents in this current study were degree holders with 56.0 per

cent and then followed by Diploma holders with 18.7 per cent. Moreover, with respect to job

status, the majority of the respondents were students with 32.0 percent, followed by middle

management with 16.7 per cent, and others with jobs such as rectifiers, sales assistant, and

entrepreneurs who represented the smallest percentage (2.0 per cent). On the other hand, 37.7

per cent of the respondents had an income level below than RM 1,000 per month, 66


mobile telcos‟


respondents were able to have monthly salary between RM 2,001 to RM 3,000, and only 6.3

per cent respondents had a monthly salary above than RM 4000 per month.

Table 1: Description of Respondents

Item Description Frequency n = 300 Percentage

Gender Male 134 44.7

Female 166 55.3

Age

18 to 21 years old

75

25.0

22 to 25 years old 108 36.0

26 to 29 years old

30 to 33 years old

73

44

24.3

14.7

Ethnicity Malay 186 62.0

Chinese 70 23.3

Indian 42 14.0

Sabahan 1 0.3

Kadazan 1 0.3

Level of Education SPM/MCE 30 10.0

STPM/HSE 17 5.7

Diploma 56 18.7

Degree 168 56.0

Masters/PHD 25 8.3

Certificate 3 1.0

Matriculation 1 0.3

Job Position Professionals 26 8.7

Top Management 26 8.7

Middle Management 67 22.3

Lower Management 50 16.7

Admin and Technical Support 29 9.7

Student 96 32.0

Rectifier 1 0.3

Entrepreneur 2 0.7

Sales Assistant 3 1.0

Income Level Below 1000 113 37.7

1001 to 2000 58 19.3

2001 to 3000 66 22.0

3001 to 4000 44 14.7

4001 and above 19 6.3

Marital Status Single 210 70.0

Married 90 30.0


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Factor analysis was used in the current study to reduce the large set of the variables into a

smaller and manageable number of dimensions or factors. There are two main approaches to

factor analysis such as exploratory and confirmatory. For this current study, the exploratory

factor analysis was selected and conducted due to the need for the researcher to examine

whether the items produce proposed factors and whether the individual items are loaded on

their appropriate factors as intended. This analysis was conducted separately for each variable

by running rotation matrix of direct oblimin. The items for every variable are grouped

separately and principle component analysis (PCA) using SPSS version 19.0 was executed on

it.

From Table 2 shown below, the overall Kaiser-Meyer-Oklin (KMO) value for all variables is

0.935, which exceeds the recommended value of 0.6 (Kaiser 1970, 1974) and the Bartlett‟s

Test of Sphericity (BTOS) reached the statistical significance, which supports the factorability

of the correlation matrix. Furthermore, the communalities value or the estimates of shared

variance among twelve items of brand equity dimensions, six items of corporate social

responsibility, and five items for brand equity are shown to be greater than 0.5. The factor

analysis for all variables revealed the presence of five components of eigenvalues exceeding 1

and contributing 71.32 per cent to item variance. One item was deleted during the process due

to redundant values for two different components. All factors are labeled as brand loyalty,

perceived quality/brand awareness/associations, brand association, corporate social

responsibility, and brand equity. Since the reliability coefficient for brand association is below

the acceptable level as suggested by Pallant.J (2007), where the values above 0.7 are

considered acceptable and values above 0.8 are considered preferable. Thus, the variable is

dropped from further analysis.


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Table 2: Result of Exploratory Factor Analysis

Loading TVE KMO BTOS

Brand Awareness/Associations/Perceived Quality

I am aware of my Telco .674

I can recognize my Telco among other competing Telcos‟ .763

In terms of overall quality, I'd rate this Telco as an exceptionally good one

for the industry .679

The quality of services at my Telco is very high .733

Some characteristics of my Telco come to my mind quickly .600

My Telco's performance is first class .751

I think my Telco has far better quality than other Telcos‟ .676

I know how my Telco looks like .720

I can quickly recall the symbol or logo of my Telco .576

Brand Loyalty

I consider myself loyal to my Telco .746

My Telco would be my first choice .807

I would not switch to others, even if I had a problem with my Telco .676

Corporate Social Responsibility

My Telco has shown strong support for Malaysian traditional culture .675

My Telco devotes a lot of time and money to help wide sections of Malaysian .710

My Telco is very active in supporting environmental protection activities .712

My Telco is already committed using a substantial portion of its profits to

help community groups .686

My Telco's business practices are better than industry codes of conduct .681

My Telco's reputation for socially responsible behavior is above average for

the industry .702

Brand Equity

Even if another Telco has a better range of services as my Telco, I strongly

prefer to use my Telco .800

If there is another Telco that offers more convenient services, I still prefer to

use my Telco for everything .752

I have a very strong preference for my Telco .813

My Telco would easily be my first choice for telecommunication services .800

It makes sense to do all telecommunication with my Telco, even if other

Telcos‟ have slightly better services .690

71.32 0.935 0.000

Notes: TVE = total variance explained,

KMO = Keyser-Meyer-Oklin, BTOS = Bartlett‟s Test of Speherecity

In conjunction with the results of exploratory factor analyses, the hypotheses also were

restated as follows:


mobile telcos‟


H1a : The higher allocation of corporate social responsibility among Malaysian Mobile

Telcos‟, the higher the brand awareness/associations/perceived quality linked to

Malaysian Mobile Telcos‟.

H1b : The higher allocation of corporate social responsibility among Malaysian Mobile

Telcos‟, the higher the brand loyalty linked to Malaysian Mobile Telcos‟.

H2a : The higher the brand awareness/associations/perceived quality of Malaysian Mobile

Telcos‟, the higher the brand equity.

H2b : The higher the brand loyalty of Malaysian Mobile Telcos‟, the higher the brand equity.

H3 : Corporate social responsibility will have a positive relationship with brand equity

through the mediating effects of brand awareness/associations/perceived, and brand

loyalty.

The reliability analysis was conducted to test the reliability of the variables. The results

depicted in Table 3 indicate that the variables, namely brand awareness/association/perceived

quality, corporate social responsibility, and brand equity have good internal consistency with

the Cronbach Alpha coefficient reported at 0.925, 0.902, and 0.912 respectively. These

coefficients are considered very good, as suggested by Pivot, Diener, Colvin and Sandvik

(1991) where the scale of good internal consistency is 0.85 and above. The values of

Cronbach Alpha above 0.8 are considered preferable as suggested by Pallant J. (2007). These

are the variable, namely brand loyalty.

Table 3: Summary Result of Reliability Test

Variables Cronbach Alpha Variables Cronbach Alpha

Brand Awareness/Association/

Perceived Quality 0.925

Corporate Social

Responsibility 0.902

Brand Loyalty 0.837 Brand Equity 0.912

Table 4 below summarizes the results of the regression analysis of corporate social

responsibility on dimensions of brand equity, i.e. brand awareness/associations/perceived

quality, and brand loyalty. The results show that the adjusted R2

was 31.0 per cent. This result

shows that only 31.0 percent of variation of brand awareness/associations/perceived quality is

explained by the model using corporate social responsibility as a predictor. The remaining

69.0 per cent remains unexplained and it may be due to other predictors which are more

related to brand awareness/associations/perceived quality. Furthermore, the model is

significant at p (0.01 indicating 99 per cent confidence) in explaining the dependent variable.

Thus, the results support hypothesis 1a and are in line with the previous study conducted by

Chomvilailuk and Butcher (2010). Moreover, adjusted R2

for corporate social responsibility

on brand loyalty was 27.0 per cent. This shows that only 27.0 per cent variation of brand

loyalty is explained by the model using corporate social responsibility as a predictor. The

remaining 73.0 per cent remains unexplained and may be due to other predictors which are

related to brand loyalty. Besides, the model is significant at p (0.01 indicating 99 per cent

confidence) in explaining the dependent variable. Thus, the results support hypothesis 1b.

This result shows that corporate social responsibility is contributing to brand loyalty of

Malaysian Mobile Telcos‟ among Gen Y users. Hence, the Malaysian Mobile Telcos‟ should


mobile telcos‟


be able to allocate more corporate social responsibility activities in getting brand loyalty

among their users.

Table 4: The Relationship of Corporate Social Responsibility on Dimensions of Brand Equity

DV Standardized Beta T Sig. Adjusted R² Sig. F

Brand Awareness/Associations/

Perceived Quality 0.558 11.623 0.000 0.310

0.000

Brand Loyalty 0.522 10.567 0.000 0.270 0.000

The results of regression analysis of brand equity dimensions of brand equity as depicted in

Table 5 above shows that 38.3 per cent variations of brand equity are explained by the model

using brand awareness/associations/perceived quality and brand loyalty as predictors. The

remaining 61.7 percent remains unexplained. It may be due to other predictors which are

related to brand equity. Based on the result of the regression analysis, there is enough

evidence to conclude that the independent variables, i.e. brand

awareness/associations/perceived quality, and brand loyalty have significant influence on

overall brand equity. Based on the models of brand equity dimension to the overall brand

equity (refer table 4.5), this indicates that brand equity is influenced by the level of brand

awareness/associations/perceived quality. The results support hypothesis 2a and this is

consistent with the earlier studies developed by Yoo, Donthu, and Lee (2000). However, the

previous study developed by Atilgan et al., (2005), shows inconsistency with this study

pertaining to perceived quality on brand equity. Therefore, the marketers for Malaysian

Mobile Telcos‟ are encouraged to create brand awareness, brand association and provide a

high quality service to their target markets. In addition, brand loyalty is also found significant

and it may indicate that brand equity is influenced by the level of brand loyalty. Thus, the

results support the hypothesis 2b and this is consistent with the previous studies by Abdul

Rahman and Norjaya (2011), Norjaya et al., (2007), Gil et al., (2007), Atilgan et al., (2005)

and Yoo, Donthu, and Lee (2000) which found that there is a positive relationship between

brand loyalty and brand equity.

Moreover, adjusted R2

for corporate social responsibility was 42.3 per cent. This result shows

that only 42.3 per cent variation of overall brand equity is explained by the model using

corporate social responsibility as a predictor. The remaining 57.3 per cent remains

unexplained and it may be due to other predictors which are more related to brand equity.

Furthermore, the model is significant at p (0.01 indicating 99 per cent confidence) in

explaining the dependent variable and are in line with past study conducted by Ming and

Wang (2010).

Table 5: The Influence of Dimensions of Brand Equity and Corporate Social Responsibility

on Brand Equity

DV Standardized Beta T Sig. Adjusted R² Sig. F

Brand Awareness/Associations/

Perceived Quality 0.286 4.433 0.000 0.383

0.000

Brand Loyalty 0.386 5.986 0.000

Corporate social responsibility 0.652 0.000 0.423 0.000


mobile telcos‟


Mediating effects of brand awareness/associations/perceived quality, and brand loyalty.

Hierarchical multiple regression was used to test the mediating effect and the results are

shown in Table 6 below.

Table 6: Relationship between Corporate Social Responsibility and Brand Equity Mediated

by the Dimensions of Brand Equity

Independent

Variable

Mediating Variable (Dimensions

of Brand Equity)

Model 1

(IV and DV)

Std. Beta

Model 2

(IV and DV with MV)

Std. Beta

CSR Brand awareness/associations/

perceived quality

0.652* 0.118**

CSR Brand Loyalty 0.652* 0.274* Note: * p < 0.001; ** p < 0.05

Model 1 shows the relationship between corporate social responsibility and brand equity.

Model 2 is the mediated regression that shows the relationship between corporate social

responsibility and brand equity with the inclusion of the mediating variable (brand

awareness/associations/perceived quality, and brand loyalty). For mediating effect to exist, the

beta coefficients in Model 2 should be less in Model 1. From the results shown in Table 6,

corporate social responsibility is found to be significant in the regressions mediated by brand

awareness/associations/perceived quality, and brand loyalty, with a decrease in the beta

coefficients. This indicates that brand awareness/associations/perceived quality, and brand

loyalty have partial mediating effects on the linkages between corporate social responsibility

and brand equity. Therefore, this result supports hypothesis 3.

LIMITATION AND FUTURE RESEARCH

Based on the results discussed above, several limitations must be acknowledged for future

study. With regards to the generalizations of the study, the sample of 300 (limited to Gen Y)

is not considered sufficient to represent a full or total Malaysian perspective. However, this

study can be used as a basis to employ generalization purposes of study. Future research

should add more constructs or statements on each variable to ensure that all the dimensions

can be measured in order to support the hypotheses and gain parallel results with previous

studies. Besides, identifying the new non marketing mix elements can also be added in order

to have the customer-based brand equity measurement. Finally, future research should

consider the applicability of findings in other countries and cultures.

CONCLUSION

The result shows that corporate social responsibility has a significant influence on dimensions

of brand equity, i.e. brand awareness/associations/perceived quality, brand loyalty, and as

well as brand equity. In addition, all dimensions of brand equity have a significant influence

in developing brand equity. This study has provided managerial implications that benefit to

Malaysian Mobile Telcos‟ operators in many ways. First, corporate social responsibility is an

important marketing tool for companies as it influences brand equity dimensions. Second, this

study provides them with a tool to measure the brand equity of their current subscribers from


mobile telcos‟


the view of corporate social responsibility. Third, a customer-based brand equity measure

may help Malaysian Mobile Telcos‟ marketers to investigate the effectiveness of their

marketing programs through the implementation of corporate citizenship activities. Fourth,

this kind of measurement (customer-based brand equity) is reasonable and useful for

managers to measure the brand equity over time and may help the Mobile Telcos‟ operators to

surpass the competitors. Furthermore, an understanding of the dynamics between marketing

actions (corporate social responsibility) and brand equity from a customer perspective could

help the Malaysian Mobile Telcos‟ operators in managing and rebuilding the brand equity.

REFERENCES

Aaker, D (1991), Managing Brand Equity: Capitalizing on the Value of a Brand Name, Free

Press, New York, NY

Aaker, D (1993), “Are brand equity investments really worthwhile?'', in Aaker D.A. and

Biel,A. (Eds), Brand Equity and Advertising; Advertising's Role in Building

Strong Brands, Erlbaum, Hillsdale, NJ, pp. 333-41

Aaker, D (1996), Building Strong Brands, Free Press, New York, NY, p. 150

Ailawadi KL, Lehmann DR, Neslin SA. (2003) Revenue premium as an outcome measure of

brand equity. Journal of Marketing, Vol. 67(October), pp. 1–17

Amine, A. (1998), “Consumers‟ True Brand Loyalty: The Central Role of Commitment”,

Journal of Strategic Marketing, 6, 305-319

Atilgan, E., Aksoy, S. and Akinci, S. (2005), “Determinants of the brand equity. A

verification approach in the beverage industry in Turkey”, Marketing Intelligence and

Planning, Vol. 23 No. 3, pp. 237-48

Berens, G., van Riel, C.B.M. and van Bruggen, G.H. (2005), „„Corporate associations and

consumer product responses: the moderating role of corporate brand dominance‟‟,

Journal of Marketing, Vol. 69, pp. 35-48

Berry LL., (2000), Cultivating service brand equity. Journal of the Academy of Marketing

Science, Vol. 28(1), pp. 128–37

Bevan, S., Isles, N., Emery, P. and Hoskins, T. (2004), “Achieving high performance: CSR at

the heart of business”, available at: www.theworkfoundation.com [Accessed 3 April

2012]

Buil I, et al, (2011), “Examining the role of advertising and sales promotions in brand equity

creation”, Journal of Business Research, doi:10.1016/j.jbusres.2011.07.030

Campbell, M.C. (2002), “Building brand equity”, International Journal of Medical Marketing,

Vol. 2 No. 3, pp. 208-18

Chaudhuri, A. and Holbrook, M. B. (2001) The Chain of Effects from Brand Trust and Brand

Effect to Brand Performance: The Role of Brand Loyalty, Journal of Marketing, 65,

81-93

Chebat, J.C., El Hedhli, K., Sirgy M. J. (2009), “How does shopper-based mall equity

generate mall loyalty? A conceptual model and empirical evidence”, Journal of

Retailing and Consumer Services, Vol. 16, pp. 50-60

Chen, H. (2001). “Using Free Association to Examine the Relationship between the

Characteristics of Brand associations and Brand Equity”. Journal of Product and

Brand Management, Vol. 10, No. 7, pp. 439-51

Cobb-Walgren, C.J., Ruble, C.A. and Donthu, N. (1995), “Brand equity, brand preference and

purchase intent”, Journal of Advertising, Vol. 24 No. 3, pp. 25-41

http://www.theworkfoundation.com/


mobile telcos‟


Commission of the European Communities (2001), Promoting a European framework for

Corporate Social Responsibility, available at: http://europa.eu.int/eur-

lex/en/comgpr/2001/ [Accessed 4 April 2012]

Chovilailuk, R. and Butcher, K. (2010), “Enhancing brand preference through corporate

social responsibility initiatives in the Thai banking sector”, Asia Pacific Journal of

Marketing and Logistics, Vol. 22, No. 3, pp. 397-418

Dean, D.H. (2003/2004), „„Consumer perception of corporate donations‟‟, Journal of

Advertising, Vol. 32, No. 4, pp. 91-102

Delgado-Ballester, E. and Munuera-Aleman, J. (2005), “Does brand trust matter to brand

equity?”, Journal of Product & Brand Management, Vol. 14 No. 3, pp. 187-96

Erdem, T., Swait, J. and Valenzuela, A. (2006), „„Brands as signals: a cross-country

validation study‟‟, Journal of Marketing, Vol. 70, pp. 34-49

Ernst & Young (2002), “Corporate social responsibility”, available at: www.ey.nl/download/

publication/download/corporate_social_responsibility.pdf

Fang, Y. C., Yu, H. C., and Yi, H. L. (2012), “Customer perceptions of airline social

responsibility and its effect on loyalty”, Journal of Air Transport Management, Vol 20,

pp. 49-51

Farquhar, P.H. (1989), ``Managing brand equity'', Marketing Research, Vol. 1 pp. 24-33

Farquhar, Peter H., Julia Y. Han, and Yuji Ijiri. (1991). "Recognizing and Measuring Brand

Assets" Marketing Science Institute Working Paper Series. Report No. 91-119.

Cambridge,/VIA: Marketing Science Institute

Feldwick P. Do we really need „brand equity‟? Journal of Brand Management 1996;4(1):9–

29

Foot and Stoffman, (2000), Entre le Boom et L‟Écho. Éditions du Boréal: Collection Info

Presse

Gil, R. B., Andres, F.E., and Salinas M. E. (2007), “Family as a source of customer-based

brand equity”, Journal of Product & Brand Management, Vol.16, No. 3, pp. 188-189

Hart, S. and Murphy, J. (Eds) (1998), Brands: The New Wealth Creators, New York

University Press, New York, NY

Herrmann A, Huber F, Shao AT, Bao Y. (2007), Building brand equity via product quality.

Total Quality Management, Vol. 18(5), pp. 531–44

Keller, K.L. (1993), “Conceptualizing, measuring, and managing customer-based brand

equity'', Journal of Marketing, Vol. 57, pp. 1-22

Keller, K. L. (2003), Strategic Brand Management: Building, Measuring and Managing

Brand Equity. Prentice Hall: New Jersey

Keller, K. L. (2008), Strategic Brand Management: Building, Measuring and Managing

Brand Equity. Prentice Hall: New Jersey

Kim, H. B., & Kim, W. G. (2005). The relationship between brand equity and firms‟

performance in luxury hotels and chain restaurants. Tourism Management, Vol. 26, pp.

549-560

Kotler P. and Armstrong G. (2001), “Principles of Marketing”, Ninth Ed., Prentice Hall

International Inc

Krishnan, H.S. (1996), “Characteristics of memory associations: a consumer-based brand

equity perspective”, International Journal of Research in Marketing, Vol. 13, pp. 389-

405

Lane, V. & Jacobson, R. (1995). “Stock Market Reactions to Brand Extension

Announcements: The Effects of Brand Attitude and Familiarity”. Journal of

Marketing, Vol. 59, No.1

Lassar, W., Mittal, B. and Sharma, A. (1995), “Measuring customer-based brand equity”,

Journal of Consumer Marketing, Vol. 12 No. 4, pp. 11-19

http://europa.eu.int/eur-lex/en/comgpr/2001/

http://europa.eu.int/eur-lex/en/comgpr/2001/

http://www.sciencedirect.com/science/article/pii/S0969698910001062#bbib18


mobile telcos‟


Lee, J. S., & Back, K. J. (2008). Attendee-based brand equity. Tourism Management, Vol. 29,

pp. 331-344

Lichtenstein, D.R., Drumwright, M.E. and Braig, B.M. (2004), „„The effect of corporate

social responsibility on consumer donations to corporate-supported nonprofits‟‟,

Journal of Marketing, Vol. 68 No. 4, pp. 16-32

Mahajan, V., Rao, V. R., and Srivastara, R. K. (1994). “An approach to assess the importance

of brand equity in acquisition decisions”. Journal of Product Innovation Management,

Vol. 11, Issue 3, June 1994, Pages 221-35

Maignan, I. and Ferrell, O.C. (2001), “Corporate citizenship as a marketing instrument”,

European Journal of Marketing, Vol. 35 Nos 3/4, pp. 457-84

Mallenbaker (2005), Corporate Social Responsibility – What Does it Mean?, available at:

www.mallenbaker.net/csr/CSRfiles/definition.html

Marketing Science Institute (2002), “Research priorities 2004-2006”, Marketing Science

Institute, Cambridge, available at: www.msi.org/msi/pdf/MSI_RP04-06.pdf

Martin Craig A, Turley LW. (2004). Malls and consumption motivation: an exploratory

examination of older Generation Y consumers. International Journal of Retail

Distribution Management, 32(10):464–75

Ming, H., and Wang, D. (2010), “Corporate social performance and financial-based brand

equity”, Journal of Product & Brand Management, Vol. 19, No. 5, pp. 335-345

Moore, E.S., Wilkie, W.L. and Lutz, R.J. (2002), “Passing the torch: intergenerational

influences as a source of brand equity”, Journal of Marketing, Vol. 66, April, pp.17-

37

Neuborne Ellen, Kerwin Kathleen, (1999).GenerationY. Business Week:81–8 (February 15).

Norjaya Mohd Yasin, Mohd Naseer Noor and Osman Mohamad (2007), “Does image of

country of origin matter to brand equity?”, Journal of Product & Brand Management,

No. 16/1, pp. 38-48

Norjaya Mohd Yasin and Abdul Rahman Zahari (2011), “Does family and viral marketing

have any effect on brand equity?”, Contemporary Marketing Review, Vol. 1 (8), pp. 1-

13: Available online at http://www.businessjournalz.org/cmr

Pallant J. (2007), “SPSS Survival Manual”, 3rd

Ed., Mc Graw Hill

Pappu R, Quester PG. (2008), Does brand equity vary between department stores and clothing

stores? Results of an empirical investigation. The Journal of Product and Brand

Management, Vol. 17(7), pp. 425–35

Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1985), “A conceptual model of service

quality and its implications for future research”, Journal of Marketing, Vol. 49 No. 4,

pp. 41-50

Pavot, W., Diener, E., Colvin, C.R. & Sandvik, E. (1991). Further Validation of the

Satisfaction with Life Scale: Evidence for the Cross Method Convergence of

Wellbeing Measures. Journal of Personality Assessment, 57, 149-61

Rangaswamy, A., Burke, R., and Oliva A. (1993), “Brand Equity and the extendibility of

brand names”. International Journal of Research in Marketing, Vol. 1, Issue 1, pp.

61-75

R. Bravo Gil, E. Fraj Andres and E. Martinez Salinas, (2007) “Family as a source of

consumer based brand equity”, Journal of Product & Brand Management, pp. 188-

199

Ricks, J.M. (2005), „„An assessment of strategic corporate philanthropy on perceptions of

brand equity variables‟‟, Journal of Consumer Marketing, Vol. 22 Nos. 2/3, pp. 121-

34

http://www.sciencedirect.com/science/journal/07376782

http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235973%231994%23999889996%23320791%23FLP%23&_cdi=5973&_pubType=J&view=c&_auth=y&_acct=C000026339&_version=1&_urlVersion=0&_userid=524468&md5=d0c9ed87d4a1b8f2730f79a52533de3a

http://www.msi.org/msi/pdf/MSI_RP04-06.pdf

http://www.businessjournalz.org/cmr


mobile telcos‟


Rio, A., Vazquez, R., and Iglesias, V. (2001), “The Effects of Brand Associations on

Consumer Response”. Journal of Consumer Marketing, Vol. 18, No. 5, pp. 410-25

Schiffman L.G. & Kanuk L.L. (2010), Consumer Behavior, 10th

Edition, Pearson Education,

Inc., Upper Saddle River, New Jersey

Shocker, A. and Weitz, B. (1988), “A perspective on brand equity principles and issues”,

Defining, Measuring and Managing Brand Equity: Conference Summary (Report no

88-104), Marketing Science Institute, Cambridge, MA

Simon CJ, Sullivan NW. The measurement and determinants of brand equity: a financial

approach. Marketing Science 1993;12, (November):28–52

Wolburg Joyce M, Pokrywczynski James (2001). A psychographic analysis of Generation Y

college students. Journal of Advert Res., 2001;41(5):33–53

Wood, D.J. (1991), “Corporate social performance revisited”, Academy of Management

Review, Vol. 16, pp. 691-718

Yoo, B., Donthu, N. and Lee, S. (2000), “An examination of selected marketing mix elements

and brand equity”, Journal of the Academy of Marketing Science, Vol. 28 No. 2, pp.

195-211

Zeithaml, V.A. (1988), “Consumer perceptions of price, quality and value: a means end

model and synthesis of evidence”, Journal of Marketing, Vol. 52, July, pp. 2-22

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