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Rambeli, N., Hashim, E., & Marikan, D. A. A. (2016b). Relationship Between Education
Expenditure, Capital, Labor Force And Economic Growth In Malaysia. International
Journal Of Academic Research In Business And Social Sciences, 6(12), 459-468.
Rambeli, N., Hashim, E., Leh, F. C., Hashim, A., & Jalil, N. A. (2020). The Role Of Education
Expenditure On Economic Growth Under Recovery Regime Of World Economic Crisis.
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Rambeli, N., Marikan, D. A. & Hashim, E.(2016a). The Effect of Foreign Direct Investment,
Exports and Employment on Economic Growth Model. International Journal of Academic
Research in Business and Social Sciences 2016, Vol. 6(11) 361-376. DOI:
10.6007/IJARBSS/v6-i11/2405 URL: http://dx.doi.org/10.6007/IJARBSS/v6-i11/2405
Rambeli, N., Podivinsky, J. M., & Jalil, N. A. (2019). The Re-Examination Of The Dynamic
Relationship Between Money, Output And Economic Growth In Malaysia. International
Journal Of Innovation, Creativity And Change, 5(2), 1812-1834.
Rambeli, N., Podivinsky, J. M., Hashim, A., & Hashim, E. (2014). Issues On Exchange Rate
Volatility & Exports Nexus – “A Case For ASEAN”. Management Research Journal, 3,
164-184. UPSI
Singh, N. K. H., Sieng, L. W., & Saukani, M. N. M. (2018). Impact of education levels on
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CM024
AN EMPIRICAL STUDY ON EDUCATION LEVEL AND HEALTH
EXPENDITURE ON ECONOMIC GROWTH IN MALAYSIA
Mohd Shahrol Nizam Mt Nor
Phd Student, Faculty of Management and Economics, Universiti Pendidikan Sultan Idris(UPSI)
Email: [email protected]
Norimah Rambeli
Associate Professor, Faculty of Management and Economics, Universiti Pendidikan Sultan Idris(UPSI)
Email: [email protected]
Normala Zulkifli
Senior Lecturer, Faculty of Management and Economics, Universiti Pendidikan Sultan Idris(UPSI)
Email: [email protected]
Shahrun Nizam Abdul Aziz
Senior Lecturer, Faculty of Management and Economics, Universiti Pendidikan Sultan Idris(UPSI)
Email: [email protected]
ABSTRACT
The objective of this empirical study is to examine the link between education level and health expenditure on economic
growth. This study used the time series data obtained from Statistic Department of Malaysia. By using the Cobb Douglas
Function, four economic growth models are formed and analyse through Multiple Linear Regression Model. Under this
approach, two analysis criteria are utilised namely Economic Criteria and Statistic Criteria. According to the results, the
Primary education level and Tartary education level are significant in explaining the economic growth under Model 1 and
Model 2. Model 3 revealed that, the government spending in health is significant in influencing the economic growth at
95% significant level. For Model 4, the results suggest that the government spending in education and health are
significant in explaining the economic growth in long term at 95% significant level. Therefore in conclusion, across for
model under observation, has proved the role of government education spending is the led growth indicator for Malaysia.
KEYWORDS
Education Level, Health Expenditure, Economic Growth, Human Capital
1. INTRODUCTION
Health and education are two most essential elements upon the development of human capital. Hence, health
and education have positive impact upon the growth of productivity. These fundamental factors towards
economic growth have gain interest a few previous researchers. These exists bidirectional causality between
education and economic growth. In the course of development, an increase in health has positive impact to a
better education hence it will lead to health conscious. Education in Malaysia is an invaluable asset for the
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country. Malaysia is also moving forward in the field of education. Therefore the country is in need of quality
man power in all aspects including physical, mental and social well.
Health and education to economic growth has attracted several of the past. Economists have previously
been interested in the relationship between health and economic growth. Thus, health is a key element to
economic growth. In fact, life expectancy is influenced by the health of the population of a country.
Employment have better health to increase productivity and output. Further increases in productivity and
output as well as affect the Gross Domestic Product (GDP). At the same time, economic growth enables
people to buy food, get a clean environment, comfortable living, and medical care are essential to health.
1.1 Objectives
The purpose of this study is to analyze the long-term relationship between health and GDP percapita on the
economic growth. In addition, this study was also carried out to look at education, which is to take into
account those with primary, secondary and higher education levels, and health expenditure is a driver of the
economic growth. Two main hypotheses are tested;
1. Health affects economic growth is a long-term phenomenon will be tested.
2. Health and education have a direct relationship to work productivity and economic growth.
2. LATERATURE REVIEW
Previous studies that have been done by some researchers who have studied the education and health of a
country's economic growth. Previous research includes studies done abroad and some studies in
Malaysia.Wang & Ni (2015) in his study relating to the composition of human capital and economic growth
in China using time series data showed a significant correlation between low and high level of education to
economic growth based on the Solow model. Empirical studies also found that there is a positive correlation
between family educational background with human capital. Aslan, Menegaki & Tugcu (2016) who
conducted a study using time series data (1980-2009) to the high-income countries show that there are
relevant relationships between health and economic growth. Islam, Kinyondo & Nganga (2015), in studying
the relationship between real wages and productivity in Tanzania found that education, age, occupation and
location are important factors to earnings.
Aisa & Pueyo (2014), in studies of the effect of public health expenditure on the lifetime proves that
public health spending plays an important role in longevity. However, the size of the public health service
sector in economic growth, which reached a maximum of 8 percent, resulting in a decrease. Meanwhile,
Arabi & Abadalla (2013), investments in education and health will have a positive effect on the quality of
education and health, which led economic growth. Hossain and Lamb (2012) who conducted the survey data
sources Aboriginal People Survey (APS) 2006, human capital measured by education level and health status
had a significant impact on the income of workers in Canada.
Tsamadias & Prontzas (2012) Model Mankiw, Romer and Weil (2012) found that education correlates
positively and affect the economy in Greece. Fredman (2014), in studies of human capital and human
capabilities that have found their first qualifier will get a job with a high percentage. Whereas if a person
went to a higher level, then it will add skills. Grange (2011) in his writings regarding the same issue
conducted by Fredman (2014), skills, talents, knowledge and personal ability is an important factor to the
investment in education that will contribute to economic growth. Classical production function Cobb-
Douglas is often used in studies of the relationship between education and health to economic growth. It
includes variables such as government spending on health, labor, capital, education and skills levels.
Wagner's principles and emphasize Keynesian hypothesis about the relationship between government
spending and economic growth is correct. Govindaraju, Rao & Anwar (2010), there is clear evidence that in
the long run relationship between economic growth, capital, labor and government expenditure includes
expenditure in education. However, most studies previously ignored about testing on Wagner's hypothesis.
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3. MODEL SPESIFICATION
In economics, the Cobb-Douglas functional form of production functions is widely used to represent the
relationship of an output to inputs. It was proposed by Knut Wicksell (1851 - 1926), and tested against
statistical evidence by Charles Cobb and Paul Douglas in 1928. In 1928 Charles Cobb and Paul Douglas
published a study in which they modeled the growth of the American economy during the period 1899 -
1922. They considered a simplified view of the economy in which production output is determined by the
amount of labor involved and the amount of capital invested. While there are many other factors affecting
economic performance, their model proved to be remarkably accurate. Perhaps the simplest framework in
which to look at the effects of education and health spending on economic growth is offered by the growth
accounting framework. For ease of exposition it is assumed that there are two inputs, labour, L, and capital,
K, with only one aggregate output, Y .
The function used to model production was of the form:
P(L, K) = bLαK β
where:
P = total production (the monetary value of all goods produced in a year)
L = labor input (the total number of person-hours worked in a year)
K = capital input (the monetary worth of all machinery, equipment, and buildings)
b = total factor productivity
α and β are the output elasticities of labor and capital, respectively. These values are constants determined
by available technology.
4. FINDINGS
In this section in-depth discussion regarding the multiple linear regression will explained. There will be four
model under discussion, Model 1, Model 2, Model 3 and Model 4.
4.1 Model 1
The first model consists of independent variables that affect the dependent variable, economic growth (GDP)
in Malaysia. The data is in the form of time series data from 1982 up to 2015. In the formation of this first
estimation model, the independent variables are the level of education at the primary level (primary-PRI),
secondary (Secondary-SEC) and high (tertiary-TER ) which will affect economic growth. The establishment
of this model based on Shaari (2014). Here is a general equation for estimating the model.
GDPt = β 0 + β1PRIt + β 2SECt + β 3TERt + et (1)
According to data that have been used, the following are the results of the model estimates;
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GDPt = 9.483 −1.275PRIt − 0.57SECt + 0.813TERt (2)
Se = (2.713)(0.172)(0.301)(0.142)
t = (3.495)(−7.431) **(−0.522)(5.713) **
where;
** Significant at the 95% significant level
F-test = 863.319
R 2 = 0.989
R 2 = 0.987
DW = 1.228
4.1.1 Criteria Statistic Result
F-test
According to equation (2), it was found that the coefficient of determination ( R 2 = .989 ) shows 98.9% of
the variation of the independent variables can explain the dependent variable. This value, however, only a
relative value which is used as a comparative strength of the relationship between variables only. In addition,
a total of 1.1% explained by other variable that is not taken into account in this estimation. Based on the
results of the analysis of the goodness of fit model (F-test) showed that the independent variables can explain
the dependent variable at 95% significant level. This shows that the model was matched with good.
T-test
According to equation (2) results of the analysis suggest that the PRI is an important variable in explaining
economic growth at a 95% significant level. The results were obtained on the TER variables. However, based
on standardized beta values, clearly show that the main variables in explaining the TER over economic
growth than the PRI variables. In addition, the results for model 1 also suggested that the SEC is an important
variable in explaining variables economic growth at a 95% significant level.
4.1.2 Criteria Economic Results
Notation Analysis
Based on the coefficient obtained PRI and SEC variables which impact negatively on economic growth.
Meanwhile, the TER variables provide a positive impact on economic growth. This means, if the number of
those with tertiary education (TER), an increase then it will have a positive impact on economic growth. In
other words, if the number of educated highest increase of 100% then, the country's economic growth will
rise to 81.3%. Here is a summary of the results of the economic criteria for the notation of the first model;
Table 1 : The notation of the variables studied in the first model.
Variable Signs Findings by Shaari (2014) Results
Primary Education (PRI) - + Opposite
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Secondary Education (SEC) - - Support
Tertiary Education (TER) + + Support
According to Table 1 clearly shows that there are two variables, namely the SEC and the TER which was in
line with the decision by Shaari (2014).
Elasticity Analysis
Table 2 : Analysis of the elasticity of the variables studied in the first model.
Variable Elasticity Results
Primary Education (PRI) 1.275
Secondary Education (SEC) 0.157 Any increase in the PRI will cause an increase of 1.275 in
the economic growth. Then the results obtained are elastic.
Tertiary Education (TER) 0.813
Any increase in the SEC will cause an increase of 0.157 in
the economic growth. Then the results obtained are not
elastic.
Any increase in the TER will cause an increase of 0.813 in
the economic growth. Then the results obtained are not
elastic.
4.2 Model 2
The second model consists of independent variables that affect the dependent variable, economic growth
(GDP) in Malaysia. The data is in the form of time series data from 1982 up to 2015. In the formation of this
first estimation model, the independent variables are the level of education at the primary level (PRI),
secondary (SEC), high (TER ) and health expenditure (HEAL), which will affect economic growth. The
establishment of this model based on Shaari (2014) but new research adds variables of health expenditures.
Here is the general equation for estimating the model.
GDPt = β 0 + β1PRIt + β 2SECt + β 3TERt + β 4HEALt + et (3)
According to data that have been used, the following are the results of the model estimates;
GDPt = 9.216 −1.085PRIt − 0.338SECt + 0.631TERt + 0.222HEALt (4)
Se = (2.319)(0.516)(0.263)(0.132)(0.064)
t = (3.974)(−6.939) **(−1.289)(4.761) **(3.479) **
where;
** Significant at the 95% significant level
F test * = 890.114
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R 2 = 0.992
R 2 = 0.991
DW = 1.243
4.2.1 Criteria Statistic Result
F-test
Based on the equation (4), it was found that the coefficient of determination (0.992) shows that 99.2% of the
variation of the independent variables can explain the dependent variable. This value, however, only a
relative value which is used as a comparative strength of the relationship between variables only. In addition,
a total of 0.8% explained by other variable that is not taken into account in this estimation. Based on the
results of the analysis of the goodness of fit model (F-test) showed that the independent variables can explain
the dependent variable at 95% significant level. This shows that the model was matched with good.
T-test
According to equation (4) results of the analysis suggest that the PRI is an important variable in explaining
economic growth at a 95% significant level. The results were obtained on variables SEC, TER and HEAL.
However, based on standardized beta values, clearly show that the main variables in explaining the TER over
economic growth than the PRI variables and HEAL. In addition, the results for the second model also
suggested that the SEC variables are important variables in explaining economic growth at a 95% significant
level.
4.2.2 Criteria Economic Results
Notation Analysis
Based on the coefficient obtained PRI and SEC variables which impact negatively on economic growth.
Meanwhile, the TER and HEAL variables provide a positive impact on economic growth. This means, if the
number of those with the highest (TER) and health spending increases, it will have a positive impact on
economic growth. In other words, if the number of educated highest increase of 100% then, the country's
economic growth will rise to 63.1%. Similarly, if health spending increased by 100% then, the country's
economic growth will increase by 22.2%. Here is a summary of the results of the economic criteria for
notation analysis for second model;
Table 3 : The notation of the variables studied in second model.
Variable Signs Findings by Shaari (2014) Results
And Mohsen, Musai & Amiri (2010).
Primary Education (PRI) - Opposite
Secondary Education (SEC) - + Support
Tertiary Education (TER) + - Support
Health Expenditure (HEAL) + + Support
+
Based on the table 3 clearly shows that there are three variables, namely the SEC and the TER which was in
line with the decision by Shaari (2014). Similarly, new variables were added by the researcher which was in
line with the decision by the Mohsen, Musai & Aamiri (2010).
Elasticity Analysis
Table 4 : Analysis of the elasticity of the variables studied in second model.
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Variable Elasticity Results
Primary Education (PRI) 1.085
Secondary Education (SEC) 0.338 Any increase in the PRI will cause an increase of 1.085 in
Tertiary Education (TER) 0.632 the economic growth. Then the results obtained are
Health Expenditure 0.222 elastic.
Any increase in the SEC will cause an increase of 0.338
in the economic growth. Then the results obtained are not
elastic.
Any increase in the TER will cause an increase of 0.632
in the economic growth. Then the results obtained are not
elastic.
Any increase in HEAL will cause an increase of 0.222 in
the economic growth. Then the results obtained are not
elastic.
4.3 Model 3
The third model consists of three independent variables that affect the dependent variable on economic
growth (GDP) in Malaysia. The data is in the form of time series data from 1982 up to 2015. In the formation
of this estimation model, the independent variables are of the total level of education at all three levels (EDU)
and health expenditure (HEAL), which will affect economic growth , The establishment of this model based
on Shaari (2014) but new research adds variables of health expenditures. Here is the general equation for
estimating the model.
GDPt = β 0 + β1EDUt + β 4HEALt + et (5)
According to data that have been used, the following are the results of the model estimates;
GDPt = −2.633 − 0.224EDUt + 0.843HEALt (6 )
Se = (4.287)(0.410)(0.059)
t = (−0.614)(−0.547)(14.347) **
where;
** Significant at the 95% significant level
F-test * = 346.898
R 2 = 0.957
R 2 = 0.954
DW = 0.670
4.3.1 Criteria Statistic Result
F-test
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Based on the equation (6), it was found that the coefficient of determination (0.957) shows 95.7% of the
variation of the independent variables can explain the dependent variable. This value, however, only a
relative value which is used as a comparative strength of the relationship between variables only. In addition,
a total of 0.43% explained by other variable that is not taken into account in this estimation. Based on the
results of the analysis of the goodness of fit model (F-test) showed that the independent variables can explain
the dependent variable at 95% significant level. This shows that the model was matched with good.
T-test
According to equation (6) the results of the analysis suggest that HEAL variables are important in explaining
the country's economic growth at a 95% confidence level. In addition, the results for the third model also
suggested that the variable EDU is an important variable in explaining economic growth at a 95% significant
level.
4.3.2 Criteria Economic Results
Notation Analysis
Based on the available variables EDU coefficient negative impact on economic growth. Meanwhile, HEAL
variables provide a positive impact on economic growth. This means, if the total health expenditure
increases, it will have a positive impact on economic growth. In other words, if the total health expenditure
increased by 100% then, the country's economic growth will increase by 84.3%. Here is a summary of the
results of the economic criteria for third model notation;
Table 5 : The notation of the variables studied in third model.
Variable Signs Findings by Shaari (2014) Results
And Mohsen, Musai & Amiri (2010).
Total Education (EDU) - Opposite
Health Expenditure (HEAL) + + Support
+
According to Table 5 clearly shows that there are variables, namely the decision HEAL line with the decision
by the Mohsen, Musai & Aamiri (2010).
Elasticity Analysis
Table 6 : Analysis of the elasticity of the variables studied in third model.
Variable Elasticity Results
Total Education (EDU) 0.224
0.843 Any increase in the EDU will cause an increase of 0.224
Health Expenditure (HEAL) in the economic growth. Then the results obtained are not
elastic.
Any increase in HEAL will cause an increase of 0.843 in
the economic growth. Then the results obtained are
elastic
4.4 Model 4
The model four consists of independent variables affect the dependent variable of Economic Growth (GDP)
in Malaysia. The data is in the form of time series data from 1982 up to 2015. In the formation of this
estimation model, the independent variables are the total level of education (Education-EDU), health
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expenditures (health-HEAL) and duration (time- T) which will affect economic growth. The establishment of
this model based on Shaari (2014). Here is a general equation for estimating the model.
GDPt = β 0 + β1EDUt + β 2HEALt + β 3Tt + et (7)
According to data that have been used, the following are the results of the model estimates;
GDPt = 5.647 − 0.462EDUt − 0.118HEALt + 0.066Tt (8)
Se = (2.167)(0.194)(0.074)(0.006)
t = (2.606)(−2.379) **(1.584)(10.487) **
where;
** Significant at the 95% significant level
F-test * = 1080.8
R 2 = 0.991
R 2 = 0.990
DW = 0.743
4.4.1 Criteria Statistic Result
F-test
Based on the equation (8), it was found that the coefficient of determination (0.991) shows 99.1% of the
variation of the independent variables can explain the dependent variable. This value, however, only a
relative value which is used as a comparative strength of the relationship between variables only. In addition,
a total of 0.9% explained by other variable that is not taken into account in this estimation. Based on the
results of the analysis of the goodness of fit model (F-test) showed that the independent variables can explain
the dependent variable at 95% significant level. This shows that the model was matched with good.
T-test
According to equation (8) results of the analysis suggest that T is an important variable in explaining
economic growth at a 95% confidence level. The results were obtained on variables EDU. However, based
on standardized beta values, clearly show that the main variables in explaining the T more economic growth
than the variable EDU. In addition, the results for the model 4 also suggested that variables HEAL is an
important variable in explaining economic growth at a 95% significant level.
4.4.2 Criteria Economic Results
Notation Analysis
Based on the available variables EDU coefficient negative impact on economic growth. Meanwhile, HEAL
and T variables provide a positive impact on economic growth. This means that if health spending (HEAL),
an increase then it will have a positive impact on economic growth. In other words, if health spending
increased by 100% then, the economic growth will rise to 11.8%. Similarly, the term, economic growth is
influenced by the period. Term impact of 6.6%. Here is a summary of the results of the economic criteria for
notation fourth model;
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Table 7 : The notation of the variables studied in fourth model.
Variable Signs Findings by Basir, Herath & Results
Total Education (EDU) - Gebremedhin (2012) and
Okunade (2004) Opposite
Support
+ Support
Health Expenditure (HEAL) + +
Time (T) ++
According to the schedule of 7 clearly shows that there are two variables, namely HEAL and T are in line
with the decision by Basir, Herath & Gebremedhin (2012) and Okunade (2004).
Elasticity Analysis
Table 8 : Analysis of the elasticity of the variables studied in fourth model.
Variable Elasticity Results
Total Education (EDU) 0.462 Any increase in the EDU will cause an increase of 0.462
Health Expenditure (HEAL) 0.118 in the economic growth. Then the results obtained are not
0.066 elastic.
Time (T)
Any increase in HEAL will cause an increase of 0118 in
the economic growth. Then the results obtained are not
elastic.
Any increase in HEAL will cause an increase of 0066 in
the economic growth. Then the results obtained are not
elastic.
5. DISCUSSION AND CONCLUSION
The findings will be discussed based on the objectives set. The discussion will be guided by the tables and
figures and the results from the analysis. The findings are also supported by studies conducted by researchers
before. From the results of the regression carried out, it can be noted that most of the results obtained are
consistent with the hypothesis of the study was provided. Most of the budget for all variables showed a
positive and significant. Important conclusions from the findings obtained within this study based on
regression analysis conducted found that low levels of education, secondary, and higher spending on the
health sector is a major contributor to the increase of the Gross Domestic Product (GDP).
In first model study found that if an increase in the labor force in primary level and tertiary economic
growth will also increase. In second model, if an increase in the labor force in tertiary level and increase
government spending in the health sector, the economic growth will also increase. Next, in the third model,
in the event of an increase in government spending in the health sector, the economic growth will also
increase. Finally in the fourth model, in the event of an increase in government spending in the health sector,
the economic growth will also increase. Important variable in all four models is the Higher Education and
Health Spending contributed to economic growth.
Of the findings in the first model, primary and tertiary education are important variables. While in the
second model, low education, and health spending are important variables. The third model found health
spending variables important in contributing to economic growth. Next in the fourth model, education at all
levels and an important period of economic growth. Based on the results of the study showed that the model
is well and good at significant level of 95%.
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Results of the data analysis is done only using regression analysis, economic criteria and statistical
criteria. However, such in-depth study of econometric criteria is not running. In general, econometric criteria
are intended to determine whether the assumptions used in the estimation of an econometric theory meet the
requirements or otherwise. Thus, the growth model is constructed must be tested with a view econometric
criteria. The econometric criteria is autocorrelation, heteroskedasticity and multicollinearity.
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https://DOI:10.15764/ER.2014.01005
Tsamadias, C., & Prontzas, P. (2012). The effect of education on economic growth in Greece over the 1960-2000
period. Education Economics, 20,(5), 522-537.
Wang, Y., & Ni, C. (2015). The role of the composition of the human capital on the economic growth.: With the
spatial effect among province in China. Modern Economy, 6, 770-781.
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CM025
THE JOHANSEN JUSELIUS COINTEGRATION ANALYSIS ON EXCHANGE
RATE MODEL
Gevivian Anak Giri
Master Student, Faculty of Management and Economics, Sultan Idris University of
Education (UPSI)
Email: [email protected]
Norimah Rambeli
Associate Professor, Faculty of Management and Economics, Sultan Idris University of
Education (UPSI)
Email: [email protected]
Siti Zubaidah Mohd Ariffin
Senior Lecturer, Faculty of Management and Economics, Sultan Idris University of
Education (UPSI)
Email: [email protected]
Sri Utami
Lecturer, Universiti Negeri Semarang (UNNES)
Email: [email protected]
ABSTRACT
The objective of this study is to analyze the selected macroeconomic variables namely interest rate,
inflation and export on the exchange rate in Malaysia. This study investigates the long-term
equilibrium relationship by adapting the Johansen Co-integration test to estimate the long-run
relationship between the variables. In addition, the Vector Autoregression (VAR) and the
Augmented Dickey Fuller (ADF) unit root test are also applied for preliminary time series analysis.
By utilizing the aggregate data, this study also used the annual time series data spinning from 1985
to 2018. The findings of the study show that all the time series variables used in this study are I(1)
data. In other words, it is stationary at the first difference level. In addition, based on the Akaike
value (AIC) on the Vector Autoregression (VAR) test, it shows that the optimal lag is at lag 10.
Based on the cointegration test, proves that there is at least one cointegration vector between the
selected macroeconomic variables and the exchange rate. Overall, these findings show that there is
a long-term equilibrium between the variables in the long term between interest rate, inflation and
export on the exchange rate in Malaysia. Further examination is needed using dynamic modeling
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namely the error correction model and vector error correction model in order to investigate the
long-and short-term relationship between the variable.
KEYWORDS
Exchange rate, macroeconomic factors, Malaysia
1.0 INTRODUCTION
The exchange rate is defined as the number of domestic currency units, and the need to
purchase one foreign currency unit. The exchange rate is very important because it allows
the conversion of a national currency into another currency, thus facilitating international
trade in goods and services and the transfer of funds between countries and also allows the
comparison of prices of goods in different countries at the same time. Developing
economies are more prone to financial and currency crises and their impacts. The smaller
size of their economies makes them more susceptible to internal and external shocks. They
may also face challenges in developing policies to mitigate exchange rate risks and boost
economic growth (Wandeda, 2014). The Malaysian Government has specifically identified
the exchange rate as one of the challenges in attaining a competitive, robust, dynamic and
resilient economy in line with Vision 2020 (Mohamad, 1999). It is therefore a very
important topic to determine the exchange rate. In determining the currencies, factors
affecting the exchange rate are critical. Due to the sharp depreciation and devaluation in
domestic currency, Ringgit Malaysia (MYR), Malaysia is now a hot topic with regard to
foreign exchange. MYR started depreciating at the end of 2014 and broke at
4.46MYR/USD on 29 September 2015. These affect not only the daily expenditure of
consumers in that country but also the economy in Malaysia. To help maintain MYR's
stability, understanding the determinants that affect it is necessary. Other than that, a stable
and predictable rate of exchange will help a country’s economy grow. Therefore, in this
study, macroeconomic factors such as interest rate, inflation and export are chosen to
examine the relationship between macroeconomic factors and the exchange rate in
Malaysia. This study is based on a study conducted by Abdoh, Yusuf, Zulkifli, Bulot, &
Ibrahim (2016). The main objective of the study is to analyze the long-term relationship
between the selected macroeconomic factors, namely interest rate, inflation and export
with the exchange rate in Malaysia. Therefore, the research hypothesis is as follows;
H0: There is no long-run relationship between selected macroeconomic factors and the
exchange rates.
H1: There is a long-term relationship between selected macroeconomic factors and the
exchange rate.
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To achieve the main purpose of the study, the time series test approach used by Rambeli,
Podivinsky & Jalil (2019) will be used in this study.
2.0 LITERATURE REVIEW
In this section, we will discuss in detail the past studies conducted by previous researchers.
The issues and discussion points are based on the overall findings of the study.
According to Yung (2021); Tomiwa (2018) and Utami & Inanga (2009), there is a positive
relationship between interest rates and exchange rates. However, Andrieș, Capraru, Ihnatov
& Tiwari (2017), stated that the relationship between interest rate and the exchange rate is
negative in the short run, while in the long run, the relationship is positive. The study done
by Wilson, & Sheefeni (2014) shows the opposite result. The empirical results have been
unable to detect a clear systematic relationship between interest rates and exchange rates.
There are also studies that show a negative relationship between macroeconomic variables
and the exchange rate, such as the study conducted by Morosan & Zubaş (2015); and Cruz
(2013). The results show that the relationship between the exchange rate and interest rate is
determined in inverse proportion.
Asari, Baharuddin, Jusoh, Mohamad, Shamsudin, Jusoff (2011) conducted a study related
to the exchange rate volatility in Malaysia. This study used a time-series Vector Error
Correction Model (VECM) approach of stationarity test, cointegration test, stability test
and Granger causality test to estimate the relationship between interest rate, inflation rate
and exchange rate volatility. Taking into account a long-run relationship, the interest rate
moves positively while the inflation rate goes negatively toward the exchange rate. The
VECM has also been applied by Ali, Mahmood, & Bashir (2015) to investigate the impact
of inflation, interest rate and money supply on the volatility of the exchange rate in
Pakistan. The findings reveal that inflation and exchange rate volatility have both short-run
and long-run relationships. Increased interest rates and a high money supply raise the price
levels (inflation), which causes an increase in exchange rate volatility. Nor, Masron, &
Alabdullah, (2020) has an EGARCH (exponential generalized autoregressive conditional
heteroskedastic) model to investigate the effect of macroeconomic factors on the volatility
of the exchange rate in Somalia. The results reveal that macroeconomic fundamentals like
money supply, imports, and short-term capital flows have a significant influence on the
volatility of Somali exchange rates.
3.0 MODEL SPECIFICATIONS
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The model specification used in this study was inspired by Abdoh et al. (2016).
ln = + ln + ln + ln
where,
= Exchange rate for ‘i’ and time ‘t’
= Interest rate for ‘i’ and time ‘t’
= Inflation for ‘i’ and time ‘t’
= Export for ‘i’ and time ‘t’
i = Error term for ‘i’ and time ‘t’
= coefficient
In this study, the research approach used by Rambeli, Podivinsky & Jalil (2019) will be
applied. According to this approach, the analysis involved is the Augmented Dickey Fuller
unit root test (ADF), vector autoregression analysis (VAR) and the Johansen Juselius
cointegration test will be used.
4.0 FINDINGS
In this section, the analysis results will be discussed including the Augmented Dickey Fuller unit
root test (ADF), vector autoregression analysis (VAR) and Johansen Juselius cointegration test.
Unit Root Test (ADF)
Table 1:
Results Of the Stationarity Test of The Relationship Between Selected
Macroeconomic Factors and The Exchange Rate in Malaysia
Level 1st Difference
Time series data Trend and Trend and
intercept intercept
Intercept Intercept
Exchange rate -2.129871 -3.077858 -3.638191 -4.935063
(Lag 9) (Lag 9) (Lag 9) (Lag 6)
Interest rate -1.921320 -2.530979 -5.071603 -5.215122
(Lag 12) (Lag 8) (Lag 9) (Lag 9)
Inflation -2.453865 -2.832133 -3.523666 -8.010332
(Lag 9) (Lag 9) (Lag 9) (Lag 7)
Export -1.640974 -0.775962 -3.519251 -4.686854
(Lag 9) (Lag 9) (Lag 11) (Lag 9)
Note: Numbers in parentheses are lag orders selected based on AIC.
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Table 1 shows the results of ADF unit root tests, both at levels and at first differences. This
unit root test takes into account the random walk model with intercept and trend and
intercept. Table 1 also shows the statistical values from the t-test for all data series used.
The results of the study show that all the time series data used are non-stationary. In other
words, this shows that the series is not stationary at level. Therefore, all the time series data
used are not I(0) data. Therefore, the test continues on the first difference. When the ADF
test is conducted for each variable at the first difference, the null hypothesis of non-
stationarity is easily rejected at the 99% level as shown in Table 1.
Vector Autoregression-VAR
Table 2:
Results of a Vector Autoregression (VAR) Test Study on the Relationship between
Selected Macroeconomic Factors and Exchange Rate in Malaysia
VAR AIC
MALAYSIA
2 11.75795
3 11.80090
4 11.95901
5 10.20148
6 9.350545
7 9.522380
8 9.731306
9 9.351035
10 8.962444
11 9.117224
12 9.278052
Note: Numbers in bold are the lowest sequences
selected based on AIC.
Based on Table 2, the results of each lag from Lag 2 to Lag 12 are presented using the
Akaike Information Criteria (AIC) value. The optimal AIC value for Malaysia is at lag 10
with an AIC value of 8.962444.
Johansen Juselius Cointegration Analysis
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Table 3:
Results of the Johansen Juselius Test Study on the Relationship between Selected
Macroeconomic Factors and the exchange rate in Malaysia.
Hypothesis 5% critical 1% critical 5% critical 1% critical
H0 H1
λ Trace value value λ Max value value
r=0 r=0 52.63172*(**) 47.21 54.46 29.81339*(**) 27.07 32.24
r≤1 r>1 22.81832 29.68 35.65 12.55305 20.97 25.52
r≤2 r>2 10.26528 15.41 20.04 8.281961 14.07 18.63
Note that, the notation ‘r’ denotes the number of cointegrating vectors. The superscript (*) indicates statistically
significant at 95% and (**) at 99% levels. The critical values for the Johansen Juselius test were obtained from
(Osterwald-Lenum, 1992)
The results of the cointegration test are shown in Table 3. The cointegration test used takes
into account the assumption that the cointegration equation only contains intercepts. The
optimal lag value for this cointegration test was determined using Akaike's criterion (AIC)
as was used in the unit root test. Both test statistic values and are significant at the 5%
significance level and the null hypothesis that there is no cointegration is successfully
rejected. This result also proves that there is at least one cointegration vector between
selected macroeconomic variables and the exchange rate in Malaysia. This shows that
there is a long-term balance between the variables. The existence of this cointegration
means that the relationship that exists between selected macroeconomic variables and the
price of gold is not 'spurious' and the balance exists in the long term.
5.0 CONCLUSION
The exchange rate is an important indicator of the economic growth of a country and its
volatility has a significant impact on international trade. Therefore, this research was
conducted to explore the influence between interest rate, inflation, and exports on the
exchange rate in Malaysia. This research is important to enrich literature because it can
help support many researchers who have relevant research in the future to provide solid
evidence for the economic development of a country. Future researchers are encouraged to
increase the number of observations in monthly data which can greatly increase the
number of observations. This is because it can explain more about the volatility of the
variables within a year which may affect the findings. As for the government, the relevance
of this study can contribute useful information to the government in identifying. managing
problems and developing relevant policies to ensure the stability of the national economy.
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REFERENCES
Abdoh, W. M. Y. M., Yusuf, N. H. M., Zulkifli, S. A. M., Bulot, N., & Ibrahim, N. J. (2016).
Macroeconomic factors that influence exchange rate fluctuation in ASEAN countries. International
Academic Research Journal of Social Science, 2(1), 89-94.
Ali, T. M., Mahmood, M. T., & Bashir, T. (2015). Impact of interest rate, inflation and money supply on
exchange rate volatility in Pakistan. World Applied Sciences Journal, 33(4), 620-630
Andrieș, A. M., Căpraru, B., Ihnatov, I., & Tiwari, A. K. (2017). The relationship between exchange rates
and interest rates in a small open emerging economy: The case of Romania. Economic Modelling,
67, 261-274.
Asari, F. F. A. H., Baharuddin, N. S., Jusoh, N., Mohamad, Z., Shamsudin, N., & Jusoff, K. (2011). A vector
error correction model (VECM) approach in explaining the relationship between interest rate and
inflation towards exchange rate volatility in Malaysia. World applied sciences journal, 12(3), 49-56.
Cruz-Rodriguez, A. (2013). Choosing and assessing exchange rate regimes: A survey of the literature.
Revista de Análisis Económico–Economic Analysis Review, 28(2), 37-62.
Mohamad, M. (2012). Malaysia on Track for 2020 Vision.
Moroşan, G., & Zubaş, I. M. (2015). Interest rate, exchange rate and inflation in Romania: Correlates and
interconnection. Journal of Public Administration, Finance and Law, 8, 146-160.
Nor, M. I., Masron, T. A., & Alabdullah, T. T. Y. (2020). Macroeconomic fundamentals and the exchange
rate volatility: empirical evidence from Somalia. SAGE Open, 10(1), 2158244019898841.
Rambeli, N., Podivinsky, J. M., & Jalil, N. A. (2019). The Re-Examination Of The Dynamic Relationship
Between Money, Output and Economic Growth In Malaysia. International Journal Of Innovation,
Creativity And Change, 5(2), 1812-1834.
Tomiwa, S. (2018). The impact of real interest rate on real exchange rate: Empirical evidence from Japan.
2018 Awards for Excellence in Student Research and Creative Activity
Utami, S. R., & Inanga, E. L. (2009). Exchange rates, interest rates, and inflation rates in Indonesia: The
International Fisher Effect Theory. International Research Journal of Finance and Economics, 26,
151-169.
Wandeda, O. D. (2014). The impact of real exchange rate volatility on economic growth in Kenya. A
Research Project Submitted to the School of Economics, University of Nairobi, in Partial Fulfilment
of the Requirements for the Award of the Degree of Master of Arts in Economics, 1-70.
Wilson, L., & Sheefeni, J. P. S. (2014). The relationship between interest rate and exchange rate in Namibia.
Journal of Emerging Issues in Economics, Finance and Banking, 3(1), 947-961.
Yung, J. (2021). Can interest rate factors explain exchange rate fluctuations? Journal of Empirical Finance,
61, 34-56.
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CM026
THE DEMOGRAPHIC AND PERSONALITY DETERMINANTS OF
HAPPINESS IN MALAYSIA
Vinothiny Subramaniam
Universiti Pendidikan Sultan Idris (UPSI),Tanjung Malim, Perak
Norasibah Abdul Jalil
Universiti Pendidikan Sultan Idris (UPSI),Tanjung Malim, Perak
Norimah Rambeli @Ramli
Universiti Pendidikan Sultan Idris (UPSI),Tanjung Malim, Perak
Zainizam Zakariya
Universiti Pendidikan Sultan Idris (UPSI),Tanjung Malim, Perak
Asmawi Haji Hashim
Universiti Pendidikan Sultan Idris (UPSI),Tanjung Malim, Perak
ABSTRACT
This paper examines the determinants of happiness under the category of demographic and personality factors using World
Values Survey 7 (2017-2022) data on a sample size of 1313 individuals. Descriptive statistics and generalized ordered logit
regression (gologit2) was used to analyze the data. Results from gologit2 analysis stated that health, financial situation,
importance of god, and employment status having strong and positive association while age, and education having negative
relationship respectively. This study proves the Easterlin Paradox in which negative association between income and
happiness. However, this relationship is not significant. Other than that, age and happiness confirms U-shape relationship
partially. Results also reveal that higher educated people tend to be unhappy because positive effect of education is offset
by frustration and expectation. These findings suggest that Malaysia government should implement policies that aim to
increasing individual happiness by considering relevant and significant determinants of happiness.
KEYWORDS
Happiness, demographic and personality factors, Income-Happiness Paradox, Malaysia, gologit2
1. INTRODUCTION
What is happiness? This question may puzzled many people in relating happiness with economics. Being happy
is one the few goals that people have and happiness economics research finding solution to this aspect of human
behavior. Happiness have been studied broadly over few decades in psychology, but recently received much
attention in economics. Policymakers worldwide has seen the happiness as an important element objective in
a public policy. Almost all countries has started to measure the happiness of their people in annual basis with
encouragement from OECD (Helliwell et al, 2022). Report by “The Commission on the Measurement of
Economic Performance and Social Progress” (CMEPSP)” identify the limits of GDP function as an indicator
for economic performance and social progress in a nation. The research by commission is included with the
limit of Gross Domestic Product GDP and its measurement and relevant indicators of social progress; to assess
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the viability of alternative measurement tools, and to discuss how to present the statistical information in an
appropriate way (Stiglitz, Sen, & Fitoussi, 2009; Vaghefi, 2022).
The growing dissatisfaction from GDP per capita as an indicator of well-being pushes scholars to
explore better ways to measure well-being (Eren and Aşıcı, 2015; Boo, et al., 2016; Frey & Stutzer, 2002a and
2002b). Although happiness is a relatively new topic in Economics literature, it is quickly growing (Kahneman
& Krueger, 2006). One of the primary studies whether economic growth increase the happiness is conducted
by Easterlin (1974), which gave birth to famous theory called Easterlin Paradox, which suggests that increasing
income should not necessarily lead to an increase in happiness (Easterlin, 1995, 2001 & Boo, Yen, & Lim,
2016). OECD (2018) indicates that, a large number of developed countries has been or will start collecting
subjective well-being data to guide for good policymaking. Moreover, the results of those surveys have been
attracting attention of researchers as it is claimed that happiness research is important for policymaking
purposes whether economic growth able to increase happiness and better well-being in the society (Bruni,
2007).
The government of Bhutan has constructed measurement of “Gross National Happiness” in which this
index represent the collective happiness of the people in a country replacing “Gross National Product”. GNH
uses nine areas which are psychological wellbeing, time use, community vitality, culture, health, education,
environmental diversity, living standard and governance (Ura, Alkire, Zangmo, & Wangdi, 2012). Gallup
World Poll (GWP) is one of the happiness well-being index in which introduced in 2005 and this index has
been conducted semiannually, annually, or quarterly determining country-by-country basis. The GWP index is
comprising thirteen different indicators of happiness including business and economics, citizen engagement,
communications and technology, education and families, environment and energy, food and shelter,
government and politics, health, law and order, religion and ethics, social issues, well-being, and work. Happy
Planet Index (HPI) is the simplest form of index, in which it utilizing only three domains to measure wellbeing
or happiness. The first publication of HPI index in 2006, now ranks 151 countries in terms of happiness across
the world. The Happy Planet Index is comprising of four features to display how efficiently people of different
nations are using environmental resources to lead long, and happy lives. HPI is a combination of well-being,
life expectancy, inequality of outcomes, and ecological footprint.
Layard (2011) in his book has argued that economics field really need “revolution” whereby we need to
understand the determinants of happiness and it should be the prime goal to government to maximize the people
happiness. According to Frey & Stutzer (2000) has split the determinants of happiness into three major sets:
(a) institutional and political factors (b) macroeconomics factors such as inflations, unemployment, real GDP
and GINI coefficient, and (c) demographic and personality factors such as age, gender, education, and health.
According Frey and Stutzer (2002a; 2010), stated that “instead of trying to determine what happiness is from
outside, one can ask the individuals how far they happy in their life”. It is a very common and practical tradition
in happiness economics to directly ask persons about their satisfaction with their lives, and this information is
used as a proxy for person’s happiness. The individual appears to be the best judges of the overall happiness
in their life, and by asking them straightforward method to access this well-being information. Major studies
in happiness economics is done through questionnaires that ask them how they perceived their life as overall.
This technique call as “reported subjective well-being”, or “individual evaluation” whether they are
experiencing positive or negative affect, and life satisfaction as a whole (Nikolaev, 2013).
The terms subjective well-being (SWB), life satisfaction and happiness are often used interchangeably, as
empirically they seem to measure a very similar concept and mutually inter-related (Caner, 2015a and 2015b;
Buitrago, 2018; and Stevenson & Wolfers, 2008). In Malaysia, the Department of Statistics Malaysia (DOSM)
has published Malaysia Happiness Index Report 2021 to measure the level of happiness among Malaysian
combining physical, social, emotional, and spiritual aspects. This is a first happiness related survey conducted
by DOSM (DOSM, 2021). The happiness survey has been collected from September to November 2021 by
using household perspectives covering only selected areas in Malaysia. The happiness index score for the year
2021 is 6.48. Initially, Economic Planning Unit has published Malaysian Quality of Life Index (1990 – 2007)
replacing much newer version is Malaysian Well-Being Index (MWI) Report (2000-2018) to measure the well-
being of people. The MWI has included both economic and social elements such as transport, communication,
education, income and distribution, working life, housing, leisure, governance, public safety, social
participation, culture, health, environment, and family. The construct of many index related to happiness and
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well-being shows that government policy framework has started to include qualitative elements such human,
social, and environmental perspectives (Social Indicators, EPU).
The studies on happiness economics has studied extensively on Western countries but those focusing on
non-Western countries such as Malaysia is limited (Selin, & Davey, 2012; Oishi, et al, 2013). Malaysia is
ranked the 70th happiest nation in World Happiness Report 2022 by United Nations Sustainable Development
Solution Network, an enormous drop compares with year 2018 when it was ranked in 35th place (Helliwell et
al, 2022, and Helliwell, et al, 2018). Malaysia has been facing plummeting in happiness level over the last four
years from 35th place in 2018, dropping to 80th place in 2019, 82nd in 2020, and 81st in 2021. Yet relatively little
is known empirically about how household’s happiness is affected by demographic and personality factors.
This study aim to examine demographic and personality factors affecting happiness in Malaysia.
The paper has been organized as follows. Section 2 cover the literature review on determinants of
individual’s happiness. Section 3 comprises of data and methodology while section 4 discusses the empirical
output followed by conclusion in section 5.
2. LITERATURE REVIEW
Happiness has been explored by many philosophers, psychologists, sociologists and economists. Over the past
decades, economists gained impressive insights into the determinants of happiness. The happiness economics
is studied by social psychologists than economists did (Frey and Stutzer, 2010; Tetaz, 2012; Diener, Oishi, and
Tay, 2018). Numerous studies done by researchers, and it is shows that national income per capita is not directly
linked to people’s happiness level. In the social science, happiness economics research becoming an
outstanding example of an interdisciplinary approach and effective way to learn people’s happiness. Neo-
classical economics theory shows that human happiness can be measured, and this measurement can be used
as approximation to the theoretical concept of utility (White, 2015; Arampatzi, 2013; Caner, 2015). Hicks and
Robbins, the important scholar in the 1920s and 1930s were concluded that it is difficult to measure the utility.
Therefore, the microeconomic theory that developed not focus on measuring the utility and this approach made
it empirically evaluate the demand function. But, over time it is understood that this approach cannot handle
behavioral theories. These inadequacies is partly overcome by happiness economics research which starts from
well-founded assumption that happiness can be captured by self-reported happiness (Frey, 2019). As results,
the literature in happiness economics especially the determinants of happiness in different domains are growing
in fast phase (Helliwell and Aknin, 2018).
Easterlin in his research wrote in 1974 “Does Economic Growth Improve the Human Lot? Some
Empirical Evidence” discussed the relationship between happiness and income level. Easterlin pointed that
people with high income people tend to be happier compare to those with lower income in a nation and the
research only done by using small number of nations due to absence of datasets. However, in the long run,
happiness level doesn’t increase when national income increases. This finding known as Easterlin paradox or
happiness-income paradox and it is indeed very important concept in happiness economics (Easterlin 1974,
1995). Easterlin used two types of empirical data including Gallup-poll type survey and data from research,
which carried out in 1965 by the humanist psychologist Hadley Cantril. Easterlin (1974) discussed in his book
that he get inspiration to start the research in happiness from essay named “ The Welfare Interpretation of
National Income and Product” by Moses Ambramovitz. Ambramovitz concluded that “we must be highly
skeptical of the view that long term changes in the rate of growth of welfare can be gauged even roughly from
the changes in the rate of growth of output” and called for “further thought about the meaning of secular
changes in the rate of growth of national income and empirical studies that can fortify and lend substances to
analysis [pp. 89]”.
Again in 1995, Easterlin examine the relationship between happiness and income from the time series
data collected on several countries such as United States, European Countries, and Japan. The research has
concluded positive relationship existed. Nevertheless, the positive impact from increasing income on happiness
tend to offset by negative impact of higher standard of living. The Easterlin Paradox theory suggested that
increase in Gross National Product (GNP) did not increase the average happiness of the people (Easterlin,
1995). In conclusion, the “Easterlin Paradox” or “Happiness Paradox” shows that in average income given, the
happiness were correlated over time. On the other hand, happiness does not catch up with increasing economic
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growth (Easterlin, 2001; 2009). Until today, Easterlin still found the long-term relationship between happiness
and income that not required to related but in short-term the happiness and income were positively correlated
(Easterlin, 2013). Veenhoven & Vergunst (2014) has found contradicting results as to Easterlin results in which
they use time series data from World Database of Happiness encompassing 67 countries from 10 to 40 years.
The result shows positive relationship between GDP and happiness in many countries . For example, a 1%
growth in GDP per capita per year is tallied by increase of happiness by 0.00335 on a scale of 0-10.
Other factors also able to affect the happiness among individual including age, employment status,
education level, financial situation, number of children, health status, family, and leisure. These factors could
influence happiness supported by domain satisfaction approach. The domain satisfaction approach is defined
as a person’s well-being and happiness is related to many aspects of their life situations. The domains of life
refer to concrete areas where a person functions as a human being. In addition, a person’s life can be
approached as general construct of many specific domains and that happiness can be understood as the result
of satisfaction in the domains of life. Cummins (2005) based on his earlier study argued that seven domains
contribute to happiness: health, productivity, intimacy, safety, community and emotional well-being.
Many researchers found out that age is U-shaped in which minimum between 40 and 50 years of age
depends on the independent variable used in happiness model (Blanchflower & Oswald, 2004 and 2008; Frey
& Stutzer, 2002; Gerdtham and Johannesson, 2001; Ngoo, 2017 and Hsieh & Li, 2022). Another latest research
by Beja (2018) reconfirm that happiness and age is U-shaped by using data come from the 3rd to the 6th waves
of the World Values Surve (WVS) with combined data represents about 90 percent of the world’s average
population for the period 1995–2014 comprising 42 are upper-income, 46 are middle-income, and 7 are low-
income societies. Results shows that happiness decreases from a high-point in young adulthood, reaches a low-
point in midlife, and thereafter increases to arrive at another high-point in old age.
Blanchflower & Bryson (2022) found that women tend to experience much lower downward hits, and
their happiness affected by good and bad events compare to men. However, women turn out to be more resilient
and revert to their previous happiness level. Perovic & Golem (2010) in the study on transition countries shows
that female is happier compare to male. In detail, successful women are the happiest, and their happiness is
effect by macroeconomic variables. Nikolaev (2018) in a recent study has reaffirm the relationship between
education attaintment and happiness. Unlike normal research, this study has used relationship between higher
education and three different measures of SWB including life satisfaction and its different sub-domains
(evaluative), positive and negative affect (hedonic), and engagement and purpose (eudaimonic). The results
shows that individual with higher education tend to be happier because they perceived their life to be more
meaningful and experience more positive emotions compare to negative emotions. In addition, highly educated
people are mostly satisfied with many aspects of life including financial status, employment opportunities,
neighborhood and local community but at the same time, they report lower happiness level with the leisure
time that they have (Ngoo, 2017).
Van Praag and Carbonell (2011) suggested that health condition could become one of the important
determinants for happiness. Easterlin (2004) also supports that health condition has a long lasting effect. Money
does not guarantee happiness. Steptoe (2019) suggested that most people who spend most of their time in
making money should devote less time in making money and pay attention on more to non-pecuniary goals. In
Indonesia, Landiyanto, Ling, Puspitasari, and Irianti (2011) put more attention to health and found it to be
significant in influencing happiness. Study by Liu et al (2016) shows that happiness correlated with increasing
wealth, health, social support, and freedom of expression. Study by Boo, Yen and Lim (2016) shows that
Malays are more satisfied with their lives compared to ‘Indian and others’ but they are not significantly happier.
The Chinese are less happier compared to ‘Indians and others’ as shown by the negative relationship with
happiness and it is not significant.
Employment of an individual reflects the economic security for them. Gerdtham and Johannesson
(2001) used unemployment to represent the socio-economic variables. This is because employment creates
positive externalities like job satisfaction, income and happiness. However, unemployment can increase the
number of suicides, which creates negative externalities. Positive externalities supports happiness but negative
externalities provides unhappiness among people. This is supported by the study conducted by Luechinger et.
al (2010), unemployment can produce negative externality because it induces negative anticipatory feelings of
angst and stress due to economic insecurity. Data from 12 European countries between 1975 and 1992, states
that aggregate unemployment decreases average life satisfaction and average income. Di Tella et al. (2001)
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differentiates effect of unemployment either to direct and indirect effects. Direct effects of unemployment is
crime and public finance, changes in working hours and salaries but indirectly reports job insecurity. The
findings support the moment-to-moment outlook theory (Yiap and Geetha, 2017).
Study by Lee and Kawachi (2019) shows that individuals that prioritizing religion were most likely
to be happier, while those prioritizing extrinsic achievements were less likely to be happy. Thus, the rank order
of happiness across personal values concerning the life domains, from the highest to lowest likelihood, are
spirituality, social relationships, physical self, and extrinsic achievements. Even though previous studies have
constantly shown that religion is positively associated with happiness, but this findings have newly shown that
individual prioritizing religion are most likely to be happy than others. This paper aims at investigating the
effects of demographic and personality factors in determining the happiness in Malaysia.
3. DATA AND METHODOLOGY
The data used are from the 7th World Values Survey (WVS) (Haerpfer, et al, 2022). The target population was
Malaysian citizen over 18 years old. This survey has been conducted in February 2018 and stratified sampling
covering all states with 1313 adults. The whole country covered in the sampling frame including both rural and
urban areas. The respondent selection was based on screening criteria and quota assigned to the interviewer.
The survey was conducted by using mixed-technology including online panel in urban areas and face-to-face
method with Computer-Assisted Personal Interviews (CAPI) in rural areas. Face-to-face interviews conducted
in rural areas using self-completion method to ensure the consistency of sampling. For further details a copy
of the questionnaire, see Haerpfer, et al. (2022). The focus of this study is to examine the relationship between
demographic and personality factors in determining the happiness in Malaysia. The happiness dependent
variable question was ‘Taking all things together, would you say you are: Very happy, Quite happy, Not very
happy [or] Not at all happy?’. A definition and measurement of dependent and independent variables are
presented in appendix 1.
The analysis was conducted by using Stata SE15 (StataCorp, 2017). Initially, chi-square tests of
distribution were tested to determine the significant relationship between two categorical variables and
presented in appendix 2. Given the ordinal nature of both dependent and independent variables, generalized
ordered logit regression (gologit2) was used (StataCorp, 2017, Williams, 2007). This method is commonly
used in social research especially involving World Values Survey data related analysis (Craemer, 2009;
Amable, 2009; Bartram, 2011). The gologit2 model constraints the independent to meet the parallel lines
assumption. In addition, the gologit2 model were weighted by using survey function, analysis were up to robust
variance estimators.
4. FINDINGS
The aim of this study to examine the demographic and personality factors affecting happiness of
Malaysians. Initially, ordinal logistic regression model (ologit) used to regress the model. However, brant test
output shows that parallel regression assumption has been violated in which at least one of the variable (leisure)
does not meet parallel lines assumption. Therefore, we proceed this study with gologit2 model by using partial
proportional odds method were used in this study. The sample characteristics of dependent and independent
variables are summarized in Table 1 and Table 2.
Table 1. Categorical Variables by percentages (%)
Variables Category Percentage
Gender:
Female 50.04%
Employment: Male 49.96%
Employed 74.11%
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Education: Unemployed 25.89%
Lower 52.25%
Financial Situation: Middle 18.66%
Children: Higher 29.09%
Satisfied 66.72%
Health: Dissatisfied 33.28%
No children 40.44%
Family: Having more than one 59.56%
Leisure: children
God: Good 67.71%
Materialist: Fair 28.48%
Ethnic: Poor 3.81%
Important 96.34%
Continuous Variables Not important 3.66%
Income1 Important 92.92%
Age Not important 7.08%
Important 88.35%
Not important 11.65%
Post materialist 72.05%
Materialist 27.95%
Malay 67.33%
Chinese 24.90%
Indian and others 7.77%
Mean Standard Deviation
4.60 2.05
38.33 13.21
Table 2. Feeling of happiness
Happiness (Ordinal scale) Frequency Percentages
Very happy 242 18.43%
67.17%
Quite happy 882 14.39%
Not very happy & 189
Not at all happy
The individuals in the sample equally distributed in gender category, with being male 49.96% and being female
is 50.04% and mean age is 38.33 years old. The mean income is 4.6 and standard deviation us 2.05. From the
sample, the breakdown showed lower education level to be 52.25%, middle education 18.66%, and higher
education is 29.09%. The measurement of happiness shows that 18.43% and 67.17% declared that they were
very happy and quite happy accordingly, while 14.39% not very happy as per table 2.
1 The scale of income is measured on a scale of 10-point ordinal scale in WVS. Therefore, the income variable
will be treated as a continuous variables as suggested by previous researchers (Rhemtulla et al, 2012, and
Sarracino, 2008). The scale 1 income is lower scale of income, while scale 10 is higher scale in income.
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Table 3. Generalized ordered logit estimates for all variables, OR (SE)
Variables Odds ratios Robust S.E.
Gender 1.0024 0.1211
Age 0.9289 0.0322*
Employment 1.3800 0.1477*
Middle education 0.6117 0.4914**
Higher education 0.6038 0.5045***
Income 0.9927 0.03186
Financial 2.7489 0.1420***
Children 1.1128 0.1479
Fair health 0.2125 0.1557***
Poor health 0.1493 0.3184***
Family 1.0261 0.3122
Leisure 0.8558 0.2283
God 1.6211 0.1953*
Materialist 0.8331 0.1333
Malay 0.7154 0.2482
Chinese 0.9869 0.7377
Notes:
1. Exponentiated coefficients ***, **, and * significant at 1%, 5%, and 10%,
respectively.
2. Pseudo 2 for happiness is 0.1248.
3. VIF (the highest value) is 1.52
Output are reported as estimated coefficients transformed into exponentiated odds ratios (OR). The positive
regression coefficient will result to an odds ratio less than 1. Therefore, one unit increase in independent
variable corresponds to decrease in the odds being happier while holding other independent variables constant.
The negative regression coefficient will result to an odds ratio more than 1. Therefore, one unit increase in
independent variable corresponds to increase in the odds of being happier while holding other independent
variables constant (Liu, 2015). There is positive but not significant relationship between being female and
happiness. It means that being female are happier compared to male (Boo, et al, 2016). There was statistically
significant negative relationship between age and happiness (OR=0.9289, p=0.0322). For one unit increase in
the age, the odds of being in higher happiness is reduce by factor of 0.9289 when holding other independent
variables constant. In other words, one unit increase in the age, happiness reduced by 7.11%. This confirms the
U- shape relationship in which increasing age reduce the happiness, and at one point of time the happiness
started to increase in older age of people. It is noted that “increase” in the independent variables that used in
this study referring to cross-sectional variations and not changes happen over time. For employment status,
being employed and happiness having significant positive relationship (OR=1.38, p=0.1477). Comparing to
those unemployed, being employed contributing to higher level of happiness. Unemployed is the main factor
contributing to unhappiness (Oswald, 1997, and Selim, 2008).
Comparing the respondents with lower education level, middle education and higher education level having
significant negative relationship (OR=0.6117, p=0.004) and (OR=0.6038, p=0.001) respectively. Being highly
educated, the odds of being in higher happiness is reduced by factor 0.6038 when holding other independent
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variables constant. Our findings are also consistent with that of Ruiu and Ruiu (2019) in which increase in the
realized income contributing positive effect on happiness from higher education is offset by frustration and
expectations. Besides that, highly educated people may facing illusionary superiority bias, the people wrongly
convinced that high education will provide them good jobs but they end up in an opposite situation. Therefore,
there will be a mismatch between realized income and income expectations that reduce the happiness of highly
educated people. The findings shows that income (treated as continuous) was not a significant and negative
relationship with happiness with an OR of 0.9927 (0.818). This result supports the Easterlin paradox which
shows the negative correlation between happiness and income.
Being in good financial situation was positively associated with higher happiness level (OR=2.7489,
p=0.0001). Being in a good financial status, will strengthen the economic situation of people leads to higher
level of happiness (Boo, et al, 2016). However, the number of children was not a significant variable in
predicting happiness. Reporting being in a good health was strongly and positively related to increase in
happiness. For one unit increase in being good health, the expected odds ratios for happiness increased by
1.5487 (p=0.0001). Malaysia is multi-racial and multi-religious country consisting of Malay, Chinese and
Indian community (Muhamat et al, 2012). Chinese and Malay ethnic group are less happy compared to “Indian
and other’ ethnic group, however this findings is not significant. This finding is partially consisted to study by
Boo, et al. (2016) conducted in Malaysia in which Chinese are less happy compared to ‘Indian and other’ ethnic
group. At the same, giving importance to god make Malaysians much happier and this is consistent to Malaysia
as a multi-religious country.
5. CONCLUSION
Using World Values Survey 7 data, this study attempted to examine the determinants of demographic and
personality factors affecting happiness in Malaysia. Results of the estimated generalized ordered logit
regression (gologit2) suggest that in Malaysia, the significant determinants of demographic and personality
factors are: age, employment status, education level, satisfaction with financial situation, health status, and
importance of god. The findings of this study on the relationship between income and happiness in Malaysia
is confirming very well-known theory, Easterlin paradox or happiness-income paradox. We can conclude that
this study supports the Easterlin Paradox theory, in which negative relationship between happiness and income
and this relationship is not significant. The limitations of this study is insufficient data to perform long-term
analysis of income and happiness using time-series data. Other factor that showing contrast results to previous
researchers is strong and negative relationship between education and happiness. Initially, investing in
education process contribute to positive economic benefits by the time they finish their studies. However, by
the time landing in job they facing mismatch between realized income and income expectations leads to
frustrations and unhappiness among highly educated people. Therefore, government policies should focus on
improving happiness by looking at the appropriate determinants of demographic and personality factors in
Malaysia.
ACKNOWLEDGEMENT
I would like to thank my supervisor Associate Professor Dr. Norasibah Binti Abdul Jalil for her expert
advice and encouragement throughout this project.
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Appendix 1. Definition and measurements of variables
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Variable Name Definition
Dependent Variable Taking all things together, would you say you are;
Happiness Scale “1” to “3”, 1=very happy, 2= Quite happy, and 3=Not very happy [or]
Not at all happy
Independent
variables:
Scale of Income
(Income): Continuous variable starting from 1-10
Age: Continuous variable starting from 18-80 years old
Gender:
Female
Male*
Employment:
Employed Being full time, part time & self employed (1=yes)
Unemployed* Being retired/pensioned, homemaker, student, unemployed (0=no)
Education:
Lower* No formal education & complete primary school
Middle Secondary school, vocational type, and university preparatory type
Higher Bachelor’s. Master’s, & Doctoral
Financial Situation:
Satisfied Being satisfied (1=yes)
Dissatisfied* Being dissatisfied (0=no)
Children:
No children* No children (1=yes)
Having more than Having more than one children (0=no)
one children
Health:
Good* Being in good health
Fair Being in fair health
Poor Being in poor health
Importance of family
(family): For each of the following aspects, indicate how important it is in your life.
Important (1=yes)
Not important* (0=no)
Importance of god
(god): How important is God in your life?
Important
Not important*
Post-materialist index
(materialist):
Post materialist (1=yes)
Materialist (0=no)
Importance of
Leisure Time
(leisure): For each of the following aspects, indicate how important it is in your life.
Important (1=yes)
Not important* (0=no)
Ethnic:
Malay
Chinese
Indian and others*
Note: * refers to reference group
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Appendix 2. Chi-square test and Cramer's V test results
Variables Pearson Chi(2) Probability Cramer’V
Gender 0.2993 0.861 0.0151
Employment 0.9251 0.630 0.0265
Education 15.1686 0.004 0.004
Financial 108.7022 0.000 0.2877
Income 16.3764 0.566 0.079
Children 3.5608 0.169 0.0521
Health 181.5131 0.000 0.2629
Family 5.1216 0.077 0.0625
Leisure 0.6568 0.720 0.0224
God 20.8011 0.0000 0.1259
Materialist 0.8449 0.655 0.0254
Ethnic 8.7608 0.067 0.0578
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CM027
DETERMINANTS OF BEHAVIOR TO PERFORM SELF-PROTECT:
MEDIATING ROLE OF INTENTION TO SELF-PROTECT
Nurul Hidayah Mohd Yusof
Faculty of Management and Economics, Universiti Pendidikan Sultan Idris
[email protected]
Ahmad Zainal Abidin Abd Razak
Faculty of Management and Economics, Universiti Pendidikan Sultan Idris
[email protected]
ABSTRACT
Cybercrime issues has widely discussed around the globe. The cases are increasing and cause concern among many parties
not only government, businesses and also individual. A lots of action had been taken by the government and agencies to
mitigate the issues such as strengthening the cyber security and improving public awareness. However, the cases are still
increasing. Technology alone is seen not enough to fend off the cybercrime threat. It needs support from the internet users
to apply self-protection to double up the shield. The objective of this study is to determine factors that contributes to self-
protection behavior in reducing cybercrime risk. This paper discuss about the issues, concept and objectives for forthcoming
study through various literatures. The conceptual paper signifies to supplement literatures for academics as reference for
their future research. The results of this future research may provide information to government, businesses, financial
institution and policy makers in making planning to reduce cybercrime cases. It will also help to prepare a secure digital
environment in the future.
KEYWORDS
behavior, cybercrime, fraud, intention to self-protect, self-efficacy
1. INTRODUCTION
Internet has become an essential things in human’s life. People rely on technology to manage their daily needs.
The usage of internet in Malaysia are increasing, especially during and after Movement Control Order (MCO).
The significant increase clearly can be seen for services relating to education, health, government services,
business and entertainment industries. According to the statistic released by Department of Statistics, Malaysia
(2021), the internet usage in Malaysia has increase from 90.1% in the year 2019 to 91.7% in the year 2020.
In 2018, the percentage of internet user was 87.4% of the total Malaysian population, with the highest
duration of usage by people age in their 20s. Most of the usage are for communication and social networking
such as Whatsapp (98.1%) and Facebook (97.3%) (Malaysian Communications and Multimedia Commission,
2018). Meanwhile, for the year 2020, the usage percentage has increased to 98.7% for Whatsapp, followed by
social networking activities such as Facebook (91.7%) and Youtube (80.6%) (Malaysian Communications and
Multimedia Commission, 2020). The increase of internet usage was believed to be associated with the
enforcement of MCO by the government due to the COVID-19 (Department of Statistics, Malaysia, 2021).
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During the MCO, there are a lots of restriction imposed by the government to reduce virus spreading such
as encouraging people to stay at home and reduce physical contact with other people (Shah et al., 2020). During
this period, the World Health Organization (WHO) recommend people to avoid using cash and replacing it
with e-payment when possible to avoid the virus spreading (Brown, 2020). This is supported by a research
conducted by Kaur, 2020 highlighting that the usage of e-wallet are believed helpful to flatten the Covid-19
curve. In Malaysia, at the time, there are variety types of e-payment introduced in the market such as e-wallet,
cards and online banking (LAI, 2016).
On 19 February 2021, the Prime Minister of Malaysia, Tan Sri Muhyiddin Yassin has launch Malaysia
Economy Blueprint which focusing on the government effort to move towards digitalization. E-commerce is
one of the area emphasized in the plan. The aim was to enhance the public to use electronic payment and
reducing public reliance on cash. The timeline for implementing the first phase is between year 2021 to year
2022 (Economic Planning Unit, 2021).
This clearly shows the seriousness of Malaysian government in realizing e-payment in the country. To
ensure the success of this plan, the financial security systems must be in place and secure enough for the user.
This is important to creates feeling of safety and encourage a good response from the public (LAI, 2016). This
study will investigate deeply on cybercrime issues in the community level which probably contribute to
negative impact for the successfulness of Malaysia Economy Blueprint implementation.
2. BODY OF PAPER
2.1 Problem statement
The world nowadays is becoming more challenging with the rapid growth of technology. As a result of the
high reliance on the internet especially during pandemics, it opens up a wider space for cyber fraudsters to
exploit society’s sudden and unplanned transition to online operations (Olofinbiyi & Singh, 2020). Companies
and individual are exposed to a bigger threat which is cybercrime. Cybercrime has becomes fourth rank in the
economic crime internationally and swiftly growth in frequency, severity and sophistication (Younies & Al-
Tawil, 2020).
The causes of cybercrime issues are varied, starting from the technology weakness in terms of its low
supervision level, improper implementation, and to the security level of the service itself. Aside from that,
humans may also become the major contributor to the issue. For example, individual attitudes that
underestimate the severity of the cyber threat and poor implementation of self-security can also contributes to
the vulnerabilities (Constantin et al., 2020). The increasing number of internet users opens up vast opportunities
for cybercrime activities (Younies & Al-Tawil, 2020). Thus, issue related to cybercrime cases has been debated
widely due to increasing number of global internet penetrations consequent by Covid-19.
In 2017, the cybercrime cases in UAE was reported increase to 3.72 million compared to year 2016, with a
total of 2.5 million (McKinsey and Co., 2020). Meanwhile, in USA, it was also reported a huge scale of cyber-
attack happens in 2017. In addition to that, in the past quarter of year 2018, there are 500 million user account
in USA are affected by cyber scammers (Symantec, 2020).United States of America had been listed as the first
top twenty countries that have the highest cybercrime cases (23%), followed by China (9%), Germany with
6%, Britain (5%), Brazil 4% and others countries (Jain & Gupta, 2020).
The same trend was also happen in Malaysia. In 2017, there are 7,962 cybercrime cases were reported. This
amount increased in the year 2018 with 10,699 cases, the year 2019 with 10,772 cases, and the year 2020 with
10,790 cases. For the year 2021, a total of 4615 cases were reported from January to May and are expected to
increase until the end of the same year (Cyber Security Malaysia, 2021).
According to Boussi and Gupta (2020), Malaysia is listed among the best cyber security in the world, with
the high level of protections together with United Kingdom, United States of America, France, Singapore,
Japan and Canada. Generally, various actions had been taken by firms to mitigate the cyber risk. For example,
improving IT security systems by buying latest technology, using secure software platforms, setting security
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procedure, enhancing employee awareness towards cyber risks, information sharing and collaborative risk
management and adaptability {Formatting Citation}.
As for banks in Malaysia, in 2018 they embarked embarked on a Cyber Security Strategy to strengthen the
protection of organizational critical information assets (Wan Azizah & Ann Teck, 2019). These efforts are
supported by the governance and operational structures that have been established to drive oversight of cyber
risk management across banks. Banks in Malaysia have established Cyber Security and Data Protection units
in the Department of Digital and Technology to manage cyber security at the organizational level (Hamzah,
2021).
Aside from that, many laws and act are also enforced by Malaysia Government in protecting the customer
from cybercrime activities. This is including Consumer Protection Act 1999 (CPA), Personal Data Protection
Act 2010 (PDPA) and Electronic Commerce Act 2006 (Nurul Atikaf et al., 2020). All these efforts are done
by Malaysian Government to enhance customer protection during online buying. This effort is aligned with
National Consumer Policy (NCP) whereby the intent is to empower the consumer to protect themselves against
cyber risk (Yi & Arif, 2021).
Other than that, the Ministry of Communications and Multimedia Malaysia (KKMM) has taken immediate
action by launching an awareness campaign and communication crime prevention in 2019. Agencies and
departments under KKMM such as MCMC, Bernama, RTM, Information Department (JaPen), National Film
Development Corporation Malaysia (Finas) and the Strategic Communications Division of KKMM also comes
together to hold the responsibilities by spreading the information and awareness about cybercrime to the public
(Hamzah, 2021).
The cybercrime awareness campaign was also extended to school and university students since this group
are also become the scammer target. Public is urged to always get the latest information and follow the
development of the prevention campaign by browsing social media with the keywords #BeSmart
#JanganTerpedaya and #Scammers (Official Portal of the Ministry of Communications & Multimedia, 2019).
The government also has strategic collaboration with various agencies and public authorities such as Royal
Malaysia Police (PDRM), various ministries and non-governmental organizations (NGOs) and community
leaders to increase the effectiveness of this campaign (Syamsiah & Rozlin, 2019).
Besides that, various acts that have been gazetted by the Malaysian government to be applied for online
offenses (social media) such as the Communications and Multimedia Act 1998, Sedition Act 1948, and
Defamation Act 1957 (Muhd Adnan & Siti Zobidah, 2019). All these actions done with hope that it can
successfully reduce the cybercrime cases. Yet, the cybercrime issues still happen. This prove that cybercrime
is not an easy issues to overcome (Bailey et al., 2014). The numbers of cyber-attack continuously increase in
frequency and severity (Cambridge Centre for Risk Studies, 2018).
According to Susskind (2014), businesses and non-businesses firms cannot depend solely to the law to
overcome cybercrimes. In most online crime cases, although the police and agencies are accountable to reduce
the crime, but online users are also responsible to take precaution actions to avoid themselves from becomes
victims.
From a research done, frequently, the cases of cybercrime are not reported by the victims to the police (Hot
& Bossler, 2014; Wall, 2007; Whitty & Buchanan, 2012). This is due to embarrassing feelings faced by the
victims because of being fooled by deceivers (Webster & Drew, 2017). This is supported by a study made by
Malaysian Communications and Multimedia Commission (MCMC) in the year 2020, whereby, it was found
that, 44% of Malaysian did not take any action after an experience of cybercrime (MCMC, 2020). This creates
problems for the police to handle the cybercrime issues (Drew & Farrell, 2018).
Muhd. Adnan et al. (2017) was argued that, one of the factors that contribute to the increase of cybercrime
issues in Malaysia is lack of knowledge and understanding about cyber law and insensitivity about individual
rights. Generally, self-protection levels applied by Malaysian are still worrisome that cause huge risk upon
cyber threat (Laily et al., 2017). This fact is supported by a study conducted by Nurul Nadiah, Elistina, Afida
Mastura, Saodah and Zuroni (2019) which states that Malaysian are still at the moderate levels in self-
protection towards cyber risk.
In order to initiate self-protective measures, it is believed that it is important for individuals to have
knowledge of actual risk (Drew & Farrell, 2018). This is because, ignorance about the cyber risks can cause
the neglect of preventive measures and the likelihood of becoming a victim will be higher (Whitty & Buchanan,
2012). Thus, to make it possible, internet user knowledge and prevention behaviors will be in question (Drew
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& Farrell, 2018). The reason is because, individual’s complacent behavior, unfounded confident, and poor
cyber hygiene are also found contribute to cyber threat (Hudson, 2016).
The impact of cybercrime issues not only on individual financial, but also emotional and psychological
(Australian Government Attorney-General’s Department, 2011; Button et al., 2014;Webster & Drew, 2017).
Therefore, with the increasing numbers of cybercrime cases, it shows an urgent focus on self-protective
behavior should be taken especially for those who has experience victimized by cyber scammers (Drew &
Farrell, 2018). The victims needs to be educate on the best practice to reduce re-victimization (House of
Representatives Standing Committee on Communications, 2010).
This shows an urgency of mitigating the cybercrime issues. A strategic action should be taken to find the
solutions to reduce the evolving risk (Mills, 2020). Thus, the government's goal in realizing the MyDigitals
action plan will become a reality.
2.3 Research Purpose
Purpose of this study is to examine the factors that contribute to public intention and behavior to take self-
protective measures in preventing cybercrime.
2.4 Research objectives
a. To determine the relationship between Individual Attitude, Subjective Norms, Perceived Behavioral
Control, Self-efficacy and Intention to Self-protect.
b. To determine the relationship between Individual Attitude, Subjective Norms, Perceived Behavioral
Control, Self-efficacy and Behavior to Perform Self-protect.
c. To determine the relationship between Intention to Self-protect and Behavior to Perform Self-protect.
d. To determine the mediating role of Intention to Self-protect in the relationship between Individual
Attitude, Subjective Norms, Perceived Behavioral Control, Self-efficacy and Behavior to Perform Self-
protect.
2.5 Research questions
a. Is there a relationship between Individual Attitude, Subjective Norms, Perceived Behavioral Control,
Self-efficacy and Intention to Self-protect?
b. Is there a relationship between Individual Attitude, Subjective Norms, Perceived Behavioral Control,
Self-efficacy and Behavior to Perform Self-protect?
c. Is there a relationship between Intention to Self-protect and Behavior to Perform Self-protect?
d. Does Intention to Self-protect mediates the relationship between Individual Attitude, Subjective Norms,
Perceived Behavioral Control, Self-efficacy and Behavior to Perform Self-protect?
2.6 The hypotheses
There are twelve hypotheses were formulated for this study. The hypotheses are used to predict the expected
results from the empirical data and help to provide solutions to the research problem.
Hypothesis 1:
There is a positive relationship between Individual Attitude and Intention to Self-protect.
Hypothesis 2:
There is a positive relationship between Subjective Norms and Intention to Self-protect.
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Hypothesis 3:
There is a positive relationship between Perceived Behavioral Control and Intention to Self-protect.
Hypothesis 4:
There is a positive relationship between Self-efficacy and Intention to Self-protect
Hypothesis 5:
There is a relationship between Individual Attitude and Behavior to Perform Self-protect.
Hypothesis 6:
There is a positive relationship between Subjective Norms and Behavior to Perform Self-protect.
Hypothesis 7:
There is a positive relationship between Perceived Behavioral Control and Behavior to Perform Self-protect.
Hypothesis 8:
There is a positive relationship between Self-efficacy and Behavior to Perform Self-protect.
Hypothesis 9:
Intention to Self-protect mediates the relationship between Individual Attitude and Behavior to Perform Self-
protect.
Hypothesis 10:
Intention to Self-protect mediates the relationship between Subjective Norms and Behavior to Perform Self-
protect.
Hypothesis 11:
Intention to Self-protect mediates the relationship between Perceived Behavioral Control and Behavior to
Perform Self-protect.
Hypothesis 12:
Intention to Self-protect mediates the relationship between Self-efficacy and Behavior to Perform Self-protect.
2.7 Significant of the study
This study hopes to provide information to governments, businesses, financial institutions and policy
makers on public attitudes and the level of technology literacy towards self -protective behavior in reducing
cybercrime cases. Such information is important to identify the cause of an individual being the target of a
cybercrime victim. Thus, a strategic action can be planned to reduce risk, and at the same time, a secure digital
market environment can be created in the future.
2.8 Limitation of the study
This conceptual paper is delimited by insufficient readings of literatures from various resources in order to
get a wider scope of taught from the past studies. This conceptual paper also has its investigative limitations
such as it is more to a review from other journal and reports since the data is yet to be collected. Other than
that, the topics is not fully explored due to time constraints.
2.9 Theoretical frameworks
Theory of Planned Behaviour (TPB) are widely used by previous researcher in understanding human
intention and behaviour (Ajzen, 2001). This theory was improvised from the original theory, Theory of
Reasoned Action (TRA) by Ajzen (1985). Additional variable was introduced to the framework, which is
Perceived Behavioural Control (PBC), which leads to a new name for the theory, called TPB. Among the
research that used TPB in their studies are Aloysius et al., (2019) a study in understanding cybercrime in retail
stores, Li et al., (2009) a study in understanding the relationship between attitude and behaviour, Reni &
Anggraini (2016) a study in understanding Auditors’s Intention in Conveying Unethical Behaviour and many
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more. This proof that TPB was recognised by many past researcher as an appropriate theory used in studying
human behaviour.
According to TPB, human behaviour was determined by Intention to perform the behaviour, with the
influenced of three factors which is, Attitude, Subjective Norms and Perceived Behavioural Control as the
dependent variables (Aertsens et al., 2011). Different with the original theory of TRA, Perceived behavioural
control was found contribute huge impact in explaining intention and self-prediction towards certain behaviour,
since in general, people have high intention to engage in behaviours when that they feels the results is
achievable (Bandura, 1982). Perceived Behavioural Control can be defined as individual’s belief that the
behaviour is within his control (Ajzen, 1991).
Other variables in TPB is Subjective Norms. It refers to individual’s perception towards other people like
his families and friends that possibly influence him to perform a behaviour (Ajzen, 1991). It was believed that,
important people can shape local culture and individual’s intention (Valliere, 2017). With the high levels of
intention, it will create high efforts to perform a behaviour (Ajzen, 2002). Last variable for TPB is Attitude.
Attitude means an overall assessment of individuals towards performing a behaviour (Pavlou & Fygenson,
2006). Intention is usually used by previous researcher to show the effects of attitude on behaviour (Bressan et
al., 2021).
For this study, the researcher plan to combine TPB and Self-efficacy to determine the human behaviour to
perform self-protection against cybercrime. Self-efficacy can be defined as individual’s self-assessed on his
capability to exercise control in performing certain behaviour (Bandura, 1991). Self-efficacy is unlike PBC
whereby Self-efficacy is an internal control about believing own capability to perform certain behaviour.
Meanwhile PBC is more to external control, whereby individual belief that he has control in performing the
behaviour (Valliere, 2017). It was believed that, through combination of both of these internal and external
factors, it can creates a higher level of behavioural control (Ajzen, 2002).
2.10 Conceptual frameworks
The conceptual framework for this study is adapted from the combination of Theory of Planned Behavior and
Self-efficacy Theory.
Individual Intention to Behavior to perform
Attitude self-protect self-protect
Subjective
Norms
Perceived
Behavioral
Control
Self-efficacy
Figure 2.1 Research Framework adapted from Bandura (1982) and Ajzen (1985).
3. CONCLUSION
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As a conclusion, from the disclosure, it shows that serious actions should be made promptly to tackle the
problem of cybercrime. It is not only for the sake of the customer, but also to ensure the sound of Malaysia’s
economy. With the achievement of controlling this issue, it is possible to create a safe business environment in
the country and can build confidence among investors and businesses to invest more. Employment
opportunities will also grow along with the economy.
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CM028
CHARACTERISTICS, COMPETENCIES AND WORK
EXPERIENCE AS SUCCESS FACTORS OF SME
WOMEN TECHNOPRENEURS IN MALAYSIA
Jothi A/P Munusamy *1, Kesavan Nallaluthan 2, Vikenesware A/P Singaravelu 3, Ahmed
Mohamed Mostafa Mohamed4
Sultan Idris Education University, 35900 Tanjong Malim, Perak, Malaysia
ABSTRACT
Technopreneurship development was a hotly debated issue during the COVID-19 pandemic, not only locally but also
globally. The purpose of this article is to identify the factors that influence the success of female technopreneurs. The
concept of entrepreneurial characteristics, entrepreneurial competence and work experience are discussed in detail in this
article.
KEY WORDS:
Technopreneurship, Small and Medium Enterprises, characteristics, competencies and work experience
1. INTRODUCTION
Entrepreneurship is one of the significant driving forces to the economic development of a nation. It creates
job opportunities and productivities for people in most nations. In today’s competitive global environment,
technology-based industries become a major concern, hence, entrepreneurial activities also become
technology-oriented (Radha, 2021). As its name, technology entrepreneurship combines both technological
ability and entrepreneurial competency. It also combines technical and commercial worlds as a foundation for
the innovation process (Burgelman et al., 2006).
Individuals who possess both technological knowledge and entrepreneurial skills are considered
technopreneurs. A technopreneur is tech-savvy, innovative, creative, and dynamic in his/her businesses that
can benefit the community (Ibrahim et al., 2015). Technopreneurs have personality traits of dare to be different,
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not afraid of failures, looking at things differently, and seeking challenges (Depositario et al. 2011). As such,
Technical and Vocational Education and Training (TVET) can be identified as a career path to nurture
technopreneurs (Majumdar, 2013; Adegbe & Aji, 2012; Burut, 2018). Hence, TVET graduates are expected to
support the economy by generating incomes and creating job opportunities, especially for the small and
medium industries (Rosly et al., 2015; Suradi et al., 2017; Romy, 2019).
The transformation of the global economy towards free trading has increased global competitiveness and
new business collaborations through technological entrepreneurship. According to Ernst et al. (1998), there are
six elements of technological capabilities. They include production, investment, minor change, marketing,
linkage, and major change. In the ASEAN (Association of South-East Asian Nations) countries, for instance,
the implementation of the Asian Free Trade Area (AFTA) has promoted industrial and regional collaborations
among its members. In particular, the impact of AFTA for Malaysia can be seen in the advancement of the
automotive industry as compared to other industrial sectors.
2. BACKGROUND OF THE STUDY
Literature on entrepreneurship has been conducted enormously. Most of the studies on entrepreneurship
are related to personal attributes such as behaviours (Weber, 2002), characteristics and personalities
(McClelland 1961; McClelland & Winter, 1971), and skills and motivation (Chandler & Redlick, 1961). The
notions of creativity and innovation are rarely touched in the conventional entrepreneurship literature.
Schumpeter (1912) was the first to introduce new insights into the field of entrepreneurship. According to him,
entrepreneurs are the prime innovators and technology leaders in market exploitation. His works focus on the
ability to carry out innovation and related activities by combining technological capabilities and entrepreneurial
capacities in order to create competitive advantages (Schumpeter, 1928).
Literature on technology entrepreneurship in Malaysia revealed some significant issues in achieving high-
tech venture success. Abu Bakar (2006), for instance, stated that the failures of many firms are due to a lack of
technopreneurship skills. According to him, a technopreneur possesses technological knowledge in addition to
others skills like emotional, intellectual, and spiritual intelligences. Additionally, Abdul Rahman and Monroe
(2006) highlighted the need for technopreneurship training in all Malaysian government agencies.
Other studies outlined strategies to encourage technology-based entrepreneurship by developing business
plan competitions among entrepreneurs (Tan, Egge & Osman, 2003; 2010). In order to nurture new
technopreneurs, there is a need for a support system like business incubator centres, venture capital funds, and
various grant schemes from the government (Jusoh, 2006). More specifically, Foo et al. (2006) investigated
the personal traits and leadership styles of women technopreneurs to be successful in society.
2.1 Success Factors of Entrepreneurs
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Studies on the success of entrepreneurs showed the growth of small and medium enterprises in Malaysia.
Yaakub and Adnan (2018), for instance, highlighted the success factors of Muslim entrepreneurs and
Bumiputera in Malaysia. They found internal and external factors that play important roles in the success
factors of Majlis Agama Islam Melaka entrepreneurs. The internal factors include practicing Islamic teaching
in life such as charity, five-time prayers, sincerity, attitude, and interest. While external factors consist of capital
assistance, entrepreneurship knowledge, and supports from family members and partners.
Furthermore, Johnes et al. (2018) discussed determinant factors for the success of traditional cookies
entrepreneurs in Sabah, Malaysia. Using the SWOT (Strengths, Weaknesses, Opportunities and Threats)
analysis, they found internal and external factors that contribute to the success of small-scale entrepreneurs.
For the strength (S), it involves the factors: level of knowledge, business attitude, recipes, business financial
capital, and communication skills; For weaknesses (W), it includes factors: weak in basic entrepreneurship,
business capital constraints, negative attitudes in business, lack of communication skills, and weak in
management; For opportunities (O), it shows the factors of financial assistance, the use of social media,
entrepreneurship programs, customer demand, and target markets; Finally, the analysis of the threats (T)
revealed the factors of product competition, large company domination, product quality, production cost, and
resource shortage.
Meanwhile, Zainon et al. (2019) explored factors influencing the success of local entrepreneurs in Pahang,
Malaysia. They found the three most important factors in the success of entrepreneurs, which are business
management skills, leadership capabilities, and environmental factors. The results of the above-mentioned
studies are relevant to be used to develop strategic plans for small and medium enterprises locally and globally.
More specifically, there are also various success factors of women entrepreneurs. Among others are having
interest, motivation, a confident personality, willingness to take risks, past work experiences, and innovative
ideas. In addition, there are also several contributing factors of entrepreneurial success, which are age when
starting a business, management and financial control, planning skills, marketing skills, and the level of
education (Faradillah Iqmar Omar & Samsudin Rahim, 2016). Other studies on women entrepreneurship and
their success factors in online business have been conducted in Malaysia such as Hamed and Deraman (2002),
Alam et al. (2011), Manaf et al. (2012), and Nurdin et al. (2017). The findings revealed that motivation, interest,
competencies, entrepreneurial characteristics, experience and innovation are among the main factors
contributing to women entrepreneurs’ success globally.
2.2 The Development of Technopreneurship in Malaysia
The development of entrepreneurship in Malaysia can be categorized into four phases: Phase I (1957-1970),
Phase II (1971-1980), Phase III (1981-1990), and Phase IV (1991-2005). Phase I is Pre-New Economic Policy
Era. This phase was ruled by two political leaders, i.e. Prime Ministers Tunku Abdul Rahman and Tun Abdul
Razak. The emphasis of this era is import substitution (1st Stage) and agriculture. The achievements of this era
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could be seen in the National Amenities and socio-economic development. Phase II is Pre-Mahathir
Mohammad Era. This phase was ruled by two political leaders, i.e. Prime Ministers Tun Abdul Razak and Tun
Hussein Onn. The emphasis of this era is export orientation and electronics industries. The achievements of
this era could be seen in the development of entrepreneurship and small and medium industries.
Phase III is Mahathir Mohammad Era. This phase was under Prime Minister Tun Mahathir Mohammad.
The emphasis of this era is import substitution (2nd Stage) and heavy industries, cluster-based strategy. The
achievements of this era could be seen in the development of technology entrepreneurship and heavy industries
(HICOM). Phase IV is Post New Economic Policy Era. This phase was ruled by two political leaders, i.e. Prime
Ministers Tun Mahathir Mohammad and Dato Seri Abdullah Ahmad Badawi. The emphasis of this era is a
high technology, K-economy, and high value-added. The achievements of this era could be seen in the
development of technology entrepreneurship and high-tech industries (Syahida Abdullah, 2008).
From the above description, it can be seen that there were minimum activities of entrepreneurial activities
in Phase I and Phase II. The practice of technology entrepreneurship specifically became apparent in Malaysia
starting from Phase III. With the introduction of the New Economic Policy (NEP) in 1971, the government of
Malaysia mainly focused on eradicating poverty eradication and restructuring society. The creation of several
enterprises showed government effort towards the development of entrepreneurship. In the second half of the
NEP period, the economic concentration was gradually geared towards industrialization through the
manufacturing sector. The focus was then narrowed down to heavy industries. However, the economic
recession in the 1980s hinders the growth of this new industrial sector in Malaysia. As a result, the national
automotive industry is the only one that survived.
To support the development of technology entrepreneurship, the government of Malaysia has established
some technology-based projects and technopreneurship programs carried out by different ministries and
agencies. Among the programs are the Cradle Investment Programme (CIP), Technopreneur Development
Flagship (TDF), Start Your Own Business (SYOB), and PHASER programme (Syahida Abdullah, 2008).
The CIP was initiated in 2003 by the Ministry of Finance. It is managed by the Malaysia Venture Capital
Management (MAVCAP) to stimulate the growth of technopreneurs and generate ideas for innovative and
knowledge societies. In particular, the CIP provides funding and support, generates new ideas from individuals
and higher learning institutions, creates employment, and commercializes products. The main focus of CIP’s
investment is in the development of information communication technology (ICT) including software and
information services such as e-commerce, mobile data, and the Internet. Almost similarly, TDF is managed
under Multimedia Super Corridor (MSC) to provide business plan competition, advisory services, and
technopreneurship projects.
Furthermore, the SYOB is organized by the Multimedia Development Corporation (MDC) to help ICT
graduates become technopreneurs. They are trained and prepared with both technological and business
knowledge through a series of workshops. It is expected that the participants could pave the way for their own
employment opportunities. Specifically, PHASER programme is organized under the Ministry of Entrepreneur
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and Cooperative Development (MECD) to support local Malaysian (Bumiputera) entrepreneurs through
experiential learning to be competitive in local and global markets.
3. STATEMENT OF THE PROBLEM
In the years following independence, Malaysia has shown impressive economic growth. The government
of Malaysia implemented policies for effective economic growth by eradicating poverty and restructuring
society through income and wealth distribution. These initiatives have led to the creation of many enterprises
and fostered the development of entrepreneurship activities that are technological-oriented. The development
of technological capabilities and the enhancement of entrepreneurial skills can be seen as the success factors
for industrialization in Malaysia.
On the other hand, local vendors are struggling to compete in the domestic and global markets. The major
obstacles are they lack technological abilities and entrepreneurship skills to commercialize their products in
the market. Specifically, women entrepreneurs face deficiencies in terms of characteristics, competencies, and
working experience. Hence, the current study attempts to examine the success factors of women
technopreneurs in Malaysia.
More specifically, the existing literature on technology entrepreneurship in Malaysia focused on the issues
of information technology and related fields. Thus, the current study will discuss the issues of technology
entrepreneurship as perceived by women technopreneurs and the key success factors of their businesses. This
new area of analysis is believed would contribute to the existing literature in the field of technopreneurship and
policymakers in developing government policies and future plans. In particular, the current study is significant
in providing insights to women entrepreneurs to improve their technological entrepreneurship capabilities.
Statistics from the Global Entrepreneurship Monitor (GEM) in 2019 also show that male entrepreneurs in
Malaysia have a high percentage of business establishments (6.7 %) compared to female entrepreneurs (5.7
%). This shows that, although women have high participation in entrepreneurship in the early stages, due to
some difficulties and challenges, they start to slow down or stop from continuing their business. The main
challenge often associated with women is their responsibility towards family and work (Aggrawwal, 2019;
Syamimi, 2021). This is because women involved in the field of entrepreneurship need to balance family and
career needs in order to be competitive (Aggrawwal, 2019). As a result, women choose to keep their businesses
on a small scale because it is easier to handle and manage both the demands of work and family at the same
time (Ayu, 2021).
Based on the trend analysis of women's involvement in entrepreneurship, it was found that women's
involvement is only limited to small-scale businesses or micro-enterprises compared to small and medium-
sized enterprises (SME Report, 2020). As of December 2020, the percentage of registered micro enterprises is
78.4% compared to small (20%) and medium enterprises (1.6%) (SME Report, 2021). A total of 26% of small
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businesses run by women were reported to be at risk of permanent closure within three months, compared to
18% of companies not run by women, suggesting that women-led companies are more vulnerable and unable
to cope with this impact. due to a lack of experience, skills, lack of innovative products, diversity, social
networks and access to resources (SME Corp. Special Report, 2021).
The entrepreneurial characteristics are suggested as one of the factors that need to be studied to determine
the characteristics possessed by female entrepreneurs to achieve their success (Mazdan, 2021; Rohayu, 2021;
NurAfifah, 2021; Zulhafizi, 2021; Amirul, 2020; Abdul Basit, 2020; Faradillah, 2019; Hana, 2019). It is very
important to study because the characteristics associated with entrepreneurs are not the same as the
characteristics that lead to successful entrepreneurs. Until now, studies on the characteristics and success of
entrepreneurs in Malaysia are still lacking, especially among female SME technopreneurs because most studies
focus on entrepreneurs in general. Previous studies claim that there is still uncertainty about the characteristics
that influence the success of entrepreneurs (Hana, 2019). In order to fill this gap, this study will be conducted
to identify the characteristics of entrepreneurship that are related to the success of women's entrepreneurship.
Based on the literature review, there are several studies on the success of SME female entrepreneurs with
competence (Zulhafizi, 2021; NurAfifah, 2021; Noorasiah, 2019; Muhaheed, 2018) in general. Nevertheless,
there are also some previous studies that state that there is still a lack of research related to the competence of
women technologists in SMEs (Khurram 2019; Romy, 2019; Janain, 2018; Bushra 2016). In order to fill this
gap, this study will be conducted to identify entrepreneurial competencies that are related to the success of
women technopreneurs in SMEs.
Another personal factor that affects entrepreneurial success is work experience. According to Quinones,
Ford and Teachout (2001), work experience is the most relevant categorization of an individual's life
experience to predict work performance. In addition, Rao, Joshi and Venkatachalm (2013) stated that female
entrepreneurs with previous experience in the same sector reported success compared to those without work
experience. This point is supported by Ekpe (2011) who also found that prior exposure is necessary for the
success of female entrepreneurs. In line with that, Mai and Gu (2012) suggest that more studies be conducted
to find the relationship between work experience and the success of entrepreneurs. Therefore, to fill this gap,
work experience is included as one of the factors that have been examined in this study.
Therefore, the scope of this study will cover the understanding of technology entrepreneurship and key
success factors of individual women entrepreneurs’ capabilities in Malaysia. In this regard, an entrepreneur in
the current study will refer to an individual woman who integrates her technological knowledge with
entrepreneurial skills as an innovator and entrepreneur.
4. DEFINITION OF ENTREPRENEURIAL SUCCESS
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Previous studies have discussed entrepreneurial success in several terms such as business success, venture
performance, entrepreneur performance, venture growth, and venture survival (Teoh & Chong, 2007; Shane &
Nicolaou, 2013; Gottschalk, Greene, Hower, & Muller, 2014; Ekpe et al., 2010). Entrepreneurial success is
generally defined from two different perspectives, namely the economic perspective (Buttner and Moore,
1997), and the non-economic perspective (Paige and Littrell, 2002; Cooper and Artz, 1995; Seligman &
Csikszentmihalyi, 2000). Ostgaard and Birley (1996) mention that the economic perspective usually refers to
return on assets, sales, profits, employees, and survival rates while the non-economic perspective refers to
customer satisfaction, personal development and personal achievement.
In other studies, conducted by Fuad and Bohari (2011) and Edwards (2008), business continuity is used to
measure success, which refers to no less than three years. In addition, a study conducted by Hana (2018) also
defines successful entrepreneurs as female entrepreneurs who have a life span of three years or more. Therefore,
this study determines the success of women's techno from a non-economic point of view. An SMI business
that operates continuously and successfully survives for more than three years is used as a dimension to
determine success.
5. ENTREPRENEURIAL CHARACTERISTICS
In the context of entrepreneurship, entrepreneurship is characterized by qualities, characteristics, or
behaviours that distinguish entrepreneurs from non-entrepreneurs. McClelland (1961) was the first person to
study the characteristics of entrepreneurs. He emphasized that most successful entrepreneurs have the desire to
pursue excellence, moderate risk, and strong internal self-control. Many characteristics of entrepreneurs are
associated with success. McClelland and McBer developed the characteristics of successful entrepreneurs
based on cultural studies in Malawi, India and Ecuador in 1985 (Nawawi, 1992). The characteristics of
successful entrepreneurs are seeing and seizing opportunities, persistence, finding information, initiative,
making systematic plans, high quality of work, commitment to work agreements, self-confidence, persistence-
oriented, solving problems creatively, and decisively, using strategies- influencing strategies and convincing
others.
Based on past studies, there are various views and descriptions regarding the characteristics of successful
entrepreneurship. Kozubíková, Belás, Bilan, & Bartos (2015), stated that the most important entrepreneurial
characteristics in an entrepreneur are courage, independence, responsibility, seriousness, perseverance,
proactiveness, and creativity. Kesavan (2022), on the other hand, states that in order to face any kind of
challenge, entrepreneurs nowadays need to be proactive. Furthermore, Janain (2018) asserted in his research
that characteristics such as entrepreneurial drive, love of innovation, willingness to take risks, purpose,
initiative and perseverance, and commitment to high-quality work influence the innovative thinking of
indigenous people. Technology entrepreneurs are successful and entrepreneurial characteristics, namely the
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need for achievement, innovation, willingness to take risks, control points and self-confidence are factors of
entrepreneurial success (Mohamad Ayub, 2019). According to Zaleha (2021), entrepreneurial characteristics
such as interest, innovative thinking, and risk-taking skills drive the success of women SMEs in Kelantan. In
addition, Mazdan Ali (2021) stated that entrepreneurial characteristics such as networking, aggressiveness, risk
and accountability have a positive relationship with the success of an entrepreneur.
So overall, the characteristics of entrepreneurship often change between researchers and there are no
standard characteristics that have been set since it is too subjective and depends on a person's personal views
and experiences. Norashidah Hashim (2009) in his book entitled "Basics of Entrepreneurship" states that
successful entrepreneurs have certain characteristics and profiles that can distinguish them from unsuccessful
entrepreneurs
6. ENTREPRENEURIAL COMPETENCE
Spencer and Spencer (1993) in their book titled "Competence at Work: Models for Superior Performance"
define competence as basic characteristics that refer to a person's ability to form a personality. Meanwhile,
Hoffman (1999) sees competence from three different angles, namely output, standard performance results and
attributes of a person such as knowledge, skills and ability. Typically, competence refers to competence where
it combines the knowledge, skills and abilities of a person needed to do a job successfully (Kaur & Bains 2013;
Rosmani & Nor Aishah 2018).
In the field of entrepreneurship, the concept of competence began to gain attention and become popular in
the late 1980s following the acceptance of Boyatzis (1982) through his book entitled "The Competent Manager:
A Model for Effective Performance" (Noor Hazlina 2007). Noor Hazlina (2007) states that competence refers
to the combination of attitudes and behaviours that enable an entrepreneur to achieve and maintain success in
his business. Ghina and Gustomo (2017) stated that competence can consist of personality traits or the
individual's motivation towards certain knowledge and skills in performing tasks well.
7. WORK EXPERIENCE
Generally, experience is defined as an event that occurs in an individual's life. Work experience is the most
relevant categorization of an individual's life experience in helping to predict a person's work performance.
Entrepreneurs who have previous work experience can be classified as having unique knowledge, and are
considered a real asset to the firm (Abdul Basit, 2020). Knowledge acquired by entrepreneurs, whether implicit
or explicit is very important to empower skills and influence accurate decision-making. In addition, Santarelli
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and Tran (2012) found that entrepreneurs who have work experience will always work hard to increase their
operating profit.
Previous experience is the most important criterion among female entrepreneurs to estimate their chances
of success (Cohoon, Wadhwa & Mitchell, 2010). It is supported by Endi (2019) who states that one of the
factors women are less successful when compared to men is because they have a lack of previous work
experience in business. In summary, it can be concluded that women need to gather as much relevant
experience as possible before starting their work to become successful entrepreneurs.
Actually, there are two different dimensions used to measure work experience. Some researchers use time
in work and some measure it by counting the number of certain tasks that have been done. DeRue (2009) states
that work experience can be seen in two different angles, namely quantity (time period) and quality (tasks
carried out). However, most research on work experience is focused on the quantitative angle that is based on
time, specifically in terms of the number of years of work experience in a particular job, time in a company, or
the amount of time spent in a job (Abdul Basit, 2020). Accordingly, this study adapted the measurement of
work experience with the number of years of their work experience. Due to the importance of experience
towards the success of entrepreneurs, there is a need for further research on the relationship between work
experience and entrepreneurial success (Montadzah & Jonathan, 2017).
8. CONCLUSION
Based on the objectives of the study, the technopreneurship capabilities of women technopreneurs in
Malaysia will be determined to understand their success factors in practicing technology entrepreneurship.
Specifically, the analysis will identify three main capabilities of technopreneurship: innovative thinking,
creative thinking, and entrepreneurship education.
Consequently, the current study is expected to contribute to the existing knowledge of technology
entrepreneurship in Malaysia. Some suggestions and recommendations will be offered to identify key success
factors of women technopreneurs. The findings from the analysis should contribute towards improving the
technopreneur capabilities of women technopreneurs in Malaysia.
REFERENCE
Abu Bakar, Ahmad Zaki. (2006). Technopreneurship as the New Paradigm for E-Business. Malaysia: University of
Technology Malaysia.
Adegbe, N., & Aji, M. O. 2012. Technical and Vocational Education and Training (TVET): A tool for poverty reduction
and national development. International Journal of Vocational Studies 6 (5): 1-11.
Alam, S. S., Jani, M. F. M., & Omar, N. A. (2011). An empirical study of success factors ofwomen entrepreneurs in
Southern region in Malaysia. International Journal of Economics and Finance, 3(2), 166-175.
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