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FOREWORD

It is my great pleasure and honor to present the Volume 4, Issue 2, 2018 of

International Journal of Business Development and Research (IJBDR). It has

been created to provide academics and practitioners a platform for exploration of

new ideas, concepts, systems and practices in the areas of business innovation,

applied technologies, and industrial & organizational management right across

the world. The world is changing; there is a continuation of needs in exploring

new ideas. For this, we must hear from individuals who are dynamic in

professional management, business development and research. Theory and

practice are interrelated, and we want to bridge the gaps.

This issue covers the areas of real situations of business development and existing

practices in a numerous areas such as: Risk (credit, financial, and liquidity),

Supply Chain Management, Organizational Performance, and Global Change

Assessment Model.

We hope that the research featured here will set up new milestones. We have had

an overwhelming response from very eminent editors and researchers globally to

support as editorial team. I look forward to make these endeavors very

meaningful. Let me take this opportunity to express my appreciation and

indebtedness for the contribution of authors and editorial board members to the

journal. Their work, either by contributing articles, reviewing them or by working

as a board member, has framed the journal leading to accomplishment of its goal.

Editor-in-chief

2

International Journal of

Business Development

and Research

Editors:

Editor-in-Chief: Dr. Haruthai Numprasertchai

Associate Editor: Dr. Sasivimol Meeampol

Contents

A Literature Review of Supply Chain Risks: 4

A Content Analysis

Nor Zawani Ibrahim, Razli Che Razak

Determinants of Financial Risk in Conventional Banks: 19

Does Technical Efficiency Matter?

Normaizatul Akma Saidi, Annuar Md Nassir

Exploring Generation Y’s Purchase Intention 41

Towards Counterfeit Product in Malaysia

Nur Haslina Ramli, Rosfatihah Che Mat, Mazlina Mamat

The Carbon Dioxide Emission Reduction in Vietnam’s 64

Power Sector using GCAM

Ha Tran Lan Huong

ISSN: 2286-6213

Volume 4 Issue 2

2018

3

A Literature Review of Supply Chain Risks:

A Content Analysis

Nor Zawani Ibrahim Faculty of Entrepreneurship and Business, Universiti Malaysia Kelantan,

Kelantan 16100 Malaysia.

Email:[email protected]

Razli Che Razak

Centre For Postgraduate Studies Universiti Malasyia Kelantan,

Universiti Malaysia Kelantan, Kelantan 16100 Malaysia


ABSTRACT

The integration field of supply chain risks has been rapidly growth and

become a major concern for organization to improve their organizational

performance. Upon increasing to the number of literatures in this field, the

objective of this study is to review 50 supply chain risks literatures since

year 2003 until 2016. By using a content analysis types of methodology, the

analysis begins by determine the frequency and percentage of year of study,

geographical area, types of methodology and types of journal. SPSS 22 was

employed to classify the 50 articles. The finding shows that 10 articles of

supply chain risks have been published in year 2015, which is the highest

number of published articles compared to other related years. Based on

geographical area, most of supply chain risk’s studies have been done in

Europe. Conceptual and Empirical types of study are the most common

research methodology for supply chain risks. This study also discovers the

most common journal that published supply chain risks articles is the Supply

Chain Management: An International Journal. The limitation of this study

was only 50 literatures were reviewed in this study. Future research must

involving at least 100 literatures of supply chain risk to obtain a better image

of the trend for each analysis.

Keywords: Supply Chain Risks, Supply Chain Management, Literature

Review, Organizational Performance, and Content Analysis.

4

1) INTRODUCTION

In this day and age, supply chain management has become an essential

component in order to improve both economic and environmental performance

(Mutuerandu, 2014; Salazar, 2012; Li et al., 2004). However, the risk in the

supply chain has become the most endangerment factors that caused the

organization fails to achieve high performance. Bavarsad et al (2014) and

Hendrick and Singal (2005) found that supply chain risks have a negative effect

on the organizational performance It explains that the high risk in the supply chain

leads to poor performance of the organization. In addition, based on the report by

Business Continuity Institute (2011), 85% of the companies from the entire world

go through at least one of the supply chain risk within 12 months. The issues of

supply chain risks in India, Malaysia, and United States have been underlined in

this study. The oil spill incident in Bhopal, India in 1984 were disturbed the global

chemical sectors in terms of economic deficiencies and environmental damages

(Kleindorfer & Saad, 2005). In Malaysia, according to Trade and Economic

Section (2012), the Malaysian automobile sector which is PROTON Holding

facing economic suffer due to Japan Tsunami in 2011. Moreover, Volvo Cars

Company in United States facing on 28% of sales drop in 2008 compared to year

2007 due to devaluation of the dollars (Musa, 2012). Due to that, PROTON

Holding and Volvo Cars Company faced the production and sales drop (Business

Forward Foundation, 2014), high cost of disruption recovery, heading to fewer

revenues, problem in time delivery, increased downtime (Marchese &

Paramasivam, 2013), and reduced environmental reputation (Lintukangas et al.,

2014; Freise &Seuring, 2015; Mangla et al., 2015).

In terms of body of knowledge, there are numbers of publications focused on the

topic of supply chai risks and economic performance (Bavarsad et al., 2014;

Florian & Constangioara, 2013; Tomas et al., 2013; Wieland & Wallenburg,

2012; Manuj & Mentzer, 2008; Hendricks & Singhal, 2005). Furthermore, some

of the studies are concentrating on the relationship of supply chain risks and

environmental performance (Freise & Seuring, 2015; Rao & Goldsby, 2009;

Seuring & Muller, 2008). Each study has presented a comprehensive information

about this field, but reviewing literatures using a content analysis types of

methodology can provide thoroughly information of supply chain risks and

identifying gap for the future research.

5

2) LITERATURE REVIEW

2.1) Definition of supply chain risks

There are multi-definitions of supply chain risks. This study has composed the

definition from Mangla et al. (2015), Qazi et al. (2015), Bavarsad et al. (2014),

Vilko et al. (2014), Kleindorfer and Saad (2005), where supply chain risks can

be defined as the unexpected event that gives negative sense to the performance.

This study intends to use the definition by Zhang and Song (2011), which they

highlighted supply chain risk as a negative deviation causing undesirable result

to the performance of the organization. They explained that the supply chain

activity are not able to be efficient due to the existence of supply chain risk.

Meanwhile, the performance of the organization also will be affected due to the

problem in the supply chain activity. In the simplest form, the statement implies

if the organization involves with high supply chain risk, the performance of the

organization will be low. Jiang (2011) supported the supply chain risk causes the

predictors fails to prevent the unexpected incidents in the organization. Therefore,

the practitioners must be aware about the existence of risk in the supply chain in

order to improve the performance of the organization.

Upon distinguishing the definition of supply chain risk from many academicians,

thus this study categorized the definitions from 2005 until 2013 as Table 1.

Table 1: Definition of supply chain risk

Author(s) Years Definition

Buddress (2013) Supply chain risk refers to the potential incident happened in supply

events that has a significant negatively impact to the purchasing firm.

Zhao et al. (2013) Supply chain risk is involves uncertainty of demand and supply,

unexpected event or disruption and arise turbulent environment.

Zhang and Song

(2011)

“Supply chain risk is the negative deviation from the expected value

of a certain performance measure, resulting in undesirable

consequences for the focal firm in the supply chain”.

Qun (2010) “Supply chain risk is the outcome based on material flow over the

supply chain network, the production and circulation of large

enterprise customers have commercial, logistics and the flow of

information related to transportation, storage and handling, transport,

packaging, distribution processing, distribution, information

processing, and so on the course, any one aspect of the problem

would lead to the risk of the supply chain, affecting its normal

operation”.

Manuj and Mentzer

(2008)

Supply chain risk involved two major components which are

potential losses and possibility of those losses. Potential losses is

consider as risk that already realized by the practitioners, so that the

risk will be 5nalysed. Possibility of those losses identified as the risk

6

Author(s) Years Definition

is still not be realized by the practitioners. The practitioners has an

initial expectation that leads to realize the risk.

Zsidisin and Ritchie

(2008)

Supply chain risk is the potential occurrence of a failure to seize

opportunities with inbound supply, in which its outcomes result low

economic performance of firm.

Bogataj and Bogataj

(2007)

“The potential occurrence in supply chain that decrease the value

added at any activity cell in the chain, where the outcome is

explained through the quantity and quality of goods in any location

and time in a supply chain flow”.

Goh, Lim, and Meng

(2007)

Internal supply chain risk- supply risk, demand risk, and trade credit

risk.

External supply chain risk-risk occur at interactions between the

supply chain network, and risk occur at supply chain environment.

Kleindorfer and Saad

(2005)

Supply chain risk is comes from the problem of supply and demand,

and also from unexpected disruption of normal activity.

2.2) Real issues regarding to supply chain risks

The real issue regarding to logistic activity has been highlighted by Gaonkar and

Viswanadham (2004) about the incident of 11th September, 2001 in United States

caused the logistic activity in supplying components from Asia to US has been

disrupted. The company in US has to turn off the production process due to

logistic problem. As reported by Sodhi et al. (2011), due to 11th September, 2001

incident, Ford has to be shut down their operation in 5 plants for the reason of air

transportation’s delay for several days. (Kleindorfer & Saad, 2005). The 1998

Hurricane Mitch in South America caused the damaged of banana plantation, and

following to that, Dole Food Company has involved high deficit of revenue

(Sodhi et al., 2011). Besides, 2011 Japan Tohoku earthquake and at the same year,

Thai flood disrupted the production process and supply chain activity for hard

disc drives and semiconductor materials (Auyong, 2013).

As composed by Aghapour et al. (2015), in last three years, agriculture sector in

Philippine loss USD 52 million due to Typhoon Haiyan disaster. As written by

Lane and Edgerton (2015) in his article which entitled “Hotel performance after

natural disasters” stated that the natural disaster have long-term negative impact

to economic performance. This article also revealed the economic activity (Gross

Metropolitan Product) in United States after the hurricanes since year 1979 until

2015 as Table 2. The Table 2 shows the supply chain risk is highly impact towards

economic performance especially in business environment.

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Table 2: Gross Metropolitan Product after hurricane in United States

Market Year Long run average change in GMP (1979-

2015)

Hurricane Andrew in Miami 1992 6.60%

Hurricane Hugo in Charleston 1989 6.35%

Hurricane Sandy in New York City 2012 5.54%

Hurricane Katrina in New Orleans 2005 4.73%c

Source: Lane and Edgerton (2015)

Besides that, disease and epidemics also involved as supply chain risks that will

affecting the supply chain activity (Olson & Wu, 2010). According to the report

from Lee and McKibbin (2004), Severe Acute Respiratory Syndrome (SARS)

disease affected the Asian Economy on 2003. Due to that, this disease dampened

the economist to predict the economic growth and affected the business global

activity. Besides that, Zsidisin (2008) in his book “Supply Chain Risk. A

Handbook of Assessment, Management, and Performance” highlighted about the

bacterial contaminations was attacked Chiron’s plant in Liverpool on 2004.

Chiron is produce flu-vaccine, and export to the US market. Due to bacterial

contamination, 48 million of vaccine (doss) cannot be export to US market, and

short 50% from consumer demand. Labor disputes, war, policy or regulations and

terrorism are part of supply chain risks. Berument et al. (2006) revealed in details

about four major issue happened in Turkey since year 1991 until year 2000. The

financial crisis due to unmanaged domestic debt by the Turkish government in

1994 disrupted the business activity in Turkey and their suppliers. Besides that,

Phung (2016) in his article “What is political risk and what can a multinational

company do to minimize exposure?” reported on the crisis happened to American

companies after Fidel Castro’s government had ruled Cuba in 1959. Due to

business relationship with Cuba, American companies loss hundred millions of

dollars.

3) REVIEW METHODOLOGY

This study uses a content analysis to review the previous literatures on supply

chain risks. A content analysis is an accurate research method to analyse the

literatures in certain area with a systematic way. This study follows a content

analysis used by Ibrahim et al. (2015), whereby it is consists of analysis of

articles, and ascertaining the research gap. Follow the methodology undertaken

by Ibrahim et al. (2015), Figure 1 shows the stages of content analysis involved

for this study.

8

Figure 1: Stages in content analysis

At the first stage, this study has setting the time horizon to select the articles which

from Year 2003 until Year 2016 to avoid outdated articles of supply chain risks.

Consistent with the stages utilized by Ibrahim et al. (2015), this study choose the

supply chain risk articles from three databases which are from Emerald, IEEE,

and Taylor and Francis by using keyword “supply chain risk”. 50 articles of

supply chain risks have been selected from these three databases and this study

were classified the 50 articles by several categories which are 1) Year of study,

2) country 3) research methodology, and 4) name of journals. This classification

is expected to reveal the research gap for the future research by undertaking the

analysis and discussion.

4) ANALYSIS OF LITERATURE REVIEW

Since the main purpose of this study is to extensively review the articles of supply

chain risks, this study analysed 50 articles to identify gaps for the future research

and providing information to the readers. The analysis of this study will divided

into four sections whereby Section 1 is to analyse the year of publication. The

geographical distribution area of this study is presented in Section 2. Section 3

will classified the research methodology to identify the most research

methodology approach by the scholars regarding to supply chain risks, and

journal of publication will presented in Section 4.

Time horizon of literatures: 2003 until 2016

Database selection: Emerald, IEEE, Taylor and Francis

Journal selection: 50 articles of Supply Chain Risks

Classifications of articles: Year of study, country, review methodology, name of journal

Analysis of articles

Discussions and ascertaining the research gap

9

4.1) Section 1 (Year of study)

Figure 2: Number of articles per year

In this section, 50 articles has been analysed based on year of publication to

identify the trend regarding the number of articles per year. Figure 2 revealed the

trend of 50 supply chain risks’ articles in Year 2003 until Year 2016.

Based on Figure 2, the trend of the articles’ number is fluctuated. The number of

supply chain risks’ articles in has been increasing from 2003 until 2004, but

declined until 2006. Then, it has been increasing in Year 2007 and declined in

2008. It is continue fluctuated until 2016. 2015 shows the highest number of

publication compared to others where 10 supply chain risk’s articles has been

published. A possible reason for the fluctuation of the trend is because the

scholars has giving attention when unexpected event happened and crippled the

global supply chain. Florida Hurricane has disturb the supply chain activity in all

over the world and the bacterial contaminations was attacked Chiron’s plant in

Liverpool in 2004. Japan Tsunami on 2011 has affected many global supply

chain, and flood in Thailand on 2011 has crippled almost global economy and

world supply chain activity. Typhoon Haiyan in Philippines on 2012 also has

disturb the global economy in terms of agriculture.

4.2) Section 2 Geographical area (Country)

In this section, many scholars in all over the world had giving attention on the

supply chain risks’ topic. Due to that, this study observed the geographical area

of supply chain risks articles which consumes six geographical area: Africa, Asia,

Australia, Europe, United States, and Global. Figure 3 revealed the percentage of

supply chain risks articles based on geographical area. As shown in Figure 3

below, Europe shows the highest percentage of published supply chain risks’

articles with 28%, while the least region article is Africa with only 2% of

0

2

4

6

8

10

12

2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003

Number of articles per year

Number of articles

10

published article. From the observation, scholars in develop region has concerned

about the impact of supply chain risks towards global supply chain activity in

Year 2003 until 2008. Therefore, most of the studies have been done in area of

Europe and United States. Continue with the observation, in year 2009 and above,

most of the studies have been completed in developing countries mostly in Asia

specifically in Indonesia, India, China, Thailand, and Malaysia.

Figure 3: Geographical area of articles

4.3) Section 3 (Methodology)

This study follows the classification of research methodology from Ibrahim et al.

(2015), where their study refers to Malhotra and Grover (1998) while they divided

the research methodology into six categories: conceptual, descriptive, empirical

(modelling), empirical (survey/ cross-sectional), explanatory (exploratory

longitudinal), exploratory (case study), perspective, and review. Therefore, the

observation of methodology used by 50 supply chain risks’ articles has revealed

in Fig.4.

*1 article used mixed methodologies approach

Figure 4: Methodology

Africa2% Asia

22%Australia

4%

Europe28%

united states18%

Global26%

Geographical Area of Article

Africa Asia Australia Europe united states Global

10

1

910

9

7

1

4

0

2

4

6

8

10

12

0

0.2

0.4

0.6

0.8

1

1.2

Methodology

11

Based on the analysis, the most common research methodology for supply chain

risks that used by academicians are conceptual study (10 articles), and empirical

study which narrowly on survey and exploratory cross-sectional study (10

articles). According to Malhotra and Grover (1998), conceptual methodology

discuss the fundamental and elementary concept of the focus area of study.

Exploratory cross-sectional or survey is a method that collect the data “at a single

point in time” (Zikmund, 2003). Based on Figure 4, there is still limited of study

in supply chain risks used descriptive and perspective research methodology,

whereby based on the analysis, from 50 articles of supply chain risks, only one

article which is in 2015 has formulates a framework or research model of the

supply chain risks area. In addition, only one article which is in 2004 has focus

on the perceptions by previous authors about the supply chain risks.

4.4) Section 4 (Name of Journal)

The following analysis is focus on journal involves in supply chain risks articles.

From the observation of 50 articles, as shown in Appendix 1, this study found

that there is 36 journals that published the supply chain risks’ articles. From the

observation, it shows that Supply Chain Management: An International Journal

is the most prevalent journal of publishing the supply chain risks articles

compared to other journals. Besides, 5 articles has been published in International

Journal of Physical Distribution & Logistic Management, follow 3 articles from

Production and Operations Management, 2 articles from International Journal of

Production Research, The International Journal of Logistic Management, and

International Journal of Operations & Production Management. Other 29 journals

have published 1 article of supply chain risks.

5) DISCUSSION AND CONCLUSION

Overall, the topic of supply chain risks has gained attention by both academicians

and practitioners since this risks has been recognized by the experts about their

impact to the supply chain activity. Based on the observation of 50 articles, most

of recent articles especially in year 2015 has discussed about the strategies

involvement in order to reduce the supply chain risks. Therefore, the future

research should giving more attention on identify the best strategy that fit to the

issue of supply chain risks. From the observation, this study also found that there

is still lack of study conducted in area of Africa and Australia. Since Australian

Logistic Council (2013) discussed about ten issues regarding to supply chain

activity in Australia, the academia requires to conduct the research regarding to

supply chain risks in Australia since there is minimal of articles have been done

in that country. Besides, since Africa is looking as a potential to outstrip the

economy in Asia, Europe, and American (Avasthy et al. 2015), it is important for

the researcher to conduct the study of supply chain risk in Africa in order to

12

contribute to their economy. In terms of methodology part, there is still neglecting

of articles that have been conducted using descriptive, and perspective types of

methodology. Descriptive and perspective articles are important for the academia

and practitioners to express idea and sharing knowledge about supply chain risks.

The definition of supply chain risks and real issue regarding to global supply

chain risks have been discussed in this study. This paper is expecting to contribute

for those seeking for research gap in supply chain risks. There are a few

limitations of this paper. First, we are only review 50 articles of supply chain risks

starting from year 2003 until 2016. Second, we did not discuss thoroughly about

the objectives of each articles. The dimension of supply chain risks also has not

been discussed in this study. Therefore, future research should take into account

about these limitations.

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Appendix APPENDIX 1

JOURNAL OF PUBLISHING SUPPLY CHAIN RISK’S ARTICLES AND YEAR OF PUBLISHED

No. Journals 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Total

1 Academic Journal of Interdisciplinary Studies

1 1

2 Benchmarking: An International Journal 1 1

3 Business Process Management Journal 1 1

4 Computer Engineering and Management

Sciences (ICM) 1 1

5 Computer Science and Service System 1 1

6 Decision Sciences 1 1

7 European Journal of Operational

Research 1 1

8 Industrial Engineering and Systems

Management (IESM) 1 1

9 Industrial Management & Data Systems 1 1

10

Information Technology, Computer

Engineering and Management Sciences (ICM)

1 1

11 International Conference on E-business

and Information System Security 1 1

12 International Journal of Academic Research in Business and Social

Sciences

1 1

13 International Journal of Business Science and Applied Management

1 1

14 International Journal of Disaster Risk

Reduction 1 1

15 International Journal of Logistics: Research & Applications

1 1

16 International Journal of Production

Economics 1 1

17 International Journal of Operations and Logistic Management

1 1

18 International Journal of Operations &

Production Management 1 1 2

19 International Journal of Physical Distribution & Logistic Management.

2 1 1 1 5

20 International Journal of Production

Research 1 1 2

14

21 Journal of Applied Research and Technology

1 1

22 Journal of Business Logistic 1 1

23 Journal of Cleaner Production 1 1

24 Journal of Enterprise Information

Management 1 1

25 Journal of Manufacturing Technology

Management 1 1

26 Journal of Supply Chain Management 1 1

27 Kybernetes 1 1

28 Logistic Research 1 1

29 Production and Operations Management 2 1 3

30 Robotics and Automation 1 1

31 Singaporean Journal of Business

Economics and Management Studies 1 1

32 Supply Chain Management: An

International Journal 1 1 2 1 1 6

33 Technology Management in the Energy Smart World (PICMET)

1 1

34 The International Journal of Logistics

Management 1 1 2

35 The Journal of Developing Areas 1 1

36 World Congress of Software Engineering 1 1

TOTAL 50

15

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19

Determinants of Financial Risk in Conventional Banks:

Does Technical Efficiency Matter?

Normaizatul Akma Saidi* Putra Business School, Universiti Putra Malaysia

Serdang, Malaysia

[email protected]

Annuar Md Nassir

Faculty of Economics and Management, Universiti Putra Malaysia

Serdang, Malaysia

[email protected]

*Corresponding author

ABSTRACT

The banking stability is vital to the health of economy as a whole. This study is

keen to determine the effect of technical efficiency on financial risk of

conventional banks in the Middle East, Southeast Asia and South Asia. The Data

Envelopment Analysis (DEA) and Ordinary Least Square (OLS) are used to

analyze the panel data. Overall, the bank size and capitalization are found to affect

credit risk of conventional banks but not the technical efficiency. Concerning the

liquidity risk, all variables which are bank size, capitalization and technical

efficiency are found to have an effect on the liquidity risk. Nevertheless, the

impact of technical efficiency on credit risk is significant and negative in Middle

East, and South Asia, but significant and positive in Southeast Asia. Then, the

effect of technical efficiency on the liquidity risk in Middle East is significant and

positive. In South Asia and Southeast Asia, the technical efficiency is significant

and negatively affect the liquidity risk. Hence, the emphasize should be given to

those variables in order to maintain bank soundness.

Keywords: Credit Risk, Conventional Banks, Financial Risk, Liquidity Risk,

and Technical Efficiency.

20

1) INTRODUCTION

In today’s competitive and uncertain economic environment, the financial

institutions are becoming crucial. The idle funds are pump to various productive

channels of the economy by the financial institutions. Thus, efficient financial

institutions are essential for continuous growth for every country. The

maximization of outputs and minimization of input costs is vital for financial

institutions in order to improve their efficiency. There are variety of dimensions

which explain various types of efficiency concepts; the ability to minimize the

input used to produce the maximum amount of outputs is refers to technical

efficiency. Meanwhile, profit efficiency determines the level of profitability of

the firm as compared to its competitors. Then, cost efficiency examine how

different the firm’s cost as compared to the best performer’s cost (Afza & Asghar,

2014).

The instability of the banking system due to the recent financial crisis has

attracted increasing attention since it can give drawbacks to the economy

(Agnello and Sousa, 2011). Thus, it’s attract the researchers to investigate in

details the most influential determinants of banking crisis. In addition, the

financial stability can be maintained by the regulatory authorities if those

determinants are explored and examined especially in the context of credit

problems, so that the responsible management can be pursued by the banks

(Chaibi & Ftiti, 2015).

Then, the economic growth is stimulated by banks’ liquidity creation, but in

developing markets this effect has not been halted by the recent financial crisis

(Fidrmuc, Fungacova, & Weill, 2015). Nevertheless, banks are expose to the

liquidity risk since the process of liquidity creation by banks depends on a

maturity mismatch between assets and liabilities (Diamond and Dybvig, 1983).

Based on Fiordelisi and Mare (2013), there is evidence that the bank risk-taking

is reduced if the efficiency is higher (Berger and DeYoung, 1997; Fiordelisi et

al., 2011), therefore the survival time of a bank is increases if the exposure to the

risky assets is reduced. Thus, the bank soundness is maintained through higher

level of efficiency. Nevertheless, empirical evidence supporting this expectation

is very limited and thus, managerial ability to save cost (cost efficiency), revenue

maximization (revenue efficiency), and maximization of profits (operating and

interest efficiency) will determine the survival of the bank.

The relationship of bank efficiency and risk-taking have been analysed by most

of the previous studies (Berger & DeYoung, 1997; Fiordelisi et al., 2011) but the

effect of different managerial skills on the occurrence of bank failure have not

being studied directly. This is because the bank performance is determined by the

21

efficiency (Fiordelisi, 2007; Fiordelisi and Molyneux, 2010) and also guarantees

the bank survival. Moreover, the contribution of the management towards the

bank survival is significant due to the recent crises of credit institutions (in terms

of costs minimization, revenues maximization, or maximization various measures

of profits). Hence, it is vital for practitioners, investors, academics and regulators

to have accurate prediction of bank survival.

Studies on bank efficiency which focus on cost, or profit, or cost and profit

efficiency have been done in other part of the world. But there is limited study on

other measures of efficiency, such as technical efficiency on its effect to the credit

risk and liquidity risk. Technical efficiency refers to how bank produce maximum

amount of outputs with the limited amount of inputs. Meanwhile, the allocative

efficiency refers to minimization of costs which can be attained if the right mix

of inputs chosen by the bank (Isik and Hassan, 2002). Hence, this study is keen

to determine the effect of technical efficiency on financial risk of conventional

banks in the Middle East, Southeast Asia and South Asia. This is because

different countries will have different and specific elements although financial

risk is affected by common factors in most countries that experience a banking

crisis (Chaibi & Ftiti, 2015).


There are little studies which focus on bank-specific determinants (Ahmad &

Ariff, 2007; Berger & DeYoung, 1997; Podpiera & Weill, 2008). In the study

done by Berger and DeYoung (1997), the relationship of bank-specific

characteristics to the problem loans have been analysed in which the study

focuses on variety of efficiency indicators based on sample of United State banks

for the period of 1985 to 1994. From the study, they formulate several

mechanisms, specifically bad luck, bad management, skimping and moral hazard

which link to the capital adequacy and efficiency. Hence, they conclude that

future problem loans and problem banks can be affected by the cost efficiency.

2.1) Bank Specific Determinants

2.1.1 Bank Size Stern and Feldman (2004) argue that large banks take excessive risks under the

“too big to fail” presumption. This is because no market discipline is imposed by

the banks’ creditors as they expect that the government will protect them in the

event of bank failure. Therefore, it leads banks to increase their leverage and thus

increase in loans to bad borrowers, which result to increase in non-performing

loans. In addition, Zribi and Boujelbene (2011), highlight that larger banks are

22

more diversified, which leads to better risk management skills, and could manage

bad borrowers more effectively.

In addition, bank size matters because of the economy of scope and scale;

concerning liquidity, a large bank might have better access to the interbank

markets because it has a larger network of regular counterparties or a wider range

of collateral (Fecht, Nyborg and Rocholl, 2010).

2.1.2 Capitalization The emerging economy banking systems are compared with developed

economies on the study done by Ahmad and Ariff (2007) which focus on bank-

specific determinants. From the study, they found that the banking systems which

offers variety of products will depend heavily on regulatory capital and loan-

dominant banks in emerging economies are rely on quality of management.

In other study by Chen et al. (2015) the capital also plays an important role in

driving liquidity (Berger and Bouwman, 2009; Cornett, McNutt, Strahan and

Tehranian, 2011, Hovarth, Seidler and Weill, 2016). The extant literature offers

two contrary opinions on the association concerning liquidity creation and capital,

namely, the financial fragility-crowding out hypothesis and the risk absorption

hypothesis. The former argues that capital can crowd out deposits and thus reduce

liquidity creation; the latter argues that more capital implies a higher capacity to

absorb risk, thus increasing liquidity. With regard to developing countries,

Fungacova et al. (2010) found that the relationship between capital and liquidity

creation in the Russian banking sector is negative.

2.1.3 Technical Efficiency In study done by Podpiera and Weill (2008), the relationship between efficiency

and bad loans has been examined on the panel of Czech banks from 1994 to 2005.

The study applies generalized methods of moments (GMM) dynamic panel

estimators in extension of the Granger causality model developed by Berger and

DeYoung (1997). From the study they found that the cost efficiency is reduced

when non-performing loans is increases.

Then, although the Islamic and conventional banks are operated based on

different principles, both are competitive since their main motives are still to

maximize profit and shareholder wealth (Khan and Bhati, 2008 and Olson and

Zoubi, 2008). Thus, they need to be efficient to utilize the minimum inputs and

produce maximum outputs. The banking literature in the early 1990s has

emphasized on the significance of the banking efficiency (Berger and Humphrey,

1997) in which it’s contributing to the overall economic growth (Levine, 1997;

Rajan and Zingales, 1998). In addition, it is believed that the efficiency in the

banking system will minimize the chance of the financial crisis to happen again

23

since many experts believe that this crisis happens due to the short-term liquidity

problem rooted by the financial markets. The higher the efficiency is, the better

the performance is (Rahman, 2011).

Figure 2.1 below present the conceptual framework for this study. From the figure

it shows the effect of bank size, capitalization and technical efficiency on

financial risk.

Figure 2.1: Conceptual Framework

3) METHODOLOGY

The bank-level data of conventional bank in the Middle East, Southeast Asia and

South Asia from 2006 to 2014 are collected from Bankscope, a commercial

database produced by Bureau van Dijk. The annual balance sheet, income

statement and financial ratios information for the selected banks are gathered for

this study. The bank specific information is mainly obtained from the Bankscope

database produced by Fitch/IBCA/Bureau Van Dijk, because the researches in

banking consider this database as the most comprehensive.

The selections of the data are from the Middle East, Southeast Asia and South

Asia for the period of 2006 to 2014. These three regions are chosen because of it

roles as the main hub in the world for the Islamic banking and finance. The total

of the sample are 300 conventional banks from 18 countries and all finance

companies, insurance companies and investment banks are excluded from the

sample in order to maintain the homogeneity.

This study applied two stages of analysis. In the first stage of analysis, the

technical efficiency of conventional banks is examined by the Data Envelopment

Analysis (DEA). The variables used in the technical efficiency are summarized

by the Table 1 below. The Multivariate Panel Regression (MPRA) is used by the

study in the second stage of analysis as the estimation method in order to identify

the variables of determinants for conventional banks that may influence the credit

Bank Size

Capitalization

Technical

Efficiency

Financial Risk

24

risk. The technical efficiency is then included in this stage of analysis in order to

know its effect for conventional banks especially on credit risk.

Table 1: Variables of Outputs and Inputs

Variable Symbol

Variable

Name Definition

Inputs x1 Deposits Total deposits,

money market and

short-term funding

Berger, Hancock & Humphrey

(1993a)

x2 Fixed

Assets

Book value of fixed

assets

Kumar & Gulati (2008)

x3 Labour Personnel Expenses Berger, Hancock & Humphrey,

1993a; Kumar & Gulati, 2008

Outputs y1 Loans Net loans and

interbank lending

Mamatzakis et al., 2008; Kasman

& Yildirim, 2006

y2 Total

Securities

Total investments in

financial market

Sealey & Lindley, 1977; Rosman,

Abd Wahab, & Zainol, 2014; See

& He, 2015

The proxy used to measure the credit risk is the loan loss provisions to total loans

(LLP/TL) ratio (Sufian and Chong, 2008; Mamatzakis, 2015; Chaibi and Ftiti,

2015). Hence, high loan loss provisions specify high NPLs. Al-Harbi (2017) used

loan to total assets ratio to measure the liquidity as in line with previous

researches (Bunda and Desquilbet, 2008; Munteanu, 2012; Roman and Sargu,

2015). The higher the ratio, the less bank liquidity. Meanwhile, the natural log of

assets is used as the measurement for the bank size in this study and the

relationship is expected to be positive or negative. Previous studies like Avery

and Hanweck (1984) and Demsetz and Strahan (1995) argue that large banks may

not be failed. Then, the capitalization is proxies by equity to total assets ratio and

the negative and positive coefficient is expected (Angkinand et al., 2013; Tan and

Floros, 2013; Miah and Sharmeen, 2015).

The determinants of credit risk in the conventional banks in selected regions will

be analysed through the basic model as below:

〖𝑙𝑛 𝐿𝐿𝑃𝑇𝐿〗_(𝑖, 𝑡) = 𝛼 + 𝛽_1 〖𝑙𝑛 𝑇𝐴〗_(𝑖, 𝑡) + 𝛽_2 〖𝑙𝑛 𝐸𝑇𝐴〗_(𝑖, 𝑡) + 𝛽_3

〖𝑙𝑛 𝑇𝐸〗_(𝑖, 𝑡) + 𝜂_𝑖 + 𝐸_(𝑖, 𝑡) Eq. (1)

〖𝑙𝑛 𝐿𝐿𝑅𝐺𝐿〗_(𝑖, 𝑡) = 𝛼 + 𝛽_1 〖𝑙𝑛 𝑇𝐴〗_(𝑖, 𝑡) + 𝛽_2 〖𝑙𝑛 𝐸𝑇𝐴〗_(𝑖, 𝑡) + 𝛽_3

〖𝑙𝑛 𝑇𝐸〗_(𝑖, 𝑡) + 𝜂_𝑖 + 𝐸_(𝑖, 𝑡) Eq. (2)

25

〖𝑙𝑛 𝐿𝐷𝑅〗_(𝑖, 𝑡) = 𝛼 + 𝛽_1 〖𝑙𝑛 𝑇𝐴〗_(𝑖, 𝑡) + 𝛽_2 〖𝑙𝑛 𝐸𝑇𝐴〗_(𝑖, 𝑡) + 𝛽_3

〖𝑙𝑛 𝑇𝐸〗_(𝑖, 𝑡) + 𝜂_𝑖 + 𝐸_(𝑖, 𝑡) Eq. (3)

〖𝑙𝑛 𝑁𝐿𝑇𝐴〗_(𝑖, 𝑡) = 𝛼 + 𝛽_1 〖𝑙𝑛 𝑇𝐴〗_(𝑖, 𝑡) + 𝛽_2 〖𝑙𝑛 𝐸𝑇𝐴〗_(𝑖, 𝑡) + 𝛽_3

〖𝑙𝑛 𝑇𝐸〗_(𝑖, 𝑡) + 𝜂_𝑖 + 𝐸_(𝑖, 𝑡) Eq. (4)

Where;

lnLLPTL is a loan loss provision to total loans (credit risk)

lnLLRGL is a loan loss reserves over gross loans (credit risk)

lnLDR is loans over deposits (liquidity risk)

lnNLTA is net loans over total assets (liquidity risk)

lnTA is total assets (bank size)

lnETA is equity to total assets (capitalization)

lnTE is technical efficiency of the i-th bank in the period t

obtained from the DEA Model

i is an individual bank

t is a time period

α is a constant term

β is the vector of coefficient

η is an unobserved bank-specific effect

ℰi,t is a normally distributed disturbance term

4) RESULTS AND DISCUSSION

The results of estimating eq. (1), eq. (2), eq. (3) and eq. (4) on the data sets

described above using static panel estimation are reported in this section. The

main results of the paper are presented in Tables 2, 3, 4, 5, 6, 7, 8 and 9. The

tables contain the estimates of credit risk and liquidity risk regressions by using

the static panel estimator. Table 2 present the static panel regressions for all

regions in which loan loss provision to total loans (LLPTL) and loan loss reserves

to gross loans (LLRGL) as the dependent variables, respectively. Referring to the

Model 1 in Table 2, it shows that only the variable of capitalization affects the

credit risk negatively at the 1% significant level. Meanwhile, the variable of bank

size and technical efficiency do not have significant relationship with the credit

risk and both variables indicate negative sign. Then, in Model 2 the variable of

bank size shows negative and significant relationship with the credit risk at the

1% level. Concerning to the liquidity risk in all regions in which loans to deposits

ratio (LDR) and net loans to total assets ratio (NLTA) as the dependent variables,

respectively. The Table 3 shows that only the variable of capitalization is

positively and statistically significant at the 1% level in affecting the liquidity risk

for Model 1. Nevertheless, all variables are statistically significant in affecting

26

the liquidity risk as shown by Model 2. The relationship of bank size with the

liquidity risk is positive and statistically significant at the 1% level. Meanwhile,

the variable of capitalization exhibits positive and significant at the 5% level with

the liquidity risk. Then, the variable of technical efficiency shows a negative

relationship with liquidity risk and significant at the 5% level.

Tables 4, 5, 6, 7, 8 and 9 present the regressions results of the determinants of

credit risk and liquidity risk in specific regions which are Middle East (ME)

region, South Asia (SA) region and Southeast Asia (SEA) region, respectively.

Referring to the Model 1 in Table 4 (the determinants of credit risk in the ME

region), it shows that the variable of capitalization is statistically significant at the

1% level and the relationship with the credit risk is positive. Then, the variable of

technical efficiency also gives a significant relationship with the credit risk at the

1% level and the effect is negative. Meanwhile, Model 2 shows that the

relationship of bank size is significant at the 1% level and negative with the credit

risk. The relationship of capitalization with the credit risk is also significant at the

1% level and its effect is positive. Then, the technical efficiency is negative and

statistically significant at the 1% level to the relationship with credit risk. The

Table 5 shows the determinants of liquidity risk in the ME region. From the Table,

the Model 1 shows that the variable of capitalization (significant at the 1% level)

and technical efficiency (significant at the 10% level) are positively significant in

affecting the liquidity risk. Meanwhile, in Model 2, it shows that only the variable

of capitalization affects the liquidity risk positively at the 1% significant level.

In SA region, referring to the Table 6 (the determinants of credit risk), only the

variable of capitalization shows a negative and significant relationship with the

credit risk at the 1% significant level in Model 1. While, in Model 2, the variable

capitalization and technical efficiency shows significant relationship with the

credit risk at the 5% and 1% significant level, respectively. Both variable present

negative coefficient. Concerning to the Table 7 (the determinants of liquidity

risk), all variables are statistically significant at the 1% level in affecting the

liquidity risk as shown in Model 1. The variable of bank size and technical

efficiency shows negative coefficient while variable capitalization shows a

positive coefficient with the liquidity risk. In Model 2, only the variable of bank

size (1% significant level) and capitalization (10% significant level) shows

significant effect to the liquidity risk. Both variables exhibit negative relationship

to the liquidity risk.

In SEA region, referring to the Model 1 in Table 8 (the determinants of credit

risk), the variable of capitalization exhibits significant relationship with the credit

risk at the 1% level, and its effect is negative. Meanwhile, in Model 2, the variable

of bank size exhibit negative and significant relationship with the credit risk at

the 1% level. Nevertheless, the relationship of technical efficiency with the credit

27

risk exhibit positive effect in this region as in Model 2 and significant at the 1%

level. Concerning to Table 9 (the determinants of liquidity risk), all variables are

significant in affecting the liquidity risk as shows by Model 1. Then, in Model 2,

only the variable of bank size (positive coefficient and significant at the 1% level)

and technical efficiency (negative coefficient and significant at the 1% level) are

significant in affecting the liquidity risk.

In summary, concerning to the effect of bank size on credit risk in all regions, the

findings from Model 2 in Table 2 is inconsistent with Chaibi and Ftiti (2015)

which found positive relationship of bank size with the non-performing loans that

support too big to fail presumption. Besides that, good risk management and

diversification is expected by the large bank. But, the riskiness of their assets are

increases although large banks could benefit from safety net and too-big-to-fail

policies (systemic risk concerns) (Camara et al., 2013). Then, referring to the

effect of capitalization on credit risk, the finding from Model 1 in Table 2 exhibit

contradict results with the result found by Camara et al., (2013) in which argue

that more stringent capital rule lead to an increase in banks’ default risk under

certain condition. The result is differing due to different choices of bank’s

portfolio which rely on its ex ante regulatory capital position, leads to variety

level of portfolio risk (increase or decrease) as adjustment to the minimum capital

requirement is needed (Calem and Rob, 1999). Lastly, the effect of technical

efficiency on credit risk is insignificant in all regions.

Then, concerning to the determinants of liquidity risk in all regions, the findings

from Model 1 and Model 2 in Table 2 indicates that the banks size is positively

significant in affecting liquidity risk. The result is inconsistent with the previous

finding which found that, the bigger bank will have a low level of liquidity risk

(Ahmed et al., 2011; Akhtar et al., 2011; Iqbal, 2012). The theory of too big to

fail is then not supported by this study. The variable of capitalization in Model 2

as shown by Table 3 exhibit positive relationship with the liquidity risk. The

result indicates that the higher level of capital hold by the banks will result to

higher level of liquidity ratio (high liquidity risk). the result is in line with

previous study which found that the relationship between capital adequacy ratio

with liquidity ratio is found to be positive and significant for both conventional

and Islamic banks (Akhtar et al., 2011; Iqbal, 2012; Vithessonthi & Tongurai,

2016). Then, the variable of technical efficiency shows a negative relationship

with the liquidity risk as shown by Model 2. The result indicates that the higher

efficiency of the banks contributes to lower liquidity risk of the banks. The result

is consistent with previous studies which stated that the better bank efficiency

leads to better performance of the banks and thus alleviate them from financial

crisis (Rahman, 2011).

28

Table 2: Static Panel Regression for Credit Risk (All Regions)

VARIABLES

ALL REGIONS

Model 1 (lnLLPTL) Model 2 (lnLLRGL)

OLS FEM REM OLS FEM REM

Constant 4.069*** 4.979*** 4.240*** 2.148*** 2.253**

*

1.857**

*

Std. Error (0.0587) (0.107) (0.0675) (0.523) (0.568) (0.53)

Bank-Specific Variables

lnTA 0.00093 -0.00469 0.000444

-

0.0236**

*

-

0.167**

*

-

0.0978*

**

Std. Error (0.000874) (0.00338) (0.00109) (0.00792) (0.0188) (0.0139)

lnETA -0.129*** -

0.358***

-

0.173*** -0.146 0.0972 0.0477

Std. Error (0.0154) (0.0274) (0.0177) (0.137) (0.144) (0.136)

lnTE -

0.0204*** -0.00957

-

0.0176** 0.329*** -0.0686 0.0232

Std. Error (0.00653) (0.00987) (0.00716) (0.0589) (0.053) (0.0504)

Obs. 2,253 2,253 2,253 2,272 2,272 2,272

R2 0.035 0.081 0.017 0.039

Adj R2 0.034 0.015

F-statistic 27.45*** 12.72***

No. of bank 291 291 293 293

Diagnostics

F-statistics 57.32*** 27.03**

*

Wald Chi2 102.00**

*

51.89**

*

BPLM 45.24*** 3087.20

***

Hausman 86.03*** 40.23**

*

Notes: The notations used are defined as follows: LLPTL is a measure of credit risk calculated

as the ratio of loan loss provision divided by total loans; LLRGL is a measure of credit risk

calculated as the ratio of loan loss reserves divided by gross loans; lnTA is a proxy measure

of bank size calculated as natural logarithm of total bank assets; lnETA is a measure of

capitalization, calculated as equity over total assets; lnTE is a measure of technical efficiency

calculated by using the DEA. *Significance at the 10% level. ∗∗Significance at the 5% level. ***Significance at the 1% level.

29

Table 3: Static Panel Regression for Liquidity Risk (All Regions)

ALL REGIONS

VARIABLES Model 1 (lnLDR) Model 2 (lnNLTA)


Constant 1.886*** 2.289*** 2.151*** 6.069*** 3.143*** 3.324***

Std. Error (0.262) (0.23) (0.222) (0.285) (0.218) (0.213)


lnTA 0.0387*** -0.00163 0.0125** 0.0265**

* 0.0219*** 0.0240***

Std. Error (0.0041) (0.00737) (0.00616) (0.00446) (0.00699) (0.00608)

lnETA 0.514*** 0.495*** 0.496*** -

0.696*** 0.119** 0.0593

Std. Error (0.0685) (0.0584) (0.0563) (0.0744) (0.0554) (0.054)

lnTE 0.03 -0.00621 0.00691 -

0.159*** -0.0468** -0.0499**

Std. Error (0.0301) (0.0217) (0.021) (0.0326) (0.0206) (0.0201)

Obs. 2,346 2,346 2,346 2,346 2,346 2,346

R2 0.056 0.034 0.06 0.011

Adj R2 0.055 0.059

F-statistic 46.48*** 50.17***

No. of bank 297 297 297 297

Diagnostics

F-statistics 24.15*** 7.69***

Wald Chi2 80.13*** 26.61***

BPLM 4905.92*

**

5543.61**

*

Hausman 12.25*** 24.72***

Notes: The notations used are defined as follows: lnLDR is a measure of liquidity risk

calculated as the ratio of loans divided by deposits; lnNLTA is a measure of liquidity risk

calculated as the ratio of net loans divided by total assets; lnTA is a proxy measure of bank

size calculated as natural logarithm of total bank assets; lnETA is a measure of capitalization,

calculated as equity over total assets; lnTE is a measure of technical efficiency calculated by

using the DEA. *Significance at the 10% level. ∗∗Significance at the 5% level. ***Significance

at the 1% level.

30

Table 4: Static Panel Regression for Credit Risk (ME Region)

VARIABLES

ME REGION



Constant 3.278*** 3.351*** 3.351*** 0.667 -2.523** -1.142

Std. Error (0.041) (0.0935) (0.062) (0.688) (1.13) (0.943)


lnTA 0.000131 -0.00276 -0.00055 -

0.0480***

-

0.0909*

**

-

0.0531**

*

Std. Error (0.000505) (0.00194) (0.000893) (0.00867) (0.0261) (0.0166)

lnETA 0.0856*** 0.0729**

* 0.0676*** 0.330*

1.186**

* 0.738***

Std. Error (0.0108) (0.0245) (0.0163) (0.181) (0.294) (0.245)

lnTE -0.0221***

-

0.0252**

*

-0.0234*** -0.000625

-

0.624**

*

-

0.495***

Std. Error (0.00435) (0.00598) (0.00507) (0.0755) (0.0746) (0.0706)

Obs. 1,114 1,114 1,114 1,118 1,118 1,118

R2 0.074 0.027 0.03 0.087

Adj R2 0.072 0.028

F-statistic 29.62*** 11.59***

No. of bank 147 147 149 149

Diagnostics

F-statistics 8.84*** 30.49**

*

Wald Chi2 39.07*** 68.38***

BPLM 238.72*** 1218.80*

**

Hausman 1.78 32.88***







31

Table 5: Static Panel Regression for Liquidity Risk (ME Region)

ME REGION



Constant -1.473*** -0.47 -0.664* 5.660**

*

0.988*

* 1.405***

Std. Error (0.337) (0.389) (0.366) (0.441) (0.412) (0.396)


lnTA 0.0693*** -0.0028 0.0226**

*

0.0505*

** 0.0142 0.0253***

Std. Error (0.00447) (0.00883) (0.00732) (0.00585

)

(0.0093

4) (0.00814)

lnETA 1.317*** 1.213*** 1.195***

-

0.660**

*

0.681*

** 0.534***

Std. Error (0.0882) (0.101) (0.0942) (0.115) (0.107) (0.102)

lnTE 0.159*** 0.0506* 0.0724**

* -0.0392

0.0026

7 0.0127

Std. Error (0.0378) (0.0281) (0.0275) (0.0495) (0.0298

) (0.0293)

Obs. 1,163 1,163 1,163 1,163 1,163 1,163

R2 0.292 0.13 0.089 0.041

Adj R2 0.291 0.086

F-statistic 158.98*** 37.61**

*

No. of bank 153 153 153 153

Diagnostics

F-statistics 50.26*** 14.41*

**

Wald Chi2 177.96**

* 37.09***

BPLM 1866.47*

** 2755.65***

Hausman 28.91*** 23.68***







at the 1% level.

32

Table 6: Static Panel Regression for Credit Risk (SA Region)

VARIABLES

SA REGION



Constant 3.950*** 4.413**

* 3.972*** -0.952

4.449**

*

3.535**

*

Std. Error (0.0864) (0.133) (0.0884) (1.277) (1.117) (1.078)


lnTA -0.00218 -0.0101 -0.00235 0.052 -0.0781 -0.0199

Std. Error (0.00249) (0.0086) (0.0026) (0.0375) (0.0757) (0.0584)

lnETA -0.0833***

-

0.198**

*

-

0.0892**

*

0.683** -0.692** -

0.545**

Std. Error (0.0221) (0.034) (0.0227) (0.327) (0.283) (0.275)

lnTE 0.0145 -0.00959 0.0136 0.485**

*

-

0.423**

*

-

0.269**

Std. Error (0.00976) (0.0154) (0.00994) (0.146) (0.134) (0.123)

Obs. 387 387 387 383 383 383

R2 0.041 0.1 0.04 0.045

Adj R2 0.034 0.032

F-statistic 5.51*** 5.25***

No. of bank 50 50 50 50

Diagnostics

F-statistics 12.33**

* 5.18***

Wald Chi2 17.63*** 8.41**

BPLM 0.67 559.55*

**

Hausman 21.51*** 28.32**

*







33

Table 7: Static Panel Regression for Liquidity Risk (SA Region)

SA REGION



Constant 5.215*** 5.285*** 4.835*** 6.094*** 5.109*** 5.251***

Std. Error (0.471) (0.434) (0.434) (0.334) (0.317) (0.305)


lnTA -

0.171***

-

0.434***

-

0.299***

-

0.0641***

-

0.0792**

*

-0.0685***

Std. Error (0.0141) (0.0302) (0.0238) (0.00996) (0.0221) (0.0163)

lnETA 0.0947 0.548*** 0.420*** -0.485*** -0.144* -0.208***

Std. Error (0.121) (0.111) (0.111) (0.0854) (0.0808) (0.0782)

lnTE 0.0157 -

0.319***

-

0.176*** -0.232*** -0.00441 -0.0294

Std. Error (0.0559) (0.0542) (0.0513) (0.0396) (0.0396) (0.036)

Obs. 399 399 399 399 399 399

R2 0.29 0.394 0.184 0.064

Adj R2 0.285 0.177

F-statistic 53.79*** 29.61***

No. of bank 50 50 50 50

Diagnostics


* 7.92***

Wald Chi2 175.36*

** 26.90***

BPLM 403.79*

** 383.05***

Hausman 42.51**

* 63.66***







at the 1% level.

34

Table 8: Static Panel Regression for Credit Risk (SEA Region)

VARIABLES

SEA REGION



Constant 5.078*** 5.707**

* 5.137***

2.884*

**

2.893**

* 2.515***

Std. Error (0.151) (0.221) (0.157) (0.881) (0.783) (0.767)


lnTA -0.00317 -0.00969 -0.00347 0.0156

-

0.207**

*

-0.131***

Std. Error (0.00279) (0.00811

) (0.00299)

(0.0163

) (0.0285) (0.0238)

lnETA -0.396***

-

0.544**

*

-0.410***

-

0.578*

**

-0.0372 -0.12

Std. Error (0.0381) (0.0547) (0.0394) (0.221) (0.194) (0.19)

lnTE -0.0305* -0.00678 -0.0284* 0.419*

**

0.570**

* 0.609***

Std. Error (0.0157) (0.0247) (0.0163) (0.0898

) (0.0863) (0.0826)

Obs. 752 752 752 771 771 771

R2 0.135 0.132 0.039 0.17

Adj R2 0.131 0.035

F-statistic 38.85*** 10.36*

**

No. of bank 94 94 94 94

Diagnostics


*

45.98**

*

Wald Chi2 114.54*** 109.44***

BPLM 0.45 717.15***

Hausman 18.54*** 34.14***







35

Table 9: Static Panel Regression for Liquidity Risk (SEA Region)

SEA REGION



Constant 3.639*** 3.074*** 3.090*** 5.151*** 3.663*

** 3.772***

Std. Error (0.514) (0.356) (0.35) (0.524) (0.342) (0.339)


lnTA 0.0268*** 0.0252* 0.0279** 0.0188* 0.0335

*** 0.0312***

Std. Error (0.00954) (0.0129) (0.0114) (0.00972) (0.012

4) (0.0111)

lnETA 0.06 0.221** 0.212** -0.432*** -

0.0403 -0.0659

Std. Error (0.129) (0.0877) (0.0859) (0.131) (0.084

4) (0.083)

lnTE -0.130**

-

0.0988**

*

-

0.0993**

*

-0.288***

-

0.150*

**

-0.162***

Std. Error (0.0522) (0.0383) (0.0369) (0.0532) (0.036

8) (0.0357)

Obs. 784 784 784 784 784 784

R2 0.018 0.029 0.061 0.046

Adj R2 0.014 0.057

F-statistic 4.65*** 16.88***

No. of bank 94 94 94 94

Diagnostics

F-statistics

6.90*** 11.08*

**

Wald Chi2 22.93*** 38.02***

BPLM

1899.62*

** 2041.69***

Hausman 0.60 4.01







at the 1% level.

36

Table 10: Results Summary (Significant Results)

Variables

Credit Risk Liquidity Risk

AR ME SA SEA AR ME SA SEA

M

1

M

2

M

1

M

2

M

1

M

2

M

1

M

2

M

1

M

2

M

1

M

2

M

1

M

2

M

1

M

2

lnTA - - - + - - + +

lnETA - + + - - - + + + + + - +

lnTE - - - + - + - - -

5) CONCLUSION

From the results presented above, it can be seen that different regions will exhibit

different determinants of financial risk as can be seen from results summary in

Table 10. Overall, the impact of technical efficiency on credit risk is significant

and negative in Middle East, and South Asia, but significant and positive in

Southeast Asia. Meanwhile, the effect of technical efficiency on the liquidity risk

in Middle East is significant and positive. Then, in South Asia and Southeast

Asia, the technical efficiency is significant and negatively affect the liquidity risk.

In addition, different financial risk measurement used also will produce different

results as the effect are very sensitive to the choices of measurement. In terms of

regulation and policy implications, the findings indicate that, there is evidence

that financial risk in these three regions are significantly more affected by bank-

specific determinants. This implies that regulatory authorities should focus more

on risk management systems, managerial performance, and measures to identify

banks with potential impaired loans and possible financial instability. Finally, this

empirical finding provides additional knowledge for the academicians who wish

to take up new research in this area to fill the gap of existing studies on the

importance of investigating the financial risk. The new findings on the potential

internal determinants of the financial risk in the conventional banks in the Middle

East, Southeast Asia, and South Asia provide new information to the

academicians. Eventually, the findings on the effect of technical efficiency to the

financial risk reveal the new dimensions in the literature. Therefore, it could give

new area to the academicians to explore more interesting topic and attained new

findings.

6) ACKNOWLEDGEMENT

This paper has greatly benefitted from funding by the Putra Business School,

Universiti Putra Malaysia. The authors are responsible for all remaining errors

and omissions.

37

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41

Exploring Generation Y’s Purchase Intention

Towards Counterfeit Product in Malaysia

Nur Haslina Ramli* Faculty of Business Management, Universiti Teknologi MARA Kelantan,

Bukit Ilmu, Machang, Kelantan, Malaysia

Email: [email protected]

Rosfatihah Che Mat

Faculty of Business Management, Universiti Teknologi MARA Kelantan,



Mazlina Mamat

Faculty of Business Management, Universiti Teknologi MARA Kelantan,



ABSTRACT

The supply of counterfeit products has been growing tremendously over the year

due to current demand which attract the attention of young consumer. This paper

investigates the impact of social influence, product, pricing and previous

experience on generation Y’s purchase intention towards counterfeit products.

Data were collected from a sample of 100 respondents age 21-35 representing the

generation Y via questionnaire. Findings shows social influence and product have

an influence to the purchase intention of counterfeit luxury product. Specifically,

product is ranked as number one for the factors followed by social influence,

previous experience and lastly price.

Keywords: Counterfeit Luxury Product, Generation Y, Purchase Intention,

and, Young Consumer.

mailto:[email protected]

42

1) INTRODUCTION

Counterfeits can be found in almost everything and the list keep on going every

day. Susta (2008) mention about plethora of counterfeit or fake products such as

imitations of medicines, cosmetic items, textiles, household and electrical

appliances, food, mobile phones, CDs, software, music, automobile (spare) parts

and other equipment that are introduced on the market under the name of famous

brands and sold at attractive prices. According to Grossman and Sharpiro, (1998)

counterfeiting is defined as a product that copy a real product with their brand

name. It’s might be happen because of the shape and the material that counterfeits

use is indistinguishable from the original, and is sold at a lower price.

There are many factors that influence consumer especially generation Y to buy

counterfeit products. Study by Phau and Teah, (2009); Nordin, (2009) explain that

attitude of consumer towards counterfeit products have positive influence to

purchase intention. This mean that attitude of buying behaviour can influence

them and leading curiosity to buy counterfeit products. Phau et al, (2009) point

out the attitudes of consumer to buy counterfeit products is high if their friends or

people around them support their decision. Apart from that, they feel the prestige

of the original brand because the physical appearances of the counterfeit product

look similar to each other very much (Kalyoncuoglu and Sahin, 2017).

Based on the study by Macharia, (2014) the effect of counterfeit products on

consumer is they paying high price for the product and it’s not worth to compare

with the quality that they get. The quality of counterfeit products is rather very

low but they selling to their customer with high price. The consumer also did not

get any warranty from counterfeit manufacturer even they buying the products

with a high price and manufacturer also will not be responsible for any damage

towards counterfeit products.

Counterfeiting activities have contributes negative impact to luxury brands itself.

Producer of luxury brand products suffer direct loss in number of sales because

consumer are easily buy counterfeit products. Some markets are even dominated

by counterfeits,creating barriers of entry for the producers of the genuine product.

In addition, Kapferer and Bastien, (2009) noted manufacturers of luxury brands

suffer the risk of damage to their brands' reputation. If reputation of the brands is

down, the consumers will not respect and believe on their brands. For instance,

those consumers who believed they were buying a genuine item when in fact it

was a fake, will be likely to blame the manufacturer of the genuine product if the

fake does not fulfil expectations, thus resulting in a loss of goodwill. Even worst,

this situation encourage more criminal activities to appear as counterfeiter’s

supply chains being linked with organized crime, sweatshops and other illegal

operations (Cesareo and Sto¨ttinger, 2015).

43

Other than that, counterfeit products also give bad impact on economic of the

country. Across the globe, manufactures of original product find themselves in a

same battle field with the counterfeit products. Susta (2008) point out that the

counterfeit goods market in 2008 amounted to € 500 billion euro, representing

about 7% up to 10% of the world trade. Hence, the continuous battle againt

counterfeit products is becoming a major consent for many country around the

world. The number of product counterfeiting increase in terms of volume,

sophistication, range of goods, and countries affected (ICC, 2005; Staake, Thiesse

and Fleisch (2012). It has been said that both increased production and demand

caused counterfeiting growth (Astray, 2011). According to (Economist, 2003)

counterfeiting of luxury, consumer and industrial products has become a global

economic and societal problem. Direct consequences from this illegal activities is

lower in sales and lead to employess lay off. The manufacturer can’t afford to pay

their salary because the competition with counterfeit manufacturer is too high.

Other that, Verma, Kumar and Philip, (2014) discuss about major effects of

counterfeiting is decrease in national income as manufacturer of counterfeit did

not pay for the tax such as sales tax, income tax, and custom duty.

There is an increase of counterfeits product because of the demand of this products

is high even though manufactured offered with low quality and low prices and it

had sell in a broad market. The continuous battle against counterfeit products is

now becoming a major concern for many country around the world. Based on

statistics from Ministry of Domestic Trade, Co-operatives and Consumerism

(KPDNKK), on 2014 there are 2,156 cases regarding counterfeit product. While

on 2015 and 2016 there are 750 and 656 cases. According to Saurabh Evrma,

Rajender Kumar and P.J Philip (2014), the major effects of counterfeiting is

decrease in national income and tarnish Malaysia`s reputation in the eyes of

tourists. The situation of counterfeit products in Malaysia is getting critical. The

statistics from Ministry of Domestic Trade, Co-operatives and Consumerism

(KPDNKK) shows that increasing in seize counterfeit products from 2014, 2015

and 2016 which is worth RM13,394,398 , RM 20,134,783 and RM 25,329,750

respectedly. Latest, in 2017, from January to April, the total seize of counterfeit

products have reach RM 3, 183,235 in value.

From the table 1, it shows that many sellers nowadays selling a counterfeit

products in Malaysia and consumers also intent to buy counterfeit products rather

than buying genuine products. So, it is vital to analyse and determine the factors

which influence the intentions of consumers to buy counterfeits product in order

to measure the demands of the consumers.

44

Table 1: Number of counterfeit product seize by KDNKK from 2014 to 2017

(Jan-April 2017)

Sources: Ministry of Domestic Trade, Co-operatives and Consumerism (KPDNKK

So, the objectives of the study is to determine association consumers attitude

towards consumer intention buying counterfeits products by looking at the

relationship between both parts. Second objectives is to rank the factors of

purchase intention towards counterfeit product. Born between 1980-1994, this so

call Generation Y consume more counterfeit products. Study done by Francis,

Burgess and Lu, (2015), show that, Generation Y’s consumption more closely to

counterfeit products rather than old folks, and they buying counterfeits products

based on their attitude rather than price consideration. Because of that, we cannot

ignore the needs, wants and attitudes of this generation.


2.1) Counterfeit Product

Several definition of counterfeit product is found based on previous literature.

First definition of counterfeit by Cordell (1996) identified counterfeit product as

“any unauthorized manufacturing of goods whose special characteristics are

protected as intellectual property rights (trademarks, patents and copyrights)

constitutes product counterfeiting. Meanwhile (Chaudhary and Walsh, 1996; Bian

and Veloutsou,2007) agreed that counterfeiting products define as trade products

that were identical to be differentiated from the registered trademark, so violating

the rights of the trademark’s owner. According to Grossman and Sharpiro, (1998)

45

counterfeiting is defined as a product that copy a real product with their brand

name. The manufactured imitate the products such as copy their design and

attributes of genuine products into their products and they claim the products is

theirs.

Malaysia has a counterfeit market value of $378 million, with software

dominating $289 million of that market value (Havocscope Global Market

Indexes, 2008). Commuri, (2009) explain on the reputation of luxury brands that

will be turnish because of counterfeit luxury products and the exclusivity and

uniqueness of the brand also destroyed or loss. When many consumer buying

counterfeit luxury products, it has an impact to the owner or luxury brands it

which is the sale of luxury brand will decrease and exclusivity of the products will

be loss. It is because consumer can get the same products with the same attributes,

design, and quality if they buying counterfeit products. Counterfeit occurred when

retailers did not pay the taxes of product and the price of the product can get the

lower price and the product exactly same with original ones. Thus, retailer can

make the higher profit rather than original products.

Past study by (Grossman and Shapiro, 1988; Lai and Zaichowsky, 1999; Sharma

and Chan, 2011) agrees that counterfeit products offered to a broad market by

supplier with low quality and low prices. Budiman, (2012) explain the great

possibility of demand counterfeit products have two reasons which are the price

is cheaper than genuine products or the consumer get economical benefits from

buying counterfeit products. Some of the consumer have their own economic

problem which is the money that they have did not enough to buy luxury products,

with buying counterfeit products it can help consumer buying their needs and

wants. Based on Lynch, (2002) counterfeit products consider a good potential

market to consumer who have low income and not able to buy the original

product. People who constantly strive to be fashionable and to possess the latest

gadgets and products will be more ready and eager to accept such counterfeit

products, thereby gaining access to international trademarks that otherwise could

not have been easily obtained at a fair price (Wee, Tan & Cheok, 1995). Accepting

or rejecting fake products depends on the extent to which people believe they can

gain recognition from their fellows, a status or high social prestige (Eastman et

al., 1999). Other previous research on counterfeit products emphasize the

importance of moral beliefs and ethical judgments on consumer attitudes

(Chaudhry and Stumpf, 2011), motivations (Penz and Stöttinger, 2012;

Wiedmann et al., 2012) as well as purchase intentions and behaviors (Wilcox et

al., 2009).

46

2.2) Generation Y

According to Williams and Page, (2011) generation Y born into an era of

electronic,technological, and wireless society and wireless society where global

boundaries have been blurred. Geyzel, (2009) said that generation Y are well-

educated, confident, passionate, upbeat, and socially conscious with high

integrity. Born between 1980-1994, this so call generation Y consume more

counterfeit products because of price considerations. Study done by Francis,

Burgess and Lu, (2015), to 251 Gen Y shows that their intent to buy counterfeit

products rather than old folks, and they buying counterfeits products based on

their attitude rather than price consideration. Der Hovanesian, (1999) notice that

generation Y spend their money for buying consumer goods and personal

services. Once generation Y get their money or salary, they will spend their money

for buying daily basis goods and for their healthcare.

2.3) Generation Y’s purchase intention

According to the theory of planned behaviour (TPB), purchase intention is a factor

of purchase behaviour, in which the purchase intention can be determined by

attitudes (Phau and Teah, 2009). Based on several studies by (Phau and Teah,

2009; Nordin, 2009; De Matos, 2007; Huang, 2004) the attitude of consumer is

playing an important positive relationship towards purchase intention. Thus,

making consumers’ intention to purchase counterfeit products at least once is the

compelling need to understand buying patterns (Romani, Gistri and Pace, 2012)

According to Wee, (1995) the higher consumer attitudes towards counterfeiting,

the higher chances of consumer will buying counterfeits products. Nguyen and

Tran, (2013) discuss about behavioural of purchasing is influence by purchase

intention and it is also can influenced by attitudes. Purchase behaviour and

attitudes influence consumer in their purchase intention towards counterfeit

products whether they want buying counterfeiting or the original products.

Purchase intention of consumers is affected by some attributes including their past

experience, preferences, and other information from other sources (Schiffman,

Kanuk, and Wisenblit, 2010). According to Lianto, (2015) the more effective

those factors affecting consumers’ intention, the possibility of those consumers

purchasing certain goods is increasing. Purchase intention is the trigger of a

consumer to purchase a product (Schiffman & Kanuk, 2000). Four factors of

purchase intention of generation Y is been discuss in this paper. The factors

include, social influence, products, price and previous experience.

47

2.3.1 Social influence According to Muhammad Rizwan, (2014) surrounding people can influence

consumer to purchase counterfeit products or non-counterfeit products. It is

normal for the consumer to refer to groups and consulting before making their

purchasing behaviour. Thus, Daniels, (2007) notice the influence word-of-mouth

is no longer comes from family or their friends, but it comes from the members

on their online network. These generation using social network and because of

that they are more influences to buy based on what their online network’s member

said rather than what their family said. This is how big the effect of online network

to the Generation Y’s buying decision. Consumer's choice is influenced by others

whether they acknowledge about it or not, on the other hand, consumers are

interested in impressing or influencing others (Ang, 2001). According Phau et al.

(2009), consumers have supportive attitudes if their friends or relationships

around them supporting it and vice versa. Other than that, consumer buying

counterfeit products because to influence others people and also to impress

themselves about their social status (Ang, 2001).

2.3.2 Products According to (Tom et al, 1998; Wee et al, 1995) consumer intent to buy

counterfeit products because of the products itself. So consumers choosing their

products based on functional of the product and compare each other. The

functionality and attributes of the product is an important thing before consumer

make their chooses and it is one of the factor why people intent to buy counterfeit

products. (Wee et al., 1995; Penz and Stottinger, 2005) the similarity of quality

and perceived attributes of genuine products and counterfeits influence consumer

to purchase counterfeiting. Based on (Stumpf et al, 2011) consumer not intent on

quality but they buying counterfeit products based on their personality and

fashionable.

2.3.3 Price One of the most important issues in affecting consumer intention to counterfeits

products is price to Phau et al (2009). Lai and Zaichkowsky, (1999) highlight

about counterfeit products that are illegal, cheap, and poor quality duplications of

prestigious branded products whereas genuine products are high priced and have

premium quality. However, the counterfeit products can meet needs and wants of

consumer who unaffordable to buy original products which is they offered with

low price but same attributes with genuine items (Chuchinprakarnm, 2003;

Chaudhry et al., 2009). Triandewi and Tjiptono (2013) indicates that in most

developing countries, consumer do not mind purchasing low quality pirated

products especially those who love fashion but cannot afford to purchase original

designer clothing. For them, this is the opportunity to enjoy the prestige of the

luxury and popular brand.

48

2.3.4 Previous experience Previous knowledge is one of the factor that enhances the attitude of consumer to

buy counterfeit products (Wang et al., 2005; Tom et al., 1998). Ponto, (2015)

mentions purchase intentions is when consumer will buying the products again

once they trying or evaluate those products and they found that it is worth to buy

that products; same goes with the consumer that intent to buy counterfeit products,

they will buying the products once again after they feel satisfied with the quality

or pricing of counterfeit products. However, study by Yoo and Lee (2009) shows

consumer prefer genuine products rather than counterfeit products based on their

experiences that using those products. In contrast, consumers with experience of

purchasing counterfeit products believe that there is nothing wrong with buying

counterfeit goods. They think that genuine products is overprice and it is better to

buy counterfeit products, since it have the same quality with the genuine one

(Walthers & Buff, 2008; Nia & Zaichkowsky, 2000).

3) METHODOLOGY

The target population of this research includes young consumer representing

generation Y who live in Kelantan, Malaysia. The age of respondent is around 21-

35 years old. The sample was selected in the streets and places close to the points

where counterfeited products were being sold. This generation Y was selected as

they have middle level income and tent to buy counterfeit products. The

respondent of this research study is 80 people. The survey-based research using

the face-to-face questionnaire administration attempted to assess on a five-point

Likert to which consumers are willing to buy counterfeit products and the factors

influence the purchase intention. The questionnaire were consists of three (3)

sections which are section A, B and C. For section A, there are 5 question

regarding demographic profiles, section B consists 5 question regarding purchase

intention of counterfeit products and for section C, by using Likert scale, there

are 20 question consists 5 question each social influence, products, pricing and

previous experience.

After obtaining the data, it will be analysed by using the Statistical Package for

Social Science (SPSS) program. In order to answer the research objective, the

researcher decided to employ four statistical modes. First is, to measure of

frequency in analysing the data. Second is, reliability analysis to show stability of

data and third, to look on correlation analysis used for. Four, multiple linear

regression model analysis was selected to test the relationship between dependent

variable and independent variable.

49

Figure 1: Theoretical Framework

Independent variable 1 = Social Influence

According to (Ang et al, 2001) whether the friend or family did not have

acknowledge about the products,it’s still influenced consumers choice.

Independent variable 2 = Product

According to (Budiman, 2012) product attributes is the factor that influence the

decision of the customer to purchase counterfeit products. The attribute of the

counterfeit products is likely same with the genuine products and because of that

the consumer attract to buy those products.

Independent variable 3 = Pricing

Based on (Norum & Cuno, 2011) price is the main factors influencing consumer’s

intent to buy counterfeit products. The price of counterfeit products is cheaper

than genuine products and because of that people tend to buy counterfeit products.

Independent variable 4 = Previous Experience

According to (Yoo & Hee Lee, 2009) the positive influence towards buying

counterfeit products is previous experience. If the experience of consumer on

buying counterfeit products is good, they intent to buy those product again.

Social Influence

Product

Purchase Intention

of Counterfeit

Products Pricing

Previous

Experience

50

4) FINDINGS

4.1) Demographic Profile

Table 2: Demographic Profile

Characteristic Frequency Percentage %

Gender

Male 28 35%

Female 52 65%

Total 80 100%

Age

21-24 28 35%

25-28 20 25%

29-32 12 15%

33-35 12 15%

Total 80 100%

Education

Diploma 12 15%

Degree 36 45%

Master 24 30%

Phd 8 10%

Total 80 100%

Status

Single 44 55%

Married 36 45%

Total 80 100%

Frequency

1-3 24 30%

4-6 40 50%

7-9 8 10%

10-12 8 10%

Table 2 shows the frequency of analysis in terms of gender, age, education, status,

and frequency of buying counterfeit products. The most respondents in this

research is female which is 65% and male is 35%. The highest age of the

respondents is 35% in range of 21-24 years old. The second highest is 20% in

range of 25-28 years old and 29-32 years old, the lowest is 12% in range of 33-

35 years old. The highest education of the respondents is 36% which is degree

holder, the second one is master holder with the percentage is 24%,the second

lowest is diploma holder which is the percentage is 12% and the lowest is Phd,

8%.

The highest percentage of respondents status is single, 55% and the lowest one is

married, 45%. The highest respondents of buying counterfeit products is 50% in

the range 4-6 products. The second highest is 30% which the range of buying

51

products is 1-3 products. The lowest is 10% and the ranges are 7-9 and 10-12

products.

4.2) Reliability Analysis

Table 3: Reliability Analysis

Variables

Cronbach’s Alpha Number of Items Strength of

Association

Purchase Intention .777 5 Good

Social Influence .766 5 Good

Products .807 5 Very Good

Pricing .740 5 Good

Previous Experience .754 5 Good

The table 3 above shows the internal consistency of purchase intention is good.

The data is a reliable and all 5 questions for dependent variable are acceptable

which is purchase intention was 0.777. For the first independent variable is social

influence are good which is 0.766, and the products is very good because the

result is 0.807, pricing is good which is the result is 0.740, and previous

experience is good, 0.754. All the value of four independent is reliable and the

entire question are acceptable.

4.3) Descriptive Statistics

Table 4: Descriptive Statistics

Variables N Minimum Maximum Mean Std. Deviation

Purchase intention 80 3.00 5.00 4.52 .454

Social influence 80 3.00 5.00 4.31 .471

Product 80 3.60 5.00 4.54 .373

Price 80 3.40 5.00 4.31 .392

Previous experience 80 2.40 4.80 4.23 .448

Valid N (listwise) 80 3.00 5.00 4.52 .454

*Scale: 1=Strongly Disagree, 2= Disagree, 3=Neutral, 4=Agree, 5=Strongly Agree

(Sources: Sample): 80

The highest mean for independent variable that contribute to the potential factor

to purchase counterfeit products is 4.54 which is products. The second one is

social influence and pricing which is same value 4.31, next is previous experience

with 4.23.

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4.4) Correlation Coefficient analysis

Table 5: Correlation Coefficient

Purchase

Intention

Social

Influence

Product Price Previous

Experience

pi Pearson

Correlation

1 .581** .560** .129 .244*

Sig. (2-tailed) .000 .000 .254 .029

N 80 80 80 80 80

SI Pearson

Correlation

.581** 1 .239* .167 .291**

Sig. (2-tailed) .000 .033 .139 .009

N 80

80 80 80 80

PDT Pearson

Correlation

.560** .239* 1 .058 .169

Sig. (2-tailed) .000 .033 .608 .133

N 80 80 80 80 80

PRCE Pearson

Correlation

.129 .167 .058 1 .220*

Sig. (2-tailed) .254 .139 .608 .049

N 80 80 80 80 80

PE Pearson

Correlation

.244* .291** .169 .220* 1

Sig. (2-tailed) .029 .009 .133 .049

N 80 80 80 80 80

Based on the table 5, it shows the ‘R’ value of each independent variable that

influences consumer purchase counterfeit products. This analysis meets an

objective of the research which is to determine association consumer’s attitude

towards consumer intention buying counterfeit products. The r value for social

influence is moderate which is 0.581 and the r value for products is 0.560 which

is the strength of association also moderate. Next is pricing, r value for this

independent variable is 0.129 it shows that the value is very weak. Lastly is

previous experience, the r value is 0.244 and the association is weak.

53

4.5) Multiple Regressions analysis

Table 6: Multiple Regressions (model summary)

Model R R Square Adjusted R

Square

Std. Error of

the Estimate

Durbin-Watson

1 .726a .527 .502 .32040 2.035

Table 6 shows that R-Square is equal to 0.527 which indicates 52.7% of the

variances in dependent variable which is the purchase intention of counterfeit

products can be explained by changes in independent variables. However, it is

still left 47.3% unexplained and cannot be described by independent variable.

Table 7: Multiple Regressions (ANOVA)

Model Sum of

Squares

df Mean Square F Sig.

1 Regression 8.589 4 2.147 20.916 .000b

Residual 7.699 75 .103

Total 16.288 79

Table 7 shows the multi regression which is ANOVA table. Result from that table

shows that the F value is 20.916 with significant value is 0.000, which less than

alpha value 0.05. It can be conclude that this model is valid and all the

independent variables significant and reliable with dependent variable.

Table 8: Multiple Regressions (Coefficients)

Model Unstandardized

Coefficients

Standardized

Coefficients

T-value Sig.

B Std. Error Beta

1 (Constant) -.081 .623 -.130 .897

Social

influence

.446 .082 .463 5.434 .000

Product .540 .100 .443 5.389 .000

Price .022 .095 .019 .236 .814

Previous

experience

.031 .086 .030 .358 .721

Table 7 shows the coefficient of independent variables that influence customer

purchase intention towards counterfeit products. The coefficient for social

influence is 0.446. The result show social influence has a positive influence

towards purchasing counterfeit products and it is significant because the p-value

54

is 0.000 which is lower than 0.05. The coefficient for products is 0.540. The

products of counterfeit has a positive influence towards purchase intention on

counterfeiter. It have positive influence because the p-value is 0.000 and it’s

significant. Next is pricing, it is not significant which means the price is a negative

influence with the coefficient value is 0.022 and the p-value is 0.814. The last

independent variable is previous experience, the coefficient of this variable is

0.31 and the p-value is 0.721. It is not significant because the p value is higher

than 0.05.

4.6) Hypothesis testing

4.6.1 Hypothesis 1 H0: There is no significant relationship between social influence and purchase

intention. H1: There is significant relationship between social influence and

purchase intention.

Based on the table 8, the result shows the coefficient of social influence is equal

to 0.000 and it is significant. Hypothesis 1 is accepted. According to (Phau and

Teah, 2009), who asserted that consumers’ intention to buy counterfeited

products depend on their attitude towards counterfeit product, which, in turn, is

influenced by status consumption. Wee et al. (1995) also supported the positive

relationship between status consumption and attitude towards counterfeit

products. Based on (Phuong V. Nguyen and Toan T.B. Tran, 2013) social

influence has significant impact on the attitude towards counterfeits of fashion

product.

4.6.2 Hypothesis 2 H0: There is no significant relationship between products and purchase intention.

H1: There is significant relationship between products and purchase intention.

Based on the table 8, the result shows the coefficient of product is equal to 0.000

and it is significant. Hypothesis 1 is accepted. According to (Yoo & Lee,2005)

counterfeit products provide high quality it is difficult to differentiate between

counterfeit and genuine products.

4.6.3 Hypothesis 3 H0: There is no significant relationship between pricing and purchase intention.

H1: There is significant relationship between pricing and purchase intention.

Based on the table 8, the result shows the coefficient of pricing is 0.814. It shows

that negative relationship between pricing and purchase intention. According to

(Yee and Sidek, 2008), pricing is not effect on consumer purchase intention

55

towards counterfeit products. Based on (Rashid Khan) low price is not a

significant factor on purchase intention towards counterfeit products.

4.6.4 Hypothesis 4 H0: There is no significant relationship between previous experience and

purchase intention. H1: There is significant relationship between previous

experience and purchase intention.

Based on the table 8, the result shows the coefficient of pricing is 0.721. It shows

that negative relationship between previous experience and purchase intention.

According to (Phuong V. Nguyen and Toan T.B. Tran, 2013) people that already

have previous experience on buying counterfeit products have no effect on

purchase intention towards counterfeit products.

Table 9: Unstandardized Coefficients Beta

Model Unstandardized Coefficients

Ranking B Std. Error

1 (Constant) -.081 .623

Social influence .446 .082 2

Product .540 .100 1

Price .022 .095 4

Previous experience .031 .086 3

The table 9 shows the ranking that conclude by researcher to know the factors of

consumer intention to buy counterfeit products. The highest beta for independent

variable that contributes to the potential factor to purchase counterfeit products is

0.540 which are products. Its mean that, product is the most factor that influence

consumer. The second one is social influence with the beta 0.446 followed by and

previous experience which is beta value 0.031. Finally the lowest mean score is

price with 0.022.

5) CONCLUSION

In Malaysia, its shows that many sellers nowadays selling a counterfeit products

and consumers also intent to buy counterfeit products rather than buying genuine

products. To prevent this issue getting worse, researcher carry out a research and

experiment regarding factors that influence consumer among generation Y

buying counterfeit products and it makes demand of counterfeit in this country.

By conducting a field survey with the aid of questionnaires this study identified

critical factors in relation purchasing for counterfeits product among respondents

which are generation Y.

56

According result on data analysis, from the findings obtained, the mean shows

the rank of potential factor to purchase counterfeit products. It’s shown the

independent variable is much influenced intentions to purchase counterfeit

products. The table shows result of factors according to the rank.

Objective 1: To find out the most and least factor of consumer intention to buy

counterfeit product.

Table 10: Main factors that influence intentions by Unstandardized Coefficients

Unstandardized Coefficients

Ranking B Std. Error

(Constant) -.081 .623

Social influence .446 .082 2

Product .540 .100 1

Price .022 .095 4

Previous experience .031 .086 3

Unstandardized Coefficients has been used in order to answer the first research

objective. The highest beta for independent variable that contributes to the

potential factor to purchase counterfeit products is 0.540 which are products. This

prove that product is the most important factor to Generation Y’s intention toward

buying counterfeit product. The second one is social influence with the beta 0.446

followed by and previous experience which is beta value 0.031 Whereas, the

lowest beta is price, 0.022. Price is the least factor influencing respondent toward

buying counterfeit product.

To examine the relationship between independent variable and dependent

variable also has been explained in this research, it shows the ‘R’ value of each

independent variables that influence consumer purchase counterfeit products.

The r value for social influence is moderate which is 0.581 and the r value for

products is 0.560 which is the strength of association also moderate. Next is

pricing, r value for this independent variable is 0.129 it shows that the value is

very weak. Lastly is previous experience, the r value is 0.244 and the association

is weak.

To meet second objective, regression has been used to show the model of the

research. For the coefficient table, the researcher want analyse the independent

variable whether significant or not. This analysis made by researcher because to

examine the relationship between dependent variable and independent variable.

For the first independent variable is social influence, this variable is significant

because the p-value is 0.000. The products is also significant which is the p-value

is 0.000. This two variable has strong relationship with dependent variable which

is purchase intention. The third variable is pricing. It is not significant or it have

57

no relationship between purchase intentions because the p-value is 0.814. The last

one is previous experience, it has no relationship between independent variable

and dependent variable. The p-value is 0.721.

Table 11: Objective 2: To examine relationship between independent variables

(social influence, product, pricing and previous experience) and dependent

variables (purchase intention of buying counterfeit products)

Table 11: To examine relationship between independent variables and dependent

variables

As a conclusion, two of independent variables which is social influence and price

are playing importance roles that influencing consumer intention for buying

counterfeit products.

6) RECOMMENDATION

The finding in this study whereby the researcher can give the recommendation

with the hope would give a contribution for positive changes in the future. It

might be useful to other researcher or the manufacture that want to use the

independent variables as a references that already carry in this research study.

The recommendation were given based on the findings and results that have been

found.

6.1) The Government

Based on research study, some suggestion that can give to the government which

is the government should enforce the counterfeit policy to combat counterfeiting

business. The members of KPDNKK need to monitor the place that have potential

to sell counterfeit products such as at night market and uptown. KPDNKK need

to find the new enforcement or law to decrease this problem such as catch the

58

counterfeiter distributor that distribute their product to unauthorized seller. Other

than that, the government should seizing the counterfeits products and confiscated

that premise. Last but not least, Kementerian Perdagangan Dalam Negeri Dan

Kepenggunanan (KPDNKK) should responsible in this matter like suspending

the business license of the unauthorized seller. They also should higher the rate

of fine to decreased unethical activities. The government needs to work with the

original brand manufacturers to discourage consumers from buying counterfeit.

Strong law enforcement needs to be applied to all of the parties related such as

manufacturers, distributors, sellers, and also the buyers.

6 2) The Manufacturer

The manufacturer should have a uniqueness of the products which means the

design, attributes and features of the original product that cannot easily copy by

counterfeiter manufacturer. The owner of original products should ensure the

quality of their products is high rather than counterfeit products. If the

manufacturer use the high quality materials, it’s make difficult to counterfeiter to

duplicate their products. The manufacturer need to do the customer service for

their business. Customer service such as give their guarantee for the product in a

life time, give a discount for membership holder, and give a half price for next

purchase. With all the customer service that offered by genuine manufacturer

consumer will decrease their intention to buy counterfeit products.

Advertisement is one of the important factor to advertise original products. So

that manufacturer should advertise the attributes of the genuine products and also

the differentiation between counterfeit and genuine products. The consumer can

easily differentiate the genuine products and counterfeits products. Consumer

have a knowledge about original products and they know how to buy the genuine

products. Other than that, manufacturer also need to explain to consumer the

negative side of counterfeits. Manufacturer can do the social events to give a

speech about negative side of counterfeiter. From social events also manufacturer

can educate consumer do not buying counterfeit products. Tell the consumer the

risk of using counterfeit products for their health and their life. To reduce

consumer buying counterfeit products, the manufacturer should reduce the price

of genuine products. Consumer will attracted to buy counterfeiter if the price of

the genuine products is cheaper or manufacturer always do the discount such as

three time in a year. It helps consumer to buy their favourite genuine products

because they have affordable on buying those products. Manufacturer also need

to create the products or design that would make the consumer eager of owning

rather than buying counterfeit products. The design of genuine products needs to

have sentimental value or exclusive value to ensure consumer eager on buying

those products.

59

6.3) To Future Researcher

The further study would be conducted by the future researchers. The suggestion

that given by researcher is for better knowledge for future researcher. The

suggestion is future researcher should gain more variables that influence

customer on purchase intention towards counterfeit products. As recommended

for the future researcher, the large sample size can be used by the future

researcher. . This is particularly on this study it is quit hard to estimate accurately

the number of population in Kelantan area. So that, this study just focuses at

Kelantan. Hopefully for future study the researcher can increase the number of

sample size to get more accurate results regarding factors that influencing

consumer purchase intention towards halal cosmetic products. Use other method

by the future researcher to collect data instead of relying fully on questionnaire.

Most questionnaires only skimmed the surface of problem. Other method such as

interviews seems as the best option to be used by researcher side by side with the

questionnaire. By doing interview, the researcher can collect and get more data

from the respondent as the explanation will be more detailed and comprehensive.

The future researcher can suggest a few suggestion to genuine manufacturer such

as do the sale on genuine products in order to decrease the intention of consumer

buying counterfeit products, build the factory outlet which is open the shop that

all the prices of luxury brands item directly comes from factory. The price is

cheaper than products in exclusive outlet, it helps consumer to choose genuine

products rather than counterfeit products.

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Wee, C.H., Tan, S.J., Cheok, K.H. (1995), „Non-price determinants of

intention to purchase counterfeit goods. An exploratory study”,

International Marketing Review, Vol. 12, No 6, pp.19-46.

Walthers, A., & Buff, C. L. (2008). Attitudes towards counterfeiting and

counterfeit products: have they Changed? Journal of International Business

and Economics, 79-87.

Williams, K. C., & Page, R. A. (2011). Marketing to the generations. Journal

of Behavioral Studies in Business, 3, 1.

Wiedmann, K.-p., Hennigs, N. & Klarmann, C. (2012). Luxury consumption

in the trade-off between genuine and counterfeit goods: what are the

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Wilcox, K., Kim, H.M. & Sen, S. (2009). Why do consumers buy counterfeit

luxury brands?. Journal of Marketing Research. 46(2). 247-259.

64

The Carbon Dioxide Emission Reduction in Vietnam’s

Power Sector using GCAM

Ha Tran Lan Huong Ajou University, Republic of Korea


ABSTRACT

This study is conducted to analyse the carbon dioxide emission reduction in

power sector to promoting economic growth and social development in Vietnam

for more than 10-year perspective plan. The analysis is based on the revised

Vietnam Power Development Plan VII and Intended Nationally Determined

Contribution submitted by Vietnam government in 2016. Global Change

Assessment Model (GCAM) - an integrated assessment model - is adopted for

analysis. A reference scenario and five alternative scenarios, which are combined

of socioeconomics, nuclear, renewable, and geothermal, are developed. The

research findings showed that the electricity generation in integrated energy

scenarios increase more than in its single energy scenario. Scenarios with

renewable representative is not much effective in CO2 emission reduction

compared to nuclear scenario because of its small proportion. Vietnam, therefore,

should produce an alternative to renewable sources such as biomass, wind and

solar. And to meet the INDC’s target of 10% renewable sources in the share of

power sources in total power production by 2030, the volume of renewable

consumption is 0.106 EJ, and the reduction of coal, gas, oil estimates 1.2%, 1.3%,

15%, respectively, as well.

Keywords: CO2 Emission, Energy Balance, Energy System, GCAM (Global

Change Assessment Model), and Power Sector.

65

1) INTRODUCTION

1.1) Background and Motivation

Vietnam has been one of the dynamic and fastest growing economies among

developing countries in the region and in the world for recent years (averaging

7% of real annual growth). The priority of the Vietnamese government policy is

to achieve not only a fast economy development but also a sustainable one as

well.

The energy system has played a significant role in promoting and keeping the

sustainable development of economy in which all participants and sectors

(building, industry and transportation) demand and spend energy as their inputs

or final consumption. The energy sector, therefore, must be cable of securing the

required future energy supply sources by conserving plentiful energy resources

and utilizing more renewable energy sources as well as controlling its negative

environmental impacts from carbon-dioxide emissions through new and

advanced technologies.

Fossil fuel-based electricity, in fact, still has dominated the Vietnam power

generation system for a long term (nearly 67.3% in 2010). The share of coal and

gas in the power generation capacity was 20.7% and 46.5%, respectively, in 2010.

And 29% of total electricity production in Vietnam (27,600 out of 94,903 GWh)

(IEA, 2017) was accounted mainly from hydro energy (27,550 out of 27,600

GWh, meaning wind with only 0.05%). To come up with that trend, electricity

from renewable energy resources’ share is expected to further increase in the

coming years based on the revised power development plan of Vietnam VII:

“Prioritize the development of renewable energy sources for electricity

production; increase the proportion of electricity generated from renewable

energy sources (excluding large-scale, medium-scale and pumped storage hydro

power) up to around 7% in 2020 and above 10% in 2030.”

In term of final energy consumption, a fast growth rate in economy in Vietnam is

an eventual corresponding increase in power consumption. In 2010, the largest

share is in the industry sector (54.6%) followed by residential (36.2%) and

commercial and public services (9.2%), especially no electricity consumption in

transportation sector. Additionally, a project called “Power sector vision:

Alternatives for power generation in the Greater Mekong Sub-region” which

outlines one of the objectives is to contribute to reduction of global Greenhouse

gas emissions (reduction by more than 80% based on 1990 levels by 2050)

(Intelligent Energy Systems and MeKong Economics, 2016). To meet this

challenge, the Vietnamese government needs to devise strategic policies focusing

on clean technologies.

66

1.2) Purpose of Research

This study is being conducted to model Vietnam energy system and analyse its

power sector, then, to promoting economic growth and social development in

Vietnam through drafting background paper for the formulation of more than 10-

year perspective plan. The focus of power sector analysis is based on the revised

Vietnam power development plan VII and Intended National Determined

Contribution’s target (United Nations Climate Change, 2017).

Specific objectives are as follows:

1. To achieve the updated Vietnam energy system modeling.

2. To demonstrate Vietnam power sector and to compare with the Vietnam

power development plan VII’s target.

3. To analyze and compare scenarios’ CO2 emission reduction.

4. To discuss the potential research areas, applications, and future work

emerging from this study.


A group of Researchers titled “Modeling Energy systems for developing

countries” in which selected 12 energy models for comparison of their

appropriateness for developing countries. The results indicated that only few of

the main characteristics of developing countries’ energy systems and economics

such as electrification, traditional biofuels and the urban-rural divide were

evaluated by some of those models like AIM (Asian-Pacific Integrated Model),

LEAP (Long-range Energy Alternatives Planning System), MARKAL (MARKet

ALlocation Model) and MESSAGE (Model for Energy Supply Strategy

Alternatives & their General Environmental impact) which are bottom-up or

hybrid models, in contrast, others are top-down optimization ones. Other

characteristics were not properly addressed or evaluated only implicitly such as

power sector, subsidies, supply shortages and investment decisions, etc. (Urban

et al., 2007).

“A Review of energy system models” found that the applied energy system

models did not adequately capture characteristics of the developing countries.

The researchers used some kinds of energy system models like EFOM (Energy

Flow Optimization Model), MARKAL, LEAP, POLES (Prospective outlook on

Long-term Energy Systems), RES (Reference Energy System), WASP (Wien

Automatic System Planning), etc. The reasons for that inappropriateness are their

inflexible data input and optimal solution toward. They preferred the accounting-

type end-use models which have more flexible input and scenarios-making for

developing countries’ energy system modeling. ( (Bhattacharyya and Timilsina,

67

2010).

A comparison between France and Malaysia – a developed, and a developing

country was studied for modeling in energy sector, specifically on the energy

production and consumption. The LEAP model was applied to represent the

difference between them in term of energy sources consumed for power

production and final use consumption through their energy balances of France

and Malaysia, from 2016 to 2030. Two scenarios were developed, one for energy

demand in the residential sector of France, the another for residential energy

balance of Malaysia. Results showed that the final energy demand is consumed

less due to the advanced technologies. Moreover, the raising of the number of

household using electric cooker instead of gas ones and electric devices lead to

the electricity consumption increase 0.5% annually in France. In contrast, the

energy will be consumed more due to the urbanization of Malaysia, especially in

electricity utilization. The researchers also indicated some reasons in getting

accurate forecast using energy modeling such as developing countries’ capacities

limitation and growing demand for energy services, advanced technology option

for non-renewable energy depletion (Coyard et al., 2016).

The researcher Ouedraogo from United Nations – Economic Commission for

Africa (UNECA) spent LEAP for modeling sustainable long-term electricity

supply-demand in Africa. It assessed possible future paths for the regional power

sectors through making three scenarios to examine renewable energy and energy

efficiency. An electricity demand increases in 2040 and supply shortages were

showed while Greenhouse Gas emission is bigger. The renewable energy is not

the best choice for sustainable electrification of Africa, however, the energy

efficiency could be considered as a sustainable pathway for electrification

(Ouedraogo, 2017).

Global Change Assessment Model (GCAM) was adopted for Ethiopia’s energy

system analysis through scenarios development. They are integrated among

socioeconomics, biomass efficiency and transmission & distribution loss. The

high GDP growth scenario (Alt-1) showed that biomass remains as the major

energy fuel consumed in the building sector until 2035. The hydropower

generation is less than the electricity demand which is leaded by GDP growth.

Oil and renewable sources take the large share to fill up for the remaining

shortage. Energy demand in the industry sector increases over 12% while total

final energy and electricity demand grow at 7.71% and 15%, respectively.

Secondly, the high biomass efficiency scenario (Alt-2) indicated that to reduce

biomass demand it could be done with increasing the biomass efficiency through

highly efficient technology. Thirdly, electricity is cheaper than other kinds of

energy when the transmission and distribution loss is lower. Therefore, the target

68

of biomass demand reduction can be solved through the higher electricity demand

(Kim and Yurnaidi, 2017).

3) RESEARCH METHODOLOGY

In addition to the drafting of background study, an attempt is given to create a

national energy model for Vietnam, utilizing Global Change Assessment Model

(GCAM) which was developed by the Joint Global Change Research Institute

(JGCRI), a joint research group between pacific Northwest National Laboratory

(PNNL) and the University of Maryland (Kim et al., 2006). An assessment model

that integrates the human and earth system by showing the interaction among

economy, energy, agriculture, land use, clime, and even water, for which

technological detailed representations are being updated.

Figure 1 Schematic of major Energy Pathways in GCAM (Calvin et al.,, 2014)

Figure 1 shows the flow of energy system from supply side (primary and

secondary fuels) and end-use sectors (buildings, industry and transportation),

specifically it is categorized into resource production, energy transformation and

end-use sectors. The resources to produce final energy are oil, bioenergy, coal,

natural gas, nuclear, hydro, solar, wind and geothermal. The energy

transformation is techniques to convert the resources into final energy like refined

liquids, delivered gas, hydrogen and electricity.

GCAM utilizes conditional logit type of equation to model the technology

competition. The technologies within a sector compete among each other in order

to satisfy the output demand of that sector. The technology competition in GCAM

is governed by the so-called logit equation, which was originally developed by

McFadden (1974). The competition formula is shown below:

69

, ,

, ,

, , , ,

, ,

, , , ,

s h t

s h t

s h t s h t

s h t

s h t s h t

h

cS

c

=

Where:

: Share of each technology

: Share-weight

: Service cost of each technology

: logit exponent

(Joint Global Change Research Institute, 2017)

GCAM is an equilibrium model which adjusts prices until the supplies and the

demands are equal. The following algorithm helps the model solved:

1. To set initial energy prices;

2. To compute the model resource supplies and the end-use demands;

3. To determine the energy needed to satisfy the end-use demands;

4. To check if the supplies meet the demands; if yes, the model is solved. If

not, above prices are changed, and the steps are iterated.

To model Vietnam energy system and analyse its power sector under scenarios

design, GCAM is considered as one of the appropriate tools to apply.

Data from the Vietnam balance of International Energy Agency (IEA) and the

Vietnam PDP VII are utilized for power sector analysis. The data for Vietnam

energy balance is available from 1990 to 2015. Using the nuclear data information

from the Vietnam PDP VII, a new scenario is determined. The approach is to see

the difference between the reference scenario assessed in GCAM and the nuclear

one in 2030.

Lastly, the Vietnam carbon emissions in GCAM should be analysed and the

significance of power sector is shown. In accordance with the recent effort to

reduce carbon emissions, carbon policy is analysed to meet the reduction target

simulated.

In general, the approach in this study is showcase the new scenarios to compare

with the reference one.

, ,s h tS

, ,s h t

, ,s h tc

, ,s h t

70

GCAM is adopted for this study since it has many advantages. GCAM is available

to all users without charge. It is flexible and open source based to model a

national, regional and global energy system or even every detailed energy flow

of the system such as electric power generation, building sector, industry sector

and transportation sector. GCAM utilizes logit equations for technology

competition while other linear programming models are with corner solutions.

GCAM finds out the cost-efficient technology or emission trading volume.

Table 1. Emissions Trading Volume and Reduction Cost by Scenarios in 2030

Scenario Korea China Japan Total

Emissions (MtCO2) Reference 753 12,862 1,202 14,817

CO2 Trading Emission

Volume (MtCO2)

uETS - - -

pETS 96 -140 44 0

rETS 144 -186 41 0

Net Benefit

(Billion $, 2010)

pETS 11.4 -9.8 6.7 8.4

rETS 22.3 -13.9 6.0 14.5

(Baek, Modeling Regional Emission Trading System of Norhteast Asian Countries Including

Korea, China and Japan., 2017)

4) RECENT ENERGY SECTOR TREND

4.1) Recent Energy Sector Trends

Figure 2 shows the historical trend of final energy consumption by different

sectors. More than 50% of total final energy consumption came from the

residential sector, and the trend has not changed much until 2005. Then, the

industry sector has increasingly consumed final energy a little bit ever year, from

28% in 1990s to more 36% in 2010s. The following sector is transport with the

trend of annual increase from 8% in 1990s to approximately 20% in 2010s. In

contrast, other sectors like commercial and public services, agriculture/ forestry

and non-energy use have spent a small quantity of final energy consumption, not

more than 10% in total.

71

Figure 2: Final Energy Consumption by Sectors

Figure 3 and figure 4 show past historical trends of exports and imports,

respectively. The data indicates that while Vietnam has exported a big amount of

crude oil, it has imported back more than that amount of oil product. The cause

of this status is lack of advanced technologies in exploitation oilfields and

building refinery plant. Since 2004, Vietnam has started to export natural gas to

abroad, and has imported electricity and some amount of coal from China, too.

Even though, it exploited a huge quantity of coal during 2000s, and decreasing

after 2010. This was happened since Vietnam’s industrialization and

modernization was at the peak in 2006, the final energy produced domestically

was less than the demand of final energy consumption, in contrast exporting more

crude oil for more investment capital.

Figure 3: Energy Import

Figure 4: Energy Export

For the building sector which includes buildings for both residential and

commercial use, the residential sector accounted for more than 50% of final

energy consumption. Out of this share of the residential sector, most of the energy

used in the residential is from bio-fuels and waste. Other types of fuel like coal

and oil product’s trend increase fast recent years. Especially, it shows an

increasingly growing portion of electricity because of Vietnam’s growing

economy with a high urbanization rate.

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72

It would be important to disaggregate the urban and rural energy consumption

patterns in conjunction with the urbanization rate in the future. And the large

portion of biomass usage is properly explained within bio-fuels and waste as well.

Figure 5: Residential Energy Use

Figure 6: Commercial and Public

Service Energy Use

It is normally the case that the commercial and public service sector becomes

more important as a given country’s economy booming, and Vietnam should not

be an exception. Figure 5 shows energy consumption history within the

commercial and public sector. It shows extensive use of oil product and indicates

an increasingly portion of electricity and coal, especially electricity demand

growth is noticeable.

Figure 7: Industry sector Energy Use

Figure 8: Transportation Sector

Energy Use

Figure 9 shows the electricity demand by final sectors. Power demand is found

to grow rapidly for industry and residential. Like other developing countries,

Vietnam has no electricity demand for transportation yet.

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Figure 9: Electricity Use in Final

Energy Sectors

Figure 10: Electricity Generation by

Technology

Figure 10 indicates the power production by technology, mainly from

hydropower and gas with a small proportion from coal which increases evenly in

2000s. The use of bio-fuels and wind for power generation appears small amount

since 2005 and 2008.

4.2) Current Structure of the Whole Energy System

According to IEA Energy Balance, the primary energy supply of Vietnam is

mainly composed of coal, oil products and bio-fuels and waste, reaching 24,954

ktoe, 11,252 ktoe, and 15,514 ktoe respectively out of 73,804 ktoe of the total

primary energy supply in 2015. The remaining parts are then crude oil (7,561

ktoe), natural gas (9,549 ktoe) and electricity (136 ktoe).

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74

Figure 11: IEA Energy balance for Vietnam (2015, kTOE)

(International Energy Agency, 2017)

75

Figure 12: Vietnam Energy balance’s in Comparison (International Energy Agency, 2017)

76

Oil products of 18,015 ktoe are accounted for the greatest amount of the total final

energy consumption of 58,180 ktoe, constituting 31%. Nearly 60% of this energy

was consumed by the transportation sector (10,672 ktoe), followed by the industry

9.4% and building sector 9.3% (1,685 ktoe and 1,668 ktoe, respectively). Bio-fuels

and waste are the second most-used final energy with 14,579 ktoe, and more than

80% of them were used in the residential sector (11,851 ktoe). Meanwhile, the main

resource used in electricity generation is coal. The electricity plants sector

transforms 13,200 ktoe of coal, 7,885 ktoe of natural gas, 4,827 ktoe of

hydropower, 255 ktoe of oil products, 16 ktoe bio-fuels and waste and 10 ktoe of

solar, etc to produce 13,182 ktoe of electricity. After losses and usage in the energy

industry own use, 12,340 ktoe of electricity was consumed in the final sector. Out

of these, 6,629 ktoe or 53.7% was consumed in the industry sector, 4,333 ktoe or

35.1% is consumed in the residential sector, and 1,179 ktoe or 9.6% was consumed

in the commercial and public services.

5) RESEARCH RESULTS AND DISCUSSION

The reference scenario is used as the base of for other scenarios that follow. This

scenario is defined as the combination of:

Table 2: Scenarios

5.1) Scenario: Alternative-1 (High GDP)

The Alternative-1 scenario modifies the socioeconomics parameter from the

reference case, which is GDP.

GDP Nuclear Renewable

Energy

Geothermal Coal/gas

Reference GCAM No GCAM No Based in

2015

Scen 1-GDP High

growth

No GCAM No Based in

2015

Scen 2-Nuclear GCAM 5.7% in

2030

GCAM No Based in

2015

Scen 3-Renewable GCAM No 10.7% in

2030

No Based in

2015

Scen 4-Nuclear +

Renewable

GCAM 5.7% in

2030

10.7% in

2030

No Based in

2015

Scen 5-Nuclear +

Renewable +

Geothermal

GCAM 5.7% in

2030

10.7% in

2030

GCAM Based in

2015

77

Figure 13: Vietnam’s Real GDP comparison

Economic growth, measured by GDP, is usually correlated with energy or

electricity consumption growth. High energy growth can induce high energy

consumption due to the increase in purchasing power. On the other hands,

economic growth can be modeled as a function of production inputs, including

energy. To support and achieve a high level of economic growth, the supply of

energy should also be increased.

Figure 14: Energy Consumption in

Comparison

Figure 15: Electricity Consumption

in Comparison

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Reference

78

Figure 16: Total Final Energy consumption by End-use sector

5.2) Scenario: Alternative-2 (Nuclear 2030 Scenario Compared to

Reference Case)

Figure 17: Electricity Generation by Aggregate Technology

Nuclear utilization from 2030 causes reducing the consumption of coal, gas and

oil, especially gas for electricity production. The same for CO2 emission reduced

from approximately 3% in 2030 to nearly 12% in 2050.

0

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Hydro

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Biomass

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Gas

Coal

79

Figure 18: CO2 Emission Comparison in Nuclear Scenario

5.3) Scenario: Alternative-3 (Renewable Scenario Compared to

Reference Case)

Like the case of the nuclear scenario, renewable sources are replaced for part of

coal, gas and oil consumption. However, CO2 emission reduces small amount

compared to its in the nuclear scenario, specifically, 2% and 4% reduction in 2030

and 2050, respectively.


85.44

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136.14

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C

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Ref

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0.798 0.729

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1.040 0.972

0.2039 0.2000

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Renewable

Hydro

Biomass

Oil

Gas

Coal

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Figure 20: CO2 Emission by Region

5.4) Scenario: Alternative-4 (Nuclear and Renewable Scenario)


85.44

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n4

Ref

Sce

n4

Ref

Sce

n4

Ref

Sce

n4

Ref

Sce

n4

2025 2030 2035 2040 2045 2050

EJ


Renewable

Hydro

Nuclear

Biomass

Oil

Gas

Coal

81

Figure 22: CO2 Emission by Region

Figure 22 shows that it is better to utilize the integrated nuclear and renewable

sources to effectively reduce the CO2 emission. 4% and up to 14% CO2 emission is

deducted in this scenario.

5. 5) Scenario: Alternative-5 (Renewable, Nuclear and Geothermal

Scenario).

Since the geothermal source occupies a negligible amount, therefore, it does

not affect much to other sources and the electricity generation as well. The

CO2 emission in both scenario 4 and 5 nearly the same, the biggest different

to 0.6 MTC in 2050.


55.62

70.06

85.44

100.54

114.73

128.73

142.02 153.49

55.46

69.63

84.46

96.76 105.55

115.48 124.80

132.66

0

20

40

60

80

100

120

140

160

2015 2020 2025 2030 2035 2040 2045 2050

MTC

Year

CO2 Emission by Region

Ref

Scen4

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Ref

Scen

5

Scen

4

Ref

Scen

5

Scen

4

Ref

Scen

5

Scen

4

Ref

Scen

5

Scen

4

Ref

Scen

5

Scen

4

Ref

Scen

5

Scen

4

2025 2030 2035 2040 2045 2050

EJ


Renewable

Hydro

Geothermal

Nuclear

Biomass

Oil

Gas

Coal

82

Figure 24: CO2 Emission in

Comparison

Figure 25: CO2 Emission in

Comparison

6) CONCLUSION AND SUGGESTIONS

The reference case shows that the electricity consumption is driven by high GDP

growth. In term of final energy consumption by fuel, liquid takes the largest share,

followed by electricity and coal. By sectors, industry sector is expected to demand

more than 50% of final energy consumption.

Preliminary simulation results on the nuclear scenario are presented in Figure 19

show that the share of nuclear is expected to relatively reduce the CO2 emission.

Vietnam government, in fact, decided to stop building the Ninh Thuan nuclear

power plant by the end of 2016 (Vietnam Energy, 2017). In the long term up to

2050, it should be considered like a potential solution for Vietnam power

generation. For the financing of the investment for the power sector, many types

of funding sources can be considered including ODA, UNDP, UNEP, World Bank.

Biomass is regarded as a renewable energy source emitting no additional

greenhouse gas, it would be important for Vietnam to promote biomass use that be

cheap, affordable and be produced within the village or district. Furthermore,

efficiency improvement in biomass utilization should be promoted to domestic

customers. On the other hand, domestic consumer should be aware of the value

they are paying for the service provided for sustainable development of energy

sector and the economy.

The effects of renewable policy are analysed, a carbon cap is aimed at reaching a

30% reduction of CO2 emission by 2030 by Vietnam’s INDC report (United

Nations Climate Change, 2017). The pathway is a reduction of fossil fuels

consumption like coal, gas, and replacing the share of renewable energy like wind

power, solar power, electricity generated from biomass. Parallel with that,

55.

70.06

85.44

100.54

114.73

128.73

142.02

153.49

55.3

69.43

84.15

96.37 105.11

114.97 124.23

132.03

55.46

69.63

84.46

96.76 105.55

115.48 124.80

132.66

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

2015 2020 2025 2030 2035 2040 2045 2050

MTC

Year

CO2 Emission in Comparison

Ref

Scen5

Scen4

0

20

40

60

80

100

120

140

160

2025 2030 2035 2040 2045 2050

MT

C

Year

CO2 Emission in ComparisonRef

Scen5

Scen4

Scen3

Scen2

83

advanced gas power generation and carbon neutral technologies are being adopted

gradually.

The future studies, researchers may consider further CO2 emission and CO2

emission intensity reduction scenarios analysis. Another path is modeling of the

international market of power sector which is the linkage between Vietnam and

other countries’ power production and consumption. Modeling the trilemma of

energy security, saving and energy efficiency, and environment emission reduction

is also significant for assessing sustainable development in Vietnam.

7) REFERENCES

Bhattacharyya, Subhes C. and Timilsina, Govinda R. (2010), A review of Energy

system Models, International journal of Energy Sector Management, Vol. 4

No. 4, pp. 494-518.

Calvin, Kate , Leon Clarke, Page Kyle, Marshall Wise (2014), Introduction to the

Global Change Assessment Model (GCAM), Joint GCAM Community

Modeling Meeting and GTSP Technical Workshop, Joint Global Change

Research Institute, College Park, maryland, USA.

Coyard. V, Darras. L, Ahmad. A. R, Hashim. W, Ibrahim. N.A.I (2016),

Modeling in Energy Sector: A Comparison Between Developing and

Developed Countries, 2016 7th International Conference on Intelligent

Systems, Modeling and Simulation in Bangkok, Thailand, IEEEXplore, pp

250-255.

Http://github.com/JGCRI/gcam-core: http://jgcri.github.io/gcam-doc/index.html.

International Energy Agency (2017), www.iea.org, available at

http://www.iea.org/statistics/statisticssearch/report/?country=Vietnam&pro

duct=balances&year=2010.


http://www.iea.org/policiesandmeasures/pams/vietnam/name-157136-

en.php?return=PG5hdiBpZD0iYnJlYWRjcnVtYiI-PGEgaHJlZj0iLyI-

SG9tZTwvYT4gJnJhcXVvOyA8YSBocmVmPSIvcG9saWNpZXNhbmRt

ZWFzdXJlcy8iPlBvbGljaWVzIGFuZCBNZWFzdXJlczwvYT4gJnJhcXV

vOyA8YSBocmVmPSIvcG9saWNpZ.

84


https://www.iea.org/statistics/statisticssearch/report/?country=Vietnam&pr

oduct=Balances&year=2015.

Joint Global Change Research Institute (2017), www.globalchange.umd.edu,

available at http://jgcri.github.io/gcam-doc.

Kim, S.H., J. Edmonds, J. Lurz, S.J. Smith, and M. Wise (2006), The Objects

Framwork for Integrated Assessment: Hybrid Modeling of Transportation,

Energy Journal (Special issue #2), pp 51-80.

Kim, Suduk and Yurnaidi, Zulfikar (2017), Strengthening the capacity of

NPC/CD: Energy Sector, Ajou University, Department of Energy System

Research, 2017.

Ouedraogo, Nadia S. (2017), Modeling sustainable long-term electricity supply-

demand in Africa, Applied Energy, pp 1047-1067.

United Nations Climate Change (2017), Retrieved from unfccc.int:

http://unfccc.int/home/items/6078.php?searchbutton.x=0&searchbutton.y=

0&searchbutton=send&q=vietnam

Urban, F., Benders, R.M.J., and Moll, H.C (2007), Modeling energy systems for

developing countries, Energy Policy, Vol. 35 No. 6, pp 3473-3482.

Vietnam Energy (2017), nangluongvietnam.vn, available at

http://nangluongvietnam.vn/news/vn/nhan-dinh-phan-bien-kien-nghi/nhan-

dinh-du-bao/nang-luong-viet-nam-trong-lo-trinh-doi-moi-tong-the.html

APPENDIX Unit Conversion

EJ GWh ktoe

1 277777.7778 23,884.59

0.0000036 1 0.085984523

0.000041868 11.63 1

AUTHOR GUIDELINES

Unique attributes

The International Journal of Business Development and Research

(IJBDR) is a fully refereed journal with editorial members from

various countries. It aims to present relevant information on current and emerging practices in business and industry as well as research

in the areas of business innovation, applied technologies, and

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Regular readers of IJBDR gain a better understanding of key issues in business development and practices. By providing new

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or a business student, researchers, corporate and academic

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development and existing practices.

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