literature review – E-commerce industry – efficient supply chain management

The proposal form has accepted by department office, and office suggest that my project title could be following:

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Declaration:

I declare that the proposed project meets the requirements of my degree (* Tick the appropriate
box). You can then delete those awards that are not relevant to you.

P Engineering Business Management
The project should normally be related to

the management of:

• companies in the engineering sector;

• the engineering function within a non-
engineering company or

• the supply chain within the engineering
sector.

The project could address many different
aspects such as operational, financial,
human resourcing or strategic management
issues.

If the focus of the project is outside the
above industrial spectrum it MUST contain
considerable comparative analysis of
practices in the engineering sector.

Manufacturing Systems
Engineering and Management
The project should relate to product or

process technology, operations or
management within a manufacturing
context.

Supply Chain and
Logistics Management
The project should relate to a logistics

related topic eg purchasing/outsourcing,
material/production control, inventory
reduction, material flow, warehousing and
distribution, supply chain management or
transport planning.

My project relates to this definition in the following way:

For Engineering Business Management:

Engineering:

The logic or model of entire supply chain in e-commerce industry is vital to company to achieve a
both effectively and efficiently internal environment. This satisfies the Engineering component of
EBM though the analysis of supply chain.

Business:

The competitiveness discussion of fresh food e-commerce supply chain and normal e-commerce
supply chain directly relates to business since online retailers are businesses and have to compete in
order to survive. This satisfies the Business component of EBM.

Management:

Companies have to be both effectively and efficiently managed if they are to compete in their
industry. This satisfies the Management component of EBM through the analysis of customer
satisfactory factors in supply chain.

P

P

P

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PROJECT OUTLINE

The Area and Idea of your Project
This should be a brief summary of the idea of your Project, indicating its broad scope of the issues
to be investigated.
• People used to shop in the offline physical store. But now people transfer their shopping style

to online shopping or combine of online and offline shopping. Almost every product we can
buy it online instead. So, we can see that retailers have to adjust the business strategy combine
with Internet otherwise retailers will face a tough time.

• There is a fast-speed growth in e-commerce field in China over last 10 years. Especially the 5G
is developing more mature, shopping online will no doubts be a future trend and be more
mature. China now has several listed leading e-commerce companies (Alibaba/ JD.com) which
are possess the largest market share over the world. Also, there is another type of e-commerce
(missfresh) which operates fresh food or vegetable differs from the supply chain management
of the normal e-commerce. Because of the uniqueness of fresh products that they provide, fresh
online retailers have a larger challenge in supply chain management (faster delivery/ high
quality/ freshness). It is because the large amount of frequent demand for fresh food from
domestic market. There are some reasons for domestic e-commerce companies well-managed
the entire supply chain to handle the huge amount of orders. Also, companies need to find out
the mean customer satisfactory factors in supply chain, then to solve the customer pain points
to maximize the customer satisfaction.

The Objective(s) of your Project
This should state the purpose of your Project; i.e. what is it that you want to achieve?

The objective of this project is to identify what are the business strategies/ competitiveness of
leading e-commerce companies in supply chain. How different the fresh food e-commerce supply
chain from the normal e-commerce supply chain. How companies manage the supply chain
efficiently. How companies meet their customer need to maximize the customer satisfaction.

The Title of your Project
This should be as Concise as possible but should also Reflect the focus of the work to be
investigated.
A study of e-commerce industry manages supply chain efficiently to meets customer needs

Strategic Importance / Business Relevance of your Project to the Company or
industry (from which the project source was derived)
Consultation with appropriate company personnel if appropriate, particularly for company-based
projects.

Currently, e-commerce industry has highly competitive pressure since each company wants to have
the competitive advantages. But how do company achieve the competitiveness when e-commerce
market selling almost the same products. One of the breakthrough points is the health and efficient
supply chain. For example, you will lose customers if your company do not deliver products
quickly and has an unorganized supply chain management. And these customers will go to your
competitor online stores to find a better shopping experience. So, each company is optimizing their
supply chain in order to maximize the customer satisfaction and keep customer for a long-time
shopping. In other words, it is vital to companies identify the customer satisfactory factors.

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Background Reading
List the main literature that is necessary to both develop your knowledge of the field of study to be
investigated and that which is relevant to the Objective(s) of your Project.

Substantial reading will be required to ascertain the supply chain management model of leading e-
commerce (normal/ fresh) over the last several years, and relevant customer satisfactory factors in
each steps of supply chain process. It is expected that these data will be obtained from:
• Internet
• Journals which have analysed this field
• Annual reports of leading e-commerce companies

Also, books and publications on how resources can be used efficiently through the use of the
following techniques and their application to fresh food e-commerce supply chain:
• Just-in-time (JIT)
• Supply chain management and logistics
• Inventory management
• Business strategies/ competitiveness

Overall Methodology of your Project
A description of the overall approach and methods, techniques, or tools by which you will achieve
the Project’s Objective(s).

Step 1: collecting information and data for normal e-commerce supply chain and fresh food e-
commerce supply chain to the following issues:
• How is the online retailers’ supply chain operating?
• How is the supply chain of fresh food online retailers differing than the normal supply chain?
• What are the customer satisfactory factors in supply chain management?

Step 2: Make a comparison between normal online retailers’ supply chain and the fresh food online
retailers’ supply chain with regard to the issues listed in Step 1.

Step 3: Conduct a survey with regard to the expectations of customers that frequent shopping
online (both normal products as well as fresh food/vegetables) so that their expectations are known.
This probably to be done by a Questionnaire survey

Step 4: Relate what customer expectations are (from Step 3), how they can be satisfied, to
what extent they are missing (from Step 1 and Step 2).

Step 5: Examine how the techniques listed previously (see Background Reading) can be used
by fresh food e-commerce to manage the supply chain.

Step 6: Discuss how the company or industry meet customer need, find out customers pain point,
and maximize the customer satisfaction in supply chain management

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Data Collection and Analysis Strategy
Detail should be provided on the method for collecting and analysing the data evidence for
addressing your research objectives / hypotheses.

These issues are briefly explained in the previous sections see Background Reading” and
“Overall Methodology/Research Design”

Resource Requirements
Identify the expected resource requirements (if any) to undertake the Project. Are any specialist
resources needed (laboratory facilities, special computer software, etc.)?

There are no specialist resources necessary for this project, other than the time required to collect
and analyse the relevant information and data.

Project Plan
Provide rough detail of the major activities you envisage plus their approximate time scale.
Alternatively, this can be represented as a Gantt chart using such as Microsoft Project and including
as an Attachment.
I prefer to attend the oral examination on 30 November 2020

2020
June & July
Collecting information about the customer satisfactory factors in the supply chain management
logics in e-commerce industry.

August & September
Completion of Chapter 1 Introduction, Chapter 2 Literature Review, Chapter 3 Research
Methodology, and Chapter 4 – differences & competitiveness/ business strategy of e-commerce
industry

October & November
Completion of Completion of Chapter 5 customer satisfactory factors in supply chain, Chapter 6
customer survey, Chapter 7 discussion, and Chapter 8 Conclusions and future outlook

December
Finally organising the report, making amendments and preparation for the oral examination.

Significant Risks to Successful Completion
Identify any significant risks which may affect the completion of your project and specify any
possible contingency plans in response.

• The likely difficulty of collecting all the relevant information and data (these may not be

available, and assumptions may need to be made), especially for those unlisted company (do
not have annual report or related company own description)

• A large enough sample size for the Customer survey see Item 3 Overall Methodology/Research

Design)

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Initial Dissertation Structure
Identify the main chapter headings and sub-headings of your planned dissertation, with a brief
summary of the content in each.
(The following are the chapters envisaged, they may change later after the project has commenced)
Title page

Abstract
Table of Contents

List of Figures
List of Tables

List of Abbreviations
Acknowledgements

Declaration

Chapter 1 – Introduction
1.1 The e-commerce industry in China (external environment, development)
1.2 The brief introduction of leading company (pick 3-5 companies)
1.3 The importance of supply chain management in business
1.4 Layout of Report

Chapter 2 – Literature Review
Information about the supply chain management strategies to each leading company. The difference
between normal e-commerce supply chain and the fresh food e-commerce supply chain. The related
customer satisfactory factors in supply chain. Information to current management technologies such
as inventory management (smart warehouse), JIT.

Chapter 3 – Methodology (or Research Methodology)
The approach by which the Objective of the Project will be achieved

Chapter 4 – differences & competitiveness/ business strategy of e-commerce industry
The type of services provided, the uniqueness structure
• Pick 2-3 normal e-commerce companies, and pick 1-2 fresh food e-commerce companies
• By using SWOT analysis

Chapter 5 – customer satisfactory factors in supply chain
How does fresh food e-commerce supply chain meet the customer needs?

Chapter 6 – customer survey
Design of questionnaire, collection and analysis of data

Chapter 7 – Discussion

Chapter 8 – Conclusions and future outlook

Appendices
References

Bibliography

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Research Article

An exploratory study of investment
behaviour of investors

Mark KY Mak1 and WH Ip2

Abstract
The financial industry plays a significant role in Mainland Chinese and Hong Kong economies and has aroused increasing
managerial and academic interests in recent decades. Individual investors are becoming more cautious towards financial
investment which makes it difficult for financial service providers to formulate marketing strategies after experiencing
several financial crises. Prior research has suggested that financial investment behaviour would be affected by various
factors, including the demographic characteristics of individuals; however, they seldom study the differences in financial
investment behaviour between Mainland Chinese and Hong Kong investors or provide an easy-to-use approach for
practical usage. This exploratory study aims at filling the identified research gap by proposing linear regression models of
the financial investment behaviour of Mainland Chinese and Hong Kong investors. Based on the results of regression
analyses, (i) there exist significant differences in financial investment

behaviour between Mainland Chinese and Hong Kong

investors, and (ii) investors’ psychological, sociological and demographic factors are significant predictors of their
investment behaviour/preferences. Thus, financial service providers are able to predict the investment behaviour/pre-
ference of its customers and formulate marketing and strategic decisions, such as customizing the financial investment
portfolio of customers on the basis of regression models built.

Keywords
Regression, exploratory study, investor behaviour, statistical analysis, financial industry

Date received: 24 January 2017; accepted: 12 April 2017

Introduction

As an international financial centre, Hong Kong offers a

variety of financial products, such as mutual funds, stocks

and bonds, for individual investors to invest. Due to the

close proximity, low tax rate, similarities of language and

culture and global access, Hong Kong remains the top off-

shore investment destination for Mainland Chinese inves-

tors.
1

These facts encourage the financial industry in Hong

Kong to review marketing strategies for targeting this fast-

growing market segment, that is, Mainland Chinese inves-

tors investing in the offshore Hong Kong market.

At the same time, individual investors are becoming

more cautious towards financial investment and make it

difficult for financial service providers to formulate mar-

keting strategies after experiencing several financial

crises.
2

Indeed, financial service providers face the chal-

lenge of understanding the investment behaviour and

preferences of their customers for long-term benefits.
2

In

order to have better market analysis and customer relation-

ship management, various finance theories have been pro-

posed by researchers.

Traditional finance theories assume that investment

behaviour is rational.
3

However, well-known events such

as the financial tsunami between 2007 and 2008, displaying

apparently irrational behaviour, have caused a rethink in

1
Lerado Financial Group Company Limited, Hong Kong Island, Hong

Kong
2 Department of Industrial and Systems Engineering, The Hong Kong

Polytechnic University, Hung Hom, Kowloon, Hong Kong

Corresponding Author:

Mark KY Mak, Lerado Financial Group Company Limited, Hong Kong

Island, Hong Kong.

Email: brother820820@gmail.com

International Journal of Engineering
Business Management

Volume 9: 1–12
ª The Author(s) 2017

DOI: 10.1177/1847979017711520
journals.sagepub.com/home/enb

Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License

(http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without

further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/

open-access-at-sage).

mailto:brother820820@gmail.com

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the domain, and the emerging field of behavioural finance

has become a popular field of study in an attempt to explain

this irrational behaviour. Under the theory of behavioural

finance, researchers suggest that the investment behaviour

of individual investors in real life is influenced by a com-

bination of specific psychological factors, such as overcon-

fidence,
4,5

representativeness
4

and herding

behaviour.

6

The research focusing on psychological investment

behaviour, however, steers away from sociological factors

and personality traits. It seems investment behaviour is a

complicated domain that combines both rational and emo-

tional elements rather than just one. Furthermore, beha-

vioural finance is not purely based on psychological

factors but also sociological factors in the study of invest-

ment behaviour.
7

Moreover, demographic factors such as

age and gender are also important in explaining investor

behaviour.
8

Behavioural finance seems to explain reality

and to provide a better framework in the way the investors

behave. It is crucial to take psychological, sociological and

demographic factors into account to explore the major attri-

butes of how investors behave.
7,9

A review of the existing literature demonstrated that

researchers focus mainly on identifying factors influencing

investor behaviour and/or examining their impact on

investment decisions.
10–12

Studies seldom investigated

how to predict investors’ preference based on the factors

influencing their behaviour. This gap is probably due to

researchers lacking access to the huge volumes of strictly

confidential financial transaction data required to draw

such conclusions from studying real behaviour.

In order to gain a deep understanding of investment beha-

viour of individual investors in Hong Kong and Mainland

China, statistical analyses are applied in this study, aimed at

identifying the differences in investment behaviour/prefer-

ence between Mainland Chinese investors and Hong Kong

investors and explaining investment behaviour determined

by rational, emotional as well as demographic factors. Over-

all, several research questions are identified, including

� What are the factors in the difference of the beha-
viour identified in the previous literature?

� What are the major attributes to explain investment
behaviour?

� How do the major attributes identified predict inves-
tors’ behaviour/preferences in Hong Kong and

Mainland China?

Literature review and hypotheses
development

Importance of understanding consumer behaviour
in financial market

In today’s increasingly competitive business environment,

a clear understanding of sophisticated consumer behaviour

is a key element for ensuring success. There are many

scholars who have examined the definition of consumer

behaviour. In general, consumer behaviour is the study of

customers and the processes they use to choose, consume

and dispose of products and services that satisfy their needs

and influence their experience.
13

Understanding the underlying mechanisms that to lead

to these customers’ responses, therefore, helps business

organizations make better managerial decisions, regarding

providing the right product or service to their customers.
14

An in-depth understanding of consumer behaviour further

helps business organizations to plan for the future buying

behaviour patterns of customers and formulate the appro-

priate marketing strategies in order to build long-term cus-

tomer relationships.

In financial markets, investors are the customers or con-

sumers. Exploring the behaviour of investors is therefore

important for financial institutions to devise appropriate

strategies and to market appropriate financial products or

offer new financial products to investors in order to better

satisfy their needs. To study investor behaviour, research-

ers have largely adopted the concept of behavioural finance

during the last decade.
3

Overview of behavioural finance

Behavioural finance refers to the application of psychology

to finance.
15

Behavioural finance offers an alternative tool

to study investor behaviour and the causes of market

anomalies. Scholars have applied behavioural finance to

explain financial market anomalies such as stock market

bubbles, overreaction and underreaction to new informa-

tion
16,17

that do not conform the traditional finance theory.

For example, Shefrin and Statman
18

found that excessive

optimism creates speculative bubbles in financial markets.

Researchers also widely applied behavioural finance to

explain emotional investor behaviour in recent years.

Frankfurter and McGoun
19

also indicated that psychology

and sociology is the essence of behavioural finance. How-

ever, according to the available literature described above,

researchers have emphasized the importance of psycholo-

gical factors and overlooked other factors in the concept of

behavioural finance.

Other researchers support the view that sociological and

demographic factors are also important to explain investor

behaviour.
7,8

Though some researchers have studied the

impacts of other factors such as gender or age on invest-

ment behaviour, these studies only explored the influences

with regard to investor behaviour but did not discuss any

findings about the financial decision-making process of

investors or predict their preference on financial products.

For example, Yang
20

investigated, through case studies, the

influence of both gender and age differences towards finan-

cial investment behaviour in terms of overconfidence,

account-open time and trade frequency. These studies

within the field of behavioural finance provide evidence

2 International Journal of Engineering Business Management

that demographic factors such as age and gender should be

considered when studying investor behaviour.

Overall, in order to make the research closer to reality

and to better comprehend the way the investors behave, this

study took psychological, sociological and demographic

factors into account to explore how and why investors

behave differently. Identifying the major attributes to

explain investment behaviour by leveraging psychological,

sociological and demographic factors is thus essential for

this study in order to address the gap in knowledge.

Key attributes influencing financial
investment behaviour

In general, research in behavioural finance provides evi-

dence that investors’ decisions are affected by behavioural

factors.
21

Researchers found that investors do not behave in

a merely rational manner across financial markets and there

are a variety of factors influencing their decision-making in

investment; among those factors, psychological factors,

sociological factors and demographic factors are the major

elements.
22–24

To study behaviour under more realistic

conditions and to better categorize the way investors

behave, this study identifies and evaluates the major attri-

butes in the literature explaining investment behaviour

under three constructs, namely, the psychological factor,

sociological factor and demographic factor, and how these

attributes impact on the investors’ decision-making.

Key psychological attribute. Regarding the psychological fac-
tor, individual investors are driven by experience or

through an investment appraisal process to make invest-

ment decisions.
25–27

Past experience, as a consequence,

affects investors’ risk perception in terms of attitude to risk

and risk tolerance.
28

This is also supported by Byrne,
29

indicating the positive correlation between investment

experience and risk. Researchers further pointed out that

accumulated investment experience significantly affects

investment decisions of individual investors in terms of

anchoring bias and overconfidence.
30

These indicate that

the investment experience of individual investors forms a

strong basis for investment decisions and is therefore

included in this study by considering it as an important

psychological attribute that influences financial investment

behaviour.

Key demographic attributes. According to Maditinos et al.31

and Sadi et al.,
32

the demographic factor is one of the

behavioural factors that plays a significant role in deter-

mining the behaviour and decisions of investors. For

example, demographic factors influence one’s choice of

investment products.
33–35

Kabra et al.
36

found that the

main factors affecting investment behaviour and inves-

tors’ decisions are age and gender. According to Huber-

man and Jiang,
37

age and the amount of funds held tend to

indicate a negative correlation. Age is always an essential

factor and has a significant relationship with investment

behaviour according to the literature and thus is included

in this study.

Gender is another crucial demographic attribute that

affects the investment decision-making process and

investor behaviour.
38

Many researchers suggested that

there are gender differences in risk attitude and thus in

the choices of financial investment products.
34,39

Many

existing studies supported that female investors are more

conservative than male investors when investing and

tend to show greater risk aversion than male inves-

tors.
39,40

For financial service providers to offer finan-

cial products which are best suited for investors of

different genders, understanding the gender difference

in the investment behaviour of individuals is crucial and

thus is taken into account in this study.

Key sociological attributes. According to the extant literature,
education level,

41,42
income level

3,42
and marital sta-

tus
9,42

are found to be significant sociological attributes

determining investors’ behaviour and influencing their

investment decision. Al-Ajmi
41

conducted an exploratory

study and concluded that income level and education level

are positively correlated with risk tolerance. Shaikh and

Kalkundrikar
42

conducted an exploratory study and con-

firmed that income level, education level and marital sta-

tus are factors affecting investors’ behaviour and

decision-making. Fares and Khami
43

identified that the

education level of investors is statistically significant to

investment decision. Rizvi and Fatima
3

also found a sig-

nificant positive correlation between income and invest-

ment frequency. More studies revealing the significant

relevance of these sociological factors, including educa-

tion level, income level and marital status, in investment

decisions and investor behaviour can be found in the lit-

erature.
43,44

With the support of the literature review,

these three attributes, income level, education level and

marital status, are considered in this study.

Table 1 summarizes all the psychological, sociological

and demographic factors and attributes of behavioural

finance considered in this study.

The literature discussion above highlighted that finan-

cial investment behaviour is commonly influenced by

demographic, psychological and sociological factors, and

Table 1. Factors and key attributes considered.

Factor Key attribute(s)
Review reference

sources

Psychological Investment experience 25–30

Demographic Age
36,37,44

Gender 34,36,38–40,44

Sociological Education level
41–44

Income level 3,41,42,44

Marital status
9,42

Mak and Ip 3

the major attributes that explain investment behaviour are

age, income level, educational level, gender, investment

experience and marital status. However, little research

has been devoted to the behaviour of individual inves-

tors.
45,46

Furthermore, Hong Kong is the top offshore

investment destination for the Mainland Chinese

investors.

Investors in Hong Kong are mainly mixed, with Mainland

Chinese investors and local investors having different char-

acteristics. In order to answer the third research question

identified in Introduction, the following hypotheses are

proposed:

H1: The investment behaviour/preference of the Main-

land Chinese and Hong Kong investors, when consid-

ered together, can be predicted by the six attributes –

age, income level, educational level, gender, investment

experience and marital status.

H2: The fund share amount held by the Mainland Chi-

nese and Hong Kong investors, when considered sepa-

rately, can be pre

dicted by the six attributes.

H3: The choice of country-specific financial investment

options selected by the Mainland Chinese and Hong

Kong investors, when considered separately, can be pre-

dicted by the six attributes.

Regression models and data

Regression models

To explore the problems in understanding financial

investment behaviour as well as to study the effects of

the six variables, as identified from literature review, on

investment behaviour of the Mainland Chinese and

Hong Kong investors, a quantitative interpretation of the

literature review was conducted for subsequent explora-

tory study. Based on the hypothesis, a table of variables

is created (Table 2) and three regression models are

constructed.

Model 1a

fundholdT ¼aþb1 ageT þ b2 incomelevelT
þb3 educationlevelT þ b4 genderT
þb5 investmentexperienceT
þb6 maritalstatusT

where the variable of the fund share amount held

( fundholdT ) is a function of age ( ageT ), income level

( incomelevelT ), gender ( genderT ), educational level

( educationallevel T ), investment experience (invertment-

experience T ) and marital status ( maritalstatusT ). a
represents the regression constant. bj ðj ¼ 1; 2 . . . ; 6Þ
denotes the regression coefficients for each independent

variable.

Model 1b

fundcurrencyT ¼aþb1 ageT þb2 incomelevelT
þb3 educationlevelT þ b4 genderT
þb5 investmentexperienceT
þb6 maritalstatusT

where the variable of the choice of country-specific finan-

cial investment options selected ( fundcurrencyT ) is a func-

tion of age ( ageT ), income level ( incomelevelT ), gender

( genderT ), educational level ( educationallevel T ), invest-

ment experience ( investmentexperienceT ) and marital sta-

tus ( maritalstatusT ). a represents the regression constant.
bj ðj ¼ 1; 2 . . . ; 6Þ denotes the regression coefficients for
each independent variable.

The first regression model helps give a general picture

for understanding whether and how the six key attributes

identified affect and predict the investment behaviour of

both the Mainland Chinese and Hong Kong investors. In

order to have an in-depth examination on the differences in

investment behaviour between Mainland Chinese and

Hong Kong investors, the second and third models are

constructed to analyse how the six attributes identified

affect investors specifically in Mainland China and Hong

Kong in terms of the fund share amount held and choice of

country-specific financial investment option selected,

respectively.

Model 2a

fundholdCN ¼aþb1 ageCN þ b2 incomelevelCN
þb3 educationlevelCN þ b4 genderCN
þb5 investmentexperienceCN
þb6 maritalstatusCN

Model 2b

fundholdHK ¼aþ b1 ageHK þ b2 incomelevelHK
þ b3 educationlevelHK þ b4 genderHK
þ b5 investmentexperienceHK
þ b6 maritalstatusHK

Model 3a

fundcurrencyCN ¼aþb1 ageCN þb2 incomelevelCN
þb3 educationlevelCN þ b4 genderCN
þb5 investmentexperienceCN
þb6 maritalstatusCN

Model 3b

fundcurrencyHK ¼aþ b1 ageHK þ b2 incomelevelHK
þ b3 educationlevelHK þ b4 genderHK
þ b5 investmentexperienceHK
þ b6 maritalstatusHK

4 International Journal of Engineering Business Management

Data

To analyse how the major attributes identified under demo-

graphic, psychological and sociological constructs affect

investors in both Hong Kong and Mainland China, finan-

cial transaction data and investors’ characteristics are col-

lected and analysed to support this research. The data of

customers from 2012 to 2014 were collected from a finan-

cial services provider listed in the Hong Kong Stock

Exchange. In recent years, the number of its customers

from Mainland China has been considerably increasing,

and thus the institution desires to learn about the investment

behaviour of mainlanders and understand the differences in

investment preference between mainland Chinese and

Hong Kong investors. This is the reason why the institution

supports this research by providing the confidential data

about its customers and makes this research possible by

overcoming the obstacle – failing to access to the huge

volume of financial transaction data that is confidential.

As the research aims to explore the individual investor

behaviour, a relative large sample size is recommended in

this kind of exploratory research for generating valid

results. As mentioned by Saunders et al.,
47

a larger sample

size can help produce more reliable results as the samples

can be more representative. In this research, 142,496 sam-

ples were collected from a financial services provider listed

in the Hong Kong Stock Exchange, of which 87,057 sam-

ples were from Mainland Chinese investors and 55,439

were from Hong Kong investors.

Table 3 provides descriptive statistics for investors’

characteristics.

Table 2. Notation of variables.

Notation Representation Notation Representation Notation Representation

fundholdT The fund share
amount held
by investors

fundholdCN The fund share
amount held by
Mainland
Chinese
investors

fundholdHK The fund share
amount held
by

Hong Kong
investors

fundcurrencyT The choice of
country-
specific
financial
investment
options
selected by
investors

fundcurrencyCN The choice of
country-specific
financial
investment
options selected
by Mainland
Chinese
investors

fundcurrencyHK The choice of
country-
specific
financial
investment
options
selected by
Hong Kong
investors

ageT Age of
investors

ageCN Age of Mainland
Chinese
investors

ageHK Age of Hong
Kong
investors

incomelevelT Income level of
investors

incomelevelCN Income level of
Mainland
Chinese
investors

incomelevelHK Income level of
Hong Kong
investors

educationlevelT Education level
of investors

educationlevelCN Education level of
Mainland
Chinese
investors

educationlevelHK Education level
of Hong Kong
investors

genderT Gender of
investors

genderCN Gender of
Mainland
Chinese
investors

genderHK Gender of Hong
Kong
investors

investmentexperienceT Investment
experience
of investors

investmentexperienceCN Investment
experience of
Mainland
Chinese
investors

investmentexperienceHK Investment
experience of
Hong Kong
investors

maritalstatusT Marital status
of investors

maritalstatusCN Marital status of
Mainland
Chinese
investors

maritalstatusHK Marital status of
Hong Kong
investors

a Regression
constant

bj ðj ¼ 1; 2 . . . ; 6Þ Regression
coefficient

Mak and Ip 5

Assumption analysis

To ensure the validity of the regression models, several

requirements in applying multiple regression models,

including linearity, multivariate normality, homogeneity

of variance and multicollinearity, are tested. As men-

tioned by Poole and O’Farrell
48

and Antonakis and

Dietz,
49

the models are only valid when these require-

ments are tested and satisfied. Before conducting the

actual regression analyses, preliminary analyses are con-

ducted to ensure the requirements for the regression

models are fulfilled.

Linearity test. Figure 1 shows the significance of the linear
relationship for model 1a. From Figure 1, the p value for all

variables are <0.0001, indicating that significant linear

relationships between all independent variables and depen-

dent variable exist. Thus, the assumption of linearity for

regression is fulfilled.

Multivariate normality test. Figure 2 shows the histogram of
the standardized residuals for model 1a. According to Ste-

vens,
50

if residuals fit a normal curve, multivariate normal-

ity is not a problem. The histogram of the residuals of

model 1a shows a symmetrical bell-shape and fairly normal

distribution. Thus, the assumption of multivariate normal-

ity is fulfilled.

Homogeneity of variance. Figure 3 shows the scatter plot of
the residuals for model 1a. According to Osborne and

Waters,
51

if residuals scatter randomly and close to the zero

axis, homogeneity of variance is not a problem. Homoge-

neity of variance is not a problem for model 1a in our study

as an almost horizontal band of points is scattered around

and close to the zero axis, as shown in Figure 3. Thus, the

assumption of homogeneity of variance is fulfilled.

Multicollinearity. Figure 4 shows the values of the coeffi-
cients of determination (R

2
) and variance inflation factors

Table 3. Descriptive statistics.

Mainland Chinese Hong Kong

Frequency Percentage

Amount of fund share
held

Frequency Percentage

Amount of fund
share held

Mean Variance Mean Variance

Marital status Divorced 1736 2.0 320.20 536,282.26 1156 2.1 15.82 311.31
Married 48,423 55.6 121.76 2,213,504.50 14,703 26.5 51.09 156,248.17
Single 36,898 42.4 29.81 12,524.81 39,580 71.4 29.08 13,370.21
Total 87,057 100.0 86.74 1,250,327.26 55,439 100.0 34.64 51,089.72

Gender Female 57,294 65.8 90.61 1,843,410.28 24,337 43.9 27.71 3330.20
Male 29,763 34.2 79.29 108,577.21 31,102 56.1 40.07 88,395.31
Total 87,057 100.0 86.74 1,250,327.26 55,439 100.0 34.64 51,089.72

Age 0–24 3991 4.6 20.96 3214.61 4564 8.2 16.74 767.42
25–29 24,607 28.3 31.96 18,667.56 19,558 35.3 23.04 1942.98
30–34 20,773 23.9 47.04 18,838.95 14,085 25.4 31.00 4086.63
35–39 15,977 18.4 73.83 51,271.84 8144 14.7 38.87 16,685.62
40–44 10,148 11.7 126.30 117,385.82 4340 7.8 55.78 94,869.81
45–49 6420 7.4 220.76 261,766.61 1886 3.4 158.18 1,133,002.77
50–54 3651 4.2 460.34 28,281,137.04 2467 4.4 30.64 3402.27
55 and over 1490 1.7 97.32 202,074.94 395 0.7 61.51 15,648.61
Total 87,057 100.0 86.74 1,250,327.26 55,439 100.0 34.64 51,089.72

Educational level Elementary 191 0.2 657.67 1,895,934.40 442 0.8 40.64 6357.03
Junior 9763 11.2 144.14 10,512,259.97 25,639 46.2 27.36 17,769.82
Senior and above 77,103 88.6 78.06 74,801.48 29,358 53.0 40.91 80,777.85
Total 87,057 100.0 86.74 1,250,327.26 55,439 100.0 34.64 51,089.72

Household’s net
worth

Below
HKD100,000

25,091 28.8 27.31 19,121.63 37,751 68.1 24.11 1857.15

HKD100,000–
300,000

33,214 38.2 35.57 6033.15 12,508 22.6 41.66 38,737.45

HKD300,000–
500,000

8019 9.2 58.43 18,073.38 4150 7.5 38.00 4321.43

HKD500,000–
1M

5813 6.7 110.23 73,910.99 491 0.9 66.01 13,164.51

HKD1M and
above

14,920 17.1 86.74 1,250,327.26 539 1.0 555.36 3,906,787.08

Total 87,057 100.0 306.67 7,151,080.17 55,439 100.0 34.64 51,089.72

6 International Journal of Engineering Business Management

(VIF) calculated for model 1a. According to Hart and

Sailor,
52

if tolerance (T), which is defined as T ¼ 1 �
R

2
, is below 0.20, the multicollinearity problem is a

severe problem. Furthermore, the attributes are moder-

ately correlated if the value of VIF is between 1 and 5.
53

In our study, correlations are at acceptable levels as the

T values for the six key attributes are over 0.20 and the

VIF values are between 1 and 5. These indicate low

multicollinearity and thus the assumption of multicolli-

nearity is fulfilled.

Having fulfilled all the requirements, the multiple

regression model 1a is confirmed to be valid and actual

regression analysis is then conducted. Similar assump-

tion analyses as discussed for model 1a are also con-

ducted for models 1b, 2a, 2b, 3a and 3b, and all the

requirements are fulfilled. In the next section, the results

are reported.

Results and analysis

With the help of the 142,496 observation samples, regres-

sion analyses are conducted in this section to assess the

relationships between the key attributes identified and the

investment behaviour/preferences between Mainland Chi-

nese and Hong Kong investors.

Tables 4 and 5 show the results for the standardized

coefficients and adjusted R
2
.

Effects of key attributes on investment
behaviour/preference

From the regression results of model 1a (Table 4), all

the six key attributes identified have a significant (at

0.01 level) effect on the fund share amount held when

Mainland Chinese and Hong Kong investors are consid-

ered together. Thus, age, income level, education level,

gender, investment experience and marital status are

Figure 2. SPSS output for histogram of residuals.

Figure 3. SPSS output for analysis of residuals.

Figure 1. IBM SPSS Statistics (SPSS) output for multiple correlation coefficient.

Mak and Ip 7

statistically significant predictors of the fund share

amount held by Mainland Chinese and Hong Kong

investors.

From the regression results of model 1b (Table 4), five

out of the six key attributes, excluding gender (p value ¼
0.223 > 0.1), identified have a significant (at 0.01 level)

effect on the choice of the country-specific financial invest-

ment option selected when Mainland Chinese and Hong

Kong investors are considered together. Thus, only age,

income level, education level, investment experience and

marital status are statistically significant predictors of the

choice of the country-specific financial investment option

selected by Mainland Chinese and Hong Kong investors.

By leveraging the results shown in Table 4, it is con-

cluded that the financial investment behaviour/preference

of Mainland Chinese and Hong Kong investors, when con-

sidered together, are inseparable with regard to their demo-

graphic factor (i.e. age), psychological factor (i.e.

investment experience) and sociological factor (i.e. income

level, education level and marital status).

Effects of key attributes on the fund share
amount held

From the regression results of model 2a (Table 4), all the

six key attributes identified have a significant (at 0.01

level) effect on the fund share amount held by Mainland

Chinese investors. Thus, it is confirmed that age, income

level, education level, gender, investment experience and

marital status are statically significant predictors of the

fund share amount held by Mainland

Chinese investors.

From the regression results of model 2b (Table 4), five

out of the six key attributes, excluding education level (p

value ¼ 0.107 > 0.1), identified have a significant (at 0.01
level) effect on the fund share amount held by Hong Kong

investors. Thus, only age, income level, gender, investment

experience and marital status are statistically significant

predictors of the fund share amount held by Hong Kong

investors.
By leveraging the results shown in Table 4, it is con-

cluded that the fund share amount held by Mainland Chi-

nese and Hong Kong investors, when considered together,

are inseparable with their demographic factor (i.e. age and

gender), psychological factor (i.e. investment experience)

and sociological factor (i.e. income level and marital sta-

tus). The top three most significant attributes are age,

income level and investment experience. The standardized

coefficient of age is �0.030 for Mainland Chinese inves-
tors and 0.024 for Hong Kong investors. The standardized

coefficient of income level is 0.277 for Mainland Chinese

Table 4. Results of regression analyses for models 1 and 2.

Model 1a Model 1b Model 2a Model 2b

age �0.022*** 0.025*** �0.030*** 0.024***
incomelevel 0.273*** 0.030*** 0.277*** 0.241***
educationlevel 0.013*** 0.014*** 0.016*** �0.007
gender �0.012*** 0.004 �0.011*** 0.021***
investment

experience
�0.014*** 0.119*** �0.023*** �0.035***

maritalstatus �0.013*** �0.020*** �0.021*** 0.015***
R2 0.074 0.018 0.075 0.060

*Statistical significance at the 0.1 level.
**Statistical significance at the 0.05 level.
***Statistical significance at the 0.01 level.

Table 5. Results of regression analyses for model 3.

Model 3a Model 3b

age 0.015*** �0.027***
incomelevel �0.020*** 0.032***
educationlevel 0.006 0.015***
gender 0.003 0.011***
investmentexperience 0.140*** 0.121***
maritalstatus 0.014*** 0.005
R2 0.021 0.006

*Statistical significance at the 0.1 level.
**Statistical significance at the 0.05 level.
***Statistical significance at the 0.01 level.

Figure 4. SPSS output for the measure of tolerance.

8 International Journal of Engineering Business Management

investors and 0.241 for Hong Kong investors. The standar-

dized coefficient of investment experience is �0.023 for
Mainland Chinese investors and �0.035 for Hong Kong
investors. In spite of the differences in the magnitude of

the three most significant attributes, the directions of the

relationship are similar, except for age. For example,

income level has a positive effect on the fund share amount

held by Mainland Chinese and Hong Kong investors. How-

ever, age has a negative effect on the fund share amount

held by Mainland Chinese investors but a positive effect on

Hong Kong investors. In other words, younger Mainland

Chinese and older Hong Kong investors tend to hold a

higher fund share.

Effects of key attributes on the choice of the country-
specific

financial investment option selected

From the regression results of model 3a (Table 5), four out

of six key attributes, excluding education level (p value ¼
0.141 > 0.1) and gender (p value ¼ 0.325 > 0.1), identified
have a significant (at 0.01 level) effect on the choice of the

country-specific financial investment option selected by

Mainland Chinese investors. Thus, it is confirmed that age,

income level, investment experience and marital status are

statically significant predictors of the choice of the country-

specific financial investment option selected by Mainland

Chinese investors.

From the regression results of model 3b (Table 5), five

out of the six key attributes, excluding marital status (p

value ¼ 0.127 > 0.1), identified have a significant (at
0.01 level) effect on the choice of the country-specific

financial investment option selected by Hong Kong inves-

tors. Thus, only age, income level, education level, gender

and investment experience are statistically significant pre-

dictors of the choice of the country-specific financial

investment option selected by Hong Kong investors.

By leveraging the results shown in Table 5, it is con-

cluded that the choice of the country-specific financial

investment options selected by Mainland Chinese and

Hong Kong investors, when considered together, are

closely correlated with the demographic factor (i.e. age),

psychological factor (i.e. investment experience) and

sociological factor (i.e. income level and marital status).

The three most significant attributes are the age, income

level and investment experience. The standardized coeffi-

cient of age is 0.015 for Mainland Chinese investors and

�0.027 for Hong Kong investors. The standardized coeffi-
cient of income level is �0.020 for Mainland Chinese
investors and 0.032 for Hong Kong investors. The standar-

dized coefficient of investment experience is 0.140 for

Mainland Chinese investors and 0.121 for Hong Kong

investors.

Investment experience has a positive effect on the

choice of country-specific financial investment options

selected by both Mainland Chinese and Hong Kong inves-

tors. On the contrary, age and income level have different

effects on the choice of the country-specific financial

investment options selected by Mainland Chinese and

Hong Kong investors. For example, younger Mainland Chi-

nese investors and older Hong Kong investors tend to have

the same choice of the country-specific financial invest-

ment option.

Table 6 summarizes the impacts of key factors and attri-

butes on the investment behaviour of Mainland Chinese

and Hong Kong investors. The table summarizes the rela-

tionship (direction and magnitude) between the key attri-

butes, the fund share amount held and the choice of the

country-specific financial investment options selected by

investors.

Discussion

Practical and strategic importance of this research

With reference to the results of regression models 1 (Table

4), 2 (Table 4) and 3 (Table 5), there exist significant dif-

ferences in the financial investment behaviour/preference,

in terms of the fund share amount held and the choice of the

country-specific financial investment option selected,

between Mainland Chinese and Hong Kong investors. For

example, the impact of age on the fund share amount held

Table 6. Impacts of key attributes.

Factor Attribute

Fund share amount held
Choice of the country-specific

financial investment option selected

Mainland Chinese
investors

Hong Kong
investors
Mainland Chinese
investors
Hong Kong
investors

Psychological Investment experience �0.023 �0.035 þ0.140 þ0.121
Demographic Age �0.030 þ0.024 þ0.015 �0.027

Gender �0.011 þ0.021 N/A þ0.011
Sociological Education level þ0.016 N/A N/A þ0.015

Income level þ0.277 þ0.241 �0.020 þ0.032
Marital status �0.021 þ0.015 þ0.014 N/A

þ: relationship in the positive direction; �: relationship in the negative direction; N/A: absence of a significant relationship.

Mak and Ip 9

by and choice of the country-specific financial investment

option selected by Mainland Chinese and Hong Kong

investors is opposite. Similarities between the investment

behaviour of Mainland Chinese and Hong Kong investors

are also found. Particularly, the three most significant attri-

butes, age, income level and investment experience, influ-

encing investment behaviour for both Mainland Chinese

and Hong Kong investors are the same, though the influ-

ence may in the opposite directions. Also, income level has

a positive effect, while investment experience has a nega-

tive effect on the fund share amount held by investors.

In view of the similarities in investment behaviour for

both Mainland Chinese and Hong Kong investors, financial

service providers can utilize the findings to design and

promote different financial investment products based on

the demographic, psychological and sociological attributes,

particularly income level and investment experience, of

individual investors from Mainland Chinese and Hong

Kong. The following targeted marketing strategy can be

formulated:

Target group 1: High-income investors

From the regression results of models 2a and 2b (Table

4), income level has a significant (at 0.01 level) and posi-

tive effect (0.277, 0.241) on the fund share amount held by

both Mainland Chinese and Hong Kong investors. Finan-

cial service providers should therefore invest more money

in advertising and strengthening their products, as well as

designing a wider range of financial investment portfolios

so as to attract these high-income investors to invest.

Target group 2: Less experienced investors

From the regression results of models 2a and 2b

(Table 4), investment experience has a significant (at

0.01 level) and negative effect (�0.023, �0.035) on the
fund share amount held by both Mainland Chinese and

Hong Kong investors. The possible reasons are listed

below. As individual investors become more experi-

enced, they become more conservative and show less

enthusiasm in fund investment. Therefore, high-risk

funds with a potential of offering higher returns are only

attractive to less experienced investors. Financial service

providers should then design higher yield funds and

allocation funds so as to raise the investment interests

of investors with less investment experience for the

highest profits.

Furthermore, in view of the differences in investment

behaviour between Mainland Chinese and Hong Kong

investors, financial service providers can utilize the find-

ings to design and promote different financial investment

products based on the demographic attribute of age. The

following strategy can be formulated for financial service

providers to better tackle Mainland Chinese and Hong

Kong investors:

Target group 3: Hong Kong investors aged 45–49

The results of regression analyses of models 2a and 2b

(Table 4) indicate that age is a statistically significant pre-

dictor for the fund share amount held by both Mainland

China and Hong Kong investors. However, the results indi-

cate that younger investors from Mainland China and older

investors from Hong Kong hold a higher fund share. The

results of descriptive analysis (Table 3) further identified

that Hong Kong investors aged between 45 and 49 show

great enthusiasm for investment and hold the highest fund

share on average (i.e. 158.18 unit). Thus, Hong Kong inves-

tors aged between 45 and 49 should be the key age group

for development and they are investors of great potential.

Financial service providers should then invest more money

in advertising and strengthen their products, as well as

designing higher yield funds so as to attract these enthusi-

astic and high purchasing power Hong Kong investors aged

between 45 and 49 to invest more in terms of frequency and

money. In the following subsection, limitations of this

research are discussed.

Limitations

In this research, financial transaction data and investors’

characteristics are collected from a single case company.

Despite the fact that 142,496 samples were collected and

used in the regression analyses, the empirical results may

not represent fully the financial investment behaviour or

investment preferences of all Mainland Chinese and Hong

Kong investors, given the limited number of case compa-

nies. In the future, more financial transaction data and

investors’ characteristics should be collected from other

Hong Kong-based financial service providers to make the

results more generalized and convincing.

Conclusions

The financial industry plays a significant role in the Main-

land China and Hong Kong economies and has aroused

increasing managerial and academic interest in recent

decades. Unfortunately, after the financial crisis of 2008

and the global crisis of 2009, investors are becoming more

cautious towards investments, especially in high-risk finan-

cial products. Furthermore, Hong Kong is the top offshore

investment destination for the Mainland Chinese investors.

Investors in Hong Kong are mainly mixed, with Mainland
Chinese investors and local investors having different char-

acteristics. These make it more difficult for financial ser-

vice providers to understand customers’ financial

investment behaviour and investment preferences.

Attempting to address the real-world challenges and

research gap, this study has (i) empirically identified that

demographic, psychological and sociological factors cause

different investment behaviour and (ii) identified that the

major attributes that explain and predict investment

10 International Journal of Engineering Business Management

behaviour/preferences of Mainland Chinese and Hong

Kong investors are age, income level, educational level,

gender, investment experience and marital status. Regres-

sion analyses and data provided by one of the Asia’s lead-

ing financial service providers were used to help the

financial industry formulate strategic and marketing

strategies.

This exploratory study helps to fill the identified

research gap and enable financial service providers to bet-

ter understand their customers’ financial investment beha-

viour and investment preferences from the perspective of

investors’ characteristics. With the huge volumes of confi-

dential transaction data and investors’ characteristics avail-

able, the research results are believed to be able to reflect

the real behaviour of individual investors from Mainland

China and Hong Kong and can offer financial service pro-

viders a foundation for sustainable strategies formulation.

In future research, it is suggested to extend the regression

results to build a data mining model to market the most

appropriate products to individual investors from Mainland

Chinese and Hong Kong and to gain a better understanding

of their financial investment behaviour in an effective and

efficient manner.

Acknowledgements

The authors thank the editors and reviewers for their valuable

comments and suggestions that have improved the quality of the

article. The authors would like to thank the Department of Indus-

trial and Systems Engineering, The Hong Kong Polytechnic Uni-

versity for their support in this work.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with

respect to the research, authorship, and/or publication of this

article.

Funding

The author(s) received no financial support for the research,

authorship, and/or publication of this article.

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Research Article

Factors affecting the organizational
performance of manufacturing firms

Ahmad Adnan Al-Tit

Abstract
Numerous studies have been conducted to explore the individual effects of organizational culture (OC) and supply chain
management (SCM) practices on organizational performance (OP) in different settings. The aim of this study is to
investigate the impact of OC and SCM on OP. The sample of the study consisted of 93 manufacturing firms in Jordan. Data
were collected from employees and managers from different divisions using a reliable and valid measurement instrument.
The findings confirm that both OC and SCM practices significantly predict OP. The current study is significant in reliably
testing the relationship between SCM practices and OP; however, it is necessary to consider cultural assumptions, values
and beliefs as the impact of OC on OP is greater than the impact of SCM practices. Based on the results, future studies
should consider the moderating and mediating role of OC on the relationship between SCM practices

and OP.

Keywords
Organizational culture, supply chain management practices, organizational performance, manufacturing firms

Date received: 9 November 2016; accepted: 4 May 2017

Introduction

Research on organizational performance (OP), either with

regard to its financial or its operational aspects, has

revealed different factors that have significant effects on

OP. Examples of these factors include enterprise risk man-

agement,
1

multidivisional structures of organizations,
2

CEO charisma,
3

stakeholders’ involvement and support,
4

intellectual capital,
5

human capital,
6

CEOs’ social net-

works,
7

organizational learning,
8

the strategic integration

of human resource management,
9

managerial practices

related to strategies, performance measurement, corporate

governance, innovation and development, along with the

external environment,
10

adoption of green supply chain

management (SCM) practices,
11

human resource prac-

tices,
12

knowledge management capacity,
13

supportive

organizational climate,
14

supply chain quality manage-

ment,
15

supply chain innovation,
16

human capital disclo-

sure
17

and knowledge creation.
18

Concerning the relationship between organizational cul-

ture (OC) and OP,

Yesil

and Kaya
19

carried out a study to

explore the impact of OC (clan, adhocratic, market

and

hierarchical cultures) on financial OP using a sample con-

sisting of managers of Turkish companies. Their results

indicated that none of these dimensions were related to the

financial dimensions of OP. On the other hand, Prajogo and

McDermott
20

found a positive relationship between OC

and OP.

In a study on the impact of human resources on SCM

and OP, Gómez-Cedeño et al.
21

found a direct influence of

an SCM implementation on SCM outcomes and an indirect

influence on OP of firms from different industries in Spain.

Using a sample of manufacturing and service firms from

Malaysia,

Chong et al.
22

asserted the positive impact of

SCM practices on OP.

Business Administration Department, College of Business and Economics

(CBE), Qassim University, Al Malida, Kingdom of Saudi Arabia

Corresponding Author:

Ahmad Adnan Al-Tit, Business Administration Department, College of

Business and Economics (CBE), Qassim University, Al Malida, Buraidah

15452, Qassim, Kingdom of Saudi Arabia.

Emails: aa.altit@qu.edu.sa; ahmteet@yahoo.com

International Journal of Engineering
Business Management

Volume 9: 1–9
ª The Author(s) 2017

DOI: 10.1177/1847979017712628
journals.sagepub.com/home/enb

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(http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without

further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/

open-access-at-sage).

mailto:aa.altit@qu.edu.sa

mailto:ahmteet@yahoo.com

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Evidence from China has confirmed the positive impact

of supply chain integration (internal, customer and supplier

integration) on OP. Li et al.
23

investigated the impact of

four practices of SCM (supplier and customer partnership,

the level and quality of information sharing and postpone-

ment) on OP, measured by market and financial perfor-

mance. Their results pointed to a significant influence of

these practices on OP dimensions.

Miguel and Brito
24

ana-

lysed data collected from companies in different industries

in Brazil to explore the relationship between SCM and OP.

They concluded that SCM practices exert positive influ-

ences on OP.

Okongwu et al.
25

investigated the impacts of quality of

information sharing and supplier–customer partnerships on

the OP of industrial firms in France. Their results supported

the hypothesis that SCM practices positively predict OP. In

light of the aforementioned findings, the aim of this study is

to explore factors affecting financial and non-financial per-

formance via investigating the impact of OC dimensions

and SCM practices on OP.

The remainder of the article is organized as follows:

‘Literature review and hypothesis development’ section

provides a literature review and hypothesis development;

this is followed by the presentation of the conceptual

model for the study in section ‘Conceptual model’. The

‘Research methodology’ section addresses the research

methodology, and results are presented in section ‘Data

analysis and results’. A discussion of the findings and

conclusion are provided in the sixth section. The final

section highlights the research implications and provides

future research directions.

Literature review and hypothesis
development

Organizational culture

Scholars have defined OC as shared values and beliefs

held by individuals that form the basis for patterns of

behaviour in solving problems.
26

Denison
27

argued that

the core content of OC covers beliefs, values and assump-

tions held by individuals within organizations. In contrast,

Schein
28

described OC as a behaviour that determines

how an organization grasps and reacts to the external and

internal environments, thus embedding the reaction to the

organizational environment in the definition of OC. Many

attributes concerning OC emerge in the literature. It has

been considered to guide individual communications

within an organization
29

and to be a critical antecedent

factor for the success of knowledge management initia-

tives
30

and a predictor of OP.
31

In terms of the dimensions of OC, studies such as that of

Balthazard et al.
32

have used the Organizational Culture

Inventory
®

(OCI), (# 2012 Human Synergistics International)

developed by Robert Cooke and J. Clayton Lafferty, which

covers three types of OC: aggressive/defensive, passive/

defensive and constructive cultures. The OCI measures 12

behavioural norms called 1–12 o’clock positions. Chang

and Lin
33

plotted OC on four axes (flexibility, internal,

external and effectiveness), which cover four types of

OC: cooperative, innovative, consistent and effective.

According to these authors, cooperation, information

sharing, empowerment and teamwork distinguish a coop-

erative culture. Adaptability and creativity are the major

features of innovative cultures. Rules and regulations, as

well as efficiency, are the dimensions included in a con-

sistency culture.

Finally, the main focus of the effectiveness, culture is on

competitiveness, goal achievement and effectiveness. In

their study of the relationship between OC, total quality

management and operational performance, Baird et al.
34

used the organizational culture profile to measure OC. The

profile consists of six dimensions: teamwork/people

respect, outcome orientation, innovation, stability, atten-

tion to detail and aggressiveness. For this study, two OC

dimensions were adopted: adaptability
26

and performance

orientation.
35

According to Ahmad,
26

customers, risks and

mistakes drive an adaptable organization. Performance

orientation refers to the accountability of members towards

results and high levels of performance.
35

Table 1 shows

examples of the OC dimensions used in the

literature.

Supply chain management

Chong et al.
22

defined supply chain management (SCM)

based on two approaches: supply management and logistics

management. The focus of the supply management is inte-

gration, while the focus of logistics management is inven-

tory reduction. According to Park and Krishnan,
38

cited in

Chong et al.,
22

SCM can be defined as activities aimed at

integrating partners in the supply chain to produce the right

quantity of a product to be distributed in the right place at

the right time.

Huang et al.
39

classified SCM research into three cate-

gories: (i) an operational approach that relates to produc-

tion, inventory and operational tools; (ii) a design approach

that deals with operational systems and information and

(iii) a strategic approach that refers to relationships and

competitive advantage. Huang et al.
40

used information

sharing and technological interdependence to measure the

level of integration in the supply chain. Okongwu et al.’s
25

study explored the relationship between SCM practices and

OP. They measured SCM practices in terms of information

sharing, supplier partnerships, customer relationships and

information quality. Two of these dimensions (supplier

partnerships and customer relationships) were adopted to

meet the purposes of this study (Table 2).

Organizational performance

Performance indicates to the achievement level of the mis-

sion at the work place that develops an employee job.
44

2 International Journal of Engineering Business Management

Treacy and Wiersema,
45

cited in Zack et al.,
46

suggested

three OP-related capabilities that provide a baseline for

competitive advantage: customer intimacy, product leader-

ship and operational excellence. Product leadership refers

to competition based on product and service innovation.

Customer intimacy relates to the competition in terms of

the strength of customer satisfaction and retention. On the

other hand, operational excellence relates to competition by

virtue of the efficiency of internal processes.
44

In the SCM domain, Arif-Khan et al.
41

identified three

categories of OP related to SCM: flexibility, output and

resource performance. According to these authors, flexi-

bility in performance relates to an organization’s respon-

siveness, output performance pertains to an organization’s

ability to deliver a superior level of customer service and

resource performance concerns an organization’s ability

to achieve efficiency. Using a sample consisting of 652

firms in Singapore, Chia et al.
47

examined performance

measurements used by SC managers. They found that the

most usable indicators were cost reduction, gross revenue,

pre-tax profit and customer satisfaction. Table 3 shows

examples of the OP dimensions used in the existing

literature.

Relationship between OC and OP

On the association between OC and OP, Yesil and Kaya
19

provided evidence from Turkey using a sample consisting

of 300 companies operating in the textile, food and service

industries. Measuring OC in terms of adhocratic, clan, hier-

archical and market cultures and OP by sales growth and

return on assets, they found no significant relationship

between their OC dimensions and OP indicators. Prajogo

and McDermott
20

examined the relationship between OC

and OP using four cultural dimensions adopted from Quinn

and Spreitzer
50

– group culture, developmental culture,

hierarchal culture and relational culture – and four dimen-

sions of performance, namely, product and process quality,

product and process innovation. Their findings indicated a

Table 1. Organizational culture dimensions used in the literature.

Dimensions of organizational culture Researcher (s)

Clan culture
Adhocracy culture
Market culture
Hierarchy culture

Yesil and Kaya
19

Cooperativeness
Innovativeness
Consistency
Effectiveness

Chang and Lin
33

and
Akhavan et al.30

Aggressive/defensive cultures
Passive/defensive culture
Constructive cultures

Balthazard et al.
32

Adaptability culture
Consistency culture
Involvement culture
Mission culture

Ahmad26

Culture management
Conflict resolution
Change disposition
Employee participation
Goal clarity
Identification with the organization
Organization focus and integration
Authority locus
Management style
Customer orientation
Human resource orientation
Task orientation
Performance orientation

Erwee et al.35

Information flow
Involvement
Meetings
Staff perceptions of teamwork
Staff perceptions of teamwork

supervision

Sikorska-Simmons36

Results-oriented vs. process-oriented
cultures

Tightly controlled vs. loosely controlled
cultures

Job-oriented vs. employee-oriented
cultures

Closed system vs. open system cultures
Professional vs. parochial cultures

Chang and Lin37

Teamwork/people respect
Outcome orientation
Innovation, stability
Attention to details
Aggressiveness

Baird et al.34

Table 2. Supply chain management dimensions used in the
literature.

Dimensions of supply chain management Researcher (s)

Customer relationship
Information sharing
Information technology
Internal operation
Strategic supplier partnership
Training

Chong et al.22

Collaborative distribution
Distribution flexibility
IT-enabled distribution
Inventory management
Order commitment
Transparency in the distribution process

Arif-Khan et al.41

Supply chain integration
Information sharing
Strategic relationships with suppliers and

customers
Support customer order

Jabbour et al.42

Information sharing, information quality
Supplier partnership
Customer relationship

Okongwu et al.25,
Al-Tit43

Technological interdependence
Information sharing

Huang et al.40

Al-Tit 3

positive relationship between developmental culture and

three of the OP dimensions (product quality, product inno-

vation and process innovation).

Al-Tit
51

conducted a study to investigate the mediating

role of OC between Human Resource Management (HRM)

practices and OP. It was found that OC moderated the rela-

tionship between HRM practices and OP. Lee and Yu
31

investigated the relationship between OC and OP using a

sample of companies from three sectors: high-tech firms,

hospitals and insurance companies. Their results

confirmed

the positive impact of OC on OP.

In Jordan, Bashayreh
52

investigated the relationship

between OC and OP. It was found that there is a relationship

between OC (policies and procedures) and OP. Based on 240

valid questionnaires collected from insurance companies,

Al-Nsour
53

investigated the role of OC in improving employ-

ees’ performance in the Jordanian banking sector. The results

identified there is a relationship between OC components

(expected Organization) and Employees’ Performance. Con-

sequently, the following hypothesis is proposed:

H1: OC (cultural adaptability and performance orienta-

tion) predicts OP.

Relationship between SCM and OP

Chong et al.
22

collected data from a sample consisting of

163 manufacturing and service companies in Malaysia to

test the relationship between SCM practices and OP (opera-

tional and innovative performance). They found a direct

influence of SCM practices, on both the operational and

the innovative performance of Malaysian companies.

Based on 128 valid questionnaires collected from different

manufacturing companies in India, Arif-Khan et al.
41

investigated the relationship between agile SCM practices

and OP. The results identified four SCM practices related

to the agile supply chain: collaborative distribution, distri-

bution flexibility, inventory management and order com-

mitment. In addition, they confirmed the association

between these practices and OP. Using the four dimensions

of SCM (information sharing, cooperation, long-term rela-

tionships and process integration) and four dimensions for

OP (cost, delivery, flexibility and quality), Miguel and

Brito’s
24

results supported the positive relationship

between SCM and OP. In addition, in a study of the rela-

tionship between SCM and OP with a sample of 450 man-

ufacturing companies in France, Okongwu et al.
25

found

direct and indirect impacts of SCM practices on OP. There-

fore, the following hypothesis is proposed:

H2: SCM (supplier partnership and customer relation-

ship) predicts OP.

Conceptual model

Figure 1 shows the study variables and the relationships

postulated between them. The conceptual model consists

of three variables: OC, SCM and OP. Two potential relation-

ships between the variables are assumed: OC is significantly

related to the OP, and SCM is significantly related to the OP.

Research methodology

Research sample and data collection

The study population comprises manufacturing firms oper-

ating in Amman, the capital city of Jordan. Of these firms, a

Table 3. Organizational performance dimensions used in prior literature.

Dimensions of organizational performance Researcher (s)

� Sales-based performance Ismail et al.48, Al-Tit43, Chong et al.22 and Lee and Yu31
– Sales revenue, profitability, return on investment
– Return on assets, manufacturing productivity
– Product added-value, employee added-value
– Sales growth and market share

� Organizational-based performance
– Product leadership (product and service innovation)
– Product and service quality
– Customer intimacy (customer satisfaction and retention)
– Operational excellence (internal processes efficiency)
– Employee development, and job satisfaction

� Supply chain-based performance Arif-Khan et al.41
– Flexibility performance
– Output performance
– Resource performance
– Cost reduction
– Gross revenue
– Profit before tax
– Customer satisfaction

Treacy and Wiersema45

– Profitability, revenue, sales volume and growth
– New customers, customer satisfaction, company reputation

Tan and Sousa49

4 International Journal of Engineering Business Management

sample of 300 firms was randomly selected. The study

sample intentionally involved employees from different

departments because OC might differ among organiza-

tional units. A questionnaire-based survey was carried out

to collect data from the participants. The response rate was

34% (102) due to the low percentage of firms that agreed to
participate in the study. Of the questionnaires returned,

nine were incomplete. This left 93 questionnaires usable

for data analysis.

Measures

The OC measure comprises two dimensions: adaptability
26

and performance orientation.
35

Four items were developed

to measure this variable. SCM practices were measured

using two dimensions adapted from Okongwu et al.
25

and

Flynn et al.
54

: supplier partnerships (information networks,

market information sharing, inventory level sharing,

demand forecast sharing) and customer relationships

(information networks, market information sharing,

computer-based orders, customer feedback and com-

plaints). Also based on these authors, mutual collaboration

and inventory management were used to evaluate supplier

partnerships, while practices directed towards the manage-

ment of customer complaints and building long-term rela-

tionships with customers were used to evaluate customer

relationships. Eight items were developed to measure this

variable. In addition, following Okongwu et al.
25

and

Quinn and Spreitzer,
50

employee satisfaction, customer

satisfaction and the introduction of new products were used

to measure non-financial performance, based on Hallavo
55

and Quinn and Spreitzer.
50

Five items were developed to

measure this variable. Therefore, the total number of items

in the questionnaire was 17 items. The questionnaire was

anchored based on a 5-point Likert-type scale that con-

sisted of from 1 point (strongly disagree) to 5 point

(strongly agree). Table 4 summarizes the measurements

used to evaluate the study variables.

Validity and reliability

Construct validity was assured as a measure previously

developed and validated. Reliability testing is defined as

a measure that ensures the stability and consistency of

results over time.
56

The findings of validity and reliability assessments, as

displayed in Table 5, confirm the acceptability of the mea-

surements used in the current study as recommended
55,57,58

(Cronbach’s a values above 0.7, w2/df < 2.0, RMSEA < 0.080, and CFI > 0.9).

Data analysis and results

Intercorrelation matrix

The Pearson’s correlation coefficients in Table 6 indicate

that all the study variables are associated with each other.

There are significant relationships between OC, SCM prac-

tices and OP indicators.

Hypothesis testing

The results of the paths postulated for this study, as summar-

ized in Table 7 and portrayed in Figure 2, provide support for

H1 and H2. The OC dimensions explain 45% of the variance
in OP and have a significant positive impact on OP (Cultural

adaptability, b ¼ 0.367, t ¼ 4.897, p value � 0.05; Perfor-
mance orientation, b ¼ 0.321, t ¼ 4.132, p value � 0.05).
The SCM dimensions explain 40% of the variance in OP and
have a significant positive impact on OP (Supplier partner-

ship, b ¼ 0.281, t ¼ 3.897, p value � 0.05; Customer rela-
tionship, b ¼ 0.275, t ¼ 3.712, p value � 0.05).

Discussion and conclusion

This study aimed to investigate factors affecting OP by

exploring the effect of OC and SCM practices on the OP

Figure 1. Research model.

Table 4. Measurements used in the study.

Variables Dimensions Researcher (s)

OC

Cultural
adaptability

Performance
orientation

Ahmad
26

and Erwee et al.
35

SCM

Supplier
partnership

Customer
relationship

Okongwu et al.
25

and Quinn and
Spreitzer50

OP Operational
performance

Okongwu et al.
25

and Al-Tit
51

and
Quinn RE and Spreitzer50

OC: organizational culture; SCM: supply chain management; OP: organi-
zational performance.

Al-Tit 5

of manufacturing firms from Jordan. The findings of the

study indicate that both OC and SCM practices signifi-

cantly predict OP. Concerning the relationship between

OC and OP, the results in the literature are mixed. In a

study of the relationship between the same constructs, Yesil

and Kaya
19

revealed a non-significant relationship between

OC and OP. On the other hand, Lee and Yu
31

confirmed

that OC positively predicts OP. The findings of

this study

are consistent with Abu-Jarad et al.,
59

suggesting that OC is

a key dimension in studies intending to investigate OP,

particularly in non-Western settings.

On the relationship between SCM practices and OP,

Chong et al.,
22

Arif-Khan et al.,
41

Miguel and Brito
24
and
Okongwu et al.
25

found a positive effect of SCM practices

and OP. Consistent with Quinn and Spreitzer,
50

this study

found a significant relationship between customer partner-

ship and operational performance. The results of Quinn

and Spreitzer
50

rejected the hypothesis that supplier

partnerships are related to operational performance. How-

ever, they explained that this was due to the introduction of

internal integration in the model. In this study, the ultimate

aim of which was to investigate factors affecting OP, the

results show that both OC and SCM practices are examples

of such factors. Overall, the study concludes that organiza-

tions driven by customers, partners, risk and mistakes and

oriented towards high levels of employee performance will

experience more enhanced levels of OP.

Implications and future research
directions

Despite the significant contribution of SCM practices to

OP,
41,22,24,25

the findings of this study indicate that the

impact of OC on OP is greater than the impact of SCM

practices on the same construct. Therefore, both research-

ers and managers should give importance to organizational

beliefs, values and assumptions along with other variables.

Hence, future research should examine the moderating and

mediating role of OC on the relationship between supply

chain practices and OP. The aim of this study is to explain

the direct relationship between SCM, OC and OP in the

absence of previous studies conducted in Jordanian

settings. However, the intended direct relationship is

Table 5. Reliability and validity of measurements.

Construct Items Mean SD a w2/df RMSEA CFI p Value

OC Cultural adaptability 2 3.74 0.90 0.83 1.22 0.061 0.94 0.00
Performance orientation 2 3.80 0.89 0.81

SCM Supplier partnership 4 3.86 0.88 0.78 1.63 0.074 0.91 0.00
Customer relationship 4 3.98 0.91 0.78

OP Operational performance 5 3.81 0.81 0.80 1.47 0.067 0.96 0.00

OC: organizational culture; SCM: supply chain management; OP: organizational performance.
p � 0.05.

Table 6. Intercorrelation of variables.

1 2 3 4 5

1 1.00
2 0.42 1.00
3 0.52 0.40 1.00
4 0.61 0.39 0.46 1.00
5 0.66 0.71 0.63 0.69 1.00

1: Cultural adaptability; 2: performance orientation; 3: supplier partner-
ship; 4: customer partnership; 5: operational performance.
p � 0.05.

Table 7. Hypothesis testing.

Hypotheses Dimensions r2 b T Result

H1: OC
predicts OP

Cultural
adaptability

0.446 0.367 4.897* Accepted

Performance
orientation

0.321 4.132*

H2: SCM
predicts OP

Supplier
partnership

0.397 0.281 3.897* Accepted

Customer
relationship

0.275 3.712*

OC: organizational culture; OP: organizational performance; SCM: supply
chain management.
*p Value � 0.05.

Figure 2. Final model.

6 International Journal of Engineering Business Management

considered an initial point to develop new models on

direct–indirect relationship between these variables in the

same context. Hence, neither mediating nor moderating

effects were studied in the current study. As recommended,

future research is required to examine such casual effects of

mediating and moderating variables.

The sample used in this study is limited to manufac-

turing firms in Amman, the capital city of Jordan. This

study is limited by its low response rate due to firms’

refusal to participate in the study, since they regarded the

required data, as secrets should be preserved from com-

petitors. Consequently, the findings should be considered

with caution based on the declined response rate. Accord-

ing to Holbrook et al.,
60

a lower response rate will only

affect the survey estimates.

Future studies should assess the impact of OC and sup-

ply chain practices on the OP of other manufacturing

firms in other countries. Finally, the research model

should include additional variables that contribute to OP

level to explore more factors that may affect OP in

Jordanian settings.

Acknowledgement

The author would like to thank the Jordanian firms who partici-

pated in this research. He would also like to thank the Deanship of

Scientific Research in Qassim University, Saudi Arabia.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with

respect to the research, authorship, and/or publication of this

article.

Funding

The author(s) received no financial support for the research,

authorship, and/or publication of this article.

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Reviewing the Literature

Agenda

 Why Search?

 What information is required?

 Sources of information

 Searching these sources and following-up references

 Maintaining references

 Writing the literature review

 Sources of help

What is Required at MSc/Doctorate level?

 A comprehensive and thorough review of the

literature which must be critical

NOT

 A simple literature search / survey

The Review will Enable you to:

 Develop background knowledge of your subject area

 Be critical of the published research work in your field

& define the state-of-the-art

 Identify where you can make a contribution to the

knowledge

 Test & defend your ideas relative to published work

“Searches for Sale”

 “The British Patent Office has notched up the 10 000 th search

under a private service that began in 1986.

 The office estimates that around 30% of all scientific research in

Europe is wasted because it covers ground that is already well

documented in patents and other literature.

 The private service aims to help researchers to avoid this

duplication of effort”.

New Scientist 29 May 1993

Information Required

 Subject Specific

 Machining hardened die steel

 Manufacturing strategy

 Research Methodology

 Cutting temperature determination

 Case study research

Sources of Information

 Books

 Journals & Conferences Papers

 PhD & MPhil Theses

 Patents

 Government Publications



Statistics



Standards



Codes of Practice



Company Information

 The Internet

Books

 Books that are recognised as good, authoritative

works in the field are valuable sources of

information on well established topics



However

, books are not a good source of

information on the latest research in the field

Journal and Conference Papers

Searching for papers

 Most libraries have electronic databases which can be

searched with key words, author names etc.

 You can also gain access to the databases such as the

Web of Science, Science Direct and EDiNA via the

Internet

 All these databases will provide an abstract and the

information needed to obtain a copy of the full article

Obtaining a Copy of the Paper

Obtaining a copy of the papers

 Increasingly, databases are providing on-line versions of the

full papers for you to download and print

 BUT do not just restrict yourself to the papers that are easy for

you can obtain in this way

 You must also obtain copies of papers which are not available

electronically

Journal and Conference Papers
Obtaining a copy of the papers

 If the journal or conference proceedings are in the library

you can photocopy the paper (for your research)

 If it is not in the your library you may be able to obtain a

copy from other local university libraries

OR

 Put in a request to the Inter-Library Loan office who will

obtain a copy for you

PhD and MPhil Theses

Searching for relevant theses

 Index to Theses

 UK research degrees – PhD, MPhil (via the Internet)

 Dissertation Abstracts

 USA research degrees – PhD, MPhil (CD-ROM & Internet trial)

 Current Research in Britain

 Information on ongoing research (CD-ROM)

PhD and MPhil Theses

Obtaining a copy of the thesis

 You will need to request the thesis via the ILL system

 Universities will not generally allow their theses to leave the

campus

 The thesis will generally be provided in electronic or

microfilm format

Statistics

 Statistics can provide useful data, but you must

consider their source

 Official Statistics

 Un-official Statistics

Standards

 National and inter

national standards

can be

important if they have an impact on your field of

research

 For example, in some areas of research, any

proposed solution may need to comply with certain

national standards

Codes of Practice

 As with standards, codes of practice can be

important if they have an impact on your field of
research

 For example, some professions such as

accountancy are subject to strict codes of practice

which control the way that they operate

Company Information

 Companies can publish some useful information

although such factors as potential bias and the quality

of the data will need to take into account

 If the information is provided informally by an

employee, there are also issues associated with

referencing and others obtaining access to the data

 Vast amount of interest and publicity about information

on the Internet

 There is a an important difference between obtaining

information on such things as leisure interests and

obtaining information for your literature review

The Internet

However

Academic Concerns with the Internet



Referencing

 Anyone can produce a Web site



Refereeing

 Designed to allow others to obtain a copy

the work

that

you refer to in your dissertation

 The library system for books and papers has existed for

many years and works very well

 Information on the Internet might be gone the next day

 If the reader can not obtain a copy of the work that you

are referring to, the reference is useless

Referencing

 Anyone here could produce a web site containing

information which they have simply invented

 People may do this for fun or to see how many people

they can fool

 Other information might be part of a deliberate attempt

to deceive people for commercial reasons

Anyone can Produce a Web Site

 This is related to the last point that anyone can place

information on the web

 There is no refereeing system such as that used for

journals to ensure the quality of the information

presented

Refereeing

Web-Based Misinformation in the Context of Higher
Education

Abstract:

Misinformation on the Web has the potential to distort the learning of higher

education students.

Research with faculty research students and taught students showed that

higher education students are naïve about the problem of misinformation.

They believe they can identify it and do not make extra effort to check the

sources of their information. *

Philip J. Calvert 1999

* Full abstract and reference in the notes [5]

Traceability of Information

 As a general rule it must be possible for others to locate

and obtain a copy of anything that you reference for a

reasonable period of time

 Maintaining accurate records of your references is

therefore extremely important

Typical Information Required

For a Book:

 Author(s)/editor(s)

 Title

 Edition

 Number of the volume

 Publisher

 Place of publication

 Date of publication

For a Journal Article:

 Author(s)

 Title of paper

 Name of journal

 Volume

 Issue

 Year

 Page numbers

Storage of Reference Information

 The compilation of the reference list for your

dissertation will be much easier if you store all the

details of the reference in a computer based system when

you obtain the reference

Referencing Systems

 You must use a recognised system and be consistent

throughout

 You must include ALL the information that the reader needs

to obtain a copy of your reference otherwise the reference is

useless!

Most Widely Used Systems

The use of through-tool cooling has been shown to reduce tool

wear [1] .

References

1. Barnes, S. and Pashby, I. R. : “Through-Tool Coolant

Drilling of Aluminium/SiC Metal Matrix Composite”, Journal

of Engineering Materials and Technology, October 2000, Vol.

122, No. 4, pp. 384-388.

Most Widely Used Systems
The use of through-tool cooling has been shown to reduce tool

wear (Barnes, 2000) .

References

Barnes, 2000: Barnes, S. and Pashby, I. R. “Through-Tool

Coolant Drilling of Aluminium/SiC Metal

Matrix omposite”, Journal of Engineering Materials and

Technology, October 2000, Vol. 122, No. 4, pp. 384-388.

Issues in Reading

 Nothing has been written on my research topic

 There’s too much

 It’s all been done

 How many references do I need

Writing the Literature Review

Structure Your Review Early

 Do not just start writing!

 Think about the areas that you need to cover

 Develop a draft contents list

 Discuss the contents list with your Mentors

Start Writing Early

 You can always find reasons not to

 You need feedback on your writing ASAP

 It will always take much longer than you think

Being Critical

 Normally, being critical is associated with a dressing down

or personal attack

 In research, critical reading, critical thinking and critical

assessment are used to produce a considered and justified

examination of the available literature

 At this level, you are required to present a critical review of

the literature and not simply report the work of others

Being Critical – Some Main Points (1)

 Agreeing with, or defending a position through an evaluation of its

strengths and weaknesses

 Conceding that an existing approach has some merit, but that others

need to be rejected

 Focusing on ideas, theories and arguments and not on the author of

those arguments

 Selecting elements from existing arguments and reformulating them

to form a new point of view

Being Critical – Some Main Points (2)

 Being aware of your own critical stance; identifying your reasons

for selecting the work and recognising the weakness in your

critique

 Finding fault in an argument by identifying fallacies,

inadequacies, lack of evidence or lack of plausibility

 Identifying errors in a criticism made by another to provide

correction and balanced criticism

Open and Fair Criticism

 Although you need to be critical, there is also a convention that

requires you to treat the work of others with due respect

 Summarise the views and arguments of others in a way that is fair

and which acknowledges the points that you agree with

 It is not enough to simply list what you have found deficient in an

argument. In order for your criticism to be legitimate you need to

provide a structured explanation showing what is wrong

Legitimacy (1)

 Remember that when being critical, it your responsibility

to use this work in a balanced, fair and legal way

 To avoid criticism of your review you must use your

sources properly and there are accepted standards in

academic work which you need to comply with

 Violation of these principles will not only put your work

into question, but, may also result in the examiners failing

the work

Legitimacy (2)

 Falsification

 misrepresenting the work of others

 Fabrication

 presenting speculations as if they were facts

 Sloppiness

 not providing correct citations

Legitimacy (3)

 Nepotism

 citing references of colleagues that are not

directly related to your work

 Plagiarism

 the act of knowingly using another person’s work

and passing it off as your own

Supporting Evidence (1)

 Select appropriate sources of evidence to support your

argument

 The works that you cite will influence your credibility

 If are discussing a medical treatment, an article from the

British Medical Journal will have more credibility than

one from a tabloid newspaper

Supporting Evidence (2)

 The date of the material can also be important.

 If you are talking about state-of-the-art research do not use

references that are 10 years old

 In any area of research, there will be workers who are

recognised as the leaders in the field, quoting their work has

obvious advantages

Academic Style and Voice (1)

 Academic style involves the correct use of tense, voice, and

grammatical structure and although some aspects vary from one

discipline to another, there are certain conventions

 The past tense is primarily used although there are variations

discussed in the notes

 Words which are regarded as imprecise should also be avoided –

“fantastic”, “crucial” , “very”, “etc.”

Academic Style and Voice (2)

 In some disciplines it is regarded as acceptable to use the active

voice (me, I, we) when describing issues such as how you came to

select your research topic

 However, use of the passive voice throughout the dissertation is

preferred by most researchers in scientific and engineering

disciplines, e.g., “the research was done”, rather than “I did my

research”

Drafts

 Some reworking and corrections are always necessary

 Do not attempt to get it perfect first time

 Get work to your Mentors early

 Proof-reading is therefore an essential process

 Read it carefully before you ask others to read it

 Do not try to use you supervisor as a spell checker

 Critically examine what you have written – there is a

check list in the notes

Length of the Review

 Remember that you will not be able to write-up everything.

 Think carefully about what is necessary to meet the aims of

your work and disregard anything that is not

 Examiners do not like to read pages and pages of interesting

but irrelevant information

The Completed Review Should:

 Shows a clear understanding of the topic

 Cite all key landmark studies and discuss them

 State clear conclusions about previous research using

appropriate

evidence

 Show the variety of approaches to the topic area

 Make recommendations using coherent arguments based on

evidence

 Show a gap in existing knowledge

 Develop a clear research problem

Sources of Help – Searching

 General Enquires

 Enquiries desk and leaflets in the library

 On-line Information

 Journals Enquires

 Periodicals Office in the library

 Specific information on your subject

 Subject Librarians

 Mentors

Sources of Help – Writing

 There are several books available on how to do a

literature review and how to research

 Remember that individual Mentors will have their

own preferences so consult them throughout

However

Summary

 Start the work early

 Get written work to you Mentors early

 Consider, make sure you understand and

implement (if appropriate) any suggestions

 Make sure that you have evidence for everything

 Pay attention to detail

 Ask questions and be critical

Summary

 It is a requirement of the degree to produce a critical

review of the literature

 A review is also essential in order to support your work

& put it into context

 If you fail to identify the fact that another worker has

published very similar ideas, but your external examiner

has found such work, you could find yourself in serious

trouble during the oral exam