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:
试用版
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
试用版
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.
试用版
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
试用版
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)
试用版
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
试用版
试用版
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
https://doi.org/10.1177/1847979017711520
http://journals.sagepub.com/home/enb
https://us.sagepub.com/en-us/nam/open-access-at-sage
https://us.sagepub.com/en-us/nam/open-access-at-sage
http://crossmark.crossref.org/dialog/?doi=10.1177%2F1847979017711520&domain=pdf&date_stamp=2017-06-08
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.
References
1. Sun C. Hong Kong ‘still top choice for China’s rich’ investing
outside the mainland and overseas. South China Morning
Post. http://www.scmp.com/news/china/money-wealth/arti
cle/1808983/hong-kong-still-top-choice-chinas-rich-invest
ing-outside (2016, accessed 1 October 2016).
2. Mak MKY, Ho GTS and Ting SL. A financial data mining
model for extracting customer behaviour. Int J Eng Bus Man-
age 2011; 3: 59–72.
3. Rizvi S and Fatima A. Behavioral finance: a study of correla-
tion between personality traits with the investment patterns in
the stock market. In: Chatterjee S, Singh N, Goyal D and
Gupta N (eds) Managing in recovering markets. New Delhi:
Springer, 2015, pp. 143–155.
4. Jain R, Jain P and Jain C. Behavioral biases in the decision
making of individual investors. IUP J Manage Res 2015; 14:
7–27.
5. Tekçe B and Yılmaz N. Are individual stock investors over-
confident? Evidence from an emerging market. J Behav Exp
Finance 2015; 5: 35–45.
6. Chiang TC, Li JD and Tan L. Empirical investigation of
herding behavior in Chinese stock markets: evidence from
quantile regression analysis. Global Finance J 2010; 21:
111–124.
7. Zhang Y and Zheng X. A study of the investment behavior
based on behavioral finance. Eur J Bus Econ 2015; 10:
1–5.
8. Fung L and Durand RB. Personality traits. In: Baker HK and
Ricciardi V (eds) Investor behavior: the psychology of finan-
cial planning and investing. Hoboken: John Wiley & Sons,
Inc., 2014, pp. 99–115.
9. Mahmood I, Ahmad H, Khan AZ, et al. Behavioural impli-
cations of investors for investments in the stock market. Eur J
Social Sci 2011; 20: 240.
10. Masini A and Menichetti E. The impact of behavioural fac-
tors in the renewable energy investment decision making
process: conceptual framework and empirical findings.
Energy Policy 2012; 40: 28–38.
11. Phan CK and Zhou J. Vietnamese individual investors’ beha-
vior in the stock market: an exploratory study. Res J Social
Sci Manage 2014; 3: 46–54.
12. Charles A and Kasilingam R. Do investor’s emotions deter-
mine their investment decisions? Drishtikon 2015; 6: 1–29.
13. Solomon MR. Consumer behaviour – buying, having and
being, 10th ed. Engelwood Cliffs: Pearson prentice Hall,
2012.
14. East R, Wright M and Vanhuele M. Consumer behaviour:
applications in marketing, 2nd ed. London: SAGE Publica-
tions Ltd., 2013.
15. Hirshleifer D. Behavioral finance. Annu Rev Financial Econ
2015; 7: 133–159.
16. Cooper MJ, Dimitrov O and Rau PR. A rose.com by any other
name. J Finance 2001; 56: 2371–2388.
17. Zhou WX and Sornette D. Fundamental factors versus herd-
ing in the 2000–2005 US stock market and prediction. Phy-
sica A 2006; 360: 459–482.
18. Shefrin H and Statman M. Behavioral finance in the financial
crisis: market efficiency, Minsky, and Keynes. In: Blinder A,
Lo A and Solow R (eds) Rethinking the financial crisis. New
York: Sage Foundation, 2013, pp. 99–135.
19. Frankfurter GM and McGoun EG. Market efficiency and
behavioral finance: the nature of the debate. J Psychol Finan-
cial Market 2000; 1: 200–210.
20. Yang Q. An empirical study of individual investors’ overcon-
fidence in stock market. J Northwest Univ 2007; 37: 64–68.
21. Jagongo A and Mutswenje VS. A survey of the factors influ-
encing investment decisions: the case of individual investors
at the NSE. Int J Hum Social Sci 2014; 4: 92–102.
22. Kumar A and Lee CM. Retail investor sentiment and return
comovements. J Finance 2006; 61: 2451–2486.
Mak and Ip 11
http://www.scmp.com/news/china/money-wealth/article/1808983/hong-kong-still-top-choice-chinas-rich-investing-outside
http://www.scmp.com/news/china/money-wealth/article/1808983/hong-kong-still-top-choice-chinas-rich-investing-outside
http://www.scmp.com/news/china/money-wealth/article/1808983/hong-kong-still-top-choice-chinas-rich-investing-outside
23. Baker M and Wurgler J. Investor sentiment in the stock mar-
ket. J Econ Perspect 2007; 21: 129–151.
24. Garling T, Kirchler E, Lewis A, et al. Psychology, financial,
decision making, and financial crises. Psychol Sci Public
Interest 2009; 10: 1–47.
25. Kaustia M and Knupfer S. Do investors overweight personal
experience? Evidence from IPO subscriptions. J Finance
2008; 63: 2679–2702.
26. Malmendier U and Nagel S. Depression babies: do macroeco-
nomic experiences affect risk-taking? J Econ 2011; 126: 373–416.
27. Seru A, Shumway T and Stoffman N. Learning by trading.
Rev Financial Stud 2010; 23: 705–839.
28. Corter JE and Chen YJ. Do investment risk tolerance attitude
predict portfolio risk? J Bus Psychol 2006; 29: 369–384.
29. Byrne K. How do consumers evaluate risk in financial prod-
ucts? J Financial Serv Market 2005; 10: 21–36.
30. Chen G, Kim K, Nofsinger J, et al. Trading performance,
disposition effect, overconfidence, representativeness bias
and experience of emergent market investors. J Behav Decis
Making 2007; 20: 425–451.
31. Maditinos DI, Sevic Z and Theriou NG. Investors’ behaviour
in the Athens Stock Exchange (ASE). Stud Econ Finance
2007; 24: 32–50.
32. Sadi R, Hassan G, Mohammad R, et al. Behavioral finance:
the explanation of investors’ personality and perceptual
biases effects on financial decisions. Int J Econ Finance
2011; 3: 234–241.
33. Charles A and Kasilingam R. Does the investor’s age influ-
ence their investment behaviour? Paradigm 2013; 17: 11–24.
34. Fellner G and Maciejovsky B. Risk attitude and market beha-
vior: evidence from experimental asset markets. J Econ Psy-
chol 2007; 28: 338–350.
35. Mittal M and Vyas RK. Personality type and investment
choice: an empirical study. ICFAI Univ J Behav Finance
2008; 5: 7–22.
36. Kabra G, Mishra PK and Dash MK. Factors influencing
investment decisions of generations in India: an econometric
study. Asian J Manage Res 2010; 1: 308–328.
37. Huberman G and Jiang W. Offering versus choice in 401(k)
plans: equity exposure and number of funds. J Finance 2006;
56: 763–801.
38. Gunay SG and Demirel E. Interaction between demographic
and financial behavior factors in terms of investment decision
making. Int Res J Finance Econ 2011; 66: 147–156.
39. Agnew JR, Anderson LR, Gerlach JR, et al. Who chooses
annuities? An experimental investigation of the role of gen-
der, framing, and defaults. Am Econ Rev 2008; 98: 418–442.
40. Speelman CP, Clark-Murphy M and Gerrans P. Decision
making clusters in retirement savings: gender differences
dominate. J Fam Econ Issues 2013; 34: 329–339.
41. Al-Ajmi JY. Risk tolerance of individual investors in an
emerging market. Int Res J Finance Econ 2008; 17: 15–26.
42. Shaikh ARH and Kalkundrikar AB. Impact of demographic
factors on retail investors’ investment decisions – an explora-
tory study. Indian J Finance 2011; 5: 35–44.
43. Fares AR and Khamis FG. Individual investors’ stock trading
behavior at Amman Stock Exchange. Int J Econ Finance
2011; 3: 128–134.
44. Geetha N and Ramesh M. A Study on relevance of demo-
graphic factors in investment decisions. Perspect Innovat
Econ Bus 2012; 10: 14–27.
45. Collard S. Individual investment behaviour: a brief review of
research. Report, School of Geographical Sciences, Bristol,
January 2009.
46. Fidelity Investments Management (Hong Kong) Limited.
Personal investment behaviour in Hong Kong. https://www.
hkupop.hku.hk/english/report/fidel05/pr (2004, accessed
6 January 2016).
47. Saunders M, Lewis P and Thornhill A. Research methods for
bus students, 5th ed. Essex: Pearson Education, 2012.
48. Poole M and O’Farrell P. The assumptions of the linear
regression model. Trans Inst Br Geogr 1971; 52: 145–158.
49. Antonakis J and Dietz J. Looking for validity or testing it?
The perils of stepwise regression, extreme-score analysis,
heteroscedasticity, and measurement error. Pers Individual
Differ 2011; 50: 409–415.
50. Stevens JP. Applied multivariate statistics for the social
sciences, 5th ed. New York: Routledge, 2009.
51. Osborne J and Waters E. Four assumptions of multiple regres-
sion that researchers should always test. Pract Assess Res
Eval 2002; 8: 1–5.
52. Hart MA and Sailor DJ. Quantifying the influence of
land-use and surface characteristics on spatial variability
in the urban heat island. Theor Appl Climatol 2009; 95:
397–406.
53. Shieh G. On the misconception of multicollinearity in detec-
tion of moderating effects: multicollinearity is not always
detrimental. Multivar Behav Res 2010; 45: 483–507.
12 International Journal of Engineering Business Management
https://www.hkupop.hku.hk/english/report/fidel05/pr
https://www.hkupop.hku.hk/english/report/fidel05/pr
<<
/ASCII85EncodePages false
/AllowTransparency false
/AutoPositionEPSFiles true
/AutoRotatePages /None
/Binding /Left
/CalGrayProfile (Gray Gamma 2.2)
/CalRGBProfile (sRGB IEC61966-2.1)
/CalCMYKProfile (U.S. Web Coated \050SWOP\051 v2)
/sRGBProfile (sRGB IEC61966-2.1)
/CannotEmbedFontPolicy /Warning
/CompatibilityLevel 1.4
/CompressObjects /Off
/CompressPages true
/ConvertImagesToIndexed true
/PassThroughJPEGImages false
/CreateJobTicket false
/DefaultRenderingIntent /Default
/DetectBlends true
/DetectCurves 0.1000
/ColorConversionStrategy /LeaveColorUnchanged
/DoThumbnails false
/EmbedAllFonts true
/EmbedOpenType false
/ParseICCProfilesInComments true
/EmbedJobOptions true
/DSCReportingLevel 0
/EmitDSCWarnings false
/EndPage -1
/ImageMemory 1048576
/LockDistillerParams true
/MaxSubsetPct 100
/Optimize true
/OPM 1
/ParseDSCComments true
/ParseDSCCommentsForDocInfo true
/PreserveCopyPage true
/PreserveDICMYKValues true
/PreserveEPSInfo true
/PreserveFlatness false
/PreserveHalftoneInfo false
/PreserveOPIComments false
/PreserveOverprintSettings true
/StartPage 1
/SubsetFonts true
/TransferFunctionInfo /Apply
/UCRandBGInfo /Remove
/UsePrologue false
/ColorSettingsFile ()
/AlwaysEmbed [ true
]
/NeverEmbed [ true
]
/AntiAliasColorImages false
/CropColorImages false
/ColorImageMinResolution 266
/ColorImageMinResolutionPolicy /OK
/DownsampleColorImages true
/ColorImageDownsampleType /Average
/ColorImageResolution 175
/ColorImageDepth -1
/ColorImageMinDownsampleDepth 1
/ColorImageDownsampleThreshold 1.50286
/EncodeColorImages true
/ColorImageFilter /DCTEncode
/AutoFilterColorImages true
/ColorImageAutoFilterStrategy /JPEG
/ColorACSImageDict <<
/QFactor 0.40
/HSamples [1 1 1 1] /VSamples [1 1 1 1]
>>
/ColorImageDict <<
/QFactor 0.76
/HSamples [2 1 1 2] /VSamples [2 1 1 2]
>>
/JPEG2000ColorACSImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 30
>>
/JPEG2000ColorImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 30
>>
/AntiAliasGrayImages false
/CropGrayImages false
/GrayImageMinResolution 266
/GrayImageMinResolutionPolicy /OK
/DownsampleGrayImages true
/GrayImageDownsampleType /Average
/GrayImageResolution 175
/GrayImageDepth -1
/GrayImageMinDownsampleDepth 2
/GrayImageDownsampleThreshold 1.50286
/EncodeGrayImages true
/GrayImageFilter /DCTEncode
/AutoFilterGrayImages true
/GrayImageAutoFilterStrategy /JPEG
/GrayACSImageDict <<
/QFactor 0.40
/HSamples [1 1 1 1] /VSamples [1 1 1 1]
>>
/GrayImageDict <<
/QFactor 0.76
/HSamples [2 1 1 2] /VSamples [2 1 1 2]
>>
/JPEG2000GrayACSImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 30
>>
/JPEG2000GrayImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 30
>>
/AntiAliasMonoImages false
/CropMonoImages false
/MonoImageMinResolution 900
/MonoImageMinResolutionPolicy /OK
/DownsampleMonoImages true
/MonoImageDownsampleType /Average
/MonoImageResolution 175
/MonoImageDepth -1
/MonoImageDownsampleThreshold 1.50286
/EncodeMonoImages true
/MonoImageFilter /CCITTFaxEncode
/MonoImageDict <<
/K -1
>>
/AllowPSXObjects false
/CheckCompliance [
/None
]
/PDFX1aCheck false
/PDFX3Check false
/PDFXCompliantPDFOnly false
/PDFXNoTrimBoxError true
/PDFXTrimBoxToMediaBoxOffset [
0.00000
0.00000
0.00000
0.00000
]
/PDFXSetBleedBoxToMediaBox false
/PDFXBleedBoxToTrimBoxOffset [
0.00000
0.00000
0.00000
0.00000
]
/PDFXOutputIntentProfile (U.S. Web Coated \050SWOP\051 v2)
/PDFXOutputConditionIdentifier (CGATS TR 001)
/PDFXOutputCondition ()
/PDFXRegistryName (http://www.color.org)
/PDFXTrapped /Unknown
/CreateJDFFile false
/Description <<
/ENU
>>
/Namespace [
(Adobe)
(Common)
(1.0)
]
/OtherNamespaces [
<<
/AsReaderSpreads false
/CropImagesToFrames true
/ErrorControl /WarnAndContinue
/FlattenerIgnoreSpreadOverrides false
/IncludeGuidesGrids false
/IncludeNonPrinting false
/IncludeSlug false
/Namespace [
(Adobe)
(InDesign)
(4.0)
]
/OmitPlacedBitmaps false
/OmitPlacedEPS false
/OmitPlacedPDF false
/SimulateOverprint /Legacy
>>
<<
/AllowImageBreaks true
/AllowTableBreaks true
/ExpandPage false
/HonorBaseURL true
/HonorRolloverEffect false
/IgnoreHTMLPageBreaks false
/IncludeHeaderFooter false
/MarginOffset [
0
0
0
0
]
/MetadataAuthor ()
/MetadataKeywords ()
/MetadataSubject ()
/MetadataTitle ()
/MetricPageSize [
0
0
]
/MetricUnit /inch
/MobileCompatible 0
/Namespace [
(Adobe)
(GoLive)
(8.0)
]
/OpenZoomToHTMLFontSize false
/PageOrientation /Portrait
/RemoveBackground false
/ShrinkContent true
/TreatColorsAs /MainMonitorColors
/UseEmbeddedProfiles false
/UseHTMLTitleAsMetadata true
>>
<<
/AddBleedMarks false
/AddColorBars false
/AddCropMarks false
/AddPageInfo false
/AddRegMarks false
/BleedOffset [
9
9
9
9
]
/ConvertColors /ConvertToRGB
/DestinationProfileName (sRGB IEC61966-2.1)
/DestinationProfileSelector /UseName
/Downsample16BitImages true
/FlattenerPreset <<
/ClipComplexRegions true
/ConvertStrokesToOutlines false
/ConvertTextToOutlines false
/GradientResolution 300
/LineArtTextResolution 1200
/PresetName ([High Resolution])
/PresetSelector /HighResolution
/RasterVectorBalance 1
>>
/FormElements true
/GenerateStructure false
/IncludeBookmarks false
/IncludeHyperlinks false
/IncludeInteractive false
/IncludeLayers false
/IncludeProfiles true
/MarksOffset 9
/MarksWeight 0.125000
/MultimediaHandling /UseObjectSettings
/Namespace [
(Adobe)
(CreativeSuite)
(2.0)
]
/PDFXOutputIntentProfileSelector /DocumentCMYK
/PageMarksFile /RomanDefault
/PreserveEditing true
/UntaggedCMYKHandling /UseDocumentProfile
/UntaggedRGBHandling /UseDocumentProfile
/UseDocumentBleed false
>>
]
/SyntheticBoldness 1.000000
>> setdistillerparams
<<
/HWResolution [288 288]
/PageSize [612.000 792.000]
>> setpagedevice
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
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:aa.altit@qu.edu.sa
mailto:ahmteet@yahoo.com
https://doi.org/10.1177/1847979017712628
http://journals.sagepub.com/home/enb
https://us.sagepub.com/en-us/nam/open-access-at-sage
https://us.sagepub.com/en-us/nam/open-access-at-sage
http://crossmark.crossref.org/dialog/?doi=10.1177%2F1847979017712628&domain=pdf&date_stamp=2017-06-12
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.
References
1. Abdul Rasid S, Isa C and Ismail W. Management accounting
systems, enterprise risk management and organizational per-
formance in financial institutions. Asian Rev Accounting
2014; 22(2): 128–144. DOI:10.1108/ARA-03-2013-0022.
2. Avdelidou-Fischer N. The relationship between organiza-
tional structures and performance: the case of the fortune
500. Int Finance Rev 2007; 7: 169–206. DOI:10.1016/
S1569-3767(06)07008-7.
3. Bacha E. The relationships among organizational perfor-
mance, environmental uncertainty, and employees’ percep-
tions of CEO charisma. J Manage Dev 2010; 29(1): 28–37.
DOI:10.1108/02621711011009054.
4. Broad ML. Improving performance in complex organiza-
tions. Ind Comm Train 2006; 38(6): 322–329. DOI:10.
1108/00197850610685833.
5. Chen MC. Intellectual capital and competitive advantages:
the case of TTY. J Bus Chem 2004; 1(1): 14–20.
6. Felı́cio J, Couto E and Caiado J. Human capital, social capital
and organizational performance. Manage Decis 2014; 52(2):
350–364. DOI:10.1108/MD-04-2013-0260.
7. Fernández-Pérez V, Garcı́a-Morales V and Bustinza-Sánchez Ó
The effects of CEOs’ social networks on organizational perfor-
mance through knowledge and strategic flexibility. Person
Rev 2012; 41(6): 777–812. DOI:10.1108/00483481211263719.
8. Garcı́a-Morales VJ, Matı́as Reche F and Hurtado Torres N.
Influence of transformational leadership on organizational
innovation and performance depending on the level of orga-
nizational learning in the pharmaceutical sector. J Organ
Change Manage 2008; 21(2): 188–212.
9. Gautam D. Strategic integration of HRM for organizational
performance: Nepalese reality. South Asian J Global Bus Res
2015; 4(1): 110–128. DOI:10.1108/SAJGBR-10-2012-0119.
10. Gavrea C, Ilies L and Stegerean R. Determinant of organiza-
tional performance: the case of Romania. Manage Market
Challeng Knowg Soc 2011; 6(2): 285–300.
11. Green K Jr, Zelbst P, Meacham J, et al. Green supply chain
management practices: impact on performance. Suppl Chain
Manage Int J 2012; 17(3): 290–305. DOI:10.1108/
13598541211227126.
12. Hooi L and Ngui K. Enhancing organizational performance
of Malaysian SMEs. Int J Manpow 2014; 35(7): 973–995.
DOI:10.1108/IJM-04-2012-0059.
13. Hsiao Y, Chen C and Chang S. Knowledge management
capacity and organizational performance: the social interac-
tion view. Int J Manpow 2011; 32(5/6): 645–660. DOI:10.
1108/01437721111158242.
14. Jing F, Avery G and Bergsteiner H. Organizational climate
and performance in retail pharmacies. Leader Organ
Develop J 2011; 32(3): 224–242. DOI:10.1108/0143773
1111123898.
15. Kuei C, Madu C and Lin C. The relationship between supply
chain quality management practices and organizational per-
formance. Int J Quality Reliabil Manage 2001; 18(8):
864–872. DOI:10.1108/EUM0000000006031.
16. Lee S, Lee D and Schniederjans M. Supply chain innovation
and organizational performance in the healthcare industry. Int
J Operat Product Manage 2011; 31(11): 1193–1214. DOI:10.
1108/01443571111178493.
17. Lin L, Huang I, Du P, et al. Human capital disclosure and
organizational performance. Manage Decis 2012; 50(10):
1790–1799. DOI:10.1108/00251741211279602.
18. Migdadi M and Abu Zaid M. The role of communication
satisfaction in enhancing the effect of knowledge creation
on organizational performance. Dirasat Administr Sci 2009;
36(2): 547–567.
19. Yesil S and Kaya A. The effect of organizational culture on
firm financial performance: evidence from a developing
country. Proc Social Behav Sci 2013; 81: 428–437. DOI:10.
1016/j.sbspro.2013.06.455.
20. Prajogo D and McDermott C. The relationship between mul-
tidimensional organizational culture and performance. Int J
Operat Product Manage 2011; 31(7): 712–735. DOI:10.
1108/01443571111144823.
Al-Tit 7
21. Gómez-Cedeño M, Castán-Farrero J, Guitart-Tarrés L, et al.
Impact of human resources on supply chain management and
performance. Ind Manage Data Syst 2015; 115(1): 129–157.
DOI:10.1108/IMDS-09-2014-0246.
22. Chong A, Chan F, Ooi K, et al. Can Malaysian firms improve
organizational/innovation performance via SCM?. Ind Man-
age Data Syst 2011; 111(3): 410–431. DOI:10.1108/
02635571111118288.
23. Li S, Ragu-Nathan B, Ragu-Nathan T, et al. The impact of
supply chain management practices on competitive advan-
tage and organizational performance. Int J Manage Sci
(Omega) 2006; 34: 107–124.
24. Miguel P and Brito L. Supply chain management measure-
ment and its influence on operational performance. J Operat
Suppl Chain Manage 2011; 4(2): 56–70.
25. Okongwu U, Brulhart F and Moncef B. Causal linkages
between supply chain management practices and perfor-
mance. J Manuf Technol Manage 2015; 26(5): 678–702.
DOI:10.1108/JMTM-01-2013-0002.
26. Ahmad S. Impact of organizational culture on performance
management practices in Pakistan. Bus Intell J 2012; 5(1):
50–55.
27. Denison D. Organizational culture: can it be a key lever for
driving organizational change. In: Cooper CL, Cartwright S
and Earley PC (eds) The international handbook of organiza-
tional culture and climate. Chichester, UK: John Wiley &
Sons, 2001, pp. 347–372.
28. Schein EH. Organizational culture and leadership. 4th ed.
San Francisco, CA: John Wiley & Sons, 2010.
29. Ribiere V and Sitar A. Critical role of leadership in nurturing
a knowledge-supporting culture. Knowg Manage Res Prac
2003; 1(1): 39–48.
30. Akhavan P, Ramezan M, Moghaddam Y, et al. Exploring the
relationship between ethics, knowledge creation and organi-
zational performance. VINE J Inform Knowg Manage Syst
2014; 44(1): 42–58. DOI:10.1108/VINE-02-2013-0009.
31. Lee S and Yu K. Corporate culture and organizational per-
formance. J Manag Psychol 2004; 19(4): 340–359. DOI:10.
1108/02683940410537927.
32. Balthazard P, Cooke R and Potter P. Dysfunctional culture,
dysfunctional organization. J Manag Psychol 2006; 21(8):
709–732. DOI:10.1108/02683940610713253.
33. Chang S and Lin C. Exploring organizational culture for
information security management. Industr Manage Data Syst
2007; 107(3): 438–458. DOI:10.1108/02635570710734316.
34. Baird K, Hu K and Reeve R. The relationships between orga-
nizational culture, total quality management practices and
operational performance. Int J Operat Product Manage
2011; 31(7): 789–814. DOI:10.1108/01443571111144850.
35. Erwee R, Lynch B, Millet B, et al. Cross-cultural equiva-
lence of the organizational culture survey in Australia. J Ind
Psychol 2001; 27(3): 7–12. DOI:10.1016/j.jom.2009.06.
001.
36. Sikorska-Simmons E. Predictors of organizational commit-
ment among staff in assisted living. Gerontologist 2005;
45(2): 196–205.
37. Chang C and Lin T. The role of organizational culture in the
knowledge management process. J Knowlg Manage 2015;
19(3): 433–455. DOI:10.1108/JKM-08-2014-0353.
38. Park D and Krishnan D. Supplier selection practices among
small firms in the United States: Testing three models.
J Small Bus Manage 2001; 39)3(: 259–271. DOI:10.1111/
0447-2778.00023.
39. Huang S, Uppal M and Shi J. A product driven approach to
manufacturing supply chain selection. Suppl Chain Manage Int
J 2002; 7(4): 189–199. DOI:10.1108.13598540210438944.
40. Huang M, Yen G and Liu T. Reexamining supply chain inte-
gration and the supplier’s performance relationships under
uncertainty. Suppl Chain Manage Int J 2014; 19(1): 64–78.
DOI:10.1108/SCM-04-2013-0114.
41. Arif-Khan K, Bakkappa B, Metri B, et al. Impact of agile supply
chains’ delivery practices on firms’ performance: cluster analysis
and validation. Supp Chain Manage Int J 2009; 14(1): 41–48.
42. Jabbour A, Filho A, Viana A, et al. Measuring supply chain
management practices. Measur Busin Excell 2011; 15(2): 18–31.
43. Al-Tit A. The impact of lean supply chain on productivity of
Saudi manufacturing firms in AL-QASSIM region. Polish J Man-
age Stud 2016; 14(1): 18–27. DOI:10.17512/pjms.2016.14.1.02.
44. Cascio WF. Managing human resources: productivity, qual-
ity of life, profits. 10th ed. McGraw-Hill Irwin, 2015, p. 61.
45. Treacy M and Wiersema F. The discipline of market leaders:
choose your customers, narrow your focus, dominate your
market. Reading, MA: Addison-Wesley, 1995.
46. Zack M, McKeen J and Singh S. Knowledge management
and organizational performance: an exploratory analysis. J
Knowlg Manage 2009; 13(6): 392–409. DOI:10.1108/
13673270910997088.
47. Chia A, Goh M and Hum S. Performance measurement in sup-
ply chain entities: balanced scorecard perspective. Bench Int J
2009; 16(5): 605–620. DOI:10.1108/14635770910987832.
48. Ismail A, Rose R, Abdullah H, et al. The relationship between
organizational competitive advantage and performance mod-
erated by the age and size of firms. Asian Acad Manage J
2010; 15(2): 157–173.
49. Tan Q and Sousa C. Leveraging marketing capabilities into
competitive advantage and export performance. Int Marketing
Rev 2015; 32(1): 78–102. DOI:10.1108/IMR-12-2013-0279.
50. Quinn RE and Spreitzer GM. The psychometrics of the com-
peting values culture instrument and an analysis of the impact
of organizational culture on quality of life. Res Organ
Change Develop 1991; 5: 115–142.
51. Al-Tit A. The mediating role of knowledge management and
the moderating part of organizational culture between HRM
practices and organizational performance. Int Bus Res 2016;
9(1): 43–54. DOI:10.5539/ibr.v9n1p43.
52. Bashayreh A. Organizational culture and effect on organiza-
tional performance: study on Jordanian insurance sector. Int J
Knowlg Syst Sci 2014; 5(2): 35–48. DOI:10.4018/ijkss.
2014040103.
53. Al-Nsour M. Role of organizational culture in improving
employees’ performance in the Jordanian banking sector.
IUG J Econom Busin 2012; 20(2): 187–210.
8 International Journal of Engineering Business Management
54. Flynn B, Huo B and Zhao X. The impact of supply chain
integration on performance: a contingency and configuration
approach. J Oper Manag 2009; 28: 58–71.
55. Hallavo V. Superior performance through supply chain fit: a
synthesis. Suppl Chain Manage Int J 2015; 20(1): 71–82.
DOI:10.1108/SCM-05-2014-0167.
56. Al-Tit A. The effect of service and food quality on customer
satisfaction and hence customer retention. Asian Soc Sci
2015; 11(23): 129–139. DOI:10.5539/ass.v11n23p129.
57. Sekaran U and Bougie R. Research methods for business: a
skill-building approach, 6th ed. New York: John Wiley and
Sons, 2013.
58. Nunnally J and Bernstein I. Psychometric yheory, 3rd ed.
New York: McGraw-Hill, 1994.
59. Abu-Jarad I, Yusof Y and Nikbin D. A review paper on
organizational culture and organizational performance. Int J
Bus Soc Sci 2010; 1(3): 26–46.
60. Holbrook A, Krosnick J and Pfent A. The causes and conse-
quences of response rates in surveys by the news media and
government contractor survey research firms. In: James M.
Lepkowski, Clyde Tucker, J. Michael Brick, Edith de Leeuw,
Lilli Japec, Paul J. Lavrakas, Michael W. Link and Roberta L.
Sangster (eds) Advances in telephone survey methodology.
Hoboken, NJ: John Wiley & Sons, Inc., 2008, pp. 499–528.
Al-Tit 9
<<
/ASCII85EncodePages false
/AllowTransparency false
/AutoPositionEPSFiles true
/AutoRotatePages /None
/Binding /Left
/CalGrayProfile (Gray Gamma 2.2)
/CalRGBProfile (sRGB IEC61966-2.1)
/CalCMYKProfile (U.S. Web Coated \050SWOP\051 v2)
/sRGBProfile (sRGB IEC61966-2.1)
/CannotEmbedFontPolicy /Warning
/CompatibilityLevel 1.4
/CompressObjects /Off
/CompressPages true
/ConvertImagesToIndexed true
/PassThroughJPEGImages false
/CreateJobTicket false
/DefaultRenderingIntent /Default
/DetectBlends true
/DetectCurves 0.1000
/ColorConversionStrategy /LeaveColorUnchanged
/DoThumbnails false
/EmbedAllFonts true
/EmbedOpenType false
/ParseICCProfilesInComments true
/EmbedJobOptions true
/DSCReportingLevel 0
/EmitDSCWarnings false
/EndPage -1
/ImageMemory 1048576
/LockDistillerParams true
/MaxSubsetPct 100
/Optimize true
/OPM 1
/ParseDSCComments true
/ParseDSCCommentsForDocInfo true
/PreserveCopyPage true
/PreserveDICMYKValues true
/PreserveEPSInfo true
/PreserveFlatness false
/PreserveHalftoneInfo false
/PreserveOPIComments false
/PreserveOverprintSettings true
/StartPage 1
/SubsetFonts true
/TransferFunctionInfo /Apply
/UCRandBGInfo /Remove
/UsePrologue false
/ColorSettingsFile ()
/AlwaysEmbed [ true
]
/NeverEmbed [ true
]
/AntiAliasColorImages false
/CropColorImages false
/ColorImageMinResolution 266
/ColorImageMinResolutionPolicy /OK
/DownsampleColorImages true
/ColorImageDownsampleType /Average
/ColorImageResolution 175
/ColorImageDepth -1
/ColorImageMinDownsampleDepth 1
/ColorImageDownsampleThreshold 1.50286
/EncodeColorImages true
/ColorImageFilter /DCTEncode
/AutoFilterColorImages true
/ColorImageAutoFilterStrategy /JPEG
/ColorACSImageDict <<
/QFactor 0.40
/HSamples [1 1 1 1] /VSamples [1 1 1 1]
>>
/ColorImageDict <<
/QFactor 0.76
/HSamples [2 1 1 2] /VSamples [2 1 1 2]
>>
/JPEG2000ColorACSImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 30
>>
/JPEG2000ColorImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 30
>>
/AntiAliasGrayImages false
/CropGrayImages false
/GrayImageMinResolution 266
/GrayImageMinResolutionPolicy /OK
/DownsampleGrayImages true
/GrayImageDownsampleType /Average
/GrayImageResolution 175
/GrayImageDepth -1
/GrayImageMinDownsampleDepth 2
/GrayImageDownsampleThreshold 1.50286
/EncodeGrayImages true
/GrayImageFilter /DCTEncode
/AutoFilterGrayImages true
/GrayImageAutoFilterStrategy /JPEG
/GrayACSImageDict <<
/QFactor 0.40
/HSamples [1 1 1 1] /VSamples [1 1 1 1]
>>
/GrayImageDict <<
/QFactor 0.76
/HSamples [2 1 1 2] /VSamples [2 1 1 2]
>>
/JPEG2000GrayACSImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 30
>>
/JPEG2000GrayImageDict <<
/TileWidth 256
/TileHeight 256
/Quality 30
>>
/AntiAliasMonoImages false
/CropMonoImages false
/MonoImageMinResolution 900
/MonoImageMinResolutionPolicy /OK
/DownsampleMonoImages true
/MonoImageDownsampleType /Average
/MonoImageResolution 175
/MonoImageDepth -1
/MonoImageDownsampleThreshold 1.50286
/EncodeMonoImages true
/MonoImageFilter /CCITTFaxEncode
/MonoImageDict <<
/K -1
>>
/AllowPSXObjects false
/CheckCompliance [
/None
]
/PDFX1aCheck false
/PDFX3Check false
/PDFXCompliantPDFOnly false
/PDFXNoTrimBoxError true
/PDFXTrimBoxToMediaBoxOffset [
0.00000
0.00000
0.00000
0.00000
]
/PDFXSetBleedBoxToMediaBox false
/PDFXBleedBoxToTrimBoxOffset [
0.00000
0.00000
0.00000
0.00000
]
/PDFXOutputIntentProfile (U.S. Web Coated \050SWOP\051 v2)
/PDFXOutputConditionIdentifier (CGATS TR 001)
/PDFXOutputCondition ()
/PDFXRegistryName (http://www.color.org)
/PDFXTrapped /Unknown
/CreateJDFFile false
/Description <<
/ENU
>>
/Namespace [
(Adobe)
(Common)
(1.0)
]
/OtherNamespaces [
<<
/AsReaderSpreads false
/CropImagesToFrames true
/ErrorControl /WarnAndContinue
/FlattenerIgnoreSpreadOverrides false
/IncludeGuidesGrids false
/IncludeNonPrinting false
/IncludeSlug false
/Namespace [
(Adobe)
(InDesign)
(4.0)
]
/OmitPlacedBitmaps false
/OmitPlacedEPS false
/OmitPlacedPDF false
/SimulateOverprint /Legacy
>>
<<
/AllowImageBreaks true
/AllowTableBreaks true
/ExpandPage false
/HonorBaseURL true
/HonorRolloverEffect false
/IgnoreHTMLPageBreaks false
/IncludeHeaderFooter false
/MarginOffset [
0
0
0
0
]
/MetadataAuthor ()
/MetadataKeywords ()
/MetadataSubject ()
/MetadataTitle ()
/MetricPageSize [
0
0
]
/MetricUnit /inch
/MobileCompatible 0
/Namespace [
(Adobe)
(GoLive)
(8.0)
]
/OpenZoomToHTMLFontSize false
/PageOrientation /Portrait
/RemoveBackground false
/ShrinkContent true
/TreatColorsAs /MainMonitorColors
/UseEmbeddedProfiles false
/UseHTMLTitleAsMetadata true
>>
<<
/AddBleedMarks false
/AddColorBars false
/AddCropMarks false
/AddPageInfo false
/AddRegMarks false
/BleedOffset [
9
9
9
9
]
/ConvertColors /ConvertToRGB
/DestinationProfileName (sRGB IEC61966-2.1)
/DestinationProfileSelector /UseName
/Downsample16BitImages true
/FlattenerPreset <<
/ClipComplexRegions true
/ConvertStrokesToOutlines false
/ConvertTextToOutlines false
/GradientResolution 300
/LineArtTextResolution 1200
/PresetName ([High Resolution])
/PresetSelector /HighResolution
/RasterVectorBalance 1
>>
/FormElements true
/GenerateStructure false
/IncludeBookmarks false
/IncludeHyperlinks false
/IncludeInteractive false
/IncludeLayers false
/IncludeProfiles true
/MarksOffset 9
/MarksWeight 0.125000
/MultimediaHandling /UseObjectSettings
/Namespace [
(Adobe)
(CreativeSuite)
(2.0)
]
/PDFXOutputIntentProfileSelector /DocumentCMYK
/PageMarksFile /RomanDefault
/PreserveEditing true
/UntaggedCMYKHandling /UseDocumentProfile
/UntaggedRGBHandling /UseDocumentProfile
/UseDocumentBleed false
>>
]
/SyntheticBoldness 1.000000
>> setdistillerparams
<<
/HWResolution [288 288]
/PageSize [612.000 792.000]
>> setpagedevice
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