· Chapter 5, Formulating the Research Design”· Section 5.2, Ch.docx

· Chapter 5, “Formulating the Research Design”
· Section 5.2, “Choice and Coherence in Research Design” (pp.
163–165)
· Section 5.3, “Methodological Choice: The Use of a
Quantitative, Qualitative or Mixed Methods Research Design”
(pp. 165–174)
5.2 Choice and Coherence in Research Design
Your research design is the general plan of how you will go
about answering your research question(s) (the importance of
clearly defining the research question cannot be
overemphasised). It will contain clear objectives derived from
your research question(s), specify the sources from which you
intend to collect data, how you propose to collect and analyse
these, and discuss
The cover photographs of recent editions of this book have
indicated that the research process is like a journey – a journey
along a road with you as the driver of the vehicle. Like many
such journeys, there is generally a choice of roads to travel
along. When you are thinking about setting out on a new
journey of some distance, you will probably find a road map and
look at the options to get to your destination. A number of
factors may influence your decision about which route to take,
including speed, time, cost and your preference between taking
the shortest route or staying on the motorway network and main
roads. The route you plan is likely to be as coherent as you can
work out from the map in front of you given your travel criteria.
As you actually undertake your journey you will find yourself
interacting with the reality of your planned route. Some parts of
the journey will go according to plan; other parts may not and
you may need to alter your route. You may change your route
because a better option presents itself as you travel along. In
many ways, designing research is like planning a journey.
Formulating the most appropriate way to address your research

question is similar to planning the route to your destination,
your research objectives are a little like your planning criteria,
the need for coherence is the same in each situation and the
journey itself, like the research process, will necessarily prove
to be an interactive experience.
Travelling downriver
Source: © Jan Thornhill 2015
Figure 5.1 The research onion
Source: © 2015 Mark Saunders, Philip Lewis and Adrian
Thornhill
ethical issues and the constraints you will inevitably encounter
(e.g. access to data, time, location and money). Crucially, it
should demonstrate that you have thought through the elements
of your particular research design.
The first methodological choice is whether you follow a
quantitative, qualitative or mixed methods research design.
Each of these options is likely to call for a different mix of
elements to achieve coherence in your research design. We
return to consider what this involves in Section 5.3. The nature
of your research project will also be either exploratory,
descriptive, explanatory, evaluative or a combination of these,
and we discuss the role of these in your research design
in Section 5.4. Within your research design you will need to use
one or more research strategies, to ensure coherence within your
research project. We discuss research strategies, their fit to
research philosophy and to quantitative, qualitative or mixed
methods methodological choices in Section 5.5. Your
methodological choice and related strategies will also influence
the selection of an appropriate time horizon, and we consider
this in Section 5.6. Each research design will lead to potential
ethical concerns and it will be important to consider these, in
order to minimise or overcome them. We briefly consider
ethical issues related to research designs in Section 5.7, before
discussing these in greater detail in Sections 6.5 and 6.6. It is

also important to establish the quality of your research design,
and we discuss the ways in which this may be
considered in Section 5.8. Finally, we recognise that practical
constraints will affect research design, especially the nature of
your own role as researcher, and briefly consider this
in Section5.9.
These aspects of your research design are vital to understand
what you wish to achieve and how you intend to do so, even if
your design changes subsequently. You are likely to be assessed
at this stage of your research project by your university or
examining institution and your research design will need to
achieve a pass standard before you are allowed to proceed. You
therefore need to have a clear design with valid reasons for each
of your research design decisions. Your justification for each
element should be based on the nature of your research
question(s) and objectives, show consistency with your research
philosophy and demonstrate coherence across your research
design.
It is useful at this point to recognise a distinction between
design and tactics. Design is concerned with the overall plan for
your research project; tactics are about the finer details of data
collection and analysis – the centre of the research onion.
Decisions about tactics will involve you being clear about the
different quantitative and qualitative data collection techniques
(e.g. questionnaires, interviews, focus groups and secondary
data) and subsequent quantitative and qualitative data analysis
procedures, which are discussed in later chapters.
We first outline the nature of quantitative, qualitative and
mixed methods research and how these may be combined to help
you to choose and design your research.
5.3 Methodological Choice: The Use of a Quantitative,
Qualitative or Mixed Methods Research Design
One way of differentiating quantitative research from
qualitative research is to distinguish between numeric data
(numbers) and non-numeric data (words, images, video clips
and other similar material). In this way, ‘quantitative’ is often

used as a synonym for any data collection technique (such as a
questionnaire) or data analysis procedure (such as graphs or
statistics) that generates or uses numerical data. In contrast,
‘qualitative’ is often used as a synonym for any data collection
technique (such as an interview) or data analysis procedure
(such as categorising data) that generates or uses non-numerical
data. This is an important way to differentiate this
methodological choice; however, this distinction is both
problematic and narrow.
It is problematic because, in reality, many business and
management research designs are likely to combine quantitative
and qualitative elements. This may be for a number of reasons.
For example, a research design may use a questionnaire but it
may be necessary to ask respondents to answer some ‘open’
questions in their own words rather than ticking the appropriate
box, or it may be necessary to conduct follow-up interviews to
seek to explain findings from the questionnaire. Equally, some
qualitative research data may be analysed quantitatively, or be
used to inform the design of a subsequent questionnaire. In this
way, quantitative and qualitative research may be viewed as two
ends of a continuum, which in practice are often mixed. A
research design may therefore mix methods in a number of
ways, which we discuss later.
The distinction drawn earlier between quantitative research and
qualitative research is also narrow. The purpose
of Chapter 4 was to ask you to consider your research question
through a philosophical lens. Given the way in which your
philosophical assumptions inform your methodological choice,
the initial distinction drawn earlier between numeric and non-
numeric data appears insufficient for the purpose of designing
research. From this broader perspective, we can reinterpret
quantitative and qualitative methodologies through their
associations to philosophical assumptions and also to research
approaches and strategies. This will help you to decide how you
might use these in a coherent way to address your research
question. We now briefly outline some of these key

associations.
Quantitative Research Design
Research Philosophy
Quantitative research is generally associated with positivism,
especially when used with predetermined and highly structured
data collection techniques. However, a distinction needs to be
drawn between data about the attributes of people, organisations
or other things and data based on opinions, sometimes referred
to as ‘qualitative’ numbers (Box 5.1). In this way, some survey
research, while conducted quantitatively, may be seen to fit
partly within an interpretivist philosophy. Quantitative research
may also be used within the realist and pragmatist philosophies
(see ‘Mixed methods research design’ later).
Approach to Theory Development
Quantitative research is usually associated with a deductive
approach, where the focus is on using data to test theory.
However, it may also incorporate an inductive approach, where
data are used to develop theory.
Characteristics
Quantitative research examines relationships between variables,
which are measured numerically and analysed using a range of
statistical and graphical techniques. It often incorporates
controls to ensure the validity of data, as in an experimental
design. Because data are collected in a standard manner, it is
important to ensure that questions are expressed clearly so they
are understood in the same way by each participant. This
methodology often uses probability sampling techniques to
ensure generalisability (Section 7.2). The researcher is seen as
independent from those being researched, who are usually
called respondents.
A quantitative research design may use a single data collection
technique, such as a questionnaire, and corresponding
quantitative analytical procedure. This is known as a mono
method quantitative study(Figures 5.1 and 5.2). A quantitative
research design may also use more than one quantitative data
collection technique and corresponding analytical procedure.

This is known as a multi-method quantitative
study (Figures 5.1 and 5.2). You might, for example, decide to
collect quantitative data using both questionnaires and
structured observation, analysing these data using statistical
(quantitative) procedures. Multi-method is the branch
of multiple methods research that uses more than one
quantitative or qualitative method but does not mix the two
(Figure 5.2).
Use of multiple methods has been advocated within business
and management research (Bryman 2006) because it is likely to
overcome weaknesses associated with using only a single or
mono method, as well as providing scope for a richer approach
to data collection, analysis and interpretation.
Box 5.1 Research in the News
Middle-Aged Are so Downbeat about Money
By Norma Cohen
The 45 to 54-year-old cohort have high but increasingly
unrealistic expectations and struggle to make sense of their
financial futures, reports Norma Cohen.
Early middle age, it seems, is the new winter of our discontent.
According to a new study from fund managers Black-Rock,
people aged 45 to 54 are the most negative about their financial
future and the least confident about their ability to control their
finances, pay for their children’s education or make the right
decisions about investments.
That is not what you might expect. The young, embarking on
their careers while saddled with heavy debts, and facing a
struggle to get on the housing ladder, are more optimistic and in
some ways better prepared. Those closest to retirement are
content with their lot. But the group who appear to have the best
odds of managing their way out of tough times and into a
reasonable retirement are thoroughly miserable – in the UK and
in other countries.
A close look at data from Britain’s Office for National Statistics
backs up the hunch that this group is doing fine. On average,
wealth is highest among the 45 to 64-year-old age group,

remains relatively high among the 65-plus age group, but is
lower for households with adults aged 25 to 44 in which
children or young adults live, the ONS says in its latest report
on household wealth. Roughly a quarter of 45 to 54-year-olds
have total household wealth of between £500,000 and £1m –
and a further fifth have wealth of more than £1m. That is much
higher than younger age groups – and not much lower than for
over-65s.
Neither has this group suffered from unemployment; the
unemployment rate for those aged 35 to 49 and those aged 50 to
64 has straddled 5 per cent against a national average of 7.7–8.0
per cent. And of the younger group, 92 per cent are
participating in work. But this group is much less satisfied with
its income than those aged 60 and over, although marginally
happier with it than are younger groups. The ONS found they
are much more likely to describe their financial situation as
“quite or very difficult” than those aged over 55.
Greg Davies, head of behavioural finance at Barclays Wealth,
says that the reason the 45 to 54-year-old age group might feel
miserable and gloomy may not be because objectively, its
finances are deteriorating. Rather, it is just a tough age to be
generally.
“This is a pattern we see globally,” Mr Davies says, noting that
this age group appears glum in happiness surveys in many
countries (as it did in the BlackRock one). “There is a U-shaped
curve in happiness. It may have nothing to do with their
finances.”
Source of extracts: Cohen, N. (2013) ‘Middle aged Britons are
so downbeat about money’, Financial Times, 02 November.
Copyright The Financial Times Limited.
Figure 5.2 Methodological choice
Post an analysis of ethical considerations for target populations

within the doctoral research process. Your analysis should
include the following:
· Briefly describe a target population within your Doctoral
Study, including any relevant factors that could be scrutinized
by an IRB committee.
· Identify specific ethical considerations for the target
population within your Doctoral Study, including access, data,
or publication restrictions, for example.
· Explain how this population and its ethical considerations
impact both the process and the overall value of your doctoral
research study.
Be sure to support your work with a minimum of two specific
citations from this week’s Learning Resources and at least
one additional scholarly source.
Michael
Researchers must act ethically when finding and studying
people, especially when working with vulnerable populations. A
scholarly study may include interviews, surveys, and
questionnaires that highlight personal or identifying
information, so it is imperative that researchers keep in mind
the privacy of the participants. In this week’s discussion, I
describe a target population for my Doctoral Study and some
factors that could be scrutinized by an Institutional Review
Board (IRB) committee. I then identify specific ethical
considerations for my target population. Finally, I explain how
the population and ethical considerations impact my research.
Target Population and the IRB
The target population for my study will primarily be low-skilled
workers without college degrees. This group is not a vulnerable
population, but some issues could draw scrutiny from an IRB
committee. Walden University (2015a) noted that questions that

could potentially get someone fired or that are self-
incriminating benefit from a preemptive ethics consultation. I
plan to create a questionnaire that questions the employee’s
motivation and how the employer could increase it by offering
certain fringe benefits. I do not believe the questions could hurt
the employee, but I will do everything I can to protect the
identity of the respondents.
Ethical Considerations
One consideration that I must be careful with is the fact that I
sell employee benefits to other businesses. My questions will
specifically ask which benefits the employee desires and it
might be challenging for me not to intervene. I do not currently
know the exact organizations I will contact to gain access to
employees, but I live in a tourist town in Florida with many
hotels. I see them as an opportunity as well as the small
restaurants in my area. Physical access may take weeks or even
months to arrange and in some cases does not guarantee access
(Saunders, Lewis, & Thornhill, 2015). I believe a quick meeting
with a gatekeeper should suffice when it comes to connecting
with the organization. I will not need any personal data and plan
to use an internet-based survey, which should make it easier.
My prediction is that I will complete a couple of meetings to
gain their trust.
Considering Ethical Impacts
I do not believe the previously stated ethical considerations will
influence my Doctoral Study. I foresee the most challenging
part of the process as gaining access to employees. I do not see
everyone opening his or her establishment to my questions,
which could slow down the process, but I do not think it will
become a hindrance. I believe that the value of my study is
increased by me being knowledgeable about employee benefits.
It allows me to ask questions that are more pertinent and not
become overburdened by having to learn additional information.
I do not see any conflict of interest here because I will not
personally contact the employees and plan to not share the
survey results with the gatekeeper, if possible. Conflict of

interest occurs when someone’s financial, personal,
professional, or political interests interfere or potentially
interfere with their judgment (Ethical Considerations, 2017).
Conclusion
Research studies use many different populations to build
statistics to support a study’s report. Each person participating
deserves a certain level of respect and an amount of anonymity
throughout the entire process. It is critical that scholars use the
data in an ethical way that demonstrates the integrity of the
study performed. This discussion examined a target population
for my Doctoral Study. It then identified some factors the IRB
may consider when looking at my target population. Lastly, it
explained how the population and ethical consideration might
influence my paper. It is critical that researchers protect the
identities of participants and use their information ethically. If
done incorrectly, it could negatively impact a report and
discourage other people from participating in future studies.
References
Ethical Considerations for Successfully Navigating the Research
Process. (2017). Journal of the Academy of Nutrition and
Dietetics. https://doi-
org.ezp.waldenulibrary.org/10.1016/j.jand.2017.02.011
Saunders, M. N. K., Lewis, P., & Thornhill, A. (2015). Research
methods for business students (7th ed.). Essex, England:
Pearson Education Limited.
Walden University Center for Research Quality. (2015a).
Research ethics & compliance: Guides and FAQs. Retrieved
from
http://academicguides.waldenu.edu/researchcenter/orec/guides

Guillermo
Target Population
My research includes buyers and sellers of capital goods. For
this research, capital goods are products that are not purchased
frequently by an organization and require careful consideration
due to technical complexity, the importance of the product to
the customer's operation, the long-life span of the product, and
the investment of financial and other resources to install and
operate. Capital goods often are selected by cross-functional
teams defined as decision-making units (DMU). The DMU for
buyers and sellers is the target population for the research and
is composed of middle and upper management, and the focus is
on processes, roles and influence factors that are rarely
confidential. The IRB may need a description of the key roles,
responsibilities, and limitations in the DMU to avoid possible
conflict between the members of the DMUs and their employer.
Ethical Considerations
Two ethical challenges may be present: Confidentiality and
conflict of interest. Confidentiality is critical because the
decision-making process is often confidential due to the
strategic nature of capital goods, in some cases the company’s
sign non-disclosure agreements to keep the transaction’s details
private. Detailed information needs to be sufficiently conveyed
to support findings without revealing proprietary or confidential
information about the specific respondent’s organizations. As
an academic practitioner, the risk of conflict of interest
increases. The process of academic study often provides insight
into a firm’s decision-making process, policy, and guiding
principles, and this information can be used to guide, educate or
influence the transaction. For example, the researcher can find
that a particular organization places more importance to cash

flow than price and may influence a potential seller to
proactively offer non-standard term to charge a higher price
with generous payment terms and gain an unfair advantage over
competitors.
Value of Doctoral Study
The understanding of how DMU’s gather, analyze and evaluate
potential suppliers of capital goods, and the role of
relationships with the incumbent supplier, can have a positive
impact by guiding the time and resource investment for building
and maintaining customer relationships. Customer relationship
management (CRM) has been shown to play an essential role in
B2B transactions and that relationships are both valid and
necessary if competitive advantage is understood to drive
commitment and loyalty between customers and sellers to their
mutual benefit (Martensen & Mouritsen, 2017). Additional
research can generate positive social change by helping
organizations make more efficient use of their resources.
References
Martensen, A., & Mouritsen, J. (2017). Prioritizing marketing
activities in different types of marketing functions. Total
Quality Management & Business Excellence, 28(11-12), 1264-
1284. doi:http://dx.doi.org/10.1080/14783363.2015.1135726
Walden University Center for Research Quality. (2015a).
Research ethics & compliance: Guides and FAQs. Retrieved
from
http://academicguides.waldenu.edu/researchcenter/orec/guides
· Chapter 5, “Formulating the Research Design”
· Section 5.2, “Choice and Coherence in Research Design” (pp.
163–165)

· Section 5.3, “Methodological Choice: The Use of a
Quantitative, Qualitative or Mixed Methods Research Design”
(pp. 165–174)
5.2 Choice and Coherence in Research Design
Your research design is the general plan of how you will go
about answering your research question(s) (the importance of
clearly defining the research question cannot be
overemphasised). It will contain clear objectives derived from
your research question(s), specify the sources from which you
intend to collect data, how you propose to collect and analyse
these, and discuss
The cover photographs of recent editions of this book have
indicated that the research process is like a journey – a journey
along a road with you as the driver of the vehicle. Like many
such journeys, there is generally a choice of roads to travel
along. When you are thinking about setting out on a new
journey of some distance, you will probably find a road map and
look at the options to get to your destination. A number of
factors may influence your decision about which route to take,
including speed, time, cost and your preference between taking
the shortest route or staying on the motorway network and main
roads. The route you plan is likely to be as coherent as you can
work out from the map in front of you given your travel criteria.
As you actually undertake your journey you will find yourself
interacting with the reality of your planned route. Some parts of
the journey will go according to plan; other parts may not and
you may need to alter your route. You may change your route
because a better option presents itself as you travel along. In
many ways, designing research is like planning a journey.
Formulating the most appropriate way to address your research
question is similar to planning the route to your destination,
your research objectives are a little like your planning criteria,
the need for coherence is the same in each situation and the
journey itself, like the research process, will necessarily prove
to be an interactive experience.

Travelling downriver
Source: © Jan Thornhill 2015
Figure 5.1 The research onion
Source: © 2015 Mark Saunders, Philip Lewis and Adrian
Thornhill
ethical issues and the constraints you will inevitably encounter
(e.g. access to data, time, location and money). Crucially, it
should demonstrate that you have thought through the elements
of your particular research design.
The first methodological choice is whether you follow a
quantitative, qualitative or mixed methods research design.
Each of these options is likely to call for a different mix of
elements to achieve coherence in your research design. We
return to consider what this involves in Section 5.3. The nature
of your research project will also be either exploratory,
descriptive, explanatory, evaluative or a combination of these,
and we discuss the role of these in your research design
in Section 5.4. Within your research design you will need to use
one or more research strategies, to ensure coherence within your
research project. We discuss research strategies, their fit to
research philosophy and to quantitative, qualitative or mixed
methods methodological choices in Section 5.5. Your
methodological choice and related strategies will also influence
the selection of an appropriate time horizon, and we consider
this in Section 5.6. Each research design will lead to potential
ethical concerns and it will be important to consider these, in
order to minimise or overcome them. We briefly consider
ethical issues related to research designs in Section 5.7, before
discussing these in greater detail in Sections 6.5 and 6.6. It is
also important to establish the quality of your research design,
and we discuss the ways in which this may be
considered in Section 5.8. Finally, we recognise that practical
constraints will affect research design, especially the nature of
your own role as researcher, and briefly consider this

in Section5.9.
These aspects of your research design are vital to understand
what you wish to achieve and how you intend to do so, even if
your design changes subsequently. You are likely to be assessed
at this stage of your research project by your university or
examining institution and your research design will need to
achieve a pass standard before you are allowed to proceed. You
therefore need to have a clear design with valid reasons for each
of your research design decisions. Your justification for each
element should be based on the nature of your research
question(s) and objectives, show consistency with your research
philosophy and demonstrate coherence across your research
design.
It is useful at this point to recognise a distinction between
design and tactics. Design is concerned with the overall plan for
your research project; tactics are about the finer details of data
collection and analysis – the centre of the research onion.
Decisions about tactics will involve you being clear about the
different quantitative and qualitative data collection techniques
(e.g. questionnaires, interviews, focus groups and secondary
data) and subsequent quantitative and qualitative data analysis
procedures, which are discussed in later chapters.
We first outline the nature of quantitative, qualitative and
mixed methods research and how these may be combined to help
you to choose and design your research.
5.3 Methodological Choice: The Use of a Quantitative,
Qualitative or Mixed Methods Research Design
One way of differentiating quantitative research from
qualitative research is to distinguish between numeric data
(numbers) and non-numeric data (words, images, video clips
and other similar material). In this way, ‘quantitative’ is often
used as a synonym for any data collection technique (such as a
questionnaire) or data analysis procedure (such as graphs or
statistics) that generates or uses numerical data. In contrast,
‘qualitative’ is often used as a synonym for any data collection
technique (such as an interview) or data analysis procedure

(such as categorising data) that generates or uses non-numerical
data. This is an important way to differentiate this
methodological choice; however, this distinction is both
problematic and narrow.
It is problematic because, in reality, many business and
management research designs are likely to combine quantitative
and qualitative elements. This may be for a number of reasons.
For example, a research design may use a questionnaire but it
may be necessary to ask respondents to answer some ‘open’
questions in their own words rather than ticking the appropriate
box, or it may be necessary to conduct follow-up interviews to
seek to explain findings from the questionnaire. Equally, some
qualitative research data may be analysed quantitatively, or be
used to inform the design of a subsequent questionnaire. In this
way, quantitative and qualitative research may be viewed as two
ends of a continuum, which in practice are often mixed. A
research design may therefore mix methods in a number of
ways, which we discuss later.
The distinction drawn earlier between quantitative research and
qualitative research is also narrow. The purpose
of Chapter 4 was to ask you to consider your research question
through a philosophical lens. Given the way in which your
philosophical assumptions inform your methodological choice,
the initial distinction drawn earlier between numeric and non-
numeric data appears insufficient for the purpose of designing
research. From this broader perspective, we can reinterpret
quantitative and qualitative methodologies through their
associations to philosophical assumptions and also to research
approaches and strategies. This will help you to decide how you
might use these in a coherent way to address your research
question. We now briefly outline some of these key
associations.
Quantitative Research Design
Research Philosophy
Quantitative research is generally associated with positivism,
especially when used with predetermined and highly structured

data collection techniques. However, a distinction needs to be
drawn between data about the attributes of people, organisations
or other things and data based on opinions, sometimes referred
to as ‘qualitative’ numbers (Box 5.1). In this way, some survey
research, while conducted quantitatively, may be seen to fit
partly within an interpretivist philosophy. Quantitative research
may also be used within the realist and pragmatist philosophies
(see ‘Mixed methods research design’ later).
Approach to Theory Development
Quantitative research is usually associated with a deductive
approach, where the focus is on using data to test theory.
However, it may also incorporate an inductive approach, where
data are used to develop theory.
Characteristics
Quantitative research examines relationships between variables,
which are measured numerically and analysed using a range of
statistical and graphical techniques. It often incorporates
controls to ensure the validity of data, as in an experimental
design. Because data are collected in a standard manner, it is
important to ensure that questions are expressed clearly so they
are understood in the same way by each participant. This
methodology often uses probability sampling techniques to
ensure generalisability (Section 7.2). The researcher is seen as
independent from those being researched, who are usually
called respondents.
A quantitative research design may use a single data collection
technique, such as a questionnaire, and corresponding
quantitative analytical procedure. This is known as a mono
method quantitative study(Figures 5.1 and 5.2). A quantitative
research design may also use more than one quantitative data
collection technique and corresponding analytical procedure.
This is known as a multi-method quantitative
study (Figures 5.1 and 5.2). You might, for example, decide to
collect quantitative data using both questionnaires and
structured observation, analysing these data using statistical
(quantitative) procedures. Multi-method is the branch

of multiple methods research that uses more than one
quantitative or qualitative method but does not mix the two
(Figure 5.2).
Use of multiple methods has been advocated within business
and management research (Bryman 2006) because it is likely to
overcome weaknesses associated with using only a single or
mono method, as well as providing scope for a richer approach
to data collection, analysis and interpretation.
Box 5.1 Research in the News
Middle-Aged Are so Downbeat about Money
By Norma Cohen
The 45 to 54-year-old cohort have high but increasingly
unrealistic expectations and struggle to make sense of their
financial futures, reports Norma Cohen.
Early middle age, it seems, is the new winter of our discontent.
According to a new study from fund managers Black-Rock,
people aged 45 to 54 are the most negative about their financial
future and the least confident about their ability to control their
finances, pay for their children’s education or make the right
decisions about investments.
That is not what you might expect. The young, embarking on
their careers while saddled with heavy debts, and facing a
struggle to get on the housing ladder, are more optimistic and in
some ways better prepared. Those closest to retirement are
content with their lot. But the group who appear to have the best
odds of managing their way out of tough times and into a
reasonable retirement are thoroughly miserable – in the UK and
in other countries.
A close look at data from Britain’s Office for National Statistics
backs up the hunch that this group is doing fine. On average,
wealth is highest among the 45 to 64-year-old age group,
remains relatively high among the 65-plus age group, but is
lower for households with adults aged 25 to 44 in which
children or young adults live, the ONS says in its latest report
on household wealth. Roughly a quarter of 45 to 54-year-olds
have total household wealth of between £500,000 and £1m –

and a further fifth have wealth of more than £1m. That is much
higher than younger age groups – and not much lower than for
over-65s.
Neither has this group suffered from unemployment; the
unemployment rate for those aged 35 to 49 and those aged 50 to
64 has straddled 5 per cent against a national average of 7.7–8.0
per cent. And of the younger group, 92 per cent are
participating in work. But this group is much less satisfied with
its income than those aged 60 and over, although marginally
happier with it than are younger groups. The ONS found they
are much more likely to describe their financial situation as
“quite or very difficult” than those aged over 55.
Greg Davies, head of behavioural finance at Barclays Wealth,
says that the reason the 45 to 54-year-old age group might feel
miserable and gloomy may not be because objectively, its
finances are deteriorating. Rather, it is just a tough age to be
generally.
“This is a pattern we see globally,” Mr Davies says, noting that
this age group appears glum in happiness surveys in many
countries (as it did in the BlackRock one). “There is a U-shaped
curve in happiness. It may have nothing to do with their
finances.”
Source of extracts: Cohen, N. (2013) ‘Middle aged Britons are
so downbeat about money’, Financial Times, 02 November.
Copyright The Financial Times Limited.
Figure 5.2 Methodological choice
· Chapter 4, “Understanding Research Philosophy and
Approaches to Theory Development”

4.2 The Philosophical Underpinnings of Business and
Management
What Is Research Philosophy?
The term research philosophy refers to a system of beliefs and
assumptions about the development of knowledge. Although this
sounds rather profound, it is precisely what you are doing when
embarking on research: developing knowledge in a particular
field. The knowledge development you are embarking upon may
not be as dramatic as a new theory of human motivation, but
even answering a specific problem in a particular organisation
you are, nonetheless, developing new knowledge.
Whether you are consciously aware of them or not, at every
stage in your research you will make a number of types of
assumption (Burrell and Morgan 1979). These include
assumptions about human knowledge (epistemological
assumptions), about the realities you encounter in your research
(ontological assumptions) and the extent and ways your own
values influence your research process (axiological
assumptions). These assumptions inevitably shape how you
understand your research questions, the methods you use and
how you interpret your findings (Crotty 1998). A well-thought-
out and consistent set of assumptions will constitute a credible
research philosophy, which will underpin your methodological
choice, research strategy and data collection techniques and
analysis procedures. This will allow you to design a coherent
research project, in which all elements of research fit
together. Johnson and Clark (2006) note that, as business and
management researchers, we need to be aware of the
philosophical commitments we make through our choice of
research strategy, since this will have a significant impact on
what we do and how we understand what it is we are
investigating.
Prior to undertaking a research methods module, few of our
students have thought about their own beliefs about the nature
of the world around them, what constitutes acceptable and
desirable knowledge, or the extent to which they believe it

necessary to remain detached from their research data. The
process of exploring and understanding your own research
philosophy requires you to hone the skill of reflexivity, that is,
to question your own thinking and actions, and learn to examine
your own beliefs with the same scrutiny as you would apply to
the beliefs of others (Gouldner 1970). This may sound daunting,
but we all do this in our day-to-day lives when we learn from
our mistakes. As a researcher, you need to develop your
reflexivity, to become aware of and actively shape the
relationship between your philosophical position and how you
undertake your research (Alvesson and Sköldberg 2000).
You may be wondering about the best way to start this reflexive
process. In part, your exploration of your philosophical position
and how to translate it into a coherent research practice will be
influenced by practical considerations, such as the time and
finances available for your research project, and the access you
can negotiate to data. However, there are two things that you
can do to start making a more active and informed philosophical
choice:
· begin asking yourself questions about your research beliefs
and assumptions;
· familiarise yourself with major research philosophies within
business and management.
This section introduces you to the philosophical underpinnings
of business and management, and Section 4.3 to the five
research philosophies most commonly adopted by its
researchers. We will encourage you to reflect on your own
beliefs and assumptions in relation to these five philosophies
and the research design you will use to undertake your research
(Figure4.2). The chapter will also help you to outline your
philosophical choices and justify them in relation to the
alternatives you could have adopted (Johnson and Clark 2006).
Through this you will be better equipped to explain and justify
your methodological choice, research strategy and data
collection procedures and analysis techniques.
At the end of the chapter in the section ‘Progressing your

research project’, you will find a reflexive tool (HARP)
designed by Bristow and Saunders to help you think about your
values and beliefs in relation to research. This will help you to
make your values and assumptions more explicit, explain them
using the language of research philosophy, and consider the
potential fit between your own beliefs and those of the five
major philosophies used in business and management research.
Is There a Best Philosophy for Business and Management
Research?
You may be wondering at this stage whether you could take a
shortcut, and simply adopt ‘the best’ philosophy for business
and management research. One problem with such a shortcut
would be the possibility of discovering a clash between ‘the
best’ philosophy and your own beliefs and assumptions.
Another problem would be that
Figure 4.2 Developing your research philosophy: a reflexive
process
Source: © Alexandra Bristow and Mark Saunders 2015
business and management researchers do not agree about one
best philosophy (Tsoukas and Knudsen 2003). In terms of
developing your own philosophy and designing your research
project, it is important to recognise that philosophical
disagreements are an intrinsic part of business and management
research. When business and management emerged as an
academic discipline in the twentieth century, it drew its
theoretical base from a mixture of disciplines in the social
sciences (e.g. sociology, psychology, economics), natural
sciences (e.g. chemistry, biology), applied sciences (e.g.
engineering, statistics), humanities (e.g. literary theory,
linguistics, history, philosophy) and the domain of
organisational practice (Starbuck 2003). In drawing on these
disciplines it absorbed the various associated philosophies
dividing and defining them, resulting in the coexistence of
multiple research philosophies, paradigms and approaches and
methodologies we see today.

Business and management scholars have spent long decades
debating whether this multiplicity of research philosophies,
paradigms and methodologies is desirable, and have reached no
agreement. Instead, two opposing perspectives have emerged:
pluralism and unificationism. Unificationists see business and
management as fragmented, and argue that this fragmentation
prevents it from becoming more like a true scientific discipline.
They advocate unification of management research under one
strong research philosophy, paradigm and methodology.
Pluralists see the diversity of the field as helpful, arguing it
enriches business and management (Knudsen 2003).
In this chapter, we take a pluralist approach and suggest that
each research philosophy and paradigm contributes something
unique and valuable to business and management research,
representing a different and distinctive ‘way of seeing’
organisational realities (Morgan 1986). However, we believe
that you need to be aware of the depth of difference and
disagreements between these distinct philosophies. This will
help you to both outline and justify your own philosophical
choices in relation to your chosen research method.
4.3 Five Major Philosophies
In this section, we discuss five major philosophies in business
and management: positivism, critical realism, interpretivism,
postmodernism and pragmatism (Figure 4.1).Positivism
We introduced the research philosophy of positivism briefly in
the discussion of objectivism and functionalism earlier in this
chapter. Positivism relates to the philosophical stance of the
natural scientist and entails working with an observable social
reality to produce law-like generalisations. It promises
unambiguous and accurate knowledge and originates in the
works of Francis Bacon, Auguste Comte and the early
twentieth-century group of philosophers and scientists known as
the Vienna Circle. The label positivism refers to the importance
of what is ‘posited’ – i.e. ‘given’. This emphasises the positivist
focus on strictly scientific empiricist method designed to yield
pure data and facts uninfluenced by human interpretation or bias

(Table4.3). Today there is a ‘bewildering array of positivisms’,
some counting as many as 12 varieties (Crotty 1998).
If you were to adopt an extreme positivist position, you would
see organisations and other social entities as real in the same
way as physical objects and natural phenomena are real.
Epistemologically you would focus on discovering observable
and measurable facts and regularities, and only phenomena that
you can observe and measure would lead to the production of
credible and meaningful data (Crotty 1998). You would look for
causal relationships in your data to create law-like
generalisations like those produced by scientists (Gill and
Johnson 2010). You would use these universal rules and laws to
help you to explain and predict behaviour and events in
organisations.Table 4.3 Comparison of five research
philosophies in business and management research
Ontology (nature of reality or being)
Epistemology (what constitutes acceptable knowledge)
Axiology (role of values)
Typical methods
Positivism
Real, external, independent
One true reality (universalism)
Granular (things)
Ordered
Scientific method
Observable and measurable facts Law-like generalisations
Numbers
Causal explanation and prediction as contribution
Value-free research
Researcher is detached, neutral and independent of what is
researched
Researcher maintains objective stance
Typically deductive, highly structured, large samples,
measurement, typically quantitative methods of analysis, but a
range of data can be analysed
Critical realism

Stratified/layered (the empirical, the actual and the real)
External, independent Intransient
Objective structures
Causal mechanisms
Epistemological relativism
Knowledge historically situated and transient
Facts are social constructions
Historical causal explanation as contribution
Value-laden research
Researcher acknowledges bias by world views, cultural
experience and upbringing
Researcher tries to minimise bias and errors
Researcher is as objective as possible
Retroductive, in-depth historically situated analysis of pre-
existing structures and emerging agency. Range of methods and
data types to fit subject matter
Interpretivism
Complex, rich
Socially constructed through culture and language
Multiple meanings, interpretations, realities
Flux of processes, experiences, practices
Theories and concepts too simplistic
Focus on narratives, stories, perceptions and interpretations
New understandings and worldviews as contribution
Value-bound research
Researchers are part of what is researched, subjective
Researcher interpretations key to contribution
Researcher reflexive
Typically inductive. Small samples, in-depth investigations,
qualitative methods of analysis, but a range of data can be
interpreted
Postmodernism
Nominal
Complex, rich
Socially constructed through power relations
Some meanings, interpretations, realities are dominated and

silenced by others
What counts as ‘truth’ and ‘knowledge’ is decided by dominant
ideologies
Focus on absences, silences and oppressed/repressed meanings,
interpretations and voices
Exposure of power relations and challenge of dominant views as
contribution
Value-constituted research
Researcher and research embedded in power relations
Some research narratives are repressed and silenced at the
expense of others
Researcher radically reflexive
Typically deconstructive – reading texts and realities against
themselves
In-depth investigations of anomalies, silences and absences
Range of data types, typically qualitative methods of analysis
Pragmatism
Complex, rich, external
‘Reality’ is the practical consequences of ideas
Flux of processes, experiences and practices
Practical meaning of knowledge in specific contexts
‘True’ theories and knowledge are those that enable successful
action
Focus on problems, practices and relevance
Problem solving and informed future practice as contribution
Value-driven research
Research initiated and sustained by researcher’s doubts and
beliefs
Following research problem and research question
Range of methods: mixed, multiple, qualitative, quantitative,
action research
Emphasis on practical solutions and outcomes
As a positivist researcher you might use existing theory to
develop hypotheses. These hypotheses would be tested and

confirmed, in whole or part, or refuted, leading to the further
development of theory which then may be tested by further
research. However, this does not mean that, as a positivist, you
necessarily have to start with existing theory. All natural
sciences have developed from an engagement with the world in
which data were collected and observations made prior to
hypotheses being formulated and tested. The hypotheses
developed, as in Box 4.5, would lead to the gathering of facts
(rather than impressions) that would provide the basis for
subsequent hypothesis testing.
As a positivist you would also try to remain neutral and
detached from your research and data in order to avoid
influencing your findings (Crotty 1998). This means that you
would undertake research, as far as possible, in a value-free
way. For positivists, this is a plausible position, because of the
measurable, quantifiable data that they collect. They claim to be
external to the process of data collection as there is little that
can be done to alter the substance of the data collected.
Consider, for example, the differences between data collected
using an Internet questionnaire (Chapter 11) in which the
respondent self-selects from responses predetermined by the
researcher, and in-depth interviews (Chapter 10). In the Internet
questionnaire, the researcher determines the list of possible
responses as part of the design process. Subsequent to this she
or he
4.4 Approaches to Theory Development
We emphasised that your research project will involve the use
of theory (Chapter 2). That theory may or may not be made
explicit in the design of the research (Chapter 5), although it
will usually be made explicit in your presentation of the
findings and conclusions. The extent to which you are clear
about the theory at the beginning of your research raises an
important question concerning the design of your research
project. This is often portrayed as two contrasting approaches to
the reasoning you adopt: deductive or inductive. Deductive

reasoning occurs when the conclusion is derived logically from
a set of premises, the conclusion being true when all the
premises are true (Ketokivi and Mantere 2010). For example,
our research may concern likely online retail sales of a soon-to-
be-launched new games console. We form three premises:
· that online retailers have been allocated limited stock of the
new games consoles by the manufacturer;
· that customers’ demand for the consoles exceeds supply;
· that online retailers allow customers to pre-order the consoles.
If these premises are true we can deduce that the conclusion that
online retailers will have ‘sold’ their entire allocation of the
new games consoles by the release day will also be true.
In contrast, in inductive reasoning there is a gap in the logic
argument between the conclusion and the premises observed,
the conclusion being ‘judged’ to be supported by the
observations made (Ketokivi and Mantere 2010). Returning to
our example of the likely online retail sales of a soon-to-be-
launched new games console, we would start with observations
about the forthcoming launch. Our observed premises would be:
· that news media are reporting that online retailers are
complaining about only being allocated limited stock of the new
games consoles by manufacturers;
· that news media are reporting that demand for the consoles
will exceed supply;
· that online retailers are allowing customers to pre-order the
consoles.
Based on these observations, we have good reason to believe
new games consoles by the release day. However, although our
conclusion is supported by our observations, it is not
guaranteed. In the past, manufacturers have launched new
games consoles which have been commercial failures
(Zigterman 2013).
There is also a third approach to theory development that is just
as common in research, abductive reasoning, which begins with
a ‘surprising fact’ being observed (Ketokivi and Mantere 2010).

This surprising fact is the conclusion rather than a premise.
Based on this conclusion, a set of possible premises is
determined that is considered sufficient or nearly sufficient to
explain the conclusion. It is reasoned that, if this set
of premises was true, then the conclusion would be true as a
matter of course. Because the set of premises is sufficient (or
nearly sufficient) to generate the conclusion, this provides
reason to believe that it is also true. Returning once again to our
example of the likely online retail sales of a soon-to-be-
launched new games console, a surprising fact (conclusion)
might be that online retailers are reported in the news media as
stating they will have no remaining stock of the new games
console for sale on the day of its release. However, if the online
retailers are allowing customers to pre-order the console prior
to its release then it would not be surprising if these retailers
had already sold their allocation of consoles. Therefore, using
abductive reasoning, the possibility that online retailers have no
remaining stock on the day of release is reasonable.
Building on these three approaches to theory development
(Figure 4.1), if your research starts with theory, often developed
from your reading of the academic literature, and you design a
research strategy to test the theory, you are using a deductive
approach (Table 4.4). Conversely, if your research starts by
collecting data to explore a phenomenon and you generate or
build theory (often in the form of a conceptual framework), then
you are using an inductive approach (Table 4.4). Where you are
collecting data to explore a phenomenon, identify themes and
explain patterns, to generate a new or modify an existing theory
which you subsequently test through additional data collection,
you are using an abductive approach (Table 4.4).
The next three sub-sections explore the differences and
similarities between these three approaches and their
implications for your research.
Table 4.4 Deduction, induction and abduction: from reason to
research

Deduction
Induction
Abduction
Logic
In a deductive inference, when the premises are true, the
conclusion must also be true
In an inductive inference, known premises are used to generate
untested conclusions
In an abductive inference, known premises are used to generate
testable conclusions
Generalisability
Generalising from the general to the specific
Generalising from the specific to the general
Generalising from the interactions between the specific and the
general
Use of data
Data collection is used to evaluate propositions or hypotheses
related to an existing theory
Data collection is used to explore a phenomenon, identify
themes and patterns and create a conceptual framework
themes and patterns, locate these in a conceptual framework and
test this through subsequent data collection and so forth
Theory
Theory falsification or verification
Theory generation and building
Theory generation or modification; incorporating existing
theory where appropriate, to build new theory or modify
existing theory
Deduction
As noted earlier, deduction owes much to what we would think
of as scientific research. It involves the development of a theory
that is then subjected to a rigorous test through a series of
propositions. As such, it is the dominant research approach in
the natural sciences, where laws present the basis of
explanation, allow the anticipation of phenomena, predict their

occurrence and therefore permit them to be controlled.
Blaikie (2010) lists six sequential steps through which a
deductive approach will progress:
1. Put forward a tentative idea, a premise, a hypothesis (a
testable proposition about the relationship between two or more
concepts or variables) or set of hypotheses to form a theory.
2. By using existing literature, or by specifying the conditions
under which the theory is expected to hold, deduce a testable
proposition or number of propositions.
3. Examine the premises and the logic of the argument that
produced them, comparing this argument with existing theories
to see if it offers an advance in understanding. If it does, then
continue.
4. Test the premises by collecting appropriate data to measure
the concepts or variables and analysing them.
5. If the results of the analysis are not consistent with the
premises (the tests fail!), the theory is false and must either be
rejected or modified and the process restarted.
6. If the results of the analysis are consistent with the premises
then the theory is corroborated.
Deduction possesses several important characteristics. First,
there is the search to explain causal relationships between
concepts and variables. It may be that you wish to establish the
reasons for high employee absenteeism in a retail store. After
reading about absence patterns in the academic literature you
develop a theory that there is a relationship between absence,
the age of workers and length of service. Consequently, you
develop a number of hypotheses, including one which states that
absenteeism is significantly more likely to be prevalent among
younger workers and another which states that absenteeism is
significantly more likely to be prevalent among workers who
have been employed by the organisation for a relatively short
period of time. To test this proposition you collect quantitative
data. (This is not to say that a deductive approach may not use
qualitative data.) It may be that there are important differences
in the way work is arranged in different stores: therefore you

would need to specify precisely the conditions under which your
theory is likely to hold and collect appropriate data within these
conditions. By doing this you would help to ensure that any
change in absenteeism was a function of worker age and length
of service rather than any other aspect of the store, for example
the way in which people were managed. Your research would
use a highly structured methodology to facilitate replication, an
important issue to ensure reliability, as we shall emphasise
in Section 5.8.
An additional important characteristic of deduction is that
concepts need to be operationalised in a way that enables facts
to be measured, often quantitatively. In our example, one
variable that needs to be measured is absenteeism. Just what
constitutes absenteeism would have to be strictly defined: an
absence for a complete day would probably count, but what
about absence for two hours? In addition, what would constitute
a ‘short period of employment’ and ‘younger’ employees? What
is happening here is that the principle of reductionism is being
followed. This holds
· Chapter 4, “Understanding Research Philosophy and
Approaches to Theory Development”
4.2 The Philosophical Underpinnings of Business and
Management
What Is Research Philosophy?
The term research philosophy refers to a system of beliefs and
assumptions about the development of knowledge. Although this
sounds rather profound, it is precisely what you are doing when
embarking on research: developing knowledge in a particular
field. The knowledge development you are embarking upon may

not be as dramatic as a new theory of human motivation, but
even answering a specific problem in a particular organisation
you are, nonetheless, developing new knowledge.
Whether you are consciously aware of them or not, at every
stage in your research you will make a number of types of
assumption (Burrell and Morgan 1979). These include
assumptions about human knowledge (epistemological
assumptions), about the realities you encounter in your research
(ontological assumptions) and the extent and ways your own
values influence your research process (axiological
assumptions). These assumptions inevitably shape how you
understand your research questions, the methods you use and
how you interpret your findings (Crotty 1998). A well-thought-
out and consistent set of assumptions will constitute a credible
research philosophy, which will underpin your methodological
choice, research strategy and data collection techniques and
analysis procedures. This will allow you to design a coherent
research project, in which all elements of research fit
together. Johnson and Clark (2006) note that, as business and
management researchers, we need to be aware of the
philosophical commitments we make through our choice of
research strategy, since this will have a significant impact on
what we do and how we understand what it is we are
investigating.
Prior to undertaking a research methods module, few of our
students have thought about their own beliefs about the nature
of the world around them, what constitutes acceptable and
desirable knowledge, or the extent to which they believe it
necessary to remain detached from their research data. The
process of exploring and understanding your own research
philosophy requires you to hone the skill of reflexivity, that is,
to question your own thinking and actions, and learn to examine
your own beliefs with the same scrutiny as you would apply to
the beliefs of others (Gouldner 1970). This may sound daunting,
but we all do this in our day-to-day lives when we learn from
our mistakes. As a researcher, you need to develop your

reflexivity, to become aware of and actively shape the
relationship between your philosophical position and how you
undertake your research (Alvesson and Sköldberg 2000).
You may be wondering about the best way to start this reflexive
process. In part, your exploration of your philosophical position
and how to translate it into a coherent research practice will be
influenced by practical considerations, such as the time and
finances available for your research project, and the access you
can negotiate to data. However, there are two things that you
can do to start making a more active and informed philosophical
choice:
· begin asking yourself questions about your research beliefs
and assumptions;
· familiarise yourself with major research philosophies within
business and management.
This section introduces you to the philosophical underpinnings
of business and management, and Section 4.3 to the five
research philosophies most commonly adopted by its
researchers. We will encourage you to reflect on your own
beliefs and assumptions in relation to these five philosophies
and the research design you will use to undertake your research
(Figure4.2). The chapter will also help you to outline your
philosophical choices and justify them in relation to the
alternatives you could have adopted (Johnson and Clark 2006).
Through this you will be better equipped to explain and justify
your methodological choice, research strategy and data
collection procedures and analysis techniques.
At the end of the chapter in the section ‘Progressing your
research project’, you will find a reflexive tool (HARP)
designed by Bristow and Saunders to help you think about your
values and beliefs in relation to research. This will help you to
make your values and assumptions more explicit, explain them
using the language of research philosophy, and consider the
potential fit between your own beliefs and those of the five
major philosophies used in business and management research.
Is There a Best Philosophy for Business and Management

Research?
You may be wondering at this stage whether you could take a
shortcut, and simply adopt ‘the best’ philosophy for business
and management research. One problem with such a shortcut
would be the possibility of discovering a clash between ‘the
best’ philosophy and your own beliefs and assumptions.
Another problem would be that
Figure 4.2 Developing your research philosophy: a reflexive
process
Source: © Alexandra Bristow and Mark Saunders 2015
business and management researchers do not agree about one
best philosophy (Tsoukas and Knudsen 2003). In terms of
developing your own philosophy and designing your research
project, it is important to recognise that philosophical
disagreements are an intrinsic part of business and management
research. When business and management emerged as an
academic discipline in the twentieth century, it drew its
theoretical base from a mixture of disciplines in the social
sciences (e.g. sociology, psychology, economics), natural
sciences (e.g. chemistry, biology), applied sciences (e.g.
engineering, statistics), humanities (e.g. literary theory,
linguistics, history, philosophy) and the domain of
organisational practice (Starbuck 2003). In drawing on these
disciplines it absorbed the various associated philosophies
dividing and defining them, resulting in the coexistence of
multiple research philosophies, paradigms and approaches and
methodologies we see today.
Business and management scholars have spent long decades
debating whether this multiplicity of research philosophies,
paradigms and methodologies is desirable, and have reached no
agreement. Instead, two opposing perspectives have emerged:
pluralism and unificationism. Unificationists see business and
management as fragmented, and argue that this fragmentation
prevents it from becoming more like a true scientific discipline.
They advocate unification of management research under one

strong research philosophy, paradigm and methodology.
Pluralists see the diversity of the field as helpful, arguing it
enriches business and management (Knudsen 2003).
In this chapter, we take a pluralist approach and suggest that
each research philosophy and paradigm contributes something
unique and valuable to business and management research,
representing a different and distinctive ‘way of seeing’
organisational realities (Morgan 1986). However, we believe
that you need to be aware of the depth of difference and
disagreements between these distinct philosophies. This will
help you to both outline and justify your own philosophical
choices in relation to your chosen research method.
4.3 Five Major Philosophies
In this section, we discuss five major philosophies in business
and management: positivism, critical realism, interpretivism,
postmodernism and pragmatism (Figure 4.1).Positivism
We introduced the research philosophy of positivism briefly in
the discussion of objectivism and functionalism earlier in this
chapter. Positivism relates to the philosophical stance of the
natural scientist and entails working with an observable social
reality to produce law-like generalisations. It promises
unambiguous and accurate knowledge and originates in the
works of Francis Bacon, Auguste Comte and the early
twentieth-century group of philosophers and scientists known as
the Vienna Circle. The label positivism refers to the importance
of what is ‘posited’ – i.e. ‘given’. This emphasises the positivist
focus on strictly scientific empiricist method designed to yield
pure data and facts uninfluenced by human interpretation or bias
(Table4.3). Today there is a ‘bewildering array of positivisms’,
some counting as many as 12 varieties (Crotty 1998).
If you were to adopt an extreme positivist position, you would
see organisations and other social entities as real in the same
way as physical objects and natural phenomena are real.
Epistemologically you would focus on discovering observable
and measurable facts and regularities, and only phenomena that
you can observe and measure would lead to the production of

credible and meaningful data (Crotty 1998). You would look for
causal relationships in your data to create law-like
generalisations like those produced by scientists (Gill and
Johnson 2010). You would use these universal rules and laws to
help you to explain and predict behaviour and events in
organisations.Table 4.3 Comparison of five research
philosophies in business and management research
Ontology (nature of reality or being)
Epistemology (what constitutes acceptable knowledge)
Axiology (role of values)
Typical methods
Positivism
Real, external, independent
One true reality (universalism)
Granular (things)
Ordered
Scientific method
Observable and measurable facts Law-like generalisations
Numbers
Causal explanation and prediction as contribution
Value-free research
Researcher is detached, neutral and independent of what is
researched
Researcher maintains objective stance
Typically deductive, highly structured, large samples,
measurement, typically quantitative methods of analysis, but a
range of data can be analysed
Critical realism
Stratified/layered (the empirical, the actual and the real)
External, independent Intransient
Objective structures
Causal mechanisms
Epistemological relativism
Knowledge historically situated and transient
Facts are social constructions
Historical causal explanation as contribution

Value-laden research
Researcher acknowledges bias by world views, cultural
experience and upbringing
Researcher tries to minimise bias and errors
Researcher is as objective as possible
Retroductive, in-depth historically situated analysis of pre-
existing structures and emerging agency. Range of methods and
data types to fit subject matter
Interpretivism
Complex, rich
Socially constructed through culture and language
Multiple meanings, interpretations, realities
Theories and concepts too simplistic
Focus on narratives, stories, perceptions and interpretations
New understandings and worldviews as contribution
Value-bound research
Researchers are part of what is researched, subjective
Researcher interpretations key to contribution
Typically inductive. Small samples, in-depth investigations,
qualitative methods of analysis, but a range of data can be
interpreted
Postmodernism
Nominal
Complex, rich
Socially constructed through power relations
Some meanings, interpretations, realities are dominated and
silenced by others
What counts as ‘truth’ and ‘knowledge’ is decided by dominant
ideologies
Focus on absences, silences and oppressed/repressed meanings,
interpretations and voices
Exposure of power relations and challenge of dominant views as
contribution

Value-constituted research
Researcher and research embedded in power relations
Some research narratives are repressed and silenced at the
expense of others
Researcher radically reflexive
Typically deconstructive – reading texts and realities against
themselves
In-depth investigations of anomalies, silences and absences
Range of data types, typically qualitative methods of analysis
Pragmatism
Complex, rich, external
‘Reality’ is the practical consequences of ideas
Flux of processes, experiences and practices
Practical meaning of knowledge in specific contexts
‘True’ theories and knowledge are those that enable successful
action
Focus on problems, practices and relevance
Problem solving and informed future practice as contribution
Value-driven research
Research initiated and sustained by researcher’s doubts and
beliefs
Following research problem and research question
Range of methods: mixed, multiple, qualitative, quantitative,
action research
Emphasis on practical solutions and outcomes
As a positivist researcher you might use existing theory to
develop hypotheses. These hypotheses would be tested and
confirmed, in whole or part, or refuted, leading to the further
development of theory which then may be tested by further
research. However, this does not mean that, as a positivist, you
necessarily have to start with existing theory. All natural
sciences have developed from an engagement with the world in
which data were collected and observations made prior to
hypotheses being formulated and tested. The hypotheses
developed, as in Box 4.5, would lead to the gathering of facts

(rather than impressions) that would provide the basis for
subsequent hypothesis testing.
As a positivist you would also try to remain neutral and
detached from your research and data in order to avoid
influencing your findings (Crotty 1998). This means that you
would undertake research, as far as possible, in a value-free
way. For positivists, this is a plausible position, because of the
measurable, quantifiable data that they collect. They claim to be
external to the process of data collection as there is little that
can be done to alter the substance of the data collected.
Consider, for example, the differences between data collected
using an Internet questionnaire (Chapter 11) in which the
respondent self-selects from responses predetermined by the
researcher, and in-depth interviews (Chapter 10). In the Internet
questionnaire, the researcher determines the list of possible
responses as part of the design process. Subsequent to this she
or he
4.4 Approaches to Theory Development
We emphasised that your research project will involve the use
of theory (Chapter 2). That theory may or may not be made
explicit in the design of the research (Chapter 5), although it
will usually be made explicit in your presentation of the
findings and conclusions. The extent to which you are clear
about the theory at the beginning of your research raises an
important question concerning the design of your research
project. This is often portrayed as two contrasting approaches to
the reasoning you adopt: deductive or inductive. Deductive
reasoning occurs when the conclusion is derived logically from
a set of premises, the conclusion being true when all the
premises are true (Ketokivi and Mantere 2010). For example,
our research may concern likely online retail sales of a soon-to-
be-launched new games console. We form three premises:
· that online retailers have been allocated limited stock of the
new games consoles by the manufacturer;
· that customers’ demand for the consoles exceeds supply;

· that online retailers allow customers to pre-order the consoles.
If these premises are true we can deduce that the conclusion that
new games consoles by the release day will also be true.
In contrast, in inductive reasoning there is a gap in the logic
argument between the conclusion and the premises observed,
the conclusion being ‘judged’ to be supported by the
observations made (Ketokivi and Mantere 2010). Returning to
our example of the likely online retail sales of a soon-to-be-
launched new games console, we would start with observations
about the forthcoming launch. Our observed premises would be:
· that news media are reporting that online retailers are
complaining about only being allocated limited stock of the new
games consoles by manufacturers;
· that news media are reporting that demand for the consoles
will exceed supply;
· that online retailers are allowing customers to pre-order the
consoles.
Based on these observations, we have good reason to believe
new games consoles by the release day. However, although our
conclusion is supported by our observations, it is not
guaranteed. In the past, manufacturers have launched new
games consoles which have been commercial failures
(Zigterman 2013).
There is also a third approach to theory development that is just
as common in research, abductive reasoning, which begins with
a ‘surprising fact’ being observed (Ketokivi and Mantere 2010).
This surprising fact is the conclusion rather than a premise.
Based on this conclusion, a set of possible premises is
determined that is considered sufficient or nearly sufficient to
explain the conclusion. It is reasoned that, if this set
of premises was true, then the conclusion would be true as a
matter of course. Because the set of premises is sufficient (or
nearly sufficient) to generate the conclusion, this provides
reason to believe that it is also true. Returning once again to our

example of the likely online retail sales of a soon-to-be-
launched new games console, a surprising fact (conclusion)
might be that online retailers are reported in the news media as
stating they will have no remaining stock of the new games
console for sale on the day of its release. However, if the online
retailers are allowing customers to pre-order the console prior
to its release then it would not be surprising if these retailers
had already sold their allocation of consoles. Therefore, using
abductive reasoning, the possibility that online retailers have no
remaining stock on the day of release is reasonable.
Building on these three approaches to theory development
(Figure 4.1), if your research starts with theory, often developed
from your reading of the academic literature, and you design a
research strategy to test the theory, you are using a deductive
approach (Table 4.4). Conversely, if your research starts by
collecting data to explore a phenomenon and you generate or
build theory (often in the form of a conceptual framework), then
you are using an inductive approach (Table 4.4). Where you are
collecting data to explore a phenomenon, identify themes and
explain patterns, to generate a new or modify an existing theory
which you subsequently test through additional data collection,
you are using an abductive approach (Table 4.4).
The next three sub-sections explore the differences and
similarities between these three approaches and their
implications for your research.
Table 4.4 Deduction, induction and abduction: from reason to
research
Deduction
Induction
Abduction
Logic
In a deductive inference, when the premises are true, the
conclusion must also be true
In an inductive inference, known premises are used to generate
untested conclusions

In an abductive inference, known premises are used to generate
testable conclusions
Generalisability
Generalising from the general to the specific
Generalising from the specific to the general
Generalising from the interactions between the specific and the
general
Use of data
Data collection is used to evaluate propositions or hypotheses
related to an existing theory
themes and patterns and create a conceptual framework
themes and patterns, locate these in a conceptual framework and
test this through subsequent data collection and so forth
Theory
Theory falsification or verification
Theory generation and building
Theory generation or modification; incorporating existing
theory where appropriate, to build new theory or modify
existing theory
Deduction
As noted earlier, deduction owes much to what we would think
of as scientific research. It involves the development of a theory
that is then subjected to a rigorous test through a series of
propositions. As such, it is the dominant research approach in
the natural sciences, where laws present the basis of
explanation, allow the anticipation of phenomena, predict their
occurrence and therefore permit them to be controlled.
Blaikie (2010) lists six sequential steps through which a
deductive approach will progress:
1. Put forward a tentative idea, a premise, a hypothesis (a
testable proposition about the relationship between two or more
concepts or variables) or set of hypotheses to form a theory.
2. By using existing literature, or by specifying the conditions
under which the theory is expected to hold, deduce a testable

proposition or number of propositions.
3. Examine the premises and the logic of the argument that
produced them, comparing this argument with existing theories
to see if it offers an advance in understanding. If it does, then
continue.
4. Test the premises by collecting appropriate data to measure
the concepts or variables and analysing them.
5. If the results of the analysis are not consistent with the
premises (the tests fail!), the theory is false and must either be
rejected or modified and the process restarted.
6. If the results of the analysis are consistent with the premises
then the theory is corroborated.
Deduction possesses several important characteristics. First,
there is the search to explain causal relationships between
concepts and variables. It may be that you wish to establish the
reasons for high employee absenteeism in a retail store. After
reading about absence patterns in the academic literature you
develop a theory that there is a relationship between absence,
the age of workers and length of service. Consequently, you
develop a number of hypotheses, including one which states that
absenteeism is significantly more likely to be prevalent among
younger workers and another which states that absenteeism is
significantly more likely to be prevalent among workers who
have been employed by the organisation for a relatively short
period of time. To test this proposition you collect quantitative
data. (This is not to say that a deductive approach may not use
qualitative data.) It may be that there are important differences
in the way work is arranged in different stores: therefore you
would need to specify precisely the conditions under which your
theory is likely to hold and collect appropriate data within these
conditions. By doing this you would help to ensure that any
change in absenteeism was a function of worker age and length
of service rather than any other aspect of the store, for example
the way in which people were managed. Your research would
use a highly structured methodology to facilitate replication, an
important issue to ensure reliability, as we shall emphasise

in Section 5.8.
An additional important characteristic of deduction is that
concepts need to be operationalised in a way that enables facts
to be measured, often quantitatively. In our example, one
variable that needs to be measured is absenteeism. Just what
constitutes absenteeism would have to be strictly defined: an
absence for a complete day would probably count, but what
about absence for two hours? In addition, what would constitute
a ‘short period of employment’ and ‘younger’ employees? What
is happening here is that the principle of reductionism is being
followed. This holds
Post an analysis of the relationship between your personal
research philosophy and quantitative and qualitative
methodologies. Your analysis should include the following:
· Identify the key concepts, propositions, precepts, etc., of your
personal research philosophy, including any rationale for your
choice.
· Analyze the relationship between your research philosophy
and the chosen research methodology for your Doctoral Study.
· Analyze how the choice of methodology can impact a Doctoral
Study, as well as influence later research decisions and results.
Be sure to support your work with a minimum of two specific
citations from this week’s Learning Resources and at least
one additional scholarly source.
Guillermo
My doctoral study aims to understand the role of customer
relationship management in driving repeat business for capital
goods, when products are purchased infrequently, i.e., more
than five years between purchases.

Personal Research Philosophy
My personal research philosophy follows the principles of
pragmatism as explained by Saunders, Lewis, & Thornhill
(2015). Throughout my career in marketing, research is intended
to provide enough reliable information to drive product
development and sales strategy effectively, and this means that
research must consider the most salient aspects of each research
approach to ensure a positive outcome and avoid costly
mistakes. Positivism is needed to ensure the data is objective
and unbiased. Critical realism helps account for different
behaviors and attitudes because a “one size fits all” solution
rarely works. Interpretivism allows the understanding of the
best customers and identifies similarities in other customer
groups, or segments, to leverage existing strengths as avenues
for growth. The postmodernist philosophy is useful to help
reconcile data to views and expectations of different
stakeholder with different vantage points. For example,
engineering needs different inputs than accounting or sales
when it comes to customer preferences.
Research Methodology for Doctoral Study
The doctoral study will use a two-phase approach for inquiry.
The exploratory phase will rely on qualitative research to
understand the process and decision factors that different
facilities use to determine how to choose potential suppliers;
this stage will help identify the common attributes sought by
potential customers. The descriptive phase will quantify the
relative importance of the factors used is supplier selection to
determine the relative importance of relationship in the supplier
selection. This combined approach is intended to help identify
and qualify the relative importance of a CRM system as a tool
for customer retention over long periods.
Research Decisions and Results
The use of a combined approach is expected to help avoid bias
and pre-conceived idealizations that may hinder substantive
inquiry (Borgianni, Cascini, & Rotini, 2015). The topic of CRM
is widely understood, and the role of relationships is considered

a significant contributor for repeat business in on-going
relationships, but questions exist about the role of relationships
when the time between interactions is long (Dowell, Morrison,
& Heffernan, 2015). As proposed by Onwuegbuzie and Leech
(2005) the combined approach will help improve descriptive and
empirical precision in a sub-segment of the population where
research is limited.
References
Borgianni, Y., Cascini, G., & Rotini, F. (2015). Business
Process Reengineering driven by customer value: A support for
undertaking decisions under uncertainty conditions. Computers
in Industry, 68, 132–
147. https://doi.org/10.1016/j.compind.2015.01.001
Dowell, D., Morrison, M., & Heffernan, T. (2015). The
changing importance of affective trust and cognitive trust across
the relationship lifecycle: A study of business-to-business
relationships. Industrial Marketing Management, 44, 119–
130. https://doi.org/10.1016/j.indmarman.2014.10.016
Ketokivi, M., & Mcintosh, C. N. (2017). Addressing the
endogeneity dilemma in operations management research:
Theoretical, empirical, and pragmatic considerations. Journal of
Operations Management, 52, 1–
14. https://doi.org/10.1016/j.jom.2017.05.001
Onwuegbuzie, A. J., & Leech, N. L. (2005). Taking the "Q" out
of research: Teaching research methodology courses without the
divide between quantitative and qualitative paradigms. Quality
and Quantity, 39(3), 267–295. https://doi:10.1007/s11135-004-
1670-0

Michael
Scholars have many different ways to conduct and write
research papers. The decision must occur early in the process,
so the study reads succinctly. As Walden University Doctorate
of Business Administration students, we must consider each
method and choose one that best represents our chosen business
problem. In this week’s discussion, I identify my particular
research philosophy and the rationale for its use. Then I analyze
the relationship between the research philosophy and my chosen
research methodology for my Doctoral Study. Finally, I examine
how the choice of the methodology can impact a Doctoral Study
and future research decisions.
Personal Research Philosophy and Rationale
Pragmatism research starts with a problem and aims to
contribute practical solutions for the future (Saunders, Lewis, &
Thornhill, 2015). This philosophy analyzes the problem with a
perspective that something is wrong and needs a correction
using a single method or multiple methods to undertake the
research. I believe this best fits my Doctoral Study because of
the criteria we students must use at Walden University. We
begin with a business problem that a business leader has the
power to mitigate. The business problem is essential to the
Doctoral Study, so it is equally vital to the research. A
researcher has two distinct tasks, the first is to be specific to
what they want to find out, and the second is to determine the
best way to do it (Abutabenjeh & Jaradat, 2018). Using
pragmatism also allows the researcher to follow a qualitative or
quantitative approach or both as the methodology (Saunders et
al., 2015).
Research Philosophy and Research Methodology
Qualitative and quantitative methods present a different view of

the studied phenomenon and use different means to persuade the
reader of the validity of the conclusions drawn (Firestone,
1987). As noted above, while using a pragmatic approach, I
could use a qualitative, quantitative, or mixed methodology. So
far, I have chosen the quantitative because of the use of
numerical data and my ability to find enough to support the
study. The foundation of the Doctoral Study will be the problem
so supporting it with statistics seems reasonable, and it matches
my chosen philosophy. More specifically, my research objective
is confirmatory meaning the goal of the study is to test or prove
a hypothesis (Onwuegbuzie & Leech, 2005).
Impact of Research Methodology
Research methodology has a significant impact on the Doctoral
Study because it affects future decisions that occur throughout
the fact-gathering process. A quantitative research design may
use questionnaires as data collection whereas qualitative is
more personal and may use interviews (Saunders et al., 2015).
Both will require access to the participants, but the qualitative
method uses a more intimate approach and more time
investment than a simple one-page questionnaire. It is also
worthy to point out because of the time investment, and it might
be difficult to find people to participate in a one-on-one
interview compared to just completing a questionnaire. An ideal
quantitative researcher remains detached to avoid research bias,
but the qualitative researcher becomes immersed in the
phenomenon of interest (Firestone, 1987).
Conclusion
Completing a scholarly paper such as a Doctoral Study is quite
an academic achievement. Throughout the time spent, there are
common decisions an author must make that have a profound
effect on the outcome of the study. This discussion looked at
the philosophy that I will incorporate as part of my Doctoral
Study. It then reviewed the relationship between research
philosophy and methodology. Lastly, it explained how
methodology choice might affect later research decisions and
results. Scholars should evaluate how philosophy and

methodology apply to their research and develop sound reasons
to support how to conduct their study.
References
Abutabenjeh, S., & Jaradat, R. (2018). Clarification of Research
Design, Research Methods, and Research Methodology: A
Guide for Public Administration Researchers and
Practitioners. Teaching Public Administration, 36(3), 237–258.
doi:10.1177/0144739418775787
Firestone, W. A. (1987). Meaning in method: The rhetoric of
quantitative and qualitative research. Educational Researcher,
16(7), 16–21. Retrieved from
http://files.eric.ed.gov/fulltext/ED292816.pdf
Onwuegbuzie, A. J., & Leech, N. L. (2005). Taking the “Q” out
of research: Teaching research methodology courses without the
divide between quantitative and qualitative paradigms. Quality
and Quantity, 39(3), 267–295. doi:10.1007/s11135-004-1670-0

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