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.

To prepare for this Discussion, review Chapters 4 and 5 in Saunders, Lewis, and Thornhill (2015), and consider how the different research philosophies can influence choice in research methodologies, as well as how these choices can impact a doctoral research study.

Saunders, M. N. K., Lewis, P., & Thornhill, A. (2015). Research methods for business students (7th ed.). Essex, England: Pearson Education Limited.

· 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 Flux of processes, experiences, practices	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 Researcher reflexive	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 online retailers will have ‘sold’ their entire allocation of the 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	Data collection is used to explore a phenomenon, identify 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

*subject: HCM520: Quality and Performance Improvement in Healthcare

*Homework: Critical thickening

*Note: 1-attached book and articles required to read  ,2- Need the plagiarism below 18%

Required:

• Chapters 16 & 17 in The Healthcare Quality Book: Vision, Strategy, and Tools
• Chapter 20 in Patient Safety and Healthcare Improvement at a Glance
• Bishop, A. C., & Cregan, B. R. (2015). Patient safety culture: Finding meaning in patient experiences. International Journal of HealthCare Quality Assurance, 28(6), 595-610.
• Webair, H. H., Al-assani, S. S., Al-haddad, R. H., Al-Shaeeb, W. H., Bin Selm, M. A, & Alyamani, A. S. (2015). Assessment of patient safety culture in primary care setting, Al-Mukala, Yemen. BMC Family Practice, 16, 1-9.
• ===========================================Assignment :Implementing Quality as the Core Organizational Strategy
  Case Study: Ministry of Health (MOH) Vision and Strategy (100 points)
  For  this assignment, you will write a research paper exploring the Ministry  of Health’s vision and strategy to improve healthcare delivery for all  citizens throughout the Kingdom.
  Choose three out of the four topics below:
  • Interoperable electronic health records,
  • Equitable care standards,
  • Quality and performance standards, or
  • Governance and accountability.
  • For each selected topic:
  • Provide an overview of the MOH vision and strategy for that topic.
  • Explain the importance of the topic and how it will improve healthcare quality and safety in Saudi Arabia.
  • Your well-written paper should meet the following requirements:
  • Four to five pages in length, not including the cover or reference pages.
  • Formatted per APA and Saudi Electronic University standards.
  • Provide  support for your work with in-text citations from a minimum of four  scholarly articles. Two of these sources may be from the class readings,  textbook, or lectures, but two must be external. The Saudi Digital  Library is a good place to find these references.
  • Provide full APA references for the sources used, along with appropriate in-text citations.
  • Utilize headings to organize the content in your work.