Introduction to Business Research 3

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1 Synopsis Introduction to Business Research 3 1. Orientation By the time the candidate has completed this module, he or she should understand: what has to be submitted for the viva voce examination; what the final format of the thesis should be; what should be included in the research design and methodology section of the thesis; what should be included in the data collection and analysis section of the thesis; how to reappraise the literature and develop the research theory; how to generate research results and conclusions; the continuing relationship with the supervisor; the role of the DBA Research Committee in assessing the thesis; and the requirement for continued progress reporting. 1.1 Introduction 1.2 The IBR Courses Process Model 1.3 What Has To Be Submitted? 1.4 The Aims and Objectives of the Research Design Section 1.5 The Aims and Objectives of the Data Collection Techniques Section 1.6 The Aims and Objectives of the Analysis and Evaluation Section 1.7 The Aims and Objectives of the Outcomes Section 1.8 The Aims and Objectives of the Contribution Section 1.9 The Validation Study 1.10 The Supervisor, the Senior Supervisor and the DBA Research Committee 1.11 Progress Reports This module has described the main components of the thesis, its origins in the documents that have been previously submitted, and the role of the supervisor in supporting its preparation. The chief points to remember are as follows. The thesis is submitted for formal assessment based on two elements: the thesis document itself, organised according to a standard set of headings, and a formal, 1

2 face-to-face viva examination with external and internal examiners who have read the document. The document is the result of material initiated in the original research proposal and intermediate submission (as described in IBR1 and IBR2), amplified by the inclusion of the account of empirical procedures and their outcomes, as described in the IBR process model. It is important to be clear about the design and its methodology, for they act as a bridge between the literature in the field and the empirical work that will, hopefully, make a further contribution to it. The data collection and analysis are intended to result in material of known reliability and validity, obtained by appropriate techniques that are suited to the epistemological assumptions and research methods outlined in IBR1. The required contribution to knowledge needs to be discussed by addressing the empirical findings in the light of the literature, and with an explicit awareness of the difference that the findings may make to that literature. It is equally important, in a professional doctorate like the DBA, for the contribution to professional practice to be discussed, in a critical way and with the practical constraints addressed so far as this is possible. The role of the DBA Research Committee and the supervisor is to give constructive advice and feedback on all of this; the former cannot hold up progression but the latter can provide firm guidance on when submission for examination is merited. The remainder of the present volume is devoted to the various techniques of data collection and analysis, their presentation as part of a complete thesis document, and the candidate s preparation for final assessment and viva. 2. Forms of Analysis: Quantitative and Qualitative By the time the candidate has completed this module, he or she should understand: the basic characteristics of quantitative research; the basic characteristics of qualitative research; the primary differences between the two approaches; the relative advantages and disadvantages of each approach; how the two approaches can be used together in the same design; and the implications of adopting either approach or a combined approach. 2.1 Introduction 2.2 The Concept of Quantitative Research 2.3 The Concept of Qualitative Research 2.4 Relationships between Quantitative and Qualitative Research Approaches 2

3 2.5 In Summary: The Case for a Combined Quantitative and Qualitative Research Approach 2.6 Frequently Asked Questions on the Quantitative Qualitative Issue This module has presented an overview of the basic elements of the quantitative and qualitative research approaches. Each has been considered individually and the concept of a combined quantitative qualitative approach has been advanced. Candidates should now understand: the basic characteristics of quantitative research; the basic characteristics of qualitative research; the primary differences between the two approaches; the relative advantages and disadvantages of each approach; how the two approaches can be used together in the same design; and the implications involved of adopting either approach or a combined approach. Qualitative analysis is associated with the phenomenological approach, while quantitative analysis is associated with the positivist approach, but the association between form of analysis and epistemological approach is not perfect, and the various distinctions between qualitative and quantitative analysis are frequently a matter of degree rather than kind. Clearly, qualitative analysis and quantitative analysis deal with two distinct forms of information. The former creates meaning by examining the content of phenomena and the latter by working with their frequency of occurrence. However, the two forms overlap whenever content is categorised, and in that sense both forms of information are always coexistent. Both approaches are appropriate for a wide range of research purposes, but the balance between them can vary depending on the application, and on the phase or stage reached in the candidate s investigation. To some extent the choice of approach dictates the range of research tools available to the candidate. Piloting may require more qualitative forms of technique, while a main study may be based on a more quantitative approach; the early stage may comprise an orientation D exploratory form but may lead to an orientation A hypothesis-testing stage. Both approaches work by examining the evidence for research assertions posited by the researcher, these being expressed in the forms of hypotheses in the case of a quantitative analysis, and in the form of belief statements in the case of a qualitative analysis; both forms require a disintegration of the research assertions, and an operationalisation of the basic terms, if the evidence is to result in a successful test of the assertions. 3

4 3. Preparation for Data Collection By the time the candidate has completed this module, he or she should be able to: anticipate the practical demands of the data capture techniques to be used; prepare the stance to be adopted when approaching a variety of data sources; understand the data capture and recording implications of the different research methods and the techniques associated with them; explain the arrangements required if findings are to be generalised; understand how to maximise response rates; choose one or more appropriate data collection and analysis techniques, bearing in mind the implications for design, elicitation of responses, analysis, and presentation and argument; handle a combination of qualitative and quantitative techniques. 3.1 Introduction 3.2 Data Sources 3.3 Accessing Data 3.4 Representation: Replication and Sampling for Generalisation 3.5 Determining the Size of Sample 3.6 Maximising Response Rates 3.7 A Standard Framework for Choice and Use of Techniques Before devising and deploying data collection techniques, the researcher must make arrangements for accessing the data in question while working within the resource constraints of social support, money and time. How the researcher enters the field, what arrangements are made for data collection, and how these are presented to the organisations being studied will make a great difference to how easily the research questions will be answered. The eventual rigour of the answers depends on a number of fairly humble procedures by which the data are recorded, as well as on the sophistication of the data analysis; and the procedures should be thought through in some detail when choosing the techniques to deploy, since they will vary depending on the assumptions that underlie the research method adopted. Thus the information available from a series of interviews in an interpretive study requires different data recording techniques from those used in a set of structured survey interviews, for example. The information available once the data have been analysed should be generalised beyond the location in which the data were collected, and the accuracy and value of this generalisation will depend on the way in which the population of potential respondents is sampled (or how the questions are replicated across different grouping of respondents where case study method is used). 4

5 Considerable thought should be devoted to the arrangements for maximising response rates, in order to reduce bias in the eventual generalisations the researcher wishes to make. This will also serve to reduce costs. It is helpful to follow a standard structure relating to the design, elicitation, analysis, and presentation and argument when considering the techniques to be used for data collection. 4. Semi-Structured Data Collection and Analysis Techniques By the time the candidate has completed this module, he or she should be able to: select appropriate techniques for data collection; support the choice with an appropriate methodology; understand the relationship between the chosen techniques and their place within the different stages and phases of the empirical programme; produce a preliminary analysis of the data. 4.1 Introduction 4.2 The Research Conversation and Storytelling 4.3 The Semi-Structured Individual Interview 4.4 The Key Informant Interview 4.5 The Focus Group 4.6 Ethnographic Observation and Netnography Every technique requires a modicum of qualitative and quantitative treatment, whatever its main emphasis, and so the presentation of techniques in this and the next module focuses on structure rather than on a division into qualitative and quantitative forms. No research technique is entirely unstructured, and so the term unstructured technique is not used here. Structure is a matter of degree; some techniques are more structured, some less so. What is important is that the design, elicitation, analysis, and presentation and argument follow the procedure found to be appropriate for the kinds of information being presented. This module has outlined the details of five commonly used semi-structured techniques: the research conversation/storytelling approach; the semi-structured individual interview; the key informant interview; the focus group; and ethnographic/netnographic observation. The way in which each can be used to conduct a complete study providing answers to a research question, or to pilot data in preparation for a further, main study using more structured techniques, has been described. 5

6 Content analysis is the main analytic tool in semi-structured work, and different variants, suited to the requirements of the five techniques, exist. It is important to establish the reliability of categorisation and coding: without this, validity and accuracy are impossible to establish, but this is done in different ways in each case. 5. Fully Structured Data Collection and Analysis Techniques By the time the candidate has completed this module, he or she should be able to: select appropriate techniques for data collection; support the choice with an appropriate methodology; understand the relationship between the chosen techniques and their place within the different stages and phase of the empirical programme; produce a preliminary analysis of the data. 5.1 Introduction 5.2 Structured Observation 5.3 The Structured Questionnaire 5.4 The Structured Interview 5.5 The Repertory Grid This module has presented procedural details for four structured techniques: structured observation, the structured questionnaire, the structured interview and the repertory grid, and has described some of the forms of argument that a structured approach facilitates. It is possible to quantify the issues being investigated in a way that simplifies the extraction of information obtained from the data; further, a structured format makes it simple to conduct a variety of differential analyses, examining differences between different kinds of respondents, whether these have been pregrouped by means of a stratified sampling scheme or are recognised as having characteristics in common on the basis of their responses. This process is simplified by the use of a range of software packages that can be used to access respondents, collect data and analyse data. However, as with the software aids available for semi-structured work, it is often faster to conduct the simpler forms of analysis manually. Certainly, no statistical software should be used unless the researcher knows enough about the tests involved to be able to conduct them manually and arrive at meaningful results. Indeed, it is wise to compute a trial analysis on a subset of data by hand to make sure that the results obtained match those provided by the software, and this kind of check is essential if Excel is the package in question. 6

7 The procedures used with the structured questionnaire and the structured interview have much in common, particularly their reliance on a previously prepared schedule of questions and potential responses. Where they vary is in the degree of social skill involved, and in their advantages and disadvantages when different media are used to collect the data. Both techniques are closely associated with survey method, and careful planning is required in order to ensure an adequate return rate from the sample of respondents approached. This issue has been extensively researched, and the candidate should be familiar with the recommendations and follow them within the circumstances of the particular research being undertaken if the results are to be of any use in representing the population being addressed. The repertory grid technique is frequently used in initial pilot work but can be used as the basis of a complete main study, depending on the nature of the research question and whether the research question requires that it should be triangulated against some other technique. It is particularly valuable for identifying respondents tacit knowledge, and in situations in which (unlike the structured questionnaire and interview) it is important to elicit the respondent s own constructs rather than supplying the researcher s. 6. Statistical Data Analysis By the time the candidate has completed this module, he or she should understand: how to use the statistical tables; how to estimate a population parameter using a confidence interval; how to carry out and interpret appropriate hypothesis tests. 6.1 Introduction 6.2 Statistical Distributions 6.3 Estimation 6.4 Hypothesis Testing 6.5 Hypothesis Testing Using Non-Parametric Tests This module has provided a summary of some of the frequently used techniques for data analysis. Four of the standard theoretical statistical distributions have been described, and examples illustrate how key values can be extracted from the tabulations. Worked examples show how sample data can be used to calculate estimates of the population parameters in the form of confidence intervals. Examples have also been given of the hypothesis tests that are frequently used for business data: these are summarised in Table 1.1Table 1. 7

8 Table 1 Summary of hypothesis tests Variable No of samples One tail Two tails Hypothesis on Test statistic Degrees of freedom Mean 1 μ z or t (n 1) Comment Mean 2 μ z or t (n 1 + n 2 2) Mean (paired sample) 1 μ diff z or t (number of pairs 1) Use average difference Mean 3 or more μ F (treatments, error) (blocks, error) ANOVA Variances 1 σ 2 χ 2 (n 1) Variances 2 σ 2 F = (s 2 1 /s 2 2 ) (n 1 1), (n 2 1) Assumes s > s 2 Proportions 1 π z n 30 Proportions 2 π z n 1 + n 1 30 Association Association χ 2 (r 1)(c 1) r = rows c = columns n = sample size 7. Statistical Data Modelling By the time the candidate has completed this module, he or she should be able to: carry out and interpret a bivariate regression analysis; calculate and interpret a correlation coefficient; understand the requirements for multivariate regression analysis and interpret the computer output; analyse and interpret the residuals; carry out and interpret a Spearman s rank correlation test; understand the key components of time series analysis; perform and interpret time series analyses. 8

9 7.1 Introduction 7.2 Bivariate Linear Regression 7.3 Multivariate Regression 7.4 Spearman s Rank Correlation Coefficient 7.5 Time Series Analysis 7.6 Structural Equation Modelling This module has indicated some of the ways in which data can be modelled. Examples have been given of bivariate linear regression, multivariate regression, Spearman s rank correlation coefficient, time series analysis and structural equation modelling. However, there are many other techniques and combinations of methods that could be used. It is important to graph the data, identify the main features and try to build an appropriate model. Several different models should be investigated. The final selection should be made by analysing the residuals and using either the mean squared error or the mean absolute deviation to indicate the model that provides the best fit to the data. In the sections on regression analysis, some hypothesis tests were used. These are summarised in Table 2. Table 2 Variable Regression model Regression coefficient Correlation coefficient Summary of hypothesis tests for data modelling No of One Two Hypothesis on Test samples tail tails statistic Proportion of variance explained F Degrees of freedom (regression, error) β t (error) ρ t (error) Comment Residuals Randomness z or number of runs Spearman s rank Correlation coefficient (n 2) 2 ρ s t where n = sample size Normal dist. or Table A1.5 9

10 8. The Challenge of Writing By the time the candidate has completed this module, he or she should understand: why writing a doctoral thesis is challenging; what the examiners will be looking for in terms of style and presentation; why it is important to start writing as soon as possible; why it is important not to be afraid of criticism or failure; how to develop the thesis structure; the basic elements of style; the editing process and its importance. 8.1 Introduction 8.2 The Challenge of Writing a Thesis 8.3 Addressing the Audience 8.4 The Details of Writing Style 8.5 Editing 8.6 In Conclusion: Content and Style By now the candidate should understand why writing a doctoral thesis is challenging and what the examiners will be looking for in terms of style and presentation. It is important to start writing as soon as possible and to be open to criticism as provided in the feedback from the supervisor and the Research Committee. A variety of psychological barriers exists, but these can generally be overcome with a little thought, practice and supervisor advice. It is helpful to develop three things: a routine for originating material; a set of techniques for integrating material taken from a variety of sources; and a consistent approach to ongoing review as the volume of written work increases. Successful writing requires the candidate to structure the material carefully, and the WBS approach may be helpful in this regard. The candidate should be sensitive to the process of his or her own growing enlightenment about the material as familiarisation develops through ongoing reading and writing. It is important to address the needs of the audience, bearing in mind that a small fragment of the potential audience is very important to success: the examiners! The candidate needs to be familiar with the minutiae of style and follow conventions scrupulously. This may require a substantial commitment if the candidate has little experience of academic writing. The editing process, important in providing a final polish to earlier drafts, requires its own commitment. 10

11 9. Thesis Structure and Format; the Viva Voce Examination By the time the candidate has completed this module, he or she should be able to: devise a structure for the thesis; define the scope and content of the abstract, introduction and main chapters; write the thesis in a clear and logical way; design the thesis so that it complies with University requirements; structure the thesis appropriately; appreciate what is required in the viva voce examination. 9.1 Introduction 9.2 Writing Up 9.3 Thesis Format 9.4 Thesis Structure 9.5 Section Sequencing 9.6 Submitting the Thesis 9.7 The Viva Voce Examination Different authors have their own different style, and develop their own voice when writing the thesis. What they all have in common is the need to follow a generally accepted scheme of writing; checking structure, content and expression; and following a standard format and layout by which the thesis is finally presented. Although this basic scheme should be adhered to, several options are available for the way in which material is sequenced within chapters and sections. What matters is that a clear argument is produced for an examiner who will be very familiar with the field but who is encountering the author s basic theory, and the data that test the theory, for the first time. After addressing the feedback provided by the supervisor and the Research Committee reviewers and making a final check of the outcome, the candidate should familiarise himself or herself with the regulations for submission and prepare for the viva. The EBS DBA viva does not require an initial presentation by the candidate. Candidates are expected to enter into a discussion of the provenance of the thesis topic, the meaning of the material presented, and a defence of the findings and how they were arrived at. This is done in depth and in considerable detail. The process requires preparation, and this module provides suggestions about what to expect and how it should be done. Several outcomes are possible, most of which will require follow-up action. The candidate should be familiar with the possibilities as outlined in the documentation available from the DBA Administrator, and should liaise with the supervisor accordingly. 11