Methodological Issues in Management Research Advances Challenges and the Way Ahead 1st Edition by Rabi Subudhi, Sumita Mishra ISBN 9781789739756 1789739756

Chapter 1: The Methodological Domain in Management Research

Introduction

The Nature of Management Research

Dominant Research Paradigms

The Methodological Domain

Quantitative and Qualitative Research Methods

Triangulation in Management Research

Bridging the Quantitative Qualitative Divide

Contributions and Organization of the Book

References

Chapter 2: Reviewing the Literature

Introduction

Why Undertake a Literature Review?

Types of Literature Reviews

Systematic Literature Reviews

Integrative Literature Reviews

Narrative Literature Reviews

Pointers on Making Literature Reviews Effective

Evaluating Information Resources

Evaluating Articles Published in Scholarly Journals

Evaluating Books

Evaluating Web Resources

Referencing in Literature Reviews

Avoiding Plagiarism

Conclusion

Key Points

Questions

References

Chapter 3: Purpose and Process of Research

Learning Outcomes

3.1. Introduction

3.2. Three Purposes of Research – Exploration, Description, and Explanation

3.2.1. Exploratory Research

3.2.2. Descriptive Research

3.2.3. Explanatory Research

3.3. Thinking about a Suitable Research Area

3.4. Formulating Research Questions

3.5. Research Concepts and Constructs

3.6. Research Propositions and Hypothesis

3.7. Verifying Theory

3.8. Theory Building

3.9. Conclusion

Questions

References

Chapter 4: Research Design

Introduction

Developmental Research Designs

Longitudinal Design

Cross-sectional Design

Manipulation-guided Designs

Experimental Design

Quasi-experimental Design

Correlational Design

Pretest–Posttest Designs

Epilogue

Questions

References

Chapter 5: Questionnaire Design

5.1. Introduction

5.2. Types of Questionnaires

5.2.1. Exploratory Questionnaires/Unstructured Questionnaires with Open-ended Questions

5.2.2. Formal Standardized Questionnaires/Structured Questionnaires with Close-ended Questions

5.2.3. Semi-structured Questionnaires with Partially Structured Questions

5.3. The Steps Preceding Questionnaire Design

5.4. Preliminary Decisions in Questionnaire Design

5.4.1. Deciding on the Information Required

5.4.2. Define the Target Respondents

5.4.3. Choose the Method(s) of Reaching Target Respondents

5.4.4. Decide on Question Content

5.4.5. Develop the Question Wording

5.4.6 Opening Questions

5.4.7 Question Flow

5.4.8 Closing Questions

5.4.9. Physical Appearance of the Questionnaire

5.4.9.1. Use of Booklets

5.4.9.2. Simple, Clear Formats

5.4.9.3. Use of Color Coding

5.4.9.4. Interviewer Instructions

5.4.9.5. Piloting the Questionnaires

5.5. Open-ended Versus Close-ended Questions in the Questionnaire

5.6. Attributes of Writing a Good Questionnaire

5.6.1. Introduce Yourself

5.6.2. Explain the Purpose of the Questionnaire

5.6.2.1 Reveal What Will Happen with the Data You Collect

5.6.2.2. Estimate How Long the Questionnaire Will Take

5.6.2.3. Describe Any Incentives That May Be Involved

5.6.3. Make Sure Your Questionnaire Looks Professional

5.7. Distributing Questionnaire

5.7.1. Pilot Study

5.7.2. Disseminate Your Questionnaire

5.7.3. Include a Deadline

5.8. Use of Internet in Questionnaire Design for Smart Online Questionnaire

Useful Questionnaires

References

Appendix 1. South Asian Network for Development and Environmental Economics (Sandee) Research Projec

Chapter 6: Qualitative Interviewing

Introduction

Types of Interviews

Seven Steps of Interviewing Preparation

Preparing for Interviewing

References

Chapter 7: Focus Group Discussions

Introduction

Ontological and Epistemological Positions

Appropriateness of FGDs

Exploratory Focus

Explanatory Focus

Contextual Factors

When Not to Use FGD

Design of FGD

FGD Discussion Guide

Group Composition and Size

Facilitation Skills

Competencies Required for FGDs

Dealing with Silences

Recoding and Transcribing FGDs

Comparing FGDs and Interviews

Internet and FGDs

Ethical Considerations

Key Points

Questions

References

Chapter 8: Quantitative Data Analysis

Introduction

Scales of Measurement

Data Analysis Strategy

Univariate Analysis

Univariate Statistics

Multivariate Statistics

Cross-tabulation

Cross-tab Example

Independent Samples t-Test for Means

Paired Sample t-Test for Means

Pearson Correlation Coefficient

Partial Correlation and Rank Correlation

Regression Analysis

Multicollinearity

Factor Analysis

Steps of Designing a Factor Analysis

Factor Loading

Factor Rotation

Cluster Analysis

Applications of Cluster Analysis

Steps in Cluster Analysis

Outliers in Cluster Analysis

Assumptions in Cluster Analysis

Conjoint Analysis

Applicability of Conjoint Analysis

Steps in Conjoint Analysis

Assumptions in Conjoint Analysis

Multidimensional Scaling

Objectives and Application of MDS

Chapter 9: Testing of Hypothesis: Concepts and Applications

Introduction

Statistical Hypothesis

Types of Tests of Hypothesis

Concepts Basics to the Hypothesis-testing Procedures

Sample Testing

Type I and Type II Errors

Test Criterion

Usual Formulae for Sample Testing

Large Sample Tests

Cases.

Small Sample Tests

t-Test

Chi-Square (χ2) Test

Hypothesis for ANOVA

Critical Region and Tails of Tests

Example

Hypothesis Testing for a Population Proportion

Example

Two-tailed and One-tailed Tests of Hypothesis

Hypothesis Testing of Proportions: Large Samples

Two-tailed Tests of Proportions

Hypothesis Testing of Means When the Population Deviation Is Not Known

How Does It Work?

Solved, Application-oriented Examples

Example 1:Case of Sky-rocketing Real-estate Price

Example 2:Case of “Campus-placements and On-job-probation”

Exercise

1: Case: TV – viewers’ survey

2: Case: How Effective Is Mass Education Program of Govt. of India, Named, SSA and Other Interventio

Solution

Self-study Exercises

Self-practice Questions

References

Appendix 1

Chapter 10: Nature of Qualitative Research

Introduction

Meaning of Research

Research Is about Learning and Knowing

Good Research Happen When It Is Linked with Your ‘Being and Quest for Learning’

Concepts in Qualitative Research

Characteristics of Qualitative Research

Researcher Subject Relationship in Qualitative Research: Researcher and Research Are Inseparable

Main Steps in Qualitative Research

Broad Research Question

Broad Literature Review

Unit of Study

Eliciting Human Experiences

Data Churning

Interpretation of the Data

Conceptual and Theoretical Linkages and Significance of the Findings

Conclusion

Challenges in Conducting Qualitative Research

Doing of Qualitative Research: Flexibility and Immersion

Trustworthiness and Quality in Qualitative Research

Relevance of Qualitative Research: Discovery of New Phenomena and Self

Limitations of Qualitative Research

Key Points

Questions

References

Chapter 11: Case Study Research

Introduction

Components of a Case Study Design

Types of Case Study Design

Unit of Analysis

Sampling in Case Study Design

Replication in Case Study

Data Collection

Interviews

Focus Groups Discussions (FGDs)

Archival Data

Qualitative Analysis in Case Study

Structuring a Case Study

Methodological Issues in Case Research

Judging the Quality of Case Study Research

Conclusion

Key Points

References

Chapter 12: Grounded Theory: A Method of Research Inquiry

12.1. Emergence of Grounded Theory

12.1.1 Relevance of GT in Today’s Context

12.2. GT as a Method

12.2.1 Data Analysis

12.2.1.1 Theoretical Sampling

12.2.1.2 Theoretical Saturation

12.2.1.3 Theoretical Sensitivity

12.2.2 Coding

2.2.1 Open Coding – Categorizing Strategy

12.2.2.2 Axial Coding – Pattern Matching

12.2.2.3 Selective Coding

12.2.3 Memo Writing

12.2.4 Constant Comparison

12.3. Things to Remember for Beginners and Critical Evaluation

12.4. Conclusion and Future Scope of Research in GT

References

Chapter 13: Supplementary Research Methods: DEA, ISM, AHP and Non-Parametric Statistics

13.1 Non-Parametric Statistics

13.1.1 The χ2 (Pronounced Chi-squared) Tests

13.1.1.1 The One-sample Chi-squared Test

13.1.1.2 The Two-sample Chi-squared Test

13.1.1.3 Related-sample Chi-squared Test (or McNemar Test for Significance of Changes)

13.1.1.4 The Multi-sample Chi-squared Test

13.1.1.5 Using Chi-square for Contingency Coefficient

13.1.2 The Kolmogorov–Smirnov (K-S) Tests

13.1.2.1 One-sample K-S Test

13.1.2.2 The Two-sample K-S Test

13.1.3 The Runs Test

13.1.4 Mann–Whitney U-test

13.1.5 Spearman Rank Correlation Coefficient ()

13.1.6 Kendall Rank Correlation Coefficient (

13.1.7 Kendall Coefficient of Concordance

13.2 Interpretive Structural Modeling

13.2.1 The ISM Process Steps

13.2.2 An Illustrative Example

13.2.2.1 Listing the Factors Contributing to the Study Objective

13.2.2.2 Finding Contextual Relationships between Pairs of Variables

13.2.2.3 Converting the SSIM into a Reachability Matrix

13.2.2.4 Checking for Transitivity and Modifying the Reachability Matrix

13.2.2.5 Drawing the Digraph for the Reachability Matrix

13.2.2.6 Deriving the Model Levels by Iterative Partitioning of the Reachability Matrix (Level Parti

13.2.2.7 Draw the Factor Hierarchy

13.2.2.8 Making a MICMAC Analysis

13.3 Analytic Hierarchy Process

13.3.1 An Example Problem

13.3.2 The Weighted Average Method of Decision-making

13.3.3 Decomposing the Problem into a Hierarchy

13.3.4 Ratio Scale of Measurement

13.3.5 Paired Comparison

13.3.6 Priority Weights for the Criteria

13.3.6.1 The Method of Geometric Mean

13.3.6.2 The Eigenvector Method

13.3.7 Paired Comparison of Alternatives Against Each Criterion

13.3.8 Synthesizing the Priorities

13.3.9 Consistency of the Priority Weights

13.3.10 Extensions to the Basic AHP

13.3.10.1 Levels of Criteria

13.3.10.2 Group Decision-Making

13.3.10.3 Analytic Network Process

13.3.10.4 Dedicated Software Packages

13.4 Data Envelopment Analysis

13.4.1 Productivity and Technical Efficiency

13.4.2 The Charnes, Cooper, Rhodes (CCR) Model (Constant Returns to Scale)

13.4.2.1 Equivalence of the Fractional LP Problem with the LP Problem

13.4.2.2 Same Units of Measure

13.4.2.3 CCR Efficiency and Efficient Frontier

13.4.2.4 Reference Set or Peer Group

13.4.2.5 Interpretation of Optimal Weights

13.4.3 Examples

13.4.3.1 The Case of Multi-inputs and Multi-outputs

13.4.3.2 The BCC Model (Variables Returns to Scale)

References

Chapter 14: Special Section: Sample Research Papers

Chapter 14.1: Working with Second-order Construct in Measurement Model: An Illustration Using Empiri

14.1.1. Introduction

14.1.2. Research Methodology

14.1.2.1. Sample and Data Collection

14.1.2.2. Research Measures

14.1.3. Results and Findings

14.1.3.1. Reliability and Correlation of Constructs

14.1.3.2. Establishing Construct Validity

14.1.3.3. Conducting an EFA to Check Factor Structure

14.1.3.4. Conducting CFA to Establish Construct Validity

14.1.3.4.1. Reliability, Convergent, and Discriminant Validity of ­Constructs

14.1.3.4.2. CFA Model 3: EmpPerf as a Uni-dimensional First-order ­Construct

14.1.3.4.3. CFA Model 4: Measurement Model with TP and CP as Two First-order Constructs

14.1.3.4.4. CFA Model 5: EmpPerf as a Second-order Factor

14.1.4. Discussion and Conclusion

References

Chapter 14.2: Role of Self-Congruity in Predicting Travel Intention

14.2.1. Introduction

14.2.2. Literature Review

14.2.3. Methodology

14.2.4. Results

14.2.5. Findings and Discussion

14.2.6. Conclusion

References

Chapter 14.3: Structural Equation Modeling: Threshold Criteria for Assessing Model Fit

14.3.1. Introduction

14.3.2. Fit Indices

14.3.2.1 Absolute Fit Indices

14.3.2.1.1. Chi-square

14.3.2.1.2 Normed Chi-Square

14.3.2.1.3 Goodness-of-Fit Index (GFI)

14.3.2.1.4 Root Mean Square Error of Approximation (RMSEA)

14.3.2.2 Incremental Fit Indices (IFI)

14.3.2.2.1. Normed Fit Index (NFI)

14.3.2.2.2 Tucker Lewis Index (TLI)

14.3.2.2.3 Comparative Fit Index (CFI)

14.3.2.3 Parsimony Fit Indices (PFI)

14.3.2.3.1. Adjusted Goodness-of-Fit Index (AGFI)

14.3.2.3.2 Parsimony Normed Fit Index (PNFI)

14.3.3. GOF Values and Validity Test of Our Research Data Set

14.3.3.1 Confirmatory Factor Analysis (CFA)

14.3.4. Conclusion

References

Chapter 14.4: Socio-economic Development Disparity in India: An Inter-state Analysis

Introduction

Data and Selection of Variables

Methodology

Results and Findings

Conclusion

References

Chapter 14.5: Stages and Methods for Cleaning Large Secondary Data Using R

14.5.1. Introduction

14.5.2. Data Context

14.5.3. Data Collection

14.5.4. Data Cleaning Methodologies

14.5.4.1 Visual Inspection of Row and Columns

14.5.4.1.1 Rows and Columns Extent

14.5.4.1.2 Apparent Anomalies

14.5.4.1.3 Complete Case of Data

14.5.4.1.4 Review of Data and Check for Data-Type

14.5.4.2 Working with Variables

14.5.4.2.1 Modifying/Deleting Column Names

14.5.4.2.2 Logical Inconsistencies

14.5.4.2.3 Decisions in Dropping Observations

14.5.4.2.4 Outlier Treatments

14.5.5. Testing the Assumptions Required for Analysis

14.5.5.1. Multicollinearity

14.5.5.2 Normality

14.5.5.3 Heteroscedasticity

14.5.6. Conclusion

References

Appendix 1

Appendix 2: [R-Syntax]

Appendix: Statistical Tables

Index