Methodological Issues in Management Research: Advances, Challenges, and the Way Ahead

The introductory paper begins with the issue about the relevance of research in management. It emphasizes the need for scholars to adopt methodologies best suited to the research problem of their choice. This paper contains sections on the nature of management research, dominant research paradigms, the methodological domain, quantitative versus qualitative research, and triangulation in using multiple methodologies. The paper provides a background to the purpose of the book and summarizes in brief the purpose of each the subsequent papers.

Management research is a discipline characterized by heterogeneity in viewpoints, the application of research to real-life problems in the organization and the multidisciplinary nature of research problems. The need for a good literature review is paramount in doctoral dissertations with a view to justifying research agendas and help interested scholars use synthesized organization of extant work. The paper aims to provide an overview of the types of review, pointers for effective review, evaluating sources of information, referencing the sources cited, and avoidance of plagiarism in writing literature reviews. The paper is intended to make doctoral scholars understand the importance of literature reviews, the organization and synthesis of ideas involved, and the rigor in detailing references and avoiding plagiarism to increase the quality of the finished output.

This chapter explains the “reason” and “procedure” of research. It also pertains to the substances of undertaking research including hypothesis building, conceptual framework, and theory advancement. It is intended to serve as a fundamental resource to equip the researcher with a manual for research.

This paper covers different types of research designs, like, longitudinal, cross-section and sequential design, experimental design (including factorial experimental design), and correlational design, with illustrative examples. This paper will help a scholar to know how choice of research design depends on a number of parameters, highlighted by author.

This chapter covers the attributes of a well-designed questionnaire and on how to adopt a framework for developing questionnaires. Different types of questionnaires are discussed exhaustively, with tips on structure, procedures, and standard format examples. The author gives an elaborate example of a survey questionnaire, closely related to one of his major research project.

Much qualitative research is interview based, and this paper provides an outline of qualitative interview techniques. This paper explains the rationale for using interviewing as a qualitative technique. Various types of qualitative interviews, seven stages of interviewing, preparations needed for conducting interview as well as the skills of interviewers are discussed.

In this paper on focus group discussion (FGD), the author reviews the origins of FGD during World War II to its current usages, epistemological positions underpinning FGDs that shape its design and implementation including composition and group size, competencies required for facilitators, recruitment of participants, recording and transcribing FGDs, technology-supported virtual group designs, and ethical considerations of data collection in a social setting.

This paper is the main section on quantitative data analysis. It explains the concepts at a greater detail to help non-Math/Stat scholars to understand the basics easily. Proper data analysis is critical to any research. If data are not properly analyzed, then it may give results which either cannot be properly interpreted or wrongly interpreted. This section covers univariate, multivariate analysis and then, factor analysis, cluster analysis, conjoint analysis, and multidimensional scaling (MDS) techniques.

Testing of hypothesis, also known as sample-testing, is a common feature with almost every social and management research. We draw conclusion on population (characteristics) based on available sample information, following certain statistical principles. This paper will introduce the fundamental concepts with suitable examples, mostly in Indian context. This section is expected to help scholar readers, to learn, how hypothesis tests for differences means (or proportions) take different forms, depending on whether the samples are large or small; and also to appreciate hypothesis-testing techniques, on how it could be used in similar decision-making situations, elsewhere.

Current paper is an overview of qualitative research. It starts with discussing meaning of research and links it with a framework of experiential learning. Complexity of socio-political environment can be captured with methodologies appropriate to capture dynamism and intricacy of human life. Qualitative research is a process of capturing lived-in experiences of individuals, groups, and society. It is an umbrella concept which involves variety of methods of data collection such as interviews, observations, focused group discussions, projective tools, drawings, narratives, biographies, videos, and anything which helps to understand world of participants. Researcher is an instrument of data collection and plays a crucial role in collecting data. Main steps and key characteristics of qualitative research are covered in this paper. Reader would develop appreciation for methodiness in qualitative research. Quality of qualitative research is explained referring to aspects related to rigor, worthiness of topic in interpretivist research. This paper presents challenges of qualitative research in terms of thinking of qualitative research, doing of qualitative research, and trustworthiness.

Case study research, most often associated with qualitative inquiry has gained significance as an effective approach to investigate complex issues in real-world settings. Conducting case research is considered to be appropriate when a contemporary phenomenon is to be studied. This chapter covers all related concepts, relating to this unique method of research. The focus is on bringing about rigor in case study research.

Grounded theory (GT) is a very crucial qualitative tool in research inquiry. It embraces systematic, inductive, and comparative inquiry method to construct a theory. GT is mostly appropriate to investigate organizational phenomena, which involves a change process. In this chapter, the authors focus on the emergence of GT as a research inquiry tool with the focus how GT evolves from classis grounded theory to constructivist ground theory. In the detailed method of GT, a focus is given on coding method along with theoretical sampling and theoretical saturation points. Despite being a powerful technique, GT has drawn a number of criticisms. Majority GT researchers consider the technique as an inductive method with a few exceptions, where it has been deliberated as a deductive method. However, in the line of Corley (2015), it can be argued that GT should be considered as a methodological approach to study inductive phenomena having less understanding of theoretical perspective. Chapter concludes with identifying future scope of study in the field of GT.

This chapter introduces four research methods that are not covered in the previous chapters. They are (1) non-parametric statistics, (2) interpretive structural modeling, (3) analytic hierarchy process, and (4) data envelopment analysis. The methods are discussed with examples. The discussion, however, is introductory; so we urge the reader to go through the pertinent references for details.

With human resource (HR) roles evolving to encompass wider responsibilities, HR decision-making in organizations has become more complex than ever. This has compelled researchers in the area to move beyond simplistic models to testing models that involve studying the relationship between multiple independent and dependent variables in the presence of moderators and mediators, in order to make relevant contribution to managerial decision-making. Thus, research in the field is heavily dependent on multivariate techniques that can run several regressions simultaneously and can study the influence of one variable on the other, in presence of the other variables in the model. Structural equation modeling is the most widely used multivariate technique and involves two phases – measurement model to test reliability and validity of study constructs and structural model that involves path diagrams to test the causal relationships between these constructs. At times, however, the researcher might run into trouble with validity issues of constructs in the measurement model; especially when dimensions of a larger construct are used as independent constructs in the study. Introducing a second-order construct in such a case could be the solution to proceed further. Using empirical data, this chater illustrates the case of such a problematic measurement model and details the research methodology of introducing and working with a second-order construct in a step-wise manner, starting with an exploratory factor analysis and subsequently, moving toward a confirmatory factor analysis, highlighting the best practices to be followed while using these statistical techniques.

Purpose of this study was to understand intention of tourists to visit a destination by exploring factors related to destination image and self-congruity of tourists with destination image. A quantitative survey-based methodology was employed for gathering data. Study used a convenience sample of 225 students and faculty members from a leading university in India. Regression analysis was carried out for testing the main effect and moderation impact. The results revealed that cognitive destination image and self-congruity had a direct impact on destination image. However, the results did not establish a moderating effect of self-congruity on relationship between destination image and return intention. The study findings have direct implication for destination marketing managers for drafting a positioning strategy for their destinations.

Nowadays, structural equation modeling is a buzz word in the arena of research in management, social sciences, and other equivalent fields. Although the theoretical base bears its significance in building the measurement and structural models, assessing different goodness-of-fit indices (GOFI) equally retains its importance for model validity and conformity. There are various alternative GOFI available for the researchers and the threshold values of each differ. The present paper discussed all the well-accepted and reported GOFI and their threshold value, which will be a great help to researchers and practitioners who use structural equation modeling in research. The author has also presented the different GOF values and validity results of her current research carried out in an Indian power transmission organization in Odisha, India.

Socio-economic development is multi-faceted. The major facets consist of: income, level of education, level of health, quality of infrastructure, sex ratio, level of employment, industrial and agricultural development, and so on. In India, the progress of socio-economic development among the states is not even. This study makes a modest attempt to measure the socio-economic development among the states of India and highlight the disparity among them. In the same line of thought, a composite index based on several facets of socio-economic development has been developed in a holistic manner and the states are arranged accordingly based on the indices. Instead of studying the disparity of a particular facet across states, a composite index is a better measure. This study has utilized the varied dimensions of socio-economic development and a taxonomic approach to construct a composite index without making any assumptions on the raw data. The findings of the study also support the general perception of large disparity in socio-economic development status of the states in India.

Data, either in primary or secondary form, represent the core strength of quantitative research. However, there is significant difference between collected data and the final researchable data. The data collection is driven by objectives of the research. The data also could be in various formats at different sources. The collected data in its original form may contain systematic and random errors. Such errors need to be cleaned from the data which is termed as data cleaning process.

The present chapter discusses about the different methodologies and steps that may be helpful for fine tuning the data into researchable format. The discussions are instantiated with the applications of methodologies on a set of financial data of companies listed in Bombay Stock Exchange. Various steps involved in transformation of collected data to researchable data are presented. A schematic model including data collection, data cleaning, working with variables, outlier treatment, testing the assumption of statistical test, normality, and heteroscedasticity is presented for the benefit of research scholars. Beyond this generic model, this paper focuses exclusively on financial data of listed companies in the Bombay Stock Exchange. The challenges involved in various sources, data gathering and other pre-analysis stages are also considered. This is also applicable for research based on secondary data sources in other fields as well.