Pub Date : 2019-11-11DOI: 10.1108/978-1-78973-973-220191013
P. Mohapatra
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.
{"title":"Supplementary Research Methods: DEA, ISM, AHP and Non-Parametric Statistics","authors":"P. Mohapatra","doi":"10.1108/978-1-78973-973-220191013","DOIUrl":"https://doi.org/10.1108/978-1-78973-973-220191013","url":null,"abstract":"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.","PeriodicalId":375437,"journal":{"name":"Methodological Issues in Management Research: Advances, Challenges, and the Way Ahead","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121553078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-11DOI: 10.1108/978-1-78973-973-220191010
Richa Awasthy
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.
{"title":"Nature of Qualitative Research","authors":"Richa Awasthy","doi":"10.1108/978-1-78973-973-220191010","DOIUrl":"https://doi.org/10.1108/978-1-78973-973-220191010","url":null,"abstract":"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.","PeriodicalId":375437,"journal":{"name":"Methodological Issues in Management Research: Advances, Challenges, and the Way Ahead","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125244261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-11DOI: 10.1108/978-1-78973-973-220191016
Malabika Sahoo
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.
{"title":"Structural Equation Modeling: Threshold Criteria for Assessing Model Fit","authors":"Malabika Sahoo","doi":"10.1108/978-1-78973-973-220191016","DOIUrl":"https://doi.org/10.1108/978-1-78973-973-220191016","url":null,"abstract":"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.","PeriodicalId":375437,"journal":{"name":"Methodological Issues in Management Research: Advances, Challenges, and the Way Ahead","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134400761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-11DOI: 10.1108/978-1-78973-973-220191009
R. Subudhi
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.
{"title":"Testing of Hypothesis: Concepts and Applications","authors":"R. Subudhi","doi":"10.1108/978-1-78973-973-220191009","DOIUrl":"https://doi.org/10.1108/978-1-78973-973-220191009","url":null,"abstract":"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.","PeriodicalId":375437,"journal":{"name":"Methodological Issues in Management Research: Advances, Challenges, and the Way Ahead","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125176758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-11DOI: 10.1108/978-1-78973-973-220191017
A. Raichoudhury
{"title":"Socio-economic Development Disparity in India: An Inter-state Analysis","authors":"A. Raichoudhury","doi":"10.1108/978-1-78973-973-220191017","DOIUrl":"https://doi.org/10.1108/978-1-78973-973-220191017","url":null,"abstract":"","PeriodicalId":375437,"journal":{"name":"Methodological Issues in Management Research: Advances, Challenges, and the Way Ahead","volume":"8 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125997709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-11DOI: 10.1108/978-1-78973-973-220191011
Srilata Patnaik, S. C. Pandey
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.
{"title":"Case Study Research","authors":"Srilata Patnaik, S. C. Pandey","doi":"10.1108/978-1-78973-973-220191011","DOIUrl":"https://doi.org/10.1108/978-1-78973-973-220191011","url":null,"abstract":"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.","PeriodicalId":375437,"journal":{"name":"Methodological Issues in Management Research: Advances, Challenges, and the Way Ahead","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134615323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-11DOI: 10.1108/978-1-78973-973-220191015
Joydeep Biswas, R. Shabbirhusain
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.
{"title":"Role of Self-Congruity in Predicting Travel Intention","authors":"Joydeep Biswas, R. Shabbirhusain","doi":"10.1108/978-1-78973-973-220191015","DOIUrl":"https://doi.org/10.1108/978-1-78973-973-220191015","url":null,"abstract":"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.","PeriodicalId":375437,"journal":{"name":"Methodological Issues in Management Research: Advances, Challenges, and the Way Ahead","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133814578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-11DOI: 10.1108/978-1-78973-973-220191014
Subhra Pattnaik
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.
{"title":"Working with Second-order Construct in Measurement Model: An Illustration Using Empirical Data","authors":"Subhra Pattnaik","doi":"10.1108/978-1-78973-973-220191014","DOIUrl":"https://doi.org/10.1108/978-1-78973-973-220191014","url":null,"abstract":"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.","PeriodicalId":375437,"journal":{"name":"Methodological Issues in Management Research: Advances, Challenges, and the Way Ahead","volume":"244 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123013715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-11DOI: 10.1108/978-1-78973-973-220191008
P. Dhall
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.
{"title":"Quantitative Data Analysis","authors":"P. Dhall","doi":"10.1108/978-1-78973-973-220191008","DOIUrl":"https://doi.org/10.1108/978-1-78973-973-220191008","url":null,"abstract":"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.","PeriodicalId":375437,"journal":{"name":"Methodological Issues in Management Research: Advances, Challenges, and the Way Ahead","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126630385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-11DOI: 10.1108/978-1-78973-973-220191019
{"title":"Index","authors":"","doi":"10.1108/978-1-78973-973-220191019","DOIUrl":"https://doi.org/10.1108/978-1-78973-973-220191019","url":null,"abstract":"","PeriodicalId":375437,"journal":{"name":"Methodological Issues in Management Research: Advances, Challenges, and the Way Ahead","volume":"162 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129184090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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