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The purpose of this paper is to provide a methodology by which qualitative analyses serve as rich source materials for discovery of theoretically cogent interrelations between latent variables.

In an illustrative case, qualitative data are collected from US franchisee managers from a single branded franchise of automotive repair outlets. Qualitative analysis of franchisee experiences and attitudes is critical for construction of a causal model used to predict conflict intensity between franchisee managers and franchisors.

The model is based on franchisees' normative expectations for resource allocation within the franchise; and their perceptions of franchisor normative violations, which are determinative of grievances, distrust, and hostility. This theoretical orientation serves to generate a system of interrelated empirically testable propositions.

In principle, the primary limitation of using qualitative analysis for the construction of causal models is the fruitfulness of the theoretical orientation shared by the qualitative analyst and the causal modeler.

The methodological approach advanced in this paper advances qualitative research and causal modeling beyond the individual contributions. Qualitative analysis infuses variables and process imagery into causal modeling. In turn, causal modeling elaborates the qualitative analysis and makes explicit logical connections between variables.

This paper advances a methodology by which qualitative analysis and causal model construction may be usefully integrated. Theory‐based qualitative analysis may be formalized to map latent concepts and their interrelations. Further, operational measures of these concepts may be adduced from the analysis of textual data.

The analysis of unstructured information, particularly in the form of text, has long been a technique in the armory of social scientists, who have to deal with conversational records, historical documents, unstructured interviews, and the like. Unsurprisingly, a considerable amount of methodological literature has developed on the subject. The methods of “qualitative data analysis” have now spread to areas of information analysis as diverse as market research and legal evidence analysis. Related computer techniques, from database management systems and word‐processors to specialized qualitative data analysis software, have been pressed into use. This article discusses the information processing methodology and theory assumed by computer‐based qualitative data analysis software; and, in particular, describes and analyzes the methodology of the NUDIST system developed by the authors.

The critical incident technique (CIT) is widely used in many disciplines; however, scholars have acknowledged challenges associated with analyzing qualitative data when using this technique. Therefore, the purpose of this article is to address the data analysis issues that have been raised by introducing some different contemporary ways of analyzing qualitative critical incident data drawn from recent dissertations conducted in the human resource development (HRD) field.

This article describes and illustrates different contemporary qualitative re-storying and cross-incident analysis approaches with examples drawn from previously and recently conducted qualitative HRD dissertations that have used the CIT.

Qualitative CIT analysis comprises two processes: re-storying and cross-incident analysis. The narrative inquiry–based re-storying approaches the authors illustrate include poetic narrative and dramatic emplotting. The analytical approaches we illustrate for cross-incident analysis include thematic assertion, grounded theory, and post-structural analysis/assemblages. The use of the aforementioned approaches offers researchers contemporary tools that can deepen meaning and understanding of qualitative CIT data, which address challenges that have been acknowledged regarding the difficulty of analyzing CIT data.

The different contemporary qualitative approaches that we have introduced and illustrated in this study provide researchers using the CIT with additional tools to address the challenges of analyzing qualitative CIT data, specifically with regard to data reduction of lengthy narrative transcripts through re-storying as well as cross-incident analyses that can substantially deepen meaning, as well as build new theory and problematize the data through existing theory.

A strength of the CIT is its focus on actual events that have occurred from which reasoning, behaviors, and decision-making can be examined to develop more informed practices.

The CIT is a very popular and flexible method for collecting data that is widely used in many disciplines. However, data analysis can be especially difficult given the volume of narrative qualitative data that can result from data collection. This paper describes and illustrates different contemporary approaches analyzing qualitative CIT data, specifically the processes of re-storying and cross-incident analysis, to address these concerns in the literature as well as to enhance and further evolve the use of the CIT method.

To explore the cases for and against the use of computer‐assisted qualitative data analysis software (CAQDAS) in qualitative organisation and management research.

Reflecting the debate inherent in the questions raised about the use of CAQDAS, a dialogue between the authors is used.

There are risks associated with using CAQDAS without considering its underpinning principles and assumptions about data analysis. If these are considered explicitly as part of a research methodology, then CAQDAS may be a valuable analytical tool. If not, there is risk of distortion and bias in results from the use of CAQDAS.

The paper addresses a commonly posed question for qualitative researchers, in a format and structure that is likely to stimulate further debate.

Discussions of the use of computer‐assisted qualitative data analysis software (CAQDAS) in social science research studies are still few and isolated. In fact, much of the literature takes an uncritical approach to CAQDAS programs based on unrealistic expectations of the software as a methodology in and of itself. This has significant implications for learning and teaching qualitative data analysis software and the way it is used in research. The study on which this article is based combined formal narrative analysis, thematic coding, and deconstruction techniques to analyse the data using a dedicated qualitative data analysis software NVivo. A discussion is given of how the qualitative analysis software was used in a social constructionist study, particularly outlining how it fitted with the methodological perspective adopted, and where, why and in what way rigour fitted with the underpinning epistemological position. Thus, this article illuminates how rigour can be integrated with relevance with the aid of the power and possibilities that qualitative research software possesses. In addition, an attempt is made to demonstrate how the use of CAQDAS enhances the validity of a qualitative project.

Discusses ways in which qualitative techniques might be incorporated in quantitative research and quantitative techniques in qualitative research ‐ a transfer of techniques. Explores the use of neuro‐linguistic programming (NLP) and projective techniques in quantitative research. Reports the results of customizing a self‐completion questionnaire to a respondent’s preferred representational system (PRS). This application of NLP produced encouraging findings. Provides suggestions for further research. Describes an example of how NLP and projective techniques can benefit a quantitative study with a case study in which TRBI’s BrandWorks was used. Suggests that, although the adoption by qualitative researchers of techniques used in quantitative research focuses on computer applications, the recent academic interest in the use of text analysers has not been matched by practitioners. Discusses issues related to quality, validity, transparency and value, and reports the findings of a survey of the largest qualitative marketing research suppliers. Finally, examines the use of correspondence analysis and describes ways in which correspondence analysis might benefit the qualitative researcher.

Novice researchers experience difficulties in analysing qualitative data. To develop the skills necessary for qualitative data analysis, theoretical manuals are often insufficient. Supervisors supporting students in analysing qualitative data stress the need for practical guidance, including exercises and feedback. The purpose of this paper is to present and discuss QualiBuddy, an interactive online support tool in answer to this need.

An online support tool was developed in answer to existing problems regarding analysing qualitative data. The tool provides a learning trajectory of 11 stages of analysis, which all contain examples, exercises, feedback, verification questions and questions for reflection. This tool is developed from a multidisciplinary perspective and is constructed around various steps. During the development process, internal feedback from the members of the project team, as well as external feedback from an international steering group with experts in qualitative research were taken into account.

The tool QualiBuddy is based on an empirically and theoretically grounded approach to qualitative data analyses. Pilot tests with experienced qualitative researchers suggest that the tool potentially allows novice researchers from various domains to develop and improve their skills in conceptualising interview data, specifically within a grounded theory approach.

QualiBuddy is a newly developed interactive online education tool based on and complementary to existing guides for qualitative data analysis.

To provide guidance for the formalised analysis of qualitative data and observations, to raise awareness about systematic analysis and illustrate promising avenues for the application of qualitative methodologies in international marketing research.

Conceptually, the nature of qualitative research, globalisation and its implications for the research landscape, text‐data as a source for international research and equivalence issues in international qualitative research are discussed. The methodology section applies these concepts and analysis challenges to a real‐world example using N*Vivo software.

A 14‐step analytic design is developed, introducing procedures of data analysis and interpretation which help to formalise qualitative research of textual data.

The use of software programs (e.g. N*Vivo) helps to substantiate the analysis and interpretation process of textual data.

Step‐by‐step guidance on performing qualitative analysis of textual data and documenting findings.

The paper is valuable for researchers and practitioners looking for guidance in analysing and interpreting textual data from interviews. Specific support is given for N*Vivo software and its application.

In a context where human–machine interaction is growing, understanding the limits between automated and human-based methods may leverage qualitative research. This paper aims to compare human and machine analyses, highlighting the challenges and opportunities of both approaches.

This study applied qualitative secondary analysis (QSA) with machine learning-based text mining on qualitative data from 25 interviews previously analyzed with traditional qualitative content analysis.

By analyzing both techniques' strengths and weaknesses, this study complements the results from the original research work. The previous human model failed to point to a particular aspect of the case, while the machine analysis did not recognize the sequence of time in the interviewee's discourse.

This study demonstrates that combining content analysis with text mining techniques improves the quality of the research output. Researchers may, therefore, better handle biases from humans and machines in traditional qualitative and quantitative research.

The purpose of this paper is to provide insightful evidence of phenomena in organization and management theory. Textual data sets consist of two different elements, namely qualitative and quantitative aspects. Researchers often combine methods to harness both aspects. However, they frequently do this in a comparative, convergent, or sequential way.

The paper illustrates and discusses a hybrid textual data analysis approach employing the qualitative software application GABEK-WinRelan in a case study of an Austrian retail bank.

The paper argues that a hybrid analysis method, fully intertwining qualitative and quantitative analysis simultaneously on the same textual data set, can deliver new insight into more facets of a data set.

A hybrid approach is not a universally applicable solution to approaching research and management problems. Rather, this paper aims at triggering and intensifying scientific discussion about stronger integration of qualitative and quantitative data and analysis methods in management research.