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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.

Explores the appropriateness of quantifying focus group discussions. Basic problems involved in quantification of text are addressed. Prior approaches are discussed and briefly evaluated. A new neural network algorithm for analyzing excerpts from in‐depth interviews is presented. Keywords (brand names, values, etc.) are identified by the analyst. The entire text is scanned and a “covariance” matrix with weights expressing pairwise associations between words is established. This matrix can be used as input data set for advanced statistical analysis. The small illustrative study involves a focus group dealing with a holiday cruise. Results are inspected using multivariate analysis. Findings seem to make good sense. It is doubtful, though, that quantitative techniques will replace well‐established qualitative approaches. On the contrary, quantitative methods for textual analysis may supplement and improve insight gained from qualitative scrutiny. Recommendations for future research are discussed.

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 make two main contributions: first, showcase the diversity of service research in terms of the variety of used theories and methods, and second, explain (post-publication) success of articles operationalized as interest in an article (downloads), usage (citations), and awards (best paper nomination). From there, three sub-contributions are derived: stimulate a dialogue about existing norms and practices in the service field, enable and encourage openness amongst service scholars, and motivate scholars to join the field.

A mixed method approach is used in combining quantitative and qualitative research methods while analyzing 158 Journal of Service Management (JOSM) articles on several criteria such as their theory, methodology, and main descriptive elements (e.g. number of authors or references) and then using automated text analysis (e.g. investigating the readability of articles, etc.).

The results show that the JOSM publishes a large variety of articles with regard to theories, methods of data collection, and types of data analysis. For example, JOSM has published a mixture of qualitative and quantitative articles and papers containing firm-level and customer-level data. Further, the results show that even though conceptual articles create the same amount of interest (downloads), they are used more (citations).

This paper presents many descriptive results which do not allow for making inferences toward the entire service research discipline. Further, it is only based on one service research journal (JOSM) through a five-year span of publication.

The results have a number of implications for the discipline that are presented and discussed. Amongst them are that: the discipline should be more open toward conceptual articles, service research shows an imbalance toward theory testing, there is more potential to work with transactional data, and writing style should be more accessible (i.e. readable).

This paper is the first to conduct an in-depth analysis of service research articles to stimulate dialogue about common publishing practices in the JOSM and to increase the openness of the field.

The purpose of this paper is to propose new directions for human resource management (HRM) research by drawing attention to online data as a complementary data source to traditional quantitative and qualitative data, and introducing network text analysis as a method for large quantities of textual material.

The paper first presents the added value and potential challenges of utilising online data in HRM research, and then proposes a four-step process for analysing online data with network text analysis.

Online data represent a naturally occuring source of real-time behavioural data that do not suffer from researcher intervention or hindsight bias. The authors argue that as such, this type of data provides a promising yet currently largely untapped empirical context for HRM research that is particularly suited for examining discourses and behavioural and social patterns over time.

While online data hold promise for many novel research questions, it is less appropriate for research questions that seek to establish causality between variables. When using online data, particular attention must be paid to ethical considerations, as well as the validity and representativeness of the sample.

The authors introduce online data and network text analysis as a new avenue for HRM research, with potential to address novel research questions at micro-, meso- and macro-levels of analysis.

This chapter will examine the interplay among actors who took part in the process of consensus building towards a post-2015 education agenda via different channels of global governance, including both formal and informal channels.

Most of the forums and entities established as part of the global governance structure are composed of representatives from UN or UNESCO member states, civil society organizations (CSOs) and UN agencies. However, each of these categories has diverse constituent groups; representing these groups is not as straightforward a task as the governance structure seems to assume. Therefore, based on interviews and qualitative text analysis, this chapter will introduce major groups of actors and their major issues of concern, decision-making structure, mode of communication and relationship with other actors. Then, based on an understanding of the characteristics of the various channels and actors, it will present the structural issues that arose during the analysis of post-2015 discourse and the educational issues that emerged as the shared concerns of the ‘education community’. While most of the analysis to untangle the nature of discourse relies on qualitative analysis of texts and interviews, the end of this chapter will also demonstrate the trends of discourse in quantitative terms.

What was the post-2015 discourse for the so-called education community, which in itself has an ambiguous and virtual existence? The keywords post-2015 and post-EFA provide us with an opportunity to untangle how shared norms and codes of conduct were shaped at the global scale.

This study aims to understand and a lay a foundation of how analytics has been used in depression management, this study conducts a systematic literature review using two techniques – text mining and manual review. The proposed methodology would aid researchers in identifying key concepts and research gaps, which in turn, will help them to establish the theoretical background supporting their empirical research objective.

This paper explores a hybrid methodology for literature review (HMLR), using text mining prior to systematic manual review.

The proposed rapid methodology is an effective tool to automate and speed up the process required to identify key and emerging concepts and research gaps in any specific research domain while conducting a systematic literature review. It assists in populating a research knowledge graph that does not reach all semantic depths of the examined domain yet provides some science-specific structure.

This study presents a new methodology for conducting a literature review for empirical research articles. This study has explored an “HMLR” that combines text mining and manual systematic literature review. Depending on the purpose of the research, these two techniques can be used in tandem to undertake a comprehensive literature review, by combining pieces of complex textual data together and revealing areas where research might be lacking.

This chapter argues that the primary reason for the underdeveloped state of microfoundations research in language and communications studies is methodological. It asserts that the long-standing methodological division between micro and macro analyses (traditionally small-scale and large-scale, respectively) has led to their continued theoretical separation. The paper draws from Giddens’ theory of structuration and newly developed computational methods to outline an alternative, mixed-methods framework for discourse analysis that the author calls Recursive Analysis (RA). The author demonstrates the application of the RA framework through a case study of the electric vehicle industry that aligns small-scale and large-scale textual analysis to generate theoretical insights.

This paper develops a generalizable, machine-learning-based method for measuring established marketing constructs using passive analysis of consumer-generated textual data from service reviews. The method is demonstrated using topic and sentiment analysis along dimensions of an existing scale: lodging quality index (LQI).

The method induces numerical scale ratings from text-based data such as consumer reviews. This is accomplished by automatically developing a dictionary from words within a set of existing scale items, rather a more manual process. This dictionary is used to analyze textual consumer review data, inducing topic and sentiment along various dimensions. Data produced is equivalent with Likert scores.

Paired t-tests reveal that the text analysis technique the authors develop produces data that is equivalent to Likert data from the same individual. Results from the authors’ second study apply the method to real-world consumer hotel reviews.

Results demonstrate a novel means of using natural language processing in a way to complement or replace traditional survey methods. The approach the authors outline unlocks the ability to rapidly and efficiently analyze text in terms of any existing scale without the need to first manually develop a dictionary.

The technique makes a methodological contribution by outlining a new means of generating scale-equivalent data from text alone. The method has the potential to both unlock entirely new sources of data and potentially change how service satisfaction is assessed and opens the door for analysis of text in terms of a wider range of constructs.

Contemporary comparative pedagogical discourses are becoming increasingly popular and strongly modify the policy and practice of education worldwide. Intensification of empirical studies naturally leads to the decrease of the research interest in purely methodological issues that stand apart from practical application of comparative analysis and comparative method. This chapter attempts to fill in the methodological lacuna in the study of comparative method and its potential when doing research determined by the ideological context. The authors state two main research questions, the first one concerning the potential of comparative analysis for the detection of the technologies and facts of ideological indoctrination and the second one focusing on its functional possibilities in revealing the transformations in the vision of pedagogical reality by the theorist under the influence of the complete change of the state’s ideology. Statistical analysis of the units, content and comparative analysis, quantification, interpretation, and analogy were used for the comprehensive comparative study of the small volume of text by A. S. Makarenko Beseda s rabochim aktivom na zavode ‘Sharikopodshipnik’ (Conversation with Working Active Members at the Plant ‘Ball-bearing’) with comments published in Russian and its analogue issued in German by Makarenko-Referat laboratory and two text versions of “Zadachi i metody narodnoj shkoly” (“Objectives and Methods of National School”) by P. P. Blonsky issued under the same title in 1916 and 1917. General outcomes of the research vividly demonstrate how micro- and macro contexts may widen the horizons of the comparative method and significantly differentiate comparative research schemes.