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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.

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.

Content analysis is a family of systematic, rule-guided techniques used to analyze the informational contents of textual data (Mayring, 2000). It is used frequently in nursing research, and is rapidly becoming more prominent in the medical and bioethics literature. There are several types of content analysis including quantitative and qualitative methods all sharing the central feature of systematically categorizing textual data in order to make sense of it (Miles & Huberman, 1994). They differ, however, in the ways they generate categories and apply them to the data, and how they analyze the resulting data. In this chapter, we describe a type of qualitative content analysis in which categories are largely derived from the data, applied to the data through close reading, and analyzed solely qualitatively. The generation and application of categories that we describe can also be used in studies that include quantitative analysis.

The purpose of this paper is to review the basic principles of qualitative analysis, and examine the practical application of these principles to analyze student assignments using the ATLAS.ti software.

Student comments from an assignment are prepared for import into ATLAS.ti. The comments are coded, and then analyzed for patterns using ATLAS.ti and its mechanisms for exploring data and data patterns.

ATLAS.ti offers myriad analytic tools that allow the researcher to quantify qualitative information through coding, data query, cross‐tabulation, and networked visualization of project design. By developing both technical expertise with the software, and developing familiarity with qualitative methodology, librarians can wield an effective means to assess and evaluate text‐based data, such as student assignments or surveys.

Because the ATLAS.ti software, and the qualitative analysis process itself is so complex, this article can only outline the most prominent aspects of the tool.

The document can serve as a “jumping‐off” point for other researchers wishing to either explore the qualitative analysis process, particularly as conducted with ATLAS.ti. The reader will become more familiar with the basic concepts of qualitative analysis as reflected in the organization and functions of the ATLAS.ti, as well as the process of preparing and analyzing textual information with ATLAS.ti.

Because there is a general lack of literature on setting up a project with the software, the article is potentially valuable to anyone wishing to expand and improve their evaluative skills using ATLAS.ti or similar tools.

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.

This article aims to explore how the mapping strategies between user requirements expressed by the humanities researchers lead to a better customization of user-driven digital humanities tools and to the creation of innovative functionalities, which can directly affect the way of doing research in a digital context.

It describes the user-driven development of a tool that helps researchers in the quantitative and qualitative analysis of large textbook collections.

This article presents an exemplary user journey map, which shows the different steps of the digital transformation process and how the humanities researchers are involved for (1) producing innovative research solutions, comprehensive and personalized reports, and (2) customizing access to content data used for the analysis of digital documents. The article is based on a case study on a German textbooks collection and content analysis functionalities.

The focus of this article is the reiterative research process, in which humanists (from the human centred point of view) starts from an initial research question, using quantitative and qualitative data and develops both the research question and the answers to it by with the aim to find patterns in the content and structure of educational media. Thus, from the viewpoint of digital transformation the humanist is part of the interaction between digitization and digitalization processes, where he/she uses digital data, metadata, reports and findings created and supported by the digital tools for research analysis.

This paper aims to report the findings of a study into the automated text analysis of student feedback comments to assist in investigating a high volume of qualitative information at various levels in an Australian university. It includes the drawbacks and advantages of using selected applications and established lexicons. There has been an emphasis on the analysis of the statistical data collected using student surveys of learning and teaching, while the qualitative comments provided by students are often not systematically scrutinised. Student comments are important, as they provide a level of detail and insight that are imperative to quality assurance practices.

The paper outlines the process by which the institution researched, developed and implemented the automated analysis of student qualitative comments in surveys of units and teaching.

The findings indicated that there are great benefits in implementing this automated process, particularly in the analysis of evaluation data for units with large enrolments. The analysis improved efficiency in the interpretation of student comments. However, a degree of human intervention is still required in creating reports that are meaningful and relevant to the context.

This paper is unique in its examination of one institution’s journey in developing a process to support academics staff in interpreting and understanding student comments provided in surveys of units and teaching.

The purpose of this paper is to illustrate how qualitative data may be analysed using a method that can be considered as rigorous/scientific as any statistical analysis of quantitative data.

An artificial neural network programme CATPAC II™ was used to evaluate selected portions of two accounting standards: the Financial Reporting Standards Board of New Zealand's standard on consolidation; and the equivalent standard developed by the International Accounting Standards Committee and revised by the International Accounting Standards Board.

The analysis of the concepts of control in the two standards identifies the differences that exist between the two standards. These differences are illuminated through the use of a hierarchical cluster analysis of 40 unique concepts in each of the two standards and 2D representation of the concepts. The extent of the differences in the concepts was established through a rotational analysis of the two datasets.

This research is limited to the analysis of the concept of control and associated commentary paragraphs and supporting documents associated with two accounting standards. Different results may have been obtained had the whole standard been analysed.

Artificial neural network software can be used to support the intuitive textual understanding of the differences that exist in qualitative data. In this paper, the differences identified in the concepts of control may result in different interpretations being taken by the accounting standard users when determining what reporting entities to include in consolidated financial statements. Some additional uses for artificial neural network software in accounting research are also identified.

This paper is the first in the discipline to use artificial neural network software to analyse and compare different texts.