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The purpose of this study is to assess how intergovernmental panel on climate change’s (IPCC’s) sixth assessment report of Working Group I (WGI), a Summary for Policymakers (SPM), has evaluated the current climate change situation. The author uses the qualitative content analysis (QCA) method (i.e. summative content analysis [SCA]) to identify critical points of the SPM.

To better understand the WGI report and its SPM, the author uses the thematic analysis, often called the QCA method. This study takes more steps and uses one of the main qualitative content analysis approaches (i.e. SCA). Therefore, QCA (SCA) can help the author count the occurrence of certain words using computer-assisted qualitative data analysis software that quantifies the words in the data. This process helps the author to understand codes and patterns (e.g. the concept in the results section).

Interestingly, the AR6 says more about adaptation for policy makers in sections C (Climate information for risk assessment) and D (Mitigation of future climate change) than in sections A and D. Finally, this study concludes that the IPCC WGI SPM has addressed evidence on global climate change policymaking for SPMa, SPMd (mitigation-based strategies and mitigation policy [MP]) and SPMb, SPMc (adaptation policy and adaptation-based strategy).

First, this study refers only to the contribution of WGI, a SPM. The findings of this study do not necessarily provide a full understanding of what the AR6 WGI SPM says about climate change. It points out that the QDA Miner software and Voyant tool do not include all variables and examples where mitigation and adaptation-based strategies are discussed. The guidance for coding is based on the approved version of IPCC AR6 WGI SPM. The final limitation is that the relatedness of key words (e.g. confidence, high and warming) is sometimes ambiguous; even experts may disagree on how the words are linked to form a concept. Thus, this method works at the keyword level. A more intelligent method would use more meaningful information than keywords.

This study used qualitative data analysis (SCA) to explore what was said about climate change in four sections of the IPCC AR6 WGI SPM, which may influence current and future global climate change policymaking.

Focus groups are becoming more widely used in libraries as a means of gathering data from the users’ point‐of‐view about how they find and use information. This article discusses the major issues in planning a focus group project and describes the use of a computer assisted qualitative data analysis software package, The Ethnograph, for data analysis. An annotated bibliography of useful focus group literature accompanies the article.

This paper aims to explore the changes imposed by the general data protection regulation (GDPR) on software engineering practices. The fundamental objective is to have a perception of the practices and phases that have experienced the greatest changes. Additionally, it aims to identify a set of good practices that can be adopted by software engineering companies.

This study uses a qualitative methodology through four case studies involving Portuguese software engineering companies. Two of these companies are small and medium enterprises (SMEs) while the other remaining two are micro-companies. The thematic analysis is adopted to identify patterns in the performed interviews.

The findings indicate that significant changes have occurred at all stages of software development. In particular, the initial stages of identifying requirements and modeling processes were the stages that experienced the greatest changes. On the opposite, the technical development phase has not noticeably changed but, nevertheless, it is necessary to look at the importance of training software developers for GDPR rules and practices.

Two relevant limitations were identified as follows: only four case studies involving micro-companies and SMEs were considered, and only the traditional software development methodology was considered. The use of agile methodologies was not explored in this study and the findings can only be mainly applied to the waterfall model.

This study offers mainly practical contributions by identifying a set of challenges that are posed to software engineering companies by the implementation of GDPR. Through their knowledge, it is expected to help these companies to better prepare themselves and anticipate the challenges they will necessarily face.

The open science movement calls for transparent and retraceable research processes. While infrastructures to support these practices in qualitative research are lacking, the design needs to consider different approaches and workflows. The paper bases on the definition of ontologies as shared conceptualizations of knowledge (Borst, 1999). The authors argue that participatory design is a good way to create these shared conceptualizations by giving domain experts and future users a voice in the design process via interviews, workshops and observations.

This paper presents a novel approach for creating ontologies in the field of open science using participatory design. As a case study the creation of an ontology for qualitative coding schemas is presented. Coding schemas are an important result of qualitative research, and reuse can yield great potential for open science making qualitative research more transparent, enhance sharing of coding schemas and teaching of qualitative methods. The participatory design process consisted of three parts: a requirement analysis using interviews and an observation, a design phase accompanied by interviews and an evaluation phase based on user tests as well as interviews.

The research showed several positive outcomes due to participatory design: higher commitment of users, mutual learning, high quality feedback and better quality of the ontology. However, there are two obstacles in this approach: First, contradictive answers by the interviewees, which needs to be balanced; second, this approach takes more time due to interview planning and analysis.

The implication of the paper is in the long run to decentralize the design of open science infrastructures and to involve parties affected on several levels.

In ontology design, several methods exist by using user-centered design or participatory design doing workshops. In this paper, the authors outline the potentials for participatory design using mainly interviews in creating an ontology for open science. The authors focus on close contact to researchers in order to build the ontology upon the expert's knowledge.

This chapter explains the research design. An interpretive methodology was considered most suitable for the study. Informed by an institutional framework, the interpretive methodology was selected for this monograph for its strengths of focusing on the research context, interactive processes, and meanings that are not measurable by quantitative approach. The interpretive methodology is also consistent with the ontological and epistemological positions of the researchers. Data were collected from interviewing four groups of key persons and a document survey. The data triangulation and multiple perspectives helped increase the reliability and validity of the study. Also, conducting data collection in a natural setting produced a rich data source. This enabled the provision of an enhanced understanding of the operation and effectiveness of corporate governance and financial reporting practice in a real setting. In addition, the systematic set of data analysis procedures helped improve research rigor and develop conceptual and theoretical understanding of issues of interest.

This paper aims to offer insights into the ways two computer-aided qualitative data analysis software (CAQDAS) applications (QSR NVivo and Leximancer) can be used to analyze big, text-based, online data taken from consumer-to-consumer (C2C) social media communication.

This study used QSR NVivo and Leximancer, to explore 200 discussion threads containing 1,796 posts from forums on an online open community and an online brand community that involved online brand advocacy (OBA). The functionality, in particular, the strengths and weaknesses of both programs are discussed. Examples of the types of analyses each program can undertake and the visual output available are also presented.

This research found that, while both programs had strengths and weaknesses when working with big, text-based, online data, they complemented each other. Each contributed a different visual and evidence-based perspective; providing a more comprehensive and insightful view of the characteristics unique to OBA.

Qualitative market researchers are offered insights into the advantages and disadvantages of using two different software packages for research projects involving big social media data. The “visual-first” analysis, obtained from both programs can help researchers make sense of such data, particularly in exploratory research.

The paper provides practical recommendations for analysts considering which programs to use when exploring big, text-based, online data.

This paper answered a call to action for further research and demonstration of analytical programs of big, online data from social media C2C communication and makes strong suggestions about the need to examine such data in a number of ways.

The key objective of the study is to understand the best processes that are currently used in managing talent in Australian higher education (AHE) and design a quantitative measurement of talent management processes (TMPs) for the higher education (HE) sector.

The three qualitative multi-method studies that are commonly used in empirical studies, namely, brainstorming, focus group discussions and semi-structured individual interviews were considered. Twenty-three individuals from six Australian universities participated in this study.

The qualitative study explored three key themes and ten subthemes of TMPs that are used in AHE. These were: (1) talent attraction, (2) talent development and (3) talent retention.

This study only targeted one country (Australia) and one sector (HE).

This study offers three major contributions as follows: theoretical, practical and policy aspects. Theoretically, the study provides a value-add to Talent Management (TM) theory through designing a guide (conceptual model) of TMPs for the HE sector. Practically, it collects original qualitative data regarding TM in the HE domain. From a policy point of view, this study adds more debate around adding new ideas to Australian education strategic plans for HE.

This study has a unique methodology because of strengthening the effect of an in-depth case study. For instance, two different techniques were used for data analysis for the same research objective as follows: (1) both manual methods and content analysis software (NVivo 11) and (2) the three-stage approach. Using these techniques for the same purpose in one study can provide greater flexibility to examine the relationship between theory and data.

The paper's first objective is to develop a new conceptual framework for categorizing and designing cognitive, specifically comparative, causal mapping (CCM) research by building upon the theory‐centred and participant‐centred perspectives. The second purpose is to enable the discerned study prototypes by introducing a new CCM software application, CMAP3.

Building upon the distinction between theory‐centred (etic) and participant‐centred (emic) perspectives in social research, we first construct and apply a conceptual framework for analysing and categorising extant CCM studies in terms of their objectives and basic design. Next, after noting the important role and basic tasks in computerising causal mapping studies, we present a new CCM software application.

The theory‐centred/participant‐centred perspectives define four causal mapping study prototypes, each with different goals, basic designs and methodological requirements. Noting the present lack of widely accessible software for qualitatively oriented CCM studies, we introduce CMAP3, a new non‐commercial Windows application, and summarise how it is used in related research.

The framework and the studies representing the prototypes demonstrate the versatility of CCM methods and that the proposed framework offers a new, systematic approach to categorising and designing CCM studies. Research technically, CMAP3 can support the defined CCM‐prototypes, based on a low‐structured (inductive/qualitative) or a structured (nomothetic/quantitative) methodological approach/stance, and having therefore different needs of data acquisition, processing, coding, aggregation/comparison, and analysis of the emerging aggregated cause maps’ contents or structure.

The purpose of this paper is to determine whether TAMS Analyzer and Viewshare are viable free and open source software data sharing and creation tools for those with limited funding and technological skills.

The participant observer method was used to collect experiential evidence while applying the tools to a collection of text-, image-, and video-based digital cultural records.

TAMS Analyzer was found to be a low barrier to entry tool for those with coding and qualitative data analysis experience. Those with general experience will be able to create datasets with the support of manuals and tutorials, while those with limited experience may find it difficult to use. Viewshare was found to be a low barrier to entry tool for sharing data online, and accessible for all skill levels.

TAMS Analyzer supports Mac and Linux platforms only, so a low-cost software recommendation was made for those in Windows environments.

Librarians can use these tools to address data access gaps while promoting library digital collections.

With a greater understanding of data tools, librarians can be advisors, collaborators, agents for data culture, and relevant participants in digital humanities scholarship.

The research evaluates both the capabilities of the tools and the barriers to using or accessing them, which are often neglected. The paper addresses a need in the literature for greater scrutiny of tools that are a critical component of the data ecology, and will further assist librarians when connecting scholars to tools of inquiry in an environment with limited funding and technical support.

This chapter examines the application of exploratory sequential mixed methods design in the context of small accommodation enterprises (i.e., home-stay). This study, therefore, discusses the exploratory sequential mixed methods of data collection and analysis and provides practical illustrations based on a study of small tourism enterprise sustainability practices in Ghana. The findings demonstrate that mixed methods overcome the weaknesses of a mono-method and offer an in-depth understanding of tourism and hospitality phenomena. In addition to providing a practical guide to emerging tourism scholars, the current study highlights the ability of mixed methods to develop emerging practitioners' skills in both qualitative and quantitative data. In conclusion, the exploratory sequential mixed methods design offers pragmatic data collection techniques that are non-existent in mono-methods. Accordingly, it is recommended for exploring research questions when there is limited information and high flexibility is needed.