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The quality of doctoral research has long been debated in the field of public administration, along with discussions about the need for improved methodological preparation. What is lacking, however, are discussions in public administration pedagogy about conceptual understandings regarding the use of computer-aided qualitative data analysis software (CAQDAS), pedagogical strategies, and student and faculty perspectives and experiences about the use of such software programs. This article attempts to fill this gap by focusing on ways in which CAQDAS can be integrated into doctoral public administration education, the possibilities and limitations of such software, and strategies that faculty and students can use in teaching and employing such software. We also draw on lessons learned from a collaborative research project that used a qualitative data analysis software program.

This paper provides a comprehensive overview and synthesis of the various definitions of corporate environmental performance (CEP) in conceptual and empirical papers. Based on an overview of existing conceptual and empirical studies of CEP, we analyze the complex nature of this multidimensional construct. In a first step, we apply content analysis to the relevant literature to identify definitions of CEP and conduct a bibliometric analysis using the software HistCite. We found only few studies that provide a clear definition of CEP. In a second step, we use a semantic mapping methodology by applying Leximancer, a computer-aided qualitative data analysis tool to organize the large literature on CEP and to explore the definitional and conceptual complexity of CEP. To our knowledge, this is a new and unique approach in the field of environmental management. This paper contributes to research on CEP in three ways. First, it collects and summarizes definitions and measurements of CEP used in the organizational literature so far. Second, it provides a bird’s eye view on the different contexts in which CEP is discussed. Third, a parsimonious model of CEP derived from computer-aided qualitative data analysis and consisting of five major elements is presented and discussed.

Nowadays we have a wide variety of computer-assisted qualitative data analysis software, CAQDAS, to choose from, and almost every qualitative researcher uses one or two of these programs to analyse his/her data. This demand for CAQDAS has brought not only more sophistication in the newest programs and updates but also the discussion about its methodological implications and the need for more training courses and workshops. A lot has been written about the relation between CAQDAS and qualitative methodology. Nevertheless, the ways the training courses and workshops have been developed and carried out have not been outlined. Who are these courses planned for? Is there any prerequisite that the attendants must fulfil? What must the main goal of these training courses be? This article discusses some facts I have found in my experience as a social researcher and CAQDAS user and trainer in a country where this kind of software is not widespread. The article also focuses on some of the problems that arise when training people in the use of CAQDAS and the consequences the globalisation of training courses and workshops focused on the acquisition of mechanical code-and-retrieve skills have for qualitative methodology. Finally, I propose some critical issues that CAQDAS trainers and qualitative researchers should bear in mind when teaching or learning the use of any qualitative data analysis software. URN: urn:nbn:de:0114-fqs0202147

The increasing popularity of Extensible Markup Language (XML) and the availability of software capable of reading and editing XML documents present opportunities for Qualitative Data Analysis (QDA) facilities to be incorporated into \"groupware\" applications such as collaborative workspaces and \"document bases\", and to be made available across networks both within organisations and across the Internet. Collaborative systems have, in the past, characteristically, been geared to retrieve and present whole documents, and while annotation and discussion of documents has been possible within such systems, the \"pencil-level\" analysis commonplace in CAQDAS (Computer Assisted Qualitative Data Analysis Software) has been lacking. XML, when combined with a scripting language such as Perl, can be used to offer basic QDA functionality--retrieval by text and codes, attachment of memos to text fragments, and the generation of summary data--via a standard web-browser. URN: urn:nbn:de:0114-fqs0202134

As we stand poised to enter the next millennium there is perhaps no better opportunity to reflect on the beliefs, values and techniques that are shared and debated by qualitative researchers throughout the United States. This paper explores some of the challenges facing those who pursue qualitative inquiry in the course of completing a graduate research degree: how we learn about research methodology and how we think about, use, and support the use of computer software research tools. The paper explores some of the assumptions inherent in the language of inquiry and discusses critical issues that qualitative researchers struggle with and continue to debate. URN: urn:nbn:de:0114-fqs000136

This chapter contains sections titled: The GIS Wars A Short History of the GIS Wars What Have We Learned: After Critique After Critique? Extending Possibilities for GIScience

This research examines how European citizens decide to disclose and protect their personal data and thereby reveals cultural and generational divides. Focus group discussions featured either young people, aged 15 to 24 years, or adults, between 25 and 70 years of age, and were conducted in seven EU member states. The results of a computer-aided text analysis with two complementary software packages suggest similarities and differences in participants' views and privacy concerns (PC). Responsibility is relevant to personal data management, which represents a hotly contested issue. A geographical north-south divide appears for the importance of responsibility as opposed to trust. Moreover, people regard disclosure differently in the south (as a choice) and east (as forced) of Europe. Younger people express more positive attitudes toward data management, feel more responsible, and are more confident in their ability to prevent possible data misuse. Their lower PC and greater protective behaviours (i.e., a potential reversed privacy paradox) may help explain contradictory results in prior literature. These results offer significant and useful theoretical, managerial, and policy implications.

The manufacturing landscape is ever-changing, and one of the most significant driving forces is the emergence of additive manufacturing (AM), which enables cost-effective and small-scale production towards sustainability. To better align AM with manufacturing in suitable applications, this study proposes a business model in terms of the cost pattern and scaling production supported by three key concepts: standardisation, localisation and collaboration. The ambiguity of the cost calculation is one of the key factors slowing down AM progress, and a lack of a cost pattern affects decision-making when applying AM to appropriate applications. The business model in this study is focused on applying the data collected from previous research, the collection-recycling-manufacturing (CRM) model, to discover the implications of AM processes on the road to sustainable manufacturing. The novel business model envisions the nature of AM characteristics and their linkages to cost patterns, so AM applications can be integrated into a cost-effective process. This study contributes qualitative analysis to the cost patterns’ integration. Through this integration, the business model mediates the gap between technologies and applications via the formulas of cost patterns, so AM can perform its appropriate role in the industry mainstream. The cost modelling proposed in this study derives generic formulas via the unit cost of tooling, moulding, machine, materials, design, miscellaneous cost and the batch size. The business model applies the “divide-and-conquer” concept, convergence effect and data analysis to support quantitative analysis. The model can calculate the total cost per unit, and its accuracy is close to 100%. Through the novelty of this model, AM and conventional manufacturing (CM) cost benchmarking and decision support functions are enabled to aid in stakeholder decision-making. Eventually, appropriate AM technologies and processes can synchronise with localisation, standardisation and collaboration and, ultimately, the impact of AM towards sustainable manufacturing.

With the advent of smart manufacturing, companies are adapting their business processes and procedures to match concepts and requirements related to this industrial revolution. In this scenario, maintenance sector should evolve to keep pace with new technological trends by shifting from traditional approaches to a smart paradigm. Some consolidated methodologies rely on standard evaluation, like Risk Priority Number, to plan maintenance actions on machines and components. However, contemporary and smart approaches should be characterized by data-driven (quantitative knowledge) as well as operator’s experience (qualitative knowledge) in order to provide a whole understanding and visibility of the system health status. In this context and with a view of digital twin concepts (under the Industry 4.0 technology), a decision support methodology (or framework) plays an essential role by providing knowledge and assisting operators in the decision-making process. With this concept in mind, this paper focuses on integrating several and not alike fields of study (process mining, multicriteria decision-making (MCDM), and data fusion) to enable the assessment of a risk and criticality analysis to rank industrial machines in order to carry out/plan maintenance tasks on them. Therefore, merging those fields of study aims at accomplishing a dynamic, flexible, and real-time evaluation to perform a robust, accurate, and responsive analysis of the system. The results from different analysis scenarios show the influence of maintenance indicators on the machine ranking process, highlighting the feasibility and flexibility of the approach, as well as expanding the application in modern systems by optimizing quality of maintenance decisions.