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This paper aims to close the gap between the generic concept of knowledge activities (KAs) and implementing them in the context of software engineering organisations concentrating on the non-technical aspects, such as team organisation and practices.

This qualitative research used a questionnaire with practitioners such as software developers and team leads who were asked to provide feedback on a set of team practices and measures typically used in software engineering projects and assess their relation to the activities of acquiring, codifying, storing, maintaining, transferring and creating knowledge. The obtained results were analysed using frequency analysis and further descriptive statistics yielding a matrix linking the investigated team practices and measures to KAs.

Team practices and measures commonly applied in software engineering can be facilitated to trigger particular KAs. While most of these team practices and measures originate from agile methods, they are not restricted to these. A purposeful composition can help in assembling a balanced set of KAs aimed at fostering given knowledge goals in software engineering organisations.

By bridging the communication and terminology gap between knowledge management research and software engineering practitioners, this work lays the foundation for assessing software teams’ knowledge profiles more easily and creating prerequisites for implementing knowledge management by facilitating common practices and measures often already part of their daily work. Hence, overhead can be avoided when implementing knowledge management.

To the best of the authors’ knowledge, this is the first study investigating application and relevance of KAs in the software industry by linking them to practices and measures well-accepted in software engineering, thus providing the necessary vocabulary for the implementation of knowledge management in software development teams.

Role-taking is a basic social process underpinning much of the structural social psychology paradigm – a paradigm built on empirical studies of human interaction. Yet today, our social worlds are occupied by bots, voice assistants, decision aids, and other machinic entities collectively referred to as artificial intelligence (AI). The integration of AI into daily life presents both challenges and opportunities for social psychologists. Through a vignette study, the authors investigate role-taking and gender in human-AI relations.

Participants read a first-person narrative attributed to either a human or AI, with varied gender presentation based on a feminine or masculine first name. Participants then infer the narrator's thoughts and feelings and report on their own emotions, producing indicators of cognitive and affective role-taking. The authors supplement results with qualitative analysis from two open-ended survey questions.

Participants score higher on role-taking measures when the narrator is human versus AI. However, gender dynamics differ between human and AI conditions. When the text is attributed to a human, masculinized narrators elicit stronger role-taking responses than their feminized counterparts, and women participants score higher on role-taking measures than men. This aligns with prior research on gender, status, and role-taking variation. When the text is attributed to an AI, results deviate from established findings and in some cases, reverse.

This first study of human-AI role-taking tests the scope of key theoretical tenets and sets a foundation for addressing group processes in a newly emergent form.

Nobody concerned with political economy can neglect the history of economic doctrines. Structural changes in the economy and society influence economic thinking and, conversely, innovative thought structures and attitudes have almost always forced economic institutions and modes of behaviour to adjust. We learn from the history of economic doctrines how a particular theory emerged and whether, and in which environment, it could take root. We can see how a school evolves out of a common methodological perception and similar techniques of analysis, and how it has to establish itself. The interaction between unresolved problems on the one hand, and the search for better solutions or explanations on the other, leads to a change in paradigma and to the formation of new lines of reasoning. As long as the real world is subject to progress and change scientific search for explanation must out of necessity continue.