Background: Traditionally, research integrity studies have focused on research misbehaviors and their explanations. Over time, attention has shifted towards preventing questionable research practices and promoting responsible ones. However, data on the prevalence of responsible research practices, especially open methods, open codes and open data and their underlying associative factors, remains scarce.Methods: We conducted a web-based anonymized questionnaire, targeting all academic researchers working at or affiliated to a university or university medical center in The Netherlands, to investigate the prevalence and potential explanatory factors of 11 responsible research practices.Results: A total of 6,813 academics completed the survey, the results of which show that prevalence of responsible practices differs substantially across disciplines and ranks, with 99 percent avoiding plagiarism in their work but less than 50 percent pre-registering a research protocol. Arts and humanities scholars as well as PhD candidates and junior researchers engaged less often in responsible research practices. Publication pressure negatively affected responsible practices, while mentoring, scientific norms subscription and funding pressure stimulated them.Conclusions: Understanding the prevalence of responsible research practices across disciplines and ranks, as well as their associated explanatory factors, can help to systematically address disciplinary- and academic rank-specific obstacles, and thereby facilitate responsible conduct of research.
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Expectations are high for digital technologies to address sustainability related challenges. While research into such applications and the twin transformation is growing rapidly, insights in the actual daily practices of digital sustainability within organizations is lacking. This is problematic as the contributions of digital tools to sustainability goals gain shape in organizational practices. To bridge this gap, we develop a theoretical perspective on digital sustainability practices based on practice theory, with an emphasis on the concept of sociomateriality. We argue that connecting meanings related to sustainability with digital technologies is essential to establish beneficial practices. Next, we contend that the meaning of sustainability is contextspecific, which calls for a local meaning making process. Based on our theoretical exploration we develop an empirical research agenda.
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To elucidate how authoritative knowledge is established for better dealing with unstructured urban problems, this article describes how collaborations between researchers and officials become an instrument for conceptualizing and addressing policy problems. A case study is used to describe a research consortium evaluating the controversial practice of ‘Lifestyle’ based housing allocation in the Dutch domain of social-housing. Analyzing this case in key episodes, we see researchers and policymakers selectively draw on established institutional practices—their so called ‘home practices’—to jointly (re-)structure problems. In addition, we find that restructuring problems is not only intertwined with, but also deliberately aimed at (re-)structuring the relations within and between the governmental practices, the actors are embedded in. It is by selectively tinkering with knowledges, values, norms, and criteria that the actors can deliberately enable and constrain the ways a real-world problem is addressed.
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This paper presents a comprehensive study on assisting new AI programmers in making responsible choices while programming. The research focused on developing a process model, incorporating design patterns, and utilizing an IDE-based extension to promote responsible Artificial Intelligence (AI) practices. The experiment evaluated the effectiveness of the process model and extension, specifically examining their impact on the ability to make responsible choices in AI programming. The results revealed that the use of the process model and extension significantly enhanced the programmers' understanding of Responsible AI principles and their ability to apply them in code development. These findings support existing literature highlighting the positive influence of process models and patterns on code development capabilities. The research further confirmed the importance of incorporating Responsible AI values, as asking relevant questions related to these values resulted in responsible AI practices. Furthermore, the study contributes to bridging the gap between theoretical knowledge and practical application by incorporating Responsible AI values into the centre stage of the process model. By doing so, the research not only addresses the existing literature gap, but also ensures the practical implementation of Responsible AI principles.
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Over the past few years, there has been an explosion of data science as a profession and an academic field. The increasing impact and societal relevance of data science is accompanied by important questions that reflect this development: how can data science become more responsible and accountable while also responding to key challenges such as bias, fairness, and transparency in a rigorous and systematic manner? This Patterns special collection has brought together research and perspective from academia, the public and the private sector, showcasing original research articles and perspectives pertaining to responsible and accountable data science.
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This paper describes a study into consumers' reasons for buying socially responsible (SR) products, such as Fair Trade products and organic meat. As opposed to other studies, we use a qualitative approach based on 25 in-depth interviews and include several different products in the research. This leads to several new results, such as: (1) buying SR products is perceived as an imperfect moral duty; (2) low quality of SR products is a dissatisfier, but high quality not a satisfier; (3) the attitude towards SR products is related to the reputation of charitable funds; (4) the demand for SR products is negatively related to the frequency of purchasing SR products; (5) reflection on SR products raises the demand for SR products; (6) consumers that have witnessed the social problems that SR products aim to alleviate purchase more SR products. Finally, we find that the demand for different SR products is correlated: if a consumer buys one SR product, it is more likely that (s)he purchases other SR products as well.
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This paper assesses the impact of perceived HRM practices on organisational citizenship behaviour (OCB) and whether leader membership exchange (LMX) mediates this relationship. The required research data were retrieved from four different departments within a logistics and supply chain management organisation. The results show that there is a significant relationship between the HRM practices as perceived by a subordinate and their level of organisational citizenship behaviour. The relationship that subordinates have with their frontline manager (LMX) acts as a significant mediator. In the final section, of this paper the findings are discussed and recommendations for future research and practical implications are given.
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Many have suggested that AI-based interventions could enhance learning by personalization, improving teacher effectiveness, or by optimizing educational processes. However, they could also have unintended or unexpected side-effects, such as undermining learning by enabling procrastination, or reducing social interaction by individualizing learning processes. Responsible scientific experiments are required to map both the potential benefits and the side-effects. Current procedures used to screen experiments by research ethics committees do not take the specific risks and dilemmas that AI poses into account. Previous studies identified sixteen conditions that can be used to judge whether trials with experimental technology are responsible. These conditions, however, were not yet translated into practical procedures, nor do they distinguish between the different types of AI applications and risk categories. This paper explores how those conditions could be further specified into procedures that could help facilitate and organize responsible experiments with AI, while differentiating for the different types of AI applications based on their level of automation. The four procedures that we propose are (1) A process of gradual testing (2) Risk- and side-effect detection (3) Explainability and severity, and (4) Democratic oversight. These procedures can be used by researchers and ethics committees to enable responsible experiment with AI interventions in educational settings. Implementation and compliance will require collaboration between researchers, industry, policy makers, and educational institutions.
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This dissertation presents the results of a research project on unraveling the dynamics of facilitating workplace learning through pedagogic practices in healthcare placements. Supervisors are challenged to foster safe learning opportunities and fully utilize the learning potential of placement through stimulating active participation for students while ensuring quality patient care. In healthcare placements, staff shortages and work pressure may lead to stress when facilitating workplace learning. Enhancing pedagogic practices in healthcare placements seems essential to support students in challenging experiences, such as emotional challenges. This dissertation proposes approaches for optimizing learning experiences for students by highlighting the value of day-to-day work activities and interactions in healthcare placements, and shedding light on agency in workplace learning through supervisor- and student-strategies.
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