Sustainable development (SD) -that is, ''Development that meets the needs of current generations without compromising the ability of future generations to meet their needs and aspirations'' -can be pursued in many dierent ways. Stakeholder relations management (SRM) is one such way, through which corporations are confronted with economic, social, and environmental stakeholder claims. This paper lays the groundwork for an empirical analysis of the question of how far SD can be achieved through SRM. It describes the so-called SD-SRM perspective as a distinctive research approach and shows how it relates to the wider body of stakeholder theory. Next, the concept of SD is operationalized for the microeconomic level with reference to important documents. Based on the ensuing SD framework, it is shown how SD and SRM relate to each other, and how the two concepts relate to other popular concepts such as Corporate Sustainability and Corporate Social Responsibility. The paper concludes that the significance of societal guiding models such as SD and of management approaches like CSR is strongly dependent on their footing in society.

Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these 'stakeholders' desiderata') in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability of artificial systems and reviews their desiderata.

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Digital interviews are a potentially efficient new form of selection interviews, in which interviewees digitally record their answers. Using Potosky's framework of media attributes, we compared them to videoconference interviews. Participants (N = 113) were randomly assigned to a videoconference or a digital interview and subsequently answered applicant reaction questionnaires. Raters evaluated participants’ interview performance. Participants considered digital interviews to be creepier and less personal, and reported that they induced more privacy concerns. No difference was found regarding organizational attractiveness. Compared to videoconference interviews, participants in digital interviews received better interview ratings. These results warn organizations that using digital interviews might cause applicants to self‐select out. Furthermore, organizations should stick to either videoconference or digital interviews within a selection stage.

Technological advancements allow the automation of every part of job interviews (information acquisition, information analysis, action selection, action implementation) resulting in highly automated interviews. Efficiency advantages exist, but it is unclear how people react to such interviews (and whether reactions depend on the stakes involved). Participants (N = 123) in a 2 (highly automated, videoconference) × 2 (high‐stakes, low‐stakes situation) experiment watched and assessed videos depicting a highly automated interview for high‐stakes (selection) and low‐stakes (training) situations or an equivalent videoconference interview. Automated high‐stakes interviews led to ambiguity and less perceived controllability. Additionally, highly automated interviews diminished overall acceptance through lower social presence and fairness. To conclude, people seem to react negatively to highly automated interviews and acceptance seems to vary based on the stakes. Open Practices This study was pre‐registered on the Open Science Framework (http://osf.io/hgd5r) and on AsPredicted (https://AsPredicted.org/i52c6.pdf).