This article researches factors that influence price fairness judgments. The empirical literature suggests several factors: reference prices, the costs of the seller, a self-interest bias, and the perceived motive of sellers. Using a Dutch sample, we find empirical evidence that these factors significantly affect perceptions of fair prices. In addition, we find that the perceived fairness of prices is also influenced by other distributional concerns that are independent of the transaction. In particular, price increases are judged to be fairer if they benefit poor people or small organizations rather than rich people or big organizations.
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This paper researches perceptions of the concept of price fairness in the Dutch coffee market. We distinguish four alternative standards of fair prices based on egalitarian, basic rights, capitalistic and libertarian approaches. We investigate which standards are guiding the perceptions of price fairness of citizens and coffee trade organizations. We find that there is a divergence in views between citizens and key players in the coffee market. Whereas citizens support the concept of fairness derived from the basic rights approach, holding that the price should provide coffee farmers with a minimum level of subsistence, representatives of Dutch coffee traders hold the capitalistic view that the free world market price is fair.
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Learning Analytics en bias – Learning analytics richt zich op het meten en analyseren van studentgegevens om onderwijs te verbeteren. Bakker onderscheidt hierin verschillende niveaus, zoals student analytics en institutional analytics, en focust op inclusion analytics, waarin gekeken wordt naar kansengelijkheid. Bias – systematische vooroordelen in data – kan vooroordelen in algoritmen versterken en zo kansenongelijkheid veroorzaken. De onderzoeksmethode maakt gebruik van het 4/5-criterium, waarbij fairness in uitkomsten gemeten wordt door te kijken of de kansen voor de beschermde groep minstens 80% zijn van die van de bevoorrechte groep.Onderzoeksaanpak – Bakker gebruikt machine learning om retentie na het eerste studiejaar te voorspellen en onderzoekt vervolgens verschillen tussen groepen studenten, zoals mbo-en vwo-studenten. Hij volgt drie stappen: (1) Data voorbereiden en modellen bouwen: Data worden opgesplitst en opgeschoond om accurate voorspelmodellen te maken. (2) Variabelen analyseren: Invloed van kenmerken op uitkomsten wordt beoordeeld voor verschillende groepen. (3) Fairness berekenen: Het 4/5-criterium wordt toegepast op metrics zoals accuraatheid en statistische gelijkheid om bias en ongelijkheden te identificeren. Resultaten, aanbevelingen en vervolgonderzoek – Uit het onderzoek blijkt dat kansengelijkheid bij veel opleidingen ontbreekt, met name voor mannen en mbo-studenten, die een hogere kans op uitval hebben. Bakker adviseert sensitieve kenmerken zoals migratieachtergrond mee te nemen in analyses op basis van informed consent. Daarnaast pleit hij voor meer flexibiliteit in het beleid, geïnspireerd door maatregelen tijdens de coronacrisis, die een positief effect hadden op studiesucces.Toekomstvisie – Bakker benadrukt dat niet elke ongelijkheid het gevolg is van discriminatie en roept op tot data-informed interventies om sociale rechtvaardigheid in het onderwijs te bevorderen. Zijn methode wordt open access beschikbaar gesteld, zodat ook andere instellingen deze kunnen toepassen en kansengelijkheid systematisch en bewust kunnen onderzoeken.
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As artificial intelligence (AI) reshapes hiring, organizations increasingly rely on AI-enhanced selection methods such as chatbot-led interviews and algorithmic resume screening. While AI offers efficiency and scalability, concerns persist regarding fairness, transparency, and trust. This qualitative study applies the Artificially Intelligent Device Use Acceptance (AIDUA) model to examine how job applicants perceive and respond to AI-driven hiring. Drawing on semi-structured interviews with 15 professionals, the study explores how social influence, anthropomorphism, and performance expectancy shape applicant acceptance, while concerns about transparency and fairness emerge as key barriers. Participants expressed a strong preference for hybrid AI-human hiring models, emphasizing the importance of explainability and human oversight. The study refines the AIDUA model in the recruitment context and offers practical recommendations for organizations seeking to implement AI ethically and effectively in selection processes.
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Supply chain collaboration, in which two or more autonomous firms work together to plan and execute supply chain operations, is becoming ever more important to remain competitive in business. Yet, through collaboration concerns arise about whether the benefits and risks of collaboration are split in an acceptable and fair manner. This research illustrates the role of fairness (organizational justice theory) in creating and appropriating value from supply chain collaborations. We therefore analyze an extensive case study in the Dutch floricultural industry, in which six companies enter a supply chain collaboration. We conclude that fairness considerations are very important for explaining the outcomes of supply chain collaborations. Asymmetries in perceived value appropriation can be offset if the collaboration is deemed fair on distributive, procedural, interpersonal and informational justice dimensions. Firms may improve the success rate of supply chain collaborations if the fairness perceived is considered to be adequate.
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Content moderation is commonly used by social media platforms to curb the spread of hateful content. Yet, little is known about how users perceive this practice and which factors may influence their perceptions. Publicly denouncing content moderation—for example, portraying it as a limitation to free speech or as a form of political targeting—may play an important role in this context. Evaluations of moderation may also depend on interpersonal mechanisms triggered by perceived user characteristics. In this study, we disentangle these different factors by examining how the gender, perceived similarity, and social influence of a user publicly complaining about a content-removal decision influence evaluations of moderation. In an experiment (n = 1,586) conducted in the United States, the Netherlands, and Portugal, participants witnessed the moderation of a hateful post, followed by a publicly posted complaint about moderation by the affected user. Evaluations of the fairness, legitimacy, and bias of the moderation decision were measured, as well as perceived similarity and social influence as mediators. The results indicate that arguments about freedom of speech significantly lower the perceived fairness of content moderation. Factors such as social influence of the moderated user impacted outcomes differently depending on the moderated user’s gender. We discuss implications of these findings for content-moderation practices.
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Although governments are investing heavily in big data analytics, reports show mixed results in terms of performance. Whilst big data analytics capability provided a valuable lens in business and seems useful for the public sector, there is little knowledge of its relationship with governmental performance. This study aims to explain how big data analytics capability led to governmental performance. Using a survey research methodology, an integrated conceptual model is proposed highlighting a comprehensive set of big data analytics resources influencing governmental performance. The conceptual model was developed based on prior literature. Using a PLS-SEM approach, the results strongly support the posited hypotheses. Big data analytics capability has a strong impact on governmental efficiency, effectiveness, and fairness. The findings of this paper confirmed the imperative role of big data analytics capability in governmental performance in the public sector, which earlier studies found in the private sector. This study also validated measures of governmental performance.
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This research investigates to what extent lecturers at universities of applied sciences do regard differentiated rewards(intended to develop and/or display professionalism)to be fair, and to what extent, and in which form, do these stimulate their willingness to (further) professionalise and/or display professionalism. This was a case study research design, and a factorial survey measurement technique was used to collect data. We argue that lecturers believe it is fair that forms of differentiated rewards are used and applied in order to have them develop and/or display more professionalism. Especially the viewpoints/practices that relate to coordination, consultation, and consideration for personal circumstances have an influence on the justice perceived. This paper contributes to the HRM literature confirming that lecturers appreciate financial stimuli enhancing their professionalism; however, elements such as consultation, respect, coordination, and communication are appreciated even more. It appeals to HRM to design new practices which have more stimulating effect on personal and professional growth in subject-specific knowledge.
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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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