Despite constant efforts of organisations to ensure a fair and transparent personnel selection process, hiring is still characterised by systematic inequality. The potential of algorithms to produce fair and objective decision outcomes has attracted the attention of academic scholars and practitioners as a conceivable alternative to human decision‐making. However, applicants do not necessarily consider an objective algorithm as fairer than a human decision maker. This study examines the conditions under which applicants perceive algorithms as fair and establishes a theoretical foundation of algorithmic fairness perceptions. We further propose and investigate transparency and anthropomorphism interventions as strategies to actively shape these fairness perceptions. In an online application scenario with eight experimental groups (N = 801), we analyse determinants for algorithmic fairness perceptions and the impact of the proposed interventions. Embedded in a stimulus‐organism‐response framework and drawing from organisational justice theory, our study reveals four justice dimensions (procedural, distributive, interpersonal, informational justice) that determine algorithmic fairness perceptions. The results further show that transparency and anthropomorphism interventions mainly affect dimensions of interpersonal and informational justice, highlighting the importance of algorithmic fairness perceptions as critical determinants for individual choices.

In order to induce the shift in consumer behavior necessary for the mitigation of diet-related diseases, front-of-package labels (FoPL) such as the Nutri-Score that support consumers in their efforts to identify nutritionally valuable products during grocery shopping have been found to be effective; however, they remain non-compulsory in most regions. Counter-intuitively, a similar stream of research on digital web-based FoPL does not yet exist, even though such digital labels hold several advantages over physical labels. Digital FoPL can provide scalable and personalized interventions, are easier to implement than physical labels, and are especially timely due to the recent increase in online grocery shopping. The goal of this study was to demonstrate the technical feasibility and intervention potential of novel, scalable, and passively triggered health behavior interventions distributed via easy-to-install web browser extensions designed to support healthy food choices via the inclusion of digital FoPL in online supermarkets. To that end, we developed a Chrome web browser extension for a real online supermarket and evaluated the effect of this digital food label intervention (i.e., display of the Nutri-Score next to visible products) on the nutritional quality of individuals’ weekly grocery shopping in a randomized controlled laboratory trial (N = 135). Compared to the control group, individuals exposed to the intervention chose products with a higher nutritional quality (e.g., 8% higher healthy trolley index (HETI), 3.3% less sugar, 7.5% less saturated fat). In particular, users with low food literacy seemed to benefit from the digital FoPL (e.g., 11% higher HETI, 10.5% less sugar, 5.5% less saturated fat). Furthermore, participants exposed to the food label advocated its introduction more strongly than the control group (p = 0.081). Consumers worldwide could easily install such applications to display digital food labels on their end devices, and would thus not have to wait for stakeholders in the food industry to eventually reach consensus on mandatory food label introduction.

The economic potential of human resources (HR) algorithms requires organizations to ensure their adoption, but HR employees are often reluctant to use them. One driver of this reluctance may be employees' overconfidence in their own judgments and the sensitive nature of HR decisions, which directly affect human beings. Interventions that aim to increase technology adoption can help organizations overcome this issue. Drawing on the theoretical lens of information and incentive interventions, we conducted a survey-based incentive-compatible online experiment with 276 participants to determine the effects of interventions on the adoption of algorithms in an organizational HR context. We distinguish between adoption in the short and long runs. Our study, which contributes to research on the use of algorithms in HR and induced technology adoption, has practical implications for HR leaders who seek to increase HR employees' adoption of algorithms in completing HR-related tasks.