Research 

Abstract

Does the scientific community sanction sexual misconduct? Using a sample of scientists at U.S. universities involved in substantiated cases of sexual misconduct that became public, we find that their prior work is cited less after the allegations become known. The effect weakens with distance in the coauthorship network, suggesting that researchers primarily learn about misconduct through their peers. Among the closest peers, male authors react more strongly. In male-dominated fields, the effects on citations appear muted. Accused scientists are more likely to leave academic research, to move to non-university institutions, and to publish less. 

Abstract

 We study how the timing and the type of donation decisions affect charitable giving. In an online real-effort experiment with a subsistence income constraint, participants could donate to a charity either before or after they worked to generate income. We find no evidence that timing affects the propensity to donate or the amounts donated. However, if a donation is expressed as a share of future income rather than an absolute amount, more people donate and they donate larger amounts. Advance commitment does not appear to have a motivational effect on effort. Our findings suggest that requesting donations as a share of future income may enhance charitable giving.

Abstract

As technology-assisted decision-making is becoming more widespread, it is important to understand how the algorithmic nature of the decisionmaker affects how decisions are perceived by the affected people. We use a laboratory experiment to study the preference for human or algorithmic decision makers in re-distributive decisions. In particular, we consider whether algorithmic decision maker will be preferred because of its unbiasedness. Contrary to previous findings, the majority of participants (over 60%) prefer the algorithm as a decision maker over a human—but this is not driven by concerns over biased decisions. Yet, despite this preference, the decisions made by humans are regarded more favorably. Participants judge the decisions to be equally fair, but are nonetheless less satisfied with the AI decisions. Subjective ratings of the decisions are mainly driven by own material interests and fairness ideals. For the latter, players display remarkable flexibility: they tolerate any explainable deviation between the actual decision and their ideals, but react very strongly and negatively to redistribution decisions that do not fit any fairness ideals. Our results suggest that even in the realm of moral decisions algorithmic decision-makers might be preferred, but actual performance of the algorithm plays an important role in how the decisions are rated.

Abstract

Are people indeed algorithm averse? How does human-in-the-loop design affect uptake of algorithmic recommendations and how does it affect accuracy? With the constantly growing number of applications where algorithmic recommendations are available, ongoing public and legal discussion about regulation of algorithmic decision-making, these questions are of utmost importance.  We address them in an online experiment and find that 66% of participants prefer algorithmic recommendations to human recommendation of equal quality if they are to delegate the decision completely.  Keeping human-in-the-loop, that is allowing participants to monitor and amend the recommendations, increases the uptake of an algorithm by further 6 to 9 percentage points. Our results document a trade off: allowing people to amend algorithmic recommendation increases uptake, but reduces the accuracy of the produced predictions due to corrections made by human monitors. Raising concerns about human-in-the-loop systems, participants are less likely to interfere with least accurate recommendations and correct them less.

Abstract

Using a survey with 57 German firms, we evaluate the level of digitalization of the HR management function and document perceived benefits and barriers of technology adoption from organizational and individual users’ perspectives. The results give a reason for optimism. Most of the companies report that the core HR processes are digitized. We do not observe adverse effects of the digital HRM tools on users’ job satisfaction and work stress. Still, more than half of companies do not yet use digital tools for strategic HRM decisions. Respondents appreciate the increased speed and cost-efficiency of digital HRM processes and associate it with a competitive advantage in talent acquisition. The most prominent adoption barriers are lack of qualified professionals, high costs, and uncertainty regarding the legal framework. Moreover, we test if small and medium-sized enterprises differ systematically from larger organizations in how they use digital HRM tools.

Abstract 

Addressing the treatment gap for mental health issues around the globe and the "hidden" pandemic aggravated by COVID, digital mental health apps attempt to provide help at a low cost, conveniently, and privately. We partner with one of the largest mental health apps in India to consider how users react to automating some of the routine processes to allow operating at scale. In a field study, a prompt to complete a psychological assessment was either labeled as delivered by a system or by a human mediator. If the engagement of a human mediator was highlighted, participants were 21.75% more likely to comply by following the prompt and starting the assessment.Conditional on starting the assessment, users were equally likely to complete it. Our results highlight the fact that apart from the technical feasibility of automation, behavioral responses of users need to be taken into account when deciding what processes should be automated or if human involvement should be emphasized.

 Abstract

Today, humans interact with automation frequently and in a variety of settings ranging from private to professional. Their behavior in these interactions has attracted considerable research interest across several fields, with sometimes little exchange among them and seemingly inconsistent findings. In this article, we review 138 experimental studies on how people interact with automated agents, that can assume different roles. We synthesize the evidence, suggest ways to reconcile inconsistencies between studies and disciplines, and discuss organizational and societal implications. The reviewed studies show that people react to automated agents differently than they do to humans: In general, they behave more rationally, and seem less prone to emotional and social responses, though this may be mediated by the agents’ design. Task context, performance expectations and the distribution of decision authority between humans and automated agents are all factors that systematically impact the willingness to accept automated agents in decision-making - that is, humans seem willing to (over-)rely on algorithmic support, yet averse to fully ceding their decision authority. The impact of these behavioral regularities for the deliberation of the benefits and risks of automation in organizations and society is discussed.

Accepted manuscript here.

Abstract

This article provides an overview of the latest empirical research on thegender  gap  in  knowledge-intensive  occupations  from  an  economicsperspective.  It  studies  contributing  factors  both  from  an  institutionaland behavioral perspective, and considers potential solutions. The persisting gender gap is not only an issue of social fairness; it also hampersinnovation and has direct negative implications for individual produc-tivity as well as economic growth. The overview of the existing genderliterature, as well as our own research, emphasize that it is imperativeto fight the gender gap with well-tailored policies and to support gender equality at the institutional and personal level.

Abstract

A large body of experimental studies demonstrates that redistribution leads to inefficiencies due to distorted work incentives. Yet, this finding is typically obtained in environments where people are unconstrained in their labor-leisure allocation decisions. In this paper we study labor supply decisions in a framework with a subsistence income constraint and a redistribution system that supports disadvantaged members of a society in meeting the constraint. We document that while high-talent taxpayers perform equally well in all conditions, the less talented ones significantly decrease their performance in response to the introduction of the tax. The negative effect of taxation is mitigated if an income threshold is present and the tax is spent meaningfully.

Abstract

When asked, people dislike punishment institutions, although punishment is more effective than rewards to maintain cooperation in social dilemmas. Which institution do they choose in the long run? We study migration patterns in a laboratory experiment that allows participants to migrate continuously between punishment and reward communities. The majority of participants initially chooses the reward institution, but a substantial number of subjects joins the less profitable punishment community subsequently. In this case, the mere threat of punishment establishes high contributions. Income differences and missing compensations for cooperators in the reward community are the key factors for the decision to migrate.

Abstract

The integration of AI into scientific work holds significant potential to accelerate innovation. We surveyed researchers in two leading German research organizations  to examine AI adoption, barriers, and perceived impact on research. Researchers are widely using AI tools – often for primary and creative tasks – and many expect the technology to be transformative for research. Effective use, however, requires training, both through hands-on experience and dedicated learning resources. A persistent gender gap in AI use can largely be explained by differences in familiarity, pointing to a clear opportunity for organizational intervention. Legal uncertainty and privacy concerns also emerge as major barriers, with researchers calling for clear, high-level regulatory guidance. Overall, our findings underscore the importance of institutional action to support equitable and effective AI adoption in research.

Abstract

We examine the impact of reputational concerns on seeking advice. While seeking can improve performance, it may affect how others perceive the seeker's competence. In an online experiment with white-collar professionals (N=2,521), we test how individuals navigate this tradeoff and if others' beliefs about competence change it. We manipulate visibility of the decision to seek and stereotypes about competence. Results show a sizable and inefficient decline in advice-seeking when visible to a manager. Higher-order beliefs about competence cannot mediate this inefficiency. We find no evidence that managers interpret advice-seeking negatively, documenting a misconception that may hinder knowledge flows in organizations.

Abstract

What is the effect of labour market adjustment to automation on political participation? We study the consequences of the introduction of industrial robots across US commuting zones on voter turnout in US counties between 2000 and 2016. We first replicate prior results showing negative effects of exposure to robots on employment and household incomes at local labour markets and then show that an increase in the exposure by one robot per thousand workers leads to a 0.5 percentage point lower voter turnout at US presidential elections. We contrast this result with the effect of the exposure to Chinese imports, for which we do not find a negative effect on political participation. The effect persists despite controlling for migration responses to labor market changes and intensity of political campaigning in the region. To understand why the effect is not uniform, we conduct an online survey experiment. We find that the nature of the shock matters beyond the mere economic consequences. While the government is seen as instrumental in addressing the trade shock, it is perceived less effective in the case of automation. Our findings highlight an important behavioral aspect of the political economy of technological change.

Abstract

We examine the impact of labor market power on firms’ adoption of automation technologies. We develop a model that incorporates labor market power into the task-based theory of automation. We show that, due to higher marginal cost of labor, monopsonistic firms have stronger incentives to automate than wage-taking firms, which could amplify or mitigate the negative employment effects of automation. Using data from US commuting zones, our results show that commuting zones that are more exposed to industrial robots exhibit considerably larger reductions in both employment and wages when their labor markets demonstrate higher levels of concentration. 

Abstract

The prevalence of artificial intelligence (AI) tools supporting decision-making, particularly in collaboration with humans, has witnessed significant growth. While the promise of AI in alleviating workplace disparities, especially in talent acquisition, is recognized, concerns persist regarding potential biases in recruitment processes. The crucial consideration of AI’s impact on perpetuating stereotypes held by decision-makers adds complexity to this evolving landscape. To address the research question of whether the collaboration of humans and AI is more beneficial or detrimental to diverse job applicants, we conducted two online experiments. Our investigations focused on how the type of screening process (human only vs. AI-supported human) influences the likelihood of diverse applicants being invited for an interview. In Study 1, participants used a CV screening software biased against women and ethnic minorities, while Study 2 incorporated AI scores favouring these underrepresented groups. The findings highlight the significance of the screening process type, revealing that while participants integrate AI advice in sequential decisions, they tend to adjust rankings to achieve a more balanced evaluation of the entire applicant pool. This research contributes to the diversity literature, offering insights into the implications of AI adoption in human resources practices.

Abstract

We investigate how the information about monetary outcomes influences perceptions of fairness of income redistributions. In an economic experiment, participants initially rated the fairness of a redistribution scheme without knowing the exchange rate of experimental tokens to real money. After learning the monetary value of tokens, participants adjusted their fairness ratings, generally perceiving redistributions  that generate higher income as fairer. As the redistribution itself did not change, our findings suggest that awareness of monetary consequences affects perceptions of redistribution beyond mere self-interest.