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Is it advantageous to make the first offer and to do so ambitiously? Although initial studies suggested clear advantages across cultures and contexts, recent findings have challenged the robustness of this first-mover advantage. A preregistered meta-analysis of 374 effects from 90 studies (Study 1; N = 16,334) revealed three beneficial effects of making the first offer: (a) a general first-mover advantage (g = 0.42, m = 80), (b) a positive correlation between first-offer magnitude and agreement value (r = 0.62, g = 1.56, m = 53), and (c) an advantage of ambitious (vs. moderate) first offers on agreement value (g = 1.14, m = 187). The meta-analysis also identified two detrimental outcomes of ambitious first offers: (d) fewer deals (i.e., more impasses; g = −0.42, m = 13) and (e) worse subjective value experienced by recipients (g = −0.40, m = 41). Two preregistered experiments (Study 2a-2b; N = 2,121) replicated both the beneficial and detrimental meta-analytic effects and simultaneously tested multiple psychological mechanisms driving these effects. Across the experiments, selective accessibility drove the effect of first-offer magnitude on counteroffers, while anger drove the effects on impasses and subjective value. Across both the meta-analysis and the experiments, negotiation complexity moderated both the beneficial and detrimental effects of first offers; as the number and type of issues (i.e., complexity) increased, the effects of first offers became smaller, and the mechanisms changed. Overall, the current meta-analysis and experiments collectively illuminate the direction, size, psychological pathways, and boundaries of first-offer effects in negotiations.

With permission of Elsevier

Copyright (c) 2025 Matthew S. Bothner, Richard Haynes, Ingo Marquart, Nghi Truong, Hai Anh Vu

© 2025 The Author(s). Journal of Occupational and Organizational Psychology published by John Wiley & Sons Ltd on behalf of The British Psychological Society.

Emerging crowdsourcing and crowdfunding mechanisms enable citizens to decide which research projects should be funded. By transferring control from professional scientists, these mechanisms reflect a broader shift towards more open “Mode 2” knowledge production that allows non-academic stakeholders to shape the direction of science. Although this may lead to a greater emphasis on the social impact of research, there is no systematic evidence on how crowd evaluators weigh social impact relative to other criteria such as scientific merit or team qualifications. There are also concerns that the personal financial costs associated with crowdfunding prevent certain socio-economic groups from participating, reducing the representativeness of opinions. Similarly, it is not clear what role citizens’ personal interest in particular topics plays in shaping their evaluations. We provide empirical evidence using data from over 2,300 crowd evaluators who assessed four research proposals in different fields and could express their support using a crowdsourcing mechanism (i.e., recommendation) and a crowdfunding mechanism (i.e., donation of own money). We confirm that crowd evaluators give significant weight to perceived social impact, although this weight is not larger than that of scientific merit. Compared to crowdsourcing, crowdfunding gives greater voice to citizens with higher income and education. Personal interest in a topic tends to be associated with greater project support, which may partly reflect an inflated assessment of social impact. Despite these general patterns, we also observe differences across projects – illustrating context-specificity and variability that make open Mode 2 processes difficult to predict and control.

with permission of Elsevier

How can the selection of innovation projects be designed to reduce false positives and false negatives? Prior research has provided theoretical insights into organizing to reduce errors, yet we know little about how organizations adapt selection over time and the effects of this on selection outcomes. Drawing from qualitative data from 126 interviews conducted over several years, we explore how an accelerator evolved through three selection regimes for high-stakes funding decisions, focusing on the organizational changes and their underlying reasons. We then analyze quantitative data from all 3,580 submissions they received, assessing false positives and false negatives across these regimes. Our findings reveal a persistent occurrence of both types of errors, with relatively small differences across the regimes despite deliberate efforts to enhance the process. In the final regime, which increased submission quality by emphasizing applicant track record and adding additional layers of screening, evaluators surprisingly became more prone to making selection errors. This finding stands net of accounting for (1) differences in the pool of submissions, (2) differences in treatment effects through training and resources provided, (3) learning, and (4) market evolution. By combining qualitative and quantitative data, we explain this through two mechanisms: (1) mean reversion in combination with increased emphasis on applicant track record and (2) within-type adverse selection enabled by a more stringent selection process. The study reveals that evolving an organization’s selection regime may require adjustments across multiple aspects, resulting in unintended consequences.

© 2025, INFORMS

with permission of Elsevier

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