SignificanceThe emerging field of gamified citizen science continually probes the fault line between human and artificial intelligence. A better understanding of citizen scientists’ search strategies may lead to cognitive insights and provide inspiration for algorithmic improvements. Our project remotely engages both the general public and experts in the real-time optimization of an experimental laboratory setting. In this citizen science project the game and data acquisition are designed as a social science experiment aimed at extracting the collective search behavior of the players. A further understanding of these human skills will be a crucial challenge in the coming years, as hybrid intelligence solutions are pursued in corporate and research environments.

This article presents insights from a laboratory experiment on human problem solving in a combinatorial task. I rely on a hierarchical rugged landscape to explore how human problem-solvers are able to detect and exploit patterns in their search for an optimal solution. Empirical findings suggest that solvers do not engage only in local and random distant search, but as they accumulate information about the problem structure, solvers make ‘model-based’ moves, a type of cognitive search. I then calibrate an agent-based model of search to analyse and interpret the findings from the experimental setup and discuss implications for organizational search. Simulation results show that, for non-trivial problems, performance can be increased by a low level of persistence, that is, an increased likelihood to quickly abandon unsuccessful paths.

In two studies, we investigate whether the link between entrepreneurial self-efficacy and entrepreneurial intentions depends on outcome expectations. In Study 1, we exploit the COVID-19-induced lockdown as a natural experiment in a two-wave student sample. We compare the efficacy–intention link in survey responses submitted right before and right after the lockdown. In Study 2, we conceptually replicate and extend the findings via an online vignette experiment. Together, these studies show that a disruption of stable institutionalized outcome expectations implying increasing risk and uncertainty makes self-efficacy a weaker predictor of entrepreneurial intentions, particularly among those with pessimistic perceptions.

Organizations rely on crowds for a variety of reasons, e.g. in order to evaluate (Amazon), create content (Threadless) but also to solve given problems (InnoCentive and OpenIDEO). Several studies have examined how to organize problem‐solving activities. However, most papers have examined the crowdsourcing process using a partial perspective and a wide‐ranging outlook has been missing. This study uses a computer‐based simulation model and anecdotal case studies of InnoCentive and OpenIDEO, in order to study the underlying drivers of collective problem solving behavior. Results suggest that dynamics between the number of users, number of iterations and different selection mechanisms impact the ability to find an optimal solution to the given problem.

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