While external search allows organizations to source diverse ideas from people outside the organization, it often generates a narrow set of non-diverse ideas. We theorize that this result stems from an interplay between organizations’ selection of ideas and the external generation of ideas: an organization selects ideas shared by external contributors, and the external contributors, who strive to see their ideas selected, use the prior selection as a signal to infer what kind of ideas the organization is looking for. Contributors whose ideas are not aligned with the organization’s selection tend to stop submitting ideas (i.e., self-selection) or adjust the ideas they submit so that they correspond (i.e., self-adjustment), resulting in a less diverse pool of ideas. Our central hypothesis is that the more consistent organizations are in their selection, the stronger the co-evolutionary lock-in: organizations with greater selection consistency receive future ideas with lower content variety. We find support for these predictions by combining large-scale network analysis and natural language processing across a large number of organizations that use crowdsourcing. Our findings suggest a reconceptualization of external search: organizations are not simply passive receivers of ideas but send signals that shape the pool of ideas that externals share.

With permission of the Academy of Management

We provide a canonical and tractable model of a trade platform enabling buyers and sellers to transact. The platform charges a percentage fee on third-party product sales and decides whether to be "hybrid", like Amazon, by selling its own product. It thereby controls the number of differentiated products (variety) it hosts and their prices. Using the mixed market demand system, we capture interactions between monopolistically competitive sellers and a sizeable platform product. Using long-run aggregative games with free entry, we endogenize seller participation through an aggregate variable manipulated by the platform's fee. We show that a higher quality (or lower cost) of the platform's product increases its market share and the seller fee, and lowers consumer surplus. Banning hybrid mode benefits consumers. The hybrid platform might favor its product and debase third-party products if the own product advantage is sufficiently high. We also provide some tax policy implications.

Artificial intelligence systems are increasingly demonstrating their capacity to make better predictions than human experts. Yet, recent studies suggest that professionals sometimes doubt the quality of these systems and overrule machine based prescriptions. This paper explores the extent to which a decision maker (DM) supervising a machine to make high-stake decisions can properly assess whether the machine produces better recommendations. To that end, we study a set-up in which a machine performs repeated decision tasks (e.g., whether to perform a biopsy) under the DM’s supervision. Because stakes are high, the DM primarily focuses on making the best choice for the task at hand. Nonetheless, as the DM observes the correctness of the machine’s prescriptions across tasks, she updates her belief about the machine. However, the DM is subject to a so-called verification bias such that the DM verifies the machine’s correctness and updates her belief accordingly only if she ultimately decides to act on the task. In this set-up, we characterize the evolution of the DM’s belief and overruling decisions over time. We identify situations under which the DM hesitates forever whether the machine is better, i.e., she never fully ignores but regularly overrules it. Moreover, the DM sometimes wrongly believes with positive probability that the machine is better. We fully characterize the conditions under which these learning failures occur and explore how mistrusting the machine affects them. These findings provide a novel explanation for human-machine complementarity and suggest guidelines on the decision to fully adopt or reject a machine.

© 2023, INFORMS

Connecting otherwise disconnected individuals and groups - spanning structural holes - can earn social network brokers faster promotions, higher remuneration, and enhanced creativity. Organizations also benefit through improved communication and coordination from these connections between knowledge silos. Neglected in prior research, however, has been theory and evidence concerning the psychological costs to individuals of engaging in brokering activities. We build new theory concerning the extent to which keeping people separated (i.e., tertius separans brokering) relative to bringing people together (i.e., tertius iungens brokering) results in burnout and in abusive behavior toward coworkers. Engagement in tertius separans brokering, relative to tertius iungens brokering, we suggest, burdens people with onerous demands while limiting access to resources necessary to recover. Across three studies, we find that tertius separans leads to abusive behavior of others, mediated by an increased experience of burnout on the part of the broker. First, we conducted a five-month field study of burnout and abusive behavior, with brokering assessed via e-mail exchanges among 1,536 university employees in South America. Second, we examined time-separated data on self-reported brokering behaviors, burnout, and coworker abuse among 242 employees of U.S. organizations. Third, we experimentally investigated the effects of the two types of brokering behaviors on burnout and abusive behavior for 273 employed adults. The results across three studies showed that tertius separans brokering puts the broker at an increased risk of burnout and subsequent abusive behavior toward others in the workplace.

© 2023, INFORMS

Early-stage technology startups rely critically on talented scientists and engineers to commercialize new technologies. And yet, they compete with large technology firms to hire the best workers. Theories of ability sorting predict that high ability workers will choose jobs in established firms that offer greater complementary assets and higher pay, leaving low ability workers to take lower-paying and riskier jobs in startups. We propose an alternative view in which heterogeneity in both worker ability and preferences enable startups to hire talented workers who have a taste for a startup environment, even at lower pay. Using a longitudinal survey that follows 2,394 science and engineering PhDs from graduate school into industrial employment, we overcome common empirical challenges by observing ability and stated preferences prior to first-time employment. We find that both ability and career preferences strongly predict startup employment, with high ability workers who prefer startup employment being the most likely to work in a startup. We show that this is due in part to the dual selection effects of worker preferences resulting in a large pool of startup job applicants, and startups “cherry picking” the most talented workers to make job offers to. Additional analyses confirm that startup employees earn approximately 17% lower pay. This gap is greatest for high ability workers and persists over workers’ early careers, suggesting that they accept a negative compensating differential in exchange for the non-pecuniary benefits of startup employment. This is further supported by data on job attributes and stated reasons for job choice.

© 2022, INFORMS

Problem Definition: Clients and service providers alike often consider one-on-one service delivery to be ideal, assuming – perhaps unquestioningly – that devoting individualized attention best improves client outcomes. In contrast, in shared service delivery, clients are served in batches and the dynamics of group interaction could lead to increased client engagement – which could improve outcomes. However, the loss of privacy and personal connection might undermine engagement. Practical Relevance: The engagement dynamics in one-on-one and shared delivery models have not been rigorously studied. To the extent that shared delivery may result in comparable or better engagement than one-on-one delivery, service providers in a broad array of contexts may be able to create more value for clients by delivering service in batches. Methodology: We conducted a randomized controlled trial with 1,000 patients who were undergoing glaucoma treatment over a three-year period at a large eye hospital. Using verbatim and behavioral transcripts from over 20,000 minutes of video recorded during our trial, we examine how shared medical appointments (SMAs) – in which patients are served in batches – impact engagement. Results: Patients who experienced SMAs asked 33.33% more questions per minute, made 8.63% more non-question comments per minute, and exhibited higher levels of non-verbal engagement across a wide array of measures (attentiveness, positivity, head wobbling or ‘talai tallattam’ in Tamil – a South Indian gesture to signal agreement or understanding – eye contact and end-of-appointment happiness), relative to patients who attended one-on-one appointments. Managerial Implications: These results shed light on the potential for shared service delivery models to increase client engagement and thus enhance service performance.

© 2023, INFORMS