Publication database

With the help of more than 700 reviewers, we assess the reproducibility of nearly 500 articles published in the journal Management Science before and after the introduction of a new Data and Code Disclosure policy in 2019. When considering only articles for which data accessibility and hardware and software requirements were not an obstacle for reviewers, the results of more than 95% of articles under the new disclosure policy could be fully or largely computationally reproduced. However, for 29% of articles, at least part of the data set was not accessible to the reviewer. Considering all articles in our sample reduces the share of reproduced articles to 68%. These figures represent a significant increase compared with the period before the introduction of the disclosure policy, where only 12% of articles voluntarily provided replication materials, of which 55% could be (largely) reproduced. Substantial heterogeneity in reproducibility rates across different fields is mainly driven by differences in data set accessibility. Other reasons for unsuccessful reproduction attempts include missing code, unresolvable code errors, weak or missing documentation, and software and hardware requirements and code complexity. Our findings highlight the importance of journal code and data disclosure policies and suggest potential avenues for enhancing their effectiveness.

© 2024, INFORMS

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