Information Technologies and the Search for Top Talent in Competitive Job Markets

dc.contributor.authorTilson, Vera
dc.contributor.authorJing, Xi
dc.contributor.authorSeidmann, Abraham
dc.contributor.authorGu, Bin
dc.date.accessioned2025-12-23T16:39:12Z
dc.date.available2025-12-23T16:39:12Z
dc.date.issued2026-01-06
dc.description.abstractThe search for highly talented professional employees has been dramatically reshaped by new information technologies. On the one hand, the rise of both dedicated and general-purpose job platforms has significantly reduced the cost and effort for applicants to apply to many more positions. In addition, the introduction of powerful LLM-based tools enables applicants to submit more polished and better-targeted materials, further lowering the signal-to-noise ratio in the application pool. On the other hand, recruiters now face an overwhelming number of applications per opening and have turned to more sophisticated selection rules and AI tools to identify candidates worth interviewing. Because application materials are not perfectly correlated with an applicant’s true inherent quality or fit, and because applicants often conceal their job preferences, recruiters continue to rely on personal interviews—both to better gauge candidate quality and to market their positions. Overall, while applicants can now submit large batches of applications easily and at low cost, recruiters, flooded with submissions, find that the search process has become increasingly expensive and time-consuming. This study situates the U.S. National Resident Matching Program (NRMP) within the broader problem of hiring in markets where specialized platforms dramatically increase application volume. As in other professional labor markets, residency programs compete for applicants in an environment where individuals can cheaply submit large numbers of applications, generating excess competition and noise. We focus on program-level strategies for managing the transition from applications to interviews—through adjustments in interview volume, screening accuracy, or interview cutoffs. Our analysis considers both symmetric settings, where programs are identical in applicant perceptions, and asymmetric settings, where one program is uniformly more desirable. A key and somewhat counterintuitive result is that additional effort by one program does not necessarily disadvantage its competitors and can, in some cases, improve overall match outcomes.
dc.format.extent10 pages
dc.identifier.doihttps://doi.org/10.24251/HICSS.2026.705
dc.identifier.isbn978-0-9981331-9-5
dc.identifier.otherbe35b22c-1710-425a-afaa-92751b77e4bc
dc.identifier.urihttps://hdl.handle.net/10125/112108
dc.language.isoeng
dc.relation.ispartofProceedings of the 59th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDigital Transformations of Business Operations
dc.subjectdeferred-acceptance algorithm
dc.subjectresidency matching
dc.subjectsimulation analytics
dc.subjecttwo-sided matching
dc.titleInformation Technologies and the Search for Top Talent in Competitive Job Markets
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage5957

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