{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T03:55:21Z","timestamp":1771041321235,"version":"3.50.1"},"reference-count":84,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2023,8,15]],"date-time":"2023-08-15T00:00:00Z","timestamp":1692057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JKM"],"published-print":{"date-parts":[[2024,3,18]]},"abstract":"\nPurpose<\/jats:title>\nThis study aims to develop a model of learning-by-hiring in which knowledge gains may occur at the time of recruitment but also after recruitment when other incumbent organizational members assimilate a recruit\u2019s knowledge. The author\u2019s model predicts that experienced recruits are more likely to catalyze change to their organization\u2019s core technological capabilities.<\/jats:p>\n<\/jats:sec>\n\nDesign\/methodology\/approach<\/jats:title>\nThe continuous-time parametric hazard rate regressions predict core technological change in a long panel (1970\u20132017) of US biotechnology industry patent data. The author uses over 140,000 patents to model the evolution of knowledge of over 52,000 scientists and over 4,450 firms. To address endogeneity concerns, the author uses the Heckman selection method and does robustness tests using a difference-in-difference analysis.<\/jats:p>\n<\/jats:sec>\n\nFindings<\/jats:title>\nThe author finds that a hire\u2019s prior research and development (R&D) experience helps overcome inertia arising from her or his new-to-an-organization \u201cdistant\u201d knowledge to increase the likelihood of core technological change. In addition, while the author finds that incumbent organizational members resist technological change, experienced hires may effectively induce them to adopt new ways of doing things. This is particularly the case when hires collaborate with incumbents in R&D projects. Understanding the effects of hiring on core technological change, therefore, benefits from an assessment of hire R&D experience and its effects on incumbent inertia in an organization.<\/jats:p>\n<\/jats:sec>\n\nPractical implications<\/jats:title>\nFirst, the author does not recommend managers to hire scientists with considerable distant knowledge only as this may be detrimental to core technological change. Second, the author recommends organizations striving to effectuate technological change to hire people with considerable prior R&D experience as this confers them with the ability to influence other members and socialize incumbent members. Third, the author recommends that managers hire people with both significant levels of prior experience and distant knowledge as they are complements. Finally, the author recommends managers to encourage collaboration between highly experienced hired scientists and long-tenured incumbent organizational members to facilitate incumbent learning, socialization and adoption of new ways of doing things.<\/jats:p>\n<\/jats:sec>\n\nOriginality\/value<\/jats:title>\nThis study develops a model of learning-by-hiring, which, to the best of the authors\u2019 knowledge, is the first to propose, test and advance KM literature by showing the effectiveness of experienced hires to stimulate knowledge diffusion and core technological change over time after being hired. This study contributes to innovation, organizational learning and strategy literatures.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/jkm-06-2022-0504","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T01:36:00Z","timestamp":1691976960000},"page":"874-894","source":"Crossref","is-referenced-by-count":8,"title":["The diffusion of recruit knowledge and core technological change in organizations"],"prefix":"10.1108","volume":"28","author":[{"given":"Amit","family":"Jain","sequence":"first","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2023,8,15]]},"reference":[{"issue":"6","key":"key2024031412262157200_ref001","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","year":"1974","journal-title":"IEEE Transactions of Automatic Control"},{"key":"key2024031412262157200_ref002","volume-title":"Organisational Learning: Creating, 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