JOURNAL ARTICLE
Corporate Hierarchy and Organizational Learning: Member Turnover, Code Change, and Innovation in the Multiunit Firm.
Published In: Organization Science (INFORMS), 2023, v. 34, n. 3. P. 1332 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Joseph, John; Rhee, Luke; Wilson, Alex James 3 of 3
Abstract
This article investigates how recombinant innovation—defined as the novel combination of technological components—is influenced by member turnover and organizational learning within a corporate hierarchy. Using 24 years of employee directory and patent data from Motorola, the study distinguishes the effects of arrivals and departures at both corporate headquarters and subunit levels on subunit-level innovation. Findings indicate that departures, particularly among corporate staff, have a stronger positive association with recombinant innovation than arrivals, as such departures facilitate changes in the organizational "code"—the shared beliefs, language, and routines—that reduce inertia and enable new technological recombinations. Supplementary analyses reveal that external hires (from outside the firm) and technical staff arrivals enhance innovation, while internal transfers may reinforce existing knowledge and constrain novelty. The study contributes to organizational learning, strategic human capital, and innovation literature by highlighting the critical role of corporate-level turnover and code change in fostering innovation within hierarchical firms.
Additional Information
- Source:Organization Science (INFORMS). 2023/05, Vol. 34, Issue 3, p1332
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2023
- ISSN:1047-7039
- DOI:10.1287/orsc.2022.1618
- Accession Number:163655251
- Copyright Statement:Copyright of Organization Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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