JOURNAL ARTICLE

Inverted Apprenticeship: How Senior Occupational Members Develop Practical Expertise and Preserve Their Position When New Technologies Arrive.

  • Published In: Organization Science (INFORMS), 2024, v. 35, n. 2. P. 405 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Beane, Matthew; Anthony, Callen 3 of 3

Abstract

The article examines how senior occupational members navigate the tension between their established positional expertise and the need to develop new practical expertise when disruptive technologies emerge. Through comparative ethnographic studies of urological surgery and investment banking, it identifies a process termed "inverted apprenticeships," where senior members restructure interactions with junior members to learn new technologies while maintaining their status. Four pathways characterize these inverted apprenticeships: seeking (joint learning with top juniors), stalling (private, hidden learning), leveraging (passive learning through junior instruction), and confronting (publicly challenging new methods). While all pathways help seniors preserve position, only seeking fosters deep practical expertise and supports junior learning; the others often limit juniors’ skill development and produce shallow expertise among seniors. The study highlights how task centrality and visibility influence pathway choice and underscores the complex interplay of hierarchy, learning, and technology adoption within expert occupations.

Additional Information

  • Source:Organization Science (INFORMS). 2024/03, Vol. 35, Issue 2, p405
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:1047-7039
  • DOI:10.1287/orsc.2023.1688
  • Accession Number:176363361
  • 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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