JOURNAL ARTICLE

Omnificence or Differentiation? An Empirical Study of Knowledge Structure and Career Development of IT Workers.

  • Published In: Information Systems Research (INFORMS), 2025, v. 36, n. 2. P. 1129 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Zhang, Yingjie; Zheng, Zhiqiang; Gu, Bin 3 of 3

Abstract

This article investigates how the structure of information technology (IT) knowledge among IT professionals influences their career outcomes, specifically salary and job security. It introduces two novel metrics: knowledge omnificence, which measures the breadth and well-roundedness of an individual's IT knowledge, and knowledge differentiation, which assesses how distinct an individual's knowledge is compared to peers. Analyzing extensive career data from over 800,000 Chinese IT workers between 2000 and 2016, the study finds that moderate levels of both knowledge omnificence and differentiation yield the most favorable economic returns. Knowledge omnificence is linked to higher salary potential and career mobility, while knowledge differentiation enhances job security. Additionally, these knowledge structures help reduce gender disparities in compensation, with women benefiting notably from increases in both omnificence and differentiation. The findings offer strategic guidance for IT professionals, firms, and policymakers aiming to foster dynamic and equitable IT labor markets.

Additional Information

  • Source:Information Systems Research (INFORMS). 2025/06, Vol. 36, Issue 2, p1129
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:1047-7047
  • DOI:10.1287/isre.2022.0634
  • Accession Number:187706227
  • Copyright Statement:Copyright of Information Systems Research (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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