Implicit bias and negative stereotyping in global software development and why it is time to move on!

Research output: Contribution to journalJournal articleResearchpeer-review


  • Fulltext

    Final published version, 489 KB, PDF document

Prior research documents how the use of national cultural differences when used as an argument for failed collaboration is problematic and makes information technology (IT) companies blind to the challenges in global software development (GSD). Nevertheless, we still witness how issues in GSD work are kept explained, applied, and predicted through generic descriptions of national cultural behavior. Based on two ethnographic studies conducted within two large Danish IT companies, we extend prior work on implicit bias. The paper presents empirical examples on the widespread practice of using racist and stereotypical rhetoric in GSD, which initially motivated us to look for alternative strategies for analyzing the actual and locally situated collaboration-related problems within organizations involved in GSD. Our contributions are threefold: (1) We show how the widespread practice of using negative stereotypical rhetoric is weaved into the fabric of GSD engagements; (2) we present the empirical results of attending to implicit bias as an approach to explore and combat pervasive practices that deploy static cultural narratives and stereotypes in GSD; and (3) we propose three areas in GSD that software organizations should investigate to identify and address the implicit biases that potentially challenge or shatter their distributed collaborative work.

Original languageEnglish
Article numbere2435
JournalJournal of software: Evolution and Process
Issue number5
Number of pages27
Publication statusPublished - 2023

Bibliographical note

Special Issue: Best papers of the 14th International Conference on Software and System Processes (ICSSP 2020) and 15th International Conference on Global Software Engineering (ICGSE 2020)

    Research areas

  • distributed work, global software engineering (GSE), implicit bias, interventionist ethnography, national cultural differences, racism, unconscious bias

ID: 299204735