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Implicit bias and negative stereotyping in global software development and why it is time to move on!
Author(s) -
Matthiesen Stina,
Bjørn Pernille,
Trillingsgaard Claus
Publication year - 2023
Publication title -
journal of software: evolution and process
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 29
eISSN - 2047-7481
pISSN - 2047-7473
DOI - 10.1002/smr.2435
Subject(s) - witness , argument (complex analysis) , situated , computer science , narrative , rhetoric , work (physics) , ethnography , empirical research , software , knowledge management , sociology , epistemology , artificial intelligence , linguistics , mechanical engineering , biochemistry , chemistry , philosophy , anthropology , engineering , programming language
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.