The Effect of Stereotypes on Perceived Competence of Indigenous Software Practitioners: A Study of Dress Style in Professional Photos

Abstract

Context. Potential employers can easily access job candidates’ photos online and attempt to infer a candidate’s ft or alignment based on their dress style. In this context, for candidates from marginalized groups like Indigenous people, traditional clothing holds cultural signifcance as it serves as a lively expression of belonging, participation in ceremonies, and resistance. Objective. This exploratory study aims to empirically demonstrate whether dress manipulation in a picture afects the perceived competence of equally qualifed candidates for a position like a software developer in which this cue should not be crucial. Method. We conducted a quasi-experiment based on a survey. It involved job candidates (photo models) and participants (evaluators) from IT companies located in Ecuador. The analysis was performed by ftting a linear mixed-efects (LME) model based on dress style, gender and race/ethnicity of the candidates as well as evaluators’ gender and experience in hiring. Also, a thematic analysis was conducted. Results. Findings show that dress manipulation hardly infuences the evaluators’ evaluation of candidates’ competence, as no statistically signifcant diferences were found in our sample. Most of the unexplained variance (64.461%) stems from variability in scores across evaluators. Likewise, the thematic analysis revealed notable evaluator discrepancies indicating varying judgments and outcomes that suggest idiosyncrasies, which are not noise or error. Conclusions. This study demonstrates the value of contextual factors - such as gender, race/ethnicity and cultural background— in software engineering studies and calls for valuing individual software developers and their human aspects. Perceived competence extends beyond hiring situations as it can infuence initial trust and cooperative behaviors among software development team members when interacting with unfamiliar collaborators.publishedVersio