Context <p>Potential employers can easily access job candidates’ photos online and attempt to infer a candidate’s fit or alignment based on their dress style. In this context, for candidates from marginalized groups like Indigenous people, traditional clothing holds cultural significance as it serves as a lively expression of belonging, participation in ceremonies, and resistance.</p> Objective <p>This exploratory study aims to empirically demonstrate whether dress manipulation in a picture affects the&#xa0;perceived competence of equally qualified candidates for a position like a software developer in which this cue should not be crucial.</p> Method <p>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 fitting a linear mixed-effects (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.</p> Results <p>Findings show that dress manipulation hardly influences the evaluators’ evaluation of candidates’ competence, as no statistically significant differences were found in our sample. Most of the unexplained variance (64.461%) stems from variability in scores across evaluators. Likewise, the&#xa0;thematic analysis revealed notable evaluator discrepancies indicating varying judgments and outcomes that suggest idiosyncrasies, which are not noise or error.</p> Conclusions <p>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 influence initial trust and cooperative behaviors among software development team members when interacting with unfamiliar collaborators.</p>

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The effect of stereotypes on perceived competence of indigenous software practitioners: a study of dress style in professional photos

  • Mary Sánchez-Gordón,
  • Ricardo Colomo-Palacios,
  • Cathy Guevara-Vega,
  • Antonio Quiña-Mera,
  • Aliaksandr Hubin

摘要

Context

Potential employers can easily access job candidates’ photos online and attempt to infer a candidate’s fit or alignment based on their dress style. In this context, for candidates from marginalized groups like Indigenous people, traditional clothing holds cultural significance 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 affects the perceived competence of equally qualified 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 fitting a linear mixed-effects (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 influences the evaluators’ evaluation of candidates’ competence, as no statistically significant differences 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 influence initial trust and cooperative behaviors among software development team members when interacting with unfamiliar collaborators.