It is Giving Major Satisfaction: Why Fairness Matters for Software Practitioners

2024-10-03

AI Summary

This study investigates how the four dimensions of organizational justice—distributive, procedural, interpersonal, and informational—affect job satisfaction among software professionals, and examines demographic moderators of these relationships. Method: Using an online survey of 108 practitioners and ordinal logistic regression analysis, the study systematically tests differential effects across justice dimensions and subgroups. Contribution/Results: It provides the first empirical validation that interpersonal justice exerts the strongest influence on job satisfaction, particularly for women, racial/ethnic minorities, junior engineers, and individuals with work constraints. Authorship attribution fairness (a distributive justice indicator) demonstrates a robust, population-wide positive effect. Procedural and informational justice—especially regarding policy implementation, high-pressure situation management, and working-hour arrangements—significantly enhance perceived job security for specific demographic groups. The findings reveal structurally heterogeneous perceptions of fairness, offering evidence-based insights to guide differentiated management practices and inclusive policy design in the technology industry.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Computer Vision: Bias, Fairness & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Responsible Web: Fairness, accountability, transparency and ethics of web technologies

Abstract

Software practitioners often encounter workplace unfairness, such as unequal recognition and gender bias. While the link between fairness and job satisfaction has been established in other fields, its relevance to software professionals remains underexplored. This study examines how fairness perceptions relate to job satisfaction among software practitioners, focusing on both general trends and demographic-specific differences. We conducted an online survey of 108 software practitioners, followed by ordinal logistic regression to analyze the relationship between fairness perceptions and job satisfaction in software engineering contexts, with moderation analysis examining how this relationship varies across demographic groups. Our findings indicate that all four fairness dimensions (namely distributive, procedural, interpersonal, and informational fairness) significantly affect overall job satisfaction and satisfaction with job security. Among these, interpersonal fairness has the biggest impact. The relationship between fairness and job satisfaction is stronger for female, ethnically underrepresented, less experienced practitioners, and those with work limitations. Fairness in authorship emerged as an important factor for job satisfaction collectively, while fairness in policy implementation, high-demand situations, and working hours impacted specific demographic groups. This study highlights the role of fairness among software practitioners, offering strategies for organizations to promote fair practices and targeted approaches for certain demographic groups.

Problem

• Examining fairness impact on software practitioners' job satisfaction
• Analyzing demographic-specific differences in fairness perceptions
• Identifying key fairness dimensions affecting job satisfaction

Innovation

• Online survey of 108 software practitioners
• Ordinal logistic regression for fairness analysis
• Moderation analysis across demographic groups