The Software Infrastructure Attitude Scale (SIAS): A Questionnaire Instrument for Measuring Professionals’ Attitudes Toward Technical and Sociotechnical Infrastructure
Miikka Kuutila, Paul Ralph, Huilian Sophie Qiu, Ronnie de Souza Santos, Morakot Choetkiertikul, Amin Milani Fard, Rana Alkadhi, Xavier Devroey, Gregorio Robles, Hideaki Hata, Sebastian Baltes, Vladimir Kovalenko
Abstract
Context: Recent software engineering (SE) research has highlighted the need for sociotechnical research, implying a demand for customized psychometric scales. Objective: We define the concepts of technical and sociotechnical infrastructure in software engineering, and develop and validate a psychometric scale that measures attitudes toward them. Method: Grounded in theories of infrastructure, attitudes, and prior work on psychometric measurement, we defined the target constructs and generated scale items. The items were reviewed and refined by domain experts. The scale was administered to 225 software professionals and evaluated using a split sample. We conducted an exploratory factor analysis (EFA) on one half of the sample to uncover the underlying factor structure and performed a confirmatory factor analysis (CFA) on the other half to validate the structure. Further analyses with the whole sample assessed face, criterion-related, and discriminant validity. Results: EFA supported a two-factor structure (technical and sociotechnical infrastructure), accounting for 65% of the total variance with strong loadings. CFA confirmed excellent model fit. Face and content validity were supported by the item content reflecting cognitive, affective, and behavioral components. Both subscales were correlated with job satisfaction, perceived autonomy, and feedback from the job itself, supporting convergent validity. Regression analysis supported criterion-related validity, while the Heterotrait–Monotrait ratio of correlations (HTMT), the Fornell–Larcker criterion, and model comparison all supported discriminant validity. Discussion: The resulting scale is a valid instrument for measuring attitudes toward technical and sociotechnical infrastructure in software engineering research. Our work contributes to ongoing efforts to integrate psychological measurement rigor into empirical and behavioral software engineering research. The scale can also be used by companies and organizations to assess their employees’ attitudes toward the infrastructure they provide.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 9c2ce1d4-1ff5-43be-b376-8f0357d74f85Cited by top-tier papers1
Ask how each one uses itBuilds on2
- A Systematic Literature Review of Infrastructure Studies in SIGCHIYao Lyu, Jie Cai, John M. CarrollCSCW 2025 · 19 citations
- Staying or Leaving? How Job Satisfaction, Embeddedness and Antecedents Predict Turnover Intentions of Software ProfessionalsMiikka Kuutila, Paul Ralph, Huilian Sophie Qiu, Ronnie de Souza Santos et al.ICSE 2026 · 1 citation
Related papers
- Unraveling the Drivers of Sense of Belonging in Software Delivery Teams: Insights from a Large-Scale SurveyBianca Trinkenreich, Marco Aurélio Gerosa, Igor SteinmacherICSE 2024 · 11 citations
- STEM-EF: A Scrum Teamwork Effectiveness Model Based on Emotional FactorsRamon Nóbrega dos Santos, Hyggo Almeida, Mirko Perkusich, Danyllo Albuquerque et al.ICSE 2026
- Sustainability is Stratified: Toward a Better Theory of Sustainable Software EngineeringSean McGuire, Erin Schultz, Bimpe Ayoola, Paul RalphICSE 2023 · 29 citations
- 'It's a spectrum': Exploring Autonomy, Competence, and Relatedness in Software Development Processes and ToolsNovia Wong, Nai-Yu Cheng, Bruna Oewel, Katherine E. Genuario et al.CHI 2025 · 5 citations
- Trust in Collaborative Automation in High Stakes Software Engineering Work: A Case Study at NASADavid Gray Widder, Laura Dabbish, James D. Herbsleb, Alexandra Holloway et al.CHI 2021 · 18 citations
