Demographic differences in stereotypical beliefs toward digital technology professionals
DOI:
https://doi.org/10.3846/bm.2026.2378Abstract
Digitalization is essential for enhancing enterprise efficiency and optimizing work processes; however, organizations often face barriers during implementation. One of the key challenges is employee resistance, which is frequently driven by negative stereotypical beliefs about people working in information and communication technologies (ICT). This study aims to examine demographic differences in perceptions of stereotypes toward digital technology professionals within the Slovak working population. Understanding such differences is important for organizations, as it helps identify groups that may require targeted attention during technology implementation. A quantitative survey was conducted on a sample of 492 Slovak respondents (Mean age = 30.1; SD = 12.6). The measurement scale demonstrated high internal consistency (McDonald’s omega = 0.907). The findings indicate that respondents tend to disagree with negative stereotypes, reflecting the increasing normalization of digital tools in the workplace. While no statistically significant differences with small effect sizes were identified based on age, gender, or education level, our results reveal that residents of smaller settlements are more likely to endorse negative stereotypes than those living in cities. These findings suggest that geographical factors should be considered when developing strategies to reduce resistance during digital transformation.
Keywords:
digital transformation, ICT stereotypes, employee resistance, perception of ICT professionalsHow to Cite
Akbulut-Bailey, A. Y. (2009). A measurement instrument for understanding student perspectives on stereotypes of IS professionals. Communications of the Association for Information Systems, 25(1), Article 29. https://doi.org/10.17705/1CAIS.02529
Beyer, S. (2014). Why are women underrepresented in computer science? Gender differences in stereotypes, self-efficacy, values, and interests and predictors of future CS course-taking and grades. Computer Science Education, 24(2–3), 153–192. https://doi.org/10.1080/08993408.2014.963363
Ceci, S. J., Williams, W. M., & Barnett, S. M. (2009). Women’s underrepresentation in science: Sociocultural and biological considerations. Psychological Bulletin, 135(2), 218–261. https://doi.org/10.1037/a0014412
Chambers, D. W. (1983). Stereotypic images of the scientist: The draw-a-scientist test. Science Education, 67(2), 255–265. https://doi.org/10.1002/sce.3730670213
Cheryan, S., Ziegler, S. A., Montoya, A. K., & Jiang, L. (2017). Why are some STEM fields more gender balanced than others? Psychological Bulletin, 143(1), 1–35. https://doi.org/10.1037/bul0000052
Council of the European Union. (2025). ‘Path to the Digital Decade’: the EU’s plan to achieve a digital Europe by 2030. Consilium. https://www.consilium.europa.eu/en/policies/path-to-the-digital-decade-the-eu-s-plan-to-achieve-a-digital-europe-by-2030/
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
DeVellis, R. F. (2012). Scale development: Theory and applications. SAGE Publications.
Devi, M. G. S., Sivashanmugam, N., Dhanalakshmi, H. S., Rupa, H. S., & Lakshmi, M. K. (2025). Analyzing factors influencing digital technology adoption among women in STEM: A UTAUT2 model perspective from India. Cuestiones de Fisioterapia, 54(4), 7891–7904.
Edison, S. W., & Geissler, G. L. (2003). Measuring attitudes towards general technology: Antecedents, hypotheses and scale development. Journal of Targeting, Measurement and Analysis for Marketing, 12, 137–156. https://doi.org/10.1057/palgrave.jt.5740104
Eurostat. (2024a). ICT specialists – statistics on hard-to-fill vacancies in enterprises. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=ICT_specialists_-_statistics_on_hard-to-fill_vacancies_in_enterprises#:~:text=Highlights,ICT%20training%20for%20other%20staff
Eurostat. (2024b). Individuals’ level of digital skills (data code: isoc_sk_dskl_i21) [Dataset]. European Commission. https://doi.org/10.2908/ISOC_SK_DSKL_I21
Eurostat. (2025a). Employed ICT specialists – total (data code: ISOC_SKS_ITSPT) [Dataset]. European Commission. https://doi.org/10.2908/ISOC_SKS_ITSPT
Eurostat. (2025b). Employed information and communications technology (ICT) specialists by sex [Dataset]. https://doi.org/10.2908/ISOC_SKS_ITSPS
Eurostat. (2025c). Individuals’ level of digital skills (from 2021 onwards) [Dataset]. https://doi.org/10.2908/ISOC_SK_DSKL_I21
Fisher, R. J. (1993). Social desirability bias and the validity of indirect questioning. Journal of Consumer Research, 20(2), 303–315. https://doi.org/10.1086/209351
Fralick, B., Kearn, J., Thompson, S., & Lyons, J. (2009). How middle schoolers draw engineers and scientists. Journal of Science Education and Technology, 18(1), 60–73. https://doi.org/10.1007/s10956-008-9133-3
Google. (2026). Gemini 2.5 [Large language model]. https://gemini.google.com
Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74(6), 1464–1480. https://doi.org/10.1037/0022-3514.74.6.1464
Grønhøj, E. O., Smith, E., & Bundsgaard, J. (2025a). Why do so few girls aspire for a technology career? The role of social influence, motivational factors, and stereotypes. Social Psychology of Education, 28(1), Article 152. https://doi.org/10.1007/s11218-025-10084-y
Grønhøj, E. O., Wong, B., & Bundsgaard, J. (2025b). Exploring young people’s perceptions and discourses of technology occupations through descriptive drawings and a questionnaire. Computer Science Education, 35(2), 267–291. https://doi.org/10.1080/08993408.2024.2385876
Hampel, K., & Kunze, F. (2023). The older, the less digitally fluent? The role of age stereotypes and supervisor support the older, the less digitally fluent? The role of age stereotypes. Work Aging and Retirement, 9(4), 393–398. https://doi.org/10.1093/workar/waad001
Johnson, R. D., Stone, D. L., & Phillips, T. N. (2008). Relations among ethnicity, gender, beliefs, attitudes, and intention to pursue a career in information technology. Journal of Applied Social Psychology, 38(4), 999–1022. https://doi.org/10.1111/j.1559-1816.2008.00336.x
Keil, L., Batur, F., Kramer, M., & Brinda, T. (2020, January). Stereotypes of secondary school students towards people in computer science. In T. Brinda, D. Passey, & T. Keane (Eds.), IFIP advances in information and communication technology: Vol. 595. Empowering teaching for digital equity and agency. OCCE 2020 (pp. 46–55). Springer International Publishing. https://doi.org/10.1007/978-3-030-59847-1_5
Kindsiko, E., & Türk, K. (2017). Detecting major misconceptions about employment in ICT: A study of the myths about ICT work among females. World Academy of Science, Engineering and Technology International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 11(1), 107–114.
Leslie, S. J., Cimpian, A., Meyer, M., & Freeland, E. (2015). Expectations of brilliance underlie gender distributions across academic disciplines. Science, 347(6219), 262–265. https://doi.org/10.1126/science.1261375
Luo, T., So, W. W. M., Wan, Z. H., & Li, W. C. (2021). STEM stereotypes predict students’ STEM career interest via self-efficacy and outcome expectations. International Journal of STEM Education, 8(1), Article 36. https://doi.org/10.1186/s40594-021-00295-y
Mariano, J., Marques, S., Ramos, M. R., Gerardo, F., Cunha, C. L. da, Girenko, A., Alexandersson, J., Stree, B., Lamanna, M., Lorenzatto, M., Mikkelsen, L. P., Bundgård-Jørgensen, U., Rêgo, S., & de Vries, H. (2022). Too old for technology? Stereotype threat and technology use by older adults. Behaviour & Information Technology, 41(7), 1503–1514. https://doi.org/10.1080/0144929X.2021.1882577
Master, A., Meltzoff, A. N., & Cheryan, S. (2021). Gender stereotypes about interests start early and cause gender disparities in computer science and engineering. Proceedings of the National Academy of Sciences, 118(48), Article e2100030118. https://doi.org/10.1073/pnas.2100030118
Moquin, R., Rutner, P., & Giddens, L. (2020). Stereotyping and stigmatizing IT professionals toward a model of devaluation. The Journal of the Southern Association for Information Systems, 6(1), 22–44. https://doi.org/10.17705/3JSIS.00012
OpenAI. (2026). ChatGPT-5.2 [Large language model]. https://chatgpt.com
Pyrkosz-Pacyna, J., Dukala, K., & Kosakowska-Berezecka, N. (2022). Perception of work in the IT sector among men and women – A comparison between IT students and IT professionals. Frontiers in Psychology, 13, Article 944377. https://doi.org/10.3389/fpsyg.2022.944377
Rojas-Méndez, J. I., Parasuraman, A., & Papadopoulos, N. (2017). Demographics, attitudes, and technology readiness: A cross-cultural analysis and model validation. Marketing Intelligence & Planning, 35(1), 18–39. https://doi.org/10.1108/MIP-08-2015-0163
Sáinz, M., Meneses, J., López, B. S., & Fàbregues, S. (2016). Gender stereotypes and attitudes towards information and communication technology professionals in a sample of Spanish secondary students. Sex Roles, 74(3), 154–168. https://doi.org/10.1007/s11199-014-0424-2
Thomas, T., & Allen, A. (2006). Gender differences in students’ perceptions of information technology as a career. Journal of Information Technology Education: Research, 5, 165–178. https://doi.org/10.28945/241
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Washington, A. N., Grays, S., & Dasmohapatra, S. (2016, June). The computer science attitude and identity survey (CSAIS): A novel tool for measuring the impact of ethnic identity in underrepresented computer science students. In ASEE’s 123rd Annual Conference & Exposition (Article 16187). American Society for Engineering Education. https://doi.org/10.18260/p.26110
Downloads
Published
Conference Event
Section
Copyright
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
