Overcoming the Glass Ceiling Syndrome through Digitalization and Artificial Intelligence in OECD Countries

Abstract

Intelligence in OECD Countries ABSTRACT This study examines the impact of digitalization and artificial intelligence technologies on the invisible barriers affecting women's access to senior management positions within the framework of the glass ceiling syndrome in OECD countries. Sweden, Germany, South Korea, Canada, and Turkey were selected as case studies, each evaluated through in-depth analysis of the public sector, private sector, and the activity reports of relevant institutions. The study applies a comparative assessment based on gender equality indices, digitalization strategies, and artificial intelligence applications, focusing on the relationship between women’s representation in digital leadership and the perception of the glass ceiling. The findings reveal that digitalization alone does not ensure gender equality; on the contrary, in countries where artificial intelligence technologies lack gender sensitivity, existing inequalities are likely to persist or deepen. While Sweden and Canada appear to have largely overcome the glass ceiling syndrome, the examples from South Korea and Turkey suggest that cultural and structural barriers continue to reinforce it. Ultimately, this study underscores the necessity of integrating gender-sensitive artificial intelligence technologies into institutional transformation efforts to overcome the glass ceiling in the digital age. Keywords: Digitalization, Artificial Intelligence, Glass Ceiling Syndrome, Gender Equality, OECD Countries Jel Code: J16, O33, M14 Referencing Style: APA 7

Keywords

• Digitalization
• Artificial Intelligence
• Glass Ceiling Syndrome
• Gender Equality
• OECD Countries

OECD Ülkelerinde Dijitalleşme ve Yapay Zekâ ile Cam Tavan Sendromunu Aşmak

Abstract

ÖZET Bu çalışma, OECD ülkelerinde dijitalleşme ve yapay zekâ teknolojilerinin kadınların üst yönetim kademelerine erişimi üzerindeki görünmeyen engellerin etkilerini cam tavan sendromu çerçevesinde incelemektedir. OECD ülkelerinden olan; İsveç, Almanya, Güney Kore, Kanada ve Türkiye ülkeleri seçilmiş olup her ülke bir vaka örneği olarak değerlendirilmiştir. Bu anlamda ülkelerin kamu sektörü, özel sektör ile bu kurumların faaliyet raporları doğrultusunda derinlemesine analiz edilmiştir. Çalışmada toplumsal cinsiyet eşitliği endeksleri, dijitalleşme stratejileri ve yapay zekâ uygulamalarına dayalı karşılaştırmalı bir değerlendirme gerçekleştirilmiş olup kadınların dijital liderlikteki temsili ile cam tavan algısı arasındaki ilişki irdelenmiştir. Bulgular ışığında dijitalleşmenin kendi başına toplumsal cinsiyet eşitliği sağlamadığını aksine toplumsal cinsiyet duyarlılığı olmayan ülkelerdeki yapay zekâ teknolojilerinin mevcut cinsiyet eşitsizlikleri gidermeyeceğini göstermektedir. İsveç ve Kanada gibi ülkelerde cam tavan sendromunun mümkün olmadığı görülürken, Güney Kore ve Türkiye ülkelerindeki örnekler de ise kültürel ve görünmeyen engellerin varlığını sürdürdüğü cam tavan sendromuna neden olduğu belirlenmiştir. Sonuç olarak, bu çalışmada dijitalleşme sürecinde cam tavan sendorumu aşmak için toplumsal cinsiyet eşitliğini benimseyen yapay zekâ teknolojilerinin kurumsal dönüşümün ayrılmaz bir parçası hâline getirilmesi gerektiğinin önemi vurgulanmaktadır. Anahtar Kelimeler: Dijitalleşme, Yapay Zekâ, Cam Tavan Sendromu, Toplumsal Cinsiyet Eşitliği, OECD Ülkeleri.

Keywords

• Dijitalleşme
• Yapay Zekâ
• Cam Tavan Sendromu
• Toplumsal Cinsiyet Eşitliği
• OECD Ülkeleri.

References

1. Andersson, E., & Wihlborg, E. (2021). Gender mainstreaming and digitalization in Swedish local government. Public Policy and Administration, 36(3), 306–322. https://doi.org/10.1177/09520767211003225
2. Aytaç, S., & Güneş, H. (2022). Türkiye’de yapay zekâ politikaları ve toplumsal cinsiyet perspektifi. Kamu Yönetimi Dergisi, 55(1), 58–77.
3. Bıçakcı, K., Kaya, A., & Coşkun, H. (2023). Türkiye’de yapay zekâ etiği ve yönetişimi: Politika geliştirme süreci. Bilgi Toplumu ve Teknoloji Dergisi, 15(1), 1–18.
4. BMWK. (2023). Nationale Strategie für Künstliche Intelligenz: Fortschreibung 2023. Bundesministerium für Wirtschaft und Klimaschutz. https://www.bmwk.de
5. Brynjolfsson, E., & McAfee, A. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. W. W. Norton & Company.
6. Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute.
7. Cave, S., Whittlestone, J., Nyrup, R., Ó hÉigeartaigh, S. S., & Calvo, R. A. (2021). It's time for AI policy to prioritize justice. Nature Machine Intelligence, 3(5), 402–404. https://doi.org/10.1038/s42256-021-00370-0
8. Chung, H., & van der Horst, M. (2021). Women's employment patterns after childbirth and the perceived access to and use of flexitime and teleworking. Human Relations, 74(6), 966– 992. https://doi.org/10.1177/0018726720907066

9. Cotter, D. A., Hermsen, J. M., Ovadia, S., & Vanneman, R. (2001). The glass ceiling effect. Social Forces, 80(2), 655–681.
10. Craig, L., & Churchill, B. (2021). Dual-earner parent couples’ work and care during COVID- 19. Gender, Work & Organization, 28(S1), 66–79. https://doi.org/10.1111/gwao.12497
11. Criado-Perez, C. (2019). Invisible Women: Exposing Data Bias in a World Designed for Men. Chatto & Windus.
12. Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G
13. De Palma, P., & Benassi, D. (2022). Gender and digital education: Disparities in STEM participation across OECD countries. Education and Information Technologies, 27(4), 4903–4921. https://doi.org/10.1007/s10639-021-10729-3
14. Dubois, E., & McKee, R. (2023). Gendered governance in the age of AI: Canadian public sector innovations. Canadian Public Administration, 66(1), 94–112. https://doi.org/10.1111/capa.12509
15. Eagly, A. H., & Carli, L. L. (2007). Through the Labyrinth: The Truth About How Women Become Leaders. Harvard Business Press.
16. European Commission. (2022). Women in the Digital Age. https://digital-strategy.ec.europa.eu
17. European Institute for Gender Equality [EIGE]. (2021). Gender Equality Index 2021: Health. Luxembourg: Publications Office of the European Union.
18. Ferrant, G., & Kolev, A. (2022). Gender equality in the digital age: A cross-national perspective. OECD Social, Employment and Migration Working Papers, No. 276. https://doi.org/10.1787/bc7edc50-en
19. Häusermann, S., & Walter, A. (2022). Digitalization and gender inequality in executive roles: Evidence from German firms. European Management Journal, 40(5), 775–788. https://doi.org/10.1016/j.emj.2022.03.004
20. Heinrichs, A., & Gärtner, L. (2023). Gender-sensitive digital transformation in German manufacturing: Organizational adaptations. Technology in Society, 72, 102164. https://doi.org/10.1016/j.techsoc.2022.102164
21. Hofstede, G. (2001). Culture's consequences: Comparing values, behaviors, institutions and organizations across nations. Sage.
22. Hösl, M., Kleer, R., & Reischauer, G. (2023). Algorithmic fairness and public institutions: A comparative study of AI policy frameworks in OECD countries. Policy & Internet, 15(2), 267–292. https://doi.org/10.1002/poi3.328
23. Hymowitz, C., & Schellhardt, T. D. (1986). The glass ceiling: Why women can't seem to break the invisible barrier that blocks them from the top jobs. Wall Street Journal, 24 March.
24. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
25. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
26. Lai, Y., Sun, H., & Ren, J. (2021). Artificial intelligence applications in COVID-19: A review and perspective. Frontiers in Public Health, 9, 614124. https://doi.org/10.3389/fpubh.2021.614124
27. Kang, Y. (2021). Algorithmic bias and hiring in South Korea: A critical analysis of AI-supported HR systems. Asian Journal of Technology Studies, 9(2), 133–151. International Labour Organization [ILO]. (2019). A quantum leap for gender equality: For a better future of work for all. Geneva: ILO. https://www.ilo.org/global/publications/books/WCMS_650653/lang--en/index.htm
28. International Labour Organization [ILO]. (2022). The Gender Gap in Employment: Challenges and Policy Responses. Geneva: ILO Publishing.
29. International Labour Organization. (2022). Gender equality at the heart of decent work. Geneva: ILO. https://www.ilo.org/global/topics/equality-and-discrimination/lang-- en/index.htm
30. International Labour Organization [ILO]. (2022). Women in the labour market: Policies and protections. Geneva: ILO.
31. International Labour Organization [ILO]. (2023). World Employment and Social Outlook: Trends for Women 2023. International Labour Organization.
32. Kabeer, N. (2020). Gender, labour, and the future of work: A global perspective. Gender & Development, 28(2), 189–205. https://doi.org/10.1080/13552074.2020.1780306
33. Maume, D. J. (2011). Meet the new boss… same as the old boss? Female supervisors and subordinate career prospects. Social Science Research, 40(1), 287–298.
34. Melin, U., Axelsson, K., & Lindgren, I. (2022). Digital transformation in the public sector: Gender implications in Germany. Information Polity, 27(1), 91–110.
35. UNESCO. (2022). AI and Gender: Towards Equitable Artificial Intelligence. Paris: UNESCO.
36. UNESCO. (2023). Cracking the Code: Girls’ and Women’s Education in STEM. Paris: UNESCO.
37. UN Women. (2023). Gender Equality and Women’s Empowerment in the Age of AI. New York: United Nations.
38. UN Women. (2023). Digital Inclusion for Gender Equality. New York: United Nations.
39. Oakley, J. G. (2000). Gender-based barriers to senior management positions: Understanding the scarcity of female CEOs. Journal of Business Ethics, 27(4), 321–334. https://doi.org/10.1023/A:1006226129868
40. OECD. (2023). Gender Equality and the Digital Transformation. https://www.oecd.org
41. OECD. (2023). Empowering Women in the Digital Age. Paris: OECD Publishing.
42. OECD. (2023). OECD Gender Data Portal. https://www.oecd.org/gender/data/
43. OECD. (2023a). Bridging the Digital Gender Divide: Include, Upskill, Innovate. Paris: OECD
44. OECD. (2023b). Gender Equality Dashboard: Leadership Indicators. Paris: OECD Publishing. Publishing.
45. OECD. (2024). Gender Equality Dashboard: Leadership Indicators. Paris: OECD Publishing.
46. Perez, C. C. (2019). Invisible Women: Data Bias in a World Designed for Men. Abrams Press.
47. Raji, I. D., & Buolamwini, J. (2019). Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial AI products. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 429–435. https://doi.org/10.1145/3306618.3314244
48. Robinson, L., Cotten, S. R., Ono, H., Quan-Haase, A., Mesch, G., Chen, W., ... & Stern, M. J. (2020). Digital inequalities and why they matter. Information, Communication & Society, 23(1), 1–17. https://doi.org/10.1080/1369118X.2019.1668455
49. Ryan, M. K., & Haslam, S. A. (2005). The glass cliff: Evidence that women are over- represented in precarious leadership positions. British Journal of Management, 16(2), 81–90.
50. Shin, D., & Park, H. (2021). Algorithmic discrimination in South Korea’s AI hiring systems. Technology Forecasting and Social Change, 168, 120762.
51. Stake, R. E. (2005). Multiple Case Study Analysis. Guilford Press.
52. Swedish Digitalisation Agency. (2022). Gender equal digitalisation: Progress report. https://www.digg.se
53. T.C. Dijital Dönüşüm Ofisi. (2021). Ulusal Yapay Zekâ Stratejisi 2021–2025. https://cbddo.gov.tr
54. Trudeau, J., & Fontaine, A. (2022). Equitable AI governance: The Canadian public sector experience. Canadian Journal of Administrative Sciences, 39(4), 612–627.
55. Yalçınkaya, E., & Öztürk, S. (2022). Türkiye’de yapay zekâ uygulamaları ve dijitalleşme süreci: Sektörel analiz. İstanbul İşletme Araştırmaları Dergisi, 1(1), 49–64.
56. Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). SAGE.
57. WIN International & Barem. (2023). Kadın-Erkek eşitliği ve cam tavan sendromu araştırması (WIN World Survey 2023). WIN International. https://www.winmr.com
58. Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2020). Artificial intelligence and the public sector— Applications and challenges. International Journal of Public Administration, 43(9), 816–830. https://doi.org/10.1080/01900692.2019.1591621
59. World Economic Forum. (2024). Global Gender Gap Report 2024. Geneva: WEF.