The Sociology of Data Governance: Examining the Intersection of Artificial Intelligence, Metadata Management, and CSR Practices in Shaping Organizational Legitimacy
DOI:
https://doi.org/10.70112/arss-2026.15.1.4346Keywords:
Artificial Intelligence (AI), Metadata Governance, Corporate Social Responsibility (CSR) , Organizational Legitimacy, Sociology , Financial PerformanceAbstract
AI in the digital economy is changing the business of corporate governance by altering the process of managing metadata and reporting corporate social responsibility (CSR). CSR disclosures are based on metadata, which can be defined as data about data and it gives structure, comparability and traceability. AI-based metadata governance improves the quality, efficiency, and transparency of CSR reporting, which allows firms to address the growing expectations of stakeholders regarding credible environmental, social, and governance (ESG) information. Sociologically, though, the importance of AI is not limited to technical efficiency: it serves as a means to strengthen organizational legitimacy, which is a necessity for long-term survival and competitiveness. The paper will discuss the convergence point of AI, metadata governance, and CSR by considering a sociological perspective, in which structured CSR metadata reporting can have an impact on investor trust, employee well-being, and social acceptability. Based on a systematic literature review of more than 35 published studies (2018-2024), the findings demonstrate that although AI enhances transparency and comparability, it has also raised challenges. The major threats are algorithmic bias, ethical issues, infrastructural disparities, and less human control. However, AI-based governance has the potential to enhance the level of stakeholder trust, curb reputational risks, and align CSR disclosures with legitimacy expectations, which have a positive impact on financial performance. The new trends are predictive analytics to manage CSR risks, the use of blockchain authentication, and harmonization of ESG standards across the world. The researchers conclude that AI-based metadata governance is not just a technical system but a socio-technical system mediating relations between corporations and society. In order to maintain legitimacy and long-term performance, firms are therefore faced with a challenge of balancing between technological adoption and ethical safeguards and sociological sensitivity.
References
Affum, M. Q. (2023). The role of artificial intelligence in library automation. Library Philosophy & Practice.
Affum, M. Q., & Dwomoh, O. K. (2023). Investigating the potential impact of artificial intelligence in CSR reporting. Sustainability Accounting Review, 27(1), 33-49.
Al-Salmi, F. N., Al Maamari, J. S., Al Rashdi, S. S., & Matriano, M. T. (2025). Employing artificial intelligence in the service of corporate social responsibility: Case of OQ, Oman. Global Scientific Journal, 13(6), 1544-1575.
Anastasopoulos, F. (2016). Data governance design for financial services organizations. Master’s thesis, Leiden Institute of Advanced Computer Science (LIACS), Leiden University.
Carroll, A. B. (2016). Carroll’s pyramid of CSR: Taking another look. International Journal of Corporate Social Responsibility, 1(1), 1-8.
Cho, C. H., Laine, M., Roberts, R. W., & Rodrigue, M. (2015). Organized hypocrisy, organizational façades, and sustainability reporting. Accounting, Organizations and Society, 40, 78-94. https://doi.org/10.1016/j.aos.2014.12.003.
Clark, G. L., Feiner, A., & Viehs, M. (2015). From the stockholder to the stakeholder: How sustainability can drive financial outperformance. Social Science Research Network. https://doi.org/10.2139/ssrn.2508281.
Dowling, J., & Pfeffer, J. (1975). Organizational legitimacy: Social values and organizational behavior. Pacific Sociological Review, 18(1), 122-136. https://doi.org/10.2307/1388226.
Gray, R., Kouhy, R., & Lavers, S. (1995). Corporate social and environmental reporting: A review of the literature and a longitudinal study of UK disclosure. Accounting, Auditing & Accountability Journal, 8(2), 47-77. https://doi.org/10.1108/09513579510146996.
Grimmelikhuijsen, S. (2022). Explaining why the computer says no: Algorithmic transparency affects the perceived trustworthiness of automated decision-making. Public Administration Review, 1-22.
Karaduman, Ö., & Gülhas, G. (2025). Blockchain-enabled supply chain management: A review of security, traceability, and data integrity amid the evolving systemic demand. Applied Sciences, 15(9), 5168. https://doi.org/10.3390/app15095168.
Lalitha, B., Ramalakshmi, K., Gunasekaran, H., Murugesan, P., & Rajkumar, N. (2024). Anticipating AI’s impact on CSR: Ethical challenges and opportunities. Journal of Sustainable Business, 14(2), 115-132.
Mallikarjuna, C. (2024). An analysis of integrating artificial intelligence in academic libraries. DESIDOC Journal of Library & Information Technology, 44(2), 124-129. https://doi.org/10.14429/djlit.44.02.18958.
Margolis, J. D., & Walsh, J. P. (2003). Misery loves companies: Rethinking social initiatives by business. Administrative Science Quarterly, 48(2), 268-305. https://doi.org/10.2307/3556659.
Meyer, J. W., & Rowan, B. (1977). Institutionalized organizations: Formal structure as myth and ceremony. American Journal of Sociology, 83(2), 340-363. https://doi.org/10.1086/226550.
Monyela, M., & Tella, A. (2024). Leveraging artificial intelligence for sustainable corporate reporting. South African Journal of Libraries and Information Science, 90(2), 1-11.
Olateju, D. J., Olateju, O. A., Adeoye, S. V., & Ilyas, I. S. (2021). A critical review of the application of the legitimacy theory to corporate social responsibility. International Journal of Managerial Studies and Research, 9(3), 1-6.
Oyighan, D., Ukubeyinje, E. S., David-West, B. T., & Oladokun, B. D. (2024). The role of AI in transforming metadata management: Insights on challenges, opportunities, and emerging trends. Asian Journal of Information Science and Technology, 14(2), 20-26. https://doi.org/10.70112/ajist-2024.14.2.4277.
Pizzi, S., Caputo, A., Venturelli, A., & Caputo, F. (2022). Embedding and managing blockchain in sustainability reporting: A practical framework. Sustainability Accounting, Management and Policy Journal, 13(3), 545-567. https://doi.org/10.1108/SAMPJ-07-2021-0288.
Ragheb-Hashem, R. E. E., Mubarak, A. I., & Abu-Musa, A. E. (2023). The impact of blockchain technology on audit process quality: An empirical study on the banking sector. International Journal of Auditing and Accounting Studies, 5(1), 87-118. https://doi.org/10.47509/IJAAS.2023.v05i01.04.
Rahman, M. H., Yeoh, W., & Pal, S. (2024). Exploring factors influencing blockchain adoption’s effectiveness in organizations for generating business value: A systematic literature review and thematic analysis. Enterprise Information Systems, 18(8). https://doi.org/10.1080/17517575.2024.2379830.
Shaban, O. S., & Omoush, A. (2025). AI-driven financial transparency and corporate governance: Enhancing accounting practices with evidence from Jordan. Sustainability, 17(9), 3818. https://doi.org/10.3390/su17093818.
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104(7), 333-339. https://doi.org/10.1016/j.jbusres.2019.07.039.
Suchman, M. C. (1995). Managing legitimacy: Strategic and institutional approaches. Academy of Management Review, 20(3), 571-610. https://doi.org/10.5465/amr.1995.9508080331.
Uwhejevwe-Togbolo, S. E., Ubogu, F. E., & Ofomaja, N. I. (2024). Ethical use of data in the metaverse for corporate social responsibility. Metaverse Basic and Applied Research, 3(1), 22-36. https://doi.org/10.57009/mbars.2024.3.1.50.
Varma, A., Dawkins, C., & Chaudhuri, K. (2022). Artificial intelligence and people management: A critical assessment through the ethical lens. Human Resource Management Review, 33(1), 100923.
Verma, V. K., & Gupta, S. (2022). Artificial intelligence and the future libraries. World Digital Libraries, 15(2), 151-166. https://doi.org/10.18329/09757597/2022/15210.
Wang, H., Xu, Z., Fujita, H., & Liu, S. (2016). Towards felicitous decision making: An overview on challenges and trends of big data. Information Sciences, 367-368, 747-765.
Yoon, J., Andrews, J. E., & Ward, H. L. (2022). Perceptions on adopting artificial intelligence and related technologies in corporate reporting. Journal of Business Technology, 40(6), 1893-1915. https://doi.org/10.1108/JBT-06-2022-0153.
Zavodna, L. S., Überwimmer, M., & Frankus, E. (2024). Barriers to the implementation of artificial intelligence in small and medium-sized enterprises: Pilot study. Journal of Economics & Management, 46, 331-352. https://doi.org/10.22367/jem.2024.46.13.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Centre for Research and Innovation
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
