نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران،
2 شکده/گروه مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
With the growing centrality of data-driven practices in organizations and governments, data governance has emerged as a core pillar of digital transformation, public policymaking, and socio-economic value creation. However, empirical experience suggests that many of the persistent challenges and failures associated with data governance are not primarily caused by the absence of formal frameworks or technical mechanisms, but rather stem from hidden and insufficiently problematized conflicts of interest operating beneath the surface of organizational and institutional arrangements. While the existing literature has extensively addressed visible dimensions of data governance—such as data quality, architecture, ownership, security, and data sharing—it has paid considerably less attention to a more fundamental question: why conflicts of interest in data governance often remain unseen, unrecognized, or systematically reduced to technical issues. This gap is particularly pronounced in developing-country contexts and in the Iranian institutional setting, where governance structures, organizational roles, and regulatory boundaries are frequently overlapping and ambiguous.
The primary objective of this study is to identify and explain the hidden causes of conflicts of interest in data governance and to uncover the mechanisms through which such conflicts are structurally reproduced while remaining largely invisible in formal policies and decision-making processes. Rather than focusing on explicit or isolated instances of conflict of interest, the study adopts a deeper analytical lens, examining the latent cognitive, institutional, and conceptual foundations that normalize and conceal these conflicts within everyday organizational practices.
This research employs a qualitative methodology based on the grounded theory approach, following the Strauss and Corbin paradigm. Data were collected through semi-structured interviews with 30 managers, experts, and practitioners specializing in data governance, data management, information technology, information security, and policymaking in large Iranian organizations. Sampling was conducted using a purposive and snowball strategy and continued until theoretical saturation was reached at the twentieth interview. Interviews, each lasting approximately 30 to 45 minutes, were conducted between March 2023 and November 2024. Data analysis proceeded through open, axial, and selective coding. To ensure the rigor and trustworthiness of the findings, several validation strategies were employed, including test–retest coding reliability, triangulation, and expert review. The test–retest reliability, calculated through repeated coding of selected interviews with a ten-day interval, yielded an average agreement rate of 71.05%, exceeding the commonly accepted threshold for qualitative reliability.
The findings reveal that conflicts of interest in data governance are largely shaped by a set of hidden and non-obvious factors that are rarely recognized as governance problems in their own right. These factors operate across three interrelated levels. At the conceptual level, ambiguity in the understanding of data governance—often conflated with data integration, technical control, or operational data management—leads to the marginalization of governance-related values such as accountability, transparency, and ethical oversight. At the institutional level, the overlap of regulatory, executive, and commercial roles within the data value chain, combined with insufficient role separation and weak regulatory mechanisms, generates structural conflicts that are routinely normalized as legitimate organizational practices. At the contextual level, growth pressure, acceleration-oriented organizational cultures, legal ambiguity, and rapid datafication processes reinforce these conflicts and contribute to their concealment behind dominant discourses of innovation, efficiency, and development.
Axial analysis indicates that these hidden causes interact and mutually reinforce one another, giving rise to consequences that extend far beyond technical inefficiencies. These consequences include increased ethical, legal, and socio-political risks; erosion of public trust; constrained data sharing; stagnation of data-driven innovation; algorithmic bias; and weakened institutional accountability. In the selective coding phase, “managing conflicts of interest in data governance” emerged as the core category, around which an integrated conceptual model was developed. This model systematically explains the relationships among causal conditions, contextual factors, intervening conditions, action–interaction strategies, and resulting outcomes.
Overall, this study demonstrates that conflicts of interest in data governance are neither incidental nor exceptional phenomena, but rather structurally embedded and perpetuated through hidden mechanisms within organizational and institutional systems. Without explicitly identifying and addressing these latent causes, technical and policy-oriented interventions are unlikely to achieve meaningful or sustainable outcomes. By foregrounding the hidden dimensions of conflict of interest, this research contributes a context-sensitive and theoretically grounded framework to the data governance literature and underscores the necessity of moving beyond purely technical approaches toward reflexive, governance-oriented interventions.
کلیدواژهها [English]
)
ارسال نظر در مورد این مقاله