Journal of Human Resource Management

Journal of Human Resource Management

Designing and Congruence Analysis of a Data-Driven Culture Model in the Oil Industry: A Multi-Method Research Approach

Articles in Press

Document Type : Original Article

Authors
Marjan Rostamzad Mansour
Ali Asghar Pourezzat
Abbas Nargesiyan
Mohammadreza Esmaeili Givi
University of Tehran
10.22034/jhrs.2026.577261.2559
Abstract
Background & Purpose:

Despite substantial investments in digital infrastructure within Iran’s oil industry, achieving data-drivenness requires cultural transformation beyond technological deployment. This study aimed to construct, evaluate, and contextualize a data-driven culture model within the institutional setting of Iran’s oil sector.
Methodology:
A qualitative two-phase design was adopted. First, a systematic review of articles published between 2000 and 2025 in Web of Science and Scopus was conducted. After Q1 quality screening, 69 articles were analyzed through congruence analysis. Second, an explanatory multiple-case study was carried out across three major oil-sector organizations. Data were collected through document analysis and semi-structured interviews.
Findings:
Core dimensions of data-driven culture values, underlying assumptions, artifacts, and symbols are identifiable within the oil industry; however, they manifest in a conditional, hierarchical, and security-oriented form. Data often serve a legitimizing role rather than functioning as binding foundations for decision-making.
Conclusion: Data-drivenness in the oil industry is primarily an institutional and cultural phenomenon rather than merely a technological one. The study proposes a contextualized model aligned with the structural and governance realities of this strategic public sector.
Keywords
Data-driven culture
Oil industry
Hatch model
Data governance
Congruence analysis
Multiple-case study
Journal of Human Resource Management

Articles in Press, Accepted Manuscript
Available Online from 26 April 2026

  • Receive Date 24 February 2026
  • Accept Date 04 March 2026