Enterprise Data Asset Quality: A Management-Standard Conformity-Benefit Realization Framework and Formation Mechanisms

2026-06-24

AI Summary

This study addresses the lack of a unified standard for assessing enterprise data asset quality and the unclear mechanisms linking such quality to value realization. It proposes an integrative three-dimensional framework—encompassing management capability, standards compliance, and benefit realization—and uniquely combines grounded theory, LDA topic modeling, PLS-SEM, necessary condition analysis (NCA), and fuzzy-set qualitative comparative analysis (fsQCA) to simultaneously examine structural relationships, necessary conditions, and configurational pathways. The findings confirm significant positive relationships among the three dimensions, identify necessary conditions for high-quality data assets, and uncover multiple equifinal configurations that reveal a dual-path chain mechanism driven by both governance orientation and benefit-driven imperatives.

Abstract

Motivated by the limited standardization of enterprise data asset quality evaluation and the unclear relationship between assessment outcomes and value realization, this study develops a three-dimensional framework comprising Data Asset Management Capability, Data Quality Standard Conformity, and Data Asset Benefit Realization Capability, based on grounded theory and LDA topic modeling. To examine the formation mechanisms of data asset quality, this study adopts a multi-method approach combining PLS-SEM, Necessary Condition Analysis (NCA), and fuzzy-set Qualitative Comparative Analysis (fsQCA), to capture net effects, capability thresholds, and configurational paths. The results show that significant positive relationships exist among the three dimensions, with Data Asset Management Capability exerting the strongest effect on Data Quality Standard Conformity and further promoting Data Asset Benefit Realization Capability, forming a chain mechanism of management foundation-standard enhancement-value realization. In addition, all three dimensions constitute critical necessary conditions for achieving high data asset quality, and multiple equivalent configurational paths reflecting different combinations of Management, Standard, and Benefit are identified, such as governance-oriented and benefit-driven mechanisms. This study integrates structural (PLS-SEM), necessary-condition (NCA), and configurational (fsQCA) analyses within a unified framework, providing a systematic approach to understanding data asset quality formation and offering practical insights for enterprise data governance and data factor market development.

Problem

• Data Asset Quality
• Standardization
• Value Realization
• Enterprise Data Governance
• Data Quality Evaluation

Innovation

• Data Asset Quality
• PLS-SEM
• Necessary Condition Analysis (NCA)
• fuzzy-set Qualitative Comparative Analysis (fsQCA)
• Configurational Analysis