Barriers and Optimization Paths of Big Data Technology Acceptance for Improving Accounting Information Quality from the Perspective of Trust Moderation

Authors

  • Yuhong Wang 1,* https://orcid.org/0009-0008-1453-1917
    • Data Curation
    • Methodology
    • Visualization
    • Conceptualization
    • Formal Analysis
    • Investigation
    • Project Administration
    • Software
    • Supervision
    • Validation
    • Writing – Original Draft Preparation
    • Writing – Review & Editing
  • Ooi Kok Loang 1 https://orcid.org/0000-0003-0412-8899
    • Validation
    • Investigation
    • Data Curation
    • Software
    • Resources
    • Visualization
    • Writing – Review & Editing
1 Faculty of Business, Information and Human Sciences, Kuala Lumpur University of Science and Technology, Kajang, Malaysia 43000
Corresponding author: 691288267@qq.com

DOI:

https://doi.org/10.63646/ZLPW9746

Keywords:

Big Data Technology Acceptance， Accounting Information Quality，Trust Moderation，BDAC，Old Industrial Bases in Northeast China

Abstract

Big data analytics can expand the timeliness and scope of accounting information, yet investment in digital systems does not by itself establish that users will accept the resulting reports. This paper develops a problem-oriented conceptual analysis of that gap, using the old industrial bases of Northeast China as an application context. It integrates the technology acceptance model, the resource-based view of big data analytics capability, and research on organizational and technology-related trust. The analysis distinguishes actual accounting information quality from stakeholders' perceptions and separates system adoption from informed reliance on specific outputs. Three interacting barriers are identified: an imbalance between technical infrastructure and complementary human and organizational resources; insufficient or poorly calibrated algorithmic, organizational, and institutional trust; and differences in the information requirements of finance personnel, investors, auditors, and regulators. Trust is proposed to accelerate the acceptance-to-quality-perception relationship when users can verify relevant capabilities, while potentially buffering temporary implementation difficulties when correction procedures remain credible. Neither effect is treated as an empirically estimated result, and excessive trust is recognized as a possible source of automation bias. The paper proposes proportionate model documentation, accountable data governance, capability-based resource allocation, stakeholder-specific explanations, and staged implementation with independent evaluation. Its contribution is a set of connected mechanisms and practical assessment criteria rather than evidence that regional firms share a uniform adoption rate or trust deficit. The framework provides a basis for subsequent comparative case research, stakeholder surveys, and longitudinal evaluation of whether digital accounting improvements are both demonstrable and credible.

How to Cite

Wang, Y., & Loang, O. K. (2026). Barriers and Optimization Paths of Big Data Technology Acceptance for Improving Accounting Information Quality from the Perspective of Trust Moderation. Journal of Business and Data Analytics, 4(9), 110-128. https://doi.org/10.63646/ZLPW9746

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