数据收集策略与数据质量如何影响数字技术采纳的结果？
How Do Data Collection Strategy and Data Quality Influence the Outcomes of Digital Technology Adoption?
- University of Southern Denmark（南丹麦大学）
- Aalborg University（奥尔堡大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究基于86家制造企业数据，采用PLS-SEM验证数据收集策略与数据质量共同影响数字技术采纳绩效，其中数据质量起完全中介作用，为数据治理提供实证依据。
AI中文摘要：
在工业4.0（I4.0）时代，数据已成为数字化转型的基本基础，然而许多组织仍难以将数据实践与数字绩效结果联系起来。本研究调查了数据收集策略和数据质量如何共同影响制造企业数字技术采纳（DTA）的成功。基于86家企业的调查数据，本研究采用偏最小二乘结构方程模型（PLS-SEM）来检验数据收集策略、数据质量、实施绩效和运营绩效之间的关系。结果表明，数据收集策略和数据质量都显著影响实施绩效和运营绩效。然而，数据收集策略对实施绩效的影响是间接的，完全由数据质量中介。研究还发现，数据收集策略影响数据质量。这些发现表明，数据质量是上游数据实践与下游数字结果之间的关键桥梁。本研究通过实证验证数据质量的核心作用，为数字化转型和数据管理文献做出贡献，并为管理者战略性地设计和治理数据流程提供实践见解。它还为未来关于数据治理框架的研究奠定了基础，这些框架整合数据质量保证、标准化和生命周期管理，以维持数据驱动和数字化转型。
英文摘要：
In the era of Industry 4.0 (I4.0), data has become the essential foundation for digital transformation, yet many organizations still struggle to link data practices with digital performance outcomes. This study investigates how data collection strategy and data quality jointly influence the success of digital technology adoption (DTA) in manufacturing firms. Drawing on survey data from 86 firms, the research employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships among data collection strategy, data quality, implementation performance, and operational performance. The results show that both data collection strategy and data quality significantly influence implementation and operational performance. However, the effect of data collection strategy on implementation performance is indirect, fully mediated by data quality. The study also finds that data collection strategy influences data quality. These findings demonstrate that data quality acts as a critical bridge between upstream data practices and downstream digital outcomes. The study contributes to the digital transformation and data management literature by empirically validating the central role of data quality and offering practical insights for managers to design and govern data processes strategically. It also sets a foundation for future research on data governance frameworks that integrate data quality assurance, standardization, and lifecycle management to sustain data-driven and digital transformation.
