🤖 AI Summary
研究通过PLS-SEM方法探讨了数据收集策略和数据质量如何共同影响制造业企业数字技术采纳的成功，发现数据质量在其中起到关键中介作用。
📝 Abstract
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.