Data quality links strategy to success in digital technology adoption
How Do Data Collection Strategy and Data Quality Influence the Outcomes of Digital Technology Adoption?
Databases
Summary
Many factories want to use new digital tools but struggle to get good results. The paper finds that how companies collect data and keep that data accurate both affect how well these tools work. But the quality of the data is the key factor that connects the way data is collected with the success of digital projects. The authors show that focusing on keeping data good helps factories improve their operations after adopting new technology.
What this means in practice
- •For manufacturing managers: Design data processes with an emphasis on data quality to improve digital technology implementation success and operational outcomes.
- •For data governance teams: Create data governance frameworks that assure quality, standardize practices, and manage data lifecycle to sustain digital transformation efforts.
Authors
Xuejiao Li, Cheng Yang
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.