Data Quality Improvement Supports Digital Transformation in Industry 5.0

Data quality is known as the fitting degree of data content and formats to functions and it plays a crucial role in firms’ digital transformation. This study focuses on Industry 5.0, draws on Deming’s Profound Knowledge System on quality, and identifies four key influencing factors on data quality that align with Industry 5.0 concepts, i.e., data variation, employee resilience, system integration, and digital variation knowledge management. A structural model among these factors was established to support the digital transformation. An empirical study with a 301-participant questionnaire survey was adopted to test the model using SEM. The results show the following: (1) employee resilience and system integration each exert a positive effect on data variation and digital variation knowledge management; (2) data variation and digital variation knowledge management both positively affect digital transformation; and (3) employee resilience mediates system integration’s effects on data variation and digital variation knowledge management. Based on the results, this paper proposes a novel approach to enhancing data quality in digital transformation with a sustainable view: (1) employee resilience and system integration should be bundled, and emphasis should be put on the mediating role of employee resilience, forming a resilient firm capability and (2) digital variation knowledge management safeguards data variation, can prevent and respond to data quality variation risks, and helps firms form a better decision-making capacity. The proposed model can convert resource identification into capabilities generation and then to value creation with the resource orchestration view. It can help firms achieve more sustainable development during digital transformation.