Impact of data-driven decision-making in Lean Six Sigma: an empirical analysis

Data-driven decision-making refers to the approaches business firms, and managers are adopting in decision-making on the strength of verifiable data. This study attempted to evaluate the Technology Acceptance Model (TAM) in a Lean Six Sigma (LSS) organisation. The research model developed included external variables that capture perceptions of managers on knowledge management, data quality, technology readiness and performance expectancy. The study examined the moderating role of complexity perceptions of managers. And, data were collected from 173 practising managers in Indian industries. A PLS-based structural equation modelling was carried out. The model revealed the critical linkages among the constructs to draw valid insights about manager’s perceptions in an LSS environment. The study found out that antecedents significantly predict usefulness and ease of using feeling among managers, resulting in adoption intentions. The complexity perceptions act as a significant moderator to adoption intentions in the framework. This study underlines the need for an adequate infrastructure capable of collecting and disseminating reliable data among decision-makers for imparting confidence among managers to adopt data-driven approaches. The main contribution of the study is that the managers hold a favourable view towards new technology adoption and prefers the ease of use for better decision-making.