This study aims to examine the relationships among AI capability, decision-making quality, and innovation performance, and to investigate the moderating effect of algorithmic transparency on the relationship between AI capability and decision-making quality.
A questionnaire survey was conducted using the Credamo data platform. To reduce common method bias, a time-lagged survey design was adopted. Data on AI capability, algorithmic transparency, decision-making quality, and innovation performance were collected from 435 participants. Established scales from authoritative foreign journals were used for measurement, and appropriate translation and verification procedures were carried out.
(1) AI capability is positively associated with innovation performance. Decision-making quality mediates the relationship between AI capability and innovation performance. (2) Algorithmic transparency positively moderates the relationship between AI capability and decision-making quality.
This study enriches AI capability research by incorporating engineering perspectives. It extends organizational learning theory by examining how AI capability shapes decision-making processes within engineer–AI collaboration contexts, identifying decision-making quality as a mediator and algorithmic transparency as a moderator. The findings offer practical insights for construction firms to enhance innovation performance through effective AI integration while helping engineers better leverage AI tools in design and project management workflows.
