Manufacturing firms face persistent digital upgrading bottlenecks, yet existing literature overlooks the co-evolutionary dynamics between organizational learning (OL), multi-dimensional data capabilities and agility in driving digital transformation (DT). This study aims to undertake a systematic empirical investigation of how OL (exploratory vs exploitative), big data capabilities (BDCs) and organizational agility (OA) synergistically shape the trajectory of DT. By synthesizing the Resource-Based View and Dynamic Capability Theory, this study further clarifies the mediating role of BDC dimensions and the boundary condition of OA in this process.
A quantitative research design was adopted using a structured questionnaire distributed to manufacturing managers across China. After a pre-survey validation, 505 valid responses were collected through online and offline channels. The proposed model was tested using partial least squares structural equation modeling via SmartPLS to examine direct, mediating and moderating effects.
Both exploratory and exploitative OL significantly advance DT, operating primarily through improvements in data analysis and integration capability and data application capability. OA positively moderates the impact of BDCs on transformation outcomes. Data acquisition capability alone shows no significant direct/indirect influence.
This study develops a framework that articulates the interactive mechanisms between OL, BDCs and agility in driving manufacturing DT, addressing the gap of treating these factors as isolated drivers in prior studies. This study empirically validates the differential mediating roles of three BDC dimensions and the moderating effect of agility.
