The dual drivetrain model of digital transformation: role of industrial big-data-based affordance
Purpose: To better understand the role of industrial big data in promoting digital transformation, the authors propose a theoretical framework of industrial big-data-based affordance in the form of an illustrative metaphor – what the authors call the “organizational drivetrain.” Design/methodology/approach: This study investigates the effective use of industrial big data in the process of digital transformation based on the technology affordance–actualization theoretical lens. A software platform and services provider with more than 4,000 industrial enterprise clients in China was selected as the case study object for analyzing the digital affordance and actualization driven by industrial big data. Findings: Drawing on a revelatory case study, the authors identify three affordances of industrial big data in the organization, namely developing data-driven customized projects, provisioning equipment-data-driven life cycle services, establishing data-based trust and determining affordance actualization actions driven by technology and market. In addition, the authors reveal the underlying drivetrain mechanisms to advance industrial big data affordance and actualization: stabilizing, enriching and pioneering. Originality/value: This study builds a drivetrain model on digital transformation by industrial big data affordance actualization. The authors also provide practical implications that can help practitioners to implement digital transformation effectively and extract value from their investment.
Digital Object Identifier (DOI)
Liu, Y., Wang, W., & Zhang, Z.J. (2020). The dual drivetrain model of digital transformation: role of industrial big-data-based affordance. Management Decision. 60(2), 344-367. https://doi.org/10.1108/MD-12-2019-1664