Industry 4.0 Adoption in Large Manufacturing Companies: The Readiness–Adoption Gap in an Emerging Economy

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Authors: Darío Jesús Quiroga-Parra, Beatriz Elena Hernández Arias, Lainiver Mendoza Munar, Harry Valencia Ayala, Luis Omar Alpala
Cited by: 1
References: 34

Abstract

Industry 4.0 is widely recognized as a strategic enabler of productivity, flexibility, and competitiveness in manufacturing systems. However, in emerging economies, declared preparedness for digital transformation often exceeds reported implementation. This study examines the readiness–adoption gap of Industry 4.0 in surveyed large manufacturing companies in Valle del Cauca, Colombia. An empirical cross-sectional study was conducted through a structured survey administered to large manufacturing companies in the region. The instrument was grounded in the Technology Acceptance Model and informed by a sociotechnical understanding of Industry 4.0 adoption. It captured perceptions of ICT infrastructure, workforce capabilities, expected benefits, implementation challenges, ease of use, and the reported implementation status of 20 Industry 4.0 technologies. Data from 59 valid responses were analyzed using descriptive statistical techniques, cross-technology comparisons, and an aggregate descriptive synthesis of readiness and adoption indicators. The findings suggest that adoption remains concentrated in foundational technologies such as cybersecurity, cloud computing, and data analytics, while advanced technologies, including artificial intelligence, cyber–physical systems, digital twins, and immersive technologies, show limited implementation despite favorable readiness perceptions. The aggregate synthesis points to a medium-to-high readiness proxy and a substantially lower average adoption level, suggesting the presence of a readiness–adoption gap. The study contributes empirical evidence from large manufacturing companies and derives two practical outputs: a readiness–adoption matrix and a staged roadmap for capability building and technology prioritization. These outputs are presented as context-specific exploratory tools rather than universally validated models.

Fields of Study & Subjects

Digital Transformation in Industry IoT and Edge/Fog Computing Big Data and Business Intelligence

Publication details

Journal / source
Systems
Publisher
MDPI AG
Volume
14
Issue
7
Pages
799–799
Publication date
8 July 2026
Language
EN