@misc{10481/105111,
year = {2025},
month = {6},
url = {https://hdl.handle.net/10481/105111},
abstract = {This study explores how the sequence and timing of Industry 4.0 technology adoption affect sustainable innovation in manufacturing firms. Using longitudinal data from the State Society of Industrial Participations, we track the adoption patterns of eight technologies, including industrial IoT, cloud computing, RFID, machine learning, robotics, additive manufacturing, autonomous robots, and generative AI. Sequence analysis reveals five distinct adoption profiles: data-centric foundations, automation pioneers, holistic integrators, cautious adopters, and product-centric innovators. Our results show that these adoption pathways differentially impact sustainability outcomes such as circular material innovation, energy transition, operational eco-efficiency, and emissions reduction. Mediation analysis indicates that data orchestration capabilities significantly enhance resource productivity in holistic integrators, generative design competencies accelerate biomaterial innovation in product-centric innovators, and cyber-physical integration reduces lifecycle emissions in automation pioneers. By highlighting how temporal complementarities among technologies shape sustainability performance, this research advances dynamic capabilities theory and emphasizes the path-dependent nature of sustainable innovation. The findings provide practical guidance for firms to align digital transformation with sustainability objectives and offer policymakers insights into designing timely support mechanisms for industrial transitions. This work bridges innovation timing with ecological modernization, contributing a new understanding of capability development for sustainable value creation.},
publisher = {MDPI},
keywords = {technology adoption sequences},
keywords = {Sustainable innovation},
keywords = {Industry 4.0},
keywords = {Digital transformation},
keywords = {Sequence analysis},
title = {Technological Adoption Sequences and Sustainable Innovation Performance: A Longitudinal Analysis of Optimal Pathways},
doi = {10.3390/su17135719},
author = {Bautista Carrillo, Francisco Gustavo and Arias-Aranda, Daniel},
}