Emerging Technology Adoption Models for Accelerating Sustainable Innovation Across Global Value Chains
Keywords:
emerging technology, sustainable innovation, global value chains, technology adoption, digital transformation, sustainability integration, GVCs, blockchain, AI, IoT, organizational readiness, policy alignment, technology ecosystem, innovation diffusionAbstract
The integration of emerging technologies into global value chains (GVCs) has the potential to accelerate sustainable innovation, yet adoption remains uneven across sectors and regions. This paper explores the strategic, technological, and institutional models that facilitate the successful adoption of emerging technologies—such as artificial intelligence (AI), blockchain, and the Internet of Things (IoT)—to drive sustainability in global production and distribution networks. We examine organizational readiness, policy frameworks, and ecosystem-based approaches as enablers of sustainable technological integration. The paper further identifies structural gaps in current GVC configurations and proposes a framework for multi-level technology adoption to enhance circularity, efficiency, and transparency. Literature and recent empirical data, this study advances a comprehensive adoption model that links technology maturity with sustainable innovation outcomes across sectors.
References
Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: Toward a unified view. MIS Quarterly 27(3):425–478
Gujjala, P.K.R. (2022). Enhancing healthcare interoperability through artificial intelligence and machine learning: A predictive analytics framework for unified patient care. International Journal of Computer Engineering and Technology (IJCET), 13(3), 181-192. https://doi.org/10.34218/IJCET_13_03_018
Rogers EM (2003) Diffusion of innovations, 5th edn. Free Press, New York
Tornatzky LG, Fleischer M (1990) The processes of technological innovation. Lexington Books
Oleti, C.S. (2022). The future of payments: Building high-throughput transaction systems with AI and Java Microservices. World Journal of Advanced Research and Reviews, 16(03), 1401-1411. https://doi.org/10.30574/wjarr.2022.16.3.1281
Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13(3):319–340
Christopher M (2016) Logistics & supply chain management. Pearson Education
Iansiti M, Lakhani KR (2017) The truth about blockchain. Harvard Business Review 95(1):118–127
Oleti, C. S. (2022). Serverless intelligence: Securing J2EE-based federated learning pipelines on AWS. International Journal of Computer Engineering and Technology, 13(3), 163-180. https://doi.org/10.34218/IJCET_13_03_017
Gujjala, P.K.R. (2023). Advancing Artificial Intelligence and Data Science: A Comprehensive Framework for Computational Efficiency and Scalability. International Journal of Research in Computer Applications and Information Technology, 6(1), 155–166. https://doi.org/10.34218/IJRCAIT_06_01_012
Frank AG, Mendes GHS, Ayala NF, Ghezzi A (2019) Servitization and Industry 4.0 convergence in the digital transformation of product firms: A business model innovation perspective. Technol Forecast Soc Change 141:341–351
Del Giudice M, Scuotto V, Garcia-Perez A, Petruzzelli AM (2021) Shifting wealth and innovation: The role of smart technologies and innovation ecosystems. Technol Forecast Soc Change 170:120896
Bag S, Pretorius JH, Gupta S, Dwivedi YK (2021) Role of institutional pressures and resources in the adoption of big data analytics by sustainable manufacturing firms. Prod Plan Control 32(5):396–411
Bocken NMP, Short SW, Rana P, Evans S (2014) A literature and practice review to develop sustainable business model archetypes. J Clean Prod 65:42–56
Porter ME, Heppelmann JE (2014) How smart, connected products are transforming competition. Harv Bus Rev 92(11):64–88
Luthra S, Kumar A, Zavadskas EK, Mangla SK, Garza-Reyes JA (2016) Industry 4.0 as an enabler of sustainability: A systematic literature review. Sustainability 8(5):641
Geissdoerfer M, Vladimirova D, Evans S (2018) Sustainable business model innovation: A review. J Clean Prod 198:401–416
Srai JS, Kumar M, Graham G, Phillips W (2016) Distributed manufacturing: Scope, challenges and opportunities. Int J Prod Res 54(23):6917–6935
Li L (2018) China’s manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0”. Technol Forecast Soc Change 135:66–74
Kamble SS, Gunasekaran A, Gawankar SA (2018) Sustainable Industry 4.0 framework: A systematic literature review. Identifying the current trends and future perspectives. Comput Ind Eng 127:253–272
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