The DSC-R Framework: Integrating Technology Transfer, Anthropotechnology, And Industry 4.0 For Digital Supply Chain Resilience
DOI:
https://doi.org/10.4067/s0718-2724202600025068Keywords:
Digital Supply Chain Resilience, Technology Transfer, Anthropotechnology, Industry 4.0, Sociotechnical Systems, Dynamic CapabilitiesAbstract
Digital supply chains (DSCs) operate in increasingly volatile environments, demanding resilience capabilities that extend beyond technological integration. Although the literature extensively addresses Technology Transfer (TT), Anthropotechnology (AT), and Industry 4.0 (I4.0) as independent domains, a systematic search of the SCOPUS and Web of Science databases revealed no study simultaneously integrating these four constructs, namely TT, AT, I4.0, and DSC, within a single conceptual model, confirming a critical gap in the field. To address this gap, this study proposes the DSC-R Framework, an integrative model that extends the articulation of TT, AT, and I4.0 into the domain of digital supply chain resilience through a five-layer architecture that unifies human, technological, and organizational dimensions. A qualitative integrative literature review was conducted using Boolean operators across four thematic axes, followed by critical analysis, theoretical triangulation, and cross-validation of constructs. The resulting framework is organized into five interconnected layers, comprising strategic guidelines, an integrating triad (TT-AT-I4.0), resilience capabilities, vulnerability assessment, and consolidated results, supported by 14 operational resilience capabilities, key performance indicators, technology transfer pathways, and practical implementation tools. Four conceptual propositions demonstrate how dynamic learning, sociotechnical adaptation, and continuous innovation interact to sustain DSC resilience over time. The DSC-R Framework advances the state of the art by integrating traditionally isolated domains, providing theoretical foundations and managerial guidelines for building robust and adaptive digital supply chains. The model is particularly relevant for petrochemicals, Oil and Gas, fertilizers, and digital manufacturing, establishing a basis for future empirical validation through case studies, surveys, and quantitative modeling.
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