Bayes Business School (formerly Cass), City, U. of London, United Kingdom
Due to the accelerating global climate crisis, supply chains are now under pressure to transform towards sustainability, necessitating precise tracing, monitoring, and reporting of greenhouse gas (GHG) emissions. The emergence of digital technologies such as IoT (Internet of Things), barcodes, RFIDs, and blockchains has made supply chain traceability easier, faster, more accurate, and more cost-effective. This research investigates the adoption of digital traceability systems for managing greenhouse gas (GHG) emissions in supply chains. Building on technology adoption and supply chain complexity literature, we categorise influencing factors into technology characteristics and supply chain complexity, exploring their impact through a choice-based conjoint experimental (CBC) design involving managers in the manufacturing sector. Results reveal that firms consider implementation when technology costs are lower, implementation time is shorter, and the full scope of implementation includes all Scope 1, 2, and 3 emissions. Detail-numerousness and detail-variety in both internal operations and the upstream supply chain positively influence adoption, whereas the impact of the dynamic dimension is nuanced. Detail-numerousness emerges as the most critical factor for internal operations, while detail-variety takes precedence for the upstream supply chain. This emphasises the multifaceted nature of supply chain complexity. The findings contribute to technology adoption and supply chain complexity literature, offering practical insights for stakeholders in sustainable supply chain management.
