Timing and interdependencies in blockchain capabilities development for supply chain management: a resource-based view perspective
Bibliographic record
Abstract
Purpose Using the resource-based view (RBV), our study aims to provide theoretical and empirical insights into blockchain capabilities’ (BCs) compounded and sequential effects on supply chain competitive advantages (CA). Design/methodology/approach We combined a systematic literature review and an expert interview. Interpretive Structural Modelling and a Matrix of Cross-Impact Multiplications Applied to Classification were used to determine the relationship between the capabilities. Simple Additive Weighting assessed each capability’s relative importance and impact. Findings We reveal a sequential development path for BCs. Foundational capabilities, such as cybersecurity, provide immediate performance benefits, establishing a unique, valuable and inimitable resource. As firms progress to advanced capabilities, the compounded value of these capabilities generates a stronger, dynamic resource for sustained CA. Moreover, the study underscores the strategic importance of timing in adopting and developing BCs, as early adoption can secure a competitive edge difficult for later entrants to replicate. Practical implications Our proposed framework guides managers in incorporating blockchain technology into supply chain management (SCM) processes once it demonstrates that firms can enhance their CA by prioritizing the technical basics BC, leveraging the informational capabilities in level two and enabling effective problem-solving through level three. Our framework also shows that a learning process occurs as BCs are used and their results are explored. Originality/value Our study extends the RBV by demonstrating BCs’ cumulative and interdependent nature in SCM. It emphasizes the synergistic interactions between these capabilities, which collectively enhance CA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".