Timing and interdependencies in blockchain capabilities development for supply chain management: a resource-based view perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it