The Impact of Instrumental Stakeholder Management on Blockchain Technology Adoption Behavior in Agri-Food Supply Chains
Bibliographic record
Abstract
Coffee is the second most important commodity in terms of global trade value, with its global market value exceeding $460 billion in 2020. Its supply networks, which encompass multiple stakeholders, are complex and nontransparent. Blockchain is a trust technology, and some coffee firms have embraced this technology to provide trust attributes to consumers while making their supply chain more transparent. For businesses to gain the expected productivity advantages, a technology must be adopted and used. As theoretical and empirical research on blockchain technology adoption is scarce, this article attempts to identify behavioral intentions of stakeholders in the supply network toward its adoption. Based on exploratory interviews, this article develops a blockchain technology adoption model based on factors relevant to individuals’ use behavior. The results provide evidence that a normative stakeholder management approach positively impacts use behavior. Managers can use the model to benchmark and improve their corporate social responsibility strategy to obtain better returns on blockchain investments. This study closes a research gap as, to the best of the authors’ knowledge, no research has been conducted so far on the impact of an instrumental stakeholder management approach on blockchain technology adoption behavior. Understanding how stakeholder management can compensate for the lack of consensus mechanisms in private and consortium blockchains, as well as understanding the factors influencing behavioral intentions toward the use of a technology, can provide for managerial guidance toward the development of an effective stakeholder management strategy, which eventually can result in a competitive advantage.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".