The internet of things in the food supply chain: adoption challenges
Bibliographic record
Abstract
Purpose The food supply chain (FSC) challenges coupled with global disruptions, such as the recent coronavirus disease 2019 (COVID-19) pandemic outbreak, exacerbate its vulnerability. The Internet of things (IoT) is one of the disruptive technologies being adopted in food supply chain management (FSCM). This study aims to address the challenges of IoT adoption in the FSC by systematically analyzing the prior pertinent literature. Design/methodology/approach A structured literature review was used to collate a list of peer-reviewed and relevant publications. A total of 72 out of 210 articles were selected for the final evaluation. Findings The literature review findings suggest five themes: technical, financial, social, operational, educational and governmental related challenges. A total of 15 challenges were devised from the review related literature of IoT adoption. The study concludes with future research recommendations for scholars and practical implications for practitioners. Research limitations/implications While this study focuses on the overall FSC, further research should address other domains in the FSC such as cold supply chain, agriculture and perishable food to gain a better contextual understanding of the specific case. Originality/value The topic of IoT adoption in the FSCM is still considered emerging. Therefore, the present work contributes to the limited studies and documentation on the level of IoT implementation in the FSCM. This study should help organizations to assimilate how to adopt and manage the IoT application by addressing the factors and challenges presented in this research.
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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".