For Better or For Worse: Impacts of IoT Technology in e‐Commerce Channel
Bibliographic record
Abstract
Internet of Things (IoT) technology utilizes sensors and other internet‐enabled devices to collect and share data. It is widely regarded as a disruptive technology that brings tremendous opportunities to supply chain members. This study uses a game‐theoretical model to study an e‐commerce setting in which an online platform provides IoT infrastructure and a manufacturer sells its products on the platform. Our work examines the interaction among the manufacturer's IoT investment decision, the platform's choice of pricing models, and the platform's transfer payment strategy. We solve the model analytically and obtain several interesting findings. Our study shows that the manufacturer in a wholesale pricing model is more likely to invest, and invests more, in IoT technology than in an agency one. One surprising finding is that both the manufacturer and the channel performance could be hurt by an increase in IoT technology value in certain situations. Also surprisingly, even having the option of investing in IoT technology by the manufacturer can make both the manufacturer and the channel performance worse off. Therefore, the advancement of IoT technology might not benefit either manufacturers or the whole industry, although e‐commerce platform giants and the news media have been advocating the benefits of IoT technology enthusiastically in recent years. Our results should concern both device manufacturers who contemplate adopting or have adopted IoT technology and policymakers who are interested in overall channel performance.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".