Channel Encroachment and Supply Chain Performance: The Effects of Internet of Things Data Sharing
Bibliographic record
Abstract
The advancement of Internet of Things (IoT) technology has enabled IoT device manufacturers to collect consumer usage data (IoT data) whenever their devices are in use. Our paper examines the following setting: A manufacturer collects IoT data and decides whether to share it with a retailer, while consumers remain concerned about their privacy; the retailer, in turn, can leverage the shared data for cross-selling by investing in data-mining efforts that transform raw data into actionable insights. This aspect of data mining differentiates our study from traditional research on information sharing. Beyond selling through the existing retail channel, the manufacturer also has the option to establish a direct channel, thereby encroaching on the retailer’s market. Our analysis reveals several key insights. First, when the manufacturer both encroaches and shares IoT data, we observe a counterintuitive positive effect of the channel substitution rate: As the substitution rate increases, both the manufacturer and the retailer may see higher profits. Second, while the manufacturer always chooses to encroach when data sharing is absent, its motivation to do so weakens when it shares IoT data. Finally, we find that an increase in the value of IoT data can unexpectedly lead to a decline in the retailer’s profit.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".