Behavior-Dependent Pricing: An IoT-Enabled Pricing Model Under Servicizing
Bibliographic record
Abstract
The benefits of the servicizing business model, in which a firm sells the use or functionality of a product rather than the product itself, extend beyond attracting new customers and driving economic growth. Aligned with circular economy principles, servicizing promotes sustainability by encouraging firms to enhance product durability and customers to be more mindful of their amount of usage. However, the lack of product ownership may lead to product misuse, negatively affecting both economic and environmental outcomes. This study addresses product misuse as a major risk to servicizing firms’ performance and investigates whether, and under what conditions, adopting Behavior-Dependent Pricing (BDP) can mitigate this risk. Leveraging digital technologies such as the Internet of Things (IoT), we develop a BDP model in which a firm monitors customers’ usage behavior and provides monetary incentives for more sustainable use. We identify conditions under which BDP leads to a win–win–win outcome by increasing firm profits, enhancing customer utility, and reducing environmental impacts. This study provides firms with insights on how and when servicizing can be less vulnerable to product misuse risk that could undermine profitability, thereby encouraging adoption of the servicizing business model and generating economic and environmental benefits.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".