Value Creation by Dynamic Pricing through Digitization and Industry-Wide Perspective
Bibliographic record
Abstract
Dynamic Pricing (DP), also known as surge pricing or dynamic price management, is the adjustment of prices for goods and services depending on the current market situation. Its purpose is to maximize profit, and the practice is getting more and more common. Dynamic pricing was first spotted in online retail; Also in offline retail, it can be found, e.g. as electronic price tags, as well as in on-demand services in mobility and smart meters in the energy industry. Dynamic pricing offers opportunities for vendors. The goal of this paper is to examine the current status and the new opportunities and risks of Dynamic Pricing in retail, mobility and the energy sector, made possible by digitization. This is done on the one side with expert interviews and on the other with an online research. 5 experts were interviewed and 238 respondents answered a questionnaire. 12 hypotheses were formulated, out of which 9 were confirmed, 1 was completely rejected and 2 were partially rejected. The unexpected results were: (1) Electronic price labels in stationary retail trade do not worry consumers with regard to momentary price changes. (2) Consumers do not prefer dynamic pricing models in car sharing. (3) Consumers can benefit from dynamic pricing models in the case of the aviation industry. The aim of this work to provide readers with insights as on how to utilize DP in their respective industries.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".