Information Technology and Pricing Decisions: Price Adjustments in Online Computer Markets1
Bibliographic record
Abstract
Determining prices is a key management task for a merchant. IT-enabled electronic markets facilitate price discovery by both buyers and sellers compared to traditional, physical markets. Recent research on electronic markets has revealed that IT has increased market transparency due to increased accessibility and availability of market information. However, what online sellers do in terms of strategic pricing decisions, in particular price adjustment behavior over time, has not been fully investigated. Due to the ease of making price changes, electronic sellers can execute a number of different pricing strategies, including setting the frequency and amount of price changes. We investigate the “opaque” side of electronic markets by exploring online sellers’ price adjustment patterns over time. More specifically, we identify four questions related to pricing decisions, which lead to hypotheses about how managers determine prices in electronic markets. The paper tests the hypotheses with data from the online computer commodity market. We found, through a simulation analysis, that this market exhibits synchronized price changes, not random changes that are frequently found in traditional markets. Interestingly, small price increases occur more frequently than decreases, while the frequency of price adjustment is significantly associated with a product’s price dispersion. A ranking analysis suggests that online sellers change their price strategies frequently, which makes it difficult for consumers to respond appropriately. The paper discusses the implications of our findings for management and for future research on market transparency and strategic pricing in electronic markets.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".