Internal Controls, Routine Activity Theory (RAT), and Sustained Online Auction Deception: A Longitudinal Analysis
Bibliographic record
Abstract
ABSTRACT This paper adapts and extends routine activity theory (RAT) to investigate the co-evolution of eBay's controls with mundane crime in the rapidly growing auction market of 1997–2005. A suspected deceptive seller's eight-year account history indicates the presence of the three market characteristics that RAT identifies as essential for deception: (1) a motivated offender, (2) suitable targets, and (3) an absence of capable guardians (i.e., regulation and eBay controls). The results document the co-evolution of a deceptive seller's tactics with eBay's controls. The investigation introduces (1) a new market, i.e., the early online auctions, (2) a new theory, i.e., RAT, and (3) new data, i.e., of a long-term deceptive seller, to the accounting controls literature. Contributions include tracing the evolving eBay control system, considering eBay's feedback system as an emergent form of continuous monitoring, and investigating the potential of RAT as an alternative theory for understanding control violations and informing accounting control analysis and design. Data Availability: The archival data are available from public sources. The primary data are available to scholars willing to sign agreements that protect the confidentiality of the sources.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".