LEADS US NOT INTO TEMPTATION : KNOWLEDGE WORKERS , BUSINESS INTELLIGENCE SYSTEMS , AND OCCUPATIONAL FRAUD
Bibliographic record
Abstract
This paper explores how uncertainty reduction due to increased forecasting accuracy, which is one of main benefits associated with the adoption of Business Intelligence (BI) systems, will affect the behavior of knowledge workers and how this change in their behavior will impact the appropriation of benefits from BI investments. The study uses a micro-economic model in order to show that higher forecasting accuracy is likely to create the conditions for knowledge workers to behave in a morally hazardous fashion. The result of this opportunistic behavior is that knowledge workers can appropriate for themselves a relatively larger portion of the firm’s rents from BI investments that should accrue to firm and ultimately to external stakeholders. Studies that measure the payoffs from IT investments that enable more accurate forecasts, such as BI, are likely to underestimate the total benefits by the portion that knowledge workers will appropriate for themselves through their opportunistic behavior.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".