Virtual Progress: The Effect of Path Characteristics on Perceptions of Progress and Choice
Bibliographic record
Abstract
In goal–oriented services, consumers want to get transported from one well–defined state (start) to another (destination) state without much concern for intermediate states. A cost–based evaluation of such services should depend on the total cost associated with the service—i.e., the price and the amount of time taken for completion. In this paper, we demonstrate that the characteristics of the path to the final destination also influence evaluation and choice. Specifically, we show that segments of idle time and travel away from the final destination are seen as obstacles in the progress towards the destination, and hence lower the choice likelihood of the path. Further, we show that the earlier such obstacles occur during the service, the lower is the choice likelihood. We present an analytical model of consumer choice and test its predictions in a series of experiments. Our results show that in choosing between two services that cover the same displacement in the same time (i.e., identical average progress), consumer choice is driven by the perception of progress towards the goal (i.e., by virtual progress). In a final experiment, we show that the effects of virtual progress may outweigh the effects of actual average progress.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".