Experimental comparison of Web, electronic and mail survey technologies in operations management
Bibliographic record
Abstract
Abstract With the growing acceptance of the Web (Internet) and electronic mail, it is no surprise that researchers are using an increasingly diverse set of survey technologies to gather data from managers. However, the effectiveness of these electronic technologies has not been rigorously assessed, especially for gathering data from establishment‐level surveys (i.e. firm‐ or plant‐level). To that end, a stratified sample of large and small, service and manufacturing firms was constructed, followed by random assignment to one of four survey technologies: mail, fax, PC disk‐by‐mail and Web‐page survey (combined with e‐mail notification). For each treatment, managers are queried about their use of forecasting characteristics, yielding a sample of 118 firms. Unfortunately, only a low percentage (34%) of firms and managers assigned to the Web technology treatment both reported access to e‐mail and were willing provide their e‐mail addresses; they tended to be large firms and from the service sector. Moreover, those that did offer e‐mail addresses were only about half as likely to respond to the Web‐based survey as those targeted by other survey technologies. However, Web, fax and disk‐by‐mail technologies yielded higher item completion rates than mail. Limited statistical evidence indicated that respondents using computer‐based survey technologies (i.e. Web or disk‐by‐mail) generally reported forecasting characteristics that are associated with firms exhibiting best practices. Thus, a multi‐technology survey approach using the Web and fax can yield a strong combination of benefits over a traditional mail survey.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".