Enhancing Self-Administered Questionnaire Response Quality Using Code of Conduct Reminders
Bibliographic record
Abstract
A key challenge for self-administered questionnaires (SaQ) is ensuring quality responses in the absence of a marketing professional providing direct guidance on issues as they arise for respondents. While numerous approaches to improving SaQ response quality have been investigated including validity checks, interactive design, and instructional manipulation checks, these are primarily targeted at situations where expected responses are of a factual nature or stated preferences. These interventions have not been evaluated in scenarios that require higher levels of engagement and judgment from respondents. While professional marketers are guided by codes of conduct, there is no equivalent code of conduct for SaQ respondents. This is particularly salient for SaQ that require higher levels of reflection and judgment, since in the absence of professional guidance, respondents rely more on their individual ethical ideologies and experience, leaving SaQ responses potentially devoid of the standards that normally set the expectations around data quality for marketing professionals. As marketing professionals are unable to provide guidance directly in a SaQ context, the approach used in this study is to offer varying levels of professional marketing guidance indirectly through specific codes of conduct reminders that are easily consumable by SaQ participants. We demonstrate that reminders and ethical ideologies moderate the relationship between the participant’s experience with SaQ and compliance with a code of conduct. Specifically, SaQ respondents produce fewer code of conduct infractions when receiving reminders than the control group, and this improves even more when the reminders coincide with the SaQ task. The paper concludes with implications for theory and practice.
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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 | 0.169 | 0.408 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".