Designing robust electronic surveys in marketing research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While electronic surveys are popular methods among marketing researchers, limited work surrounds how they can be effectively developed. Consequently, this article provides editorial guidance on designing robust electronic surveys to help marketing academics and graduate students to overcome notable pitfalls. Several best practices are outlined, commencing with initial issues, like formatting and interactivity. Then, some factors linked to measures and robustness checks are evaluated, including capturing instruments to test for common method variance and endogeneity bias, plus accounting for reliability and validity. Afterwards, key methodological benefits of pre-testing, conducting field interviews, pre-registration activities, and underpinning electronic surveys with appropriate theoretical lenses are discussed. Next, the paper features some final considerations, such as selecting suitable empirical contexts and respondents, adhering to ethical procedures, and managing expenses. This article ends with various summary points, alongside a checklist to minimize poor-quality survey data being collected and analyzed, facilitating advancements to marketing theory and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.127 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it