An empirical study of the anticipated consumer response to RFID product item tagging
Bibliographic record
Abstract
Purpose This empirical study of consumer/shopper response to radio frequency identification (RFID) product item tagging anticipates what is likely to take place in the retail marketplace. Using the theories of procedural justice/fairness, expected utility, and prior literature on personal privacy the purpose of this study is to use the survey method to measure consumer willingness to purchase RFID‐tagged product items within the Canadian context. Procedural justice/fairness is operationalized using the implementation of the Personal Information Protection and Electronic Documents Act (PIPEDA) enacted in Canada on January 1, 2004. Design/methodology/approach This study used the survey questionnaire method after the sample participants (N=381) were exposed to an experimental treatment. Students and faculty members of the Faculty of Business Administration, University of New Brunswick Fredericton, Canada participated in this study. Findings Consumers responded positively to the procedural justice concept using PIPEDA law in Canada. The less privacy sensitive group valued the specific RFID benefits, was willing to buy the tagged items to obtain specific benefits, was willing to pay more for these items, and was also less concerned about selected RFID issues. Practical implications Practical suggestions are given to retailers thinking of implementing product item RFID tagging to make their initiatives more successful. Originality/value This is one of the first empirical studies on the likely consumer response to product item tagging based on solid theoretical foundations.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".