INVESTIGATING A MODERATED MEDIATION MODEL OF THE IMPACT OF PERSONALIZED MESSAGE APPEAL AND PRIVACY THREAT EXTENT ON CONSUMER BEHAVIOUR
Bibliographic record
Abstract
As electronic commerce is prevailing progressively, more personal data of customers is getting \nshared with businesses since this information is an indispensable resource for effective \npersonalized advertising in ecommerce context. However, in the event of an information leakage, \nthis win-win strategy would be subject to change and demands precise employment of advertising \nelements that does not escalate privacy distress among consumers. More specifically, building on \nConservation of Resources theory, this study proposes three dimensions for privacy threats and by \nconducting three empirical experiments demonstrates that although rational and emotional \nmessage appeals have similar impacts in high-personalized advertising messages, they play \ndifferent roles when various types of a privacy threat are announced to customers. Results prove \nthat customers’ psychological comfort mediates the relationship between high-personalized \nadvertising and consumer’s response to the advertising when privacy threat is high. Additionally, \nwhen the perceived severity and distance of the announced privacy threat are high and low \nrespectively, high-personalized rational advertising message would lead to more psychological \ncomfort, while this holds true for emotional appeal when the perceived scope of the threat is high. \nThis study contributes to the literature on customers privacy by providing a better understanding \nof the privacy threat construct and introduces and empirically examines a new boundary condition \nin which the influence of message appeals in high-personalized advertising may differ across each \ndimension of privacy threat.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".