Marketing implications of privacy concerns in the US and Canada
Bibliographic record
Abstract
Purpose Increasing availability of data obtained via the internet and the proliferation of direct mail advertising provides tremendous opportunities for marketers to reach their customers. However, increased risks to the personal privacy of consumers, and attention in the media to these risks, provide unique challenges. Companies and especially direct marketers are finding that they need to change their tactics to deal with the increase in consumer concerns and privacy‐protecting behaviors. This paper aims to address these issues. Design/methodology/approach Using the results of a multinational privacy survey, the paper examines consumer privacy concerns and privacy‐protecting behaviors in the USA and Canada. It uses factor analysis and multiple regression techniques to analyze the data. Findings While consumer concerns about privacy are essentially the same between the two countries, the privacy‐protecting behaviors differed significantly. The paper also suggests that demographic variables influence a consumer's level of concern and likelihood to take privacy‐protecting behaviors. Research limitations/implications The behaviors in the paper are self‐reported and therefore potentially subject to self‐desirability bias. Also, missing data limited the ability to test for the impact of income. Practical implications The paper provides recommendations for marketers to address customer concerns and behaviors such as providing greater transparency and use of privacy seals. Originality/value International companies face even greater challenges with regard to privacy issues and related customer behaviors due to cultural and governmental policy differences. This paper provides some guidelines for companies that need to provide privacy protection to customers from a variety of cultures.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".