Social media marketing: Who is watching the watchers?
Bibliographic record
Abstract
The ready access to and availability of social media has opened up a wealth of data that marketers are leveraging for strategic insight and digital marketing. Yet there is a lack of professional norms regarding the use of social media in marketing and a gap in understanding consumers’ comfort with marketers’ use of their social media data. This study analyzes a census-balanced sample of online adults (n = 751) to identify consumers’ perceptions of using social media data for marketing purposes. The research finds that consumers’ perceived risks and benefits of using social media have a relationship with their comfort with marketers using their publicly available social media data. The research extends the applicability of communication privacy management theory to social media and introduces marketing comfort —a new construct of high importance for future marketing research. Marketing comfort refers to an individual's comfort with the use of information posted publicly on social media for targeted advertising, customer relations, and opinion mining. In the context of the construct development, we find that targeted advertising is the strongest contributing component to marketing comfort, relative to the other two dimensions: opinion mining and customer relations. By understanding what drives consumer comfort with this emerging marketing practice, the research proposes strategies for marketers that can support and mitigate consumers’ concerns so that consumers can maintain trust in marketers’ digital practices.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".