Cultural Differences in User Privacy Behavior on Social Networking Sites : An Empirical Study comparing German and Swedish Facebook Users
Bibliographic record
Abstract
Social Networking Sites (SNSs), such as Facebook, are becoming increasingly popular. Their worldwide accessibility is attracting billions of SNS users from all over the globe, which results in a variety of different cultures meeting on the respective platforms. Apart from their growing popularity, privacy issues represent a downside of SNSs attracting strong media and research attention. Considering SNS users’ cultural diversity, recent studies show that a culture influences the privacy concerns of its respective members. Only little research investigating cultural differences in User Privacy Behavior (UPB) on SNSs has been carried out so far. Furthermore, the majority of existing studies include the United States (US) when comparing different cultures. Taking the fact into account that, for instance, more than three-fourths of all Facebook users are outside the US and Canada, this study builds on the current state of research by comparing the UPB of Ger-man and Swedish Facebook users based on their different cultural characterization. In this context, two of Hofstede’s (1980) cultural dimensions, Masculinity and Uncertainty Avoidance, serve as a foundation for the creation of hypotheses and the development of the research model. The chosen research approach had a quantitative design and was conducted as a web-survey. The sample consisted of 791 valid respondents (667 German college students and 124 Swedish college students). The findings reveal several significant differences between German and Swedish SNS users. According to that, Swedes, as mem-bers of a low masculinity culture, have more SNS friends and a greater amount of strangers within these friends, compared to Germans, as members of a culture character-ized by a higher masculinity. As a result of their comparatively low uncertainty avoidance, Swedish SNS users have more trust in both SNS providers and other SNS members than Germans, characterized by a higher uncertainty avoidance. On the other hand, Germans reveal higher privacy concerns and disclose themselves to a lower extent than Swedes. Besides, Swedish SNS users perceive the provided control over personal information on SNSs as sufficient, while Germans indicate a need for more control. Moreover, Germans apply the provided control settings to a greater extent than Swedes. The results provide relevant insights for SNS providers and help them in adapting their services in consider-ation of the respective cultural attitudes in terms of UPB.
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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 | 0.001 |
| 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 | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".