Investigating Privacy Decision-Making Processes Among Nigerian Men and Women
Bibliographic record
Abstract
The privacy calculus framework and trust heuristics has been used to understand people’s privacy decision-making processes. However, most existing studies are mainly focused on people from developed countries. In this study, we use the privacy calculus in combination with trust heuristics to analyse how people from a developing African nation make decisions. Specifically, we conduct a web-based experiment in which 232 participants from Nigeria used a financial planning prototype app to respond to a number of disclosure questions. We examined how their perceived benefit, perceived sensitivity, and trust in the app influenced their disclosure decisions. In addition, we investigated possible moderating effects of gender and used Partial Least Squares path modelling to analyze our data. Our results show that perceived sensitivity (risks) and perceived benefits influenced the decision-making process of our participants. In addition, women were more likely to change their perception of sensitivity and benefits based on trust, while men were more likely to disclose information based on their perception of benefits. We also found that women were less likely to disclose their information to the app than men. Based on our findings, we make recommendations for educators, financial institutions, designers, and policymakers that aim to raise privacy awareness and design interventions in Nigeria and Africa at large.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".