Data-Privacy Concerns and Its Influence on Consumer Purchasing Intention in Bangladesh and India
Bibliographic record
Abstract
The research studies the factors of customers’ awareness of their online behavior. This paper aims to highlight the gaps in customer awareness and views regarding data privacy, marketing research, and factors that impact the purchasing intentions in developing nations like India and Bangladesh. Previously it can be seen that there has been much research done in this area, but mostly those were for developed nations. However, in developing nations, like Bangladesh and India, which are outstanding and growing markets for online marketers, the concept of data privacy is not observed meticulously. Descriptive, relational, and quantitative research methodologies were applied for the study. Inferential statistics via SPSS were used to accomplish the purpose of the research. The target population was infinite, and the sampling frame was focused on Bangladesh and India’s internet users. 440 respondents were approached, and the received response rate was close to 81%, 354 responses. Unlike this study, commonly explorative and qualitative research methodologies were considered for similar research before. The research outcomes demonstrate that consumers in Bangladesh and India are mindful of data privacy, and the result of this research supports the current literature. The outcomes showed a significant positive relationship between the consumers’ view of how online platforms of companies handle their data with their buying intentions. Nevertheless, in these recommended relationships from the framework, customer commitment’s moderating influence plays a significant role in forming buying intention. The research is particularly important for marketing professionals to understand customers’ expectations from the companies regarding customize marketing. This research envisioned to cause some influence the literature and executive implications with development of a new research framework, adjusting new variables, and hypotheses, which were solely based on keeping in mind, the customers of Bangladesh and India.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".