Mobile Marketing: The Influence of Trust and Privacy Concerns on Consumers’ Purchase Intention
Bibliographic record
Abstract
The rapid proliferation of mobile phones along with the consumers’ acceptance and usage has created new mobile marketing opportunities to business industry. These new technologies and communication devices emerged as new ways of conducting business known as mobile marketing. Mobile marketing allows consumers to access products and services conveniently. It has become new information and an important channel on how consumers gather, and exchange information that has created a huge potential marketing opportunities for business organizations. This development also offered marketers opportunity to promote their services and attract customers’ anytime and anywhere irrespective of distance. The adoption and effective usage have been hindered by issues that bothered on consumers’ trust and privacy concerns. The study found that consumer privacy risk positively influences mobile electronic marketing negatively. The study also found that consumers’ trust and privacy risk will reduce the perception of risks on intention to use mobile marketing as mobile tools. It was further noted that lack of trust was found to be the major hindering factor on mobile marketing applications. To enhance consumers’ trust and privacy concerns about the use of mobile marketing devices, this study suggests that personalization of services will strengthen mobile marketing among consumers’. The paper is structured into three parts: introduction, literature, a conclusion which entails marketing implications and further suggestion.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".