Bibliographic record
Abstract
Purpose Smartphone adoption by consumers is increasing exponentially, and presents marketers with many new opportunites to reach and serve customers. However, are consumers ready for mobile marketing through their smartphones? This study aims to investigate consumers' willingness to accept marketing through their smartphones. Design/methodology/approach The study is based on an online survey of 428 respondents. The data is analyzed through ANOVA and regression analysis. Findings The results indicate that consumers' shopping style, brand trust, and value are key motivations for engaging in mobile marketing through their smartphones. Further research should focus on specific tactics marketers use to engage customers beyond marketing messages, that is, how they engage customers in dialogue to build relationships, encourage purchases and build loyalty. This could reveal how customers really want to engage in mobile marketing. Research limitations/implications This research adds to the growing body of evidence on acceptance of mobile marketing. Practical implications This study found that successful enagement of customers in mobile marketing requires that marketers focus their strategies and tactics around value creation; getting customers to engage with their brand in an authentic way; and respecting customers' shopping style, i.e. engaging customers the way they want to be engaged. Marketers must listen to their customers and develop appropriate strategies rather than simply adapting existing marketing strategies. Originality/value The topic of mobile marketing through smartphones is important to both marketing executives and marketing researchers. To date, this topic has attracted little research attention and marketing executives are simply basing their decsions on anecdotal case studies and reports in the popular press. This study contributes to fulfilling the need for research evidence.
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 | 0.001 | 0.003 |
| 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 | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".