Mobile Marketing and Consumer Behavior Current Research Trends
Bibliographic record
Abstract
This article provides a direction for future research in Mobile Marketing and specifically Consumer Behavior by developing a research agenda based on a census of recent articles published between 2008 and 2010. 126 articles were categorized and analyzed revealing 53 articles that dealt with Consumer Behavior and whose research questions were extracted to provide an overview of future research in the field. Consumer Behavior articles were classified in sub-categories: (1) Acceptance & Adoption, whose most common articles dealt with SMS, Mobile Advertising, Mobile Shopping, Conceptual and, Technology specific articles, (2) the role of Trust, (3) Satisfaction & Loyalty, (4) Attitudes towards mobile marketing and, (5) Value Creation. Comparing our data with previous results enables us to comment on the last 10 years of mobile marketing research and conclude that (1) the quantity of mobile marketing research is generally stable, (2) it is gaining widespread interest and, (3) it is still an emerging research field thus is rich in research opportunities. Our data also indicates that recent articles have mostly omitted to exploit newer technologies such as Bluetooth, Near Field Communications (NFC) and location-based services using GPS as potential research topics. This article strongly encourages mobile marketing research in these areas.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".