Effect of Mobile Commerce Interaction Characteristics on Game Advertising Effect and Game Re-purchase Intent
Bibliographic record
Abstract
Background/Objectives: This study is to present marketing strategies and implications for mobile game companies’ intention to buy games again.Methods/Statistical analysis: The questionnaire was distributed to then collected from 260 people who enjoyed playing games. The collected data verified the suitability of the structural equation model and the causal relationship to each concept.Findings: First, mobile game companies should try to use websites or famous websites to provide information about their own products and utilise trusted models and formulate advertisement copies, in order to build trust on their games and incidental products.Second mobile game companies will have to strive to show consistency between product advertising and the community through continuous management and providing information.Third, mobile game companies will have to restructure their homepage to focus on user-oriented design, and further care about the convenience of connecting to their homepage and the convenience of searching for information in famous sites (considering the location of banner ads) even among popular sites that game users often use.Fourth, mobile game companies will have to pay close attention to the trust and acceptance of advertising by paying attention to their promises with users, continuous website updates, swift Q&A activities, and the consistency between advertising and products.Finally, mobile game companies should spread information such as, gameplays, game item and character introduction, and create videos and programs that gamers would like to interact with in order to expose their websites and information on famous websites.Improvements/Applications: Companies that develop and sell games are analysing the characteristics of people who intend to repurchase games, which shows suitable direction of marketing strategies for sustainable management in the mobile game market.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".