The Impact of Marketing Strategy on Consumers' Purchasing Decisions in the Computer Gaming Aspect
Bibliographic record
Abstract
Video game marketing strategies, particularly concerning social media and E-sports culture, have become a focal point of contemporary research. Researchers have highlighted the potential and progress in utilizing these digital platforms to engage consumers and enhance game popularity. However, there remains a significant gap in understanding the specific mechanics of how such strategies lead to commercial success and the extent to which they influence consumer behaviour. This study delves into the multifaceted dynamics shaping consumer behaviour in digital gaming. Drawing from the Cognitive Evaluation Theory, it explores how intrinsic motivations like autonomy and engagement drive consumer behaviour. It also examines innovative in-game purchase strategies, notably the Battle Pass and loot boxes, and their profound influence on player spending. Social media is scrutinized as a critical marketing tool, with user and expert reviews shaping purchase decisions. The importance of vibrant gaming communities and E-sports culture is highlighted, underlining their contribution to a game's cultural significance. Lastly, the study considers brand building in E-sports, shedding light on how renowned brands foster emotional bonds with consumers, ultimately securing loyalty and enhancing their market position. The goal is to provide industry players with a holistic understanding of the evolving digital gaming industry, aiding them in devising effective strategies to navigate this complex terrain.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".