Social tie strength and virtual goods purchase decisions of online game players
Bibliographic record
Abstract
This paper investigates how social ties among online game players can influence their in-game purchase of virtual goods that are representative of the personalities and status of the players. Using the data provided by the operator of an online game, this paper finds that a player is more likely to purchase such a product if more of her peers (with either a strong tie or a weak tie) possess that same or similar product, with the strong-tie peers having a bigger influence on the purchase than that of the weak-tie ones. This paper also finds that, due to the product-sharing activities among strong-tie friends, a player is less likely to purchase a more expensive virtual good if more of his strong-tie friends already own a similar one. In terms of methodology, this paper uses generalised linear mixed models to identify strong and weak ties, and models customer purchase behaviours. This paper contributes to the literature of social connections and purchase decisions, and offers managerial implications on how to utilise social media that builds on the different strengths of social ties to promote consumer purchase.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".