Rolling in Fun, Paying the Price: A Thematic Analysis on Purchase and Play in Tabletop Games
Bibliographic record
Abstract
The tabletop games industry has experienced remarkable growth in recent years. A deeper exploration of the factors motivating players to invest time and money in these games would help game companies better cater to their target audience and increase profits. We conducted a reflexive thematic analysis of 20 semi-structured interviews with tabletop game players. Our analysis revealed five themes concerning purchasing decision influences: (1) childhood past experiences and cultural norms, (2) representation and inclusivity, (3) social connections and shared fun, (4) overcoming gameplay hindrances with digital assistance, and (5) economic constraints. These findings suggest that game companies should focus on presenting easily understandable information, minimizing idle time, and exploring opportunities for inclusivity in digital interactions to effectively engage players and drive sales. Based on our valuable insights into player motivations, we offer actionable recommendations for the tabletop games industry.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".