The Process of Entering Flow and the Outcomes of Flow in Product Trials
Bibliographic record
Abstract
Flow is a psychological state that is considered to be an ‘optimal experience.’ Given its power in eliciting positive experiences, flow has been suggested to be an important topic for future research. However, the literature lacks a nuanced understanding of flow and it has yet to expand beyond the online context in consumer behavior research. This dissertation addresses these related problems through two essays. The first essay addresses the underlying problem by helping understand the process of entering flow. I demonstrate how the two component parts of flow- fluency and absorption, combine to elicit flow. Across three studies I demonstrate that fluency-related aspects of an experience facilitate the absorption-related experiences, which mediate perceptions of being in flow overall. In Study 1 I demonstrate that the perceived fluency of listening to a song increases absorption which mediates perceptions of being in flow. In Study 2 I replicate the flow process model in the context of reading. Study 3 is dedicated to shutting down the relationship between fluency and absorption. I shut down the relationship between fluency and absorption by having people work on an easy Sudoku puzzle. The second essay builds from the findings of the first to facilitate flow in product trials and demonstrate the positive consequences it has for product attitudes and the desire to engage with the products again. I use three studies to achieve these goals. In Study 1 I demonstrate that flow experienced in the trial of a rowing machine mediates the desire to engage with the rowing machine again. In Study 2, I demonstrate that manipulating curiosity before the trial of an augmented reality game facilitates flow while playing the game. In Study 2 I also demonstrate that flow mediates an increase in attitudes towards the game and the desire to play the game again. In Study 3 I demonstrate that the relationship between curiosity and flow is moderated by the valence of information that elicits curiosity. Again, flow mediated the desire to listen to the song again in the future.
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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 | 0.009 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".