How Is Flow Induced? From the Perspective of Online and Offline Channels
Bibliographic record
Abstract
The flourish of internet technology has contributed to the trend of online shopping but also threatened the operation of brick-and-mortar channels. This study investigated how bookstores can use experiential value to influence the repurchase behavior of consumers from the perspective of experiential value. The significant findings of this study are as follows. 1). When channel type is not considered, the experiential values playfulness, escapism, and educational experiences are the most crucial elements for the achievement of flow. 2). Playfulness, educational experiences, and customer return on investment are relatively more important for flow inducement in online channels, while the hedonic experiential values playfulness, escapism, and aesthetics are more prominent in offline channels. This shows that playfulness is an extremely crucial strategic experiential value for bookstores. 3). The analysis of moderating effect revealed that aesthetics and service excellence can give brick-and-mortar channels a unique advantage under the threat of online channels. This study included brick-and-mortar channels, unlike past studies that focused on online channels. We believe that the findings, managerial implications, and suggestions in this study are particularly meaningful and valuable for brick-and-mortar stores, which are in gradual decline.
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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 | 0.002 | 0.007 |
| 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 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".