Empathy in action: how product set granularity and streamer type can shape consumer purchase intentions through cognitive and emotional pathways
Bibliographic record
Abstract
Purpose This study investigates how product set granularity (fine vs coarse) and streamer type (enterprise vs celebrity) shape consumer purchase intentions through the cognitive and emotional empathy pathways, moderated by consumer mindset (local vs holistic processing). Design/methodology/approach This study employed a pre-test to examine consumers’ perception of product set granularity and streamer type. It also conducted three scenario experiments (n = 818) to examine the interaction effects of product set granularity and streamer type on purchase intentions. Mediation and moderation analyses were conducted to explore the roles of cognitive empathy, emotional empathy and consumer mindset. Findings Enterprise (celebrity) streamers enhance purchase intentions for fine-grained (coarse-grained) product sets by stimulating cognitive (emotional) empathy. Local (holistic) processing mindsets amplify emotional (cognitive) empathy for celebrity (enterprise) streamers. Research limitations/implications The study is limited to Chinese consumers and specific product categories. Future research could explore cross-cultural contexts and additional streamer types. Practical implications Businesses should align product set granularity with the streamer type: enterprise streamers for fine-grained products and celebrity streamers for coarse-grained products. Tailoring strategies to consumer mindsets can further optimize marketing effectiveness. Originality/value Drawing on the empathy perspective, this study confirms the existence of an interaction between the granularity of product set granularity and streamer type. This correlation affects consumers’ purchase intentions through cognitive and emotional empathy. This finding fills the research gap in the field of e-commerce live streaming recommendation strategies. It also highlights the importance of consumer mindset and provides new insights for related fields.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".