Effects of streamer, context and product on consumers’ purchase and continuous watching intentions in livestreaming commerce
Bibliographic record
Abstract
Purpose This study examines consumer behavior in livestreaming shopping. It investigates how streamer characteristics, contextual elements and product attributes affect consumers’ feelings of relatedness, competence and autonomy, and subsequently, their purchase and continuous watching intentions. Design/methodology/approach This study draws on the stimuli-organism-response model and self-determination theory. An online survey was conducted with Amazon Live users. PLS-SEM was used to test the research model and hypotheses. Findings The findings indicate that streamers’ perceived interactivity, similarity and expertise substantially enhance these psychological constructs. Contextual factors – visual complexity, time pressure and perceived social herding – also significantly influence such psychological constructs. Additionally, price attractiveness and product diversity are critical in influencing consumers’ sense of relatedness and autonomy. These psychological constructs are central to consumer behavior, impacting both purchase and continuous watching intentions. Originality/value This study reveals relatedness, competence and autonomy as drivers of consumer behavior in livestreaming shopping, suggesting that the fulfillment of these psychological motivations can lead to higher continuous watching and purchase intentions.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".