Marketing Tactics Underlying Scenes: Viewers’ Purchase Motivations in Li Jiaqi’s Live Streaming
Bibliographic record
Abstract
Based on Joshua Meyrowitz’s media situation theory and Halliday’s functional grammar, this paper takes Li Jiaqi’s live streaming of “55 cost-effective festivals” on May 5, 2022, as its research object to explore the scene’s construction tactics and anchor’s influence on scenes construction. By doing this, it intends to reveal the hidden marketing tactics in live streaming for the sake of helping viewers to be more rational in the process of watching live streaming and providing certain reference values for those who want to enter this industry. This paper aims to address the following two questions: 1) What types of scenes are constructed in the process of live streaming? 2) How the anchor influences the scene construction? The results show that 1) four scene types are found in Li Jiaqi’s live streaming, namely, virtual online consumption scene; artificially presupposed usage scene; real-time interaction scene, and elaborate business scene in which relevant marketing tactics are used wisely; 2) the anchor relies on his personal style, personas, and narrative methods to affect scene construction. Through the analysis of these scenes and the anchor’s influence, this paper finds that by right of grasping viewers’ attention and enhancing their trust and narrowing the distance between each other, Li Jiaqi successfully influences viewers’ purchase motivations, then a sales purpose is achieved.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".