Alexa – What’s Your Personality? The Personification Of Amazon’s Alexa Through Television Advertisements
Bibliographic record
Abstract
Brand personification has been widely used in marketing strategies for decades, and many research studies have confirmed its efficacy in shaping consumers’ brand attitudes and behaviours. The aim of this research paper is to explore how voice-activated virtual assistants are personified in commercial advertisements. Previous research has investigated why artificial intelligence-powered devices are personified; however fewer scholars have explored how these devices are anthropomorphized in commercial advertisements. Considering that advertisements are a “contribution to the complex symbol which is the brand image” (Ogilvy, 1951, p. 178), it is useful to study how brands advertise their products with the goal of influencing consumers’ positive perceptions of the brand. This paper analyzes four Super Bowl commercials for Amazon’s virtual agent, Alexa. I consider how language, characters, voice, and other visual elements contribute to the personification of Alexa and attempt to deduce the implication of this advertisement strategy for consumer brands. Considering that voice activated virtual assistants are a rapidly growing consumer technology, this study expands the extant knowledge on how these agents are anthropomorphized, and what this means for the consumer-brand relationship.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".