Alexa – What’s Your Personality? The Personification Of Amazon’s Alexa Through Television Advertisements
Bibliographic record
Abstract
<p>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.</p><div><br></div>
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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 | 0.001 | 0.000 |
| 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 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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 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".