How emotions influence anthropomorphism
Bibliographic record
Abstract
The purpose of this dissertation is to explore the relations between emotion, consumers anthropomorphism, and related consequences. Current literature examines this relationship by the perspective that anthropomorphic brand designs can elicit certain emotions and increase brand evaluations subsequently (Aggarwal and McGill 2007; Aggarwal and McGill 2012; Kim et al. 2016; Yuan and Dennis 2019). Minimal amount of research investigates this relationship from the direction that emotion can induce anthropomorphism. To fill this void, I examine the effect of emotional valence and arousal separately in this dissertation. It is valuable to scrutinize the effect of these two dimensions respectively (Di Muro and Murray 2012) since they are independent from each other. In fact, the results across six studies revealed that valence and arousal did not influence consumers' anthropomorphism in the same way. While the initial proposal was based on the suggestion that emotional arousal would influence anthropomorphism, my conclusion based on the studies reported herein is that both emotional arousal and emotional valence play a significant role. Positively valenced emotions tend to have significant effects in all three studies. Emotional arousal seems more complicated. I never observed a significant affect from the manipulation of emotional arousal to the dependent variables, however, measured felt arousal appears to be positively and significantly related to anthropomorphism. While I cannot claim that emotions drive anthropomorphism to the exclusion of cognitive operations, it is clear that emotions can play an important role in anthropomorphism. Additionally, study 3a and 3b suggest that compared with the brand with low preexisting anthropomorphism, likability to the brand with high preexisting anthropomorphism stays in a relatively high level regardless of consumers' emotion. Hence, I suggest that high preexisting anthropomorphism can be a buffer for a brand.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".