Bibliographic record
Abstract
Purpose The literature to-date has focused on dimensions of emotions based on emotions’ affective state (captured by valence, arousal and dominance, PAD). However, it has ignored that emotional reactions also depend on emotions’ functionality in serving to solve recurrent adaptive problems related to survival and reproduction. Evolutionary psychology suggests that relationships with others are the key that helps individuals reach both goals. The purpose of this paper is to conceptualize, measure and validate the temperature dimension of emotions that underlies such human relationships, as suggested by frequent verbalization of emotional states via temperature-related terms (“cold fear” and “warm love”). Design/methodology/approach Across three studies (nStudy1a = 71; nStudy1b = 33; and nStudy2 = 317) based on samples from two countries (Germany and the USA) and using two different methods (semantic and visual), the temperature dimension of emotions is conceptualized and measured. Across a wide spectrum of emotions, factor analyses uncover temperature as an emotional dimension distinct from PAD and assess the dimension’s face, discriminant, convergent, nomological and criterion validity. Findings Emotional temperature is a bipolar dimension of an affective state that underlies human relationships, ranging from cold to warm, such that social closeness is linked to emotional warmth and social distance to emotional coldness. Emotional temperature is uncovered as a dimension distinct from PAD, that is, it is correlated with but separate from PAD. Research limitations/implications In this research, a portfolio of 17 basic emotions relevant in everyday consumption contexts was examined. Future research could further refine the emotional temperature dimension by analyzing more complex emotions and their position on the temperature map. In general, this paper sets the stage for additional work examining emotional temperature and its effects on consumer behavior. Practical implications The results have strategic implications for marketers on which emotions to select for campaigns, depending on factors like the climate or season. Social implications This research provides a better foundation upon which to understand the effect of emotions that invoke warmth or coldness. Originality/value To the best of the authors’ knowledge, this research is the first to conceptualize, measure and comprehensively validate the temperature dimension of emotions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".