Bibliographic record
Abstract
Guest editorial special sectionemotions in service interactionsConsumption emotions are the set of positive and negative emotional reactions felt by consumers during product usage and service consumption (Westbrook and Oliver, 1991).The role of emotions has gained the interest of both academics and practitioners as a critical element in understanding consumer behavior and consumption experience (Bagozzi et al., 1999;Han and Jeong, 2013;Mattila and Enz, 2002).Emotions influence information processing, attitudes, preferences, satisfaction and dissatisfaction, complaining behavior, and loyalty (e.g.Bagozzi et al., 1999;Bigné et al., 2008;Han and Jeong, 2013;Joireman et al., 2013).The recognition of the role of emotions has increased within the development of the experiential approach, emphasizing that consumers are rational and emotional human beings motivated by the pursuit of fun and feelings.Past research focussed mainly on the cognitive component of service experience, largely supporting the relationships between service quality, satisfaction, perceived value, and service users' behavioral intentions.However, many services researchers reported that cognitive models are limited in their ability to explain service encounter assessment (Brunner-Sperdin et al., 2012;Dong and Siu, 2013).They suggest that service experience evaluation is both cognitive and emotional (Edvardsson, 2005;Han and Jeong, 2013).Favorable emotional reactions to service experience enhance service evaluation, loyalty, and recommendation, whereas negative emotional reactions increase dissatisfaction, negative word-of-mouth, complaining behavior, and exit.In some service settings (e.g.river rafting, movies, etc.), the relationships may not be valence-congruent.Despite their valuable role in modeling consumer attitudes and behaviors in service settings, the focus on customers' emotional reactions to service encounters is currently lacking (Brunner-Sperdin et al. 2012;Ladhari, 2009).This special section intends to provide a few insights.In the first paper, Carla Ruiz-Mafé et al. (2016) study the influences of individual and social antecedents of emotions as well as the impact of emotions on attitude and loyalty toward online travel communities.They found that perceived privacy and security risk elicit negative emotions such as stress, frustration, and fear toward the online travel community.Normative influences and feeling the presence of other community members boost positive emotions toward the online travel community.Positive and negative emotions influence attitudes toward the online travel community.Subjective norm and attitudes influence loyalty toward an online travel community.The study results confirm previous research grounded in social impact theory and theory of reasoned action.In the second paper, Lorena Blasco-Arcas et al. (2016) examine the role of emotions in developing customer engagement and brand image during virtual service interactions.This study confirms that during interactions in online platform customer engagement with the firm influences brand image.Indeed, based on the P-A-D model, the study reports that pleasure and arousal experienced by customers influence their engagement with the firm while dominance influences brand image.Finally, customer engagement and brand image have a positive effect on purchase behavior.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.040 | 0.030 |
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".