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Record W2098512717 · doi:10.1177/0018726712451762

The problem-solving service worker: Appraisal mechanisms and positive affective experiences during customer interactions

2012· article· en· W2098512717 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHuman Relations · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsCarleton University
Fundersnot available
KeywordsHappinessCognitive appraisalPsychologyAppraisal theoryVariety (cybernetics)CognitionSocial psychologyCustomer serviceService (business)Thematic analysisNegative emotionEvent (particle physics)Qualitative researchMarketingBusinessComputer science

Abstract

fetched live from OpenAlex

Affective Events Theory suggests customer interactions elicit event appraisals that, in turn, prompt affective reactions in employees. A qualitative diary study was used to examine the daily events and cognitive appraisals that elicit positive emotions during customer service interactions. Thematic analysis of the diary contents of 276 sales employees from a variety of industries (874 positive events) showed helping customers solve their problem was the event most likely to trigger positive emotions. The data and resulting model revealed that particular configurations of employees’ appraisals predicted particular emotion(s). Within-person differences in cognitive appraisals also helped explain why some initially negative events may ultimately become a positive experience. Emotional contagion was found, where the positive emotions of the sales employees, or those of the customer, influenced the emotion of the other. The implications of the study for employees’ happiness and well-being, and for enhanced customer service relations, are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.352
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it