Rethinking functionality and emotions in the service consumption process: the case of funeral services
Bibliographic record
Abstract
Purpose The purpose of this paper is to contribute to the service literature by investigating post-consumption evaluation in the context of unwanted services. In particular, it intends to delineate the main characteristics of funeral services. Design/methodology/approach Given the lack of substantive literature on funeral services, a qualitative exploratory design was used from in-depth interviews with ten managers of funeral services companies in Quebec (Canada). Findings The study shows that compared to other traditional services, funeral services are characterized by their strong emotiveness, non-recurrence, irreversibility, uncommonness, high level of symbolism and personalization and emotion control of the service provider. The study also argues that funeral services quality is strongly dependent on funeral houses’ integrated logistics, proximity and integrity. Practical implications Because of consumers’ lack of competency, funeral companies need to guide and educate consumers about the criteria they should use to evaluate the service quality. Because funeral consumers are strongly emotion-driven at the purchase time, funeral services providers should find the right balance of emotions to express. Thus, more staff training is needed. Originality/value Because funeral services are emotionally challenging and deal with grief and distressed clients, the present study contributes in shedding light on service quality assessment in the funeral industry. Although they have some characteristics of traditional services (intangibility, perishability and variability), funeral services are also different in many ways.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".