Dying to understand how historical trends and influential intermediaries impact the future of sustainable deathcare
Bibliographic record
Abstract
Purpose Consumers often prefer sustainable goods and services but fail to follow through with purchases that reflect these espoused values. The green intention–outcome gap is studied in many contexts but has yet to inform deathcare decisions. Industry reports suggest that most Americans prefer sustainable deathcare options, yet unsustainable corpse dispositions dominate the market. The purpose of this paper is to understand how history informs this phenonea. Design/methodology/approach This study looks to the past – using historical narrative analysis of deathcare trends and influential intermediaries – to understand the future of sustainable deathcare and the prospective role that marketers can play in bridging the gap between decedents’ preferences and survivors’ purchase outcomes. Findings Historical ritualization, medicalization and commercialization have resulted in the monopolization of traditional deathcare services. Mortuary professionals remain unresponsive to consumer preferences for sustainable alternatives. Social implications Socioeconomic shocks can allow humanity to reflect and transition from consumerism to sustainability. COVID-19 has led to greater awareness of self-mortality, and death has become less taboo. The slow market penetration of sustainable deathcare services suggests a lack of communication between a decedent and their survivors. Marketing scholars need to help marketing practitioners bridge the preference-outcome gap. Originality/value To the best of the authors’ knowledge, this study is amongst the first to examine how history informs the sustainable action–outcome gap for deathcare preferences in a post-COVID environment and the role that marketers can play in perpetuating change.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".