Value co-destruction: a typology of resource misintegration manifestations
Bibliographic record
Abstract
Purpose Actors who participate in co-created service experiences typically assume that they will experience improved well-being. However, a growing body of literature demonstrates that the reverse is also likely to be true, with one or more actors experiencing value co-destruction (VCD), rather than value co-creation, in the service system. Building on the notion of resource misintegration as a trigger of the VCD process, this paper offers a typology of resource misintegration manifestations and to present a dynamic conceptualization of the VCD process. Design/methodology/approach A systematic, iterative VCD literature review was conducted with a priori aims to uncover the manifestations of resource misintegration and illustrate its connection to VCD for an actor or actors. Findings Ten distinct manifestations of resource misintegration are identified that provide evidence or an early warning sign of the potential for negative well-being for one or more actors in the service system. Furthermore, a dynamic framework illustrates how an affected actor uses proactive and reactive coping and support resources to prevent VCD or restore well-being. Originality/value The study presents a typology of manifestations of resource misintegration that signal or warn of the potential for VCD, thus providing an opportunity to prevent or curtail the VCD process.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".