Comparing Conventional versus Immersive Service Prototypes: An Empirical Study
Bibliographic record
Abstract
For years, Immersive Technologies and 3D printing, demonstrated their capacity to quickly build product prototypes in order to reach a common understanding among all stakeholders, especially potential users. Service prototyping is a novel agile process intended to accelerate the service development, while improving the overall anticipated service experience. The use of Immersive Technologies in service prototyping is intended to enable a co-creative and explorative service experience, even before the service really exists. Service prototyping transforms intangible processes into a real experience. Immersive Technologies are already deployed in several industrial applications ranging from product design to product and service exploration. They are also used for conducting training even before the product or service exists. The main concern remains in the fact that there is a lack of study for comparing and selecting the most appropriate form of Service Prototypes (SP) to explore a new service. This paper presents our empirical study comparing different SP forms and the results of two experiment sessions that were conducted at ENSAM Laval and Angers campuses. These results reveal that participants preferred immersive forms rather than conventional forms. However, it also unveils some difficulties in properly handling Immersive Technologies.
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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 | 0.027 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".