Design fiction diegetic prototyping: a research framework for visualizing service innovations
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a design fiction diegetic prototyping methodology and research framework for investigating service innovations that reflect future uses of new and emerging technologies. Design/methodology/approach Drawing on speculative fiction, the authors propose a methodology that positions service innovations within a six-stage research development framework. The authors begin by reviewing and critiquing designerly approaches that have traditionally been associated with service innovations and futures literature. In presenting their framework, authors provide an example of its application to the Internet of Things (IoT), illustrating the central tenets proposed and key issues identified. Findings The research framework advances a methodology for visualizing future experiential service innovations, considering how realism may be integrated into a designerly approach. Research limitations/implications Design fiction diegetic prototyping enables researchers to express a range of “what if” or “what can it be” research questions within service innovation contexts. However, the process encompasses degrees of subjectivity and relies on knowledge, judgment and projection. Practical implications The paper presents an approach to devising future service scenarios incorporating new and emergent technologies in service contexts. The proposed framework may be used as part of a range of research designs, including qualitative, quantitative and mixed method investigations. Originality/value Operationalizing an approach that generates and visualizes service futures from an experiential perspective contributes to the advancement of techniques that enables the exploration of new possibilities for service innovation research.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".