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Record W3194443576 · doi:10.1504/jdr.2021.10040621

Using the creativity support index to evaluate a product-service system design toolkit

2021· article· en· W3194443576 on OpenAlexaff
Celine Latulipe, Ivo Dewit, Francis Dams, Alexis Jacoby

Bibliographic record

VenueJ of Design Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCreativityProduct-service systemComputer scienceService (business)Product (mathematics)Field (mathematics)New product developmentDesign educationIterative designIndex (typography)Product designProcess (computing)Systems engineeringService designProcess managementEngineering design processValue (mathematics)Engineering managementEngineeringService delivery frameworkOperations managementWorld Wide WebBusinessPsychology

Abstract

fetched live from OpenAlex

The design of product-service systems is one of the more recent evolutions in the field of design and innovation. The approach for designing products and services in an integrated way holds the opportunity for developing more value for the user and the entire value chain. Despite the existence of various PSS design tools and methods to optimise this creative development process, it remains unclear to what extent the full array of tools supports the design team in their creative work. In this paper, we present the results of four years of iterative evaluation of a PSS Design Toolkit deployed in a graduate education setting, using the creativity support index (CSI), a psychometrically-validated instrument. By using the CSI longitudinally, the results enabled us to iteratively improve the PSS Design Toolkit to better support future generation designers for the challenges that come with designing these product-service systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.385
GPT teacher head0.415
Teacher spread0.030 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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