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Record W4413735523 · doi:10.1017/pds.2025.10047

Adapting the engineering design process to develop a business model for service-oriented living labs: a case study of PISCES

2025· article· en· W4413735523 on OpenAlexaff
Anubhab Majumder, Titing Reza Fahrisa, Spyridoula Gerassimidou, Gatot Yudoko, Susan Jobling, Eleni Iacovidou, Vishal Singh, Amaresh Chakrabarti

Bibliographic record

VenueProceedings of the Design Society · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsLearning Partnership
FundersUK Research and Innovation
KeywordsProcess (computing)Process managementLiving labEngineering design processService (business)Engineering managementBusiness modelBusiness processService designEngineeringService delivery frameworkComputer scienceKnowledge managementBusinessOperations managementWork in processMarketingHuman–computer interactionMechanical engineering

Abstract

fetched live from OpenAlex

ABSTRACT: This paper focuses on the development of a viable business model for the PISCES Living Lab, which seeks to address plastic pollution in Indonesia. The overarching aim is to transition it from a project-based initiative to a self-sustaining service enterprise. The paper introduces a new modified engineering design process as a workshop template to guide an interdisciplinary team in creating a business model for a service-oriented living lab. A four-day workshop was conducted in Banyuwangi, Indonesia, involving a diverse group of stakeholders from the project, and the final outcome was the creation of a Business Model Canvas outlining the core components of the PISCES Living Lab’s business model. The findings demonstrate the effectiveness of integrating the engineering design process with business model innovation, offering a structured yet flexible approach to developing self-sustaining Living Labs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0040.003
Open science0.0030.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.048
GPT teacher head0.257
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations0
Published2025
Admission routes1
Has abstractyes

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