MétaCan
Menu
Back to cohort
Record W2317575890 · doi:10.1504/ijpmb.2016.075600

A visual process model for improved technology-based service design

2016· article· en· W2317575890 on OpenAlexaff
Jennifer Percival

Bibliographic record

VenueInternational Journal of Process Management and Benchmarking · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsComputer scienceProcess managementBlueprintBusiness Process Model and NotationProcess (computing)Business process modelingService (business)Business processConceptual modelProcess modelingKnowledge managementBusiness modelCorrectnessSoftware engineeringEngineeringWork in processBusinessOperations managementDatabase

Abstract

fetched live from OpenAlex

This paper presents a new method for business process modelling in the services sector that integrates the client focus of service blueprints with the operational analytics of lean value stream mapping. The approach incorporates a simple and innovative use of colour to improve the correctness of the models. The conceptual model is analysed using the guidelines of business process modelling and benefits are demonstrated through an illustrative example. The proposed approach provides a mechanism to facilitate the effective design and implementation of technology-based innovations in service delivery by enabling a diverse set of stakeholders to participate in the modelling. This modelling approach is based on best practices from existing business process modelling notations and was developed to support change management and innovation in services. The approach results in intuitive models that can be used by novice modellers for clear communication and technology-infused service design.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.002

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.

Opus teacher head0.026
GPT teacher head0.289
Teacher spread0.263 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Process Management and BenchmarkingSame topicService and Product InnovationFrench-language works237,207