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Record W1970464914 · doi:10.1002/hfm.20517

Characterizing organizations as service systems

2012· article· en· W1970464914 on OpenAlexaff
Kelly Lyons, Stephen Tracy

Bibliographic record

VenueHuman Factors and Ergonomics in Manufacturing & Service Industries · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsService designService (business)Service systemService guaranteeScope (computer science)Service delivery frameworkService product managementCompetence (human resources)BusinessKnowledge managementService providerService organizationService level objectiveProcess managementValue (mathematics)Computer scienceMarketingManagementEconomics

Abstract

fetched live from OpenAlex

Abstract Over the past 50 years, the service sector has grown in most advanced industrialized economies to be the dominant economic activity. Researchers are working to understand service activities and define scientific concepts and methods of service under an emerging research area called service science. Service is defined as the application of competence and knowledge to create value. Value is realized through interactions and cocreation among service systems. Service systems vary in scope (from individuals to businesses, organizations, governments, and nations) and adapt dynamically and connect to other service systems through value propositions. The service system has been proposed as an abstraction for service science, and yet it is not clear how to characterize a given organization as a service system. In this article, we present a review of literature on service system concepts and define a service system framework developed from the literature that can be used to characterize an organization as a service system. © 2012 Wiley Periodicals, Inc.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.004
Science and technology studies0.0020.007
Scholarly communication0.0080.008
Open science0.0010.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.038
GPT teacher head0.236
Teacher spread0.198 · 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 designTheoretical or conceptual
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

Citations34
Published2012
Admission routes1
Has abstractyes

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