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INSIGHTS INTO SERVICE OPERATIONS MANAGEMENT: A RESEARCH AGENDA

2003· article· en· W2135910982 on OpenAlexaff
Aleda V. Roth, Larry J. Menor

Bibliographic record

VenueProduction and Operations Management · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsWestern University
Fundersnot available
KeywordsKey (lock)Service (business)Order (exchange)Knowledge managementProcess managementComputer scienceBusinessManagement scienceMarketingEngineering

Abstract

fetched live from OpenAlex

This paper offers insights regarding an agenda for service operations management (SOM) research. First, we motivate the need for an SOM research agenda. Second, we offer a research framework that paints a broad‐based picture of key architectural elements in the SOM research landscape. The framework builds upon prior and emerging research for designing, delivering and evaluating services. Third, in order to stimulate future research in SOM, we use this framework to hone in on five understudied and emerging research themes that underpin our proposed SOM research agenda.

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)0.009
metaresearch head score (Gemma)0.009
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: Review · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.012
Science and technology studies0.0040.011
Scholarly communication0.0170.025
Open science0.0020.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0090.001

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 head0.069
GPT teacher head0.320
Teacher spread0.251 · 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
GenreReview

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

Citations607
Published2003
Admission routes1
Has abstractyes

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