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Record W3160042592 · doi:10.1051/itmconf/20213802002

A Service Science Perspective on Resilience of Service Organisations

2021· article· en· W3160042592 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueITM Web of Conferences · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsResilience (materials science)Service (business)BureaucracyService designCorporate governancePerspective (graphical)BusinessOrder (exchange)Service delivery frameworkService systemProcess managementKnowledge managementPoliticsPublic relationsMarketingComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Nowadays, different types of unexpected turbulence and disruptions lead to challenges and changing conditions of the environment that organisations operate. The previous studies related to crisis response or service recovery have addressed many aspects of the governance of an organisation in reacting to crisis or failure situations, including innovation and bureaucracy, science and politics, and decision-making speed. However, there is still little attention on supporting service organisations to revise and adapt their business services in a coherent manner to overcome the challenges from disruptive events. In order to improve organisational resilience, this paper presents an approach based on the service science perspective for service organisations to adapt their services at the three levels of service science, including the network of service systems, service system, and service levels. The paper also presents a case study of using the proposed approach in cultural organisations and ends with a discussion and some conclusions.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.278
Teacher spread0.243 · 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