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Record W2979563308 · doi:10.20381/ruor-23940

Modelling Resource Configurations in Ict-Enabled Service Systems

2019· dissertation· en· W2979563308 on OpenAlexaboutno aff
Daoyang Xiao

Bibliographic record

VenueuO Research (University of Ottawa) · 2019
Typedissertation
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyResource (disambiguation)Service (business)Computer scienceKnowledge managementEngineering managementBusinessProcess managementEngineeringWorld Wide WebMarketingComputer network

Abstract

fetched live from OpenAlex

Telehealth, the use of information and communications technologies (ICTs) to support care delivery at a distance, is increasingly used in health systems worldwide. A service system is defined as a configuration of people, technologies, and other resources that interact with other service systems to create mutual value. Adopting a service system perspective thus allows understanding a telehealth service as an ICT-enabled service system. Adequately configuring resources, both tangible (e.g., hardware) and intangible (e.g., knowledge), is key to co-creating value through service systems. However, existing service system engineering methods and tools are not yet able to comprehensively capture the nature, role, and status of resources within service systems. In particular, while conceptual modelling is recognized as an excellent tool of understanding, designing, and monitoring for service engineering, existing conceptual modelling notations have limited abilities to express configurations of resources. In order to address this gap, the following research objectives are proposed: 1) Develop a conceptual framework of resource configurations as the basis for further developing a metamodel of resource configurations; 2) Develop a metamodel of resource configurations in ICT-enabled service systems that can formally express the constructs, relationships, and constraints within the domain of resource configurations; 3) Demonstrate and evaluate the metamodel by conducting a multiple-case study in the field of telehealth. This study will focus on telehealth as a representative instance of ICT-enabled service systems. The research design is guided by the Design Science Research Methodology (DSRM). DSRM provides a well-structured process for developing and evaluating information systems artifacts, such as the proposed metamodel, that can solve practical problems while contributing to a knowledge base. A multiple-case study of telehealth services at a Canadian hospital will support the evaluation and refinement of the metamodel. The results of this research project include both conceptual and practical contributions. The metamodel of resource configurations derived from the reviewed literature and conceptual framework will provide a formal understanding of resource configurations in ICT-enabled service systems. The metamodel may also be adopted as a tool for professionals to capture and analyze resource configurations in the domain of ICT-enabled services such as telehealth.

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)0.006
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: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0050.006
Open science0.0020.003
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 head0.272
Teacher spread0.234 · 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
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
Published2019
Admission routes1
Has abstractyes

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