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Record W2129357896

A Time-Driven Activity Cost Approach for the Reduction of Cost of IT Services: A Case Study in the Internet Service Industry

2012· article· en· W2129357896 on OpenAlexaff
Adenle Adeoti, Raul Valverde

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsConcordia University
Fundersnot available
KeywordsActivity-based costingCost reductionService (business)Computer scienceUnit (ring theory)Unit costIncident managementOperations managementScope (computer science)Operations researchBusinessEngineeringComputer securityMarketing
DOInot available

Abstract

fetched live from OpenAlex

This study aims to show that application of Time-Driven Activity Based Costing (TDABC) to the management of cost of Information Technology (IT) Services Operations and how it can be used to achieve significant cost reduction. To achieve this; a case study organization was used and the scope of activities was limited to Technical Services department Operations units. Interviews were conducted with the unit managers and their operations staff. From the interview, a list of services were developed and linked to activities and time to execute each was provided by the operational staff. Time equations were developed from the activity groups that supported each type of service. A TDABC model was then simulated with Microsoft Excel; which incorporated the activities, the time to deliver each and the capacity cost rate to derive the cost of delivering a service. The result of the test showed that two variations (out of six) of that incident type cost more than 75% of the overall cost of that incident type, though they constitute about 30% of the incident type. This study showed that TDABC is an effective tool in identification of costly processes which may then allow IT operations managers and supervisors to take critical decisions about cost control, charge-back or costing of services.

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 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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.052
GPT teacher head0.282
Teacher spread0.230 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2012
Admission routes1
Has abstractyes

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