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Record W1623356587 · doi:10.19030/jbcs.v4i12.4825

Business Process Outsourcing: Lessons From Case Studies In India, Poland, And Canada

2011· article· en· W1623356587 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Business Case Studies (JBCS) · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Downsizing and Restructuring
Canadian institutionsConcordia University
Fundersnot available
KeywordsOutsourcingBusinessVendorProductivityProcess (computing)MarketingKnowledge process outsourcingBest practiceProcess managementEconomicsManagementComputer science

Abstract

fetched live from OpenAlex

The objective of this article is to study the effectiveness of the company-partner relationship when outsourcing business processes in a large aerospace company. The intent is to supplement existing anecdotal evidence with data collected through a structured methodology in an effort to highlight process inefficiencies that may lead to hidden costs. Recommendations are provided to management as a means of addressing the process gaps to improve productivity. A literature review was conducted and a selection of findings from relevant papers and studies were retained as best practices for a successful outsourcing venture. These findings were then used to generate questions as part of a survey. The latter was distributed to 90 employees and managers from both the company and the vendor with the purpose of identifying gaps with the literature. A mismatch between the survey results and the literature would signal an improvement opportunity requiring management of attention. Although the overall health of the outsourcing process is satisfactory, several aspects of the working relationship were found to be deficient and the cause of inefficiencies (i.e. loss time, frustration, increased cost ). In particular, employees from both sides found a lack in upfront planning, communication of expectations, and information sharing. Furthermore, both employees and managers expressed concern about the need for training to better deal with cultural differences and motivation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.270
Teacher spread0.223 · 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