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Record W2051856813 · doi:10.1108/so-11-2013-0022

On the attractiveness of the UK for outsourcing services

2014· article· en· W2051856813 on OpenAlexaboutno aff
Ilan Oshri, M. N. Ravishankar

Bibliographic record

VenueStrategic Outsourcing An International Journal · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsnot available
Fundersnot available
KeywordsOutsourcingBusinessCompetition (biology)AttractivenessCompetitive advantageKnowledge process outsourcingMarketingExcellenceIndustrial organizationOffshoring

Abstract

fetched live from OpenAlex

Purpose – Outsourcing is in a new era: an era of value-adding services, innovation and transformation. An era that shifts competition to skills and expertise where the main focus of key players in the industry is on the strategic impact of outsourcing services. As the outsourcing landscape is changing, so competition between countries for outsourcing work is reconstructing. It is no longer competition for low costs, but a search for superior skills, both technical and managerial, that provides the strategic guidance and operational excellence needed in the twenty-first century. While the professional and academic literature has extensively studied the comparative advantage of low-cost locations such as India, we know very little about the attractiveness of Western countries, such as the UK, for outsourcing services. To contribute to this end, the purpose of this paper is to examine the UK attractiveness in light of three key trends in the outsourcing industry: the maturity of the outsourcing industry drives more client firms to seek impact on business and strategic performance from their vendors; client firms and vendors deploy complex sourcing models that increase the importance of sourcing managerial capabilities, such as relationship management, vis-à-vis technical and delivery capabilities; locations with promising entry points to lucrative markets are becoming attractive for outsourcing investments as part of the firm's growth strategy. Design/methodology/approach – The empirical base of this study is based on a comparative analysis of eight European destinations (UK, Germany, France, The Netherlands, Spain, Ireland, Czech Republic and Poland) to conclude that the UK, as a talent-base, value-adding country that also offers advanced sourcing capabilities, has positioned its economy to attract investments from both outsourcing vendors and client firms. While the authors acknowledge the relative high-cost base of the UK economy, they assert that the high service standards, access to skills, entry point to mainland Europe and the USA, government support and supportive infrastructure are superior value propositions offered by the UK in the context of outsourcing services. Findings – The findings of this study highlight the contribution of Western economies to outsourcing and their fairly strong comparative position to specific line of services such as contact centers, research and development and specific business process outsourcing services. Research limitations/implications – The main limitation of this study is the use of a country attractiveness framework which has been mainly used for low-cost countries. The authors therefore acknowledge the need to develop a country attractiveness framework which is suitable for Western countries. Practical implications – This study offers decision makers an extensive tool to assess their outsourcing investments by considering both low-cost and Western countries based on the value expected from each investment. Originality/value – This is the first study on the attractiveness of a Western country, such as the UK, which the authors defined as a talent-based, value-adding and advanced sourcing (TAVAAS) country. Through the examination of its comparative attractiveness the authors highlight the potential of the UK and many other Western countries such as USA, Germany or Canada to attract outsourcing investments.

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)0.002
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 score0.331
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.026
GPT teacher head0.253
Teacher spread0.227 · 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 designTheoretical or conceptual
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

Citations7
Published2014
Admission routes1
Has abstractyes

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