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Record W1487247468

International Outsourcing in Information Technology

2005· article· en· W1487247468 on OpenAlexaboutno aff
Manuel G. Serapio

Bibliographic record

VenueResearch-Technology Management · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsnot available
Fundersnot available
KeywordsOutsourcingOffshoringBusinessInformation technologyValue (mathematics)Principal (computer security)MarketingFinanceCommerce
DOInot available

Abstract

fetched live from OpenAlex

Offshoring is prevalent among Colorado information technology companies, and IT jobs have been and will be lost to international outsourcing. However, the number of IT jobs lost from offshoring is likely to be less than the tens of thousands of jobs being predicted by Colorado's popular press. This is a principal finding of a study I recently completed on offshoring activities by Colorado IT firms. In 2003, Colorado ranked first among the 50 states in concentration of high-tech workers, and there has been considerable concern over losses of domestic high-tech jobs. Study respondents were cautiously optimistic that IT jobs lost to international outsourcing can and will be replaced-provided that Colorado high-tech workers are willing to move up the value chain and acquire new higher-value-added skills to make them more competitive in their business. The study, sponsored by the Colorado Institute of Technology (CIT), addressed the drivers of international outsourcing, how these companies' international outsourcing operations are performing, what IT jobs these companies are offshoring, and how their international outsourcing activities are impacting IT employment in Colorado. Forty executives from 34 companies were interviewed between February and November 2004. These included firms based in Colorado, as well as companies headquartered outside of Colorado and the United States with a meaningful presence in the state. The firms represent different industries, such as financial services, IT services and consulting, computer equipment and peripherals, telecommunications, software, and others. Several of Colorado's largest employers and a few entrepreneurial firms participated in the study. Drivers of International Outsourcing Of the 34 companies in the study, 22 were engaged in offshoring. Fifty-four percent of these companies' offshoring operations started prior to 2001, signifying that this is not a recent development. These companies' offshore operations were located in over a dozen countries (e.g., China, Germany, UK, South Africa) but the lion's share was in India. Figure 1 shows the key factors driving offshoring. Lower labor costs, staff augmentation, access to high-quality employees, and access to technology were identified as the most important drivers of international outsourcing. Firms that outsourced in order to take advantage of lower labor costs did so under different conditions before and after 2001. Companies that off shored work prior to 2001 did so in the context of strong employment. Labor shortages, escalating labor costs, and the need to handle bursts in IT workloads prompted these companies to offshore. In contrast, most firms that engaged in offshoring after 2001 did so as part of cost-cutting efforts under difficult business conditions. Access to technology and to high-quality employees/contractors were important drivers for several companies, particularly those that engaged in development and research. For example, one company launched a project in Canada to conduct R&D in wireless technologies. A second company has maintained an offshore facility in Japan to work on software development projects for manufacturing. Companies that conducted software development in India noted the advantages provided by their CMM (Capability Maturity Model) providers. According to these companies, their Indian providers are very good in process management and improvement and in executing projects that have well-defined specifications. Magnitude and Scope of Offshoring The majority of the companies' offshoring operations had fewer than 50 employees or contractors dedicated to the offshoring company. However, the notable exceptions were seven companies that had more than 300 employees or contractors working on offshore projects. Of these, the largest offshore outsourcers were companies in IT services, software and telecommunications. Figure 2 lists the different types of offshored work. …

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0040.004
Scholarly communication0.0050.004
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.020
GPT teacher head0.287
Teacher spread0.268 · 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 designNot applicable
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

Citations5
Published2005
Admission routes1
Has abstractyes

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