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International Outsourcing in Information Technology

2005· article· en· W1487247468 sur OpenAlex

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Notice bibliographique

RevueResearch-Technology Management · 2005
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueOutsourcing and Supply Chain Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOutsourcingOffshoringBusinessInformation technologyValue (mathematics)Principal (computer security)MarketingFinanceCommerce
DOInon disponible

Résumé

récupéré en direct d'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. …

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Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,935
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0110,004
Études des sciences et des technologies0,0000,000
Communication savante0,0000,002
Science ouverte0,0010,002
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,020
Tête enseignante GPT0,287
Écart entre enseignants0,268 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle