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Record W168995569 · doi:10.25300/misq/2013/37.1.14

Information Technology Outsourcing and Non-It Operating Costs: An Empirical Investigation1

2013· article· en· W168995569 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.

Bibliographic record

VenueMIS Quarterly · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsOutsourcingBusinessPanel dataInformation technologyKnowledge process outsourcingIndustrial organizationEmpirical researchSet (abstract data type)Operations managementEconomicsComputer scienceMarketingEconometricsStatisticsOperating systemMathematics

Abstract

fetched live from OpenAlex

Does information technology outsourcing reduce non-IT operating costs? This study examines this question and also asks whether internal IT investments moderate the relationship between IT outsourcing and non-IT operating costs. Using a panel data set of approximately 300 U.S. firms from 1999 to 2003, we find that IT outsourcing has a significant negative association with firms’ non-IT operating costs. However, this finding does not imply that firms should completely outsource their entire IT function. Our results suggest that firms benefit more in terms of reduction in non-IT operating costs when they also have higher levels of complementary investments in internal IT, especially IT labor. Investments in internal IT systems can make business processes more amenable to outsourcing, and complementary investments in internal IT staff can facilitate monitoring of vendor performance and coordination with vendors. We discuss the implications of these findings for further research and for practice.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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