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The Complexity of Outsourced Services and the Role of International Business Travel

2014· article· en· W2123136320 on OpenAlexaff
Runjuan Liu, Barry Scholnick, Adam Finn

Bibliographic record

VenueAcademy of Management Proceedings · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOutsourcingBusiness travelBusinessOffshore outsourcingTransaction costService (business)International businessDatabase transactionOffshoringKnowledge process outsourcingBusiness modelCommerceMarketingIndustrial organizationFinanceEconomicsTourismManagementComputer science

Abstract

fetched live from OpenAlex

Face-to-face communication between international business partners is a valuable mechanism for reducing transaction costs, even in contexts where electronic communication is pervasive. This paper empirically examines whether face-to-face interactions (as measured by international business travel) have greater impacts where transaction costs are higher, e.g. where services with greater complexity are outsourced offshore. Matching data from the Survey of International Air Travelers with Bureau of Economic Analysis data on US service outsourcing, we find that international business travel has a significantly positive impact on service outsourcing, and that this impact is significantly larger for outsourcing more complex services. We further provide more evidence on the role of international business travel by showing that: (a) business travel by managers leads to relatively more outsourcing; (2) business travel by non-diasporas leads to relatively more outsourcing; (3) the role of international business travel remains the same before and after the digital revolution. All these findings are supported by IV estimation where we use leisure travel or terrorism incidents as instruments for business travel.

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 score0.768
Threshold uncertainty score0.446

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.013
GPT teacher head0.209
Teacher spread0.197 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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