Machine learning approaches to understand IT outsourcing portfolios

Yingda Lu (), Anjana Susarla (), Kiron Ravindran () and Deepa Mani ()
Additional contact information
Yingda Lu: University of Illinois Chicago
Anjana Susarla: Michigan State University
Kiron Ravindran: IE Business School
Deepa Mani: Indian School of Business

Electronic Commerce Research, 2024, vol. 24, issue 4, No 14, 2547-2577

Abstract: Abstract The outsourcing of IT services poses a conundrum to the traditional theories of the firm. While there are many prescriptive sourcing metrics that are geared towards the evaluation of tangible and measurable aspects of vendors and clients, much of the information that is traditionally important in making such decisions is unstructured. To address this challenge, we train and apply our own NLP model based on deep learning methods using doc2vec, which allows users to create semi-supervised methods for representation of words. We find two novel constructs, vendor–client alignment and vendor–task alignment, that shape partner selection and the alternatives faced by clients in IT outsourcing, as opposed to agency or transaction cost considerations alone. Our method suggests that NLP and machine learning approaches provide additional insight, over and above traditionally understood variables in academic literature and trade and industry press, about the difficult-to-elicit aspects of vendor–client interaction.

Keywords: Outsourcing; Machine learning; Natural language processing; Vendor selection (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10660-022-09663-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:elcore:v:24:y:2024:i:4:d:10.1007_s10660-022-09663-4

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10660

DOI: 10.1007/s10660-022-09663-4

Access Statistics for this article

Electronic Commerce Research is currently edited by James Westland

More articles in Electronic Commerce Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().