Machine Learning Approaches to Understand IT Outsourcing Portfolios

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

JEL Classification: C11, C15, C18, C33, D23, L14, L84, M20, M21, M55