Machine learning approaches to understand IT outsourcing portfolios

Lu, Y and Susarla, A and Ravindran, K and Mani, D (2023) Machine learning approaches to understand IT outsourcing portfolios. Electronic Commerce Research. ISSN 1389-5753

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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.

Item Type: Article
Subjects: Information Systems
Date Deposited: 30 Aug 2023 09:37
Last Modified: 30 Aug 2023 09:37

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