[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122516-en":3,"doc-seo-122516-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122516,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Machine Learning Approaches to Understand IT Outsourcing Portfolios - Research overview","Machine learning and natural language processing support uncovering data patterns and revealing contextual signals in inter-firm IT outsourcing arrangements. Traditional firm-theory metrics often emphasize measurable sourcing factors, while key decision information is embedded in unstructured discussions and contracting disclosures. Using an NLP deep-learning approach with doc2vec, the work extracts semi-supervised representations from press releases and vendor-client information. It identifies vendor-client alignment and vendor-task alignment as constructs shaping partner selection beyond capabilities and transaction-cost explanations, with implications for both research and practice.","Machine Learning Approaches to Understand IT Outsourcing Portfolios  \nAbstract  \nAdvances in machine learning (ML) and natural language processing (NLP) holds out tremendous insights both for discovering patterns in data and for better understanding the distinct context of inter-firm arrangements. The outsourcing of information technology (IT) services poses a conundrum to the traditional theories of the firm. While there are a lot of 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, as it is captured through discussion and analyses summarized in contracting decisions, self-disclosed aspects of such arrangements explicated in press releases of outsourcing arrangements. NLP methods can parse through such press releases and disclosures , and learn vendor and client specific information. 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. The question we ask is: How can we employ machine learning and natural language processing to capture the difficult to elicit aspects of vendor selection in outsourcing IT services? 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 as well as trade and industry press, about the difficult to elicit aspects of vendor-client interaction. Implications for research and practice are discussed.  \n1. INTRODUCTION  \nIndustry surveys indicate robust and continued interest in the spending on IT outsourcing. Per the recent KPMG CIO survey of 20191 , 41% of the 3600 respondents state they plan to increase their IT Outsourcing spending next year for the primary reason of accessing skills not available inhouse. However, a challenge in understanding outsourcing for IT services is that such outsourcing initiatives do not fit the continuum of make-vs-buy relationships prescribed in prior theories. Recent decades have likewise witnessed an unprecedented rise in vertical de-integration and outsourcing of complex products and services (Linder et al. 2003, Miozzo and Grimshaw 2005) . A remarkable aspect of such inter-firm collaborations is that they are governed through a blend of explicit and implicit obligations enmeshed within a formal structure of exchange (Gilson et al. 2009) . Such obligations can be described neither as arm’s length arrangements nor relational contracting and offer a contrast to traditional theories of the firm.  \nManagerial and economic motivations for exchange posit that partner selection is driven by capabilities and transaction costs. Industry analysts and business press have advocated various attributes that clients may adopt in choosing vendors. It has been posited that a vendor’s domain expertise, financial backing, ramp up capability and etc. should be the dominant reasons for selecting vendors. However, IT outsourcing initiatives are not standardized, and vendors are heterogeneous in their capabilities, making it difficult to generalize such advice beyond a few large and reputed IT vendors. Prior research employing a contract theoretic perspective contends that uncertainty in operations (Aubert et al. 2004) and in vendor capabilities (Banerjee and Duflo 2000), and post-contractual holdup by the vendor (e.g., Susarla et al. 2010) critically influence client’s vendor selection decision. Theories of relational governance predict depth of relationships  \n1 A Changing Perspective, Harvey Nash / KPMG CIO survey 2019, accessible at  \n[https://assets.kpmg/content/dam/kpmg/n","cbCaib7K0ow9w81w","https://ap.wps.com/l/cbCaib7K0ow9w81w","pdf",1232945,1,42,"English","en",105,"# Introduction\n## Industry demand for IT outsourcing\n## Limits of traditional make-vs-buy theories\n## Governance, uncertainty, and partner selection\n## Portfolio and network perspectives\n## Long-tail and multi-sourcing patterns","[{\"question\":\"Why do traditional theories of the firm struggle to explain IT outsourcing partner selection?\",\"answer\":\"IT outsourcing initiatives often do not match the make-vs-buy continuum, and obligations in these arrangements combine explicit and implicit elements that differ from arm’s length or purely relational contracting models.\"},{\"question\":\"What data sources does the approach use to model outsourcing decisions?\",\"answer\":\"The method focuses on unstructured text from press releases and disclosures that summarize contracting decisions and self-disclosed aspects of IT outsourcing arrangements.\"},{\"question\":\"Which constructs does the study propose to explain vendor selection?\",\"answer\":\"It identifies vendor-client alignment and vendor-task alignment as constructs that shape partner selection and client alternatives, extending beyond agency and transaction-cost considerations alone.\"}]","Machine Learning Approaches to Understand IT Outsourcing Portfolios - 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