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Design/methodology/approach – Machine learning algorithms evaluate whether moving from basic to advanced offerings yields optimal performance using two USA manufacturing surveys from 2021 and 2023. Findings – The base-intermediate-advanced services pathway does not best predict optimal performance, while developing advanced services first and later adding less complex ones performs better. Practical implications – Service development follows heterogeneous pathways; non-servitised firms should choose context-fit starting offerings and may benefit from beginning with a single service.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/testing-service-infusion-in-manufacturing-through-machine-learning-techniques-looking-back-and-forward-290969/290969/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/testing-service-infusion-in-manufacturing-through-machine-learning-techniques-looking-back-and-forward-290969/290969.png","ImageObject",300,407,{"name":92,"@type":93},"Seraphina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What core assumptions does the paper challenge about service infusion?","Question",{"text":113,"@type":114},"It challenges assumptions that manufacturing firms follow a product-to-service progression and that profits increase linearly with that progression, arguing these are inconsistent with behavioural and learning theory premises.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How are machine learning methods used in the study?",{"text":118,"@type":114},"Machine learning algorithms test whether successive stages of service offerings, from basic to advanced, lead to optimal performance based on survey datasets.",{"name":120,"@type":111,"acceptedAnswer":121},"What service development pathway is found to support better performance?",{"text":122,"@type":114},"The analysis indicates that starting by developing advanced services and then later adding less complex offerings supports better performance than the base-intermediate-advanced pathway.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290969,1790624617,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},962075114101,"https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165","Edinburgh Research Explorer  \nTesting service infusion in manufacturing through machine learning techniques  \nLooking back and forward  \nCitation for published version:  \nBustinza, OF, Vendrell-Herrero, F, Davies, P & Parry, G 2024, 'Testing service infusion in manufacturing through machine learning techniques: Looking back and forward', International Journal of Operations & Production Management, vol. 44, no. 13, pp. 127-156. [https://doi.org/10.1108/IJOPM-02-2023-0121](https://doi.org/10.1108/IJOPM-02-2023-0121)  \nDigital Object Identifier (DOI):  \n10.1108/IJOPM-02-2023-0121  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nPublished In:  \nInternational Journal of Operations & Production Management  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 04. Jul. 2024  \nThe current issue and full text archive of this journal is available on Emerald Insight at:  \n[https://www.emerald.com/insight/0144-3577.htm](https://www.emerald.com/insight/0144-3577.htm)  \nTesting service infusion in manufacturing through machine learning techniques: looking back  \nand forward  \nOscar F. Bustinza  \nDepartment of Management, University of Granada, Granada, Spain  \nFerran Vendrell-Herrero  \nThe University of Edinburgh Business School, Edinburgh, UK  \nPhilip Davies  \nDepartment of Business Informatics, Systems and Accounting, University of Reading Henley Business School Whiteknights Campus, Reading, UK, and  \nGlenn Parry  \nDepartment of Digital Economy, Entrepreneurship and Innovation, Surrey Business School, Guildford, UK  \nAbstract  \nPurpose – Responding to calls for deeper analysis of the conceptual foundations of service infusion in manufacturing, thispaper examines the underlying assumptions that:(i)manufacturing firms incorporating services follow a pathway, moving from pure-product to pure-service offerings, and (ii) profits increase linearly with this process. We propose that these assumptions are inconsistent with the premises ofbehavioural and learning theories. Design/methodology/approach – Machine learning algorithms are applied to test whether a successive process, from a basic to a more advanced offering, creates optimal performance. The data were gathered through two surveys administered to USA manufacturing firms in 2021 and 2023. The first included a training sample comprising 225 firms, whilst the second encompassed a testing sample of 105 firms.  \nFindings – Analysis shows that following the base-intermediate-advanced services pathway is not the best predictor of optimal performance. Developing advanced services and then later adding less complex offerings supports better performance.  \nPractical implications–Manufacturing firms follow heterogeneous pathways in their service development journey. Non-servitised firms need to carefully consider their contextual conditions when selecting their initial service offering. Starting with a single service offering appears to be a superior strategy over providing multiple services.  \nOriginality/value – The machine learning approach is novel to the field and captures the key conditions for manufacturers to successfully servitise. Insight is derived from the adoption and implementation year datasets  \n© Oscar F. Bustinza, Ferran Vendrell-Herrero, P","cbCaicYxHsHLSOjr","https://ap.wps.com/l/cbCaicYxHsHLSOjr","pdf",1306659,31,"English","# Abstract\n## Purpose\n## Design/methodology/approach\n## Findings\n## Practical implications\n## Originality/value\n# Introduction\n## Background on service infusion and servitization","[{\"question\":\"What core assumptions does the paper challenge about service infusion?\",\"answer\":\"It challenges assumptions that manufacturing firms follow a product-to-service progression and that profits increase linearly with that progression, arguing these are inconsistent with behavioural and learning theory premises.\"},{\"question\":\"How are machine learning methods used in the study?\",\"answer\":\"Machine learning algorithms test whether successive stages of service offerings, from basic to advanced, lead to optimal performance based on survey datasets.\"},{\"question\":\"What service development pathway is found to support better performance?\",\"answer\":\"The analysis indicates that starting by developing advanced services and then later adding less complex offerings supports better performance than the base-intermediate-advanced pathway.\"}]","Testing service infusion in manufacturing through machine learning techniques - Looking back and forward | PDF",1789643067,78]