[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127909-en":3,"doc-seo-127909-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127909,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Testing Service Infusion in Manufacturing Through Machine Learning Techniques - Looking Back and Forward","Purpose – This study examines the conceptual assumptions behind service infusion in manufacturing, specifically the idea that firms move from pure-product to pure-service offerings and that profits increase linearly. It argues these assumptions conflict with behavioural and learning theories. Design/methodology/approach – Machine learning models evaluate whether progressing from basic to advanced offerings improves performance using two USA manufacturing surveys from 2021 and 2023. Findings – The base-intermediate-advanced pathway is not the best performance predictor; starting with advanced services and later adding simpler offerings yields better results. Practical implications – Firms follow heterogeneous pathways and should choose contextual-fit service starts, with a single-service entry often outperforming multiple services.","The 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, Philip Davies and Glenn Parry. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4 .0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at [http://creativecommons.org/licences/by/4.0/](http://creativecommons.org/licences/by/4.0/)[ ](http://creativecommons.org/licences/by/4.0/)legalcode  \nOscar F. Bustinza acknowledges support from the Ministry of Universities of Spain within the framework of the State Plan for Scientific, Technical and Innovation Research 2021-2023 (Ref. PRX22/ 00176) . This research also received support from the UK Engineering and Physical Science Research Council through the Digitally Enhanced Advanced Services NetworkPlus funded by grant ref EP/ R044937/1, which this research is a part of. Funding for open access charge was provided by Universidad de Granada/CBUA.  \nTesting service  \ninfusion in manufacturing  \n127  \nReceived 20 February 2023 Revised 19 November 2023  \n18 January 2024  \n13 March 2024 Accepted 10 April 2024  \nInternational Journal of Operations & Production Management  \nVol. 44 No. 13, 2024  \npp. 127-156 Emerald Publishing Limited 0144-3577  \nDOI 10. 1108/IJOPM-02-2023-0121  \nIJOPM foasrs1e7sstyoptheesr opfrserviocessc-es describedbased modelisn pinrreelviouatedsqumaanlaitative sgementtfudieieldss .(The ge","cbCaiiz5Gl7B9E6B","https://ap.wps.com/l/cbCaiiz5Gl7B9E6B","pdf",1164131,1,30,"English","en",105,"# Abstract\n## Purpose\n## Design/methodology/approach\n## Findings\n## Practical implications\n## Originality/value\n# Introduction","[{\"question\":\"What core assumptions about service infusion does the paper challenge?\",\"answer\":\"The paper challenges assumptions that manufacturing firms follow a pure-product to pure-service pathway and that profits increase linearly along that process.\"},{\"question\":\"How does the study test service-infusion pathways?\",\"answer\":\"It applies machine learning algorithms to assess whether moving from basic to more advanced offerings produces optimal performance, using two surveys of USA manufacturing firms from 2021 and 2023.\"},{\"question\":\"Which service pathway is associated with better performance?\",\"answer\":\"Developing advanced services first 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 - 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