[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119433-en":3,"doc-seo-119433-105":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},119433,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","The application of machine learning for demand prediction under macroeconomic volatility - a systematic literature review","In a contemporary context characterised by shifts in macroeconomic conditions and global uncertainty, predicting the future behaviour of demanders is critical for marketing management science. Despite recognised machine-learning potential, prior work lacks systematic reviews focused on demand prediction under volatile environments. This systematic literature review fills the gap using a rigorous protocol and a hybrid methodological approach, analysing n = 64 studies to map the field’s conceptual structure, publication trends, geographic activity centres, and leading sources. It further discusses implementation strategies, including integrating forward-looking data with economic indicators, modelling demand via coefficient of variation, and combining algorithms and artificial neural networks for accurate predictions.","The application of machine learning for demand prediction under macroeconomic volatility: a systematic literature review  \nManuel Muth1 · Michael Lingenfelder1 · Gerd Nufer2  \nReceived: 20 November 2023 / Accepted: 23 May 2024 © The Author(s) 2024  \nAbstract  \nIn a contemporary context characterised by shifts in macroeconomic conditions and global uncertainty, predicting the future behaviour of demanders is critical for management science disciplines such as marketing. Despite the recognised potential of Machine Learning, there is a lack of reviews of the literature on the application of Machine Learning in predicting demanders’ behaviour in a volatile environment. To fill this gap, the following systematic literature review provides an interdisciplinary overview of the research question: “How can Machine Learning be effectively applied to predict demand patterns under macroeconomic volatility?” Following a rigorous review protocol, a literature sample of studies (n = 64) is identified and analysed based on a hybrid methodological approach. The findings of this systematic literature review yield novel insights into the conceptual structure of the field, recent publication trends, geographic centres of scientific activity, as well as leading sources. The research also discusses whether and in which ways Machine Learning can be used for demand prediction under dynamic market conditions. The review outlines various implementation strategies, such as the integration of forward-looking data with economic indicators, demand modelling using the Coefficient of Variation, or the application of combined algorithms and specific Artificial Neural Networks for accurate demand predictions.  \nKeywords Machine learning · Macroeconomic volatility · Demand forecasting · Marketing predictions · Systematic literature review  \nJEL Classification C53 · E32 · C45 · M31  \n* Manuel Muth  \nmuthman@students.uni-marburg.de  \n1 School of Business and Economics, Philipps-Universität Marburg, Universitätsstr. 24, 35037 Marburg, Germany  \n2 ESB Business School, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany  \n1 3  \n1 Introduction  \nIn light of geopolitical instabilities in Eastern Europe and the Middle East, changes in inflation and interest rates, and disruptions in global supply chains (Dörner et al. 2023 ; European Central Bank 2023), various business functions face a complex task in accurately predicting the behaviour of demanders. The economic circumstances influencing managerial operations have hence undergone profound changes and many of the existing prediction approaches rely on substantially different circumstances than those currently prevailing (Durst et al. 2022 ; Durugbo and Al-Balushi 2022) . As a result,“contemporary organizations face environments with unprecedented levels of volatility, uncertainty, complexity, and ambiguity”(Troise et al. 2022), presenting new challenges for predicting the future behaviour of demanders.  \nIn scientific literature, several authors emphasise the general potential of Machine Learning (ML) for predictive analytical tasks in important business areas like marketing (Huang and Rust 2021 ; Ma and Sun 2020 ; Verma et al. 2021) . However, there is an existing knowledge gap in terms of predicting future demand with ML in volatile environments (Ghoddusi et al. 2019), leaving a significant need for further research. The lack of comprehensive literature reviews on this particular question is substantiated by preliminary searches in the Web of Science-database across 12,000 available journals and screening the 205 query results that include the title words “systematic literature review” and “machine learning”, along with additional investigations in further electronic resources. While some existing literature reviews address the overarching intersection of ML and applied management disciplines, there is a tendency to focus more on general questions such as primary application categories or global trend","cbCaiq8Mkbrjdnqj","https://ap.wps.com/l/cbCaiq8Mkbrjdnqj","pdf",3510868,1,44,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What research gap does the systematic literature review address?\",\"answer\":\"It addresses the lack of comprehensive literature reviews on using machine learning to predict demanders’ behaviour in volatile, macroeconomic environments.\"},{\"question\":\"How many studies are included in the literature sample and how are they analysed?\",\"answer\":\"The review identifies and analyses a literature sample of n = 64 studies using a rigorous review protocol with a hybrid methodological approach.\"},{\"question\":\"What implementation strategies for demand prediction does the review discuss?\",\"answer\":\"It discusses strategies such as integrating forward-looking data with economic indicators, demand modelling using the coefficient of variation, and applying combined algorithms and specific artificial neural networks for accurate demand predictions.\"}]","The application of machine learning for demand prediction under macroeconomic volatility - a systematic literature review | PDF",1785724267,111,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"the-application-of-machine-learning-for-demand-prediction-under-macroeconomic-volatility-a-systematic-literature-review","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-application-of-machine-learning-for-demand-prediction-under-macroeconomic-volatility-a-systematic-literature-review/119433/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What research gap does the systematic literature review address?","Question",{"text":76,"@type":77},"It addresses the lack of comprehensive literature reviews on using machine learning to predict demanders’ behaviour in volatile, macroeconomic environments.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How many studies are included in the literature sample and how are they analysed?",{"text":81,"@type":77},"The review identifies and analyses a literature sample of n = 64 studies using a rigorous review protocol with a hybrid methodological approach.",{"name":83,"@type":74,"acceptedAnswer":84},"What implementation strategies for demand prediction does the review discuss?",{"text":85,"@type":77},"It discusses strategies such as integrating forward-looking data with economic indicators, demand modelling using the coefficient of variation, and applying combined algorithms and specific artificial neural networks for accurate demand predictions.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]