{"title":"Utilising Artificial Intelligence to Enhance Firm Circular Economy Maturity: A Thematic Review via Machine Learning","authors":"Donghao Huang, Yuanzhu Zhan, Chris Lonsdale","doi":"10.1002/bse.4291","DOIUrl":null,"url":null,"abstract":"Despite the environmental imperative of a transition to a circular economy (<jats:sc>CE</jats:sc>), the current literature finds that globally firms are being slow to engage with such a transition. In this context, our thematic review explores how artificial intelligence (AI) might accelerate firm <jats:sc>CE</jats:sc> transition. In a departure from the dominant approach that adopts the product life cycle as the unit of analysis, the authors contribute to the literature by framing the potential of AI in terms of how it might accelerate greater <jats:sc>CE</jats:sc> maturity on the part of firms. A maturity model is advanced, identifying four stages of firm <jats:sc>CE</jats:sc> maturity, and then, different AI techniques are applied to each of the stages, providing guidance to adopting the correct AI technique for each stage in the maturity journey. The paper makes a further contribution to the literature by utilising a novel literature review method, whereby the authors themselves utilise AI in the form of a machine learning algorithm that optimises manual classification outcomes. This method provides greater objectivity to a review of 601 papers and reveals its future research potential.","PeriodicalId":9518,"journal":{"name":"Business Strategy and The Environment","volume":"238 1","pages":""},"PeriodicalIF":12.5000,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Business Strategy and The Environment","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1002/bse.4291","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
Despite the environmental imperative of a transition to a circular economy (CE), the current literature finds that globally firms are being slow to engage with such a transition. In this context, our thematic review explores how artificial intelligence (AI) might accelerate firm CE transition. In a departure from the dominant approach that adopts the product life cycle as the unit of analysis, the authors contribute to the literature by framing the potential of AI in terms of how it might accelerate greater CE maturity on the part of firms. A maturity model is advanced, identifying four stages of firm CE maturity, and then, different AI techniques are applied to each of the stages, providing guidance to adopting the correct AI technique for each stage in the maturity journey. The paper makes a further contribution to the literature by utilising a novel literature review method, whereby the authors themselves utilise AI in the form of a machine learning algorithm that optimises manual classification outcomes. This method provides greater objectivity to a review of 601 papers and reveals its future research potential.
期刊介绍:
Business Strategy and the Environment (BSE) is a leading academic journal focused on business strategies for improving the natural environment. It publishes peer-reviewed research on various topics such as systems and standards, environmental performance, disclosure, eco-innovation, corporate environmental management tools, organizations and management, supply chains, circular economy, governance, green finance, industry sectors, and responses to climate change and other contemporary environmental issues. The journal aims to provide original contributions that enhance the understanding of sustainability in business. Its target audience includes academics, practitioners, business managers, and consultants. However, BSE does not accept papers on corporate social responsibility (CSR), as this topic is covered by its sibling journal Corporate Social Responsibility and Environmental Management. The journal is indexed in several databases and collections such as ABI/INFORM Collection, Agricultural & Environmental Science Database, BIOBASE, Emerald Management Reviews, GeoArchive, Environment Index, GEOBASE, INSPEC, Technology Collection, and Web of Science.