[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125569-en":3,"doc-seo-125569-105":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},125569,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Towards a precise understanding of social entrepreneurship - An integrated bibliometric - machine learning based review and research agenda","This paper advances a more precise and comprehensive understanding of social entrepreneurship (SE) research through an integrated bibliometric analysis and unsupervised machine learning framework. Latent Dirichlet Allocation (LDA) topic modelling is used to extract latent themes from a large corpus. Using Scopus and Web of Science, the study analyzes 3,844 texts (titles, abstracts, and keywords) and identifies key trends, organizing them into three major categories with 21 sub-topics to support future research.","Towards a precise understanding of social entrepreneurship: An integrated bibliometric – machine learning based review and research agenda  \nAuthors  \n1. Vineet Kaushik  \nPhD student  \nIndian Institute of Management Kashipur, Uttarakhand, India  \n[Email:](Email: vineet.phd1904@iimkashipur.ac.in)[ ](Email: vineet.phd1904@iimkashipur.ac.in)[vineet.phd1904@iimkashipur.ac.in](Email: vineet.phd1904@iimkashipur.ac.in)  \n[2](2). Shobha Tewari (Corresponding author)  \nAssistant Professor  \nIndian Institute of Management Kashipur, Uttarakhand, India  \nEmail: [shobha.tewari@iimkashipur.ac.in](shobha.tewari@iimkashipur.ac.in)  \n3. Sreevas Sahasranamam  \nAssociate Professor  \nHunter Centre for Entrepreneurship, Strathclyde Business School, Glasgow, UK.  \nEmail: [sreevas.sahasranamam@strath.ac.uk](sreevas.sahasranamam@strath.ac.uk)  \n[4](4). Pradeep Kumar Hota Associate Professor  \na) LM Thapar School of Management, Thapar Institute of Engineering and Technology, Punjab, India  \nb) Research School of Management, Australian National University, Australia [Email:](Email: pradeep.hota@thapar.edu)[ ](Email: pradeep.hota@thapar.edu)[pradeep.hota@thapar.edu](Email: pradeep.hota@thapar.edu)  \nAuthor Accepted Manuscript for publication in Technological Forecasting and Social  \nChange  \nAbstract:  \nThis paper focuses on building a more precise and comprehensive understanding of the state of social entrepreneurship (SE) research by using an integrated bibliometric and unsupervised machine learning approach. Bibliometric analysis, along with Latent Dirichlet Allocation (LDA) for topic modelling enables us to identify key trends and themes in the SE domain. This approach is superior to tools and methods used in the past, which primarily employed systematic literature reviews and bibliometric analysis. While systematic manual literature reviews become impractical as the literature grows, bibliometric analysis focuses on the most cited articles, ignoring recent influential work, suffering from citation biases, and giving more weight to impact over thematic discovery. The methodology used by us overcomes these issues by first extracting large amounts of information through advanced computational methods and then using unsupervised machine learning to discover the latent themes and topics in this large collection of publications. This research uses the Scopus and Web of Science (WoS) databases to extract corpora of 3844 texts (titles, abstracts, and keywords) from published research on SE. We decipher the key trends in the literature and segregate them into three broad categories – individual attributes and motivation, organizational actions, and institutional conditions and development with 21 sub-topics to enhance the understanding of this field of inquiry. This study is the first in the entrepreneurship domain to use this integrated approach to review the literature, and the findings lay the groundwork for future research.  \nKeywords: Social Entrepreneurship, Literature Review, Bibliometric Analysis, Unsupervised Machine Learning, Topic Modeling, Latent Dirichlet Allocation (LDA)  \n1. Introduction:  \n“As a body of literature develops, it is useful to stop occasionally, take inventory for the work that has been done, and identify new directions and challenges for the future ” (Low & MacMillan, 1988, p.139)  \nEntrepreneurship for economic development and growth has received a great deal of scholarly attention. However, social entrepreneurship as a means of social development and progress has only recently evoked the interest of researchers (Mair & Marti, 2006; Saebi, Foss, & Linder, 2019) . While definitional clarity still eludes SE (Choi & Majumdar, 2014; Nicholls, 2010), it can be broadly considered as an entrepreneurial and novel way to improve the social and economic situation of the marginalised segments of the population that are excluded from the mainstream and unable to improve their conditions without such external help (Saebi et al., 2019; Seel","cbCaimczW9e3bEuy","https://ap.wps.com/l/cbCaimczW9e3bEuy","pdf",1397206,1,50,"English","en",105,"# Introduction\n## Social entrepreneurship research problem and motivation\n## Limitations of systematic manual reviews\n## Limitations and biases in bibliometric studies\n# Methodology overview\n## Data sources and corpus construction\n## Bibliometric analysis and topic modelling (LDA)\n# Findings framework\n## Three broad categories and 21 sub-topics\n# Research agenda and contribution","[{\"question\":\"What is the main goal of this integrated bibliometric and machine learning study?\",\"answer\":\"To build a more precise and comprehensive understanding of the current state and key directions in social entrepreneurship research by combining bibliometric methods with unsupervised machine learning and topic modelling.\"},{\"question\":\"How does the paper address shortcomings of traditional systematic literature reviews?\",\"answer\":\"It argues that manual systematic reviews become impractical as the literature expands and cannot efficiently process large volumes of data.\"},{\"question\":\"Why use both bibliometric analysis and LDA topic modelling?\",\"answer\":\"Bibliometrics helps reveal prominent trends, while LDA enables thematic discovery of latent topics; together they mitigate biases where older, highly cited work can otherwise dominate and obscure recent influential research.\"}]","Towards a precise understanding of social entrepreneurship - An integrated bibliometric - machine learning based review and research agenda | PDF",1785899937,126,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"towards-a-precise-understanding-of-social-entrepreneurship-an-integrated-bibliometric-machine-learning-based-review-and-research-agenda","",{"@graph":36,"@context":85},[37,54,68],{"@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/towards-a-precise-understanding-of-social-entrepreneurship-an-integrated-bibliometric-machine-learning-based-review-and-research-agenda/125569/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this integrated bibliometric and machine learning study?","Question",{"text":75,"@type":76},"To build a more precise and comprehensive understanding of the current state and key directions in social entrepreneurship research by combining bibliometric methods with unsupervised machine learning and topic modelling.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper address shortcomings of traditional systematic literature reviews?",{"text":80,"@type":76},"It argues that manual systematic reviews become impractical as the literature expands and cannot efficiently process large volumes of data.",{"name":82,"@type":73,"acceptedAnswer":83},"Why use both bibliometric analysis and LDA topic modelling?",{"text":84,"@type":76},"Bibliometrics helps reveal prominent trends, while LDA enables thematic discovery of latent topics; 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