{"title":"新兴市场跨国企业跨境收购完成情况再探:来自机器学习分析的归纳证据","authors":"Jianhong Zhang , Arjen van Witteloostuijn , Chaohong Zhou , Shengyang Zhou","doi":"10.1016/j.jwb.2024.101517","DOIUrl":null,"url":null,"abstract":"<div><p>Existing empirical studies of cross-border acquisition completion by emerging market multinational enterprises remain highly contextual, yielding inconsistent evidence regarding the determinants of deal success or failure. We apply machine learning to expose underlying complexities. The learning results of LightGBM, from data on 24,693 cross-border acquisition deals involving 29 emerging countries, unveil a comprehensive picture of the relative importance and impact patterns of 59 predictors that were fragmentally, inconsistently, or not at all presented in the extant literature. Our findings offer fresh insights into the deal completion of cross-border acquisitions by emerging market multinational enterprises, suggesting novel future research priorities.</p></div>","PeriodicalId":51357,"journal":{"name":"Journal of World Business","volume":null,"pages":null},"PeriodicalIF":8.9000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1090951624000014/pdfft?md5=2bafe38e685541eaac05379ea44fd252&pid=1-s2.0-S1090951624000014-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Cross-border acquisition completion by emerging market MNEs revisited: Inductive evidence from a machine learning analysis\",\"authors\":\"Jianhong Zhang , Arjen van Witteloostuijn , Chaohong Zhou , Shengyang Zhou\",\"doi\":\"10.1016/j.jwb.2024.101517\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Existing empirical studies of cross-border acquisition completion by emerging market multinational enterprises remain highly contextual, yielding inconsistent evidence regarding the determinants of deal success or failure. We apply machine learning to expose underlying complexities. The learning results of LightGBM, from data on 24,693 cross-border acquisition deals involving 29 emerging countries, unveil a comprehensive picture of the relative importance and impact patterns of 59 predictors that were fragmentally, inconsistently, or not at all presented in the extant literature. Our findings offer fresh insights into the deal completion of cross-border acquisitions by emerging market multinational enterprises, suggesting novel future research priorities.</p></div>\",\"PeriodicalId\":51357,\"journal\":{\"name\":\"Journal of World Business\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2024-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1090951624000014/pdfft?md5=2bafe38e685541eaac05379ea44fd252&pid=1-s2.0-S1090951624000014-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of World Business\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1090951624000014\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of World Business","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1090951624000014","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
Cross-border acquisition completion by emerging market MNEs revisited: Inductive evidence from a machine learning analysis
Existing empirical studies of cross-border acquisition completion by emerging market multinational enterprises remain highly contextual, yielding inconsistent evidence regarding the determinants of deal success or failure. We apply machine learning to expose underlying complexities. The learning results of LightGBM, from data on 24,693 cross-border acquisition deals involving 29 emerging countries, unveil a comprehensive picture of the relative importance and impact patterns of 59 predictors that were fragmentally, inconsistently, or not at all presented in the extant literature. Our findings offer fresh insights into the deal completion of cross-border acquisitions by emerging market multinational enterprises, suggesting novel future research priorities.
期刊介绍:
The Journal of World Business holds a distinguished position as a leading publication within the realm of International Business. Rooted in a legacy dating back to 1965, when it was established as the Columbia Journal of World Business, JWB is committed to disseminating cutting-edge research that reflects significant advancements in the field. The journal actively seeks submissions that propel new theoretical frameworks and innovative perspectives on International Business phenomena. Aligned with its domain statement, submissions are expected to possess a clear multinational, cross-border, or international comparative focus, while remaining pertinent to the study of management and organizations. JWB particularly encourages submissions that challenge established theories or assumptions, presenting pioneering or counterintuitive findings. With an inclusive approach, the journal welcomes contributions from diverse conceptual and theoretical traditions, encompassing allied social sciences and behavioral sciences. Submissions should either develop new theories or rigorously test existing ones, employing a variety of qualitative, quantitative, or other methodological approaches. While JWB primarily caters to scholars and researchers, it values contributions that explore implications for Multinational Enterprises and their management, as well as ramifications for public policy and the broader societal role of business.