{"title":"了解贸易政策效应不确定性对企业创新投资的影响","authors":"Daniel Chen, Nan Hu, Peng Liang, Morgan Swink","doi":"10.1002/joom.1285","DOIUrl":null,"url":null,"abstract":"<p>Drawing on real options and resource dependence theories, this study examines how firms adjust their innovation investments to address trade policy effect uncertainty (TPEU), a type of firm-specific, perceived environmental uncertainty capturing managers' difficulty in predicting the impacts of potential policy changes on business operations. To develop a context-dependent, time-varying measure of TPEU, we apply bidirectional encoder representations from transformers, an advanced deep learning technique. We analyze the texts of mandatory management discussion and analysis sections of annual reports from 3181 publicly listed Chinese firms. Our sample comprises 22,669 firm-year observations spanning the years 2007 to 2019. The econometric analyses show that firms experiencing higher TPEU will reduce innovation investments. This effect is stronger for firms facing lower competition, involving more foreign sales, and not owned by the state. These findings provide clarity on previously inconclusive results by showcasing the significant influence of policy effect uncertainty, as opposed to policy state uncertainty, on firms' decisions regarding innovation investments. Additionally, these findings underscore the importance of resource dependence factors as crucial contextual factors in this decision-making process.</p>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"70 2","pages":"316-340"},"PeriodicalIF":6.5000,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Understanding the impact of trade policy effect uncertainty on firm-level innovation investment\",\"authors\":\"Daniel Chen, Nan Hu, Peng Liang, Morgan Swink\",\"doi\":\"10.1002/joom.1285\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Drawing on real options and resource dependence theories, this study examines how firms adjust their innovation investments to address trade policy effect uncertainty (TPEU), a type of firm-specific, perceived environmental uncertainty capturing managers' difficulty in predicting the impacts of potential policy changes on business operations. To develop a context-dependent, time-varying measure of TPEU, we apply bidirectional encoder representations from transformers, an advanced deep learning technique. We analyze the texts of mandatory management discussion and analysis sections of annual reports from 3181 publicly listed Chinese firms. Our sample comprises 22,669 firm-year observations spanning the years 2007 to 2019. The econometric analyses show that firms experiencing higher TPEU will reduce innovation investments. This effect is stronger for firms facing lower competition, involving more foreign sales, and not owned by the state. These findings provide clarity on previously inconclusive results by showcasing the significant influence of policy effect uncertainty, as opposed to policy state uncertainty, on firms' decisions regarding innovation investments. Additionally, these findings underscore the importance of resource dependence factors as crucial contextual factors in this decision-making process.</p>\",\"PeriodicalId\":51097,\"journal\":{\"name\":\"Journal of Operations Management\",\"volume\":\"70 2\",\"pages\":\"316-340\"},\"PeriodicalIF\":6.5000,\"publicationDate\":\"2023-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Operations Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/joom.1285\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Operations Management","FirstCategoryId":"91","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/joom.1285","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
Understanding the impact of trade policy effect uncertainty on firm-level innovation investment
Drawing on real options and resource dependence theories, this study examines how firms adjust their innovation investments to address trade policy effect uncertainty (TPEU), a type of firm-specific, perceived environmental uncertainty capturing managers' difficulty in predicting the impacts of potential policy changes on business operations. To develop a context-dependent, time-varying measure of TPEU, we apply bidirectional encoder representations from transformers, an advanced deep learning technique. We analyze the texts of mandatory management discussion and analysis sections of annual reports from 3181 publicly listed Chinese firms. Our sample comprises 22,669 firm-year observations spanning the years 2007 to 2019. The econometric analyses show that firms experiencing higher TPEU will reduce innovation investments. This effect is stronger for firms facing lower competition, involving more foreign sales, and not owned by the state. These findings provide clarity on previously inconclusive results by showcasing the significant influence of policy effect uncertainty, as opposed to policy state uncertainty, on firms' decisions regarding innovation investments. Additionally, these findings underscore the importance of resource dependence factors as crucial contextual factors in this decision-making process.
期刊介绍:
The Journal of Operations Management (JOM) is a leading academic publication dedicated to advancing the field of operations management (OM) through rigorous and original research. The journal's primary audience is the academic community, although it also values contributions that attract the interest of practitioners. However, it does not publish articles that are primarily aimed at practitioners, as academic relevance is a fundamental requirement.
JOM focuses on the management aspects of various types of operations, including manufacturing, service, and supply chain operations. The journal's scope is broad, covering both profit-oriented and non-profit organizations. The core criterion for publication is that the research question must be centered around operations management, rather than merely using operations as a context. For instance, a study on charismatic leadership in a manufacturing setting would only be within JOM's scope if it directly relates to the management of operations; the mere setting of the study is not enough.
Published papers in JOM are expected to address real-world operational questions and challenges. While not all research must be driven by practical concerns, there must be a credible link to practice that is considered from the outset of the research, not as an afterthought. Authors are cautioned against assuming that academic knowledge can be easily translated into practical applications without proper justification.
JOM's articles are abstracted and indexed by several prestigious databases and services, including Engineering Information, Inc.; Executive Sciences Institute; INSPEC; International Abstracts in Operations Research; Cambridge Scientific Abstracts; SciSearch/Science Citation Index; CompuMath Citation Index; Current Contents/Engineering, Computing & Technology; Information Access Company; and Social Sciences Citation Index. This ensures that the journal's research is widely accessible and recognized within the academic and professional communities.