{"title":"在荟萃分析中使用机器学习方法:消费者对肉类替代品接受度的经验应用","authors":"Jiayu Sun, Vincenzina Caputo, Hannah Taylor","doi":"10.1002/aepp.13446","DOIUrl":null,"url":null,"abstract":"Meta‐analyses are widely used in various academic fields, including applied economics. However, the high labor intensity involved in paper searching and small sample sizes remain two dominant limiting factors. We conducted a meta‐analysis of studies on consumer preferences for plant‐based and lab‐grown meat alternatives using machine‐learning techniques at both the data collection and the data analysis phases. We demonstrated that machine learning reduces the workload in the manual title‐abstract screen phase by 69% accounting for 24% of total workload in data collection. We also found that machine learning improves out‐of‐sample of sample prediction accuracy by 48–78 percentage points when compared to econometric model. Notably, we showed that integrating machine learning can also improve the predictive performance of econometric methods, thereby improving their out‐of‐sample predictions. Our empirical findings further revealed that demand for meat alternatives is higher among younger consumers, especially when the products displayed benefit information.","PeriodicalId":8004,"journal":{"name":"Applied Economic Perspectives and Policy","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using machine‐learning methods in meta‐analyses: An empirical application on consumer acceptance of meat alternatives\",\"authors\":\"Jiayu Sun, Vincenzina Caputo, Hannah Taylor\",\"doi\":\"10.1002/aepp.13446\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Meta‐analyses are widely used in various academic fields, including applied economics. However, the high labor intensity involved in paper searching and small sample sizes remain two dominant limiting factors. We conducted a meta‐analysis of studies on consumer preferences for plant‐based and lab‐grown meat alternatives using machine‐learning techniques at both the data collection and the data analysis phases. We demonstrated that machine learning reduces the workload in the manual title‐abstract screen phase by 69% accounting for 24% of total workload in data collection. We also found that machine learning improves out‐of‐sample of sample prediction accuracy by 48–78 percentage points when compared to econometric model. Notably, we showed that integrating machine learning can also improve the predictive performance of econometric methods, thereby improving their out‐of‐sample predictions. Our empirical findings further revealed that demand for meat alternatives is higher among younger consumers, especially when the products displayed benefit information.\",\"PeriodicalId\":8004,\"journal\":{\"name\":\"Applied Economic Perspectives and Policy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Economic Perspectives and Policy\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1002/aepp.13446\",\"RegionNum\":2,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AGRICULTURAL ECONOMICS & POLICY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Economic Perspectives and Policy","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1002/aepp.13446","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURAL ECONOMICS & POLICY","Score":null,"Total":0}
Using machine‐learning methods in meta‐analyses: An empirical application on consumer acceptance of meat alternatives
Meta‐analyses are widely used in various academic fields, including applied economics. However, the high labor intensity involved in paper searching and small sample sizes remain two dominant limiting factors. We conducted a meta‐analysis of studies on consumer preferences for plant‐based and lab‐grown meat alternatives using machine‐learning techniques at both the data collection and the data analysis phases. We demonstrated that machine learning reduces the workload in the manual title‐abstract screen phase by 69% accounting for 24% of total workload in data collection. We also found that machine learning improves out‐of‐sample of sample prediction accuracy by 48–78 percentage points when compared to econometric model. Notably, we showed that integrating machine learning can also improve the predictive performance of econometric methods, thereby improving their out‐of‐sample predictions. Our empirical findings further revealed that demand for meat alternatives is higher among younger consumers, especially when the products displayed benefit information.
期刊介绍:
Applied Economic Perspectives and Policy provides a forum to address contemporary and emerging policy issues within an economic framework that informs the decision-making and policy-making community.
AEPP welcomes submissions related to the economics of public policy themes associated with agriculture; animal, plant, and human health; energy; environment; food and consumer behavior; international development; natural hazards; natural resources; population and migration; and regional and rural development.