{"title":"明天早上送货比今天晚送货好?客户对网络卖家物流服务质量感知中的时间效应","authors":"Fei (Sophie) Song, Yuhang Xu, Heng Chen, Kunpeng Zhang","doi":"10.1002/tjo3.12017","DOIUrl":null,"url":null,"abstract":"Drawing upon energy depletion theory and expectancy disconfirmation theory, this study aims to zoom in on the effect of delivery time on customer perception of online seller logistics service quality (LSQ). We conjecture that customer rating of LSQ will vary depending on the delivery time in a day (i.e., the time‐of‐day effect). With a large sample consisting of more than 42 million orders from Alibaba, the results from mixed‐effects ordered logit model corroborate the time‐of‐day effect and that promised delivery service interacts with the time‐of‐day effect by strengthening it. Following that, machine learning techniques are employed to quantify the importance of the different predictors and results show that the time‐of‐day effect is the most important predictor. The study reveals a new time‐related attribute, contributes to the LSQ framework, and has important managerial implications for practitioners.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":" 10","pages":""},"PeriodicalIF":16.4000,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Better to deliver tomorrow morning than late today? The time‐of‐day effect in customer perception of online sellers' logistics service quality\",\"authors\":\"Fei (Sophie) Song, Yuhang Xu, Heng Chen, Kunpeng Zhang\",\"doi\":\"10.1002/tjo3.12017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Drawing upon energy depletion theory and expectancy disconfirmation theory, this study aims to zoom in on the effect of delivery time on customer perception of online seller logistics service quality (LSQ). We conjecture that customer rating of LSQ will vary depending on the delivery time in a day (i.e., the time‐of‐day effect). With a large sample consisting of more than 42 million orders from Alibaba, the results from mixed‐effects ordered logit model corroborate the time‐of‐day effect and that promised delivery service interacts with the time‐of‐day effect by strengthening it. Following that, machine learning techniques are employed to quantify the importance of the different predictors and results show that the time‐of‐day effect is the most important predictor. The study reveals a new time‐related attribute, contributes to the LSQ framework, and has important managerial implications for practitioners.\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":\" 10\",\"pages\":\"\"},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2024-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1002/tjo3.12017\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/tjo3.12017","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Better to deliver tomorrow morning than late today? The time‐of‐day effect in customer perception of online sellers' logistics service quality
Drawing upon energy depletion theory and expectancy disconfirmation theory, this study aims to zoom in on the effect of delivery time on customer perception of online seller logistics service quality (LSQ). We conjecture that customer rating of LSQ will vary depending on the delivery time in a day (i.e., the time‐of‐day effect). With a large sample consisting of more than 42 million orders from Alibaba, the results from mixed‐effects ordered logit model corroborate the time‐of‐day effect and that promised delivery service interacts with the time‐of‐day effect by strengthening it. Following that, machine learning techniques are employed to quantify the importance of the different predictors and results show that the time‐of‐day effect is the most important predictor. The study reveals a new time‐related attribute, contributes to the LSQ framework, and has important managerial implications for practitioners.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.