{"title":"分析和预测中小企业采用电子商务的驱动因素:一种机器学习方法","authors":"Yomna Daoud, Aida Kammoun","doi":"10.1155/2024/7747136","DOIUrl":null,"url":null,"abstract":"<p>This paper investigated the factors in the technology–organization–environment (TOE) framework that affect the decision of whether to adopt electronic commerce (EC) or not within small- and medium-sized enterprises (SMEs). To this end, a questionnaire-based survey was conducted to collect data from 60 managers or owners of manufacturing SMEs in Tunisia. Unlike the traditional regression approaches, we referred to novel machine learning (ML) techniques and reveal that ML techniques reach a higher level of performance in forecasting driving factors to EC adoption compared to the traditional logistic regression approach. The achieved results also indicate that EC adoption within SMEs is significantly affected by eight factors, namely, IT vendors’ support, the adopted technology complexity degree, chief executive officer (CEO) innovativeness, technology readiness, customers’ pressure, firm size, infrastructure compatibility, and the innovative technology-perceived relative advantage.</p>","PeriodicalId":36408,"journal":{"name":"Human Behavior and Emerging Technologies","volume":"2024 1","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7747136","citationCount":"0","resultStr":"{\"title\":\"Analyzing and Forecasting E-Commerce Adoption Drivers Among SMEs: A Machine Learning Approach\",\"authors\":\"Yomna Daoud, Aida Kammoun\",\"doi\":\"10.1155/2024/7747136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This paper investigated the factors in the technology–organization–environment (TOE) framework that affect the decision of whether to adopt electronic commerce (EC) or not within small- and medium-sized enterprises (SMEs). To this end, a questionnaire-based survey was conducted to collect data from 60 managers or owners of manufacturing SMEs in Tunisia. Unlike the traditional regression approaches, we referred to novel machine learning (ML) techniques and reveal that ML techniques reach a higher level of performance in forecasting driving factors to EC adoption compared to the traditional logistic regression approach. The achieved results also indicate that EC adoption within SMEs is significantly affected by eight factors, namely, IT vendors’ support, the adopted technology complexity degree, chief executive officer (CEO) innovativeness, technology readiness, customers’ pressure, firm size, infrastructure compatibility, and the innovative technology-perceived relative advantage.</p>\",\"PeriodicalId\":36408,\"journal\":{\"name\":\"Human Behavior and Emerging Technologies\",\"volume\":\"2024 1\",\"pages\":\"\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7747136\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Human Behavior and Emerging Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1155/2024/7747136\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Behavior and Emerging Technologies","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/2024/7747136","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
Analyzing and Forecasting E-Commerce Adoption Drivers Among SMEs: A Machine Learning Approach
This paper investigated the factors in the technology–organization–environment (TOE) framework that affect the decision of whether to adopt electronic commerce (EC) or not within small- and medium-sized enterprises (SMEs). To this end, a questionnaire-based survey was conducted to collect data from 60 managers or owners of manufacturing SMEs in Tunisia. Unlike the traditional regression approaches, we referred to novel machine learning (ML) techniques and reveal that ML techniques reach a higher level of performance in forecasting driving factors to EC adoption compared to the traditional logistic regression approach. The achieved results also indicate that EC adoption within SMEs is significantly affected by eight factors, namely, IT vendors’ support, the adopted technology complexity degree, chief executive officer (CEO) innovativeness, technology readiness, customers’ pressure, firm size, infrastructure compatibility, and the innovative technology-perceived relative advantage.
期刊介绍:
Human Behavior and Emerging Technologies is an interdisciplinary journal dedicated to publishing high-impact research that enhances understanding of the complex interactions between diverse human behavior and emerging digital technologies.