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

IF 4.3 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Yomna Daoud, Aida Kammoun
{"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,&nbsp;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":null,"pages":null},"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,&nbsp;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\":null,\"pages\":null},\"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}
引用次数: 0

摘要

本文研究了技术-组织-环境(TOE)框架中影响中小企业决定是否采用电子商务(EC)的因素。为此,我们开展了一项问卷调查,向突尼斯 60 家制造业中小企业的经理或业主收集数据。与传统的回归方法不同,我们采用了新颖的机器学习(ML)技术,结果表明,与传统的逻辑回归方法相比,ML 技术在预测采用 EC 的驱动因素方面具有更高的性能。研究结果还表明,中小企业采用电子通信技术受到八个因素的显著影响,即信息技术供应商的支持、所采用技术的复杂程度、首席执行官(CEO)的创新能力、技术准备程度、客户压力、企业规模、基础设施兼容性和创新技术感知的相对优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Analyzing and Forecasting E-Commerce Adoption Drivers Among SMEs: A Machine Learning Approach

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Human Behavior and Emerging Technologies
Human Behavior and Emerging Technologies Social Sciences-Social Sciences (all)
CiteScore
17.20
自引率
8.70%
发文量
73
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信