企业卖家对企业对企业跨境电子商务平台的满意度：以阿里巴巴网站为例

IF 3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems Pub Date : 2024-01-24 DOI:10.1016/j.is.2024.102348

Jhong-Min Yang , Yu-Xin Xiang , Chi-Wen Liu

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引用次数: 0

摘要

卖家对使用跨境电子商务（CBEC）平台的满意度对卖家持续使用该平台至关重要。此外，卖家满意度也是企业制定 CBEC 业务战略的评估参考。本研究以阿里巴巴公司作为企业间跨境电子商务平台，对影响企业卖家使用平台满意度的因素进行了调查。本研究共收集了 184 个有效样本。使用智能偏最小二乘法软件进行统计分析后发现，本研究的结果与文献中的结果有所不同。研究对结果及其影响进行了全面讨论。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Enterprise sellers’ satisfaction with business-to-business cross-border e-commerce platforms: Alibaba.com as an example

Seller satisfaction of using a cross-border e-commerce (CBEC) platform is crucial for the platform's continual use by sellers. Furthermore, seller satisfaction is an evaluation reference for companies developing a CBEC business strategy. In this study, factors that affect enterprise sellers’ satisfaction of using a platform were investigated using Alibaba.com as a business-to-business CBEC platform. A total of 184 valid samples were collected in this study. Statistical analysis using Smart Partial least squares software revealed that the results of this study differed from those in the literature. The results and their implications were discussed comprehensively.

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来源期刊

Information Systems 工程技术-计算机：信息系统

CiteScore

9.40

自引率

2.70%

发文量

112

审稿时长

53 days

期刊介绍： Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.