یک تحلیل موردی درباره شیوه توزیع لجستیک تجارت الکترونیک
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یک تحلیل موردی درباره شیوه توزیع لجستیک تجارت الکترونیک

عنوان فارسی مقاله: شیوه توزیع لجستیک تجارت الکترونیک در زمینه داده های بزرگ: یک تحلیل موردی از JD.COM
عنوان انگلیسی مقاله: E-commerce logistics distribution mode in big-data context: A case analysis of JD.COM
مجله/کنفرانس: مدیریت بازاریابی صنعتی - Industrial Marketing Management
رشته های تحصیلی مرتبط: مدیریت، مهندسی صنایع
گرایش های تحصیلی مرتبط: تجارت الکترونیک، مدیریت کسب و کار، بازاریابی، مدیریت استراتژیک، مدیریت صنعتی، سیاست های تحقیق و توسعه، لجستیک و زنجیره تامین
کلمات کلیدی فارسی: تجارت الکترونیک، داده های بزرگ، نحوه توزیع، فرایند تحلیل سلسله مراتبی، روش آنتروپی، تاپسیس
کلمات کلیدی انگلیسی: E-commerce، Big data، Distribution mode، AHP، Entropy method، TOPSIS
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.indmarman.2019.10.009
دانشگاه: Logistics and E-Commerce College, Zhejiang Wanli University, Ningbo 315100, China
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 6/511 در سال 2019
شاخص H_index: 114 در سال 2020
شاخص SJR: 2/375 در سال 2019
شناسه ISSN: 0019-8501
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 9
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: دارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14843
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Literature review


3- Methodologies


4- Analyzing E-commerce distribution mode in a big-data context: a case analysis of JD.Com


5- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


This paper analyzes the existing distribution modes adopted by China's e-commerce enterprises. Based on the empirical analysis of the electronic mall at JD.com (Jing-Dong), this paper compares and investigates the different logistics distribution modes faced by e-commerce enterprises embracing the new features, new challenges, and new advantages of big data. The Analytic Hierarchy Process (AHP) method and entropy value are applied to investigate the e-commerce enterprise distribution choice mode and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to verify the model. Our research analysis and results bear strong managerial insights for e-commerce logistics distribution practitioners.


Introduction


Business consumers in the digital age have become increasingly informed, challenging industrial marketing and sales teams to adapt their traditional marketing strategies to fully embrace buyers' preferences and expectations. Forrester, in its latest research report, predicts that > 20% of companies will begin to apply modern technologies in their industrial marketing platforms in order to optimize the engagement between business buyers and sellers (Robertson et al., 2018). Technologies such as artificial intelligence (AI) and big data analytics have created unprecedented opportunities for companies to exploit their data assets for business-to-business (B2B) market initiatives. For instance, incorporating Lattice Engine's predictive analytics into its marketing initiatives, the industrial marketing team at Akamai was able to better segment its customers and to send personalized messages, sextupling its lead-to-opportunity conversion rate (Anderson 2018). In addition, companies including Google, Amazon, Facebook, and Apple have all made great efforts in the field of industrial marketing through collecting and utilizing big data. All of this underlines the importance of big data as a crucial factor in global marketing operations (Miguel & Casado 2016). Data also plays a key role in making different decisions about business supply chains and logistics operations that are closely related to the industrial marketing field. Supply chain management deals with creating and maintaining linkages between different entities with specific responsibilities, ranging from raw material procurement to enduser product interactions. Logistics management ensures that relevant work support methods, such as traffic management, warehouse management, inventory management, packaging, and order tracking, are in place. Employing a large and diverse range of data in logistics and supply chain management, companies can understand the needs and preferences of their customers. Electronic commerce (e-commerce) giants such as Amazon, Flipkart, and Snapdeal have been collecting and exploring data from customers, orders, inventory, and other information (Meena, 2017). The success of e-commerce companies now depends largely upon how efficiently they capture, store, and use data. The advent of the big-data era has further strengthened the relationship between logistics distribution and e-commerce, and this presents new opportunities, such as the expansion of enterprise information, the sharing of distribution channels, and the integration of data resources. Particularly, e-commerce enterprises can accurately predict the future needs of customers and can fulfill personalized services to customers. In addition, they can organize and coordinate the distribution activities beforehand in a well-planned way, allowing for better selection and innovation of distribution modes.

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