مقاله انگلیسی رایگان در مورد شیوه توزیع لجستیک تجارت الکترونیک در زمینه داده های بزرگ – الزویر ۲۰۲۰

elsevier

 

مشخصات مقاله
ترجمه عنوان 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
انتشار مقاله سال ۲۰۲۰
تعداد صفحات مقاله انگلیسی ۹ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس میباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۶٫۵۱۱ در سال ۲۰۱۹
شاخص H_index ۱۱۴ در سال ۲۰۲۰
شاخص SJR ۲٫۳۷۵ در سال ۲۰۱۹
شناسه ISSN ۰۰۱۹-۸۵۰۱
شاخص Quartile (چارک) Q1 در سال ۲۰۱۹
مدل مفهومی دارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط مدیریت، مهندسی صنایع
گرایش های مرتبط تجارت الکترونیک، مدیریت کسب و کار، بازاریابی، مدیریت استراتژیک، مدیریت صنعتی، سیاست های تحقیق و توسعه، لجستیک و زنجیره تامین
نوع ارائه مقاله
ژورنال
مجله  مدیریت بازاریابی صنعتی – Industrial Marketing Management
دانشگاه Logistics and E-Commerce College, Zhejiang Wanli University, Ningbo 315100, China
کلمات کلیدی 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
شناسه دیجیتال – doi
https://doi.org/10.1016/j.indmarman.2019.10.009
کد محصول E14843
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فهرست مطالب مقاله:
Abstract

۱- Introduction

۲- Literature review

۳- Methodologies

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

۵- Conclusion

References

بخشی از متن 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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