RETRACTED: E-commerce logistics distribution mode in big-data context: A case analysis of JD.COM

Zheng, Kangning; Zhang, Zuopeng; Song, Bin

doi:10.1016/j.indmarman.2019.10.009

Cited by 86 publications

(51 citation statements)

References 52 publications

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“…Normalization is the normal operation of standardizing things. The direct influence matrix is standardized to obtain the standardized direct influence matrix by using formula (9). Then, according to the formula T = G(E-G) -1 , MATLAB software is used for matrix calculation, and the comprehensive influence matrix was obtained, as shown in Table 5.…”

Section: Resultsmentioning

confidence: 99%

“…Although the above studies have different emphases, they ignore the mutual coordination and limitations among different links of the value chain, which is not conducive to the overall grasp of the operation of platform enterprises and is more detrimental to the overall design of the platform business model. In addition, in terms of the application of big data in platform enterprises, most studies have adopted the method of case analysis and focused on well-known platform enterprises, such as Amazon [8], JD [9] and Taobao [10], lacking a universal integration framework. E-commerce platform is a virtual network used to carrying out transactions over the Internet and a management environment for ensuring a smooth business operation.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the application of big data technology in platform business model: A hierarchical framework

Du¹,

Gao²,

Chang³

et al. 2020

PLoS ONE

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This research aims to create a hierarchical framework for the development of a platform business model based on big data. However, this hierarchical framework must consider unnecessary attributes and the interrelationships between the aspects and the criteria. Hence, fuzzy set theory is used for screening out the unnecessary attributes, a decisionmaking and trial evaluation laboratory (DEMATEL) is proposed to manage the complex interrelationships among the aspects and attributes, and interpretive structural modeling (ISM) is used to divide the hierarchy and finally construct a hierarchical framework. The results reveal that (1) value proposition and community building in value production are fundamental links; (2) information technology and information management in value production are technical supports; (3) customer development in value marketing is the power source; and (4) value acquisition is the last link, which is established on the basis of and influenced by value marketing and value network. This hierarchical framework aims to guide the platform toward the application of big data. This study also proposes engagement of stakeholders for promoting value creation and establishing a sound business model from multiple levels and links.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the application of big data technology in platform business model: A hierarchical framework

Du¹,

Gao²,

Chang³

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Big data analytics can provide valuable insights in real-time about how the market is responding to products and campaigns [127]. According to obtained results, companies can adjust their prices, forecasting demand and its distribution, adjusting production, update distribution strategies, promotions, and campaign placements accordingly [128][129][130][131]. Therefore, to know the consumer mindset, it is necessary to apply smart decisions derived from big data [132].…”

Section: Marketing and E-commercementioning

confidence: 99%

Big Data: Analytical Solutions, Research Challenges and Trends

Ali¹,

Novikov²

2020

Proceedings of ISP RAS

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The term Big Data refers to an extensive collections of digital data generating every second. Produced datasets come in structured, semi-structured, and unstructured formats throughout the world, which is difficult for the traditional database management systems to analyze. Recently, big data analytics emerges as an essential research area due to the popularity of the Internet and the advent of new Web technologies. This growing area of research represents a multidisciplinary that attracts researchers from various research fields. Interested researchers are invited to design, develop, and implement several tools, technologies, architecture, and platforms for analyzing these large volumes of data. This paper begins with a brief introduction to big data and related concepts, including the main characteristics of big data, followed by discussions of the most significant open research challenges and emerging trends. Next, we review a study of big data analytics, the advantages of using big data solutions, and the preliminary assessments required before migrating from traditional solutions. Finally, we present a review of the recent main applications to obtain a broad perspective of big data analytics.

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“…In recent years, some relevant results on the selection of logistics service mode for last mile delivery can be seen. For the research on the concept and characteristics of the logistics service mode for last mile delivery, some scholars have obtained some results [6][7][8]. For example, Lim et al [6] found that the last mile delivery service is a part of supply chain with high cost and low operation efficiency, and showed that the suitable logistics service mode can reduce costs, improve operation efficiency and further optimize the delivery process.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Lim et al [6] found that the last mile delivery service is a part of supply chain with high cost and low operation efficiency, and showed that the suitable logistics service mode can reduce costs, improve operation efficiency and further optimize the delivery process. Zheng [8] proposed the concepts of self-run delivery, third-party delivery, and mixed delivery for last mile delivery, and analyzed the characteristics and advantages of these three modes.…”

Section: Introductionmentioning

confidence: 99%

The Logistics Service Mode Selection for Last Mile Delivery Considering Delivery Service Cost and Capability

et al. 2020

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The last mile delivery service is an important part in the logistics service process of express enterprises. How to select a suitable logistics service mode for last mile delivery to maximize the delivery service capacity and minimize delivery service cost is a noteworthy problem, but studies on this problem are still lacking. In this paper, we first analyze three potential logistics service modes for last mile delivery, i.e., self-run mode, outsourcing mode, and alliance mode, and then propose a selection framework of logistics service mode for last mile delivery based on a two-dimensional matrix decision model according to the two dimensions of delivery service cost advantage and delivery service capability advantage. Next, we give the calculation formulas for the delivery service cost and delivery service capability. Furthermore, we propose a method for logistics service mode selection for last mile delivery according to delivery service costs and delivery service capabilities of three potential logistics service mode. Finally, we show the feasibility and effectiveness of the proposed method by a case analysis.

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RETRACTED: E-commerce logistics distribution mode in big-data context: A case analysis of JD.COM

Cited by 86 publications

References 52 publications

Assessing the application of big data technology in platform business model: A hierarchical framework

Assessing the application of big data technology in platform business model: A hierarchical framework

Big Data: Analytical Solutions, Research Challenges and Trends

The Logistics Service Mode Selection for Last Mile Delivery Considering Delivery Service Cost and Capability

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