Analysis of circular economy of E-commerce market based on grey model under the background of big data

Xu, Peng; Li, Xiang; Xiao, Yang

doi:10.1108/jeim-01-2021-0015

Cited by 20 publications

(11 citation statements)

References 30 publications

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“…, 2012). The advent of “Big Data” has transformed the firms' working styles (Peng et al. , 2022; Contreras Pinochet et al., 2021) and enforces a firm's top management to bring changes in their production system to capture a competitive advantage in the highly globalized markets (Aydiner et al ., 2019).…”

Section: Introductionmentioning

confidence: 99%

“…The importance of BDU in organizations has tremendously increased because of its innovative nature in strategically transforming business operations (Peng et al ., 2022; Narwane et al. , 2021; Aydiner et al.…”

Section: Introductionmentioning

confidence: 99%

“…Hadoop and MapReduce), which are used to create valuable insights from the large amount of dataset, are referred as big data use (BDU) (Sun and Shi, 2021;Chen et al, 2012). The advent of "Big Data" has transformed the firms' working styles (Peng et al, 2022;Contreras Pinochet et al, 2021) and enforces a firm's top management to bring changes in their production system to capture a competitive advantage in the highly globalized markets (Aydiner et al, 2019). Yang et al (2022) highlighted that big challenges using big data can be resolved to minimize the financial risk in the digital economy.…”

Section: Introductionmentioning

confidence: 99%

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The trickle-down effect of big data use to predict organization innovation: the roles of business strategy alignment and information sharing

Wang

Ali

Mehreen

et al. 2022

JEIM

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PurposeIn this age of digitalization, organizations are generating large data from the organizations' manufacturing processes that are valuable for capturing a competitive edge. Chinese small and medium enterprises (SMEs) can bring organizations radical innovation by investing in innovation projects (i.e. big data use; BDU) using the SMEs' scarce resources. Thus, the authors' research aims to predict Chinese SMEs' radical innovation (RI) through BDU using the theoretical lenses of the resource-based view. Moreover, the authors' study also pursues to realize the undermining mediating process of business strategy alignment (BSA) and the buffering role of information sharing in BDU–RI linkage.Design/methodology/approachIn total, 297 Chinese SMEs entrepreneurs and managers were recruited from the Yangtze River Economic Belt, China using a survey approach. In Mplus 7.4, the authors tested the proposed hypotheses.FindingsThe sample of 297 shows that BDU is directly and indirectly (via BSA) related to RI. Further, information sharing moderates the linkage between BDU and BSA and BSA and RI. The association between BSA and RI is only significant and stronger when information sharing is high.Practical implicationsThis research is beneficial for SME entrepreneurs/managers to enhance the understanding of BDU, eliminate challenges of BSA via BDU and align business strategies to bring RI to Chinese SMEs.Originality/valueSMEs always search for new ways to enhance SMEs' productivity using scarce resources. This is the first research that advances big data and innovation literature by predicting firm RI through BDU using a resource-based view. Moreover, this study is novel because the study investigates the mediation role of BSA and the moderating role of information sharing in the linkage between BDU and firm RI in Chinese SMEs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The trickle-down effect of big data use to predict organization innovation: the roles of business strategy alignment and information sharing

Wang

Ali

Mehreen

et al. 2022

JEIM

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“…Machine learning was one of the topics that we published some important articles (Feng and Qu, 2021; Peng et al , 2021; Sun and Shi, 2021; Yang et al , 2021c; Zhao and Chen, 2021). For example, Wu et al (2021) discussed how to realize the sustainable development of the social economy through the innovation of green computing technology.…”

Section: Article Classificationsmentioning

confidence: 99%

Guest editorial: Editorial for special issue on the role of big data on the transition to circular economy and sustainable operations management

Mardani

Weber

Khan

2022

JEIM

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“…The results of the study showed that customer behavior has a strong correlation with the time dimension. From the above literature review, it can be seen that most of the studies are mainly aimed at the classification of data in the field of e-commerce [ 18 – 20 ]. They mainly use decision trees and support vector machines, but they do not further predict the data in the field of e-commerce.…”

Section: Introductionmentioning

confidence: 99%

E-Commerce Information System Management Based on Data Mining and Neural Network Algorithms

Zhang

Abdullah

Chong

et al. 2022

Computational Intelligence and Neuroscience

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The rapid development of artificial intelligence technology has led to rapid development in various fields. It has many hidden related customer behavior information and future development trends in the e-commerce information system. The data mining technology can dig out useful information and promote the development of e-commerce. This research analyzes the significance and advantages of data mining technology in the application of e-commerce management systems and analyzes the related technologies of data mining and future trend prediction. This research has taken the advantages of clustering and naive Bayesian methods in data mining to classify product information and purchase preferences and other information and mine the associated data. Then, the nonlinear data processing advantages of neural networks are used to predict future purchasing power. The results show that data mining technology and neural networks have high accuracy in predicting future consumer purchasing power information. The correlation coefficient between real consumption data and predicted consumption data reached 0.9785, and the maximum relative average error was only 2.32%. It fully shows that data mining technology can obtain some unrecognizable related information and future consumption trends in e-commerce systems, and neural networks can also predict future consumption power and consumption patterns well.

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Analysis of circular economy of E-commerce market based on grey model under the background of big data

Cited by 20 publications

References 30 publications

The trickle-down effect of big data use to predict organization innovation: the roles of business strategy alignment and information sharing

The trickle-down effect of big data use to predict organization innovation: the roles of business strategy alignment and information sharing

Guest editorial: Editorial for special issue on the role of big data on the transition to circular economy and sustainable operations management

E-Commerce Information System Management Based on Data Mining and Neural Network Algorithms

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