A cognitive buying decision-making process in B2B e-commerce using Analytic-MLP

Vincent, Olufunke Rebecca; Makinde, Ayodeji Samuel; Akinwale, Adio T.

doi:10.1016/j.elerap.2017.08.002

Cited by 12 publications

(6 citation statements)

References 44 publications

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“…The many types of features that have been included in the paper are intended to enhance their usefulness. Principal component analysis, which was also used to statistically analyze the quality obtained by the usability prediction algorithms [1], was utilized to minimize the number of data. One aspect of e-commerce websites that may be evaluated based on information interchange on the Internet is usability.…”

Section: Introductionmentioning

confidence: 99%

Retracted: Performance optimization of unstructured E‐commerce log data for activity and pattern evaluation using web analytics

Shukla¹,

Mishra

Sinha

et al. 2022

IET Communications

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The above article from IET Communications, published online on 4 November 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

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Section: Introductionmentioning

confidence: 99%

Retracted: Performance optimization of unstructured E‐commerce log data for activity and pattern evaluation using web analytics

Shukla¹,

Mishra

Sinha

et al. 2022

IET Communications

View full text Add to dashboard Cite

show abstract

“…CNN is also applied as a content generator [48], and it is also used for text and image detection in social networks, brands, and retail [49]- [52]. Moreover, further applications of deep learning emerge in decision-making processes such as buying, which uses a multilayer perceptron neural network (MPL-NN) [53], or ranking products with hierarchical deep learning [47], [54]. Furthermore, the ANN is one of the most used deep learning algorithms.…”

Section: A Deep Learningmentioning

confidence: 99%

“…In the same way, a study focused on customers, studying their loyalty and asset management in hotels [85], the inconsistencies in their opinions in a cognitive purchase decision-making process [53], the classification of their elements directly for predictive recommendation [39], and their experiences using chatbots [23]. Publicity and campaigns have also used ML techniques to combine means of publicity (television and online) [72], estimate when, what, and how much to spend on publicity to increase profits [142], efficiently evaluate online publicity [88], and optimize micro-focalized techniques of campaigns [143].…”

Section: B Recommender Systemsmentioning

confidence: 99%

Machine Learning and Marketing: A Systematic Literature Review

2022

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Even though machine learning (ML) applications are not novel, they have gained popularity partly due to the advance in computing processing. This study explores the adoption of ML methods in marketing applications through a bibliographic review of the period 2008-2022. In this period, the adoption of ML in marketing has grown significantly. This growth has been quite heterogeneous, varying from the use of classical methods such as artificial neural networks to hybrid methods that combine different techniques to improve results. Generally, maturity in the use of ML in marketing and increasing specialization in the type of problems that are solved were observed. Strikingly, the types of ML methods used to solve marketing problems vary wildly, including deep learning, supervised learning, reinforcement learning, unsupervised learning, and hybrid methods. Finally, we found that the main marketing problems solved with machine learning were related to consumer behavior, recommender systems, forecasting, marketing segmentation, and text analysis-content analysis.

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“…Transnational consumption has become regulated; thus, more users have begun to purchase products from other countries through imported cross-border ecommerce channels. For this reason, with the growth of global demand, transnational online shopping has become an important way for people to consume on the Internet platform [4]. Tmall International, NetEase, Vipshop, and http://JD.com/ seized good opportunities to enter the cross-border shopping market, and the multinational ecommerce market developed rapidly [5,6].…”

Section: Introductionmentioning

confidence: 99%

Influence of Cultural Differences on the Establishment of Consumer Trust in a Socialized Cross-Border E-Commerce

Huang

2022

Mobile Information Systems

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This research paper examines the necessity of identifying cultural issues in online globalization and emphasizes that payment and logistic systems, as well as language, are the most important variables that any company should consider during their online globalization process. With the rapid development of the network and the continuous integration of the world economy, cross-border e-commerce has become more important and convenient. However, there are a number of electronic transaction problems that hinder the smooth functioning of this concept, thus creating distrust between the parties to the transaction, such as non-delivery, inaccurate quantities of goods, fraudulent transactions and low-quality goods, which are related to cultural differences, quality problems of the goods themselves and many other aspects. There is huge room for cross-border e-commerce development, and there are trust problems such as fraud and false transactions in all development processes. Based on the factors of cultural differences and consumer trust, this paper establishes an evaluation model of consumer trust in cross-border e-commerce, including honesty, goodwill, and ability. This article uses deep learning to establish a credit evaluation model, analyzes credit evaluation indicators, and obtains the factors that affect cross-border trade under different cultures through experimental testing and analysis.

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A cognitive buying decision-making process in B2B e-commerce using Analytic-MLP

Cited by 12 publications

References 44 publications

Retracted: Performance optimization of unstructured E‐commerce log data for activity and pattern evaluation using web analytics

Retracted: Performance optimization of unstructured E‐commerce log data for activity and pattern evaluation using web analytics

Machine Learning and Marketing: A Systematic Literature Review

Influence of Cultural Differences on the Establishment of Consumer Trust in a Socialized Cross-Border E-Commerce

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