Predicting Purchasing Behavior on E-Commerce Platforms: A Regression Model Approach for Understanding User Features that Lead to Purchasing

This research introduces a novel approach to improve and optimize the predictive capacity of consumer purchase behaviors on e-commerce platforms. This study presented an introduction to the fundamental concepts of the logistic regression algorithm. In addition, it analyzed user data obtained from an e-commerce platform. The original data were preprocessed, and a consumer purchase prediction model was developed for the e-commerce platform using the logistic regression method. The comparison study used the classic random forest approach, further enhanced by including the K-fold cross-validation method. Evaluation of the accuracy of the model’s classification was conducted using performance indicators that included the accuracy rate, the precision rate, the recall rate, and the F1 score. A visual examination determined the significance of the findings. The findings suggest that employing the logistic regression algorithm to forecast customer purchase behaviors on e-commerce platforms can improve the efficacy of the approach and yield more accurate predictions. This study serves as a valuable resource for improving the precision of forecasting customers’ purchase behaviors on e-commerce platforms. It has significant practical implications for optimizing the operational efficiency of e-commerce platforms.

Dynamic Pricing Model of E-Commerce Platforms Based on Deep Reinforcement Learning

With the continuous development of artificial intelligence technology,its application field has gradually expanded.To further apply the deep reinforcement learning technology to the field of dynamic pricing,we build an intelligent dynamic pricing system,introduce the reinforcement learning technology related to dynamic pricing,and introduce existing research on the number of suppliers(single supplier and multiple suppliers),environmental models,and selection algorithms.A two-period dynamic pricing game model is designed to assess the optimal pricing strategy for e-commerce platforms under two market conditions and two consumer participation conditions.The first step is to analyze the pricing strategies of e-commerce platforms in mature markets,analyze the optimal pricing and profits of various enterprises under different strategy combinations,compare different market equilibriums and solve the Nash equilibrium.Then,assuming that all consumers are naive in the market,the pricing strategy of the duopoly e-commerce platform in emerging markets is analyzed.By comparing and analyzing the optimal pricing and total profit of each enterprise under different strategy combinations,the subgame refined Nash equilibrium is solved.Finally,assuming that the market includes all experienced consumers,the pricing strategy of the duopoly e-commerce platform in emerging markets is analyzed.

A Novel Named Entity Recognition Scheme for Steel E-Commerce Platforms Using a Lite BERT

In the era of big data,E-commerce plays an increasingly important role,and steel E-commerce certainly occupies a positive position.However,it is very difficult to choose satisfactory steel raw materials from diverse steel commodities online on steel E-commerce platforms in the purchase of staffs.In order to improve the efficiency of purchasers searching for commodities on the steel E-commerce platforms,we propose a novel deep learning-based loss function for named entity recognition(NER).Considering the impacts of small sample and imbalanced data,in our NER scheme,the focal loss,the label smoothing,and the cross entropy are incorporated into a lite bidirectional encoder representations from transformers(BERT)model to avoid the over-fitting.Moreover,through the analysis of different classic annotation techniques used to tag data,an ideal one is chosen for the training model in our proposed scheme.Experiments are conducted on Chinese steel E-commerce datasets.The experimental results show that the training time of a lite BERT(ALBERT)-based method is much shorter than that of BERT-based models,while achieving the similar computational performance in terms of metrics precision,recall,and F1 with BERT-based models.Meanwhile,our proposed approach performs much better than that of combining Word2Vec,bidirectional long short-term memory(Bi-LSTM),and conditional random field(CRF)models,in consideration of training time and F1.

Digital Marketing and B2B Cross-Border E-Commerce: A Case Study of Alibaba International Station in China

The rapid development of the“Internet+”has ushered in a new era of global digital technology innovation.Concurrent with the deepening trends of economic globalization and integration,international trade is progressively undergoing extensive digitization,with cross-border e-commerce emerging as a significant manifestation of this transformation.Within this landscape,the cross-border e-commerce B2B platform is anticipated to become a pivotal driver for China’s cross-border trade growth,representing a strategic opportunity for trade transformation.This paper provides a comprehensive examination of the concepts,characteristics,and development status of cross-border e-commerce and B2B platforms,considering both global and Chinese perspectives.Focusing on a prominent case study,Alibaba International Station,a B2B cross-border e-commerce platform,the paper delves into its business model and core competencies,offering a thorough analysis of its role in the intricacies of international e-commerce and its contribution to the field.Drawing on insights gained,the paper concludes by presenting targeted recommendations from both the platform and government perspectives.These suggestions are informed by a nuanced understanding of the development opportunities and challenges facing China’s cross-border e-commerce B2B platforms.

Effects of E-Commerce Platforms on Firm Export——Evidence from China’s Industrial Enterprises

With deepening digital technology applications in international trade, e-commerce platforms have become a major avenue for firm export. Based on a multicountry, multi-product heterogeneous firms trade theory model, this paper investigates the firm export effects of e-commerce platforms, and empirically tests the inferences from the model using data from Alibaba China Station, China industrial enterprises data, and China customs data from 2000 to 2009. Our estimation result shows that overall, e-commerce platforms significantly increase firm export probability and volume;e-commerce platforms enable firms to export products of different types to more countries by reducing the cost of information and export markets threshold while raising trade efficiency. Further analysis of enterprise heterogeneity reveals that SMEs, firms in China's eastern region, and general exporters benefit more from e-commerce platforms than other types of firms do.

Application of the E-commerce Platform in the International Trade

E-commerce is a new consumption mode, which develops rapidly and is welcomed by consumers. It also plays an important role in promoting the international trade. Through this analysis, we can understand that the influence of the e-commerce on the international market has both positive and negative aspects. We need to be aware of our shortcomings, understand the gap between them and the developed countries, and formulate the relevant policies. Let China's e-commerce continue to improve and innovate in the international trade, and find a suitable strategy for its own development. Relevant Chinese departments should actively and effectively participate in the construction of e-commerce. On the one hand, they can stabilize the status of e-commerce in the international trade, and on the other hand, they can expand the consumer groups and make the electronic goods better.

Evolutionary game analysis of information service quality control of e-commerce platforms under information ecology

Due to the information asymmetry and imperfect supervision system,the problem of information service quality of e-commerce platforms is becoming increasingly prominent.Based on the perspective of information ecology,this paper constructs a three-party evolutionary game model including merchants,e-commerce platforms and governments,and analyzes the dynamic process of the threeparty game under bounded rationality,thereby characterizing the behavior and optimal strategies of information service quality control.We carry out numerical simulation using data of the Pinduoduo platform.The results show that the cost of each party,control strength of e-commerce platforms,proportion of margin deducted and other factors are important factors affecting the quality of information service;Only when the sum of the costs of passive management of e-commerce platforms,penalties for merchants,and the additional revenue generated from the active management exceeds the cost of the active management of the e-commerce platforms,it ensures effective control over the quality of information service in a stable market environment.Finally,some suggestions are put forward to optimize the e-commerce information service quality control.

Price Masking Strategy of Cross-Border E-Commerce Platform Financing

E-commerce platform financing is a new service pattern of supply chain finance.However,this pattern may bring some new issues when considering the problem of cash flow shortage and financing difficulties of small and medium-sized enterprises.When enterprises use this service,they worry about the leakage of preferential wholesale price when applying the full loan amount and providing the true transaction information.Based on the model consisting of a supplier,a retailer and a cross-border e-commerce platform,the authors design a price masking strategy to prevent the retailer’s preferential wholesale price information from leakage.The authors analyze the profit of the retailer and the platform before and after adopting the price masking strategy.The authors find that the price masking strategy always benefits the retailer.Besides,the optimal profit of the retailer and the platform are both affected by the loan interest rate.Moreover,there exists a range of loan interest rates that can benefit both the retailer and the platform if the price masking strategy is adopted.The research emphasizes that platform can expand the total business volume by allowing retailers to use price masking strategy.In other words,there will be more and more retailers attracted by the strategy,which benefits the long-term growth of cross-border e-commerce platform financing.

Leveling the Playing Field China’s first e-commerce law takes effect in an effort to regulate the industry

Gao Difan,27,was overjoyed with the Givenchy lipstick she bought herself as a Christmas gift via a daigou.on Taobao,one of the most famous e-commerce platforms in China.A daigou is the name given to an intermediary who travels or lives abroad and buys products on behalf of people in China.

Study on the Tort Liability of Electronic Commerce Platform

Today,the rapid development of electronic commerce(E-commerce)has added a new driving force for China’s economic development.However,all kinds of legal problems have followed,such as uncontrolled production and sale of fake goods,frequent malicious complaints,endless unfair competition,and illegal infringement,which have harmed the legitimate interests of consumers and seriously damaged the market order and fair competition.In order to provide some theoretical and realistic reference for the development of China’s E-commerce industry,this paper focuses on the relevant problems in the field of“infringement”of E-commerce platforms for in-depth thinking,and puts forward suggestions for solving the existing problems based on the development status of E-commerce platforms.

Comparative Analysis of the Effects of False Low-Price Marketing Strategy on Multiple Platforms

In order to make full use of the characteristics of commodity prices,merchants on e-commerce platforms have adopted the low-price marketing strategy.Regular promotional discounts can bring new vitality to the commodity sales market,but extreme discount marketing methods would lead to serious impacts on the sales of competing products,thus affecting the stable development of the online shopping market.The sales data of four electrical products using the false low-price marketing strategy on three e-commerce platforms(Taobao,JD,and Amazon)were used in this study.The sales data from different e-commerce platforms and different time periods were analyzed,and one-way ANOVA was used on the factors affecting the effect of marketing strategy.The results showed that there is a significant difference between the direct marketing of high-priced products and low-priced products on Taobao;the difference between the marketing effects of high-priced products and mid-priced products on JD and Amazon is significant.This analysis would help businesses formulate reasonable marketing strategies and promote the stable development of the online shopping market.

Generational Dynamics of Innovation Adoption in Chinese Consumer Markets: A Comprehensive Analysis

This extensive and comprehensive study delves into the intricate dynamics of generational responses to innovative marketing strategies within the dynamic landscape of the Chinese consumer market.Given China’s rapid economic growth and technological advancements,it has become imperative for businesses and marketers to grasp how different generations engage with marketing innovations.This study encompasses Baby Boomers,Generation X,Millennials,and Generation Z,each shaped by unique life experiences and societal contexts,resulting in distinct preferences and behaviors.Furthermore,the study offers a thorough analysis of how diverse generations in China interact with and respond to innovative marketing strategies,providing academic researchers and businesses operating in the rapidly evolving Chinese consumer marketing landscape with actionable insights.Understanding these generational dynamics is crucial for developing marketing strategies that resonate with diverse generational segments and harnessing the power of technology to connect with consumers across all age groups.The dynamic landscape is further enriched by the proliferation of digital technologies,social media platforms,and e-commerce ecosystems.This study scrutinizes how these generational cohorts interact with innovation in marketing,considering preferences,technological adoption patterns,cultural influences,and attitudes toward trust and privacy.Additionally,it examines generational disparities in marketing channel preferences,offering valuable insights for companies aiming to tailor their marketing strategies to effectively engage diverse generational segments in China.Importantly,this research underscores the strategic significance of understanding generational differences in marketing innovation adoption.It emphasizes that this knowledge is not solely an academic pursuit but rather a critical necessity for companies seeking to thrive in one of the most competitive consumer markets globally.By acknowledging and responding to the distinct preferences and behaviors of various generational cohorts,businesses can forge meaningful connections,optimize return on investment,and adeptly navigate the evolving consumer landscape.

Theoretical analysis of brand-building of e-commerce platforms

Brand-building is inevitable for e-commerce platforms,based on the economic theory of heterogeneous perfect competition.By investing heterogeneous factors,such as innovative entities into the platforms,their basic business can achieve a natural monopoly in the market,which constitutes a necessary condition for them to build their brands.E-commerce platforms have invalidated the traditional hypothesis of asset specificity,and its value-added business targets brand-building by selling products of multiple varieties and in small batches.

电子商务平台的设计

本文通过对电子商务平台的定位、系统功能等具体需求的分析,进行了对电子商务平台的设计,努力把电子商务平台项目建设为国内一流的电子商品交易平台,实践"互联网+"行动计划。

Impacts of Platform’s Omnichannel Coupons on Multichannel Suppliers

While coupons that can be redeemed only in online channels have been issued by e-commerce platforms for decades, a new type of platform’s coupons, i.e., omnichannel coupons, which can be redeemed in both online and store channels, is gaining popularity with the rise of the omnichannel retail mode. It is interesting to explore the conditions under which omnichannel coupons are more advantageous to platforms and multichannel suppliers that sell products through platforms and physical stores. Two game models are developed in two cases where an e-commerce platform offers single channel coupons or omnichannel coupons for a multichannel supplier. Two scenarios are considered: one in which a consumer’s valuation of a product that fits his or her need is homogeneous and another in which the valuation is heterogeneous. Equilibrium outcomes show that under the homogeneous scenario, the product price and coupon face value in both coupon modes increase with the product’s fit probability when the cross-selling revenue is high, while decrease with the product’s fit probability when the cross-selling revenue is low. However, under the heterogeneous scenario, the price in both modes increases with the product’s fit probability only when the supplier’s loss from returns is low and the cross-selling revenue is high, and the coupon face value always decreases with the product’s fit probability. Compared with single channel coupons, omnichannel coupons may lead to a higher product price under certain conditions. Furthermore, omnichannel coupons can lead to higher total demand and benefit both the platform and the supplier if and only if the product’s fit probability is low and the supplier’s loss from returns is high. An extension shows that the platform’s preference for omnichannel coupons is weakened when the supplier offers a partial refund policy.

The Mobile Personalized Recommendation Model Containing Implicit Intention

Because mobile e-commerce is limited by the mobile terminal,network environment and other factors,accurate personalized recommendations become more and more important.We establish a large data intelligence platform,aiming at the characteristics of mobile e-commerce;we put forward a personalized recommendation model with implicit intention further.Firstly,create an intelligence unit with the virtual individual association set,virtual demand association set and virtual behavior associated set;Secondly,calculate the complex buying behavior prediction engine;Finally,give the predictive value of complex buying behavior.This method takes full account of factors such as hidden wishes perturbations that affect the predict of complex buying behavior,which to some extent solve a long-span composite purchasing behavior prediction.It shows that this method improves the purchasing behavior prediction accuracy effectively through experiments.

Recycling decision,fairness concern and coordination mechanism in EC-CLSC

Taking the e-commerce platform’s fairness concern into account,three decision-making models of e-commerce closed-loop supply chain are constructed.Then,the recycling decisions under three models are studied and the impact of fairness concern is analysed.The‘effort costs sharing contract’is proposed to realise the coordination of the supply chain.Finally,the conclusions are verified by numerical analysis.The results show that the e-commerce platform’s fairness concern reduces own profit and the remanufacturer’s profit,and decreases system efficiency.The‘effort costs sharing contract’can realise the centralised model where recycling price of waste products is the highest,commission is the lowest,and system profit is the highest.In the coordinated system,the higher the proportion of remanufacturer sharing effort costs,the higher the profit of remanufacturer.

GOLDEN FLEECERS

A system error that struck at midnight on January 20 proved a gold mine for several users of Pinduoduo (PDD), China's third-largest e-commerce platform: The glitch allowed an unlimited number of 100 RMB 44red packet" coupons to be sold at the bargain price 0.4 RMB each.