User Purchase Intention Prediction Based on Improved Deep Forest

Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection.To address this issue,based on the deep forest algorithm and further integrating evolutionary ensemble learning methods,this paper proposes a novel Deep Adaptive Evolutionary Ensemble(DAEE)model.This model introduces model diversity into the cascade layer,allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns.Moreover,this paper optimizes the methods of obtaining feature vectors,enhancement vectors,and prediction results within the deep forest algorithm to enhance the model’s predictive accuracy.Results demonstrate that the improved deep forest model not only possesses higher robustness but also shows an increase of 5.02%in AUC value compared to the baseline model.Furthermore,its training runtime speed is 6 times faster than that of deep models,and compared to other improved models,its accuracy has been enhanced by 0.9%.

Promoting sustainable consumption and circular economy:the intention of Vietnamese youth consumers to purchase products made from recycled plastics

Recycling is viewed as a key component in a circular economy and serves as an ideal solution for promoting sustainability.During the global plastic crisis,plastic recycling practices have been adopted worldwide,leading to the production of various products made from recycled plastics(PRP).Nevertheless,a gap persists between consumption and demand for such products,which is primarily attributed to a lack of comprehension from the consumer perspective.Given the pivotal role consumers play in the adoption of these products,this study explores consumers’intentions to purchase PRP.This is particularly significant in Vietnam,which is an emerging economy aspiring to achieve the objectives of a circular economy and sustainable development.Utilizing an integrated cognitive-emotional framework comprising the Valence Theory and the Norm Activation Model,data from 564 Vietnamese students were gathered and analyzed using structural equation modeling.The results show that awareness of consequences is a major driver of consumer purchase intentions,followed by perceived ease of application and monetary incentives.The results also indicate that health concerns have the strongest effect on purchase intention and in the negative side,meaning that the health-related risk is the primary concern for consumers during the decision-making process.This research holds substantial value for academics and managers,as it aids in the theoretical exploration and the formulation of strategies to improve consumer acceptance of PRP.

The Impact of E-Commerce Live Broadcasts on Consumers’Purchase Intention

E-commerce live broadcast has an important influence on consumers’purchase intention.The three dimensions of live broadcast content in the broadcast room are the number of comments,product quality,and live content as independent variables.A theoretical model is constructed with perceived value and risk as intermediaries and the consumers’purchase intention as the dependent variable,and corresponding hypotheses are put forward.We designed the scale,collected relevant data,and tested the model hypothesis using Statistical Package for Social Sciences(SPSS)and Analysis of Moment Structure(AMOS)software.The study found that the number of comments and product quality had a significant impact on perceived value,perceived risk,and consumers’purchase intention.From this conclusion,it is suggested that businesses should control the number of comments,strengthen the product quality of comments,and distinguish the repetition degree of the content of live broadcasts.

Theoretical Analysis of Consumers’Purchase Motivation in the Context of Environmental Protection

Through reviewing and summarizing the existing research on consumer motivation,and combining the characteristics of human behavior and the objective needs of environmental protection,this paper analyzes and defines consumers’purchase motivation.In the context of environmental protection,purchase motivation is divided into demand motivation,value recognition motivation,recognition motivation,and interest motivation.Their definitions are given in this paper,which provides a conceptual basis for further research on the influence of purchase motivation on consumer behavior in the context of environmental protection.

A Study on the Factors Influencing Consumer Purchase Decision Under the Live-Streaming Sales Model

In recent years,with the rapid development and popularization of Internet information technology,many new media platforms have risen rapidly,and major e-commerce companies have begun to explore the mode of livestreaming.Especially during the COVID-19 pandemic,due to the lockdown,live-streaming has become an important means of economic development in many places.Owing to its remarkable characteristics of timeliness,entertainment,and interactivity,it has become the latest and trendiest sales mode of e-commerce channels,reflecting huge economic potential and commercial value.This article analyzes two models and their characteristics of live-streaming sales from a practical perspective.Based on this,it outlines consumer purchasing decisions and the factors that affect consumer purchasing decisions under the live-streaming sales model.Finally,it discusses targeted suggestions for using the live-streaming sales model to expand the consumer market,hoping to promote the healthy and steady development of the live-streaming sales industry.

Prediction of users online purchase behavior based on selective ensemble learning

A probabilistic multi-dimensional selective ensemble learning method and its application in the prediction of users' online purchase behavior are studied in this work.Firstly, the classifier is integrated based on the dimension of predicted probability, and the pruning algorithm based on greedy forward search is obtained by combining the two indicators of accuracy and complementarity.Then the pruning algorithm is integrated into the Stacking ensemble method to establish a user online shopping behavior prediction model based on the probabilistic multi-dimensional selective ensemble method.Finally, the research method is compared with the prediction results of individual learners in ensemble learning and the Stacking ensemble method without pruning.The experimental results show that the proposed method can reduce the scale of integration, improve the prediction accuracy of the model, and predict the user's online purchase behavior.

Optimization of Electricity Purchase and Sales Strategies of Electricity Retailers under the Condition of Limited Clean Energy Consumption

In the process of my country’s energy transition,the clean energy of hydropower,wind power and photovoltaic power generation has ushered in great development,but due to the randomness and volatility of its output,it has caused a certain waste of clean energy power generation resources.Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption,this paper establishes a quantitative model of clean energy restricted electricity fromthe perspective of power system supply and demand balance.Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid-to long-term electricity market and the day-ahead market.Through the multi-scenario analysis method,the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene,and the typical scene is obtained by using the hierarchical clustering algorithm.This paper establishes a electricity retailer’s risk decisionmodel for purchasing and selling electricity in themid-and long-term market and reduce-abandonment market,and takes the maximum profit expectation of the electricity retailer frompurchasing and selling electricity as the objective function.At the same time,in themediumand longterm electricity market and the day-ahead market,the electricity retailer’s purchase cost,electricity sales income,deviation assessment cost and electricity purchase and sale risk are considered.The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies,on the premise of balancing profits and risks.

Nonfarm employment, agricultural machinery service purchase and agricultural production efficiency:An empirical study based on China's major wheat-producing areas

Based on the cross-sectional data of the survey conducted in China's main wheat producing areas at the end of 2019,this paper uses the translog production function model to estimate agricultural technical efficiency,and studies the relationship between nonfarm employment,agricultural machinery service purchase and agricultural production efficiency.The results show that the household non-agricultural employment ratio and non-agricultural income have a significant positive impact on the purchase of agricultural machinery services.In addition to the effect of scale efficiency,non-agricultural employment does not have a significant impact on agricultural technical efficiency,and labor transfer does not have a significant negative impact on agricultural production.

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.

Consumer Psychology in the Digital Age:How Online Environments Shape Purchasing Habits

The advent of the digital age has profoundly changed consumers’mindsets and habits.The rapid development of e-commerce and the widespread use of mobile applications have created enormous demand for individual recommendation systems based on mass data.This system not only increases the convenience of purchases and conversions but also alters the purchasing behavior of consumers,leading them to make choices subconsciously.Potential risks associated with large-scale data sharing and usage have heightened consumer concerns regarding privacy,thereby weakening the foundational trust in platforms and deterring them from shopping.Additionally,the rapid growth of e-commerce in the digital age,coupled with changing market circumstances,has intensified psychological pressure on consumers,making their decision-making processes more complex and difficult.Furthermore,the program will explore issues related to improving customer experience,developing individual marketing strategies,and designing customer loyalty plans.It will also address questions of privacy in a digital environment,the dilemmas of excessive or disruptive consumption behavior,and the complexity and diversity of consumer behavior in the face of digital change.The objective of this study is to develop a fear study that will enable a better understanding of the impact of online shopping on consumer behavior and provide a strategic guide for retailers to meet the challenges and opportunities presented by the digital age.

Optimization purchase price and the profit policy undervendor managed inventory

The optimization policy of the purchase price and the profit under vendor managed inventory(VMI) is studied. For a salable product, supply chain mode of VMI is established, which is based on deterministic demand, having initial stock and having stock-out cost. With the further analysis of the mode, VMI is found to increase profits of the buyer in the short-term motivation. But VMI will reduce profits of the supplier under the matching condition. And in the short-term motivation, VMI will increase the purchase price to compensate the transfer cost of the supplier. As a result, the foundation of theory is provided to implement VMI in the supply chain, and have some definituded project significance.

The Influence of Perceived Risk on Purchase Intention——A Case study of Taobao Online Shopping of Fresh Fruit

Based on perceived risk,the impacts of different kinds of risks on consumer's purchase intention are tested by using the investigation data of 2016,and perceived risks of consumers from different groups have different impacts on purchase intention. Via interviews and online questionnaires,425 copies of effective samples were obtained. The structural equation modeling is used for research. Results display that product risk significantly affects purchase intention,and perceived risk among different groups has significant difference. According to research conclusion,the authors think that consumer should actively collect all kinds of information and take effective risk avoidance measures; Taobao platform should continuously perfect purchase rule,punish the behavior of leaking buyers' information,and continuously update Alipay software to prevent hacker intrusion; business should guarantee product quality,open information and make scientific marketing strategy.

The Impacts of Agricultural Machinery Purchase Subsidies on Mechanized Crop Residue Recycling

Crop residue recycling can improve the quality of the cropland,and it has multiple economic and ecological benefits.However,such practice is with low adoption due to different constraints.In this paper,we use the survey data from Baoding,Hebei province,and use the probit model to explore how the agricultural machinery purchase subsidies affect the mechanized crop residue recycling.The results showed that several factors that affect farmers in adopting the practice of mechanized crop residue crop recycling.Among these factors,the cost of adopting such practice is significant.The agricultural machinery purchase subsidies can effectively reduce the cost of such practice,as well as promote mechanized crop residue recycling.The paper also proposed several actions in the future.They include increasing the subsidies on agricultural machinery purchase and increasing farmers' awareness on crop residue recycling.

Empirical Analysis on Factors Affecting Mobile Internet Users’ Purchase Intention for 3G Handsets

The research on consumers' purchase intention for 3G handsets has important theoretical and practical value. This research puts forward four key factors which influence consumers' purchase intention based on the cue utilization theory, situational theory and social impact theory, then it establishes the study model for consumers' purchase intention of 3G handsets. Based on the result of the questionnaire survey and empirical analysis, the study shows that social influence is the most prominent factor in purchase intention. Moreover, as different from results of previous studies, the situational influence factors have no significant impact on purchase intention in the 3G handsets market. The results of the model provides a forceful evidence for operators' and handset manufacturers' decision-making of 3G handsets design and marketing strategies, and it will improve their social impact.

Demand prediction and purchase optimization decision model for alloys in steel making

In this study,related models of alloy purchasing decision system in the Baoshan base of Baosteel are discussed.First,the corresponding relationship between steel grades and alloy consumption is established through metallurgical-mechanism modeling and statistical analysis.Then,the alloy-demand prediction model based on alloy unit consumption and time series analysis is developed by combining sales plans and historical data.Finally,the alloy purchasing and inventory optimization model is developed to minimize the total cost of purchase and storage by combining inventory optimization theories.

Consumers'Purchase Intention of Geographical Indication Agricultural Products under the Background of Live-streaming Sales:An Exploratory Research Based on Grounded Theory

Exploring the mechanism for the formation of consumer purchase intentions of geographical indication agricultural products in the context of live-streaming sales can provide an important reference for brand marketing of geographical indication agricultural products.In this study,in-depth interviews were conducted with consumers of geographical indication agricultural products.Based on grounded theory,open coding,axial coding and selective coding were performed for interview text.Finally,21 concepts,7 subcategories and 3 main categories were obtained,and a model of the formation mechanism of the purchase intention of geographical indication agricultural products under the background of live-streaming sales was constructed,that is,"consumer cognition-consumer attitude-consumer behavior".Among them,consumer cognition includes two dimensions:the type of geographical indication agricultural products and the live-streaming appeal strategy,i.e.,the personal cognition of consumer and the promotion of live-streaming host's strategy.Consumer attitude is value perception of consumers,mainly including two dimensions of functional value and emotional value.Consumer behavior is the consumer's willingness to buy.It has been concluded that the types of geographical indication agricultural products interact with the live-streaming appeal strategies.Through the intermediary of consumers'value perception,consumers'purchase intention is generated.Among them,resource-oriented geographical indication agricultural products adopt rational live-streaming appeal strategies,which can enhance the consumer's perception of functional value,thereby promoting their purchase intention;and cultural and creative geographical indication agricultural products brands adopt perceptual live-streaming appeal strategies,which can enhance the emotional value perception of consumers,thereby promoting their purchase intention.

Influence Mechanism of Clothing Anchor Features on Consumers'Purchase Intention

Relying on the field of e-commerce live broadcast,the features of clothing anchors are divided into four dimensions:charm features,recommendation features,display features and interaction features.The intermediate variable of social presence is introduced to construct a theoretical model of e-commerce live broadcast consumers'purchase intention.Based on the questionnaire survey data collection,using SPSS 23.0 software and Amos 23.0 software to analyze the data,it is concluded that the features of charm,recommendation,display and interaction of the clothing anchors have a significant positive impact on consumers'purchase intentions.Social presence plays a mediating role in the influence of the features of charm,recommendation,display and interaction on consumer'purchase intention.

Purchase willingness of new energy vehicles: A case study in Jinan City of China

Speeding up the promotion of new energy vehicles is an important measure to optimize the energy structure,promote energy conservation and emission reduction,and develop the economy sustainability.The research uses a questionnaire survey to analyze the residents’willingness to purchase new energy vehicles in Jinan Gty of China,and utilizes the binomial logistic regression model and Global Moran's I to explain the impact of three factors(including responden ts'personal characteristics and subjective cognition,products,and social environment)on the purchase willingness of new energy vehicles.According to the survey,75.12%of the responden ts consider buying new energy vehicles in the future,but only 11.66%of the respondents know new energy vehicles well.It can be seen that the respondents in Jinan City generally have an insufficient understanding of new energy vehicles.It may lead to a decline in residents’trust in new energy vehicles,which will in turm affect their purchase willingness.Based on the survey,we find that women who live far from the city center enjoy high incomes and have a low-carbon awareness,generally exhibit a higher willingness to purchase new energy vehicles.Spatial distribution of the purchase willingness has certain aggregation characteristics,showing a positive spatial correlation pattern.Purchase willingness has a certain positive diffusion effect in space,and areas with a higher purchase willingness have a positive driving effect on their surrounding regions.Spatial distribution of the purchase willingness can be used as one of the breakthroughs in promoting new energy vehicles.In addition,safety,price,after-sales service,and infrastruicture of new energy vehicles are important determinants of people's purchase willingness.Among the types of subsidies,financial subsidy is most effective on the residents’purchase wilingness.Our research provides an impor tant information for the promotion of new energy vehicles in the region.

Dynamic Promotional Resource Allocation for Segment Specific and Spectrum Effect of Promotion for a Product Line Incorporating Repeat Purchase Behavior

Promotion is an essential element in the marketing mix. It is used by businesses to inform, influence and persuade customers to adopt the products and services they offer. Without promotion, business would be stagnant and lack substantial growth because the brands would have low visibility in the market. Moreover, today’s vast and assorted markets comprise of customers with different needs and varied behavior. So it is rarely possible for companies to satisfy all customers by treating them alike. Thus there arises a need to divide the market into segments having customers with similar traits/characteristics. After identifying appropriate market segments, firms can design differentiated promotional campaigns for each segment. At the same time there can be a mass market promotional campaign that reaches different segments with a fixed spectrum. Also since promotional effort resources are limited, one must use them judiciously. In this paper, we formulate mathematical programming problem under repeat purchase scenario, which optimally allocates mass promotional effort resources and differentiated promotional effort resources across the segments dynamically in order to maximize the overall sales obtained from multiple products of a product line under budgetary and minimum sales aspiration level constraint on each product under consideration in each segment. The planning horizon is divided into multi periods, the adoption pattern of each product in each segment is observed in every subinterval and accordingly promotional effort allocations are determined for the next period till we reach the end of planning period. The optimization model has been further extended to incorporate minimum aspiration level constraints on total sales for each product under consideration from all the segments taken together. The non linear programming problem so formulated is solved using differential evolution approach. A numerical example has been discussed to illustrate applicability of the model.

A Grey Decision Model Used in Bidding for Equipment Purchase

Bidding has long been used as a method for allocating and procuring goods and services. An acurrate and comprehensive evaluation of bidders is the key to make the bidding a successful one for tenderee. However, evaluation of bidders is a tough work and the result of evaluation is always affected by the evaluator's understanding of the standards set for evaluation, and the evaluator's expertise, experience and preferrence. We make an effort to find a method for selecting bidders as accurately as possible. A grey decision model is presented, and an example is illustrated to make the practioners know how to use the model.