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.

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.

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.

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.

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.

Research on the Relationship Between Average Cigarette Price per Box and Government Procurement in City A Based on a Regression Model

This study aims to analyze and predict the relationship between the average price per box in the cigarette market of City A and government procurement,providing a scientific basis and support for decision-making.By reviewing relevant theories and literature,qualitative prediction methods,regression prediction models,and other related theories were explored.Through the analysis of annual cigarette sales data and government procurement data in City A,a comprehensive understanding of the development of the tobacco industry and the economic trends of tobacco companies in the county was obtained.By predicting and analyzing the average price per box of cigarette sales across different years,corresponding prediction results were derived and compared with actual sales data.The prediction results indicate that the correlation coefficient between the average price per box of cigarette sales and government procurement is 0.982,implying that government procurement accounts for 96.4%of the changes in the average price per box of cigarettes.These findings offer an in-depth exploration of the relationship between the average price per box of cigarettes in City A and government procurement,providing a scientific foundation for corporate decision-making and market operations.

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.

Enhancing Enterprise Efficiency through Cross-Domain Supply Chain Linkage Management

In recent years,with the complexity and variability of international finance,debt,and geopolitical risks,the growth rate of domestic economic operations has slowed down.More enterprise groups are facing the transformation and upgrading from domestic leadership to internationalization,achieving revenue growth of nearly 10 billion yuan per year against the trend.The severe and complex internal and external environment has brought many challenges to the supply chain for enterprises.This article focuses on the goal of an“enterprise supply chain system focusing on value creation,strengthening system linkage,and improving overall chain efficiency,”aiming to find solutions to problems such as strong demand variability,frequent emergency needs,coexistence of project shortages and inventory surplus,long material turnover cycles,and high organizational complexity.By searching for key activities in the entire business chain of demand,design,production,service,and finance,implementing source planning,process control,system linkage,analysis and summary,and team collaboration,we have achieved more scientific demand forecasting,more accurate planning,more effective procurement cost reduction,more stable production performance supply,more convenient inventory sharing services,and more efficient integration of business and financial policies.

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.

YKK achieves 100% renewable energy purchasing at 31 sites worldwide

YKK Corporation announces that the number of YKK locations that have switched 100%of their electricity consumption to renewable energy in fiscal year 2022 has increased by 20 locations from FY2021 to FY2022,reaching 31 locations globally.

65% Aussie clothes buyers actively prioritise ethical factors

Over 65 per cent of respondents actively consider ethical factors when purchasing clothes,while nearly a third explicitly consider the human rights of garment workers,as per a survey.The safety of garment work­ers are top priorities for 88 per cent of Australian clothing buyers.Younger generations are more likely to make ethical purchasing decisions.

赋能供应链成本管理能力 助力车企降本增效方法研究

站在供应链的高度,以促进供应商降本为抓手,发挥供应链降本优势。本文以供应链降本应用于车企实现成本控制各种方法出发,对汽车成本控制的有效策略进行分析,以期为相关工作提供理论的参考与借鉴。

The Role of Rivers in the United States 19th Century Territorial Expansion from the Mississippi River to the Pacific Ocean

This study explores the key role of rivers in the westward expansion of the United States during the 19th century. In the early 1800s, President Jefferson envisioned a United States that extended from the Atlantic to the Pacific Ocean. At the time, the entire United States territory was located between the Atlantic Ocean and the Mississippi River. Much of the land west of the Mississippi River was claimed by Spain, France, or Canada. In 1803, President Jefferson was able to purchase the Missouri River watershed from France via the Louisiana Purchase. This allowed the United States to extend its land claim west from the confluence of the Missouri and Mississippi Rivers to the head waters of the Missouri River at the continental divide in the Rocky Mountains. President Jefferson commissioned William Clark and Meriwether Lewis, in 1803, to explore, discover and describe the Missouri River watershed and find a water route to the Pacific Ocean. The Lewis and Clark Corps of Discovery trip found no such waterway link but did continue to explore the Pacific Northwest lands north and west of the continental divide. The only way that the Pacific Northwest could be claimed as part of the United States was for Americans to settle there before the Canadians did. Starting in the 1820s, many Americans traveled via the Oregon Trail to the Willamette Valley (Land of Flowing Milk and Honey) in Oregon. The primary objectives of this study are to document how the United States: 1) extended its land claims west from the Mississippi River to the North American continental drainage divide;2) established an American claim to the Pacific North West territory;and 3) fulfilled President Jefferson’s vision of a United States extending from the Atlantic to the Pacific Ocean.

A model of targeted advertising with customer recognition

A two-period duopoly model is developed to examine the competitive effects of targeted advertising with customer recognition （TACR）. In the model, two competing firms sell goods to end consumers in the first period, during which customer recognition is obtained. In the second period, advertising can be targeted toward different consumer types. Advertising is assumed to be persuasive in the way that consumer valuation is increased. Equilibrium decisions and profits in each period are derived, showing that the firm who loses the current competition will win in the future. As a result, forward-looking firms price less aggressively so that their long-term profits can be enhanced with the help of TACR. Particularly, TACR improves profits through three important effects： valuation increasing, customer poaching, and anti-competition. Finally, this paper investigates the welfare issues, showing that firms enhance profits at the expense of consumer surplus. It is, therefore, suggested that public sectors take a step to protect consumers with the rapid development of targeting technology.

药品带量采购的核心要素分析

明确药品带量采购的核心要素及相关理论,深入分析和比较我国实施的3批药品集中采购的相关政策,厘清我国药品带量采购模式的演进历程和发展趋势,以期为优化我国医药采购制度、增进人民群众健康福祉提供参考。

Purchasing policy model based on components/parts unification

This paper presents a mathematical model for components/parts unification (CPU) policy. This model considers two components/parts that are functionally interchangeable but purchased from suppliers with different prices and quality characteristics. Because of the buyer's quality preference and suppliers' discount rates for bulky purchases, the model assists the procurement manager to determine how best to purchase the components/parts to meet its demand while minimizing the total acquisition costs.

Research on Optimization of Component Stock

A set of model is established to optimize BSW Company’s component stock. By analyzing the company’s current part stock condition in terms of the occupation of capitals in the precondition of continuous production, it describes how to control the purchase parameters of import parts. The model describes how to adjust slightly product output sequence and how to control the components’ purchase parameters: purchasing risk time and purchase order quantity. Then simulation is developed to illustrate the model.