Research on the Influence of Anchor Attributes on Consumers’Online Behaviors in Social E-Commerce Platforms:The Moderating Effect of Platform Contextual Factors

As e-commerce continues to mature,the advantages of live streaming within the industry have become increasingly apparent,offering significant growth opportunities.Social e-commerce platforms,which are user-centered,integrate social networks with e-commerce by leveraging social interactions to drive product sales and enhance the overall consumer shopping experience.This type of e-commerce fosters engagement and promotes products by merging online communities with shopping behavior,creating a more interactive and dynamic marketplace.It not only retains the traditional e-commerce trading and marketing functions but also adds a social dimension,making live stream anchors crucial figures connecting consumers with products.These anchors can attract consumers with their appearance and charm,and use their expertise on live streaming platforms to guide consumers by recommending live content.They can also interact with their audiences and potentially influence them to purchase the recommended goods.It is evident that the attributes of anchors in live streaming rooms significantly impact consumers’online behavior.Therefore,researching how platform contextual factors regulate consumers’online behavior is of great practical significance.This study employs multilevel regression analysis to support its hypotheses using data.The findings indicate that contextual factors of the platform significantly influence online behavior,enhancing the positive relationship between user attachment and online activities.

Will Online Car-Hailing Affect Consumers’ Decisions about Automobile Purchase?—An Empirical Study Based on Questionnaire Investigation

The online car-hailing industry, which provides the right of use, has a certain impact on the traditional automobile market, but there is no unified theory on whether it has a positive impact or a negative impact. Based on 362 consumer questionnaire data, this study builds a structural equation model to discuss the driving factors of residents’ choice of online car-hailing and whether the development of online car-hailing will have a certain substitution impact on the sales of private cars. From the perspective of consumers’ purchase intention, the research results show that consumers’ price consciousness, convenience consciousness, environmental protection consciousness and possession tendency will affect their choice of travel mode, and the use of online car-hailing is positively correlated with consumers’ willingness to replace private car ownership with online car-hailing.

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.

Impact of Online Comments on Purchase Intention of College Student Consumers under Online Shopping

With the rapid development of network technology,online shopping has been constantly growing,and how to make better use of the dissemination effect of online comment has become an imperative and practical issue. Based on review of the existing literature,this paper established the theoretical research model and used the literature research and empirical research combined method to analyze the impact of online comment on purchase intention of college student consumers under online shopping. The research results indicate that the quantity,quality,valence and timeliness of online comments exert a significant impact on the purchase intention of college student consumers.

Selling Luxury Brands in Online Private Sales Multibrand Retailers--An Exploratory Study of Consumers’Perspective

With Internet changing the luxury business landscape,new players have emerged such as the Online Private Sales Retailers(OPSRs).These offer online buyers with a choice of limited-time sales to help companies get rid of their overstocks.Luxury brands are no exception.No research has been conducted about how luxury consumers relate with such websites,hence this paper.In an exploratory fashion,interviews with luxury buyers who also buy online on OPSRs,are conducted to get insights on consumers’perceptions and luxury brand equity that selling through OPSRs may have.We find that appropriate product and brand help consumers forget that they are buying brands’unsold stocks,that transferring the luxury webmospheres would be positively perceived,that consumers from these websites are looking for benefits such as freedom of use and brand discovery,rather than personalized offers,that multiple discounts on several OPSRs may damage the luxury-perception of a brand,that the private sales members consider the service to be good enough for the demanded price,and that personalized invitations can help increase online consumers’feelings of desirability and exclusivity.The paper concludes with practical recommendations for both luxury companies and OPSRs.

Online Shopping: What Factors Determine Consumers to Buy?

This study analyzed factors influencing consumers in the process of making a decision in online shopping. The findings could inspire suggestions for online marketers in developing either media or infrastructures accordingly. The variables in the study involved product information, price, service, transaction safety, environment, age, gender, educational background, and income rate as independent variables, while the process of purchasing decision served as a dependent variable. Primary data were gathered in 10 locations within Jabodetabek areas involving 270 respondents which were analyzed using multiple regression analysis. The study revealed that price, information product, and service as significant variables influencing consumers in online shopping. Service became the most important factor for marketers to be considered as it emerged as the most dominant variable influencing the process of making a decision in online shopping.

A Study on consumers＇ continuing to use online group-buying platforms： The impact of price performance expectations

In this study, we aim to understand the characteristics of online group-buying consumers and to investigate salient factors which influence the continuance intention of online group-buying platforms （OGBP） to bridge this knowledge gap. An expectation-confirmation model of information systems （IS） continuance is adapted to construct a research model in online group-buying contexts. A total of 289 complete and valid responses were collected. Our findings contribute to academics and practitioners in two ways： Firstly, our respondents show that they are young （93% of the respondents＇ ages range between 19 and 28 years old）, female （88% of the respondents）, and thrifty （82% of the respondents＇ transaction amounts are below US$16）. Secondly, based on our results, price performance expectations have a direct impact on confirmation. In addition, in contrast to the IS continuance model （Bhattacherjee, 2001）, the effect of perceived usefulness on satisfaction is not supported. Thus, in online group-buying settings, confirmation is the key antecedent of satisfaction. Satisfaction and perceived usefulness are significantly associated with OGBP continuance intention. Consequently, in addition to offering a wide assortment of merchandise and a convenient online shopping experience to enhance customers＇ perceived usefulness of OGBP, OGBP managers should aim low-price marketing strategies at this female, young, thrifty and price-sensitive segment to transcend consumers＇ price expectations and attract consumers＇ continued intention to visit OGBP.

Effects of Negative Online Reviews on Consumers＇ Attitudes and Behavioral Intentionstoward Online Products

Online shopping has become an important new channel because of its rapid development and broad application of the Internettechnology. As consumer information search and release gradually shift from offline to online, online reviews of products havebecome more valuable. Research shows that most online shoppers view online reviews from product users before purchasing. Asone of the most important forms of spreading awareness, online product reviews has an increasing impact on customer purchasedecisions and has gradually become an urgent issue in network marketing research. This phenomenon impels businesses to realizethat online reviews significantly affect trading volume. Businesses have attempted to manipulate online reviews by providing asignificant number of positive comments that could lead to consumer confidence and purchase of products. Internet users inChina are more interested in reading negative comments compared with the rest of the global Internet users. Thus, the effects ofstructural characteristics of potential customers, negative attitudes, and behavioral intentions have not yet aroused global concernbecause this phenomenon has been limited to the local scale.Based on literature, the main objective of negative online reviews is the positioning of fashion products. The perception ofnegative online reviews, purchase attitudes, and behaviors are the factors considered in the present study. Negative online reviewsof clothing and accessories sold online and their influence on consumer purchase intention and attitudes are analyzed. The studyaims to confirm that negative online reviews have an effect on consumer purchase intention, attitudes, and behaviors. Moreover,buying attitudes influence behavior intention.

Effects of Explanation Type on Consumers' Perceived Usefulness of Online Reviews and Their Boundary Conditions

In making purchasing decisions, consumers look to online reviews posted by others who have used or experienced the product/service. Most existing studies on online reviews only focused on their content without addressing how their explanation type influences purchasing decisions, not to mention the boundary conditions for such effects. Based on three experimental studies, this paper examines how explanation type affects the perceived usefulness of online reviews and purchasing decisions, and discusses the boundary conditions for such effects. Our findings show that positive explanation type has a positive effect on the perceived usefulness of online reviews, while negative explanation has an insignificant impact. For search products, there is a positive correlation between the online reviews of explained actions and the perceived usefulness of consumer reviews. For experience products, there is a positive correlation between the online reviews of explained reactions and the perceived usefulness of consumer reviews. Further research found that temporal distance may moderate the effects of explanation type and product type on the perceived usefulness of online reviews. This study also provides a new approach for research on the word-of-mouth(WOM) with meaningful implications for e-commerce firms.

TPACK框架下GeoScene Online与地理教学融合的实践

技术革新影响学科教学方式选择,TPACK模式为解决当下学科教学应用新技术“两张皮”问题提供了新思路。本文从科勒和米什拉的理论出发,尝试将GeoScene Online平台与地理课堂教学融合,提出循序渐进、跨学科、基于真实情境和交互式的融合原则,进而在TPACK模式下将GeoScene Online功能特点与高中地理必修内容进行融合分析,构建以PCK、TCK、TPK三条子路径为导向的GeoScene Online与地理教学融合模式,并以“耕地”为主题进行案例实践探索。

Early warning method for thermal runaway of lithium-ion batteries under thermal abuse condition based on online electrochemical impedance monitoring

Early warning of thermal runaway(TR)of lithium-ion batteries(LIBs)is a significant challenge in current application scenarios.Timely and effective TR early warning technology is urgently required considering the current fire safety situation of LIBs.In this work,we report an early warning method of TR with online electrochemical impedance spectroscopy(EIS)monitoring,which overcomes the shortcomings of warning methods based on traditional signals such as temperature,gas,and pressure with obvious delay and high cost.With in-situ data acquisition through accelerating rate calorimeter(ARC)-EIS test,the crucial features of TR were extracted using the RReliefF algorithm.TR mechanisms corresponding to the features at specific frequencies were analyzed.Finally,a three-level warning strategy for single battery,series module,and parallel module was formulated,which can successfully send out an early warning signal ahead of the self-heating temperature of battery under thermal abuse condition.The technology can provide a reliable basis for the timely intervention of battery thermal management and fire protection systems and is expected to be applied to electric vehicles and energy storage devices to realize early warning and improve battery safety.

Efficient unequal error protection for online fountain codes

In this paper,an efficient unequal error protection(UEP)scheme for online fountain codes is proposed.In the buildup phase,the traversing-selection strategy is proposed to select the most important symbols(MIS).Then,in the completion phase,the weighted-selection strategy is applied to provide low overhead.The performance of the proposed scheme is analyzed and compared with the existing UEP online fountain scheme.Simulation results show that in terms of MIS and the least important symbols(LIS),when the bit error ratio is 10-4,the proposed scheme can achieve 85%and 31.58%overhead reduction,respectively.

Modulated-ISRJ rejection using online dictionary learning for synthetic aperture radar imagery

In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.

Online Consensus Control of Nonlinear Affine Systems From Disturbed Data

Dear Editor,In this letter, we introduce a novel online distributed data-driven robust control approach for learning controllers of unknown nonlinear multi-agent systems(MASs) using state-dependent representations.

基于Online-GRU信道预测的星上自适应功率控制方法

针对传统卫星功率控制方法存在资源浪费、时延长的问题,提出一种基于在线-门控循环单元(Online-GRU)信道预测的星上自适应功率控制方法,通过在线训练更新网络参数来解决离线预测算法存在的累积误差的问题。仿真结果表明,提出的在线训练算法比离线算法预测精确度提升了38.30%,相比在线-长短期记忆网络(Online-LSTM)节约了63.21%的训练时间;提出的自适应功率控制方法比固定发射功率的方法节约了55.74%的发射功率;同时,相比基于地面定时反馈信道状态的自适应功率控制方法具备更好的鲁棒性。

Optimized operation scheme of flash-memory-based neural network online training with ultra-high endurance

With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attracted increasing attention in recent years.In this work,to provide a feasible CIM solution for the large-scale neural networks(NN)requiring continuous weight updating in online training,a flash-based computing-in-memory with high endurance(10^(9) cycles)and ultrafast programming speed is investigated.On the one hand,the proposed programming scheme of channel hot electron injection(CHEI)and hot hole injection(HHI)demonstrate high linearity,symmetric potentiation,and a depression process,which help to improve the training speed and accuracy.On the other hand,the low-damage programming scheme and memory window(MW)optimizations can suppress cell degradation effectively with improved computing accuracy.Even after 109 cycles,the leakage current(I_(off))of cells remains sub-10pA,ensuring the large-scale computing ability of memory.Further characterizations are done on read disturb to demonstrate its robust reliabilities.By processing CIFAR-10 tasks,it is evident that~90%accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN.Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training,which pave the way for further development of artificial intelligence(AI)accelerators.

QoE oriented intelligent online learning evaluation technology in B5G scenario

Students'demand for online learning has exploded during the post-COvID-19 pandemic era.However,due to their poor learning experience,students'dropout rate and learning performance of online learning are not always satisfactory.The technical advantages of Beyond Fifth Generation(B5G)can guarantee a good multimedia Quality of Experience(QoE).As a special case of multimedia services,online learning takes into account both the usability of the service and the cognitive development of the users.Factors that affect the Quality of Online Learning Experience(OL-QoE)become more complicated.To get over this dilemma,we propose a systematic scheme by integrating big data,Machine Learning(ML)technologies,and educational psychology theory.Specifically,we first formulate a general definition of OL-QoE by data analysis and experimental verification.This formula considers both the subjective and objective factors(i.e.,video watching ratio and test scores)that most affect OLQoE.Then,we induce an extended layer to the classic Broad Learning System(BLS)to construct an Extended Broad Learning System(EBLS)for the students'OL-QoE prediction.Since the extended layer can increase the width of the BLS model and reduce the redundant nodes of BLS,the proposed EBLS can achieve a trade-off between the prediction accuracy and computation complexity.Finally,we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE values.Through timely interventions,their OL-QoE and learning performance can be improved.Experimental results verify the effectiveness oftheproposed scheme.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Research on the Collaborative Governance of Social Responsibility in Online Audiovisual Enterprises

This paper aims to analyze the present conditions of the social responsibility ecosystem in online audiovisual enterprises in the digital age. It focuses on the governance of social responsibility in these enterprises and conducts an in-depth analysis of the problems and influencing factors related to the social responsibility aberrations of online audiovisual enterprises. Drawing upon social responsibility theory and collaborative governance theory, this research constructs a social responsibility guidance and governance system guided by the public, supported by the voluntary fulfillment of responsibilities by online audiovisual enterprises, and based on the collaborative participation of diverse stakeholders. It explores and optimizes the implementation pathways of this system, providing theoretical support and practical guidance for promoting the sustainable development of online audiovisual enterprises. Furthermore, it aims to contribute to the creation of a harmonious Internet ecosystem.