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.

Study of The Technical Index of Online Learning Behavior Analysis of Nursing Majors on The Superstar Platform Based on The Kirkpatrick Evaluation Model

Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis.Methods:The online learning behavior data of Physiology of nursing students from 2021-2023 and the first semester of 22 nursing classes(3 and 4)were collected and analyzed.The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation.Results:The study found that the demand for online learning of nursing students from 2021-2023 increased and the effect was statistically significant.Compared with the stage assessment results,the online learning effect was statistically significant.Conclusion:The main indicators for evaluating and classifying online learning behaviors were summarized.These two indicators can help teachers predict which part of students need learning intervention,optimize the teaching process,and help students improve their learning behavior and academic performance.

An Empirical Study of the Influence of Online Learning Behaviors on Learning Outcomes in Blended Teaching Mode—Taking College English Listening as an Example

Blended teaching, which integrates the advantages of online and offline teaching, has become the main direction of higher education teaching reform. In the era of education big data, research on the online learners’ behavior based on data mining has attracted more and more attention from higher education researchers. However, in the field of foreign language teaching, research on the relationship between online learning behaviors and learning outcomes in the blended teaching mode is still at an early stage. Taking the course College English Listening in Zhejiang Yuexiu University (ZYU) as an example, this study conducted a comprehensive data analysis of online learning behaviors of 152 students of ZYU to explore the influence of online learning behaviors on learning outcomes in the blended teaching mode by utilizing Microsoft Excel and SPSS.20 statistic software. The result shows that the number of course login, the quantity and the quality of forum replies, the number of note submission, the quality of the notes, the average score of vocabulary tests, the number of the times of taking listening tests and the average score of listening tests are all significantly and positively correlated with students’ learning outcomes, while the study does not find a correlation between students’ learning outcomes and the number of the times of taking vocabulary tests, the total length of online learning and the length of video viewing. Based on the study results, implications are put forward to give reference for the teaching design and the management of the foreign language blended courses.

Buying Behavior Under Coronavirus Disease(COVID-19)Pandemic Situation:A Online Perspective Case in Bangladeshi Shoppers

Coronavirus disease(COVID-19)is an highly infectious respiratory disease caused by a newly discovered coronavirus.Most people infected by the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment;a portion of infected people may die.Under coronavirous disease pandemic situation,human normal life,movement and business has been disturbed due to lockdown and closing of shopping malls and business centers in the city.Nowadays,e-commerce is a vigorous tool for diminishing streaming business processes,cycle time,organizational costs,stay at home,maintain social distancing,protect from virus,and enlightening associations with both shoppers and business partners.The research investigated the buying behavior of Bangladeshi shoppers under coronavirus disease(COVID-19)pandemic situation in case of online perspective.The research reconnoitered the impact of five aspects:health aspect,price aspect,product aspect,trust aspect,and place aspect on online buying behavior under coronavirus disease(COVID-19)pandemic situation in Bangladesh.Data were collected through a structured questionnaire by online survey method from 155 samples which encompass online shoppers in country.Simple random sampling technique were used.Data were analyzed using factor analysis,reliability analysis,and multiple regression analysis.Findings revealed that four out of five aspects:health aspect,price aspect,product aspect,and place aspect had a positive and significant influence on online buying behavior under coronavirus disease(COVID-19)pandemic situation in the perspective of Bangladesh.The assessment generates responsiveness among online practicing companies,researchers,managers,shoppers,and prospects online buyers.Online functioning businesses could be a successful leading aspects for explaining online buying behavior under coronavirus disease(COVID-19)pandemic situation in the context of Bangladesh.

Significance of Live Streaming in Shaping Business: A Critical Review and Analytical Study

With the rise of live streaming on social media, platforms like Facebook, Instagram, and YouTube have become powerful business tools. They enable users to share live videos, fostering direct connections between businesses and their customers. This critical literature review paper explores the impact of live streaming on businesses, focusing on its role in attracting and satisfying consumers by promoting products tailored to their needs and wants. It emphasizes live streaming’s crucial role in engaging customers, a key to business growth. The study also provides viable strategies for businesses to leverage live streaming for growth and customer engagement, underscoring its importance in the business landscape.

Implementation of Generic AHP Evaluation System and Its Application to Impact Factors of On-Line Consumption Behavior

To implement the computation of AHP （Analytic Hierarchy Process） automatically, make the implementation independence on the platform, and to convenient the service composition and service computation, this study implemented AHP model as a Web service and stored the evaluation information as XML form. This paper introduces the overall demands of AHP evaluation system firstly, and then presents some key technologies to implement AHP evaluation system, including mainly AHP evaluation index architecture based on XML, the class of AHP model, the Web service encapsulation of AHP, the publication and invocation of AHP and the client application. At last, the system is used to analyze the impact factors of online consumption behavior.

How Facial Expressions of Recipients Influence Online Prosocial Behaviors?-Evidence from Big Data Analysis on Tencent Gongyi Platform

Cyberspace has significantly influenced people’s perceptions of social interactions and communication.As a result,the conventional theories of kin selection and reciprocal altruism fall short in completely elucidating online prosocial behavior.Based on the social information processing model,we propose an analytical framework to explain the donation behaviors on online platform.Through collecting textual and visual data from Tencent Gongyi platform pertaining to disease relief projects,and employing techniques encompassing text analysis,image analysis,and propensity score matching,we investigate the impact of both internal emotional cues and external contextual cues on donation behaviors.It is found that positive emotions tend to attract a larger number of donations,while negative emotions tend to result in higher per capita donation amounts.Furthermore,these effects manifest differently under distinct external contextual conditions.

Who Benefits More from Online Learning?An Empirical Study on Postgraduates’Online Knowledge-Sharing Behavior

Online knowledge-sharing is the key link to individuals in the digital era.Postgraduates are the core members of the future knowledge-based society.Exploring factors affecting postgraduates’online knowledge-sharing behavior is of significance promoting their fair enjoyment of digital dividends and contributing to the construction of Digital China.However,little literature exists on this topic.This study examines factors resulting in postgraduates’differentiated online knowledge-sharing behavior from two dimensions:social structure and individual initiative.The results of the questionnaire survey of 501 postgraduates show that:First,not all postgraduates are aborigines of the digital age;and structural factors(gender,school type,location,father’s occupation,and father’s education)have a significant impact on postgraduates’online knowledge-sharing behavior.These factors also have a greater impact on the quality of online knowledge-sharing than in a quantity sense.Second,the individual initiative factor(information literacy)also has a significant impact on postgraduates’online knowledge-sharing behavior.It has a greater impact on the quantity of online knowledge-sharing than in a quality sense.Third,there is a Matthew effect under the internet context,and certain effects of structural factors on online knowledge-sharing behavior are indirectly generated through individual initiative factors.Participants with privileged structural status show a higher level of information literacy,which further encourages them to be more active in online knowledge-sharing behavior and facilitates their high-quality production.

Anger as a Predictor of Active Public Protest： Extended Application of the Situational Theory of Publics to a Health Crisis

This study applies the situational theory of publics （STP） to a health crisis by exploring the effect of anger on public behavior. The results of a survey administered on the case of the candlelight rallies on the U.S. beef imports in South Korea in 2008 demonstrated that involvement, problem recognition, and constraint recognition predicted communication behaviors as well as various issue-related online and offline behaviors. In addition, anger was found to be a significant driving force for publics to perform the behaviors. The segmented publics showed significantly different levels of anger, information processing and seeking, online behaviors, and participation in the candlelight rallies

Quantifying the Influence of Websites Based on Online Collective Attention Flow

The availability of network big data, such as those from online users＇ surfing records, communication records, and e-commerce records, makes it possible for us to probe into and quantify the regular patterns of users＇ long-range and complex interactions between websites. If we see the Web as a virtual living organism, according to the metabolic theory, the websites must absorb ＂energy＂ to grow, reproduce, and develop. We are interested in the following two questions： 1） where does the ＂energy＂ come from？ 2） will the websites generate macro influence on the whole Web based on the ＂energy＂？ Our data consist of more than 30 000 online users＇ surfing log data from China Internet Network Information Center. We would consider the influence as metabolism and users＇ attention flow as the energy for the websites. We study how collective attention distributes and flows among different websites by the empirical attention flow network. Different from traditional studies which focused on information flow, we study users＇ attention flow, which is not only a ＂reversed＂ way to study Web structure and transmission mode, but also the first step to understand the underlying dynamics of the World Wide Web. We find that the macro influence of websites scales sub-linearly against the collective attention flow dwelling time, which is not consistent with the heuristics that the more users＇ dwelling time is, the greater influence a website will have. Further analysis finds a supper-linear scaling relationship between the influence of websites and the attention flow intensity. This is a websites version of Kleiber＇s law. We further notice that the development cycle of the websites can be split into three phases： the uncertain growth phase, the partially accelerating growth phase, and the fully accelerating growth phase. We also find that compared with the widespread hyperlinks analysis models, the attention flow network is an effective theoretical tool to estimate and rank websites.

Predicting Student Rankings Based on the Dual-Student Performance Comparison Model

Currently,learning early warning mainly uses two methods,student classification and performance regression,both of which have some shortcomings.The granularity of student classification is not fine enough.The performance regression gives an absolute score value,and it cannot directly show the position of a student in the class.To overcome the above shortcomings,we will focus on a rare learning early warning method-ranking prediction.We propose a dual-student performance comparison model(DSPCM)to judge the ranking relationship between a pair of students.Then,we build the model using data including class quiz scores and online behavior times and find that these two sets of features improve the Spearman correlation coefficient for the ranking prediction by 0.2986 and 0.0713,respectively.We also compare the process proposed with the method of first using a regression model to predict scores and then ranking students.The result shows that the Spearman correlation coefficient of the former is 0.1125 higher than that of the latter.This reflects the advantage of the DSPCM in ranking prediction.