Effects of Online English Movie Reviews on Improving Students' Writing

This study aims to study the effectiveness of online English movie reviews for improving university students’English writing in China.English movie reviews can be profitably undertaken to improve university students’writing ability by reading English movie reviews online,discussing topics related to English movie reviews and writing English movie reviews collaboratively.University students have easy access to English movie reviews massively available on the Internet,which renders it possible and feasible for English teachers to use them to improve students’English writing.Online English movie reviews provide students with enough input of model texts,hence they can acquire some appropriate expressions before writing within a short period of time.In addition,discussion on English movie reviews through Emails and QQ platform can activate students’critical thinking to stimulate their original ideas for English movie review writing.Writing English movie reviews collaboratively with the help of Internet can develop students’confidence in writing because of peer feedback and less pressure.

Tourism Development Mode of Historical and Cultural Blocks from the Perspective of Urban Renewal Based on Online Review Data:A Case Study of Nanluoguxiang in Beijing

Under the background of urban renewal, this paper re-explores the tourism development modeof Nanluoguxiang historical and cultural block based on online review data, and puts forward correspondingdevelopment strategies. As a cultural label of a city, historical and cultural blocks should be updated first inorder to achieve sustainable development. By using multi-source big data review and qualitative researchmethods, the perception evaluation of tourists in Nanluoguxiang is obtained, and the shortcomings ofcurrent tourism development mode are analyzed. Furthermore, corresponding improvement strategies andsuggestions are put forward, in order to provide some effective ideas for the sustainable development ofhistorical and cultural blocks in the future.

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.

Review of Arbitral Award of Network Loan and Analysis of Construction of Implementation Review Rules

The network arbitration cases arising from the network lending disputes are pouring into the courts in large numbers.It is reported that the network arbitration system of some arbitration institutions even“can accept more than 10,000 cases every day,”while online lending is booming,it has also caused a lot of contradictions and disputes,and traditional dispute resolution methods have failed to effectively respond to the need for efficient and convenient resolution of online lending disputes.This paper tries to study the arbitral award of online loans and proposes the construction of implementation review rules.

Teacher feedback, peer comment and online review in L2 writing

It is apparent that writing has played an irreplaceable role in research and pedagogy because of the recognition of English as lingua franca and also the growing number of students going abroad for higher education.In the context of China,various kinds of feedback can be considered as significant factors to enhance Chinese English learners'writing ability.

Pricing Decision of E-Commerce Supply Chains with Return and Online Review of Product Quality

The online review function helps consumers grasp more real product information and reduce the possibility of returning, but it may also damage firms’ reputations or profits. However, few studies considered the relationship between online reviews and consumer returns. Based on this, we develop an e-commerce supply chain(E-SC) game model consisting of a single manufacturer and a single e-platform, aiming to explore the relationship between consumer returns and online reviews and to analyze the impact on both the decision-making of E-SC members and their profits. We find that there is a negative relationship between consumer returns and online reviews of product quality, and consumer returns make the pricing decisions in the two scenarios of yes/no online reviews move toward two different directions. Only when the online review is positive and higher than a certain threshold will it have a positive impact on sales and E-SC members’ profits. Finally, we design a new “commission joint returns and quality improvement costs sharing” contract to optimize the decentralized model with online reviews, and we find that the higher the accuracy of product information, the less conducive the contract applied to E-SC.

E-Commerce Fraud Detection Based on Machine Learning Techniques:Systematic Literature Review

The e-commerce industry’s rapid growth,accelerated by the COVID-19 pandemic,has led to an alarming increase in digital fraud and associated losses.To establish a healthy e-commerce ecosystem,robust cyber security and anti-fraud measures are crucial.However,research on fraud detection systems has struggled to keep pace due to limited real-world datasets.Advances in artificial intelligence,Machine Learning(ML),and cloud computing have revitalized research and applications in this domain.While ML and data mining techniques are popular in fraud detection,specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking depth.Existing reviews provide broad overviews but fail to grasp the intricacies of ML algorithms in the e-commerce context.To bridge this gap,our study conducts a systematic literature review using the Preferred Reporting Items for Systematic reviews and Meta-Analysis(PRISMA)methodology.We aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape.Understanding the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated costs.Through our investigation,we identify research opportunities and provide insights to industry stakeholders on key ML and data mining techniques for combating e-commerce fraud.Our paper examines the research on these techniques as published in the past decade.Employing the PRISMA approach,we conducted a content analysis of 101 publications,identifying research gaps,recent techniques,and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.

E-commerce development in rural and remote areas of BRICS countries

E-commerce plays an essential role in modern trade today.It is expected that e-commerce volume amounted to 29 trillion USD in the world in 2017,and would grow with the spread of the Internet and information and communication technologies(ICTs).Brazil,Russia,India,China and South Africa(BRICS),together with many others,consider e-commerce a means to facilitate rapid,inclusive and sustainable economic growth,improving the living standards and alleviating poverty.This article examines areas for potential cooperation by BRICS countries in e-commerce development across rural and remote areas to fight poverty.It analyses the current state of e-commerce development in rural and remote areas in each of the BRICS countries,including cases of public and private initiatives to support it.The article also defines the opportunities which e-commerce brings to people living in rural and remote areas.Moreover,it evaluates the existing challenges and risks.The article concludes that despite the rapid e-commerce development in BRICS countries,and significant opportunities created,there are still issues of disproportionate e-commerce in varied regions and the lack of BRICS cooperation in this sphere.Based on a comparative and normative in-depth,systematic analysis,the article develops a set of recommendations for deepening BRICS countries'cooperation in the following areas:infrastructure in rural and remote regions;education;consumer protection;online dispute resolution;coordinated policy in the international scene,including representation of BRICS countries in international indexes,such as the Organization of Economic Co-operation and Development(OECD)Digital Services Trade Restrictiveness Index(STRI).

Data Analytics for the Identification of Fake Reviews Using Supervised Learning

Fake reviews,also known as deceptive opinions,are used to mislead people and have gained more importance recently.This is due to the rapid increase in online marketing transactions,such as selling and purchasing.E-commerce provides a facility for customers to post reviews and comment about the product or service when purchased.New customers usually go through the posted reviews or comments on the website before making a purchase decision.However,the current challenge is how new individuals can distinguish truthful reviews from fake ones,which later deceives customers,inflicts losses,and tarnishes the reputation of companies.The present paper attempts to develop an intelligent system that can detect fake reviews on ecommerce platforms using n-grams of the review text and sentiment scores given by the reviewer.The proposed methodology adopted in this study used a standard fake hotel review dataset for experimenting and data preprocessing methods and a term frequency-Inverse document frequency(TF-IDF)approach for extracting features and their representation.For detection and classification,n-grams of review texts were inputted into the constructed models to be classified as fake or truthful.However,the experiments were carried out using four different supervised machine-learning techniques and were trained and tested on a dataset collected from the Trip Advisor website.The classification results of these experiments showed that na飗e Bayes(NB),support vector machine(SVM),adaptive boosting(AB),and random forest(RF)received 88%,93%,94%,and 95%,respectively,based on testing accuracy and tje F1-score.The obtained results were compared with existing works that used the same dataset,and the proposed methods outperformed the comparable methods in terms of accuracy.

An E-Commerce Recommender System Based on Click and Purchase Data to Items and Considered of Interest Shifting of Customers

A well-performed recommender system for an e-commerce web site can help customers easily find favorite items and then increase the turnover of merchants, hence it is important for both customers and merchants. In most of the existing recommender systems, only the purchase information is utilized data and the navigational and behavioral data are seldom concerned. In this paper, we design a novel recommender system for comprehensive online shopping sites. In the proposed recommender system, the navigational and behavioral data, such as access, click, read, and purchase information of a customer, are utilized to calculate the preference degree to each item; then items with larger preference degrees are recommended to the customer. The proposed method has several innovations and two of them are more remarkable: one is that nonexpendable items are distinguished from expendable ones and handled by a different way; another is that the interest shifting of customers are considered. Lastly, we structure an example to show the operation procedure and the performance of the proposed recommender system. The results show that the proposed recommender method with considering interest shifting is superior to Kim et al(2011) method and the method without considering interest shifting.

TOOR： A Novel Product Title Optimization Method Based on Online Reviews in E-commerce

Titles of online products play an important role in attracting consumers and promoting product sales in e-commerce. However, current online product titles only cover basic features and cannot reflect the preferences of consumers exactly. To address this problem, this research proposed an online title optimization method based on the analysis of online reviews, which is called TOOR （Title Optimization based on Online Reviews）. In this research, we analyzed and compared product features extracted from online product titles and online reviews from the point of view of consumers and applied features extracted from reviews to title optimization. In order to verify the effectiveness of the proposed method, two experiments were conducted in this paper, selecting four typical smartphones as experiment samples and Taobao.com as the data resources. The experimental results indicated that features extracted from online reviews can better reflect the consumers＇ concern, and the tires optimized by the TOOR method are more appealing to consumers and have higher click-through rates.

A Study on Fraud Reviews: Incentives to Manipulate and Effect on Sales

With the broad reach of Internet, online reviews have become an important source of electronic Word-of-Mouth. Fraud reviews that are deliberately posted by businesses are a type of online reviews. This paper discusses the incentives of fraud reviews and the effect of fraud reviews on consumer behavior through empirical research. Using book download data at Amazon, we find that a book is more likely to manipulate fraud reviews when it has few online reviews posted by real consumers, higher proportion of negative reviews, longer average length of negative reviews, lower average rating scored by real users and higher price. And fraud reviews change the review environment and have a significant impact on the consumer purchasing decisions. More number, higher proportion, longer word count and higher promotion of rating of fraud reviews lead to higher sales. The results also show consumers can discern the manipulation of fraud reviews to a certain extent.

Multi-Dimensional Customer Data Analysis in Online Auctions

In this paper, we designed a customer-centered data warehouse system with five subjects： listing, bidding, transaction, accounts, and customer contact based on the business process of online auction companies. For each subject, we analyzed its fact indexes and dimensions. Then take transaction subject as example, analyzed the data warehouse model in detail, and got the multi-dimensional analysis structure of transaction subject. At last, using data mining to do customer segmentation, we divided customers into four types： impulse customer, prudent customer, potential customer, and ordinary customer. By the result of multi-dimensional customer data analysis, online auction companies can do more target marketing and increase customer loyalty.

Comparison of Online Distribution Channels of the Thailand Community Enterprise Products

This research aims to study an online distribution of community enterprise products,to design the management system for an e-commerce distribution channel,and to compare the efficiency of distribution channels through a developed e-commerce system and ready web.A personal interview survey method was used to collect data on the production,marketing,and online selling issues.In total 150 community enterprise entrepreneurs in five product categories(food,appliance/home decoration,beverage,garment/ornament and non-food herb)and 50 consumers were equally selected by judgment sampling method.The e-commerce channel that was developed is our website and social network website that was selected to compare the efficiency of distribution channels is Facebook.The research found that only 14.67%of community enterprise entrepreneurs sell products through online channel.The product which was sold through the developed website and maximized gross profit is food(35.53%of total revenue).The others,are beverage,non-food herb,appliance/home decoration,and garment/ornament,earn gross profit 31.50%,22.39%,9.78%and 0.80%of total revenue,respectively.The result of distribution channels comparison between our website and webpages on Facebook found that Facebook has the ability to reach customers 2,811 times more than the website 64.75%,which has only 991 times.Regarding to sales,it is found that revenue from Facebook is higher than the website in all product categories.

Online Shopping:Antecedents of Attitude,Intention and Use

Consumer behavior in electronic commerce has been the theme of hundreds of studies conducted by researchers of many nationalities in the past twenty years.The purpose of this study was to review and classify the concepts used in papers published between 2003 and 2014 to explain the consumer behavior in electronic commerce.A systematic search of the literature in nine databases was performed and 136 papers published in double-blind peer reviewed journals were selected.Reference models were prepared based on a classification of the concepts found.This article reports only the concepts that displayed statistical significance in the studies analyzed.Finally,we suggest new studies that can be conducted.

Contemporary Education Research——Online Education from the Perspective of Teachers

This paper examines the development process of online education,the advantages and disadvantages of online education from the perspective of teachers,proposes on how teachers should transform in order to adapt to online education from the perspective of classroom teaching,and finally gives thoughts on the future and application of online education.