Online E-commerce Sites Research Articles

Business to Consumer Interaction in the Post COVID Era. A Case Study on Customer Reviews for Electronics and Cosmetics

The current paper discusses the topic of business to consumer interaction in the post COVID era. It briefly reviews key literature on aspects related to business communication practices and trends, and analyses a corpus of customer reviews selected from leading online e-commerce sites to see how customers interact with service/product providers to express satisfaction and dissatisfaction. The analysis will try to identify how the language used reflects the relation between participants, their credibility or lack of credibility in front of each other, and whether such aspects may be said to influence online sales trends.

Online counterfeit purchase behaviour: moderating effect of perceived anonymity and moral disengagement

PurposeThe continuous evolution of e-commerce with young consumers’ growing interest in online shopping has transformed the retail landscape across the world. With the surge in online sales, counterfeits of luxury goods have also found themselves from brick-and-mortar shelves to online e-commerce sites. Against this backdrop, this study aims to understand and analyse young consumers’ online counterfeit purchase behaviour (OCPB). Additionally, it also aims at identifying the determinants that influence their purchase decisions.Design/methodology/approachFollowing an extensive review of the literature, the present study pursued a quantitative approach in exploring critical demographic, psychographic, behavioural and situational factors influencing OCPB. The study was conducted in India through an online survey using a structured questionnaire.FindingsThe findings indicate that young consumers’ OCPB is significantly related to influencing factors like brand consciousness, fashion involvement, face consciousness, impulsive buying tendency, acquisition centrality and utilitarian shopping values. Furthermore, moderating effects of perceived anonymity (PA) and moral disengagement (MD) on OCPB were also observed and validated.Research limitations/implicationsThe study examined the critical factors and their linkages while building upon a structural framework on OCPB, keeping India as a representative sample. The proposed framework will bring more clarity and further insights that will help scholars expand the research domain with more cross-cultural studies and aid brand e-marketers to strategize their action towards developing strong brand aesthetic values.Originality/valueThe study contributes towards the literature by introducing PA and MD vis-à-vis building a framework for studying young consumers’ OCPB.

Deep Neural Network Based Multi-Review Summarization System

E-commerce is prevalent everywhere now-a-days. While shopping from these sites, users generally go through the reviews of the product posted by other users. For a given product, thousands of reviews may be available and it is cumbersome for the user to analyze each and every review. This paper proposes a multi-review summarization method to get a summarized review of products. A deep neural network-based model is employed to create an extractive summary of the reviews collected from online e-commerce sites i.e. Amazon and Flipkart. The deep neural network has been used to obtain the features of the product from multi reviews and cluster the sentences based on learned features. After clustering, a ranking of sentences is done and hence, an extractive summary is generated by selecting top n sentences from each of the clusters formed.

Machine Learning-Based Price Prediction for Cows

As Bangladesh is an agricultural country, cows have a great influence on our economy. However, there is no cow-related work or dataset accessible online in the fields of machine learning and artificial intelligence. This study aims to predict cow price ranges using any cow picture. Cow images were collected from different online e-commerce sites which are selling cows and mainly attempted to predict the price range of cows based on the images of the cows. Cows are divided into four classes based on their price range namely low, medium, high, and very high classes. A machine learning-driven approach has been taken for the prediction where convolutional neural network (CNN) is used as an image classifier and linear regression is used for predicting the prices. Our result shows that the price range of a cow can be predicted with an accuracy of 70%.

FACTORS WHICH INFLUENCE ON-LINE BUYING BEHAVIOR DURING FESTIVE SEASON

Rising incomes in the hands of a young population, a growing economy, expansion in the availability of products and services and easy availability of credit all have given rise to new consumer segments and rising acceptability of debt, whether it is mobile phones, credit cards, apparel or organized retail, people clearly seem to be spending more, particularly on discretionary items. The credit facility from business houses has been increasing at a rapid rate. This shows the terrific cut-throat competition in the ever-changing market. Consumers in large metros are opting for online retail and e-commerce for most of their purchases, the trend is slowly penetrating in non-metro cities as well. Festival Sales are the latest fad in India contributing tremendously to the growth of the online sales. All marketing retailers use the festival time to promote their products – either new or stock clearance products at heavy discounts or other offers (freebies, cash-backs, buy 1 get 1, etc), The major shopping festival in India comes around the period of the period of October- November when Diwali is celebrated and most of the online e-commerce sites provide big ticket offers during this period has created unique names for such shopping events – for example – Big billion days by Flipkart, Great Indian Shopping Festival by Amazon to name some. This study is an attempt to understand consumer buying behavior in India during the festive season.

Perspectives of the performance metrics in lexicon and hybrid based approaches: a review

Online social media and social networking services experience a drastic development in the present scenario. Contents generated by hundreds of millions of users are used for communication in general. Users mark their opinion and review in various applications such as Twitter, Facebook, YouTube, Weibo, Flicker, LinkedIn, Online-e commerce sites, Microblogging sites, etc. User generated text is spread rapidly on the web, and it has become tedious to analyze the opinionated text in order to arrive at a decision. Sentiment analysis, a sub-category of text mining is the major active research domain in current era due to greater quantity of opinionated text present in the Internet. Semantic detection is the sub-class in the sentiment analysis which is used for measuring the sentiment orientation in any text. Opinionated text is used for analyzing and making the decision simple. This interdisciplinary field draws various techniques from data mining, machine learning, natural language processing, lexicon based and hybrid based approaches. This paper provides a broad perspective with the highlight of the current state-of art techniques emphasizing the various research challenges and gaps present. The performance metrics in terms of detection rate, precision, recall, f-measure/score, average mean, auto-Pearson correlation, cosine similarity and ratio of time on various algorithms is discussed in detail. An analysis of the text mining approaches in different domains is presented.

Analysis of usability factors affecting purchase intention in online e-commerce sites

The growing number of internet users plays a significant role in the emergence of a variety of online e-commerce sites to meet the needs of Indonesians. However, there are still some problems faced by the users in using e-commerce sites. Therefore, a research related to user experience on their purchase intention to foster e-commerce sites is required. This study is conducted to find out the relationship between usability factors on e-commerce users’ purchase intention using a case study by using SEM to analyse the usability of the website. The result of this study shows that credibility, readability and telepresence are usability factors that directly affect purchase intention, while simplicity, consistency and interactivity are usability factors that indirectly affect purchase intention. Therefore, we can conclude that Indonesian consumers are on the Early Majority phase in adopting Company A.

Advertisement Posting Based on Consumer Behaviour

Abstract Social networking and other online e-commerce sites allow consumers and sellers to communicate directly regarding their items to be purchased, sold, brands and products. The advertisements posted in these websites often not interesting to the consumers as these sites pour in unnecessary advertisements. In this paper, we have proposed a consumer behaviour model using which relevant advertisements can be posted to the consumer whenever they visit the website. This model analyzes the process and activities people engage in when searching for, selecting, purchasing, using, evaluating and disposing of products and services so as to satisfy their needs and desires. We have developed a scalable prototype system based on this consumer behaviour model using Hadoop Framework which selects and displays relevant advertisements in the web site to the satisfaction of the consumers.