Items Online Research Articles

MINING ONLINE REVIEWS TO SUPPORT CUSTOMERS’ DECISION-MAKING PROCESS IN E-COMMERCE PLATFORMS: A NARRATIVE LITERATURE REVIEW

ABSTRACT By dint of the massive daily production of user-generated content (textual reviews) in E-commerce platforms, the need to automatically process it and extract different types of knowledge from it becomes a necessity. In this work, an attempt has been made to summarize some studies that aim to propose systems, which automatically mine textual reviews expressed in natural languages for the purpose of supporting customers’ decision-making process in E-commerce (buying, renting, and booking). The given review is the first work of this type and it includes 44 studies (30 aspect/feature-based summarizers and 14 reputation systems) published from 2004 to 2021. First, it investigates aspect and feature-based summarizers that aim to help customers in making an informed decision toward online entities (products, movies, hotels, services …). Second, it introduces reputation generation systems that seek to provide valuable information about online items. Finally, it provides recommendations for future research directions and open problems.

STUDENTS’ DIFFICULTIES IN SOLVING PROBLEMS USING MATRICES: A CASE STUDY

Linear algebra is an important focus in the study of university mathematics. It is also used in other fields such as engineering, physics, and economics. Utilizing responses from online diagnostic quizzes, this study investigated the difficulties first-year mathematics students at a university encountered when solving problems using matrices. This paper reports on the analysis of students’ responses to four types of questions on matrices and their applications. To understand those students’ difficulties, this study focused on online items that tested: matrix multiplication; transpose of a matrix in the context of matrix multiplication; the inverse of a matrix, and the application of matrices. However, participating students had a high ability to multiply two matrices. This was supported by the interviewed student, who described matrix multiplication as very basic.

Feasibility of HPV self-sampling pathway in Kathmandu Valley, Nepal using a human-centred design approach

Cervical cancer is preventable and curable yet causes almost 2000 deaths in Nepali women each year. The present study aims to explore the feasibility and acceptability of a self-sampling-based approach for cervical cancer screening in urban and peri-urban Nepal and develop pathways for self-sampling using a co-design methodology. An iterative design approach was applied. Semi-structured in-depth interviews were conducted with 30 healthy women and four women who had had a prior cancer diagnosis on topics which included: sexual and reproductive health knowledge and human papillomavirus (HPV); use of the internet/social media platforms; their views regarding acceptability and usability of the self-sampling kit and the proposed user journey. Data collection was done between December 2020 and January 2021. Seven medical experts were also interviewed to explore the current service configuration for cervical cancer screening in Nepal. Knowledge regarding HPV and its association with cervical cancer was absent for the majority of participants. Although 70% (n = 21/30) had purchased items online previously, there was a general lack of trust in online shopping. Half of the women (n = 17/30; 56.7%) expressed a willingness to self-sample and provided recommendations to improve the clarity of the instructions. The proposed user journey was considered feasible in the urban area. There is a clear unmet need for information about HPV and alternative cervical screening options in Nepal. An online pathway for self-sampling service delivery to urban women is feasible but will need to be optimally designed to address barriers such as confidence in self-sampling and trust in online purchasing.

A comprehensive E-commerce customer behavior analysis using convolutional methods

E-commerce is one of the most widely preferred business models all around the world. People can order items and have them in days or maybe in hours without visiting the shops. They count on the reviews of the online items to purchase them, so reviews have become one of the most significant metrics. Modern day algorithms and models can be made to decide the reviews seen by the user to judge an item fairly. We have attempted to make models that can judge the sentiment of the user writing the reviews from the text of the review, classifying it into positive, negative or neutral. Edge Computation is an emerging field in this scenario that can be very beneficial alongside capsule networks for processing the data as data capsules. In this work, we have performed a comparison of the effect of convolutional layers, attention layers and capsule network layers on base models for GRU, LSTM, Bi-GRU, and Bi-LSTM. These are done with the purpose to support the functioning of Edge Computation. The final results show that Bi-LSTM-Capsule Model (BiCapsModel) gives highest accuracy of 94.8%. The final outcome is very promising and can be used in various domains in journalism, product review and stock market. For classification, an Amazon review dataset has been considered. The dataset has various types of classified reviews based on which the emotional text analysis can be performed efficiently covering each aspect.

E-commerce now Big Commerce: E-commerce Industry and Consumer Internet Sector & its Effect on the World Today

Abstract: The Covid19 pandemic has made a huge impact on the ecommerce industry. The industry has experienced some benefits and problems during the pandemic. People tend to buy more items online as it can be more secure when it comes to the spread of disease. The repocommerce industry is having an increased sales growth. Many methods and techniques are being used by the e rts are saying that overall ecommerce industry to make Covid19 pandemic. This sup the negative impacts that happened to online business during the study is done to deeply analyse the problems and factors that are affecting the Online businesses and to know about the situations of ecommerce business in different parts of the world.The COVID-19 pandemic has had a significant impact on the Indian economy, which saw one of the largest lockdowns globally. The Indian internet economy, which was one of the most attractive markets worldwide, saw a 90% decline in April 2020, crippled by the shutdowns.1The decline was a result of steep falls in business for e-commerce and travel, the largest sectors in the internet ecosystem. However, the pandemic helped accelerate growth for segments such as hyperlocal delivery, edtech, healthtechand online payments as Indian consumers moved online to fulfil their daily needs. Despite a steep GMV fall in the initial months of the lockdown, India’s internet economy is likely to be a net beneficiary in longer run, driven by multi-fold increase in digital interactions and adoption of digital platforms. The number of first time online users has witnessed an increase across segments, along with an increase in online activity from tier 2 and tier 3 cities, which indicates a strong opportunity for enterprises and start-ups to cater to these cohorts digitally as the new normal. What is also expected to drive growth for digitally native start-ups is rising adoption by small and medium businesses in the country. Small merchants across cities are now adopting mobile channels, implementing online payments and exploring collaborations with technologydriven start-ups. Keywords: Ecommerce, Eshops, Covid19, Lockdowns, etc.

Online Recommender System

As the size of the e-commerce market grows, the consequences of it are appearing throughout society.The business Environment of a company changes from a product center to a user center and introduces a recommendation system. However, the existing research has shown a limitation in deriving customized recommendation information to reflect the detailed information that users consider when purchasing a product. Therefore, the proposed system reflects the users subjective purchasing criteria in the recommendation algorithm. And conduct sentiment analysis of product review data. Finally, the final sentiment score is weighted according to the purchase criteria priority, recommends the results to the user. Recommender system (RS) has emerged as a major research interest that Aims to help users to find items online by providing suggestions that Closely match their interest. This paper provides a comprehensive study on the RS covering the different recommendation approaches, associated issues, and techniques used for information retrieval.

Impact of Online Purchase of Food Items on Diet Pattern of Adults between 25-35 Years of Age in Central Mumbai

In the context of online purchasing of Grocery and other food items it was observed that the major advantage was the ability to purchase things from any location at any time with convenience and reduced use of time which made it more practical than visiting stores and very easy. Objectives:- To know the type and quantity of food purchased online and assess the effect of online food purchasing on body composition and Body Mass Index. Methodology:- The respondents being studied belonged to the age group of 25-35 years, the samples were selected by purposive random sampling. The number of participants were 100. Food frequency questionnaire was the tool used and The frequency of the food products that the participants purchase online and consume included categories such as groceries, frozen food, canned foods, ready to cook meals, packaged foods, chocolates and candies, desserts, jams and conserves, beverages, fast food. Results:- It was observed from BMI (Body Mass Index) that 23% of the participants were overweight and 51% of were Pre obese indicating that their consumption of processed energy dense food was high. It was observed that more than (50%) participants preferred to Purchase food items online on a monthly basis and (28%) preferred it on a weekly basis. Thus Indicating that the participants are used to purchasing food items online and find it more convenient Than offline shopping. When BMI was correlated with the frequency of Online purchase of food items it was not significant (p-value=0.29). Also participants mostly preferred to purchase Fast food (34%) online followed by Grocery (29%) on the other hand the least purchased food item Was frozen food indicating that the consumption of Frozen food is not much. The difference in Percentage of Body Mass Index (BMI) in relation to the purchase of groceries and other food items was not significant (p-value= 0.43). It was also noted that (41%) of the participants gained weight after purchasing food items online however there was no significant relation in the BMI and weight status of the respondents.

The Mediating Role of Organizational Commitment on the Relationship Between Emotional Labor and Work Engagement of Teachers

This study aims to investigate the mediating role of teachers’ organizational commitment (OC) on the relationship between their emotional labor (EL) and work engagement (WE). The study employed a cross-sectional design. The sample of the study consisted of the teachers working in Sakarya province of Turkey. They participated in the study voluntarily and responded scale items online. The findings showed that teachers’ perceptions of EL, OC and WE is relatively high. Also, there are statistically significant and positive correlations among variables. On the other hand, the findings confirmed the hypotheses. Teachers’ EL predicts their OC and WE. Additionally, OC predicts WE and plays a mediating role on the relationship between EL and WE. Based on the findings some suggestions were made.

The Effect of Price Perception and Game Features in Online Virtual Game Item Purchase Decisions

This study tries to answer the research objectives, namely to find out how the influence of Price Perception and Game Features on Online Game Virtual Item Purchase Decisions. The results of hypothesis testing using multiple regression analysis with two independent variables and one dependent variable indicate that: The proposed H1 states that Price Perception has no effect on Online Game Virtual Item Purchase Decisions, it can be seen if the tcount value is 0.896 or less than ttable (α : 5%, df : 37 = 2.026192), in addition, the significance value is 0.376 or greater than the model significance value of 0.05. Then H1 is rejected, meaning that this cannot affect the Online Game Item Virtual Purchase Decision. The proposed H2 states that Game Features have an effect on Online Game Virtual Item Purchase Decisions, it can be seen if the tcount value of 1.908 is smaller than ttable (5%, df : 37 = 2.026192), but the significance value is 0.064 or greater than the value the significance of the model is 0.05. Then H2 is rejected, meaning that this cannot affect the Online Game Item Virtual Purchase Decision. The proposed H3 states that Price Perception and Game Features simultaneously affect the Decision to Purchase Virtual Items Online Game, from table 4.12 the Fcount value is 4.751 or greater than the Ftable value (α : 5%, dk numerator : 2, dk denominator : 39 -2-1, Ftable: 3.26), besides the significance value is 0.015 or less than the model significance (0.05), it can be concluded if H3 is accepted, meaning that Price Perception and Game Features simultaneously affect Virtual Purchase Decisions Online Game Items.

What drives generation-y women to buy fashion items online?

ABSTRACT Fashion retailers, marketers, and app designers must captivate their dominant consumers: Generation-y women. Nevertheless, existing research and design practice is grounded in older apps and older generations: Generation-x. Through 27 semi-structured interviews, we define generation-y’s unique interaction with – and perception of – fashion m-commerce to enhance fashion retailers’ ability to design apps targeted at their dominant consumers. We prove that efficiency and convenience drive app engagement, official marketing social media is more effective than friend’s social media posts, and interactive design features are irrelevant in purchase motivation. We finish by communicating our findings through six design recommendations, subdivided by market sector.

Comparing Online and In-Store Grocery Purchases

ObjectiveTo describe the grocery shopping patterns of people who shopped both online and in-store and evaluate whether shoppers purchased fewer unhealthy, impulse-sensitive items online. DesignSecondary analysis of 44 weeks of grocery transaction data collected for fruit and vegetable incentive trials in 2 Maine supermarkets. ParticipantsA total of 137 primary household shoppers who shopped at least once in-store and online (curbside pickup) for 5,573 total transactions Main Outcome Measures and AnalysisPaired t tests and descriptive analyses compared online and in-store transactions with respect to frequency, total spending, number of items purchased, and spending on 10 food groups and 34 subgroups. Mixed-effects regression models estimated differences in online vs in-store spending on 5 unhealthy, impulse-sensitive subgroups. ResultsWhen shopping online, participants spent 44% more per transaction ($113.58 vs $78.88, P < 0.001) and purchased more items (38.3 vs 26.6 items/transaction, P < 0.001). Compared with in-store, shopping online was associated with reduced spending per transaction on candy (−$0.65, P < 0.001), cold or frozen desserts (−$0.52, P < 0.001), and grain-based desserts (−$1.29, P < 0.001). Conclusions and ImplicationsOnline shopping was associated with lower spending on certain unhealthy, impulse-sensitive foods. Grocery-based healthy eating initiatives might leverage online ordering platforms to increase their reach and effectiveness.

ONLINE SALE OF FASHION GOODS

The main channel for the distribution of retail goods is gradually becoming the Internet. It is also a binding distribution channel for the sale of fashion goods. It allows the retailer to take opportunity not only to post photos, video and audio material showcasing their latest collections, but also to sell the goods directly to the consumer through a website. In Latvia, the purchase of goods in the Internet is growing very fast, especially during the Covid - 19 restrictions, when the purchase of goods in stores is restricted. Throughout Europe and especially in Latvia the crisis caused by Covid-19 has led to the questions, such as - what to do now, what should change and what decisions should be made by fashion retailers so that employees and buyers are not so vulnerable in such unpredictable force majeure situations? According to data from the Interactive Media Retail Group, for example, the UK's online retail turnover increased by 42% in September 2020, by 35.7% in October and by 39% in November compared to the previous year. The aim of this article is to analyze researches about online fashion retailers conducted by US and British researchers and the results of surveys among Chinese and Latvian students. The questions included in the researches and surveys are: Who sells fashion items online? Who helps to create a good fashion website? How do fashion retailers design websites? Who buys online? Conclusions and proposals have been made for retail web developers in Latvia.

Coronavirus Impact On Online Purchases Behavior in Japan

The COVID-19 outbreak has impacted almost every aspect of people’s lives in Japan. With the ageing society that more vulnerable to the disease that needs most people to stay home, the Japanese have been adapting to both the outbreak and the regulations. The declaration of a state of emergency since April makes the businesses and stores deemed non-essential to limit their operations. To limit places where people could gather, all schools, institutes of higher learning, and houses of worship were ordered to be closed during the period. Japanese have been spending more time online and have shown more willingness to purchase items online now. Changes in behavior such as needing to work from home or exploring new hobbies while staying at home also present new opportunities for eCommerce. COVID-19 is having a catastrophic impact on many non-essential products in Japan. With the environment being so different to pre-COVID time, COVID-19 has impacted the Japanese eCommerce industry in some ways. COVID-19 pandemic creates a new environment for the Japanese shopping and spending behavior that needs to be discussed further.

Sharing is caring: How non-financial incentives drive sustainable e-commerce delivery

As more and more people are buying more and more items online, limiting the ecological footprint of e-commerce deliveries is pressing. Research suggests several initiatives for retailers and logistics service providers to take, but consumer-involvement is key. This research investigates how to encourage consumers’ sustainable decision-making in the web-shop’s check-out page by using non-financial incentives only. To this end, we carry out an online experiment among Belgian consumers to assess and compare the impact of four incentives: (1) information, (2) options order, (3) social media share and (4) social norm. The experiment demonstrates that providing information on the ecological footprint of delivery options is the most decisive incentive. The impact of social media shares and social norms are positive, while reversing the order of delivery options has limited effects. This research highlights the potential of consumer behaviour in enhancing sustainability and encourages further exploration of this topic.

FACTORS INFLUENCING CONSUMER PURCHASE INTENTION WHILE BUYING ONLINE

Motivation/Background: Today, it is very imperative to study the factors that effect and predict the purchase intention of consumer online shopping. E-tailers and e-commerce is expanding very quickly, especially clothing and fashion brands have shifted to electronic channels to reach out their clients. This is one of the reasons that apparel is amongst the most sold items online. The purpose of this research is to identify the factors that influence consumer choices and which factors have significant effect on consumer purchase intention while online shopping fashion apparel.&#x0D; Method: This research has adopted quantitative approach and explanatory research purpose to establish relationship between four independent variables: Convenience, Price, Information and Security on the dependent variable (Consumer purchase intention). Survey response from 300 consumers was collected through online questionnaire and distributed through convenience sampling.&#x0D; Results: Data collected was analyzed on SPSS. For two hypotheses, correlation test one-sample t-test was conducted. Further, two hypothesis were analyzed by regression analysis. All four hypotheses were accepted. Results conclude that there is a significant relationship between online purchase intention of consumer and its factors such as price, convenience, security and information about product or service.&#x0D; Conclusions: This study has many implications for small retailers to focus on factors that can make it easier selling online. Security measures to protect user data are very vital to bring user back on the e-tailing website or webpage.

E-Commerce industry and its effect on the world today

The Covid-19 pandemic has made a huge impact on the e-commerce industry. The industry has experienced some benefits and problems during the pandemic. People tend to buy more items online as it can be more secure when it comes to the spread of disease. The reports are saying that overall e-commerce industry is having an increased sales growth. Many methods and techniques are being used by the e-commerce industry to make-up the negative impacts that happened to online business during the Covid-19 pandemic. This study is done to deeply analyse the problems and factors that are affecting the Online businesses and to know about the situations of e-commerce business in different parts of the world.

Purchaser Attributes And Their Impact On Tolerating Internet Shopping, With Regards To Various Item Types

Web based shopping is among the most famous exercises of the web, yet the reasons why buyers purchase online are as yet hazy. In spite of the fact that it is inferred that purchaser acknowledgment of web based shopping is influenced by various items very few examinations have embraced this view. This examination endeavors to analyze buyers' disposition when making on the web buys with regards to various item types. A hypothetical system is proposed dependent on the determinants of buyer conduct and client acknowledgment of internet shopping, just as online item grouping. The elements that were chosen to be tried are Close to home Ingenuity of Data Innovation, Self-adequacy, Seen security, Protection, Item association and how they influence shopper demeanor towards internet shopping. Connection examination, from the outset, to decide the connections among the factors and relapse investigation a short time later to confirm the degree of the variable cooperation were utilized to test the theories. In view of the previously mentioned investigations, results were attracted and contrasted with the outcomes found by Lian and Lin in a comparative report. It has been discovered that PIIT, saw security and item association affect the demeanor towards web based shopping, yet the outcomes fluctuate among the diverse item types.

A Two-Step Approach for Improving Sentiment Classification Accuracy

Sentiment analysis is a method for assessing an individual’s thought, opinion, feeling, mentality, and conviction about a specific subject on indicated theme, idea, or product. The point could be a business association, a news article, a research paper, or an online item, etc. Opinions are generally divided into three groups of positive, negative, and unbiased. The way toward investigating different opinions and gathering them in every one of these categories is known as Sentiment Analysis. The enormously growing sentiment data on the web especially social media can be a big source of information. The processing of this unstructured data is of deep interest for researchers in the field of data science. The proposed article is an effort to find a model which can improve the classification accuracy of sentiment data. In this paper, a complete model is introduced to improve the accuracy of binary-class sentiment data. The model is reliable as it has been validated on three distinctive datasets with various sample sizes. In this regard, Amazon reviews, Yelp reviews, and IMDB reviews are taken into account. This model is based on completely referenced datasets and considerable improvement in accuracy is seen as compared to individual classifiers. Various heterogeneous classifiers are trained on the above-mentioned datasets at the base level. These base-level classifiers ought to be chosen from various groups of classifiers. At that point, the output of these base-level classifiers is consolidated to create a new dataset that acts as input for meta-level classifiers. At the meta-level, the base-level classifier with the best outcomes is selected with the new dataset framed. The model accordingly framed can be used to predict any sample query.

Extraction and Summarization of Reviews using Lexicon based Approach

Online service providers have enabled users to freely comment in the form of review on an online items. These large scale of reviews contains valuable information and poses the need of automatic summarization. In this paper the extraction and summarization of reviews expressed by customers on Samsung mobiles has been carried out. The proposed approach extracts the Samsung reviews using custom build scraper from Flipkart and classify them as positive, negative and neutral using Lexicon based approach (SentiWordNet).

Feedback System Using Sentiment Analysis

– Today, when looking at the quality of an online item, the feedback itself plays a very important role. Based on the feedback we can decide whether the desired item is good or not, get a picture of the seller and so on. Many companies that have online shops display the most positive feedback while hiding bad ones or display only a few of them. In this research, we will help people by automating the process of deciding whether a feedback is positive or negative, which will give them time for other jobs and save money for hiring people who will work on the feedback. Since feedback on online articles is very important today, the process of determining positive and negative feedback should be made as quick and easy as possible. In this research, we will show a very simple and fast way to classify feedback as positive or negative, which means that the main question of this research is how to facilitate and speed up the process of determining the polarity of the feedback. We will use NLP using Python’s library called TextBlob. The used algorithm is called Naïve Bayes, it gave the accuracy of around 80%.