Online Retail Sector Research Articles

Sentiment Analysis through Big Data in online Retail Industry: A Conceptual Quantitative Study on linkage of Big-Data and Assortment Proactive of Online Retailers

Big-Data is the recent trend in data sciences prevailing all over the globe. The tool aids significantly in optimization of knowledge and has predominant use in optimization of knowledge and productivity. However, there is lack of understanding of concept and its application in Pakistan as indicated by Gallup Pakistan (2018) and stream of data is going to be doubled in two years’ time Tankard (2012). Therefore, there is a definite need of research which optimizes understanding associated with technology and its application from the context of Pakistan. Hence considering the application of big-data in retail sector this study aims to explore the impact of sentiment analysis through relating impact of big-data with effective assortment s of online stores. Although data has been collected from IT experts associated with online retail sector via quota sampling and SMART-PLS has been incorporated for the purpose of analysis. Results of the study highlights that big-data is perceived as the major tool for the betterment of assortment in online retail stores although data scientist and their applicability might diminish the impact of the use of big-data.

Mediating Role of Company Information in the Relationships among Perceived Risks and Purchase Intentions in an Online Retailing Context

The exponential growth of the online retail sector has attracted the attention of researchers across the globe. Understanding the consumer decision-making process in an online retail context is of vital importance to all online retailers. The objective of this study was to examine the direct and indirect impacts of perceived risks on consumers’ purchase intentions in an online shopping context. This study applied structural equation modeling to test the study model with data from 234 samples. We found a significant negative full mediating impact of performance risk, financial risk, physical risk, and psychological risk on consumers’ purchase intentions. Moreover, we found a partial mediating impact of social risk on purchase intentions. However, we did not find a mediating impact of time risk on purchase intentions. These empirical results may help online retailers to better understand their consumers, their intentions to purchase, and their level of risk perceptions. Accordingly, online marketers can frame contemporary strategies to attract retail customers, leading to greater profitability of the organization.

Alternative e-commerce delivery policies

Contemporary shopping habits are undergoing rapid change, with more and more consumers purchasing goods online. The rapid growth of the online retail sector provides great opportunities for both wholesalers and transporters in servicing this newly emergent type of customer. With both consumers and corporations acutely aware of the environmental impact of business activities, one of the most relevant research questions is how to organize the operations of a e-commerce delivery business while simultaneously minimizing its environmental impact. The present paper addresses the e-commerce delivery problem, a mathematical formulation and fast heuristics which enable the simulation of various e-commerce delivery scenarios. The effects of the scenarios regarding more environmentally friendly e-commerce concerns are tested upon real-world data. In particular, the impact of new green(er) technology (such as electric bicycles and cars), aggregated collection points, carrier bundling, and changing delivery times is investigated. The obtained results are suitable for implementation at an organizational or operational level within both e-commerce delivery companies and transporters.

E Word of Mouth in the E-Retail Sector-Methodological and Thematic Review

The rise of social media in today's world has led to researchers taking a major interest in the topic. With all the tools available for research, the article focuses on how the word of mouth affects the online retail sector. The article aims at creating a systematic literature review of the effect of word of mouth on the retail sector online and identifying the main themes revolving around it. A total of 31 articles were included in this paper involving five major themes like relationship marketing, service quality, e-loyalty, e-satisfaction and social media. In terms of the methodological review it was found that e-loyalty and the actual effect of the word of mouth is ambiguous in nature. It needs further research for various other approaches to finally come to the conclusion if word of mouth actually makes a huge impact and if the companies should invest and bank on the same for their sales

A CLASSIFICATION APPROACH FOR NAÏVE BAYES OF ONLINE RETAILERS

Many small online retailers and new entrants to the online retail sector are keen to practice data mining and consumer-centric marketing in their businesses yet technically lack the necessary knowledge and expertise to do so. In this article a case study of using data mining techniques in customer-centric business intelligence for an online retailer is presented. The main purpose of this analysis is to help the business better understand its customers and therefore conduct customer-centric marketing more effectively. On the basis of the Recency, Frequency, and Monetary model, customers of the business have been segmented into various meaningful groups using the classification and naive bayes algorithm, and the main characteristics of the consumers in each segment have been clearly identify ed. Accordingly a set of recommendations is further provided to the business on consumer-centric marketing.

Effect of eWOM Valence on Online Retail Sales

Online retail sector in India has witnessed a phenomenal growth in recent times. Online shopping has also become a popular trend among the younger generation in India. Increasing number of shoppers visit online retailer websites and read online reviews before making their purchase decision. The online reviews or electronic word of mouth (eWOM) becomes an important guiding tool for the online shoppers with its intrinsic product information and evaluation characteristics. The present study aims to analyze the effects of various eWOM antecedents on online sales by considering the effects of positive and mixed-neutral eWOM (MNWOM) valence (stimuli) on sales. It also explores the role of market-level eWOM factors, such as price, on online sales of security products like antivirus software. The confirmatory bias of these factors was noted, while the elaboration likelihood model (ELL) has been used to understand the relative importance of these factors in influencing customers’ purchase decision and sales. Further, a content analysis method supplemented by a multiple regression method was used to analyze 205 real-time online sales (reviews from verified purchasers) data pertaining to popular and top-selling antivirus products taken from two leading e-commerce websites. The study contributes as a pioneering effort in the domain with the use of innovative methodology of capturing real-time online data with a subsequent kappa statistics validation. The results showed a new insightful perspective of eWOM valence and price on sales, and provided further research directions.

Payment Methods and Purchase Intention from Online Stores

Online retail stores are a new booming phenomenon in the Arab world. Recent years witnessed a significant increase in the number of Arabic online stores launched for different kinds of products and services. Stores offer grocery products, fashion and life style items, and electronics and technology devices. Regardless of the growing investments in the Arabic online retail sector, Arabic consumers are still doubtful about online shopping. This research investigated the factors affecting Jordanians' purchase intentions from online stores by extending the technology acceptance model (TAM) to include payment method. The model included payment method (PM), perceived ease of use (PEOU) and perceived usefulness (PU), as direct predictors of the intention to use online stores and moderated by consumers' level of trust of such factors. Results indicated that TAM predictors, and payment methods do affect consumers' intention to purchase online. However, PM and PEOU relationships with the intention to shop online are the only relationships moderated by trust.

The role of e-service quality management in the delivery business value

In sharp contrast to the extensive debate that has been conducted in the contemporary literature, with regard to consumers' perceptions of e-service quality, the voice of those managing e-service quality has remained relatively silent. Against this backdrop, the primary aim of the research presented in this paper is to address the gap in the literature with regard to the role of electronic service quality management in leveraging business value from on-line retailing strategies. The study was undertaken using a quantitative research methodology, based upon questionnaires, which resulted in a sample of 225 responses, completed by senior managers from across the UK's on-line retail sector. The results of the statistical analyses have demonstrated that the perceived success of a retailer's e-commerce operations is strongly associated with both the management approaches to e-service quality and the level of e-commerce adoption. Of particular interest is our finding that as the scale and scope of a retailer's e-commerce operations grow, there is a concomitant need for its e-service quality operations to develop, if it is to realise the full value from its on-line activities. The article adds to the body of knowledge regarding the management of electronic service quality while also seeking to stimulate critical debate concerning the role of service quality practises within prevailing IT value discourses.

Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining

Many small online retailers and new entrants to the online retail sector are keen to practice data mining and consumer-centric marketing in their businesses yet technically lack the necessary knowledge and expertise to do so. In this article a case study of using data mining techniques in customer-centric business intelligence for an online retailer is presented. The main purpose of this analysis is to help the business better understand its customers and therefore conduct customer-centric marketing more effectively. On the basis of the Recency, Frequency, and Monetary model, customers of the business have been segmented into various meaningful groups using the k-means clustering algorithm and decision tree induction, and the main characteristics of the consumers in each segment have been clearly identified. Accordingly a set of recommendations is further provided to the business on consumer-centric marketing. SAS Enterprise Guide and SAS Enterprise Miner are used in the present study.

Shopping for security: Securing the e-tail market

As the online retail sector continues to expand, encouraging a wider audience to shop online, it's important that online retailers protect their brand by securing their customers' credit card details. Cath Everett reports