E-commerce Retailers Research Articles

A Study on E- Commerce in India

E-commerce stands for electronic commerce. E-commerce is doing commerce online and electronically. The E-commerce has totally revolutionized the customary concept of trade. E - commerce bargains with offering and acquiring of products and administrations through web and computer systems. This paper endeavors to highlight the diverse challenges confronted by the E-commerce in India, get it the basic development components required for E-commerce, depicts the success of E-Commerce in India and retail E-Commerce deals in India. The think about found that, within the world of E-commerce, the presence of the wholesalers is at the most prominent hazard since the maker can effectively overlook them and offer their items to the retailers and the customers. Wholesalers can take the advantage of E-commerce in setting up contracts with rumored makers and connecting their trade with the online. The think about too found that, E-commerce gives the different sorts of openings to the wholesalers.

Scheduling heterogeneous delivery tasks on a mixed logistics platform

Large e-commerce retailers usually establish their own logistics systems. Such systems make use of their own dedicated fleets but will also use a crowdsourced delivery mode by hiring occasional fleets. These mixed logistics systems with both dedicated and occasional fleets serve both retailers’ internal delivery tasks and external tasks requested by local businesses. This paper studies the problem of scheduling heterogeneous (internal and external) delivery tasks on a mixed logistics platform with multiple depots and two types of vehicles (dedicated and occasional). A delivery task is executed by either a dedicated vehicle or an occasional vehicle. The dedicated vehicles depart from and return to the platform's depots; the occasional vehicles depart from their original location and pick up goods from depots or external pickup locations, fulfill the delivery tasks, and finish their route at the final delivery location. We propose mixed integer programming models and column generation-based solution methods to solve the problem. A computational study is conducted based on a series of randomly generated instances and real-world instances involving 15 depots, 120 internal customers, 15 external delivery tasks, and 38 dedicated and occasional vehicles. The results obtained demonstrate the efficiency of the column generation-based solution methods. Moreover, the effectiveness of the proposed models is validated by a significant cost saving in comparison to intuitive decision rules. A sensitivity analysis is also conducted to derive a number of managerial implications.

The integrated orderline batching, batch scheduling, and picker routing problem with multiple pickers: the benefits of splitting customer orders

Fast delivery is one of the most popular services in e-commerce retail. It consists in shipping the items ordered on-line in short times. Customer orders in this segment come with deadlines, and respecting this latter is pivotal to ensure a high service quality. The most time-consuming process in the warehouse is order picking. It consists in regrouping orders into batches, assigning those batches to order pickers, sequencing the batches assigned to each order picker such that the orders deadlines are satisfied, and the picking time is minimized. To speed up the order picking operations, e-commerce warehouses implement new logistical practices. In this paper, we study the impact of splitting the orders (assigning the orderlines of an order to multiple pickers). We thus generalize the integrated orders batching, batch scheduling, and picker routing problem by allowing the orders splitting and propose a route first-schedule second heuristic to solve the problem. In the routing phase, the heuristic divides the orders into clusters and constructs the picking tours that retrieve the orderlines of each cluster using a split-based procedure. In the scheduling phase, the constructed tours are assigned to pickers such that the orders deadlines are satisfied using a constraint programming formulation. On a publicly available benchmark, we compare our results against a state-of-the-art iterated local search algorithm designed for the non-splitting version of the problem. Results show that splitting the customer orders using our algorithm reduces the picking time by 30% on average with a maximum reduction of 60%.

Efficiency Loss and Coordination in the Online Shopping Supply Chain with Competitive Shipping Companies

This paper studies efficiency loss and coordination mechanism in a supply chain with one online retailer and multiple competitive shipping companies in the presence of congestion effects. We build a three-level game between customers, shipping companies and the retailer. The equilibrium market structure is determined. The optimal volume–investment ratio for each shipping company is the same for both centralized and decentralized supply chains, and it is dependent on the delivery time function, independent of competitors’ decisions. The efficiency loss of the decentralized supply chain with one retailer and one shipping company is found to be 1/4, independent of the delivery time function and the demand function. The loss is reduced when competition is introduced into shipping companies and an upper bound of efficiency loss with multiple homogeneous shipping companies is derived. Revenue-sharing contracts may be designed to allow the decentralized supply chain to perform as well as a centralized one. The necessity of coordination is reduced when competition is introduced and the number of competitive companies increases. Finally, we extend the model to consider heterogeneous shipping companies and find that the disparity between shipping companies increases the efficiency loss because in the decentralized supply chain less efficient shipping companies also provide service. This research explicates the relationship options between e-commerce retailers and shipping companies, providing managerial insights for industry practitioners.

E-COMMERCE RETAIL CUSTOMERS REPURCHASE FACTORS INFLUENCING IDENTIFICATION

With the fast-paced technological development era and the importance of using the Internet in our daily lives, e-commerce as shopping no longer seems new or unusual. Regardless of type or size, companies are using e-commerce advantage to compete in the market. Each of these companies needs a circle of independent and loyal customers. When the customer is satisfied, he gives positive feedback about the company and makes a repeat purchase. In this way, he attracts new customers to the company and provides an independent income for the company. Certain factors influence the customer's attitude and behavior. It is essential to determine what influences their customers' choices when competing to earn profit in the market; the customer must be satisfied in order for him to want to make a repurchase. Repurchases from customers indicate a loyalty to the company. Customer loyalty can be the result of a company consistently meeting and exceeding its customer expectations. Customer loyalty can have a significant impact on business growth. To assess and identify the factors influencing customer satisfaction, they are identified and offered a conceptual reflection of the current situation and offer a conceptual model of Identification Causes and Effects of Customer Satisfaction Framework (IceCSF) in e-commerce retail.

Clustering of potencial customer in digital trade transaction

The growth of digital trade transaction data in Indonesia is very rapid. Retail ecommerce in Indonesia is currently growing rapidly although it is still smaller than other countries, growing by around 30% every year for the last five years. Customer grouping is carried out based on different types of customer data, including customer value data, customer profile data, customer transaction data, and customer purchase order data. In the following sections, we briefly introduce each type. This study used data mining that was collected from potential customers by online questionnaires. The customer set data used were 309 respondents. The data collection that the authors do it by distributing questionnaires online through Google form. Clustering process used neural network algorithm. The competitive network used data clustering. The research result based on the results of data collection, the data grouping can be obtained with cluster 3 epoches 1000 data with kohonen parameter 0.01 and cluster 3 epoches 1000 data with kohonen parameter 0.01.

Online or Offline? How Smog Pollution Affects Customer Channel Choice for Purchasing Fresh Food.

Due to fresh foods' unique characteristics, where quality, freshness, and perishability are the main concerns, consumers are more inclined to choose offline channels for purchasing foods. However, it is not well-understood how these behaviors are affected by the adverse external environment, e.g., smog pollution. Fine particulate matters (PM2.5) on smog days would irritate the respiratory tract and pose health risks to people, triggering negative emotions such as sadness and depression. People tend to stay in a clean indoor environment on smog days. An adverse external environment is causing a gradual change in people's habits and emotions. Still, its impact on shopping behaviors is a complex process in need of further study. The study fills this gap by examining the impact of smog pollution on customer channel choice. Based on data from an e-commerce retailer that operates in both online and offline channels. We find that (1) the degree of smog pollution has a significant positive effect on online channel purchasing at aggregated store-, product-, and individual- levels; (2) moreover, the retailer's in-store interactive activities would restrain this positive relationship; (3) variation of product pricing and customers' healthy eating tendency would pronounce the positive association between smog and online purchasing. These results can serve as a reference for retailers to adjust channel strategies in the face of harsh external conditions.

All's well that ends well: Effective recovery from failures during the delivery phase of e-retailing process

E-commerce retail has been on an exponential growth since late-1990s and now with COVID-19 crisis it is more popular and prevalent than ever before. But failures during the e-retailing process, specially when the business is under pressure, is a severe challenge with deteriorative impacts on customers' attitude and behavioral intention. While the majority of failures during e-retail process takes place at the ‘product delivery phase’ (the last phase of e-commerce process), little attention has been paid towards investigating how e-retailers can effectively recover from service failure encounters during the delivery phase. Seeking to address this gap, the present study draws on cognitive dissonance theory to examine the determinants of effective recovery from service failure encounters during the delivery phase of e-retailing. In doing so, a scenario-based experiment was conducted to collect data from 320 online customers. The findings provide empirical insights on how perceived recovery endeavor interacts with criticality of situation and brand equity to mitigate the negative consequences of service failure at delivery phase. The theoretical and managerial implications of the findings are discussed along with avenues for future research.

Influence of Mall Shopping Culture on Online Shopping Preferences: An Emerging Economy Perspective using the Technology Acceptance Model

Growth of e-commerce activities are keeping up pace with the rapid progress in the development and availability of internet. However, the availability of internet technology and its penetration does not automatically imply consumer preference of online shopping. This study aims to examine the factors influencing the decisions of consumers for engaging in online shopping in the context of UAE, and inquires whether the prevalence of mall culture has an impact on the online shopping adoption by consumers. The Technology Acceptance Model (Davis 1989) was used to assess consumer perceptions and intentions towards online shopping. Data was collected through online surveys using convenience sampling method. From the analysis of sample data, there is sufficient evidence to conclude that online shopping is still not very popular in the UAE as residents prefer to do shopping in malls. The findings of the study are relevant to the e-commerce retail entities operating in countries where the mall culture is an important influence in the retail scenario, as this will help them in formulating their strategy to accommodate the shopping preferences of both online and in-store customers. The impact of the COVID-19 pandemic is also considered on the objectives and findings of the study.

The Latest Generation of SEZs: Consumer-Oriented Unilateralism in China’s E-Commerce Trade

ABSTRACT World Trade Organization multilateralism is driven by manufacturers. However, in China, Cross-border E-commerce Retail Import has spurred a new, consumer-oriented trade unilateralism. Cross-border E-commerce Retail Import prospers within China’s National Cross-Border E-commerce Pilot Cities, which are special economic zones aimed at using unilateral trade liberalization to meet consumers’ growing demands for high-quality foreign products. Cross-border E-commerce Retail Import enhances consumer benefits beyond reducing customer formalities and tax rates and lowering product prices. It re-conceptualizes consumer protection by treating consumers as diverse individuals rather than as a homothetic group. It also empowers consumers by making them ‘importers’ to minimize behind-the-border trade barriers. Cross-border E-commerce Retail Import warrants a rethinking of World Trade Organization multilateralism from its initial focus on corporations and capital owners to a revised focus on consumers.

Smartfit - Artificial Intelligence Based Virtual Dressing Room

In the current existing busy and modern lifestyle had led many people to purchase ready-made clothes from e-commerce stores or retail stores without actually knowing the fitting of the clothes, in the expectation of perfect fit. To improve the accessibility and increase the time efficiency of clothes trying on we have created a virtual clothes trial room environment. In this, we have introduced a virtual try-on strategy which is based on generative adversarial networks(GAN).Our technique will firstly obtain masking of cloth region of a person with the help of human parsing model. Next, the cloth region will be removed from the person's image and another desired cloth image is added on the region. This application will also be more useful for the shops which requires to show every clothes to customer and hence time consuming and requires more Human Effort.

Case Article—GreatDeal and NewChicken Merger: Designing an Omni-Channel Supply Chain

In this case study, we consider the design and redesign of an omni-channel supply chain, following a merger of two existing companies. Prior to the merger, each company has their existing supply chain and operational strengths. Specifically, one company is a more established brick-and-mortar chain and the other is a web-based, e-commerce retailer. Postmerger, they need to evaluate the supply chain structure in terms of sales channels, distribution and fulfillment strategies, and inventory management (especially for three specific groups of products). The case study presents information regarding the market conditions that forced the two companies to merge and offers information to evaluate two competing proposals from senior executives regarding navigating the integration of the online and physical supply chains. The case article discusses the role of this integrated problem within a supply chain management or an inventory management course. In the case article, we present a literature review regarding the positioning of the posed problem among the existing works. We also highlight our experiences in teaching the case. The case “Teaching Note” presents a number of discussion questions depending on the usage of the case, offers answers to specific discussion questions, and provides guidance regarding classroom discussion. We also have a complementary Microsoft (MS) Excel template that can be shared with students and a complete MS Excel spreadsheet presenting the solutions.

The Role of Mobile Application Acceptance in Shaping E-Customer Service

Most retailers are integrating their practices with modern technologies to enhance the effectiveness of their operations. The adoption of technology aims to enable businesses to accurately meet customer needs and expectations. This study focused on examining the role of mobile application (app) acceptance in shaping customer electronic experience. A mixed method was adopted, in which qualitative data were collected using interviews, and quantitative data were gathered using the questionnaires. The results indicate that mobile app acceptance contributes to a positive customer experience while purchasing products and services from online retailers. Mobile apps are associated with benefits, such as convenience, ease of use, and the ability to access various products and services. With the rapid development in technology, e-commerce retailers should leverage such innovations to meet customer needs.

Outsourcing Reverse Logistics for E-Commerce Retailers: A Two-Stage Fuzzy Optimization Approach

On the heels of the online shopping boom during the Covid-19 pandemic, the electronic commerce (e-commerce) surge has many businesses facing an influx in product returns. Thus, relevant companies must implement robust reverse logistics strategies to reflect the increased importance of the capability. Reverse logistics also plays a radical role in any business’s sustainable development as a process of reusing, remanufacturing, and redistributing products. Within this context, outsourcing to a third-party reverse logistics provider (3PRLP) has been identified as one of the most important management strategies for today’s organizations, especially e-commerce players. The objective of this study is to develop a decision support system to assist businesses in the selection and evaluation of different 3PRLPs by a hybrid fuzzy multicriteria decision-making (MCDM) approach. Relevant criteria concerning the economic, environmental, social, and risk factors are incorporated and taken into the models. For obtaining more scientific and accurate ranking results, linguistic terms are adopted to reduce fuzziness and uncertainties of criteria weights in the natural decision-making process. The fuzzy analytic hierarchy process (FAHP) is applied to measure the criteria’s relative significance over the evaluation process. The fuzzy technique for order preference by similarity to an ideal solution (FTOPSIS) is then used to rank the alternatives. The prescribed method was adopted for solving a case study on the 3PRLP selection for an online merchant in Vietnam. As a result, the most compatible 3PRLP was determined. The study also indicated that “lead time,” “customer’s voice,” “cost,” “delivery and service,” and “quality” are the most dominant drivers when selecting 3PLRLs. This study aims to provide a more complete and robust evaluation process to e-commerce businesses and any organization that deals with supply chain management in determining the optimized reverse logistics partners.

On deep neural network for trust aware cross domain recommendations in E-commerce

Over the years, cross-domain and trust-based recommendation systems are proven to be very helpful in solving issues pertaining to data sparsity and cold start. Many e-commerce sites used recommender systems as business tools for increasing their sale productivity and help their customers in finding suitable products. However, due to sparse rating and lack of historical information, such systems cannot generate effective recommendations. Matrix factorization and deep learning techniques have been the focus of research community for the last few years to solve the problem of data sparsity and cold start. In this paper, we have proposed a model called Trust Aware Cross-Domain Deep Neural Matrix Factorization (TCrossDNMF) that predicts rating of an item for an active user and solves user cold start problem in the cross-domain scenario of ‘User Overlap’ in e-commerce system. TCrossDNMF model is divided into four main steps: i) Features learning that learns the users’ features using a latent factor model and then finds the similarity between users of source and target domains. As the users are shared between two domains, the proposed model learns the common information and transfers the knowledge from a source to target domain. ii) Ranking that finds set of similar users (neighbors), and then filters out the dissimilar users based on similarity threshold θ, and then generates a bipartite trust graph from these reduced set of users and executes Ant Colony Optimization, to find trustworthy neighbors for an active user. iii) Weighting computes the trust degree between an active user and his or her top-k neighbors. iv) Prediction that trains the TCrossDNMF model using multilayer perceptron (MLP) and generalized matrix factorization (GMF) by representing user-item interactions in higher dimensions and ensembles the GMF and MLP with trust information for rating prediction. We evaluated proposed model on a real dataset collected from a popular e-commerce retail service ‘AliExpress’. We used categories available in ‘AliExpress’ as source domain and a target domain. For observing the performance of proposed model, we took six domains that have a higher ratio of sparsity. The proposed model is evaluated by using MAE, RMSE and F-measure metrics and compared it with baselines. The experiments show that the proposed model is a viable solution for the mentioned problem with significant improvements in results.

Reselling or Agency Selling? Consumer Valuation, Quality Design, and Manufacturers' Competition

The retail e-commerce yields lucrative revenue in many industries. In this work, we provide a strategic perspective by investigating how consumer valuation, product quality, and manufacturers’ competition influence the choice of selling agreement using stylized models. The utility and valuations of consumers are involved in analyzing strategic decisions under different selling agreement that is not trivial to analyze. We first show that agency selling agreement induces a higher retail price, but this can benefit the entire channel; under this agreement, the agency fee and consumer valuation present a nonmonotonic (approximately U-shaped) relation. Even though double marginalization exists, reselling agreement induces the manufacturer to make low-quality product compared to the agency selling agreement, but such a low-quality product is only profitable for the manufacturer. Second, when introducing manufacturers' competition, agency selling agreement can induce a wider quality difference: higher high-quality and lower low-quality. Agency selling agreement also hurts the manufacturer and the entire channel, but this is a preferred format for the low-quality manufacturer. Both the high-quality manufacturer and the entire channel and are profitable to choose the reselling agreement; offering a high-quality product can hurt the manufacturer remains robust. Third, the first-mover manufacturer can get a lower profit than the follower retailer. Moreover, when comparing scenarios with alternative sequences of making decisions, first-mover can lead to disadvantage, in that both supply chain parties prefer to act later rather than earlier. Our work also explains the rationale about why e-commerce firms in China now pay more attention to low-end consumers.

Optimization algorithm and simulation of supply chain coordination based on cross-border E-commerce network platform

With the rapid development of cross-border e-commerce, the improvement of consumer satisfaction has become the focus of cross-border e-commerce platform optimization. Relying on advanced algorithm technology, it can realize the accurate and efficient matching between massive information and users, which is conducive to improving the user experience. Based on the consideration of consumer satisfaction, this paper constructs a dual channel supply chain composed of cross-border suppliers, cross-border e-commerce enterprises, retailers and consumers and studies the revenue and cost sharing contract of the supply chain under the conditions of centralized decision and decentralized decision. The research shows that cross-border e-commerce enterprises can choose to form revenue and expenditure sharing contracts through online and offline channels, optimize decentralized decision-making, and achieve win–win cooperation among supply chain entities.

The Impact of the Covid-19 Pandemic on Retail Consumer Behavior

The COVID-19 pandemic that occurred throughout 2020 has an impact on economic sector. Consumers tend to use online channels to reduce face-to-face contact with marketers or other consumers. On the other hand, the consumer's need to see, touch and feel a product directly is only available in physical stores. This study aims to analyze the impact of the COVID-19 pandemic on retail consumer behavior. This study uses quantitative methods with secondary data sources obtained from several countries including the United States, England, Germany, France, Canada and Latin America. The results show that the shopping trends during the COVID-19 pandemic are webrooming and pure online shopping. Retail sales data in these countries shows that retail sales in physical stores exceed 70% of total retail sales and retail e-commerce sales are less than 30% of total retail sales. This research is expected to be useful for marketers in improving retail marketing strategies during the COVID-19 pandemic

Analysis of sentiment expressions for user-centered design

Devising intelligent systems capable of identifying the idiosyncratic needs of users at scale and translating them into attribute-level design feedback and recommendations is a key prerequisite for successful user-centered design processes. Recent studies show that 49% of design firms lack systems and tools for monitoring external platforms, and only 8% have adopted digital, data-driven approaches for new product development despite acknowledging them as a high priority. The state-of-the-art attribute-level sentiment analysis approaches based on deep learning have achieved promising results; however, these methods pose strict preconditions, require manually labeled data for training and pre-defined attributes by experts, and only classify sentiments intro predefined categories which have limited implications for designers. This article develops a rule-based methodology for extracting and analyzing the sentiment expressions of users on a large scale, from myriad reviews available on social media and e-commerce platforms. The methodology further advances current unsupervised attribute-level sentiment analysis approaches by enabling efficient identification and mapping of sentiment expressions of individual users onto their respective attributes. Experiments on a large dataset scraped from a major e-commerce retail store for apparel and indicate 74.3%–93.8% precision in extracting attribute-level sentiment expressions of users and demonstrate the feasibility and potentials of the developed methodology for large-scale need finding from user reviews.

Your reviews or mine? Exploring the determinants of "perceived helpfulness" of online reviews: a cross-cultural study.

E-commerce platforms allow customers to post online reviews about their products, but many of these reviews remain non-voted by existing customers. Current literature on the perceived helpfulness of online reviews has overlooked the effect of these non-voted reviews. Additionally, current studies lack the cross-cultural perspective to help analyze the influence of cultural factors during an analysis of online reviews in an e-commerce platform. This study proposes a novel cross-cultural framework using online consumer reviews from a global e-commerce retailer to investigate these shortcomings. Our study has twofold contributions: firstly, we identified the significant predictors (such as review-title, review sentiments, star rating, social context, and temporal features) of the count of helpful votes received by online reviews – both voted and non-voted. Secondly, we identified a strong moderating effect of national culture on these predictors during the perception and evaluation of global consumers’ reviews. We also performed a quantile regression across the entire distribution of the count of helpful votes. Findings from our study will guide reviewers who publish helpful and comprehensive reviews. Additionally, customers and sellers from global e-commerce firms will learn to account for cross-cultural differences on the provided features to prevent the distortion of the information content of online reviews.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12525-020-00452-1.