Should I Buy Now, Pay Later? An Empirical Study of Consumer Behavior in E-Commerce

The Buy Now, Pay Later (BNPL) payment service in FinTech has rapidly gained popularity as a new payment option for consumers. However, its effect on consumer behavior remains unclear. This study investigates the effect of BNPL adoption on consumer purchase behaviors using a proprietary dataset from a large e-commerce platform. We find that BNPL adoption increases monthly spending by 11.2%, leads to a shift in purchase channel usage towards the mobile channel, and has a cannibalization effect on cash, debit cards, and credit cards payment methods. Our further analyses shed light on the mechanisms behind these effects. We find that the increased consumer spending as a result of BNPL adoption is driven by increased credit accessibility, mobile device ubiquity, and induced consumption effects. Our findings contribute to the growing body of literature on BNPL in FinTech and provide various practical implications for e-commerce and FinTech service operators

Increasing the Sale of Long Tail Items on E-Commerce Websites

The advent of e-commerce has transformed the retail industry, offering a platform for retailers to reach a vast audience, enabling consumers to shop from anywhere in the world at any time. However, most e-commerce websites are dominated by sales of popular or mainstream items, referred to as the head of the distribution. The long-tail items, which make up the tail of the distribution, are often overlooked, leading to reduced sales and low revenue for online retailers. This research paper aims to explore strategies that online retailers can use to increase the sale of long-tail items on their websites. It also includes a review of existing literature, a qualitative analysis of consumer behavior, and a quantitative analysis of sales data. The findings indicate that online retailers can increase the sale of long-tail items by optimizing their website design, improving search functionality, using data-driven pricing strategies, and implementing targeted marketing campaigns. These strategies have the potential to improve the visibility of long-tail items, increase consumer engagement, and boost revenue for online retailers. A study has also been conducted on the Retail Rocket e-commerce dataset from Kaggle which involved a meticulous examination of various aspects of user interactions within the online retail platform. The dataset provided a rich source of information, including events such as page views, add-to-cart actions, and completed purchases. The analysis aimed to uncover specific patterns and trends related to these events, shedding light on how users engage with both popular and long-tail items

Conversational commerce: anthropomorphic chatbots in e-commerce and their effect on consumer behavior

Conversational agents are becoming increasingly popular in today’s technology-driven world, thus a better understanding of factors that enhance customer experience with this technology is crucial. Our study provides insights about the impact of anthropomorphism on consumer behavior in a conversational interface usage scenario. This is the first experimental study to fill the research gap in investigating customer satisfaction with anthropomorphic chatbots in food e-commerce. A sample of 426 participants was tested to verify the proposed hypotheses. The test group interacted with a standard chatbot with out human-like characteristics, while the control group communicated with the anthropomorphically designed agent. The results confirm the tremendous potential of anthropomorphic cues in chatbot applications and show that they are positively associated with customer satisfaction and mediated by the variables enjoyment, attitude, and trust

Stimulating E-Business Capabilities and Digital Marketing Strategies on Business Performance in E-Commerce Industry

This study investigates how e-business capabilities and digital marketing strategies jointly influence business performance in the e-commerce industry, which has experienced unprecedented growth driven by technological advancements and changing consumer behavior. E-business capabilities encompass the use of technology and digital infrastructure, while digital marketing strategies are employed to attract and retain online customers. The study examines the effect of e-business capabilities through digital marketing strategies on the customer satisfaction and loyalty of UAE e-commerce industry. The research is descriptive and explanatory, and uses a structured 5-scale Likert questionnaire to collect data from the HR managers of 135 e-commerce companies based in UAE. The statistical analysis is performed using structural equation modeling. The findings highlight the significant relationship of the study variables. Consequently, the results of this study provide valuable insights for e-commerce businesses, elucidating the need to invest in robust e-business capabilities to support effective digital marketing efforts. Additionally, the research underscores the significance of tailoring digital marketing strategies to align with specific business objectives and customer segments. The study also contributes to the literature on digital transformation, by demonstrating how e-business capabilities and digital marketing strategies can enable e-commerce firms to adapt and thrive in the dynamic and competitive digital environment

Analytical Modeling Framework to Assess the Economic and Environmental Impacts of Residential Deliveries, and Evaluate Sustainable Last-Mile Strategies

In the last decade, e‐commerce has grown substantially, increasing business‐to‐business, business‐to‐consumer, and consumer‐to‐consumer transactions. While this has brought prosperity for the e-retailers, the ever-increasing consumer demand has brought more trucks to the residential areas, bringing along externalities such as congestion, air and noise pollution, and energy consumption. To cope with this, different logistics strategies such as the introduction of micro-hubs, alternative delivery points, and use of cargo bikes and zero emission vehicles for the last mile have been introduced and, in some cases, implemented as well. This project, hence, aims to develop an analytical framework to model urban last mile delivery. In particular, this study will build upon the previously developed econometric behavior models that capture e-commerce demand. Then, based on continuous approximation techniques, the authors will model the last-mile delivery operations. And finally, using the cost-based sustainability assessment model (developed in this study), the authors will estimate the economic and environmental impacts of residential deliveries under different city logistics strategies.View the NCST Project Webpag

An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection

The biological immune system (BIS) is characterized by networks of cells, tissues, and organs communicating and working in synchronization. It also has the ability to learn, recognize, and remember, thus providing the solid foundation for the development of Artificial Immune System (AIS). Since the emergence of AIS, it has proved itself as an area of computational intelligence. Real-Valued Negative Selection Algorithm with Variable-Sized Detectors (V-Detectors) is an offspring of AIS and demonstrated its potentials in the field of anomaly detection. The V-Detectors algorithm depends greatly on the random detectors generated in monitoring the status of a system. These randomly generated detectors suffer from not been able to adequately cover the non-self space, which diminishes the detection performance of the V-Detectors algorithm. This research therefore proposed CSDE-V-Detectors which entail the use of the hybridization of Cuckoo Search (CS) and Differential Evolution (DE) in optimizing the random detectors of the V-Detectors. The DE is integrated with CS at the population initialization by distributing the population linearly. This linear distribution gives the population a unique, stable, and progressive distribution process. Thus, each individual detector is characteristically different from the other detectors. CSDE capabilities of global search, and use of L´evy flight facilitates the effectiveness of the detector set in the search space. In comparison with V-Detectors, cuckoo search, differential evolution, support vector machine, artificial neural network, na¨ıve bayes, and k-NN, experimental results demonstrates that CSDE-V-Detectors outperforms other algorithms with an average detection rate of 95:30% on all the datasets. This signifies that CSDE-V-Detectors can efficiently attain highest detection rates and lowest false alarm rates for anomaly detection. Thus, the optimization of the randomly detectors of V-Detectors algorithm with CSDE is proficient and suitable for anomaly detection tasks

Increasing online shop revenues with web scraping: a case study for the wine sector

Purpose – Wine has been produced for thousands of years and nowadays we have seen a spread in the wine culture. E-commerce sales of wine have increased considerably and online customer’s satisfaction is influenced by quality and price. This paper presents a case study of the company “QuieroVinos, S.L.”, an online wine shop founded in 2015 that sells Spanish wines in two main marketplaces. Design/methodology/approach – With the final target of increasing the company profits it has been designed and developed an application to track the prices of competitors for a set of products. This information will be used to set the product prices in order to offer the products both competitively and profitably in each Marketplace. This application must check, by tacking into account information such as the product cost or the minimum product margin, if it is possible to decrease the price in order to reach the top cheapest position and as a consequence, increase the sales. Findings – The application improved in a notorious way the company’s results in terms of sales and shipping costs. It must be said that without the use of the presented application, performing the price comparison process within each one of the marketplaces would have taken a long time. Moreover, as prices change very frequently, the obtained information has a very limited time value, and the competitors prices should be analyzed daily in order to take accurate decisions regarding the company’s price policy. Originality/value – Although the application has been designed for the wine sector and the two named marketplace, it could be exported to other sectors. For that, it should be implemented new modules to collect information regarding the competitor’s price of the products selling on each corresponding marketplaceThis work was supported by the Ministerio de Economía y Competitividad under contract TIN2017- 84553-C2-2-R. Also, the authors are members of the research group 2017-SGR363, funded by the Generalitat de Catalunya

Channels, consumers and communication: online and offline communication in service consumption

This paper reports on a study that investigated consumer use of e-services in a multichannel context. To develop a deeper understanding of what makes consumers decide to use the online channel, and contrary to most HCI studies on the use of e-services that focus on the use of the online channel in relative isolation, this study examined consumer channel-choice beyond the instances of internet use. The consumption behaviour of its participants was investigated across channels in an in-depth qualitative study. The analysis of the elicited rich data focused specifically on the investigation of voluntary consumer movements between online and offline channels during the course of a consumption process. The results indicate that participants often use multiple channels in parallel and frequently switch between channels. Literature from marketing and consumer research was used as the perspective to explore the rationale for the complex and dynamic reported consumer behaviour