• Consumers can be encouraged to choose more sustainable delivery options. • Extra surcharges work best to get consumers to choose greener delivery. • Messages about the environment help a little, but not as much as surcharges. • Consumers feel better about extra surcharges when they think they’re fair. Logistic providers sometimes nudge consumers to participate in the delivery process and stimulate them to choose more sustainable delivery options to reduce the environmental impacts of last-mile delivery. Nevertheless, online retailers are wary of such interventions, fearing that this will reduce consumer satisfaction and jeopardize their competitive position vis-à-vis other online retailers and brick-and-mortar shops. Based on fairness theory, we developed and tested interventions to nudge consumers towards more sustainable delivery options while maintaining consumer satisfaction. A vignette-based experiment following a 2 (surcharge) × 3 (message) design was used to test the impact of surcharges in combination with a message grounded in fairness theory. Using logistic regression, we found that surcharges were the most effective in increasing the probability that the (free) more sustainable delivery option would be selected. A message focusing on the sustainability implications was also effective, but to a lesser extent. The combination of surcharges and messaging was found to decrease this probability. Using causal mediation analysis, perceived fairness was found to mediate the effect of the surcharge on satisfaction. The study findings support the notion that it is possible to design interventions that will stimulate consumers to select more sustainable delivery options without significantly affecting their satisfaction. Interventions based on price (dis)incentives remain the most effective when influencing consumer behavior in the last mile. Their negative effects need to be mitigated through appropriate communication, however.

• Individual carbon footprints from online shopping are estimated using survey data. • The top 20% of emitters generate between 65% and 70% of emissions. • Top emitters shop more, travel farther, use cars more, and have higher emissions. • Environmental footprints rise as travel linked to online shopping increases. • Density’s effect on distance and emissions varies with delivery conditions. What is the environmental footprint of shopping-related mobility and how are emissions distributed among individuals? We approach this question by estimating individual pollutant footprints, using a survey of travel practices linked to goods purchases by French households, which we combine with emission factors for NOx, PM 2.5 and CO 2 . The results show that the top 20% of emitters account for between 65% and 74% of emissions from representative trips. At the individual level, these top emitters have higher purchase frequencies, travel longer distances, are highly dependent on cars, and have high emission intensities when traveling by car. The characteristics most strongly associated with high emission levels are living in a high-density area, being female, having a low income, and preferring home delivery. In addition, increased frequency of trips to relay points is correlated with higher emissions of local pollutants (NOx, PM2.5). In summary, individual environmental footprints associated with online purchases increase with the intensification of shopping trips, which is itself driven by a higher frequency of online purchases and a greater propensity to use the car.

Enhanced relationship marketing anchored on digital live selling behaviors and Uses and Gratification Theory.

How teleworking affects online shopping and home delivery: a joint modeling perspective from post-pandemic New York City

Smart choices, green voices: The role of AI and social influence in eco-friendly online shopping.

Live-streaming encroachment and blockchain resistance in online retail channels

The increasing integration of digital technologies into daily life has significantly altered student behavior, giving rise to concerns about screen time addiction. This review article presents findings from a survey conducted among 1,200 students to assess patterns, causes, and consequences of excessive screen use. Results show that 70.8% of students primarily use digital devices for social media, followed by 25% for entertainment, 2.5% for academic purposes, and 0.8% each for gaming and online shopping. Device preference patterns revealed that 52% regularly use smartphones, 20.3% laptops, 15.5% TV or gaming consoles, and 12.3% smartwatches. Daily screen time was reported as 4–6 hours by 56.3% of students, 6–8 hours by 19.4%, 2-4 hours by 16.2%, over 8 hours by 4.2%, and less than 2 hours by only 4%. Notably, 70.4% of respondents often felt anxious or restless without digital access, while 17.7% experienced it sometimes, 7.7% always, and 3.5% never. Despite awareness of the problem, 61.8% had unsuccessfully tried to reduce screen time. Alarmingly, 80.7% admitted to staying up late daily due to device use. Additionally, 65% of students reported difficulty concentrating without checking their phones, 84.5% used screens during meals or social settings, and 73.3% experienced sleep disturbances. Other reported health issues included eye strain (8.6%), neck/back pain (3.5%), and anxiety or irritability (3.3%). Most students (63.6%) slept for 4-6 hours, 32.3% for 6–8 hours, 2.2% for over 8 hours, and 2% for 4 hours or less. Alarmingly, 85.5% were unaware of the term "screen time addiction," and 96.9% did not consider it a serious issue. These findings highlight a significant behavioral health challenge in student communities, emphasizing the need for awareness campaigns, digital hygiene education, and institutional interventions to promote healthier screen habits.

Growing competition among online retail platforms has made it considerably challenging for buyers to locate the most affordable option for a given product within a practical time frame. Prices, discounts, ratings, and inventory availability for identical products often differ substantially across e-commerce websites, compelling shoppers to visit numerous platforms individually before making a purchase decision. This manual process is tedious, repetitive, and susceptible to missed opportunities. PriceWise AI is a browser-based price aggregation tool built to streamline this experience through automated product discovery and intelligent multi-source comparison. Users can initiate a search either by typing a product name or by submitting a direct product link. The platform then gathers matching product data from several major online stores Amazon, Flipkart, and Croma and consolidates the results within a single, cohesive display. Key features include automatic identification of the cheapest available listing, flexible sorting by price or user rating, and one-click navigation to the respective retailer's product page. Beyond its comparison core, PriceWise AI integrates user account management, profile customization, and a conversational AI chatbot to improve overall accessibility. The system is developed with React, TypeScript, Tailwind CSS, Supabase, and serverless edge functions. Through the convergence of automated data retrieval, unified result presentation, and user-centric design, PriceWise AI delivers a robust and extensible solution for enhancing transparency and efficiency in digital purchasing

Human fingers exhibiting remarkable dexterity are ideal for natural human-machine interaction. Traditional methods require at least one device per finger and extensive labeled data, often limiting models to a single user and task. Here, we propose a wearable thumb sleeve integrated with self-supervised learning, which exhibits user independence and data efficiency, enabling recognition of various finger-related tasks. The thumb sleeve is equipped with only two stretchable sensors at the thumb joints and learns latent features from unlabeled random thumb movement data. By using fine-tuning with five-shot labeled data, it can rapidly adapt to new users and tasks, including eight directional commands and 10 knuckle key inputs. It allows free switching between tasks without the need to reconstruct or retrain the model. The proposed approach demonstrates strong potential for real-world applications, serving as a substitute for a mouse and keyboard to enable tasks such as online shopping.

This study will examine how price endings influence discerned parsimony among college students using e-commerce applications, focusing on how common digital pricing strategies shape consumer self-perception in online shopping. In particular, the study will analyze the use of charm pricing focusing on .99/.95 and even pricing .00 and their impact on students’ perception of thriftiness and value judgment in the act of buying. To gather data, the researchers will adopt purposive sampling and exploratory quantitative design and will distribute an online questionnaire to 100 college students within FEU Roosevelt Marikina who are frequent users of e-commerce platforms like Shopee, Amazon, and Shein. The instrument will measure perceived savings, purchase satisfaction, and self-reported spending awareness through the examination of different price-ending conditions. Data will be analyzed using exploratory statistical analysis and mean comparison techniques to examine differences in perceived parsimony across various price formats. The study is expected to find that charm-priced items focusing on .99/.95 will generate higher perceived savings scores compared to even-priced items .00. Moreover, respondents are expected to be more inclined to describe cheaper purchases as “practical” rather than “unnecessary.” However, even pricing is anticipated to produce higher perceived quality ratings, suggesting that price endings will influence not only thrift perception but also value interpretation. The findings are expected to demonstrate that price-ending tactics remain effective in online settings, especially among college students who are highly conscious of their budgets. The research will suggest that e-commerce sites can use price endings as a strategy to influence consumers’ without changing the actual price, while future studies may further examine the long-term impact of these tactics on spending attitudes and financial behavior.

Understanding consumer behavior in the context of online shopping is critical for businesses to adapt to evolving market trends. Customer reviews serve as a rich source of information reflecting consumer sentiments and preferences. Sentiment analysis of these reviews has become a powerful tool to uncover underlying consumer emotions and purchasing trends. However, traditional methods relying on shallow lexical features and classical machine learning algorithms often fall short in capturing the intricate and contextual patterns present in textual data. In this study, we propose the use of the large language model RoBERTa-Large to enhance sentiment classification performance by imposing its advanced contextual embeddings and attention mechanisms. This approach enables the capture of complex semantic relationships beyond surface-level word frequencies. Alongside sentiment analysis, we apply topic modeling using Latent Dirichlet Allocation (LDA) on publicly available datasets to identify prevalent themes and topics within consumer feedback. We perform a comprehensive comparison of RoBERTa against traditional machine learning and ensemble models using TF-IDF features, as well as deep learning architectures utilizing sentence embeddings and transformer-based models. Experimental results demonstrate that RoBERTa-Large achieves the highest accuracy of 93.59%, significantly outperforming baseline models. To enhance model transparency and trustworthiness, we apply SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) interpretability techniques, providing meaningful explanations of model predictions at both global and local levels.

Customer reviews are a vital component of modern e-commerce platforms, offering meaningful insights into customer satisfaction, product quality, and overall user experience. With the rapid growth of online shopping, a massive volume of textual feedback is generated daily. Traditional evaluation methods, such as average ratings and review counts, provide only a general overview and fail to capture the deeper sentiments expressed in textual reviews. As a result, a large portion of valuable customer feedback remains underutilized. Moreover, manual analysis of large-scale review data is time-consuming and inefficient, emphasizing the need for automated analytical solutions. To address these challenges, this research proposes a machine learning-based framework for analysing customer review data and predicting both recommendation outcomes and product ratings. The system incorporates data preprocessing, exploratory data analysis, and text representation using Term Frequency–Inverse Document Frequency (TF-IDF). Multiple machine learning models are implemented, including Restricted Boltzmann Machine (RBM) combined with Logistic Regression (LR) for classification, RBM with Ridge Regressor (RR) for regression, Gradient Boosting (GB), Extreme Gradient Boosting (XGB), and Multi-Task Neural Network with Extra Trees (MTNN-ET) these models are works with Classification and Regression Tree (CART) Model. Experimental results demonstrate that the proposed MTNN-ET-CART model achieves superior performance, with a classification accuracy of 0.9640 and a regression R² score of 1.0000. The framework effectively processes large-scale review datasets and generates reliable predictions, enabling enhanced decisionmaking and improved customer experience in e-commerce platforms

Shipping experience improvement has been an essential business strategy in e-commerce. Beyond investing directly in improving shipping speed, online retailers have recently expanded their focus on other shipping strategies, such as offering consumers the option to pick up orders at a local station. This paper uses the opening of hundreds of such pickup stations as a natural experiment to study the impact of these stations on consumers. We find that the introduction of pickup stations increased total sales by [Formula: see text]. In contrast with past literature, we show that shipping time reduction is not the driving factor in the impact of pickup stations. Yet, the logistic flexibility introduced by pickup stations explains the sales impact. To explicitly examine how logistic flexibility affects consumers’ decisions on purchases, we develop and estimate a structural model of consumer choice. In our model, consumers value two types of logistics flexibility—the flexibility to pick up their items at their preferred times, referred to as the value of preferred time flexibility, and the flexibility to delay pickup decisions to the last moment, referred to as the value of last-minute choice flexibility. We show that the value of preferred time flexibility accounts for [Formula: see text] of the impact on sales, whereas the value of last-minute choice flexibility accounts for the remaining [Formula: see text]. Using our estimated model, we develop a counterfactual strategy in building pickup stations that could achieve the sales lift with [Formula: see text]–[Formula: see text] fewer stations. Last but not least, using our estimated time flexibility, we also develop a novel shipping strategy without pickup stations that could improve sales by [Formula: see text]. Our estimates suggest that our counterfactual logistic strategies could increase consumer welfare by [Formula: see text]–[Formula: see text]. This paper was accepted by Elena Katok, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00163 .

This research focuses on Online Impulse Buying Intentions, defined as the motivation to purchase items without planning while browsing online sites, often triggered by product recommendations. The study employs the Self-Congruency Theory framework, which explains how the alignment between consumer identity and perceptions of influencers or products facilitates impulsive behavior. A contradiction exists in the literature regarding the relationship between Influencer–Product Congruence and Online Impulse Buying Intentions: some studies find a direct influence, while others do not. To address this, a research model was developed to test the influence of Consumer – Influencer Congruence, Consumer – Product Congruence, and Influencer – Product Congruence on Online Impulse Buying Intentions. This model integrates Wishful Identification as a moderating variable and Consumer Confidence in Online Shopping as a mediating variable. The main contribution of this study is to expand the theoretical model by incorporating Consumer Confidence in Online Shopping as a mediating variable. This addition aims to provide comprehensive insights into the psychological mechanisms underlying impulsive behavior in online shopping. These findings suggest that direct congruence between the consumer and the influencer, and between the consumer and the product, is a strong driver of Online Impulse Buying Intentions. The recommendation is that marketers should focus on creating strong congruence between consumer values and the influencer’s image, as well as product suitability with the consumer's lifestyle, as these factors directly trigger Online Impulse Buying Intentions

In the digital age, online shopping has become a dominant mode of purchasing, yet product prices vary significantly across e-commerce platforms. Consumers often face challenges comparing prices manually, leading to inefficient decisionmaking and potential overspending. This project presents a centralized Price Comparison Website designed to aggregate prices of the same product from multiple online retailers and display them in a structured, user-friendly interface. The system automates price retrieval using web scraping APIs, product metadata analysis, and dynamic search querying. A ranking engine analyzes product attributes, price trends, and availability to present users with the most cost-effective option. The project aims to enhance transparency, convenience, and accuracy in online shopping. The proposed solution incorporates backend technologies such as Python, Flask/Django, or Node.js, with data extraction techniques using BeautifulSoup or API-based price fetchers. The frontend is designed for simplicity, enabling users to search products, compare prices, and view detailed product specifications. The system supports real-time updates, ensuring that the displayed prices remain accurate and competitive. Evaluation results demonstrate that the platform significantly reduces user time spent on manual price checking while improving purchasing confidence. This project highlights the importance of automation and data-driven decision-making in modern e-commerce environments.

On-demand digital services provide convenient and fast fulfilment of consumption. From their emergence in media, on-demand digital services have expanded in ride-hailing, food delivery and retail. While there is abundant research on digitalisation in various consumption sectors, few have identified the interrelations between digital services across multiple consumption sectors and their implications for consumption behaviours. Using domestication theory, this article explores the interconnections between households’ cognitive, practical and symbolic learning of various on-demand digital services to understand how they shape the understanding of and responses to convenience in consumption. Findings from qualitative interviews in the United Kingdom show linkages between how households learn about, use, and develop meanings around online media, food delivery, ride-hailing and retail services. Learning from one service affects households’ awareness, skills, routinisation and meaning-making of other services. These multi-sited learnings engender shared expectations of convenience within and across digital services that are interpreted and negotiated through households’ identities. The dynamic process between households and technologies organised around convenience shapes households’ temporal and spatial coordination of their consumption. Recognising the similarities and interdependencies in how households engage with various digital services in media, retail, food and ride-hailing helps us understand digitalisation as an overarching transformation of consumption.

ABSTRACT Online reviews are a key information source for consumers, yet limited research explores how perceived credibility and usefulness shape decision outcomes and ongoing engagement. Drawing on Expectation-Confirmation Theory and information adoption perspectives, this study develops and tests a model linking perceived review credibility, usefulness, and product certainty to satisfaction and continuance intention. Survey data from 342 recent online shoppers support the model. Results show that perceived usefulness is the dominant driver of decision certainty, satisfaction, and continuance intention. While credibility exerts no direct effect, it indirectly influences outcomes through usefulness. Product certainty increases satisfaction but does not directly affect continuance intention, underscoring the mediating role of usefulness. Theoretically, the findings extend expectation-confirmation research by showing that information usefulness, not only satisfaction, predicts continued review use. Practically, platforms can foster loyal, satisfied customers by enhancing both the credibility and usefulness of reviews, thereby sustaining participation and long-term engagement.

ABSTRACT Online fashion retailers often use lenient return policies to compensate for the lack of physical product experience, effectively turning customers’ homes into fitting rooms. This convenience, however, contributes to high return rates. Digital nudges are widely used in e-commerce to influence decision-making. When these nudges promote environmentally friendly behavior, they are referred to as digital green nudges. Drawing on self-determination theory, this study examines how and whether digital green nudges influence return motivation. Within e-commerce loyalty programs, digital green nudging is often combined with other measures, such as gamification or financial incentives. Therefore, we analyze and compare how and whether combining digital green nudges with gamification or gamification and monetary incentives influences return motivation. Using structural equation modeling, we analyze data from a survey-based online experiment with US online shoppers (n=1949). Among other results, we show that these measures directly affect return motivation, indicating their role as extrinsic motivators. This study is the first to compare digital green nudges and their combinations with gamification and monetary incentives in the context of online returns. Overall, our results show that all three measures can help mitigate the challenges of lenient return policies. However, we observe side effects on intrinsic motivation that reduce purchase motivation.

The rapid expansion of digital payments and e-commerce has significantly increased the risk of credit card fraud, exposing the inadequacy of traditional authentication mechanisms such as passwords, PINs, and CVV codes. These credential-based methods verify only what a person possesses or knows, not who they physically are — a distinction that fraudsters readily exploit. This project presents a secure online shopping and payment platform that addresses this gap by integrating facial biometric authentication with blockchain-based transaction recording. The Grassmann algorithm is applied for face recognition, enabling robust identity verification across real-world variations in lighting, facial expression, and head orientation. At the point of payment, the system captures a live facial image, processes it through the Grassmann subspace matching framework, and compares it against the registered cardholder's stored biometric profile. Only a verified match permits the transaction to proceed.

With the rapid development of the internet, online shopping has become one of the main channels for consumers. Among these channels, influencer live sales have emerged as a particularly popular business model, in which limited-time, limited-quantity discounts are commonly used as marketing tactics. This study examines how these two scarcity-based strategies influence consumers' impulse purchasing behavior through emotional and psychological mechanisms, along with other related factors. This study employed a mixed-methods approach, gathering data from consumers who regularly engage in live shopping through questionnaires and interviews. The data were analyzed using descriptive statistics, correlation analysis, and thematic analysis.