Search Results (19)

As an emerging e-commerce model, live streaming e-commerce integrates instant interaction, content marketing, and online sales to bring consumers a new shopping experience. However, there are many risks in the process of live e-commerce transactions. Identifying key risk factors and implementing targeted control measures are crucial for promoting the sustainable and healthy development of live streaming e-commerce. This paper firstly constructs a business model of live streaming e-commerce transactions according to the transaction scenario and summarizes 24 risk factors from the three dimensions of live streaming e-commerce platforms, merchants, and anchors based on relevant national standards and other relevant literature. Secondly, the Delphi method is employed to modify and optimize the initial risk factors. On this basis, the social network model of risk factors is constructed to determine the influence relationship among risk factors. By calculating the degree centrality, factor types are segmented, and key risk factors as well as influence paths are identified. Finally, corresponding countermeasures and suggestions are proposed. The results indicate that Credit Evaluation System Perfection, Service Evaluation System Perfection, Qualification Audit Mechanism Perfection, Dispute Complaint Handling Channels Perfection, Risk Identification Mechanism Perfection, Platform Qualification, Merchant Qualification, and Merchant Credit are the critical risk factors affecting live streaming e-commerce transactions. Full article

This study examines the strategic decisions and profit dynamics of suppliers marketing their products through both offline and online channels, alongside online e-commerce platforms providing their own consumer credit services. We develop a model that incorporates consumer disposable income, channel preferences, and credit utility. Four supply chain scenarios are analyzed: wholesale and agency models with either private or open credit strategies. Using Stackelberg game theory, we explore suppliers’ sales model choices and the conditions under which platforms extend credit to offline channels. Our results show that increasing credit utility generally leads to higher equilibrium prices, while higher service fees compel suppliers to adjust their prices, favoring lower-cost channels. Notably, suppliers are more likely to adopt the wholesale model to secure platform credit for offline sales, especially when credit service fees or credit utility are high. Furthermore, platform credit strategies are strongly influenced by suppliers’ sales model choices: In wholesale models, platforms are more inclined to extend credit to offline channels under specific conditions of high disposable income (DPI) and credit utility, whereas in agency models, open credit strategies are only adopted when both DPI proportions and credit utility are low. This research provides new insights into how platforms can tailor credit offerings based on supplier strategies, offering a theoretical foundation for consumer credit policies in multi-channel sales environments and valuable guidance for managers in determining optimal channel strategies and credit service offerings. Full article

Over-indebtedness is an increasingly serious issue among young people in China. Using Atlas.ti, this study analyzes textual data from the online community “Debtors Avengers” on Douban.com, applying a combined framework of life cycle and credit liberalization hypotheses. The findings reveal that youth indebtedness is not solely driven by irrational consumer behavior but is closely linked to economic activities during specific life stages. Structurally, it reflects sociofinancial digitization and normalized credit use. Factors such as life circumstances, financial literacy, labor market instability, and public safety risks contribute to a “debt spiral”. Addressing these challenges requires the refinement of financial policies, enhanced education, and intervention in financial aggression. Full article

Online consumer credit services play a vital role in the contemporary consumer market. To foster their sustainable development, it is essential to establish and strengthen the relevant risk management mechanism. This study proposes an intelligent management framework called the consumer default risk portrait (CDRP) to mitigate the default risks associated with online consumer loans. The CDRP framework combines traditional credit information and Internet platform data to depict the portrait of consumer default risks. It consists of four modules: addressing data imbalances, establishing relationships between user characteristics and the default risk, analyzing the influence of different variables on default, and ultimately presenting personalized consumer profiles. Empirical findings reveal that “Repayment Periods”, “Loan Amount”, and “Debt to Income Type” emerge as the three variables with the most significant impact on default. “Re-payment Periods” and “Debt to Income Type” demonstrate a positive correlation with default probability, while a lower “Loan Amount” corresponds to a higher likelihood of default. Additionally, our verification highlights that the significance of variables varies across different samples, thereby presenting a personalized portrait from a single sample. In conclusion, the proposed framework provides valuable suggestions and insights for financial institutions and Internet platform managers to improve the market environment of online consumer credit services. Full article

The purpose of this paper is to analyse the cybersecurity in online travel agencies (OTAs) and hotel sectors to protect users’ private data in smart cities. Methodologically, this research uses a sample of information about cyberattacks that occurred during the period of 2000–2023 in companies operating as OTAs and in the travel, tourism, and food sectors, which was obtained from research articles. Then, we had to expand the research to include updated information about cyberattacks from digital newspapers, regulatory sources, and state data breach notification sites like CSIS, KonBriefing, EUROCONTROL, and GlobalData. The findings of the current research prove that hotels and OTAs were constantly exposed to cyberattacks in the period analysed, especially by data breaches and malware attacks; in fact, this is the main novelty of this research. In addition, these incidents were severe for both guests and tourism companies because their vulnerabilities and consequences affect the reputation of companies and smart cities where these firms operate, as well as consumer confidence. The results also showed that most of the cyberattacks examined in this manuscript were aimed at stealing information about the companies’ and users’ private data such as email addresses; credit card numbers, security codes, and expiration dates; and encoded magstripe data; among many other types of data. Cyberattacks and cyberthreats never disappear completely in the travel and tourism sectors because these illegal activities are closely related to the hacker’s thirst for power, fame, and wealth. Full article

This article is based on the findings of a longitudinal study examining the behavior of Polish e-consumers. A series of methodically planned and executed surveys was conducted to elucidate the evolution of online consumer behavior over the past decade. The objective of this article is to diagnose changes in the behavior of selected types of e-consumers from Generations X and Y over the last decade, and link them to the concept of sustainable consumption. The 2020 study employed a sample possessing attributes similar to the 2010 research study, maintaining the sample distribution in terms of gender, education, and age. Characterizations of the identified consumer types were derived from prevalent psychographic traits, attitudes towards oneself and others, behavioral patterns, and determinants of buying behavior. Four distinct types of Polish e-consumers were identified: HQ_type (those seeking the Highest Quality products), MS_type (those intending to Save Money during shopping), LT_type (those dedicating the Least amount of Time to shopping), and DP_type (those Deriving Pleasure from the shopping experience). The identified Polish e-consumer types were subjected to a comparative analysis spanning a decade. The findings reveal significant alterations in the behavior of e-consumers representing various types, with a trend favoring sustainable consumption. Generally, Polish e-consumers are shown to demonstrate conscious and sustainable consumption behaviors, such as effective financial management, especially evident in the MS_type group. They typically purchase products within their means, avoiding expensive credits to fulfill their “needs.” Half of the HQ_type, MS_type, and LT_type e-consumers recognize the importance of budgeting and spending tracking for informed consumption, whereas the DP_type, who enjoy shopping, tend to overlook active financial management, perceiving it as a burdensome task. Over the years, a positive shift in the HQ_type’s attitude towards spending management has been observed. A significant aspect of conscious and sustainable consumption involves planned purchases. More than half of the HQ_type, MS_type, and LT_type e-consumers adhere to ready-made shopping lists, while the more impulsive DP_type frequently opts to shop in-store without any preceding planning. This research carries both theoretical and practical implications. The repeatability of the studies renders them a fundamental source of knowledge about the studied populations over time, and serves as an exclusive means of learning about changes in market phenomena and processes in a scientific manner. The findings of this article may prove valuable for e-commerce managers in devising strategies for effective sales promotion and customer communication for different types of e-consumers. Full article

The Millennials and Generation Z use online shopping for a holistic experience and buy more expensive or better-quality products with buy now pay later payment methods for their highly demanding needs. The authors aimed to deepen understanding of this phenomenon by finding related knowledge fields and discovering the type of economy that will represent an increasing market share for the method of domestic e-commerce payments. The methodology used combined computer-assisted review, descriptive statistics, and linear regression to explain the market share of 23 economies worldwide. Student credit card use, myopic consumer law, buying tendencies. and dark financial triangles were identified as related topics. Logistics performance, ease of doing business, and postal development were found to be significant factors. Finally, economies with medium ranks are inclined to adopt this kind of payment easily. Hence, major implications, both managerial and academic, must be addressed. High responsibility should be borne by industry associations, which should run information campaigns by collaborating with public institutions. From the point of view of theoretical implications, studying the buy now and pay later concept and its outcomes might deepen understanding of consumer behaviour, decision-making processes, risk perception mitigation, debt behaviours, and credit adoption. Full article

It is widely recognised that the ability of e-commerce businesses to predict conversion probability, i.e., acceptance probability, is critically important in today’s business environment. While the issue of conversion prediction based on browsing data in various e-commerce websites is broadly analysed in scientific literature, there is a lack of studies covering this topic in the context of online loan comparison and brokerage (OLCB) platforms. It can be argued that due to the inseparable relationship between the operation of these platforms and credit risk, the behaviour of consumers in making loan decisions differs from typical consumer behaviour in choosing non-risk-related products. In this paper, we aim to develop and propose statistical acceptance prediction models of loan offers in OLCB platforms. For modelling, we use diverse data obtained from an operating OLCB platform, including on customer (i.e., borrower) behaviour and demographics, financial variables, and characteristics of the loan offers presented to the borrowers/customers. To build the models, we experiment with various classifiers including logistic regression, random forest, XGboost, artificial neural networks, and support vector machines. Computational experiments show that our models can predict conversion with good performance in terms of area under the curve (AUC) score. The models presented are suitable for use in a loan comparison and brokerage platform for real-time process optimisation purposes. Full article

In the context of carbon neutrality, plastic ban, and green development, this paper aims to maximize the comprehensive interest of manufacturers in building a sustainable logistic network. It proposes a reverse logistics network model of a dual-channel model with multiple objectives and a random environment for the construction of recycling boxes projects and uses a Stackelberg game to develop pricing strategies for dual-channel recyclers and a genetic algorithm to optimize reverse logistics networks. This paper found the following: multi-objective evaluation is more conducive to sustainable development; when consumers are price-sensitive, a stronger rebate can significantly increase revenue; online platform recyclers should invest more in marketing than traditional recyclers; retailers’ willingness to cooperate in recycling has a significant impact on the overall benefits; the impact of government subsidies is marginal and needs to be controlled to a certain extent; the impact of green credit is insignificant, and the strength of commercial banks’ scrutiny of companies has little effect; an increase in environmental uncertainty within a certain range can lead to an overall loss of benefits, and an excessive impact can be out of line with extreme values. It provides a basis for decision-making on strategies for enterprises to build a logistics network of recycling boxes, government subsidies, green credit from banks, and dual-channel recyclers. Full article

Recently, there has been a growing effort to reduce the environmental impact of products throughout their life cycle, particularly during the end-of-life (EoL) stage. To incentivise consumers’ recycling/reusing behaviours and enhance their environmental awareness, a novel ICT-based system for recycling and eco-shopping has been developed in this paper. The recycling of EoL products is conducted based on information-communication technologies to remotely monitor and manage the recycled products (such as electronics or household bio-wastes), enabling consumers’ recycling process over the Internet. Consumers are awarded the eco-credits, which can be used for various forms of eco-incentives, such as shopping discounts, tree planting donations, and exchanges for theatre and museum tickets. The eco-costs reflect the environmental impact of a product throughout its life cycle. The consumer is informed about the eco-costs through eco-shopping, which are displayed on a payment receipt. Both eco-costs and eco-credits are recorded in the consumer’s eco-account. To develop the recycling and eco-shopping system, multiple information-communication technologies are utilised, such as hardware digital monitoring/control, Internet-based communication services, traceability media (bar-code and QR code), user identity recognition and privacy protection, and multi-language supports. A case study is conducted, including online tracking of the recycling process and then implementing incentive activities with the eco-credits and eco-costs. The system has been successfully validated via illustrating recycling, eco-shopping, and eco-incentives in public places (e.g., schools, urban cultural centres), as well as promoting the consumer’s participation in recycling and enhancing their environmental awareness, which proved the successful implementation of the novel contribution of this research. Full article

The health crisis caused by COVID-19 has affected consumption and payment patterns worldwide. Consumers have had to change their habits and deal with new sanitation guidelines and have often struggled with lengthy infrastructure closures. These factors significantly influenced both the choice of payment methods and purchase decisions made by consumers. Still, consumption patterns during the pandemic as a new social situation have not yet been thoroughly investigated. As the unsustainable consumption of resources is an important issue, this paper aims to analyze the relationship between credit card use, hedonic motivations, and its impact on the impulsive buying behavior in physical fast fashion stores during the COVID-19 pandemic in Spain. For this purpose, an online survey was conducted on a sample of 300 regular fast fashion buyers in physical stores. Structural equation modelling was used for the data analysis. The results show that there is a significant relationship between credit card use and impulse buying behavior for fast fashion in physical stores, as well as between credit card use and social shopping. It is also observed that hedonic motivations such as gratification shopping, value shopping, novelty-seeking shopping, and adventure-seeking shopping are related to impulse buying behavior. Therefore, this evidences the unsustainable overconsumption, thereby having a higher negative environmental and social impact. With the increased popularity of cashless payment methods, including credit cards relaxing tighter budgets during the pandemic, fast fashion impulse buying should be considered an important issue in individual, social and environmental well-being. Consequently, the need for more responsible consumption and sustainability-focused value orientation arises so as to mitigate the environmental impact of the fast fashion industry. Full article

Internet of Things (IoT) environments produce large amounts of data that are challenging to analyze. The most challenging aspect is reducing the quantity of consumed resources and time required to retrain a machine learning model as new data records arrive. Therefore, for big data analytics in IoT environments where datasets are highly dynamic, evolving over time, it is highly advised to adopt an online (also called incremental) machine learning model that can analyze incoming data instantaneously, rather than an offline model (also called static), that should be retrained on the entire dataset as new records arrive. The main contribution of this paper is to introduce the Incremental Ant-Miner (IAM), a machine learning algorithm for online prediction based on one of the most well-established machine learning algorithms, Ant-Miner. IAM classifier tackles the challenge of reducing the time and space overheads associated with the classic offline classifiers, when used for online prediction. IAM can be exploited in managing dynamic environments to ensure timely and space-efficient prediction, achieving high accuracy, precision, recall, and F-measure scores. To show its effectiveness, the proposed IAM was run on six different datasets from different domains, namely horse colic, credit cards, flags, ionosphere, and two breast cancer datasets. The performance of the proposed model was compared to ten state-of-the-art classifiers: naive Bayes, logistic regression, multilayer perceptron, support vector machine, K*, adaptive boosting (AdaBoost), bagging, Projective Adaptive Resonance Theory (PART), decision tree (C4.5), and random forest. The experimental results illustrate the superiority of IAM as it outperformed all the benchmarks in nearly all performance measures. Additionally, IAM only needs to be rerun on the new data increment rather than the entire big dataset on the arrival of new data records, which makes IAM better in time- and resource-saving. These results demonstrate the strong potential and efficiency of the IAM classifier for big data analytics in various areas. Full article

Within the context of Instagram celebrity-based brand endorsement, this study investigates the effects of parasocial interaction, perceived ad message authenticity, and the match-up between brand and celebrity. An online survey was administered to 253 college students taking introductory advertising or PR courses. Respondents, in return, received course credits. As hypothesized, study results reveal that parasocial interaction positively influences perceived ad message authenticity, the match-up between brand and celebrity, and attitude toward ads. In addition, perceived ad message authenticity and the match-up between brand and celebrity have a positive impact on consumers’ attitudes toward ads. The current study provides advertising practitioners with implications when it comes to creating advertising campaign messages and selecting celebrity endorsers. Full article

Digital transformation of businesses has seen tremendous growth recently, moreover, COVID-19 has increased online shopping. However, it is important for businesses that customers’ online shopping experience is secure and joyful. In this paper, customers’ security perception regarding some leading mobile commerce applications in Saudi Arabia is explored. Our survey questions focused on three aspects, namely: Consumer rating, trustworthiness, and credit card security. The results highlight that consumers perceive that mobile commerce applications in Saudi Arabia need further improvement in security. In this work, a model to improve customers’ security perception in Saudi Arabia is presented. This model can be generalized to other geographical regions as well. The model outlines different actions for practitioners and policymakers that help in improving security infrastructure, authentication mechanisms, and trustworthiness. Full article

Internet consumer finance platforms (ICFPs), as a new Internet financial model, have emerged and been widely adopted by Chinese as well as global online shoppers following the rapid growth of e-commerce. This emerging financial tool is, however, luring younger generations deep into debt. It jeopardizes the Sustainable Development Goal (SDG) 1—No poverty, bringing the sustainability of this consumer financial model into question. To aid the sustainable development of ICFP industry, this paper distinguishes two antecedents of continuous use intention for ICFP users: (1) satisfaction, as a sustainable determinant; and (2) impulsive buying, as a non-sustainable determinant. We found satisfaction (b = 0.452) has a larger positive effect on continuous use intention compared to impulsive buying (b = 0.229). Therefore, ICFPs should weigh heavier on technology innovation instead of seducing consumers’ impulsive buying behavior. We also found that credit limit misconception poses the largest impact (b = 0.483) on impulsive buying. In the near term, governments and ICFPs may initiate public programs to improve ICFP users’ financial literacy in order to restrain their unsustainable impulsive buying behavior and cultivate their sustainable satisfaction on ICFP technology and service. Market regulators may build up institutional frameworks to tighten the abuse of financial platforms on credit issuing power and better foster sustainable entrepreneurship in this new financial platform business. Full article