Search Results (59)

Fashion live commerce has rapidly emerged as a compelling format that blends entertainment, real-time interaction, and product promotion. However, limited research has examined how specific experiential and perceptual factors influence consumer behavior in this context. This study aims to identify the key psychological and environmental drivers of satisfaction, continued platform use, and purchase intention among viewers of fashion live commerce. Using the stimulus–organism–response framework, this research focuses on the effects of perceived credibility, social media influencer characteristics, informativeness, internal shop environment, and monetary savings. Data were collected from 300 users of fashion live commerce platforms and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that all predictor variables significantly influence either satisfaction or current use, and both satisfaction and current use significantly predict purchase intention. Among the factors, satisfaction plays a central role, acting as a strong predictor for both current engagement and future buying decisions. These findings offer theoretical insights into consumer engagement in live commerce and provide practical guidance for streamers, marketers, and platform designers aiming to improve user experience and conversion rates. This study contributes to understanding the evolving dynamics of digital shopping environments shaped by social and emotional interactions. Full article

With the rapid development of the digital economy and the evolving shopping preferences of consumers, e-commerce platforms have been enhancing their competitiveness by launching private label brands and optimizing their sales channel strategies. This study focuses on an online sales system comprising a strong brand and an e-commerce platform. Four game modes were constructed: agency selling only (NN), agency selling combined with reselling (NS), agency selling combined with private labels (IN), and reselling combined with agency selling under the introduction of private labels (IS). Under the coupon promotion strategy, this study focused on the introduction strategy for private labels (PLs) and the selection strategy for platform sales modes. Our research produced the following findings: (1) Regardless of whether the platform introduces its own brand, adopting a reselling mode can significantly enhance the profits of both the brand owner and platform. (2) Irrespective of whether the reselling mode is implemented, the platform’s profits are always increased when introducing its own brand. (3) When the coupon redemption rate is higher, the brand owner achieves greater profitability in the absence of PL introduction. Conversely, when the coupon redemption rate is low, an increase in the commission rate leads to reduced profit margins for the brand owner due to competition from a private label. (4) When the coupon redemption and commission rate are both high, the coupon face value without a PL is larger. Otherwise, when these rates are both low, the coupon face value is higher under the introduction of a PL. This study offers a theoretical foundation and decision-making support for e-commerce platforms to optimize sales mode selection, introduce private-label brands, and develop coupon strategies. Full article

With the rapid development of e-commerce, Return Freight Insurance (RFI) has emerged as a vital tool for retailers and customers to mitigate the financial burden posed by high return rates in online shopping. This paper investigates the strategic decision-making of RFI providers (insurers), examining whether they should offer fixed-compensation insurance at a lower price or full-coverage insurance at a higher price under varying market conditions. Specifically, we develop game-theoretic models to analyze insurers’ strategic decisions in both monopoly and duopoly markets, accounting for customer return cost and product mismatch probability heterogeneity. Our findings reveal that in a monopoly market, when customer return costs are relatively low and concentrated, insurers benefit from offering fixed compensation insurance (i.e., pre-set reimbursement amounts) at a lower price to attract a broader customer base. By contrast, when return costs are more dispersed and higher, full-coverage insurance (which reimburses actual freight costs) becomes more profitable, as the insurer can utilize its flexible pricing power to attract higher-paying customer segments. In a duopoly market, customer return cost heterogeneity significantly influences market equilibrium. When heterogeneity is high, full-coverage insurance dominates, as insurers can leverage precise market segmentation to justify higher premiums. Conversely, when return costs are more uniform, fixed compensation insurance is preferred for its affordability, appealing to a wider range of customers. Full article

As digital platforms reshape the commercial landscape, brands increasingly collaborate with these platforms to enhance product sales. Many adopt livestream as a strategic tool to attract more traffic, typically choosing between Artificial Intelligence (AI) or Key Opinion Leader (KOL) approaches. Meanwhile, platforms operate under either an agency or a resale mode. However, the relative effectiveness of these strategies remains unclear. This study investigates an e-commerce supply chain comprising a single brand and platform, examining how AI and KOL livestream influence supply chain decisions across different sales modes and identifying optimal strategies for the brand and platform. Results show that when the platform’s revenue sharing rate is low, the agency mode consistently yields a Pareto improvement over resale, regardless of the livestream scheme. Moreover, when the KOL promotion fee rate is low, KOL livestream outperforms AI livestream under both sales modes. When the revenue sharing rate is high, the brand’s optimal strategy is “resale mode and KOL livestream”, while the platform prefers “agency mode and KOL livestream”. Conversely, when the revenue sharing rate is low, the platform’s best strategy is “resale mode and KOL livestream”, while the brand favors the agency mode, with livestream preferences shaped by KOL promotion fee rate. Full article

His baptismal name, Fernando de Bulhões (1195–1231), may say little to many people, but his religious name, St. Anthony, certainly says a lot, especially to the Portuguese. In fact, the cult of St. Anthony is indelibly inscribed in the religious landscape of Portugal, with particular intensity in Lisbon. This study, of an ethnographic and historical nature, is an innovative approach to this emblematic medieval figure, capable of so many miracles, veneration, revelry festivals, and processions among the people of Lisbon and the Portuguese in general, right up to the present day. The history of this Saint and his cult, as an expression of popular religiosity, will be discussed, favouring the dimensions of marriage and commerce in a comparative approach between two ethnographic contexts: Lisbon and Viana do Castelo. The notions of popular religion, syncretism, cult of the saints, and pilgrimage–procession will, therefore, be instrumental. The data collected and analysed are based on a mixed methodological triangulation of qualitative data complemented by quantitative data, using direct participant observations (ethnographic) and indirect observations (collected through semi-structured interviews and informal conversations), as well as documentary sources. Full article

The study investigates how information asymmetry affects customer satisfaction in the tourism industry by examining trust and perceived value as mediating factors. The research implements an integrated model to test and prove information asymmetry’s direct and mediating effects on customer satisfaction by examining the literature gap. The research used a quantitative approach based on opinion polls distributed to 408 customers of hotels, tourism companies, and travel agencies who were in Egypt. SmartPLS 3 software implemented the data analysis process using partial least squares structural equation modeling (PLS-SEM). Previous studies have developed scales to measure information asymmetry and its related constructs, including customer trust, perceived value, and customer satisfaction. Multiple tests showed that the measurement tools possess both reliability and validity. Results strongly support all hypotheses: information asymmetry demonstrated significant direct negative effects on customer satisfaction (β = −0.187), trust (β = −0.520), and perceived value (β = −0.453). Conversely, customer satisfaction received significant positive direct effects from both trust (β = 0.273) and perceived value (β = 0.263). Importantly, trust (indirect effect β = −0.142) and perceived value (indirect effect β = −0.119) acted as powerful mediators, confirming that information asymmetry diminishes satisfaction largely by eroding these crucial factors. Crucially, the results demonstrate that the negative impact of information asymmetry on customer satisfaction is significantly mediated jointly through two parallel pathways: the erosion of customer trust and the impairment of perceived value. The research adds theoretical support to information asymmetry theory with its findings while also extending trust theory, perceived value theory, and expectancy disconfirmation theory in the field of e-commerce. E-commerce entities must establish clear communication to gain customer trust and create perceived value that helps compensate for information asymmetry to create enhanced customer loyalty and superior market position. Full article

Due to the rapid expansion of e-commerce and urbanization, Last-Mile Delivery (LMD) faces increasing challenges related to cost, timeliness, and sustainability. Artificial intelligence (AI) techniques are widely used to optimize fleet management, while augmented and mixed reality (AR/MR) technologies are being adopted to enhance warehouse operations. However, existing approaches often treat these aspects in isolation, missing opportunities for optimization and operational efficiency gains through improved information visibility across different roles in the logistics workforce. This work proposes the adoption of novel technological solutions integrated in an LMD framework that combines AI-based optimization of shipment allocation and vehicle route planning with a knowledge graph (KG)-driven decision support system. Additionally, the paper discusses the exploitation of relevant recent tools, including large language model (LLM)-powered conversational assistants for managers and operators and MR-based headset interfaces supporting warehouse operators by providing real-time data and enabling direct interaction with the system through virtual contextual UI elements. The framework prioritizes the customizability of AI algorithms and real-time information sharing between stakeholders. An experiment with a system prototype in the Apulia region is presented to evaluate the feasibility of the system in a realistic logistics scenario, highlighting its potential to enhance coordination and efficiency in LMD operations. The results suggest the usefulness of the approach while also identifying benefits and challenges in real-world applications. Full article

AI-personalized recommendation technology offers more accurate and diverse choices to consumers and increases click-through rates and sales on e-commerce platforms. Yet, data on consumers’ experiences of AI-personalized recommendations and their impact path on clicking intention are scarce. This article addressed these issues through three studies. In study 1, we adopted the Grounded Theory approach to conduct in-depth interviews with 30 Chinese consumers and constructed a scale to measure the impact of consumer experience on click intention. In study 2, we adopted the empirical research method to conduct reliability and validity tests on 347 valid questionnaires to finalize the scale officially. In study 3, based on the SOR theory, we constructed a model and formulated hypotheses and then conducted empirical analysis using 1097 valid questionnaires. We found that the relevance, inspiration, and insightful experiences of AI-personalized recommendations can significantly promote consumers’ clicking intention. Moreover, immersive experience mediates between the former three factors and clicking intention, and technology acceptance mediates between relevance, inspiration, and clicking intention. When consumers perceive a high degree of information privacy infringement, the immersive experience’s positive impact on clicking intention will be weakened. Meanwhile, the promoting effect of technology acceptance on clicking intention will also be inhibited. When information quality improves, the positive impact of technology acceptance on clicking intention will be enhanced. This research fills the gap in the literature on consumers’ experiences of AI-personalized recommendations and clarifies how these experiences affect the clicking intention. It offers valuable insights for e-commerce platforms to continuously optimize personalized recommendation algorithms and boost the click conversion rate of online shopping. Full article

This paper analyzes how consumer preferences for eco-friendly private labels affect platform selling formats and manufacturer channel strategies. We construct a game-theoretic model that encompasses both an e-commerce platform and a manufacturer. In this model, the platform chooses its selling format—either wholesale or agency—while the manufacturer determines whether to launch an online direct channel. Our analysis takes into account consumer preferences for both product brands and sales channels. We compare and analyze the equilibrium outcomes across four different scenarios, leading to the following conclusions: (1) When the platform utilizes the wholesale selling format, the manufacturer is consistently motivated to launch the direct channel if the costs associated with it are low. Conversely, when the platform employs the agency selling format, the manufacturer’s decision to establish an online direct marketing channel depends on the relative strength of consumers’ eco-friendly and channel preferences. (2) The platform’s choice of selling format is affected by the manufacturer’s channel strategy, leading the platform to modify its selling format based on varying manufacturer channel approaches. (3) The choices of the platform’s selling format and the manufacturer’s channel strategy are not always in conflict. When consumer preferences for the manufacturer’s direct channel are strong and the cost of introducing it is moderate, the platform’s decision to adopt the agency selling format can create a win-win outcome for both the platform and the manufacturer. And the establishment of online channels by manufacturers further enhances the sustainable growth of eco-friendly products. Full article

As an important tool for brand promotion and marketing, the status of virtual streamers is gradually improving, especially in the Chinese market with a huge Internet user base. Virtual streamer anthropomorphism has gradually become an important research content in the field of consumer behavior. However, the specific mechanism by which the multidimensional anthropomorphic characteristics of virtual streamers affect consumer trust and purchase intention requires further investigation. Therefore, based on the avatar theory, this research explores how the anthropomorphic characteristics of virtual streamers affect consumer purchase intention through cognitive trust. The analysis was performed using SPSS 27.0 and AMOS 24.0, establishing a structural equation model. Through the analysis of questionnaire data from 503 Chinese consumers, it was found that behavioral anthropomorphism, cognitive anthropomorphism, and emotional anthropomorphism all exert a notable influence on cognitive trust. Appearance anthropomorphism and emotional anthropomorphism directly affect purchase intention, and cognitive trust has a significant impact on purchase intention. Moreover, cognitive trust fully mediates the effects of behavioral anthropomorphism and cognitive anthropomorphism on purchase intention and partially mediates the effects of emotional anthropomorphism on purchase intention. This study enriches the application of avatar theory in virtual streamers in live e-commerce and provides theoretical backing for virtual streamer development and enterprise marketing strategies. It also offers practical insights to help brands optimize virtual streamers and improve consumer participation and purchase conversion rates. Full article

This paper examines the effects of optimism bias within online retail supply chains. Here, optimism refers to a cognitive bias wherein the third-party seller and the e-commerce platform underestimate the likelihood of facing low market potential. The analysis begins by exploring the impacts of each party’s respective biases. The results indicate that while seller optimism generally leads to self-detrimental outcomes, it can also benefit both the platform and the overall system. Conversely, platform optimism does not permanently harm the platform itself but consistently disadvantages the seller and negatively impacts the supply chain. This work then investigates the combined effects of seller and platform optimism on overall system performance, revealing that the entire channel can, in fact, gain from optimism bias. This research offers insights into strategic approaches that may enhance the efficiency of online retail supply chains. Full article

To investigate the dynamic complexity of chain-to-chain output decisions in a closed-loop supply chain system of cross-border e-commerce (CBEC), this study decomposes the system into four product–market (PM) chains, based on the e-commerce platform’s information-sharing strategy and the manufacturer’s selected logistics mode (direct mail or bonded warehouse). By combining game theory with complex systems theory, discrete dynamic models for output competition among PM chains under four scenarios are constructed. The Nash equilibrium solution and stability conditions of the models are derived according to the principles of nonlinear dynamics. The stability of the model under the four scenarios, as well as the impacts of the initial output level and comprehensive tax rates on the stability and stability control of the system, are analyzed using numerical simulation methods. Our findings suggest that maintaining system stability requires controlling the initial output levels, the output adjustment speeds, and tariff rates to remain within specific thresholds. When these thresholds are exceeded, the entropy value of the model increases, and the system outputs decisions to enter a chaotic or uncontrollable state via period-doubling bifurcations. When the output adjustment speed of the four PM chains is high, the direct-mail logistics mode exhibits greater stability. Furthermore, under increased tariff rates for CBEC, the bonded warehouse mode has a stronger ability to maintain stability in system output decisions. Conversely, when the general import tax rate increases, the direct-mail mode demonstrates better stability. Regardless of the logistics mode, the information-sharing strategy can enhance the stability of system output decisions, while increased e-commerce platform commission rates tend to reduce stability. Interestingly, the use of a non-information-sharing strategy and the direct-mail logistics mode may be more conducive to increasing the profit levels of overseas manufacturers. Finally, the delayed feedback control method can effectively reduce the entropy value, suppress chaotic phenomena in the system, and restore stability to output decisions from a fluctuating state. Full article

Although digital hoarding behavior does not directly affect physical space, with the popularization of cloud storage services, its impact on energy consumption has become increasingly significant, posing a challenge to environmental sustainability. This study focuses on the factors influencing consumer digital hoarding behavior on e-commerce platforms, aiming to provide management decision-making references for e-commerce enterprises to deal with consumer digital hoarding phenomena and improve transaction effectiveness. Based on the Motivation–Opportunity–Ability (MOA) Theory and through the Adversarial Interpretive Structure Modeling Method (AISM), this study systematically identifies and analyzes the influencing factors. The findings reveal that emotional attachment, burnout, and fear of missing out are the main motivational factors directly affecting consumer digital hoarding behavior, with strong interconnections between these factors. Perceived usefulness and platform interaction design are significant opportunity factors, indirectly affecting digital hoarding behavior by improving user experience and satisfaction. E-commerce platform convenience, anticipated ownership, perceived economic value, emotional regulation ability, auxiliary shopping decision-making, perceived behavioral control, and information organization ability are the foundational and intermediate factors. The research results emphasize the importance of understanding consumer digital hoarding behavior in the context of sustainable development. This is not only conducive to optimizing the shopping cart function and data management strategy of e-commerce platforms and improving transaction conversion rates but also provides a reference for policymakers to formulate data management and privacy protection policies. Full article

As companies increasingly undergo digital transformation, the value of their data assets also rises, making them even more attractive targets for hackers. The large volume of weblogs warrants the use of advanced classification methodologies in order for cybersecurity specialists to identify web traffic anomalies. This study aims to implement Isolation Forest, an unsupervised machine learning methodology in the identification of anomalous and non-anomalous web traffic. The publicly available weblogs dataset from an e-commerce website underwent data preparation through a systematic pipeline of processes involving data ingestion, data type conversion, data cleaning, and normalization. This led to the addition of derived columns in the training set and manually labeled testing set that was then used to compare the anomaly detection performance of the Isolation Forest model with that of cybersecurity experts. The developed Isolation Forest model was implemented using the Python Scikit-learn library, and exhibited a superior Accuracy of 93%, Precision of 95%, Recall of 90% and F1-Score of 92%. By appropriate data preparation, model development, model implementation, and model evaluation, this study shows that Isolation Forest can be a viable solution for close to accurate web traffic anomaly detection. Full article

The fusion of livestreaming e-commerce and AI technology is booming, and many firms have started to replace human streamers with AI streamers. Despite their popularity, the acceptance of AI streamers by consumers varies widely and the signaling effects of AI streamers still remain unclear. We build an analytical model and compare scenarios where the acceptance level is either exogenously given or endogenously determined, highlighting the implications for firms’ optimal separation strategy. Our findings suggest that in markets with moderate information asymmetry, using both price and acceptance level as joint signals can be more profitable for high-quality firms. Conversely, in highly asymmetric markets, firms must incur additional costs to distinguish their high-quality products, regardless of the signaling strategy employed. Our paper provides strategic insights for firms aiming to leverage AI streamers in diverse market conditions. Full article