Search Results (151)

With the increase in the global aging population, the demand for elderly-friendly game products is growing rapidly. To address existing limitations, particularly in user demand extraction and design parameter setting, this study proposed a design framework integrating the BTM–AHP–AD–TOPSIS methods. The goal was to accurately identify the core needs of elderly users and translate them into effective design solutions. User reviews of elderly-friendly game products were collected from e-commerce platforms using Python 3.8-based web scraping. The Biterm Topic Model (BTM) was employed to extract user needs from review texts. These needs were prioritized using the Analytic Hierarchy Process (AHP) and translated into specific design parameters through Axiomatic Design (AD). Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied to comprehensively evaluate multiple design schemes and select the optimal solution. The results demonstrate that the proposed design path offers a holistic method for progressing from need extraction to design evaluation. It effectively overcomes previous limitations, including inefficient need extraction, limited scope, unclear need weighting, and unreasonable design parameters. This method enhances user acceptance and satisfaction while establishing rigorous design processes and scientific evaluation standards, making it well suited for developing elderly-friendly products. Full article

In today’s competitive environment of online service industries, particularly e-commerce, meeting consumer expectations is essential for service providers to ensure service quality. However, service failures are unavoidable, leading to unfavorable consequences for businesses. Understanding the mechanisms for customer recovery after negative service experiences is crucial. Using cognitive–emotional personality systems theory and benign violation theory, this study constructed a theoretical model. A total of 351 samples were collected through a situational simulation experiment for a linear regression analysis. A self-mocking response strategy positively influenced brand trust through perceived brand authenticity regarding the dimensions of credibility, integrity, and symbolism. Simultaneously, brand trust was identified as a key driver of post-recovery satisfaction. This study proposes a chain mediation model, which incorporates perceived authenticity and brand trust, to fully comprehend the mechanisms underlying consumers’ satisfaction after service recovery. Our findings provide empirical evidence for the effects of self-mockery on post-recovery satisfaction, as well as suggestions for marketers seeking efficient means to meet consumers’ emotional and cognitive demands during service recovery situations. Full article

Online travel agencies (OTAs) function as e-commerce platforms that facilitate transactions between accommodation providers and consumers, enabling users to efficiently search for, compare, and book travel and lodging services. As the number of OTAs continues to grow, delivering superior service quality has become essential for increasing customer repurchase intentions. Despite its significance, existing research has primarily focused on factors such as website quality, pricing strategies, brand image, and perceived value as determinants of repurchase intention. However, relatively little attention has been paid to the alignment between online information disclosure and customers’ actual offline experiences. To address this gap, the present study introduces the concept of the information disclosure gap and examines its effects on search cost, customer satisfaction, and trust, as well as the subsequent influence of these variables on repurchase intention. A questionnaire-based survey method was conducted with individuals in Taiwan who had prior experience using OTAs, yielding 365 valid responses. This study offers practical insights and recommendations for both OTAs and accommodation providers aimed at reducing the information disclosure gap and strengthening customer repurchase intention. Full article

In an increasingly digitalized marketplace, understanding Generation Z’s (Gen Z) online consumer behavior has become a critical priority, particularly in relation to newly launched technological products. Although online consumer behavior has been widely studied, a gap remains in understanding how the location of the e-shop (domestic vs. international) moderates this behavior. Addressing this gap, the present study adopts a quantitative, cross-sectional design with data from 302 Gen Z participants, using a hybrid sampling method that combines convenience and systematic techniques. A structured questionnaire, grounded in 19 well-established behavioral theories, was employed to examine the influence of six key factors, behavioral and attitudinal traits, social and peer influences, marketing impact, online experience, brand perceptions, and Gen Z characteristics, across various stages of the consumer journey. Moderation analysis revealed that e-shop location significantly affects the strength of relationships between these factors and both purchase intention and post-purchase behavior. Notably, Gen Z’s values and marketing responsiveness were found to be more predictive in the context of international e-shops. These findings highlight the importance of marketing strategies that are both locally relevant and globally informed. For businesses, this research offers actionable insights into how digital engagement and brand messaging can be tailored to meet the unique expectations of Gen Z consumers across diverse e-commerce contexts, thereby enhancing consumer satisfaction, loyalty, and brand advocacy. Full article

The integration of generative artificial intelligence (AI) in e-commerce is constantly increasing and in different forms, while transforming content creation. Its impact on user experience remains underexplored. This study examines user perceptions of AI-generated e-commerce content, focusing on functionality, aesthetics, and security. A survey was conducted where 223 participants were requested to browse through the pages of an online store developed using ChatGPT and DALL·E and evaluate it, providing feedback through a constructed questionnaire. The collected data was subjected to descriptive statistical analysis, exploratory factor analysis (EFA), and comparative statistical tests to identify key user experience dimensions and possible demographic variances in satisfaction. Factor analysis extracted two main components influencing user experience: “Service Quality and Security” and “Design and Aesthetics”. Further analysis highlighted a slight variation in user evaluations between male and female participants. Although security-related questions were addressed with caution, the rest of the findings indicate that AI-generated content was well-received and highly rated. Clearly, generative AI is a valuable tool for businesses, AI developers, and anyone seeking to optimize AI-driven processes to enhance user engagement. It can be confidently concluded that it positively contributes to the development of a functional and aesthetically appealing e-commerce platform. Full article

(1) Background: With the deep integration of e-commerce and video technology, live-streaming marketing has emerged globally and maintained rapid growth. However, most of the current research on live-streaming e-commerce marketing focuses on merchants’ sales strategies and consumers’ purchase intentions, and there is relatively little research related to the risks of live-streaming e-commerce marketing. Nevertheless, with the development of live-streaming e-commerce marketing and its integration with technologies such as artificial intelligence and virtual reality (VR), live-streaming e-commerce marketing still faces challenges such as unclear subject responsibility, difficulty in verifying the authenticity of marketing information, and uneven product quality. It also harbors problems such as the ethical misbehavior of AI anchors and the excessive beautification of products by VR technology. (2) Methods: This study systematically analyzes the scenarios of live-streaming marketing to elucidate the mechanisms of risk formation. Utilizing fault tree analysis (FTA) and risk checklist methods, risks are identified based on the three core elements of live-streaming marketing: “people–products–scenes”. Subsequently, the Delphi method is employed to refine the initial risk indicator system, resulting in the construction of a comprehensive risk indicator system comprising three first-level indicators, six second-level indicators, and 16 third-level indicators. A hesitant fuzzy multi-attribute group decision-making method (HFMGDM) is then applied to calculate the weights of the risk indicators and comprehensively assess the live-streaming marketing risks in live broadcast rooms of three prominent celebrity anchors in China. Furthermore, a detailed analysis is conducted on the risks associated with the six secondary indicators. Based on the risk evaluation results, targeted recommendations are proposed. This study aims to enhance consumers’ awareness of risk prevention when conducting live-streaming transactions and pay attention to related risks, thereby safeguarding consumer rights and fostering the healthy and sustainable development of the live-streaming marketing industry. (3) Conclusions: The results show that the top five risk indicators in terms of weight ranking are: Ethical Risk of the AI Anchor (A4), VR Technology Promotion Risk (F3), Anchor Reputation (A1), Product Quality (D1), and Logistics Distribution Service Quality (D2). The comprehensive live-streaming marketing risk of each live broadcast room is Y > L > D. Based on the analysis results, targeted recommendations are provided for anchors, MCN institutions, merchants, supply chains, and live-streaming platforms to improve consumer satisfaction and promote sustainable development of the live-streaming marketing industry. Full article

Consumer satisfaction critically determines the operational sustainability of fresh food e-commerce platforms, yet integrated investigations combining multi-source data remain scarce. This study develops a theory–data fusion framework to identify key satisfaction drivers in China’s fresh e-commerce sector. Utilizing Python-based crawlers, we extracted 1252 online reviews of Aksu apples from a certain fresh produce e-commerce platform alongside 509 validated questionnaires. Through systematic literature synthesis, three core dimensions—perceived value (price–performance balance), platform experience (interface usability), and perceived quality (freshness assurance)—were operationalized into measurable indicators. The final structural equation model reveals that perceived value, platform experience, and perceived quality all have significant positive impacts on consumer satisfaction. This study pioneers a methodological paradigm integrating computational text mining (Octopus Collector + SPSS Pro) with traditional psychometric scales, achieving superior model fit (RMSEA = 0.023, CFI = 0.981). These findings empower platforms to implement a precision strategy. The validated framework provides a theoretical basis for omnichannel consumer research while addressing the data-source bias prevalent in prior studies. Full article

This study investigates consumer reactions to mascots on e-commerce websites, focusing on how anthropomorphic visual cues influence website satisfaction, revisit intention, and purchase intention. Specifically, we examine how mascot movement affects consumers’ sense of social presence and engagement, as well as the role of team identification in these effects. A 3 (mascot type: none, static, animated) × 2 (team identification: high, low) between-subjects experiment was conducted with 203 participants recruited from Amazon Mechanical Turk. Our findings show that the presence of mascots significantly impacts consumer evaluations, with social presence and engagement acting as sequential mediators. Notably, high team identification moderates the effect of animated mascots on revisit and purchase intentions but does not affect website satisfaction. These results provide valuable theoretical and practical insights for marketing, highlighting the importance of mascot design and movement in enhancing e-commerce experiences. Full article

Driven by digital technology, live streaming business is becoming increasingly common worldwide. Unlike traditional online shopping, live streaming commerce integrates real-time interaction, social communication, and e-commerce. It also eliminates the limitations of one-way information transmission and promotes purchasing behavior by conveying signals such as products, brands, and the personal charm of live streamers to consumers through real-time communication. The core issues explored in this study are whether the cues between the signal transmitters and receivers are consistent and how they affect consumers’ purchasing behavior. In this study, the consistency of the signal is measured by five dimensions, namely self–product fit, live streamer–product fit, live content–product fit, danmaku content–product fit, and self–live streamer fit. In order to study this problem, we constructed a structural equation modeling (SEM) model. In the causal relationship between signal consistency and purchase intention, performance responses have also been discussed as mediating variables. Accordingly, signal consistency enables consumers to perceive performance expectancy, whereby consumers believe that products that perform well positively influence satisfaction. To verify these hypotheses, 443 randomly collected valid questionnaires were used in empirical analyses. The results showed that most of our hypotheses were validated, aside from the relationship between self–live streamer fit and perceived performance expectancy. The findings suggest that signal consistency cues such as live streamer–product fit, live content–product fit, danmaku content–product fit, and self–product fit positively influence consumers’ perceived performance expectancy and satisfaction, which in turn promote purchase intention. These findings not only enrich the application of signaling theory in the field of live streaming commerce but also expand consumer behavior theory. Moreover, they also inspire practitioners in live streaming commerce by helping them to improve live streaming sales and bolster their market competitiveness through signal display optimization, content planning, and precision marketing. Full article

With the rapid development of e-commerce, the importance of logistics distribution is becoming increasingly prominent. In particular, the last-mile delivery is particularly important because it serves customers directly. Improving customer satisfaction is one of the important factors to ensure the quality of service in delivery and also an important guarantee for improving the market competitiveness of logistics enterprises. In the process of last-mile delivery, flexible delivery locations and variable delivery times are effective means to improve customer satisfaction. Therefore, this paper introduces a Vehicle Routing Problem with flexible time windows and delivery locations, considering customer satisfaction (VRP-CS), which considers customer satisfaction by using prospect theory from two aspects: the flexibility of delivery time and delivery locations. This VRP-CS is formally modeled as a bi-objective optimization problem, which is an NP-hard problem. To solve this problem, a Multi-Strategy Adaptive Large Neighborhood Search (MSALNS) method is proposed. Operators guided by strategies such as backtracking and correlation are introduced to create different neighborhoods for ALNS, thereby enriching search diversity. In addition, an acceptance criterion inspired by simulated annealing is designed to balance exploration and exploitation, helping the algorithm avoid being trapped in local optima. Extensive numerical experiments on generated benchmark instances demonstrate the effectiveness of the VRP-CS model and the efficiency of the proposed MSALNS algorithm. The experiment results on the generated benchmark instances show that the total cost of the VRP-CS is reduced by an average of 14.22% when optional delivery locations are utilized compared to scenarios with single delivery locations. Full article

Postal services have re-emerged across numerous emerging economies worldwide as essential logistics providers, harnessing their vast coverage and dependability in the face of expanding e-commerce platforms and technological innovations. This study investigates India Post, one of the largest postal networks globally, to determine the key logistics service parameters prioritized by customers in southern India. Quantitative data obtained from 255 India Post end-users were evaluated using the logistics service quality (LSQ) scale, assessing eight dimensions including information quality, timeliness, ordering procedure, order accuracy, order condition, personal contact quality, order discrepancy handling, and order release quantities. The Analytical Hierarchy Process (AHP) ranked these elements, while Quality Function Deployment (QFD) bridged customer expectations with service improvements. The findings highlight the need to improve sorting and distribution processes to meet customer demands for timely, high-quality delivery. By refining logistics efficiency, this study provides suggestions and recommendations for boosting satisfaction and profitability, shedding light on service-led economic advancement for postal services in emerging economies. These insights equip postal service providers to cultivate loyalty and maintain competitiveness within the dynamic logistics landscape. 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

The rapid growth of e-commerce has intensified the need for efficient and sustainable last-mile delivery solutions in urban environments. This paper explores the integration of electric vehicles (EVs) and artificial intelligence (AI) into a combined framework to enhance the environmental, operational, and economic performance of urban logistics. Through a comprehensive literature review, we examine current trends, technological developments, and implementation challenges at the intersection of smart mobility, green logistics, and digital transformation. We propose an operational framework that leverages AI for route optimization, fleet coordination, and energy management in EV-based delivery networks. This framework is validated through a real-world case study conducted in Lisbon, Portugal, where a logistics provider implemented a city consolidation center model supported by AI-driven optimization tools. Using key performance indicators—including delivery time, energy consumption, fleet utilization, customer satisfaction, and CO₂ emissions—we measure the pre- and post-AI deployment impacts. The results demonstrate significant improvements across all metrics, including a 15–20% reduction in delivery time, a 10–25% gain in energy efficiency, and up to a 40% decrease in emissions. The findings confirm that the synergy between EVs and AI provides a robust and scalable model for achieving sustainable last-mile logistics, supporting broader urban mobility and climate objectives. Full article

The rapid growth in the global apparel market and the rise of online consumption underscore the necessity for intelligent clothing recommendation systems that balance visual compatibility with personalized preferences, particularly price sensitivity. Existing recommendation systems often neglect nuanced consumer price behaviors, limiting their ability to deliver truly personalized suggestions. To address this gap, we propose DeepFMP, a multimodal deep learning framework that integrates visual, textual, and price features through an enhanced DeepFM architecture. Leveraging the IQON3000 dataset, our model employs ResNet-50 and BERT for image and text feature extraction, alongside a comprehensive price feature module capturing individual, statistical, and category-specific price patterns. An attention mechanism optimizes multimodal fusion, enabling robust modeling of user preferences. Comparative experiments demonstrate that DeepFMP outperforms state-of-the-art baselines (LR, FM, Wide & Deep, GP-BPR, and DeepFM), achieving AUC improvements of 1.6–12.2% and NDCG@10 gains of up to 3.2%. Case analyses further reveal that DeepFMP effectively improves the recommendation accuracy, offering actionable insights for personalized marketing. This work advances multimodal recommendation systems by emphasizing price sensitivity as a pivotal factor, providing a scalable solution for enhancing user satisfaction and commercial efficacy in fashion e-commerce. Full article

E-commerce applications have emerged as key drivers of digital transformation, reshaping consumer behavior and driving demand for seamless online transactions. Despite the growth of smart mobile technologies, existing methods rely on fixed UI content that cannot adjust to local cultural preferences and fluctuating user behaviors. This paper explores the combination of generative Artificial Intelligence (AI) technologies with Model-Driven Development (MDD) to enhance personalization, engagement, and adaptability in e-commerce. Unlike static adaptation approaches, generative AI enables real-time, adaptive interactions tailored to individual needs, providing a more engaging and adaptable user experience. The proposed framework follows a three-tier architecture: first, it collects and analyzes user behavior data from UI interactions; second, it leverages MDD to model and personalize user personas and interactions and third, AI techniques, including generative AI and multi-agent reinforcement learning, are applied to refine and optimize UI/UX design. This automation-driven approach uses a multi-agent system to continuously enhance AI-generated layouts. Technical validation demonstrated strong user engagement across diverse platforms and superior performance in UI optimization, achieving an average user satisfaction improvement of 2.3% compared to GAN-based models, 18.6% compared to Bootstrap-based designs, and 11.8% compared to rule-based UI adaptation. These results highlight generative AI-driven MDD tools as a promising tool for e-commerce, enhancing engagement, personalization, and efficiency. Full article