Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (1,487)

Search Parameters:
Keywords = user intention

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
16 pages, 1085 KiB  
Article
The Innovativeness–Optimism Nexus in Autonomous Bus Adoption: A UTAUT-Based Analysis of Chinese Users’ Behavioral Intention
by Qiao Liang, Qianling Jiang and Wei Wei
Vehicles 2025, 7(3), 87; https://doi.org/10.3390/vehicles7030087 - 22 Aug 2025
Abstract
This study extended the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating affective constructs (innovativeness, optimism, and hedonic motivation) to examine user adoption of autonomous bus (AB) in China, where government-supported deployment creates unique adoption dynamics. Analyzing 313 responses, collected [...] Read more.
This study extended the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating affective constructs (innovativeness, optimism, and hedonic motivation) to examine user adoption of autonomous bus (AB) in China, where government-supported deployment creates unique adoption dynamics. Analyzing 313 responses, collected via stratified sampling using SmartPLS 4.0, we identified innovativeness as the dominant driver (total effect, β = 0.347), directly influencing behavioral intention (β = 0.164*) and indirectly shaping optimism (β = 0.692*), effort expectancy (β = 0.347*), and hedonic motivation (β = 0.681*). Our findings highlight contextual influences in public service systems. Performance expectancy (β = 0.153*) exerts a stronger effect than hedonic or social factors (H6/H3 rejected), while optimism demonstrates a dual scaffolding effect (OPT→EE, β = 0.189*; OPT→PE, β = 0.401*), reflecting a “calculative optimism” pattern where users balance technological interest with pragmatic utility evaluation in policy-supported deployment contexts. From a practical perspective, these findings suggest targeting high-innovativeness users through incentive programs, emphasizing system reliability over ease of use, and implementing adapted designs. This study contributes to the literature both theoretically, by validating the hierarchical role of innovativeness in UTAUT, and practically, by offering actionable strategies for China’s ongoing AB deployment initiative, including ISO-standardized UX and policy tools such as municipal Innovator Badges. Full article
17 pages, 1523 KiB  
Article
AI in Fracture Detection: A Cross-Disciplinary Analysis of Physician Acceptance Using the UTAUT Model
by Martin Breitwieser, Stephan Zirknitzer, Karolina Poslusny, Thomas Freude, Julia Scholsching, Karl Bodenschatz, Anton Wagner, Klaus Hergan, Matthias Schaffert, Roman Metzger and Patrick Marko
Diagnostics 2025, 15(16), 2117; https://doi.org/10.3390/diagnostics15162117 - 21 Aug 2025
Abstract
Background/Objectives: Artificial intelligence (AI) tools for fracture detection in radiographs are increasingly approved for clinical use but remain underutilized. Understanding physician attitudes before implementation is essential for successful integration into emergency care workflows. This study investigates the acceptance of an AI-based fracture [...] Read more.
Background/Objectives: Artificial intelligence (AI) tools for fracture detection in radiographs are increasingly approved for clinical use but remain underutilized. Understanding physician attitudes before implementation is essential for successful integration into emergency care workflows. This study investigates the acceptance of an AI-based fracture detection tool among physicians in emergency care settings, using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Methods: A cross-sectional, pre-implementation survey was conducted among 92 physicians across three hospitals participating in the SMART Fracture Trial (ClinicalTrials.gov: NCT06754137). The questionnaire assessed the four core UTAUT constructs—performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC)—and additional constructs such as attitude toward technology (AT), diagnostic confidence (DC), and workflow efficiency (WE). Responses were collected on a five-point Likert scale. Structural equation modeling (SEM) and confirmatory factor analysis (CFA) were performed to assess predictors of behavioral intention (BI). Results: PE was the strongest predictor of BI (β = 0.5882, p < 0.001), followed by SI (β = 0.391, p < 0.001), FC (β = 0.263, p < 0.001), and EE (β = 0.202, p = 0.001). These constructs explained a substantial proportion of variance in BI. WE received the lowest ratings, while internal consistency for SI and BI was weak. Moderator analyses showed prior AI experience improved EE, whereas more experienced physicians were more skeptical regarding WE and DC. However, none of the moderators significantly influenced BI. Conclusions: Physicians’ intention to use AI fracture detection is primarily influenced by perceived usefulness and ease of use. Implementation strategies should focus on intuitive design, targeted training, and clear communication of clinical benefits. Further research should evaluate post-implementation usage and user satisfaction. Full article
Show Figures

Figure 1

28 pages, 2845 KiB  
Article
The Impact of Social Presence on Purchase Intentions of Knowledge Products Among Knowledge-Based Short Video Users: A Moderated Mediation Model
by Can Zheng, Shuai Ling, Yarong Huang and Xinxiang Li
Behav. Sci. 2025, 15(8), 1140; https://doi.org/10.3390/bs15081140 - 21 Aug 2025
Abstract
Despite the rapid growth of knowledge-based short videos, monetizing this content remains a significant challenge. Grounded in social presence theory, this study investigates how social presence influences users’ purchase intentions by incorporating the mediating effects of cognitive engagement and expectation, as well as [...] Read more.
Despite the rapid growth of knowledge-based short videos, monetizing this content remains a significant challenge. Grounded in social presence theory, this study investigates how social presence influences users’ purchase intentions by incorporating the mediating effects of cognitive engagement and expectation, as well as the moderating effects of knowledge anxiety. Using data from 663 users of knowledge-based short videos in China, the proposed model demonstrates strong explanatory power for purchase intention (R2 = 54.6%). The findings show that social presence significantly enhances users’ intention to purchase knowledge products by fostering cognitive engagement and expectations, creating a serial mediation effect. Furthermore, knowledge anxiety positively moderates the impact of social presence on purchase intention, with a more pronounced effect for individuals with higher anxiety. This research provides a novel theoretical perspective for understanding user behavior in knowledge-based short videos and offers practical guidance for platforms and creators to enhance monetization. Full article
(This article belongs to the Section Behavioral Economics)
Show Figures

Figure 1

17 pages, 2418 KiB  
Article
InstructSee: Instruction-Aware and Feedback-Driven Multimodal Retrieval with Dynamic Query Generation
by Guihe Gu, Yuan Xue, Zhengqian Wu, Lin Song and Chao Liang
Sensors 2025, 25(16), 5195; https://doi.org/10.3390/s25165195 - 21 Aug 2025
Abstract
In recent years, cross-modal retrieval has garnered significant attention due to its potential to bridge heterogeneous data modalities, particularly in aligning visual content with natural language. Despite notable progress, existing methods often struggle to accurately capture user intent when queries are expressed through [...] Read more.
In recent years, cross-modal retrieval has garnered significant attention due to its potential to bridge heterogeneous data modalities, particularly in aligning visual content with natural language. Despite notable progress, existing methods often struggle to accurately capture user intent when queries are expressed through complex or evolving instructions. To address this challenge, we propose a novel cross-modal representation learning framework that incorporates an instruction-aware dynamic query generation mechanism, augmented by the semantic reasoning capabilities of large language models (LLMs). The framework dynamically constructs and iteratively refines query representations conditioned on natural language instructions and guided by user feedback, thereby enabling the system to effectively infer and adapt to implicit retrieval intent. Extensive experiments on standard multimodal retrieval benchmarks demonstrate that our method significantly improves retrieval accuracy and adaptability, outperforming fixed-query baselines and showing enhanced cross-modal alignment and generalization across diverse retrieval tasks. Full article
Show Figures

Figure 1

33 pages, 732 KiB  
Article
Perceptions of Greenwashing and Purchase Intentions: A Model of Gen Z Responses to ESG-Labeled Digital Advertising
by Stefanos Balaskas, Ioannis Stamatiou, Kyriakos Komis and Theofanis Nikolopoulos
Risks 2025, 13(8), 157; https://doi.org/10.3390/risks13080157 - 19 Aug 2025
Viewed by 214
Abstract
This research examines the cognitive and psychological mechanisms underlying young adults’ reactions to ESG-labeled online advertisements, specifically resistance to persuasion and purchase intention. Based on dual-process theories of persuasion and digital literacy theory, we develop and test a structural equation model (SEM) of [...] Read more.
This research examines the cognitive and psychological mechanisms underlying young adults’ reactions to ESG-labeled online advertisements, specifically resistance to persuasion and purchase intention. Based on dual-process theories of persuasion and digital literacy theory, we develop and test a structural equation model (SEM) of perceived greenwashing, online advertising literacy, source credibility, persuasion knowledge, and advertising skepticism as predictors of behavioral intention. Data were gathered from 690 Greek consumers between the ages of 18–35 years through an online survey. All the direct effects hypothesized were statistically significant, while advertising skepticism was the strongest direct predictor of purchase intention. Mediation tests indicated that persuasion knowledge and skepticism partially mediated perceptions of greenwashing, literacy, and credibility effects, in favor of a complementary dual-route process of ESG message evaluation. Multi-group comparisons revealed significant moderation effects across gender, age, education, ESG familiarity, influencer trust, and ad-avoidance behavior. Most strikingly, women evidenced stronger resistance effects via persuasion knowledge, whereas younger users and those with lower familiarity with ESG topics were more susceptible to skepticism and greenwashing. Education supported the processing of source credibility and digital literacy cues, underlining the contribution of informational capital to persuasion resilience. The results provide theoretical contributions to digital persuasion and resistance with practical implications for marketers, educators, and policymakers seeking to develop ethical ESG communication. Future research is invited to broaden cross-cultural understanding, investigate emotional mediators, and incorporate experimental approaches to foster consumer skepticism and trust knowledge in digital sustainability messages. Full article
(This article belongs to the Special Issue ESG and Greenwashing in Financial Institutions: Meet Risk with Action)
Show Figures

Figure 1

20 pages, 757 KiB  
Article
Exploring Twitch Viewers’ Donation Intentions from a Dual Perspective: Uses and Gratifications Theory and the Practice of Freedom
by José Magano, Manuel Au-Yong-Oliveira and Antonio Sánchez-Bayón
Information 2025, 16(8), 708; https://doi.org/10.3390/info16080708 - 19 Aug 2025
Viewed by 184
Abstract
This study examines the factors that motivate viewers to financially support streamers on the Twitch digital platform. It proposes a conceptual framework that combines the uses and gratifications theory (UGT) with Michel Foucault’s concept of the practice of freedom (PF). Using a cross-sectional [...] Read more.
This study examines the factors that motivate viewers to financially support streamers on the Twitch digital platform. It proposes a conceptual framework that combines the uses and gratifications theory (UGT) with Michel Foucault’s concept of the practice of freedom (PF). Using a cross-sectional quantitative survey of 560 Portuguese Twitch users, the model investigates how three core constructs from UGT—entertainment, socialization, and informativeness—affect the intention to donate, with PF acting as a mediating variable. Structural equation modeling confirms that all three UGT-based motivations significantly influence donation intentions, with socialization exhibiting the strongest mediated effect through PF. The findings reveal that Twitch donations go beyond mere instrumental or playful actions; they serve as performative expressions of identity, autonomy, and ethical subjectivity. By framing PF as a link between interpersonal engagement and financial support, this study provides a contribution to media motivation research. The theoretical integration enhances our understanding of pro-social behavior in live streaming environments, challenging simplistic, transactional interpretations of viewer contributions vis-à-vis more political ones and the desire to freely dispose of what is ours to give. Additionally, this study may lay the groundwork for future inquiries into how ethical self-formation is intertwined with monetized online participation, offering useful insights for academics, platform designers, and content creators seeking to promote meaningful digital interactions. Full article
Show Figures

Figure 1

23 pages, 1553 KiB  
Article
Assessing Chatbot Acceptance in Policyholder’s Assistance Through the Integration of Explainable Machine Learning and Importance–Performance Map Analysis
by Jaume Gené-Albesa and Jorge de Andrés-Sánchez
Electronics 2025, 14(16), 3266; https://doi.org/10.3390/electronics14163266 - 17 Aug 2025
Viewed by 199
Abstract
Companies are increasingly giving more attention to chatbots as an innovative solution to transform the customer service experience, redefining how they interact with users and optimizing their support processes. This study analyzes the acceptance of conversational robots in customer service within the insurance [...] Read more.
Companies are increasingly giving more attention to chatbots as an innovative solution to transform the customer service experience, redefining how they interact with users and optimizing their support processes. This study analyzes the acceptance of conversational robots in customer service within the insurance sector, using a conceptual model based on well-known new information systems adoption groundworks that are implemented with a combination of machine learning techniques based on decision trees and so-called importance–performance map analysis (IPMA). The intention to interact with a chatbot is explained by performance expectancy (PE), effort expectancy (EE), social influence (SI), and trust (TR). For the analysis, three machine learning methods are applied: decision tree regression (DTR), random forest (RF), and extreme gradient boosting (XGBoost). While the architecture of DTR provides a highly visual and intuitive explanation of the intention to use chatbots, its generalization through RF and XGBoost enhances the model’s explanatory power. The application of Shapley additive explanations (SHAP) to the best-performing model, RF, reveals a hierarchy of relevance among the explanatory variables. We find that TR is the most influential variable. In contrast, PE appears to be the least relevant factor in the acceptance of chatbots. IPMA suggests that SI, TR, and EE all deserve special attention. While the prioritization of TR and EE may be justified by their higher importance, SI stands out as the variable with the lowest performance, indicating the greatest room for improvement. In contrast, PE not only requires less attention, but it may even be reasonable to reallocate efforts away from improving PE in order to enhance the performance of the more critical variables. Full article
Show Figures

Figure 1

31 pages, 1381 KiB  
Article
Exploring Generation Z’s Acceptance of Artificial Intelligence in Higher Education: A TAM and UTAUT-Based PLS-SEM and Cluster Analysis
by Réka Koteczki and Boglárka Eisinger Balassa
Educ. Sci. 2025, 15(8), 1044; https://doi.org/10.3390/educsci15081044 - 14 Aug 2025
Viewed by 293
Abstract
In recent years, the rapid growth of artificial intelligence (AI) has significantly transformed higher education, particularly among Generation Z students who are more open to new technologies. Tools such as ChatGPT are increasingly being used for learning, yet empirical research on their acceptance, [...] Read more.
In recent years, the rapid growth of artificial intelligence (AI) has significantly transformed higher education, particularly among Generation Z students who are more open to new technologies. Tools such as ChatGPT are increasingly being used for learning, yet empirical research on their acceptance, especially in Hungary, is limited. This study aims to explore the psychological, technological, and social factors that influence the acceptance of AI among Hungarian university students and to identify different user groups based on their attitudes. The methodological novelty lies in combining two approaches: partial least-squares structural equation modelling (PLS-SEM) and cluster analysis. The survey, based on the TAM and UTAUT models, involved 302 Hungarian students and examined six dimensions of AI acceptance: perceived usefulness, ease of use, attitude, social influence, enjoyment and behavioural intention. The PLS-SEM results show that enjoyment (β = 0.605) is the strongest predictor of the intention to use AI, followed by usefulness (β = 0.167). All other factors also had significant effects. Cluster analysis revealed four groups: AI sceptics, moderately open users, positive acceptors, and AI innovators. The findings highlight that the acceptance of AI is shaped not only by functionality but also by user experience. Educational institutions should, therefore, provide enjoyable and user-friendly AI tools and tailor support to students’ attitude profiles. Full article
Show Figures

Graphical abstract

23 pages, 17882 KiB  
Article
When Generative AI Meets Abuse: What Are You Anxious About?
by Yuanzhao Song and Haowen Tan
J. Theor. Appl. Electron. Commer. Res. 2025, 20(3), 215; https://doi.org/10.3390/jtaer20030215 - 14 Aug 2025
Viewed by 364
Abstract
The rapid progress of generative artificial intelligence (AI) has sparked growing concerns regarding its misuse, privacy risks, and ethical issues. This study investigates the interplay between Generative AI Abuse Anxiety, trust, perceived usefulness, acceptance, and the intention to use it. Using variance-based partial [...] Read more.
The rapid progress of generative artificial intelligence (AI) has sparked growing concerns regarding its misuse, privacy risks, and ethical issues. This study investigates the interplay between Generative AI Abuse Anxiety, trust, perceived usefulness, acceptance, and the intention to use it. Using variance-based partial least squares (PLS-SEM), we analyze 318 valid survey responses. The findings reveal that Generative AI Abuse Anxiety negatively impacts trust, perceived usefulness, acceptance, and the intention to use generative AI. Additionally, different subdimensions of trust play significant roles in influencing users’ technology acceptance and intention to use it, though the specific mechanisms differ. This research extends the applicability of the technology acceptance model to the generative AI context and enriches the multidimensional framework of trust studies. Full article
Show Figures

Figure 1

27 pages, 4099 KiB  
Article
Reimagining Urban Cemeteries: Behavioral Patterns, Perceptions, and Intentions in Tokyo’s Public Burial Landscapes
by Yunchen Xu, Ruochen Ma and Katsunori Furuya
Land 2025, 14(8), 1638; https://doi.org/10.3390/land14081638 - 13 Aug 2025
Viewed by 325
Abstract
Once confined to mourning and burial, urban cemeteries are now being reimagined as multifunctional public spaces integrated into everyday urban life. Responding to this evolving role, this study investigates how metropolitan cemeteries in Tokyo are used, perceived, and socially negotiated. Although institutional initiatives [...] Read more.
Once confined to mourning and burial, urban cemeteries are now being reimagined as multifunctional public spaces integrated into everyday urban life. Responding to this evolving role, this study investigates how metropolitan cemeteries in Tokyo are used, perceived, and socially negotiated. Although institutional initiatives have promoted the integration of cemeteries into green infrastructure, empirical research on user behavior, perception, and willingness remains limited—particularly in East Asian contexts. To address this gap, the study combines unstructured user-generated data (Google Maps reviews and images) with structured questionnaire responses to examine behavioral patterns, emotional responses, perceived landscape elements, and behavioral intentions across both urban and suburban cemeteries. Findings reveal that non-commemorative uses—ranging from nature appreciation and cultural engagement to recreational walking—are common in urban cemeteries and are closely associated with positive sentiment and seasonal perception. Factor analysis identifies two dimensions of behavioral intention—active and passive engagement—and reveals group-level differences: commemorative visitors show greater inclination toward active engagement, whereas multi-purpose visitors tend toward passive forms. Urban cemeteries are more frequently associated with non-commemorative behaviors and higher willingness to engage than suburban sites. These results underscore the role of cultural norms, prior experience, and spatial typology in shaping cemetery use, and offer practical insights for managing cemeteries as inclusive and culturally meaningful components of the urban landscape. Full article
Show Figures

Figure 1

20 pages, 1180 KiB  
Article
The Role of Social Influence as a Moderator in Evaluating Factors Affecting the Intention to Use Digital Wallets
by Aivars Spilbergs
Businesses 2025, 5(3), 34; https://doi.org/10.3390/businesses5030034 - 12 Aug 2025
Viewed by 234
Abstract
Digital wallets (DWs) have experienced significant growth in recent years. Still, at the same time, there are substantial differences in the level of adoption of these financial technologies between EU Member States. This research investigates the key factors affecting the intention to use [...] Read more.
Digital wallets (DWs) have experienced significant growth in recent years. Still, at the same time, there are substantial differences in the level of adoption of these financial technologies between EU Member States. This research investigates the key factors affecting the intention to use DWs by analyzing previous research and applying an extended Technology Acceptance Model. In total, 418 respondents from the Baltic states participated in the online survey in 2024. Using partial least squares–structural equation modeling (PLS-SEM), the analysis revealed that the factors studied, such as perceived usefulness, perceived ease of use, social influence, facilitating conditions, and perceived trust, significantly influenced users’ intent to use DWs for financial services. Perceived trust emerged as the strongest predictor, and social influence moderated perceived ease of use and facilitated conditions that impacted users’ intent to adopt DWs. This study provides important insights into the factors that shape users’ intentions to use DWs and the interactions between these factors. In addition, the extension of the TAM strengthened the theoretical framework for the study of DW adoption. Full article
Show Figures

Figure 1

31 pages, 3266 KiB  
Article
Context-Driven Recommendation via Heterogeneous Temporal Modeling and Large Language Model in the Takeout System
by Wei Deng, Dongyi Hu, Zilong Jiang, Peng Zhang and Yong Shi
Systems 2025, 13(8), 682; https://doi.org/10.3390/systems13080682 - 11 Aug 2025
Viewed by 305
Abstract
On food delivery platforms, user decisions are often driven by dynamic contextual factors such as time, intent, and lifestyle patterns. Traditional context-aware recommender systems struggle to capture such implicit signals, especially when user behavior spans heterogeneous long- and short-term patterns. To address this, [...] Read more.
On food delivery platforms, user decisions are often driven by dynamic contextual factors such as time, intent, and lifestyle patterns. Traditional context-aware recommender systems struggle to capture such implicit signals, especially when user behavior spans heterogeneous long- and short-term patterns. To address this, we propose a context-driven recommendation framework that integrates a hybrid sequence modeling architecture with a Large Language Model for post hoc reasoning and reranking. Specifically, the solution tackles several key issues: (1) integration of multimodal features to achieve explicit context fusion through a hybrid fusion strategy; (2) introduction of a context capture layer and a context propagation layer to enable effective encoding of implicit contextual states hidden in the heterogeneous long and short term; (3) cross attention mechanisms to facilitate context retrospection, which allows implicit contexts to be uncovered; and (4) leveraging the reasoning capabilities of DeepSeek-R1 as a post-processing step to perform open knowledge-enhanced reranking. Extensive experiments on a real-world dataset show that our approach significantly outperforms strong baselines in both prediction accuracy and Top-K recommendation quality. Case studies further demonstrate the model’s ability to uncover nuanced, implicit contextual cues—such as family roles and holiday-specific behaviors—making it particularly effective for personalized, dynamic recommendations in high-frequency scenes. Full article
Show Figures

Figure 1

25 pages, 1287 KiB  
Article
A Multi-Dimensional Psychological Model of Driver Takeover Safety in Automated Vehicles: Insights from User Experience and Behavioral Moderators
by Ruiwei Li, Xiangyu Li and Xiaoqing Li
World Electr. Veh. J. 2025, 16(8), 449; https://doi.org/10.3390/wevj16080449 - 7 Aug 2025
Viewed by 334
Abstract
With the rapid adoption of automated driving systems, ensuring safe and efficient driver takeover has become a crucial challenge for road safety. This study introduces a novel psychological framework for understanding and predicting takeover behavior in conditionally automated vehicles, leveraging an extended Theory [...] Read more.
With the rapid adoption of automated driving systems, ensuring safe and efficient driver takeover has become a crucial challenge for road safety. This study introduces a novel psychological framework for understanding and predicting takeover behavior in conditionally automated vehicles, leveraging an extended Theory of Planned Behavior (TPB) model enriched by real-world driver experience. Drawing on survey data from 385 automated driving system users recruited in Shaoguan City, China, through face-to-face questionnaire administration covering various ADS types (ACC, lane-keeping, automatic parking), we demonstrate that driver attitudes, perceived behavioral control, and subjective norms are significant determinants of takeover intention, collectively explaining nearly half of its variance (R2 = 48.7%). Importantly, our analysis uncovers that both intention and perceived behavioral control have robust, direct effects on actual takeover behavior. Crucially, this work is among the first to reveal that individual user characteristics—such as driving experience and ADS (automated driving system) usage frequency—substantially moderate these psychological pathways: experienced or frequent users rely more on perceived control and attitude, while less experienced drivers are more susceptible to social influences. By advancing a multi-dimensional psychological model that integrates personal, social, and experiential moderators, our findings deliver actionable insights for the design of adaptive human–machine interfaces, tailored driver training, and targeted safety interventions in the context of automated driving. Using structural equation modeling with maximum likelihood estimation (χ2/df = 2.25, CFI = 0.941, RMSEA = 0.057), this psychological approach complements traditional engineering models by revealing that takeover behavior variance is explained at 58.3%. Full article
Show Figures

Graphical abstract

21 pages, 767 KiB  
Article
Promoting Sustainable Mobility on Campus: Uncovering the Behavioral Mechanisms Behind Non-Compliant E-Bike Use Among University Students
by Huihua Chen, Yongqi Guo and Lei Li
Sustainability 2025, 17(15), 7147; https://doi.org/10.3390/su17157147 - 7 Aug 2025
Viewed by 311
Abstract
Electric bikes (e-bikes) offer a low-carbon, space-efficient solution for campus mobility, yet their sustainable potential is increasingly challenged by patterns of non-compliant use, including speeding, informal parking, and unauthorized charging. This study integrates the Theory of Planned Behavior (TPB) and the Technology Acceptance [...] Read more.
Electric bikes (e-bikes) offer a low-carbon, space-efficient solution for campus mobility, yet their sustainable potential is increasingly challenged by patterns of non-compliant use, including speeding, informal parking, and unauthorized charging. This study integrates the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to examine the cognitive and contextual factors that shape such behaviors among university students. Drawing on a survey of 408 e-bike users and structural equation modeling, the results show that non-compliance is primarily driven by perceived usefulness, ease of action, and behavioral feasibility, with affective and normative factors playing indirect, reinforcing roles. Importantly, actual behavior is influenced not only by intention but also by students’ perceived capacity to act within low-enforcement environments. These findings highlight the need to align behavioral perceptions with sustainability goals. The study contributes to sustainable mobility governance by clarifying key psychological pathways and offering targeted insights for designing perception-sensitive interventions in campus transport systems. Furthermore, by promoting compliance-oriented campus mobility, this research highlights a pathway toward enhancing the resilience of transport systems through behavioral adaptation within semi-regulated environments. Full article
Show Figures

Figure 1

18 pages, 655 KiB  
Article
Examining Consumer Impulsive Purchase Intention in Virtual AI Streaming: A S-O-R Perspective
by Tao Zhou and Songtao Li
J. Theor. Appl. Electron. Commer. Res. 2025, 20(3), 204; https://doi.org/10.3390/jtaer20030204 - 6 Aug 2025
Viewed by 635
Abstract
Virtual AI-driven streamers have been gradually used in live commerce, and they may affect consumer impulsive purchase intention. Drawing on the stimulus–organism–response (S-O-R) model, this research examined consumer impulsive purchase intention in virtual AI streaming. Based on survey data from 411 predominantly young [...] Read more.
Virtual AI-driven streamers have been gradually used in live commerce, and they may affect consumer impulsive purchase intention. Drawing on the stimulus–organism–response (S-O-R) model, this research examined consumer impulsive purchase intention in virtual AI streaming. Based on survey data from 411 predominantly young and educated virtual AI streaming users recruited through snowball sampling, we found that perceived responsiveness, perceived likeability, perceived expertise, and perceived anthropomorphism of virtual AI streamers are associated with trust and flow experience, both of which predict consumers’ impulsive purchase intentions. The fsQCA identified two paths that lead to impulsive purchase intention. The results imply that live streaming platforms need to engender consumers’ trust and flow experience in order to increase their impulsive purchase intention. Full article
Show Figures

Figure 1

Back to TopTop