Search Results (36)

Customers’ repurchase intention is a key driver of sustained profitability for companies. However, the influencing factors of customers’ repurchase intention and their direct mechanisms of action are not yet clear. This study empirically examines the relationships among intrinsic quality, perceptual quality, brand equity, customer satisfaction, online word-of-mouth, and customers’ repurchase intention using a structural equation model. A customer repurchase intention model is constructed with customer satisfaction as the mediating variable and online word-of-mouth as the moderating variable. The empirical results show that intrinsic quality, perceptual quality, and brand equity have a significant impact on customer satisfaction. Notably, intrinsic quality exerts an indirect effect on customers’ repurchase intention primarily through customer satisfaction, with no significant direct impact, while perceptual quality and brand equity have significant direct effects on repurchase intention. Customer satisfaction plays a partial mediating role between intrinsic quality, perceptual quality, brand equity, and repurchase intention. Online word-of-mouth has a significant moderating effect on the relationship between customer satisfaction and customers’ repurchase intention. These findings provide actionable insights for smartphone enterprises to optimize product development, brand management, and online reputation strategies, thereby enhancing customer loyalty and market competitiveness. Full article

Mobility-as-a-Service (MaaS) represents a transformative shift in transportation, enabling users to plan, book, and pay for diverse mobility services via a unified digital platform. While previous research has explored factors influencing MaaS adoption, few studies have addressed users’ perspectives, particularly concerning data privacy and cyber security. To address this gap, we conducted an online survey with 320 UK-based participants recruited via Prolific. This study examined psychological, demographic, and perceptual factors influencing individuals’ willingness to adopt MaaS, focusing on cyber security and privacy attitudes, as well as perceived benefits and costs. The results of a hierarchical linear regression model revealed that trust in how commercial websites manage personal data positively influenced willingness to use MaaS, highlighting the indirect role of privacy and security concerns. However, when additional predictors were included, this effect diminished, and perceptions of benefits and costs emerged as the primary drivers of MaaS adoption, with the model explaining 54.5% of variance. These findings suggest that privacy concerns are outweighed by users’ cost–benefit evaluations. The minimal role of trust and security concerns underscores the need for MaaS providers to proactively promote cyber security awareness, build user trust, and collaborate with researchers and policymakers to ensure ethical and secure MaaS deployment. Full article

The spatial distribution mechanism of industrial agglomeration has long been a central topic in urban economic geography. With the increasing availability of street view imagery and built environment data, effectively integrating multi-source spatial information to identify key drivers of firm clustering has become a pressing research challenge. Taking Shanghai as a case study, this paper constructs a street-level Built Environment (BE) database and proposes an interpretable spatial analysis framework that integrates SHapley Additive exPlanations with Multi-Scale Geographically Weighted Regression. The findings reveal that: (1) building morphology, streetscape characteristics, and perceived greenness significantly influence firm agglomeration, exhibiting nonlinear threshold effects; (2) spatial heterogeneity is evident in the underlying mechanisms, with localized trade-offs between morphological and perceptual factors; and (3) BE features are as important as macroeconomic factors in shaping agglomeration patterns, with notable interaction effects across space, while streetscape perception variables play a relatively secondary role. This study advances the understanding of how micro-scale built environments shape industrial spatial structures and offers both theoretical and empirical support for optimizing urban industrial layouts and promoting high-quality regional economic development. Full article

The formation mechanism of Virtual Reality (VR) Immersive Experience (VRIE) is notably complex; this study aimed to dynamically decode its underlying drivers by innovatively integrating Flow Theory and Privacy Calculus Theory, focusing on Perceptual-Interactive Fidelity (PIF), Consumer Willingness to Immerse in Technology (CWTI), and the applicability of Loss Aversion Theory. To achieve this, we analyzed approximately 30,000 user reviews from Amazon using Latent Semantic Analysis (LSA) and regression analysis. The findings reveal that user attention’s impact on VRIE is non-linear, suggesting an optimal threshold, and confirm PIF as a central influencing mechanism; furthermore, CWTI significantly moderates users’ privacy calculus, thereby affecting VRIE, while Loss Aversion Theory showed limited explanatory power in the VR context. These results provide a deeper understanding of VR user behavior, offering significant theoretical guidance and practical implications for future VR system design, particularly in strategically balancing user cognition, PIF, privacy concerns, and individual willingness. Full article

Autonomous vehicles are a promising solution to traffic congestion, air pollution, accidents, wasted time, and resources. However, remote driver intervention may be necessary in extreme situations to ensure safe roadside parking or complete remote takeover. In these cases, high-quality real-time video streaming is crucial for remote driving. In a preliminary study, we presented a region of interest (ROI) High-Efficiency Video Coding (HEVC) method where the image was segmented into two categories: ROI and background. This involved allocating more bandwidth to the ROI, which yielded an improvement in the visibility of classes essential for driving while transmitting the background at a lower quality. However, migrating the bandwidth to the large ROI portion of the image did not substantially improve the quality of traffic signs and lights. This study proposes a method that categorizes ROIs into three tiers: background, weak ROI, and strong ROI. To evaluate this approach, we utilized a photo-realistic driving scenario database created with the Cognata self-driving car simulation platform. We used semantic segmentation to categorize the compression quality of a Coding Tree Unit (CTU) according to its pixel classes. A background CTU contains only sky, trees, vegetation, or building classes. Essentials for remote driving include classes such as pedestrians, road marks, and cars. Difficult-to-recognize classes, such as traffic signs (especially textual ones) and traffic lights, are categorized as a strong ROI. We applied thresholds to determine whether the number of pixels in a CTU of a particular category was sufficient to classify it as a strong or weak ROI and then allocated bandwidth accordingly. Our results demonstrate that this multi-category ROI compression method significantly enhances the perceptual quality of traffic signs (especially textual ones) and traffic lights by up to 5.5 dB compared to a simpler two-category (background/foreground) partition. This improvement in critical areas is achieved by reducing the fidelity of less critical background elements, while the visual quality of other essential driving-related classes (weak ROI) is at least maintained. Full article

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

Grounded in information processing theory, this study explores how ecotourism decisions were formed within the rural district of Akdağmadeni (Türkiye), integrating both heuristic and systematic decision-making processes. The research adopts a two-phase mixed-methods design: First, it employs a survey-based factorial analysis involving 383 participants to examine preferences for nature-based activities such as trekking, cycling, and cultural tourism. Second, it uses in-depth interviews to investigate participants’ strategic evaluations of local landscape and heritage assets. The results reveal that individuals flexibly switch between intuitive and analytical judgments based on contextual factors. Key decision drivers identified include alignment with local development, ecological integrity, and socioeconomic contribution. This dual-process insight is operationalized through a novel “feature-based evaluation model” that synthesizes landscape identity values with cognitive-perceptual cues, providing a new lens for assessing geoheritage-based tourism behavior. It was determined that participants used both intuitive and systematic information processing strategies in their decision-making processes, and factors such as harmony with nature, economic contribution, and local identity were found to affect preferences. The study draws attention to the need to develop sustainable tourism policies, raise public awareness, and support infrastructure investments, and provides a road map for the effective use of the region’s ecotourism potential. Full article

Vehicle color coatings have long been recognized as a factor influencing road safety, particularly regarding their impact on speed perception and crash risk. This study aims to examine how different vehicle color coatings affect drivers’ perception of speed under nighttime driving conditions, with a specific focus on sustainability and visibility. A controlled laboratory experiment was conducted using a driving simulator to replicate realistic night traffic scenarios. A total of 161 participants evaluated passenger vehicles in four distinct color treatments, white (high-reflective paint), yellow (matte safety film), blue (glossy metallic finish), and black (low-reflective coating), at two speeds: 30 km/h and 50 km/h. Participants’ perceived speeds were collected and analyzed using standardized statistical methods. Results indicated a consistent pattern: speed was overestimated at 30 km/h and underestimated at 50 km/h across all vehicle colors. Lighter-colored vehicles (white and yellow) were perceived as moving faster than darker-colored vehicles (blue and black), with significant differences between black and yellow (30 km/h), yellow and blue (30 km/h), and black and white (50 km/h). Additionally, female participants tended to estimate higher speeds than male participants across most conditions. Other individual factors, such as place of residence, driver’s license type, driving experience, and frequency of driving, also showed measurable effects on speed perception. By using a simulator and accounting for diverse demographic characteristics, the study highlights how perceptual biases related to vehicle color can influence driver behavior. These findings emphasize the importance of considering vehicle color in traffic safety strategies, including driver education, vehicle design, and policy development aimed at reducing crash risk. Full article

With the increasing complexity of urban roads and rising traffic flow, traffic safety has become a critical societal concern. Current research primarily addresses drivers’ attention, reaction speed, and perceptual abilities, but comprehensive assessments of cognitive abilities in complex traffic environments are lacking. This study, grounded in cognitive science and neuropsychology, identifies and quantitatively evaluates ten cognitive components related to driving decision-making, execution, and psychological states by analyzing video footage of drivers’ actions. Physiological data (e.g., Electrocardiogram (ECG), Electrodermal Activity (EDA)) and non-physiological data (e.g., Eye Tracking (ET)) are collected from simulated driving scenarios. A dual-branch Transformer network model is developed to extract temporal features from multimodal data, integrating these features through a weight adjustment strategy to predict driving-related cognitive abilities. Experiments on a multimodal driving dataset from the Computational Physiology Laboratory at the University of Houston, USA, yield an Accuracy (ACC) of 0.9908 and an F1-score of 0.9832, confirming the model’s effectiveness. This method effectively combines scale measurements and driving behavior under secondary tasks to assess cognitive abilities, providing a novel approach for driving risk assessment and traffic safety strategy development. Full article

The acoustic environment of urban forests is indispensable for urban residents’ nature-based recreation opportunities and experience of green spaces, and the perceptual and physical sound features in time and space serve as determinants during this process. However, their spatiotemporal variation mechanisms and influential landscape characteristics are still underexplored in urban forests. Thus, this study aims to explore the spatiotemporal variability of perceptual and physical sound features and their relationship with landscape characteristics in urban forests. For this purpose, we measured perceptual sound features using the indicators of the sound harmonious degree (SHD) and soundscape pleasantness and eventfulness. The physical acoustic features were determined using sound-level parameters for measuring the sound level intensity (L_Aeq, L₁₀, L₉₀) and fluctuation (L_10–90). Perceptual and physical sound data collection was based on on-site questionnaire surveys and acoustic instrument measurements, respectively. The landscape characteristics were classified using the principal components of four main categories, including the terrain, area proportion of land cover types, distance to land cover types, and landscape patterns. The results showcase that significant spatiotemporal variation was found in most perceptual and physical sound features, whereas soundscape pleasantness and eventfulness did not vary significantly across time. In general, the variabilities of both perceptual and physical sound features were affected more by the types of spatial functions than by diurnal patterns. Human activities that generate sounds (e.g., hawking, playing, and exercise) may be the key drivers for spatiotemporal changes in physical acoustic features. The components of landscape patterns, including landscape structural diversity and shape complexity persistently, affected specific sound features in all periods. However, no landscape component had persistent cross-spatial influences on the sound features. This study offers critical insights into the spatiotemporal patterns of the acoustic environment and its relationship with landscape characteristics in urban forests. The findings underscore the practical importance and implications of integrating acoustic considerations into urban forest management. By providing a scientific foundation, these results can usefully inform dynamic resource management, functional zoning optimization, and sustainable landscape development in urban forests. Full article

Mission-based routes for various occupations play a crucial role in occupational driver safety, with accident causes varying according to specific mission requirements. This study focuses on the development of a system to address driver distraction among law enforcement officers by optimizing the Driver–Vehicle Interface (DVI). Poorly designed DVIs in law enforcement vehicles, often fitted with aftermarket police equipment, can lead to perceptual-motor problems such as obstructed vision, difficulty reaching controls, and operational errors, resulting in driver distraction. To mitigate these issues, we developed a driving simulation platform specifically for law enforcement vehicles. The development process involved the selection and placement of sensors to monitor driver behavior and interaction with equipment. Key criteria for sensor selection included accuracy, reliability, and the ability to integrate seamlessly with existing vehicle systems. Sensor positions were strategically located based on previous ergonomic studies and digital human modeling to ensure comprehensive monitoring without obstructing the driver’s field of view or access to controls. Our system incorporates sensors positioned on the dashboard, steering wheel, and critical control interfaces, providing real-time data on driver interactions with the vehicle equipment. A supervised machine learning-based prediction model was devised to evaluate the driver’s level of distraction. The configured placement and integration of sensors should be further studied to ensure the updated DVI reduces driver distraction and supports safer mission-based driving operations. Full article

This paper investigates the impact of four demographic variables and four perceptual drivers identified through a review of the existing literature on adopting subscription-based e-commerce models. Seven hypotheses were tested on a convenience sample of 202 respondents from Croatia. Significant differences in subscription model acceptance were observed across age groups, while education level, employment status, and disposable income showed no significant relation to subscription model adoption in Croatia, although studies in other countries have indicated otherwise. This study also examined four factors (perceived trust, risk, usefulness, and ease of use) described with 21 critical success dimensions. The results showed positive relationships with perceived trust, usefulness, and ease of use and a negative relationship with perceived risk. Enhancing trust, usefulness, and ease of use while reducing perceived risks can boost subscription-based e-commerce adoption. Significant differences in perceived trust, risk, and usefulness were found between users of multiple products/services and non-users but not in perceived ease of use. These findings provide valuable insights for future scientific research on subscription-based models, given their growing popularity in e-commerce and the limited existing research. Additionally, this paper offers practical implications for businesses by enhancing their understanding of customers and the Croatian e-commerce market and by proposing innovative strategies and promotional approaches based on the research outcomes. Full article

Distracted driving significantly affects the efficiency and safety of traffic flow. Modeling distracted driving behavior in microscopic traffic flow simulation is essential for understanding its critical impacts on traffic flow. However, due to the influence of various external factors and the considerable uncertainties in behavior characteristics, modeling distracted driving behavior remains a challenge. This study proposed a model which incorporates distraction features into the microscopic traffic flow model to simulate distracted driving behavior. Specifically, the study first examines the characteristics of distracted driving, including the intervals and durations of distraction events, as well as the patterns and environments of distraction. It then introduces distraction parameters into the Intelligent Driver Model (IDM), including reaction time delays and perception deviations in both speed difference and following distance. These parameters are quantified by probabilistic distributions to reflect the uncertainty and individual differences in driving behavior. The model is calibrated and validated using 772 distracted following events from the Shanghai Naturalistic Driving Study (SH-NDS) data. Three patterns of distraction (excessive, moderate, mild) are distinguished and modeled separately. The results show that the model’s accuracy surpasses that of the IDM under various road types and traffic volumes, with an average improvement in model accuracy of about 11.30% on expressways with high traffic volume, 4.54% on expressways with low traffic volume, and 4.46% on surface roads. Meanwhile, the model can effectively simulate the variations in reaction times and perceptual deviations in both speed and following distance for different distraction modes at the individual level, maintaining consistency with reality. Finally, the study simulates distracted driving behavior under different road environments and traffic volumes to explore the impact of distracted driving on traffic flow. The simulation results indicate that an increase in the proportion of distraction reduces the efficiency and safety of traffic flow, which is consistent with real-world observations. Since the model considers human distraction factors, it can generate more dangerous driving scenarios in simulations, which holds significant importance for safety-related research. The findings from this study are expected to be helpful for understanding distracted driving behavior and mitigate its negative influence on the efficiency and safety of traffic flow. Full article

The aim of this study was to assess whether road risk, road hazard perception skills, and attitudes towards risky driving are significant psychological antecedents of different driving styles. The study sample consisted of 446 non-professional drivers (with an average age of 32.6 years) and 200 professional drivers (with an average age of 47.7 years) from Lithuania. The study questionnaire included demographic questions, a multidimensional driving style assessment, a Lithuanian version of a hazard prediction test, a risk perception scale, and a subjective evaluation of driving competenc3 (perceptual, motor, and safety driving skills), as well as an evaluation of attitudes towards risky driving. The results confirmed that cognitive factors, together with attitudes towards driving and demographic factors, are important for understanding the origins of different driving styles. Cognitive factors like hazard perception and risk perception skills were found to be significant predictors of anxious, careless, and angry driving styles, mainly for professional drivers. Attitudes towards risky driving together with demographic characteristics and cognitive factors were found to important in predicting anxious, careless, and angry driving styles among professional as well as non-professional drivers. The subjective evaluation of driving competence (driving skills) was found to be crucial in predicting all four driving styles, but only in the non-professional drivers sample. Full article

Risky driving is a major factor in traffic incidents, necessitating constant monitoring and prevention through Intelligent Transportation Systems (ITS). Despite recent progress, a lack of suitable data for detecting risky driving in traffic surveillance settings remains a significant challenge. To address this issue, Bayonet-Drivers, a pioneering benchmark for risky driving detection, is proposed. The unique challenge posed by Bayonet-Drivers arises from the nature of the original data obtained from intelligent monitoring and recording systems, rather than in-vehicle cameras. Bayonet-Drivers encompasses a broad spectrum of challenging scenarios, thereby enhancing the resilience and generalizability of algorithms for detecting risky driving. Further, to address the scarcity of labeled data without compromising detection accuracy, a novel semi-supervised network architecture, named DGMB-Net, is proposed. Within DGMB-Net, an enhanced semi-supervised method founded on a teacher–student model is introduced, aiming at bypassing the time-consuming and labor-intensive tasks associated with data labeling. Additionally, DGMB-Net has engineered an Adaptive Perceptual Learning (APL) Module and a Hierarchical Feature Pyramid Network (HFPN) to amplify spatial perception capabilities and amalgamate features at varying scales and levels, thus boosting detection precision. Extensive experiments on widely utilized datasets, including the State Farm dataset and Bayonet-Drivers, demonstrated the remarkable performance of the proposed DGMB-Net. Full article