Search Results (26)

As robotaxis transition from technological validation to commercial operation, converting first-time tryers into long-term users becomes pivotal for achieving sustainable development. Existing research mainly examines factors affecting initial adoption intention for robotaxis from a net-effect perspective, yet little is known about the factors affecting continuance intention and their nonlinear causal mechanisms. This study integrates the Expectation–Confirmation Model (ECM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to construct a systematic analytical framework and employs fuzzy-set Qualitative Comparative Analysis (fsQCA) for configurational analysis. Using survey data from 327 users in China with actual robotaxi experiences, the findings unveil four factor configurations driving high continuance intention and two causing non-high continuance intention. Regarding the interplay of factors driving high continuance intention, post-usage usefulness, satisfaction, and perceived safety constitute a complementary mechanism, whereas expectation confirmation and personal innovativeness form a substitutive mechanism that depends on the specific patterns of factor configurations. This study contributes to the robotaxi adoption literature by extending the research context to the post-adoption phase, developing a tailored theoretical framework, and applying a configurational approach rooted in complex systems analysis paradigms. The findings offer implications for governments to formulate synergistic policy mixes and for robotaxi companies to design user retention strategies. Full article

The successful integration of autonomous vehicle (AV) technologies into future mobility systems depends not only on technological maturity but also on user acceptance and perceived value. While existing research has identified several demographic determinants of AV acceptance, the role of educational background—particularly differences between humanities and STEM graduates—has received limited attention within the context of user-centred mobility research. This study examines how educational background and gender influence attitudes toward autonomous vehicle technologies using a large-scale survey conducted in Hungary (N = 8663). The analysis combines non-parametric statistical tests with effect size measures, exploratory factor analysis, and structural equation modelling (SEM) to capture both group differences and underlying attitudinal mechanisms. The results indicate no meaningful differences between humanities and STEM graduates in overall acceptance of autonomous vehicles or trust in the technology. Statistically significant differences are observed only in two dimensions: willingness to spend on autonomous driving features and expectations regarding improved travel speed. However, effect size analyses reveal that these differences are negligible in practical terms, indicating substantial overlap in user attitudes. SEM results show that educational background does not directly determine acceptance of autonomous vehicle technologies. Instead, its influence is mediated through three latent attitude dimensions relevant for electric and autonomous mobility adoption: willingness to invest, functional expectations (e.g., time savings and convenience), and safety orientation. Humanities graduates—especially men—exhibit slightly higher financial openness toward autonomous features, whereas STEM graduates place greater emphasis on functional performance. Safety-related attitudes play a central mediating role, with gender-specific patterns. By integrating large-sample effect size interpretation with SEM-based modelling, this study provides a nuanced understanding of user acceptance of autonomous vehicle technologies. The findings suggest that differences between educational groups reflect variations in attitudinal emphasis rather than fundamental divides, offering relevant insights for user-centred AV development, mobility policy design, and communication strategies in the transition toward automated and electric mobility systems. Full article

Cytokine and chemokine profiling is central to understanding inflammatory processes and the mechanisms driving diverse diseases. We introduce InCytokine, an open-source tool for semiquantitative analysis of cytokine and chemokine data generated by protein array technologies. InCytokine features robust and modular image-processing workflows, including automated spot detection, template alignment, normalization, quality control measures, and quantitative intensity summarization to deliver consistent and reliable readouts from profiling assays. We evaluated InCytokine by profiling wild-type microglia, TREM2 knockout, and Alzheimer’s disease-associated TREM2 R47H variant cells in response to lipopolysaccharide and sulfatide exposure. Differential expression analysis revealed unique sulfatide-specific and genotype-specific cytokine signatures in TREM2 variants. We also report an intriguing modulation of DPP4 and a divergent expression pattern of ENA-78 in TREM2 variants in response to lipopolysaccharide and sulfatide treatment. Such distinct expression signatures raise the possibility that TREM2 variants may play a role in modulating inflammatory signaling relevant to cardio-metabolic and Alzheimer’s disease. These signatures were corroborated using transcriptional profiling of the same microglia cells, revealing also a good concordance between protein array and RNA sequencing technologies. Taken together, InCytokine is an interactive, user-friendly web application for rapid, reproducible, and scalable analysis of protein array data, proven to generate meaningful insights for drug and biomarker discovery campaigns in pharmaceutical settings. Full article

Alongside technological innovations, the energy transition requires notable behavioral changes in the residential sector. Smart technologies (STs) can support this shift by promoting transparency, energy-conscious behavior, and automated efficiency gains; their adoption depends on user acceptance. This study investigates the determinants shaping adoption patterns of different STs in German households. Based on a standardized online survey of 284 participants within the SmartQuart project (2022 and 2023), the analysis examined the motivational, sociodemographic, and housing-related factors influencing usage. The investigation was guided by a conceptual framework adapted from the Unified Theory of Acceptance and Use of Technology 2. The results revealed that efficiency- and control-related motives mainly drive the adoption of energy-oriented technologies, such as energy monitoring and home energy management systems. In contrast, indoor air quality monitoring and smart home systems are primarily used to enhance residential comfort. Regression analyses demonstrated that education and building type have a significant impact on energy-oriented technologies, while income, age, and living space influence comfort-oriented applications. The findings highlight the importance of differentiated communication and user-centered technology design. Despite limited generalizability, this study offers relevant insights into the target group-specific adoption dynamics essential for promoting behavioral energy efficiency in the residential sector. Full article

Digital platforms for intangible cultural heritage (ICH) function as vibrant electronic marketplaces, yet they grapple with content overload, high search costs, and under-leveraged social networks of heritage custodians. To address these electronic-commerce challenges, we present GCHS, a custodian-aware, graph-based deep learning model that enhances ICH recommendation by uniting three critical signals: custodians’ social relationships, user interest profiles, and content metadata. Leveraging an attention mechanism, GCHS dynamically prioritizes influential custodians and resharing behaviors to streamline user discovery and engagement. We first characterize ICH-specific propagation patterns, e.g., custodians’ social influence, heterogeneous user interests, and content co-consumption and then encode these factors within a collaborative graph framework. Evaluation on a real-world ICH dataset demonstrates that GCHS delivers improvements in Top-N recommendation accuracy over leading benchmarks and significantly outperforms in terms of next-N sequence prediction. By integrating social, cultural, and transactional dimensions, our approach not only drives more effective digital commerce interactions around heritage content but also supports sustainable cultural dissemination and stakeholder participation. This work advances electronic-commerce research by illustrating how graph-based deep learning can optimize content discovery, personalize user experience, and reinforce community networks in digital heritage ecosystems. Full article

In recent years, the remarkable versatility of large language models (LLMs) has spurred considerable interest in leveraging their capabilities for recommendation systems. Critically, we argue that the intrinsic aptitude of LLMs for modeling sequential patterns and temporal dynamics renders them uniquely suited for sequential recommendation tasks—a foundational premise explored in depth later in this work. This potential, however, is tempered by significant hurdles: a discernible gap exists between the general competencies of conventional LLMs and the specialized needs of recommendation tasks, and their capacity to uncover complex, latent data interrelationships often proves inadequate, potentially undermining recommendation efficacy. To bridge this gap, our approach centers on adapting LLMs through fine-tuning on dedicated recommendation datasets, enhancing task-specific alignment. Further, we present the temporal Integration Enhanced Fine-Tuning of Large Language Models for Sequential Recommendation (TisLLM) framework. TisLLM specifically targets the deeper excavation of implicit associations within recommendation data streams. Its core mechanism involves partitioning sequential user interaction data using temporally defined sliding windows. These chronologically segmented slices are then aggregated to form enriched contextual representations, which subsequently drive the LLM fine-tuning process. This methodology explicitly strengthens the model’s compatibility with the inherently sequential nature of recommendation scenarios. Rigorous evaluation on benchmark datasets provides robust empirical validation, confirming the effectiveness of the TisLLM framework. Full article

Parking management and operation represent a major challenge for both users and administrators, who seek to ensure efficient utilization, accommodate as many demands as possible, and reduce maintenance costs. This paper presents a theoretical model for an integrated IT system designed for parking management and administration. The modeling process involved designing a parking facility using the AutoCAD Vehicle Tracking v25.00.2775 software package, in accordance with current design standards. To simulate system operation, a dedicated Python v2025.12.0 program was developed to assign parking spaces to arriving vehicles based on specific allocation criteria. Three allocation strategies were applied: random allocation, allocation aimed at minimizing the driving distance within the parking lot, and allocation aimed at reducing the walking distance from the assigned space to the destination. The simulation results show that, in the absence of allocation criteria, parking spaces are utilized in a quasi-uniform manner. The calculated values of variance and standard deviation are significantly lower in this case, increasing as allocation restrictions are introduced, but then returning to reduced values as the occupancy rate grows, since under intensive use the potential for controlled allocation decreases. The relationship between the number of allocations of each parking space and the applied allocation strategies was examined using Pearson and Spearman correlation coefficients. The results reveal a direct linear dependence under moderate demand and an inverse dependence under high demand—patterns consistent with situations observed in practice. The proposed software application provides a practical tool for effective parking management, contributing to the rational use of parking spaces, reduced travel distances within the facility, lower fuel consumption, and consequently, reduced pollution. Full article

City-building games are very popular, on both digital and analog platforms. However, analog games named after cities are a tradition in modern board games. These games, resulting from the game design innovations of the last decades, are engaging a growing number of players worldwide. We wanted to understand what drives players and game designers to develop games that have a direct connection with cities or urban matters. We intend to explore them and identify their design patterns in order to support game-based planning support tools, mostly for participatory and collaborative planning. Planners have been using game-based processes, and analog games seem to be the easier solution. We analyzed the top-ranking city-building games (CBGs) and games named after cities (GNACs) from Board Game Geek (BGG) and then ran a survey with BGG users (n = 102). The results show that GNACs do not deeply portray cities but tend to focus on a specific dimension. CBGs are better at mimicking an urban planning process but with many simplifications. Despite this, mastering the design of these two types of games is useful for planners who wish to use game-based planning processes. However, the engagement level might depend on the target audience. Full article

Micromobility vehicles, e-scooters and e–bicycles in particular, gain an increasing popularity but also receive criticism, mainly due to road safety issues and their carbon footprint, particularly in relation to their Li-ion batteries. Available field data are not sufficient to explore those issues. Important input variables, such as riders’ reaction time, the impact of human factors on riders’ safety, battery performance degradation with time, remain unknown. This paper presents the design, development, initial calibration and validation of two novel driving simulators, one for an e-scooter and one, for an e-bicycle. The simulators are already operational and used to acquire new knowledge on driving behavior and battery performance. By enabling a better understanding of e-vehicle performance and safety, these simulators contribute to reducing the environmental impact of micromobility by optimizing battery usage and improving vehicle design for sustainability. The paper describes the overall configuration and the main technical specifications of both simulators and provides a thorough description of all their mechanical and electromechanical components. It documents the initial calibration process before launching the experiments and presents the validation methodology with the participation of over 100 users. The outcomes of future experiments are expected to be beneficial to (i) researchers who will gain new insights on e-vehicle performance, (ii) users, enabling them to make informed decisions on vehicle choice and riding patterns, (iii) urban planners on improving urban infrastructure design, (iv) vehicle manufacturers on identifying customer needs and enhancing vehicle design for sustainability, and (v) Public Authorities on adjusting vehicle and infrastructure specifications to reduce the carbon footprint of urban mobility. Full article

This study investigates user experiences of interactions with two types of robots: Pepper, a social humanoid robot, and Double 3, a self-driving telepresence robot. Conducted in a controlled setting with a specific participant group, this research aims to understand how the design and functionality of these robots influence user perception, interaction patterns, and emotional responses. The findings reveal diverse participant reactions, highlighting the importance of adaptability, effective communication, autonomy, and perceived credibility in robot design. Participants showed mixed responses to human-like emotional displays and expressed a desire for robots capable of more nuanced and reliable behaviors. Trust in robots was influenced by their perceived functionality and reliability. Despite limitations in sample size, the study provides insights into the ethical and social considerations of integrating AI in public and professional spaces, offering guidance for enhancing user-centered designs and expanding applications for social and telepresence robots in society. Full article

In this paper, we analyze the Reddit social network with the goal of identifying “highly interconnected” subreddits. Intuitively, a subreddit is highly interconnected if the users in the subreddit interact a lot with users from other subreddits in the Reddit ecosystem. To identify the highly interconnected subreddits, we used the communication patterns of the users on the Reddit platform. We definde an “interconnectedness score” that was obtained from user interactions across subreddits. This score was used to identify the highly interconnected subreddits. We also leveraged the interactions among users within the subreddits to identify implicit leader–follower relationships within them. Intuitively, an implicit leader in a subreddit is someone who receives a lot of attention from other users, who are the followers. We inferred the implicit leaders using only the responses they received on their posts from other users in the subreddit. Finally, we studied the role played by these implicit leaders within the interconnected subreddits using the idea of a “leaderness score”. For the analysis, we used data obtained from Reddit in 2022 with a custom-built crawler. We analyzed a total of 125,000 subreddits for this work and identified the group of highly interconnected subreddits using the idea of the interconnectedness score. We manually evaluated the content of the posts on the identified interconnected subreddits in order to understand the nature of these subreddits. Our analysis showed that the highly interconnected subreddits discuss content considered to be “not safe/suitable for work” (NSFW). We also observed that though these subreddits were highly interconnected among themselves, they were sparsely connected with other non-NSFW subreddits. Furthermore, we found that the implicit leaders in these subreddits drove majority of the conversations in these groups. These results are socially significant as they can be used to make online social networks safe for the underage population. Thus, our results can be used for enforcing age-based restrictions on access to these NSFW subreddits. Finally, our results also open up the possibility of moderating the content on these subreddits by enforcing content moderation rules on the implicit leaders who drive the conversation in these groups. Finally, though these results are specific to Reddit, the insights obtained from this analysis can be used for analyzing other large-scale online social networks with similar goals to this study. Full article

An Open Government Data (OGD) portal assessment is necessary to track and monitor the progress of the OGD initiative and to drive improvement. Although OGD benchmarks typically focus on assessing and ranking OGD portals, few have been developed specifically for internal process improvement within the portal. This paper proposes a gap analysis framework to support the Plan–Do–Check–Act (PDCA) cycle to guide OGD portal improvement. The framework adopted the Importance–Performance Analysis (IPA) to identify gaps in an OGD portal. The analysis measured the performance and importance of an OGD portal based on data provision and consumption activities. Several factors related to data provision and consumption activities are examined, including dataset creation, updates, views, searches, high-value datasets, resource formats, and user data requests. Gap analysis assessment results can help to identify the current situations of different areas on the portal and their gaps in achieving the objectives. A case study of the Data.go.th portal was conducted to exemplify and validate the framework’s adoption. The analysis results of the case study revealed existing patterns of relationships between data provision and consumption activities that can guide the improvement of similar OGD portals. Full article

Social media platforms play a significant role in facilitating business decision making, especially in the context of emerging technologies. Such platforms offer a rich source of data from a global audience, which can provide organisations with insights into market trends, consumer behaviour, and attitudes towards specific technologies, as well as monitoring competitor activity. In the context of social media, such insights are conceptualised as immediate and real-time behavioural responses measured by likes, comments, and shares. To monitor such metrics, social media platforms have introduced tools that allow users to analyse and track the performance of their posts and understand their audience. However, the existing tools often overlook the impact of contextual features such as sentiment, URL inclusion, and specific word use. This paper presents a data-driven framework to identify and quantify the influence of such features on the visibility and impact of technology-related tweets. The quantitative analysis from statistical modelling reveals that certain content-based features, like the number of words and pronouns used, positively correlate with the impressions of tweets, with increases of up to 2.8%. Conversely, features such as the excessive use of hashtags, verbs, and complex sentences were found to decrease impressions significantly, with a notable reduction of 8.6% associated with tweets containing numerous trailing characters. Moreover, the study shows that tweets expressing negative sentiments tend to be more impressionable, likely due to a negativity bias that elicits stronger emotional responses and drives higher engagement and virality. Additionally, the sentiment associated with specific technologies also played a crucial role; positive sentiments linked to beneficial technologies like data science or machine learning significantly boosted impressions, while similar sentiments towards negatively viewed technologies like cyber threats reduced them. The inclusion of URLs in tweets also had a mixed impact on impressions—enhancing engagement for general technology topics, but reducing it for sensitive subjects due to potential concerns over link safety. These findings underscore the importance of a strategic approach to social media content creation, emphasising the need for businesses to align their communication strategies, such as responding to shifts in user behaviours, new demands, and emerging uncertainties, with dynamic user engagement patterns. Full article

Sustainable food consumption is critical for addressing global environmental challenges and promoting health and ethical practices. Understanding what drives sustainable food choices among younger generations, particularly Generation Z, is essential for developing effective strategies to encourage sustainable consumption patterns. Using the Theory of Planned Behavior as the theoretical framework, this study aims to explore how the variables of the theory (personal attitude, subjective norms, and perceived behavioral control), along with consumer knowledge, trust, and health concerns, affect Generation Z’s intentions to buy sustainable food. The research was carried out in Poland via the online interview method (CAWI), with 438 users ranging between the ages 18 and 27. The results show that attitudes and knowledge are significant predictors of sustainable food consumption among Generation Z, while subjective norms, perceived behavioral control, health consciousness, and trust do not significantly affect purchase intentions. This research underscores the importance of educational campaigns and marketing strategies that enhance consumer knowledge and shape positive attitudes towards sustainable food. These insights offer valuable implications for policymakers, marketers, and educators aiming to encourage sustainable practices. Understanding the drivers of Generation Z’s sustainable food consumption behaviors can provide valuable insights for developing effective strategies to promote sustainable consumption patterns. This study adds to the body of knowledge on sustainable food consumption by highlighting the specific factors that drive Generation Z’s purchasing intentions. Full article

Small-format mobility services have been introduced in many cities to promote sustainable urban development. In some cities, these services are primarily seen as entertainment rather than significant transport modes. Research has studied the roles of experiential/hedonic and functional/instrumental motivations in users’ adoption intent for such services. However, there is still a limited understanding of how actual spatial experiences of mobility travels shape travel behaviors. This study explores the role of spatial experience in mobility travels. Specifically, the research question revolves around whether better spatial knowledge leads to better spatial experiences, thereby satisfying users’ functional/instrumental and experiential/hedonic values for mobility trips. Additionally, we examine how spatial knowledge affects travel behaviors regarding trip chaining and vehicle charging. To assess road users’ spatial knowledge, we use sketch maps to examine changes after three months of using battery-sharing two-wheelers. A mixed-methods approach and multiple data sources are employed to provide deeper insights, including sketch maps, questionnaire surveys on attitudes, and a panel data analysis on activity-travel patterns. The results indicate that spatial experience significantly influences perceived values and, consequently, travel behaviors. Improved knowledge leads to greater satisfaction with mobility travel. Furthermore, an interaction effect is found between cognitive distance and cognitive direction concerning users’ satisfaction with the driving range and charging issues of electric vehicles. Full article