Search Results (2,135)

Global navigation satellite systems (GNSSs) are essential for navigation in aviation, transportation, and autonomous systems, yet they remain vulnerable to intentional interference such as jamming and spoofing. Unlike prior studies that primarily focus on positioning error, this work emphasizes acquisition-phase behavior, analyzing the impact of interference on time-to-first-fix (TTFF) and post-attack reacquisition time. A controlled and repeatable laboratory testbed based on software-defined radio (SDR) was developed to emulate Global Positioning System (GPS) L1 and Galileo E1 signals under multiple interference scenarios, including narrowband jamming, static spoofing, and dynamic spoofing. Five commercial GNSS receivers were evaluated under identical conditions. The results show that jamming causes an immediate loss of positioning capability, reducing the empirical navigation-fix probability to near zero and significantly increasing reacquisition time, with recovery-phase empirical fix probabilities ranging from 0.062 to 0.991 depending on receiver class. In contrast, spoofing maintains high attack-phase empirical navigation-fix probabilities ranging from 0.730 to 0.907 while introducing persistent and undetected errors. Static position spoofing was found to produce position offsets that persisted into the recovery phase, delaying the return to the authentic navigation solution. For most receivers, however, correct positioning was restored within the observation window. Multi-constellation spoofing further increases attack effectiveness, raising fix continuity by more than 0.15 compared to single-constellation cases. Multi-band receivers demonstrate increased resilience by delaying spoof acceptance by more than 4 min in extended scenarios, rather than preventing it entirely. The proposed methodology enables reproducible evaluation of GNSS receiver robustness and demonstrates that navigation-fix continuity alone is not a reliable indicator of navigation integrity during spoofing attacks. Overall, the results demonstrate that navigation-fix continuity alone cannot be regarded as a reliable indicator of navigation integrity and highlight the importance of complementary integrity-monitoring mechanisms for GNSS-dependent systems. The reported observations were obtained under controlled laboratory conditions and should be interpreted within the context of the adopted experimental methodology rather than as a direct representation of operational performance in real-world environments. Full article

Interest in electric vehicles (EVs) is rising as the world shifts toward sustainable transportation, yet consumer adoption remains highly uneven, particularly in developing countries. This study examines how five dimensions of consumption value—functional, social, emotional, novelty, and conditional—influence consumers’ purchase intention toward EVs in Indonesia, while also testing the moderating role of infrastructure readiness. Using a quantitative approach, data were collected through an online survey with purposive sampling, yielding 455 valid responses. Partial least squares structural equation modeling (PLS-SEM) was applied to assess the measurement and structural models. The results reveal that functional, social, emotional, and conditional values significantly influence consumers’ purchase intention toward EVs, whereas novelty value has no significant effect. Infrastructure readiness also significantly moderates most consumption values, with negative coefficients indicating that limited charging access and inadequate maintenance support weaken the positive impact of consumer values on EV adoption. The findings show that although consumers value performance, social image, emotional appeal, and situational factors, poor charging infrastructure hinders purchase intention toward EVs. This study contributes to EV adoption literature by integrating consumption value theory with infrastructure readiness as a moderator. The results emphasize that developing charging infrastructure, expanding service availability, and maintaining supportive government policies are critical steps for accelerating EV adoption in emerging markets. Full article

Digital agricultural technologies can support the living conservation and sustainable development of agricultural heritage sites, but farmers’ adoption behavior in these contexts remains underexplored. This study examines the endogenous drivers and external contextual conditions shaping such adoption. Integrating the Theory of Planned Behavior (TPB), Perceived Value Theory, and Context Effects Theory, this study develops an “external context–endogenous driving factors–adoption intention–adoption behavior” framework. Based on 399 household surveys from the Qitai Dryland Farming System in Xinjiang, the data were analyzed using PLS-SEM and multi-group analysis. The results show that subjective norms, perceived behavioral control, behavioral attitude, and perceived value positively affect adoption intention, with behavioral attitude having the strongest effect. Adoption intention promotes adoption behavior and fully mediates the perceived value–behavior relationship. Technical training, economic incentives, and policy regulations strengthen key internal driving paths, whereas publicity and education enhance attitude-based intention formation but may weaken perceived behavioral control when disseminated information diverges from farming realities. Adoption pathways differ across livelihood types, supporting differentiated promotion strategies. This study is among the first to apply PLS-SEM and multi-group analysis to farmers’ adoption of digital technologies in agricultural heritage sites, providing empirical evidence for integrating digital agriculture, heritage conservation, and sustainable development. Full article

Growing environmental concerns have intensified interest in sustainable packaging; however, consumer adoption of beverages with environmentally friendly packaging remains relatively low in developing markets. This study investigates the determinants of consumers’ purchase intentions toward such beverages in Central Vietnam using the extended Theory of Planned Behaviour (TPB), integrated with the Stimulus–Organism–Response (S-O-R) framework. Data were collected from 475 consumers and analysed using structural equation modelling (SEM). The results indicate low consumer familiarity with eco-friendly packaging labels, such as the Forest Stewardship Council (FSC), Green Dot Logo, and Compostable Logos, and only moderate adoption of sustainable packaging materials, with particularly low willingness to purchase beverages in fully recycled packaging. SEM findings show that attitude, perceived behavioural control, and perceived consumer effectiveness are significant direct predictors of purchase intention. Subjective norms and perceived environmental packaging knowledge show positive but marginal direct effects, whereas packaging functions have no significant direct effect. Attitude and perceived behavioural control act as key mediators. The study highlights the need to strengthen consumer awareness, improve recognition of eco-labels, and enhance social and psychological support to promote the adoption of environmentally friendly beverage packaging. Full article

Residential buildings have been responsible for the increasing percentage of final energy consumption in Libya, which is mainly due to the cooling of buildings in hot arid climatic conditions, but there is still a constant discrepancy between the projected and actual energy performance. This review critically synthesises the available literature to find out the knowledge gaps underlying residential energy overconsumption, and it concentrates on the systemic loss of contact between the architectural design intent, material and envelope performance, construction execution, and occupant behaviour throughout the building lifecycle. The literature employed to complete the study is peer-reviewed journals, conference papers, and technical reports concerning Libya and similar hot-arid and Mediterranean settings, which are arranged into four overlapping areas, namely, design practices, materials and construction quality, regulatory enforcement, and occupant energy use patterns. The results show that the cumulative and reinforcing inefficiencies are caused by the lack of adoption of climate-sensitive designs, inattentive view of thermal material properties, ineffective control over the quality of construction, ineffective regulation enforcement, and neglect of the occupant behaviour in performance analysis, which contribute to the increase in cooling loads and expansion of the performance gap. Building a coherent conceptual structure using disjointed evidence, the review offers recommendations that can be applied in practice by architects, engineers, policymakers and housing stakeholders, highlighting the importance of lifecycle-based design methodologies, improved governance and performance energy strategies with behavioural elements in Libyan residential buildings. Full article

Despite increasing scholarly attention to short-video platforms as tourism communication tools, the mechanism through which sustainability-oriented persuasive cues shape Sustainable Tourism Intention remains insufficiently theorized. This article integrates the Elaboration Likelihood Model (ELM) and the Stimulus–Organism–Response (SOR) framework to explain how short-video persuasive cues may influence Sustainable Tourism Intention through Environmental Attitude. Methodologically, the paper adopts a theory synthesis and conceptual integration approach to connect insights from short-video persuasion, sustainability communication, environmental attitude research, and tourism intention studies. The framework treats short-video sustainability communication as a cue-dense persuasive environment, identifies key cue families, organizes selected cues according to likely central/peripheral processing tendencies, and positions Environmental Attitude as the sustainability-specific organismic evaluative state through which cue exposure translates into intention. It proposes that both central route-oriented and peripheral route-oriented cues may strengthen Environmental Attitude and may thereby shape Sustainable Tourism Intention, although they do so through different processing tendencies. The article concludes that integrating ELM and SOR provides a clearer and more bounded explanation of short-video sustainability persuasion and offers a structured basis for future empirical research. Full article

Consumer Internet of Things (IoT) devices are increasingly central to Industry 4.0 commercialisation, but repeat demand depends on more than technical connectivity. This study examines how product design, ease of use, product quality and social status influence repurchase intention for consumer IoT devices in New Zealand, and whether trust and customer satisfaction mediate these relationships. A quantitative cross-sectional survey of 93 New Zealand IoT consumers was analysed using reliability testing, exploratory factor assessment, Pearson correlation, multiple regression and Hayes PROCESS Model 4 mediation. Trust and customer satisfaction jointly explained 63.3% of the adjusted variance in repurchase intention. Product design and product quality were significant direct predictors of both trust and customer satisfaction. Ease of use and social status were not significant direct predictors in the simultaneous regression models, but all four product attributes showed significant indirect effects on repurchase intention through trust and customer satisfaction. The findings suggest that consumer IoT repurchase in New Zealand is best understood as a post-adoption confidence process: functional, usability and symbolic product attributes become commercially meaningful when they are converted into trusted and satisfying product experiences. The study contributes an integrated model of IoT repurchase intention and offers practical guidance for IoT manufacturers, retailers, installers and policy actors seeking to strengthen consumer retention, privacy assurance and sustainable connected-product adoption. Full article

The integration of AI-generated content into higher education has intensified interest in how students form and calibrate trust toward algorithmic outputs and whether pedagogical relationships can serve as conduits for that trust. Grounded in Trust Transfer Theory, this between-subjects randomized experimental study (N = 320) investigated whether teacher endorsement shapes students’ perceptions of AI-generated educational content across four dimensions: Perceived AI Competence, Academic Integrity, Perceived Human-Mediated Reliability, and Behavioral Intention to adopt. Participants from Saudi Arabian universities were randomly assigned to an endorsed or non-endorsed vignette condition and responded to a validated 13-item Trust and Acceptance Scale. Independent-samples t-tests confirmed statistically significant differences across all four dimensions in favor of the endorsed condition, with effect sizes ranging from small to large, and findings remained robust after controlling for gender via ANCOVA. Within-condition regression analyses further established Perceived Human-Mediated Reliability as a structurally stable positive predictor of trust outcomes in both conditions, with predictive power consistently amplified under endorsement. Postgraduate students placed greater emphasis on human oversight, while no disciplinary differences emerged, confirming the cross-disciplinary universality of the trust transfer mechanism. These findings are consistent with positioning the teacher as a trust guarantor in AI-mediated learning environments and carry direct implications for pedagogical design and institutional AI governance. Full article

This paper adopts a multisemiotic perspective to examine the significations of monuments in city spaces in Calabar, Nigeria. Data were generated through photo documentation, participant observation and semi-structured interviews in a three-year fieldwork involving 34 participants. Drawing from interactions between the designers, the monuments and the social audience, findings present the monuments as multisemiotic significations of the benefits derived from Western education, gains from missionaries’ activities and their interface with the locals, abolition of the slave trade, sociocultural heritage, histories and identity, potential in tourism and carnivals, and an emerging shift from African traditional religion to Anglo-Christian beliefs. These representations are dialogic expressions of lived experiences and histories of residents that (re)construct memorials of their past and provide a guide for the present and project expectations of the future. The symbolic images integrate localised intentions of the designers drawn from the environment and the people, with appropriate interpretations by the social audiences to situate their indigenous significations within varying global discourses on monuments. Full article

As generative artificial intelligence (GenAI) increasingly permeates educational contexts, understanding the factors driving learners’ Behavioral Intention (BI) toward GenAI-powered tools has become critical. This study integrates the Technology Acceptance Model (TAM), the Task-Technology Fit (TTF) framework, and privacy and ethical risk considerations to explore the determinants of Chinese higher education students’ Behavioral Intention to adopt these tools. Data were collected from 716 students via a structured self-reported questionnaire. A multi-stage analytical approach was employed by integrating structural equation modeling (SEM) with artificial neural networks (ANN) and support vector regression (SVR). SEM was first utilized to validate the theoretical hypotheses and the measurement model. Subsequently, ANN and SVR models were constructed to explore non-linear relationships and rank the importance of core predictors for Behavioral Intention, including Perceived Ease of Use (PEU), Privacy and Ethical Concerns (PEC), Perceived Technical Features (PTF), and TTF. The modeling performance of the two algorithms was then rigorously compared. The SEM results indicate that PTF exerts an indirect impact on Behavioral Intention via the sequential mediation of Task-Technology Fit and Perceived Usefulness (PU), while PEU positively influences both Perceived Usefulness and Behavioral Intention. Notably, PEC did not exhibit a significant negative effect on users’ Attitude (ATT) or Behavioral Intention. These findings were further elucidated by the machine learning analyses, where PTF and PEU emerged as the dominant predictors, whereas the non-linear contribution of PEC was marginal. Furthermore, SVR outperformed ANN in terms of predictive accuracy and model stability. This study demonstrates the efficacy of combining theoretical modeling with machine learning techniques to elucidate the adoption mechanisms of GenAI in higher education. In addition, preliminary teaching observations in undergraduate mathematics and logistics management courses link quantitative results with actual learning scenarios. We acknowledge that future research should validate these patterns using observed behavioral data. Full article

Mobile applications are increasingly promoted as instruments for improving agricultural information access, advisory delivery, market participation, and decision support, particularly for smallholder farmers in developing nations. Research, however, has been dominated by general technology-acceptance and diffusion constructs, while the design-sensitive and infrastructural mechanisms studied in human–computer interaction (HCI) have received comparatively little attention. In this paper we develop a parsimonious HCI model of mobile application use in agriculture. Drawing on the technology acceptance model, the unified theory of acceptance and use of technology, diffusion of innovations, socio-technical systems theory, and human–computer interaction for development (HCI4D), the model proposes that agricultural application use is driven by five antecedent domains: perceived agronomic value, inclusive usability and accessibility, contextual and cultural fit, trust and transparency, and social and institutional embeddedness. Each plays a distinct role across three use stages: adoption intention, sustained use, and decision impact. Contextual constraints (infrastructure and farmer characteristics) moderate these relationships. We develop six testable propositions from the model. The model is conceptual: it is offered as a framework for empirical testing rather than as a validated account of farmer behavior. The paper contributes an HCI-sensitive specification of mobile application use under agricultural field conditions: a “field-ready” conception of mobile HCI. Full article

Private vehicle overreliance in Klang Valley, Malaysia contributes to severe traffic congestion, air pollution, and carbon emissions, yet public transport modal shift remains far below national policy targets. This study extends the Theory of Planned Behaviour (TPB) by incorporating two external constructs, environmental concern (EC) and technology adoption (TA), to investigate the behavioural intention of 483 private vehicle users to switch to rail transit. Hypotheses were tested using partial least squares structural equation modelling (PLS-SEM). The results show that, with the exception of subjective norm ($\beta =0.064$, $p=0.180$), all constructs exert significant positive effects on behavioural intention. Technology adoption emerged as the strongest direct predictor ($\beta =0.417$), followed by perceived behaviour control ($\beta =0.340$). Environmental concern operated exclusively through indirect pathways, mediated by attitude and perceived behaviour control, and exhibited the largest effect size on the TPB components ($f^2$ = 0.380–0.449, large effect). Full article

Artificial intelligence (AI) adoption within logistics organizations remains uneven despite increasing digital transformation initiatives in emerging economies. This study investigates respondent-perceived organizational AI readiness profiles in Thailand’s logistics industry using an integrated analytical framework combining TOE–UTAUT predictors, clustering analysis, supervised machine learning, and explainable artificial intelligence techniques. Data were collected from 520 logistics and supply chain professionals in Thailand using a structured questionnaire. K-means clustering was applied to identify internally derived respondent-perceived AI readiness profiles, while Random Forest, Support Vector Machine (SVM), XGBoost, and LightGBM models were developed to classify readiness-profile membership. A weighted voting ensemble model was additionally employed to assess classification robustness and profile-differentiation stability across multiple learning algorithms. The findings identified three internally derived respondent-perceived AI readiness profiles representing relatively low, moderate, and advanced readiness patterns within the TOE–UTAUT feature space. Among the evaluated models, the SVM classifier achieved the strongest classification performance, obtaining the highest accuracy and AUC values. SHAP analysis indicated that Actual Use, Technological Factors, Facilitating Conditions, and Behavioral Intention exhibited the largest feature-attribution contributions within the readiness-profile classification framework. The study contributes to AI adoption research by integrating clustering-based segmentation, machine-learning classification, and explainable artificial intelligence into a unified readiness-profiling framework. The findings provide practical insights for managers and policymakers seeking to understand respondent-perceived organizational AI readiness patterns and support digital transformation initiatives within logistics professional contexts. Full article

The rise in energy demand in Thailand due to constant economic growth coupled with reliance on limited natural gas and oil resources has led to an increased demand for alternative sources of energy. Therefore, this study aims at examining factors that influence the intention of adopting solar rooftop energy among households in Thailand through the integration of the theory of planned behavior (TPB) and the technology acceptance model (TAM). A quantitative research approach was adopted whereby data were obtained from 255 households in all parts of Thailand through questionnaires. The results reveal that attitude (β = 0.484, p < 0.001), perceived usefulness (β = 0.271, p < 0.05), and subjective norms (β = 0.257, p < 0.001) positively and significantly influence intention to use solar rooftop energy, collectively explaining 61% of the variance (R² = 0.61). Attitude proved to be the most significant predictor in this regard, underscoring the significance of the evaluative process of cognition and emotion for adopting certain behavior. This research makes a valuable contribution to the body of knowledge on renewable energy in that the TPB-TAM model has been empirically validated in a Thai household setting. In addition, the findings provide suggestive evidence of a mediating role of attitude in the link between perceived usefulness and intention, although this mediation finding should be interpreted with caution due to the conceptual overlap between these constructs and the absence of bootstrap confidence intervals. Future research is recommended to confirm this mediation pathway using formal bootstrap procedures. Full article

This paper examines the association between two Financial Technology (FinTech)-related policy windows and the profitability of Qatari commercial banks over a twenty-year horizon (2005–2024). The analysis is anchored by two structural breaks: in 2017, the Qatar Central Bank (QCB) established its FinTech task force and lifted restrictions on the implementation of a regulatory sandbox and centralised electronic know your customer (e-KYC) framework; and during the digital-acceleration period in 2020 in response to the COVID-19 pandemic and the issuance of digital banking licences. FinTech adoption is not measured directly at the bank level; the two policy windows are used as intent-to-treat proxies. Using return on assets (ROA) and return on equity (ROE), bank performance is measured and influenced by bank size (log of total assets), bank age and type (Islamic and conventional). Multiple diagnostics of Hausman and Breusch–Pagan support the use of fixed-effects (FE) panel regressions with cluster-robust standard errors on an unbalanced panel of 125 bank–year observations. The results show a positive coefficient on the post-2017 dummy in the ROE model (β = 0.0306, p = 0.054, cluster-robust) and no detectable change in ROA (β = −0.00058, p = 0.868). For the post-2020 phase, both coefficients are positive but do not reach conventional significance (ROA: β = 0.00218, p = 0.539; ROE: β = 0.0224, p = 0.150). There is no systematic difference between Islamic and conventional banks that is offered by the interaction terms in either phase. Given the small sample (nine banks, eight effective clusters after the FE singleton drop; 125 observations) and the use of policy-window proxies rather than direct bank-level FinTech measures, the design cannot isolate the effect of the FinTech-related reforms from concurrent macroeconomic, sectorial or pandemic-related developments, and the cluster-robust p-values should be read as approximate. The results are therefore presented as preliminary and indicative. Read in light of these design constraints, the results are consistent with incremental rather than transformative change around the FinTech-related policy windows in Qatar, with results influenced more by timing, scale economies, and regulatory saturation than by bank type. The country-specific empirical findings can help restore context to the literature on the Gulf Cooperation Council (GCC) average, and provide measured guidance for bank managers and regulators working toward the Qatar National Vision 2030 digital aspiration. Full article