Search Results (2,497)

In the rapid digital era, artificial intelligence (AI) tools have progressively arisen to shape the education environment. In this context, large language models (LLMs) (i.e., ChatGPT vs. 4.0 and Gemini vs. 2.5) have emerged as powerful applications for academic inclusion. This paper investigated how using and trusting LLMs can impact the academic success and quality of life (QoL) of visually impaired university students. Quantitative research was conducted, obtaining data from 385 visually impaired university students through a structured survey design. Partial Least Squares Structural Equation Modelling (PLS-SEM) was implemented to test the study hypotheses. The findings revealed that trust in LLMs can significantly predict LLM usage, which in turn can improve QoL. While LLM usage failed to directly support the academic success of disabled students, but its impact was mediated through QoL, suggesting that enhancements in well-being can contribute to higher academic success. The results highlighted the importance of promoting trust in AI applications, along with developing an accessible, inclusive, and student-centred digital environment. The study offers practical contributions for educators and policymakers, shedding light on the importance of LLM applications for both the QoL and academic success of visually impaired university students. Full article

In the rapidly evolving global business landscape, servitization and digitalization have emerged as key strategies for enhancing firm competitiveness and performance. This study examines their impact, along with the moderating role of government support, in the Indonesian shipping industry. Drawing on the resource-based view (RBV), servitization and digitalization are conceptualized as internal drivers of performance, while Resource Dependence Theory (RDT) positions government support as an external factor that reduces environmental uncertainty and strengthens these relationships. Using data from 345 shipping companies, analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM), the results show that both servitization and digitalization positively affect competitiveness and performance. Furthermore, government support significantly enhances these effects by providing resources such as infrastructure and financial incentives, facilitating the adoption of digital strategies and service-based models. Beyond firm outcomes, these transformations align with broader sustainability objectives by improving resource efficiency, reducing waste and delays, and potentially lowering the environmental footprint of logistics activities. This study advances theoretical understanding by demonstrating the central role of external resources—particularly government support—in enabling successful digital and service transformations. For policymakers, the findings emphasize the need for targeted incentives and infrastructure to accelerate industry-specific innovation and sustainability goals. For practitioners, they highlight the importance of aligning strategic initiatives with government policies to maximize the benefits of servitization and digitalization. Full article

While integrated Artificial Intelligence and Business Analytics (AI-BA) represents a significant advancement in marketing analytics and greatly influences firms’ innovations, there is a considerable gap in current research regarding its impact on technological innovation. This study addresses this gap by exploring how AI-BA affects data-driven and technological innovation, considering the mediating roles of integration capabilities and digital platforms. A theoretical model has been developed based on the dynamic capability view (DCV) and organizational information processing theory (OIPT). The model has been validated using data from enterprises in Saudi Arabia, and Partial Least Squares Structural Equation Modeling (PLS-SEM) has been employed for analysis. The findings demonstrate that AI-BA directly enhances both technological and data-driven innovation. Additionally, it was discovered that data-driven innovation, integration capabilities, and digital platforms mediate these effects, thereby enhancing technological innovation within the respective industries. These findings provide both theoretical and practical insights into the relationship between AI-BA, data-driven innovation, and technological innovation. They enrich the existing literature and provide actionable guidance for practitioners aiming to align their AI-BA with improved technological innovation outcomes. Full article

Student trust plays a pivotal role in shaping the future integration of artificial intelligence (AI) in higher education. This study investigates how AI Facilitating Conditions (FCs), Performance Expectancy (PE), and task type influence students’ System-like Trust (AST) and Human-like Trust (AHT) in AI and further examines the mediating role of human-like trust in fostering the willingness to continue AI-assisted learning. Drawing on valid data collected from 466 Chinese university students, we employed partial least squares structural equation modeling (PLS-SEM) in combination with artificial neural networks (ANN) to test the hypothesized relationships, mediating mechanisms and the relative importance of influencing factors. The findings indicate that AI facilitating conditions significantly enhance both system-like trust and usage intention; performance expectancy exerts a positive effect on both forms of trust, with particularly strong effects observed in subjective tasks. Moreover, system-like trust positively promotes human-like trust, and together, these dimensions jointly strengthen students’ intention to engage in AI-assisted learning. Results from the ANN analysis further highlight that performance expectancy, system-like trust, and facilitating conditions are the primary determinants of system-like trust, human-like trust, and usage intention, respectively. This study extends the application of interpersonal trust theory to the AI domain and offers theoretical insights for fostering more positive and effective patterns of AI adoption in higher education. Full article

Sedum alfredii is a cadmium (Cd) hyperaccumulator, but the regulatory mechanisms linking phytohormones and redox balance to Cd transporter expression remain unclear. In this study, we omitted external cadmium (Cd) stress to isolate and examine the interplay between phytohormone and reactive oxygen species (ROS) signaling. Exogenous treatments with abscisic acid (ABA), indole-3-acetic acid (IAA), gibberellic acid (GA₃), trans-zeatin (t-Z), and H₂O₂ were combined with analyses of hormone levels, antioxidant enzyme activities, and transporter gene expression. Correlation and PLS-SEM analyses identified the CAT–H₂O₂ module as a key node: ABA and IAA enhanced CAT activity and alleviated ROS-mediated repression of transporters, while GA₃ and t-Z exerted opposite effects. Functional validation using an H₂O₂ scavenger revealed that the regulation of HMA3 and Nramp5 by ABA and t-Z is H₂O₂-dependent. In contrast, IAA modulates Nramp5 through a ROS-independent pathway, while the regulatory effects of GA₃ were negligible. Functional validation under Cd exposure suggests a model wherein HMA3 and Nramp5 act in a complementary manner to sequester and redistribute Cd in leaves, thereby supporting hyperaccumulation. These findings highlight hormone-specific ROS pathways as central to transporter regulation and provide mechanistic insights to improve phytoremediation efficiency. Full article

This study scrutinizes the effects of generative artificial intelligence (GenAI) on sustainable performance (SP) in Chinese manufacturing firms through the mediating role of novelty-centered and efficiency-centered business model innovations (BMIs). It also explores the moderating effect of AI regulation on the GenAI–BMIs and GenAI–SP relationships. Data were collected from 1192 middle-level managers across 500 Chinese manufacturing firms using a two-wave survey design. Partial least squares structural equation modeling (PLS-SEM) was employed to test direct, mediating, and moderating relationships. The findings show that GenAI adoption has a significant positive effect on novelty-centered BMI, efficiency-centered BMI and sustainability performance. The GenAI–SP relationship is mediated by both BMIs, indicating that GenAI contributes to sustainability both directly and through innovative business practices. Moreover, AI regulation significantly strengthens the effects of GenAI on both BMI and SP, emphasizing the importance of regulatory alignment in maximizing technological benefits. This research shows that firms should emphasis AI tools and strategies to innovate their business model for better sustainable outcomes. Firms need to follow regulations and rules embedded into digitalization to ensure a sustainable competitive position in the market. Full article

Virtual Reality (VR), particularly when integrated with Building Information Modeling (BIM), is transforming architectural practice in developed economies. However, its adoption in developing countries remains limited due to infrastructural, economic, and organizational challenges. This study addresses this gap by empirically evaluating VR readiness among emerging architectural professionals in Oman through a novel integrated framework. This framework combines the Unified Theory of Acceptance and Use of Technology (UTAUT), which focuses on functional drivers like usefulness, with Presence Theory, which captures experiential drivers like immersion. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze the survey data and assess VR readiness. The analysis revealed that prior VR exposure significantly predicts adoption intention, a relationship that is partially mediated by perceived usefulness. Organizational support emerged as a key moderator, effectively mitigating the impact of technical barriers on adoption decisions. The model explained the variance in adoption intention, highlighting that experiential familiarity, functional evaluation, and institutional support were critical for advancing digital transformation. The findings provide actionable insights for educational institutions, policymakers, and industry stakeholders aiming to prepare the next generation of architects in Oman and similar economies for VR adoption. By validating a dual-pathway adoption framework, this research contributes both theoretically and practically to understanding immersive technology assimilation in resource-constrained professional contexts. Full article

Background: Despite growing interest in supply chain resilience (SCRes), theoretical overlap between dynamic capabilities (DC) and supply chain agility (SCA) has complicated empirical analysis of their distinct roles. Additionally, the contextual role of information asymmetry in shaping resilience remains underexplored. This study addresses both issues by modeling DC hierarchically and examining IA as a moderator. Methods: Data were collected through a cross-sectional survey of 157 U.S.-based supply chain professionals. Partial least squares structural equation modeling (PLS-SEM) was used to examine the relationships among DC, SCA, IA, and SCRes. Results: SCA was a strong, direct predictor of SCRes. In contrast, DC showed no direct effect in the full model; however, in a hierarchical component model (HCM), DC, a higher-order construct, emerged as significant predictor of SCRes. IA exerted a dual negative influence: it directly weakened SCRes and negatively moderated the relationship between DC and SCRes. Conclusions: This study makes two novel contributions. First, it resolves ambiguity between DC and SCA by empirically modeling DC as a higher-order construct that encompasses but remains distinct from SCA. Second, it introduces IA as a multidimensional barrier to resilience, demonstrating its direct and interactive effects. These findings provide new insight into capability design and contextual adaptation for SCRes in uncertain, information-constrained environments. Full article

Growing consumer interest in personal health and environmental sustainability has driven a significant number of companies to enter the organic food market. While this offers valuable opportunities, companies face substantial challenges in making marketing decisions which are aligned with the specific characteristics of this sector. This paper studies the impact of three external drivers—environmental customer pressure, environmental competitive intensity, and environmental regulatory pressure—on companies’ decision to adopt a dominant green positioning strategy within the Romanian organic food market. To this end, an online survey was conducted among 77 companies, including producers, processors, distributors, and retailers, all of which own an organic food brand. Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied to assess the measurement model and test the hypothesised structural relationships. Our findings confirm that customer pressure plays a significant role in shaping green brand positioning decisions. Accordingly, companies must be responsive to consumer expectations, even in the absence of strict regulations in Romania’s organic food sector guiding organisational behaviour. Furthermore, competitive dynamics were also found to be vital, as evidenced by the positive and direct relationship between environmental competitive intensity and the strategic green positioning decision examined. Full article

Fe–porphyrin/g-C₃N₄ composites have emerged as promising visible-light photocatalysts, but their performance remains limited by inefficient charge separation and low reactive oxygen species (ROS) yield. Here, iron–tetra(4-carboxyphenyl) porphyrin (Fe–TCPP) was coupled with g-C₃N₄ nanotubes (CNNTs) via a facile self-assembly strategy, creating a morphology-coordinated system. Comprehensive characterization (XRD, FTIR, SEM/TEM, BET, UV–Vis diffuse reflectance, PL, XPS, and EPR) confirmed the structural integrity, electronic coupling, and ROS generation capability of the composites. Fe–TCPP incorporation narrowed the bandgap from 2.78 to 2.56 eV, prolonged the average carrier lifetime from 6.3 to 7.5 ns, and significantly enhanced the generation of •OH and ¹O₂. The optimized 1 wt% Fe–TCPP@CNNTs achieved complete Rhodamine B degradation within 30 min under visible light, with the highest two-stage apparent rate constants (k₁ = 0.0964 min⁻¹, k₂ = 0.328 min⁻¹). In addition, the hybrids retained over 90% activity after six consecutive runs, confirming their stability and recyclability. The synergistic effect of Fe–N coordination and nanotubular architecture thus promotes light harvesting, charge separation, and ROS utilization, offering a promising design principle for high-performance photocatalysts in environmental remediation. Full article

Background: In an increasingly digital marketplace, social media marketing activities (SMMAs) have become vital for building consumer–brand relationships, particularly among Generation Z (Gen Z) consumers. Coffee shops offer a unique context because they are lifestyle-oriented and highly dependent on repeat visits, making them especially responsive to digital engagement. This study examines the impact of SMMAs on brand loyalty in the Saudi Arabian coffee shop sector, with a particular focus on the mediating role of consumer brand preference and the moderating role of brand awareness. Drawing on the Stimulus–Organism–Response (S-O-R) framework and Generational Marketing Theory, the research explores how Gen Z consumers respond to social media efforts that are informative, interactive, trendy, and personalized. Methods: Data were collected using convenience sampling via an online survey of 412 Gen Z consumers in Saudi Arabia who follow at least one local or international coffee shop brand on social media. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to test the hypothesized relationships, mediation, and moderation effects. Results: The findings revealed that SMMAs have a substantial direct effect on both brand loyalty and consumer brand preference. Moreover, consumer brand preference partially mediates the relationship between SMMAs and brand loyalty, underscoring its importance as a psychological mechanism in the formation of loyalty. Additionally, brand awareness was found to significantly moderate the SMMAs–brand loyalty relationship, with more potent effects observed among consumers with higher levels of brand familiarity. Implications: The study contributes theoretically by extending the S-O-R framework with Generational Marketing Theory, demonstrating the partial mediating role of brand preference and the moderating direct effect of brand awareness. Practically, the results suggest that coffee shop marketers should design social media strategies that are informative, interactive, trendy, and personalized while also investing in awareness-building campaigns to amplify loyalty among Gen Z consumers. Full article

As a pivotal carrier of emerging human–computer interaction technologies, artificial intelligence (AI) conversational agents (CAs) hold critical significance for research on the mechanisms of users’ continuance usage behaviour, which is essential for technological optimization and commercial transformation. However, the differential impact pathways of multidimensional perceived interactivity on continuance usage intention, particularly the synergistic mechanisms between technical and affective dual-path dimensions, remain unclear. This study investigates the personalized AI-based CAs project “Dialogue with Great Souls,” launched on a Chinese social platform, using survey data from 305 users. A hybrid approach combining partial least squares structural equation modelling (PLS-SEM) and artificial neural networks (ANN) was employed for empirical analysis. The results indicate that technical dimensions, such as control and responsiveness, are key factors influencing trust, while affective interactive dimensions, including communication, personalization, and playfulness, significantly affect social presence, thereby shaping users’ continuance usage intention. ANN results corroborated most PLS-SEM findings but revealed inconsistencies in the predictive importance of personalization and communication on social presence, highlighting the complementary nature of linear and nonlinear interaction mechanisms. By expanding the interactivity model and adopting a hybrid methodology, this study constructs a novel framework for AI CAs. The empirical findings suggest that developers should strengthen socio-emotional bonds in anthropomorphic interactions while ensuring technical credibility to enhance users’ continuance usage intention. This research not only advances theoretical perspectives on the integration of technical and affective dimensions in agent systems but also provides practical recommendations for optimizing the design and development of AI CAs. Full article

This study investigates how Development and Operations (DevOps) practices impact Project Management Office (PMO) governance within the technology sector of the United Arab Emirates (UAE). It addresses the need for agile-aligned governance frameworks by exploring how DevOps principles affect traditional PMO structures. A quantitative cross-sectional survey was conducted, and data was collected from 321 DevOps and PMO professionals in UAE organizations. The analysis, using Partial Least Squares Structural Equation Modelling (PLS-SEM), revealed a moderate positive correlation between specific DevOps practices—such as microservices, Minimum Viable Experience (MVE) culture, continuous value streams, automated configuration, and continuous delivery—and effective PMO governance. The study’s novel theoretical contribution is the integration of the Dynamic Capabilities Framework (DCF) with the Agile DevOps Reference Model (ADRM) to examine this alignment, bridging strategic agility and operational execution. This research offers actionable insights for UAE organizations and policymakers seeking to enhance governance and digital maturity. Full article

Plant functional traits (PFTs) serve as key predictors of plant survival and adaptation to environmental gradients. Studies on intraspecific variation in PFTs are crucial for evaluating species’ adaptation to projected climate change and developing long-term conservation strategies. This study systematically investigated PFT responses in Polyspora chrysandra (Theaceae, Yunnan, China) through an integrated multivariate analysis of 20 leaf functional traits (LFTs) and 33 environmental factors categorized into geographical conditions (GCs), climate factors (CFs), soil properties (SPs), and ultraviolet radiation factors (UVRFs). To disentangle complex environmental–trait relationships, we employed redundancy analysis (RDA), hierarchical partitioning (HP), and partial least squares structural equation modeling (PLS-SEM) to assess direct, indirect, and latent relationships. Results showed that the intraspecific coefficient of variation (CV) ranged from 7.071% to 25.650%. Leaf tissue density (LTD), specific leaf area (SLA), leaf fresh weight (LFW), leaf dry weight (LDW), and leaf area (LA) exhibited moderate intraspecific trait variation (ITV), while all other traits demonstrated low ITV. Reference Bulk density (RBD) and Silt emerged as significant factors driving the variation. Latitude (Lat), altitude (Alt), and mean warmest month temperature (MWMT) were also identified as key influences. HP analysis revealed Silt as the most important predictor (p < 0.05). Latent variable analysis indicated descending contribution rates: SPs (31.51%) > GCs (11.52%) > CFs (11.04%) > UVRFs (10.29%). Co-effect analysis highlighted significant coupling effects involving RBD and cation exchange capacity of clay (CECC), as well as organic carbon content (OCC) and UV-B seasonality (UVB2). Path analysis showed SPs as having the strongest influence on leaf thickness (LT), followed by GCs and UVRFs. These findings provide empirical insights into the biogeographical patterns of ITV in P. chrysandra, enhance the understanding of plant environmental adaptation mechanisms, and offer a theoretical foundation for studying community assembly and ecosystem function maintenance. Full article

Despite a substantial volume of literature on the consequences of the recent pandemic, the relationship between psychological constructs that affect individual mindset and confidence, as well as travel intention, is missing. This study seeks to examine the travelers’ behavioral intentions and psychological constructs. The study was conducted among 398 Malaysian medical students. The study was conducted from the end of 2022 to the middle of 2023. The study followed PLS-SEM to estimate relationships between variables and predict dependent variables. The results revealed a negative correlation between travel risk, severity, travel barriers, and travel intention, while COVID-19 vaccination effectiveness and self-efficacy positively influenced travel intention. The present study reveals that individuals with higher levels of self-efficacy or confidence in overcoming obstacles and coping with the challenges of new circumstances exhibit a stronger intention to travel (ITT). This study contributes to understanding the cognitive process of individuals’ intentions to travel and the coping mechanisms during the post-pandemic. Utilizing the health belief model, this study validates how individual health behavior regarding perceived risk affects travel decisions or intentions. This study provides valuable insight into consumer behavior for decision-making in the aviation and tourism industries and for policymakers after the global health crisis. Full article