Search Results (341)

This paper introduces a unified and systematic design methodology for robotic systems that is generalizable across a wide range of applications. It integrates rigorous mathematical formalisms such as kinematics, dynamics, control theory, and optimization with advanced simulation tools, ensuring that each design decision is grounded in provable theory. The approach defines clear phases, including mathematical modeling, virtual prototyping, parameter optimization, and theoretical validation. Each phase builds on the previous one to reduce unforeseen integration issues. Spanning from conceptualization to deployment, it offers a blueprint for developing mathematically valid and robust robotic solutions while streamlining the transition from design intent to functional prototype. By standardizing the design workflow, this framework reduces development time and cost, improves reproducibility across projects, and enhances collaboration among multidisciplinary teams. Such a generalized approach is essential in today’s fast-evolving robotics landscape where rapid innovation and cross-domain applicability demand flexible yet reliable methodologies. Moreover, it provides a common language and set of benchmarks that both novice and experienced engineers can use to evaluate performance, facilitate knowledge transfer, and future-proof systems against emerging application requirements. Full article

This study examines the factors that influence individuals’ intentions to create socially oriented ventures, emphasizing the joint role of social and technical systems. Grounded in Socio-Technical Systems Theory, the research investigates how perceptions of social legitimacy and technological infrastructure shape social entrepreneurial intention (SEI) and how these effects are conditioned by generational cohort, familiarity and intent to use artificial intelligence (AI), and social proximity to entrepreneurial peers. Based on survey data from 388 respondents in China who expressed interest in both entrepreneurship and social problem-solving, the study applies a conditional process structural equation model to capture the complex interplay between external systems and individual-level readiness. The results show that both social and technical systems significantly and positively influence SEI, particularly among younger generations (Millennials and Generation Z). Furthermore, AI familiarity and social proximity operate as moderated mediators, differentially transmitting and shaping systemic influences on SEI. These findings advance the theoretical understanding of socio-technical determinants of social entrepreneurship and offer practical insights for fostering inclusive, generationally responsive entrepreneurial ecosystems. Full article

This qualitative case study explores Missouri-based consumers’ ethical beliefs regarding restaurant sourcing from minority farmers. Guided by the Hunt–Vitell theory of ethics (H-V model), it applies the model in a new context: culturally inclusive restaurant sourcing. Based on 15 semi-structured interviews conducted between September 2024 and October 2024, the study explores how ethical beliefs shape dining intentions. Participants generally viewed support for minority farmers as ethically appropriate. Thematic analysis revealed six key themes: (1) community-oriented social values (e.g., social responsibility toward local businesses); (2) cultural identity (e.g., traditional farming methods); (3) consumer values—food-oriented (e.g., quality); (4) consumer values—people-oriented (e.g., financial support for ethical sourcing); (5) trust-building mechanisms (e.g., sourcing transparency); and (6) barriers (e.g., lack of awareness). These findings highlight limited consumer awareness of minority farmers and the need for transparent communication and cultural education. The study contributes theoretically by extending the H-V model to the intersection of ethics, culture, and restaurant sourcing. Practically, it offers guidance for restaurant managers, marketers, and policymakers to support minority farmers, build trust, and promote inclusive and socially responsible dining. One key limitation of this study is its reliance on a small, Missouri-based consumer sample, which limits generalizability and excludes perspectives from other stakeholders. However, as a regional case study, it provides important depth and contextual insight into an underexplored aspect of sustainable sourcing. This study also highlights the need for multi-stakeholder engagement to advance equity in the food system. Full article

The rapid development of generative artificial intelligence (AI) systems such as ChatGPT (GPT-4) could transform teaching and learning. Yet, integrating these tools requires insight into what drives students to adopt them. Research on ChatGPT acceptance has so far focused on university settings, leaving school contexts underexplored. This study addresses the gap by surveying 506 upper secondary students in Baden-Württemberg, Germany, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Performance expectancy, habit and hedonic motivation emerged as strong predictors of behavioral intention to use ChatGPT for school purposes. Adding personality traits and personal values such as conscientiousness or preference for challenge raised the model’s explanatory power only marginally. The findings suggest that students’ readiness to employ ChatGPT reflects the anticipated learning benefits and enjoyment rather than the avoidance of effort. The original UTAUT2 is therefore sufficient to explain students’ acceptance of ChatGPT in school contexts. The results could inform educators and policy makers aiming to foster the reflective and effective use of generative AI in instruction. Full article

We present a brief overview of the field of charge density waves (CDW) in condensed systems with focus set to the underlying mechanisms behind the CDW ground state. Our intention in this short review is not to count all related facts from the vast volume of literature about this decades-old and still developing field, but rather to pinpoint the most important, mostly theoretical ones, presenting the development of the field. Starting from the “early days”, mainly based on weakly coupled, chain-like quasi-1D systems and Peierls instability, in which the Fermi surface nesting has been the predominant and practically paradigmatic mechanism of the CDW ground state stabilisation, we track the change in paradigms while entering the field of layered quasi-2D systems, with Fermi surface far away from the nesting regime, in which rather strong, essentially momentum-dependent interactions and particular reconstructions of the Fermi surface become essential. Examples of real quasi-1D materials, such as organic and inorganic conductors like Bechgaard salts or transition metal trichalcogenides and bronzes, in which experiment and theory have been extremely successful in providing detailed understanding, are contrasted to layered quasi-2D materials, such as ${\mathrm{high-T}}_c$ superconducting cuprates, intercalated graphite compounds or transition metal dichalcogenides, for which the theory explaining an onset of the CDWs constitutes a frontier of this fast-evolving field, strongly boosted by development of modern ab initio calculation methods. Full article

Against the backdrop of evolving global climate patterns, the frequency and intensity of extreme weather events have increased significantly, posing unprecedented threats to agricultural production. This change has particularly profound impacts on agricultural systems in developing countries, making the enhancement of farmers’ capacity to withstand extreme weather events a crucial component for achieving sustainable agricultural development. As an essential safeguard for agricultural production, agricultural insurance plays an indispensable role in risk management. However, a pronounced gap persists between policy aspirations and actual adoption rates among farmers in developing economies. This study employs the integrated theory of planned behavior (TPB) and protection motivation theory (PMT) to construct an analytical framework incorporating psychological, socio-cultural, and risk-perception factors. Using Jiangsu Province—a representative high-risk agricultural region in China—as a case study, we administered 608 structured questionnaires to farmers. Structural equation modeling was applied to identify determinants influencing insurance adoption decisions. The findings reveal that farmers’ agricultural insurance purchase decisions are influenced by multiple factors. At the individual level, risk perception promotes purchase intention by activating protection motivation, while cost–benefit assessment enables farmers to make rational evaluations. At the social level, subjective norms can significantly enhance farmers’ purchase intention. Further analysis indicates that perceived severity indirectly enhances purchase intention by positively influencing attitude, while response costs negatively affect purchase intention by weakening perceived behavior control. Although challenges such as cognitive gaps and product mismatch exist in the intention-behavior transition, institutional trust can effectively mitigate these issues. It not only strengthens the positive impact of psychological factors on purchase intention, but also significantly facilitates the transformation of purchase intention into actual behavior. To promote targeted policy interventions for agricultural insurance, we propose corresponding policy recommendations from the perspective of public intervention based on the research findings. Full article

The complexities inherent in resource management within cross-domain digital innovation ecosystems have significantly intensified, giving rise to heightened challenges in collaborative interactions among diverse stakeholders, thereby directly impacting systemic stability. Conventional governance frameworks for innovation ecosystems are inadequate in effectively managing the uncertainties and risks inherent in these environments. To address the collaborative governance dilemma and enhance governance efficiency, this paper aims to construct an effective collaborative governance mechanism for a cross-domain digital innovation ecosystem and explore the optimal strategy choices of key governance stakeholders, including the government, digital platform enterprises, and other relevant parties. This research utilizes evolutionary game theory to construct a model comprising three governing entities: the government, digital platform enterprises, and stakeholders. It investigates the evolutionary dynamics of collaborative governance strategies among these entities and the factors that influence governance. Following this, a system dynamics methodology is employed for simulation analysis. The results reveal the following: (1) As the initial intentions of the governing entities evolve, governance decisions within the system tend to stabilize, characterized by a strategic combination of proactive regulation, active cooperative governance, and engaged participation. This equilibrium governance strategy significantly fosters the stable advancement of cross-domain digital innovation ecosystems. (2) The punitive measures enacted by the government and the internal incentive structures of the system positively influence the evolution of governance decisions towards collaborative governance. (3) The cost–benefit assessment of the primary governing entity, the digital platform enterprise, demonstrates a detrimental effect on the evolution of governance decisions towards collaborative governance. These findings are vital for refining the collaborative governance frameworks of cross-domain digital innovation ecosystems and for promoting the robust and stable progression of the system. Full article

In the current context, where environmental concerns are gaining increased attention, the transition toward sustainable urban mobility stands out as a necessary and responsible step. Technological advancements over the past decade have brought private autonomous vehicles, particularly those defined by the Society of Automotive Engineers Levels 4 and 5, into focus as promising solutions for mitigating road congestion and reducing greenhouse gas emissions. However, the extent to which Autonomous Vehicles can fulfill this potential depends largely on user acceptance, patterns of use, and their integration within broader green energy and sustainability policies. The present paper aims to develop an integrated conceptual model that links behavioral determinants to environmental outcomes, assessing how individuals’ intention to adopt private autonomous vehicles can contribute to sustainable urban mobility. The model integrates five psychosocial determinants—perceived usefulness, trust in technology, social influence, environmental concern, and perceived behavioral control—with contextual variables such as energy source, infrastructure availability, and public policy. These components interact to predict users’ intention to adopt AVs and their perceived contribution to urban sustainability. Methodologically, the study builds on a narrative synthesis of the literature and proposes a framework applicable to empirical validation through structural equation modeling (SEM). The model draws on established frameworks such as Technology Acceptance Model (TAM), Theory of Planned Behavior, and Unified Theory of Acceptance and Use of Technology, incorporating constructs including perceived usefulness, trust in technology, social influence, environmental concern, and perceived behavioral control, constructs later to be examined in relation to key contextual variables, including the energy source powering Autonomous Vehicles—such as electricity from mixed or renewable grids, hydrogen, or hybrid systems—and the broader policy environment (regulatory frameworks, infrastructure investment, fiscal incentives, and alignment with climate and mobility strategies and others). The research provides relevant directions for public policy and behavioral interventions in support of the development of clean and smart urban transport in the age of automation. Full article

Insider threats pose significant challenges to organizations, seriously endangering information security and privacy protection. These threats arise when employees with legitimate access to systems and databases misuse their privileges. Such individuals may alter, delete, or insert data into datasets, sell customer or client email addresses, leak strategic company plans, or transfer industrial and intellectual property information. These actions can severely damage a company’s reputation, result in revenue losses and loss of competitive advantage, and, in extreme cases, lead to bankruptcy. This study presents a novel solution that examines how organizational factors such as job satisfaction and security, organizational support, attachment, commitment, involvement in information security, and organizational norms influence employees’ attitudes and intentions, thereby mitigating insider threats. A key strength of this research is its integration of two foundational theories: the Social Bond Theory (SBT) and the Theory of Planned Behavior (TPB). The results reveal that job satisfaction and security, affective and normative commitment, information security training, and personal norms all contribute to reducing insider threats. Furthermore, the findings indicate that employees’ attitudes, perceived behavioral control, and subjective norms significantly influence their intentions to mitigate insider threats. However, organizational support and continuance commitment were not found to have a significant impact. Full article

VR technology is increasingly being integrated into daily life, with virtual book communities emerging as novel platforms for immersive reading and interaction. This study investigates how internal and external factors jointly influence users’ usage intention from psychological and behavioral science perspectives. A multivariate structural equation model based on three-dimensional perception theory was developed and tested through a survey of individuals with prior VR reading experience. The model examines the roles of task–technology fit, privacy and security risks, emotional resonance, self-expression, and the sense of belonging. The results reveal that task–technology fit positively influences usage intention, while privacy and security risk has a negative effect. Internally, emotional resonance and a sense of belonging significantly enhance usage intention. Furthermore, emotional resonance mediates the relationship between self-expression and both sense of belonging and usage intention, while sense of belonging also mediates between emotional resonance and usage intention. These findings underscore the critical interplay between technical attributes and affective factors in shaping engagement with VR-based reading platforms. This study offers new insights into user acceptance mechanisms in virtual book communities, and provides a theoretical foundation and practical implications for enhancing user experience and adoption in digital library systems. Full article

As healthcare systems face mounting pressure to modernise payment infrastructure, fintech innovations have emerged as potential tools to improve affordability and efficiency. However, the adoption of these technologies in clinical settings remains limited. This study investigated the perceptions and resistance patterns of healthcare professionals toward Buy-Now-Pay-Later technology and blockchain in healthcare finance, using Innovation Resistance Theory as the guiding framework. Survey data collected from medical practitioners (N = 366) were analysed to identify knowledge gaps, perceived risks, and tradition-related barriers that influence adoption intent. The findings reveal that while interest in financial innovation exists, resistance is driven by institutional conservatism, regulatory uncertainty, and limited familiarity with decentralised finance systems. This research contributes to the literature by offering a theory-based explanation for why even high-potential financial tools face behavioural and structural resistance in healthcare environments. Full article

This paper examines the philosophy of “Dao follows nature” (道法自然) and investigates how Daoist clothing transforms abstract cosmological concepts into a “wearable interface for spiritual practice” through the use of materials, colors, and patterns. By integrating symbol system analysis, material culture theory, and the philosophy of body practice, this study uncovers three layers of symbolic mechanisms inherent in Daoist attire. First, the materials embody the tension between “nature and humanity”, with the intentional imperfections in craftsmanship serving as a critique of technological alienation. Second, the color coding disrupts the static structure of the Five Elements system by dynamically shifting between sacred and taboo properties during rituals while simultaneously reconstructing symbolic meanings through negotiation with secular power. Third, the patterns (such as star constellations and Bagua) employ directional arrangements to transform the human body into a miniature cosmos, with dynamic designs offering a visual path for spiritual practice. This paper introduces the concept of a “dynamic practice interface”, emphasizing that the meaning of Daoist clothing is generated through the interaction of historical power, individual experience, and cosmological imagination. This research fills a critical gap in the symbolic system of Daoist art and provides a new paradigm for sustainable design and body aesthetics, framed from the perspective of “reaching the Dao through objects”. Full article

Background/Objectives: Integrating Artificial Intelligence Clinical Decision Support Systems (AI-CDSSs) into healthcare can improve diagnostic accuracy, optimize clinical workflows, and support evidence-based medical decision-making. However, the adoption of AI-CDSSs remains uneven, influenced by technological, organizational, and perceptual factors. This study, conducted between November 2024 and February 2025, analyzes the determinants of AI-CDSS adoption among healthcare professionals through investigating the impacts of perceived benefits, technological costs, and social and institutional influence, as well as the transparency and control of algorithms, using an adapted Path Dependence-Augmented–Unified Theory of Acceptance and Use of Technology model. Methods: This research was conducted through a cross-sectional study, employing a structured questionnaire administered to a sample of 440 healthcare professionals selected using a stratified sampling methodology. Data were collected via specialized platforms and analyzed using structural equation modeling (PLS-SEM) to examine the relationships between variables and the impacts of key factors on the intention to adopt AI-CDSSs. Results: The findings highlight that the perceived benefits of AI-CDSSs are the strongest predictor of intention to adopt AI-CDSSs, while technology effort cost negatively impacts attitudes toward AI-CDSSs. Additionally, social and institutional influence fosters acceptance, whereas perceived control and transparency over AI enhance trust, reinforcing the necessity for explainable and clinician-supervised AI systems. Conclusions: This study confirms that the intention to adopt AI-CDSSs in healthcare depends on the perception of utility, technological accessibility, and system transparency. The creation of interpretable and adaptive AI architectures, along with training programs dedicated to healthcare professionals, represents measures enhancing the degree of acceptance. Full article

Blockchain technology has recently emerged as a transformative innovation with the potential to enhance transparency, accountability, and efficiency, key pillars of sustainable financial and accounting systems. Despite its relevance to sustainable digital transformation, the adoption of blockchain in accounting practices remains limited, particularly in developing economies such as Vietnam. This study investigates the behavioral factors influencing the intention to adopt blockchain in financial accounting among manufacturing enterprises, drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Theory of Planned Behavior (TPB). Survey data from 320 Vietnamese manufacturing firms were analyzed using Cronbach’s Alpha, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM) to examine both direct effects and mediating pathways. The results reveal that performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived cost significantly affect attitudes, which in turn strongly predict intentions to adopt blockchain. Attitudes also partially mediate these relationships, underscoring their central role in shaping sustainable technology adoption behavior. Notably, trust in technology does not exert a significant influence, suggesting that practical and organizational enablers outweigh individual-level trust in this context. This study contributes to the emerging literature on blockchain-enabled sustainable accounting by extending the UTAUT–TPB framework and offering insights for policymakers, technology providers, and managers aiming to foster sustainability-driven digital transformation in financial practices. Full article

Globally, many school systems face leadership shortages and challenges in building strong principal pipelines, making career intentions to pursue school leadership a critical area of study. This study examines how key features of educational leadership preparation programs (ELPPs) influence graduates’ intentions to become school leaders. Guided by Social Cognitive Career Theory (SCCT), we analyzed data from 2994 graduates across 51 U.S.-based ELPPs collected between 2016 and 2020, using structural equation modeling and estimation thinking to assess direct and mediated relationships among program features and career outcomes. Findings reveal that internship quality plays a pivotal role as both a direct predictor of career intentions and a mediator for other program features, including faculty quality, program rigor and relevance, and peer relationships. Faculty quality influences intentions primarily through rigor and relevance, while cohort participation contributes indirectly by fostering peer relationships and internship quality. Graduate attributes, including prior leadership experience, also shape career aspirations. This study extends SCCT by demonstrating how ELPP features shape candidates’ career intentions through interconnected pathways, offering insights that inform policy and program design aimed at strengthening pathways into school leadership. Full article