Search Results (1,021)

Agile teams often struggle to translate business requirements into maintainable, high-quality software due to the persistent ambiguity in the roles and relationships of behavior-driven development (BDD), Acceptance Test-driven Development (ATDD), and Test-driven Development (TDD). These approaches are frequently misunderstood, inconsistently applied, and only loosely connected within a unified delivery lifecycle. This article introduces BLOW (Behavior-Driven Development in a Layered Organization of Work-Centers), a systematic approach that establishes BDD as the coordinating methodology between ATDD (business-focused) and TDD (technology-focused). BLOW structures scenario-driven development across layered domains of accountability with clearly defined roles and responsibilities, organizing delivery through nested work-centers that transform user stories into executable specifications and production code. This approach integrates two complementary collaboration practices: the Three Amigos for discovering and formulating business scenarios, and the proposed Technical Three Amigos for linking those scenarios to Technical Domain Contexts, identifying required Enablers, and deriving technical scenarios when additional architectural support is needed. The proposed operating model emphasizes observability through executable scenarios as first-class artifacts, introducing native, test-anchored metrics that support reasoning about progress, technical effort, and value delivery within scenario-driven development. An exploratory longitudinal case study, consisting of a single-sprint proof of concept followed by an 18-month production deployment, reports patterns in which technical enablement precedes business value delivery and reusable infrastructure supports sustained growth of business scenarios over time. The findings also indicate that changes in the applied operating model are associated with measurable shifts in scenario evolution and internal quality indicators. Overall, BLOW provides a governance-compatible, end-to-end approach for organizing scenario driven development and improving alignment between stakeholder intent and technical implementation in complex software systems. Full article

This study examines user responses to an AI travel assistant in a cross-border tourism service system. Moving beyond adoption-centered technology acceptance research, it conceptualizes these responses as a staged appraisal process in which social and experiential cues shape performance expectancy and effort expectancy, which then influence attitude and two behavioral outcomes: users’ willingness to accept AI and objections to AI. Cosmopolitanism is introduced as an individual-level boundary condition. Survey data were collected from 499 Chinese tourists holding valid South Korean tourist visas after they evaluated Visit Seoul AI, an official AI-based travel-planning tool. The hypotheses were tested using partial least squares structural equation modeling. The results show that social influence, hedonic motivation, and perceived anthropomorphism significantly affect performance expectancy and effort expectancy, which in turn shape attitude. Attitude increases usersf’ willingness to accept AI and reduces objections to AI, with a stronger effect on users’ willingness to accept AI. Cosmopolitanism strengthens the negative effect of hedonic motivation on effort expectancy. This study extends AIDUA to cross-border AI service systems and shows that users may both accept and object to AI travel assistants. Full article

Background/Objectives: Artificial Intelligence (AI) integration in healthcare promises improved diagnostic accuracy, patient safety, and operational efficiency. However, AI acceptance among healthcare workers remains limited due to knowledge gaps, risk concerns, and governance challenges, particularly in developing countries like Saudi Arabia, where rapid healthcare modernization faces unique infrastructure, organizational, and cultural challenges. This research investigates the factors influencing AI acceptance among medical practitioners, nurses, administrators, and students in Saudi Arabian hospitals to identify key determinants and barriers to adoption. Methods: This cross-sectional study employed an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework integrated with ethical considerations from the Model for Ethical Assessment and Analysis of AI in Medicine (MEAAM). A structured bilingual questionnaire was administered to 119 healthcare professionals and students across Saudi Arabia, measuring constructs including Awareness and Knowledge, Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influence, Trust, Perceived Risk, Ethical Governance, and Price Value. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for quantitative analysis, supplemented by thematic analysis of open-ended qualitative responses. Results: The PLS-SEM analysis explained 59.8% of variance in behavioral intention to adopt AI (R² = 0.598). Awareness and Knowledge emerged as the strongest predictor (β = +0.505, p < 0.001), followed by Performance Expectancy (β = +0.229, p < 0.05) and Social Influence (β = +0.123). Perceived Risk functioned as the primary barrier (β = −0.185, p < 0.05). Qualitative findings identified infrastructure gaps, regulatory ambiguities, and training deficiencies as major implementation barriers, while emphasizing opportunities in diagnostic accuracy and remote monitoring. Conclusions: AI acceptance in Saudi healthcare is primarily driven by knowledge, with perceived usefulness and peer support as secondary facilitators, while safety and accountability concerns remain substantial obstacles. Successful AI integration requires coordinated efforts in education, transparent governance frameworks, and institutional support. This study contributes theoretically by validating extended UTAUT in a non-Western healthcare context and practically by providing evidence-based strategies for sustainable AI adoption that enhance healthcare quality while respecting professional roles and ethical principles. Full article

Digital transformation in construction small and medium-sized enterprises (SMEs) depends on employees’ willingness to use information systems in their daily work. This study examines the role of transformational leadership (TL) and perceived organizational support (POS) in employees’ behavioral intention to use information systems in Chinese engineering construction SMEs. It also considers the mediating role of perceived usefulness (PU) and perceived ease of use (PEOU) and the moderating role of organizational culture. A total of 361 valid responses were collected from employees in Chinese engineering construction SMEs. The results show that TL and POS both act as organizational drivers of employees’ adoption intention. TL influences BI by improving employees’ cognitive evaluation of information systems through PU and PEOU. POS provides resource-based support to help employees feel more confident using these systems. OC further conditions how employees respond to leadership and support signals during digital transformation. These findings suggest that technology acceptance in engineering construction SMEs is shaped by both individual technology beliefs and organizational conditions. This study extends technology acceptance research by making the Theory of Planned Behavior more concrete through managerial and support mechanisms. It also provides practical guidance for SME managers seeking to support digitalization through clear leadership communication, targeted resource support, and a learning-oriented culture. Full article

Digital transformation among small and medium enterprises (SMEs) in developing countries is limited by a persistent gap between prevailing adoption frameworks and the sociocultural realities of target populations. Frameworks such as the Technology Acceptance Model (TAM), the Technology-Organization-Environment (TOE) framework, UTAUT, and IDT were originally developed for industrialized contexts and do not adequately account for the cultural factors influencing adoption behavior in structurally distinct environments. A systematic mapping of 256 articles revealed that only 14 consider cultural behavior as a variable, and none utilize a validated cultural measurement instrument. This study introduces the Culturally Contextualized Digital Transformation Model (CC-DTM), a four-layer theoretical architecture that integrates the TOE framework, TAM constructs, and Hofstede’s cultural dimensions, operationalized as individual-level espoused values rather than national aggregate scores. The model incorporates a novel meso-level construct, Ecosystem Density, which mediates the relationship between environmental context and organizational readiness. The CC-DTM specifies 22 constructs and 15 directional hypotheses, organized into an initial empirical agenda (H1–H12) and deferred extensions (H13–H15). Additionally, a three-configuration typology based on internal SME attributes is developed. A two-phase validation roadmap, consisting of expert-panel content assessment and configurational case illustration across ten Chilean SMEs, is proposed. Full article

This study investigates how AI-assisted plagiarism changes dishonest academic behavior in a socially interactive learning environment under different educational conditions. To this end, this study develops a scenario-based simulation to examine how AI-assisted plagiarism influences dishonest academic behavior in socially interactive learning environments. The model represents students as autonomous agents embedded in local peer networks who adapt their weekly behavior under academic pressure, institutional intervention, and available cheating options. Two behavioral scenarios are considered: a conventional plagiarism environment, in which agents choose between honest submission and direct copying, and an AI-augmented environment, in which AI-assisted plagiarism is introduced as an additional dishonest strategy. Intervention is modeled through environmental and institutional conditions, specifically detection probability and sanction severity, rather than through direct internal reward manipulation. Q-learning is used as a simplified adaptive mechanism for repeated agent choice. Experimental results show that the possibility of producing and assessing a simulation to see the availability of AI-assisted plagiarism substantially changes the behavioral composition of misconduct by increasing total dishonest behavior and shifting a large share of it toward the AI-assisted category. In the simulation, active intervention reduces dishonest behavior overall but does not eliminate AI-assisted plagiarism as the dominant dishonest strategy in the AI-augmented environment. These observations in the simulation suggest that academic misconduct in the AI era should be understood not only as a problem of deterrence but also as a problem of behavioral adaptation under changing technological and institutional conditions. To support the realism assessment of the simulation design, the study also conducts a structured qualitative plausibility review using multiple large language models under a shared prompt. Across these reviews, the model is judged to be acceptable as a first-stage stylized baseline, while important limitations are identified in agent heterogeneity, social influence depth, and the use of Q-learning as a simplified adaptive heuristic to reproduce the behaviors of actors in there. Full article

This study investigates the determinants of driver acceptance of “M-Flow”, Thailand’s first Advanced Traffic Management solution utilizing Multi-Lane Free Flow (MLFF) technology. While designed to eliminate toll plaza bottlenecks through AI-driven automated billing, the system’s operational efficiency is hindered by a “trust gap” caused by a stringent ten-fold penalty for late payment compliance. By integrating the Technology Readiness Index (TRI 2.0) with the Technology Acceptance Model (TAM), this research explores how psychological readiness dictates the success of smart traffic infrastructures. Data from 485 drivers were analyzed using Structural Equation Modeling (SEM). The results reveal that while technological optimism and innovativeness act as motivators, Insecurity (β = −0.723) emerges as the dominant psychological barrier, directly suppressing the perceived ease of use and triggering behavioral resistance. The findings demonstrate that technical efficiency and diverse payment options alone are insufficient to ensure mass adoption if the regulatory climate fosters financial anxiety. To maximize system throughput, this study recommends that policymakers shift from punitive enforcement to “trust engineering.” By enhancing financial transparency, simplifying the registration-to-payment workflow, and mitigating the “penalty trap” perception, authorities can achieve the psychological seamlessness that is a strict prerequisite for a fully trusted smart transportation infrastructure in Thailand. Full article

The adoption of large language models (LLMs) is reshaping how organizations approach automation, decision-making, and user engagement across sectors. This study investigates the trends, theoretical frameworks, and adoption factors influencing the integration of LLMs in five key domains: education, commerce, banking, healthcare, and service. By employing a systematic literature review and meta-analysis, this paper synthesizes research published between 2022 and early 2026, corresponding to the period when LLMs became widely accessible for public and enterprise use, to evaluate both conceptual and empirical dimensions of LLM adoption. The review identifies the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology, including its extensions, as the most frequently applied frameworks. It also highlights the growing incorporation of complementary models such as the diffusion of innovation, the information system success model, and self-determination theory. The meta-analysis examines 59 pairwise relationships drawn from 154 studies with a cumulative sample size of 88,886 participants. Using correlation coefficients, $I^2$ statistics, and Egger’s test, the analysis reveals strong, consistent associations between behavioral intention and both use behavior and actual use, while also identifying high heterogeneity across contexts. Constructs such as trust, hedonic motivation, and personal innovativeness emerged as influential but were underrepresented in the theoretical modeling. The study underscores the importance of facilitating conditions, infrastructure, and organizational readiness for enabling sustained use while also drawing attention to gaps in addressing perceived risks, privacy concerns, and ethical implications. Full article

Developing-country construction sectors continue to record disproportionately high occupational accident rates, partly attributable to the slow adoption of digital safety technologies, including Building Information Modeling (BIM) and Internet of Things (IoT) systems. While prior empirical research has established the population-level factors that explain stakeholder adoption intention through survey-based frameworks, the ability to classify individual stakeholder readiness for targeted, pre-deployment intervention remains methodologically unaddressed. This study fills that gap by applying three supervised machine learning classifiers (Random Forest [RF], XGBoost (XGB), and Support Vector Machine (SVM)) to a dataset of 107 construction professionals purposively sampled from large-scale infrastructure projects in Pakistan, including China−Pakistan Economic Corridor (CPEC) packages and the Barakahu Bypass project. Five construct-level features derived from an integrated Technology Acceptance Model and Technology−Organization−Environment (TAM-TOE) survey instrument were used to classify stakeholders into High, Moderate, and Low readiness tiers. XGBoost achieved the best classification performance (accuracy = 93%, macro F1 = 0.93), followed by RF (91%, F1 = 0.91) and SVM (87%, F1 = 0.87). The convergent performance across three structurally different algorithm families indicates that the readiness signal reflects a consistent attitudinal pattern rather than an artifact of any single modeling assumption. Feature importance analysis consistently identified Perceived Benefits (32%) and Technology Awareness (25%) as the dominant predictive features, followed by Organizational Readiness (20%), Perceived Barriers (15%), and Respondent Profile (8%). Attitudinal readiness mapping classified 62% of stakeholders as High readiness, 28% as Moderate, and 10% as Low, providing an exploratory attitudinal segmentation framework to assist construction managers in prioritizing capacity-building investments, subject to longitudinal behavioral validation. The study also finds that awareness of digital technology consistently outpaces Organizational Readiness for implementation, a pattern consistent with findings from analogous developing-country construction contexts. Full article

This study explores the multifaceted attitudes of high school students toward the use of artificial intelligence (AI) and large language models (LLMs) like ChatGPT in educational contexts. Drawing upon a tripartite model of attitudes, our research evaluates affective, cognitive, and behavioral dimensions to offer a nuanced understanding of students’ perceptions. The affective dimension assesses emotional responses to AI tools, the cognitive dimension examines beliefs about the utility and ethical considerations of AI, and the behavioral dimension evaluates actual usage patterns of AI technologies. Utilizing a newly developed survey instrument tailored for the educational context, data was collected from 93 high school students across different regions of Italy in the period that ranged from February 2024–March 2024. Exploratory factor analysis (EFA) was employed to explore the underlying structure of the survey instrument and identify underlying factors influencing AI acceptance. The analysis reveals three distinct factors—Mindful AI Learning, Embracing AI Effects, and LLM as Learning Companion, highlighting the complexity of students’ attitudes toward AI. Results indicate a cautious but optimistic reception of AI in education, offering crucial insights into Information Intelligence for enhanced learning and the design of personalized learning pathways. The study contributes to the literature by offering a novel scale to measure attitudes toward artificial intelligence, specifically focusing on both general AI and Generative AI large language models, such as ChatGPT. Moreover, it highlights the critical need for AI literacy, ethical digital learning frameworks, and robust institutional policies to bridge the digital divide. Consequently, this work is framed as a preliminary exploratory investigation. Ultimately, these findings advance our knowledge of transformative digital learning processes and inform future strategies for human–machine integration in educational systems. Full article

This paper describes design, implementation and initial evaluation of Digital Twin Metaverse Classroom for higher education. Digital Twin Metaverse Classroom refers to highly realistic digital replicas or virtual replicas or prototypes of university classrooms or learning spaces. This paper focuses on creating high-fidelity digital replica of typical university lecture room. The main purpose of the Digital Twin Metaverse Classroom is to support teaching and learning in addition to traditional videoconferencing. The pilot involved thirty-two undergraduate students. A single-group pre-test/post-test quiz measured short-term learning, while the Technology Acceptance Model (TAM) measured acceptance through perceived usefulness, perceived ease of use, attitude toward use, and behavioral intention. A single session raised the mean quiz score from 6.41 to 9.19, a within-session gain that reached statistical significance, while all four TAM constructs scored highly. Because the sample was small and confined to one institution, with neither a control group nor a follow-up, these findings are best read as early evidence of feasibility, short-term improvement, and favorable acceptance rather than as proof of comparative effectiveness. Full article

The existing literature highlights that artificial intelligence (AI) creates both hope and threat perceptions among managers and workers, particularly due to concerns about potential job losses and the negative effect on continued professional development. Employee trust in AI-based systems varies depending on their features and performance. Furthermore, regardless of the performance of such systems, some individuals are inherently opposed to AI, a phenomenon known as AI aversion. In this study, an Integrative AI Adoption Framework is developed, drawing upon principles from established theories, including the technology acceptance model, behavioral decision theory, and emotion-based frameworks, to assess how perceived usefulness and perceived ease of use, along with perceived threat, trust, and AI aversion, influence human resources (HR) professionals’ attitudes and behavioral intentions to use AI-based recruitment systems. In doing so, the study conceptualizes AI-based recruitment as a socio-technical system in which a technical subsystem (the system’s instrumental and AI-specific properties) and a social subsystem (the affective and trust-related responses of HR professionals) must be jointly considered to explain adoption. The model was tested using the partial least squares structural equation modeling (PLS-SEM) approach through survey-based data collected from 242 HR professionals. The study’s findings indicate that attitude plays an important role in shaping behavioral intention, and perceived usefulness is a key driver of attitude. AI aversion negatively influences attitudes, while trust has a twofold effect of reducing AI aversion and positively influencing attitude. Additionally, perceived threat significantly increases AI aversion, which is driven by concerns over job replacement and personal development. Full article

Chitosan, produced through deacetylation of chitin from crustacean byproducts and, increasingly, fungal biomass and insects, is attracting food-sector interest because it combines antimicrobial activity, antioxidant capacity, biodegradability, and film-forming behavior in a single polymer. This review discusses how source, molecular weight (MW), degree of deacetylation, solubility, and charge density shape its performance in food systems. The paper then follows the main technological routes now tested or used: edible films and coatings, hydrogels, cryogels, nanoparticles, microcapsules, and hybrid matrices. These formats can protect fresh produce, meat, poultry, fish, seafood, and dairy foods, while also supporting beverage clarification, emulsion control, release of natural antimicrobials or antioxidants, and freshness monitoring in active or intelligent packaging. The evidence indicates strong promise, especially where microbial growth, lipid oxidation, moisture transfer, and short shelf life remain limiting factors. Yet, wider industrial use is still slowed by water sensitivity, sensory effects, raw-material variation, cost, process scale-up, and regulatory alignment. Future work should move beyond laboratory efficacy and address reproducible production, food-specific validation, and consumer acceptance. Full article

Inadequate management of municipal solid waste represents a critical challenge for the sustainability of modern cities, characterized by low citizen participation rates due to the lack of direct incentives. Unlike existing approaches that isolate hardware classification or fleet monitoring, this article presents RENOVA as a socio-technical closed-loop system based on the Internet of Things (IoT) and artificial intelligence (AI). This system integrates an IoT-enabled smart bin, a gamified mobile application for citizens, and an administrative web panel for merchant redemption, all interconnected via a REST API. The system employs computer vision through the GPT-4o (OpenAI, San Francisco, CA, USA) multimodal model for the automatic classification of recyclable materials (PET plastic and Aluminum) and integrates a gamified rewards program to incentivize citizen participation. The methodology follows an applied technological development approach under the agile Scrum framework. Prototype validation demonstrated successful real-time communication between the IoT device and the cloud platform, achieving classification accuracy exceeding 95% under controlled conditions. A diagnostic survey applied to a convenience sample of 51 participants revealed that 94.1% accepted the proposed gamification model, while user experience evaluation (n = 74; consisting primarily of university-affiliated individuals aged 15–24) yielded a mean overall satisfaction score of 4.77/5.0 (SD = 0.48), with 79.7% of participants assigning the maximum rating. These findings reflect stated user acceptance and behavioral intention under prototype conditions rather than observed long-term behavioral change, and should not be generalized to broader urban populations without further validation. The proposed solution directly contributes to Sustainable Development Goals 11 (Sustainable Cities) and 12 (Responsible Consumption), suggesting a potentially scalable framework. Full article

The rapid development of generative artificial intelligence (GAI) has exerted extensive and far-reaching impacts on college students’ learning, making it a topic worthy of in-depth investigation. This study aims to explore the impact of GAI usage on college students’ innovative learning behaviors, drawing on the theoretical framework of the Unified Theory of Acceptance and Use of Technology (UTAUT). Specifically, the research explores the mediating mechanisms of effort expectancy and performance expectancy, as well as the moderating role of growth mindset in this process. Based on a sample of 430 Chinese college students recruited from diverse academic majors, the proposed moderated mediation model is empirically examined through latent structural equation modeling analysis. The results indicate that using GAI in learning significantly enhances students’ perceptions of effort expectancy and performance expectancy, thereby fostering their subsequent innovative behavior. Notably, the findings reveal that while performance expectancy mediates the relationship between GAI usage and innovative behavior, a growth mindset weakens this indirect pathway. The practical implications of this study are also discussed for both students and universities. Full article