Search Results (280)

Housing stock is observed to be associated with high carbon emissions, high fuel poverty and low comfort levels in the UK. Retrofitting the housing stock is one of the best solutions to address these problems. This paper directly corresponds with human and planetary health in terms of climate change, human health and mental health by addressing the challenges of housing retrofit at scale. Retrofitting houses can also contribute to social equity, reduced use of planetary resources and better financial and physical comfort. Despite the availability of the right technology, government grants and the potential to acquire supply chain and skilled labour, the progress of retrofit is extremely poor. Importantly, the UK is off track to achieve net zero by 2050, and the housing stock contributes 18.72% of the total emissions. The problem is further exacerbated by the 30.4 million units of housing stock. Robust strategies are required to retrofit the housing stock at scale. The study uses a qualitative modelling method under the diffusion of innovations theory to formulate a retrofit-at-scale strategy for the UK. Findings recommend focusing on skill development, show homes, research and innovation, supply chain development, business models, government grants and regulatory tools in a trajectory from 2025 to 2050. The proposed strategy is aligned with the segments of the diffusion of innovation theory. Although the analysis was performed with reference to the UK, the findings are transferable, considering the broader and urgent concerns related to human and planetary health. Full article

Although green housing is widely regarded as an effective solution to energy and environmental challenges, its actual rate of adoption remains lower than expected. In the context of increasingly prominent sustainable development goals, promoting residents’ adoption of green housing has become a key issue in advancing sustainable transformation within the housing sector. Consequently, enhancing residents’ willingness to adopt green housing is critical to its broader diffusion. Drawing on diffusion of innovation theory, attitude theory, and perceived value theory, this study develops a multidimensional integrated model to identify factors influencing the adoption of green housing. The model examines how the innovation attributes of green housing and residents’ psychological evaluations jointly shape adoption intention. A questionnaire survey was conducted among 387 residents in Nanjing, China, and the data were analysed using partial least squares modelling. The results indicate that the five attributes derived from diffusion of innovation theory are significant antecedents of residents’ attitudes. Relative advantage, compatibility, trialability, and observability exert significant positive effects on residents’ attitudes toward adopting green housing, with relative advantage emerging as the most influential factor. Complexity has a negative, though comparatively weaker, effect on residents’ attitudes toward green housing adoption. Residents’ attitudes and perceived value are identified as significant predictors of green housing adoption intention. These findings contribute to a clearer understanding of residents’ green housing adoption intentions for both researchers and practitioners. More importantly, the study offers general policy and managerial implications for governments and developers seeking to enhance the uptake of green housing. Full article

Western Macedonia, the leading power-producing region in Greece, has long depended on thermoelectric plants and lignite mining. To reach climate neutrality by 2050, Greece is undergoing a delignitization process aiming to shut down all lignite plants. This structural reconstruction of the energy model will mainly affect society, the economy, the environment, and agriculture. Strengthening efforts to support lignite-dependent areas are essential for this transition. Bioeconomy could be one of the main pillars for the post-lignite era in the Western Macedonia Region (WMR). This paper explores the gender dimension in the adoption of bioeconomy practices and innovativeness among farmers in the Region of Western Macedonia. Based on 331 structured questionnaires and a Two-Step Cluster Analysis, the research identifies five farmer clusters and then correlates the clusters with Rogers’s theory of diffusion of innovations. The findings identify a dynamic group of young female farmers leading the diffusion of innovation, emphasizing their role in promoting sustainable agricultural transitions and the need for gender-responsive policies in regional bioeconomy strategies. Full article

The dynamic evolution of collective emotions across the news dissemination life-cycle is a powerful yet underexplored signal in affective computing. While phenomena like the spread of fake news depend on eliciting specific emotional trajectories, existing methods often fail to capture these crucial dynamic affective cues. Many approaches focus on static text or propagation topology, limiting their robustness and failing to model the complete emotional life-cycle for applications such as assessing veracity. This paper introduces C-STEER (Cycle-aware Sentiment-Temporal Emotion Evolution), a novel framework grounded in communication theory, designed to model the characteristic initiation, burst, and decay stages of these emotional arcs. Guided by Diffusion of Innovations Theory, C-STEER first segments an information cascade into its life-cycle phases. It then operationalizes insights from Uses and Gratifications Theory and Emotional Contagion Theory to extract stage-specific emotional features and model their temporal dependencies using a Bidirectional Long Short-Term Memory (BiLSTM). To validate the framework’s descriptive and predictive power, we apply it to the challenging domain of fake news detection. Experiments on the Weibo21 and Twitter16 datasets demonstrate that modeling life-cycle emotion dynamics significantly improves detection performance, achieving F1-macro scores of 91.6% and 90.1%, respectively, outperforming state-of-the-art baselines by margins of 1.6% to 2.4%. This work validates the C-STEER framework as an effective approach for the computational modeling of collective emotion life-cycles. Full article

A system dynamics approach is described to explore the path of Carbon Capture diffusion. The proposed model, in principle, follows the Bass diffusion of innovation theory and includes all major influencing factors. The primary contribution of this paper is the modification of Bass’s model to reflect parameters affecting the adoption of Carbon capture and storage technology. Consequently, it differs from other extensions to Bass’s model. The underpinning of this work is the system dynamics (SD) approach, which can open a pathway for further research into CCS acceptance. The proposed model’s behaviour is illustrated for various transition pathways of the technology, for different regimes. By modifying the proposed model, the paper also allows consideration of various capturing technologies on their merit. The proposed framework enables the examination of the impact of intervention policies on the adoption of CCS by individual investors. The purpose is to identify the parameters of these policies to support the under-resourced CCS technology and reduce the need for government participation. It is worth noting that the SD is primarily a descriptive method used for scenario analysis to illustrate what the future would look like. Full article

As artificial intelligence (AI) becomes embedded in entrepreneurial practice, an unresolved question is whether age shapes founders’ perceptions of its opportunities and risks. Drawing on diffusion-of-innovations and technology adoption theories, this study examines whether age cohorts differ in their perceived benefits of AI, perceived risks, and short-term expectations regarding AI’s business impact. Using data from the 2024 Global Entrepreneurship Monitor (GEM) survey for Slovenia, we analyze ordinal indicators across all three domains. Bivariate comparisons using Mann–Whitney U tests with effect sizes are complemented by multivariate ordinal logistic regression models controlling for sector, education, and gender. The analysis reveals an asymmetrical age gap in AI perceptions. Younger entrepreneurs report higher perceived benefits and more positive impact expectations, while AI-related risk perceptions do not vary by age. Multivariate analyses show that age effects on perceived benefits are context-dependent, whereas age remains a robust predictor of future-oriented impact expectations. The study offers a theoretically grounded and methodologically transparent analysis integrating technology adoption frameworks with entrepreneurial psychology. Practically, it underscores the need for differentiated AI-readiness initiatives that address age-related differences in strategic orientation and preparedness. Future research could further explore the roles of capabilities, industry context, and entrepreneurial experience. Full article

Despite significant research interest, the understanding of how to systematically implement Lean practices in supply chains remains limited. Therefore, this study analyzes the impact of blockchain technology on implementing Lean principles within supply chain networks. A theoretical model was developed based on a comprehensive literature review, utilizing innovation diffusion theory, agency theory, and transaction cost economics. The LDA topic modeling, based on big data from the past decade, was employed to explore key areas and essential industry practices related to blockchain technology. By cross-validating big data analysis and survey results, we also developed reliable metrics that can be used to study blockchain utilization in SCM. The hypotheses were empirically tested using survey data from 219 US enterprises that have adopted blockchain technology. The empirical results revealed that blockchain adoption significantly improved Lean management practices within supply chain networks. Furthermore, research has shown that blockchain can significantly enhance operational performance, including cost reduction, quality improvement, delivery capacity, and greater flexibility. These compelling results suggest that blockchain has the potential to serve as a powerful platform for systematically integrating and orchestrating Lean management practices across the entire supply chain network, thereby achieving operational excellence. An in-depth discussion of the study’s practical implications and theoretical contributions is presented. Full article

In the era of sustainable digital transformation, organizations increasingly rely on artificial intelligence (AI) to enhance efficiency, innovation, and long-term competitiveness. However, employees’ psychological barriers, including technostress and innovation resistance, continue to constrain successful and sustainable AI adoption. Grounded in Social Exchange Theory (SET), Conservation of Resources Theory (COR), Diffusion of Innovation Theory (DOI), and the Technology Acceptance Model (TAM), this study develops an integrated model linking perceived organizational support (POS)—comprising emotional, informational, and instrumental dimensions—to employees’ sustainable AI adoption through the dual mediating roles of technostress and innovation resistance. Based on 426 valid responses collected from multiple industries, a triadic hybrid approach combining Structural Equation Modeling (SEM), Artificial Neural Networks (ANNs), and Necessary Condition Analysis (NCA) was applied to capture both linear and nonlinear mechanisms. The results reveal that Informational Support (IFS) is the most influential factor and constitutes the sole necessary condition for high-level AI adoption, while emotional and instrumental support indirectly promote sustainable adoption by mitigating employees’ stress and resistance. This study contributes to sustainable management and AI adoption research by providing insights into the potential hierarchical and threshold patterns of organizational support systems in digital transformation. It also provides managerial implications for designing transparent, empathetic, and resource-efficient support ecosystems that foster employee-driven intelligent transformation. Full article

Renewable energy efficiency (REE) remains critically low across many Sub-Saharan African (SSA) countries, yet the existing literature provides limited empirical clarity on how governance quality shapes efficiency outcomes and through which mechanisms these effects operate. This study addresses this gap by examining the influence of governance quality on REE in 23 SSA countries from 2005 to 2023, drawing on institutional theory and innovation diffusion theory. The analysis investigates three mediating channels, renewable investment, green policy, and green technology, using a multidimensional empirical framework that integrates the Malmquist Productivity Index (MPI), Two-Step System GMM, Generalized Estimating Equations (GEE), Generalized Least Squares (GLS), and Panel-Corrected Standard Errors (PCSE). Results consistently show that governance quality significantly enhances REE through investment, policy, and technological pathways. To capture nonlinearities and heterogeneous responses often overlooked in traditional models, we complement the econometric estimations with causal machine-learning simulations (Double Machine Learning and Causal Forests). These counterfactual analyses reveal that governance improvements and renewable-policy adoption produce the highest efficiency gains in mid-governance countries with stronger absorptive capacity. While the study offers policy-relevant insights, limitations remain, due to data constraints, unobserved institutional dynamics, and the uneven maturity of green-technology systems across the region. Nevertheless, the findings underscore that strengthening governance and fostering innovation are fundamental to accelerating a sustainable and inclusive green-energy transition in Sub-Saharan Africa. Full article

This study investigates the factors that influence the acceptance of 3D concrete printing technology in South Africa. The purpose is to provide evidence-based insights to guide regulators in developing clear standards and certification pathways for 3DCP in South Africa. In a mixed-method research design, the study gathered data from professionals including architects, civil engineers, quantity surveyors, project managers, contractors, regulators, and local municipalities using a modified Unified Theory of Acceptance and Use of Technology framework, adapted to the institutional and infrastructure contextual nuances of South Africa. The findings indicate significant variability in awareness, exposure, and openness to 3DCP across professions and regions. Regulatory actors express caution due to the absence of national standards but also recognize the potential alignment with sustainable construction goals. Major enablers of acceptance include access to demonstrable case studies, technical training, and policy incentives. Barriers include a lack of local performance benchmarks, cost perceptions, and uncertainty regarding compliance pathways. By incorporating institutional variables such as regulatory clarity and policy maturity, the study advances a theoretical understanding of construction technology diffusion in the global south. The study offers a robust, context-specific model that can be adapted in similar economies seeking to balance innovation with regulatory oversight. Full article

Three-dimensional printed food has rapidly positioned itself at the intersection of food technology and personalized nutrition, opening up new perspectives for sustainable production, creative customization, and more efficient resource use. Although global interest in this innovation continues to grow, consumer acceptance remains largely underexplored in Central and Eastern Europe. This study analyzes how Romanian consumers approach the adoption of 3D-printed food by applying an extended UTAUT2 framework to a sample of 608 urban respondents. Using structural equation modeling, it examines the influence of expected effort, performance expectancy, social influence, and perceived compatibility on adoption intention, while price perception is introduced as a key mediating variable—a novel and meaningful contribution to the literature on food technology acceptance. Given the non-probabilistic sampling design, the difficulties encountered in measuring Hedonic Motivation and Facilitating Conditions, and the early diffusion stage of 3D food printing in Romania, the present work should be viewed as a robust exploratory investigation based on Structural Equation Modeling (SEM) among urban Romanian consumers, providing first empirical evidence on 3D-printed food acceptance in Eastern Europe rather than definitive conclusions for the entire population. The results highlight that utilitarian and social factors are decisive: expected effort enhances perceived performance, while performance, social influence, and compatibility significantly strengthen perceptions of price fairness. In turn, price perception strongly predicts consumers’ behavioral intention to adopt 3D-printed food. Hedonic motivation and facilitating conditions were not statistically significant and were therefore removed from the final model. These findings show that, in emerging food markets, consumers tend to make adoption decisions based more on rational value assessments than on novelty or convenience. The study contributes to theory by embedding price perception into the UTAUT2 framework and to practice by identifying the key elements that can boost market readiness—transparent pricing and closer alignment with consumer values. By filling an important gap in the empirical literature from Eastern Europe and focusing on price as a cognitive bridge between technological and psychological drivers, this paper offers a timely and relevant contribution to ongoing research on consumer perception and acceptance of food innovations. For Eastern European food innovation research, this study provides one of the first quantitative analyses of 3D-printed food acceptance that explicitly links technology-related beliefs to price perception in a regional, price-sensitive context. Full article

This study investigates the impact of Internet of Things (IoT) demand on Digital Economic Growth (DEG) in Saudi Arabia between 2015 and 2023, employing both linear regression and a panel Autoregressive Distributed Lag (ARDL) model. The results show a long-term, significant, and positive association between IoT adoption and DEG, supported by the Technology Organization Environment (TOE) framework, highlighting the relevance of technology readiness and organizational capacity. Moreover, Internet penetration is a significant driver of digital transformation, aligned with the Diffusion of Innovations (DOI) theory, which emphasizes the role of connectivity in facilitating the adoption of digital devices. IoT will have little or no impact in the short term, but in the long run, the benefits are clear. Furthermore, despite the long- and short-term benefits of 5G deployment indicated by the results, a divergence between 5G deployment and electricity consumption is signaled by the significance of the error-correction term, which may be attributed to infrastructure and deployment prerequisites. Additionally, as an extension of the Resource-Based View (RBV) paradigm, the ultimate drivers of DEG through innovation and strategic resources highlight the importance of Research and Development (R&D) investment and Foreign Direct Investment (FDI) in inducing its growth. In contrast, inflation has an adverse impact on DEG, confirming macroeconomic instability as an obstacle to digital advancement, which relates to the environmental pillar of TOE. Policymakers can maximize Saudi Arabia’s digital economic growth on a sustainable, stronger path by investing in IoT infrastructure, increasing internet access and adoption, enhancing R&D and institutional support, and addressing challenges related to macroeconomic stability and 5G deployment. This study adds to the extant research by empirically evaluating the short- and long-term effects of IoT adoption on Saudi Arabia’s digital economic development, thereby providing insights into the roles of innovation, infrastructure, and institutional support in driving digital transformation. Full article

The aim of the paper is to recognize the role of external institutions in supporting the Transport, Shipping, and Logistics (TSL) sector in the transformation towards sustainable and low-emission operations in Poland. In the context of the EU’s decarbonization agenda and accelerating climate challenges, the study explores how regulatory, financial, and normative mechanisms affect the electrification of transport fleets. A mixed-methods approach was applied, combining qualitative content analysis of European and national policy frameworks with a quantitative CATI survey among logistics enterprises. The results reveal that legal and normative instruments remain the dominant institutional drivers of fleet electrification, while fiscal incentives—subsidies and tax reliefs—play a supportive but still secondary role. Sectoral and financial pressures from banks and market stakeholders are emerging as new, complementary forces of change. Firm size, ownership structure, and market scope significantly moderate these perceptions. The paper contributes to institutional and innovation-diffusion theory and offers policy insights for designing coherent and multi-level frameworks. Full article

The rapid advancement of digital technologies is compelling the field of cultural heritage preservation to shift from static conservation toward dynamic transmission and innovative transformation. In response, this study proposes a Generative AI (GenAI) application approach based on a cultural cognitive model. First, a cognitive structure of cultural symbols is constructed based on symbolic interactionism, and grounded theory is applied to analyze how specific user groups interpret and internalize these symbols, thereby establishing a cultural cognition system. An enhanced Delphi method is then employed to synthesize expert judgments and develop a multi-level cultural-symbol dataset. The dataset is integrated into generated models through stable diffusion models and Low-Rank Adaptation (LoRA) to strengthen their capacity for recognizing and generating culturally significant features. The feasibility and effectiveness of the proposed model are evaluated through expert-based assessments. To further examine its generalizability, the study conducts a case application using Shanghai-style furniture design. The results demonstrate substantial improvements in output quality and alignment with design requirements. This research provides a reproducible methodology for the digital safeguarding and innovative development of cultural heritage, while expanding the application scenarios of AI technologies in the protection of intangible cultural heritage. Full article

The application of Building Information Modeling (BIM) in construction projects can significantly improve project efficiency, accuracy, and collaboration among stakeholders. However, construction professionals, particularly design engineers, exhibit behavioral resistance to BIM implementation, which has hindered the achievement of expected benefits. To explore the behavioral resistance to BIM implementation, this study integrates the status quo Bias (SQB) theory and the innovation diffusion theory and proposes a factor model for predicting the behavioral resistance of architectural engineering design engineers to BIM implementation. The model was empirically tested through partial least squares structural equation modeling (PLS-SEM) on survey data collected from design engineers in BIM-based construction projects in China. The results indicate the following: (1) resistance to change (β = 0.383), BIM compatibility, and BIM user satisfaction play prominent but independent roles in predicting behavioral resistance to BIM implementation, and (2) resistance to change is motivated by inertia (β = 0.473), self-efficacy, and perceived distributive equity. The proposed framework provides a more nuanced account of resistance to BIM implementation. Specifically, deep-seated cognitive biases and innovation-specific perceptions independently and jointly shape behavioral resistance, a distinction often overlooked in prior research. Practically, this research provides recommendations for effectively addressing resistance behaviors within construction project settings. Full article