Sustainable Adoption Research Articles

This study examined the influence of sustainable supply chain practices on the competitive performance of manufacturing firms in Oyo State, Nigeria, framed by the Triple Bottom Line. It addressed the critical gap between global sustainability pressures and local adoption barriers. A mixed-methods research design was employed, combining quantitative surveys with qualitative insights. Data were collected from 81 manufacturing firms using a structured questionnaire and were analyzed via SPSS for correlation and regression, complemented by thematic analysis. The key findings revealed that economic sustainability practices significantly enhanced cost-effectiveness (r=0.395, p<0.001), while social practices substantially improved customer loyalty and brand reputation (r=0.742, p<0.001). Compliance with global standards also positively impacted international competitiveness (r=0.634, p<0.001). Conversely, environmental practices showed an insignificant negative effect (r=-0.212, p=0.058), primarily due to infrastructural deficits. Qualitative data corroborated these results, with firms reporting 5–10% cost savings from lean production but facing significant hurdles in implementing green initiatives. The study concluded that while economic and social sustainability were potent drivers of competitive advantage, environmental sustainability was hampered by systemic constraints. It was recommended that firms prioritize cost-effective social and economic practices, while policymakers and industry associations should facilitate infrastructure sharing, simplify global certifications, and provide government incentives to close the sustainability adoption gap.

Artificial intelligence and infectious diseases: Scope and perspectives.

The construction industry is one of the largest consumers of resources and a significant contributor to environmental degradation in Europe, accounting for 50% of natural resource use, 34% of waste generation, and 5–12% of greenhouse gas emissions. In response to growing environmental pressures and regulatory demands, the sector needs to adopt sustainable material alternatives. This study examines the potential adoption of rice straw ash in the European construction sector. The research applies a PRISMA-based systematic literature review, integrated with the Multi-Level Perspective (MLP) framework, PESTLE, and SWOT analyses to provide a comprehensive assessment of the socio-technical dynamics influencing its adoption. The findings identify barriers including the absence of standards, fragmented supply chains, and inconsistent material quality. However, it highlights strategic opportunities such as the declining availability of conventional SCMs, alignment with the EU’s regulations and circular economy principles, and growing public awareness of sustainable materials. The study concludes that advancing the transition to RSA will require regulatory support, the development of standards, and coordinated collaboration among stakeholders to achieve large-scale implementation. By integrating multi-dimensional transition factors, this research contributes actionable insights for advancing sustainable material adoption.

ABSTRACT This study examines the impact of information systems management (ISGOV) on the maturity of FINTECH (Fintechm) in EU companies operating in developing economies focusing on the role of risk management (Itriskeff). Using the 550 panel of fixed years in 2012–2023, we use OLS, fixed effects, high-dimensional fixed effects, 2SL, and GMM system models to take into account solid heterogeneity, endogeneity, and persistence in digital transformation results. The finding shows that the stronger managing is significantly increased by the maturity of Fintech, reflecting improved operating resistance, risk mitigation, and strategic digital acceptance. It is not evenly significant, but it suggests that the structures of management themselves do not have to fully utilize the benefits of mitigating risks. Control variables such as IT infrastructure, organizational digital culture, and digital talent consistently affect the results. The results emphasize the critical role of integrated are the management and management of IT risk in achieving sustainable adoption of Fintech and continuity of business in contexts of developing economies.

Africa’s healthcare systems face profound structural challenges, including fragmented infrastructure, systemic data vulnerabilities, and unreliable medical supply chains, necessitating innovative, context-specific solutions. Blockchain technology has emerged as a transformative tool in global healthcare, yet research remains disproportionately focused on high-income economies, with less than 5% addressing Africa’s unique socio-technical landscape. This study employs a multi-method approach, combining bibliometric analysis, latent semantic analysis (LSA), and the PRISMA framework, to map blockchain-healthcare research in Africa, revealing critical gaps and proposing B4HC (Blockchain for Healthcare), a novel conceptual model tailored to resource-constrained settings. Our findings highlight blockchain’s potential to enhance data security in electronic health records (EHRs), optimize pharmaceutical supply chains, and empower patient-centric innovations like digital health wallets and decentralized telemedicine platforms. By addressing ethical and equitable adoption, this research challenges Eurocentric technological determinism, integrates social determinants of health, and aligns with decolonization agendas to foster inclusive health ecosystems. We provide policymakers with a roadmap for sustainable blockchain adoption and outline future research directions to bridge theoretical and practical gaps in decentralized health systems.

BackgroundClinical decision support (CDS) tools are critical for improving care delivery and guideline adherence but are associated with clinician burnout when inadequately designed and implemented. User-centered design (UCD) and implementation science (IS) methods are evidence-based approaches to optimizing CDS tools, but are infrequently used in part due to limited guidance on how to apply them within resource-constrained health systems.ObjectiveThis paper focuses on pragmatic application of an integrated UCD–IS approach, demonstrating how it can be adapted to meet operational constraints through two real-world case studies.MethodsWe applied an integrated UCD–IS approach guided by the Practical Robust Implementation and Sustainability Model (PRISM) to two CDS projects within a large regional health system: (1) adapting a CDS for improving prescribing of goal-directed medical therapy in patients with heart failure during virtual visits, and (2) expanding a naloxone co-prescribing CDS across outpatient settings. Each project followed iterative phases—partner engagement, design, prototyping, deployment, and evaluation tailored to time and resource constraints of the health system. Methods used included interviews, focus groups, surveys, and usability testing.ResultsMultilevel partner engagement surfaced critical insights that informed design adaptations. The heart failure CDS was adapted using minimal changes while the naloxone CDS underwent more extensive design iterations. Both projects balanced rigor and pragmatism, enabling timely implementation and rigorous design evaluation while supporting feasibility and sustainability. Iterative evaluations of both CDS are ongoing and structured to inform real-time refinements that support patient, clinician, and system-level outcomes.ConclusionThis work provides practical guidance on applying an integrated UCD–IS approach to CDS design and evaluation in time and resource–constrained health system environments. By flexibly applying this integrated approach, health systems can better address multilevel partner needs, ensure contextual relevance, and support sustained adoption.

ABSTRACT Social sustainability remains underexplored in the context of startups. While much attention is given to economic and environmental performance, the social dimensions are frequently overlooked. This gap is particularly significant for startups, as their early‐stage decisions can shape long‐term cultural and social impacts both within their organizations and in the broader ecosystems they influence. This study investigates how emerging ventures implement social sustainability through ethical practices, inclusive business models, and responsible supply chain management, moving beyond traditional economic performance metrics. Adopting a mixed‐methods design, the research combines qualitative insights from structured interviews with quantitative validation. Based on a sample of 56 startups from Italy and the United States, the study examines: (1) the drivers shaping the adoption of social sustainability, (2) the types of activities and policies implemented and (3) the relationship between sustainability practices and startup performance. Findings reveal that concrete sustainability actions, rather than mere awareness or formal statements, are the strongest predictors of positive economic impact. The study contributes empirical evidence that highlights social sustainability as a strategic lever for entrepreneurial resilience and innovation, offering insights for founders, investors, and policymakers.

This study examines the interaction between cognitive demands and generative artificial intelligence (GenAI) technologies in shaping the quality and influence of academic research. While GenAI tools such as ChatGPT and Elicit are increasingly adopted to ease information processing and automate repetitive tasks, their broader impact on researchers’ cognitive performance remains underexplored. Using data from 998 researchers and applying structural equation modeling (SEM-PLS), we examined the effects of cognitive load, task fatigue, and resilience on research outcomes, with GenAI immersion as a higher-order moderator. Results reveal that both cognitive load and fatigue negatively affect research quality, while engagement and resilience offer partial protection. Unexpectedly, high immersion in GenAI intensified the negative impact of cognitive strain, suggesting that over-reliance on AI can amplify mental burden rather than reduce it. These results enhance the design and responsible integration of AI technologies in academic environments by demonstrating that sustainable adoption necessitates a balance between efficiency and human creativity and resilience. The study provides evidence-based insights for researchers, institutions, and policymakers seeking to optimize AI-supported workflows without compromising research integrity or well-being.

Blood transfusions are vital in health care, yet maintaining an adequate and safe blood supply remains a significant challenge. To address blood donation-associated challenges, this review explores how integrating artificial intelligence (AI) technologies can improve donor recruitment, retention and management. For instance, robotic process automation can streamline repetitive administrative tasks, allowing staff to focus on more critical activities and improving efficiency. When augmented with AI techniques such as machine learning (ML) and natural language processing, it transitions from static rule-based automation to intelligent process automation. This combination enables dynamic decision making, handling unstructured data and optimizing workflows, thus extending its role in improving efficiency and decision making in donor management. ML algorithms can analyse large datasets to predict future donation patterns, identify donor behaviour trends and forecast blood demand more accurately. By applying these predictive models, blood banks can plan more effectively, avoid shortages and precisely target recruitment efforts. Additionally, AI-driven chatbots are gaining traction as a tool for improving communication with potential and existing donors, ultimately fostering better retention rates. Beyond routine donor management, AI also shows promise in supporting rare donor identification and targeted engagement strategies. While these innovations hold great potential, their implementation faces challenges such as data availability and quality, ethical issues concerning AI utilization, the necessity for clinical and technical expertise, a robust infrastructure, environmental impact and cybersecurity risks. Addressing these issues through practical strategies and thoughtful integration will be the key to ensuring the responsible, effective and sustainable adoption of AI in blood banking systems.

Artificial intelligence (AI) offers significant potential to enhance writing instruction in primary education. However, its sustained adoption by English language teachers remains insufficiently understood. This study examines the factors influencing teachers’ acceptance and continued use of AI-powered learner corpora, drawing on the Technology Acceptance Model (TAM) and Expectation Confirmation Model (ECM). A structured questionnaire was administered to 355 primary school English teachers in Malaysia to assess variables related to external support (e.g., facilitating conditions, teacher-learner interaction), individual characteristics (e.g., perceived self-efficacy, expectancy effects, growth mindset, interest) and technology perceptions (e.g., perceived ease of use, perceived usefulness, satisfaction, continuance intention). Data were analyzed using exploratory and confirmatory factor analysis, followed by Structural Equation Modeling. Results highlight perceived self-efficacy and interest as key predictors of perceived usefulness and continuance intention. They underscored the role of intrinsic motivation. In contrast, facilitating conditions did not significantly affect perceived ease of use, possibly due to increased digital familiarity among teachers. These findings validate the applicability of TAM and ECM in the primary education context and offer practical insights for designing AI tools that align with teachers’ pedagogical needs, enhance user experience and support the sustainable integration of AI in writing instruction.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-21205-0.

The integration of deep learning in education has the potential to enhance pedagogical practices, personalized learning, and adaptive instruction. However, Islamic schools face unique challenges in adopting AI-driven educational models due to technological limitations, digital literacy disparities, and regulatory constraints. This study assesses the readiness of Islamic school teachers in Indonesia to implement deep learning-based curricula, analyzing knowledge, attitudes, barriers, and demographic influences on AI adoption. A structured questionnaire was administered to 1,120 teachers across madrasahs, pesantrens, and Islamic private schools, with data analyzed using the Rasch measurement model to ensure psychometric validity. Differential Item Functioning (DIF) analysis was conducted to examine variations in readiness across gender, age, education level, teaching experience, and ICT knowledge. The results reveal moderate teacher readiness, with significant gaps in deep learning comprehension and practical implementation. Female teachers, mid-career educators (36–45 years), and secondary school teachers exhibit higher AI readiness, while novice and older teachers face greater barriers. ICT literacy emerges as the strongest predictor of readiness, underscoring the need for targeted digital training programs. Findings highlight infrastructure deficits, professional development gaps, and policy misalignment as primary obstacles to deep learning adoption. While urban teachers demonstrate higher AI engagement, rural educators require greater institutional support. The study emphasizes the necessity of differentiated professional development programs that cater to teachers at different career stages and digital literacy levels. These insights provide critical implications for policymakers, educational leaders, and curriculum developers in designing AI-driven pedagogical strategies for Islamic schools. Future research should explore mentorship initiatives and hybrid training models to foster sustainable AI adoption in religious education settings.

Purpose: This study examines the relationship between sustainability disclosure quality and financial indicators among mining companies listed on the Johannesburg Stock Exchange (JSE), providing insights into an African emerging-market context. Design/Methodology/Approach: Sustainability disclosure quality was measured using an index based on the Global Reporting Initiative (GRI) Mining and Metals Sector Supplement. The analysis employed a balanced panel dataset (2012–2021) of 36 firms and applied fixed-effects panel regression to assess the associations with three financial indicators, namely liquidity, leverage, and profitability, as well as company size. Findings: The results show significant positive relationships between disclosure quality, leverage, and profitability, while no significant associations were found with liquidity or company size. These findings suggest that transparent, high-quality disclosures may be more relevant to financing structures and profitability than to short-term liquidity or firm size. Originality/Value: By applying a sector-specific disclosure index within the South African mining industry over a decade of reporting, this study contributes to understanding the financial relevance of sustainability disclosure in an emerging-market setting. Practical and Social Implications: The findings suggest that the early adoption of sustainability reporting regulations can strengthen transparency, improve financing outcomes, and foster accountability in South Africa’s mining sector, with broader lessons for resource-intensive industries in Africa. Research Implications: Future research should extend to other sectors, incorporate additional financial and market-based measures, and evaluate the effects of evolving sustainability standards, including IFRS S1 and S2 and the GRI 14: Mining Sector Standard.

This article examines the application of Universal Design for Learning (UDL) in highly heterogeneous classrooms from the teacher's perspective, placing the problem in the gap between the principles of UDL and their translation into everyday practice. The study, which is qualitative and based on a literature review, focuses on teachers' understanding, assessment, and use of UDL in highly diverse contexts. The findings summarize: (a) uneven knowledge of the framework, with confusion about its three principles; (b) positive assessment of UDL, although with operational doubts about designing, evaluating, and managing heterogeneity; (c) institutional and training obstacles—time, resources, support—that strain sustained adoption; and (d) the presence of compatible practices not always recognized as UDL. It is concluded that its effective implementation requires moving from isolated actions to a systematic pedagogical design, supported by continuous training, peer collaboration, and organizational support, in order to close the gap between inclusive discourse and classroom reality.

ABSTRACT Background Educational robotics can enrich STEM learning in preschool and primary education. Implementation is often constrained by school infrastructure, limited time, costs, curriculum alignment, and access to support. Objective To identify educators’ perceived challenges to implementing educational robotics in preschool and primary settings and to examine whether these perceptions vary by educator characteristics and training. Sample Greek preschool and primary educators, n = 302, selected through stratified random sampling across diverse school contexts. Design and methods Cross-sectional survey using the validated Attitude, Knowledge, and Application of Educational Robotics instrument (AKAER). Internal consistency was α = .757. Descriptive statistics summarized perceived barriers. Pearson chi square tests with α = .05 examined associations with specialization, gender, relevant coursework, and ER or STEM training. Results Reported barriers included infrastructure gaps, limited instructional time, limited teacher knowledge, curriculum constraints, high cost or limited availability of kits, and frequent need for technical support. Agreement rates were 85.5% for infrastructure, 34.4% for time, 49.3% for knowledge, 52.0% for curriculum, 59.3% for cost or availability, and about 81% for technical support. Pearson chi square tests showed associations of physics coursework with views on laboratory feasibility, robotics coursework or training with time and curriculum constraints, environmental coursework with knowledge and cost concerns, and gender with perceived need for technical support. Some items showed no differences by educator characteristics. Conclusion Barriers to implementation are primarily infrastructural, temporal, financial, and support related rather than rooted in individual educator profiles. Prioritizing dependable infrastructure, affordable kits, protected instructional time, responsive technical support, and targeted professional learning can enable sustainable adoption and richer STEM learning in preschool and primary classrooms.

This study presents the design, fabrication, and evaluation of a modified corn sheller to improve post-harvest operations for small-scale farmers in Surigao City, Philippines. The prototype integrates a rotating drum driven by a 1.5-HP single-phase electric motor, a perforated separation platform, a belt-and-pulley transmission, and an enclosed frame built from locally available mild steel and hardwood. Development followed iterative stages: needs assessment via farmer interviews and field observation, conceptual design, materials selection for cost and durability, fabrication, and performance testing. Trials measured shelling efficiency, throughput (kg/hr), kernel damage (%), and power consumption across cobs with varying moisture. Results showed shelling efficiencies above 90% in most runs, throughput markedly higher than manual shelling, and kernel damage generally under 5% — comparable to other low-cost designs while optimized for local parts and maintenance. User evaluation (n=25) indicated very high acceptability across technical, economic, environmental, and social criteria, highlighting safety features, replaceable parts availability, and affordability. The study discusses critical design variables (roller diameter, spike arrangement, drum speed, and feed clearance), moisture’s effect on performance, and operational recommendations to minimize kernel breakage. Finally, it situates the device within smallholder mechanization initiatives in the Philippines and advocates scalable dissemination approaches such as machinery pooling and local fabrication hubs to broaden access and socioeconomic impact. Findings indicate that well-designed, locally fabricated shellers can reduce drudgery, increase throughput, and improve rural livelihoods when accompanied by training, after-sales support, and context-sensitive dissemination. Future work should include field scaling, cost-benefit analysis, and training program evaluation for sustainable adoption

Sustainable food development is crucial for minimizing environmental impacts and ensuring the capacity to provide sufficient food for both present and future generations. Many eco-friendly production methods have been adopted world-wide, including organic farming, regenerative agriculture, and plant-based alternatives, aimed at reducing greenhouse gas emissions, conserving water and soil resources, and promoting biodiversity. However, despite this development in sustainable production, consumer awareness and adoption of sustainable food choices remain limited, preventing full environmental and health impacts of these practices from being realized. This paper represents the design of an AI-powered chatbot, offering nutrition guidance, promoting sustainable and healthy daily food choices, while also addressing ethical considerations such as user privacy, fairness, and transparency in its design. The chatbot integrates artificial intelligence and large language models, adapted with domain-specific data on nutrition and sustainability, to engage users in conversations about healthy eating, food waste reduction, and eco-friendly diets. Its design combines a user-friendly interface, a curated knowledge base, and personalized recommendations informed by user preferences. Early evaluations suggest that the system can increase awareness and encourage more sustainable food choices. Ethical aspects such as privacy, transparency, and fairness are embedded in its development to promote responsible use of AI. Future enhancements may include integrating image-based calorie estimation to provide personalized nutritional feedback alongside sustainability guidance.

This study aims to analyze the impact of the implementation of the Global Reporting Initiative (GRI) and the Sustainability Accounting Standards Board (SASB) on the quality of Sustainable Development Goals (SDGs) disclosure in six ASEAN countries: Indonesia, Malaysia, Thailand, Singapore, Vietnam, and the Philippines. A quantitative approach was employed, utilizing secondary data from sustainability reports for 2022-2023, analyzed using panel data regression with a fixed effects model. The results indicate that both independent variables have a significant positive partial effect on SDGs disclosure. The SASB variable demonstrated a significant influence with an error rate of 14%, while the GRI variable was significant with a 3.7% error rate. These findings indicate that the adoption of specific sustainability reporting standards SASB with its focus on industry-specific material topics and GRI with its comprehensive framework effectively enhances corporate transparency and accountability in achieving sustainable development objectives. This research provides practical implications for policymakers and regulators in the ASEAN region to promote the harmonization and adoption of standardized reporting frameworks to improve the quality of SDGs disclosure, which can ultimately attract investment, create jobs, and improve societal welfare. The originality of the study lies in its application of the Sustainability Maturity Model theoretical framework and the use of panel data regression analysis to investigate the complex interactions between these variables within the ASEAN context.

Background: The global financial sector is increasingly prioritizing sustainability, with green banking and investment emerging as key strategies to support environmentally responsible economic growth. Despite growing interest, awareness and adoption remain limited in developing economies like Nepal, particularly among younger generations who will shape future financial trends. Objective: This study examines undergraduate students' awareness, perceptions, and willingness to engage in green banking and investment, assessing potential gender differences and identifying barriers to sustainable finance adoption. Methods: A quantitative approach was employed, using a structured questionnaire administered to 217 undergraduate students from public and private colleges. Data were analyzed using descriptive statistics, independent samples t-tests, and ANOVA to compare responses across genders and academic disciplines. Findings: Results indicated moderate awareness of green finance concepts, with no significant gender differences in green banking perceptions (p=0.220). However, male students showed marginally higher interest in green investment (p=0.066). Key barriers included limited financial products, insufficient policy support, and lack of technical knowledge. Conclusion: While students demonstrate foundational awareness of green finance, targeted educational initiatives and policy incentives are needed to deepen engagement. The findings underscore the importance of integrating sustainability into financial curricula and fostering institutional support for green finance in Nepal. Novelty: This study contributes to limited research on youth perspectives toward green finance in developing economies, offering actionable insights for policymakers and educators to bridge awareness gaps and promote sustainable financial behaviors.

This study investigates the key determinants influencing the adoption of Artificial Intelligence (AI) by Small and Medium Enterprises (SMEs) in the e-commerce sector and examines its impact on marketing performance. The research integrates the Technology–Organization–Environment (TOE) framework and the Technology Acceptance Model (TAM) to explain the relationships among technological readiness, organizational support, environmental pressure, perceived usefulness (PU), perceived ease of use (PEOU), and AI adoption level. A quantitative approach employing Partial Least Squares–Structural Equation Modeling (PLS-SEM) was conducted on 155 e-commerce SMEs operating in Jakarta, Indonesia. Empirical results reveal that technology, organization, and environment significantly affect perceived usefulness, which in turn strongly drives AI adoption. Perceived usefulness is identified as the most powerful determinant of adoption, whereas perceived ease of use exerts an indirect influence via usefulness perception. Furthermore, AI adoption has a positive and significant effect on marketing performance, particularly in enhancing digital campaign effectiveness, product innovation, and customer loyalty. The findings emphasize that internal readiness and external competitive pressures jointly foster AI-driven digital transformation within SMEs. The study provides both theoretical validation of the TOE–TAM integration and practical guidance for policymakers and business owners to design effective, ethical, and sustainable AI adoption strategies.

Purpose The purpose of this study is to examine the motivators and demotivators influencing millennials’ purchase of sustainable clothing, providing insights into their consumer behavior. The burgeoning sustainable clothing market promises to address sustainability challenges within the fashion industry, making a nuanced understanding of these factors crucial. Design/methodology/approach This study uses interpretative phenomenological analysis (IPA) based on a sample size of 17 millennials in India. It is a qualitative approach that delves into consumers’ personal experiences. By focusing on individual perspectives, this technique enables researchers to extract common themes from the data, providing profound insights. Findings Millennials are driven to purchase sustainable clothing by moral responsibility, environmental concern, biodiversity conservation, pricing, brand appeal, social recognition, self-satisfaction, comfort, quality, durability, well-being, timeless designs and waste reduction. However, limited variety, color options and awareness hinder their adoption. Practical implications Fashion brands must expand variety, provide clear sustainability labels, educate consumers, leverage social influencers and align marketing with consumers’ ethical values to boost sustainable fashion adoption. Moreover, educators should embed sustainability in interdisciplinary learning to build environmental awareness and ethical responsibility. Originality/value This study provides pioneering insights into the purchasing decisions of millennials, framed within the value–belief–norm theory. This is a novel study to examine motivators and demotivators related to sustainable clothing and suggest solutions to encourage the purchase of sustainable clothing to attain sustainable development goal 12. To the best of the authors’ knowledge, this is also one of the first studies on sustainable clothing, using IPA as the research methodology.