International Journal of Multidisciplinary Research and Creative Innovation Ideas
The International Journal of Multidisciplinary Research and Creative Innovation Ideas (The MIR) is a scholarly publication dedicated to fostering high-quality research across diverse academic disciplines. This journal serves as a platform for: Multidisciplinary research in pure sciences and various educational fields. Studies on the development of educational applications, including comprehensive analysis and evaluation. Exploration of creative and innovative ideas that contribute to advancements in research and practical implementations. The MIR welcomes original research articles, case studies, and review papers that align with its scope, aiming to bridge the gap between theoretical knowledge and real-world applications.
AI-Enhanced Vocational School Management Framework: A Systematic Review and Novel Implementation Model for Indonesian SMK Daily Operations.
The integration of artificial intelligence (AI) in vocational school management presents transformative opportunities for enhancing daily operational efficiency, particularly in resource-constrained contexts. This systematic literature review examines AI applications in educational management and proposes a novel AI-Enhanced Vocational School Management Framework (AI-VSMF) specifically designed for Indonesian Sekolah Menengah Kejuruan (SMK) daily operations. Through a comprehensive analysis of 129 high-quality Scopus-indexed publications (2020-2026), we identified key AI technologies and management applications across administrative automation, student performance monitoring, resource optimization, and decision support systems. The proposed AI-VSMF framework integrates four core modules: (1) Intelligent Administrative Operations, (2) Smart Student Management, (3) Adaptive Learning Analytics, and (4) Resource Optimization Engine. Contextualized for SMK Bondowoso, Indonesia, the framework emphasizes cost-effective, scalable solutions suitable for resource-limited vocational schools. This study contributes a practical implementation roadmap that addresses infrastructure constraints, technical capacity, and cultural adaptation needs specific to Indonesian vocational education contexts. The framework offers actionable pathways for SMK administrators to leverage AI technologies for improving operational efficiency, student outcomes, and institutional competitiveness in the evolving digital education landscape.
Copyright (c) 2026 International Journal of Multidisciplinary Research and Creative Innovation Ideas
Navigating AI Integration in South African Higher Education: A Systematic Review of Tools, Challenges, and Opportunities.
This systematic literature review explores the adoption, application, and impact of artificial intelligence (AI) tools in South African higher education institutions, with emphasis on everyday teaching and learning practices. Through qualitative analysis of 30 Scopus-indexed studies published between 2020 and 2025, this review identifies key AI applications including generative AI assistants (ChatGPT), adaptive learning systems, automated assessment tools, and AI-powered teaching assistants. The novelty of this study lies in its focused examination of the South African context, revealing unique challenges including infrastructure constraints, digital inequality, policy gaps, and academic integrity concerns, alongside innovative local solutions such as mobile-first platforms and bilingual AI assistants. Thematic analysis uncovers mixed perceptions among students and educators: while AI enhances engagement, personalization, and scalable feedback, concerns persist regarding equity, preparedness, and ethical governance. This review contributes practical recommendations for South African universities, including context-aware tool adoption, inclusive policy development, assessment redesign, and regional collaboration frameworks. The findings advance understanding of AI integration in resource-constrained higher education environments and provide actionable insights for institutions like Durban University of Technology seeking to harness AI’s transformative potential while addressing contextual barriers
Artificial Intelligence Integration in Thai Education: A Systematic Literature Review of Everyday Learning Applications
This systematic literature review examines the integration of artificial intelligence (AI) in everyday educational practices within the Thai educational context. Through a comprehensive analysis of 30 peer-reviewed studies published between 2020 and 2026, this review identifies key AI applications, adoption patterns, implementation challenges, and opportunities specific to Thailand’s educational landscape. The findings reveal that generative AI tools, intelligent chatbots, and adaptive learning systems are increasingly integrated across educational levels, with higher education showing more advanced adoption than K-12 settings. Key challenges include infrastructure limitations, digital literacy gaps, language barriers for non-native English speakers, and concerns about academic integrity and over-reliance on technology. The novelty of this review lies in its focused examination of AI in everyday Thai educational practices, revealing unique cultural and contextual factors that influence AI adoption, including the tension between technological innovation and traditional pedagogical values. This study provides evidence-based recommendations for policymakers, educators, and institutions to enhance AI integration while preserving the essential human elements of teaching and learning in the Thai context
Smart Agriculture In Bali: A Systematic Review of Artificial Intelligence Applications for Tropical Farming
This systematic literature review examines artificial intelligence (AI) applications in agriculture with specific implications for Bali’s unique agricultural landscape. Through a comprehensive analysis of 20 Scopus-indexed articles (2019-2026), this study identifies key AI technologies, applications, and adoption barriers relevant to Balinese farming contexts. The review reveals that AI-driven solutions—including deep learning for disease detection (achieving 92-99% accuracy), IoT-based precision irrigation (reducing water use by 30%), and machine learning recommendation systems (99% accuracy)—offer transformative potential for Bali’s rice-dominated agriculture. However, adoption faces significant barriers including infrastructure limitations, digital literacy gaps, economic constraints, and socio-institutional challenges. This study contributes novel insights by contextualizing global AI innovations within Bali’s traditional subak irrigation system, mixed cropping patterns, and tourism-agriculture integration. Recommendations include developing lightweight edge-computing models, participatory technology design with farmer communities, and policy frameworks supporting responsible AI deployment that preserves Bali’s agricultural heritage while enhancing productivity and sustainability
Artificial Intelligence Applications in Agribusiness: Transforming Daily Agricultural Operations for Sustainable Food Systems
The integration of artificial intelligence (AI) in agribusiness has revolutionized daily agricultural operations, offering transformative solutions for sustainable food production. This study examines the practical applications of AI technologies in modern farming, focusing on precision agriculture, crop monitoring, pest detection, and resource management. Through comprehensive analysis of recent implementations, we demonstrate that AI-powered systems achieve 89.5-99.94% accuracy in crop disease detection and agricultural decision support. Computer vision technologies enable real-time identification of plant diseases and pests, while IoT-AI integration reduces water usage by 20-50% and increases crop yields by 15-20%. Machine learning algorithms optimize fertilizer application, reducing costs by 15-30% while maintaining productivity. Despite these advances, implementation challenges persist, including high initial costs, limited technical expertise, and infrastructure constraints in developing regions. This research presents a comprehensive framework for practical AI deployment in agribusiness, emphasizing edge computing solutions, farmer training programs, and collaborative partnerships. The findings demonstrate that strategic AI integration in daily agricultural operations can significantly enhance productivity, sustainability, and resource efficiency while addressing global food security challenges
Transforming Daily Agricultural Food Systems.
Artificial intelligence in Education: Perspective from South African higher education context.
This systematic literature review examines the integration of artificial intelligence (AI) in everyday educational practices within South African contexts, with particular emphasis on teacher and student experiences at institutions like STADIO College of Education and Durban University of Technology, Durban. Through a comprehensive analysis of 30 Scopus-indexed studies published between 2020 and 2026, this research reveals that South African educational institutions employ a diverse array of 57 different technologies, blending mainstream communication platforms (WhatsApp, Zoom, Microsoft Teams) with emergent AI-enabled systems, including automated assessment tools, adaptive learning platforms, and intelligent tutoring systems. The study identifies critical contextual factors unique to South Africa—including infrastructure constraints, the digital divide, socioeconomic disparities, and policy-practice misalignments—that significantly shape AI adoption patterns. While COVID-19 accelerated digital transformation, it simultaneously exposed profound inequities in access and readiness. Teachers demonstrate positive attitudes toward AI integration but face substantial barriers related to skills development, resource limitations, and ethical concerns. This research contributes novel insights by synthesizing South African-specific evidence on everyday AI use, highlighting the tension between technological potential and contextual realities, and proposing equity-centered recommendations for policy and practice
Artificial Intelligence in Daily Banking and Finance: Transforming Customer Experience and Operational Excellence
Artificial intelligence (AI) has fundamentally transformed the financial services landscape, particularly in daily banking operations and customer interactions. This study examines the current state and emerging applications of AI technologies in the banking sector, focusing on practical implementations that directly impact both financial institutions and individual customers. Through a comprehensive literature review of 95 peer-reviewed articles published between 2022 and 2025, this research identifies key AI technologies including natural language processing, deep learning, graph machine learning, and reinforcement learning that are reshaping banking services. The findings reveal that AI-powered applications such as intelligent chatbots, fraud detection systems, automated credit scoring, and personalized financial advisors have significantly enhanced operational efficiency and customer experience. Specifically, advanced fraud detection systems using graph embeddings achieve F1 scores of 67.1%–73.4%, while explainable AI models for ATM fraud detection demonstrate AUC values of 0.963. However, challenges including data privacy, model explainability, regulatory compliance, and algorithmic bias remain significant barriers to adoption. This research contributes to the understanding of AI's practical applications in banking and provides insights for financial professionals at institutions like Akademi Keuangan dan Perbankan Denpasar to prepare for the AI-driven future of financial services
Strategic Management of Artificial Intelligence Integration in Daily Life: A Framework for Personal Productivity Enhancement
The integration of Artificial Intelligence (AI) into daily life has transformed from a futuristic concept into an omnipresent reality, yet the strategic management of these technologies at the individual level remains largely unexplored. This research introduces a novel framework—the Personal AI Management Model (PAMM)—that synthesizes management principles with AI adoption patterns to optimize individual productivity and decision-making in everyday contexts. Through a mixed-methods approach combining surveys of 385 Indonesian respondents and in-depth interviews with 30 active AI users, this study reveals that effective personal AI management requires five critical dimensions: strategic selection, integration planning, performance monitoring, continuous adaptation, and ethical governance. The findings demonstrate that individuals who apply structured management principles to their AI tool adoption achieve 47% higher productivity gains and 63% better satisfaction rates compared to ad-hoc users. This research contributes to management literature by extending organizational AI management frameworks to the personal domain and provides practical insights for individuals, educators, and policymakers in developing countries. The PAMM framework offers a systematic approach for navigating the complex landscape of AI tools in daily life, addressing the critical gap between AI availability and effective utilization.
Artificial Intelligence Integration in Daily Agribusiness Operations: A Paradigm Shift for Tropical Smallholder Farming Systems
The integration of artificial intelligence (AI) in daily agribusiness operations represents a transformative shift in modern agricultural management, particularly for tropical smallholder farming systems. This study examines the novel applications of AI technologies in routine agricultural business activities, focusing on practical implementations that bridge technological innovation with operational feasibility. Through comprehensive analysis of recent developments (2023-2025), we identify four critical domains where AI demonstrates high impact: (1) IoT-enabled precision resource management, (2) autonomous crop monitoring and intervention systems, (3) intelligent farm decision support frameworks, and (4) market-aware supply chain optimization. Field evidence from tropical agriculture settings demonstrates that low-cost AI solutions combining sensor networks, machine learning algorithms, and mobile applications can achieve substantial improvements in resource efficiency (20-30% water savings), productivity (15-20% yield increases), and profitability through optimized crop selection. This research contributes to the growing body of knowledge by synthesizing cutting-edge AI applications specifically contextualized for resource-constrained tropical agribusiness environments, offering actionable insights for smallholder farmers, agricultural extension services, and agribusiness policymakers in developing economies
Democratizing Financial Services: The Role of Artificial Intelligence in Transforming Daily Banking and Personal Finance Management
The integration of artificial intelligence (AI) in banking and finance has fundamentally transformed how individuals interact with financial services in their daily lives. This study examines the democratization of financial services through the application of AI technologies, focusing on machine learning, deep learning, natural language processing, and computer vision. Through systematic analysis of recent literature (2020-2025), this research identifies six core daily-life applications: mobile banking personalization, real-time fraud detection, alternative credit scoring, robo-advisory services, conversational banking interfaces, and intelligent personal finance management. Results indicate that AI enables unprecedented accessibility, personalization, and efficiency in financial services, particularly benefiting underserved populations through the use of alternative data and 24/7 digital access. However, challenges persist regarding data privacy, algorithmic bias, explainability requirements, and regulatory compliance. The study contributes to understanding how AI-driven financial democratization impacts consumer behavior, banking operations, and financial inclusion, with implications for emerging markets seeking to leverage technology for broader economic participation. This research provides practical insights for financial institutions, policymakers, and educators in adapting to the AI-transformed financial landscape.
Implementation of Artificial Intelligence in Intelligent Agricultural Systems for Agrotechnological Transformation for Daily Life
The development of artificial intelligence (AI) technology has brought a significant transformation in the agrotechnology sector, transforming conventional agricultural practices into efficient and sustainable smart farming systems. This research aims to examine the implementation of AI in agrotechnology for farmers' daily life applications. The systematic literature review method was used by analyzing 170 scientific publications indexed by Scopus for the 2022-2025 period. The results of the study show that AI has been implemented in various aspects of agrotechnology including: (1) computer vision-based plant disease detection system with an accuracy of up to 98%, (2) an integrated IoT platform for real-time monitoring of land and plant conditions, (3) a machine learning-based recommendation system for irrigation and fertilization management, (4) UAV technology with deep learning for land mapping and scouting, and (5) digital twin for simulation and optimization of production systems. The implementation of federated learning enables collaboration between farmers in the development of models without compromising data privacy. In conclusion, AI provides practical and affordable solutions to improve productivity, resource efficiency, and sustainability of agriculture in daily life. The implications of this study provide guidance for farmers, practitioners, and academics in adopting AI technology for the transformation of agrotechnology towards smart and sustainable agriculture
Artificial intelligence (AI) has emerged as a transformative technology in modern agrotechnology, addressing critical challenges in food security and sustainable agriculture. This study examines recent innovations in AI applications for daily agricultural practices, focusing on practical implementations that enhance farmer decision-making and operational efficiency. Through a comprehensive analysis of current literature (2023-2025), we identify five key AI application domains: automated irrigation control, crop disease detection, yield prediction, precision farming systems, and explainable AI for crop recommendations. Deep learning techniques, particularly convolutional neural networks (CNNs) and hybrid models, demonstrate superior performance in image-based disease detection (>95% accuracy) and yield forecasting. Edge computing implementations enable real-time decision support even in connectivity-limited environments. The integration of Internet of Things (IoT) sensors with machine learning algorithms facilitates continuous monitoring and automated interventions, reducing labor requirements by 30-40% while improving resource efficiency. Key challenges include data quality, farmer trust, and infrastructure limitations in developing regions. This review synthesizes cutting-edge AI methodologies applicable to Indonesian agricultural contexts, particularly for smallholder farmers, and proposes a framework for sustainable AI adoption in tropical agrotechnology systems. The findings indicate that explainable AI and privacy-preserving federated learning represent critical pathways for widespread adoption, enabling farmers to understand and trust AI-driven recommendations while maintaining data sovereignty
The effectiveness of AI as an adaptive learning tutor to improve student understanding
The development of artificial intelligence (AI) technology presents significant opportunities for adaptive learning innovation, enabling materials to be tailored to individual student needs. However, the effectiveness of AI as an adaptive tutor in increasing student motivation, engagement, and understanding still needs to be analyzed in depth. This research aims to analyze how the use of AI as an adaptive tutor can increase student motivation, engagement, and understanding in the learning process. The research results show that the use of adaptive AI tutoring has a significant positive effect on student motivation and engagement. Motivation acts as a mediator that strengthens the relationship between AI use and student understanding. These findings indicate that AI not only provides appropriate material but also increases students' emotional involvement in learning. This study concludes that AI as an adaptive learning tutor is effective in increasing students' understanding through motivation and engagement. The implications of this research encourage the development and application of AI that is more personal and interactive to support quality and inclusive learning processes.
Model Adaptif Respons Cepat Krisis Deepfake: Kolaborasi Pemerintah–Media Dalam Menghadapi Dampak Perkembangan Kecerdasan Buatan.
Perkembangan teknologi kecerdasan buatan telah memunculkan fenomena deepfake yang mampu memanipulasi informasi secara realistis dan masif, sehingga menimbulkan krisis kepercayaan publik terhadap media dan institusi pemerintah. Kondisi ini menuntut adanya model respons cepat yang adaptif dan kolaboratif agar penyebaran disinformasi dapat diminimalkan secara efektif. Penelitian ini bertujuan untuk merancang dan menganalisis model adaptif respons cepat terhadap krisis deepfake melalui kolaborasi antara pemerintah dan media sebagai strategi mitigasi dampak negatif perkembangan kecerdasan buatan. Penelitian ini menggunakan metode kualitatif dengan desain fenomenologi dan pendekatan aplikatif, yang memungkinkan eksplorasi mendalam terhadap pengalaman dan persepsi aktor kelembagaan dalam menghadapi krisis informasi digital. Populasi penelitian mencakup pejabat pemerintah di bidang komunikasi publik, jurnalis dan redaktur media nasional, serta akademisi yang memahami isu etika kecerdasan buatan. Sebanyak 80 responden dipilih dengan teknik purposive sampling, berdasarkan keterlibatan mereka dalam pengelolaan informasi publik, kebijakan digital, dan produksi konten media. Data dikumpulkan melalui wawancara mendalam, observasi, serta dokumentasi, dan dianalisis menggunakan pendekatan tematik. Hasil penelitian menunjukkan bahwa kolaborasi pemerintah–media masih bersifat parsial, dengan keterlambatan koordinasi dan kurangnya prosedur standar dalam merespons deepfake. Model adaptif yang dihasilkan menekankan tiga elemen utama: kecepatan respons lintas lembaga, transparansi komunikasi publik, dan pembaruan kebijakan berbasis evaluasi berkelanjutan. Kesimpulan penelitian menegaskan bahwa sinergi pemerintah dan media merupakan kunci utama dalam membangun ketahanan informasi publik. Implikasinya, model ini dapat dijadikan kerangka operasional dalam penguatan tata kelola komunikasi krisis digital di era kecerdasan buatan
The Effect of Limited Internet and Electricity Infrastructure on the Utilization of AI in Environmental Data Collection in Remote Areas.
Limited technological infrastructure, especially internet access and electricity supply, is still a serious challenge for remote areas in collecting environmental data effectively. This condition has a direct impact on the region's ability to utilize artificial intelligence (AI) systems that require quality and up-to-date data. This study aims to analyze the influence of limited internet and electricity infrastructure on the effectiveness of the use of AI in the process of collecting environmental data in remote areas using quantitative methods with mediation analysis through the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. Data collection was carried out on community populations and field officers involved in environmental monitoring in remote areas, with a sample of 55 respondents. Respondents were selected using purposive sampling techniques to ensure that all participants had experience or direct involvement in environmental data collection activities. The research instrument is in the form of a questionnaire that includes indicators of infrastructure quality, technology support, and the effectiveness of AI-based data collection. The results show that the limitations of the internet and electricity have a significant negative effect on the effectiveness of data collection required by AI systems. The findings also show that technology support plays a role as a mediating variable that weakens the negative impact of limited infrastructure. In addition, increased technology support has proven to be able to improve the smooth process of data acquisition and delivery, even though the infrastructure is still minimal. In conclusion, limited infrastructure is a major obstacle to maximizing the use of AI in remote areas, but strengthening technology support can reduce this impact. The implications of this study emphasize the need to develop digital infrastructure and increase technological capacity to optimize the use of AI in environmental monitoring.
Adaptation and Social Readiness Strategies in the Face of Rapid Advances in AI Technology.
The rapid development of AI technology has changed the patterns of social interaction, work, and daily life of the Indonesian people, but not all layers have the same readiness, resulting in existential anxiety as well as opportunities for social progress. This ethnographic research aims to describe and analyze in depth how adaptation strategies are built and social readiness is formed in a society that continues to change due to AI, thus providing a practical picture to face and use technology responsibly. With a purposively selected sample of 70 respondents from various layers (freelancers, housewives, online drivers, teachers, the elderly, etc.), data were collected through in-depth interviews, participatory observations in the digital-offline community, and documentation of daily activities. The results show that AI adaptation is collective, with peer-to-peer learning in communities (WA groups, forums, and social gatherings) as the most effective strategy; the majority of respondents have integrated AI as an "assistant" or "friend" despite still being accompanied by work anxiety, while the main obstacles are literacy inequalities, infrastructure, and ethics-privacy issues. In conclusion, true social readiness depends on the power of the community as a safe space for learning together. The implication is that AI literacy policies must be based on local communities, inclusive, and integrated with cultural values so that AI transformation strengthens solidarity and humanity in Indonesia.
Aligning Technological Innovation With Moral Principles For The Common Benefit Of Humanity
The background of rapid technological innovation triggers a variety of ethical issues, especially related to bias and discrimination that may arise, as well as a lack of developer accountability. This raises the need to align technological innovation with moral principles to ensure social justice and sustainable benefits for humanity. The goal of this research is to create a framework for technological innovation that not only focuses on functional progress but also pays attention to fairness, transparency, and accountability. The research used a qualitative method of literature study with content analysis techniques, while data collection was based on a population sampling of 50 respondents consisting of technology practitioners and academics in the field of ethics. Data were collected through structured interviews and questionnaires, which were then analyzed in depth to find patterns of alignment between innovation and moral principles. The results show that there is a strong need for strict supervision and regulation to manage bias and increase accountability in technological innovation. In addition, it found the importance of the role of multi-stakeholder collaboration in ensuring technology can meet ethical standards and contribute to social benefits. This study concludes that the alignment of technological innovation with moral principles requires an integrative approach involving policy, education, and community participation. The implication of this research is the development of ethical technology implementation guidelines and increased awareness of social responsibility in the world of technology for the benefit of humanity at large.
AI as a Driver of Skills Transformation in Preparing an Adaptive and Superior Future Workforce.
The rapid development of artificial intelligence (AI) has transformed the world of work by demanding the adaptation of workforce skills to be more adaptable and superior. However, the readiness of human resources and coordination between stakeholders in dealing with these changes is not optimal, while measuring the impact of AI on skills transformation still requires complex analytical methods. This study aims to examine the relationship between human resource readiness, coordination, and collaboration between stakeholders, and the influence of AI in the transformation of workforce skills in Indonesia, using a mediation analysis model. The quantitative method was applied with the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique to understand the direct and indirect influences between variables. Data was collected through sampling from the labor population, with a total of 170 respondents representing various industries in Indonesia. Data collection was carried out using a structured questionnaire that ensured the accuracy and representativeness of the samples. The results show that the readiness of human resources and the quality of coordination between stakeholders significantly mediate the role of AI in improving the transformation of workforce skills. This mediation effect confirms that synergy between parties is very important in maximizing the positive impact of AI. The study's conclusions confirm that AI-based skills transformation cannot run effectively without increasing human resource readiness and improving institutional coordination. The practical implications require governments, industry, and educational institutions to strengthen strategic collaborations and sustainably integrate digital training programs to create an adaptive and competitive future workforce.
Urban Governance and Regulation Issues in Encouraging the Implementation of Recycling Practices and Efficient Use of Resources in Developing Cities
The rapid growth of developing cities is often accompanied by the challenges of inefficient waste and resource management, which has an impact on the deterioration of environmental quality and public health. One potential solution is the implementation of recycling practices and efficient use of resources, but it is still hampered by governance constraints and less supportive regulations. This research aims to analyze and examine the constraints of urban governance and regulatory aspects in encouraging the implementation of recycling practices and efficient use of resources, as well as providing policy recommendations that support sustainable resource management in developing cities. The study used a qualitative method with a case study design, involving 56 respondents who were selected through purposive sampling and snowball sampling techniques. Data was collected through in-depth interviews, field observations, and supporting documentation. The results of the study show that the main obstacles include a lack of policy synchronization between institutions, a lack of supporting facilities and infrastructure, and low participation of the community and the private sector. In addition, existing regulations have not fully accommodated recycling practices and resource efficiency optimally. In conclusion, improving governance by harmonizing regulations and increasing the capacity of infrastructure and public education are important keys in encouraging sustainable recycling practices. The implications of this research are expected to be the basis for more effective policy formulation and resource management implementation strategies that support sustainable urban development
Copyright (c) 2025 International Journal of Multidisciplinary Research and Creative Innovation Ideas
Enhancing the Role of Educational Human Resources in Encouraging Environmentally Friendly Innovation for Sustainable City Transformation in the Industrial Transition Era
The industrial transition era creates both challenges and opportunities for sustainable urban development, requiring effective integration of environmentally friendly innovations. However, the readiness of educational human resources to adopt and implement such innovations remains uneven, hindering progress. This study aims to assess the readiness of educational human resources in urban settings to encourage environmentally friendly innovation for sustainable city transformation. This quantitative research employed a cross-sectional survey design with 130 purposively sampled respondents, including educators and educational personnel directly involved in innovation adoption. Data collection utilized a validated questionnaire focusing on motivation, technical competence, institutional support, and strategy effectiveness. Sampling targeted the population actively participating in innovation processes within educational institutions. Results indicate a good level of readiness, with 77% of respondents moderately to very ready to adopt eco-innovations. Key internal factors included motivation (mean score 4.2) and environmental awareness (4.1), while institutional support (4.0) and training effectiveness (4.3) significantly enhanced adaptation. Collaboration among stakeholders emerged as a vital enabler for capacity building. The study concludes that developing educational human resources through continuous training and institutional support is crucial for fostering environmentally friendly innovation. Practical implications highlight the need for sustainable capacity-building programs, inter-sectoral collaboration, and policy frameworks to embed green technology into the education system, ultimately contributing to greener and more socially inclusive sustainable city transformation in the industrial transition era.
Study of the Role of Green Entrepreneurship in Reducing Carbon Emissions through Eco-Friendly Agriculture and Livestock Practices
Increasingly intensive climate change demands innovation in environmentally friendly farming and livestock practices to reduce carbon emissions sustainably. Green entrepreneurship is a strategic concept that plays an important role in integrating economic, social, and ecological values through the application of green technology and efficient resource management. This study aims to identify and analyze personal, social, economic, and environmental factors—including knowledge, institutional support, and policy—that influence individuals' interest in becoming green entrepreneurs in the agriculture and livestock sectors. The research method used is a quantitative approach with a survey design. Data was collected through questionnaires from 85 respondents who are farmers, breeders, and young business actors from rural areas that have the potential for green business development. The sampling technique uses purposive sampling with the criteria of individuals who have experience and/or interest in environmentally friendly innovations in the agribusiness sector. The results showed that environmental knowledge factors and government policy support had a significant influence on green entrepreneurship interest, while economic and social factors strengthened motivation through community incentives and support. Institutional support, such as training and technical assistance, also increased respondents' confidence and commitment in implementing sustainable practices. It was concluded that capacity building, knowledge, and affirmative policies are key elements in strengthening the green entrepreneurship ecosystem. The practical implications of this study emphasize the importance of synergy between governments, educational institutions, and business actors in creating a green agriculture-livestock model oriented towards reducing carbon emissions.
Development of Renewable Energy Integration Model in Green Entrepreneurship to Support Ecological and Economic Perspectives
The increasing importance of the use of renewable energy in green businesses is to preserve the environment while supporting sustainable economic growth. However, there are still many challenges in developing an effective model so that these two aspects can run in balance. The purpose of this research is to develop a model of renewable energy integration in green entrepreneurship that is environmentally friendly and economically profitable. This study uses a quantitative method with a model study design and a survey. Data was collected from 68 respondents of green business actors who use renewable energy, with simple population sampling to get a representative picture. Data collection was carried out through questionnaires that explored the success factors and obstacles in integrating green energy into their ventures. The results of the study show that the integrated model developed is able to increase the energy efficiency and operational costs of green businesses. In addition, with an innovative approach, this green business succeeds in combining mutually supportive economic and ecological goals. Awareness and involvement of business actors are key to the success of this integration. In conclusion, the renewable energy integration model in green entrepreneurship can have a positive impact on the environment and the economy in a sustainable manner. The implications of this research encourage the development of innovative business strategies and policies that support the widespread use of renewable energy in the green business sector.
Evaluation of the Role of Circular Economy Readiness as a Mediator of the Relationship between Environmentally Friendly Product Innovation and Urban Sustainability in the City of Mojosari.
The increasing need for environmentally friendly product innovations that contribute to urban sustainability in the city of Mojosari. However, the implementation of a circular economy based on the readiness of various parties is still a challenge in realizing this sustainability. The purpose of this study is to evaluate the extent to which the readiness of the circular economy can act as a mediator in the relationship between environmentally friendly product innovation and urban sustainability in the city of Mojosari, measure the influence of environmentally friendly product innovation on urban sustainability in the city of Mojosari through circular economy readiness, and provide an empirical picture using quantitative methods with bootstrapping mediation analysis. This study uses a quantitative method with a bootstrapping-based mediation analysis approach. Data was collected through a survey using a population sampling of 188 respondents representing industry players and stakeholders in the urban area in the city of Mojosari in the city of Mojosari. The research instrument is in the form of a structured questionnaire that measures environmentally friendly product innovation, circular economy readiness, and urban sustainability indicators in the city of Mojosari. Data analysis was carried out using bootstrap mediation techniques to test the role of circular economy readiness as a mediator. The results of the study show that environmentally friendly product innovation has a significant effect on urban sustainability in the city of Mojosari. The readiness of the circular economy has proven to play a mediator role that strengthens the positive relationship between environmentally friendly product innovation and urban sustainability. These findings indicate that increasing circular economy readiness is very important to realize sustainability through product innovation. In conclusion, the readiness of the circular economy is a key factor that connects environmentally friendly product innovation with urban sustainability in the city of Mojosari. The implications of this study emphasize the need for strategies to increase circular economy readiness to support sustainable innovation and policies in the development of greener and more environmentally friendly cities.
Circular Economy Readiness Mediation Analysis on the Relationship between Green Innovation and Sustainable Development Goals (SDGs) in Indonesia
Sustainable development in Indonesia is highly dependent on the effective implementation of green innovation as an effort to achieve the Sustainable Development Goals (SDGs). However, this success is greatly influenced by the readiness of the circular economy readiness which is the main mediator in the relationship between green innovation and the achievement of the SDGs. Challenges faced include the readiness of human resources and skills, lack of coordination and collaboration between stakeholders, and complexity in the measurement and mediation analysis of these factors. This study aims to analyze the mediating role of circular economy readiness in the relationship between green innovation and SDGs in Indonesia. The research method uses a quantitative approach with mediation analysis through the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. Data was collected through a survey with a sample of 170 respondents representing various sectors related to green innovation and the circular economy in Indonesia. Data collection was carried out by distributing questionnaires directly and online, then analyzed using the PLS-SEM model to test the role of mediation in the concept model. The results show that circular economy readiness significantly mediates a positive relationship between green innovation and the achievement of the SDGs. This indicates that increasing the readiness of the circular economy can strengthen the contribution of green innovation in achieving sustainable development goals. The findings also highlight the importance of increasing human resource capacity and increasing synergy between stakeholders to overcome the complexity of measurement and analysis. The conclusion of this study emphasizes that the readiness of the circular economy is a key factor that supports the acceleration of the achievement of the SDGs through green innovation. The practical implication is the need for policies and programs that strengthen human resource capabilities and cross-sector collaboration to realize a sustainable green economy in Indonesia
Innovative Strategies to Increase Livestock Productivity Through the Integration of Traditional Herbal Medicine with a Sustainable Entrepreneurship Approach.
The need to increase livestock productivity effectively and sustainably through the integration of environmentally friendly traditional herbal medicines with a sustainable entrepreneurial approach. The research aims to develop innovative strategies that combine traditional herbal remedies to optimally and sustainably increase livestock productivity. The method employed is a mixed-methods approach with a sequential explanatory research design, where qualitative data are collected through interviews and group discussions, followed by quantitative data collection using questionnaires distributed to 50 farmer respondents as the sample population. The data analysis included descriptive statistics, mean difference tests, and regression analysis to see the influence of herbal medicine and the application of entrepreneurship on livestock productivity, as well as thematic analysis on qualitative data. The results show that the integration of traditional herbal medicine significantly improves the health and growth of livestock, while improving the knowledge and entrepreneurial skills of farmers. Additionally, the use of herbal medicines has been successful in reducing dependence on synthetic antibiotics. The application of an herbal entrepreneurship model also contributes to increasing income and the sustainability of farmers' businesses. In conclusion, the innovative strategy of integrating traditional herbal medicine with a sustainable entrepreneurial approach is effective in increasing livestock productivity as well as the welfare of farmers. The implications of this research can be used as the basis for the development of sustainability-oriented and conservation-oriented breeder empowerment programs
The Role of Knowledge Management and Organizational Learning in Improving Eco-Innovation Capabilities in Technology Start-up Companies in Indonesia.
Climate change and the environment encourage companies, especially technology start-ups, to develop eco-innovation capabilities. However, limited resources in knowledge management, difficulties in building an organizational learning culture, and communication barriers are the main problems that hinder these capabilities. This research aims to explore the role of knowledge management and organizational learning in improving eco-innovation capabilities in technology start-up companies in Indonesia. The method used is qualitative with a case study approach. Data was collected through participatory observational interviews and documentation with purposive sampling on three technology start-up companies in Indonesia that have tried to implement eco-innovation. This study examined as many as 88 respondents. Data analysis is carried out thematically to identify the patterns and dynamics underlying the knowledge management and organizational learning process. The results show that effective knowledge management and an adaptive learning culture strengthen the capabilities of eco-innovation despite limited resources. Communication and coordination barriers between team members can be overcome with supportive information systems and leadership that encourages collaboration. These findings provide an understanding of best practices and challenges faced by tech start-ups in developing eco-friendly innovations. The conclusion of this study emphasizes the importance of strengthening knowledge management and learning culture to support eco-innovation. The practical implication is a recommendation for managerial strategies that can be applied by start-ups in optimizing their sustainable innovation capabilities
The Influence of Digital Technology and Green Supply Chain on Sustainable Business Performance in the Manufacturing Sector.
The development of digital technology has brought significant changes in various aspects of business, including the supply chain in the manufacturing sector. On the other hand, awareness of the importance of environmentally friendly business practices encourages the implementation of green supply chains as a strategy to achieve sustainable business performance. This research aims to find out and analyze how the influence of digital technology applied in the green supply chain on sustainable business performance in the manufacturing sector. The study also evaluates the role of digital technology in improving operational efficiency, environmentally friendly supplier and customer relationships, and green information sharing. The research uses quantitative methods with pathway analysis and secondary data. Data was collected from 150 respondents who were employees and managers in the manufacturing sector using population sampling techniques. Data collection was carried out through questionnaires and secondary data documentation. Path analysis was used to test the relationships between the variables studied comprehensively. The results of the study show that digital technology has a positive and significant effect on the implementation of green supply chains and sustainable business performance. Green supply chains also have a strong impact on improving sustainable business performance in the manufacturing sector. In addition, digital technology plays a role in strengthening collaborative relationships between suppliers and customers and improving the efficiency of business processes. The conclusion of this study emphasizes the importance of utilizing digital technology as the main support in the implementation of green supply chains to support sustainable business performance. The implication of this research is that manufacturing companies are expected to optimize the use of digital technology to achieve business sustainability goals
Implementation Model of Green Innovation Through Entrepreneurship in Areas with High Ecological Challenges
Regions with major ecological problems face difficulties in protecting the environment and developing businesses. Green innovation through entrepreneurship can be a solution, but there are still obstacles such as a lack of supportive regulations, low public awareness, and limited business actors' capabilities. This study aims to analyze how to improve regulations to support green innovation, understand the factors that make market awareness low, and evaluate the ability of business actors to use green innovation. The method used is Mixed Methods with data from 168 business actors who were selected purposively, using surveys and interviews. The results of the study show that clear incentive regulations and policies help business actors implement green innovation. Public awareness of green products still needs to be increased through education. The limited ability of business actors can be overcome with training and access to technology. In conclusion, the success of the green innovation model requires policy improvement, increased market awareness, and strengthening business actors. This research provides recommendations for the government and business actors so that green entrepreneurship can develop and be sustainable
Eco-Innovation Implementation Strategy to Increase Competitive Advantage and Environmental Performance of Creative Craft Industry Players.
Indonesia's creative industry transformation faces obstacles in maintaining competitiveness and improving environmental performance amid changing market dynamics and global policy shifts. One of the strategic efforts currently receiving attention is the implementation of eco-innovation, which is empirically believed to integrate sustainability aspects with the creation of new economic value for creative industry players, especially in the handicraft sector. This research aims to identify relevant forms of eco-innovation in Indonesia's creative sector, along with their obstacles and opportunities for implementation. Using the Grounded Theory design qualitative method, data were collected through in-depth interviews with creative craft industry players and related literature. The sampling process was carried out purposively to ensure the diversity of perspectives of industry players, by involving 225 active respondents from various regions in Indonesia as a representation of the creative craft industry population. Triangulation techniques are used to test the validity of the data, paying attention to demographic characteristics, business patterns, and the adoption of environmentally friendly innovations. The study's results show that the dominant forms of eco-innovation include the optimization of renewable local materials, environmentally friendly product design, and cross-sector collaboration based on local knowledge. Key barriers include limited access to technical information, high initial investment, and resistance to changes in work practices. However, great opportunities open up through synergy with government policies, human resource capacity building, and growing global market demand for sustainable products. The implications of this research confirm the importance of multi-stakeholder collaborative strategies in strengthening Indonesia's green innovation ecosystem in the creative sector. The theoretical and practical contribution of this research encourages policy adjustments and capacity building to create sustainable competitive advantages and optimal environmental performance at the national industrial level
Territorial Branding Strategy to Increase the Competitiveness of the Handicraft Industry.
The handicraft industry is a vital sector that contributes significantly to the local and national economy, yet it still struggles to establish strong competitiveness in the global market. One of the factors causing this is the weak territorial branding strategy that can strengthen the position of handicrafts as a regional superior product. This study aims to analyze effective territorial branding strategies to increase the competitiveness of the handicraft industry by focusing on key problems such as weak brand identity and regional positioning, limited product quality and standardization, limited market access and marketing strategies, and limited resources and capacity of artisans. The research uses qualitative methods with case studies and interviews as data collection techniques. Sampling was carried out on 115 respondents consisting of handicraft business actors, entrepreneurs, and related stakeholders in the handicraft sector. The data was analyzed thematically to obtain a comprehensive picture of the conditions and branding strategies implemented. The results of the study show that increasing competitiveness can be achieved through strengthening brand identity according to regional characteristics, improving product quality with clear standards, utilizing relevant marketing strategies, including digital marketing, and developing the capacity of artisans through training and resource support. Integration between stakeholders is also an important factor in the successful implementation of the territorial branding strategy. In conclusion, territorial branding strategies must be designed comprehensively and be adaptive to local conditions to be able to compete sustainably. The practical implications of this study emphasize the need for cross-sector synergy in supporting the development of a highly competitive handicraft industry.