AI in sustainable higher education: an interpretive structural model and MICMAC approach

Foreword by Richard M. Clugston Preface Acknowledgements Part One: Problematics 1 The Problematics of Sustainability in Higher Education: An Introduction Peter Blaze Corcoran and Arjen E.J. Wals 2. The Evolution of Sustainability Declarations in Higher Education Tarah Wright 3. Sustainability as Emergence: The Need for Engaged Discourse Richard Bawden 4 Critical Realism: A Philosophical Framework for Higher Education for Sustainability John Huckle 5. Higher Education, Sustainability, and the Role of Systemic Learning Stephen Sterling 6. Assessing Sustainability: Criteria, Tools, and Implications Michael Shriberg 7. The Problematics of Sustainability in Higher Education: A Synthesis Peter Blaze Corcoran and Arjen E.J. Wals Part Two: Promise 8. The Promise of Sustainability in Higher Education: An Introduction Arjen E.J. Wals and Peter Blaze Corcoran 9. Environmental Education for Sustainability: A Force for Change in Higher Education Daniella Tilbury 10. The Contribution of Environmental Justice to Sustainability in Higher Education Julian Agyeman and Craig Crouch 11. Learning Our Way to a Sustainable and Desirable World: Ideas Inspired by Arne Naess and Deep Ecology Harold Glasser 12. The Contribution of Ecofeminist Perspectives to Sustainability in Higher Education Annette Gough 13. Sustainability and Transformative Educational Vision Edmund O'Sullivan 14. Teaching Interactive Approaches to Natural Resource Management: A Key Ingredient in the Development of Sustainability in Higher Education Niels Roeling 15. Living Sustainably through Higher Education: A Whole Systems Design Approach to Organizational Change James Pittman 16. Disciplinary Explorations of Sustainable Development in Higher Education Geertje Appel, Irene Dankelman and Kirsten Kuipers 17. The Promise of Sustainability in Higher Education: A Synthesis Arjen E.J. Wals and Peter BlazeCorcoran Part Three: Practice 18. The Practice of Sustainability in Higher Education: An Introduction Kim Walker, Arjen E.J. Wals and Peter Blaze Corcoran 19. Education and Sustainable Development in United Kingdom Universities: A Critical Exploration William Scott and Stephen Gough 20. Lighting Many Fires: South Carolina's Sustainable Universities Initiative Wynn Calder and Rick Clugston 21. Integrating Education for the Environment and Sustainability into Higher Education at Middlebury College Nan Jenks-Jay 22. Sustainability in Higher Education through Distance Learning: The Master of Arts in Environmental Education at Nottingham Treat University Malcolm Plant 23. A Pedagogy of Place: The Environmental Technology Center at Sonoma State University Rocky Rohwedder 24. Policy Development for Sustainability in Higher Education: The Auditing Instrument for Sustainability in Higher Education Niko Roorda 25. Curriculum Deliberation amongst Adult Learners in South African Community Contexts at Rhodes University Heila Lotz-Sisitka 26. Incorporating Sustainability in the Education of Natural Resource Managers Curriculum Innovation at the Royal Veterinary and Agricultural University of Denmark Susanne Leth and Nadarajah Sriskandarajah 27. The Practice of Sustainability in Higher Education: A Synthesis Arjen E.J. Wals, Kim Walker and Peter Blaze Corcoran Resource Links - Rogier van Mansvelt Afterword - Hans van Ginkel Abut the Editors

PurposeThe paper aims to analyse the contextual relationship and dependency amongst enablers for lean manufacturing implementation in Bulgarian small and medium-sized enterprises (SMEs).Design/methodology/approachIn this study, the interpretive structural modelling (ISM) technique was used to develop a hierarchical structural model for enablers. Also, the interpretive ranking process (IRP) was used to analyse and rank enablers with reference to performance variables. For the ISM approach, a structural self- integration matrix was developed with the help of experts’ suggestions and opinions. Cross-impact matrix multiplication applied to classification (MICMAC) analysis was used to analyse the relationship amongst enablers. A total of nine experts were chosen for collecting the primary data in which seven experts belong to the industry and two experts were academicians. The dominant relationship amongst the enablers was analysed through IRP modelling.FindingsA total of 11 enablers were identified for the purpose of this study. The model shows that “leadership and commitment by management”, “human resource management”, “customer relation management”, “supplier relation management” and “information technology system” are the most significant enablers for lean implementation in Bulgarian SMEs as these are positioned at the bottom levels in ISM model. MICMAC analysis shows that five enablers fall in the independent factor, two enablers in linkage factor and four enablers in the dependant factor while there is no enabler in the autonomous factor. ISM and IRP models show that “continuous improvement” is an essential enabler for the successful implementation of lean in Bulgarian SMEs. This study also helps to explain the comparative analysis of ISM and IRP, which indicates that IRP is a more robust modelling approach than ISM, as it incorporates the relationship of enablers with performance variables.Research limitations/implicationsISM and IRP modelling approaches are based solely on expert opinions and responses. This limitation can be overcome with the help of empirical study.Practical implicationsThis study supports the professionals/experts to prioritise and manage enablers at strategic and tactical levels while implementing lean manufacturing practices in Bulgarian SMEs. The models developed in the study will be helpful for practitioners to understand and analyse the interdependence of enablers for lean manufacturing implementation.Originality/valueThis study helps to identify and prioritise enablers that affect lean manufacturing adoption using ISM and IRP approaches. Literature shows that numerous authors have used the ISM approach but the use of IRP approach is limited. The models were developed in the study, totally dependent on data collected from the experts to ensure their real-life validity.

Purpose Artificial intelligence (AI) has emerged as a transformative force in advancing Sustainable Development Goals (SDGs) and redefining higher education. Recently, AI has garnered significant attention from academia and industry due to its potential to address SDGs and shape the future of higher education. The prime objective of this study is to critically investigate the intersection of AI, sustainability and higher education (SHE) on the existing literature. The study also aims to develop a conceptual framework toward AI driven SHE. Design/methodology/approach This study is a systematic literature review employing Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, which is based on the Scopus and web of science databases based on the time frame of 2015–2024 in the context of higher education. Following the PRISMA guidelines, a systematic review was performed, yielding 39 pertinent articles for inclusion in the analysis. Descriptive analysis, thematic analysis, cluster analysis was conducted by VOS viewer software to reach reasonable conclusion on this intersection. Findings After conducting the review analysis, the findings reveal that three key themes have emerged in AI driven higher education such as customized learning experiences (first theme), accessibility (second theme) and optimizing resources allocation (third theme). Additionally, the study has created two categories to show the challenge in implementing AI in higher education: pedagogical challenges (category one), and policy and governance challenges (category two). Originality/value Finally, this study proposes and develops a SHE conceptual framework based on AI. To fully harness the potential benefits of AI technology towards achieving SHE, universities and policymakers should pay more attention to ethical aspects of AI implications and adoption in learning content.

Education for sustainability (EfS) in higher education is an emerging specialisation within the general field of EfS. EfS encompasses cognitive, affective and behavioural aspects, and aims at enhancing a variety of learning outcomes in these domains and reaching students from all programmes. One of the main challenges for higher education educators is to design courses in a way that will effectively promote the various learning outcomes of EfS. A central question is how sustainability should be integrated into the curriculum; which topics should be taught and which pedagogies ought to be applied to improve students’ knowledge, skills and motivation to promote sustainable living. The present study aimed to contribute to the knowledge about students’ learning outcomes yielded by different designs of EfS courses. This multiple-case study of three courses used a mixed-methods design. For each course, we identified its characteristics and analysed students’ self-reported learning outcomes. We found that: (1) a course with a higher degree of participatory learning, employing a system approach, promoted the highest and most varied learning outcomes; (2) the lecture-based course yielded the fewest learning outcomes; and (3) field trips promoted learning outcomes only when accompanied by more advanced pedagogies.

PurposeThe purpose of this paper is to explore and encapsulate the enablers that can facilitate education for sustainable development in higher education (HE). The study also aims to understand the interdependence between the enablers.Design/methodology/approachThe study adopts the total interpretive structural modelling approach to comprehend the interaction and transitivity between the enablers. Cross-impact multiplication matrix analysis was applied to rank the enablers and classify them on the basis of the driving and dependence power into dependent, autonomous, independent and linkage enablers.FindingsAn extensive literature review and expert opinion helped in identifying 10 enablers that can promote sustainability in higher education. The structural model revealed government policies, media, accreditation/sustainability audit, sustainability leadership and institutional commitment as the crucial enablers that can drive sustainability and activate the enablers with high dependence and low driving power.Practical implicationsThe results of this study will assist the policymakers and management of universities and colleges in understanding important factors that can facilitate sustainability in higher education. Universities and colleges to incorporate sustainability in their system need to transform not only the core higher education activities of learning and teaching, research and engagement, also the way the colleges operate its culture, governance, structure and how it supports the staff and students.Originality/valueSo far, research on sustainability in higher education has looked into each factor in isolation. This research provides a comprehensive view of the factors and has attempted to establish a multidirectional interplay between factors facilitating sustainability in higher education (SHE).

Due to industrialization, increasing solid waste is affecting environmental integrity globally. Reverse logistics (RL) has become a significant tool to deal with environmental degradation issues, and it is being implemented in developed countries. However, RL is at the infancy stage in developing countries especially in Pakistan due to different obstacles. This study aims to identify and analyzes the interrelationship between barriers affecting RL implementation in Pakistani manufacturing industry using an integrated methodology of Interpretive Structural Modeling (ISM) and MICMAC approach. Results of ISM and MICMAC identified organizational, financial, and technological barriers as dependent barriers. However, lack of government policy incentives, lack of responsiveness about RL, lack of enforceable laws on product return, changing in regulations due to political changes, lack of environmental law awareness and lack of corporate social responsibility emerged out as top-ranked barriers driving other barriers that need to be addressed. An inter-relationship based structural model will be helpful for supply chain and RL professional in understanding major obstacles to RL implementation and develop a strategy to promote RL in the manufacturing industry.

ABSTRACTThe banking systems of emerging economies in general and India in particular are facing sustained impairment due to mounting non-performing assets (NPAs). In the absence of stringent policies and their implementation, the results will be detrimental and may eventually lead to an economic crisis. Thus, it is imperative to unearth the causal factors and mitigate the risks involved with rising NPAs. The study attempts to identify the determinants of NPAs from the existing literature and subsequently, explore the interlinkages between the identified factors. A model of these factors is developed using Interpretive Structural Modeling (ISM) and MICMAC approach. Key managerial insights were obtained by the suggested model, specific to the Indian context. The hierarchical model provides a clearer perspective about the relationship between the factors and suggests that economic conditions and political factors are the key drivers which impact the ownership pattern and adherence to the regulatory framework; these further impact the internal factors related to the banks and borrowers’ capacity to repay. The study will act as a scaffolding for policymakers and bankers. Based on these findings, better instruments and mechanisms for recovery/management of NPAs can be put in place.

The present study designed a model for the implementation of constructive leadership in the higher education system using an interpretive structural modeling approach. This study was applied in terms of aim and qualitative type of descriptive in terms of method of implementation. The statistical population of the study included experts in educational and constructive leadership in higher education at Urmia University. Based on the principle of random sampling, 7 of them were selected as a sample using convenience and snowball sampling methods. A semi-structured interview with experts whose questions were designed based on the theoretical foundations of constructive leadership in higher education with the help of professors was used as the study tool. The validity of the results was examined and confirmed by the triangulation method. Also, its face validity was confirmed by the opinions of experts who were not members of the study, and its reliability was calculated at 0.83 using the inter-coder coefficient of the agreement method. The data obtained from semi-structured interviews were analyzed with open, axial, and selective coding based on grounded theory in MAXQDA software. The results showed that the model of constructive leadership in higher education based on grounded theory included 12 components and 9 dimensions. The category of causal conditions included two dimensions of the necessity of constructive management (with two components of knowledge management and organizational innovation) and organizational culture (with two components of collaborative management and innovative atmosphere). The category of contextual conditions included two dimensions of management maturity (with the component of evaluating and applying policies for constructive management) and the system of fostering change and transformation (with the component of establishing relationships with aligned people and organizations). The category of intervening conditions included the dimension of competition with the surrounding environment and increasing progress (with the component of planning and paying attention to facilities). The category of strategies included two dimensions having facilities and requirements (with the component of financing and equipment) and responsibility and cooperation (with the components of empathy and public desire). Finally, the category of implications included two dimensions of academic development (with 2 components of individual competence development and university development) and social development (with the component of gaining public trust).

In its resolve for a sustainable future, the UN has adopted 17 ambitious goals known as the sustainable development goals (SDGs) which aim to wipe out poverty, fight inequality and tackle climate change over the next 15 years. The key challenge is how to concentrate efforts to effectively implement and achieve the SDGs by 2030. This paper aims to assess the importance of different SDGs from the perspective of India. It also aims to develop a hierarchical model for the adoption of the SDGs by India, for which the interpretive structural modelling (ISM) approach is used. ISM is a methodology for identifying and establishing relationships among the identified variables. The study variables comprise the 17 SDGs which are found to have a direct relatedness to common sustainability targets like poverty eradication, hunger elimination, reduced inequality, etc. These variables have also been mapped on driving power-dependence diagram and categorised accordingly.

Artificial intelligence (AI) is receiving increasing attention for its potential to support sustainability in higher education. As both users and developers of AI technologies, universities are well-positioned to advance environmental and social goals while ensuring that AI is used responsibly. This scoping review examines two related areas: AI for Sustainability, which involves using AI to achieve sustainability outcomes, and sustainable AI, which focuses on reducing the direct environmental and ethical impacts of AI itself. The review examines how these concepts are defined in literature, the field's evolution over time, and the potential or increasing application of AI in sustainability practices by universities. While there are promising examples of the use of AI to advance sustainability projects, including AI used for energy management, climate monitoring, and green campus programs, many of these efforts are still limited in scale and lack clear ethical or environmental guidelines. In addition, much of the current research is either conceptual or based on small-scale pilot projects, with few studies assessing long-term impact or full institutional adoption and commitment. The outcome of the review is intended to provide a structured overview of the extant of work in the area; identify research gaps, and introduce the concept of a future green campus. It argues that future research should focus on testing AI tools in real university settings, measuring their environmental impact, and developing policies that combine ethical and sustainability goals. Greater involvement from students, staff, and faculty will also be essential to ensure that AI supports a fair and sustainable future for higher education.

This research aimed to analyze the importance of artificial intelligence and sustainability in higher education according to the literature in the field and to present the relationships of this context with the United Nations Sustainable Development Goals (SDGs). The adopted research strategies included bibliometric analysis using VOSviewer software and literature review, considering the Web of Science scientific database. The bibliometric analysis resulted in the clustering of four groups. The blue cluster highlighted the emergence of interest in studies on AI and sustainability in higher education following the Covid-19 pandemic. The green cluster emphasized the importance of more efficient teaching methods adapted to the demands of higher education, as well as the need to empower teachers to use artificial intelligence in developing students' skills and competencies, emphasizing sustainability. The yellow cluster indicated the presence of artificial intelligence in higher education based on the triad of sustainable education and innovation, aiming to prepare students for future challenges. The red cluster emphasized the impact of artificial intelligence in higher education, focusing on student learning, efficiency, and sustainable performance. Finally, the literature analysis identified the main AI technologies in higher education and their relationship with the United Nations SDGs. The reflections presented here can contribute to expanding discussions on the relationship between artificial intelligence and sustainability in higher education. From a practical standpoint, it can serve as a foundation for university managers aiming to promote the integration of AI into their teaching processes, considering the context of sustainability.

Several qualitative and quantitative techniques are available for factors analysis of benchmarking of supply chain management. Fifteen variable factors of benchmarking of internal supply chain management (ISCM) has been identified and derived theoretically from various literature sources and opinions of expert's from 300 manufacturing industries. Mean score and an interpretive structural modelling (ISM) approach is applied to assign the rank of factors. In ISM approach, influence between factors is determined by considering the opinions of experts from relevant field. An industrial questionnaire method is used to collect the opinion of experts. Firstly, analysis of the interactions among factors for benchmarking of ISCM by ISM approach and matriced impacts croises multiplication appliqueeaun classement (MICMAC) analysis. Secondly, to develop the relationship among identified rank of factors. Finally, to do the classification of variable factors into clusters based on their driving power and dependence power. According to social implication and managerial point of view, this research provides help to researchers and managers to understand the mutual influence of factors. This research work is also helpful to identify those factors which support in benchmarking of ISCM of any business organisation.

Purpose – Due to country-wise lockdown and state-wise curfews in COVID-19, people were not able to make offline payments (i.e. cash payments) during purchases in India. So, people are switching their payment behavior from offline to online mode. But, as per the central bank report, the rate of adoption through mobile payments is still slow. The paper focuses on identifying critical barriers to mobile payment systems (MPSs) adoption in India. Innovation resistance theory (IRT) has been used as a base model for barriers, despite the wide range of choices of barriers available in the MPSs context. Additionally, three external variables which are out of the wider coverage of IRT constructs were incorporated in this paper. The study, on the other hand, adds to innovation resistance theory in the frame of reference of MPSs from a theoretical perspective. Interpretive structural modeling (ISM), together with MICMAC analysis is brought into play to analyse the direct and indirect relationship amongst the barriers. Research methodology – ISM approach has been used to establish the relationship among the eight (08) identified barriers, through literature and expert opinions. The key barriers to high driving power are then identified with the help of MICMAC analysis. Findings – The results reveal that value barrier (b2), image barrier (b5) and visibility barrier (b7) are the most significant variables. Interestingly, IRTs’ risk barrier (b3) and privacy barrier (b6) from the literature fall in the lowest level of the ISM model. The majority of the barriers fall under quadrant III of MICMAC analysis, indicating the high driving and dependence power. Research limitations – The developed ISM model is based on the sentiments of five (05) experts, which could be biased and influence the structural model’s final output. Due to COVID-19, data has been collected through online video conferencing mode, this may vary if data will be collected through an offline or face-to-face interview. The proposed model’s key findings aim to assist in explaining the barriers that exist during MPS adoption. Originality/Value – This study is the first attempt to use the ISM approach in conjunction with IRT to detect barriers within MPSs. The result of this paper will guide and motivate the researcher to analyse more critical barriers with IRT to contribute to the theoretical development.

Analysis of factors and their hierarchical relationships influencing building energy performance using interpretive structural modelling (ISM) approach

This paper explores the leadership of education for sustainability (EfS) in higher education, focusing specifically on the key role students can play as internal catalysts for change. It presents a case study of Plymouth University, a higher education institution with an international reputation for EfS leadership. The paper outlines the importance of seeking cultural transformation in the leadership of sustainability, highlighting the benefits of an integrated approach that encompasses teaching and learning, research, and campus and operations. This manifold and coordinated approach requires top-down strategic support in order for EfS to take root and gather momentum. However, in this paper it is argued that the bottom up empowerment of ‘students as change agents’ is just as important. Reflecting on the strengths and weaknesses of a number of student leadership initiatives at Plymouth University, this paper argues that EfS reform in HE has significant implications for staff training and the design of participatory learning spaces in order for students to have their voices heard and to be partnered with as leaders.