Generative Artificial Intelligence and the Impact on Sustainability

Purpose This conceptual paper examines what underlies decision-making in generative change processes to explore how generative artificial intelligence (GAI) might shape the future of organisational change. Design/methodology/approach This investigation draws on Snowden and Stanbridge's (2004) social complexity concept, Archer's (1995) morphogenetic/morphostatic explanatory methodology and Mackenzie and Bititci's (2023) social systems-based model for organisational change to explain how structure, culture and agency influence generative change processes in complex adaptive social systems. Findings Both human-based decision-making and machine-based decision-making have roles to play in generative change. This paper proposes that human reflexivity mediates ideas, whereas the material aspects of artificial intelligence (AI) and GAI mediate tasks. The former shapes the change interventions that take place and the latter contributes to their more effective execution. Practical implications In generative change processes, AI and GAI technologies should be focused on tasks that support human-based decision-making. Originality/value This paper explores decision-making in organisational change from a social complexity perspective and identifies the complementary roles of human reflexivity and AI and GAI materiality in delivering emergent outcomes.

Teacher education students play double role as present learners and future educators. Hence, they need targeted training to navigate the growing influence of Generative Artificial Intelligence (GenAI) on teaching, learning and professional identity. However, existing artificial intelligence (AI) literacy programmes predominantly emphasize technical AI knowledge and pre‐GenAI tools or are offered by GenAI platforms that focus on their own technologies' features, thereby lacking pedagogically structured frameworks that address non‐technical dimensions such as ethics, contextual understanding and the development of human‐centered, critical and lifelong learning mindsets to adapt to rapid technology changes. Furthermore, limitation of time in teacher education syllabus and faculty's lack of AI literacy require targeted intervention. This design‐based research (DBR) aims to fill this gap by developing and evaluating a set of design principles for GenAI literacy training in teacher education. Integrating contemporary AI competency frameworks for learners and teachers, the study implements these principles in a workshop that serves as the research prototype. The prototype was first piloted with 14 master's students and then evaluated with 29 teacher education (TE) students. Results show significant gains in participants' AI competence self‐efficacy, attitudes towards GenAI and commitment to critical, ethical and pedagogical engagement with GenAI tools. The findings highlight the need for teacher education programmes to integrate GenAI literacy that supports teachers' evolving roles as reflective practitioners, co‐creators and lifelong learners in an AI‐driven world. Practitioner notes What is already known about this topic Students' self‐efficacy and mindset influence their adoption of GenAI in learning and teaching. GenAI competency is essential for teacher education students, encompassing technological, ethical and sociocultural dimensions. Implementing AI literacy training faces challenges such as educators' limited AI literacy, the lack of pedagogical frameworks and the need to integrate technical knowledge with ethical reasoning, self‐regulated learning and metacognitive skills. Transformative learning is crucial in preparing teacher education students as co‐creators and facilitators in AI‐enhanced education. What this paper adds This study presents the design and evaluation of an AI competency training programme to enhance teacher education students' self‐efficacy, critical and human‐centered AI mindsets and human–AI interaction skills. Using design‐based research, it identifies key principles for AI literacy training, demonstrating how transformative, active and experiential learning approaches can prepare teacher education students for evolving roles in AI‐enhanced education. Implications for practice and/or policy Educational technologists can apply the proposed design principles to develop GenAI tools and AI systems that enhance AI competency and self‐regulated learning (SRL). Educators can integrate these AI training principles into their teaching strategies and enhance their task assignments and assessments to foster deep learning, self‐regulated learning and metacognitive skills in the age of GenAI. Policymakers should update teacher education curriculum to include AI competency, strengthen content and pedagogical knowledge, especially in social emotional learning, formative and authentic assessment methods and establish guidelines for selecting GenAI tools that support human intelligence and mitigate over‐reliance.

Despite increasing interest in using Generative Artificial Intelligence (GenAI) in education, little is known about how students with disability engage with GenAI to support their own learning. This study investigates the potential of ChatGPT to support the learning agency of adolescents with disability in a secondary science classroom in Australia. Guided by sociocultural and socio‐material conceptualisations of agency, the study explored the mediated choices and capabilities of three students with disability to use ChatGPT to facilitate their learning. The study was conducted in a class comprising students of varying ages clinically diagnosed with diverse learning needs. Data sources included student interviews, the students' conversations with ChatGPT, teachers' lesson worksheets and video recordings of the lesson. Thematic analyses reveal that while students expressed clear and meaningful choices to use ChatGPT to support their learning, they faced metacognitive challenges and cognitive constraints, resulting in a misalignment between their choices and actual capability. The findings identify key theoretical perspectives and practical considerations for supporting students with disability in using GenAI to develop their learning agency. The study recommends customising GenAI for specific learning needs in line with its function as a cognitive prosthesis for students with disability and for better alignment with Universal Design for Learning, thereby supporting students' learning agency. Practitioner notes What is already known about this topic The development of students' learning agency has been widely explored in the secondary context, but not for students with disability. There is increasing interest in using Generative AI (GenAI) to support inclusive education. Current theoretical frameworks can inform the exploration of the learning agency of students with disability using GenAI. What this paper adds Foregrounds the learning choices of students with disability as indicators of their emergent learning agency. Identifies learning challenges facing students with disability that misalign their learning choices and capabilities. Examines GenAI's potential as a socio‐material mediator facilitating the material and relational agency of students with disability. Implications for practice and/or policy Consulting students with disability on how GenAI can support their learning, providing opportunities for them to express their learning preferences. Customising GenAI tools in line with the Universal Design for Learning guidelines to address the specific learning challenges of students with disability. Clarifying the roles of teachers and education assistants in facilitating the learning agency of students with disability in light of customising GenAI to mediate their learning agency.

Recent advancements in generative artificial intelligence (GenAI) have drawn significant attention from educators and researchers. However, its effects on learners’ programming performance, self-efficacy and learning processes remain inconclusive, while the mechanisms underlying its efficiency-enhancing potential are underexplored. This study addresses these gaps through a quasi-experiment comparing an experimental group using GenAI for self-directed programming learning with a control group relying on alternative tools. Additionally, the experimental group was divided into high- and low-performance subgroups to examine the relationship between learning behaviour patterns and academic outcomes using process mining techniques. The findings reveal that (a) GenAI demonstrates strong potential to enhance learning outcomes and self-efficacy but negatively affects long-term knowledge transfer; (b) excessive reliance on GenAI and cognitive outsourcing impede effective knowledge acquisition; (c) high-performing learners exhibit greater epistemic agency, actively critiquing and engaging with AI-generated content to construct knowledge proactively. This study underscores the risks of over-reliance on GenAI and the detrimental effects of cognitive offloading, highlighting the critical role of cognitive engagement and epistemic agency in fostering hybrid intelligence. It provides empirical and theoretical insights to inform the design of GenAI-supported programming education strategies and interventions. Implications for practice or policy: Instructors can enhance programming self-efficacy by integrating GenAI tools like ChatGPT into self-learning activities, particularly for reinforcing academic performance. Course leaders should emphasise GenAI use in programming courses to support student engagement, though they should also prepare students for problem-solving without external resources. Educational institutions may consider developing guidelines for balanced GenAI usage to maximise learning benefits while addressing potential limitations in problem-solving skills.

Pastoralists are people who, for their livelihood, depend on livestock raising using the natural pasture. Sustainability of pastoralist production is important for their livelihood as well as to the supply of animal-based protein. However, concerns with the pastoralist production system have also been voiced, particularly, on the ecological implications of the system. As a consequence, sustainability of the pastoral system and pastoralists that operate therein is a central issue in many debates among development practitioners, academics, and policy makers). Central to this debate is whether pastoralists can be expected to adapt their livestock production strategies to the varying availability of natural resources (i.e., pasture and water). Thus, herd size management in terms of stocking and destocking of livestock by pastoralists has become a central issue in the debate. The stocking and destocking decision in anticipation of changing external conditions can be conceived of as a marketing challenge to pastoralists, not only contributing to their livelihood and profit, but also to the sustainability of the system. As such, markets (and therefore, marketing) relates not only to economic and social aspects of sustainability, but also to the ecological aspects. This study investigates whether and how marketing can contribute to the sustainability of pastoralists. For this purpose, four lines of research are explored: (1) Is pastoralism in principle a sustainable production system?; If so under which conditions is such system sustainable?, (2) How does marketing relate to the conditions under which pastoralism is sustainable?, (3) Which concepts from the marketing literature can generate insights that potentially contribute to the sustainability of pastoralism?, and (4) How do these concepts empirically relate to the social and economic aspects of sustainability and to the resource dilemma that characterizes the ecological aspect of pastoralists’ sustainability? The thesis indicates that marketing can help pastoralists to strengthen their livelihood and to adapt to changing natural conditions through strategic selling and buying of livestock. In that respect only strategic marketing with a central view on the creation of customer value can contribute to economic, social and ecological sustainability at the same time. Thus, the thesis shows evidence that if pursued with a strategic intent marketing is a viable tool also for the sustainable development of the pastoralist system. Market orientation (in terms of customer orientation and interfunctional coordination) is in that respect the key concept. The thesis also contributes to the literature on the role of marketing in emerging economies indicating that market orientation theory is not restricted to formal economies with bounded organizations, but generalizable to informal economies such as pastoralists. To help pastoralists to live in a sustainable manner, policy should support their adaptation along the lines of the market by not just strengthening market integration, but by advocating and facilitating a market orientation.

The advancements in Generative Artificial Intelligence (GenAI) can provide opportunities for enriching educational experiences, but at the same time raise concerns regarding academic integrity. Many educators have expressed anxiety and hesitation when it comes to integrating GenAI in their teaching practices. Thus, recommendations and guidance from institutions are needed to support instructors in this new and emerging GenAI era. In response to this need, this study explores different U.S. universities' academic policies and guidelines regarding the use of GenAI tools (e.g., ChatGPT) for teaching and learning, and from there, gains understanding of how these universities respond and adapt to the development of GenAI in their academic contexts. Data sources include academic policies, statements, guidelines, and relevant resources provided by the top 100 universities in the U.S. Results show that the majority of these universities adopt an open but cautious approach towards GenAI. Primary concerns lie in ethical usage, accuracy, and data privacy. Most universities actively respond and provide diverse types of resources, such as syllabus templates, workshops, shared articles, and one-on-one consultations; focusing on a range of topics, namely general technical introduction, ethical concerns, pedagogical applications, preventive strategies, data privacy, limitations, and detective tools. The findings provide four practical pedagogical implications for educators when considering GenAI in teaching practices: 1) accepting GenAI presence, 2) aligning GenAI use with learning objectives, 3) evolving curriculum to prevent misuse of GenAI, and 4) adopting multifaceted evaluation strategies. For recommendations toward policy making, the article suggests two possible directions for the use of GenAI tools: 1) establishing discipline-specific policies and guidelines, and 2) managing students' sensitive information in a transparent and careful manner.

Advances in generative artificial intelligence (GenAI) have created uncertainties and tensions in higher education, particularly concerning learning, equity and quality. Despite emerging empirical research, much current policy is based on assumptions about how and why students are using GenAI. This Pecha Kucha reports on 20 online focus groups involving 79 students from four Australian universities. Each focus group represents a mix of disciplines and levels of study (including undergraduate and postgraduate). We conducted reflexive thematic analysis, adopting a relational view of AI (Bearman & Ajjawi, 2023) that supports a nuanced examination of how AI uses are enacted, understood, and contested within educational settings. Our study shows that students use GenAI in diverse and complex ways and their beliefs about GenAI contain ambiguity, contradictions, and tensions. In this pecha kucha we focus on five interrelated tensions, identified across participants, and selected as particularly significant and challenging for educators. The salience of these tensions varied across participants but, together, they paint a complex picture of student engagement with GenAI. Tension 1 is between student perceptions of AI in terms of enhanced efficiency and concerns about academic integrity. Students reported that GenAI tools could speed up writing, editing, summarising, and simplifying complex materials. However, many also feared that short-cuts and efficiencies could lead to accusations of cheating. Tension 2 is between widespread adoption of GenAI tools and ambiguous policy around acceptable use. Many students used a diverse range of GenAI tools, yet a number of participants voiced uncertainty about allowable use of GenAI in assessments. A perceived lack of clear and detailed guidance from universities created confusion and anxiety, and the development of personal rules to avoid accusations of academic misconduct. Tension 3 is between empowerment and dependency. AI tools were sometimes seen as reducing inequalities (e.g. for international students or those requiring language support). On the other hand, some students expressed concerns about becoming dependent on GenAI tools where tasks were made too easy, undermining learning and skill development. Tension 4 is between access and equity. Closely related to tension 2, here, the reduction of barriers to academic writing and accessing educational resources is contrasted with concerns around exacerbating inequalities due to variation in access and support. These concerns are amplified through diversity of engagement, beliefs of students and educators around acceptability, and contextual pressures (e.g. fear of being left behind, time pressures, the perceived stakes of assessment). Tension 5 is between beliefs about deepened engagement with learning materials and reduced quality or accuracy of GenAI output. Some students reported that GenAI tools could provide useful perspectives on resources or simplify complex texts. However, many voiced frustration that GenAI tools sometimes provided incorrect information, required verification or “missed the point”, which could lead to significant additional work. These tensions highlight areas where students need additional support and guidance. The overlaps and entanglements of these tensions make their navigation in higher education particularly complex. These findings suggest practical implications for educators, policymakers, and institutions. For instance, to better support students, institutions should continue to develop clear, context-sensitive guidelines that resolve ambiguities around acceptable use (Tensions 1 and 2) and provide concrete strategies to balance the benefits of efficiency with concerns over academic integrity and dependency (Tensions 1 and 3). Additionally, efforts should be made to ensure equitable access to GenAI tools and support (Tension 4) while helping students critically assess the quality of AI-generated content (Tension 5).

Purpose Generative Artificial Intelligence (GenAI) is rapidly transforming business strategy, innovation processes and governance practices. While prior research has focused primarily on technological implementation and performance outcomes, limited attention has been paid to how senior executives interpret, frame and adapt to GenAI as a strategic and ethical phenomenon. This study aims to explore executives’ sensemaking and adaptive responses to GenAI and to develop a conceptual model that captures this process. Design/methodology/approach The study adopts a qualitative exploratory design based on semi-structured interviews with 15 senior executives from diverse industries in Greece. Data were analyzed using thematic analysis to identify recurring patterns in executives’ perceptions, decision rationales and ethical considerations related to GenAI adoption and integration. Findings The analysis reveals four interrelated dimensions shaping executive adaptation: strategic integration and decision-making, business value and innovation, human–AI collaboration and workforce transformation and ethics, governance and adoption barriers. Cross-thematic synthesis indicates that executives perceive GenAI as a decision-support and innovation amplifier rather than an autonomous decision-maker. These findings inform an Emergent Integrative Model of Executive Adaptation to GenAI, conceptualized as a dynamic cycle comprising strategic sensemaking (Curate), operational experimentation (Create) and ethical consolidation (Consolidate). Research limitations/implications Several executives cited technical and resource limitations as obstacles to effective GenAI implementation. Challenges include legacy systems, lack of skilled artificial intelligence (AI) engineers and limited integration between AI platforms and enterprise software. Practical implications This model advances understanding of executive cognition and adaptive intelligence in the AI era, positioning leadership as a process of continuous learning, sensemaking and ethical stewardship. Practically, the research offers a roadmap for organizations and policymakers for aligning GenAI-driven innovation with responsible governance and leadership development. Social implications By highlighting the role of executive stewardship, the study underscores how ethical leadership in GenAI adoption influences public trust, workforce well-being and organizational legitimacy. Originality/value Existing research on AI in business has predominantly focused on technological implementation, efficiency gains and economic outcomes. Studies emphasize measurable benefits such as productivity enhancement, improved decision-making speed and customer experience optimization. However, fewer studies explore how executive cognition and strategic reasoning shape the trajectory of AI adoption – particularly regarding GenAI technologies that introduce new forms of creative automation. The study advances leadership and sensemaking research by shifting the focus from GenAI adoption outcomes to executive cognition and ethical stewardship. It offers a novel integrative model that explains how strategy, innovation and governance co-evolve in the GenAI era.

To investigate the associations between social sustainability factors and people's satisfaction with their local community as a place to live now and in the future. This study used data from the Norwegian County Public Health Survey conducted in Viken County, Norway, in 2021 (N = 97,323). The survey included questions concerning physical aspects (e.g. accessibility of services and facilities) and non-physical aspects (civic participation, social support, trust, safety, place attachment and well-being) of social sustainability in local communities. A new outcome variable was constructed to capture people's satisfaction with their local community as a place to live now and in the future. Hierarchical linear regression was applied to examine the relationships between physical and non-physical factors of social sustainability and this outcome. All included factors were significantly associated with people's satisfaction with their local community as a place to live now and in the future, but the magnitude and direction of these associations varied. Place attachment (β = 0.53), safety (β = 0.15) and well-being (β = 0.11) were the strongest indicators of people's satisfaction with their local community. Non-physical factors accounted for 41% of the variance in people's satisfaction, while physical factors accounted for 14%. Both physical and non-physical factors are essential for developing socially sustainable communities. This knowledge could be valuable for academics and policymakers, helping them better understand the complex relationships between various aspects of social sustainability and informing the development of socially sustainable local communities.

New generative artificial intelligence (AI) models have created new challenges for copyright policy as such models may be trained on data that include copy-protected content. This paper examines this issue from an economic perspective and analyzes how different copyright regimes for generative AI will impact the quality of content generated and AI training. Because of transaction costs (for example, because of the large amount of content being used to train generative AI models), it is not possible for copyright holders and AI providers to engage in negotiations. The result is a characterization of the factors that would favor full copyright and no copyright protections, balancing the level of potential harm to original content providers and the importance of content for AI training quality. However, it is demonstrated that an ex post mechanism like fair use can lead to higher expected social welfare than traditional rights regimes.

Introduction: Generative Artificial Intelligence (GAI) has consolidated in recent years as a disruptive technology with the capacity to transform the tourism sector through experience personalization, content generation, and process optimization. Methods: A bibliometric approach was applied to analyze the scientific literature on GAI in tourism. Data were retrieved from the Scopus database, considering publications between 2020 and 2025. Records were processed using the Biblioshiny tool. Indicators evaluated included annual scientific production, sources, institutions, keywords, collaboration networks, and thematic maps. Results: A total of 412 documents were identified. Scientific production grew exponentially from 2022 onward, reaching over 150 articles in 2023 and 180 in 2024. Current Issues in Tourism and International Journal of Hospitality Management were the most productive journals. The University of Macau led institutional output, while China and the United States were the countries with the highest levels of international collaboration. Thematic maps revealed consolidated, specialized, and emerging topics. Conclusions: GAI in tourism is a rapidly expanding and multidisciplinary field, with notable geographic and institutional concentration. The findings provide a foundation for guiding research strategies, technological investment, and training, highlighting the need to integrate ethical and sustainability dimensions into its future development.

Purpose This research explains how mianzi – the quest for social prestige in Chinese culture – drives B2B agents to depend on international brand-owner equity, and how advanced artificial intelligence (AI) can loosen that cultural grip, enabling agents to craft independent brand strategies. Design/methodology/approach We adopted a four-study, multi-method program (aggregate N = 6,468) that spans laboratory-quality simulation and real-market observation: a VR consumer experiment, a managerial replication with MBA/EMBA executives, a large-scale field test in urban retail precincts, and a 2 × 2 × 2 pop-up-store intervention that varies AI sophistication and price salience. Across all designs we measured mianzi, perceived dependence on brand-owner equity, brand-strategy choice, and – in the latter studies – AI perception and price sensitivity. Findings High mianzi consistently elevates perceived dependence on brand-owner equity and tilts agents toward the international brand. However, when agents deploy high-level AI—characterized by real-time analytics, personalized recommendation, and simulated social presence – the mianzi effect on dependence weakens, granting greater strategic autonomy. Price sensitivity does not explain or moderate these relationships. Research limitations/implications The work is confined to sports-apparel distribution and relies on established proxy scales for mianzi. Future research should test other sectors and employ finer cultural diagnostics or cross-national comparisons. Practical implications Chinese distributors can use AI-enabled personalization to reduce over-reliance on global logos, while international brand owners should calibrate support from reputational “halo” to capability transfer. The framework generalizes to any high-prestige market where cultural values intersect with digital transformation. Originality/value By integrating a culturally specific motive (mianzi), a strategic resource (brand-owner equity), and a disruptive technology (AI) in a single explanatory chain, the study illuminates how technology can temper deeply embedded cultural constraints, advancing theory on cross-cultural marketing, AI adoption, and B2B branding.

The 17 United Nations Sustainable Development Goals (SDGs) are an urgent call for action by nearly every industry. The buildings sector has made significant efforts toward the SDGs by enhancing energy efficiency. The emergence of generative artificial intelligence (GenAI) technologies, such as advanced algorithms and models, has brought new possibilities for further enhancing energy efficiency in buildings. However, the potential of GenAI in this domain has not yet been fully explored. This study provides a comprehensive overview of the current applications of GenAI in enhancing building energy efficiency. A total of 34 studies were identified from the Scopus and arXiv databases and analyzed using content and thematic analysis. Results show that GenAI provides significant advantages in building energy data processing, energy prediction, as well as energy modeling and management. This research enriches the existing knowledge body by summarizing the applications of GenAI in enhancing building energy efficiency. Furthermore, it highlights future trends of this emerging AI technology, paving the way toward a more sustainable built environment.

This study examines the impact of generative artificial intelligence (GAI) on Chinese enterprises. 320 participants completed electronic surveys, revealing a positive relationship between GAI and corporate performance. Managerial relationships were found to play a crucial role, mediating the influence of GAI on performance. Additionally, the speed of technological change in the industry was identified as a moderator, highlighting the dynamic nature of GAI's impact on managerial relationships and firm performance. These findings underscore the strategic importance of GAI in cultivating managerial relationships and improving enterprise performance, deepening our understanding of the integration of advanced AI technologies and management practices in the Chinese market. Practical implications are offered for decision-makers in technology-intensive industries, providing valuable insights for leveraging GAI to gain a competitive edge and achieve sustainable growth in a rapidly evolving technological landscape.

Access to energy is a precursor to sustainable development. In India, at present, the number of people without access to electricity remains high, despite several efforts by the government. Non-feasibility of grid extension is one of the major reasons for this poor energy access. Even in the areas with grid connectivity, reliable electricity supply still remains a challenge. Biomass gasification based renewable distributed power generation sources may provide electricity access by generating it at the point of consumption using locally available resources. Most of the prior studies have evaluated biomass gasification system based on cost of electricity generation neglecting its impact on environment and society. Instead, sustainability aspects including social, technical, environmental, and resource should be an integral part of this practice along with economic aspect and compared in transparent way. This not only necessitates identification of appropriate indicators to reflect relevant sustainability dimensions, but also requires development of general framework comparing different qualitative and quantitative indicators. Framework should bring out various trade-offs and bounce associated with indicators. This paper analyses different indicators and assessment methodologies including single-criterion and multi-criteria decision methods discussed in the literature for sustainability assessment of power generation systems followed with specific focus on sustainability assessment methodologies suggested for biomass gasification systems. Research gaps with scope for conceptual contributions in the prevailing sustainability assessment process from selection of appropriate indicator to assessment methodologies are discussed. Based on the literature review, this article identifies appropriate criteria and indicators to be considered for assessing the sustainability of biomass gasification based power generation systems. A total of 18 indicators representing economic, technical, environment, resource, and social aspect of sustainability along with their relevance and importance for sustainability assessment are discussed. The set of indicators identified may be used for comparing sustainability of possible distributed power generation systems in Indian context.