Resilience narratives of multiethnic communities during water supply disruptions in the climate change era: evidence from Malaysia

Agile culture, collective human intelligence and AI adoption: Gender and sectoral perspectives on augmented intelligence in Europe

This article examines objections to the use of artificial intelligence (AI) in decisions affecting humans and argues that most objections rest on anthropocentric assumptions rather than evidence about decision quality. Critics often presume that human decision makers are uniquely capable of moral and contextual judgment, while AI systems are inferior, opaque or hostile. The article challenges this view arguing that modern AI systems are an expression of collective human intelligence and ethics, built on the image of human cognition, trained on curated human decisions and operating on best decision making frameworks. Drawing on empirical studies the article shows that modern AI systems offer unique advantages in decision-making, thus may already in some domains be as good at making good decisions as an average individual human. The article deconstructs main criticisms of AI decision-making and introduces the novel argument of inseparability between human and AI decisions. Human and AI contributions are increasingly intertwined, AI involvement is latent and appropriated by humans, making existing accountability frameworks based on a clear human–AI boundary obsolete. The article advocates for the development of agnostic decision-making frameworks that apply universal accountability to both human and non-human agents and provide a path to better decisions.

Groupthink has long been documented as a recurring feature of boardroom decision making, impairing collective judgment and, in some cases, contributing to major corporate failures throughout history. Artificial intelligence (AI) is increasingly introduced into governance settings, yet its implications for collective decision-making dynamics remain underexplored. Drawing on 20 interviews with Moroccan board directors, this study examines perceptions of AI's role in shaping groupthink tendencies. The findings suggest that AI-enabled decision support may enhance the informational basis of deliberation, encourage dissent through neutral feedback, direct prompting, and anonymized inputs, strengthen individual and collective evaluation processes, and augment collective intelligence in board deliberations. Nonetheless, entrenched cultural norms, hierarchical structures, and enduring human dynamics were perceived to constrain AI's influence. Building on these insights, the study introduces the concept of AI-mediated groupthink, contributing to research on small-group decision making, groupthink, and corporate governance, while informing governance actors about the potential and limits of AI-enabled decision support in boardroom decision making. • AI may mitigate some aspects of groupthink but is unlikely to eliminate it. • AI is perceived to enhance efficiency, objectivity, and board evaluation. • AI may foster dissent via neutral, prompted, and anonymous input channels. • Cultural, hierarchical, and human factors may constrain AI's influence. • AI is valued primarily for its potential to augment collective intelligence.

• The Africa Entrepreneurial Ecosystem Index (AEEI) includes 7 conditions for productive entrepreneurship across Africa. • The AEEI better predicts productive entrepreneurship prevalence in African countries than other metrics. • AEEI data aids entrepreneurial ecosystem diagnostics, monitoring and evaluation, to foster productive entrepreneurship. Productive entrepreneurship is highly needed for economic growth and development in Africa. For this to happen, the conditions for productive entrepreneurship need to be improved. However, data on the conditions for productive entrepreneurship in the African continent are scarce, which makes it very difficult to improve these conditions and interventions in a context-specific way. We tackle this challenge and source data from various public, private, global, and African sources. We use an entrepreneurial ecosystem framework to select, make sense of, and integrate this data. We use an extensive set of 21 empirical indicators to measure seven dimensions of national entrepreneurial ecosystems in Africa and to compose the Africa Entrepreneurial Ecosystem Index. This index and the underlying data aim to facilitate large-scale research on entrepreneurial ecosystems and collective learning for improving entrepreneurial ecosystems in Africa.

In today`s modern industrial societies, economy, prosperity, and well-being are resource-intensive and often shaped neither socially nor ecologically responsibly:. To follow the normative guidelines of sustainable development in an increasingly complex society, a more sustainable way of doing business is required. This depends on the motives of the actors in the economic equation of supply and demand. Therefore, the study focused on the micro-perspective of individual sustainably operating innovation actors. The goal was to derive trends in the underlying values and orientations of these actors (supply side) and to identify factors that have the potential to foster an innovation climate for an accelerated, more sustainable economy. Despite the diversity of sectors (ranging from agriculture and transport to industry), the results show central trends in the commonalities: Contrary to a purely techno-functional logic of innovation, these innovations emerge in strong, network-based cooperations founded on collective learning processes and shared values. The differences lie primarily in the innovation focus – from social and organizational innovations that question existing market logics, to technological and digital innovations for enhancing the efficiency of established industries.

ABSTRACT This study sets out to explore the potential outcomes of a novel approach to architectural education, in which students are encouraged to utilise a diverse range of media, including poetry, soundscapes and AI-generated dreamscapes, in place of conventional architectural documentation. This paper reports on an action research study that examined how interdisciplinary co-design workshops can expand spatial literacy in the field of architectural education. A two-day workshop, entitled ‘Layered Space’, engaged 46 architecture students across six subgroups in transforming themes such as ‘shelter’ and ‘dream-space’ into physical installations. The integration of analogue techniques with digital and AI tools has enabled material experimentation and epistemic diversity. The study identifies three significant outcomes: representational pluralism, which is defined as the use of collaborative artistic methods to validate marginalised spatial narratives; epistemic justice, which is defined as a co-design process that elevates philosophical and subjective knowledge to parity with technical expertise; and pedagogical prototyping, which is defined as an informal, iterative format where failure serves as a collective learning catalyst. The integration of such collaborative art-architecture practices into formal curricula is posited as a means of addressing contemporary design complexities through embodied experience, artistic intervention, and technological mediation.

Collective intelligence has emergent problem-solving capacities that are different than those of its subunits. The plethora of multi-scale systems within nature and society makes it imperative to understand the interaction policies necessary and sufficient for emergence of collective intelligences. The economy is a complex system consisting of autonomous elements at multiple scales and which exhibits adaptive problem-solving capabilities, suggesting that the economy offers an interesting, important example of collective intelligence. We identify the price system as the cognitive glue of the economy by acting as a coordinating affordance that enables members to form plans that are mutually compatible. Using the collective intelligence framework of Technological Approach to Mind Everywhere, we elaborate on various aspects of the economy that make it useful to model the economy as a collective intelligence. We argue that any cognitive glue must solve the same kind of problem that the price system solves in broadly the same way that the price system solves it, and thus the price system serves as a generic template or abstract model for all cognitive glues. Finally, we describe some research ideas that combine concepts from biology and economics in the hopes of inspiring interdisciplinary collaboration. This article is part of the theme issue 'World models in natural and artificial intelligence'.

All intelligence is collective intelligence, in the sense that it is made of parts that must align with respect to system-level goals. Understanding the dynamics that facilitate or limit navigation of problem spaces by aligned parts thus impacts many fields ranging across life sciences and engineering. To that end, consider a system on the vertices of a planar graph, with pairwise interactions prescribed by the edges of the graph. Such systems can sometimes exhibit long-range order, distinguishing one phase of macroscopic behaviour from another. In networks of interacting systems, we may view spontaneous ordering as a form of self-organization, modelling neural and basal forms of cognition. Here, we discuss necessary conditions on the topology of the graph for an ordered phase to exist, with an eye towards finding constraints on the ability of a system with local interactions to maintain an ordered target state. By studying the scaling of free energy under the formation of domain walls in three model systems-the Potts model, autoregressive models and hierarchical networks-we show how the combinatorics of interactions on a graph prevent or allow spontaneous ordering. As an application, we are able to analyse why multiscale systems like those prevalent in biology are capable of organizing into complex patterns, whereas rudimentary language models are challenged by long sequences of outputs. This article is part of the theme issue 'World models in natural and artificial intelligence'.

ABSTRACT Purpose This study examines learning processes within a reduced tillage project in Uruguay. We explored the interplay of instrumental, communicative, and emotional learning in a multi-stakeholder collaborative project and analyzed related action outcomes. Methodology This research employed qualitative methods, combining semi-structured interviews and a nominal group technique workshop. We analyzed the data through the lens of transformative learning theory. Findings Overall, the study found evidence of learning, reflection, and action among stakeholders, indicating a potential for transformative change. Stakeholders actively participated in learning about reduced tillage and reflected on participatory learning processes. Key enabling conditions include trust, inclusive dialogue, and on-farm demonstrations. Practical Implications The results of this study provide insights on how processes of learning for transformative change unfold, as well as appropriate frameworks to shape participatory projects. Theoretical Implications This study broadens the scope of transformative learning by examining its application in agricultural contexts, thereby contributing to a deeper understanding of individual and collective learning processes toward transformations. Value This study contributes to literature on the application of an expanded theory of transformative learning as a driver in fostering necessary transformative change, offering insights on the dynamics between unfolding learning processes, relationships, and actionable outcomes in sustainability-related transitions.

An unprecedented era with patients receiving high (≥100 msv) cumulative doses: collective actions needed.

Purpose This study aims to examine workplace learning and competence development in the Swedish mining industry amid the green transition and ongoing technical development, the dual transition. It explores how miners’ learning expectations are experienced, negotiated and enacted in everyday work. Design/methodology/approach Two qualitative studies were conducted at three mines operated by two companies, involving 14 interviews, six workshops and two focus group discussions with mining personnel. Findings Miners demonstrate a strong willingness to learn, driven by professional pride and a desire to contribute to both the collective and the green transition. Collective workplace learning enables adaptation, yet may also normalize uneven organizational conditions and mask structural limits to competence development. While collective workplace learning remains vital, emerging competence demands under the dual transition may exceed what informal, peer-based learning alone can sustain. Practical implications Clearer alignment between competence demands and organizational arrangements is essential to support miners’ long-term learning and competence development as mining work evolves under the dual transition. Originality/value This study empirically shows how macro-level transformations, meso-level organizational arrangements and micro-level collective practices interact to shape workplace learning under the dual transition. Furthermore, by engaging sociocultural perspectives, this study examines how collective participation and shared norms mediate the dual transition, illustrating how continuity and transformation coexist and are negotiated in everyday work practices.

Technical and Medical Challenges in the Mountain Rescue of a Fatally Injured BASE Jumper: A Case Report.

Purpose This study aims to examine how collective dimensions manifest and operate in worker cooperatives with strong collaborative orientations, analyzing the dynamic tension between structured processes and emergent adaptations in their collaborative practices. Design/methodology/approach A qualitative two-phase design was implemented with ten Aragonese cooperatives from Spain’s Alternative and Solidarity Economy Network (REAS). Phase one comprised semistructured interviews with cooperative representatives, while phase two involved a focus group with 6 participants to validate and deepen initial findings. Findings Four collective dimensions were identified operating as adaptive systems: collective intelligence, collective leadership (reconceptualized as shared governance), collective action and collective impact. These dimensions enable organizations to navigate between formal structures and emergent responses while maintaining collaborative principles. The study reveals how cooperatives develop specific organizational capabilities to manage inherent tensions in collaborative processes. Research limitations/implications The sample is limited to worker cooperatives in Aragón, Spain, potentially restricting generalizability. However, findings contribute to interorganizational collaboration theory by demonstrating how collective dimensions operate as integrated systems rather than isolated elements. Practical implications The research documents specific strategies for navigating structure-emergence tensions, developing shared governance systems and creating impact evaluation frameworks that recognize both external results and internal transformations. Social implications The study demonstrates how alternative organizational forms can maintain economic viability while preserving transformative principles, offering insights for social economy development. Originality/value This research provides empirical evidence of how collective dimensions operate in practice, contrasting theoretical frameworks with organizational realities. It identifies the critical importance of contextual factors like territorial embeddedness and emotional-relational aspects previously underexplored in interorganizational collaboration literature.

Purpose The specific purpose of this study is to co-create in partnership learning experiences that are responsive to the needs of local communities by unearthing voices of lived experiences in communities, and uniting them with academic practices of research, teaching and learning. Co-formulating a new transdisciplinary micro-credential with a novel and blended pedagogical approach, using rural digital hubs in the connected hubs network to bring the university to local communities. Micro-credentials are small, accredited courses designed to meet the demands of learners, enterprise and organisations, created by Irish Universities Association partner universities in consultation with enterprise and in this case communities. Design/methodology/approach This paper was informed and developed from a transdisciplinary, co-creation and participatory approach. It emphasises the collaborative development of knowledge across disciplines, and stepped into societal boundaries with key principles on the inclusion of the voices of rural communities in the west of Ireland. Findings This paper charts the development of this novel partnership work, exploring a critical, digital pedagogy of “problem-posing education” in which learner and teacher hierarchical relationships are replaced by individual and collective learning, nurturing pedagogies of connection, both within and outwith the University. Research limitations/implications This conceptual paper starts a conversation on a novel use of technology in a community learning setting using a blended approach that incorporates live in place and live online modes of participation in learning (Beetham et al., 2024). Using sense of community theory (McMillan and Chavis, 1996) to bring increased understanding of equitable interaction and co-creation practices in a designing a community led co-created course. Practical implications The authors explore the meaning of belonging and sustainability and the practical challenges of bringing the university to the community using technology available in rural hubs, the group approach helps to overcome the digital divide and supports knowledge exchange and relationship building. Social implications The approach values the lived experience of people in communities, supporting knowledge exchange that is equitable, together the authors aim to create shared value through respectful interaction. Originality/value This exploratory project brings together a diverse group of people with a common purpose, that of sharing knowledge to support communities to thrive sustainably. Using a novel blended pedagogy featuring rural digital hubs and community as a group participation to co-create a course.

Smallholder farmers in Kitui West Sub-county, Kitui County, rely heavily on rain-fed agriculture, making them highly vulnerable to climate variability and change. Climate-Smart Agriculture (CSA) provides a sustainable approach to enhance productivity, resilience, and environmental conservation. However, adoption of CSA practices remains uneven, particularly among women farmers, despite their significant role in agricultural production. This study examined the role of women farmer groups in facilitating the adoption of climate-smart agriculture practices under private sector-led extension systems in Kitui West Sub-county. A mixed-methods approach was adopted, involving 196 smallholder farmers selected using Cochran’s sampling formula. In addition, purposive sampling was used to select women farmer groups, extension agents, and key stakeholders for interviews and focus group discussions. Data were analysed using chi-square tests and binary logistic regression in SPSS. The findings revealed that 75.1% of respondents belonged to women farmer groups, and group membership was significantly associated with CSA adoption (χ² = 10.842, p = 0.001). Frequency of meetings, perceived benefits, and discussion topics were also significantly associated with CSA uptake (p < 0.05). A majority (81.5%) of respondents perceived women farmer groups as critical drivers of CSA adoption. The study concludes that women farmer groups are effective platforms for promoting CSA through knowledge sharing, collective learning, and access to extension services. The study recommends strengthening private sector partnerships, enhancing access to training and financial services, and promoting gender-responsive extension systems.

A rapid proliferation of industrial internet of things (IIoT) systems has increased the vulnerability of interconnected devices for sophisticated cyberattacks, which necessitates intelligent and privacy-preserving solution for security. This paper presents TrustFed-RHIO, a novel hybrid model which integrates rock hyrax intelligence optimization (RHIO) for optimal selection of feature with a trustworthy differential privacy-enhanced federated learning (TrustFed) scheme for the collaborative detection of attack. The algorithm of RHIO mimics collective intelligence of rock hyrax colonies for identifying most discriminative features, thus reducing dimensionality and enhancing efficiency of classifier. The proposed TrustFed-RHIO scheme ensures, secure, distributed learning over multiple IIoT nodes on embedding differential privacy mechanisms thus mitigating the data leakage risks and adversarial inference. A suggested scheme is thus empowered with explainable artificial intelligence (XAI) scheme termed SHAP and LIME for enhancing interpretability and trust in model predictions. Experimental estimation on benchmark IIoT dataset shows that TrustFed-RHIO attains superior performance on detection accuracy, robustness against adversarial attacks, and privacy preservation on comparing existing schemes. At last, this framework supports secure storage of cloud on detection outcomes, thus enabling scalable deployment in the real-world IIoT framework. The performance evaluation is carried on benchmark dataset CCIoT2024-DIAD and performance is estimated for various metrics like latency, accuracy, precision, recall, F1-score, specificity, training time, and so on. Overall analysis shows that the proposed model is effective in detecting IIoT attacks.

This study analyzes transformative collaborative learning as a management innovation in a community-based service ecosystem in Phrom Kiri, Thailand. Leveraging Transformative Learning Theory and Service-Dominant Logic, the study employs qualitative participatory methods (i.e., multi-stakeholder workshops, focus groups, and field observations) to document the dynamic processes through which learning, interactions, and institutional changes evolve. These findings demonstrate how collectively informed strategies for sustainability challenges engendered collective learning processes that led to an alteration of actors’ assumptions, mobilization of shared understanding, and facilitated new governance practices driven by multi-dimensional value drives in response to accumulating disconnects. These reflect the rise of participatory governance mechanisms, the intermediation between actors to create synergies, and the anchoring of institutional frameworks into local contexts to allow for value generation both in economic terms and social ones. Our case study shows that transformative learning can be more than just a cognitive change, also enabling community-level management innovations. It finds that sustainable development of local service ecosystems relies on the formation, institutionalization, and promotion of collaborative practices that facilitate the alignment of stakeholders’ interests and competencies. By conceptualizing transformative collaborative learning as a key mechanism to understand how management innovation and value co-creation unfold in community-based development, this research advances sustainability and management literature.

Collective intelligence as collective information processing.

The logistics sector in Nigeria is a key to the development of the country, however because of a robust institutional vacuum it has been characterized by infrastructural deficiencies, port congestion, insecurity, and red tape among others. These pressures interfere with operations, especially in cases where small and medium sized enterprises (SMEs) are involved but informal networks are still less researched in terms of strategic response to achieve operations resilience. The proposed qualitative multiple-case study focuses on the strategic use of informal networks by logistics companies in Nigeria and how informal networks affect the operational resilience of the company. The data were collected using an interpretivist approach through semi-structured interviews (25 participants of 12 SMEs in major hubs, i.e., Lagos, Kano, and Port Harcourt), observations, and documents. The results show that there are three types of networks: kinship-based (internal trust and redundancies), community-embedded (collective intelligence and collaboration), and regulatory facilitation (bureaucratic navigation). These enhance resilience capabilities, anticipation, robustness, agility, and adaptation, enabling effective disruption mitigation. However, they are associated with their financial and social costs, the possibility of relationship breakdown, and the existence of the vacuum. The study concludes that informal networks act as essential institutional substitutes and bricolage mechanisms, fostering resilience but hindering long-term sustainability. One such framework connects networks with resilience processes/outcomes and elucidates theory and hybrid strategies. Some implications are conscious network management among practitioners and specific reduction of the void among policymakers.