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Change agents are individuals who can successfully transform aspects of how organisations operate. In education, teachers as change agents are increasingly seen as vital to the successful operation of schools and self-improving school systems. To date, however, there has been no systematic investigation of the nature and role of teacher change agents. To address this knowledge gap, we undertook a systematic review into five key areas regarding teachers as change agents. After reviewing 70 outputs we found that current literature predominantly positions teacher change agents as the deliverers of top-down change, with the possibility of bottom-up educational reform currently neglected.

This study aims to explore how managers exercise agency in strategic investment decisions (SIDs) by drawing on their knowledgeability of the strategic context. Specifically, the authors address the role of position–practice relations and irresistible causal forces in this conduct.

The authors examine SID-making (SIDM) practices in four case organisations operating in highly competitive markets, conducting interviews with managers at various levels and analysing company documents. Drawing on strong structuration theory, the authors show how managerial decision makers draw upon their knowledge of organisational context when exercising agency in SIDs.

The authors provide insights into how SIDM behaviour, specifically agents’ conduct, is shaped by a combination of position–practice relations and the agents’ comprehension of their organisation’s context.

The authors extend the SIDM literature by surfacing the issue of how actors’ conjuncturally-specific knowledge of external structures shapes the general dispositions they draw on in exercising agency in practice.

The authors extend the SIDM literature by surfacing the issue of how actors’ conjuncturally-specific knowledge of external structures shapes the general dispositions they draw on in exercising agency in practice. Particularly, the authors contribute to this literature by identifying irresistible causal forces and illuminating why actors might not resist in SIDM processes, despite having the potential to do so.

Individual change agents for corporate sustainability can drive the transformation of organizations and foster sustainable development. Current research literature is growing and is published in a wide variety of journals. This systematic literature review provides an overview and synthesis of different understandings of individual change agents for corporate sustainability transformation. It identifies gaps and puts forward propositions to contribute to theoretical development in the field.

This paper conducts a systematic literature review and thematic content analysis of individual agency in light of corporate sustainability transformation.

The analysis identifies five research streams, three key dimensions of individual change agency for sustainability (beliefs, actions and competencies) and presents levels of individual agency for transformation. An integrated definition of change agents for sustainability is proposed.

The review concludes with implications to support individuals in fostering sustainability transformations of organizations, markets and societies. The synthesis and propositions help in identifying which dimensions are already and could in future be addressed by individual change agents. While acknowledging the usual limitations of literature reviews, this paper can inspire future empirical research on the effectiveness of individual change agents for sustainability.

By synthesizing different understandings in the literature of individual change agents for sustainability, this article contributes to the theoretical development of individual agency in the areas of understandings, research streams, dimensions and perspectives. It also develops propositions on how individual change agency can effectively contribute to sustainability transformations at individual, organizational and systems levels.

This paper aims to study the role of self-concept in consumer relationships with anthropomorphised conversational artificially intelligent (AI) agents. First, the authors investigate how the self-congruence between consumer self-concept and AI and the integration of the conversational AI agent into consumer self-concept might influence such relationships. Second, the authors examine whether these links with self-concept have implications for mental well-being.

This study conducted in-depth interviews with 20 consumers who regularly use popular conversational AI agents for functional or emotional tasks. Based on a thematic analysis and an ideal-type analysis, this study derived a taxonomy of consumer–AI relationships, with self-congruence and self–AI integration as the two axes.

The findings unveil four different relationships that consumers forge with their conversational AI agents, which differ in self-congruence and self–AI integration. Both dimensions are prominent in replacement and committed relationships, where consumers rely on conversational AI agents for companionship and emotional tasks such as personal growth or as a means for overcoming past traumas. These two relationships carry well-being risks in terms of changing expectations that consumers seek to fulfil in human-to-human relationships. Conversely, in the functional relationship, the conversational AI agents are viewed as an important part of one’s professional performance; however, consumers maintain a low sense of self-congruence and distinguish themselves from the agent, also because of the fear of losing their sense of uniqueness and autonomy. Consumers in aspiring relationships rely on their agents for companionship to remedy social exclusion and loneliness, but feel this is prevented because of the agents’ technical limitations.

Although this study provides insights into the dynamics of consumer relationships with conversational AI agents, it comes with limitations. The sample of this study included users of conversational AI agents such as Siri, Google Assistant and Replika. However, future studies should also investigate other agents, such as ChatGPT. Moreover, the self-related processes studied here could be compared across public and private contexts. There is also a need to examine such complex relationships with longitudinal studies. Moreover, future research should explore how consumers’ self-concept could be negatively affected if the support provided by AI is withdrawn. Finally, this study reveals that in some cases, consumers are changing their expectations related to human-to-human relationships based on their interactions with conversational AI agents.

This study enables practitioners to identify specific anthropomorphic cues that can support the development of different types of consumer–AI relationships and to consider their consequences across a range of well-being aspects.

This research equips marketing scholars with a novel understanding of the role of self-concept in the relationships that consumers forge with popular conversational AI agents and the associated well-being implications.

To address the challenges of the agricultural sector, innovation is necessary. This study aims to focus on knowledge circulation as a basis to facilitate innovation in viticulture in the context of climate change.

We have conducted interviews with viticulture stakeholders in Central Spain (Madrid region) on their perceptions and concerns about climate change, knowledge on practices to mitigate its effects on this crop and their relationship with each other for knowledge exchange. A map showing the knowledge nodes and their relationships with other stakeholders has been drawn based on the answers obtained.

Winegrowers have already noticed the effects of climate change, and they are changing some agricultural practices. Drip irrigation was the most frequently mentioned option to minimize these effects. The map of knowledge identifies the main nodes in the information flow. Results also highlight different approaches to climate change and interesting nuances in the maps of knowledge among winegrowers with and without winery.

This paper is focused on the Madrid region, a territory that is still consolidating its wine sector at the economic and marketing levels. We understand that regions with more consolidated or stronger sectors involve maps of knowledge more complex than that obtained in this study.

Showing the nodes of knowledge, as well as the weaknesses and strengths of the information circuit in the wine sector in the Madrid region, is very relevant to developing strategies aimed at supporting innovation in this sector. From a practical point of view, strategies for knowledge generation and circulation are only one part of the innovation process – policies for financial and technical support are key complementary measures.

Identification of key agents in the innovation process in the wine sector is essential to foster innovation processes. Ultimately, this will lead to more efficient adaptation to new challenges in the sector.

The Agriculture Knowledge and Innovation Systems (AKIS) approach has a consolidated theoretical framework that pays great attention to knowledge flows, but specific studies are needed to capture the reality of AKIS by sector and by region.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

This bibliometric mapping study aimed to provide comprehensive insights into the global research landscape of cybernetics. Utilizing the biblioshiny function in R Studio, we conducted an analysis spanning 1958 to 2023, sourcing data from Scopus. This research focuses on key terms such as cybernetics, cybernetics systems, complex adaptive systems, viable system models (VSM), agent-based modeling, feedback loops and complexity systems.

The analysis leveraged R Studio’s biblioshiny function to perform bibliometric mapping. Keyword searches were conducted within titles, abstracts and keywords, targeting terms central to cybernetics. The timespan, 1958–2023, provides a comprehensive overview of the evolution of cybernetics-related literature. The data were extracted from Scopus to ensure a robust and widely recognized source.

The results revealed a rich and interconnected global research network in cybernetics. The word cloud analysis highlights prominent terms such as “agent-based modeling,” “complex adaptive systems,” “feedback loop,” “viable system model” and “cybernetics.” Notably, the journal Kybernetes has emerged as a focal point, with significant citations, solidifying its position as a key source within the cybernetics research domain. The bibliometric map provides visual clarity regarding the relationships between various concepts and their evolution over time.

This study contributes original insights by employing advanced bibliometric techniques in R Studio to map the cybernetics research landscape. The comprehensive analysis sheds light on the evolution of key concepts and the global collaborative networks shaping cybernetics research. The identification of influential sources, such as Kybernetes, adds value to researchers seeking to navigate and contribute to the dynamic field of cybernetics. Furthermore, this study highlights that cybernetics not only provides a useful framework for understanding and managing major economic shocks but also offers perspectives for understanding phenomena in various fields such as economics, medicine, environmental sciences and climate change.

To present an analytical framework for conducting critical dialogic accounting and accountability-based participatory action research to further democratisation, social change and empowering marginalised groups, and to reflect on its application in a Bangladeshi nongovernmental organisation's microfinance program.

The framework, synthesising prior CDAA theorising and agonistic-inspired action research, is described, followed by a discussion of the methodological challenges when applying this during a ten-year, ongoing intervention seeking greater voice for poor, female borrowers.

Six methodological issues emerged: investigating contested issues rather than organisation-centric research; identifying and engaging divergent discourses; engaging marginalised groups, activists and/or dominant powerholders; addressing power and power relations; building alliances for change; and evaluating and disseminating results. The authors discuss these issues and how the participatory action research methods and analytical tools used evolved in response to emergent challenges, and key lessons learned in a study of microfinance and women's empowerment.

The paper addresses calls within and beyond accounting to develop critical, engaged and change-oriented scholarship adopting an agonistic research methodology. It uses a novel critical dialogic accounting and accountability-based participatory action research approach. The reflexive examination of its application engaging NGOs, social activists, and poor women to challenge dominant discourses and practices, and build alliances for change, explores issues encountered. The paper concludes with reflective questions to aid researchers interested in undertaking similar studies in other contentious, power-laden areas concerning marginalised groups.

This study aims to explore how the boundary between science and society can be addressed to support the transformation of higher education towards sustainable development (HESD) in the sense of the whole institution approach. It analyses students’ learning experiences in self-led sustainability projects conducted outside formal curricula to highlight their potential contribution to HESD. The students’ projects are conceived as learning spaces in “sustainability-oriented ecologies of learning” (Wals, 2020) in which five learning dimensions can be examined.

Using an iterative, grounded-theory-inspired qualitative approach and sensitising concepts, 13 in-depth semi-structured interviews were conducted exploring students’ learning experiences. Interviews were categorised in MAXQDA and analysed against a literature review.

Results revealed that students’ experiences of non-formal learning in self-led projects triggered deep learning and change agency. Trust, social cohesion, empowerment and self-efficacy were both results and conditions of learning. Students’ learnings are classified according to higher education institutions’ (HEIs) sustainability agendas, providing systematised insights for HEIs regarding their accommodative, reformative or transformative (Sterling, 2021) path to sustainable development.

The education for sustainable development (ESD) debate focuses mainly on ESD competences in formal settings. Few studies explore students’ learnings where formal and non-formal learning meet. This article investigates a space where students interact with different actors from society while remaining rooted in their HEIs. When acting as “change agents” in this hybrid context, students can also become “boundary agents” helping their HEIs move the sustainability agenda forward towards a whole institution approach. Learning from students’ learnings is thus proposed as a lever for transformation.

This study focuses on (re-)introducing computer simulation as a part of the research paradigm. Simulation is a widely applied research method in supply chain and operations management. However, leading journals, such as the International Journal of Operations and Production Management, have often been reluctant to accept simulation studies. This study provides guidelines on how to conduct simulation research that advances theory, is relevant, and matters.

This study pooled the viewpoints of the editorial team of the International Journal of Operations and Production Management and authors of simulation studies. The authors debated their views and outlined why simulation is important and what a compelling simulation should look like.

There is an increasing importance of considering uncertainty, an increasing interest in dynamic phenomena, such as the transient response(s) to disruptions, and an increasing need to consider complementary outcomes, such as sustainability, which many researchers believe can be tackled by big data and modern analytical tools. But building, elaborating, and testing theory by purposeful experimentation is the strength of computer simulation. The authors therefore argue that simulation should play an important role in supply chain and operations management research, but for this, it also has to evolve away from simply generating and analyzing data. Four types of simulation research with much promise are outlined: empirical grounded simulation, simulation that establishes causality, simulation that supplements machine learning, artificial intelligence and analytics and simulation for sensitive environments.

This study identifies reasons why simulation is important for understanding and responding to today's business and societal challenges, it provides some guidance on how to design good simulation studies in this context and it links simulation to empirical research and theory going beyond multimethod studies.