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Complex and adaptive challenges threaten human well-being and sustainability. However, our leadership graduates often lack the capacity and or commitment to address these challenges in a meaningful way. This paper details a five-year study exploring the impact of an interdisciplinary undergraduate course on the development of global capacities, complex Adaptive Leadership, Socially Responsible Agency, and systems thinking. The course instructors used innovative and intentionally divergent learning methods to challenge students to serve as stewards of their own knowledge and facilitators of their own learning through the confrontation of authentic and complex challenges. The researchers note transferable qualitative findings that demonstrate the profound impact of the noted leadership learning experience on the development of Socially Responsible Agency, along with Adaptive Leadership capacity and systems thinking.

The purpose of this paper is to deepen the understanding of adaptive governance, which is advocated for as a manner to deal with dramatic changes in society and/or environment. To re-think the possible contributions of organizations and organization theory, to adaptive governance.

Based on social systems theory this study makes a distinction between “governance organizations” and “governance communities.” Organizations are conceptualized as the decision machines which organize and (co-)steer governance. Communities are seen as the social environments against which the governance system orients its operations. This study considers the adaptive mechanisms of organizations and reflect on the roles of organizations to enhance adaptive governance in communities and societies.

Diverse types of organizations can link or couple in different ways to communities in their social environment. Such links can enhance the coordinative capacity of the governance system and can also spur innovation to enable adaptation. Yet, linking with communities can also slow down responses to change and complexify the processes of deliberation in governance. Not all adaptive mechanisms available to organizations can be used in communicating with communities or can be institutionalized, but the continuous innovation in the field of organizations can inspire continuous testing of small-scale adaptive mechanisms at higher levels. Society can thus enhance its adaptive capacity by managing the role of organizations.

The harnessing of insights in organization theory and systems theory for improving understanding of adaptive governance. The finding that both experiment and coordination at societal level are needed, toward adaptive governance, and that organizations can contribute to both.

The term “agent-based modelling” (ABM) is a buzzword which is widely used in the scientific literature even though it refers to a variety of methodologies implemented in different disciplinary contexts. The numerous works dealing with ABM require a clarification to better understand the lines of thinking paved by this approach in economics. All modelling tasks are a means and a source of knowledge, and this epistemic function can vary depending on the methodology. this paper is to present four major ways (deductive, abductive, metaphorical and phenomenological) of implementing an agent-based framework to describe economic systems. ABM generates numerous debates in economics and opens the room for epistemological questions about the micro-foundations of macroeconomics; before dealing with this issue, the purpose of this paper is to identify the kind of ABM the author can find in economics.

The profusion of works dealing with ABM requires a clarification to understand better the lines of thinking paved by this approach in economics. This paper offers a conceptual classification outlining the major trends of ABM in economics.

There are four categories of ABM in economics.

This paper suggests a methodological categorization of ABM works in economics.

Tourism areas are challenged to become adaptive areas in the context of a dynamic networked society and globalizing economy. The purpose of this paper is to contribute to an enhanced understanding and conceptualization of adaptive tourism areas by drawing attention to “fitness landscapes,” a metaphor that is used in complexity theories to visualize development trajectories of adaptive systems.

Fitness landscapes, and its underlying theories, are useful to conceptualize tourism area development as a stepwise movement through a dynamic landscape with peaks and valleys. Doing so allows us to highlight why adaptation is a crucial property for tourism areas that are embedded in dynamic contexts and offers a frame of thought for how tourism areas can be managed.

The article raises awareness about and draws attention to a set of factors and conditions that support tourism planners and managers in enhancing the capacity of tourism areas to adaptively respond to changing circumstances.

Introducing fitness landscapes contribute to the discussion on adaptive capacity building – a topic that contributes to managing uncertain futures and is likely to gain importance in the dynamic society. Moreover, it helps as well as stimulates tourism scholars to further develop this topic. Finally, it helps tourism planners to build adaptive capacity in practice.

The economic, financial and social crisis has heightened researchers' interest in the importance of organizational sustainability and resources (e.g. work engagement and psychological capital) for performance. Despite some studies demonstrating the significance of resources, such as work engagement and psychological capital, for workers’ performance, there is limited knowledge regarding the role of organizational sustainability practices (OSP) in the relationship mentioned above. Therefore, this study drew upon the social identity theory to conceptualize and test the mediating role of work engagement in the relationship between OSP and performance (both task and adaptive). Based on the conservation of resources theory, it was hypothesized that a worker's resource – psychological capital – would moderate the indirect relationship between social responsibility practices and performance (both task and adaptive).

To test the proposed model, 304 adults participated in a two-wave study.

The results supported the hypotheses, revealing that OSP had a positive and significant relationship with task and adaptive performance, occurring through work engagement. Furthermore, the indirect relationship between OSP and performance task and adaptive through work engagement was moderated by psychological capital, such that the relationship became stronger for workers with lower levels of psychological capital.

The relevance of this research lies in its ability to identify the conditions and mechanisms in which corporate social responsibility initiatives are most effective, thereby enabling a more nuanced application of these practices across different sectors and organizational cultures. This study's theoretical and practical implications may assist organizations in devising strategies for implementing social responsibility practices to enhance their outcomes.

Time headway (THW) is an essential parameter in traffic safety and is used as a typical control variable by many vehicle control algorithms, especially in safety-critical ADAS and automated driving systems. However, due to the randomness of human drivers, THW cannot be accurately represented, affecting scholars’ more profound research.

In this work, two data sets are used as the experimental data to calculate the goodness-of-fit of 18 commonly used distribution models of THW to select the best distribution model. Subsequently, the characteristic parameters of traffic flow are extracted from the data set, and three variables with higher importance are extracted using the random forest model. Combining the best distribution model parameters of the data set, this study obtained a distribution model with adaptive parameters, and its performance and applicability are verified.

In this work, two data sets are used as the experimental data to calculate the goodness-of-fit of 18 commonly used distribution models of THW to select the best distribution model. Subsequently, the characteristic parameters of traffic flow are extracted from the data set, and three variables with higher importance are extracted using the random forest model. Combining the best distribution model parameters of the data set, this study obtained a distribution model with adaptive parameters, and its performance and applicability are verified.

The results show that the proposed model has a 62.7% performance improvement over the distribution model with fixed parameters. Moreover, the parameter function of the distribution model can be regarded as a quantitative analysis of the degree of influence of the traffic flow state on THW.

This paper discusses a multifaceted approach to developing specific and general climate resilience in a state transportation system that focuses on organizations and physical infrastructure. The paper focuses on resilience building to the dynamically evolving climate-related threats and extreme events in a transportation agency. This paper aims to enable agencies to understand better how their systems are exposed to different hazards and provide the information necessary for prioritizing their assets and systems for resilience improvement.

This paper leverages long-term climate hazard databases, spatial and statistical analyses and nonprobabilistic approaches for specific and general climate resilience improvement. Spatial and temporal variability assessments were conducted on granular historical records of exposure obtained from Spatial Hazards Events and Losses Database for the United States data set to identify emerging hot spots of exposure. These were then assessed in combination with various asset specific vulnerability parameters, presented with examples of pavements and bridges. Specific metrics were obtained for the various aspects of vulnerability in the context of a given asset to estimate the overall vulnerability. A criticality-vulnerability matrix was then developed to provide a prioritization model for transportation systems.

This paper provides insights into the evolving nature of exposure, vulnerability and risk assessments and an approach to systematically account for climate change and the uncertainties associated with it in resilience planning. The Multi-Hazards Exposure, Vulnerability and Risk Assessment tool presented in this paper conducts climate hazard exposure, vulnerability and risk analysis on pavements, bridges and culverts and can be applied by any transportation agency.

This study does not address operational aspects of the transportation system nor include future climate scenario data, but uses the historical records available at hand for resilience planning. With better climate projection data available in the future, the approach should be enhanced by leveraging scenario-based planning.

This paper is of potential value to practitioners and researchers interested in developing resilience building capabilities to manage the effects of climate-related hazards and extreme events as well as unknown threats on infrastructure and organizational performance.

This paper bridges an important gap in infrastructure resilience approaches by systematically accounting for the dynamic nature of climate change and the system level context of vulnerability beyond the physical condition of assets.

Our understanding of leadership has undergone a significant shift in the early part of the 21st century. An emerging perspective, dubbed Eco-Leadership, suggests leadership is a collective process involving both leaders and followers co-creating leadership. Because our beliefs and attitudes toward leadership affect how we lead, it becomes crucial to better understand the views youth have towards leadership, as they develop into our future leaders, to improve curricula and instruction. In this study, no relationship was found between youth attitudes and beliefs towards systemic and hierarchical thinking with respect to their preferred problem-solving style. These findings indicate youth may adaptively or innovatively associate leadership equally through systemic and hierarchical thinking. Further, neither being more adaptive nor more innovative implies one to be better at leading.

Business process re-engineering (BPR) initiatives are complex endeavours which require many factors to ensure success. However, most studies focus on the organisational processes and improvement within the organisation itself and less on the project team and management dynamics. The study aimed to identify factors that enabled the completion of a BPR, in a technical firm, based on reflections on the project management style.

The study entailed a descriptive and interpretive case study with reflections from project team members. Data were analysed using descriptive statistics and content analysis.

Findings revealed that critical success factors for BPR in a technical firm include project leadership and sponsorship, organisational culture and attributes, team dynamics and the nature (activities), and duration of the process.

The findings will benefit project managers in improving their competence and project success through reflective practice. The identified factors could be used in future projects of a similar nature and size to improve how organisations execute BPR projects.

The study used reflections to identify success factors for BPR in a technical firm.

The purpose of this study is to address a critical gap in enterprise resource planning (ERP) implementation process for small and medium-sized enterprises (SMEs) by acknowledging and countering cognitive biases through a cognitive bias awareness matrix model. Cognitive biases such as temporal discounting and optimism bias often skew decision-making, leading SMEs to prioritize short-term benefits over long-term sustainability or underestimate the challenges involved in ERP implementation. These biases can result in costly missteps, underutilizing ERP systems and project failure. This study enhances decision-making processes in ERP adoption by introducing a matrix that allows SMEs to self-assess their level of awareness and proactivity when addressing cognitive biases in decision-making.

The design and methodology of this research involves a structured approach using the problem-intervention-comparison-outcome-context (PICOC) framework to systematically explore the influence of cognitive biases on ERP decision-making in SMEs. The study integrates a comprehensive literature review, empirical data analysis and case studies to develop the Cognitive Bias Awareness Matrix. This matrix enables SMEs to self-assess their susceptibility to biases like temporal discounting and optimism bias, promoting proactive strategies for more informed ERP decision-making. The approach is designed to enhance SMEs’ awareness and management of cognitive biases, aiming to improve ERP implementation success rates and operational efficiency.

The findings underscore the profound impact of cognitive biases and information asymmetry on ERP system selection and implementation in SMEs. Temporal discounting often leads decision-makers to favor immediate cost-saving solutions, potentially resulting in higher long-term expenses due to the lack of scalability. Optimism bias tends to cause underestimating risks and overestimating benefits, leading to insufficient planning and resource allocation. Furthermore, information asymmetry between ERP vendors and SME decision-makers exacerbates these biases, steering choices toward options that may not fully align with the SME’s long-term interests.

The study’s primary limitation is its concentrated focus on temporal discounting and optimism bias, potentially overlooking other cognitive biases that could impact ERP decision-making in SMEs. The PICOC framework, while structuring the research effectively, may restrict the exploration of broader organizational and technological factors influencing ERP success. Future research should expand the range of cognitive biases and explore additional variables within the ERP implementation process. Incorporating a broader array of behavioral economic principles and conducting longitudinal studies could provide a more comprehensive understanding of the challenges and dynamics in ERP adoption and utilization in SMEs.

The practical implications of this study are significant for SMEs implementing ERP systems. By adopting the Cognitive Bias Awareness Matrix, SMEs can identify and mitigate cognitive biases like temporal discounting and optimism bias, leading to more rational and effective decision-making. This tool enables SMEs to shift focus from short-term gains to long-term strategic benefits, improving ERP system selection, implementation and utilization. Regular use of the matrix can help prevent costly implementation errors and enhance operational efficiency. Additionally, training programs designed around the matrix can equip SME personnel with the skills to recognize and address biases, fostering a culture of informed decision-making.

The study underscores significant social implications by enhancing decision-making within SMEs through cognitive bias awareness. By mitigating biases like temporal discounting and optimism bias, SMEs can make more socially responsible decisions, aligning their business practices with long-term sustainability and ethical standards. This shift improves operational outcomes and promotes a culture of accountability and transparency. The widespread adoption of the Cognitive Bias Awareness Matrix can lead to a more ethical business environment, where decisions are made with a deeper understanding of their long-term impacts on employees, customers and the broader community, fostering trust and sustainability in the business ecosystem.

This research introduces the original concept of the Cognitive Bias Awareness Matrix, a novel tool designed specifically for SMEs to evaluate and mitigate cognitive biases in ERP decision-making. This matrix fills a critical gap in the existing literature by providing a structured, actionable framework that effectively empowers SMEs to recognize and address biases such as temporal discounting and optimism bias. Its practical application promises to enhance decision-making processes and increase the success rates of ERP implementations. This contribution is valuable to behavioral economics and information systems, offering a unique approach to integrating cognitive insights into business technology strategies.