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In an urban environment, different types of actors (individuals, households, community groups, institutions, governmental organizations, nongovernmental organizations (NGO), community-based organizations (CBO) and public and private organizations, etc.) play critical roles in enhancing urban resilience to adapt to the impacts of climate change. To identify and prioritize aspects of climate resilience planning, this study aims to examine the potential impacts of climate change on diverse urban systems and evaluate the capacities of various actors to adapt to climatic stress.

This research selects Mongla, a coastal as well as port town in south-western Bangladesh that faces climate risks including frequent cyclones, storm surges and salinity intrusion due to sea-level and temperature rise, as reported in the National Climate Vulnerability Index 2018. This research uses the methodology proposed by ICLEI South Asia’s Climate Resilient Cities Action Plan and accesses different urban actors’ adaptive capacity for contributing to enhancing climate resilience based on three broad characteristics: the capacity to organize and respond, availability of resources and access to information. The cumulative scores of these characteristics aid in determining the climate adaptive capacity of each urban actor.

The identified 53 actors are grouped into four categories: government (local and national); international/local NGOs, CBOs and associations; community representatives and private sectors and groups and individuals. The group of NGOs, CBOs and associations has a higher overall adaptive potential than all other actor groups, according to the study. When it comes to practicing adaptive capacity, government institutions are in the second-highest position. But, the Khulna development authority, the government agency that is in charge of overseeing all forms of urban development, scored poorly on all fragile urban systems. However, the performance of the fourth group of urban actors is very subpar when it comes to adapting to climatic stress, which emphasizes the need for focused interventions.

Variations in adaptive capacities to climate change across different actors are particularly useful for targeting actors with lower levels of climate adaptive capacities in Mongla town.

This paper particularly addresses the research gap in assessing the adapting capacity of urban actors in Mongla port-town. Policymakers and practitioners can create and carry out targeted interventions that address the particular needs and concerns of vulnerable actors by making use of the insights obtained from this kind of study, thereby assisting in the development of climate resilience in any urban area.

This study aims to introduce the Adaptive Capacity Wheel (ACW) Framework to assess the adaptive capacity of local institutions to address energy security issues. This study used two comparative cases, representing Indonesia's city level (Palembang) and the province level (South Sumatra Province).

Data used in the analysis were from face-to-face interviews, site observation and document review. This study conducted a set of semi-structured interviews with the key interviewees.

The two government institutions (city and province) report the challenges in dealing with energy security. The results found that the stakeholders at the city level are less adaptive than those at the province level. This challenge is substantially visible regarding variety, resources, room for autonomous change and leadership. On the other hand, South Sumatra Province is quite adaptive in terms of variety, resources and leadership. This study offers a potential adoption of the ACW framework as an analytical instrument to review the adaptive capacity of local institutions in addressing energy security.

Transforming data from the qualitative to the quantitative form can lead to a biased interpretation of data.

This study is the first attempt to explore the adaptive capacity of local institutions to address the energy security agenda through the lens of the ACW framework.

This article focusses on the hybrid strategy, a simultaneous combination of cost leadership and differentiation strategy. The study aims to examine the impact of hybrid strategy on firm performance through its anticipated positive effects on process and product innovation. In addition, we study the moderating role of adaptive capacity in the direct relationships of hybrid strategy with process and product innovation.

Structural equation modelling was used to analyse 1,842 Spanish firms with fewer than 250 employees. We randomly selected small and medium-sized enterprises (SMEs) operating in Spain from the Spanish Central Business Directory (2021) database. The overall sample design was based on stratified sampling.

We found that hybrid strategy is positively related to firm performance and to process and product innovation. Additionally, in firms implementing hybrid strategies, process innovation fostered firm performance. Finally, adaptive capacity strengthened the relationships of hybrid strategy with process and product innovation. This sheds light on how and when hybrid strategy is most effective in fostering SME performance.

We highlight that SMEs need to establish strategies that use diverse resources and capabilities and not just generate competitive advantage using one strategy (cost leadership or differentiation strategy). This requires an agile and flexible systems and structures.

Our research provides novel results by proposing the adoption of hybrid strategies instead of pure strategies (cost leadership and differentiation strategy) as a way for SMEs to survive during crises. Unlike “stuck in the middle” strategies, our study demonstrates the importance of hybrid strategies in a comprehensive model that links them to innovation and firm performance, with adaptive capacity being a determining factor.

The Agents of Change program is a two-year, project-based learning program to develop Extension Professionals’ capacity to engage in Adaptive and Transformative Leadership. Its primary goal is to develop the capacity of Extension Professionals to engage in leadership to create more diverse, equitable, inclusive and just Extension programs and community change initiatives. This manuscript describes the program and an initial evaluation and results.

Results of an evaluation of the first year of the program indicate that regular training sessions and support are appropriate for leadership development and that Extension Professionals are using the learning, awareness and tools from this program to address challenges with Adaptive and Transformative Leadership elements. Also, Extension professionals demonstrated commitment to personal growth, community engagement and understanding of their multifaceted roles as change agents.

Participants are sharing resources from the program with colleagues, leading meetings differently, questioning the status quo and pushing others to try new ways forward.

As part of the scale development process, this paper aims to test a scale to measure organizational resilience for the oil and gas industry. The objective is to provide stakeholders with a set of indicators to evaluate their organizations and prepare them to cope with the negative consequences of disruptions (e.g. Covid-19, shortage of resources, etc.).

The paper conducts exploratory and confirmatory factor analysis to test the suitability, dimensionality and reliability of specific indicators and their items under examination. Therefore, the goal is not to validate hypotheses by testing an organizational resilience scale in the oil and gas industry.

The study tests and proposes a scale to effectively measure organizational resilience within the oil and gas industry. A comprehensive set of ten indicators and 40 items are identified through this process. The findings of this research provide stakeholders in this sector with a rigorous set of indicators to evaluate the strengths and weaknesses of their organizations and better prepare them to handle disruptions.

This paper fills the gap in existing research by testing and proposing a scale to measure organizational resilience specifically for the oil and gas industry. It highlights the importance of organizational resilience for survival in a sector that is especially susceptible to disruptions.

This study aims to examine the effects of organizational polychronicity on individual adaptive performance. Drawing on conservation of resources theory and the perspective of individual differences in pressure perceptions, this study develops a conceptual model to test the mediating role of time pressure and the moderating role of trait regulatory focus in the relationship between organizational polychronicity and individual adaptive performance.

A three-wave survey was conducted to investigate a sample of 591 employees who engaged in innovative activities in China. The hypotheses were tested using hierarchical multiple regression and bootstrapping.

The results show that organizational polychronicity is negatively related to individual adaptive performance via time pressure. Promotion focus weakens the positive relationship between organizational polychronicity and time pressure and the mediating role of time pressure, while prevention focus augments the positive relationship between organizational polychronicity and time pressure and the mediating role of time pressure.

This study reveals the mediating role of time pressure in the relationship between organizational polychronicity and individual adaptive performance, as well as the moderating role of trait regulatory focus in this relationship, thereby deepening our understanding of organizational polychronicity from both a theoretical and a practical perspective.

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.

Recent disruptions have sparked concern about building a resilient and sustainable manufacturing supply chain. While artificial intelligence (AI) strengthens resilience, research is needed to understand how cloud adoption can foster integration, collaboration, adaptation and sustainable manufacturing. Therefore, this study aimed to unleash the power of cloud adoption and AI in optimizing resilience and sustainable performance through collaboration and adaptive capabilities at manufacturing firms.

This research followed a deductive approach and employed a quantitative method with a survey technique to collect data from its target population. The study used stratified random sampling with a sample size of 1,279 participants working in diverse manufacturing industries across California, Texas and New York.

This research investigated how companies can make their manufacturing supply chains more resilient and sustainable. The findings revealed that integrating the manufacturing supply chains can foster collaboration and enhance adaptability, leading to better performance (hypotheses H1-H7, except H5). Additionally, utilizing artificial intelligence helps improve adaptability, further strengthening resilience and sustainability (H8-H11). Interestingly, the study found that internal integration alone does not significantly impact collaboration (H5). This suggests that external factors are more critical in fostering collaboration within the manufacturing supply chain during disruptions.

This study dives into the complex world of interconnected factors (formative constructs in higher order) influencing manufacturing supply chains. Using advanced modeling techniques, it highlights the powerful impact of cloud-based integration. Cloud-based integration and artificial intelligence unlock significant improvements for manufacturers and decision-makers by enabling information processes and dynamic capability theory.

This research proposes a novel risk assessment model to elucidate the risk propagation process of industrial safety accidents triggered by natural disasters (Natech), identifies key factors influencing urban carrying capacity and mitigates uncertainties and subjectivity due to data scarcity in Natech risk assessment.

Utilizing disaster chain theory and Bayesian network (BN), we describe the cascading effects of Natechs, identifying critical nodes of urban system failure. Then we propose an urban carrying capacity assessment method using the coefficient of variation and cloud BN, constructing an indicator system for infrastructure, population and environmental carrying capacity. The model determines interval values of assessment indicators and weights missing data nodes using the coefficient of variation and the cloud model. A case study using data from the Pearl River Delta region validates the model.

(1) Urban development in the Pearl River Delta relies heavily on population carrying capacity. (2) The region’s social development model struggles to cope with rapid industrial growth. (3) There is a significant disparity in carrying capacity among cities, with some trends contrary to urban development. (4) The Cloud BN outperforms the classical Takagi-Sugeno (T-S) gate fuzzy method in describing real-world fuzzy and random situations.

The present research proposes a novel framework for evaluating the urban carrying capacity of industrial areas in the face of Natechs. By developing a BN risk assessment model that integrates cloud models, the research addresses the issue of scarce objective data and reduces the subjectivity inherent in previous studies that heavily relied on expert opinions. The results demonstrate that the proposed method outperforms the classical fuzzy BNs.

Reconstruction of old buildings is often necessary after prolonged usage, but these projects encounter various complexities, leading to their failure. However, there is a lack of studies that focus on investigating the complexity of old building reconstruction initiatives. Thus, this study aims to examine the complexity of reconstruction projects for old buildings in Vietnamese urban areas.

Through a literature review and discussions with experienced practitioners, 12 significant complexity factors were identified. The study then utilized the fuzzy decision-making trial and evaluation laboratory (DEMATEL) technique to analyze the interrelationships among these complexity factors.

The findings revealed intricate interdependencies among the complexity factors, visualized in a complexity map. The map highlighted the critical complexities of site compensation, clearance and the long project duration. Furthermore, a causal diagram categorized the complexity factors into net-effect and net-cause groups.

By examining the relationships and interactions among these factors, the study emphasizes the interconnected nature of complex systems and the influence of one factor on others. Therefore, decision-makers and stakeholders involved in reconstructing old buildings are suggested to possess a comprehensive understanding of these interdependencies to ensure effective decision-making.