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This paper aims to investigate how organizational learning (OL) affects the development of anticipatory adaptation to climate change in companies. Because the need to learn increases in circumstances of greater uncertainty such as the case of climate change, one of the processes that can explain different levels of anticipatory adaptation to climate change (AACC) by companies is OL.

The research uses a case study design. Following the procedures of qualitative sampling, an exemplary case of organizational adaptation to climate change in a sector that is extremely affected by the impacts of weather events was chosen. Empirical data collection includes semi-structured interviews and the collection of private and public documents. Such data were analyzed through thematic analysis.

The process of OL for anticipatory adaptation to climate change presents substantial differences from the traditional OL process presented by the specialized literature. In particular, the concepts of single- and double-loop learning were challenging to fit into the learning processes required for AACC.

Organizations have historically been working towards the adaption to external unforeseen events, but anticipatory adaptation to climate change presents new challenges and requires new forms of learning. Previous research has examined the interplay between learning and climate change adaptation, especially at the inter-organizational level. By developing research at the organizational level, this paper addresses a gap in the literature and shows that the required learning to adapt to climate change differs from the traditional learning, described in the management literature.

Following a successful career in industry, Dr. Douglas V. Shick, a newly minted entrepreneur, established NRS Associates, LLC, to perform consulting services based on two highly technical computer-modeling programs. Doug was heavily involved in the development of one program, an innovative computer simulation software for modeling a particular manufacturing process, through intrapreneurial activity during his corporate experience. Doug established his business on September 1, 2001, and on September 10 announced his services by e-mail to everyone he knew. The unforeseen events of the next day, September 11, produced some unexpected aftereffects that Doug had to factor into his developing business.

This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.

Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.

The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.

The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.

This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.

This paper aims to expand the emerging literature on COVID-19 and the financial markets by searching for a relationship between the uncertainty of the first phase of the COVID-19 pandemic experienced through social media and the extreme volatility of the Italian stock market.

The authors analyze the relationship between social media and stock market trends during the first phase of the COVID-19 pandemic through the lens of social theory and Baudrillard's simulacra and hyperreality theory. The authors conducted the data analysis in two phases: the emotional and Granger correlation analysis by using the KPI6 software to analyze 3,275,588 tweets for the predominant emotion on each day and observe its relationship with the stock market.

The research results show a significant Granger causality relation between tweets on a particular day and the closing price of the FTSE MIB during the first phase of the COVID-19 epidemic. The results highlight a strong relationship between social media hyperreality and the real world. The study confirms the role of social media in predicting stock market volatility.

The findings have theoretical and practical implications as they reveal the relevance of social media in our society and its relationship with businesses and economies. In an emergency, social media, as an expression of users' feelings and emotions, can generate a state of hyperreality that is strong correlated with reality. Since social media allows users to publish and share messages without any filter and mediation, the hyperreality generated is affected by highly subjective elements.

This research is different from the previous ones on the same topic because unlike previous studies, conducted under normal or simulated scenarios, this study is focused on the first phase of an unpredictable and unforeseen emergency event: the COVID-19 pandemic. This research adopts a multidisciplinary approach and integrates previous studies on the economic and financial effects generated by social media by applying well-known theories to a new and unexplored context. The study reveals the significant impact generated by social media on stock markets during a global pandemic.

There is a large amount of published literature on project management. However, there exists a gap between the existing literature and current practices in the industry for the development and execution of megaprojects. Existing literature generally focuses on individual elements applicable to project management in general. This article aims to provide an overview of the project management system components used in industrial megaprojects and identify the gaps between theory and practice, which can be used as input for further research on the topic.

The topic of megaproject management is reviewed based on available literature sources on megaproject management systems to identify the main gaps in the literature between theory and practice. Based on the findings, an analysis is provided to discuss the improvements required in distinct project management areas and phases.

There are multiple gaps associated with issues, failures, successes and challenges in industrial megaprojects. Improvements are needed in distinct management areas and over the entire project lifetime. Further guidelines are required for achieving improved megaproject management systems. Such concepts could benefit researchers and practitioners in streamlining their research toward the most relevant and critical areas of improvement of megaproject management systems.

This study addresses the literature gaps between theory and practices on megaproject management systems with an overview that provides helpful guidance for industrial applications and future research. A holistic analysis identifies gaps and critical drives in the body of knowledge, revealing avenues for future research focused on quality as the central pillar that affects the entire megaproject management system.

Unforeseen events can disrupt the operational process and negatively impact emergency resources optimization and its supply chain. A limited number of studies have addressed risk management issues in the context of emergency supply chains, and this existing research lacks inbuilt and practical techniques that can significantly affect the reliability of risk management outcomes. Therefore, this paper aims to identify and practically analyze the specific risk factors that can most likely disrupt the normal functioning of the emergency supply chain in disaster relief operations.

This paper has used a three-step process to investigate and evaluate risk factors associated with the emergency supply chain. First, the study conducts a comprehensive literature review to identify the risk factors. Second, the research develops a questionnaire survey to validate and classify the identified risk factors. At the end of this step, the study develops a hierarchical structure. Finally, the research investigates the weighted priority of the validated risk factors using the fuzzy-analytical hierarchy process (FAHP) methodology. Experts were required to provide subjective judgments.

This paper identified and validated 28 specific risk factors prevalent in emergency supply chains. Based on their contextual meanings, the research classified these risk factors into two main categories: internal and external risk factors; four subcategories: demand, supply, infrastructural and environmental risk factors; and 11 risk types: forecast, inventory, procurement, supplier, quality, transportation, warehousing, systems, disruption, social and political risk factors. The most significant risk factors include war and terrorism, the absence of legislative rules that can influence and support disaster relief operations, the impact of cascading disasters, limited quality of relief supplies and sanctions and constraints that can hinder stakeholder collaboration. Therefore, emergency supply chain managers should adopt appropriate strategies to mitigate these risk factors.

This study will contribute to the general knowledge of risk management in emergency supply chains. The identified risk factors and structural hierarchy taxonomic diagram will provide a comprehensive risk database for emergency supply chains.

The research findings will provide comprehensive and systemic support for respective practitioners and policymakers to obtain a firm understanding of the different risk categories and specific risk factors that can impede the effective functioning of the emergency supply chain during immediate disaster relief operations. Therefore, this will inform the need for the improvement of practices in critical aspects of the emergency supply chain through the selection of logistics and supply chain strategies that can ensure the robustness and resilience of the system.

This research uses empirical data to identify, categorize and validate risk factors in emergency supply chains. This study contributes to the theory of supply chain risk management. The study also adopts the fuzzy-AHP technique to evaluate and prioritize these risk factors to inform practitioners and policymakers of the most significant risk factors. Furthermore, this study serves as the first phase of managing risk in emergency supply chains since it motivates future studies to empirically identify, evaluate and select effective strategies that can eliminate or minimize the effects of these risk factors.

This paper aims to investigate aspects of flood experience, attitudes and responses of micro-, small- and medium-sized enterprises (MSMEs) in Greece and to indicate a typology of strategies associated with their relative effort to build flood resilience capacity.

A qualitative study protocol was used, based on pertinent literature that considers how business entities withstand, adapt and/or recover from non-linear climate change impacts, natural hazards and extreme weather. Data was obtained by conducting semi-structured interviews with 82 MSMEs’ owners-managers who had recently experienced flooding.

The study reports limited activities of MSMEs towards flood resilience capacity despite the threat of relevant disasters. Findings suggest that most owners-managers of these enterprises are not adequately preparing their businesses for the impacts of flooding.

The findings call for multi-level and dynamic perspectives to be examined in assessing MSME resilience capacity to floods. It is attitudinal, managerial, organisational, behavioural and regulatory (as well as other institutional) factors that merit further investigation. Such an investigation would allow a better understanding as to whether these factors hinder or enable conditions for microeconomic flood preparedness and resilience as well as how they may interact with each other or create feedback loops.

The study carries managerial implications and policy recommendations in terms of nurturing opportunities towards awareness-raising campaigns for reducing deficits in managerial knowledge and competencies. It also encapsulates practical implications in terms of emphasising supporting mechanisms from key institutional stakeholders to allow MSMEs scan available options they have in effectively reinforcing the business premises from the forces of rising waters.

Most of the related studies have examined flood impacts, responses and/or resilience capacity at the household- or community-level. Empirical work that is conducted to ascertain how MSMEs cope with flooding remains thin on the ground. In response to this, the current study and the typology of MSMEs’ strategic postures that are suggested seek to contribute to this under-researched topic.

The purpose of this paper is to study the crisis management process for project-based organizations (PBOs) by developing a comprehensive model and propositions.

This paper is based on a conceptual study. A literature review is considered a primary source for studying contemporary research, including 171 publications in total, which embody qualitative, quantitative, conceptual and theoretical studies. For data analysis, content analysis is used, which is comprised of descriptive and thematic analysis.

This study identifies five imperative elements of crisis management for PBOs which include (1) sense-making (information gathering and crisis interpretation), (2) decision-making (accurate and timely decision), (3) response (reactive response), (4) outcome (success/failure) and (5) learning. Based on these findings, this study proposes an integrative model of the interplay between sense-making, decision-making, response, outcome and learning. Furthermore, the findings lead to propositions for each of the elements. The paper contributes to the literature on dynamic capability theory.

This paper explores the crisis management process for PBOs. The proposed model deepens the understanding of the practices and processes of project-based crisis management.

This study aims to test the impact of the coronavirus (Covid-19) pandemic on large firms, with regard to a supply disruption, productivity, customer satisfaction and firm performance.

A cross-sectional survey design and stratified sampling technique were implemented for employee selection and data gathering. Confirmatory factor analysis (CFA) was used to examine the data and model fitness, while the structural equation model was used for hypotheses testing.

The pandemic triggered supply disruptions, but did not significantly impact the productivity of manufacturing firms directly. However, supply disruption positively and significantly impacted productivity. Organisational productivity had no significant impact on customer satisfaction, however, when mediating the relationship between Covid-19 and customer satisfaction, it produces a positive indirect effect. Finally, Covid-19 and supply disruption when mediated by organisational productivity both had negative significant relationships on performance.

Having a unique model, it creates a trail for future researchers to explore further. Though customer satisfaction was expected to be affected by the pandemic, it is interesting to find out that customer satisfaction when mediated by organisational productivity was positively influenced.

Disruptions are inevitable, managers must balance the pursuit of customer satisfaction and productivity so that one does not erode the other. Emphasis must be channelled towards managing the productivity of the firm to maintain customer satisfaction during these uncertain times. Deliberate steps like manufacturing flexibility investments should be initiated.

The first study to examine Covid-19, supply disruption, customer satisfaction, organisational productivity and performance in the Nigerian manufacturing sector.

This article explores the value of engaging a hybrid learning strategy in a micro-enterprise setting when responding to a global pandemic. The research question asks: “Does a hybrid learning strategy enhance a micro-enterprise's response to extreme events?”.

A micro-enterprise owner–manager (OM) reflects on their experience running a business during the coronavirus disease 2019 (COVID-19) pandemic, which resulted in the complete decimation of the organization’s revenue stream in early 2020. Captured in conversation with an academic peer, these practitioner insights provide valuable case evidence relating to micro-enterprise response to extreme commercial events, such as a global pandemic.

The journey from initial survival-mode to emergent market opportunity recognition and subsequent growth is recorded. The paper contributes to the limited research on the impact of learning strategy plans on micro-enterprise crisis response strategies and provides insights into the value of engaging a hybrid learning strategy when responding to a significant external business shock.

Drawing from these insights, the authors offer a literature-informed framework from which to consider the dynamics of an adaptive strategic response in a micro-enterprise setting, offering a means through which micro-enterprises can plan for and respond to extreme events in the future.