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The purpose of this paper is to delve into an extensive analysis of different food crops, ranging from bananas, beans, brinjals, cabbages, chillies, creepers, groundnuts, mixed vegetables, pineapples and tomatoes, over three decades. To maintain an ever-increasing population level, much stress is exerted on the production of food crops. However, till date, very little is known about how climate change is influencing the production of food crops in Mauritius, an upper-income developing country found in the Indian Ocean and highly vulnerable to climate risks.

Based on the interactions between production of crops, harvest area for crops and weather metrics, a vector autoregressive model (VAR) system is applied comprising production of each crop with their respective harvest area. Weather metrics are then entered into as exogeneous components of the model. The underlying rationale is that weather metrics are not caused by production or harvest area and should thereby be exogeneously treated. Should there be cointegration between the endogenous components, the vector error correction model (VECM) will be used. Diagnostic tests will also be entertained in terms of ensuring the endogeneity states of the presumed variables under investigation. The impact of harvest area on product is plain, as higher the harvest area, the higher is the production. However, a bi-directional causality can also manifest in the case that higher production leads towards lower harvest area in the next period as land is being made to rest to restore its nutrients to enable stable land productivity over time. Other dynamics could also be present. In case cointegration prevails, VECM will be used as the econometric model. The VAR/VECM approach is applied by virtue of the fact that traditional ordinary least squares (OLS) estimation approach will be biased and susceptible to trigger off unreliable results. Recourse is made towards the Johansen and Juselius (1990) technique. The Johansen and Juselius approach is based on the following VAR specification-bivariate VAR methodology. X1,t = A0 + A1,1X1,t – 1 + A1,2X1,t – 2+ […] .+ A1,p X1,t – p + A2,1X2,t – 1 + A2,2X2,t – 2+ […] .+ A2,pX2,t – p + ßjW + e1,t […] […]..(1) X2,t = B0 + B2,1X2,t – 1 + B2,2X2,t – 2+ […] .+ B2,p X2,t – p + B1,1X1,t – 1 + B1,2X2,t – 2+ […] .+ B1,pX2,t – p + ajW + e2,t […] […] […](2) X1,t is defined as the food crops production, while X2,t pertains to harvest area under cultivation for a given crop under consideration, both constituting the endogeneous components of the VAR. The exogeneous component is captured by W which consists of the nine aforementioned weather metrics, including the cyclone dummy. The subscript j under equation (1) and (2) captures these nine distinct weather metrics. In essence, the aim of this paper is to develop an econometric-based approach to sieve out the impacts of climate metrics on food crops production in Mauritius over three decades.

Results show weather metrics do influence the production of crops in Mauritius, with cyclone being particularly harmful for tomatoes, chillies and creepers. Temperature is found to trail behind bearish impacts on tomatoes and cabbages production, but positive impacts in case of bananas, brinjals and pineapples productions, whereas humidity enhances production of beans, creepers and groundnuts. Evidence is found in favour of production being mainly governed by harvest area. Overall, the study points out the need of weather derivatives in view of hedging against crop damages, let alone initiation of adaptation strategies to undermine the adverse effects of climate change.

To the best of the author’s knowledge, no study has been undertaken in Mauritius, let alone developing of an econometric model that properly integrates production, harvest area and weather metrics. Results show weather metrics do influence the production of crops in Mauritius, with cyclone being particularly harmful for tomatoes, chillies and creepers. Temperature is found to trail behind bearish impacts on tomatoes and cabbages production, but positive impacts in case of bananas, brinjals and pineapples productions, whereas humidity enhances production of beans, creepers and groundnuts. Evidence is found in favour of production being mainly governed by harvest area. Overall, the study points out the need of weather derivatives in view of hedging against crop damages, let alone initiation of adaptation strategies to undermine the adverse effects of climate change.

Weather events have a significant impact on airport arrival performance and may cause delays in operations and/or constraints in airport capacity. In Europe, almost half of all regulated airport traffic delay is due to adverse weather conditions. Moreover, the closer airports operate to their maximum capacity, the more severe is the impact of a capacity loss due to external events such as weather. Various weather uncertainties occurring during airport operations can significantly delay some arrival processes and cause network-wide effects on the overall air traffic management (ATM) system. Quantifying the impact of weather is, therefore, a key feature to improve the decision-making process that enhances airport performance. It would allow airport operators to identify the relevant weather information needed, and help them decide on the appropriate actions to mitigate the consequences of adverse weather events. Therefore, this research aims to understand and quantify the impact of weather conditions on airport arrival processes, so it can be properly predicted and managed.

This study presents a methodology to evaluate the impact of adverse weather events on airport arrival performance (delay and throughput) and to define operational thresholds for significant weather conditions. This study uses a Bayesian Network approach to relate weather data from meteorological reports and airport arrival performance data with scheduled and actual movements, as well as arrival delays. This allows us to understand the relationships between weather phenomena and their impacts on arrival delay and throughput. The proposed model also provides us with the values of the explanatory variables (weather events) that lead to certain operational thresholds in the target variables (arrival delay and throughput). This study then presents a quantification of the airport performance with regard to an aggregated weather-performance metric. Specific weather phenomena are categorized through a synthetic index, which aims to quantify weather conditions at a given airport, based on aviation routine meteorological reports. This helps us to manage uncertainty at airport arrival operations by relating index levels with airport performance results.

The results are computed from a data set of over 750,000 flights on a major European hub and from local weather data during the period 2015–2018. This study combines delay and capacity metrics at different airport operational stages for the arrival process (final approach, taxi-in and in-block). Therefore, the spatial boundary of this study is not only the airport but also its surrounding airspace, to take both the arrival sequencing and metering area and potential holding patterns into consideration.

This study introduces a new approach for modeling causal relationships between airport arrival performance indicators and meteorological events, which can be used to quantify the impact of weather in airport arrival conditions, predict the evolution of airport operational scenarios and support airport decision-making processes.

Research draws the distinction between noise traders and informed traders. Research also documents market biases in equity returns due to cloud cover, a non‐informational (noise) event, showing that returns decrease on cloudy days. The purpose of this paper is to investigate the trading behaviour of short‐sellers, who are considered informed traders, conditioning on the level of cloudiness, and find an increase in short selling with the level of cloudiness. Additionally, the paper finds decreases in short selling the three days prior to a cloudy day (or series of cloudy days).

The authors replicate the weather anomaly in stock returns reported in the literature for the sample period, and then study the trading behaviour of short sellers conditioned on cloud cover. Additionally the authors treat cloud cover as an event and study short selling volume in the pre‐event window.

The paper finds an increase in short selling with the level of cloudiness. Additionally, the paper finds decreases in short selling, relative to the event day(s), in the three days prior to a cloudy day (or series of cloudy days).

The authors believe that they are the first to document that weather impacts short seller's trading behaviour. The authors argue that the results point towards a behavioural bias.

This study aims to investigate the combinations of religiosity, cosmopolitanism and perceived destination image leading to satisfaction and loyalty amongst Muslim consumers within the Maldivian tourism and hospitality context.

Using fuzzy-set qualitative comparative analysis (fsQCA), this study reveals that diverse combinations of religiosity, cosmopolitanism and destination image dimensions stimulate satisfaction and loyalty in Muslim consumers.

Multiple recipes can be used to design compelling destinations that balance the desire for religiosity and cosmopolitanism characteristic of contemporary Muslim consumers. The results confirm the applicability of complexity theory in explaining Muslim consumer behaviour within the Islamic destination context.

Several implications for the hospitality and tourism industry are drawn from the results, with suggestions for future research provided. Each fsQCA recipe identifies distinct suggestions to shape the design of destination attributes and hospitality offerings to stimulate consumer satisfaction and loyalty.

Complexity theory was applied to assess the complex causal relationships among Muslim consumers’ religiosity, perceived destination image, cosmopolitanism, satisfaction and loyalty.

The purpose of this paper is to present a forward-looking case of climate change induced displacement in the Pacific Islands as a multidimensional phenomenon with a moral dimension. Instead of seeking to provide a definitive solution to an imagined problem, the authors have identified the complexity of the situation through an exploration of the accounts of place and accountability for the consequences of displacement.

The paper explores displacement from a sociological perspective. The authors use the sociology of worth (SOW) to anchor explicit and competing moral claims in an evaluation regime that considers questions of justice and the common good. The public accounts of place in the Pacific Islands provide the empirical material for a consideration of a situated crisis. While SOW is generally adopted for current crises or disputes, this study explores the pre-immigrant story and a future case of displacement. Bauman’s (1998, 2012) perspective on globalization is used to narrate the local conditions of place in a global context as reflective of a dominant social order.

Since place is a multidimensional concept and experienced according to various states of being including physical, functional, spiritual and emotion or feeling, displacement is also felt at a multidimensional level. Thus to provide an account of a lived experience and to foster a moral accountability for climate induced displacement requires a consideration of multiple accounts and compromises that need to be considered.

As with the majority of accounting research that is concerned with the suffering of those at a distance, we too must tackle this conundrum in a meaningful way. As members of a society that is the largest per capita emitter of greenhouse gas, how do we speak for our drowning neighbors? The paper concludes with some insights from Boltanski (1999) as a way forward.

The paper presents a forward-looking scenario of a looming crisis from a sociological perspective. It adds to the literature on alternative accounts by using stories, media, government reports and other sources to holistically build a narrative grounded in a current and imaged social order.

The purpose of this paper is to examine the multiplicity of views on integrated reporting and to consider the possibility of, and impediments to, reconciling these multiple rationales (“orders of worth”) and thus gain legitimacy through a compromise. This sheds light on the understanding of integrated reporting as such, as well as shows how legitimacy struggles are resolved in practice around complex accounting practices in heterogeneous environments.

This explorative paper empirically applies Boltanski and Thévenot's sociology of worth (SOW) framework to analyse integrated reporting in the Dutch reporting field. Data were collected using multiple methods, including 64 semi-structured in-depth interviews with a wide range of relevant actors, and documentary analysis. Data were coded for the presence of orders of worth and legitimating compromise mechanisms.

The author's analysis suggests that integrated reporting combines the disparate domains of industrial, market, civic and green order of worth. These different logics of valuation need to be reconciled in a compromise in order for integrated reporting to become a legitimate practice. Such a compromise requires a common interest, avoidance of clarification and maintenance of ambiguity. The author's analysis suggests these mechanisms are violated though, with the risk that integrated reporting gets captured by investors and accountants, leading to local private arrangements rather than durable legitimate compromise.

First, SOW informs the understanding of integrated reporting. It highlights in particular its fragility as fundamentally different rationales need to be reconciled, which is a challenge yet also gives rise to creative frictions. Second, the SOW framework creates the possibility for scholars to look closer at the dynamics of legitimacy and at the possible mechanisms to attain legitimacy in fragmented and heterogeneous environment.

The SOW framework offers tools for practitioners, in particular those working within a pluralistic context. The various mechanisms of compromise discussed in this paper provide practical guidelines for how to manage this complexity and gain or maintain legitimacy.

This rich empirical study combines a novel theoretical approach (the SOW framework) with an analysis of the relatively unexplored topic of integrated reporting. At the same time it introduces a conceptualisation of legitimacy that highlights communicative and constitutive dialogue and goes beyond fit and compliance.

The unique nature of the construction sector makes it fall behind other sectors in terms of productivity. Monitoring construction productivity is crucial for the construction project's success. Current practices for construction productivity monitoring are time-consuming, manned and error prone. Although previous studies have been implemented toward reducing these limitations, a gap still exists in the automated monitoring of construction productivity.

This study aims to investigate and assess the different techniques used for monitoring productivity in building construction projects. Therefore, a mixed review methodology (bibliometric analysis and systematic review) was adopted. All the related publications were collected from different databases, which were further screened to get the most relevant based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria.

A detailed review was performed, and it was found that traditional methods, computer vision-based and photogrammetry are the most adopted data acquisition for productivity monitoring of building projects, respectively. Machine learning algorithms (ANN, SVM) and BIM were integrated with monitoring tools and technologies to enhance the automated monitoring performance in construction productivity. Also, it was observed that current studies did not cover all the complex construction job sites and they were applied based on a small sample of construction workers and machines separately.

This review paper contributes to the literature on construction management by providing insight into different productivity monitoring techniques.

The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.

The research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).

Shipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.

These findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.

Resilience concepts in integrated urban transport refer to the performance of dealing with external shock and the ability to continue to provide transportation services of all modes. A robust transportation resilience is a goal in pursuing transportation sustainability. Under this specified context, while before the perturbations, robustness refers to the degree of the system’s capability of functioning according to its design specifications on integrated modes and routes, redundancy is the degree of duplication of traffic routes and alternative modes to maintain persistency of service in case of perturbations. While after the perturbations, resourcefulness refers to the capacity to identify operational problems in the system, prioritize interventions and mobilize necessary material/ human resources to recover all the routes and modes, rapidity is the speed of complete recovery of all modes and traffic routes in the urban area. These “4R” are the most critical components of urban integrated resilience.

The trends of transportation resilience's connotation, metrics and strategies are summarized from the literature. A framework is introduced on both qualitative characteristics and quantitative metrics of transportation resilience. Using both model-based and mode-free methodologies that measure resilience in attributes, topology and system performance provides a benchmark for evaluating the mechanism of resilience changes during the perturbation. Correspondingly, different pre-perturbation and post-perturbation strategies for enhancing resilience under multi-mode scenarios are reviewed and summarized.

Cyber-physic transportation system (CPS) is a more targeted solution to resilience issues in transportation. A well-designed CPS can be applied to improve transport resilience facing different perturbations. The CPS ensures the independence and integrity of every child element within each functional zone while reacting rapidly.

This paper provides a more comprehensive understanding of transportation resilience in terms of integrated urban transport. The fundamental characteristics and strategies for resilience are summarized and elaborated. As little research has shed light on the resilience concepts in integrated urban transport, the findings from this paper point out the development trend of a resilient transportation system for digital and data-driven management.

Whether they support long-term growth companies, entrepreneurial firms or turnarounds, top teams need to make bold strategic investment choices in times of boom, bust or pandemic. This paper aims to discuss firm strategies, as evidenced by their investment choices, over a 21-year period during which they led firms committed to growth through times of crisis and disruption.

The starting point for this research is Fortune magazine’s 100 Fastest Growing Companies, published in 2018 and updated in 2019. The list is based on the magazine’s ranking of the world’s top three-year performers in revenues, profits and stock returns for the four quarters preceding publication. Inclusion on the list is all about growth, not starting size (the smallest and not renown). The classification of firms by industry sector follows Fortune’s nomenclature. Comparing these firms with industry peers in the same period, the authors look at Fortune’s 100 Fastest-Growing Companies of 2018 from the vantage point of their financials from 1999 to 2017, years that included the tech boom and bust, the mortgage meltdown and the Great Recession. This period also saw a relatively long expansion which was, paradoxically, punctuated by a trade war with China and recession fears that have impacted spending for growth. Only 32 of Fortune’s 2018 list made it to Fortune’s 100 Fastest Growing Companies of 2019. The authors call them the Persistent 32 and examine their investment and performance metrics from 2018 through 2020.

The Persistent 32 – companies that have survived multiple recessions, including the COVID-19 recession, and continue to grow – have lessons to teach, although there is no silver bullet or secret formula, even within the same industry. It was found that in the group of 32, the average company lifespan is 28.75 years and astute, decisive leadership matters. Companies that persist make unique, strategic resource choices. They postpone expenditures on marketing and sales, fixed assets or R&D or all three depending on their needs, rather than fit with industry. They continue to invest in future growth. Their people are not expendable: employee retention during a recession has been a familiar strategy for the top growers covered in this investigation throughout the period (1999–2020). They cut cost of goods and services produced (COGS). The Persistent 32, loathing the idea of cutting COGS in the face of earlier recessions or recessionary threats, are cutting expenses other than personnel expenditures now. Amazon, Nvidia, Stamps.com, Lam Research, Supernus Pharmaceuticals all continue to rein in costs while simultaneously reinvesting in growth. They communicate their concerns and plans to their constituents. These companies retained and grew headcount while communicating their safety program as well as work-from-home and social-distancing strategies to employees, customers, shareholders and elected officials during the COVID-19 recession of 2020. They plan for supply disruptions. All have already articulated their plans for supply disruptions or alternative sources. Both the Federal Government and semiconductor companies are looking to jump-start the development of new chip factories in the USA as concern grows about reliance on Asia as a source of critical technology. They sense, seize, transform. David Teece’s dynamic capabilities framework is still the best way to turn every black swan event into an opportunity for business based on newly immediate needs. They work remotely. Businesses that are growing despite the recession are already committed to remote work. Join them and take the high anxiety out of work for both employees and customers.

The starting point for our research was Fortune magazine’s 100 Fastest Growing Companies, published in 2018 and updated in 2019. The list is based on the magazine’s ranking of the world’s top three-year performers in revenues, profits and stock returns for the four quarters preceding publication. Only 32 of Fortune’s 2018 list made it to Fortune’s 100 Fastest Growing Companies of 2019. The authors call them the Persistent 32 and examine their investment and performance metrics from 2018 through 2020. They sought answers to three questions: First, do the fastest growing firms invest heavily in their businesses during recessions? The authors looked at the 100 fastest growing companies from 1999 to 2017 and then the Persistent 32 from 2018 to 2020. Second, what happened to the investments and performance of the Persistent 32 during the pandemic and recession that began in the first quarter of 2020? Where did they invest or curtail investment, what plans did they make around COVID-19 and what headcount decisions did they make? Third, do growth-committed firms follow different investment strategies that can be categorized based on spending patterns?

Companies that can survive and grow through the hardest of times have lessons to teach, although there is no silver bullet or secret formula, even within the same industry.

Employee retention during a recession has been a familiar strategy for the top growers covered in this investigation throughout the period (1999–2020). This strategy is not generally common among US firms. Indeed, it says something about the growth prospects of these firms and their dependence on talent and need to leverage their prior investment in recruiting and training employees.

What is important about this topic? Whatever the industry, trying times call for top teams to try harder, identify priorities, spend to achieve them, manage stakeholder expectations and protect and build their access to top talent. The authors can help with the last four: they set up a structure for analyzing firm spending and performance metrics, based on Gulati and others writing for business practitioners; they comb the evidence for spending and performance shifts in good times and bad from 1999 to 2020; they categorize firm strategies by spending patterns versus industry; they examine the findings for insights; and finally, the authors identify key actions that set still growing firms apart.