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The purpose of this study is to examine how the lodging market and the state economy affected by Hurricane Sandy have recovered from the damages sustained. Specifically, this study examines and predicts the influence of revenue management key performance indicators (KPIs) on recovery and lodging revenue in the affected states and the states’ economies. These KPIs include average daily rate (ADR), occupancy and revenue per available room (RevPAR).

Secondary financial data were collected for the states most damaged by Hurricane Sandy. Subsequently, pooled Ordinary Least Square (OLS) regression was conducted combining time and non-time dependent variables based on the states and radius from the landfall.

The results indicate that although the lodging market and the state economies have recovered since the onslaught of Hurricane Sandy, certain KPIs still need to improve.

Managerial implications are suggested in terms of dynamic pricing, market-based recovery, the KPIs, federal aid and facility management.

Despite its importance, research on the effects of climate change in the hospitality context has not actively progressed after Hurricane Katrina. Time and non-time dependent variables are combined in this analysis to gain a richer understanding of the impacts and recovery of KPIs on the revenue in the lodging market and the revenue on states’ economies. Additional analysis based on the radius from the landfall of the hurricane was performed to examine the impact and recovery based on geographical proximity.

The purpose of this paper is to elaborate pros and cons of two coding methods: the rapid network assessment (RNA) and the manual content analysis (MCA). In particular, it focuses on the applicability of a new rapid data extraction and utilization method, which can contribute to the timely coordination of disaster and emergency response operations.

Utilizing the data set of textual information on the Superstorm Sandy response in 2012, retrieved from the LexisNexis Academic news archive, the two coding methods, MCA and RNA, are subjected to social network analysis.

The analysis results indicate a significant level of similarity between the data collected using these two methods. The findings indicate that the RNA method could be effectively used to extract megabytes of electronic data, characterize the emerging disaster response network and suggest timely policy implications for managers and practitioners during actual emergency response operations and coordination processes.

Considering the growing needs for the timely assessment of real-time disaster response systems and the emerging doubts regarding the effectiveness of the RNA method, this study contributes to uncovering the potential of the RNA method to extract relevant data from the megabytes of digitally available information. Also this research illustrates the applicability of MCA for assessing real-time disaster response networks by comparing network analysis results from data sets built by both the RNA and the MCA.

The purpose of this paper is to explore the past events where communication challenges have occurred during a disaster, and events in which amateur radio had played a vital role in creating reliable communication links during the disaster response. Furthermore, this paper identifies the modern technology available with amateur radio operators that can be used to create reliable communication networks in order to meet the high demand of disaster communication.

In this paper the author has conducted an integrative literature review, while analyzing case studies of disasters where communication challenges have occurred and the amateur radio has provided communication support during a disaster response.

In today’s world there is a heavy reliance on centralized communications infrastructure such as cell towers, which are very likely to fail during a disaster. Failure or overload of such infrastructure will cause disruptions in communications and make the disaster response inefficient. Amateur radio does not rely on centralized communications infrastructure, and has the ability to be used to meet the demand during many disaster situations. Thus, it is very important for emergency professionals to understand the value of including amateur radio operators in emergency operations plans.

Research is qualitative in nature, with an aim to write a short paper introducing the issues of Disaster Communications.

This paper is intended to provide an insight about the importance of reliable communications during a disaster. Communication interruptions can create a lot of problems and cause inadequate coordination between response agencies. In order to mitigate such challenges it is crucial to study the vital role of amateur radio in supporting the communications when all other mediums of communication fail.

This study aims to propose iStage, i.e. an intelligent hybrid deep learning (DL)-based framework to determine the stage of the disaster to make the right decisions at the right time.

iStage acquires data from the Twitter platform and identifies the social media message as pre, during, post-disaster or irrelevant. To demonstrate the effectiveness of iStage, it is applied on cyclonic and COVID-19 disasters. The considered disaster data sets are cyclone Fani, cyclone Titli, cyclone Amphan, cyclone Nisarga and COVID-19.

The experimental results demonstrate that the iStage outperforms Long Short-Term Memory Network and Convolutional Neural Network models. The proposed approach returns the best possible solution among existing research studies considering different evaluation metrics – accuracy, precision, recall, f-score, the area under receiver operating characteristic curve and the area under precision-recall curve.

iStage is built using the hybrid architecture of DL models. It is effective in decision-making. The research study helps coordinate disaster activities in a more targeted and timely manner.

There is a large literature about crisis entrepreneurship, spanning from necessity, natural disaster and long-term conflict entrepreneurship. This paper situates pandemic entrepreneurship as a unique form of crisis entrepreneurship.

The authors utilize the Kirznerian and Schumpeterian theories of entrepreneurship to understand pandemic entrepreneurship. Using evidence from the US COVID-19 pandemic, the authors argue that pandemics impact both the “identification” and “action” moments of entrepreneurship.

The Kirznerian identification moment becomes much more uncertain for entrepreneurs because of fluctuating conditions, such as public health conditions, new potential variants of the virus causing the pandemic, shifting government mandates and rules and so forth. The Schumpeterian action moment becomes more challenging because of the necessity of physical distancing and because, generally, all crises raise the cost of entrepreneurial action. That said, the authors still document considerable entrepreneurship during pandemics as entrepreneurs adapt to the increased uncertainty and costs by rely upon local and customary knowledge.

This research finds that entrepreneurs, depending upon the crisis, face differing constraints. Specifically in times of pandemic, entrepreneurs face difficulty recognizing opportunities because of shifting conditions and acting upon opportunities because of financial and political constraints. This research thus implies that there are large opportunities for alleviation of such constraints if there were to be future variants or pandemics.

Practically speaking, this research affects how people study entrepreneurship. By recognizing the differing constraints that pandemic entrepreneurs face, the authors can better understand the last several years, and can also prepare better policy wise for future pandemics or further variants of COVID-19.

Socially, entrepreneurship can be a large factor in recovery from disasters and crises. By recognizing and perhaps alleviating constraints that pandemic entrepreneurs face, future crises could have better responses and recoveries.

Although several studies have examined entrepreneurship during the COVID-19 pandemic, the extant literature on pandemic entrepreneurship remains relatively underdeveloped and has not yet focused on what distinguishes pandemic entrepreneurship from other forms of crisis entrepreneurship. The authors highlight what pandemic entrepreneurship has in common with other forms of crisis entrepreneurship and pinpoint the various ways that is distinct.

Recently, reverse logistics (RL) has become more prominent due to growing environmental concerns, social responsibility, competitive advantage and high efficiency by customers because of the expansion of product selection and shorter product life cycle. However, effective implementation of RL results in some direct advantages, the most important of which is winning customer satisfaction that is vital to a firm’s success. Therefore, paying attention to customer feedback in supply chain and logistics processes has recently increased so that manufacturers have decided to transform their RL into customer-centric RL. Hence, this paper aims to identify the features of a mobile phone which affect consumer purchasing behaviour and to analyse the interrelationship among them to develop a framework for customer-centric RL. These features are studied based on website analysis of several mobile phone manufacturers. The special focus of this paper is on social media data (Twitter) in an attempt to help the decision-making process in RL through a big data analysis approach.

A portfolio of mobile phone features that affect consumer’s mobile phone purchasing decisions has been taken from website analysis by several mobile phone manufacturers to achieve this objective. Then, interrelationships between the identified features have been established by using big data supplemented with interpretive structural modelling (ISM). Apart from that, cross-impact matrix multiplication, applied to classification analysis, was carried out to graphically represent these features based on their driving power and dependence.

During the study, it has been observed from the ISM that the chip (F5) is the most significant feature that affects customer’s buying behaviour; therefore, mobile phone manufacturers realize that this is to be addressed first.

The focus of this paper is on social media data (Twitter) so that experts can understand the interaction between mobile phone features that affect consumer’s decisions on mobile phone purchasing by using the results.

This paper aims to propose DisDSS: a Web-based smart disaster management (DM) system for decision-making that will assist disaster professionals in determining the nature of disaster-related social media (SM) messages. The research classifies the tweets into need-based, availability-based, situational-based, general and irrelevant categories and visualizes them on a web interface, location-wise.

It is worth mentioning that a fusion-based deep learning (DL) model is introduced to objectively determine the nature of an SM message. The proposed model uses the convolution neural network and bidirectional long short-term memory network layers.

The developed system leads to a better performance in accuracy, precision, recall, F-score, area under receiver operating characteristic curve and area under precision-recall curve, compared to other state-of-the-art methods in the literature. The contribution of this paper is three fold. First, it presents a new covid data set of SM messages with the label of nature of the message. Second, it offers a fusion-based DL model to classify SM data. Third, it presents a Web-based interface to visualize the structured information.

The architecture of DisDSS is analyzed based on the practical case study, i.e. COVID-19. The proposed DL-based model is embedded into a Web-based interface for decision support. To the best of the authors’ knowledge, this is India’s first SM-based DM system.

In much of the literature written in Sustainability and Environmental Justice, the focus is on the effects of government mismanagement or corporate social irresponsibility, or CSR ignored for the goal of greater profits. Certainly we have seen natural resources ripped from communities and nations for the benefit of corporate profits (Sarkar, 2013). The idea that a participatory government will lead to greater efforts for sustainability must be viewed in the light of its times and economy (Gonzalez-Perez, 2013). What happens when the man-made disaster precedes or clashes with natural disaster? The Great Recession of 2008 was stunning in the rapidity with which it spread around the globe. The recession illustrated a global acceptance of financial wisdom that had been presented as fact and yet could easily be undermined by people who understood the barriers, boundaries, and restrictions in place as well as where the financial assumptions could be deceived by introduction of new terms and definitions as in the case of credit default swaps.

In this chapter, we focus on the influence of the recession on one of the most powerful financial capitals of the world, New York City. We discuss the general effect of the recession on New York City as a whole and then take a narrower look at each of the five boroughs, Manhattan, Queens, Staten Island, Brooklyn, and the Bronx. We examine the disparate economic states of each borough and how the recession has impacted each of them. Furthermore, we discuss the implications of the general perception of Manhattan’s resilience to the recession and how is has impacted the other boroughs, such as the housing crisis in Staten Island and Queens following Hurricane Sandy, unemployment rates in the Bronx, and the rebuilding of a sustainable job market in Brooklyn.

We reviewed relevant literature, including academic research, reports issued by the State of New York, census data, articles printed in popular press outlets, and business resources to provide a thorough look at the influence of the Great Recession on New York City and each of its five boroughs. We found extensive support for the disparity amongst the five boroughs, despite the perception that New York City is thriving in the wake of the Great Recession and Hurricane Sandy. We detail the unique economic and environmental factors of each borough and explain how it influenced the impact of the Great Recession and subsequent natural disaster.

Manhattan was well insulated from the initial impact of the Great Recession, with tourism in the city remaining high through 2008 and financial firms on Wall Street experiencing record high profits well into 2009. Despite the downfall of Lehmann Brothers and Merrill Lynch, the financial bailouts and Federal Reserve credit available to Wall Street firms prevented Manhattan’s financial sector from experiencing the dramatic unemployment rates that the rest of New York and the United States were facing (DeFreitas, 2009).

The Great Recession hit disadvantaged areas, like the Bronx, harder than other areas of New York, while Hurricane Sandy halted the economic recovery in areas like Queens and Staten Island. While unemployment remains low in New York City as a whole, the recovery from the Great Recession has been uneven, further widening the gap between New York City’s boroughs, with the lower income areas at a greater disadvantage and the higher income areas souring. While Manhattan has recovered significantly, with Wall Street profits reaching record levels in 2009, other boroughs haven’t experienced the same economic upturn and are still facing significant challenges (Parrott, 2010). While the city has gained nearly 375,000 jobs, nearly twice the number of jobs that were lost during the Great Recession (Crain’s New York Business, 2013), the significant variance in wages and high costs of living has not greatly reduced the number of working poor across New York City and has not resulted in an evenly spread boost in wealth.

At the end of our chapter, we discuss “lessons learned” and, in particular, the importance of preparation for both fiscal and natural disasters. Local policy makers must ensure that the needs of its constituents are being met and will be met in the future if such hardship were to strike. Government leaders need to have a forward-looking plan, rather than simply handling immediate needs.

The originality of our content stems from a deeper look into the nuances of the economy of New York City. Statistics paint a picture of a thriving City, despite the Great Recession. However, understanding the distinct differences amongst the five boroughs illustrates that these citywide averages do not paint an accurate picture of life for New Yorkers off of Wall Street. The extent to which the high-income areas in Manhattan have recovered suggests that the economy of New York City as a whole is thriving, whereas the reality is that the middle-class has not recovered and the previously disadvantaged are now even more so. It is important to look at each of the five boroughs of New York City individually when creating policy to both recover from and prevent events such as the Great Recession and the destruction of Hurricane Sandy. Our chapter illustrates stark differences within New York City in the face of both financial and natural crises.

To look at the role of local decision-making and control in the face of a trend towards unified national and transnational disaster protocols. To look at the implications of a shifting rhetoric – from sustainability to resilience – for this issue.

This chapter draws upon the author’s case studies of the 2009 H1N1 pandemic in New York City and Hurricane Sandy (2013) in New York City, as well as studies of Hurricane Katrina (2006) in New Orleans, to discuss governance issues.

Empirical studies confirm the importance of locally based decision-making and control. There are tensions between national disaster protocols and local decision-making; urban governance matters given differences in political culture, leadership, and community participation.

We need a resilient social infrastructure as well as a resilient physical environment. Strong social institutions are an essential part of this process but communities must be given material, not only symbolic benefits.

The conclusion that the threat of natural disasters requires more rather than less autonomy in decision-making for the locality.

Furthermore, that the shift in objectives, from sustainability to resilience (mandating redundancy and sophisticated data retrieval) requires what we might call a more empowered city.