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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.

United Arab Emirates (UAE) has experienced various natural and man-made hazards. The energy sector dominates in the UAE and protection of this critical infrastructure against any impending hazards is important. The purpose of this paper is to examine the vulnerability of Emirati energy sector, explored barriers to enhance resilience and ways to overcome identified barriers.

Study followed case study research strategy and multiple case studies; three electricity generating power plant facilities – one in Abu Dhabi, one in Dubai and one in Sharjah were selected. Data collection involved a combination of a questionnaire survey and semi-structured face-to-face interviews. In total 42 questionnaire responses and nine interviews among Emirati energy sector employees were analyzed.

Results indicated terrorism, atmospheric and tectonic hazards as the three main risks of vulnerability within Emirati energy sector; whereas “lack of or absence of national government legislation” and “awareness and education” are revealed as the main barriers. Improving human resource management within Emirati energy sector through better awareness, training and practices is considered as a priority.

It is argued that the vulnerabilities and barriers identified in this study can be applicable to Emirati energy sector in general, not just the case studies represented in the study.

The feedback from the members of the Emirati energy sector is indicative of the latent failures and urgent issues that need to be addressed; predominately those of training, education and awareness. Findings of this study may help UAE energy sector to be prepared and build resilience for future hazards.

Based on the scarcity and the fragmented nature of the literature in the field of knowledge management (KM) and crisis, this paper aims to present a systematic literature review of these two constructs, interlinking the literature in KM to the prevention, preparedness, response and recovery (PPRR) phases framework. The output is a critical discussion on the state of the literature in the field, and an overview of avenues for future research.

The methodology adopted in this paper is that of a systematic literature review. Using the Scopus database, this study presents the findings that emerged from 59 publications in the field of KM and crisis.

Through the application of a systematic literature review, this paper categorizes 59 publications on KM according to the different stages comprising a crisis. The implications of each paper are discussed and critically analyzed, acting as the basis of areas for future research.

This paper is the first to offer a systematic review of the literature on KM in contexts of crisis by integrating the literature into a well-defined PPRR framework. Furthermore, the discussions presented in this review may be used by practitioners as a basis/starting point to identify relevant literature on different phases of crisis, while scholars may use this paper to further develop studies in KM and crisis management.

Emergency preparedness (EP) is one of the crucial phases of the disaster management cycle for the built environment. The body of knowledge, therefore, reports on different preparedness standards adopted by developed countries such as the United Kingdom (UK), the United States of America (USA), Canada, Japan and Australia. Other countries, however, such as the United Arab Emirates (UAE) (in the absence of its preparedness framework), have long adapted the UK preparedness standards. This has called for this study to investigate the state of EP practices in the UAE to identify the limitations and challenges it has been facing during its preparedness phase when adopting the UK preparedness standards.

Qualitative methods of data collection and documentation with the content analysis were adopted to identify the barriers faced by the preparedness phase of emergency management (EM) in the UAE. A Pilot study was therefore conducted to validate eight key elements of the EP phase identified from the literature. The state of EP phase and the extent to which the eight key elements of EP elements were practiced and the barriers in their implementation in the UAE were explored through interviews at federal (National Crisis and Emergency Management Authority) and local levels (local team of crisis and emergency management).

The study identified eight key elements of the EP phase and the associated barriers related to their implementation in the UAE. The barriers were ranked based on their severity by interviewing experts at both federal and local levels.

This paper addresses the need to investigate the state of the EP phase, its key elements and the barriers faced during its implementation in the UAE.

Due to the absence of any EP frameworks or systems in the UAE, this paper aims to validate the EP elements identified by adopting a qualitative approach.

This research aims to examine the extent to which eruptions and earthquakes are explained using religious terms of reference within Islamic countries, and those where Muslims constitute a significant minority. Is the notion that disasters represent divine punishment so pervasive that the only reaction for the devout believer is passive acceptance of suffering or, alternatively, is the innovation of policies of disaster reduction acceptable theologically?

Information is derived from: bibliographic sources; archives; questionnaires; and interviews.

There is a distinctive theological perception of natural disasters within Islamic thought. Although the inhabitants of Islamic countries may explain earthquake and eruption losses in religious terms, there is little evidence to suggest that this inhibits the introduction of programmes of planned loss reduction. Relief is a generally acceptable response for both aid donors and recipients.

The impact of volcanic eruptions and earthquakes is not as well researched as those occurring in countries with a Christian ethos. Generalizations have had to be made from a more limited set of examples. More detailed research is required both within the countries affected and in the languages used in hazard‐exposed Islamic states (e.g. Arabic, Urdu and Farsi).

Introduction of programmes of hazard reduction is generally possible in Islamic countries, but requires theological justification.

In comparison with the study of earthquake and eruption impacts in countries with a Christian ethos, little has been published on disasters and Islam.

Communication of an impending hazard to people in near real time is critical. The purpose of this paper is to develop an internet‐SMS based geo‐hazard warning communication system.

A warning system based on an internet‐resident concept and the available cellular mobile infrastructure is proposed in this study. The functionality of the system is modular in architecture.

The messages have been transmitted in a set of 20 SMSs six times, to locally‐owned mobile numbers, resulting in a total number of 120. It has been found that 58 messages got delivered within ten seconds, the rest within 40 more seconds. The threat messages reached the impending threat areas within acceptable time delay.

The paper describes the implementation of a novel and stand‐alone system for dynamic hazard warning. Cellular or mobile phone, a gadget used by common man, is expected to be the best proposition to effectively warn people individually and to propagate hazard messages to users in large regions ubiquitously. The concept allows pervasiveness and redundancy, important to withstanding hazards and bringing several original elements through the development of this “fast warning system”, as current warning strategies do not include such solutions.

This study aims to examine the current state of academic research pertaining to the role played by artificial intelligence (AI) in the achievement of a critical sustainable development goal (SDG) – poverty alleviation and describe the field’s development by identifying themes, trends, roadblocks and promising areas for the future.

The authors analysed a corpus of 253 studies collected from the Scopus database to examine the current state of the academic literature using bibliometric methods.

This paper identifies and analyses key trends in the evolution of this domain. Further, the paper distils the extant literature to unpack the intermediary mechanisms through which AI and related technologies help tackle the critical global issue of poverty.

The corpus of literature used for the analysis is limited to English language studies from the Scopus database. The paper contributes to the extant research on AI for social good, and more broadly to the research on the value of emerging technologies such as AI.

Policymakers and government agencies will get an understanding of how technological interventions such as AI can help achieve critical SDGs such as poverty alleviation (SDG-1).

The primary focus of this paper is on the role of AI-related technological interventions to achieve a significant social objective – poverty alleviation.

To the best of the authors’ knowledge, this is the first study to conduct a comprehensive bibliometric analysis of a critical research domain such as AI and poverty alleviation.

Complex crises affect tightly coupled systems making them highly unpredictable. This paper aims to determine how organizations learn from their crisis experience shaping their knowledge and transformation trajectory toward and beyond survival. A theoretical framework integrating organizational learning (OL) and knowledge management in organizational transformation (OT) in complex crises is presented.

This conceptual paper presents a systematic literature review on OT in crisis from 2000 to 2021. To achieve integration, the authors searched for studies on OT, knowledge management and OL, each paired with a crisis.

Crises highlight the emergent and decentered nature of knowing and organizing. This study suggests that OT is achieved through various changes in organizational knowledge. Different learning modes enable the transformation of knowledge in a crisis: contextual or situated learning, strategic and collective integration.

The authors' pandemic experience may have influenced the analysis. This paper does not account for new types of learning emerging due to the influence of digital technologies.

Organizations may hasten renewal through distributed crisis management facilitated by contextual and strategic learning and collective integration.

This study categorizes learning, based on its function in crisis management, into three types: contextual learning for creative problem-solving, strategic learning for leadership and direction and collective integration to evaluate their crisis journey. Through this classification, this study sheds light on the types of knowledge needed to manage crises effectively, showing that organizations can leverage their crises by transforming and innovating themselves in this turbulent period.

This paper aims to present a theoretical account of the connection between artificial intelligence (AI) enabled technologies and Muslim-friendly tourism experiences (MFTX) using the customer experience (CX) theory, reference group theory and theory of tourism consumption systems.

A model research design is adopted to build a theoretical framework that predicts relationships between constructs. Critical assessment in tourism and AI literature is used to explore AI-enabled technologies in Halal-friendly tourism.

The findings of this paper have conceptualised the CX theory for Muslim travellers satisfying their religious needs in Halal-friendly tourism by suggesting a new construct called the MFTX. It also offered a theoretical model for using AI-enabled technologies to improve the MFTX.

This study provides a new theoretical model for using AI-enabled technologies to improve the MFTX. This paper is also expected to provide suggestions for tourism operators and service providers to cater to Muslim tourists’ needs using AI technologies.

Security requirements play an important role in software development. These can be specified both in enterprise architecture models and in business processes. Enterprises increasingly carry out larger amounts of business processes where security plays a major role. Business processes including security can be automatically obtained from enterprise architecture models by applying a model-driven architecture approach, through a CIM to CIM transformation. The aim of this article is to present the specification of transformation rules for the correspondence between enterprise architecture and business process model elements focusing on security.

This work utilizes motivational aspects of the ArchiMate language to model security in the business layer of enterprise architectures. Next, a set of transformation rules defined with the Atlas Transformation Language are utilized to obtain the correspondence of the enterprise architecture elements in a business process, modelled with a security extension of BPMN.

A total of 19 transformation rules have been defined. These rules are more complex than element to element relations, as they take into consideration the context of the elements for establishing the correspondence. Additionally, the prototype of a tool that allows the automatic transformation between both models has been developed.

The results of this work demonstrate the possibility to tackle complex transformations between both models, as previous literature focuses on semantic correspondences. Moreover, the obtained models can be of use for software developers applying the model-driven approach.