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The present study investigated organizational resilience (OR) in the United Arab Emirates (UAE’s) energy sector to identify impactful technological and human variables and assess the hermeneutic effect of digital transformation on value co-creation and OR. The study also investigates the mediating role of value co-creation on a few covariates of OR.

The questionnaire was sent out to 311 professionals in the energy sector, all affiliated with governmental organizations, using quota sampling. A total of 206 collated responses corresponding to the tested variables regarding the influences of digital transformation, employee resilience, innovation readiness, cyber resilience and value co-creation on OR were analyzed using structural equation modeling. Accordingly, a model of eight constructs and their 27 indicators was tested.

Instituting flexibility and adaptability to technological advancements, as well as cyber resilience, was found to enhance digital transformation. The sense of self-efficacy of the professionals who participated in the study led them to develop innovation readiness and thus embrace creativity and encourage co-creation while maintaining collaborative efforts with customers and stakeholders. This mediated several technological and human variables, such as the importance of managers' understanding of customer needs, preferences and pain points, which involves actively seeking and valuing customer feedback to inform decision-making.

When iterative prototyping, continuous learning, and OR are integrated into an organization’s culture, they create a robust foundation for a customer-centric mindset. This mindset becomes ingrained in how employees approach their work and make purposeful decisions.

The present study drew empirical insights into OR in the UAE’s energy sector from a resource-based theory perspective. By identifying potential vulnerabilities and implementing appropriate mitigation measures, organizations can reduce the likelihood and impact of disruptions, which can ultimately help them maintain customer satisfaction and loyalty.

This paper addresses the under-explored collateral damage of economic sanctions, shedding light on the disproportionate harm endured by the most vulnerable segments of societies, which at the same time lack political influence to effect the ruling government into change. The primary objective is to review the literature on humanitarian repercussions associated with sanctions, concluding if they really are a comparably human way of international interference.

Employing a systematic literature review, adhering to the PRISMA approach, and incorporating key term definitions and clear selection criteria, this review analyses 52 studies sourced from Scopus and EconBiz.

The surveyed literature reveals profound adverse impacts of sanctions on health, economic well-being, inequality, and education. Critical gaps in the literature such as disproportional focus on extreme cases like Iran and Iraq, scarce literature on effects on education and inequality, and predominantly inadequate control groups are identified, limiting the generalizability of existing findings.

This paper is the first systematic and replicable review of the literature on the effects of sanctions using a capabilistic approach to define poverty. Highlighting gaps in the current research landscape underscores the limited generalizability of reviewed results. Providing a well-structured summary of existing literature, this work serves as a foundation for future research.

This study aims to offer insights into the state of research covering cybersecurity, cyber insurance and small- to medium-sized enterprises (SMEs). It examines benefits of insurance to an SME’s security posture, challenges faced, and potential solutions and outstanding research questions.

Research objectives were formulated, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol was used to perform a systematic literature review (SLR). A total of 19 papers were identified from an initial set of 451.

This research underscores the role of cybersecurity in the value proposition of cyber insurance for SMEs. The findings highlight the benefits that cyber insurance offers SMEs including protection against cyber threats, financial assistance and access to cybersecurity expertise. However, challenges hinder SME’s engagement with insurance, including difficulties in understanding cyber risk, lack of cybersecurity knowledge and complex insurance policies. Researchers recommend solutions, such as risk assessment frameworks and government intervention, to increase cyber insurance uptake/value to SMEs.

There is a need for further research in the risk assessment and cybersecurity practices of SMEs, the influence of government intervention and the effectiveness of insurers in compensating for losses. The findings also encourage innovation to address the unique needs of SMEs. These insights can guide future research and contribute to enhancing cyber insurance adoption.

To the best of the authors’ knowledge, this is the first SLR to comprehensively examine the intersection of cybersecurity and cyber insurance specifically in the context of SMEs.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

This study aims to identify critical barriers to design thinking (DT) implementation in healthcare and to determine hierarchical relationships among the barriers.

Through an extensive literature review and healthcare experts' opinions, 13 barriers to DT implementation in healthcare were identified. Data were collected using survey questionnaires, and an interpretive structural modeling (ISM) -MICMAC analysis was employed to produce a hierarchical model of identified barriers.

Results reveal the absence of standalone barriers, highlighting “Lack of Organizational autonomy” and “Lack of innovation mindset” as crucial barriers at the bottom of the hierarchy. Overcoming these barriers requires a shift in organizational mindset, dedicated resources, interdisciplinary collaborations, and aligning DT with healthcare regulations.

The findings aid policymakers in recognizing interconnected barriers, enabling DT implementation through strategic mitigation. Healthcare leaders and stakeholders can use this insight to formulate effective strategies for addressing these barriers.

This research presents a distinct investigation of identifying the barriers to DT implementation in the healthcare sector in India. DT’s seamless implementation in hospitals encounters diverse barriers, hindering its full potential. This research contributes to the extant literature by providing the interrelationship between the barriers and a hierarchical model for a clear understanding of the levels of barriers.

The author investigates the effect of trade protection on domestic firm innovation in China and explores the channel through which trade protection affects corporate innovation.

Using a sample of Chinese A-share manufacturing companies from 2003 to 2019, the author starts with a univariate analysis by examining the innovation output after trade protection for all samples. The author uses the natural logarithm of one plus the number of trade protection cases received by the industry to which the firm belongs in a particular year to proxy for trade protection.

The author finds that trade protection significantly encourages firms’ patent application, particularly substantive patents, which is stronger in non-state-owned enterprises. Moreover, the mitigation of financial constraint is plausible channel that allows trade protection to promote innovation.

For practitioners, they should seize the dividends of national policies. In the process of implementing trade protection, they should concentrate on improving their innovation level and enhancing their core competitiveness. When they are not subject to trade protection, they can also make profits and develop in the long run.

For policy makers, in the early stage of industry development, trade protection can be used to ease the companies’ financing constraints and improve the companies’ profits, which will help them concentrate their efforts, promote innovation and further develop. However, in the mid-term development of the industry, policy makers should reduce trade protection. Through the entry of foreign capital, companies face increased competition, which can enhance the companies’ motivation for long-term development.

Overall, this paper sheds light on the real effects of trade protection and the determinants of innovation. First, the paper sheds light on the impact of international trade on firms’ innovation. Second, this study also contributes to the emerging literature on the effect of trade policy uncertainty on financial constraint. Third, the paper adds to the stream of literature on the drivers of innovation.

This paper aims to explores the escapist journey that consumers embark on to reduce self-discrepancy, from the motivation to satisfy personal goals to the fulfilment of these goals. Escapism is a powerful concept for understanding why and how people resolve a perceived self-discrepancy. Previous research has provided rich and diverse insights into the motivations and goals, experience contexts and outcomes that are associated with escapism. However, these notions have been explored in an independent, piecemeal fashion that has hampered the development of cohesive theoretical foundations.

The authors conduct a qualitative study, relying on 33 journeys derived from 20 in-depth interviews, to understand and explain how escapism is initiated, formed and relived.

The findings document the existence of a predominantly utilitarian and a predominantly hedonic model, articulating why and how consumers pursue distinctly different types of escapist journeys. Utilitarian escapism is primarily concerned with the need to avoid negative emotional states, prompting consumers to decide impulsively to engage into routinised experience contexts to find relief. Hedonic escapism is primarily concerned with the desire to achieve positive emotional states. It involves lengthy and thoughtful anticipation, promotes consumers to pursue experiences in out of the ordinary contexts, and is realised when consumers obtain reward.

This study’s contributions present significant opportunities for further knowledge development.

The research findings have substantial practical ramifications for industry.

Understanding people’s needs to avoid the stress of everyday life (i.e. utilitarian escapism) and their desires to seek out excitement and pleasure (i.e. hedonic escapism) has important societal implications.

This research provides a robust theoretical foundation for escapism, one that is applicable across multiple settings. Findings are anchored within existing theory to articulate the notions of utilitarian and hedonic escapism, explaining why and how consumers engage in markedly different escapist journeys.

The study aims to examine the extent of the corporate social responsibility (CSR) disclosure of Canadian cannabis firms and how they view responsibility. It also explores how cannabis firms build their CSR-based organizational identity through Twitter.

Deductive and inductive content analyses were carried through on tweets for a sample of 18 firms listed on the Canadian marijuana index during the legalization period of the recreational use of cannabis.

The results of this study show that cannabis firms approach responsibility by focusing on consumer and community/local development and by raising awareness and providing product information. The findings also highlight that the firms build their organizational identity mainly around their products’ medical benefits, the scientific efforts behind product development and the continual stigmatization they experience. At the industry level, cannabis firms attempt to build a harmonized identity to neutralize stigma.

This study allowed for a comprehensive understanding on how cannabis firms position themselves within an emergent sin industry and how they create their CSR identity through Twitter. It advances our understanding on the meaning of responsibility about the specific and distinctive features of the cannabis industry. From the methodology side, this study developed two content analysis tools: a coding instrument and a dictionary. These tools could be useful for conducting future studies related to the CSR disclosure of cannabis firms worldwide.

This study presents the impact of Economic Policy Uncertainty (EPU)-induced Trade Supply Chain Vulnerability (TSCV) on the Small and Medium-Sized Enterprises (SMEs) in India by leveraging the World Bank Enterprise Survey data for 2014 and 2022. Applying econometric techniques, it examines firm size’ influence on productivity and trade participation, providing insights for enhancing SME resilience and trade participation amid uncertainty.

The econometric techniques focus on export participation, along with variables such as total exports, firm size, productivity, and capital intensity. It addresses crucial factors such as the direct import of intermediate goods and foreign ownership. Utilizing the Cobb-Douglas production function, the study estimates Total Factor Productivity, mitigating endogeneity and multicollinearity through a two-stage process. Besides, the study uses a case study of North Indian SMEs engaged in manufacturing activities and their adoption of mitigation strategies to combat unprecedented EPU.

Results reveal that EPU-induced TSCV reduces exports, impacting employment and firm size. Increased productivity, driven by technological adoption, correlates with improved export performance. The study highlights the negative impact of TSCV on trade participation, particularly for smaller Indian firms. Moreover, SMEs implement cost-based, supplier-based, and inventory-based strategies more than technology-based and risk-based strategies.

Policy recommendations include promoting increased imports and inward foreign direct investment to enhance small firms’ trade integration during economic uncertainty. Tailored support for smaller firms, considering their limited capacity, is crucial. Encouraging small firms to engage in international trade and adopting diverse SC mitigation strategies associated with policy uncertainty are vital considerations.

This study explores the impact of EPU-induced TSCV on Indian SMEs’ trade dynamics, offering nuanced insights for policymakers to enhance SME resilience amid uncertainty. The econometric analysis unveils patterns in export behavior, productivity, and factors influencing trade participation during economic uncertainty.

The study aims to explore the organizational benefits of positive employee experience and to provide a framework for measuring it. The positive employee experience has a profound impact on employees’ attitudes; thus, it is particularly important to what extent an organization can create the conditions supporting this.

The study is based on literature review and the framework needs to be empirically tested to draw final conclusions.

Organizational performance and success are influenced by employees’ well-being, commitment, job satisfaction and the high level of individual performance. However, this grouping of variables is not exhaustive, but in practice, it is often not necessary to fully understand the complex and complicated relationships among the organizational variables. However, a positive employee experience has an impact on all of these variables. According to our understanding and experience, the task of management is not to strengthen the variables describing employee attitudes individually, based on the knowledge of specific relations presented in the management literature and selected for the sake of a single research, but to create an acceptable level of the positive employee experience, which is able to strengthen these variables in a way that is useful for the organization.

In this study, the authors introduce the concept of the positive employee experience and the ways and steps to measure it. The authors review the methodology of predictive analytics, the main principles of data collection and the types of data with their possible applications. Finally, the limitations of the framework and the risks of enhancing the positive employee experience are also discussed.