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The crowdfunding concept and activities have recently been the focus of attention of many researchers and practitioners over different business contexts. However, there is a dearth of literature considering the main aspects of e-equity crowdfunding activities and their impact on the innovation performance for entrepreneurial business. Therefore, this study aims to explore how entrepreneurs' engagement in e-crowdfunding activities could enhance both knowledge acquisition and innovation performance.

The conceptual model will be proposed based on three main theoretical perspectives: relationship marketing orientation (RMO); Kirzner's alertness theory; and the DeLone and McLean model of information systems. The data of the current study were collected using an online questionnaire from a sample of 500 entrepreneurs who have actively engaged in e-crowdfunding in Saudi Arabia.

The statistical results of structural equation modelling (SEM) approved the impacting role of RMO, entrepreneurial alertness, system quality and service quality on the entrepreneurs' engagement in e-equity crowdfunding, which in turn, predicts both knowledge acquisition and innovation performance.

There are several limitations which could be addressed in future studies, for example, this study has only considered one form of crowdfunding (equity based crowdfunding) and due to its nature these findings would not be easily generalized to other kinds of crowdfunding (i.e. donation-based crowdfunding; rewards-based crowdfunding; and debt-based crowdfunding). Future studies could consider these kinds of crowdfunding activities.

This study has contributed to the understanding of e-equity crowdfunding in several aspects. For example, this study presents results that assist both researchers and practitioners in the Middle East and Saudi Arabia to develop an in-depth knowledge of e-equity crowdfunding by considering new dimensions such as RMO and information system success factors.

Utilizing the theoretical foundation of the stimulus-organism-response framework, the present work developed and investigated a conceptual model. The work explores the effects of perceived risk of COVID-19 on tourists' choice hesitation and choice confidence. Furthermore, it examines the impacts of choice hesitation and choice confidence on psychological distress, which, in turn, influences purchase intentions and risk-protective behavior. Additionally, the study assesses the boundary effects of vulnerability on the association between choice hesitation, choice confidence, and psychological distress.

An online survey was administered in China during COVID-19 to assess the postulated hypotheses. We collected 491 responses using purposive sampling, and covariance-based structural equation modeling (CB-SEM) was performed to investigate the relationships.

Results show that the perceived risk of COVID-19 positively influences the choice hesitation and negatively impact choice confidence. It was also found that choice hesitation and choice confidence positively developed psychological distress, which, in turn, negatively triggered purchase intentions and positively developed risk-protective behavior. Additionally, perceived vulnerability had a significant moderating impact on the proposed relationships, strengthening psychological distress.

In the current context, this study measures bipolar behavioral outcomes using the S-O-R model. Because cognitive processes influence participation in health preventative behavior during the spread of diseases, we highlighted how the perception of risk and vulnerability to a pandemic serves as a reliable indicator of certain behaviors. This study advances understanding of how the psychological mindset of tourists copes with such circumstances. Due to the pandemic, tourists face limitations in their choices and are placing greater emphasis on adopting protective measures to mitigate associated risks.

Shadow banks or non-bank financial intermediaries (NBFIs) are facilitators of credit, especially in emerging market economies (EMEs). However, there are certain risks associated with them, such as their unchecked leverage and interconnectedness with the rest of the financial system. In light of this, the present study analyses the impact of the growth of shadow banks on the stability of the banking sector and the overall stability of the financial system. The authors further examine the effect of the growth of finance companies (a type of NBFIs) on financial stability.

The study employs data of 11 EMEs (monitored by the Financial Stability Board (FSB)) for the period 2002–2020 to examine the above relationships. Panel-corrected standard errors method and Driscoll–Kray standard error estimation are deployed to conduct the analysis.

The results signify that the growth of the shadow banking sector and the growth of lending to the shadow banking sector are negatively associated with the stability of the banking sector and increases the vulnerability of the financial system (overall instability). This implies that the higher the growth of the shadow banks, the higher the financial fragility. Finance companies are also found to negatively affect financial stability. These findings are validated by different estimation methods and point out the risks posed by the NBFI sector.

The extant study builds a composite index (Financial Vulnerability Index (FVI)) to measure financial stability; thus, the findings contribute to the evolving literature on shadow banks.

This chapter provides an overview of the potential use of Intelligent Transport Systems (ITS) and associated artificial intelligence (AI) techniques in the land transport sector in an attempt to achieve related United Nations Sustainable Development Goals (SDGs) targets. ITS applications that have now been extensively tested worldwide and have become part of the everyday transport toolkit available to practitioners have been discussed. AI techniques applied successfully in specific ITS applications such as automatic traffic control systems, real-time image processing, automatic incident detection, safety management, road condition assessment, asset management and traffic enforcement systems have been identified. These methods have helped to provide traffic engineers and transport planners with novel ways to improve safety, mobility, accessibility and efficiency in the sector and thus move closer to achieving the various SDG targets pertaining to transportation.

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.

While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.

The four developed performance prediction models achieved an overall precision and reliability rate of over 80%.

The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.