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The UAE only officially formed in December 1971, now has seven Emirates joined together as a nation under one President. Since its establishment as a sovereign, independent country around just 50 years ago, the UAE has accelerated advancement across numerous sectors at notable speed, and science, technology, and Research & Development (R&D) sectors are no exception. Research Management and Administration (RMA) as a profession is in the relatively early stages of being recognised as a distinct and niche skill set; at present, the UAE largely imports experienced international talent to fill RMA roles. However, the country’s strides in progressing R&D infrastructure and goals of elevating ranks from regional to global R&D leader is beginning to generate a legitimate career ladder for RMA professionals across the country. This, paired with the UAE’s commitment to engage more Emirati nationals in the workforce, is cultivating a viable environment for the RMA profession to emerge more significantly from general administration and begin generating a skilled talent pipeline of RMA professionals within the UAE.

The primary purpose of this study is to integrate the stimulus-organism-behavior-accomplishment (S-O-B-A) paradigm to investigate the chain effect of university students' perceived university and family support on their entrepreneurial action (EA) with a serial mediation of their attitude and intention.

This study introduces stratified random sample to choose respondents and a cross-sectional research design. partial least square-structural equation modeling (PLS-SEM) has applied to thoroughly investigate the behavioral intention concerned with students' entrepreneurship action.

The findings explored that perceived university support and family supports positively impact students' entrepreneurship attitude, where perceived family support creates statistically more powerful implications than university support. Students' attitude toward entrepreneurship positively affects their entrepreneurial intent, and finally, the entrepreneurial pursuit has an affirmative impact on students' EA.

This study incorporates the S-O-B-A paradigm for the very first time to investigate the effects of students' environmental support on their EA with double mediation by their attitude and intention.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

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.

The employer brand is a crucial intangible asset for companies as it enhances the employer–employee relationship, leading to improved employee performance and overall company outcomes. This paper aims to investigate the contribution of the employer brand to the financial results of companies in southern Europe.

The sample consists of 266 companies operating in southern European countries during the year 2020. Secondary data on employer brand attributes, assessed from the perspective of current employees, were collected from the Glassdoor platform. Financial indicators were obtained from the companies' annual financial reports. The research hypotheses were tested using regression analysis.

The results of the regression analysis support the notion that the employer brand contributes to profitability indicators and management effectiveness indicators of southern European companies. However, the study did not find evidence supporting the contribution of the employer brand to market indicators and financial structure indicators of the observed companies.

This study is one of the first empirical investigations to assess the role of the employer brand as a human capital tool for enhancing the financial performance of companies in southern Europe. The study examines employer brand attributes from the perspective of current employees, who actively participate in shaping the employer brand and the company's image. In contrast to prior research, this study incorporates a more extensive set of financial indicators, categorized into four groups: profitability indicators, management effectiveness indicators, market indicators and financial structure indicators.

This study aims to understand consumer behaviour in the context of online food delivery (OFD), especially given the mandatory lockdown imposed in some countries that have modified the behaviour of consumers. Using model goal-directed behaviour (MGB), this study was conducted to investigate consumer perceived risk on the use of OFD services.

Responses of food delivery services users were collected online throughout April 2020 to understand their risk profile and behaviour. A total of 339 responses were collected and subsequently analysed using partial least square (PLS). Both measurement and structural model were evaluated to ensure that the structural equation modelling (SEM) is valid.

The results revealed that attitude (ATT), subjective norm (SN), positive anticipated emotion (PAE) and negative anticipated emotion (NAE) and perceived behavioural control (PBC) significantly influenced users' desire. It was also found that PBC significantly influenced users' intention. The empirical result suggests that performance, privacy, financial, physical and the risk of contracting COVID-19 negatively influenced users' desire. In contrast, only physical and the risk of contracting COVID-19 negatively influenced users' intention to use OFD services.

These findings provide OFD service providers and scholars with significant insights into what compels urbanites to adopt OFD services amid a health pandemic. It also allows OFD companies to realign their operation in addressing these concerns and changes in consumer behaviour.

Against the backdrop of the pandemic, this study provides insights for OFD providers in developing new strategies and approaches for business development and consumer retention in a post-pandemic world.

本研究擬瞭解與網上訂餐相關的消費行為；尤其當有些國家推行了改變消費者行為的強制性封鎖政策的情況下，這類研究更具意義。透過應用目標導向行為模型，本研究擬探討消費者在使用網上訂餐服務時所意識到的風險。

研究人員於 2020年4月網上收集使用訂餐服務人士的意見，以瞭解其風險狀況和行為。研究共收集了339位人士的意見，並以偏最小二乘法進行分析。測量和結構模型均加以評估，以確保結構方程模型是站得住腳的。

研究結果顯示，態度、主觀規範、預期的正面和負面情緒、以及感知的行為控制，顯著地影響了用戶的慾望。研究結果亦發現，感知的行為控制顯著地影響了用戶的意圖。研究的經驗性結果暗示了表現、私隱、金錢上的爭議、可能會導致的身體損傷和感染2019冠狀病毒病的風險，負面地影響用戶的慾望。相比之下，影響著用戶使用網上訂餐服務的意圖的因素就只有可能會導致的身體損傷和感染2019冠狀病毒病的風險。

研究的結果，為提供網上訂餐服務的營運者和研究學者提供了重要的啟示，使他們更瞭解是什麼因素會迫使都市人在與健康息息相關的大流行病期間使用網上訂餐服務。而且，提供網上訂餐服務的公司亦可藉此重新調整其營運，以能處理客戶的憂慮和應付消費行為的變化。

在大流行病肆虐的背景下，本研究為網上訂餐服務提供者給予了啟示，以便他們能在後疫情時代制定新的策略和營運方法，以拓展其業務和留住消費者。

This study provides theoretical and empirical insights into how firms can enhance their performance strategically and financially by integrating learning and transformational capabilities into research and development (R&D) activities based on the dynamic capabilities approach. To achieve this, the authors propose that transformational capabilities in R&D mediate the relationship between learning capabilities in R&D and competitive advantage.

Using a purposive sampling technique and standardized questionnaires, data were collected from 118 R&D and senior managers. To analyze the data, multiple regression analysis, along with SPSS PROCESS macro (Model 4), was used.

The results support the theoretical assumption that there are direct and indirect positive and significant effects between learning capabilities in R&D and competitive advantage as mediated by transformational R&D capabilities.

This study explores R&D from a dynamic capabilities perspective and suggests that learning capabilities should come first to enhance businesses’ competitive advantage. Furthermore, transformational R&D capabilities can explain the relationship between learning capabilities in R&D and competitive advantage. The authors recommend that researchers should investigate the contributions of R&D to promote competitive advantage.