The Arab Open University (AOU) is a non-profit university. The inception of AOU is a personal initiative by Prince Talal bin Abdulaziz Al Saud, the Chairman of the AOU Board of Trustees. In December 2000, Kuwait was designated to host the Headquarters of AOU.The AOU first phase was launched in October 2002 in three branches: Kuwait, Lebanon and Jordan. Branches in Bahrain, Egypt and Saudi Arabia were opened in 2003. Branches were opened in Oman in February 2008 and in Sudan in September 2013. In May 2015, AOU signed an agreement of opening a new branch in Palestine with the Palestinian Ministry of Higher Education.
This study examines the relationship between sustainability performance, eco-friendly production, and board characteristics and their impact on the green revenues of industrial firms. Using a panel dataset of 6084 firm-year observations from multiple countries (2018-2023), the study employs panel data analysis to assess these relationships. A dummy variable approach differentiates firms with green revenues from those without, followed by a segmented analysis of high- and low-performing green firms. The results indicate that sustainability performance positively influences green revenues, while board size and diversity have negative effects. Conversely, board expertise and sustainability compensations enhance green revenues. Interestingly, the findings highlight that high-performing green firms adopt proactive sustainability strategies, whereas low-performing firms rely on incentives. The study contributes to the literature by providing evidence from industrial firms across developed and developing countries, offering policy implications for sustainability governance and corporate incentives.
Purpose This study aims to investigate the effect of digital transformation on accounting conservatism, emphasizing the moderating role of industry type in an emerging market context. Focusing on Egypt, it examines whether differences in sectoral digital maturity shape how technological adoption influences conservative financial reporting practices, thereby highlighting the importance of institutional and operational heterogeneity across industries. Design/methodology/approach Drawing on signaling theory, agency theory and the resource-based view, the study employs a quantitative research design using panel data from non-financial firms listed on the Egyptian Exchange (EGX) over the period 2019–2023. The empirical analysis relies on multivariate regression models to test the direct effect of digital transformation and its interaction with industry characteristics. To address potential endogeneity concerns, the study applies two-step System GMM estimations, ensuring robust and reliable inferences. Findings The results indicate that digital transformation is positively associated with accounting conservatism. This relationship is significantly stronger in industries characterized by higher levels of digital maturity, suggesting that sectoral readiness conditions the extent to which firms can translate digital investments into improved governance outcomes. Overall, industry digital maturity amplifies the conservatism-enhancing effect of digital transformation. Practical implications The findings suggest that managers can strengthen conservative reporting and internal control systems through targeted digital investments. Regulators and auditors should account for industry-specific digital maturity when evaluating reporting quality and disclosure risk. Firms in technology-intensive sectors may gain particular benefits from prioritizing advanced digital capabilities aligned with governance objectives. Social implications By enhancing transparency and reducing information asymmetry, digital transformation supports accountability and investor confidence, which is especially critical in emerging markets with developing institutional frameworks. Originality/value This study contributes to the accounting literature by introducing industry type as a moderating factor in the digital transformation–conservatism nexus and by employing advanced GMM techniques. It offers context-sensitive evidence from Egypt and extends conservatism research through the use of digitalization measures derived from content analysis.
This study examines students’ perceptions of the rebranding initiative at Arab Open University (AOU), Kuwait Branch, with a particular focus on the newly introduced logo and visual identity. Using a cross-sectional quantitative survey design, data were collected from undergraduate students across different faculties. The findings indicate generally positive evaluations of the new logo in terms of modernity, professionalism, visual appeal, and alignment with institutional values. Inferential analysis further revealed a strong positive association between overall logo evaluation and students’ recommendation intention (r = .80, p < .001), with regression results showing that logo perception explained approximately 65
Accurate opponent modeling is critical for effective automated negotiation, enabling agents to adapt their strategies based on the type of opponent. This study investigates machine learning approaches for classifying negotiation agent strategies from offer sequences across three scenarios: time-dependent agents following predetermined concession functions, strategic agents adapting to opponent behavior with deadline-only termination, and strategic agents with realistic termination through mutual agreement or deadline expiration. We systematically evaluate four algorithms-Naive Bayes, Random Forest, Support Vector Machines, and Neural Networks- on a number of simulated negotiations, comparing classification performance with and without temporal feature augmentation. A key contribution of this work is the introduction of temporal feature augmentation, where quarterly concession patterns and variance metrics are used to capture adaptive negotiation behavior that raw offer sequences alone cannot reveal. The augmented features encode temporal adaptation characteristics that distinguish Boulware, Linear, Conceder, and strategic negotiation behaviors. Feature augmentation produced statistically significant improvements in 7 of 12 model-scenario combinations, with the most notable gains observed in strategic agent identification.
Side-Channel Analysis (SCA) utilizing Deep Learning has demonstrated significant potential in recovering secret keys from cryptographic implementations. However, the efficiency of these attacks is often severely compromised by hardware countermeasures such as temporal shuffling, which desynchronizes leakage traces. Existing non-profiled collision attacks successfully mitigate shuffling, but often rely on a "Grey-Box" threat model, requiring prior knowledge of the shuffle permutation to align traces before analysis. This study presents a Global Average Pooling Convolutional Neural Network (GAP-CNN) designed to exploit side-channel collisions in a strict Black-Box setting. By integrating a translation-invariant GAP layer, the proposed architecture forces the network to learn the presence of leakage signatures regardless of their temporal location, effectively neutralizing the shuffling countermeasure end-to-end without pre-processing. The methodology is evaluated on the DPA Contest v4.2 dataset, a highly protected AES-128 implementation. The empirical results demonstrate that the proposed Black-Box approach successfully recovers a majority of the target bytes, outperforming previous Grey-Box baselines. Furthermore, the study demonstrates strong cross-byte portability and crossdataset robustness against masking countermeasures (ASCAD), confirming the existence of exploitable leakage clusters that persist despite advanced randomization.
