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Cloud computing originated in central data centers that are connected to the backbone of the Internet. The network transport to and from a distant data center incurs long latencies that hinder modern low-latency applications. In order to flexibly support the computing demands of users, cloud computing is evolving toward a continuum of cloud computing resources that are distributed between the end users and a distant data center. The purpose of this review paper is to concisely summarize the state-of-the-art in the evolving cloud computing field and to outline research imperatives.

The authors identify two main dimensions (or axes) of development of cloud computing: the trend toward flexibility of scaling computing resources, which the authors denote as Flex-Cloud, and the trend toward ubiquitous cloud computing, which the authors denote as Ubi-Cloud. Along these two axes of Flex-Cloud and Ubi-Cloud, the authors review the existing research and development and identify pressing open problems.

The authors find that extensive research and development efforts have addressed some Ubi-Cloud and Flex-Cloud challenges resulting in exciting advances to date. However, a wide array of research challenges remains open, thus providing a fertile field for future research and development.

This review paper is the first to define the concept of the Ubi-Flex-Cloud as the two-dimensional research and design space for cloud computing research and development. The Ubi-Flex-Cloud concept can serve as a foundation and reference framework for planning and positioning future cloud computing research and development efforts.

This study seeks to examine the relationship between macroeconomic shock indicators, namely geopolitical risk (GPR), global economic policy uncertainty (GEPU) and financial stress (FS), and returns as well as volatilities on seven carefully selected stock markets in Africa. Specifically, the study intends to unravel the co-movement and interdependence between the respective macroeconomic shock indicators and each of the stock markets under consideration across time and frequency.

This study employed wavelet coherence approach to examine the strength and stability of the relationships across different time scales and frequency components, thereby providing valuable insights into specific periods and frequency ranges where the relationships are particularly pronounced.

The study found that GEPU, Financial Stress (FS) and GPR failed to induce significant influence on African stock market returns in the short term (0–4 months band), but tend to intensify in the long-term band (after 6th month). On the contrary, stock market volatilities exhibited strong coherence and interdependence with GEPU, FSI and GPR in the short-term band.

This study happens to be the first of its kind to comprehensively consider how the aforementioned macro-economic shock indicators impact stock markets returns and volatilities over time and frequency. Further, none of the earlier studies has attempted to examine the relationship between macro-economic shocks, stock returns and volatilities in different crisis periods. This study is the first of its kind in to employ data spanning from May 2007 to April 2023, thereby covering notable crisis periods such as global financial crisis (GFC) and the COVID-19 pandemic episodes.

Despite the growing recognition of the complex interplay between macroeconomic shock indexes and stock market dynamics, there is a significant research gap concerning their interconnectedness and return spillovers in the context of the African stock market. This leaves much to be desired, given that the financial market in Africa is arguably one of the most preferred destinations for hedge and portfolio diversification (Alagidede, 2008; Anyikwa and Le Roux, 2020). Further, like other financial markets across the globe, the increased capital flow, coupled with declining information asymmetry in Africa, has deepened intra and inter-sectoral integration within and across national borders. This has, thus, increased the susceptibility of financial markets in Africa to spillover of shocks from other sectors and jurisdictions. Additionally, while previous studies have investigated these factors individually (Asafo-Adjei et al., 2020), with much emphasis on developed markets, an all-encompassing examination of spillovers and the connectedness between the aforementioned macroeconomic shock indexes and stock market returns remains largely unexplored. This study happens to be the first to consider the impact of each of the indexes on stock returns in Africa, with evidence spanning from May 2007 to April 2023, covering notable global crisis episodes such as the Global Financial Crisis (GFC), the COVID-19 pandemic and the Russia–Ukraine war.

This study employs the novel quantile vector autoregression (QVAR) model, making it the first of its kind in literature. By applying the QVAR, the study captures the potential nonlinear and asymmetric relationship between stock returns and the factors of interest across different quantiles, i.e. bearish, normal and bullish market conditions. Thus, the approach allows for a more accurate and nuanced examination of the tail dependence and extreme events, providing insights into the behaviour of the variables under extreme events.

The study revealed that connectedness and spillovers intensified under bearish and bullish market conditions. It was also observed that, among the macroeconomic shock indicators, FSI exerted the highest influence on stock returns in Africa in both bullish and normal market conditions. Across the various market regimes, the Egyptian Exchange (EGX) and the Nairobi Stock Exchange (NSE) were net receiver of shocks.

This study happens to be the first to consider the impact of each of the indexes on stock returns in Africa, with evidence spanning from May 2007 to April 2023, covering notable global crisis episodes such as the GFC, the COVID-19 pandemic and the Russia–Ukraine war. On the methodology front, this study employs the novel QVAR model, making it one of the few studies in recent literature to apply the said method.

This paper aims to use Bayesian network (BN) methodology complemented by machine learning (ML) and what-if analysis to investigate the impact of digital technologies (DT) on logistics service quality (LSQ), employing the service quality (SERVQUAL) framework.

Using a sample of 244 Italian firms, this study estimates the probability distributions associated with both DT and SERVQUAL logistics, as well as their interrelationships. Additionally, BN technique enables the application of ML techniques to uncover hidden relationships, as well as a series of what-if analyses to extract more knowledge.

The results show that the average probability of firms investing in DT for analytics (DTA) is higher than that of investing inDT for immersive experiences (DTIE). Furthermore, adopting both offers only a moderate likelihood of successfully implementing SERVQUAL logistics. Additionally, certain technologies may not directly influence some SERVQUAL dimensions. The application of ML reveals hidden relationships among technologies, enhancing the predictions of SERVQUAL logistics. Finally, what-if analyses provide further insights to guide decision-making processes aimed at enhancing SERVQUAL logistics dimensions through DTA and DTIE.

This research delves into the influence of DTIE and DTA on SERVQUAL logistics, thereby filling a gap in the existing literature in which no study has explored the intricate relationships between these technologies and SERVQUAL dimensions. Methodologically, we pioneer the integration of BN with ML techniques and what-if analysis, thus exploring innovative techniques to be used in logistics and supply-chain studies.

This article explores the emergence of organizational digital transformation in the rapidly advancing technological era. It discusses the origins, driving forces, strategies, challenges and broader implications.

The article employs a scoping review methodology that synthesizes knowledge from the existing literature, research, case studies and other relevant sources.

The findings underscore the pivotal role that organizational digital transformation plays in an era of relentless technological advancement. Leadership, organizational culture and technological enablers are identified as crucial drivers of innovation and competitiveness within organizations. The article also emphasizes ethics as a crucial element of digital transformation, focusing in particular on concerns about data privacy and the morality of artificial intelligence. Additionally, the author talks about anticipated future trends that are anticipated to influence the future of digital transformation, such as the growing influence of artificial intelligence, the trend toward hyper-personalization and the emergence of quantum computing.

The assessment has failed to provide recommendations for the actual implementation because it has mainly concentrated on conceptual and strategic aspects. Furthermore, it does not clearly define the criteria for choosing real-world examples, which limits the representation of the different industries, size ranges of organizations and outcomes associated with digital transformation.

The article stresses the significance of paying attention to the forces driving digital transformation while navigating ethical and societal concerns. In addition to highlighting the importance of anticipating future trends for strategic planning in the rapidly changing digital landscape, it emphasizes the advantages as incentives for organizations to invest in digital initiatives.

The investigation demonstrates how technology contributes to progress while posing complex ethical and change management issues. In light of increased connectivity, data analytics and artificial intelligence, it highlights the crucial need for societal adaptability and highlights the crucial role that cooperative human–machine coexistence plays in responsible development and transformative societal evolution.

The article stands out because it examines organizational digital transformation in-depth while considering its historical roots, ethical implications and future prospects. It is a priceless contribution to the field because real-world case studies and a scoping review provide a distinctive viewpoint and a comprehensive view of the effects of digital transformation on organizations and society.

This study aims to quantify and analyse the dynamics of land use and land cover (LULC) changes over three decades in the rapidly urbanizing city of Abha, Saudi Arabia, and to assess urban growth using Morphological Spatial Pattern Analysis (MSPA).

Using the Support Vector Machine (SVM) classification in Google Earth Engine, changes in land use in Abha between 1990 and 2020 are accurately assessed. This method leverages cloud computing to enhance the efficiency and accuracy of big data analysis. Additionally, MSPA was employed in Google Colab to analyse urban growth patterns.

The study demonstrates significant expansion of urban areas in Abha, growing from 62.46 km² in 1990 to 271.45 km² in 2020, while aquatic habitats decreased from 1.36 km² to 0.52 km². MSPA revealed a notable increase in urban core areas from 41.66 km² in 2001 to 194.97 km² in 2021, showcasing the nuanced dynamics of urban sprawl and densification.

The novelty of this study lies in its integrated approach, combining LULC and MSPA analyses within a cloud computing framework to capture the dynamics of city and environment. The insights from this study are poised to influence policy and planning decisions, particularly in fostering sustainable urban environments that accommodate growth while preserving natural habitats. This approach is crucial for devising strategies that can adapt to and mitigate the environmental impacts of urban expansion.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

This paper explores the convergence of Education 4.0 and Industry 4.0 and presents a Twin Peaks model for their seamless integration.

A high-level literature review is conducted to identify and discuss the important challenges and opportunities offered by both Education 4.0 and Industry 4.0. A novel Twin Peaks model is devised for the convergence of these domains and to cope with the challenges effectively.

The proposed Twin Peak model for the convergence of Education 4.0 and Industry 4.0 suggests that the development of these two domains is interdependent. It emphasizes ethical considerations, inclusivity and understanding the concerns of stakeholders from both education and industry. We have also explained how continuous incremental adaptation within the proposed Twin Peaks model might assist in addressing concerns of one sector with the opportunities of the other.

First, Education 4.0 and Industry 4.0 are reviewed in terms of opportunities and challenges they present. Second, a novel Twin Peaks model for the convergence of Education 4.0 and Industry 4.0 is presented. The proposed discovers that the convergence is adaptive, iterative and must be ethically sound while considering the broader societal implications of the digital transformation. Third, this study also acts as a torch-bearer for the necessity for more research of this kind to guarantee that our educational ecosystem is adaptable and capable of producing the skills required for success in the era of IR4.0.

In the new digital age, enterprises are facing an increasing global competition. In this paper, we first examine how Information Technology (IT) can play an important role in giving significant competitive advantage in the modern enterprises. The business value of IT is examined, as well as the limitations and the trade-offs that its applicability faces. Next, we present the basic principles for a successful IT strategy, considering the development of a long-term IT renovation plan, the strategic alignment of IT with the business strategy, and the adoption of an integrated, distributed, and interoperable IT platform. Finally, we examine how a highly functional and efficient IT organization can be developed.

Our methodological approach was based to the answers of the following questions: 1. Does IT still matter? 2. What is the business value created by IT along with the corresponding limitations and trade-offs? 3. How could a successful IT Strategy be build up? 4. How could an effective? T planning aligned with the business strategy be build up? 5. How could a homogenized and distributed corporate IT platform be developed? and finally, 6. How could a high-performance IT-enabled enterprise be build up?

The enterprises in order to succeed in the new digital area need to: 1. synchronize their IT strategy with their business strategy, 2. formulate a long-term IT strategy, 3. adopt IT systems and solutions that are implemented with elasticity, interoperability, distribution, and service-orientation. 4. keep a strategic direction towards the creation of an exceptional organization based on IT.

This paper is original with respect to the integrated approach the overall problem is examined. There is a prototype combined investigation of all perspectives for an effective enforcement of IT in a way that causes acceleration in competitive advantage when conducting business.

The purpose of this systematic review is to critically analyze pharmaceutical industry case studies on the implementation of Lean 4.0 methodologies to synthesize key lessons, benefits and best practices. The goal is to inform decisions and guide investments in related technologies for enhancing quality, compliance, efficiency and responsiveness across production and supply chain processes.

The article utilized a systematic literature review (SLR) methodology following five phases: formulating research questions, locating relevant articles, selecting and evaluating articles, analyzing and synthesizing findings and reporting results. The SLR aimed to critically analyze pharmaceutical industry case studies on Lean 4.0 implementation to synthesize key lessons, benefits and best practices.

Key findings reveal recurrent efficiency gains, obstacles around legacy system integration and data governance as well as necessary operator training investments alongside technological upgrades. On average, quality assurance reliability improved by over 50%, while inventory waste declined by 57% based on quantified metrics across documented initiatives synthesizing robotics, sensors and analytics.

As a comprehensive literature review, findings depend on available documented implementations within the search period rather than direct case evaluations. Reporting bias may also skew toward more successful accounts.

Synthesized implementation patterns, performance outcomes and concealed pitfalls provide pharmaceutical leaders with an evidence-based reference guide aiding adoption strategy development, resource planning and workforce transitioning crucial for Lean 4.0 assimilation.

This systematic assessment of pharmaceutical Lean 4.0 adoption offers an unprecedented perspective into the real-world issues, dependencies and modifications necessary for successful integration, absent from conceptual projections or isolated case studies alone until now.