Purpose: The model of CSR devised by the American scientist ″Archie B. Carroll in 1991″ - which became well- known in academic circles as Carroll′s CSR pyramid, has been criticized by academics and researchers. The researcher firmly believes that one of the most important reasons that led to the emergence of these criticisms was Carroll's adoption of the idea of the pyramid as a form to illustrate his idea of CSR. The content of the pyramid does not match the accompanying explanations given by Carroll. Carroll's CSR pyramid does not reflect the concept of simultaneous CSRs. Also, Carroll's CSR pyramid does not reflect the logical interaction and interrelationships among CSRs. It is also noted that, Carroll's pyramid reflects the expectations of stakeholders and does not reflect the expectations of companies from their commitment to their social responsibilities. This study aimed to design a model for CSR, in which the content of its figure matches the explanations attached to it, and reflects the concept of simultaneous CSRs, in addition to the logical interaction and the interrelationship among those responsibilities. Method: The researcher used the concept of precedence as an approach to explain the logical interaction and interrelationships among CSRs. The idea of precedence is clearly found only in the railway industry. The interaction of CSRs with each other can be likened to the interaction of the components of a classic train (a cockpit and coaches). Therefore, the researcher adopted the idea of the classic train as an innovative model to address some of the shortcomings of Carroll ′s pyramid. Battal's CSR train as an innovative model- in this study, can reflect the notion of simultaneous CSRs, the logical interaction and interrelationships among CSRs, and expectations and aspirations of both stakeholders and companies. Result: By adopting the idea of the classic train work and its components, this study was able to produce a model of CSR as an alternative model for Carroll′s pyramid (1991). Originality/Value: Battal ′s CSR train is an educational model that is designed to address some of the shortcomings of Carroll's CSR pyramid "as a figure and content." The content of Battal′s CSR train matches the attached explanation, and this will help to understand how CSRs interact with each other, as well as the concept of simultaneous CSRs. It is an enrichment of the thought of CSR.

M&A is a strategic concept of business growth through consolidation, gaining market access, increasing strategic positions, and increasing operational efficiency. To understand the dynamics of M&A, this paper looks at aspects such as targeted firm identification, evaluation, bidding for the target firm, and post-acquisition integration. All forms of M&A, including horizontal, vertical, conglomerate, and acquisitions, are discussed in terms of goals and values, including synergy, cost reduction, competitive advantages, and access to better technology. However, issues such as cultural assimilation, adhesion to regulations, and calculating an inaccurate value are also resolved. The paper then goes deeper to provide insight into how predictive analytics applies to M&A, using ML to improve decision-making with forecasting benefits. Including healthcare, education, and construction industries, the presented predictive models using regression analysis, neural networks, and ensemble techniques help to make decisions. Through time series and real-time data, PDA enables sound M&A strategies, effective risk management and smooth integration.

Innovations in computing and communication technologies are reshaping finance. The seismic changes are casting uncertainty about the future of financial services. On one hand, fintech evangelists project a rosy future, asserting that the fast-moving algorithms can deliver low-cost financial services intuitively, customized to meet robust consumer expectations. On the other hand, many finance veterans fret that the traditional banking model could disintermediate, bleeding banks via a ‘death by a thousand cuts’, reducing them to passive portfolio holders with no direct customer relationship, eclipsed by digital giants which use their enormous treasure troves of customer data to offer banking as an added service with nearly free cost. Amidst the upbeat technological promises and apocalyptic forebodings, there are two constant, mostly agreed-upon, truths. The first is the vital importance of data. Advances in the internet, cloud computing, and record-keeping technologies are producing an ‘exponential growth in the volume and detail of data’. Some of this big data are personal information. Smartphones are deployed in almost all developed and emerging economies, serving as little spies; tracking, recording location histories, social networks, and app usage of their unsuspecting owners; often with a great degree of precision. ‘People are walking data-factories’ in this ‘mobile digital society’. Data are the fermentation of these global exchanges, electronic commerce and communication, and financial transactions. To just take Facebook as an example, it shares 30 million people a day through updates and posts, hosting personal information on 2.23 billion users. To the alarm of the uninformed public, much of this information is available for commercial harvest. The second constant is the rise of intelligent solutions. Consumers today—be it disclosed or not—are fed tailored clothes, music, film, holiday packages—almost anything you like, notably dynamic pricing, varying in accordance with individual profiles, or personalized search results. The availability of powerful computers has enabled comparable applications that are intended to make the system more responsive to their customer profiles and desires, or to capitalize competitive business possibilities. Such changes will transform the financial industry and occupy a prominent position among the mechanisms of policy competition, reshaping the way in which financial services are bestowed and led on the demand side.

Machine Learning (ML) and Artificial Intelligence (AI) are having an increasingly transformative impact on all industries and are already used in many mission-critical use cases in production, bringing considerable value. Data engineering, which combines ETL pipelines with other workflows managing data and machine learning operations, is also significantly impacted. The Intelligent Data Engineering and Automation framework offers the groundwork for intelligent automation processes. However, ML/AI are not the only disruptive forces; new Big Data technologies inspired by Web2.0 companies are also reshaping the Internet. Companies having the largest Big Data footprints not only provide applications with a Big Data operational model but also source their competitive advantage from data in the form of AI services and, consequently, impact the cost/performance equilibrium of ETL pipelines. All these technologies and reasons help explain why the traditional ETL pipeline design should adapt to current and emerging technologies and may be enhanced through artificial intelligence.