Faculty of Management

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Browsing Faculty of Management by Subject "Artificial Intelligence (AI)"

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    A bibliometric analysis of a four-construct framework: internationalization, business strategies, digital technologies (AI, blockchain, cloud computing), and digital transformation
    (Tritonic, 2023-10) Biea, Elena Adriana
    In the dynamic business landscape of 2023, the rapid adoption of digital tools and technologies, including ChatGPT 4 and advanced AI software, is reshaping companies' operations and strategies. This article focuses on the profound impact of these advancements on internationalization efforts. The integration of AI applications empowers businesses to enhance efficiency, optimize decision-making, and drive innovation, particularly in the context of global expansion. Cloud computing plays a pivotal role by offering scalable infrastructure and adaptability to changing market conditions. Additionally, blockchain technology ensures secure and transparent cross-border transactions, bolstering businesses' confidence in international markets. Through real-world case studies and research insights, this article highlights the transformative influence of AI, cloud computing, and blockchain on business strategies, emphasizing their potential to drive operational excellence, foster innovation, and facilitate global expansion. It provides valuable guidance for navigating the digital transformation era and seizing international opportunities. This research employs an extensive literature review to examine the intersection of internationalization, business strategies, digital technologies (AI, blockchain, cloud computing), and digital transformation. Additionally, a bibliometric analysis utilizing VOSviewer is conducted to visualize and analyze the co-occurrence of keywords related to these four concepts, providing insights into research trends and relationships within the field.
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    AI & Cybersecurity – connection, impacts, way ahead
    (Pro Universitaria Publishing House, 2024-07-16) Sarcea (Manea), Oana Alexandra
    Artificial intelligence (AI) and cybersecurity have a strong connection and impact each other in different ways. An overview is related to the following categories: detection and prevention, automated response, adversarial AI, data protection, risk assessment, privacy concerns. Looking ahead, the linkage between AI and cybersecurity will continue to evolve. Key areas of focus include: development of AI-driven security solutions, ethical AI-use, enhanced threat intelligence, human-machine collaboration. Overall, AI holds tremendous potential to revolutionize cybersecurity, but it also presents new challenges that must be addressed to ensure a secure and resilient digital environment. The relationship between AI and cybersecurity is multifaceted. AI technologies are increasingly being employed both to enhance cybersecurity defenses and to facilitate cyberattacks. In addition to the above key points, is to mention the cybersecurity skills gap: with the growing complexity of cyber threats, there is a shortage of skilled cybersecurity professionals. AI technologies can help bridge this gap by automating routine tasks and augmenting the capabilities of existing security teams. Behavioral analysis is another important element: AI-powered systems can analyze user and network behavior to identify anomalies that may indicate a security breach. By understanding typical behavior, AI can detect deviations that might signal an attack. Overall, the relationship between AI and cybersecurity is complex and evolving. While AI offers significant opportunities to enhance cybersecurity defenses, it also presents new challenges and risks that must be addressed. Ongoing research and development are essential to stay ahead of emerging threats in this rapidly evolving landscape.
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    Artificial Intelligence in modern policing : understanding the multifaceted impacts
    (Hungarian Association of Police Science, 2024) Ioan, Alexandru
    This article comprehensively examines the multifaceted impact of artificial intelligence (AI) on modern law enforcement, considering stakeholders, societal implications, and results analysis. In an era of rapid AI advancement, law enforcement increasingly leverages AI for enhanced operational efficiency, public safety, and addressing challenges. The paper begins with predictive policing, using machine learning to analyze historical crime data and predict hotspots, exploring its impact on stakeholders and societal implications. The role of AI in investigative processes, particularly facial recognition aiding suspect identification and forensics, is assessed with attention to privacy, accuracy, and transparency. AI's role in digital forensics and cybercrime detection is discussed, emphasizing stakeholder involvement, ethical concerns, and the balance between privacy and security. Integration of AI in police operations and resource management is examined, focusing on AI-driven decision-making, optimized patrols, resource allocation, and emergency responses.
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    Artificial systems and models for risk covering operations
    (EduSoft Publishing, 2017-09-11) Treapăt, Laurențiu Mihai; Gheorghiu, Anda
    Mainly, this paper focuses on the roles of artificial intelligence based systems and especially on risk-covering operations. In this context, the paper comes with theoretical explanations on real-life based examples and applications. From a general perspective, the paper enriches its value with a wide discussion on the related subject. The paper aims to revise the volatilities’ estimation models and the correlations between the various time series and also by presenting the Risk Metrics methodology, as explained is a case study. The advantages that the VaR estimation offers, consist of its ability to quantitatively and numerically express the risk level of a portfolio, at a certain moment in time and also the risk of on open position (in titles, in FX, commodities or granted loans), belonging to an economic agent or even individual; hence, its role in a more efficient capital allocation, in the assumed risk delimitation, and also as a performance measurement instrument. In this paper and the study case that completes our work, we aim to prove how we can prevent considerable losses and even bankruptcies if VaR is known and applied accordingly. For this reason, the universities in Romania should include or increase their curricula with the study of the VaR model as an artificial intelligence tool. The simplicity of the presented case study, most probably, is the strongest argument of the current work because it can be understood also by the readers that are not necessarily very experienced in the risk management field.
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    Leveraging neuroleadership and adopting AI to improve human capital development in IT&C business organizations
    (Bucharest University of Economic Studies, Faculty of Management, 2024-08) Stăneiu, Roxana Maria; Stratone, Mădălina Elena; Dabija, Dan Cristian; Mititean, Pompei
    This paper aims to explore how business organisations in the Romanian IT&C sector strategically implement neuro-leadership principles to accelerate the adoption and implementation of artificial intelligence (AI) technologies, thus enhancing human capital development and promoting sustainable competitive advantages. Applying the focus group method, dynamic discussions were held with 10 representatives of Romanian IT&C companies to highlight how these business organisations strategically implement neuroleadership principles and adopt AI technologies in human capital management. The results of the research, divided into four main themes, reveal the importance of neuroleadership in improving employee competencies as well as overcoming the challenges associated with implementing AI in organisations. Research provides new and relevant insights for leaders in business organisations to effectively adapt to the complexity of technological change caused by AI adoption in human resource management. At the same time, the results reveal the importance of fostering a culture of continuous learning and development to harness the potential benefits of AI, while mitigating the risks associated with this approach. The originality of the paper is derived from the merging of the fields of leadership, neuroscience, and AI, thus providing a comprehensive understanding of the dynamics shaping organisational responses to technological innovation.
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    Looking into the antecedents of the transformation of IT jobs : a country-based perspective
    (Faculty of Management (SNSPA), 2022) Moldoveanu, Ion
    Technology is changing at an exponential rate. As a result, it does have a disruptive life on society and our lives. It changes our personal lives, socialization, and interaction with people and businesses. Technology has changed and does continue to change the way we work. Along with the four industrial revolutions, many jobs have disappeared, more jobs have been created, and almost every job was transformed by automation. The 4th industrial revolution leading to Industry 4.0 is powered by artificial intelligence, robotics, Internet of things. The Information Technology (IT) industry and IT professionals primarily drive this transformation. While information technology specialists contribute with the technology they build to change their world, technology is transforming the profession responsible for this transformation. The paper looks at how digital transformation impacts the transformation of IT jobs, how government policies and managerial strategies impact the transformation of IT jobs and how employees and organizations are responding with investment in skills development. The research relies on a questionnaire-based survey with 132 Romanian IT professionals, students and computer science professors representing small and large organizations. Seven out of the nine hypotheses were supported by the data, confirming that digital transformation impacts the transformation of jobs, particularly IT jobs, and that this drives the need to build new technical and soft skills.
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    The impact of knowledge vulnerabilities on knowledge risks
    (Bucharest University of Economic Studies, Faculty of Management, 2024-03) Brătianu, Constantin; Bejinaru, Ruxandra; Ursache, Vlad-Mihai
    The purpose of this paper is to explore the impact of knowledge vulnerabilities on knowledge risks and to analyse their intricate connections within the knowledge management systems. It is a new topic in knowledge management which is requested by the accelerated process of digitalization and the exponential development of the AI programs. There is a lack of research in this area and efforts should be made to bridge the knowledge gap. The method we use is based on a critical analysis of knowledge vulnerabilities and knowledge risks within a generic organization and of designing tree-diagrams able to illustrate the connections between them, and the possible adverse consequences for the firm’s performance. The method of tree diagrams has been extensively used in risk analysis of the complex technological systems of airplanes and nuclear reactors. Also, it is used in the risk management applied to other domains like climate change and earthquakes. It is the first paper to investigate these phenomena and to conceptualize such kind of diagrams.
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    What managers of SMEs in the CEE region should know about challenges of artificial intelligence’s adoption? : an introductive discussion
    (Military University of Technology, Warsaw, 2022) Vidu, Cristian Mihai; Pînzaru, Florina; Mitan, Andreea
    The next step of the digital transformation is to adopt artificial intelligence (AI), even if the technology itself is still evolving. Nevertheless, discussions on AI pros and cons are vivid: managers are in the frontline of the decision-making on the best ways for such transitions. If corporations are already familiar with AI, at least partially for some processes, small and medium enterprises (SMEs) face a double pressure: their inequal degree of digital maturity, as well as the everyday constraints on how to increase competitiveness. CEE SMEs, in particular, find themselves in a complex framework, and the adoption of AI, even if challenging, could be one of the solutions to advance in terms of efficiency. Nevertheless, risks in such an approach must be carefully considered. Based on a semi-structured literature review, this opinion paper discusses the main risks that managers of SMEs in the CEE region should understand regarding AI and the consequent challenges of adopting it in business. Final considerations and future research discussions conclude the paper.