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Injury Prevention and Rehabilitation Using Machine Learning for Athletes
No full textThis chapter explores the role of machine learning (ML) in injury prevention and rehabilitation for athletes. It examines how ML models can predict injuries by analysing diverse data sources, such as biomechanics, wearables, and medical records, and highlights the potential for personalized, data-driven injury prevention strategies. The chapter also addresses how AI-driven rehabilitation programs can adapt in real-time to optimize recovery and reduce the risk of re-injury. Key challenges, such as data privacy, model complexity, and the need for explainable AI, are discussed, along with future trends like the integration of wearable technology, federated learning, and virtual reality in rehabilitation. These innovations promise to transform sports medicine by making injury prevention more accurate and rehabilitation more efficient, ultimately enhancing athlete performance and longevity
Natural Language Processing for Capability Enhancement of RPA
No full textThis chapter elaborates on how NLP pushes RPA to tasks beyond rule-based activities, to areas involving complex processing such as document analysis, real-time decision-making, automation of customer services, etc. Key areas of enhancement pertain to the integration of sentiment analysis, chatbots, and intelligent extraction of data. Technical challenges and general implications of such integration are also covered in the chapter, like infrastructure requirements, avoiding biases, and responsible deployment. Changes in the workforce and equitable access to technology are two examples of managerial and social impacts. Understanding the potential and limitations of NLP-enhanced RPA will position an organization strategically to employ such technologies and optimize their operations toward innovation
Ex-Ante Competition Regulation of Digital Markets: rethinking regulatory autonomy under the gats non-discrimination obligation
Get PDFIn light of the growing complexities of data-driven digital markets, traditional ex-post competition laws are often insufficient, prompting many jurisdictions to adopt ex-ante regulatory frameworks. This paper examines the compatibility of ex-ante competition regulations, such as the European Union’s Digital Markets Act (DMA), with the General Agreement on Trade in Services (GATS), focusing on the potential violation of national treatment and most-favoured-nation obligations. The paper critiques the Appellate Body’s narrow approach in Argentina–Financial Services, which limits the consideration of regulatory intent in the GATS non-discrimination analysis. It advocates for a broader approach that integrates regulatory purpose in assessing ‘likeness’ and ‘less favourable treatment’. The paper concludes that such a perspective would ensure that ex-ante competition regulations, like the DMA, can be justified under GATS without undermining fair competition, while allowing states to regulate digital markets effectively
Leveraging Neuromarketing Technologies to Enhance Agile Marketing Strategies: a study on consumer behavior insights and real-time adaptation
No full textThis chapter explores the intersection of neuromarketing and agile marketing, emphasizing how leveraging neuromarketing technologies can enhance agile marketing strategies. By integrating insights from neuromarketing, which studies consumer brain responses and behaviors, with the flexible, real time approach of agile marketing, brands can achieve more personalized and effective campaigns. The chapter discusses key trends, including the role of Artificial Intelligence (AI), hyper-personalization, and real-time data analytics, as well as emerging technologies like Virtual Reality (VR) and Augmented Reality (AR). It also addresses challenges such as data privacy and the need for ethical practices. By examining these elements, the chapter provides a comprehensive overview of how combining neuromarketing and agile methodologies can drive innovation and improve consumer engagement in modern marketing
AI in Talent Scouting and Player Development: role of AI and machine learning in Identifying and nurturing talent in sports organizations
No full textArtificial Intelligence (AI) is revolutionizing sports organizations by enhancing talent scouting, player development, and operational efficiency. This chapter explores the transformative role of AI and machine learning in identifying and nurturing athletic talent. It delves into the evolution of talent scouting, the integration of AI technologies such as predictive analytics and wearable sensors, and the implications for player development. Key sections address AI-driven scouting methods, player performance optimization, and the ethical and legal considerations of using AI. The chapter also discusses practical and managerial challenges in implementing AI systems, including data privacy and bias mitigation. By examining future trends and innovations, it provides a comprehensive overview of how AI is reshaping sports management and offers insights into the ethical and practical considerations that organizations must navigate
Exploiting Cloud Computing in RPA
No full textThe integration of cloud computing with Robotic Process Automation (RPA) changes the very course of the way business is conducted, bringing about improvements in business operations efficiency, scalability, and adaptability. This chapter introduces the reader to the architecture of cloud-based RPA and discusses its benefits: that is, a lowered cost of operations and rapid deployments. Discussions include the rise of cloud-native RPA platforms and tools, as well as the importance placed on security and compliance in the cloud environment. On the other side, the chapter also analyses other issues and limitations organizations may face when implementing cloud-enabled RPA, including issues with data privacy and integration complexity among others. Outlook on cloud-enabled RPA is also reviewed in terms of implications for business and the workforce. Based on this, this chapter is specifically looking at various considerations in enabling insight into the means through which organizations can transform their processes through cloud-enabled RPA while propelling innovation into the future
Developing a Sustainability Index for Implementing Big Data Analytics in the Logistics Sector
Get PDFThis study identifies Critical Success Factors (CSFs) for implementing Big Data Analytics (BDA) for sustainable logistics practicesin the context of an emerging economy. Through a combination of literature review and experts' opinions, the study identifies 18CSFs essential for the effective application of BDA in the logistics sector. The identified CSFs are further classified into four major categories: Organizational Efforts (OE), Technological Capabilities (TC), Environmental Practices (EP), and Social Factors (SF)using TOE and stakeholders theory. With the help of experts, the identified CSFs are later ranked using the Best-Worst Method(BWM). A real-life Indian logistics company is studied to comprehend its existing operations, technological abilities, workforce competencies, and organizational environment. Further, the Graph Theory Matrix Approach (GTMA) is used to develop a sustainability index for analyzing the case study and expert remarks. The prioritization of CSFs under different categories can guide logistics companies in implementing BDA to achieve sustainability in logistics. The findings from the study reflect that OE and TC are the most important CSFs. The sustainability index value guides the evaluation of the current sustainability of the case company and assists in improving performance by benchmarking the best index values of the same industry. Logistics companies can learn from benchmarked companies and can adopt their strategies for achieving goals, simultaneously considering the ranking of identified CSFs for implementing BDA
Role of ICT and Education 5.0 in improving student engagement in distance and online education programs
No full textDistance education courses and programs in higher education continue to rise significantly, with increasing demand for online-access learning. Universities and colleges are striving to meet these demands, but concerns about student engagement and the legitimacy of distance learning persist. This study aims to explore the role of information technology and Education 5.0 in enhancing student engagement and learners’ perceptions of online education. Surveys of students who completed distance learning programs identified key engagement factors and challenges based on their experiences. Results show that ICT tools improve engagement and satisfaction. The study also revealed that successful online learning is influenced by course design, learner motivation, contact classes, and comfort with online technologies. An advanced student engagement model for management-related distance programs was developed that will help institutions in creating activities that foster deeper engagement, helping students and faculty become more actively involved in the learning ecosystem, rather than simply completing courses
Redefining Work and Outsourcing in the AI Era : Challenges and Opportunities
No full textArtificial Intelligence (AI) is transforming industries by enabling machines to mimic human cognitive functions like learning, reasoning, and problem-solving. This chapter explores AI's core concepts, including machine learning, deep learning, and neural networks, along with its applications in healthcare, finance, supply chain management, and automation. AI enhances efficiency, optimizes decision-making, and drives innovation. The study examines AI-driven automation's impact on business processes, focusing on robotic process automation (RPA) in streamlining operations. Using bibliometric data and topic modeling, it identifies key trends, gaps, and future directions in AI research. Findings suggest AI will continue shaping technological advancements, influencing industries and human interactions with intelligent systems. This chapter serves as a resource for researchers, policymakers, and professionals navigating AI's evolving landscape and future implications