scholarly journals Introductory Chapter: Advanced Analytics and Artificial Intelligence Applications

Author(s):  
Ali Soofastaei
2021 ◽  
Author(s):  
Daniel Ludwig

This work examines family and non-family businesses and their use of personnel practices in times of crisis. The detailed questions that it addresses are, firstly, whether these types of businesses, in connection with crisis indicators, exert an influence on the use of personnel practices. Secondly, the study clarifies whether there are differences between family and non-family businesses and to what extent this is influenced by varying crisis indicators. The author previously worked as a research assistant, during which time, in addition to the topics covered in this work, he was primarily concerned with quantitative research methods. Since completing his dissertation, he has been working in the field of advanced analytics and artificial intelligence.


Author(s):  
Yousif Abdullatif Albastaki

There is a paradigm shift in the financial services industry. Combined with ever-changing customer expectations and preferences, emerging technologies such as artificial intelligence (AI), machine learning, the internet of things (IoT), and blockchain are redefining how financial institutions deliver services. It is an enormous task to remain competitive in this ever-changing environment. Financial institutions see FinTech as a major part of the digital future, and as proof of this, since 2015, financial institutions have invested over US$ 27 billion in FinTech and digital innovation. This chapter is an introductory chapter that explores FinTech in the literature. It focuses on how FinTech is reshaping the financial industry by describing FinTech phases and development process. The financial products and services using FinTech are also described with a highlight on Islamic FinTech. The chapter finally concludes by describing the future of FinTech.


Author(s):  
Jyh-An Lee ◽  
Reto M Hilty ◽  
Kung-Chung Liu

This introductory chapter provides an overview of the relationship between artificial intelligence (AI) and intellectual property (IP). While human beings have used various instruments and technologies to create and innovate, they themselves have been the main driving force of creativity and innovation. AI puts that into question, raising numerous challenges to the existing IP regime. Traditionally, the “intellectual” part of “intellectual property” refers to human intellect. However, since machines have become intelligent and are increasingly capable of making creative, innovative choices based on opaque algorithms, the “intellectual” in “intellectual property” turns out to be perplexing. Existing human-centric IP regimes based on promoting incentives and avoiding disincentives may no longer be relevant—or even positively detrimental—if AI comes into play. Moreover, AI has sparked new issues in IP law regarding legal subjects, scope, standards of protection, exceptions, and relationships between actors.


Author(s):  
Zhaohao Sun ◽  
Andrew Stranieri

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.


Author(s):  
Andrea M. Prud’homme ◽  
John V. Gray ◽  
Andrew C. Barley

This chapter looks at emerging technologies and their use in supply management processes as a means to improve effectiveness through improved speed and accuracy, at a reduced cost. Many technologies are finding their way into supply management, with differing levels of penetration and application and with mixed results. It may be challenging for supply management professionals to understand how, when, and where these technologies are likely to yield positive results. This chapter reviews several technologies, including artificial intelligence/machine learning, big data/advanced analytics, blockchain, cloud computing, conversational things (e.g., chatbots), immersive technologies (e.g., virtual and augmented reality), and robotic process automation. Findings indicate that the primary advantages are achieved by improving current processes and workflows, rather than that these technologies are currently disrupting or will fundamentally change supply management. Another important finding is the importance of “clean data” inputs, something that artificial intelligence can help with and that is foundational for successful robotic process automation.


Author(s):  
Ulrich Lichtenthaler

Many companies have recently started digital transformation initiatives, and they now increasingly focus on artificial intelligence (AI). By means of smart algorithms and advanced analytics, firms attempt to leverage some of the results of their ongoing digital transformation initiatives, for example with regard to data about their established business operations. A conceptual framework underscores the need for combining data management and AI initiatives in order to ensure a firm's digital readiness and to realize digital business opportunities subsequently. An overview of recent trends further illustrates how different companies respond to these managerial challenges. This paper contributes to the literature on digitalization, AI, and ‘integrated intelligence' by highlighting the role of AI for leveraging data from digital transformation initiatives. Specifically, the use of AI applications helps companies to turn data into valuable knowledge and intelligence. In addition, this paper provides new knowledge about achieving superior performance in the digital economy.


Author(s):  
John O. McGinnis

This introductory chapter analyzes the central political problem of our time, namely how to adapt democracy to the acceleration of the information age. Modern technology creates a supply of new tools for improved governance, but it also creates an urgent demand for putting these tools to use. We need better policies to obtain the benefits of innovation as quickly as possible and to manage the social problems that speedier innovation will inevitably create—from pollution to weapons of mass destruction. Our task is to place politics progressively within the domain of information technology—to use its new or enhanced tools, such as empiricism, information markets, dispersed media, and artificial intelligence, to reinvent governance. An overview of the subsequent chapters is also presented.


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