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This book has these two research questions need to be answered? (1) Can apply (AI) learning machine predict consumer behaviors? (2) Can (AI) learning machine replace human marketing research method, e.g. survey or human psychological and micro and macro economic methods to predict consumer behaviors more accurate? Nowadays, many businessmen or marketing research professional hope to apply different methods to predict consumer behaviors in order to know what will be future market activities and market changes to help them to choose to implement what kinds of marketing strategies more accurately. The methods include economic environmental change prediction method, consumer individual psychological change prediction method, micro or macro behavioral economic environmental change prediction method, marketing environmental change prediction method etc. different kinds of methods which can be applied to predict how consumer behavioral changes to influence whose behavioral consumption to the manufacturer products sale within one to two years short term or three to five years middle term, even above five years long term business plans. Hence, if the product manufacturers can apply the most suitable consumer behavioral prediction method to predict how consumers' choice will be changed to influence their products sale easily. It will have more beneficial intangible and tangible advantages to achieve the their product easier sale aim to ensure their businesses' future market share to be increased more easier to their countries' choice target sale markets. Otherwise, if they applied the inaccurate consumer behavioral prediction methods to predict how their consumers' behavioral changes wrongly. Then, it will influence their market shares to be same level, even it will decrease their market shares, when their consumer behavioral prediction inaccurately. In my this book first part, I concentrate on indicate whether any artificial intelligence (AI) tools will be one kind of good consumer behavioral prediction method to be choose to apply to predict consumer behaviors. I shall indicate some examples, cases to give reasonable evidences to analyze whether (AI) tools will be one kind suitable tool to be applied to predict when and how consumer behavioral changes. If (AI) can be one kind tool to attempt to be applied to predict when and how consumer behavioral changes. Will it replace other kinds of methods to predict consumer behaviors? Does it have weaknesses to be applied to predict consumer behaviors, instead of strengths? Can it be applied to predict consumer behaviors depending on any situations of only some situation? Finally, I believe that any readers can find answers to answer above these questions in this book. In my this book second part, I shall explain why and how human can possible apply (AI) tool to predict consumer individual emotion. I shall indicate case studies to explain how consumer individual better or worse emotion how to influence whose consumption behavior in different suitations. Finally, I shall indicate evidences to conclude how and why (AI) tool that can be used to predict consumer individual emotion and it will have direct relationship to influence consumption behavior, as well as how (AI) tool can assist businessmen to judge whether what reasons case the customer does not choose to buy its product, it is possible because the product high price factor, poor product quality or poor staff service performance or attitude etc. different factors to influence the consumer decides to choose to buy the other product consequently, when the (AI) tool can confirm consumer has good or bad emotion to judge what factors are the causes his decision making at the moment.
This handbook focuses on new threats to psychological security that are posed by the malicious use of AI and how it can be used to counteract such threats. Studies on the malicious use of AI through deepfakes, agenda setting, sentiment analysis and affective computing and so forth, provide a visual representation of the various forms and methods of malicious influence on the human psyche, and through this on the political, economic, cultural processes, the activities of state and non-state institutions. Separate chapters examine the malicious use of AI in geopolitical confrontation, political campaigns, strategic deception, damage to corporate reputation, and activities of extremist and terrorist organizations. This is a unique volume that brings together a multidisciplinary range of established scholars and upcoming new researchers from 11 countries. This handbook is an invaluable resource for students, researchers, and professionals interested in this new and developing field of social practice and knowledge.
Understanding consumer behavior in today's digital landscape is more challenging than ever. Businesses must navigate a sea of data to discern meaningful patterns and correlations that drive effective customer engagement and product development. However, the ever-changing nature of consumer behavior presents a daunting task, making it difficult for companies to gauge the wants and needs of their target audience accurately. Enhancing and Predicting Digital Consumer Behavior with AI offers a comprehensive solution to this pressing issue. A strong focus on concepts, theories, and analytical techniques for tracking consumer behavior changes provides the roadmap for businesses to navigate the complexities of the digital age. By covering topics such as digital consumers, emotional intelligence, and data analytics, this book serves as a timely and invaluable resource for academics and practitioners seeking to understand and adapt to the evolving landscape of consumer behavior.
This is an open access book. As a leading role in the global megatrend of scientific innovation, China has been creating a more and more open environment for scientific innovation, increasing the depth and breadth of academic cooperation, and building a community of innovation that benefits all. These endeavors have made new contribution to globalization and creating a community of shared future. With the rapid development of modern economic society, in the process of economic management, informatization has become the mainstream of economic development in the future. At the same time, with the emergence of advanced management technologies such as blockchain technology and big data technology, real market information can be quickly obtained in the process of economic management, which greatly reduces the operating costs of the market economy and effectively enhances the management level of operators, thus contributing to the sustained, rapid and healthy development of the market economy. Under the new situation, the innovative application of economic management research is of great practical significance. 2022 International Conference on Bigdata, Blockchain and Economic Management (ICBBEM 2022) will be held on March 25–27, 2022 in Wuhan, China. ICBBEM 2022 will focus on the latest fields of Bigdata, Blockchain and Economic Management to provide an international platform for experts, professors, scholars and engineers from universities, scientific institutes, enterprises and government-affiliated institutions at home and abroad to share experiences, to expand professional fields, to exchange new ideas face to face, to present research results, and to discuss the key challenging issues and research directions facing the development of this field, with a view to promoting the development and application of theories and technologies in universities and enterprises.
The relationship between firms and stakeholders is held together by a continuous two-way cycle of value creation. In this, how can value be managed such that the stakeholder's wellbeing is ensured? How does stakeholder wellbeing vary across business contexts? Are there varied perspectives in understanding stakeholder wellbeing? These and other pertinent questions have been addressed in this book. Particularly, this book provides a synthesis of research perspectives on value creation and stakeholder wellbeing through a collection of chapters from scholars in this area. It synthesizes research perspectives on value into three categories – firm-focused, customer-focused, and community-focused. In doing so, this book presents novel insights through these lenses and highlights best practices in ensuring stakeholder wellbeing. Responding to the rapidly changing business landscape where stakeholders are more connected, accessible, and informed than ever before, many firms are interested in creating value for all and in the process ensuring stakeholder wellbeing. This book will appeal to research scholars, practitioners, consultants, and managers looking to seek new insights and understanding on value creation. Contents: CHAPTER 1. PERSPECTIVES ON STAKEHOLDER WELLBEING AND VALUE CREATION - Bharath Rajan, Uday Salunkhe, D. N. Murthy CHAPTER 2. INFLUENCE OF SENSORY MARKETING ON CONSUMER BEHAVIOUR AND THEIR IMPACT ON BRAND EQUITY - Abhinandan N, Manasa K, Kiran G CHAPTER 3. ATTITUDE TOWARDS FEMALE ROLE PORTRAYAL IN ADVERTISING AND ITS IMPACT ON BRAND IMAGE & PURCHASE INTENTION: LINKAGES WITH FEMININE ROLE ORIENTATION - Shraddha Shivani, Evelina Sahay, Somnath Mukherjee, Sadiya Fatima CHAPTER 4. DETERMINANTS OF PURCHASE INTENTIONS TOWARDS GREEN MOBILES – AN EXTENSION OF THE THEORY OF PLANNED BEHAVIOUR (TPB) - Deepa Rohit CHAPTER 5. PREDICTING CONSUMER DECISIONS USING MODIFIED TEMPORAL MOTIVATION THEORY - Pranav Manjunath Bhat, Priyanshu M, S Shruti, Madhav Murthy CHAPTER 6. MARKETING 4.0: EMERGING TECHNOLOGIES THAT ARE REFINING DIGITAL MARKETING - Fathima Raj Kilimas, Ashish Chandra, Narendra Rustagi CHAPTER 7. MARKETING GAME CHANGERS: CAPITALIZING THE MICRO-MOMENT THROUGH AUGMENTED REALITY - Uday Salunkhe, D. Narasimha Murthy, Vijaya Kumar. B. CHAPTER 8. EXPLORATORY STUDY ON VALUE CREATION ALONG THE SUPPLY CHAIN OF ELECTRIC VEHICLES: AN OPINION MINING APPROACH - Dakshina Murthy R.A, Madhumita Guha Majumder, M. Khurrum S. Bhutta CHAPTER 9. VALUE CREATION FOR VENTURE CAPITAL-BACKED FIRMS BY AVOIDING THE.. LIKELIHOOD OF MORAL HAZARDS - Vandana Panwar, Christopher Erickson, Alan Tupicoff CHAPTER 10. APPLYING BEHAVIOURAL ECONOMICS TO BRING IN SOCIAL TRANSFORMATION: RURAL SHORING FOR STAKEHOLDER WELLBEING - Vikramaditya Kanodia and Rima Ghose Chowdhury
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
The ability to predict consumer choice is a fundamental aspect to success for any business. In the context of artificial intelligence marketing, there are a wide array of predictive analytic techniques available to achieve this purpose, each with its own unique advantages and disadvantages. Artificial Intelligence Marketing and Predicting Consumer Choice serves to integrate these widely disparate approaches, and show the strengths, weaknesses, and best applications of each. It provides a bridge between the person who must apply or learn these problem-solving methods and the community of experts who do the actual analysis. It is also a practical and accessible guide to the many remarkable advances that have been recently made in this fascinating field. Online resources: bonus chapters on AI, ensembles and neural nets, and finishing experiments, plus single and multiple product simulators.
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students.