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This book offers students an introduction to the concepts of big data and artificial intelligence (AI) and their applications in the business world. It answers questions such as what are the main concepts of artificial intelligence and big data? What applications for artificial intelligence and big data analytics are used in the business field? It offers application-oriented overviews and cases from different sectors and fields to help readers discover and gain useful insights. Each chapter features discussion questions and summaries. To assist professors in teaching, the book supplementary materials will include answers to questions, and presentation slides.

PurposeThis paper aims to present a forecast for renewable energy production in the USA. Growth curves are used to conduct the forecasts.Design/methodology/approachThe analysis is based upon a literature review, supplemented by collection of secondary data. The study then focuses on applying the Pearl growth curve.FindingsThe authors' results show that biomass energy production is growing the fastest followed by geothermal and wind. Additionally, the forecast for solar energy production shows little to no growth over the next two decades.Research limitations/implicationsIf the US government hopes to achieve its goals in renewable energy, considerable funding and incentives will have to be put forth to accelerate the growth of renewable energy. Since the biomass technology is already growing nicely it makes sense to put the additional resources behind the other three technologies to close the 10.3 percent gap being forecasted. The government also needs to put more funding into dual renewable plants such as wind or solar combines with pumped hydro, this will ensure environmental and reliability are both maintained. Finally, for renewable energies to be competitive in the long term, considerable research needs to go into driving down the cost so there is not a need for subsidies.Originality/valueThis study provides value in providing a forecast for expected future growth for renewable energy sources.

Series Foreword -- Preface -- Contents -- Part I: A Dynamic Capabilities Theory-Based Innovation Diffusion Model for Spread of Health Information Technology in the USA -- Chapter 1: Introduction to the Adoption of Health Information Technologies -- 1.1 The Healthcare Crisis in the United States -- 1.2 Government Efforts and HIT Meaningful-Use Initiative -- 1.2.1 State of Diffusion Research: General and Health IT -- References -- Chapter 2: Background Literature on the Adoption of Health Information Technologies -- 2.1 Overview of the Healthcare Delivery System -- 2.2 A Methodological Note -- 2.3 The Critical Stakeholders and Actors -- 2.3.1 Care Providers -- 2.3.1.1 Physicians, Nurses, and Medical Assistants -- 2.3.1.2 The Hospital or Clinic -- 2.3.2 Government -- 2.3.3 Patients and Their Family and Care Givers -- 2.3.4 Payers -- 2.3.5 HIT/Innovation Suppliers -- 2.3.5.1 HIT Vendors -- 2.3.5.2 Regional Health Information Organizations -- 2.4 Attributes of the Stakeholders -- 2.5 Important Factors Effecting Diffusion and Adoption for HIT -- 2.5.1 Barriers and Influences -- 2.5.2 Tools, Methods, and Theories -- 2.5.3 Policy Making -- 2.5.4 Hospital Characteristics and the Ecosystem -- 2.5.5 Adopter Attitudes, Perceptions, and Characteristics -- 2.5.6 Strategic Management and Competitive Advantage -- 2.5.7 Innovation Champions and Their Aids -- 2.5.8 Workflow and Knowledge Management -- 2.5.9 Timing and Sustainability -- 2.5.10 Modeling and Forecasting -- 2.5.11 Infusion -- 2.5.12 Social Structure and Communication Channels -- 2.6 The Need for Multiple Perspectives in Research -- 2.7 Linstone's Multiple Perspectives Method -- 2.8 The "4 + 1 View" Model for Software Architectures -- 2.9 Categorization of Important Factors in HIT Adoption Using Multi-perspectives -- References -- Chapter 3: Methods and Models