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This book reports what progress is being and should be made in marketing science by social complexity science through such means as agent-based modeling and complex networks, which is seldom addressed by books reviewing the state of the art of marketing research or marketing science. Recently, as the penetration of brand-new communication technologies such as social media or mobile phones is connecting consumers more tightly than ever, marketers should understand and harness complex social interactions between consumers as well as between consumers and firms. Traditional marketing science does not sufficiently capture such phenomena; hence, the emerging alternative approaches are reflecting advances in social complexity science. The aim of this book is to give an overview of these newly emerging research trends, focusing mainly on the following three topics: diffusion of new products, choice and relationship with existing brands and marketing communication including consumers' word-of-mouth. These topics have attracted a lot of attention among marketers in particular in rapidly growing markets such as hi-tech or services, where consumers' mutual interaction and their bounded-rational behavior play critical roles. The prospective readers of this book include researchers, graduate students and practitioners with high expertise (e.g. data scientists) in both marketing/consumer research areas and complexity science areas such as computer science, physics and mathematical social science. The book serves as a bridge by providing the evolving knowledge on social complexity modeling for marketing researchers and the accumulated knowledge on marketing/consumer research for complexity researchers. In addition to reviewing previous and ongoing studies, this book offers the agenda for future researchers to discuss what problems have not been solved yet and need social complexity science approaches.
List of contents
Chapter 1. Agent-Based Modeling for Marketing Science.- Chapter 2. New Production Diffusion and ABM.- Chapter 3. Simulation of an Agent-Based Diffusion Model.- Chapter 4. Application for Influencer Marketing.- Chapter 5. Empirical ABM for Marketing.- Chapter 6. ABM with More Proactive Agents.
About the author
Makoto Mizuno is a professor at Meiji University in Tokyo, Japan. Before transitioning to academia, he worked in the advertising industry for many years. He earned his PhD in economics from the University of Tokyo. His research spans traditional marketing science, where he analyzes consumer purchase data using choice modeling as well as computational social science, which explores digital traces from social media. Additionally, he specializes in agent-based modeling simulations, as discussed in this book. His interdisciplinary approach integrates econometrics, network analyses, and simulation techniques to deepen the understanding of consumer behavior and market dynamics.
Summary
This book reports what progress is being and should be made in marketing science by social complexity science through such means as agent-based modeling and complex networks, which is seldom addressed by books reviewing the state of the art of marketing research or marketing science. Recently, as the penetration of brand-new communication technologies such as social media or mobile phones is connecting consumers more tightly than ever, marketers should understand and harness complex social interactions between consumers as well as between consumers and firms. Traditional marketing science does not sufficiently capture such phenomena; hence, the emerging alternative approaches are reflecting advances in social complexity science. The aim of this book is to give an overview of these newly emerging research trends, focusing mainly on the following three topics: diffusion of new products, choice and relationship with existing brands and marketing communication including consumers’ word-of-mouth. These topics have attracted a lot of attention among marketers in particular in rapidly growing markets such as hi-tech or services, where consumers’ mutual interaction and their bounded-rational behavior play critical roles. The prospective readers of this book include researchers, graduate students and practitioners with high expertise (e.g. data scientists) in both marketing/consumer research areas and complexity science areas such as computer science, physics and mathematical social science. The book serves as a bridge by providing the evolving knowledge on social complexity modeling for marketing researchers and the accumulated knowledge on marketing/consumer research for complexity researchers. In addition to reviewing previous and ongoing studies, this book offers the agenda for future researchers to discuss what problems have not been solved yet and need social complexity science approaches.
