{"title":"弥合人工市场模拟与创新扩散定性研究之间的差距","authors":"Brent A. Zenobia, C. Weber","doi":"10.1142/S0219877012500265","DOIUrl":null,"url":null,"abstract":"Artificial markets (AMs) are an emerging form of agent-based simulation (ABS), in which agents represent individual consumers, firms, or industries interacting under simulated market conditions. The validity of the method depends on the ability of researchers to construct simulated agents that faithfully capture the key behavior of targeted entities. Without such a correspondence the simulation cannot be considered to be a valid representation of market dynamics. To date, no such correspondence has been established. Yet, for artificial markets to achieve their potential as a tool for marketing practice it is crucial that closer ties be forged with mainstream methods for consumer behavioral research, especially qualitative methods. The primary contribution of this article is a novel method combining qualitative marketing research (inductive case studies, grounded theory, and sequence analysis) and software engineering techniques to synthesize simulation-ready theories of consumer behavior. We provide a step-by-step explanation and a demonstrative example of theory-building from the consumer technology adoption domain. The outcome is a theory of consumer adoption behavior that is sufficiently precise and formal to be expressed in Unified Modeling Language (UML). The article concludes with a discussion of the limitations of the method, recommendations for its implementation in the study of diffusion of innovation (DOI) and suggestions for further research. The arguments and findings in this article that pertain to artificial markets can be generalized with respect to most agent-based simulations, including those applied to the study of diffusion of innovation. The results of an ABS of innovation diffusion cannot be relied upon unless the agents are based on a theory of adoption that grounded in empirical observations of the targeted entities — regardless of whether those entities are consumers, firms or industries. Qualitative research of adoption behavior is thus a useful precursor to successful agent-based approaches to studying the diffusion of innovations.","PeriodicalId":345430,"journal":{"name":"PICMET 2010 TECHNOLOGY MANAGEMENT FOR GLOBAL ECONOMIC GROWTH","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Bridging the gap between artificial market simulations and qualitative research in diffusion of innovation\",\"authors\":\"Brent A. Zenobia, C. 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The primary contribution of this article is a novel method combining qualitative marketing research (inductive case studies, grounded theory, and sequence analysis) and software engineering techniques to synthesize simulation-ready theories of consumer behavior. We provide a step-by-step explanation and a demonstrative example of theory-building from the consumer technology adoption domain. The outcome is a theory of consumer adoption behavior that is sufficiently precise and formal to be expressed in Unified Modeling Language (UML). The article concludes with a discussion of the limitations of the method, recommendations for its implementation in the study of diffusion of innovation (DOI) and suggestions for further research. The arguments and findings in this article that pertain to artificial markets can be generalized with respect to most agent-based simulations, including those applied to the study of diffusion of innovation. The results of an ABS of innovation diffusion cannot be relied upon unless the agents are based on a theory of adoption that grounded in empirical observations of the targeted entities — regardless of whether those entities are consumers, firms or industries. 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Bridging the gap between artificial market simulations and qualitative research in diffusion of innovation
Artificial markets (AMs) are an emerging form of agent-based simulation (ABS), in which agents represent individual consumers, firms, or industries interacting under simulated market conditions. The validity of the method depends on the ability of researchers to construct simulated agents that faithfully capture the key behavior of targeted entities. Without such a correspondence the simulation cannot be considered to be a valid representation of market dynamics. To date, no such correspondence has been established. Yet, for artificial markets to achieve their potential as a tool for marketing practice it is crucial that closer ties be forged with mainstream methods for consumer behavioral research, especially qualitative methods. The primary contribution of this article is a novel method combining qualitative marketing research (inductive case studies, grounded theory, and sequence analysis) and software engineering techniques to synthesize simulation-ready theories of consumer behavior. We provide a step-by-step explanation and a demonstrative example of theory-building from the consumer technology adoption domain. The outcome is a theory of consumer adoption behavior that is sufficiently precise and formal to be expressed in Unified Modeling Language (UML). The article concludes with a discussion of the limitations of the method, recommendations for its implementation in the study of diffusion of innovation (DOI) and suggestions for further research. The arguments and findings in this article that pertain to artificial markets can be generalized with respect to most agent-based simulations, including those applied to the study of diffusion of innovation. The results of an ABS of innovation diffusion cannot be relied upon unless the agents are based on a theory of adoption that grounded in empirical observations of the targeted entities — regardless of whether those entities are consumers, firms or industries. Qualitative research of adoption behavior is thus a useful precursor to successful agent-based approaches to studying the diffusion of innovations.