弥合人工市场模拟与创新扩散定性研究之间的差距

Brent A. Zenobia, C. Weber
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引用次数: 8

摘要

人工市场(AMs)是基于代理的模拟(ABS)的一种新兴形式,其中代理代表在模拟市场条件下相互作用的个人消费者,公司或行业。该方法的有效性取决于研究人员构建能够忠实地捕捉目标实体关键行为的模拟代理的能力。没有这样的对应,模拟就不能被认为是市场动态的有效表示。到目前为止,还没有建立这种联系。然而,为了实现人工市场作为营销实践工具的潜力,与消费者行为研究的主流方法,特别是定性方法建立更密切的联系是至关重要的。本文的主要贡献是一种结合定性营销研究(归纳案例研究、扎根理论和序列分析)和软件工程技术的新方法,以综合消费者行为的模拟理论。我们提供了一个循序渐进的解释和一个来自消费者技术采用领域的理论构建的示范示例。其结果是消费者采用行为的理论,该理论足够精确和形式化,可以用统一建模语言(UML)来表达。文章最后讨论了该方法的局限性,对其在创新扩散(DOI)研究中的应用提出了建议,并对进一步研究提出了建议。本文中有关人工市场的论点和发现可以推广到大多数基于主体的模拟,包括那些应用于创新扩散研究的模拟。创新扩散ABS的结果是不能依赖的,除非代理人是基于对目标实体的经验观察的采纳理论——不管这些实体是消费者、公司还是行业。因此,采用行为的定性研究是研究创新扩散的成功的基于主体的方法的有用先导。
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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.
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