An agent-based diffusion model with consumer and brand agents

Abstract

Market members interact within a complex, adaptive system to effect adoption decisions and the resulting diffusion of innovations. Agent-based modeling (ABM) is a methodology well suited for simulating this system. It complements and extends econometric approaches by incorporating interactions among system members, and adaptation in the system, revealing emergent results. Since ABM allows study at the individual unit level, heterogeneity among system members is reflected and modeling at the brand level is possible. Here an ABM with consumer and brand agents is described. The brand and product diffusion curve output allows study of diffusion at micro and macro levels, respectively.

Introduction

The diffusion of innovations by consumers is a topic of ongoing interest to researchers and marketing managers. Diffusion of innovations in the social system reflects adoption decisions made by individual consumers. These decisions are made in a complex, adaptive system and result from the interactions among individual's personal characteristics, perceived characteristics of the innovation, and social influence [12].

There are two broad approaches to modeling diffusion: econometric and explanatory. Econometric approaches, such as the Bass [1] model, “describe and forecast the diffusion of an innovation in a social system” [21, p. 1]. Econometric approaches forecast growth within a product category by modeling the timing of first-purchases of the innovation by consumers [21]. Econometric modeling is most applicable when market growth rate and market size are of primary interest [28].

Explanatory models, such as the “consumer diffusion paradigm” proposed by Gatignon and Robertson [12], establish that the diffusion of a product in a defined market is equivalent to the aggregation of individual consumer adoption decisions. Adoption is primarily a function of three sets of consumer-related factors: personal characteristics, perceived innovation characteristics, and the consumer's exposure to social influence [12], [30]. The explanatory modeling approach is most applicable when developing marketing strategies such as segmentation, targeting, and product positioning [21]. This type of model does not support diffusion curve development.

Although established models have provided a strong basis for diffusion research, they do have limitations. ABM has the potential to complement these approaches since it addresses many of their limitations. However, since outputs differ among the models, ABM does not replace econometric or explanatory models [10], [36]. In established models, the ability to reflect real-world dynamics is limited since they do not incorporate the effect of interactions that occur among members of the social system (i.e., market segment) and brands during the diffusion process [10]. Their predictive power is especially poor in cases where the consumer population is very diverse or social influence has a significant effect on the diffusion process [16]. Further, since the unit of study in econometric models is at the aggregate rather than individual unit level, differences among brands, such as marketing mix characteristics, cannot be explicitly incorporated in the model. As a result, one cannot study the effect of marketing mix strategies on diffusion at the brand level. The ability to see this effect is of particular interest to the brand manager who is charged with understanding outcomes at this level in order to make strategic decisions.

The goal of this study is to address the limitations of current diffusion models by developing an agent-based diffusion model with consumer and brand agents. Utilizing this methodology allows characteristics of the consumers and brands in the market to be incorporated in the model. Agent-based modeling (ABM) also allows interactions and emergent behaviors in the simulated market to be studied. Modeling diffusion at the brand rather than product-category level offers insights of interest from both research and management standpoints. This approach to modeling reveals the diverse set of brand-level diffusion curves that result from differences among brands. These brand-level diffusion curves aggregate to form the product-category level curve.

Agent-based modeling is an emerging methodology well suited for studying complex, adaptive systems, such as those seen in diffusion studies. Brand-level diffusion models are especially complex since there are: multiple brands; many and diverse factors define each brand; “order-of-entry effects” [2]; and consumers and brands that may adapt to changing market conditions [11]. In a real-world system, for example, such adaptation might occur as a consumer is influenced by an opinion leader. ABM overcomes the limitations of econometric models in three ways. First, members of the market can be defined in terms of their attributes. Consumers, for example, can be defined in terms of factors such as their personal characteristics while brands can be defined in terms of their marketing mix characteristics and entry timing. Second, interactions among agents may be defined and simulated. Third, the agent adaptations that occur as the result of interactions can be modeled further adding to the simulation's ability to model real-world phenomenon [35].

Perhaps because of the widespread use of agent-based models in sociology, many of the current applications of ABM in diffusion research focus on the effect of the social network on adoption [10]. As a result of the network focus, factors at the consumer and product level that directly affect diffusion are not explicitly incorporated in these models. The ABM developed in this study clearly departs from others' emphasis on social network influence. This model incorporates a comprehensive set of consumer and brand attributes to develop brand-level diffusion curves then aggregates them to develop a product-category diffusion curve.

Little diffusion research incorporating the effect of complex adaptive systems is found in the innovation literature [35]. The current research contributes to the field by developing a complex, adaptive model that simulates key factors and interactions that occur in the market during the diffusion process. Further, since this approach incorporates consumer and brand characteristics, it may be used as a decision tool to compare the outcomes of different strategic choices during the marketing strategy development process.

The remainder of this paper will discuss diffusion theory, provide an overview of ABM, describe the agent-based diffusion model used in this study, review the simulation results, and end with conclusions.