Targeting and timing promotional activities: An agent-based model for the takeoff of new products

Delre, Sebastiano A.; Jager, Wander; Bijmolt, Tammo H. A.; Janssen, Marco A.

doi:10.1016/j.jbusres.2007.02.002

Cited by 152 publications

(118 citation statements)

References 32 publications

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“…They are increasingly used in the social sciences to model social processes such as diffusion, collective action, and group influence (Smith & Conrey, 2007;Macy & Willer, 2002) as well as economic activity in general (Tesfatsion, 2003). They are also increasingly used in the marketing literature, particularly with respect to new product growth (Delre, 2007;Goldenberg , 2007;Shaikh, Rangaswamy & Balakrishnan, 2006;Garcia, 2005;Libai, Peres & Muller, 2005). Cellular automata modeling is an agent-based modeling technique that has been extensively used across disciplines to model social-based phenomena.…”

Section: An Agent-based Model (Abm) Of Network Good Growthmentioning

confidence: 99%

The chilling effects of network externalities

Goldenberg

Libai

Muller

2010

International Journal of Research in Marketing

193

132

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Conventional wisdom suggests that network effects should drive faster market growth due to the bandwagon effect. However, as we show, network externalities may also create an initial slowdown effect on growth because potential customers wait for early adopters, who provide them with more utility, before they adopt. In this study, we explore the financial implications of network externalities by taking the entire network process into account. Using an agent-based as well as an aggregate-level model, and separating network effects from word of mouth, we find that network externalities have a substantial chilling effect on the net present value associated with new products. This effect may occur not only in a competitive framework, such as a competing standards scenario, but also in the absence of competition. Drawing on the collective action literature in order to relate network effects to individual consumer threshold levels, we find that the chilling effect is stronger with a small variability in the threshold distribution, and is especially affected by the process early on in the product life cycle. We also find a "hockey stick" growth pattern by empirically examining the growth of fax machines, CB radios, CD players, DVD players, and cellular services.

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Section: An Agent-based Model (Abm) Of Network Good Growthmentioning

confidence: 99%

The chilling effects of network externalities

Goldenberg

Libai

Muller

2010

International Journal of Research in Marketing

193

132

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“…For example, Goldenberg et al (2001) used a fully connected agent-based model to study the effect of heterogeneity in the values of p and q among individuals. In other agent-based diffusion models, however, individuals could only communicate with their neighbors, and the population had a spatial structure such as a one-dimensional grid (e.g., Alkemade and Castaldi 2005), a two-dimensional grid (e.g., Goldenberg et al 2002), or a regular lattice with some added random links (e.g., Garber et al 2004, Delre et al 2007). The goal of this study is to analytically study the effect of the spatial structure on the diffusion process in agent-based models.…”

Section: Introductionmentioning

confidence: 99%

Aggregate Diffusion Dynamics in Agent-Based Models with a Spatial Structure

Fibich

Gibori

2010

Operations Research

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We explicitly calculate the aggregate diffusion dynamics in one-dimensional agent-based models of adoption of new products, without using the mean-field approximation. We then introduce a clusters-dynamics approach, and use it to derive an analytic approximation of the aggregate diffusion dynamics in multidimensional agent-based models. The clusters-dynamics approximation shows that the aggregate diffusion dynamics does not depend on the average distance between individuals, but rather on the expansion rate of clusters of adopters. Therefore, the grid dimension has a large effect on the aggregate adoption dynamics, but a small-world structure and heterogeneity among individuals have only a minor effect. Our results suggest that the one-dimensional model and the Bass model provide a lower bound and an upper bound, respectively, for the aggregate diffusion dynamics in agent-based models with "any" spatial structure.

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“…Agent-based models imitate natural market dynamics by specifying individual product choice processes and implementing word-of-mouth information, which is fed back to the individual decisionmaking process of potential consumers [6,7]. This approach supports simulations of various "what-if" scenarios in artificial markets, the results of which can be used for decision support [3,[7][8][9].…”

Section: Related Researchmentioning

confidence: 99%

Product Attribute Design Using an Agent-Based Simulation of an Artificial Market

Kim¹,

Kim²

2014

Int. j. simul. model.

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New product development is a high-risk decision-making problem in which similar products compete with each other to expand their market shares. Brand-level diffusion predictions can help product design managers to analyse how product attribute specifications impact total market shares, which can, in turn, aid managers in choosing the designs that yield maximum profits. In this paper, we develop a product attribute design method in which an artificial market consisting of consumer agents in an interaction network is created to simulate the diffusion process of products, and a genetic algorithm is integrated with the artificial market to support the product design decision-making process. The contribution of this research is that the predicted market response to product alternatives is incorporated into the product design optimisation. Two empirical experiments were conducted on the Korean laptop computer market to demonstrate the potential of this integrated method. Preliminary experiment showed that our prediction diffusion curves had an average error of 3.04 %. In the primary experiment, five designs were recommended, and a comparison with the 31 best-selling laptop computers resulted in an average error of less than 8 % when the "Price" attribute was excluded.

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Targeting and timing promotional activities: An agent-based model for the takeoff of new products

Cited by 152 publications

References 32 publications

The chilling effects of network externalities

The chilling effects of network externalities

Aggregate Diffusion Dynamics in Agent-Based Models with a Spatial Structure

Product Attribute Design Using an Agent-Based Simulation of an Artificial Market

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