An agent-based competitive product diffusion model for the estimation and sensitivity analysis of social network structure and purchase time distribution

Keeheon Lee, Shintae Kim, Chang Ouk Kim, Taeho Park

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

To maximise the possibility of success for a new product and minimise the risk and opportunity cost of a failed product, firms must understand the diffusion dynamics of competing products. The diffusion dynamics of competing products emerge from the aggregation of consumers' decisions. At the individual level, a consumer's decision consists of "which product to buy among the available products" and "when to buy a product". Individual product choices are affected by local and global social interactions among consumers. It would be helpful for firms to be able to determine the characteristics of the relevant social network for their target market and how changes in this social network influence their market shares. In addition, determining the distribution of product purchase times of consumers and how their variation affects market shares are interesting issues for firms. In this study, therefore, we propose an agent-based simulation model that generates the market share paths (market shares over time) of competing products. We apply the model to estimate the social network and purchase time distribution of the Korean netbook market. Our observation is that Korean netbook consumers tend to buy a product without hesitation, and their social network is rather regular but sparse. We also conduct sensitivity analyses with respect to the social network and the purchase time distribution.

Original languageEnglish
JournalJASSS
Volume16
Issue number1
DOIs
Publication statusPublished - 2013 Jan

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Social Sciences(all)

Fingerprint

Dive into the research topics of 'An agent-based competitive product diffusion model for the estimation and sensitivity analysis of social network structure and purchase time distribution'. Together they form a unique fingerprint.

Cite this