Abstract
Diffusions of new products and technologies through social networks can be formalized as spreading of infectious diseases. However, while epidemiological models describe infection in terms of transmissibility, we propose a diffusion model that explicitly includes consumer decision-making affected by social influences and word-of-mouth processes. In our agent-based model consumers' probability of adoption depends on the external marketing effort and on the internal influence that each consumer perceives in his/her personal networks. Maintaining a given marketing effort and assuming its effect on the probability of adoption as linear, we can study how social processes affect diffusion dynamics and how the speed of the diffusion depends on the network structure and on consumer heterogeneity. First, we show that the speed of diffusion changes with the degree of randomness in the network. In markets with high social influence and in which consumers have a sufficiently large local network, the speed is low in regular networks, it increases in small-world networks and, contrarily to what epidemic models suggest, it becomes very low again in random networks. Second, we show that heterogeneity helps the diffusion. Ceteris paribus and varying the degree of heterogeneity in the population of agents simulation results show that the more heterogeneous the population, the faster the speed of the diffusion. These results can contribute to the development of marketing strategies for the launch and the dissemination of new products and technologies, especially in turbulent and fashionable markets.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 185-202 |
| Number of pages | 18 |
| Journal | Computational and Mathematical Organization Theory |
| Volume | 13 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 2007 |
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Keywords
- Heterogeneous markets
- Innovation diffusion
- Social networks
- Threshold models
- Word-of-mouth
ASJC Scopus subject areas
- Computational Theory and Mathematics
Cite this
Diffusion dynamics in small-world networks with heterogeneous consumers. / Delre, Sebastiano A.; Jager, Wander; Janssen, Marcus.
In: Computational and Mathematical Organization Theory, Vol. 13, No. 2, 06.2007, p. 185-202.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Diffusion dynamics in small-world networks with heterogeneous consumers
AU - Delre, Sebastiano A.
AU - Jager, Wander
AU - Janssen, Marcus
PY - 2007/6
Y1 - 2007/6
N2 - Diffusions of new products and technologies through social networks can be formalized as spreading of infectious diseases. However, while epidemiological models describe infection in terms of transmissibility, we propose a diffusion model that explicitly includes consumer decision-making affected by social influences and word-of-mouth processes. In our agent-based model consumers' probability of adoption depends on the external marketing effort and on the internal influence that each consumer perceives in his/her personal networks. Maintaining a given marketing effort and assuming its effect on the probability of adoption as linear, we can study how social processes affect diffusion dynamics and how the speed of the diffusion depends on the network structure and on consumer heterogeneity. First, we show that the speed of diffusion changes with the degree of randomness in the network. In markets with high social influence and in which consumers have a sufficiently large local network, the speed is low in regular networks, it increases in small-world networks and, contrarily to what epidemic models suggest, it becomes very low again in random networks. Second, we show that heterogeneity helps the diffusion. Ceteris paribus and varying the degree of heterogeneity in the population of agents simulation results show that the more heterogeneous the population, the faster the speed of the diffusion. These results can contribute to the development of marketing strategies for the launch and the dissemination of new products and technologies, especially in turbulent and fashionable markets.
AB - Diffusions of new products and technologies through social networks can be formalized as spreading of infectious diseases. However, while epidemiological models describe infection in terms of transmissibility, we propose a diffusion model that explicitly includes consumer decision-making affected by social influences and word-of-mouth processes. In our agent-based model consumers' probability of adoption depends on the external marketing effort and on the internal influence that each consumer perceives in his/her personal networks. Maintaining a given marketing effort and assuming its effect on the probability of adoption as linear, we can study how social processes affect diffusion dynamics and how the speed of the diffusion depends on the network structure and on consumer heterogeneity. First, we show that the speed of diffusion changes with the degree of randomness in the network. In markets with high social influence and in which consumers have a sufficiently large local network, the speed is low in regular networks, it increases in small-world networks and, contrarily to what epidemic models suggest, it becomes very low again in random networks. Second, we show that heterogeneity helps the diffusion. Ceteris paribus and varying the degree of heterogeneity in the population of agents simulation results show that the more heterogeneous the population, the faster the speed of the diffusion. These results can contribute to the development of marketing strategies for the launch and the dissemination of new products and technologies, especially in turbulent and fashionable markets.
KW - Heterogeneous markets
KW - Innovation diffusion
KW - Social networks
KW - Threshold models
KW - Word-of-mouth
UR - http://www.scopus.com/inward/record.url?scp=33846104820&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33846104820&partnerID=8YFLogxK
U2 - 10.1007/s10588-006-9007-2
DO - 10.1007/s10588-006-9007-2
M3 - Article
AN - SCOPUS:33846104820
VL - 13
SP - 185
EP - 202
JO - Computational and Mathematical Organization Theory
JF - Computational and Mathematical Organization Theory
SN - 1381-298X
IS - 2
ER -