Diffusion dynamics in small-world networks with heterogeneous consumers

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 and susceptibility, we propose a diffusion model that explicitly includes consumer decision-making affected by social influences and word-of-mouth processes. In our computational model consumers' probability of adoption depends on the external marketing effort and on the internal influence that each consumer perceives in his/her personal social networks. Maintaining a given marketing effort and assuming its effect on the probability of adoption as linear, we can study 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. The speed is low in regular networks, it increases in smallworld networks and finally it becomes low again in random networks. Second, we show that heterogeneity helps the diffusion. Alteris paribus and varying the degree of heterogeneity in the population of agents results show that the more heterogeneous the population, the faster the speed of the diffusion. These results contribute to marketing strategies for the launch and the dissemination of new products and technologies

The Effects of Shared Consumption on Product Life Cycles and Advertising Effectiveness:The Case of the Motion Picture Market

Consumers frequently consume hedonic products together with other consumers and derive value from this shared experience. This article investigates the impact of shared consumption, a type of social influence that determines the enjoyment of joint experiences, in the context of a typical hedonic product: movies. The authors argue that this type of influence has important consequences for the diffusion curves of hedonic goods that are consumed together and the effectiveness of advertising in generating launch and postlaunch sales. An empirically validated agent-based model simulates the U.S. motion picture market, with new movies launching, competing, and exiting. The agent-based model serves as a means to demonstrate the essential role of shared consumption for explaining movie life cycles and tests how advertising expenditures accelerate and/or acquire movies' demand in markets with varying levels of shared consumption. The results provide key theoretical insights for the new product diffusion of hedonic products and help managers predict the financial consequences of their strategic decisions

Will It Spread or Not? The Effects of Social Influences and Network Topology on Innovation Diffusion

Innovation diffusion theory suggests that consumers differ concerning the number of contacts they have and the degree and the direction to which social influences determine their choice to adopt. To test the impacts of these factors on innovation diffusion, in particular the occurrence of hits and flops, a new agent-based model for innovation diffusion is introduced. This model departs from existing percolation models by using more realistic agents (both individual preferences and social influence) and more realistic networks (scale free with cost constraints). Furthermore, it allows consumers to weight the links they have, and it allows links to be directional. In this way this agent-based model tests the effect of VIPs who can have a relatively large impact on many consumers. Results indicate that markets with high social influence are more uncertain concerning the final success of the innovation and that it is more difficult for the innovation to take off. As consumers affect each other to adopt or not at the beginning of the diffusion, the new product has more difficulties to reach the critical mass that is necessary for the product to take off. In addition, results of the simulation experiments show under which conditions highly connected agents (VIPs) determine the final diffusion of the innovation. Although hubs are present in almost any network of consumers, their roles and their effects in different markets can be very different. Using a scale-free network with a cut-off parameter for the maximum number of connections a hub can have, the simulation results show that when hubs have limits to the maximum number of connections the innovation diffusion is severely hampered, and it becomes much more uncertain. However, it is found that the effect of VIPs on the diffusion curve is often overestimated. In fact when the influence of VIPs on the decision making of the consumers is strengthened compared with the influence of normal friends, the diffusion of the innovation is not substantially facilitated. It can be concluded that the importance of VIPs resides in their capacity to inform many consumers and not in a stronger persuasive power

Will It Spread or Not? The Effects of Social Influences and Network Topology on Innovation Diffusion

Innovation diffusion theory suggests that consumers differ concerning the number of contacts they have and the degree and the direction to which social influences determine their choice to adopt. To test the impacts of these factors on innovation diffusion, in particular the occurrence of hits and flops, a new agent-based model for innovation diffusion is introduced. This model departs from existing percolation models by using more realistic agents (both individual preferences and social influence) and more realistic networks (scale free with cost constraints). Furthermore, it allows consumers to weight the links they have, and it allows links to be directional. In this way this agent-based model tests the effect of VIPs who can have a relatively large impact on many consumers. Results indicate that markets with high social influence are more uncertain concerning the final success of the innovation and that it is more difficult for the innovation to take off. As consumers affect each other to adopt or not at the beginning of the diffusion, the new product has more difficulties to reach the critical mass that is necessary for the product to take off. In addition, results of the simulation experiments show under which conditions highly connected agents (VIPs) determine the final diffusion of the innovation. Although hubs are present in almost any network of consumers, their roles and their effects in different markets can be very different. Using a scale-free network with a cut-off parameter for the maximum number of connections a hub can have, the simulation results show that when hubs have limits to the maximum number of connections the innovation diffusion is severely hampered, and it becomes much more uncertain. However, it is found that the effect of VIPs on the diffusion curve is often overestimated. In fact when the influence of VIPs on the decision making of the consumers is strengthened compared with the influence of normal friends, the diffusion of the innovation is not substantially facilitated. It can be concluded that the importance of VIPs resides in their capacity to inform many consumers and not in a stronger persuasive power

Will It Spread or Not? The Effects of Social Influences and Network Topology on Innovation Diffusion

Innovation diffusion theory suggests that consumers differ concerning the number of contacts they have and the degree and the direction to which social influences determine their choice to adopt. To test the impacts of these factors on innovation diffusion, in particular the occurrence of hits and flops, a new agent-based model for innovation diffusion is introduced. This model departs from existing percolation models by using more realistic agents (both individual preferences and social influence) and more realistic networks (scale free with cost constraints). Furthermore, it allows consumers to weight the links they have, and it allows links to be directional. In this way this agent-based model tests the effect of VIPs who can have a relatively large impact on many consumers. Results indicate that markets with high social influence are more uncertain concerning the final success of the innovation and that it is more difficult for the innovation to take off. As consumers affect each other to adopt or not at the beginning of the diffusion, the new product has more difficulties to reach the critical mass that is necessary for the product to take off. In addition, results of the simulation experiments show under which conditions highly connected agents (VIPs) determine the final diffusion of the innovation. Although hubs are present in almost any network of consumers, their roles and their effects in different markets can be very different. Using a scale-free network with a cut-off parameter for the maximum number of connections a hub can have, the simulation results show that when hubs have limits to the maximum number of connections the innovation diffusion is severely hampered, and it becomes much more uncertain. However, it is found that the effect of VIPs on the diffusion curve is often overestimated. In fact when the influence of VIPs on the decision making of the consumers is strengthened compared with the influence of normal friends, the diffusion of the innovation is not substantially facilitated. It can be concluded that the importance of VIPs resides in their capacity to inform many consumers and not in a stronger persuasive power.</p

Will It Spread or Not? The Effects of Social Influences and Network Topology on Innovation Diffusion

Innovation diffusion theory suggests that consumers differ concerning the number of contacts they have and the degree and the direction to which social influences determine their choice to adopt. To test the impacts of these factors on innovation diffusion, in particular the occurrence of hits and flops, a new agent-based model for innovation diffusion is introduced. This model departs from existing percolation models by using more realistic agents (both individual preferences and social influence) and more realistic networks (scale free with cost constraints). Furthermore, it allows consumers to weight the links they have, and it allows links to be directional. In this way this agent-based model tests the effect of VIPs who can have a relatively large impact on many consumers. Results indicate that markets with high social influence are more uncertain concerning the final success of the innovation and that it is more difficult for the innovation to take off. As consumers affect each other to adopt or not at the beginning of the diffusion, the new product has more difficulties to reach the critical mass that is necessary for the product to take off. In addition, results of the simulation experiments show under which conditions highly connected agents (VIPs) determine the final diffusion of the innovation. Although hubs are present in almost any network of consumers, their roles and their effects in different markets can be very different. Using a scale-free network with a cut-off parameter for the maximum number of connections a hub can have, the simulation results show that when hubs have limits to the maximum number of connections the innovation diffusion is severely hampered, and it becomes much more uncertain. However, it is found that the effect of VIPs on the diffusion curve is often overestimated. In fact when the influence of VIPs on the decision making of the consumers is strengthened compared with the influence of normal friends, the diffusion of the innovation is not substantially facilitated. It can be concluded that the importance of VIPs resides in their capacity to inform many consumers and not in a stronger persuasive power

Will It Spread or Not? The Effects of Social Influences and Network Topology on Innovation Diffusion

Innovation diffusion theory suggests that consumers differ concerning the number of contacts they have and the degree and the direction to which social influences determine their choice to adopt. To test the impacts of these factors on innovation diffusion, in particular the occurrence of hits and flops, a new agent-based model for innovation diffusion is introduced. This model departs from existing percolation models by using more realistic agents (both individual preferences and social influence) and more realistic networks (scale free with cost constraints). Furthermore, it allows consumers to weight the links they have, and it allows links to be directional. In this way this agent-based model tests the effect of VIPs who can have a relatively large impact on many consumers. Results indicate that markets with high social influence are more uncertain concerning the final success of the innovation and that it is more difficult for the innovation to take off. As consumers affect each other to adopt or not at the beginning of the diffusion, the new product has more difficulties to reach the critical mass that is necessary for the product to take off. In addition, results of the simulation experiments show under which conditions highly connected agents (VIPs) determine the final diffusion of the innovation. Although hubs are present in almost any network of consumers, their roles and their effects in different markets can be very different. Using a scale-free network with a cut-off parameter for the maximum number of connections a hub can have, the simulation results show that when hubs have limits to the maximum number of connections the innovation diffusion is severely hampered, and it becomes much more uncertain. However, it is found that the effect of VIPs on the diffusion curve is often overestimated. In fact when the influence of VIPs on the decision making of the consumers is strengthened compared with the influence of normal friends, the diffusion of the innovation is not substantially facilitated. It can be concluded that the importance of VIPs resides in their capacity to inform many consumers and not in a stronger persuasive power