Identification of influencers - Measuring influence in customer networks
Decision Support Systems
Zooming In: Self-Emergence of Movements in New Product Growth
Marketing Science
Optimal Entry Timing in Markets with Social Influence
Management Science
Financing as a Marketing Strategy
Marketing Science
Firm-Created Word-of-Mouth Communication: Evidence from a Field Test
Marketing Science
Opinion Leadership and Social Contagion in New Product Diffusion
Marketing Science
Information Technology Diffusion with Influentials, Imitators, and Opponents
Journal of Management Information Systems
An approach to measuring influence and cognitive similarity in computer-mediated communication
Computers in Human Behavior
Measuring Information Diffusion in an Online Community
Journal of Management Information Systems
Are markets for vulnerabilities effective?
MIS Quarterly
Sequential and Temporal Dynamics of Online Opinion
Marketing Science
Assessing the ripple effects of online opinion leaders with trust and distrust metrics
Expert Systems with Applications: An International Journal
Studying Diffusion of Viral Content at Dyadic Level
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Whose and what chatter matters? The effect of tweets on movie sales
Decision Support Systems
Networked individuals predict a community wide outcome from their local information
Decision Support Systems
Hi-index | 0.00 |
We model the diffusion of innovations in markets with two segments: influentials who are more in touch with new developments and who affect another segment of imitators whose own adoptions do not affect the influentials. This two-segment structure with asymmetric influence is consistent with several theories in sociology and diffusion research, as well as many “viral” or “network” marketing strategies. We have four main results. (1) Diffusion in a mixture of influentials and imitators can exhibit a dip or “chasm” between the early and later parts of the diffusion curve. (2) The proportion of adoptions stemming from influentials need not decrease monotonically, but may first decrease and then increase. (3) Erroneously specifying a mixed-influence model to a mixture process where influentials act independently from each other can generate systematic changes in the parameter values reported in earlier research. (4) Empirical analysis of 33 different data series indicates that the two-segment model fits better than the standard mixed-influence, the Gamma/Shifted Gompertz, and the Weibull-Gamma models, especially in cases where a two-segment structure is likely to exist. Also, the two-segment model fits about as well as the Karmeshu-Goswami mixed-influence model, in which the coefficients of innovation and imitation vary across potential adopters in a continuous fashion.