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Multi-agent Model of Technological Shifts
Multi-Agent-Based Simulation VIII
Mining social networks using heat diffusion processes for marketing candidates selection
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Identification of influencers - Measuring influence in customer networks
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Risk Management of Contract Portfolios in IT Services: The Profit-at-Risk Approach
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Decision Support Systems
Cloud Computing: A Statistics Aspect of Users
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
Evolving viral marketing strategies
Proceedings of the 12th annual conference on Genetic and evolutionary computation
International Journal of Business Information Systems
Agent-based simulation of the diffusion of warnings
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
An agent-based diffusion model with consumer and brand agents
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Combining mathematical forecasting with intuitive techniques for better decision making
MMES'10 Proceedings of the 2010 international conference on Mathematical models for engineering science
A time-critical information diffusion model in vehicle ad hoc networks
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia
International Journal of Information Technology and Management
Simulating the Diffusion of Information: An Agent-Based Modeling Approach
International Journal of Agent Technologies and Systems
The role of organization hierarchy in technology adoption at the workplace
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Peeking into the invitation-based adoption process of OSN-based applications
ACM SIGCOMM Computer Communication Review
Organizational simulation in support of global manufacturing enterprises
Information-Knowledge-Systems Management - Enterprise Transformation: Manufacturing in a Global Enterprise
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(This article originally appeared in Management Science, January 1969, Volume 15, Number 5, pp. 215-227, published by The Institute of Management Sciences.) A growth model for the timing of initial purchase of new products is developed and tested empirically against data for eleven consumer durables. The basic assumption of the model is that the timing of a consumer's initial purchase is related to the number of previous buyers. A behavioral rationale for the model is offered in terms of innovative and imitative behavior. The model yields good predictions of the sales peak and the timing of the peak when applied to historical data. A long-range forecast is developed for the sales of color television sets.