dayflower

My interests in marketing strategy are related to entrepreneurs, entrepreneurial startups, and their perceptions. Specifically, how do they make sense of the information they perceive in the market place and how do these perceptions influence their marketing strategies for their entrepreneurial ideas.

"Monte from Montana" was born and raised near Glacier National Park. He is a strong, sober mind that likes to solve problems in order to help people. Following in his father's footsteps, he began teaching high school mathematics (BYU: mathematics with minors in Physics and Spanish). The excitement of the dot-com era led Monte to Monterey California where he became a Senior Software Engineer doing web-application development for an Internet Company. Following the bubble-burst, he returned to BYU (MBA: Marketing Research). Monte is concurrently working toward his Ph.D. in Marketing and a M.S. in Statistics at WSU in Pullman, Washington. Generally, he likes to identify innovative statistical techniques that can help solve marketing problems. Specifically, his interests are in Entrepreneurial Innovation, U.S. Patent Data, and Internet Consumer Behavior. Outside of Marketing, Monte enjoys his family, a good game of basketball, golf, and chess.

Assessing a firm's innovation portfolio is a challenge? Even more difficult is estimating its future value? This paper applies the principles of the Bass model of diffusion of innovation \citep{Bass:1969} to the estimation of forward citations, ``class-match" dampened forward citations, and the newly introduced Patent Rank Scores. The cumulative diffusion will be modeled using a generalized logistic function known as the Richards' curve \citep{Richards:1959}. To estimate the parameters of the the model, the Newton-Raphson method is used. Over 22,000 randomly selected patents from 1976--2008 will be individually modeled, and diffusion patterns will be classified based on the parameters of the model. Valuation of innovation can be objectively assessed, and future valuation can be predicted based on each innovation's specific diffusion pattern.