Monte J. Shaffer
Monte J. Shaffer is a fourth-year Ph.D. student and job market candidate (2011) in the Department of Marketing at Washington State University. Monte is currently working on his marketing dissertation in Entrepreneurial Innovations. Prior to joining Washington State University, Monte received a Bachelor in Mathematics / MBA in Marketing from Brigham Young University (BYU) in Provo, UT.
"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.
Entrepreneurial Research
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.
Entrepreneurial Innovation
Entreprenuerial perceptions are elusive. Applying principles of individual differences may help unlock the paradoxes of the risk taker/avoider? What motivates an entrepreneur to start a business? What influences an entrepreneur's decision to attempt to protect the startup idea using the patent process?
My dissertation focuses on two Kauffman datasets (1998 EOYI, most recent longitudinal study), interviews with faculty, students in entrepreneurship, interviews with agents involved in patenting process, pilot study of business students to measure motivations (entrepreneurial proclivity), etc.
U.S. Patent Data
There has been a call for 'new' patent data (Kortum - see Tellis et al. 2009). I believe that I can contribute to the field of marketing strategy by improving the data available, and describing its potential uses. The new data source allows for large and rich information regarding patents that can be used in many types of strategic analyses. The most recent run of these data consisted of 73 IT firms in the S&P 500. Collecting data from January 1996 to June 2009 provides over 192,000 patents with information about forward/backward citations, classification matches, and more. The programming process to run this list took nearly 36 hours as it had to analyze over 3 million patents to create the informative dataset. This is my definition of new data, and the process is continuous and ongoing:
All Patent Data has been harvest (8 million patents)
Parsed Data is currently being stored in database format
Firm boundary issues [IBM, Internation Business Machines, mergers, misspellings, etc.]
with an intent to do new modeling research on the patent data:
Diffusion of Radical Innovations (patents)
Patent Rank (e.g., Page Rank applied to patent network of citations) - structural and weighted ranks (e.g., classification matching)
EIQ
Race to the Patent Office
Make vs. Buy - Marketing Rationale as a Basis for Entrepreneurs' Outsourcing Configuration
The strategic decision to outsource organizational functions is an important part of marketing strategy. This research compares and contrasts the marketing rationale of entrepreneurial CEOs on the firm's outsourcing configuration: perceptions of strategic focus, the competitive environment, and internal control/tenure. A multistep-multiscale bootstrapping algorithm is used to measure the stability of a hierarchical cluster which classifies an entrepreneur's competitive outlook; competing on: costs, brand, and/or quality. A model-based Gaussian mixture algorithm is used to classify entrepreneurial CEO control: managers, partners, and control-freaks. With the marketing rationale appropriately classified, two different log-linear analyses are performed which provide complementary findings. The first analysis uses a non-hierarchical model, Poisson regression, and verifies that current outsourcing configurations relate to rationale regarding market competition, internal constraints (level of control and tenure), and the CEO's strategic focus. The second analysis applies an innovative statistical technique known as multigraph representations on a decomposable hierarchical log-linear model, generating class, to create statements of independence among the variables considered. Implications of these findings are discussed for entrepreneurial startups and firms transitioning from the founder to a new CEO.
Empirical Modeling
With a background in Mathematics, I enjoy learning about new Statistical insights that can be applied to marketing problems. Specifically, I have applied Multigraph representations of HLLM contingency tables. I have also began studying parameterized growth curves: (1) can I statistically compare multiple curves and say they are different? (2) can I classify growth curves such that the clusters represent statistically different curves?
I have also done statistical consulting for various clients: Apple growers in the state of Washington (apple growth, blossom stages, and temperature); Archaelogists with raw consumerism data from Africa foragers; Biologists studying entomology, faunal growth, etc.
Growth Models
The generalized logistic function, also known as the Richard's curve (1959), is a six-parameter nonlinear function that models growth over time. Most importantly, each of the six parameters have conceptual meaning that can help solve numerous types of problems. It is a robust form as it can be reduced to simpler models, with the simplest being the logistic function. Using R (and SAS), I fit data to variants of this nonlinear model.
Multiple Comparisons to Control
Using R, I programmed the critical values for Multiple Comparisons to Control based on the proofs of Dasgupta (1996). The assumptions of this comparison is that the different parameterized models all have the same sample size (except for the control model). We extend these findings and simulate critical values for unequal sample sizes, provide the simulation programming, and offer general "rules of thumb" to determine when the simulations are required and when assuming equal sample sizes is good enough.
The basics of Multiple Comparisons, pairwise comparisons, and nested hypotheses all are valuable tools that should be used in understanding and solving marketing problems.
Proximity to markets and consumption patterns among foragers from the Heart of Central Africa
Working with an archaelogist with interesting data, we wanted to study raw consumerism from a marketing perspective: materialism (hi/lo), I vs. we (hunting technique), distance from commercial market, hunting success, hedonic vs utilitarian goods in Forager culture, signaling with faunal remains (Bones), etc.
Heading using the h3 tag
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