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Biography

John Howell is an Assistant Professor of Marketing at Brigham Young University, where he studies quantitative models using Bayesian statistics. His primary research interests involve examining the causal motivators of individual choice. He has done extensive research on the barriers to product adoption and is especially interested in the hidden costs that consumers incur in order to evaluate, purchase, and/or use a product. The work is especially relevant to managers making pricing and promotion decisions. He has also done extensive work on preference elicitation techniques including choice-based conjoint analysis and discrete choice models.

Prior to joining academia, John worked as a consultant to Sawtooth Software, where he developed conjoint analysis solutions for a wide variety of clients in industries. John also worked as a Pricing Analyst and Software Engineer for Novell, Inc.

John received his Ph. D. from Ohio State University in 2013 and worked as an Assistant Professor of Marketing at Penn State University from 2013-2018.

Selected Publications

• "Nurse practitioner job preference: A discrete choice experiment", International Journal of Nursing Studies, Edition February 2023, Volume 138, Page 104407, Pergamon, 2022
• "Gremlins in the Data: Identifying the Information Content of Research Subjects", Journal of Marketing Research, Edition 1, Volume 58, Sage Journals, 2021
• "Patient familiarity with, understanding of, and preferences for clinical trial endpoints and terminology", Cancer, Wiley, 2020
• "A Probit Model with Structured Covariance for Similarity Effects and Source of Volume Calculations", 2018
• "Feature Valuation Using Equilibrium Analysis", Handbook of Marketing Analytics, 2018
• "Discerning the clinical relevance of biomarkers in early stage breast cancer", Breast Cancer Treatment and Research, Edition 164, Pages 89-97, Springer, 2017
• "Price Promotions in Choice Models", Edition 2, Volume 35, Pages 319-334, 2015
• "Valuation of Patented Product Features", 2014
• "Economic Valuation of Product Features", 2014
• "Analyzing Platforms Goods Using Multiple-Discrete Continuous Demand Models"