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The purpose of this paper is to introduce virtue epistemology as a complementary approach to how we learn and make wise decisions within organizations.

Drawing on a philosophic history of intellectual virtue and recent research into virtue epistemology, this article presents an applied theoretical approach for practitioners to use in developing a more robust learning environment.

With robust market and operational databases of information, organizations continue to face the difficult decision of what this data means and what they can do with it. This article suggests intellectual virtue as a tool to develop appropriate knowledge, informed practical actions and sustainable outcomes.

Volatility, uncertainty, complexity and ambiguity have led to increasing rates of change in organizations. Organizations rely increasingly on their ability to observe, analyze, interpret and ultimately make decisions and act in ways that ensure sustainable results. This article provides an alternative perspective to complement traditional problem solving and decision-making processes.

There is currently limited research into the applicability of intellectual virtue or virtue epistemology to the field of organizational development and learning.

This research examines the use of econometric models to predict the total net asset value (NAV) of an asset allocation mutual fund. In particular, the mutual fund case used is the Vanguard Wellington Fund (VWELX). This fund maintains a balance between relatively conservative stocks and bonds. The period of the study on which the prediction of the total NAV is based is the 24-month period of 2010 and 2011 and the forecasting period is the first three months of 2012. Forecasting the total NAV of a massive conservative allocation fund, composed of an extremely large number of investments, requires a method that produces accurate results. Achieving this accuracy has no necessary relationship to the complexity of the methods typically employed in many financial forecasting studies.

Pharmaceutical companies are faced with identifying development compounds for their Drug Development Processes (DDPs) that will not only gain approval for sale by the regulatory agencies, such as the Food and Drug Administration (FDA), but also establish a sustainable and profitable market presence. This identification of compounds for the DDP includes projection of objective criteria, such as ability to generate revenue and profitability (Financial) and safety and efficacy (Clinical), as well as more subjective criteria, such as determination of insurance coverage by payers, such as the Centers for Medicare and Medicaid Services and pricing (Reimbursement), ability to produce a product of consistent quality (Manufacturing), and attain approval for sale in a timely manner (Registration). The Analytical Hierarchy Process (AHP) is a multi-criteria decision model that can integrate both objective and subjective information. This study applies the AHP methodology to the identification of compounds resulting in a dynamic application of the model that can be used by pharmaceutical companies to determine the best compounds to put in the DDP, at a time when the cost of conducting clinical evaluations for development compounds is very high and global market conditions are evolving.