Using ANP in the Non-Profit Sector: Selecting a Congress Site and Predicting Conference Attendance

Abstract

Non-profit organizations operate with more tight budgets and restrictions than their private counterparts. There is always a risk that a wrong decision may have catastrophic effects for the organization. Also, decisions made by non-profit managers tend to be highly scrutinized by their constituents in terms of consistency with the organizational mission, objectives, etc. This creates the need for managers, to use a methodology that not only assists in their decision-making process but that also allows them to explain the decision criteria to the organizational stakeholders. This paper reports an on-going application of the Analytic Network Process (ANP) in the context of one such organization: The Latin American Studies Association (LASA). LASA organizes a large international conference every eighteen months and need to estimate conference attendance in advance (for logistic purposes) as well as selecting a Latin American city where the combination of hotel infrastructure, conference costs, and so on, makes it a sound financial choice. In this paper, ANP will be used to first, create a model to predict the relative number of attendees to the forthcoming 2009 LASA international conference; and second, to create a Benefit-Cost-Risk (BCR) model that will provide a framework to select the most suitable Latin American city as the conference site. This paper shows how the combination of these two ANP models, for prediction and selection, can be used together for effective decision-making in the non-profit sector. From a practical point of view, these two models will allow LASA top managers, to select a conference site for their forthcoming March 2009 International LASA congress in a rational, consistent way, based on both the expected number of attendees and organizational objectives. Also, using ANP methodology, LASA managers will be able to explain to interested constituents, the criteria used in the selection process.