Stan Lipovetsky

The relative importance of project success dimensions

Traditionally, the success of a project is assessed using internal measures such as technical and operational goals, and meeting schedule and budget. More recently, it has been recognized that several other measures should be used to define project success. These measures reflect external effectiveness: the projects impact on its customers, and on the developing organization itself. In our study of 110 defense projects performed by Israeli industry, we used a multidimensional approach to measure the success of defense projects. Based on previous studies, we defined four dimensions of success: meeting design goals; benefits to the customer; benefits to the developing organization; and benefits to the defense and national infrastructure. For each project, we asked three different stakeholders (the customer, the developing organization, and the coordinating office within the Ministry of Defense) for their views on the relative importance of these dimensions of success. Analysis of the data revealed that the dimension benefits to the customer is by far the most important success dimension. The second in importance is meeting design goals. The other two dimensions are relatively unimportant.

Robust estimation of priorities in the AHP

Abstract A pairwise comparison matrix of the Analytic Hierarchy Process (AHP) is considered as a contingency table that helps to identify unusual or false data elicited from a judge. Special techniques are suggested for robust estimation of priority vectors. They include transformation of a Saaty matrix to matrix of shares of preferences and solving an eigenproblem designed for the transformed matrices. We also introduce an optimizing objective that produces robust priority estimation. Numerical results are compared using the AHP with these differing approaches. The comparison demonstrates that robust estimations yield priority vectors not prone to influence of possible errors among the elements of a pairwise comparison matrix.

Customer Satisfaction Analysis: Identification of Key Drivers

Abstract A problem of identifying key drivers in customer satisfaction analysis is considered in relation to Kano theory on the relationship between product quality and customer satisfaction using tools from cooperative game theory and risk analysis. We use Shapley value and attributable risk techniques to identify priorities of key drivers of customer satisfaction, or key dissatisfiers and key enhancers. We demonstrate the theoretical and practical advantages of Shapley value and attributable risk concepts in elaborating optimal marketing strategy.

Interval estimation of priorities in the AHP

This paper extends and modifies the Analytic Hierarchy Process (AHP) and the Synthetic Hierarchy Method (SHM) of priority estimation to accommodate random data in the pairwise comparison matrices. It employs a Cauchy distribution to describe the pairwise comparison of alternatives in Saaty matrices, and shows how to modify these matrices in order to handle random data. The use of random data yields Saaty matrices that are not reciprocally symmetrical. Several variants of the AHP are then modified (i) to accommodate reciprocally asymmetric matrices, and (ii) to allow each priority estimate to be expressed on an interval of possible values, rather than as a single discrete point. The merits of interval estimation are illustrated by an example.

The synthetic hierarchy method: An optimizing approach to obtaining priorities in the AHP

Abstract A new method of synthesizing local and criteria priorities into global priorities is suggested. This approach is a development of the Analytic Hierarchy Process enabling the united consideration of all horizontal and vertical connections of a hierarchical system in a single optimizing objective function based on statistical models of the synthesis process. The solution can be reduced to a linear system or to an eigenproblem of a special matrix constructed as a combination of Kroneckers sums and products of pairwise judgement matrices. A numerical example shows that the optimizing approach produces a ranking of global priorities that may be different from the ranking produced by the classical AHP.

Multiobjective regression modifications for collinearity

Abstract In this work we develop a new multivariate technique to produce regressions with interpretable coefficients that are close to and of the same signs as the pairwise regression coefficients. Using a multiobjective approach to incorporate multiple and pairwise regressions into one objective we reduce this technique to an eigenproblem that represents a hybrid between regression and principal component analyses. We show that our approach corresponds to a specific scheme of ridge regression with a total matrix added to the matrix of correlations. Scope and purpose One of the main goals of multiple regression modeling is to assess the importance of predictor variables in determining the prediction. However, in practical applications inference about the coefficients of regression can be difficult because real data is correlated and multicollinearity causes instability in the coefficients. In this paper we present a new technique to create a regression model that maintains the interpretability of the coefficients. We show with real data that it is possible to generate a model with coefficients that are similar to easily interpretable pairwise relations of predictors with the dependent variable, and this model is similar to the regular multiple regression model in predictive ability.

The Flexible CES-GBC Family of Cost Functions: Derivation and Application

Recent research has indicated that flexible forms do not always generate empirically credible elasticity estimates. In this paper we present a methodology from which we derive a new family of flexible functional forms (denoted by CES-GBC) that are richer in structure than the cost functions in current use. We use the CES-GBC form to estimate the demand for electricity under time-of-use pricing. The estimation results are encouraging. The elasticity estimates are very reasonable and the fit of the estimated equations is far superior than the more commonly used generalized Leontief, generalized square root quadratic, or any other member of the generalized Box-Cox family of cost functions.

Canonical correlation analyses for three data sets: a unified framework with application to management

Abstract Researchers in management, economics and other social sciences often find that they have very large data sets (a cross section over many firms, individuals or projects), but little theoretical knowledge of how to model the data. The common solution is to resort either to partial models that use small subsets of the data or to large linear models. In the first case, the researcher runs the risk of omitting important variables from the model. In the second case, there is a risk of specifying an inappropriate functional form of the relations among the variables. In this paper we survey, classify and apply several methodologies of generalized canonical correlation analysis (CCA) and partial canonical correlation analysis (PCCA) to estimate and analyze the relationships among several data sets. We present all the methods on a uniform basis—as optimization problems with several kinds of parameter restrictions. Further, we analyze all the methods within the convenient theoretical structure of generalized nonlinear eigenvector problems. These methods are applied to data on 310 Israeli manufacturing firms.

Modelling and forecasting with robust canonical analysis: method and application

Abstract Multivariate methods often serve as an intelligent way to study the relations between two data sets. When the number of variables in one or both data sets is large, which is usually the case, the correlation matrices of the data sets may be singular or ill-conditioned. When this happens the weights obtained by multivariate methods that require the inversion of the correlation matrices are not unique, or highly unreliable. Here we present and apply a robust estimation and forecasting method that does not require us to invert the correlation matrices. This method, which we call robust canonical analysis (RCA), is a straightforward extension of the simple covariance of two variables to two data sets. As an example we use the RCA method to estimate the relations between a set of measures that describe how the firm manages its relations with its customers, and a set of variables that describe the utility of information systems applications to the firm’s operations. Scope and purpose Researchers often employ multivariate analysis when they need to represent a very large data set by several easy-to-interpret variables, or when it is necessary to relate one set of variables to another. These methods facilitate identification of effects of key variables of one data set on all or several of the variables in the othe data set. Thus, they can also be used as data reduction methods. Depending on the particular application and the available data, a multivariate method serves either as the first stage of the quantitative analysis, or as the true representation of the theoretical model. In this study we present a multivariate generalization of covariance as a first-order approximation to the true relations between two large data sets that may exhibit severe multicollinearity among the variables.

SURF — STRUCTURAL UNDUPLICATED REACH AND FREQUENCY: LATENT CLASS TURF AND SHAPLEY VALUE ANALYSES

Lazarsfelds Latent Structure Analysis (LSA) is applied to problems in marketing involving the choice of products with maximum customer coverage. The LSA is combined with Total Unduplicated Reach and Frequency (TURF) technique, and also with a tool from cooperative game theory, the Shapley Value (SV), also known as the fuzzy Choquet integral. SV is used for ordering the items by their strength in covering the maximum number of consumers, which provides more stable results than TURF. Structural Unduplicated Reach and Frequency (SURF) analysis is introduced as the LSA segmentation of customers with different preferences across the products. The blending of LSA with TURF and SV yields new abilities of the latent structured TURF and SV. The marketing strategy based on using these techniques permits the identification of the preferred combinations in media or product mix for different population segments.