Enterprise Schemes for Nonprofit Survival,Growth, and Effectiveness

The need for nonprofit organizations to develop a viable enterprise scheme is discussed in this article. An enterprise scheme indicates how the organization can meet social needs and stakeholders' interests in a self‐sustaining way over the long haul. Without a viable enterprise scheme, the organization will not survive, let alone grow, prosper, and achieve its mission.     

Using Readiness Assessment to Improve Career Services: A Cognitive Information‐Processing Approach

This article begins with a review of the use of readiness assessment measures as a strategy for improving career services. A 5‐step process model for readiness assessment is then proposed and current readiness measures are identified. Although considerable research has been conducted on career decision‐making readiness and numerous measures have been developed, there has been limited literature available on the application of readiness assessment in selecting career interventions to meet specific client needs. This article continues with a theory‐based conceptualization of readiness and then links readiness assessment to the selection of career interventions designed to meet clients' needs.     

Decision Theory Applied to an Instrumental Variables Model

This paper applies some general concepts in decision theory to a simple instrumental variables model. There are two endogenous variables linked by a single structural equation; k of the exogenous variables are excluded from this structural equation and provide the instrumental variables (IV). The reduced‐form distribution of the endogenous variables conditional on the exogenous variables corresponds to independent draws from a bivariate normal distribution with linear regression functions and a known covariance matrix. A canonical form of the model has parameter vector (ρ, φ, ω), where φis the parameter of interest and is normalized to be a point on the unit circle. The reduced‐form coefficients on the instrumental variables are split into a scalar parameter ρand a parameter vector ω, which is normalized to be a point on the (k−1)‐dimensional unit sphere; ρmeasures the strength of the association between the endogenous variables and the instrumental variables, and ωis a measure of direction. A prior distribution is introduced for the IV model. The parameters φ, ρ, and ωare treated as independent random variables. The distribution for φis uniform on the unit circle; the distribution for ωis uniform on the unit sphere with dimension k‐1. These choices arise from the solution of a minimax problem. The prior for ρis left general. It turns out that given any positive value for ρ, the Bayes estimator of φdoes not depend on ρ; it equals the maximum‐likelihood estimator. This Bayes estimator has constant risk; because it minimizes average risk with respect to a proper prior, it is minimax. The same general concepts are applied to obtain confidence intervals. The prior distribution is used in two ways. The first way is to integrate out the nuisance parameter ωin the IV model. This gives an integrated likelihood function with two scalar parameters, φand ρ. Inverting a likelihood ratio test, based on the integrated likelihood function, provides a confidence interval for φ. This lacks finite sample optimality, but invariance arguments show that the risk function depends only on ρand not on φor ω. The second approach to confidence sets aims for finite sample optimality by setting up a loss function that trades off coverage against the length of the interval. The automatic uniform priors are used for φand ω, but a prior is also needed for the scalar ρ, and no guidance is offered on this choice. The Bayes rule is a highest posterior density set. Invariance arguments show that the risk function depends only on ρand not on φor ω. The optimality result combines average risk and maximum risk. The confidence set minimizes the average—with respect to the prior distribution for ρ—of the maximum risk, where the maximization is with respect to φand ω.     

Promises and Partnership

We examine experimentally the impact of communication on trust and cooperation. Our design admits observation of promises, lies, and beliefs. The evidence is consistent with people striving to live up to others' expectations so as to avoid guilt, as can be modeled using psychological game theory. When players exhibit such guilt aversion, communication may influence motivation and behavior by influencing beliefs about beliefs. Promises may enhance trustworthy behavior, which is what we observe. We argue that guilt aversion may be relevant for understanding strategic interaction in a variety of settings, and that it may shed light on the role of language, discussions, agreements, and social norms in these contexts.     

Item‐Level RFID in the Retail Supply Chain

Analyzing the proliferation of item‐level RFID, recent studies have identified the cost sharing of the technology as a gating issue. Various qualitative studies have predicted that conflict will arise, in particular in decentralized supply chains, from the fact that the benefits and the costs resulting from item‐level RFID are not symmetrically distributed among supply chain partners. To contribute to a better understanding of this situation, we consider a supply chain with one manufacturer and one retailer. Within the context of this retail supply chain, we present analytic models of the benefits of item‐level RFID to both supply chain partners. We examine both the case of a dominant manufacturer as well as the case of a dominant retailer, and we analyze the results of an introduction of item‐level RFID to such a supply chain depending on these market power characteristics. Under each scenario, we show how the cost of item‐level RFID should be allocated among supply chain partners such that supply chain profit is optimized.     

Unweaving leaving: the use of models in the management of employee turnover

This paper offers a review of the literature on labour turnover in organizations. Initially, the importance of the subject area is established, as analyses of turnover are outlined and critiqued. This leads to a discussion of the various ways in which turnover and its consequences are measured. The potentially critical impact of turnover behaviour on organizational effectiveness is presented as justification for the need to model turnover, as a precursor to prediction and prevention. Key models from the literature of labour turnover are presented and critiqued.     

Tracking the Sexual Behavior-Specific Effects of a Foster Family Treatment Program for Children with Serious Sexual Behavior Problems

Few treatment programs exist for very young children with serious sexual behavior problems. Fewer still have produced data relating to their effectiveness, and the sparse data that have emerged have focused on global social adjustment or improvement rather than on sexual behavior- specific changes. This study tracked both frequency of problem sexual behaviors and care giver estimates of the propensity to re-offend of six initial clients referred to a treatment-intensive foster care program for sexually reactive children and pre-adolescent sexual offenders. A simple pre-post (basepoint-treatment) design was used, and in-treatment data gathered over a two-year interval are presented. Initial results indicate that the problem sexual behaviors of most of these youthful clients were effectively and immediately suppressed in the context of their treatment intensive foster placements, but that the program's impact on the clients' propensity to re-offend given the opportunity—a crude measure of treatment internalization—was much less immediate, less pronounced, and less predictable across clients. Recommendations based on these data are offered concerning the viability of foster care intervention for sexualized and offending children, as well as optimal durations for such treatments. The strengths and weaknesses of the novel progress tracking method are discussed.     

Bayes linear analysis for graphical models: The geometric approach to local computation and interpretive graphics

This paper concerns the geometric treatment of graphical models using Bayes linear methods. We introduce Bayes linear separation as a second order generalised conditional independence relation, and Bayes linear graphical models are constructed using this property. A system of interpretive and diagnostic shadings are given, which summarise the analysis over the associated moral graph. Principles of local computation are outlined for the graphical models, and an algorithm for implementing such computation over the junction tree is described. The approach is illustrated with two examples. The first concerns sales forecasting using a multivariate dynamic linear model. The second concerns inference for the error variance matrices of the model for sales, and illustrates the generality of our geometric approach by treating the matrices directly as random objects. The examples are implemented using a freely available set of object-oriented programming tools for Bayes linear local computation and graphical diagnostic display.