Measuring to Improve Versus Measuring to Prove: Understanding the Adoption of Social Performance Measurement Practices in Nascent Social Enterprises

Social enterprises are described as organizations with dual objectives—social and commercial. While the measurement of commercial performance is relatively straightforward and well understood, our understanding of the factors related to measuring social performance is more ambiguous. Is the adoption of social performance measurement (SPM) practices more related to external pressures, such as the need to demonstrate legitimacy to funders and peers, or is it more closely related to the growing rationalization within the social sector? We examine the relationship between external and internal factors and the adoption of SPM using a novel dataset of 1864 nascent social enterprises from around the world. Our findings suggest support for the argument that the adoption of SPM in social enterprise is related to the growing rationalization of the social sector, which challenges some of the past research on this topic, and provides a more nuanced perspective of SPM in social enterprise.     

An alternative estimator for multiple characteristics in PPS sampling

The problem considered relates to large-scale sample surveys. A new estimator of population total for the characteristics that are poorly correlated with the selection probabilities has been developed for the PPSWR sampling scheme. The relative efficiency of the proposed estimator has been studied under a super-population model. A numerical investigation into the performance of the estimator has also been made.     

Managing Demand Uncertainty through Core Acquisition in Remanufacturing

We discuss the optimal raw material acquisition strategy for a third party remanufacturer (3PR). We specifically investigate whether a 3PR should acquire used products or cores in bulk with uncertain quality levels, or in sorted grades with known quality levels; and whether to acquire and remanufacture cores before the demand is realized (planned acquisition), or after the demand is realized (reactive acquisition), or on both occasions (sequential acquisition). When only sorted cores are acquired, we find that, (i) it is optimal to acquire cores in multiple grades to balance acquisition and remanufacturing costs; (ii) if reactive acquisition is possible, it reduces the assortment size (number of grades in which cores are acquired) and the total inventory acquired in the planned acquisition; and (iii) the optimal portfolio of grades to acquire and the optimal acquisition and remanufacturing quantities of these grades can be determined analytically. When bulk cores are acquired in addition to sorted cores, the property of reduction in assortment size of the planned acquisition is preserved. We also show that the 3PR should acquire only a fraction of the demand in planned acquisition, and leave the rest for reactive acquisition. This fraction changes during the lifecycle of a remanufactured product. Using a combination of empirical and realistic data from a smartphone remanufacturer we show that sequential acquisition increases expected profit by up to 8% and 27% over only planned and only reactive acquisitions respectively, and reduces the inventory acquired by up to 21% over only planned acquisition.     

An Application of EM Algorithm to a Change-Point Problem

We consider the problem of change-point in a classical framework while assuming a probability distribution for the change-point. An EM algorithm is proposed to estimate the distribution of the change-point. A change-point model for multiple profiles is also proposed, and EM algorithm is presented to estimate the model. Two examples of Illinois traffic data and Dow Jones Industrial Averages are used to demonstrate the proposed methods.     

Bayes Prediction for a Heteroscedastic Regression Superpopulation Model Using Balanced Loss Function

We consider Prais–Houthakker heteroscedastic normal regression model having variance of the dependent variable same as square of its expectation. Bayes predictors for the regression coefficient and the mean of a finite population are derived using Zellner's balanced loss function. Bayes predictive expected losses are obtained and compared with those of classical predictors and Bayes predictors under squared error loss function to examine their loss robustness.     

On the mixtures of Weibull and Pareto (IV) distribution: An alternative to Pareto distribution

Finite mixture models have provided a reasonable tool to model various types of observed phenomena, specially those which are random in nature. In this article, a finite mixture of Weibull and Pareto (IV) distribution is considered and studied. Some structural properties of the resulting model are discussed including estimation of the model parameters via expectation maximization (EM) algorithm. A real-life data application exhibits the fact that in certain situations, this mixture model might be a better alternative than the rival popular models.     

FPT algorithms for Connected Feedback Vertex Set

We study the recently introduced Connected Feedback Vertex Set (CFVS) problem from the view-point of parameterized algorithms. CFVS is the connected variant of the classical Feedback Vertex Set problem and is defined as follows: given a graph G=(V,E) and an integer k, decide whether there exists F?V, |F|??k, such that G[V?F] is a forest and G[F] is connected. We show that Connected Feedback Vertex Set can be solved in time O(2 ^O(k) n ^O(1)) on general graphs and in time $O(2^{O(\sqrt{k}\log k)}n^{O(1)})$ on graphs excluding a fixed graph H as a minor. Our result on general undirected graphs uses, as a subroutine, a parameterized algorithm for Group Steiner Tree, a well studied variant of Steiner Tree. We find the algorithm for Group Steiner Tree of independent interest and believe that it could be useful for obtaining parameterized algorithms for other connectivity problems.