Social Entrepreneurship and Institutional Sustainability: Insights from an Embedded Social Enterprise

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - Mainstream enterprises function by alleviating the cognitive burdens on their members and hence generating an...     

An Exploration of New Product Development's Front‐end Knowledge Conceptualization Process in Discontinuous Innovations

Organizational knowledge creation, as reflected in new product/service development, is a vital process for firms to master. Critical to the development of new products/services is the management of the front‐end phase, which crucially determines the eventual fate of a new product/service idea. However, studies on how knowledge is created at the front‐end of the new product development process are rare. This empirical paper addresses this gap by focusing on knowledge conceptualization – where new knowledge from its generation is crystallized into a concrete and explicit form. Employing a qualitative methodology, the paper examines ten discontinuous projects and develops a front‐end knowledge conceptualization framework. The framework consists of the identified knowledge conceptualization stages and illuminates the pattern of overlaps, outcomes, contributors, knowledge, interactions and volatility across each stage. Our main contribution is a knowledge‐based appreciation of the dynamic and interactive nature of the new product/service development's front‐end phase in the context of discontinuous innovations, and thence the paper provides clear managerial learning points for the effective management of this phase.     

Dynamic factor analysis for short panels: estimating performance trajectories for water utilities

We develop a novel estimation algorithm for a dynamic factor model (DFM) applied to panel data with a short time dimension and a large cross sectional dimension. Current DFMs usually require panels with a minimum of 20 years of quarterly data (80 time observations per panel). In contrast, the application we consider includes panels with a median of 8 annual observations. As a result, the time dimension in our paper is substantially shorter than previous work in the DFM literature. This difference increases the computational challenges of the estimation process which we address by developing the “Two-Cycle Conditional Expectation - Maximization” (2CCEM) algorithm which is a variant of the EM algorithm and its extensions. We analyze the conditions under which our model is identified and provide simulation results demonstrating consistency of our 2CCEM estimator. We apply the DFM to a dataset of 802 water and sanitation utilities from 43 countries and use the 2CCEM algorithm in order to estimate dynamic performance trajectories for each utility.     

Bayesian inference for stochastic multitype epidemics in structured populations via random graphs

Summary.  The paper is concerned with new methodology for statistical inference for final outcome infectious disease data using certain structured population stochastic epidemic models. A major obstacle to inference for such models is that the likelihood is both analytically and numerically intractable. The approach that is taken here is to impute missing information in the form of a random graph that describes the potential infectious contacts between individuals. This level of imputation overcomes various constraints of existing methodologies and yields more detailed information about the spread of disease. The methods are illustrated with both real and test data.     

Distribution-free monitoring schemes based on order statistics: a general approach

In this article, we establish a new class of distribution-free Shewhart-type monitoring schemes based on order statistics. The setup of the proposed family of nonparametric control charts is presented in detail. Specific monitoring schemes, already introduced in the literature, are confirmed to be members of the new class. In addition, a new nonparametric monitoring scheme that belongs to the class is established, while explicit formulae for its basic characteristics are reached. The numerical study carried out reveals that the proposed scheme achieves adversarial in-control and out-of-control performance.KEYWORDS: Distribution-free monitoring schemes, average run length, false alarm rate, Lehmann alternatives, statistical process control, order statistics     

Piecewise deterministic Markov processes applied to fatigue crack growth modelling

In this paper, we use a particular piecewise deterministic Markov process (PDMP) to model the evolution of a degradation mechanism that may arise in various structural components, namely, the fatigue crack growth. We first derive some probability results on the stochastic dynamics with the help of Markov renewal theory: a closed-form solution for the transition function of the PDMP is given. Then, we investigate some methods to estimate the parameters of the dynamical system, involving Bogolyubov's averaging principle and maximum likelihood estimation for the infinitesimal generator of the underlying jump Markov process. Numerical applications on a real crack data set are given.     

Lottery- and survey-based risk attitudes linked through a multichoice elicitation task

We analyze the results from three different risk attitude elicitation methods. First, the broadly used test by Holt and Laury (2002), HL, second, the lottery-panel task by Sabater-Grande and Georgantzis (2002), SG, and third, responses to a survey question on self-assessment of general attitude towards risk (Dohmen et al. 2011). The first and the second task are implemented with real monetary incentives, while the third concerns all domains in life in general. Like in previous studies, the correlation of decisions across tasks is low and usually statistically non-significant. However, when we consider only subjects whose behavior across the panels of the SG task is compatible with constant relative risk aversion (CRRA), the correlation between HL and self-assessed risk attitude becomes significant. Furthermore, the correlation between HL and SG also increases for CRRA-compatible subjects, although it remains statistically non-significant.     

On a multidimensional general bootstrap for empirical estimator of continuous-time semi-Markov kernels with applications

The present paper introduces a general notion and presents results of bootstrapped empirical estimators of the semi-Markov kernels and of the conditional transition distributions for semi-Markov processes with countable state space, constructed by exchangeably weighting the sample. Our proposal provides a unification of bootstrap methods in the semi-Markov setting including, in particular, Efron's bootstrap. Asymptotic properties of these generalised bootstrapped empirical distributions are obtained, under mild conditions by a martingale approach. We also obtain some new results on the weak convergence of the empirical semi-Markov processes. We apply these general results in several statistical problems such as the construction of confidence bands and the goodness-of-fit tests where the limiting distributions are derived under the null hypothesis. Finally, we introduce the quantile estimators and their bootstrapped versions in the semi-Markov framework and we establish their limiting laws by using the functional delta methods. Our theoretical results and numerical examples by simulations demonstrate the merits of the proposed techniques.     

Terrorism and the effectiveness of security spending in Greece: Policy implications of some empirical findings

Greece has over the years faced serious security challenges from domestic as well as transnational terrorist activity. This paper examines empirically the effectiveness of counter-terrorism policy and particularly it focuses on current and investment expenditure on domestic security and public order. Using annual budget data for the 1974–2004 period, it investigates whether current and investment spending by the Ministry of Public Order has been an effective policy measure to counter terrorism. The results seem to suggest that such investment has at best a weak negative impact on internal terrorist actions. The main policy implication of this finding is that investing in counter-terrorist infrastructure and equipment can potentially prove to be an effective policy measure in the fight against terrorism. This, however, may be conditional upon a number of other factors including other anti-terrorist measures such as legislation or how efficiently such expenditure is used.