Metatheory of network management: A contingency perspective

This paper introduces a metatheoretical, contingency-based framework of inter-organizational network management. We define four basic network management functions and suggest that they are universal to all inter-organizational networks. We argue that management tasks, derived from more general management functions and contingent upon network characteristics, differ according to network type. We further argue that the roles that different actors in a network can adopt depend on their resources and capabilities. The framework contributes to the emerging theory of network management by clarifying the connections between its different conceptualizations and layers. A clear implication of the framework is that it is fundamentally useless to argue that networks cannot be managed. Networks are being managed, all of the time. However, we agree that the extent to which networks can be managed differs from one network to another along with the managerial tasks employed.     

Serial Nonparticipation in Repeated Discrete Choice Models

We consider alternative econometric strategies for addressing serial nonparticipation, that is, repeated choice of the same alternative or same type of alternative across a series of choice occasions, in data typically analyzed within the repeated discrete choice framework. Single and double hurdle variants of the repeated discrete choice model are developed and applied to choice experiment and multisite seasonal recreation demand data. Our results suggest that hurdle models can generate significant improvements in statistical fit and qualitatively different policy implications, particularly in choice experiment applications where the proper treatment of serial nonparticipation is relatively more ambiguous.     

Competition,cooperation, and corporate culture

Cooperation between workers can be of substantial value to a firm, yet its level often varies substantially between firms. We show that these differences can unfold in a competitive labor market if workers have heterogeneous social preferences and preferences are private information. In our model, workers differ in their willingness to cooperate voluntarily. We show that there always exists a separating equilibrium in which workers self‐select into firms that differ in their monetary incentives as well as their level of worker cooperation. Our model highlights the role of sorting and worker heterogeneity in the emergence of heterogeneous corporate cultures. It also provides a new explanation for the coexistence of nonprofit and for‐profit firms.

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One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient,root-<Emphasis Type="Italic">n</Emphasis> consistent StoNEZD method

Understanding the effects of operational conditions and practices on productive efficiency can provide valuable economic and managerial insights. The conventional approach is to use a two-stage method where the efficiency estimates are regressed on contextual variables representing the operational conditions. The main problem of the two-stage approach is that it ignores the correlations between inputs and contextual variables. To address this shortcoming, we build on the recently developed regression interpretation of data envelopment analysis (DEA) to develop a new one-stage semi-nonparametric estimator that combines the nonparametric DEA-style frontier with a regression model of the contextual variables. The new method is referred to as stochastic semi-nonparametric envelopment of z variables data (StoNEZD). The StoNEZD estimator for the contextual variables is shown to be statistically consistent under less restrictive assumptions than those required by the two-stage DEA estimator. Further, the StoNEZD estimator is shown to be unbiased, asymptotically efficient, asymptotically normally distributed, and converge at the standard parametric rate of order n ^−1/2. Therefore, the conventional methods of statistical testing and confidence intervals apply for asymptotic inference. Finite sample performance of the proposed estimators is examined through Monte Carlo simulations.     

Fitting dynamic factor models to non-stationary time series

Factor modelling of a large time series panel has widely proven useful to reduce its cross-sectional dimensionality. This is done by explaining common co-movements in the panel through the existence of a small number of common components, up to some idiosyncratic behaviour of each individual series. To capture serial correlation in the common components, a dynamic structure is used as in traditional (uni- or multivariate) time series analysis of second order structure, i.e. allowing for infinite-length filtering of the factors via dynamic loadings. In this paper, motivated from economic data observed over long time periods which show smooth transitions over time in their covariance structure, we allow the dynamic structure of the factor model to be non-stationary over time by proposing a deterministic time variation of its loadings. In this respect we generalize the existing recent work on static factor models with time-varying loadings as well as the classical, i.e. stationary, dynamic approximate factor model. Motivated from the stationary case, we estimate the common components of our dynamic factor model by the eigenvectors of a consistent estimator of the now time-varying spectral density matrix of the underlying data-generating process. This can be seen as a time-varying principal components approach in the frequency domain. We derive consistency of this estimator in a “double-asymptotic” framework of both cross-section and time dimension tending to infinity. The performance of the estimators is illustrated by a simulation study and an application to a macroeconomic data set.