What Do We Know About Corporate Headquarters? A Review, Integration, and Research Agenda

During the past five decades, scholars have studied the corporate headquarters (CHQ) – the multidivisional firm’s central organizational unit. The purpose of this article is to review the diverse and fragmented literature on the CHQ and to identify the variables of interest, the dominant relationships, and the contributions. We integrate, for the first time, the existing knowledge of the CHQ into an organizing framework. Based on a synthesis of the literature, we identify major shortcomings and gaps, and present an agenda for future research that contributes to our understanding of the CHQ and the multidivisional firm

Model order reduction approaches for infinite horizon optimal control problems via the HJB equation

We investigate feedback control for infinite horizon optimal control problems for partial differential equations. The method is based on the coupling between Hamilton-Jacobi-Bellman (HJB) equations and model reduction techniques. It is well-known that HJB equations suffer the so called curse of dimensionality and, therefore, a reduction of the dimension of the system is mandatory. In this report we focus on the infinite horizon optimal control problem with quadratic cost functionals. We compare several model reduction methods such as Proper Orthogonal Decomposition, Balanced Truncation and a new algebraic Riccati equation based approach. Finally, we present numerical examples and discuss several features of the different methods analyzing advantages and disadvantages of the reduction methods

Deep Bilevel Learning

We present a novel regularization approach to train neural networks that enjoys better generalization and test error than standard stochastic gradient descent. Our approach is based on the principles of cross-validation, where a validation set is used to limit the model overfitting. We formulate such principles as a bilevel optimization problem. This formulation allows us to define the optimization of a cost on the validation set subject to another optimization on the training set. The overfitting is controlled by introducing weights on each mini-batch in the training set and by choosing their values so that they minimize the error on the validation set. In practice, these weights define mini-batch learning rates in a gradient descent update equation that favor gradients with better generalization capabilities. Because of its simplicity, this approach can be integrated with other regularization methods and training schemes. We evaluate extensively our proposed algorithm on several neural network architectures and datasets, and find that it consistently improves the generalization of the model, especially when labels are noisy.Comment: ECCV 201

Stabilization by sparse controls for a class of semilinear parabolic equations

Stabilization problems for parabolic equations with polynomial nonlinearities are investigated in the context of an optimal control formulation with a sparsity enhancing cost functional. This formulation allows that the optimal control completely shuts down once the trajectory is sufficiently close to a stable steady state. Such a property is not present for commonly chosen control mechanisms. To establish these results it is necessary to develop a function space framework for a class of optimal control problems posed on infinite time horizons, which is otherwise not available.The first author was supported by Spanish Ministerio de Economía y Competitividad under project MTM2014-57531-P. The second author was supported by the Austrian Science Fund (FWF) under grant SFB F32 (SFB “Mathematical Optimization and Applications in Biomedical Sciences”) and by the ERC advanced grant 668998 (OCLOC) under the EU’s H2020 research program

Order reduction approaches for the algebraic Riccati equation and the LQR problem

We explore order reduction techniques for solving the algebraic Riccati equation (ARE), and investigating the numerical solution of the linear-quadratic regulator problem (LQR). A classical approach is to build a surrogate low dimensional model of the dynamical system, for instance by means of balanced truncation, and then solve the corresponding ARE. Alternatively, iterative methods can be used to directly solve the ARE and use its approximate solution to estimate quantities associated with the LQR. We propose a class of Petrov-Galerkin strategies that simultaneously reduce the dynamical system while approximately solving the ARE by projection. This methodology significantly generalizes a recently developed Galerkin method by using a pair of projection spaces, as it is often done in model order reduction of dynamical systems. Numerical experiments illustrate the advantages of the new class of methods over classical approaches when dealing with large matrices

Spatially dispersed corporate headquarters: a historical analysis of their prevalence, antecedents, and consequences

Our study, which complements recent works challenging the traditional conceptualization of the CHQ as a single organizational unit, has a dual purpose. First, in descriptive terms, we set out to explore the prevalence of spatially dispersed CHQs in a historical context. Second, we aim to shed additional light on the CHQ’s spatial design by exploring internal antecedents and potential consequences. Building on arguments from information-processing theory, we propose that the strategic complexity facing the CHQ (affecting its information-processing demands) is associated with the likelihood of a spatially dispersed CHQ (affecting its information-processing capacity). In line with our dual purpose, we conduct a historical study drawing on survey and archival data covering 156 public firms domiciled in four countries (Germany, the Netherlands, the UK, and the US) in the late 1990s. Our results provide empirical support for the hypothesized associations between strategic complexity and the CHQ’s spatial design. Moreover, although we find no empirical support for the expected contingency effects, the results suggest that a spatially dispersed CHQ can have negative effects on CHQ and firm performance. Overall, our theoretical arguments and empirical results advance our knowledge about complex CHQ configurations

The Corporate Headquarters in the Contemporary Corporation: Advancing a Multimarket Firm Perspective

The corporate headquarters (CHQ) is the central organizational unit in the contemporary corporation, and is critical for value creation in the overall firm. Since the early 1960s, a significant body of research on the CHQ has evolved along two separate but related streams. The first stream focuses on the CHQ in the multibusiness firm, while the second stream concerns the CHQ in the multinational firm. In this article, we promote a consistent multimarket firm perspective that draws on research in both streams. First, we describe the origins and evolution of CHQ research in each stream, and articulate the benefits of a multimarket firm perspective. Second, we integrate the conversations found in the two streams into a schematic framework, review the studies’ findings, and establish a shared language. We also propose ways in which scholars in each stream might enrich their work by incorporating some of the theories, methods, and findings of the other stream. Third, we discuss four fundamental inquiries for future research that draw upon the cumulative CHQ research in both streams. Overall, this article informs the study of the CHQ and, thereby, contributes to our understanding of the contemporary corporation

Using review articles to address societal grand challenges

We introduce a special issue of International Journal of Management Reviews that demonstrates how to use review articles to address societal grand challenges—complex, large-scale issues facing humankind, such as climate change, inequality and poverty. First, we argue that review articles possess unique features that make them particularly useful for addressing societal grand challenges. Second, we discuss three distinct but related roles of review articles in addressing societal grand challenges: (1) advancing theoretical knowledge; (2) advancing methodological knowledge; and (3) advancing practical knowledge. We conclude by providing future directions to enhance contributions of review articles for addressing societal grand challenges further by: (a) spanning disciplinary boundaries; (b) engaging practitioners; and (c) using alternative review approaches

Why do firms launch corporate change programs? A contingency perspective on strategic change

We study strategic change as a visible and substantive action by examining the circumstances under which firms launch corporate change programs. Drawing on prior literature and corroborated by insights from interviews with executives, we propose a contingency perspective on the launch of corporate change programs (i.e. that different types of programs are launched under different circumstances). To do so, we combine arguments for three general motives for launching a corporate change program with two distinct types of corporate change programs. More specifically, we argue that firms are more likely to launch growth-oriented programs when the market situation is buoyant, when they have prior experience, and when they are underperforming. Furthermore, we argue that firms are more likely to launch efficiency-oriented programs when there is a new CEO, when they are underperforming, and when they are facing high levels of organizational complexity. To test our hypotheses regarding the motives for launching programs, we conducted a large-scale empirical study. Using hand-collected data for the European financial services and insurance industry over a ten-year period, we found support for our predictions. We discuss the implications of these findings for strategic change research

Learning filter functions in regularisers by minimising quotients

Learning approaches have recently become very popular in the field of inverse problems. A large variety of methods has been established in recent years, ranging from bi-level learning to high-dimensional machine learning techniques. Most learning approaches, however, only aim at fitting parametrised models to favourable training data whilst ignoring misfit training data completely. In this paper, we follow up on the idea of learning parametrised regularisation functions by quotient minimisation as established in [3]. We extend the model therein to include higher-dimensional filter functions to be learned and allow for fit- and misfit-training data consisting of multiple functions. We first present results resembling behaviour of well-established derivative-based sparse regularisers like total variation or higher-order total variation in one-dimension. Our second and main contribution is the introduction of novel families of non-derivative-based regularisers. This is accomplished by learning favourable scales and geometric properties while at the same time avoiding unfavourable ones