Private equity and entrepreneurial management in management buy-outs

Critics claim that short-term profit orientation and high deal price strategies of private equity (PE) firms can negatively affect the ability of management buyouts to initiate and sustain entrepreneurial management. This study investigates this claim by comparing effects of majority PE backed and other buy-outs at different levels of financial leverage on post buy-out increases in entrepreneurial management. We propose that PE can be used as an organizational refocusing device that simultaneously increases entrepreneurial and administrative management. We find that majority PE-backed buy-outs significantly increase entrepreneurial management practices. Furthermore, the increased financial leverage positively affects administrative management in management buy-outs. However, the effect of high financial leverage is larger for majority PE-backed buy-outs. These results support the notion that PE firms help buy-out companies develop ambidextrous organizational change: i.e. simultaneously develop entrepreneurial and administrative management practices. The findings have important implications for practitioners and policy makers.

Deriving the Railway Mania

This article argues that the promotion boom which occurred in the railway industry during the mid 1840s was amplified by the issue of derivative-like assets, which let investors take highly leveraged positions in the shares of new railway companies. The partially paid shares which the new railway companies issued allowed investors to obtain exposure to an asset by paying only a small initial deposit. The consequence of this arrangement was that investor returns were substantially amplified, and many schemes could be financed simultaneously. However, when investors were required to make further payments it put a negative downward pressure on prices.

Common-sense knowledge for a computer vision system for human action recognition

This work presents a novel approach for human action recognition based on the combination of computer vision techniques and common-sense knowledge and reasoning capabilities. The emphasis of this work is on how common sense has to be leveraged to a vision-based human action recognition so that nonsensical errors can be amended at the understanding stage. The proposed framework is to be deployed in a realistic environment in which humans behave rationally, that is, motivated by an aim or a reason. © 2012 Springer-Verlag.

Common-sense reasoning for human action recognition

This paper presents a novel method that leverages reasoning capabilities in a computer vision system dedicated to human action recognition. The proposed methodology is decomposed into two stages. First, a machine learning based algorithm - known as bag of words - gives a first estimate of action classification from video sequences, by performing an image feature analysis. Those results are afterward passed to a common-sense reasoning system, which analyses, selects and corrects the initial estimation yielded by the machine learning algorithm. This second stage resorts to the knowledge implicit in the rationality that motivates human behaviour. Experiments are performed in realistic conditions, where poor recognition rates by the machine learning techniques are significantly improved by the second stage in which common-sense knowledge and reasoning capabilities have been leveraged. This demonstrates the value of integrating common-sense capabilities into a computer vision pipeline. © 2012 Elsevier B.V. All rights reserved.

Predicting the past: Understanding the causes of bank distress in the Netherlands in the 1920s

Why do some banks fail in financial crises while others survive? This article answers this question by analysing the effect of the Dutch financial crisis of the 1920s on 142 banks, of which 33 failed. We find that choices of balance sheet composition and product market strategy made in the lead-up to the crisis had a significant impact on banks’ subsequent chances of experiencing distress. We document that high-risk banks – those operating highly-leveraged portfolios and attracting large quantities of deposits – were more likely to fail. Branching and international activities also increased banks’ default probabilities. We measure the effects of board interlocks, which have been characterized in the extant literature as contributing to the Dutch crisis. We find that boards mattered: failing banks had smaller boards, shared directors with smaller and very profitable banks and had a lower concentration of interlocking directorates in non-financial firms.

Transnational Culture Wars

The well-known ‘culture wars’ clash in the United States between civil society actors has now gone transnational. Political science scholarship has long detailed how liberal human rights non-governmental organizations NGOs engage in extensive transnational activity in support of their ideals. More recently, US conservative groups (including faith-based NGOs) have begun to emulate these strategies, promoting their convictions by engaging in transnational advocacy. NGOs thus face off against each other politically across the globe. Less well known is the extent to which these culture wars are conducted in courts, using conflicting interpretations of human rights law. Many of the same protagonists, particularly NGOs that find themselves against each other in US courts, now find new litigation opportunities abroad in which to fight their battles. These developments, and their implications, are the focus of this article. In particular, the extent to which US faith-based NGOs have leveraged the experience gained transnationally to use international and foreign jurisprudence in interventions before the US Supreme Court is assessed.

Video Lecture Capture : Student Engagement

Video Capture of university lectures enables learners to be more flexible in their learning behaviour, for instance choosing to attend lectures in person or watch later. However attendance at lectures has been linked to academic success and is of concern for faculty staff contemplating the introduction of Video Lecture Capture. This research study was devised to assess the impact on learning of recording lectures in computer programming courses. The study also considered behavioural trends and attitudes of the students watching recorded lectures, such as when, where, frequency, duration and viewing devices used. The findings suggest there is no detrimental effect on attendance at lectures with video materials being used to support continual and reinforced learning with most access occurring at assessment periods. The analysis of the viewing behaviours provides a rich and accessible data source that could be potentially leveraged to improve lecture quality and enhance lecturer and learning performance.

Inferring and Exploiting Categories for Next Location Prediction

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called “check-in” profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.

A green perspective on Structured parallel programming

Structured parallel programming, and in particular programming models using the algorithmic skeleton or parallel design pattern concepts, are increasingly considered to be the only viable means of supporting effective development of scalable and efficient parallel programs. Structured parallel programming models have been assessed in a number of works in the context of performance. In this paper we consider how the use of structured parallel programming models allows knowledge of the parallel patterns present to be harnessed to address both performance and energy consumption. We consider different features of structured parallel programming that may be leveraged to impact the performance/energy trade-off and we discuss a preliminary set of experiments validating our claims.

Clustering-based Query Routing in Cooperative Semi-structured Peer to Peer Networks

We consider the problem of resource selection in clustered Peer-to-Peer Information Retrieval (P2P IR) networks with cooperative peers. The clustered P2P IR framework presents a significant departure from general P2P IR architectures by employing clustering to ensure content coherence between resources at the resource selection layer, without disturbing document allocation. We propose that such a property could be leveraged in resource selection by adapting well-studied and popular inverted lists for centralized document retrieval. Accordingly, we propose the Inverted PeerCluster Index (IPI), an approach that adapts the inverted lists, in a straightforward manner, for resource selection in clustered P2P IR. IPI also encompasses a strikingly simple peer-specific scoring mechanism that exploits the said index for resource selection. Through an extensive empirical analysis on P2P IR testbeds, we establish that IPI competes well with the sophisticated state-of-the-art methods in virtually every parameter of interest for the resource selection task, in the context of clustered P2P IR.