Debt financing in private and public firms

Using administrative confidential data on the universe of Canadian corporate firms, we compare debt financing choices of private and public firms. Private firms have higher leverage ratios, which are entirely driven by private firms’ stronger reliance on short-term debt. Further, private firms rely more of leverage during economic expansions, while public firms rely on equity financing. Specifically, private firms manage to increase their long-term debt during expansions, while short-term debt is used during downturns. Our findings have implications for a better understanding of the role of asymmetric information in private firms’ capital structure decisions.     

Business cycles,financial cycles and capital structure

We perform peridogram based cycle analysis of firm capital structure and find evidence that firms’ leverage is both persistent and cyclical. The cyclicality of leverage is supported by the trade-off, pecking order and market timing capital structure theories (Korajczyk and Levy in J Financ Econ 68:75–109, 2003; Bhamra et al. in Rev Financ Stud 23:645–703, 2010). Although market timing theory research supports persistence, previous literature dictates that the trade-off and pecking order theories may predict either persistent or mean reverting leverage. Our tests reject mean reversion in favor of persistent and cyclical leverage. We corroborate pecking order theory literature that predicts leverage is persistent. In these models, when firms’ investment spending is below earnings, leverage decreases. In addition, we examine whether firms change their capital structure as a result of business and financial cycles. Since financial cycles last longer than business cycles, financial cycles should have a long term effect on leverage. Our findings confirm the persistent leverage business cycle models that suggest firms change their capital structure due to financial and credit cycles (Jermann and Quadrini in Am Econ Rev 102:238–271, 2012; Azariadis et al. in Rev Econ Stud 83:1364–1405, 2016). We conclude that leverage is persistent due to the cyclicality of the financing decision.     

How supply chain architecture and product architecture impact firm performance: An empirical examination

In order to enhance performance, manufacturing firms are building various capabilities and utilizing supply chain networks that are geographically dispersed around the globe. This study examines how decisions related to supply chain architecture and product architecture influence organizational competitiveness. Building on the co-specialization perspective, we evaluate the performance implications of product modularity and supply chain agility. While, product modularity constitutes a product configuration related to sourcing and assembling of products, supply chain agility is a supply chain configuration related to velocity (sensing, comprehending, and responding) and visibility (supplier network, internal operations, and external environment). Both the product and supply chain architectural decisions can impact performance, individually and in conjunction with each other. We empirically test the hypothesized relationships using data collected from 103 manufacturing firms. Results show that supply chain agility and product modularity directly enhance responsiveness and enable organizations to reduce cost. Furthermore, supply chain agility partially mediates the relationship between product modularity and both responsiveness and cost reduction. We elaborate on the key contributions of this study for both research and practice, discuss limitations, and also offer various avenues for further research.     

Innovation performance and the role of clustering at the local enterprise level: a fuzzy-set qualitative comparative analysis approach

ABSTRACT

This study, utilizes an innovative methodological approach, fuzzy-set Qualitative Comparative Analysis (fsQCA), investigating the drivers of heterogeneous geographies characterizing English Local Economic Partnerships (LEPs). The fsQCA technique offers a novel configurational alternative to regression-based approaches investigating the effects of clustering in conjunction with firm-level innovation, university third-sector activity (TSA) and entrepreneurship, on LEPs innovation performance. The findings, offer contributions to the theories of industrial clusters and innovation, regional innovation systems, knowledge spillovers and entrepreneurial university innovation within LEPs. First, supporting fsQCAs, no individual variable generates either a positive/negative innovation outcome. Second, while all positive innovation recipes include presence of the cluster variable, for negative innovation recipes, only one does not identify absence of clustering as relevant. Given that the cluster variable does not appear in any recipes without at least one of the other variables suggests activity concentration does not exist in isolation to generate innovation outcomes without other localized conditions existing, e.g. firm-level innovation. Third, there is evidence for the non-cluster-based aspects of knowledge spillover theory of entrepreneurship with respect to university activity and the entrepreneurial university concept. Instead, roles of entrepreneurship and university TSA, while important, appear to be more peripheral and geographically context specific.     

An Approach to Security for Unstructured Big Data

Security of Big Data is a huge concern. In a broad sense, Big Data contains two types of data: structured and unstructured. Providing security to unstructured data is more difficult than providing security to structured data. In this paper, we have developed an approach to provide adequate security to unstructured data by considering types of data and their sensitivity levels. We have reviewed the different analytics methods of Big Data to build nodes of different types of data. Each type of data has been classified to provide adequate security and enhance the overhead of the security system. To provide security to a data node, and a security suite has been designed by incorporating different security algorithms. Those security algorithms collectively form a security suite which has been interfaced with the data node. Information on data sensitivity has been collected through a survey. We have shown through several experiments on multiple computer systems with varied configurations that data classification with respect to sensitivity levels enhances the performance of the system. The experimental results show how and in what amount the designed security suite reduces overhead and increases security simultaneously.