Modelling generalized firms' restructuring using inverse DEA

The key consideration for firms’ restructuring is improving their operational efficiencies. Market conditions often offer opportunities or generate threats that can be handled by restructuring scenarios through consolidation, to create synergy, or through split, to create reverse synergy. A generalized restructuring refers to a move in a business market where a homogeneous set of firms, a set of pre-restructuring decision making units (DMUs), proceed with a restructuring to produce a new set of post-restructuring entities in the same market to realize efficiency targets. This paper aims to develop a novel inverse Data Envelopment Analysis based methodology, called GInvDEA (Generalized Inverse DEA), for modeling the generalized restructuring. Moreover, the paper suggests a linear programming model that allows determining the lowest performance levels, measured by efficiency that can be achieved through a given generalized restructuring. An application in banking operations illustrates the theory developed in the paper

Supply chain sustainability performance measurement of small and medium sized enterprises using structural equation modeling

Sustainability of small and medium sized enterprises (SMEs) is significant as SMEs contribute to GDP substantially in every economy. This research develops an innovative sustainable supply chain performance measurement model for SMEs. Prior researches predominantly use balanced score card (BSC) approach that presume causal relationship of criteria and Data Envelopment Analysis (DEA), which derive efficiency of units from a few input and output criteria. While DEA is effective for policymakers, BSC is more suitable for individual SME. The proposed method that uses structural equation modeling (SEM) approach to derive the relationship of criteria and criteria weights formulates regression-type models for a specific region as well as for specific SME. The SEM-based supply chain sustainability performance measurement model is beneficial to policymakers as they can determine means for improvement at a regional level. The proposed method could also facilitate managers/owners of individual SMEs with measures for improving their supply chain sustainability performance. The method has been applied to three varied geographical locations in the UK, France and India in order to demonstrate its effectiveness

Environmentally Responsible and Conventional Market Indices’ Reaction to Natural and Anthropogenic Adversity: A Comparative Analysis

It is widely claimed that climate change has increased the magnitude and the frequency of natural phenomena such as storms, droughts, and floods with the concomitant costs in terms of damages and victims. This paper using weekly data from global stock market indices in a Fama–French model, examines how and to what extent market agents and investors react to such events. As a yardstick for comparison purposes, the possible market impact of industrial accidents is also incorporated and examined in the empirical investigation. The study uses in a comparative approach the STOXX Global ESG Environmental Leaders index and the STOXX Global index diversified across 1800 top companies. Results reported herein seem to indicate that natural and anthropogenic adversity have no immediate impact on the stock indices, while wildfires have an immediate reduction impact on market risk in the case of the ESG Environmental Leaders index. Moreover, wildfires and industrial accidents appear to cause a significant reduction of systematic risk over the next week following the incident. However, the magnitude of the effect is higher in the case of the ESG Environmental Leaders stock index. Finally, the effect on systematic risk by industrial accidents is temporarily without any lasting imprint. © 2015 Springer Science+Business Media Dordrech