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During the last years of his life, the mathematician Karl Menger worked on a biography of his father, the economist and founder of the Austrian School of Economics, Carl Menger. The younger Menger never finished the work. While working in the Menger collections at Duke University’s David M. Rubenstein Rare Book and Manuscript Library, we discovered draft chapters of the biography, a valuable source of information given that relatively little is known about Carl Menger’s life nearly a hundred years after his death. The unfinished biography covers Carl Menger’s family background and his life through early 1889. In this chapter, the authors discuss the biography and the most valuable new insights it provides into Carl Menger’s life, including Carl Menger’s family, his childhood, his student years, his time working as a journalist and newspaper editor, his early scientific career, and his relationship with Crown Prince Rudolf.

This study analyzes whether power in the supply chain, based on governance modes and network centrality, explain financial performance at different levels of analysis: buyers, suppliers and dyads.

The study employs a dual macro-micro lens based on global value chain (i.e. market, modular, relational and captive governance modes) and social network analysis (network centrality) to assess the impact of power (im)balance onto financial performance. Different from previous research, this study adopts information reliability techniques – such as information entropy – to differentiate the weights of distinct financial performance metrics in terms of the maximal entropy principle. This principle states that the probability distribution that best represents the current state of knowledge given prior data is the one with largest entropy. These weights are used in TOPSIS analysis.

Results offer insightful reflections to SCM research. We show that buyers outperform suppliers due to power asymmetry. We ground our findings both analyzing across governance modes and comparing network centrality. We show that market and modular governances (where power balance prevails) outperform relational and captive modes at the dyadic level – thus inferring that in the long run these governance modes may lead to financially healthier supply chains.

This study advances SCM research by exploring the impact of governance modes and network centrality on performance at both firm and dyadic levels while employing an innovative combination of secondary data and robust set of techniques including TOPSIS, WASPAS and information entropy.

Reviews some new trends in parallel machine scheduling (PMS). PMS, as an area of research, is governed by questions that arise in production planning, flexible manufacture systems, computer control, etc. The main characteristic of these problems is to optimize an objective, with jobs to be finished on a series of machines with the same function. Gives a short review of new developments in PMS associated with the problems of just‐in‐time (JIT) production, pre‐emption with set‐up, and capacitated machine scheduling. Discusses non‐regular objectives oriented by the JIT concept; pre‐emption with set‐up; capacitated machine scheduling; and relationships between PMS and vehicle routeing problems.

The purpose of this paper is to assess the efficiency determinants of mergers and acquisitions (M&A) in the context of Latin American airlines based on business-related variables commonly found in the literature. The idea is to identify preferable potential airline matches in light of fleet mix, ownership structure and geographical proximity.

In order to achieve the objective, all possible combinations of M&A pairs are considered in the analysis, which is developed in a two-stage approach. First, the M&A Data Envelopment Analysis model efficiency and returns-to-scale estimates are computed. Then, robust regression and multinomial logistic regression are respectively used to discriminate these estimates in terms of such business-related variables.

The results reveal that these different contextual variables significantly impact virtual efficiency and returns-to-scale levels. Private ownership, passenger focus and a better match between aircraft size and demand for flights appear to be key drivers for merged airline efficiency.

The study makes theoretical contributions, though limited to analyzing Latin American airlines only. The use of bootstrapped robust/multinominal logistic regression, compared to the methods adopted by previous literature studies, generates more accurate and robust results related to the efficiency drivers due to its special feature and ability to allow the discrimination of increasing, decreasing, and constant returns to scale in light of a given set of contextual variables.

This study examines the pure effect of the merging activity on efficiency gains. Not only private ownership but also a hybrid public–private ownership has a positive influence on virtual efficiency, suggesting an important governmental role in promoting M&A in the airline industry.

The authors present an original take on the issue of airline mergers by exploring what are the major drivers possibly involved in efficiency gains of potentially merged (virtual) airlines. The authors identify preferable potential airline matches where efficiency gains would be positive in light of business-related variables such as fleet mix, ownership structure and geographical proximity. The analysis also includes an assessment of the impact of contextual variables such as cargo type, ownership structure and geographical proximity in relation to the strategic fit of mergers considering the resulting efficiency and returns-to-scale scores of virtually merged airlines. To the authors’ knowledge, no previous research has addressed these issues in Latin American airlines. Further research directions for this industry are also discussed.

Optimum utilization of investments has always been considered one of the most crucial aspects of capital markets. Investment into various securities is the subject of portfolio optimization intent to maximize return at minimum risk. In this series, a population-based evolutionary approach, stochastic fractal search (SFS), is derived from the natural growth phenomenon. This study aims to develop portfolio selection model using SFS approach to construct an efficient portfolio by optimizing the Sharpe ratio with risk budgeting constraints.

This paper proposes a constrained portfolio optimization model using the SFS approach with risk-budgeting constraints. SFS is an evolutionary method inspired by the natural growth process which has been modeled using the fractal theory. Experimental analysis has been conducted to determine the effectiveness of the proposed model by making comparisons with state-of-the-art from domain such as genetic algorithm, particle swarm optimization, simulated annealing and differential evolution. The real datasets of the Indian stock exchanges and datasets of global stock exchanges such as Nikkei 225, DAX 100, FTSE 100, Hang Seng31 and S&P 100 have been taken in the study.

The study confirms the better performance of the SFS model among its peers. Also, statistical analysis has been done using SPSS 20 to confirm the hypothesis developed in the experimental analysis.

In the recent past, researchers have already proposed a significant number of models to solve portfolio selection problems using the meta-heuristic approach. However, this is the first attempt to apply the SFS optimization approach to the problem.

My contribution deals with the link between Wagner’s entangled political economy and Carl Menger’s economic thought. It is mainly based on what Wagner himself has called “neo-Mengerianism”: a new approach that considers economics as a discipline focused on the network-based interrelations among phenomena (based on human decisions) and political economy as embedded in a social framework no longer neutral from a political point of view.

Introduction The transformation function for two‐sector production models has been used extensively in the international trade and general equilibrium literature. It has been derived both graphically and mathematically from individual sector production functions that are assumed to be linearly homogeneous. The result has been a convex set possessing first derivatives that have frequently been used. However, there seems to be a lack of research on further properties attributable to the transformation function. The purpose of this paper is to analyze these additional properties. We begin by proving that the transformation function is linearly homogeneous in all of its arguments. We then derive the standard first derivatives of the transformation function providing their usual economic interpretation. Finally, we conclude by deriving and interpreting the second‐order derivatives of the transformation function.

Dynamic predictions of financial distress of the firms have received less attention in finance literature rather than static prediction, specifically in Malaysia. This study, therefore, investigates dynamic symptoms of the financial distress event a few years before it happened to the firms by using neural network method. Cox Proportional Hazard regression models are used to estimate the survival probabilities of Malaysian PN17 and GN3 listed firms. Forecast accuracy is evaluated using receiver operating characteristics curve. From the findings, it shown that the independent directors’ ownership has negative association with the financial distress likelihood. In addition, this study modeled a mix of corporate financial distress predictors for Malaysian firms. The combination of financial and non-financial ratios which pressure-sensitive institutional ownership, independent director ownership, and Earnings Before Interest and Taxes to Total Asset shown a negative relationship with financial distress likelihood specifically one year before the firms being listed in PN 17 and GN 3 status. However, Retained Earnings to Total Asset, Interest Coverage, and Market Value of Debt have positive relationship with firm financial distress likelihood. These research findings also contribute to the policy implications to the Securities Commission and specifically to Bursa Malaysia. Furthermore, one of the initial goals in introducing the PN17 and GN3 status is to alleviate the information asymmetry between distressed firms, the regulators, and investors. Therefore, the regulator would be able to monitor effectively distressed firms, and investors can protect from imprudent investment.

The purpose of this paper is to present a new numerical model for shallow water flows over heterogeneous sedimentary layers. It is already several years since the single-layered models have been used to model shallow water flows over erodible beds. Although such models present a real opportunity for shallow water flows over movable beds, this paper is the first to propose a multilayered solver for this class of flow problems.

Multilayered beds formed with different erodible soils are considered in this study. The governing equations consist of the well-established shallow water equations for the flow, a transport equation for the suspended sediments, an Exner-type equation for the bed load and a set of empirical equations for erosion and deposition terms. For the numerical solution of the coupled system, the authors consider a non-homogeneous Riemann solver equipped with interface-tracking tools to resolve discontinuous soil properties in the multilayered bed. The solver consists of a predictor stage for the discretization of gradient terms and a corrector stage for the treatment of source terms.

This paper reveals that modeling shallow water flows over multilayered sedimentary topography can be achieved by using a coupled system of partial differential equations governing sediment transport. The obtained results demonstrate that the proposed numerical model preserves the conservation property, and it provides accurate results, avoiding numerical oscillations and numerical dissipation in the approximated solutions.

A novel implementation of sediment handling is presented where both averaged and separate values for sediment species are used to ensure speed and precision in the simulations.