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Since, wear is the one of the most commonly encountered industrial problems leading to frequent replacement of components there is a need to develop metal matrix composites (MMCs) for achieving better wear properties. The purpose of this paper is to fabricate aluminum MMCs to improve the dry sliding wear characteristics. An effective multi-response optimization approach called the principal component analysis (PCA) was used to identify the sets of optimal parameters in dry sliding wear process.

The present work investigates the dry sliding wear behavior of graphite reinforced aluminum composites produced by the molten metal mixing method by means of a pin-on-disc type wear set up. Dry sliding wear tests were carried on graphite reinforced MMCs and its matrix alloy sliding against a steel counter face. Different contact stress, reinforcement percentage, sliding distance and sliding velocity were selected as the control variables and the response selected was wear volume loss (WVL) and coefficient of friction (COF) to evaluate the dry sliding performance. An L25 orthogonal array was employed for the experimental design. Optimization of dry sliding performance of the graphite reinforced MMCs was performed using PCA.

Based on the PCA, the optimum level parameters for overall principal component (PC) of WVL and COF have been identified. Moreover, analysis of variance was performed to know the impact of individual factors on overall PC of WVL and COF. The results indicated that the reinforcement percentage was found to be most effective factor among the other control parameters on dry sliding wear followed by sliding distance, sliding velocity and contact stress. Finally the wear surface morphology of the composites has been investigated using scanning electron microscopy.

Various manufacturing techniques are available for processing of MMCs. Each technique has its own advantages and disadvantages. In particular, some techniques are significantly expensive compared to others. Generally the manufacturer prefers the low cost technique. Therefore stir casting technique which was used in this paper for manufacturing of Aluminum MMCs is the best alternative for processing of MMCs in the present commercial sectors. Since the most important criteria of a dry sliding wear behavior is to provide lower WVL and COF, this study has intended to prove the application of PCA technique for solving multi objective optimization problem in wear applications like piston rings, piston rods, cylinder heads and brake rotors, etc.

Application of multi-response optimization technique for evaluation of tribological characteristics for Aluminum MMCs made up of graphite particulates is a first-of-its-kind approach in literature. Hence PCA method can be successfully used for multi-response optimization of dry sliding wear process.

The purpose of this study is investigating the effect of using multi-phase nanofluids, Rayleigh number and baffle arrangement simultaneously on the heat transfer rate and Predict the optimal arrangement type of baffles in the differentiation of Rayleigh number in a 3D enclosure.

Simulations were performed on the base of the L25 Taguchi orthogonal array, and each test was conducted under different height and baffle arrangement. The multi-phase thermal lattice Boltzmann based on the D3Q19 method was used for modeling fluid flow and temperature fields.

Streamlines, isotherms, nanofluid volume fraction distribution and Nusselt number along the wall surface for 104 < Ra < 108 have been demonstrated. Signal-to-noise ratios have been analyzed to predict optimal conditions of maximize and minimize the heat transfer rate. The results show that by choosing the appropriate height and arrangement of the baffles, the average Nusselt number can be changed by more than 57 per cent.

The value of this paper is surveying three-dimensional and two-phase simulation for nanofluid. Also using the Taguchi method for Predicting the optimal arrangement type of baffles in a multi-part enclosure. Finally statistical analysis of the results by using of two maximum and minimum target Function heat transfer rates.

Diesel engine can produce power more efficiently with lower exhaust emissions when operated at optimum input parameter settings. To achieve this goal, the purpose of this paper is to optimize the input parameters of diesel engine which will lead to optimum performance and exhaust emissions.

To achieve the goal of improving diesel engine performance and exhaust emissions, four input parameters were considered in the study. Five different levels of each input parameter were taken. Four response variables under no load, half load and full load conditions were recorded. Experiments were performed in random manner according to selected Taguchi L25 orthogonal array. The data were analyzed using grey relational analysis coupled with principal component analysis. Analysis of S/N ratio was performed to obtain the optimum combination of input parameters. The grey relational grade at optimum setting of the input parameters was obtained by regression analysis.

Results of the current research work give the optimum input parameter settings for no load, half load and full load conditions of diesel engine. Engine produces power more efficiently with low exhaust emissions when operated at these optimum settings.

In view of the compliance to the stringent air pollution norms of the nations and fast depleting fossil fuels, it is of the utmost importance to design and operate the engine in the optimum range of its input parameters so that it produces more power with low exhaust emissions. This paper aims at optimizing input parameters of diesel engine to improve performance and exhaust emissions. Results of the study presented in this paper are significantly useful for diesel engine-related researchers and professionals.

From the literature review, it appears that only few researchers have conducted studies pertaining to the optimization of the input parameters of diesel engine to improve performance or exhaust emissions. Although few studies related to the optimization of compression ratio, fuel injection timing, fuel injection pressure and air pressure have been reported, no work related to optimization of temperature and pressure of turbocharged air has been reported. Therefore, the main focus of the current research work is on optimizing the charge air temperature and pressure with respect to performance and exhaust emissions.

Third-party certifications promote firms' high standards and enhance their legitimacy and reputation, potentially positively impacting their performance, namely on exports.

This paper studies the differences in firms' export performance between firms with and without the “SME Leader” label, a Portuguese firm-specific label. This study data set comprises data for 5.489 SMEs, for 2014–2020, where 2.950 firms obtained their certification during the period and 2.539 are non-certified control firms. The methodology adopted circumvents the traditional endogeneity problems in this type of study.

The results indicate that the “SME Leader” label does not directly improves a firm's export performance. Additionally, industry characteristics seem relevant for the relationship between SMEs' certification and their levels of internationalization.

Since the study sample includes firms from all industrial sectors, this study results apply to various organizations.

Las certificaciones de terceros promueven los altos estándares de las empresas y mejoran su legitimidad y reputación, lo que puede tener un impacto positivo en su desempeño, especialmente en las exportaciones.

Este documento estudia las diferencias en el desempeño de las exportaciones de las empresas entre las empresas con y sin la etiqueta “Líder PYME”, una etiqueta específica de la empresa portuguesa. Nuestro conjunto de datos comprende datos de 5.489 Pymes, para 2014–2020, donde 2.950 empresas obtuvieron su certificación durante el período y 2.539 son empresas de control no certificadas. La metodología adoptada sortea los tradicionales problemas de endogeneidad en este tipo de estudios.

Los resultados indican que la etiqueta de “PYME líder” no mejora directamente el desempeño exportador de una empresa. Además, las características de la industria parecen relevantes para la relación entre la certificación de las PYMES y sus niveles de internacionalización.

Dado que nuestra muestra incluye empresas de todos los sectores industriales, nuestros resultados se aplican a varias organizaciones.

This paper aims to investigate the efficiency implications of belonging to a strategic hospital alliance (SHA) and measuring the effects over capacity utilization of such agreements in a Mexican healthcare context.

Data Envelopment Analysis (DEA) is the nonparametric methodology used, which supports both objectives. Technological gaps ratios are calculated by using DEA-metafrontier approach to compare efficiency between SHA members and a hospital’s control group. Also, hospital capacity utilization ratios are used as the maximum rate of output possible from fixed inputs in a frontier setting using directional distance functions. Data were collected from an alliance called Consorcio Mexicano de Hospitales in México, which has 29 general private hospitals and a group of 47 hospitals with same characteristics from a database made by the Instituto Nacional de Estadística y Geografía for year 2014.

The results indicate that efficiency is better at hospitals that belong to an alliance; it also shows an improvement of installed capacity management for hospital alliances in México.

The results can be useful for both private health organization managers and regulators themselves to adopt management practices that may end up having a favorable impact on cost and prices containment. Additionally, there are no previous studies neither in Mexico nor in Latin America that analyze the impact of strategic hospitality alliances on the efficiency and utilization of the capacity of private hospitals.

Este documento tiene como objetivo investigar las implicaciones de pertenecer a una alianza hospitalaria estratégica (AHE) en la eficiencia, así como cuantificar los efectos sobre la utilización de la capacidad de dichos acuerdos en el contexto mexicano de atención médica.

El Análisis Envolvente de Datos (DEA) es la metodología no paramétrica utilizada para lograr ambos objetivos. Las brechas tecnológicas se estiman empleando meta-fronteras calculadas mediante modelos DEA, comparando la eficiencia entre los miembros de la AHE y un grupo de control de hospitales. El nivel de utilización de la capacidad hospitalaria se calcula, utilizando funciones direccionales de distancia, a partir del máximo output alcanzable a partir de la dotación de inputs fijos. Los datos fueron obtenidos de la alianza Consorcio Mexicano de Hospitales en México, integrada por 29 hospitales privados generales, y de un grupo de 47 hospitales con las mismas características obtenidos de una base de datos del Instituto Nacional de Estadística y Geografía para el año 2014.

adosLos resultados indican que los niveles de eficiencia son superiores en los hospitales pertenecientes a la alianza, así como una mejor gestión de la capacidad instalada en la alianza hospitalaria en México.

Los resultados pueden ser útiles tanto para los administradores de las organizaciones de salud privadas como para los reguladores, de forma que puedan adoptar prácticas de gestión con un impacto favorable en la contención de costos y precios. Asimismo, no existen estudios previos ni en México ni en América Latina que analicen el impacto de las alianzas estratégicas hospitalarias en la eficiencia y la utilización de la capacidad de los hospitales privados.

Many observers believe that industry experience of entrepreneurs drives successful new entrepreneurial firms. However, whenever it comes to disruptive digital ventures such as Financial Technologies (Fintechs), the picture may be different due to the cross-industry nature of digital firms. The purpose of this study is to disentangle the impacts of finance, banking and information technology (IT) experiences of founders on performance of European Fintechs around venture capital (VC) investment.

Based on a data set of 105 Fintechs from European countries, including UK, which are involved in 201 VC rounds between 2006 and 2019, the authors adopt a Bayesian quantile approach to link founders’ experience with two performance measures that identify market success (return on sales) and investment outcome (return on equity).

The findings indicate that finance and IT-specific experiences seem to matter more often than banking experience and that the extent of their impact depends on level and metric of performance. More specifically, Fintechs in Europe and UK are more able to achieve market success with both finance and IT experiences of their founders, but that does not necessarily transform into higher returns for investors.

This study provides new evidence that not all aspects of industry experience matter for digital ventures, as they must fit to a certain firm, cycle and industry. For Fintech, as the name says, finance and IT experiences matter.

This paper aims to analyse the effect of mandatory corporate social responsibility expenditure (CSRE) on the performance of food and agribusiness firms in India.

This study is based on the firm-level data collected from the Prowess database of the Centre for Monitoring Indian Economy in the year 2019. The data on key characteristics, business performance and CSRE has been compiled from 362 food and agribusiness firms. The descriptive statistics, t-test for equality of means and Spearman correlation analysis have been undertaken to understand the relationship between mandatory CSRE and firm performance across food and agribusiness sectors.

Out of 362 food and agribusiness firms, 52.2% have reported expenditure in the implementation of social initiatives under their corporate social responsibility. The results show a significant difference in the firm’s characteristics vis-à-vis with and without CSRE. Further, the findings highlight a positive and significant correlation and causal impact of corporate social responsibility (CSR) on return on sales, return on assets and profit after tax.

The study provides insights for implementing strategic CSR in food and agribusiness firms and gives an adequate justification for incurring CSRE.

This paper increases the understanding of CSR in the food and agribusiness sector. Besides, provisioning mandatory CSR seems to be a beneficial proposition for enhancing a firm’s performance.

Do financial services needs depend on the firm size? To highlight the impact of different categories of financial services on firm performance, we establish a correspondence between financial services and firms' performance classified according to their size, controlling with the determinants of firm performance and the obstacles that hinder the development of each category of firm.

We have mobilized microeconomic data on 78,629 firms stratified by size and covering 135 countries, extracted from the Enterprise Surveys database. A two-stage least squares (2SLS) regression analysis with instrumental variable modeling is used.

Our empirical results show that a firm's financing behavior differs according to its size. For micro and small firms, the availability of internal financing has a positive impact on their performance. For medium-size firms, the use of debt stimulates firm performance. For large firms, the positive effect of debt diminishes as the level of debt increases, which leads this category of firms to increase their capital. We complemented our study by exploring the issue of whether barriers to firm performance differ by size. Our results bring a support to the idea that medium-size firms suffer more than micro, small, and large firms. The size of this category of firms does not allow them to operate in the informal sector as micro and small firms do, and does not allow them to influence political authorities to operate in their favor as large firms do.

Previous studies have focused on investigating the effects of access to finance and/or financing constraints on firm's performance, neglecting the issue of identifying which financial services have the most impact on firm performance depending on firms' size. This study fills the gap in the literature in two main ways. First, we identify the financial services that have the most impact on firm performance using firm-level data covering 78,629 firms by size (micro, small, medium, and large). Second, we investigate the different barriers to firm performance by size.

Facing increased asset prices and growing competition, private equity firms needed to innovate their established business model and shift from focusing on financial engineering to creating operating value. Yet, the authors understand little about how private equity firms increase the value of companies in their portfolios. This paper aims to shed light on organizational strategies, activities and governance principles that private equity firms use to create value.

This investigation combines several qualitative research approaches. Using in-depth interviews with executives in 35 private equity firms, the authors define industry-specific design principles for value creation using a Gioia methodology. They then use the Eisenhardt methodology to make in-depth case comparisons among sample firms.

Private equity firms employ one of four strategies – labeled “Infiltrator,” “Consultant,” “Organizer” or “Investor” – to create value in portfolio companies, each with a different organizational structure, level of cooperation between investor and portfolio firm and specific configuration of design elements.

To the best of the authors’ knowledge, this study is the first to focus on private equity value creation strategies from an organizational perspective. To their knowledge, no other publication has tapped this deeply into the interface between the private equity firm and the portfolio company to define the exact approach taken by the firm. This study contributes to the emerging discussion around the nonfinancial inputs to value creation. In addition, this qualitative research design is underrepresented in private equity research.

A brief review of earlier studies on the productivity scenario of Indian industry shows that most of the studies analysed are confined to either parametric approach or growth accounting approach of measuring productivity. At the same time, the few studies based on the non-parametric [namely, Malmquist productivity index (MPI)] overlook the returns to scale conditions as well as the bias involved in the estimation of distance functions. Given this backdrop, this study aims to provide a robust measure of productivity, which considers the returns to scale assumptions and correct for the bias involved in the estimation of productivity.

This study empirically tests for the returns to scale that exists in the chemical and chemical products industry in India. The test result suggests that Ray and Desli (1997) approach of MPI is the appropriate one for the present context. Initially, the conventional Ray and Desli (1997) estimation and decomposition of MPI for the period 2001 to 2017 is being used. Subsequently, to correct for the bias in the estimation of efficiency scores used for the estimation of MPI, the bootstrapping algorithm of Simar and Wilson (2007) has been extended into the context of MPI estimation.

The results from the conventional Malmquist productivity estimates testifies to an improvement of total factor productivity (TFP) in seven out of 16 years under consideration. On the contrary, TFP growth is recorded only in the four years throughout the period after the bias correction. A greater discrepancy between the two measures has been found in the case of scale change factor component of MPI.

The technical change (TC) component positively influences TFP, whereas scale change factor (SCF) deteriorates the TFP condition of this industry. It will be appropriate for these firms to identify and operate under an optimal scale of operation, along with reaping the benefits of technological change. From a methodological perspective, researchers should consider the potential bias that arise in estimation of TFP and use a larger sample whenever possible.

This paper brings in a new perspective to the existing literature on industrial productivity. As against earlier studies, this study empirically tests the returns to scale of the sector under consideration and uses the most appropriate approach to measure productivity. The effect of sampling bias on TFP and its components is analysed.