Adoption des Politiques de Protection de l'Environnement et performance des Entreprises Camerounaises

The objective of this paper is to evaluate the effect of the environmental protection policies of by Cameroonian firms on their performance. It uses the endogenous switching regression technique and propensity scores applied to micro-data from 639 firms in Cameroon. The results show that only 17% of firms adopt these measures, while on average 85% of firms produce solid, gaseous or liquid waste. The results also indicate that the adoption of these environmental protection policies increases operating costs while significantly improving the turnover and the performance of the productive capacity of the company. These increases are 39.11%, 58.6%, and 38.63% for operating costs, turnover and return on productive capacity of the company, respectively. However, firms can also suffer significant losses resulting from the non-adoption of environmental policies. In fact, firms that do not adopt environmental protection policies have their performance reduced by an average of 1.625 percentage points.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Higher Intensity,Higher Profit? Empirical Evidence from Dairy Farming in New Zealand

Over the last two decades there have been significant and well‐documented changes in the nature and structure of New Zealand dairy production. One particular feature has been a marked shift in the ‘input intensity’ of dairy farming systems through increased use of supplementary feed. These changes have generated debate about the impact of dairy farm intensity on the performance of farm businesses and the competitiveness of the New Zealand dairy sector. Using a novel econometric approach, we assess statistically the impact of three types of dairy farming systems on milk production and financial performance, using farm business data provided by DairyBase^®. Our empirical results show that higher input systems perform significantly better physically than lower input systems, but not financially. The disaggregated analysis suggests that the average treatment effects differ by region and performance quantiles.     

Efficiency,economy and effectiveness—but what about ethics? Supreme audit institutions at a critical juncture

Ethics is increasingly recognized as one of several important dimensions of performance. Yet, performance audit, or value-for-money audit, as conducted by supreme audit institutions (SAIs), does not typically include this dimension. Instead, the focus is on the classical ‘three Es’ (efficiency, economy and effectiveness). The reluctance to address issues of ethical misconduct has taken the audit practice of SAIs to a critical juncture, where the legitimacy of these audits ultimately is at stake. This paper explains why SAIs need to add a fourth ‘E’— ethical audit. Possible implications are discussed.     

The major determinants of logistic performance in a global perspective: evidence from panel data analysis

The global logistics industry has grown significantly and logistics has become an important sector of the business economic system and a major global economic activity in recent years. Logistics activities accelerate economic and productivity growth. Efficient logistics is also important to a country’s competitiveness and source of employment. The purpose of this paper is to uncover and understand the major determinants of logistics performance (LP) to further lift the LP of countries. Using unbalanced panel data of 93 selected countries from 2007 to 2014, the present research attempts to critically investigate the major determinants of LP. In estimating the model, this study prefers to use static panel data approach owing to limited data. The findings of the present study reveal that (a) countries with low level of corruption and stable political environment are likely to yield a high level of LP; (b) improvement in resources supply such as infrastructure, technology, labour, and education also have a significant positive effect on LP. Therefore, institutional reforms and upgrading resources will effectively accelerate LP.     

The effect of mandatory CSR disclosure on firm profitability and social externalities: Evidence from China

We examine how mandatory disclosure of corporate social responsibility (CSR) impacts firm performance and social externalities. Our analysis exploits China's 2008 mandate requiring firms to disclose CSR activities, using a difference-in-differences design. Although the mandate does not require firms to spend on CSR, we find that mandatory CSR reporting firms experience a decrease in profitability subsequent to the mandate. In addition, the cities most impacted by the disclosure mandate experience a decrease in their industrial wastewater and SO₂ emission levels. These findings suggest that mandatory CSR disclosure alters firm behavior and generates positive externalities at the expense of shareholders.     

A systematic approach to conducting review studies: An assessment of content analysis in 25 years of IB research

Content analysis has become a popular method for qualitative and quantitative analyses in management and international business (IB) research. It is increasingly used in literature reviews to assess extant knowledge and understand intellectual structures. However, it is often poorly understood and incorrectly applied. In this article, we identify benchmark criteria and develop coding schemes that IB scholars can use in review studies. We also demonstrate the application of content analysis through a review of content analysis–based articles, published in the top eight IB journals from 1991 to 2015.     

The importance of career goal in relation to academic performance: a case study of hospitality management students

ABSTRACT

Hospitality management programs have commonly offered exclusive learning opportunities to talented students in order to keep “brains” in the industry. While academic performance indices are often used to identify “brains” in the education sector, scant research has empirically examined whether “brains” are intended to stay in the industry. This study surveyed 304 hospitality management students and examined the relationships between their career-goal related factors and academic performance. Findings indicate that career goal and positive job valence toward the hospitality industry are significantly related to academic performance. The results of this study contribute to the limited literature on the relationship between career goal and academic performance.     

Private equity characteristics and performance: An analysis of North American venture capital and buyout funds

The paper investigates the relationship between fund performance and fund characteristics of North American private equity (PE) funds, by analyzing the interactions of fund size, fund sequence, and past fund performance on traditional fund return measures. The empirical evidence is based on both linear and polynomial regressions, on a sample of 345 venture capital (VC) and 411 buyout (BO) funds with vintage year over the period 1995–2010. We document a concave relationship between fund size and performance, persistence in PE performance, as well as a convex relationship between fund sequence and performance. We suggest both the optimal fund size and the optimal fund sequence number. Economic implications for investors and general partners are discussed as well.