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.     

基于CiteSpace 的创新链研究综述

运用CiteSpace软件，以1995—2022年中国知网（CNKI）核心期刊和中文社会科学引文索引（CSSCI）期刊为数据源，从热点关键词、发文作者、发文机构以及突现词方面对创新链相关领域文献进行可视化分析。研究发现：有关创新链的研究角度大概可以分为产业、技术、区域、模式和价值链5个方面；从发文特征来看，发文量最多的是张杰，群体内相较于群体间合作较多；从研究机构分布来看，机构间的合作比较分散，且合作大多发生在一个国家或地区；从未来演进趋势来看，文献近期表现为国家政策主导。     

What Makes Firms Competitive? States,Markets, and Organisational Embeddedness in Competitive Firm Restructuring in Postsocialist Economies

The prevailing transitions literature suggests that dynamic firms in postsocialist economies are the result of macroinstitutional reforms leading to the making of markets. This article builds on work in comparative political economy and economic sociology to show that the degree of competitive behaviour of postsocialist firms is determined not by the existence of general market institutions alone but by the kinds of organisational allies firms possess and the kinds of markets they compete in. Using firm survey data across 28 postsocialist economies, the article examines the determinants of competitive restructuring by firms, including product innovation, standards upgrade, financial transparency, and investments in research and development. The article confirms insights from comparative political economy which suggest that dynamic enterprise sectors emerge when governance is effective. However, at the firm level, the article finds that transnational ties and supportive policy environments are most significant in the making of dynamic postsocialist enterprises. The article also highlights important regional variation in firm behaviour and discusses the relationship between institutional frameworks, organisational embeddedness, and firm restructuring in postsocialist economies.     

General Purpose Technologies and local knowledge accumulation — A study on MNC subunits and local innovation centers

This research looks into the innovative activities of subunits of large non-U.S firms in the U.S from 1969 to 1995, suggesting that the innovations in the subunits of multinational corporations are closely linked to their specializations in General Purpose Technologies (GPTs). GPTs enable recombining knowledge from different domains on an international scale, and that the specialization of innovations in GPT fields within a subunit is positively associated with the innovative capacity at large, along with the degree of technological diversification of that specific subunit. As firms expand geographically to tap into local innovation centers to develop new growth alternatives, specialization of GPTs in these firms actually facilitates further geographical dispersion of innovative activities across locations. Lastly, additional specializations of innovations in GPT fields within the host innovation centers does help in attracting innovation activities of firms from a wider range of industries.     

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.     

考虑声誉效应的科技服务业与制造企业合作创新行为策略分析

采用博弈论的方法，考虑声誉效应对博弈双方效用、策略的影响，建立信号博弈模型，并分析精炼贝叶斯均衡，证明声誉水平、对声誉的态度以及技术市场价值的高低是影响建立合作关系的重要因素。探讨双方建立合作关系的行为策略，提出实现合作达成双赢的建议。     

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.