关于企业成本管理的探讨

成本管理是预算管理和绩效管理的前提,企业需要正确地将成本进行分类、计量和归集。恰当的成本管理制度可以帮助企业提升运营效率和业务绩效,同时企业的流程改善、突破和优化与成本管理密不可分。另外,管理者需要令客户对产品和服务满意,并且有效控制各项成本。企业成本管理不是单纯地降低成本,而是通过利用成本分析工具,有效地管理和规划资源,进而提高总体运营效率,保证企业总体绩效的持续改善。论文通过成本计算制度的制定方法和业务流程改善分析技术两方面对成本管控进行探讨。     

企业应收账款管理研究

我国经济已由高速增长阶段转向高质量发展阶段,“提升治理能力,实现高质量发展”是我国面对新变化、新趋势再一次作出的明确路径选择。如何加强应收账款的管理,已经成为现代企业管理中不容回避的问题。论文通过对LD公司应收账款管理的研究,提出改进方案和实施保障措施,有助于解决LD公司应收账款管理的实际问题,顺应企业高质量发展需求,提高应收账款管理能力,推动企业朝着更高质量、更可持续的方向发展。     

Unfolding the impact of leadership and management on sustainability performance: Green and lean practices and guanxi as the dual mediators

This study aims to empirically evaluate the predictors that influence sustainability performance among manufacturing firms. Leadership and management, green and lean practices, and guanxi were examined to determine whether these predictors are directly and/or indirectly affecting sustainability performance; 160 valid responses were collected and partial-least-squares-structural-equation-modeling (PLS-SEM) was used to analyze the data. The results showed that leadership and management positively influenced green and lean practices and green and lean practices positively influenced sustainability performance. Leadership and management also positively influenced guanxi. Interestingly, leadership and management and guanxi do not exert a significant direct influence on sustainability performance. The findings contributed to the development of the resource-based-view theory further by empirically exploring the significance of leadership and management coupled with green and lean practices as competencies and capability to drive sustainability performance. The testing of the dual mediators' effects further added value to this study.     

Industry 4.0 technologies in the manufacturing sector: Are we sure they are all relevant for environmental performance?

This research contributes to the debate about the relevance of Industry 4.0 technologies in improving environmental performance in the manufacturing industry. We employed a qualitative–quantitative approach in which 19 Italian operations managers were interviewed and 260 managers responded to an online questionnaire. The effects of various technologies were ranked using ordinal regression. Comments and suggestions gave context to the quantitative results. Sensors, radio-frequency identification, artificial intelligence and analytics were found to be most relevant in improving environmental performance, whereas simulation software contributed moderately. Additive manufacturing, cobots, robots, automated mobile robots and automated guided vehicles had a negative effect, augmented reality had no effect and other technologies indirectly affected environmental performance. We also found a lack of knowledge and application as well as scepticism about technologies such as artificial intelligence and augmented reality. Finally, there was concern about the disposal of electrical and electronic waste produced by these technologies.     

Alternative balanced scorecards built from paradigm models in strategic HRM and employment/industrial relations and used to measure the state of employment relations and HR system performance across U.S. workplaces

This paper constructs alternative balanced scorecards based on high‐performance work system (HPWS) and employment relations system (ERS) models. The models are depicted and compared in diagrams and used as framework skeletons for building separate HPWS and ERS scorecards, intended to provide a detailed data picture of the operational health and performance of an organization's employment/HR system and its operations, processes, and inputs/outputs. The scorecards are filled in with nationally representative data from 2,000+ U.S. workplaces using more than 50 employment/HR indicators, as reported by separate panels of managers and employees. The indicators for each workplace are aggregated into an overall HR/employment system score, ranked from low‐to‐high, and graphed as frequency distributions. These distributions provide a unique snapshot picture of the mean and dispersion of the state of employment relations and HR system performance for companies across the United State. They also reveal that “models matter” since the HPWS and ERS scorecards provide distinctly different evaluation assessments.     

Asymmetric information consolidation and price discovery: Inferring bad news from insider sales

Due to the paucity of sources of negative firm‐specific information, US capital markets have more difficulty identifying and incorporating bad news into stock prices than they do good news. Even though insider selling is a potentially important proxy for undisclosed bad news, researchers have difficulty ex ante identifying information‐based sales due to an inability to separate liquidity‐motivated from information‐based insider trades. We hypothesize that when insiders in multiple firms sell shares of one firm in which they are insiders and at the same time buy shares of other insider portfolio firms, the sale is more likely to be information‐based, since the proceeds are reinvested. Conversely, when an insider sells one firm without purchasing others or sells multiple insider firms the sale is likely liquidity‐motivated. We find that insider sales identified as information‐based using this algorithm are followed by significant negative abnormal returns. Information‐based sales are also more likely to be associated with delistings, earnings declines and earnings restatements. Analysts are also more likely to revise their earnings forecasts downwards for these firms. It is thus possible to ex ante identify insider sales with information content. Our results will be of interest to investors and also to regulators designing insider trading rules.     

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.     

Ownership discrimination and private firms financing in China

This study examines the financing/funding of private firms in China. Our results show that private firms are significantly less funded through formal financing channels such as bank loans than state-owned firms, and hence have to resort to alternative financing such as trade credit. Consistent with the theoretical expectation and literature, there is a substitution effect between trade credit and bank loans for private firms, but this effect is much weaker compared to that of state-owned firms. Moreover, while the univariate comparisons indicate that private firms obtain more notes payable than state-owned firms, the multivariate regression analyses show that the relation between bank loan and notes payable is positive and indifferent between private and state-owned firms.     

公司业绩与财务杠杆

本文从实证角度分析了我国上市公司的财务杠杆对公司业绩的影响。结果表明,往年财务杠杆和当年财务杠杆对当年公司业绩的影响截然相反。财务杠杆对公司的息税后收益和息税前收益均有影响。长期负债率和长期借款率对公司业绩的影响分别比同年的流动负债率和短期借款率对公司业