债务违约、社会责任与政府支持

转型期中国，地方国有企业作为地区经济发展的一股中坚力量，当其发生债务违约时，作为实际控制人的地方政府究竟是否愿意提供支持，会优先选择哪些企业提供支持，政府的支持行为是否会影响到信贷资源的配置效率呢?为得到验证，文章选取2007-2016年沪深A股地方国有上市企业作为研究样本，实证分析了地方国企发生债务违约后，地方政府的支持行为及其对信贷资源配置效率的影响。研究结论表明：第一，地方国企发生债务违约后，地方政府会提供更多的"支持"，且支持额度与债务违约额呈显著正相关关系；第二，债务违约前，承担了更多社会责任（环保投资、捐赠以及就业等）的地方国企，在债务违约后可相应地获得更多的地方政府支持；第三，违约企业，尤其是得到更多政府支持的违约企业可获得更多的信贷资源，而其经营业绩却往往更差。由此表明政府干预一定程度上降低了信贷资源配置效率。     

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.     

Lobbying as a potent political marketing tool for firm performance: A closer look

This study endeavors to enhance political marketing literature about the impact of lobbying on firm performance. Our sample is composed of 140 U.S. firms and spans the years 2007–2014 to encompass the 2007–2009 recession and the subsequent recovery period. Our findings indicate that lobbying expenses positively contribute to firm performance. Also, government contracts in both ways, dollar amount and number of government contracts, act as mediators between lobbying expenses and firm performance. In addition, organizational slack moderates the relationship between lobbying expenses and government contracts. The managerial implications suggest that lobbying expenses can be leveraged as a potent tool for firm performance. Firms with larger lobbying efforts acquired both, higher dollar amounts and a greater number of government contracts.     

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.     

A COMPREHENSIVE REVIEW FOR GREEN PRODUCT TERM: FROM DEFINITION TO EVALUATION

Nowadays, interest in corporate environmental strategies shifts from cleaner processes to the holistic nature of green products. The relevant literature argues that firms have the opportunity to pioneer through green product innovation, allowing them to differentiate and thus gain competitive advantage. Environmental burden of products during their entire life cycle is undeniable. Due to the weakness of the existing literature that inadequately addresses a commonly accepted green product definition, as well as the thereby caused inconclusive academic empirical results on firms' competitiveness, there are many cases of businesses greenwashing behavior. The overall contribution of this exploratory paper, on determining and evaluating the degree of greenness of a product, is twofold; first, starting with a systematic literature review, authors further contribute by proposing an integrative definition that addresses the so far existing terminological gap. Next, after reviewing the existing environmental assessment tools, authors based on the developed definition and in accordance to its dynamic dimension contribute to the existing methodology, as the paper reveals issues that need to be considered in the evaluation of green products.     

In the city: The John Lewis partnership and planned shopping centres

A defining feature of large-scale retailing during the period 1950–1980 was the emergence and evolution of planned shopping centres. During the 1950s, department stores in the United States were in the vanguard of this phenomenon. In contrast, British department stores continued operating from traditional high street sites, and had limited opportunities for expansion within planned shopping centres until the 1970s. This paper addresses the connection between department store retailing and the development of the planned shopping centre in Britain from the perspective of one enterprise: the John Lewis Partnership. The article demonstrates that the Partnership was willing to operate department stores within centrally located shopping centres, but was circumspect about operating stores in non-centrally located shopping centres.     

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.     

Effects of export product diversification on quality upgrading: an empirical study

This paper empirically examines the effects of export product diversification on overall export quality in a panel data set of 115 countries from 1970 to 2010. It uses the data sets of the overall export quality and three export diversification measures of the International Monetary Fund: the extensive margin (variation in the number of new products exported), the intensive margin (variation in export values among existing exports), and the overall (Theil) index. It finds that export quality has only been increasing with a higher variation in export values among existing exports in low- and lower-middle-income countries. It also observes that export quality has been increasing with both a higher variation in export values among existing exports and new products exported in upper-middle- and high-income countries. The results are robust to the changing measures of controls in the benchmark model, the inclusion of many other controls; i.e. various measures of globalization, country size, factor endowments, macroeconomic stance, etc., and the exclusion of outliers.