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.     

The valuation relevance of environmental performance revisited: The moderating role of environmental provisions

This study attempts to broaden our understanding of the value relevance of environmental performance by providing empirical evidence on the moderating role of financial environmental reporting. Previous studies find that firms' environmental performance can be both positively and negatively associated with market value. Such contradictory findings can be attributed to the fact that environmental performance is associated with future economic benefits and costs. This study suggests that firms with recognized environmental provisions on their balance sheets enable investors to disentangle these opposite effects either by signaling strong future financial performance or by enhancing the reliability of environmental performance information. Regardless of the mechanism by which this moderation effect is invoked, it is hypothesized that capital market participants place a positive and significantly higher value on the environmental performance ratings of firms with recognized environmental provisions than on the ratings of firms without environmental provisions. Utilizing a sample of 692 firm-year observations of French listed firms and employing a linear price-level model that associates the market value of a firm's equity with its environmental performance, I provide empirical evidence to corroborate this thesis. In addition to contributing to the academic debate on the market valuation implications of environmental performance, this study intends to provide useful insights from a country that can be considered a pioneer of environmental reporting legislation; hence, it provides valuable lessons for other jurisdictions that are in the process of developing their sustainability reporting regulations. Finally, the findings of this study support the calls for more integrated reporting showing that the interaction of financial and non-financial information has market valuation implications.     

Network structure,portfolio diversification and systemic risk

We investigate the effect of portfolio diversification on banking systemic risk, where the network effect is incorporated. We analyze three kinds of interbank networks, namely, random networks, small-world networks and scale-free networks. We show that the effect of portfolio diversification on banking systemic risk depends on interbank network structures and shock types. First, systemic risk increases first and then reduces with the increase of the level of portfolio diversification in the case of the individual shock. Second, in the case of the systemic shock, systemic risk reduces with the increases of the level of portfolio diversification. Third, banking systems with scale-free network structures are the most stable, and those with small-world network structures are the most vulnerable.     

Is the system reliability profitable for retailing and consumer service of a dynamical system under cross-price elasticity of demand?

Retailing strategy is one of the most crucial factors for industries. A proper retailing strategy can help to enhance consumer service and increase the industry's profit. An improved approach to retailing is suggested in this research to deliver superior customer service while maximizing profits in a dynamic system. The study analyzes a retailing strategy for a demand with cross-price elasticity upon the retail price. A product's cross-price elasticity and the system reliability are critical factors in retailing. Understanding the cross-price elasticity of demand between products helps retailers to make pricing decisions that maximize profits by maintaining demand. Imperfect products are produced due to an imperfect production system. The imperfect ones must be adjusted with some costs to make them perfect for better retailing. The system failure rate is crucial for retailing under cross-price elasticity of demand patterns. Production system reliability, cross-price elasticity of demand, and consumer service are all essential factors that can impact a company's success in the market. The production rate is considered time- and system failure rate-dependent. Contradictory to the literature, a dynamical system is proposed for improved retail management, which is solved using the Euler-Lagrange theory. Finally, one can achieve the expected maximum profit for this retail system with optimum selling prices for different products by reducing the system failure rate. Some numerical illustrations with graphical representations are provided to validate the current study. Numerical examples show that applying cross-price elasticity of demand for more than two identical products provides 35% more profit for the retail industry than a single type of product.     

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.     

Coping with coopetition—Facing dilemmas in cooperation for sustainable development: The case of the Dutch smart grid industry

This paper is amongst the first to examine coopetition strategy for sustainable development at the network level. Companies who want to successfully implement complex innovative technologies that support sustainable development need to collaborate with other actors of the innovation ecosystem, including their competitors, so that they can develop standards, interoperable products, pool knowledge, and resources and bundle forces to compete against other technologies. Collaboration with competitors brings benefits, but also many risks. We investigated how firms cope with these risks when establishing an innovation ecosystem to implement a new technology in society. We conducted research in the Dutch smart grids sector and explored how these firms minimize inherent risks of coopetition. We found that system‐building actors in the Dutch smart grid field not only minimize inherent risks, but from the start of their collaboration they implement so‐called enablers to prevent these risks upfront.     

Organizational career management practices and objective career success: A systematic review and framework

The theorization of the relationship between organizational investments in career development and individual success remains underdeveloped, and empirical tests of this relationship, which have been dispersed among several disciplinary areas, have produced inconsistent results. Addressing these issues, the purpose of this article is to propose a theoretical framework that illustrates why and how organizational career management practices translate into career success and under what circumstances the relationship is effective. Using a systematic review of empirical studies on career management practices and objective success, we identify three theoretical mechanisms - developmental, informational, and relational - and two groups of contingency factors that explain this relationship. Our framework advances the extant literature on organizational career management and provides suggestions to companies for designing effective career management systems.     

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.     

Exploring job crafting: Diagnosing and responding to the ways employees adjust their jobs

Recent research has developed an in-depth understanding of how workers change their jobs on their own to improve their performance and well-being, a process called job crafting. This research suggests that managers, coworkers, and organizations need a better understanding of how to manage job crafting to capitalize on its benefits and reduce costs. This article will help organizations diagnose the occurrence of job crafting, recognize the differences between goal-oriented approaches to job crafting as opposed to withdrawal-oriented avoidance job crafting, and identify the seven specific types of job crafting: work role expansion, social expansion, work role reduction, work organization, adoption, metacognition, and withdrawal. We conclude with recommendations for managers, coworkers, and organizations on how to diagnose and manage job crafting.     

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.