Intra-organizational social media platforms are expected to help build a flexible corporate stage that facilitates employees’ communication and leverages creative idea generation. However, whether and how these systems can expedite the creativity of employees remains unclear. This paper attempts to address the impacts of employees’ social relationships on intra-organizational social media on their idea generation quantity and quality. Drawn upon the extant literature in the fields of information systems and organizational behavior, a theoretical model is postulated on the basis of analyzing the attributes of social ties and exchanged information content in light of a two-dimensional framework that juxtaposes boundary spanning and work relevance. The model is empirically tested using data obtained from two internal application systems of a large telecommunications company. Test results validate that employees’ online social relationships with different attributes are discriminably associated with their group identification and proactive creativity. Meanwhile, proactive creativity and group identification both show the positive impacts on idea generation quantity, whereas only proactive creativity has a significant positive impact on idea generation quality. These findings contribute to the literature on intra-organizational social media and employee idea generation, by uncovering the links among employees’ online social relationships, mental processes, and idea generation.

Inventory management (e.g. lost sales) is a central problem in supply chain management. Lost sales inventory systems with lead times and complex cost function are notoriously hard to optimize. Deep reinforcement learning (DRL) methods can learn optimal decisions based on trails and errors from the environment due to its powerful complex function representation capability and has recently shown remarkable successes in solving challenging sequential decision-making problems. This paper studies typical lost sales and multi-echelon inventory systems. We first formulate inventory management problem as a Markov Decision Process by taking into account ordering cost, holding cost, fixed cost and lost-sales cost and then develop a solution framework DDLS based on Double deep Q-networks (DQN).

In the lost-sales scenario, numerical experiments demonstrate that increasing fixed ordering cost distorts the ordering behavior, while our DQN solutions with improved state space are flexible in the face of different cost parameter settings, which traditional heuristics find challenging to handle. We then study the effectiveness of our approach in multi-echelon scenarios. Empirical results demonstrate that parameter sharing can significantly improve the performance of DRL. As a form of information sharing, parameter sharing among multi-echelon suppliers promotes the collaboration of agents and improves the decision-making efficiency. Our research further demonstrates the potential of DRL in solving complex inventory management problems.

In China, the target setting on loading rate of domestic equipment for shipbuilding industry has not been studied from a perspective of an optimization problem. Based on the constant elasticity of substitution production function, a localization rate model for shipbuilding industry is established to investigate the government’s trade-off between the interest of shipbuilding industry and that of ship supporting industry and to set the optimal localization rate. The results show that the market capacity has a significant effect on localization rate. In particular, when the market capacity is too small, the optimal localization rate is zero. When the market capacity is too large, the optimal localization rate is one. When the market capacity is in a certain range, a regular localization rate target exists. Moreover, the optimal localization rate could be affected by the technology gap and prices of domestic and foreign marine equipments. When the market capacity is large and the technology gap between domestic and foreign marine equipments is small, or the price of domestic equipment is too low, a higher localization rate target should be set. Finally, the substitutability of domestic and foreign equipments affects the optimal set of localization rate. If the substitutability of domestic and foreign equipments is too low, the improvement of localization rate caused by technological progress or price reduction of domestic equipment will be limited. This study provides several significant policy suggestions on dynamic adjustment of localization rate, classified implementation, and core technology mastering.

Peer-to-peer (P2P) rental markets connect independent owners and price sensitive renters. Examples of P2P rental platform include home sharing, such as Airbnb and Ma Yi Duan Zu, and carsharing, such as Uber and Didi. The platform gains profit by asking owners and renters to pay the commission rates. Owners decide whether to share their durable goods, and if so, how much time to share. In this paper, we study the short-term sharing behavior and decision making process in the P2P rental markets. Specifically, we consider two settings: owner makes the rental price decision; and the platform makes the rental price decision. The former one applies to the home sharing industry, while the latter one applies to the carsharing industry. In addition, we consider two market conditions: supply shortage case (supply is less than demand); and supply surplus case (supply is larger than demand). We show that, platform earns a higher profit under supply surplus (or shortage) case if the potential owner ratio is high (or low). In addition, we show that, under supply shortage case, owner’s optimal earnings and platform’s optimal profit do not change when we compare the setting where owner makes pricing decision with the setting where platform makes pricing decision. Last, we numerically illustrate how the social welfare changes with respect to the owner’s ratio. We find that it is first decreasing, then increasing, and finally decreasing in owner’s ratio.

Credit risk assessment is an important task of risk management for financial institutions. Machine learning-based approaches have made promising progress in credit risk assessment by treating it as imbalanced binary classification tasks. However, few efforts have been made to deal with the class overlap problem that accompanies imbalances simultaneously. To this end, this study proposes a Tomek link and genetic algorithm (GA)-based under-sampling framework (TEUS) to address the class imbalance and overlap issues in binary credit classification by eliminating majority class instances with considering multi-perspective factors. TEUS first determines boundary majority instances with Tomek link, then take the distance from each majority instance to its nearest boundary as the radius and assigns the density of opposite class samples within the radius as the overlap potential of that majority instance. Second, TEUS weighs each non-borderline majority instance based on its information contribution in estimating class labels. After partitioning non-borderline majority instances into subgroups according to overlap potential and information contribution, TEUS applies GA to select samples from subgroups and merge them with the minority samples into a new training set. Innovatively, the design of the fitness function in GA and the grouping of the non-borderline majority not only trade off the multi-perspective characteristics of instances but also help reduce the computational complexity of the sampling optimization search. Numerical experiments on real-world credit data sets demonstrate the effectiveness of the proposed TEUS.

This study investigates the influence of strategic competition on peer effects in corporate investment by using a sample of 28,522 observations of Chinese listed companies from 2008 to 2020. The study develops a linear-in means model and uses an instrumental variables approach, and uses the competitive strategy measure (CSM) and Lerner index as proxies of competitive strategies and competitive positions to capture the firm-level competition. The empirical results demonstrate that when firms compete as strategic substitutes and when firms are in higher competitive positions, the peer effects in corporate investment are significant and positive. In addition, in circumstances of high information asymmetry, firms competing as strategic substitutes and firms in high competitive positions rely more on information related to investment from peer firms. Moreover, industry policies and barriers do not significantly influence peer effects in investment. This study offers new empirical evidence regarding peer effects in corporate investment in China.