Abstract:The experiential economy is an important component of service market. Given that diffusion of experiential service has distinct spatial influences，this study establishes a spatial spillover model of service contagion that incorporates the infectiveness and susceptibility between regions. In this way，the model precisely separates the spatial effects of focal region and cross-regional diffusion. Based on the mobile data of visitors to Shanghai Disneyland，this paper empirically measures the spatial diffusion effect of Disney，including the clout and susceptibility of the service diffusion at provincial and regional levels. The results indicate that diffusion of Disney service at the focal region comes from the direct influence of local tourists ( 38. 6% ) ，the spillover effect from others( 52. 4% ) ，the temporal effect ( 8. 7% ) and media coverage effect ( 0. 3% ) . The study further reveals the asymmetry of inter-regional spatial impact in the service diffusion，and provides important managerial implications on location-based service promotion.

Abstract:The prevalence of group-based behavior has gradually increased in many business scenarios，including travelling，shopping，catering，and leisure activities. Such group-based behavior has the potential to influence consumers’purchasing decision. In the era of big data，human activities are recorded in ways that are far more detailed and systematic than before，which allows us to more accurately investigate the effects of group-based behaviors. We believe that the desire for a high-quality life fundamentally and consumption upgrade motivates group-based behaviors. Leveraging the sales data from Chengdu-Chongqing Railway，we investigate the effect of group travel on the choice of railway products and services. Based on Heckman’s two-stage model with controlling for more than 1. 3 million passenger fixed effects，our results show that travel with companions will enhance the possibility of choosing high-speed trains and premium seats. Further，such an effect is moderated by gender and age. Specifically，when travelling with female passengers and the elderly，one is more likely to choose high-speed trains and premium seats. These findings not only provide new insights into the railway management，but also suggest a general desire of human being for high-quality life and consumption upgrade beneath the group-based behaviors，which shed light on a wide range of sectors including business services and public administration. Through the encouragement of group-based behaviors，companies，organizations，and governments can stimulate consumers’and citizen’s choices for high-quality product and services.

Abstract:Ambulance is one of the most crucial medical resources to save patients’lives. Appropriate allocations of limited ambulances to different emergency stations can effectively lower the response time and lift medical service quality. In view of this，we propose a reinforcement learning based scheduling structure to resolve the dynamic ambulance redeployment problems. In order to address the challenges aroused from high-dimensional state spaces，we propose RedCon-DQN by considering multiple scheduling interactive factors，which is based on Deep Q-value Network ( DQN) and can output the optimized redeployment policy given specific environment. In addition，we propose a measurement，emergency-network resilience to evaluate the influences of each individual emergency station on the global optimization objectives. Finally，we construct a environment interactive simulator based on the emergency calls and response data of Nanjing from 2016 to 2017. We validate the advantages of the proposed redeployment policy over the state-of-the-art methods，and further analyze the effectiveness and characteristics in different time periods.

Abstract:Urban traffic congestion and air pollution bring severe challenges to the sustainable development of Chinese cities. Based on the merge of DiDi’s customer ordering data，air quality and climate data in Chengdu，we use regression discontinuity and mediation variable analysis to investigate the interactive mechanism of unban traffic congestion and air pollution. Our results show that the increase of urban traffic flow leads to more air pollution，and mobility efficiency plays a mediation role in such a relation，i. e. ，the reduction of mobility efficiency or traffic congestion will increase emission and air pollution. On the other hand，air pollution has a positive impact on mobility efficiency which reduces traffic congestion through the mediation effect of traffic flow and demand reduction under air pollution. Based on the new perspective of public mobility behavior，this study sheds light on the relationship between traffic congestion and air pollution，and provides theoretical and empirical evidence to deal with the problems jointly.

Abstract:With the change of market and the rapid growth of the scale of the agency selling，the reseller will adjust the product strategy in time and selectively penetrate the market of agency selling so as to improve its own income and grab more market share in the hybrid retail platform. This paper attempts to explore the impact of reselling’s competitive entry threat on the sales revenue of agency selling，and how the agency selling will respond through price adjustment when faced with such direct product competition. Based on a set of trading data from a platform，we used the propensity score matching and difference-in-difference model for empirical analysis in this paper. Our results suggested that，first，the entry of reselling will cannibalize the sales revenue of the agency selling. Second，faced with the direct competition from reselling，agency sellers may choose to raise products’sale price. Third，market size of the agency selling will weaken the negative impact of reselling’s entry on the sales revenue of agency selling，however，it may enhance the positive impact of reselling’s entry on the sales price of agency selling. Through further supplementary studies，it was found that reselling’s entry would lead to the abandonment of the operation of some products by agency sellers，which may loss some of users as well. These findings not only expand the operation mode of two-sided platforms，the theory of inter-organizational relationship，co-opetition and entry，but also provide extremely beneficial practical guidance for the reseller and agency sellers in the hybrid retail platform.

Abstract:Gamification has been widely adopted in various online learning contexts to better engage and motivate users. However，the extant literature still lacks consistent empirical evidence on the effectiveness of online gamified learning. Based on needs-affordances-features( NAF) perspective，we draw on self-determination theory and psychological ownership theory to analyze how gamified competition，including indirect competition ( such as a leaderboard) and direct competition ( such as one-on-one player-killing，or PK in short) in online learning apps provide motivational affordances to users. We collect data from a popular online language learning app and construct a large scale panel dataset. By the econometric analysis of using propensity score matching and difference in difference method，we find that our hypotheses are supported. We are among the first to explore the effect of using gamification modules in the voluntary online learning contexts. This study enriches the relevant literature and sheds light on practice.

Abstract:More and more enterprises utilize the online communities to interact with consumers，with the purpose of generating more sales aside from providing services to consumers. However，how to deliver marketing and service content in online communities is a challenge. Based on the theory of marketing and service ambidexterity，we propose the concept of balance dimension and combination dimension of content ambidexterity， and explore how balance dimension and combination dimension affect sales performance and consumer satisfaction. Based on natural language processing technology，we recognize the conversation and coded customer satisfaction from the online community，and find that the balance dimension has a positive effect on consumer satisfaction and sales performance. However，the combined dimension has an inverted U-shape effect on sales performance and consumer satisfaction. In addition，the skill level of employees has a significant moderating effect on the effect of content ambidexterity strategy. The findings may help enterprises understand the dual relationship between marketing content and service content in online communities and guide them to optimize the content delivery strategy.

Abstract:In the existing literature on corporate governance，most of the research related to managerial characteristics has two main limitations. First，most of the papers focus on the relationship between one managerial individual characteristic and corporate performance but lack a comprehensive understanding of the potential non-linear relationship and interactions among some of the important independent variables. Second，existing research tests casual inference but ignores the predictive performance of the model. In this paper，we first examine if managerial individual characteristics can predict corporate performance by using a machine learning approach: Boosting regression trees. Using a sample of listed firms in the Chinese A-share market from 2008 to 2016，we study whether these individual characteristics could predict corporate performance. The evidence shows that: 1) The individual characteristics of Chinese executives including CEOs and chairmen could predict corporate performance only to a limited degree. 2) Among multiple individual characteristics，managerial ownership and executive age are the two most important predictors of corporate performance. 3) The relations between predictors and corporate performance are non-linear，consistent with the prior literature. This paper initiates a new，more thorough perspective in Chinese executive research using machine learning methods and has important implications for selecting executives and designing incentive mechanisms.

Abstract:The rapid development of Internet has enabled non-professional individual investors to share information and express their tendency through online financial community. Using 5 178 824 user comments on eastmoney. com，this paper uses the convolutional neural network (CNN) classification algorithm to extract and measure the support tendency of online users for the future market (i. e. bullish or bearish) and the impact of user support tendency on the market is tested from two aspects of market returns and volatility. The results show that the current user’s support tendency has a significant negative impact on future stock market returns，and the consistency of support tendency will amplify the market’s volatility. Furthermore，the user support tendency is significantly based on the historical performance of the stock market，and the support tendency has a certain“herd effect”.