期刊：Informs Journal on Computing

期刊介绍：UTD24

论文题目：Exact simulation of quadratic intensity models

作者：曲妍(本院教师); Dassios, Angelos; Liu, Anxin; Zhao, Hongbiao
摘要：

We develop efficient algorithms of exact simulation for quadratic stochastic intensity models that have become increasingly popular for modeling events arrivals, especially in economics, finance, and insurance. They have huge potential to be applied to many other areas such as operations management, queueing science, biostatistics, and epidemiology. Our algorithms are developed by the principle of exact distributional decomposition, which lies in a fully analytical expression for the joint Laplace transform of quadratic process and its integral newly derived in this paper. They do not involve any numerical Laplace inversion, have been validated by extensive numerical experiments, and substantially outperform all existing alternatives in the literature. Moreover, our algorithms are extendable to multidimensional point processes and beyond Cox processes to additionally incorporate two-sided random jumps with arbitrarily distributed sizes in the intensity for capturing self-exciting and self-correcting effects in event arrivals. Applications to portfolio loss modeling are provided to demonstrate the applicability and flexibility of our algorithms.

期刊：Technological Forecasting and Social Change

期刊介绍：中国科学院一区

论文题目：The butterfly  effect  of  cloud  computing  on  the  low-carbon  economy

作者：陈岩(本院教师); Zhang, Ruiqian; Lyu, Jiayi; Ma, Xin

摘要：

This paper investigates the long-term butterfly effect of cloud computing on the low-carbon economy against the dual backdrop of the digital age and carbon neutrality. Utilizing a full-sample and sub-sample approach, the study identifies the complex interrelationship between the China Cloud Computing Index (CCI) and the Low Carbon Index (LCI). The quantitative examination reveals how CCI impacts LCI - both favorably and unfavorably. The positive effects suggest that cloud computing serves as a motivator in the environmentally friendly economy. In case of negative impacts, however, it is not possible to always determine the incentive effects owing to the large amounts of energy consumption generated by cloud computing data centers. Meanwhile, the positive influence of LCI on CCI indicates how the pursuit of low carbon-economy (as a goal) will bring opportunities for the explosive growth of the cloud computing industry. Cloud computing has increasingly prompted the fluctuation of carbon emissions in the whole ecosystem. By exploring this butterfly effect, this study digs deeper into the complex interrelationship between the discussed indexes. It is expected that the study outcomes will offer meaningful recommendations for the two main vectors of economic development - greenization and digitalization.

 

 

期刊：Journal of Cleaner Production

期刊介绍：中国科学院一区

论文题目：How do competition and collaboration promote green technology diffusion? Evidence from the global hydropower industry

作者：许冠南(本院教师); Yanmeng Wang; Liming Wang; Yuan Zhou

摘要：

Amidst urgent global calls for sustainable development, the diffusion of green technology emerges as a pivotal challenge, especially in our interconnected and dynamic digital era. Therefore, this research explores the drivers of green technology diffusion, moving from the traditional binary view of organizational proximity to a more nuanced trend of intricate and dynamic interactions within inter-organizational networks. We introduce a conceptual model to examine the influence of competition and collaboration on green technology diffusion, employing an inter-organizational network perspective. Our empirical study, targeting the global hydropower industry from 1999 to 2018, employs multi-source heterogeneous network analysis and quadratic assignment procedure regression. The findings reveal that during the industry's growth phase, collaboration plays a more significant role in green technology diffusion than competition. Conversely, in the maturity phase, competition emerges as the primary positive influence. Additionally, geographical distance is found to positively moderate the relationship between both competition and collaboration in green technology diffusion. These insights enhance our understanding of the drivers of green technology diffusion and provide policy recommendations for promoting such diffusion through inter-organizational competition and collaboration in the new area.

 

 

 

期刊：Reliability Engineering & System Safety

期刊介绍：中国科学院一区

论文题目：Reliability analysis for a generalized sparse connection multi-state consecutive-k-out-of-n linear system

作者：高洪达(本院教师); Tengfei Tu; Qingan Qiu

摘要：

The utilization of binary states is a common practice in reliability analysis. However, in complex systems such as aviation, spaceflight, and watercraft, the prevalence of multi-state systems and units is more pronounced. Consequently, extensive research has been undertaken to explore reliability analysis with a focus on multi-state consecutive-k models across various disciplines. Notably, the concept of sparse connection has received considerable attention due to its relevance in practical applications like wireless communication, cloud computing networks, and oil pipeline systems. This paper aims to propose a generalized multi-state consecutive-k: G system model that incorporates sparse connection. Both the system and the units are allowed to be in one of the ( M + 1 ) possible states, wherein the order numbers 0 , 1 , 2 , … , M indicate the performance measurement of the system or the units, i.e., 0 denotes the worst performance state while M denotes the best performance state. We classify the model into increasing, decreasing and non-monotonic consecutive-k systems corresponding to different mission requirements in practical applications. This paper utilizes the finite Markov chain imbedding (FMCI) method to derive explicit system state distributions for three types of multi-state consecutive-k-out-of-n: G systems. Subsequently, numerical examples are provided to demonstrate the proposed models and the corresponding results.

 

期刊：IEEE Transactions on Knowledge and Data Engineering

期刊介绍：JCR Q1

论文题目：Text-Rich Graph Neural Networks with Subjective-Objective Semantic Modeling

作者：李雅文(本院教师); Yu,Zhizhi; He,Dongxiao

 

摘要：

Graph Neural Networks (GNNs), which obtain node embeddings by attribute propagates along graph topology, exhibit significant power in graph-structured data mining. However, graphs in the real world are usually text-rich, where the text can not only be represented as node attributes, but also contains valuable objective semantic structures. Moreover, the graph topology also exhibits complex subjective semantic structures, especially the heterophily where nodes from different classes are prone to build connections, making existing GNNs that work under the assumption of homophily incapable to realize generalization. To tackle aforementioned limitations, we design a new text-rich graph neural network from a unified perspective, namely SO-GNN. It can effectively enhance the expressive power of GNNs by modeling the implicit but informative subjective-objective semantics underlying the text-rich graphs. Specifically, we first introduce a new constrained Markov matrix with well-defined probabilistic diffusion dynamics to guide information propagation, where the neighbors are more appropriate and indicative in providing both local and global subjective semantics. We then construct a flexible heterogeneous text graph to gain a deeper insight into objective semantics, providing indispensable information for learning node embedding. Finally, we unite subjective and objective semantics in an end-to-end manner, so that the model can fully utilize the most relevant information for downstream tasks. Extensive experiments across various text-rich graphs with low-to-high homophily demonstrate the effectiveness and flexibility of the proposed SO-GNN over state-of-the-arts.

 

 

 

期刊：Telematics and Informatics

期刊介绍：中国科学院一区

论文题目: Insights from cross-cultural memes: An empirical study on instagram and Douban

作者：张雷瀚(本院教师)，Cao, Herui，闫强(本院教师)

 

摘要：

As one of the most prevalent types of memes, visual memes often transcend individual cultures and languages and reach broad online communities of disparate actors. However, how the intrinsic factors of visual memes shape cross-cultural diffusion and whether visual memes can positively promote digital cultural globalization remains unclear. To answer these questions, we identified 1147 visual memes with 11,729 instances from four online communities on Instagram and Douban and manually annotated the memes in three dimensions, i.e., form, content, and emotion. Then, regression and structural causal models were designed to investigate the intrinsic factors affecting cross-cultural diffusion. Empirical results reveal that memes expressing focused and positive emotions, conveying universal topics, sourcing from films, using short captions, and featuring African or Caucasian roles are more likely to attain cross-cultural diffusion. In contrast, the memes featuring female or Asian roles are just the opposite. Moreover, the structural analysis of emotions, topics, and social identities suggests that although the dominance of Western culture and male groups persists in cross-cultural memes, visual memes have the potential to challenge the hegemonic power structures. From the prism of cross-cultural diffusion, the connotation of memes is enriched-expressive repertoires using multimodal discourses that can act as bridges between different cultures and languages. In summary, this research uncovered the effects of the intrinsic factors of visual memes on cross-cultural diffusion using regression and causal models for the first time and can help perform effective memetic engagement across different communities and cultures.

 

期刊：International Review of Financial Analysis

期刊介绍：中国科学院一区

论文题目：Asymmetric impact of energy prices on financial cycles based on interval time series modeling

作者：严子淳(本院教师); Chaonan Wu; Jingjia Zhang; Zehan Wang; Ivona Lađevac

摘要：

Energy prices crucially affect financial cycles. We employ the bivariate empirical mode decomposition (BEMD) model to disaggregate the daily interval data of the volatility index (VIX) and rely on the threshold autoregressive interval model (TARI) to incorporate energy prices into the forecasting model. Furthermore, the threshold interval decomposition ensemble (TIDE) is used to forecast the VIX series with nonlinearities to improve the forecasting accuracy. Moreover, we apply the root mean square error (RMSE) and the Diebold–Mariano test (DM) to evaluate the TIDE model performance across various frequency components and the final integration results. This paper demonstrates a significant correlation between energy prices and financial cycles, along with a temporal asymmetry effect. While the impact of energy prices on improving VIX forecasting is minimal in the short term, it becomes substantial over medium and long terms. Specifically, the influence of liquefied petroleum gas (LPG) prices on the VIX is notable in both medium and long terms. Our results offer new insights and methodologies for predicting financial cycles, assisting investors in evaluating volatility-related exchange-traded products. Additionally, these findings are crucial for developing more effective policies to promote green energy development.

 

 

期刊：Expert Systems with Applications

期刊介绍：中国科学院一区

论文题目：Insight into China’s economically motivated adulteration risk in online agricultural product sales

作者：刘恒宇(本院教师); Wen Tong

 

摘要：

Quality uncertainty and inspectors’ imperfect testing capability leave agricultural products wide open to economically motivated adulteration (EMA), and online shopping demand for these products in China makes it even worse. We develop a game-theoretic framework to investigate online agricultural product sellers’ EMA behavior on an e-commerce platform (EP). We characterize the sellers’ equilibrium pricing and adulteration decisions and the EP’s optimal take rate decision and analyze how the sampling inspections and adulteration penalty jointly impact these decisions. Moreover, we investigate three managerial levers to deter EMA: (i) penalizing EP’s malpractice regarding food safety management, (ii) claiming a higher-than-law-requires adulteration penalty to consumers, and (iii) adopting the traceability systems to record reliable quality information. Finally, we use real-world data to calibrate our model and derive more managerial insights. We find that the quality-differentiated sellers’ adulteration decisions are symmetric and their ex-post pricing decisions lead them to evenly share the market on the EP. We show that the EP’s higher take rate can inhibit the sellers’ adulteration behavior, however, it may indulge their such behavior by intentionally decreasing this rate. Our results highlight a penalty-inspection-centered approach as essential to combat EMA, and that it can be supplemented by the three levers.

 

期刊：Pattern Recognition

期刊介绍：中国科学院一区

论文题目：Weakly privileged learning with knowledge extraction

作者：付赛际(本院教师); Tianyi Dong; Zhaoxin Wang; Yingjie Tian

摘要：

Learning using privileged information (LUPI) has shown promise in improving supervised learning by embedding additional knowledge. However, its reliance on the assumption of readily available privileged information may not hold true in practical scenarios due to limitations in access or confidentiality. To address these challenges, this paper presents a novel weakly privileged learning (WPL) framework, integrating knowledge extraction methods within the LUPI context. An effective strategy is proposed to implement the WPL framework, where knowledge extraction techniques generate a weight matrix as weak privileged information. Extensive experiments employing various existing knowledge extraction techniques demonstrate that the proposed WPL outperforms traditional supervised learning and approaches the performance of standard privileged learning where privileged information is given in advance. This research establishes WPL as a promising learning paradigm, addressing limitations in privileged information availability and advancing the field of machine learning in practical settings.

 

期刊：Knowledge-Based Systems

期刊介绍：中国科学院一区

论文题目：An intensification-driven search algorithm for the family traveling salesman problem with incompatibility constraints

作者：魏泽群(本院教师); Jin-Kao Hao; Jintong Ren; Qinghua Wu; Eduardo Rodriguez-Tello

摘要：

The family traveling salesman problem with incompatibility constraints (FTSP-IC) is a variant of the well-known traveling salesman problem. Given a set of candidate nodes divided into several subsets (families), the FTSP-IC is to find several routes such that the sum of their total traveling distance is minimized, while ensuring a predetermined number of nodes from each family is visited and satisfying the incompatibility constraints. The FTSP-IC has a number of real-life applications, yet it is challenging to solve the problem due to its NP-hard nature. In this work, we introduce a competitive intensification-driven search algorithm for solving this relevant problem. The proposed algorithm significantly intensifies the search by performing extensive searches in the nearby area of discovered local optima. Computational results on 63 benchmark instances from the literature show that our algorithm is able to improve 29 best-know solutions (new upper bounds) and match all the remaining 34 proven optimal solutions. The impacts of the key components of the algorithm on its performance are experimentally analyzed.

 

期刊：Computers & Industrial Engineering

期刊介绍：中国科学院一区

论文题目：Recovering farming supply chains from animal epidemics via government subsidies

作者：刘恒宇(本院教师)

 

摘要：

In recent years, animal epidemics - such as African swine fever and bird flu - break out more frequently in both developed and developing economies, causing serious economic losses and animal product shortages. To restore animal product supply, many governments offer two agricultural subsidies to farms: (i) the culling compensating subsidy (CCS), which guarantees a floor price for culled animals, and (ii) the scaled construction subsidy (SCS), which supports large-scale farms to enhance their production capacities. We develop a three-stage Stackelberg game framework to capture the strategic interactions among the government, large- and small-scale farms. In particular, the two types of farms differ in their epidemic prevention and control capabilities and must operate under yield uncertainty. We study two cases regarding whether the government requires a minimum BS for the SCS. In each case, we analyze the equilibrium decisions of the profit-seeking farms and characterize the structure of the government's optimal subsidy programs with an earmarked budget to maximize social welfare. Moreover, we identify the conditions under which the government should adopt the "SCS only strategy", "CCS only strategy", or offer the two subsidies simultaneously. Furthermore, we examine the effectiveness of the subsidy programs in terms of consumer surplus, farms payoffs, and social welfare. We show that in both cases, these programs can make the three stakeholders achieve a win-win-win situation by prioritizing a reasonable CCS according to the subsidy budget. We leverage our analyses to offer insights that can help generate policy recommendations for stakeholders in countries suffering from animal epidemics.

 

期刊：Omega - The International Journal of Management Science

期刊介绍：JCR Q1

论文题目：Analysis of the Chinese government's subsidy programs to restore the pork supply chain: The case of African swine fever

作者：刘恒宇(本院教师); Zheng, Kai

 

摘要：

Since African swine fever (ASF) was first detected in China in August 2018, it has killed more than one million pigs and caused pork prices to skyrocket. To address this, the Chinese government offers two farm subsidies: (i) the compulsory culling subsidy (CCS), which cushions losses from new outbreaks by compensating for pigs culled due to ASF, and (ii) the large-scale breeding subsidy (LBS), which maintains or increases farms' breeding scales (BSs) by requiring a minimum BS. We develop a game-theoretic model to capture the underlying dynamics between the government and farms. In particular, farms have different production capacities and must decide their BSs under yield uncertainty due to possible new outbreaks. We analyze the optimal design of subsidy programs with an earmarked budget to maximize social welfare, and we examine the impacts on different stakeholders. Our analysis reveals several insights. First, the government should offer the CCS only if the budget is very constrained; otherwise, it should simultaneously offer the two subsidies and prioritize compensating farm losses by providing a good CCS. Second, the optimal subsidy programs can increase consumer surplus regardless of the budget, and programs with a small or large budget can make all farms better off. However, small- and moderate-scale farms (that do not enroll in the LBS) will be worse off under these programs with a moderate budget. Third, the optimal subsidy programs can create positive net social value that is nondecreasing in the budget; hence, a win-win-win situation can be achieved by establishing a sufficiently large budget for these programs. Finally, we calibrate our model using Chinese pig industry data and provide further insights into ASF subsidy programs.

期刊：Neural Networks

期刊介绍：中国科学院一区

论文题目：Generalized robust loss functions for machine learning

作者：付赛际(本院教师); Xiaoxiao Wang; Jingjing Tang; Shulin Lan; Yingjie Tian

摘要：

Loss function is a critical component of machine learning. Some robust loss functions are proposed to mitigate the adverse effects caused by noise. However, they still face many challenges. Firstly, there is currently a lack of unified frameworks for building robust loss functions in machine learning. Secondly, most of them only care about the occurring noise and pay little attention to those normal points. Thirdly, the resulting performance gain is limited. To this end, we put forward a general framework of robust loss functions for machine learning (RML) with rigorous theoretical analyses, which can smoothly and adaptively flatten any unbounded loss function and apply to various machine learning problems. In RML, an unbounded loss function serves as the target, with the aim of being flattened. A scale parameter is utilized to limit the maximum value of noise points, while a shape parameter is introduced to control both the compactness and the growth rate of the flattened loss function. Later, this framework is employed to flatten the Hinge loss function and the Square loss function. Based on this, we build two robust kernel classifiers called FHSVM and FLSSVM, which can distinguish different types of data. The stochastic variance reduced gradient (SVRG) approach is used to optimize FHSVM and FLSSVM. Extensive experiments demonstrate their superiority, with both consistently occupying the top two positions among all evaluated methods, achieving an average accuracy of 81.07% (accompanied by an F-score of 73.25%) for FHSVM and 81.54% (with an F-score of 75.71%) for FLSSVM.

 

期刊： 管理科学学报

期刊介绍：国家自然科学基金委管理科学部认定的A类重要期刊

论文题目：公司和高管特征与上市公司违规行为——基于机器学习的经验证据

作者：何瑛(本院教师); 任立祺; 于文蕾; 杜亚光

摘要：

上市公司违规问题一直备受资本市场高度关注,对于其影响因素的单一维度变量因果关系研究固然关键,但构建有效的整体性预测模型研究同样具有重要意义.本研究基于内部治理视角选取重要的公司特征和高管个人特征构建上市公司违规行为预测模型,以2008年—2019年中国A股上市公司为样本,利用机器学习算法LightGBM和SHAP工具,研究两类特征对违规行为的预测能力,重要性排序及预测模式.研究结果表明:模型可以在一定程度上预测公司违规行为,公司特征比高管个人特征对预测产生的影响更大. 其中,上市公司信息透明度越高、总资产净利率越大、资产负债率越低、高管团队持股比例越高、业绩波动性越小、分析师关注度越高,模型预测违规的倾向越低;高管年龄偏小、公司存在董事长与CEO两职合一情况时,模型预测违规的倾向增高.大部分特征均与违规行为呈现非线性关系,与传统理论和实证研究结论相一致. 本研究从预测视角拓展我国公司高管特征研究,为监管部门和投资者提升监管和投资效率、企业完善内部治理机制提供经验证据。

 

期刊：中国工业经济

期刊介绍：国家自然科学基金委管理科学部认定的A类重要期刊

论文题目：数据资产化能否缓解“专精特新”中小企业融资约束

作者：何瑛(本院教师); 陈丽丽; 杜亚光

摘要：

数据资产作为企业具有核心竞争力的关键生产要素和战略资产，能够发挥降本提质、交易增值和信用担保的作用，并有助于缓解“专精特新”中小企业融资约束。本文选取2011—2021年中国专精特新“小巨人”上市公司数据，运用文本分析方法构建数据资产文本词典，并提取企业年报中的关键词，刻画出企业数据资产化程度，实证考察了数据资产化对“专精特新”中小企业融资约束的影响。研究发现，数据资产化显著缓解了“专精特新”中小企业的融资约束，将数据资产划分为自用型数据资产和交易型数据资产两种类型，均能缓解企业融资约束。作用机制检验发现，企业数据资产化可以通过优化信贷资源配置和提高资金流动性缓解“专精特新”中小企业的融资约束。基于TOE框架的异质性检验发现，企业技术能力和客户关系能够增强数据资产化对企业融资约束的缓解效应，数字金融发展水平与数据资产化在一定程度上呈现替代关系。经济后果检验发现，数据资产化通过缓解融资约束提升了“专精特新”中小企业创新能力，尤其促进了“专精特新”中小企业突破式创新。本文丰富了数据资产微观领域的研究成果，为推动“专精特新”中小企业数字化转型和高质量发展提供了证据和经验借鉴。

 

 

期刊：南开管理评论

期刊介绍：国家自然科学基金委管理科学部认定的A类重要期刊

论文题目：工作场所伙伴关系：数字经济时代员工与组织共赢的逻辑基础

作者： 赵晨(本院教师); 林晨; 周锦来; 刘军; 高中华

摘要：

工作场所伙伴关系是一种提倡员工与组织间对等合作、互利共赢的关系理念，要求基于员工与组织间的互利制度来实现双方的价值共创。近年来，伴随数字经济及平台型组织的发展，工作场所伙伴关系受到理论界和实践界的广泛探讨。鉴于现有研究存在概念模糊、结论分歧及缺乏系统性研究等问题，本研究从理念、方法及实践层面系统阐述工作场所伙伴关系的概念并给出界定，从互利基础、制度保障、合法性及员工话语权角度归纳出该理念的前提框架，探讨关系建立后的影响效应并总结出边界框架。最后在理论演进、机制探索、数字经济情境研究、中国特色理论发展和新时代制度建设五个方面给出未来的研究重点。

 

 

期刊： 中国人口·资源与环境

期刊介绍：国家自然科学基金委管理科学部认定的重要期刊

论文题目：高铁连通对企业跨区域合作创新的影响及作用机制

作者： 王雨飞(本院教师); 王云辉; 许可; 曹清峰

摘要：

创新主体之间的跨区域合作是应对并解决现有企业创新需求的有效手段，是发挥创新合力打造科技创新共同体的重要形式。该研究从企业跨区域合作创新的视角，基于2005—2019 年高铁连通与“企业-城市关系对”的合作专利申请的面板数据，采用多期双重差分法，实证检验高铁连通促进企业跨区域合作创新的影响及作用机制。研究发现：①企业所在城市与合作对象所在城市高铁连通显著提升了企业合作专利申请的数量，该结论在经过内生性检验、平行趋势检验、安慰剂检验和稳健性检验后依然稳健；②在高铁连通影响下，企业跨区域合作创新对象的数量明显增加，但主要分布在近距离及近距离的中心城市范围内，整体呈现“散中有聚”的空间收缩效应和空间分级效应；③高铁连通还使得企业合作创新质量得到提升，将近距离范围内的低质量合作创新转化为高质量的合作创新，呈现空间上的分层效应。进一步研究发现：①高铁连通主要促进了非制造业企业的合作创新，而对制造业企业的促进效果不显著；②高铁连通使国有资本背景的企业跨区域合作专利申请的数量得到提高，而对民营、外资和其他企业的影响不显著；③高铁连通更能提高规模较大企业的合作创新水平，但对规模相对较小的企业作用不显著。该研究结论可为国家推动企业牵头组建创新联合体，完善创新体系建设提供新思路；建议持续优化完善高铁主干及支线网络布局，将更多潜力城市接入高铁网络。

 

期刊：科学学研究

期刊介绍：国家自然科学基金委管理科学部认定的A类重要期刊

论文题目：关键核心技术国产替代的创新模式研究——基于CPU技术头部企业的双案例分析

作者：王砚羽(本院教师); 卢婷; 刘汝芳

摘要：

关键核心技术是国之重器。本研究以龙芯中科和天津飞腾两家CPU技术头部企业为案例，探讨关键核心技术国产替代的创新模式。得出如下结论：（1）识别出关键核心技术国产替代的两种路径，龙芯中科采用“技术引进+学习导向的消化吸收”路径，最终实现了核心技术自主可控；而天津飞腾采用“技术引进+应用导向的消化吸收”路径，存在卡脖子风险。（2）不同的国产替代创新模式塑造了不同的企业技术能力和生态能力。本研究为理解技术限制背景下的技术主权提供了新的视角，为政策制定者和行业利益相关者提供管理启示。

 

 

期刊：科研管理

期刊介绍：国家自然科学基金委管理科学部认定的A类重要期刊

论文题目：多重情境下“核心-边缘”网络关系研究——基于云计算企业分阶段成长的分析

作者：陈岩(本院教师); 张睿倩; 吴超楠; 时准

摘要：

精准发挥有为政府和有效市场两方面力量推动大中小企业协同共赢是实现中国式创新的关键着力点，揭示我国本土多重情境下大中小企业协同合作的内在复杂机制具有重要意义。基于2016—2019年云计算企业数据，本文发现了云计算企业在我国数字化、市场、制度多重情境叠加下的分阶段成长规律。结果表明，数字化情境下建立“核心-边缘”网络关系对创建期、成长期企业成长影响为负，并对存活期企业成长影响为正；多市场接触情境能强化存活期正面影响而弱化成长期负面影响，国有资本支持情境能弱化创建期负面影响而强化存活期正面影响，且市场、制度交互情境能进一步增强存活期正面影响。本研究揭示了我国本土多重情境下中小企业从依附大企业到成长的微观过程，为数字化时代大中小企业合作共赢提供策略建议，并为我国分阶段运用有为政府和有效市场两股力量，进而精准释放中国式创新红利提供了实证依据和施策配方。

 

 

期刊：科研管理

期刊介绍：国家自然科学基金委管理科学部认定的A类重要期刊

论文题目：短视频个性化推荐对用户信息采纳意愿的影响

作者：张生太(本院教师); 杨阳; 袁艺玮(本院教师); 杨洪军; 张梦桃

摘要：

“抖音5分钟，人间1小时”,近年来短视频社交平台成为用户获取信息和日常娱乐的重要途径之一，受到了学术界的广泛关注。然而，少有学者结合短视频平台特点，深入研究抖音等短视频用户对平台技术的感知和理解及其对用户心理需求和行为模式的影响，关于感知个性化和信息窄化的影响也未在抖音等短视频平台上得到验证。本文以抖音为例，借鉴刺激-机体-反应（S-O-R）模型，对用户感知个性化、信息窄化、心理抗拒对信息采纳意愿的影响进行了实证研究。结果显示，用户感知个性化会对信息窄化产生积极影响，感知个性化程度高的用户更容易陷入“信息茧房”中。其次，与以往研究不同，本研究发现用户感知个性化和信息窄化会降低用户的心理抗拒。该结果表明，短视频平台中，高水平的个性化推荐会降低用户的信息获取成本和平台使用成本，提高用户的满意度。最后，本研究发现心理抗拒在两条影响路径中均起到了部分中介的作用，表明用户对平台算法的感知会通过降低心理抗拒来提高信息采纳意愿。本研究从研究情境、研究变量及研究模型上拓宽了信息采纳意愿的研究领域。由研究结果可知，在探究用户对抖音等短视频平台的感知个性化和信息窄化时，不能完全借鉴以往结论，需单独研究。本研究为平台管理者进一步改善推荐算法提供理论支撑，也为传播界避免“劣币驱逐良币”现象产生提供了新的研究视角。

期刊：中国软科学

期刊介绍：国家自然科学基金委管理科学部认定的A类重要期刊

论文题目：数字领头雁：数字化共享愿景对企业数字化转型的影响机制

作者：赵晨(本院教师); 周锦来; 林晨; 高中华

摘要：

整合高阶理论与变革认知相关文献，构建产业链中链主与在链企业数字化共享愿景对企业数字化转型的影响机制。预研究利用二手数据研究发现，链主与在链企业的数字化共享愿景会对在链企业数字化转型产生积极影响；正式研究通过调查研究发现，数字化共享愿景在降低在链企业高管团队的变革承诺差异的同时，提高在链企业高管团队的变革承诺强度，能够促进在链企业的数字化转型；进一步研究发现，链主与在链企业的协同方式（契约治理vs关系治理）不仅调节了数字化共享愿景对高管团队变革认知结构的积极作用，而且调节了数字化共享愿景对企业数字化转型的影响机制。研究结果揭示了产业链中链主企业在数字化转型过程中的领头雁效应及其作用机制，对于发挥我国的产业链优势与推进传统制造业数字化转型具有指导作用。

 

期刊：中国软科学

期刊介绍：国家自然科学基金委管理科学部认定的A类重要期刊

论文题目：国际人才引进与中国企业技术突破

作者：袁然(本院教师); 魏浩

摘要：

吸引国际人才,加快世界重要人才中心建设,是营造具有全球竞争力开放创新生态的重要途径.利用中国A股上市公司数据与中国吸引国际人才数据,从高度细化的企业—专利层面实证考察了国际人才影响当地企业技术突破的效果与机制.研究发现:国际人才流入有助于当地企业实现技术突破,拓宽新技术领域的范围.推动跨国技术溢出、提升企业风险承担能力以及优化创新任务分工是国际人才推动企业实现技术突破的有效机制.进一步研究发现,国际人才流入是当地企业加快突破"卡脖子"关键核心技术和核心数字技术的重要力量;与没有海归高管的企业相比,国际人才流入更有助于拥有海归高管的企业拓展新技术领域的范围;国际人才流入对当地企业技术组合的影响主要表现在促进优势技术的多样化,提升优势技术在企业技术组合中的比重.研究成果对于有效应对国际科技竞争、加快建设世界科技强国具有重要参考意义.

 

期刊：管理评论

期刊介绍：国家自然科学基金委管理科学部认定的A类重要期刊

论文题目：电商直播中主播情绪沟通与认知沟通对消费者直播购买行为的影响

作者：石文华(本院教师); 黄来恩; 吕廷杰(本院教师); 胡明瑶(本院教师)

摘要：

随着电商直播模式的不断发展与成熟，主播直播时的话术内容已成为影响消费者直播购买行为的关键因素。本研究以信息加工理论为基础，探讨了主播的积极情绪沟通、消极情绪沟通以及认知沟通如何影响消费者的直播购买行为。本研究通过收集电商直播间的真实数据进行分析，并采用实验室实验做进一步验证，研究发现积极情绪沟通和认知沟通对消费者直播购买行为有正向作用，且两者存在交互效应；此外，研究还发现主播类型(企业主播和名人主播)和商品类型(私有消费品和公开消费品)在其中存在调节效应，情绪沟通更容易在名人主播和公开消费品上产生影响，认知沟通更容易在企业主播和私有消费品上发挥作用。因此，本研究丰富了电商直播领域的情绪与认知研究，拓展了信息加工理论在电商直播场景中的实践应用，研究结论对主播的直播话术制定、企业的直播策略选择以及观众的理性购物都具有重要指导意义。

 

期刊：管理评论

期刊介绍：国家自然科学基金委管理科学部认定的A类重要期刊

论文题目： 领导批判性思维能激活团队创造力吗？一个三重交互作用模型

作者：江静(本院教师); 董雅楠; 武欣; 屠兴勇

摘要：

创新驱动发展战略的深度实施和团队工作方式的盛行，使得团队创造力越来越受到学者们的广泛关注。本研究从领导批判性思维这一认知视角出发，探讨其对团队创造力的影响。以某通信公司的82个研发团队为样本，基于团队动机驱动的信息处理理论，假设并验证了领导批判性思维能够提高整个团队的创造力水平，且团队信息深度加工起到中介作用。此外，时间压力能够弱化领导批判性思维和团队信息深度加工之间的关系；任务复杂性则能强化这一关系。领导批判性思维、时间压力和任务复杂性对团队信息深度加工产生三重交互作用，即当时间压力小且任务复杂性高时，领导批判性思维对团队信息深度加工的促进作用最强。最后探讨了该研究的理论意义和管理启示。

 

 

期刊：管理评论

期刊介绍：国家自然科学基金委管理科学部认定的A类重要期刊

论文题目：上下交而志同：民营企业党组织嵌入与企业社会责任

作者：王砚羽(本院教师); 卢婷

摘要：

党组织嵌入是民营企业高质量发展的重要保障。本文基于2006—2016年中国私营企业调查数据，探讨民营企业党组织嵌入对其履行社会责任的影响。研究发现：民营企业设立党支部有利于儒家义利观通过自上而下和基层生根的方式潜移默化地影响企业，使其更多地履行社会责任。在履行社会责任的同时，民营企业党支部设立对其履行环境责任的促进作用更高。更进一步，在履行环境责任时，民营企业党支部设立更加促进企业履行事前的环境责任（未雨绸缪）而非事后环境责任（亡羊补牢）。本文从中国传统儒家义利观与嵌入理论的视角探究了民营企业党组织嵌入促进企业社会责任履行的内在机制，为全面推进民营企业党建提供理论基础和实证依据。

 

期刊：系统工程理论与实践

期刊介绍：国家自然科学基金委管理科学部认定的A类重要期刊

论文题目：面向异构非欧数据的因果关系发现方法

作者：王晓康(本院教师); 李帅戈; 王熠晖; 万岩(本院教师)

摘要：

因果关系在揭示事物发生机制及指导干预行为等方面具有相关关系不能替代的重要作用.然而,由于现有框架对模型表示和学习算法的限制,仅有少量工作在非欧几里德数据上进行因果关系发现.本文进一步针对同时包含了多种类型非欧几里得数据的集合,提出了基于多元坐标表示的因果映射过程,描述了父节点与子节点之间的数据生成机制,并创建了一个基于多元张量回归的因果生成模型.其次,在上述理论框架下,提出了基于正则化广义典型相关分析的方法的两阶段因果关系发现方法,可以将一系列具有复杂多样化特征的非欧氏数据投影至共享子空间中的一系列数值变量,在计算相关性的同时,实现了变量的综合降维与离散化表示.使用上述一致投影方向下的离散表示,可以在共享子空间内进一步发现异构非欧变量之间的因果关系.最后,在真实世界工业传感器数据的实验结果上进行了实证研究,结果表明,本文提出的异构非欧数据因果关系发现方法能够给出有意义的因果关系。

 

期刊：心理学报

期刊介绍：中国人文社会科学期刊 AMI 心理学权威期刊

论文题目：愿景沟通中负面反馈的解释水平对下属愿景追逐行为的影响

作者：赵晨(本院教师); 林晨; 周锦来; 高中华

摘要：

为了让下属更好地接受并追逐愿景, 领导会在与下属的愿景沟通中有意进行负面反馈。然而, 领导应如何通过调整负面反馈的措辞策略, 来提高愿景沟通的效果尚不明确。基于幻想实现理论开展了情景实验(研究1,N= 76)及问卷调查(研究2,N= 301; 研究3,N= 619), 结果一致表明, 愿景沟通中领导的负面反馈解释水平能够通过下属的愿景实现期望进而影响其愿景追逐行为, 同时这一中介机制被组织经营状态调节。当组织处于顺境中, 领导在采取负面反馈时应该采用较低的解释水平, 而当组织处于逆境中则应采取较高的解释水平, 这样能使负面反馈发挥出最佳效果。研究结论有助于揭示愿景沟通中负面反馈的解释水平对下属愿景认知与行为反应的作用机制, 为领导者如何在与下属的愿景沟通中有效开展负面反馈提供了实践启示。