2022年上半年上外ok138cn太阳集团 国内外顶级期刊论文回顾（一）

近年来，上外国际工商管理学紧抓学科建设，与“国际接轨，前沿同步”，形成“数据科学”+“智能科学”的数智驱动型管理学研究特色。建成“人工智能与数据科学应用实验室”、“脑与认知科学应用重点实验室”两个实验室和全球管理学院首个磁共振成像研究中心，支撑管理学全领域智能科学研究。学院加强师资队伍建设，陆续从海内外名校招聘一批优秀学者为管理学教师，19人入选长江、杰青、国家百千万、上海市领军、上海市东方学者等国家与省部级人才。

随着学科建设引领、师资队伍完善及实验室科研支撑的作用逐渐凸显，学院科研团队致力于管理学与信息科学、神经科学等交叉领域的前沿原创研究，科研成果再创新高，2022年上半年学院教师一作/通讯发表论文40余篇。其中，发表顶级期刊论文近10篇，包括UTD-24本管理学期刊、金融时报（FT）50种经济管理期刊及Tliburg-35本经济学期刊；SSCI/SCI检索一区论文近20篇；CSSCI检索的管理学顶刊（《管理学科学报》）1篇。

部分研究发表成果

 ①

在线发表：2022年2月

期刊 | MIS Quarterly

论文题目| Crowdfunding for Microfinance Institutions:The New Hope?

期刊介绍 | MIS Quarterly属于信息管理与信息系统领域的顶级期刊之一，同时也是UTD24和FT50期刊。该期刊致力于发表信息管理与信息系统领域的前沿原创成果，在信息系统和管理领域有较高的认同和影响力。

该论文由骆雪琛等合作发表

论文摘要

Online crowdfunding holds the promise of empowering entrepreneurs and small businesses as an innovative alternative financing channel. However, doubts have been expressed as to whether online crowdfunding can deliver its promise because of the lack of empirical evidence regarding its effects. In this study, we investigate the effects that prosocial crowdfunding has on traditional microfinance institutions (MFIs). Combining multiple data sources, including data from Kiva.org and the Microfinance Information Exchange Market (MIX Market), we examine how access to crowdfunding influences MFIs’ sustainability and interest rates. We find that after joining Kiva, MFIs’ sustainability improves and interest rates decrease. Further investigation suggests that the changes mainly result from efficiency improvement, rather than increased supply of low-cost funds. We propose that joining an online crowdfunding platform induces greater transparency and crowd monitoring, which motivates and empowers MFIs to improve operations and become more efficient.

 ②

在线发表：2022年6月

期刊 | MIS Quarterly

论文题目| Combining Crowd and Machine Intelligence to Detect False News in Social Media

期刊介绍 | MIS Quarterly属于信息管理与信息系统领域的顶级期刊之一，同时也是UTD24和FT50期刊。该期刊致力于发表信息管理与信息系统领域的前沿原创成果，在信息系统和管理领域有较高的认同和影响力。

该论文由张明月等合作发表

论文摘要

The explosive spread of false news on social media has severely affected many areas such as news ecosystems, politics, economics, and public trust, especially amid the COVID-19 infodemic. Machine intelligence has met with limited success in detecting and curbing false news. Human knowledge and intelligence hold great potential to complement machine-based methods. Yet they are largely underexplored in current false news detection research, especially in terms of how to efficiently utilize such information. We observe that the crowd contributes to the challenging task of assessing the veracity of news by posting responses or reporting. We propose combining these two types of scalable crowd judgments with machine intelligence to tackle the false news crisis. Specifically, we design a novel framework called CAND, which first extracts relevant human and machine judgments from data sources including news features and scalable crowd intelligence. The extracted information is then aggregated by an unsupervised Bayesian aggregation model. Evaluation based on Weibo and Twitter datasets demonstrates the effectiveness of crowd intelligence and the superior performance of the proposed framework in comparison with the benchmark methods. The results also generate many valuable insights, such as the complementary value of human and machine intelligence, the possibility of using human intelligence for early detection, and the robustness of our approach to intentional manipulation. This research significantly contributes to relevant literature on false news detection and crowd intelligence. In practice, our proposed framework serves as a feasible and effective approach for false news detection.

 ③

接收：2022年5月

期刊 | MIS Quarterly

论文题目|  The wisdom of model crowds

期刊介绍 | MIS Quarterly属于信息管理与信息系统领域的顶级期刊之一，同时也是UTD24和FT50期刊。该期刊致力于发表信息管理与信息系统领域的前沿原创成果，在信息系统和管理领域有较高的认同和影响力。

该论文由万飞等合作发表

论文摘要

We examine the impact of firm social media engagement on sales performance and answer the “whether,” “what,” and “how” questions. The study used a quasi-experimental design in a social e-commerce setting, for which propensity score matching and difference-in-differences methods quantify a mean 20.67% sales increase after firm social media adoption. We also found that firms that sell low-involvement products benefit more from social media adoption than do those that sell high-involvement products. Further, in regard to how to manage social media engagement, we find that informative content in general is sales effective, especially for selling high-involvement products; whereas promotional content, a new type of content discovered in this study, is more beneficial for sales of low-involvement products. Meanwhile, more social media followers and more blog postings both generate greater firm sales performance. We use instrumental variables and control function method to address endogeneity issues and conduct robustness checks to support our conclusion. This study sheds light on the value of firm social media, particularly in regard to industry differences and firm know-how.

 ④

出版：2022年5月

期刊 | Management Science

论文题目|  The wisdom of model crowds

期刊介绍 |  Management Science创刊于1954年，是UTD-24国际顶级期刊，美国运筹与管理学会(Institute for Operations Research and the Management Sciences)的旗舰期刊，也是管理科学、运筹学领域历史最悠久、口碑最高的顶级期刊。该期刊2020年影响因子为4.883，近五年影响因子为6.619。

该论文由何黎胜等合作发表

论文摘要

A wide body of empirical research has revealed the descriptive shortcomings of expected value and expected utility models of risky decision making. In response, numerous models have been advanced to predict and explain people’s choices between gambles. Although some of these models have had a great impact in the behavioral, social, and management sciences, there is little consensus about which model offers the best account of choice behavior. In this paper, we conduct a large-scale comparison of 58 prominent models of risky choice, using 19 existing behavioral data sets involving more than 800 participants. This allows us to comprehensively evaluate models in terms of individual-level predictive performance across a range of different choice settings. We also identify the psychological mechanisms that lead to superior predictive performance and the properties of choice stimuli that favor certain types of models over others. Moreover, drawing on research on the wisdom of crowds, we argue that each of the existing models can be seen as an expert that provides unique forecasts in choice predictions. Consistent with this claim, we find that crowds of risky choice models perform better than individual models and thus provide a performance bound for assessing the historical accumulation of knowledge in our field. Our results suggest that each model captures unique aspects of the decision process and that existing risky choice models offer complementary rather than competing accounts of behavior. We discuss the implications of our results on theories of risky decision making and the quantitative modeling of choice behavior.

 ⑤

在线发表：2022年3月

期刊 | Internet Research

论文题目|  Doctor recommendation on healthcare consultation platforms: an integrated framework of knowledge graph and deep learning

期刊介绍 |  Internet Research是计算机科学、信息系统领域的重要刊物，特别是互联网领域，的知名期刊，近五年影响因子为4.835，是SCI检索一区期刊。

该论文由袁慧等合作发表

论文摘要

Purpose Recommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps. Design/methodology/approach This research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results. Findings The proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations. Originality/value This study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.