农业创业情境下商业模式设计的影响机制研究——基于模糊集定性比较分析

农业创业者如何通过整合与管理资源与能力，设计新颖型商业模式创造更多价值，是实现乡村振兴战略的关键措施。文章基于资源编排、商业模式理论，以央视《致富经》栏目40个案例为样本，运用模糊集定性比较分析（fsQCA）方法，剖析资源编排、机会能力、创业学习所构成的前因变量组态对农业创业活动中商业模式设计的影响机制。研究发现，资源编排在农业创业活动中发挥不可或缺的关键作用；相较于资源结构化和资源捆绑，资源利用在新颖型商业模式设计中的作用更为突出；机会能力须与资源编排共同作用，方可实现新颖型商业模式设计；在资源编排作用缺乏时，创业学习的发挥有助于新颖型商业模式设计的形成。研究成果诠释了农业创业情境下资源编排、机会能力与创业学习对商业模式设计的影响机制，为开展农业创业活动提供指导。     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.     

Objective methods for graphical structural learning

Graphical models are used for expressing conditional independence relationships among variables by the means of graphs, whose structure is typically unknown and must be inferred by the data at hand. We propose a theoretically sound Objective Bayes procedure for graphical model selection. Our method is based on the Expected-Posterior Prior and on the Power-Expected-Posterior Prior. We use as input of the proposed methodology a default improper prior and suggest computationally efficient approximations of Bayes factors and posterior odds. In a variety of simulated scenarios with varying number of nodes and sample sizes, we show that our method is highly competitive with, or better than, current benchmarks. We also discuss an application to protein-signaling data, which wieldy confirms existing results in the scientific literature.     

Trading off learning and performance: Exploration and exploitation at work

Employees are increasingly given control over how they learn, and their choices for training are diverse and varied, yet employees must balance competing demands. On one hand, they are expected to be increasingly efficient in their current job duties – on the other hand, they are expected to develop new skills and competencies that enable them to adapt and respond to changing job demands. Drawing from the organizational learning literature, we propose a model of worker and work characteristics that inform choices between two mindsets related to learning at work. The first mindset is exploration, or the pursuit of learning outside one’s current knowledge domain; the second mindset is exploitation, the refinement/deepening of one’s existing knowledge stock focusing on the task at hand. We further propose that these strategic choices, or trade-offs, influence employee learning and performance in unique ways, with different implications for both routine and adaptive performance. Finally, we incorporate the notions of feedback loops and risk assessments that influence ongoing decisions between exploration and exploitation mindsets. Recommendations for future research and extensions of the theoretical model are also proposed.     

Using deep learning and visual analytics to explore hotel reviews and responses

This study aims to use computational linguistics, visual analytics, and deep learning techniques to analyze hotel reviews and responses collected on TripAdvisor and to identify response strategies. To this end, we collected and analyzed 113,685 hotel reviews and responses and their semantic and syntactic relations. We are among the first to use visual analytics and deep learning-based natural language processing to empirically identify managerial responses. The empirical results indicate that our proposed multi-feature fusion, convolutional neural network model can make different types of data complement each other, thereby outperforming the comparisons. The visualization results can also be used to improve the performance of the proposed model and provide insights into response strategies, which further shows the theoretical and technical contributions of this study.     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.     

双元视角下调节聚焦对国防研发人员创新行为的影响机制

调节聚焦作为个体实现目标的动机调节系统，对个体创新行为具有重要作用，是组织行为学领域关注的热点。基于调节聚焦等相关理论，从二维视角深入考察个体调节聚焦对其双元创新的过程机理与边界机制。对527名国防科技研发人员进行问卷调研分析，结果发现：①促进聚焦和防御聚焦均正向预测双元创新及其平衡性与互补性，促进聚焦的预测作用更强；②探索式失败学习和利用式失败学习同时中介上述关系；③正向组织差错管理氛围正向调节促进聚焦与双元创新及其互补性的关系，负向组织差错管理氛围正向调节防御聚焦与双元创新及其互补性的关系。     

创业失败经验对连续创业意愿的影响——创业失败学习与市场动荡性的作用

基于经验学习理论，探究创业失败经验如何通过创业失败学习影响创业者连续创业意愿，以及市场动荡性的调节作用。对176位有失败经历的创业者进行问卷调查，结果表明：创业失败经验对创业失败学习和连续创业意愿均有显著正向影响；创业失败学习在创业失败经验与连续创业意愿关系间起中介作用；市场动荡性负向调节创业失败学习与连续创业意愿的关系，即市场动荡性越高，创业失败学习与连续创业意愿间的关系越弱。研究结论对于拓展创业失败经验与连续创业意愿研究、帮助创业者从失败中复原，以及激励连续创业具有重要理论价值和实践意义。     

创业网络结构属性如何影响创业学习方式的选择——基于印记理论的单案例研究

创业网络对具有新进入缺陷的新创企业以及创业者而言有着至关重要的作用，不仅表现为"知识源"作用，也表现为"印记源"作用。印记理论指出个体或组织在其容易受到影响的时期，外部环境的显著特征将对其产生重大影响，个体和组织将打上环境特征的烙印，并且这种烙印将持续影响个体和组织的行为。基于此，本研究基于印记理论，利用对一家创业企业的归纳式纵向单案例分析探讨创业网络不同的结构属性经由何种印记过程，继而影响创业者学习方式的选择。结果表明：当创业网络表现为封闭、半开放以及开放的不同网络结构时，经由不同结构属性导致的结构印记和认知印记过程差异，影响着创业者实验式学习、观察式学习以及协同式学习的不同选择。     

On the existence of the ex post symmetric random entry model

This paper studies symmetry among countably infinitely many agents who randomly enter into a stochastic process, one for each period. Upon entry, they observe only the current period signal and try to draw inference about the underlying state governing the stochastic process. We show that there exist random entry models under which agents are ex post symmetric. That is, all agents have identical posterior belief about the underlying states, although they are not ex ante symmetric. The form of the posterior belief is uniquely pinned down by ex post symmetry and a stationarity condition. Our results provide a common prior foundation for the model studied in Liu and Skrzypacz (2014).