突发重大疫情网络舆论的生成逻辑､演进特征与治理机制

新形势下突发重大疫情网络舆论不仅给疫情防控带来了严峻挑战，而且给维护国家意识形态安全带来了较大压力。厘清网络舆论的生成逻辑，把握网络舆论发展的演进特征，对做好网络舆论引导与谣言治理意义重大。在现代网络社会，随着交往实践的普遍进行，从现实逻辑、实践逻辑与理论逻辑来看，突发重大疫情网络舆论的生成，是网络议题、网络交往和网络传播相互作用的结果。受网络社会交往实践主体、客体、环体和介体等多种因素影响，突发重大疫情网络舆论又呈现为非线性的发展演进趋势，极易引发“蝴蝶效应”造成舆论秩序失范，甚至导致“塔西佗陷阱”的舆论信任危机，给疫情防控带来复杂变数。必须建立健全网络舆论治理机制，有效管控舆情，提高疫情防控治理水平。     

网络外部性对双渠道供应链信息分享的影响

研究了网络外部性对双渠道供应链信息分享的影响。分别建立了存在网络外部性和不存在网络外部性下的双渠道供应链模型，通过比较零售商信息分享和不分享下其与制造商的期望利润。研究发现：当未考虑产品的网络外部性时，零售商不与制造商分享其市场需求信息，与已有研究一致。当考虑了产品网络外部性且网络外部性较小时，零售商仍不与制造商分享市场需求信息；然而，当网络外部性较大时，零售商与制造商分享其市场需求信息。此外，零售商与制造商共享其市场需求信息有助于增加制造商和供应链利润。     

“一带一路”沿线国家设施联通对贸易利益的影响

以全球价值链（GVC）参与为视角，基于2005—2015年40个“一带一路”沿线国家与我国的增加值贸易数据，采用静态面板固定效应模型实证分析交通基础设施和通信基础设施对“一带一路”沿线国家贸易利益的影响。结果发现，加强交通和通信基础设施建设有利于提高一国出口中所包含的国内增加值，增进该国的贸易利益。作用机制研究表明，交通基础设施和通信基础设施都能够通过降低贸易成本促进贸易利益的提升，其中通信基础设施还能够通过提高贸易的时间效率对贸易利益产生积极影响。因此，“一带一路”沿线国家应以设施联通为依托，加强本国的交通和通信基础设施建设，把握参与国际生产分工的主动权，以实现共同发展繁荣。     

Finite mixture of regression models for a stratified sample

Despite the popularity and importance, there is limited work on modelling data which come from complex survey design using finite mixture models. In this work, we explored the use of finite mixture regression models when the samples were drawn using a complex survey design. In particular, we considered modelling data collected based on stratified sampling design. We developed a new design-based inference where we integrated sampling weights in the complete-data log-likelihood function. The expectation–maximisation algorithm was developed accordingly. A simulation study was conducted to compare the new methodology with the usual finite mixture of a regression model. The comparison was done using bias-variance components of mean square error. Additionally, a simulation study was conducted to assess the ability of the Bayesian information criterion to select the optimal number of components under the proposed modelling approach. The methodology was implemented on real data with good results.     

On hysteretic vector autoregressive model with applications

This paper proposes a new hysteretic vector autoregressive (HVAR) model in which the regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. We integrate an adapted multivariate Student-t distribution from amending the scale mixtures of normal distributions. This HVAR model allows for a higher degree of flexibility in the degrees of freedom for each time series. We use the proposed model to test for a causal relationship between any two target time series. Using posterior odds ratios, we overcome the limitations of the classical approach to multiple testing. Both simulated and real examples herein help illustrate the suggested methods. We apply the proposed HVAR model to investigate the causal relationship between the quarterly growth rates of gross domestic product of United Kingdom and United States. Moreover, we check the pairwise lagged dependence of daily PM2.5 levels in three districts of Taipei.     

Design selection for strong orthogonal arrays

Strong orthogonal arrays (SOAs) were recently introduced and studied as a class of space‐filling designs for computer experiments. An important problem that has not been addressed in the literature is that of design selection for such arrays. In this article, we conduct a systematic investigation into this problem, and we focus on the most useful SOA(n,m,4,2 + )s and SOA(n,m,4,2)s. This article first addresses the problem of design selection for SOAs of strength 2+ by examining their three‐dimensional projections. Both theoretical and computational results are presented. When SOAs of strength 2+ do not exist, we formulate a general framework for the selection of SOAs of strength 2 by looking at their two‐dimensional projections. The approach is fruitful, as it is applicable when SOAs of strength 2+ do not exist and it gives rise to them when they do. The Canadian Journal of Statistics 47: 302–314; 2019 © 2019 Statistical Society of Canada     

A dynamic business modelling approach to design and experiment new business venture strategies

Business Modelling has evolved as a key activity to reflect new business venture strategy by framing the way a firm will operate and how it will function in achieving its goals (e.g., profitability, growth, innovation, social impact). However, scholars and practitioners have criticized the adoption of a too static perspective in the design and use of conventional Business Model representations. Such a static perspective prevents nascent entrepreneurs experimenting with their Business Models and, as a result, identifying the most effective strategies, especially in terms of business sustainability and profitability. In this paper, we argue that combining conventional Business Model schemas with System Dynamics modelling results in a strategy design tool that may overcome several limitations related to a static view of Business Model representation. Mapping the different key elements underlying value creation processes into a system of causal interdependencies – through the use of simulation – allows strategy analysts and entrepreneurs to experiment and learn how the business reacts to strategic and organizational changes in terms of performance, innovation and value creation. As such, Dynamic Business Models provide useful insights to strategy formulation and business venturing by capturing how critical Business Model elements interact to produce enduring competitive advantages over time.     

基于Petri网的微博网络信息传播模型

微博网络中的信息传播模型是分析用户行为，找出传播路径，确定领袖人物，发现舆情热点等研究的基础。虽然多种不同角度的信息传播模型已经得到广泛研究，但缺乏对信息动态传播过程的直观描述。本文基于Petri网的结构和特征，针对微博网络信息传播过程，提出了一种简单直观的描述模型，该模型首先对微博网络的信息动态传播过程中的对象进行结构化描述。本文根据微博网络的用户结构关系，并利用Petri网的相关理论，形式化解释和定义信息传播基本对象，从而更加直接描述了微博网络中的转发、评论、回复等多种用户行为。在此基础上，本文利用Petri网能够描述信息流动问题的特征，结合颜色Petri网和时延Petri网，从Petri网系统的角度表示信息动态传播路径，并研究网络的动态性质和传播条件，使得信息传播模型更加真实地模拟信息传播情况。最后本文分析信息传播算例和新浪微博真实数据实验，验证了模型的有效性和可行性，为舆情态势分析以及用户行为的研究提供帮助和支持，同时也为其它社交网络信息传播的用户行为描述提供了新的思路。     

Response strategy to environment context factors using a lean and agile approach: Implications for firm performance

Based on the environment-strategy performance perspective and dynamic capabilities framework, we develop a theoretical model and hypotheses specifying how supply chain collaboration as a response to environment context factors – competitive intensity, supply uncertainty, technological turbulence and market turbulence, using a lean and agile strategy may influence firm performance. We test the model using partial least square structural equation modelling on data collected from a field survey with responses from 152 manufacturing firms representing a variety of industries. Empirical findings generally support the relationship between collaboration and firm performance using a lean and agile strategy. Also, for firms in industries that face environments characterised by high supply uncertainty and competitive intensity with, technological turbulence, the study finds evidence of a direct relationship between these environmental factors and supply chain collaboration. The findings provide an initial strategic response framework for appropriately aligning a lean and agile supply chain strategy through collaboration with environment context factors to achieve firm performance improvements.     

汇率货币模型的非线性协整关系检验——基于深度GRU神经网络

本文采用深度门控循环单元（GRU）神经网络探讨三种汇率货币模型（弹性价格、前瞻性和实际利率差模型）的非线性协整关系。GRU技术在深度学习中具有智能记忆、自主学习和强逼近能力等优点。为此，本文运用该技术对6组典型浮动汇率制国别数据进行了非线性Johansen协整检验。结果表明，汇率与宏观经济基本面之间存在非线性协整关系，从而说明了货币模型在非线性条件下的有效性，以及先进的深度学习工具在检验经济理论中的优势。