人工智能时代企业管理变革的逻辑与分析框架

人工智能作为社会发展和技术创新的产物,已经成为新一轮科技创新和产业变革的核心驱动力,正在对世界经济、社会进步和人民生活产生极其深刻的影响。人工智能的商业应用会改变企业内部运作和生产经营的过程,为企业管理带来了诸多挑战和变革机遇。本文结合人工智能的特征与发展趋势,从管理对象、管理属性、管理决策和管理伦理四个方面对人工智能时代企业管理变革的逻辑进行了梳理,得出如下推论:第一,人工智能时代,被管理者将出现越来越多的"人工智能员工",以"社会人"为中心的管理理论和管理手段的适用性降低,如何管理"人工智能员工"以及协调人类员工与智能机器人的关系将对管理者提出挑战;第二,人工智能时代,管理属性不仅包括管理理论的科学性、管理实践的艺术性,还包括管理过程的技术性;第三,人工智能时代,将出现一批优秀的技术管理者,此时,技术管理者指的不是管理技术的人,而是掌握智能技术的管理者;第四,人工智能时代,管理者的决策环境得到优化,管理活动中的决策准则将由"满意决策"过渡为"最优决策";第五,人工智能时代,管理手段的智能化发展可能会加剧管理效率和管理伦理的冲突,管理者必须提升管理道德,经营过程中强化社会责任的履行;第六,随着人工智能技术发展,政府、社会团体和行业协会有必要从政策、法律与行业标准等多个方面构建人工智能发展伦理的多层次约束机制。     

民国时期汉语语文辞书词性标注研究——以《王云五大辞典》为例

民国时期为我国汉语语文辞书古今转型的关键时期,《王云五大辞典》是该时期第一部采用现代汉语语法体系为字词标注词性的白话词典,本文对《王云五大辞典》的词类划分依据和词性标注特点进行了较为系统的剖析,并与其他代表性辞书的词性标注加以对比,总结《王云五大辞典》在词性标注理念的时代创新性,也指出其字词词性标注中存在的不足,供当前的辞书编纂参考借鉴。     

Pathways from workplace flexibility to turnover intention: Role of work–family conflict,family–work conflict,and job satisfaction

This study examined the prevalence of workplace flexibility and the mechanisms that allow workplace flexibility to influence turnover intentions through work–family and family–work conflicts and job satisfaction among low‐wage workers in South Korea. Participants included 250 low‐wage workers whose monthly salary was less than 2 million Korean won (approx. $1,900). The study results indicate that low‐wage workers have limited access to workplace flexibility and that workplace flexibility plays a significant protective role in reducing their turnover intention, indirectly by decreasing work–family conflicts and enhancing job satisfaction. This article also discusses the implications of these findings for labor policy and social work practice.     

习近平总书记关于高校体育工作重要论述的逻辑意蕴与实践路径

习近平总书记关于高校体育工作重要论述是新时代中华民族所认同体育文化的价值取向、精神气质、思维方式与行动纲要的总和，它是大学生思想教育工作发展创新的内在需要。高校通过以中华体育文化精神为基石，融入社会主义主流价值；优化体育文化育人环境，让体育文化叫好又叫座；深入挖掘体育文化活动内涵，打造经典体育文化品牌；丰富体育文化载体形式，使体育文化育人产生倍增效果，多种实践途径践行习近平总书记关于高校体育工作的重要论述，促推中华民族体育强国梦的实现。     

基于DFA和SVR的GDP预测模型研究

首先对1980—2013年全国年度GDP数据进行消除趋势分析（DFA），发现GDP时序具有长记忆性，表明当前GDP值用来预测未来一段时间内的GDP值具有可行性，在此基础上利用具有非线性和强泛化能力特点的支持向量回归机（SVR），建立SVR预测模型,得到了较好的预测效果.     

Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery

Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.     

Cross‐Sectional Psychological and Demographic Associations of Zika Knowledge and Conspiracy Beliefs Before and After Local Zika Transmission

Perceptions of infectious diseases are important predictors of whether people engage in disease‐specific preventive behaviors. Having accurate beliefs about a given infectious disease has been found to be a necessary condition for engaging in appropriate preventive behaviors during an infectious disease outbreak, while endorsing conspiracy beliefs can inhibit preventive behaviors. Despite their seemingly opposing natures, knowledge and conspiracy beliefs may share some of the same psychological motivations, including a relationship with perceived risk and self‐efficacy (i.e., control). The 2015–2016 Zika epidemic provided an opportunity to explore this. The current research provides some exploratory tests of this topic derived from two studies with similar measures, but different primary outcomes: one study that included knowledge of Zika as a key outcome and one that included conspiracy beliefs about Zika as a key outcome. Both studies involved cross‐sectional data collections that occurred during the same two periods of the Zika outbreak: one data collection prior to the first cases of local Zika transmission in the United States (March–May 2016) and one just after the first cases of local transmission (July–August). Using ordinal logistic and linear regression analyses of data from two time points in both studies, the authors show an increase in relationship strength between greater perceived risk and self‐efficacy with both increased knowledge and increased conspiracy beliefs after local Zika transmission in the United States. Although these results highlight that similar psychological motivations may lead to Zika knowledge and conspiracy beliefs, there was a divergence in demographic association.     

Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.