The role of network embeddedness across multiple social networks: Evidence from mobile social network games

This article examines peer influences from network relationships within a social network game (i.e., embeddedness) and across such games (i.e., multiplexity). Drawing on social influence theory, we develop a bivariate Poisson model of users’ repeated visits and latent attrition that accommodates peer interaction after controlling for homophily. We estimate the model using data from two social network games with considerable overlap among network members. We find that friends who are only multiplex across games exert greater peer influence on users’ game visits than members who are embedded within a single game. We also determined that ignoring network multiplexity across games may lead firms to mistarget users due to biased peer influences of embedded friends. This result provides an unresearched explanation—strength of peer influence—for the mixed findings in previous literature on network embeddedness. We utilized our results to conduct several scenario analyses to demonstrate how firms can effectively manage users’ engagement and target users in multiple social network games.     

Can information and communication overload influence smartphone app users' social network exhaustion,privacy invasion and discontinuance intention? A cognition-affect-conation approach

Thanks to the expansion of network technologies and the coupling of smartphone hardware, younger generations have unparalleled access to a diverse range of information and interactive communication opportunities. Despite voluminous research on the causes of social network exhaustion and privacy invasion, limited academic attention has been devoted to understanding the driving forces and contributing factors for discontinuing usage of smartphone app. Drawing on the cognition-affect-conation paradigm, the present research proposes a conceptual research model to examine how information overload and communication overload effect social network exhaustion, privacy invasion, and ultimately, smartphone app users' intentions to discontinue usage. The study evaluates data from 442 smartphone app users, employing statistical analyses to demonstrate that both information overload and communication overload significantly impact users' exhaustion and privacy invasion. Additionally, social network exhaustion and privacy invasion are two major predictors of smartphone app discontinuance intention. By identifying the influential factors and causes of younger generations' discontinuous usage intention of smartphone app, this study meets the need for a thorough assessment of the social network exhaustion and privacy invasion phenomena. The findings may potentially enhance comprehension of the discontinuation intention phenomenon in the context of cognitive overload. Furthermore, they may provide novel insights for practitioners regarding effective strategies conducive to managing mobile social network adoption behavior, as well as the preservation of engagement levels among young users in mobile-saturated societies.     

全货运航空公司单分配严格枢纽航线网络优化设计

针对全货运航空公司，研究其货运航线网络的构建问题。基于单分配严格枢纽航线网络，对传统模型进行改进。以各航线上货物的委托运输成本和自有机队的运输成本，取代传统的单位运输成本和折扣因子，考虑枢纽点的中转成本和载运率对路径选择的影响。结合航空货运夜航的特征考虑各机队夜间可用时间的限制，以总成本最小为目标，构建数学模型，采用LINGO优化软件求解，验证模型的有效性，为全货运航空公司构建货运航线网络提供参考。     

基于转移概率和网络联系的辽宁省农村居民点适宜性评价

[目的]农村居民点适宜性评价在农村居民点调控、规划和预测中具有基础作用,且农村居民点适宜性评价应顾及农村居民点之间的社会经济活动联系。[方法]以辽宁省为研究区,首先运用逐步Logistic回归模型构建农村居民点转移概率,其次运用网络分析方法确定农村居民点图斑间的网络联系强度,最终通过加权求和方法确定农村居民点转化概率,进行农村居民点空间布局适宜性评价。[结果]辽宁省农村居民点可以划分为高度适宜类、较适宜类、一般适宜类和不适宜类4类,分别占农村居民点总面积的比例为28.18%、31.32%、13.26%和27.24%;从各地貌分区来看,高度适宜类主要集中分布在中部平原地区和东部山地丘陵区中的南方沿海地区,而不适宜类整体分布则较为零散;辽宁省中部平原区位条件好,东部沿海地区区位条件也较好,西部山地丘陵区地区较差。[结论]研究方法和结论有助于全面把握农村居民点社会经济与区位适宜性特征,为农村居民点科学规划提供科学指导。     

利用大数据分析技术的智能化网络故障诊断系统

随着网络技术的快速发展以及各种新网络业务的出现，网络的规模日益扩大和复杂。同时，各种网络服务对于网络可靠性的要求也逐渐提高，这就对网络的维护提出了更高的要求。在这种情况下，传统的网络维护模式逐渐不能适应现代网络发展的需求。针对现代大型网络的故障诊断问题，利用大数据分析技术，设计并完成了一套整体的智能化故障诊断系统。在此系统中，针对不同的故障类型，利用能够获取到的数据类型，针对性地设计了相关的故障诊断模块和算法。系统整体基于开源的数据处理平台构建，具备良好的可扩展性。通过实际案例数据的验证，结果表明此系统具备良好的处理实际网络故障的能力，将大数据分析技术应用于网络故障诊断领域也具备较强的可行性。     

General Purpose Technologies and local knowledge accumulation — A study on MNC subunits and local innovation centers

This research looks into the innovative activities of subunits of large non-U.S firms in the U.S from 1969 to 1995, suggesting that the innovations in the subunits of multinational corporations are closely linked to their specializations in General Purpose Technologies (GPTs). GPTs enable recombining knowledge from different domains on an international scale, and that the specialization of innovations in GPT fields within a subunit is positively associated with the innovative capacity at large, along with the degree of technological diversification of that specific subunit. As firms expand geographically to tap into local innovation centers to develop new growth alternatives, specialization of GPTs in these firms actually facilitates further geographical dispersion of innovative activities across locations. Lastly, additional specializations of innovations in GPT fields within the host innovation centers does help in attracting innovation activities of firms from a wider range of industries.     

区域耕地生态价值补偿量化研究——以新疆为例

[目的]在耕地保护形势日趋严峻的情形下,开展耕地生态价值补偿量化研究对了解耕地外部性价值的大小及深化耕地生态价值的量化方法和思路具有重要意义。[方法]文章以新疆为例,从14个地州市的耕地生态服务价值出发,将生态超载指数作为耕地生态服务价值与生态足迹在各地州市转移的测度,同时综合考虑各地州市的经济发展状况,通过构建市域生态价值补偿量化模型分别测算各地州市耕地生态价值补偿量。[结果](1)2015年新疆耕地生态服务价值整体上有盈余输出,全疆共可获得生态补偿费103.31亿元;(2)2015年新疆南北疆耕地生态服务价值比为1:1.7,生态足迹总量比为1:2.2,生态超载指数北亏南盈,表明南北疆耕地生态足迹和耕地生态服务价值间呈"空间异位"格局;(3)新疆北疆乌鲁木齐市、克拉玛依市、吐鲁番市及哈密市共需支付耕地生态补偿费55.67亿元,北疆(乌鲁木齐市、克拉玛依市、吐鲁番市及哈密市外)和南疆地区分别可获得生态补偿费为96.59亿元和62.39亿元。[结论]该文可为新疆各地州市构建耕地生态价值补偿路径提供新思路,也可为以耕地生态价值量确定耕地保护指标和构建耕地生态补偿机制提供参考。     

Spatial movement patterns among intra-destinations using social network analysis

To explore popularly visited tourist locations, travel movement patterns, and movement points, this study collected samples of 321 Chinese tourists and 337 Japanese tourists who were visiting major tourist destinations in Seoul and its vicinity in South Korea. Results of analyzing movement patterns showed that Japanese tourists tend to be clustered around popular attractions, whereas Chinese tourists tend to spread over a larger area of attractions. Some specific shopping and amusement attractions were the locations most popularly visited by both groups. The start points and end points in the two groups’ itineraries were dissimilar overall, even though their patterns were similar in regard to major preferred tourist attractions. Thus, the findings of this study have the potential to contribute to understanding spatial mobility in a tourism destination through tracking tourists’ movement patterns.     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

Big data driven cycle time parallel prediction for production planning in wafer manufacturing

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.