The dynamic effect of interactivity on customer engagement behavior through tie strength: Evidence from live streaming commerce platforms

Interactivity, which is a key characteristic of the live streaming commerce environment, fosters users’ active attitudes and behaviors in communications and transactions. However, the literature on live streaming commerce, is scarce, and few studies examine how interactivity influences customers’ non-transactional behaviors from a dynamic perspective. In this setting, based on the stimulus-organism-response (S-O-R) framework, we developed a research model using real-time data to investigate the dynamic effect of interactivity on customer engagement behavior through tie strength in live streaming commerce, which is a relatively new derivative of social commerce. This study developed a text mining method to quantify constructs using a large-scale sample of 3,500,445 online review texts. Our empirical study found that interactivity has a curvilinear relationship with customer engagement behavior. Besides, tie strength plays an intermediary role between interactivity and customer engagement behavior. It was further observed that both tenure of membership and popularity have an important moderating relationship between interactivity and tie strength. The study enriches the relationship marketing theory and live streaming commerce literature. Moreover, this study is one of the first studies to use real-time online data for live streaming commerce research.     

LTE系统中一种利用DRX降低UE功耗的视频流调度方法

提出一种利用DRX(discontinuous reception)降低UE(user equipment)功耗的视频流调度方法,该方法在保证数据包时延要求的同时,减少了InactivityTimer的开启次数,增加了UE的休眠时间.仿真结果表明,相比已有的DRX机制下的实时业务调度方法,该方法可以在丢包率相同的情况下,显著降低UE的功耗.     

A new method for fault prediction of model-unknown nonlinear system

In this paper, the subspace identification based robust fault prediction method which combines optimal track control with adaptive neural network compensation is presented for prediction the fault of unknown nonlinear system. At first, the local approximate linear model based on input-output of unknown system is obtained by subspace identification. The optimal track control is adopted for the approximate model with some unknown uncertainties and external disturbances. An adaptive RBF neural network is added to the track control in order to guarantee the robust tracking ability of the observation system. The effect of the system nonlinearity and the error caused by subspace modeling can be overcome by adaptive tuning of the weights of the RBF neural network online without any requisition of constraint or matching conditions. The stability of the designed closed-loop system is thus proved. A density function estimation method based on state forecasting is then used to judge the fault. The proposed method is applied to fault prediction of model-unknown fighter F-8II of China airforce and the simulation results show that the proposed method can not only predict the fault, but has strong robustness against uncertainties and external disturbances.     

Pachinko Prediction: A Bayesian method for event prediction from social media data

The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest. However, accounting for uncertainty in such models, particularly when using diverse, unstructured datasets such as social media, is essential to guarantee the appropriate use of such methods. Here we develop a Bayesian method for predicting social unrest events in Australia using social media data. This method uses machine learning methods to classify individual postings to social media as being relevant, and an empirical Bayesian approach to calculate posterior event probabilities. We use the method to predict events in Australian cities over a period in 2017/18.     

A perspective on optical developments in microfluidic platforms for Caenorhabditis elegans research

Microfluidics offers unique ways of handling and manipulating microorganisms, which has particularly benefited Caenorhabditis elegans research. Optics plays a major role in these microfluidic platforms, not only as a read-out for the biological systems of interest but also as a vehicle for applying perturbations to biological systems. Here, we describe different areas of research in C. elegans developmental biology and behavior neuroscience enabled by microfluidics combined with the optical components. In particular, we highlight the diversity of optical tools and methods in use and the strategies implemented in microfluidics to make the devices compatible with optical techniques. We also offer some thoughts on future challenges in adapting advancements in optics to microfluidic platforms.     

Motives for participation in Internet innovation intermediary platforms

Internet innovation intermediaries are the key role of an organization that affects on innovation processes and driving a potential contributor to economic development. Therefore, understanding what are the main motivations that drive the participation of users into these innovation intermediaries is increasingly relevant. This is why this paper aims to develop an understanding on this matter with UTAUT framework in order to examine the effects of major factors on behavioral intention and actual use of Internet innovation intermediaries and topic discussions. An empirical study was conducted in 10 Internet innovation intermediary platforms using a survey instrument targeting 735 respondents and mainly from China and Taiwan. The findings show that there is a significant relationship between facilitating conditions and usage behavior proving that intermediaries can influence the usage of Internet innovation intermediary platforms. The theoretical and practical implications of the study are discussed, offering recommendations and future research directions.     

A multimodal query expansion based on genetic programming for visually-oriented e-commerce applications

We present a novel multimodal query expansion strategy, based on genetic programming (GP), for image search in visually-oriented e-commerce applications. Our GP-based approach aims at both: learning to expand queries with multimodal information and learning to compute the “best” ranking for the expanded queries. However, different from previous work, the query is only expressed in terms of the visual content, which brings several challenges for this type of application. In order to evaluate the effectiveness of our method, we have collected two datasets containing images of clothing products taken from different online shops. Experimental results indicate that our method is an effective alternative for improving the quality of image search results when compared to a genetic programming system based only on visual information. Our method can achieve gains varying from 10.8% against the strongest learning-to-rank baseline to 54% against an adhoc specialized solution for the particular domain at hand.     

基于支持向量机的外贸出口预测

针对支持向量机(SVM)方法所具有的拟合精度高、推广能力强、全局最优且针对小样本等特点,本文将SVM回归建模方法引入到外贸出口预测中,对出口时间序列建立预测模型,并利用此法对重庆摩托车出口进行了预测,对其预测性能进行了验证比较。结果表明,SVM方法对非平稳的小样本出口时间序列数据有良好的建模和泛化能力,且可达到较高的预测精度。     

Exploring viewer participation in online video game streaming: A mixed-methods approach

Video game streaming (VGS) has attracted hundreds of millions of viewers all over the world to not only watch but also participate in a variety of VGS activities, such as interacting with streamers and other co-viewers, gift-giving, and social sharing of the viewing experience. The success of the VGS paradigm depends on the active participation of the viewers, since it creates economic, hedonic, and social values. This study applied a mixed-methods approach to explore the critical environmental stimuli evoking viewers’ cognitive and emotional state and empirically tested a research model examining viewers’ participation. Using qualitative interviews, three environmental stimuli were identified (i.e., broadcaster appeal, medium appeal, and perceived co-viewer involvement), which were adopted in the quantitative research model. The research findings suggested that environmental stimuli were positively related to both cognitive and emotional organisms, namely cognitive involvement and arousal, which in turn impacted viewers’ participation.     

组织中知识生产的知识服务平台分析

知识生产可以为组织创造新知识,提高组织的应变力和创新力。高效率的知识生产和创新是组织维持竞争优势的前提。然而高效的知识生产和知识创新需要一个良好的服务平台,以满足知识服务业发展要求和提高知识生产效率为出发点,为知识生产提供了两种服务平台,即知识生产服务模型"硬件"平台和知识生产"软件"平台。其中,"硬件"平台是由基于知识网格的六层结构模型构成,它能使网格内所有成员有效地共享和利用网格内的资源;"软件"平台对"硬件"平台起重要作用,它使整个知识服务平台能更加和谐高效地运行。     

A stock rank prediction method combining industry attributes and price data of stocks

Stock forecasting has always been challenging as the stock market is affected by a combination of factors. Temporal Convolutional Network (TCN) based on convolutional structure has been widely used in time series prediction in recent years, but the dilated causal convolution structure leaves it unable to effectively learn the dependencies between data at different time points. This paper proposes a method for stock ranking prediction. To enhance the ability of TCN to handle dependencies within series, we first develop a channel-time dual attention module (CTAM). In conjunction with TCN to process complex historical stock price data, CTAM can adaptively learn the importance of multiple price nature series of stocks and model the dependencies between the data at different times. On the other hand, due to the market industry rotation, some stocks with specific industry attributes may become market preference for a period time. To apply the industry attributes to the stock prediction, we construct an industry-stock Pearson correlation matrix and extract a vector that fully characterizes the industry attributes of stocks from it through a matrix factorization algorithm. Furthermore, the historical market preference is modeled according to the industry attribute of the stocks to generate the dynamic correlation between stocks and market preference, and this correlation is combined with the historical price features extracted by TCN for stock ranking prediction. We conduct experiments on three datasets of 950 constituent stocks of the Shanghai Stock Exchange Index, 750 constituent stocks of the Shenzhen Stock Exchange 1000 Index and 486 stocks of the S&P500 to demonstrate the effectiveness of the proposed method. On the Shanghai Stock Exchange Index dataset, the Investment Return Ratio (IRR) obtained by using the predict results of our method to guide the exchange reached 1.416, and the Sharpe Ratio (SR) reached 2.346. On the Shenzhen Stock Exchange Index dataset, the IRR reached 1.434 and the Sharpe ratio reached 2.317. On the S&P500, the IRR reached 1.491 and the Sharpe ratio reached 2.031.     

高速列车寿命预测技术专利计量分析

以描述发明新颖性和其应用价值的德温特手工代码为检索策略,以德温特数据库中与高速列车寿命预测相关的专利文献为基础,运用专利知识计量的理论和可视化软件CitesSpaceⅡ,对与重大产品和设施寿命预测相关的专利知识主体,在国家和组织间的分布及其发展的内容与特点进行可视化分析,为政府、企业、大学、科研机构等相关组织的技术创新和管理决策提供参考。     

无线Ad Hoc网络中一种网络无关的P2P流媒体优化传输方案

根据无线Ad Hoc网络环境下P2P流媒体数据传输的特点,提出一种工作在P2P数据拓扑层面的,不依赖于底层物理网络的节点配置和特定的路由算法的优化传输方案.在发送数据时,节点结合应用层视频分片的重要性和网络状况动态地调整传输层参数,从而减小重要数据的传输延时和播放超时的概率.仿真结果验证了本方案对于改善服务质量和减小控制信令开销的有效性.     

用于网络安全教学课堂实时演示的虚拟化系统

采用虚拟化技术,在一台物理计算机上虚拟多台计算机和多个网络,能够在多媒体教室用一台机器对网络安全教学中涉及的大量内容进行实时演示。设计了一个用于网络安全教学课堂实时演示的虚拟化系统VS4NST,给出了该系统的架构和各组件的配置情况,概要描述了数据包嗅探、IPSec、VPN、SQL注入、CA等教学内容的演示方法。在不同配置的物理计算机上的运行实验表明,VS4NST可被目前广泛使用的中低端个人计算机所承载,验证了该系统的普遍适用性。     

基于虚拟变量法的我国交通能源消费量预测

交通能源消费系统往往会因内外部影响因素的突变而发生结构性变动，虚拟变量法是经济计量中用于处理结构性变动的一种有效方法。本文根据虚拟变量法的原理，利用我国交通能源消费的历史数据，建立了我国未来交通能源消费系统的预测模型。该模型验证了我国交通能源消费系统中结构性变动的存在，并应用所建立的模型对2010年前，我国交通能源消费量的高、中、低三种情景进行了预测。     

Exploring payment behavior for live courses in social Q&A communities: An information foraging perspective

People are increasingly searching for information in social Q&A communities, especially through a new form of paid knowledge product, namely, live course. Such course provides a way for users to interact synchronously with content creators online. However, how this knowledge product is accepted and why users pay for it deserve attention from researchers. In this study, a research model was developed based on information foraging theory (IFT) and social information foraging (SIF) theory to analyze users’ information processing and evaluation when making payment decisions. Our research model was validated by collecting subjective and objective data from a Chinese social Q&A community that has been successful in offering live course services. We found that perceived quality of free content, perceived credibility of content creators, and perceived quantity of participants positively influence users’ willingness to pay, and thus, positively affects users’ payment behavior. Unexpectedly, social endorsement negatively moderates the relationship between willingness to pay and payment behavior. This study enhances the theoretical understanding of the drivers of users’ payment for live courses in social Q&A communities. For IS practice, our findings provide unique insights for community managers and content creators on how to operate paid knowledge products appropriately and effectively.     

A big data analytics model for customer churn prediction in the retiree segment

Undoubtedly, the change in consumers’ choices and expectations, stemming from the emerging technology and also significant availability of different products and services, created a highly competitive landscape in various customer service sectors, including the financial industry. Accordingly, the Canadian banking industry has also become highly competitive due to the threats and disruptions caused by not only direct competitors, but also new entrants to the market.The primary objective of this paper is to construct a predictive churn model by utilizing big data, including the structured archival data, integrated with unstructured data from sources such as online web pages, the number of website visits and phone conversation logs, for the first time in the financial industry. It also examines the effect of different aspects of customers’ behavior on churning decisions. The Datameer big data analytics tool on the Hadoop platform and predictive techniques using the SAS business intelligence system were applied to study the client retirement journey path and to create a churn prediction model. By deploying the above systems, we were able to uncover a wealth of data and information associated with over 3 million customers’ records within the retiree segment of the target bank, from 2011 to 2015.     

A three-phase method for patent classification

An automatic patent categorization system would be invaluable to individual inventors and patent attorneys, saving them time and effort by quickly identifying conflicts with existing patents. In recent years, it has become more and more common to classify all patent documents using the International Patent Classification (IPC), a complex hierarchical classification system comprised of eight sections, 128 classes, 648 subclasses, about 7200 main groups, and approximately 72,000 subgroups. So far, however, no patent categorization method has been developed that can classify patents down to the subgroup level (the bottom level of the IPC). Therefore, this paper presents a novel categorization method, the three phase categorization (TPC) algorithm, which classifies patents down to the subgroup level with reasonable accuracy. The experimental results for the TPC algorithm, using the WIPO-alpha collection, indicate that our classification method can achieve 36.07% accuracy at the subgroup level. This is approximately a 25,764-fold improvement over a random guess.