简谈从桌面到业务系统单点的研究及实现

红云红河烟草集团信息系统统一身份认证体系后,虽然解决了业务系统体系用户的统一身份问题,但是未解决用户计算机用户认证与业务系统认证直接的关联性。通过对数字证书与操作系统的绑定和使用,解决用户在日常办公中分别进行操作系统和业务系统认证的问题,实现一次认证操作系统即可直接身份漫游所有业务系统的效果。在提高安全性的同时增加用户的可操作性,提升办公效率。     

微博客用户特征分析及分类研究——以“新浪微博”为例

以新浪微博为例,通过网络爬虫软件抓取新浪用户的相关信息。使用计量学方法,对用户特征进行统计分析,并利用决策树分析、相关性分析和关联挖掘用户特征。结果表明:新浪微博的认证用户和非认证用户之间存在明显差异;微博客用户的特性、发布微文时间段、微文长度、关注者、粉丝量和微文数等具有统计特性,地域差异明显;微博客用户选择是否公布城市,是否允许所有人私信、评论等之间存在一定关联关系;另外,通过对用户的分类发现,其中三种类型的用户群体之和占用户总体的85%以上,具有显著特性,为微博客用户特性的深入研究提供参考依据。     

基于超网络和在线评论的产品竞争力分析

[目的/意义]探究在线评论中用户的产品特征观点的变化,以分析产品的竞争力。[方法/过程]运用超网络建模在线评论中特征观点对及其演变关系,识别最受用户关注的产品特征及其情感倾向和演化,实现产品竞争力的分析。[结果/结论]通过对不同品牌产品进行实例验证,该模型能够识别用户关注的特征观点对及其情感倾向,发掘产品的竞争优势与不足,为企业改进产品、建立品牌口碑提供决策支持。     

工程教育专业认证背景下计算机专业应用型人才培养方案的制定研究

在深入理解工程教育专业认证思想、领悟工程教育专业认证标准的基础上,以我校建设成为吉林省特色高水平应用型大学为契机,以计算机科学与技术专业为例,对其应用型人才培养模式进行分析和研究。本文从专业培养目标、毕业基本要求、专业课程体系及课程与毕业要求的对应矩阵等方面进行研究并制定,对其他新建应用型本科专业建设提供借鉴。     

知识管理学科体系研究（下）——聚类分析和多维尺度分析

通过超星图书检索199本中外文知识管理图书目录,在此基础上,构建出57个高频词的共词矩阵,运用SPSS软件进行聚类分析和多维尺度分析,得出目前知识管理学科体系包括11个方面的内容,并可以分为4种类型。     

关于计算机电磁辐射及防护的探讨

计算机已经成为人们日常工作、生活所必不可少的重要工具,计算机的电磁辐射会给计算机用户带来健康危害,如何防止和降低计算机的电磁辐射是计算机用户应该关注的重要问题。任何带电物体都会对周围辐射电磁场,作为计算机主要的带电元件,显示器和主机是计算机电磁辐射的两个主要来源,选择液晶显示器和具有优异屏蔽功能的机箱是防止和降低计算机辐射的关键。     

数字资源长期保存认证体系中用户相关指标分析

在OAIS参考模型的基础上,对3个具有代表性的长期保存系统认证体系中涉及用户的指标加以比较,分析它们在用户定义、数字对象的发现、传递及使用等方面的异同.最后指出认证体系还需要进一步加强政策的适应性、对变化的预防及用户感知的服务效果等方面的指标.     

用户关注微信公众订阅号动机研究

随着微信用户的不断增加,腾讯公司开发设计了为用户提供服务的微信公众平台,微信公众订阅号就是其中之一。本文在文献分析和问卷调查的基础上,对微信用户关注公众订阅号的动机从载体层面、媒体层面和用户层面3个角度来构建模型。再利用SPSS进行数据分析,以及利用结构方程模型软件AMOS进行模型的修正,最后得出网络用户关注微信公众订阅号动机的分析模型。为微信公众平台设计提供依据,为微信订阅号健康发展提供参考和建议。     

关于计算机电磁辐射及防护的探讨

计算机已经成为人们日常工作、生活所必不可少的重要工具,计算机的电磁辐射会给计算机用户带来健康危害,如何防止和降低计算机的电磁辐射是计算机用户应该关注的重要问题.任何带电物体都会对周围辐射电磁场,作为计算机主要的带电元件,显示器和主机是计算机电磁辐射的两个主要来源,选择液晶显示器和具有优异屏蔽功能的机箱是防止和降低计算机辐射的关键.     

基于文献计量的我国数字档案馆研究现状分析

以中国期刊全文数据库为文献来源,借助文献计量方法,从文献出版年代分布、来源期刊分布、高被引文献这几个角度进行研究;论文研究热点分析采用共词分析法,通过构建论文关键词共词矩阵,采用多维尺度分析、聚类分析等方法,分析得出近20年来我国数字档案馆领域研究相关论文的研究热点,了解数字档案馆研究领域现状和发展趋势。     

Effect of penitence on social media trust and privacy concerns: The case of Facebook

Abuse of information entrusted to organizations can result in a variety of privacy violations and trust concerns for consumers. In the event of violations, a social media brand or organization renders an apology – a form of social account – to alleviate users’ concerns and maintain user membership and engagement with the platform. To explore the link between apology offered by a social media brand or organization and the users’ trust dynamics in the brand’s services, we study how organizational integrity can contribute to reducing individuals’ privacy concerns whiles increasing or repairing their trust. Drawing on organizational behavioral integrity literature, our proposed research model suggests that the persuasiveness of an apology following a data breach affects users’ trust or spillover trust through their perceptions of the degree of alignment between the words in the apology and the actions of the violating entity. Based on a survey of Facebook users, our findings show that persuasiveness of an apology has a significant impact on users’ perceptions of the alignment between the social media brand’s (i.e. Facebook) words and subsequent actions. These perceptions impact social media brand trust (i.e. users’ trust in Facebook and allied services such as Instagram). We also find that, post data breach incidence, while integrity of the social media organization partially mediates the relationship between persuasive apology and users’ trust, it fully mediates the relationship between the persuasive apology and the privacy concerns expressed by the users. However, users’ privacy concerns do not contribute much to the repair of trust needed to maintain their membership.     

Examining branding co-creation in brand communities on social media: Applying the paradigm of Stimulus-Organism-Response

This paper examines the antecedents of branding co-creation that include social networking sites’(SNSs) participation motivations,customer participation, brand trust and brand loyalty in social media brand communities by applying the “Stimulus-Organism-Response paradigm”. The survey method was used to gather data from 407 social media users. Data were analysed using structural equation modeling techniques. The findings reveal that SNSs’ participation motivations positively influence customer participation, which in turn significantly affects brand trust and brand loyalty. Consequently, both brand trust and brand loyalty positively influence branding co-creation in brand communities on social media. Furthermore, brand trust contributes as a mediator between customer participation and brand loyalty on social media brand communities. Although studies on relationships examined through the lens of the Stimulus-Organism-Response paradigm are popular, to the authors’ surprise there is scant literature examining the relationships between SNSs’ participation motivations, customer participation in social media brand communities, brand trust, brand loyalty and branding co-creation.     

APCO框架下微信用户使用动机、隐私关注与隐私行为之间关系研究

[目的/意义]用户的微信使用行为,涉及使用动机、隐私关注和隐私行为三个问题,研究目的在于探讨三者之间的关系。[方法/过程]以隐私关注为中介变量,使用动机为前因变量,隐私行为为结果变量,构建用户使用微信的APCO模型,设计量表,测度微信用户对这三个变量的认知,进行相关性分析与回归分析,探讨三者之间的联系。[结果/结论]结果表明,使用动机与隐私关注整体上相关性较弱,但其中的关系保持和信息搜寻动机对隐私关注存在正向影响;隐私关注对隐私行为中的隐私保护存在正向影响,但在使用动机与自我披露之间的中介作用并不显著;使用动机中的关系建立对隐私行为中的自我披露具有显著正向作用。     

Emotional Text Mining: Customer profiling in brand management

The widespread use of the Internet and the constant increase in users of social media platforms has made a large amount of textual data available. This represents a valuable source of information about the changes in people’s opinions and feelings. This paper presents the application of Emotional Text Mining (ETM) in the field of brand management. ETM is an unsupervised procedure aiming to profile social media users. It is based on a bottom-up approach to classify unstructured data for the identification of social media users’ representations and sentiments about a topic. It is a fast and simple procedure to extract meaningful information from a large collection of texts. As customer profiling is relevant for brand management, we illustrate a business application of ETM on Twitter messages concerning a well-known sportswear brand in order to show the potential of this procedure, highlighting the characteristics of Twitter user communities in terms of product preferences, representations, and sentiments.     

顾客参与价值共创与品牌形象塑造的关系研究

伴随着社会化媒体的普及,企业日益注重虚拟品牌社区的建设,借此向顾客展示产品独特价值,以期形成良好的口碑传播效应。通过采用扎根理论研究方法,搜集社区评论信息作为经验资料,并进行编码分析,提炼出互动体验、社区归属感以及品牌定位等范畴,阐释了顾客参与价值共创与品牌形象塑造的关系。研究表明,产品价值共创包括价值认知以及用户需求等核心要素,且有利于形成良好的品牌文化,而品牌形象的塑造则依赖于创新产品及建构以用户价值交互为基础的生态圈,顾客参与价值共创有助于品牌形象的塑造,企业应围绕产品创新打造虚拟品牌社区,强化顾客互动体验,并建立完善的信息沟通机制,引导顾客积极参与价值共创。     

Approach or avoid? The dualistic effects of envy on social media users’ behavioral intention

People express themselves through posts or selfies with brands and activities on social networking sites (SNSs) such as Facebook and Instagram. This user-generated content provides adequate social information that may elicit the emotions of users who are exposed to such content on these online platforms. Although academic researchers have focused on factors affecting users’ emotions on SNSs, how users reconcile both positive and negative emotions to their attitude and behavior in triadic interactions remains unclear. The current study attempted to answer this research question through the lens of social comparison theory by investigating the effects of tie strength and focus of attention as two forces eliciting online social comparisons on emotional consequences measured through pleasure and envy. Moreover, the dualistic effects of the two distinct types of envy, namely benign and malicious, and the effect of pleasure on users’ approach and avoidance behavior were explored and hypothesized. The results indicated that strong ties triggered more benign envy compared with malicious envy. Furthermore, benign envy positively affected brand attitude and SNS avoidance in terms of exhaustion, whereas malicious envy negatively influenced brand attitude and increased the likelihood of SNS avoidance in terms of both exhaustion and discontinuance.     

Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter

There is a strong interest among academics and practitioners in studying branding issues in the big data era. In this article, we examine the sentiments toward a brand, via brand authenticity, to identify the reasons for positive or negative sentiments on social media. Moreover, in order to increase precision, we investigate sentiment polarity on a five-point scale. From a database containing 2,282,912 English tweets with the keyword ‘Starbucks’, we use a set of 2204 coded tweets both for analyzing brand authenticity and sentiment polarity. First, we examine the tweets qualitatively to gain insights about brand authenticity sentiments. Then we analyze the data quantitatively to establish a framework in which we predict both the brand authenticity dimensions and their sentiment polarity. Through three qualitative studies, we discuss several tweets from the dataset that can be classified under the quality commitment, heritage, uniqueness, and symbolism categories. Using latent semantic analysis (LSA), we extract the common words in each category. We verify the robustness of previous findings with an in-lab experiment. Results from the support vector machine (SVM), as the quantitative research method, illustrate the effectiveness of the proposed procedure of brand authenticity sentiment analysis. It shows high accuracy for both the brand authenticity dimensions’ predictions and their sentiment polarity. We then discuss the theoretical and managerial implications of the studies.     

基于C2C与B2C虚拟品牌社区的企业知识创新比较研究

随着互联网的发展,激发虚拟品牌社区中有利于企业的知识创新,成为企业应对动态市场环境而实施创新战略的关键。本文基于C2C与B2C虚拟品牌社区中用户探索性/应用性学习和用户创意组合管理的视角,构建了激发有利于企业知识创新的理论模型。实证发现:C2C与B2C虚拟品牌社区会对用户探索性/应用性学习方式产生差异影响,相对于B2C虚拟品牌社区有利于用户的应用性学习,C2C虚拟品牌社区倾向于用户的探索性学习,而探索性学习更有利于激发用户知识创新能力;进一步,用户知识创新能力对激发用户产生有利于企业知识创新的作用并不显著;但是,用户创意组合管理在用户知识创新能力对激发有利于企业知识创新的关系中起到显著的正向调节作用。本研究为企业实施用户知识管理创新战略提出了有价值的启示。     

Mining shape of expertise: A novel approach based on convolutional neural network

Expert finding addresses the task of retrieving and ranking talented people on the subject of user query. It is a practical issue in the Community Question Answering networks. Recruiters looking for knowledgeable people for their job positions are the most important clients of expert finding systems. In addition to employee expertise, the cost of hiring new staff is another significant concern for organizations. An efficient solution to cope with this concern is to hire T-shaped experts that are cost-effective. In this study, we have proposed a new deep model for T-shaped experts finding based on Convolutional Neural Networks. The proposed model tries to match queries and users by extracting local and position-invariant features from their corresponding documents. In other words, it detects users’ shape of expertise by learning patterns from documents of users and queries simultaneously. The proposed model contains two parallel CNN’s that extract latent vectors of users and queries based on their corresponding documents and join them together in the last layer to match queries with users. Experiments on a large subset of Stack Overflow documents indicate the effectiveness of the proposed method against baselines in terms of NDCG, MRR, and ERR evaluation metrics.     

加强黑龙江省农产品品牌建设的思考

实施农产品品牌战略是我国引领现代农业发展、提高农业产业竞争力的必然选择。黑龙江省是农业大省,现已形成了3种类型的品牌化发展模式,政府做了大量支持性工作。新时期,应进一步转变政府职能,加强政府对准公共物品的建设,采取政策支持,发挥政府在农产品品牌化进程中的重要作用。