基于GMCR的政府支持基础研究R D项目决策者冲突分析     

张鹏  周恩毅  刘启雷 《科技管理研究》2019,(23)

政府投资基础研究项目是提升我国基础创新能力的重要手段。项目参与主体利益目标不一致等引发冲突问题也不容忽视。基于第三方偏好信息,应用冲突分析的图模型方法即GMCR理论,构建分析模型模拟基础研究项目研发过程决策主体冲突状态演变与解决机制;通过稳定性求解,分析政府、高校科研院所、企业等决策主体不同偏好情境下冲突的个体稳定性和全局稳定性。研究为基础研究项目的多主体冲突问题提供了解决思路,可作为政府投资项目决策的理论依据。  相似文献   

Protective behavior in ride-sharing through the lens of protection motivation theory and usage situation theory     

《International Journal of Information Management》2021

The frequent occurrence of security incidents in ride-sharing is a challenge for the survival of IT-enabled ride-sharing platforms. Passengers’ protective behavior is an effective means to alleviate this issue, with benefits to both the passengers and the platforms. This study explores the mechanisms of passengers’ protective behavior in the ride-sharing context by combining protection motivation theory (PMT) and usage situation theory. We test our hypotheses using data (n = 346) collected from a field survey based on a real scenario. The findings reveal that PMT and usage situation theory work well to explain passengers’ protective behavior during ride-sharing. This study explains the motivation behind passengers’ protective behavior in the ride-sharing context, extends the contents of PMT by exploring its antecedents, and extends the contents of usage situation theory by introducing a new dimension. Our findings can help ride-sharing platforms take appropriate strategies to improve passengers’ protective behavior.  相似文献   

Information retrieval in the workplace: A comparison of professional search practices     

Tony Russell-Rose  Jon Chamberlain  Leif Azzopardi 《Information processing & management》2018,54(6):1042-1057

Legal researchers, recruitment professionals, healthcare information professionals, and patent analysts all undertake work tasks where search forms a core part of their duties. In these instances, the search task is often complex and time-consuming and requires specialist expertise to identify relevant documents and insights within large domain-specific repositories and collections. Several studies have been made investigating the search practices of professionals such as these, but few have attempted to directly compare their professional practices and so it remains unclear to what extent insights and approaches from one domain can be applied to another. In this paper we describe the results of a survey of a purposive sample of 108 legal researchers, 64 recruitment professionals and 107 healthcare information professionals. Their responses are compared with results from a previous survey of 81 patent analysts. The survey investigated their search practices and preferences, the types of functionality they value, and their requirements for future information retrieval systems. The results reveal that these professions share many fundamental needs and face similar challenges. In particular a continuing preference to formulate queries as Boolean expressions, the need to manage, organise and re-use search strategies and results and an ambivalence toward the use of relevance ranking. The results stress the importance of recall and coverage for the healthcare and patent professionals, while precision and recency were more important to the legal and recruitment professionals. The results also highlight the need to ensure that search systems give confidence to the professional searcher and so trust, explainability and accountability remains a significant challenge when developing such systems. The findings suggest that translational research between the different areas could benefit professionals across domains.  相似文献   

An extension of the technology acceptance model in the big data analytics system implementation environment     

Surabhi Verma  Som Sekhar Bhattacharyya  Saurav Kumar 《Information processing & management》2018,54(5):791-806

Research on the adoption of systems for big data analytics has drawn enormous attention in Information Systems research. This study extends big data analytics adoption research by examining the effects of system characteristics on the attitude of managers towards the usage of big data analytics systems. A research model has been proposed in this study based on an extensive review of literature pertaining to the Technology Acceptance Model, with further validation by a survey of 150 big data analytics users. Results of this survey confirm that characteristics of the big data analytics system have significant direct and indirect effects on belief in the benefits of big data analytics systems and perceived usefulness, attitude and adoption. Moreover, there are mediation effects that exist among the system characteristics, benefits of big data analytics systems, perceived usefulness and the attitude towards using big data analytics system. This study expands the existing body of knowledge on the adoption of big data analytics systems, and benefits big data analytics providers and vendors while helping in the formulation of their business models.  相似文献   

个性服务视域下的高校档案数据库建设     

沙敏 《档案管理》2020,(3):115-116

本文通过研究当前高校档案管理的重要性,结合实际的档案需求情况,分析了档案专题数据库基本的几种类型,来满足高校群体对档案信息的个性化需求,并通过对档案专题数据库模式的分析,制定了具体的应用措施,以便能够为相关人员提供信息参考。  相似文献   

Location extraction from tweets     

Thi Bich Ngoc Hoang  Josiane Mothe 《Information processing & management》2018,54(2):129-144

Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. These events are represented by three main dimensions: time, location and entity-related information. The focus of this paper is location, which is an essential dimension for geo-spatial applications, either when helping rescue operations during a disaster or when used for contextual recommendations. While the first type of application needs high recall, the second is more precision-oriented. This paper studies the recall/precision trade-off, combining different methods to extract locations. In the context of short posts, applying tools that have been developed for natural language is not sufficient given the nature of tweets which are generally too short to be linguistically correct. Also bearing in mind the high number of posts that need to be handled, we hypothesize that predicting whether a post contains a location or not could make the location extractors more focused and thus more effective. We introduce a model to predict whether a tweet contains a location or not and show that location prediction is a useful pre-processing step for location extraction. We define a number of new tweet features and we conduct an intensive evaluation. Our findings are that (1) combining existing location extraction tools is effective for precision-oriented or recall-oriented results, (2) enriching tweet representation is effective for predicting whether a tweet contains a location or not, (3) words appearing in a geography gazetteer and the occurrence of a preposition just before a proper noun are the two most important features for predicting the occurrence of a location in tweets, and (4) the accuracy of location extraction improves when it is possible to predict that there is a location in a tweet.  相似文献   

Neural embedding-based indices for semantic search     

Fatemeh Lashkari  Ebrahim Bagheri  Ali A. Ghorbani 《Information processing & management》2019,56(3):733-755

Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus.  相似文献   

Web document summarization by exploiting social context with matrix co-factorization     

Minh-Tien Nguyen  Viet Cuong Tran  Xuan Hoai Nguyen  Le-Minh Nguyen 《Information processing & management》2019,56(3):495-515

In the context of social media, users usually post relevant information corresponding to the contents of events mentioned in a Web document. This information posses two important values in that (i) it reflects the content of an event and (ii) it shares hidden topics with sentences in the main document. In this paper, we present a novel model to capture the nature of relationships between document sentences and post information (comments or tweets) in sharing hidden topics for summarization of Web documents by utilizing relevant post information. Unlike previous methods which are usually based on hand-crafted features, our approach ranks document sentences and user posts based on their importance to the topics. The sentence-user-post relation is formulated in a share topic matrix, which presents their mutual reinforcement support. Our proposed matrix co-factorization algorithm computes the score of each document sentence and user post and extracts the top ranked document sentences and comments (or tweets) as a summary. We apply the model to the task of summarization on three datasets in two languages, English and Vietnamese, of social context summarization and also on DUC 2004 (a standard corpus of the traditional summarization task). According to the experimental results, our model significantly outperforms the basic matrix factorization and achieves competitive ROUGE-scores with state-of-the-art methods.  相似文献   

Platform-based customer agility: An integrated framework of information management structure,capability, and culture     

《International Journal of Information Management》2021

Platform-based customer agility is the ability to leverage the voice of the customer on a platform to achieve market intelligence and to explore competitive action opportunities. Prior studies have indicated the critical role of customer agility in enabling the survival and prosperity of contemporary organizations in a turbulent business environment, although how to develop this capability is not answered. The current research attempts to fill this theoretical gap. Drawing on the information management literature, we propose an integrative information management framework to investigate the process of developing customer agility. By conducting a case study of a leading e-commerce platform in China, we identify three types of platform-based customer agility (i.e. reactive customer agility, proactive customer agility, and coactive customer agility) in different phases of the growth of the platform. Furthermore, a process model is developed from the case study. It shows that platform-based customer agility is achieved by establishing information management structure, developing information management capability, and instilling information management culture. This study contributes to the knowledge on customer agility and information management. Detailed recommendations are also provided for potential practitioners.  相似文献   

Drivers of information technology choice by individuals     

《International Journal of Information Management》2021

Technology acceptance has spawned considerable research in technology adoption, technology use, and technology switching. However, technology choice—i.e., an individual’s selection of a technology from a set of technologies that support similar tasks—has received limited attention in information systems research. This research was aimed at identifying the drivers of technology choice through a series of activities in two universities, in which students chose an information technology tool from various alternatives to complete the given tasks. A thematic analysis was conducted on the reasons for technology choice reported by 249 students, which yielded 18 technology, user, and environmental drivers that influenced individuals’ technology choice. This study provides insights into the drivers generally applicable for technology choice and drivers applicable in specific contexts. Implications for research and practice are discussed.  相似文献