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Cooperation, regardless of its nature or involvement degree, is one of the most important factors determining the companies’ success on a dynamic market. This is caused by the need to quick adaption to changes. Using the opportunities and avoid risks is a daily task of top management.
The cooperation allows the use the capital of everyone participant involved in the process. It is thus a factor directly influencing the process of gaining the competitive advantage. Each event market, including any change, can be seen in terms of the information aspect. Collaboration at any stage is also an information process. As well as company management. It can be said that the cooperation of companies focused on success, depends on the efficiency of information management.
The aim of the article is to realize the complexity of information management. It is also awareness of the need for a comprehensive and integrated approach to the issue of information in the life of the company.
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Gharib, R. K., Philpott, E., & Duan, Y. (2017). Factors affecting active participation in B2B online communities: An empirical investigation. Information & Management , 54(4), 516–530. 10.1016/j.im.2016
I have experienced increased interest in the evaluation of the SSH in China since then, not only by the invitation to hold the Luojia Lecture in Wuhan but also at meetings and conferences organized by, e.g., The Chinese Academy of Social Sciences Evaluation Studies (CASSES) and the Beijing and Chengdu branches of the National Science Library of the Chinese Academy of Sciences. The School of InformationManagement at Wuhan University has recently initiated a PhD project supervised by professor Lin Zhang with the aim of studying the publication patterns and
knowledge creation. Journal of Enterprise InformationManagement 27(1), 31–44. 10.1108/JEIM-09-2012-0063
Wagner D. Vollmar G. & Wagner H.-T. 2014 The impact of information technology on knowledge creation Journal of Enterprise InformationManagement 27 1 31 44
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Wattal S. Racherla P. & Mandviwalla M. 2009
The increasing complexity of the business environment, growing knowledge requirements, development of information technologies, and competitiveness implies the need of implementation of information management systems. Moreover, welter of information about online customers, their individual behavior, and their expectations force entrepreneurs to manage information in a personalized way. Monitoring Internet users behavior, creating their profiles (based on data about age, sex, lifestyle, interests, family, work, etc.), and controlling current traffic on the Web site give wide range of possibilities in creating a real model of potential customers preference and using it in online communication. This study concentrates on possibilities of using personalized communication in the information management by online stores in 4C model.
Yukun Zheng, Yiqun Liu, Zhen Fan, Cheng Luo, Qingyao Ai, Min Zhang and Shaoping Ma
A number of deep neural networks have been proposed to improve the performance of document ranking in information retrieval studies. However, the training processes of these models usually need a large scale of labeled data, leading to data shortage becoming a major hindrance to the improvement of neural ranking models’ performances. Recently, several weakly supervised methods have been proposed to address this challenge with the help of heuristics or users’ interaction in the Search Engine Result Pages (SERPs) to generate weak relevance labels. In this work, we adopt two kinds of weakly supervised relevance, BM25-based relevance and click model-based relevance, and make a deep investigation into their differences in the training of neural ranking models. Experimental results show that BM25-based relevance helps models capture more exact matching signals, while click model-based relevance enhances the rankings of documents that may be preferred by users. We further proposed a cascade ranking framework to combine the two weakly supervised relevance, which significantly promotes the ranking performance of neural ranking models and outperforms the best result in the last NTCIR-13 We Want Web (WWW) task. This work reveals the potential of constructing better document retrieval systems based on multiple kinds of weak relevance signals.
Filtering out irrelevant documents and classifying the relevant ones into topical categories is a de facto task in many applications. However, supervised learning solutions require extravagant human efforts on document labeling. In this paper, we propose a novel seed-guided topic model for dataless short text classification and filtering, named SSCF. Without using any labeled documents, SSCF takes a few “seed words” for each category of interest, and conducts short text filtering and classification in a weakly supervised manner. To overcome the issues of data sparsity and imbalance, the short text collection is mapped to a collection of pseudodocuments, one for each word. SSCF infers two kinds of topics on pseudo-documents: category-topics and general-topics. Each category-topic is associated with one category of interest, covering the meaning of the latter. In SSCF, we devise a novel word relevance estimation process based on the seed words, for hidden topic inference. The dominating topic of a short text is identified through post inference and then used for filtering and classification. On two real-world datasets in two languages, experimental results show that our proposed SSCF consistently achieves better classification accuracy than state-of-the-art baselines. We also observe that SSCF can even achieve superior performance than the supervised classifiers supervised latent dirichlet allocation (sLDA) and support vector machine (SVM) on some testing tasks.
A prevalent belief is that it is advantageous to have surname initials that are placed early in the alphabet (early surname initials) in academic fields in which authors are ordered alphabetically (alphabetic academic fields), because first authors are more visible. However, it is not certain that the advantage is strong enough to affect academic careers. In this paper, the advantage in having such early surname initials is analyzed by using data from 1,345 course catalogs that span a 100 years. We obtained academic titles and surname initials of 19,353 faculty members who appeared 211,816 times in these course catalogs. Two alphabetic academic fields – economics and mathematics – and four other academic fields that are not alphabetic were analyzed. We found that there are some years when faculty members who have early surname initials are more likely to be full professors. However, there are many other years when faculty members who have early surname initials are less likely to be full professors. We also analyzed the career path of each faculty member. Economists who have early surname initials are found to be more likely to become full professors. However, this result is not significant and does not extend to mathematicians.