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Enterprise social network - a proposal for an interdisciplinary, multi-theoretical and integrative approach

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DataCite Commons2021-03-25 更新2024-07-28 收录
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https://scielo.figshare.com/articles/dataset/Enterprise_social_network_-_a_proposal_for_an_interdisciplinary_multi-theoretical_and_integrative_approach/14284771/1
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ABSTRACT This research mapped how scientific theories discuss different facets of the Enterprise Social Network - ESN. The goal is to provide a study that organizes some of the existing knowledge at the intersection of media studies and network science. As a field map, it is intended to think of several types of links involved in a relational perspective on ESN. The literature review identified 15 theories, four were selected: the Social Capital theory analyzes the network's ability to link people to each other; the theory of Affordance analyzes the resources that it offers; the motivation to participate is analyzed through the theory of Social Change; and one of the main challenges of organizations in general, the location of expertise is analyzed by the theory of Transitive Memory. These theories do not exhaust the possible theoretical bases for the conceptualization of ESN, but they have proved in fact appropriate to explain important organizational processes in the search for a more integral vision. Finally, the Conceptual Map is presented with the main key concepts related to theories and their interconnections in order to help create a better understanding of the existing integration between them and serve as an instrument to establish the created knowledge.

摘要 本研究系统梳理了科学理论对企业社交网络(Enterprise Social Network,ESN)各维度的探讨。本研究旨在整合媒体研究与网络科学交叉领域的部分现有知识,形成一份领域图谱,并尝试从关系视角梳理企业社交网络所涉及的各类联结。本次文献综述共识别出15种理论,最终选取4种展开分析:社会资本理论(Social Capital theory)用于剖析网络实现人际联结的能力;可供性理论(Affordance theory)用于解析其提供的资源禀赋;社会变迁理论(Social Change theory)用于分析用户参与动机;而针对各类组织普遍面临的核心挑战之一——专业知识定位问题,则由传递性记忆理论(Transitive Memory theory)开展研究。上述理论并未穷尽企业社交网络概念化的全部理论基础,但实践证明它们适于解释诸多重要的组织过程,助力形成更为完整的研究视角。最后,本研究构建了概念图谱(Conceptual Map),收录与上述理论相关的核心关键概念及其相互关联,以期帮助研究者更清晰地理解各理论间的现有整合路径,并为已形成的研究知识提供系统化的梳理工具。
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SciELO journals
创建时间:
2021-03-24
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