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The influence of information depth and information breadth on brokers' idea newness in online maker communities

机译:信息深度和信息广度对在线制造商社区中经纪人的想法新性的影响

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Social networks provide individuals with diverse or redundant information depending on the network structure. Both types of information offer advantages for generating new ideas. At the same time, network structure and network content are independent. As a result, two individuals with the same network position can access diverse or redundant content from their social peers. In this study, we investigate the function of social networks in innovative endeavors given individuals' different kinds of information accessing behavior. In accordance with previous research, we argue that individuals with a broker status access more diverse information through non-redundant network structures and develop, on average, more novel ideas. We further propose that redundancy in content complements brokers' structural non-redundancy by providing familiar knowledge elements and therefore interpretability, while non-redundancy in both content and structure leads to information overload. Thus, we hypothesize that brokers accessing more information depth, and independently, less information breadth generate newer ideas. To test our hypotheses, we collected data from a popular online maker community containing 18,146 ideas, 19,919 profiles, and 52,663 comments. We used topic modeling (Latent Dirichlet Allocation) to extract hidden knowledge elements and social network analysis to identify brokers. In line with our hypotheses, we find that information depth (breadth) strengthens (weakens) a favorable broker position. These findings have implications for the literature on idea generation in social networks and household sector innovation.
机译:社交网络根据网络结构提供具有多样化或冗余信息的个人。两种类型的信息都提供了产生新想法的优势。同时,网络结构和网络内容是独立的。因此,两个具有相同网络位置的个人可以从社交同行中访问不同或多余的内容。在这项研究中,我们调查了社交网络在创新努力中的职责,给予个人不同类型的信息访问行为。根据以前的研究,我们认为具有经纪人状态的个人通过非冗余网络结构访问更多样化的信息,并平均开发更多的新颖思路。我们进一步提出了内容中的冗余来补充经纪人的结构性非冗余,通过提供熟悉的知识元素并因此可解释性,而内容和结构中的非冗余导致信息过载。因此,我们假设经纪人访问更多信息深度,独立地,较少的信息广度生成更新的想法。为了测试我们的假设,我们从包含18,146个想法,19,919个概况的流行在线制造商社区收集数据,以及52,663个评论。我们使用主题建模(潜在Dirichlet分配)来提取隐藏知识元素和社交网络分析来识别经纪人。符合我们的假设,我们发现信息深度(广度)增强(削弱)有利的经纪人位置。这些调查结果对社会网络和家庭部门创新的想法生成有影响。

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