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

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  • Resch, Christian
  • Kock, Alexander

Abstract

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.

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  • Resch, Christian & Kock, Alexander, 2021. "The influence of information depth and information breadth on brokers’ idea newness in online maker communities," Research Policy, Elsevier, vol. 50(8).
  • Handle: RePEc:eee:respol:v:50:y:2021:i:8:s0048733320302171
    DOI: 10.1016/j.respol.2020.104142
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    2. Chan, C.S. Richard & Pethe, Charuta & Skiena, Steven, 2021. "Natural language processing versus rule-based text analysis: Comparing BERT score and readability indices to predict crowdfunding outcomes," Journal of Business Venturing Insights, Elsevier, vol. 16(C).
    3. Manis, K.T. & Madhavaram, Sreedhar, 2023. "AI-Enabled marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues," Journal of Business Research, Elsevier, vol. 157(C).
    4. Svetlana Klessova & Sebastian Engell & Catherine Thomas, 2022. "Dynamics of couplings and their implications in inter-organizational multi-actor research and innovation projects," Post-Print hal-03690108, HAL.
    5. Ye-Chan Park & Paul Hong, 2022. "Knowledge Sharing Practices for Corporate Sustainability: An Empirical Investigation of Sharing Economy Firms in Japan," Sustainability, MDPI, vol. 14(24), pages 1-19, December.

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