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Ambiguity of network outcomes

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  • Cinelli, Matteo

Abstract

The extent to which available data is continuously growing in terms of volume is forcing organizations to contend with and seek to resolve the so-called Big Data Challenge. Big data comes or can be structured in the form of networks from which information can be extracted via statistical and computational tools. The results of such investigations can be generally referred to as network outcomes. Such outcomes, despite being often characterized by a inner ambiguity, need to be well understood and interpreted in order to exploit the potentialities of network data, especially in practical situations. For this reason, addressing the ambiguity of network outcomes becomes a key issue in business-related environments, where the possibility of rapidly interpreting and properly exploiting network data can positively affect performances. In this paper, we propose a framework to face ambiguity of network outcomes that, by means of specific solutions, allows practitioners to successfully interpret and exploit the obtained outcomes.

Suggested Citation

  • Cinelli, Matteo, 2021. "Ambiguity of network outcomes," Journal of Business Research, Elsevier, vol. 129(C), pages 555-561.
    • Handle: RePEc:eee:jbrese:v:129:y:2021:i:c:p:555-561
    DOI: 10.1016/j.jbusres.2020.02.031
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    References listed on IDEAS

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    Cited by:

    1. Matteo Cinelli & Giovanna Ferraro & Antonio Iovanella, 2022. "Connections matter: a proxy measure for evaluating network membership with an application to the Seventh Research Framework Programme," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3959-3976, July.

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