IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v129y2021icp555-561.html
   My bibliography  Save this article

Ambiguity of network outcomes

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296320301375
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2020.02.031?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Larry G. Epstein & Martin Schneider, 2008. "Ambiguity, Information Quality, and Asset Pricing," Journal of Finance, American Finance Association, vol. 63(1), pages 197-228, February.
    2. Abramo, Giovanni & D’Angelo, Ciriaco Andrea & Soldatenkova, Anastasiia, 2017. "An investigation on the skewness patterns and fractal nature of research productivity distributions at field and discipline level," Journal of Informetrics, Elsevier, vol. 11(1), pages 324-335.
    3. Einhorn, Hillel J & Hogarth, Robin M, 1986. "Decision Making under Ambiguity," The Journal of Business, University of Chicago Press, vol. 59(4), pages 225-250, October.
    4. Traud, Amanda L. & Mucha, Peter J. & Porter, Mason A., 2012. "Social structure of Facebook networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4165-4180.
    5. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    6. Hakansson, Hakan & Ford, David, 2002. "How should companies interact in business networks?," Journal of Business Research, Elsevier, vol. 55(2), pages 133-139, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yehuda Izhakian, 2012. "Ambiguity Measurement," Working Papers 12-01, New York University, Leonard N. Stern School of Business, Department of Economics.
    2. Krahnen, Jan Pieter & Ockenfels, Peter & Wilde, Christian, 2014. "Measuring ambiguity aversion: A systematic experimental approach," SAFE Working Paper Series 55, Leibniz Institute for Financial Research SAFE.
    3. Khan, Muhammad Asif & Pattnaik, Debidutta & Ashraf, Rohail & Ali, Imtiaz & Kumar, Satish & Donthu, Naveen, 2021. "Value of special issues in the journal of business research: A bibliometric analysis," Journal of Business Research, Elsevier, vol. 125(C), pages 295-313.
    4. Christoph Bühren & Fabian Meier & Marco Pleßner, 2023. "Ambiguity aversion: bibliometric analysis and literature review of the last 60 years," Management Review Quarterly, Springer, vol. 73(2), pages 495-525, June.
    5. Ge Bai & Ranjani Krishnan, 2016. "Effects of Ambiguous Common Uncertainty on Employee Preference for Relative Performance Contracts," The Japanese Accounting Review, Research Institute for Economics & Business Administration, Kobe University, vol. 6, pages 65-93, December.
    6. Haemin Dennis Park & Pankaj C. Patel, 2015. "How Does Ambiguity Influence IPO Underpricing? The Role of the Signalling Environment," Journal of Management Studies, Wiley Blackwell, vol. 52(6), pages 796-818, September.
    7. Tabesh, Pooya & Mousavidin, Elham & Hasani, Sona, 2019. "Implementing big data strategies: A managerial perspective," Business Horizons, Elsevier, vol. 62(3), pages 347-358.
    8. Fujikawa, Takemi, 2009. "The hot stove effect in repeated-play decision making under ambiguity," MPRA Paper 17647, University Library of Munich, Germany.
    9. Thibaut Mastrolia & Dylan Possamaï, 2018. "Moral Hazard Under Ambiguity," Journal of Optimization Theory and Applications, Springer, vol. 179(2), pages 452-500, November.
    10. Ivana Bassi & Federico Nassivera & Lucia Piani, 2016. "Social farming: a proposal to explore the effects of structural and relational variables on social farm results," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 4(1), pages 1-13, December.
    11. Jarratt, Denise & Ceric, Arnela, 2015. "The complexity of trust in business collaborations," Australasian marketing journal, Elsevier, vol. 23(1), pages 2-12.
    12. De Clercq, Dirk & Danis, Wade M. & Dakhli, Mourad, 2010. "The moderating effect of institutional context on the relationship between associational activity and new business activity in emerging economies," International Business Review, Elsevier, vol. 19(1), pages 85-101, February.
    13. Gad Yair & Keith Goldstein & Nir Rotem & Anthony J. Olejniczak, 2022. "The three cultures in American science: publication productivity in physics, history and economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 2967-2980, June.
    14. Ahmad Ibrahim Aljumah & Mohammed T. Nuseir & Md. Mahmudul Alam, 2021. "Traditional marketing analytics, big data analytics and big data system quality and the success of new product development," Post-Print hal-03538161, HAL.
    15. Jewitt, Ian & Mukerji, Sujoy, 2017. "Ordering ambiguous acts," Journal of Economic Theory, Elsevier, vol. 171(C), pages 213-267.
    16. Marcello Basili & Stefano Dalle Mura, 2004. "Ambiguity and macroeconomics:a rationale for price stickiness," Department of Economics University of Siena 428, Department of Economics, University of Siena.
    17. Wohl, Jennifer B. & Ravenswaay, Eileen O. van & Hoehn, John P., 1995. "The Effect of Ambiguity on Willingness to Pay for Reduced Pesticide Residues," Staff Paper Series 201202, Michigan State University, Department of Agricultural, Food, and Resource Economics.
    18. Ogulin, R. & Selen, W. & Ashayeri, J., 2010. "Determinants of Informal Coordination in Networked Supply Chains," Discussion Paper 2010-133, Tilburg University, Center for Economic Research.
    19. Aaltonen, Aleksi Ville & Alaimo, Cristina & Kallinikos, Jannis, 2021. "The making of data commodities: data analytics as an embedded process," LSE Research Online Documents on Economics 110296, London School of Economics and Political Science, LSE Library.
    20. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:129:y:2021:i:c:p:555-561. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.