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The Missing Variable in Big Data for Social Sciences: The Decision-Maker

Author

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  • Michela Arnaboldi

    (Department of Management, Economics and Industrial Engineering, Politecnico di Milano, 20156 Milano, Italy)

Abstract

The value of big data for social sciences and social impact is professed to be high. This potential value is related, however, to the capacity of using extracted information in decision-making. In all of this, one important point has been overlooked: when “humans” retain a role in the decision-making process, the value of information is no longer an objective feature but depends on the knowledge and mindset of end users. A new big data cycle has been proposed in this paper, where the decision-maker is placed at the centre of the process. The proposed cycle is tested through two cases and, as a result of the suggested approach, two operations—filtering and framing—which are routinely carried out independently by scientists and end users in an unconscious manner, become clear and transparent. The result is a new cycle where four dimensions guide the interactions for creating value.

Suggested Citation

  • Michela Arnaboldi, 2018. "The Missing Variable in Big Data for Social Sciences: The Decision-Maker," Sustainability, MDPI, vol. 10(10), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3415-:d:171959
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    References listed on IDEAS

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    Cited by:

    1. Ágnes Szukits, 2022. "The illusion of data-driven decision making – The mediating effect of digital orientation and controllers’ added value in explaining organizational implications of advanced analytics," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 33(3), pages 403-446, September.
    2. Laura Grassi & Nicolas Figini & Lorenzo Fedeli, 2022. "How does a data strategy enable customer value? The case of FinTechs and traditional banks under the open finance framework," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-34, December.
    3. Abdulrahman Housawi & Amal Al Amoudi & Basim Alsaywid & Miltiadis Lytras & Yara H. bin Μoreba & Wesam Abuznadah & Sami A. Alhaidar, 2020. "Evaluation of Key Performance Indicators (KPIs) for Sustainable Postgraduate Medical Training: An Opportunity for Implementing an Innovative Approach to Advance the Quality of Training Programs at the Saudi Commission for Health Specialties (SCFHS)," Sustainability, MDPI, vol. 12(19), pages 1-37, September.
    4. Miltiades D. Lytras & Anna Visvizi, 2019. "Big Data and Their Social Impact: Preliminary Study," Sustainability, MDPI, vol. 11(18), pages 1-18, September.
    5. Lei Wang & Mengke Yang & Zulfiqar Hussain Pathan & Shafaq Salam & Khuram Shahzad & Jianqiu Zeng, 2018. "Analysis of Influencing Factors of Big Data Adoption in Chinese Enterprises Using DANP Technique," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    6. van der Voort, Haiko & van Bulderen, Sabine & Cunningham, Scott & Janssen, Marijn, 2021. "Data science as knowledge creation a framework for synergies between data analysts and domain professionals," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    7. Keding, Christoph & Meissner, Philip, 2021. "Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions," Technological Forecasting and Social Change, Elsevier, vol. 171(C).

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