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Demystifying big data: Anatomy of big data developmental process

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  • Shin, Dong-Hee

Abstract

This study seeks to understand big data ecology, how it is perceived by different stakeholders, the potential value and challenges, and the implications for the private sector and public organizations, as well as for policy makers. With Normalization Process Theory in place, this study conducts socio-technical evaluation on the big data phenomenon to understand the developmental processes through which new practices of thinking and enacting are implemented, embedded, and integrated in South Korea. It also undertakes empirical analyses of user modeling to explore the factors influencing users׳ adoption of big data by integrating cognitive motivations as well as user values as the primary determining factors. Based on the qualitative and quantitative findings, this study concludes that big data should be developed with user-centered ideas and that users should be the focus of big data design.

Suggested Citation

  • Shin, Dong-Hee, 2016. "Demystifying big data: Anatomy of big data developmental process," Telecommunications Policy, Elsevier, vol. 40(9), pages 837-854.
    • Handle: RePEc:eee:telpol:v:40:y:2016:i:9:p:837-854
    DOI: 10.1016/j.telpol.2015.03.007
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    1. Nir KSHETRI & Lailani L. ALCANTARA & Yonghoon PARK, 2014. "Development of a Smart City and its Adoption and Acceptance: the Case of New Songdo," Communications & Strategies, IDATE, Com&Strat dept., vol. 1(96), pages 113-128, 4th quart.
    2. Shin, Dong-Hee & Yoon, Hongseok & Lee, Jaegil & Moon, Yohan & Kim, Namchul & Cho, Hoyeon, 2014. "A socio-technical framework for Internet-of-Things design," 20th ITS Biennial Conference, Rio de Janeiro 2014: The Net and the Internet - Emerging Markets and Policies 106844, International Telecommunications Society (ITS).
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    Cited by:

    1. Yadegaridehkordi, Elaheh & Hourmand, Mehdi & Nilashi, Mehrbakhsh & Shuib, Liyana & Ahani, Ali & Ibrahim, Othman, 2018. "Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 199-210.
    2. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    3. Jithesh Arayankalam & Satish Krishnan, 2023. "ICT-Based Country-Level Determinants of Social Media Diffusion," Information Systems Frontiers, Springer, vol. 25(5), pages 1881-1902, October.
    4. Md. Morshadul Hasan & Lu Yajuan & Appel Mahmud, 2020. "Regional Development of China’s Inclusive Finance Through Financial Technology," SAGE Open, , vol. 10(1), pages 21582440199, February.
    5. Shahbaz, Muhammad & Zahid, Rimsha, 2022. "Probing the factors influencing cloud computing adoption in healthcare organizations: A three-way interaction model," Technology in Society, Elsevier, vol. 71(C).
    6. Abhishek Behl & Pankaj Dutta & Stefan Lessmann & Yogesh K. Dwivedi & Samarjit Kar, 2019. "A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach," Information Systems and e-Business Management, Springer, vol. 17(2), pages 285-318, December.
    7. Kris Hartley, 2023. "Public Perceptions About Smart Cities: Governance and Quality-of-Life in Hong Kong," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 166(3), pages 731-753, April.
    8. Zhang, Yucheng & Xu, Shan & Zhang, Long & Yang, Mengxi, 2021. "Big data and human resource management research: An integrative review and new directions for future research," Journal of Business Research, Elsevier, vol. 133(C), pages 34-50.
    9. Sung-Un Park & Hyunkyun Ahn & Dong-Kyu Kim & Wi-Young So, 2020. "Big Data Analysis of Sports and Physical Activities among Korean Adolescents," IJERPH, MDPI, vol. 17(15), pages 1-11, August.
    10. Orji, Ifeyinwa Juliet & Kusi-Sarpong, Simonov & Huang, Shuangfa & Vazquez-Brust, Diego, 2020. "Evaluating the factors that influence blockchain adoption in the freight logistics industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    11. Shim, Yongwoon & Shin, Don, 2019. "Smartness in techno-nationalism? Combining actor-network theory and institutionalization to assess Chinese smart TV development," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 87-98.
    12. Shahbaz, Muhammad & Gao, Changyuan & Zhai, LiLi & Shahzad, Fakhar & Khan, Imran, 2021. "Environmental air pollution management system: Predicting user adoption behavior of big data analytics," Technology in Society, Elsevier, vol. 64(C).
    13. Kamolsook, Apinya & Badir, Yuosre F. & Frank, Björn, 2019. "Consumers' switching to disruptive technology products: The roles of comparative economic value and technology type," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 328-340.
    14. Jaklič, Jurij & Grublješič, Tanja & Popovič, Aleš, 2018. "The role of compatibility in predicting business intelligence and analytics use intentions," International Journal of Information Management, Elsevier, vol. 43(C), pages 305-318.
    15. Chunling Zhang & Zunfeng Liu, 2019. "Application of big data technology in agricultural Internet of Things," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.

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