IDEAS home Printed from https://ideas.repec.org/a/vrs/hjobpa/v11y2020i2p161-170n12.html
   My bibliography  Save this article

Is Big Data Security Essential for Students to Understand?

Author

Listed:
  • Mustofa Rochman Hadi

    (Universitas Muhammadiyah Surakarta, Indonesia)

Abstract

Big Data has become a significant concern of the world, along with the era of digital transformation. However, there are still many young people, especially in developing countries, who are not yet aware of the security of their big data, especially personal data. Misuse of information from big data often results in violations of privacy, security, and cybercrime. This study aims to determine how aware of the younger generation of security and privacy of their big data. Data were collected qualitatively by interviews and focus group discussions (FGD) from. Respondents were undergraduate students who used social media and financial technology applications such as online shopping, digital payments, digital wallet and hotel/transportation booking applications. The results showed that students were not aware enough and understood the security or privacy of their digital data, and some respondents even gave personal data to potentially scam sites. Most students are not careful in providing big data information because they are not aware of the risks behind it, socialization is needed in the future as a step to prevent potential data theft.

Suggested Citation

  • Mustofa Rochman Hadi, 2020. "Is Big Data Security Essential for Students to Understand?," HOLISTICA – Journal of Business and Public Administration, Sciendo, vol. 11(2), pages 161-170, August.
    • Handle: RePEc:vrs:hjobpa:v:11:y:2020:i:2:p:161-170:n:12
    DOI: 10.2478/hjbpa-2020-0026
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/hjbpa-2020-0026
    Download Restriction: no
    File URL: https://libkey.io/10.2478/hjbpa-2020-0026?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
    ---><---

    References listed on IDEAS

    as
    1. Moro, Sérgio & Rita, Paulo & Vala, Bernardo, 2016. "Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach," Journal of Business Research, Elsevier, vol. 69(9), pages 3341-3351.
    2. Amado, Alexandra & Cortez, Paulo & Rita, Paulo & Moro, Sérgio, 2018. "Research Trends On Big Data In Marketing: A Text Mining And Topic Modeling Based Literature Analysis," European Research on Management and Business Economics (ERMBE), Academia Europea de Dirección y Economía de la Empresa (AEDEM), vol. 24(1), pages 1-7.
    3. Anggraeni Permatasari & Mayci Kartikowati, 2018. "The influence of website design on customer online trust and perceived risk towards purchase intention: a case of O2O commerce in Indonesia," International Journal of Business and Globalisation, Inderscience Enterprises Ltd, vol. 21(1), pages 74-86.
    Full references (including those not matched with items on IDEAS)

    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. Silva, Ana Teresa & Moro, Sérgio & Rita, Paulo & Cortez, Paulo, 2018. "Unveiling the features of successful eBay smartphone sellers," Journal of Retailing and Consumer Services, Elsevier, vol. 43(C), pages 311-324.
    2. Navdeep Bohra & Vishal Bhatnagar, 2021. "Group level social media popularity prediction by MRGB and Adam optimization," Journal of Combinatorial Optimization, Springer, vol. 41(2), pages 328-347, February.
    3. 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.
    4. Keeheon Lee, 2021. "A Systematic Review on Social Sustainability of Artificial Intelligence in Product Design," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    5. Shijie Guo & Shufeng Tang & Dongsheng Zhang, 2019. "A Recognition Methodology for the Key Geometric Errors of a Multi-Axis Machine Tool Based on Accuracy Retentivity Analysis," Complexity, Hindawi, vol. 2019, pages 1-21, November.
    6. Moro, Sérgio & Lopes, Rui J. & Esmerado, Joaquim & Botelho, Miguel, 2020. "Service quality in airport hotel chains through the lens of online reviewers," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    7. Cabrera-Sánchez, Juan-Pedro & Villarejo-Ramos, Ángel F., 2019. "Fatores que afetam a adoção de análises de Big Data em empresas," RAE - Revista de Administração de Empresas, FGV-EAESP Escola de Administração de Empresas de São Paulo (Brazil), vol. 59(6), December.
    8. Aslan, Serpil & Kaya, Buket & Kaya, Mehmet, 2019. "Predicting potential links by using strengthened projections in evolving bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 998-1011.
    9. He, Yi & You, Ya & Chen, Qimei, 2020. "Our conditional love for the underdog: The effect of brand positioning and the lay theory of achievement on WOM," Journal of Business Research, Elsevier, vol. 118(C), pages 210-222.
    10. Gupta, Shivam & Justy, Théo & Kamboj, Shampy & Kumar, Ajay & Kristoffersen, Eivind, 2021. "Big data and firm marketing performance: Findings from knowledge-based view," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    11. Moro, Sérgio & Pires, Guilherme & Rita, Paulo & Cortez, Paulo, 2020. "A cross-cultural case study of consumers' communications about a new technological product," Journal of Business Research, Elsevier, vol. 121(C), pages 438-447.
    12. Moro, Sérgio & Pires, Guilherme & Rita, Paulo & Cortez, Paulo, 2019. "A text mining and topic modelling perspective of ethnic marketing research," Journal of Business Research, Elsevier, vol. 103(C), pages 275-285.
    13. Shahbaznezhad, Hamidreza & Dolan, Rebecca & Rashidirad, Mona, 2021. "The Role of Social Media Content Format and Platform in Users' Engagement Behavior," Journal of Interactive Marketing, Elsevier, vol. 53(C), pages 47-65.
    14. Siyam, Nur & Alqaryouti, Omar & Abdallah, Sherief, 2020. "Mining government tweets to identify and predict citizens engagement," Technology in Society, Elsevier, vol. 60(C).
    15. Othman Boujena & Isabelle Ulrich & Aikaterini Manthiou & Bruno Godey, 2021. "Customer engagement and performance in social media: a managerial perspective," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(4), pages 965-987, December.
    16. Anne Parlina & Kalamullah Ramli & Hendri Murfi, 2021. "Exposing Emerging Trends in Smart Sustainable City Research Using Deep Autoencoders-Based Fuzzy C-Means," Sustainability, MDPI, vol. 13(5), pages 1-28, March.
    17. Shianghau Wu, 2020. "A Fuzzy Association Rules Mining Analysis of the Influencing Factors on the Failure of oBike in Taiwan," Mathematics, MDPI, vol. 8(11), pages 1-18, October.
    18. Wei‐Min Fan & Wei Jeng & Muh‐Chyun Tang, 2023. "Using data citation to define a knowledge domain: A case study of the Add‐Health dataset," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 81-98, January.
    19. Zamoshnikova, Valeriya & Kashkin, Vasily, 2023. "Психометрические характеристики китайского клиента: тестирование программы Symanto [Psychometric Characteristics of a Chinese Client: Testing the Symanto Program]," MPRA Paper 122138, University Library of Munich, Germany.
    20. Marina Johnson & Rashmi Jain & Peggy Brennan-Tonetta & Ethne Swartz & Deborah Silver & Jessica Paolini & Stanislav Mamonov & Chelsey Hill, 2021. "Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(3), pages 197-217, September.

    More about this item

    Keywords

    Big Data; Security and Privacy; Digital Data; Financial Technology;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

    Statistics

    Access and download statistics

    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:vrs:hjobpa:v:11:y:2020:i:2:p:161-170:n:12. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    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.