Quantifying the Impact of Data Loss Incidents on Publicly-traded Organizations

被引:0
|
作者
Hsieh, Tien-Shih [1 ]
Noyes, Daniel [2 ]
Liu, Hong [2 ]
Fiondella, Lance [2 ]
机构
[1] Univ Massachusetts, Dept Accounting & Finance, Dartmouth, MA 02747 USA
[2] Univ Massachusetts, Dept Elect & Comp Engn, Dartmouth, MA USA
关键词
data loss; formatting; publicaly-traded organization; abnormal return;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the widespread adoption of electronic data collection and sharing technologies, data theft has gained prominence as a major problem. The convenience of services enabled by information technology is generally appreciated. However, improper handling of data can expose government agencies, corporate entities, and individuals to various forms of financial and identity fraud. Moreover, it is unclear if these risks are distributed among government, business, and private citizens. Given the complex technical nature of data theft and the lack of methods to systematically protect against such crime, it is natural that each of these stakeholders should seek to protect their own interests. This paper explores the hypothesis that increased investment in data security by the business sector may benefit both the company making the investment as well as their customers and clients. Toward this end, we applied statistical methods from the business research community on a historical database of data loss incidents to correlate the impact of data loss on the stock performance of publicly-traded organizations. Our results indicate that the negative impact of public disclosure of data loss is statistically significant. While the long term impacts such as decreased future sales resulting from a tarnished reputation are not addressed, our preliminary results suggest that businesses may seriously under invest in electronic data protection and that additional quantitative research is needed to help businesses identify an optimal level of investment in data security specific to the risk exposure posed by their online operations.
引用
收藏
页数:5
相关论文
共 50 条
  • [41] Impact of Korea's emissions trading scheme on publicly traded firms (vol 18, e0285863, 2023)
    Oh, Nyonho
    Miteva, Daniela A.
    Lee, Yehchan
    PLOS ONE, 2024, 19 (06):
  • [42] Quantifying the impact of public omics data
    Yasset Perez-Riverol
    Andrey Zorin
    Gaurhari Dass
    Manh-Tu Vu
    Pan Xu
    Mihai Glont
    Juan Antonio Vizcaíno
    Andrew F. Jarnuczak
    Robert Petryszak
    Peipei Ping
    Henning Hermjakob
    Nature Communications, 10
  • [43] Quantifying the impact of public omics data
    Perez-Riverol, Yasset
    Zorin, Andrey
    Dass, Gaurhari
    Manh-Tu Vu
    Xu, Pan
    Glont, Mihai
    Vizcaino, Juan Antonio
    Jarnuczak, Andrew F.
    Petryszak, Robert
    Ping, Peipei
    Hermjakob, Henning
    NATURE COMMUNICATIONS, 2019, 10 (1)
  • [44] Evaluation of the impact of traffic incidents using GPS data
    Wong, Wai
    Wong, Sze Chun
    PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT, 2016, 169 (03) : 148 - 162
  • [45] Assessing the impact of intermediate import liberalization on green innovation in pollution-prone industries: A study on publicly traded companies
    Tian, Shuo
    Elahi, Ehsan
    Liu, Lin
    Sun, Ailin
    JOURNAL OF CLEANER PRODUCTION, 2023, 425
  • [46] The Impact of Distributed Data in Big Data Platforms on Organizations
    Koren, Oded
    Binyaminov, Matan
    Perel, Nir
    PROCEEDINGS OF THE FUTURE TECHNOLOGIES CONFERENCE (FTC) 2018, VOL 2, 2019, 881 : 1024 - 1036
  • [47] APPLYING THE EXPECTED CREDIT LOSS MODEL UNDER IFRS 9 ON ISLAMIC SUKUK: EMPIRICAL EVIDENCE FROM JORDAN PUBLICLY TRADED COMPANIES
    Morshed, Amer
    15TH ANNUAL INTERNATIONAL BATA CONFERENCE FOR PH.D. STUDENTS AND YOUNG RESEARCHERS (DOKBAT), 2019, : 778 - 785
  • [48] Big Data in organizations: Exploring the adoption of Big Data applications and their impact on organizations in China and the Netherlands
    Raab, Jorg
    Pang, Yuting
    Baaijens, Joan
    Zhou, Honggeng
    BIG DATA RESEARCH, 2024, 36
  • [49] A data analytic approach to quantifying scientific impact
    Cao, Xuanyu
    Chen, Yan
    Liu, K. J. Ray
    JOURNAL OF INFORMETRICS, 2016, 10 (02) : 471 - 484
  • [50] Quantifying the impact of data replication on error propagation
    Zuhal Ozturk
    Haluk Rahmi Topcuoglu
    Mahmut Taylan Kandemir
    Cluster Computing, 2023, 26 : 1985 - 1999