Research Article
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Year 2023, , 12 - 18, 31.03.2023
https://doi.org/10.17261/Pressacademia.2023.1714

Abstract

References

  • De Chiara, A., & Manna, E. (2022). Corruption and the case for safe-harbor regulation. Economics Letters, 216, 110546. https://doi.org/10.1016/j.econlet.2022.110546
  • Dottin, E. S. (2009). Professional judgment and dispositions in teacher education. Teaching and Teacher Education, 25(1), 83-88. https://doi.org/10.1016/j.tate.2008.06.005
  • Ferris, S. P., Hanousek, J., & Tresl, J. (2021). Corporate profitability and the global persistence of corruption. Journal of Corporate Finance, 66, 101855. https://doi.org/10.1016/j.jcorpfin.2020.101855
  • Ghasemaghaei, M., & Calic, G. (2020). Assessing the impact of big data on firm innovation performance: Big data is not always better data. Journal of Business Research, 108, 147-162. https://doi.org/10.1016/j.jbusres.2019.09.062
  • Gupta, S., Justy, T., Kamboj, S., Kumar, A., & Kristoffersen, E. (2021). Big data and firm marketing performance: Findings from knowledge-based view. Technological Forecasting and Social Change, 171, 120986. https://doi.org/10.1016/j.techfore.2021.120986
  • Hollyer, J. R., Rosendorff, B. P., & Vreeland, J. R. (2014). Measuring transparency. Political Analysis, 22(4), 413-434. https://www.jstor.org/stable/24573081
  • Jeppesen, K. K. (2019). The role of auditing in the fight against corruption. The British Accounting Review, 51(5), 100798. https://doi.org/10.1016/j.bar.2018.06.001
  • Kul’ba, V. V., Shelkov, A. B., & Avdeeva, Z. K. (2021). Analysis of anti-corruption management effectiveness based on the scenario approach (on the Example of the Construction Industry). IFAC-PapersOnLine, 54(13), 150-154. https://doi.org/10.1016/j.ifacol.2021.10.436
  • Lee, K., Choi, S. O., Kim, J., & Jung, M. (2018). A Study on the factors affecting decrease in the government corruption and mediating effects of the development of ICT and E-Government—a cross-country analysis. Journal of Open Innovation: Technology, Market, and Complexity, 4(3), 41. https://doi.org/10.3390/joitmc4030041
  • Letzring, T. D. (2008). The good judge of personality: Characteristics, behaviors, and observer accuracy. Journal of research in personality, 42(4), 914-932. https://doi.org/10.1016/j.jrp.2007.12.003
  • McCarthy, D. R., & Fluck, M. (2017). The concept of transparency in International Relations: Towards a critical approach. European Journal of International Relations, 23(2), 416-440. https://doi.org/10.1177/1354066116651688
  • Melki, M., & Pickering, A. (2020). Polarization and corruption in America. European Economic Review, 124, 103397. https://doi.org/10.1016/j.euroecorev.2020.103397
  • Myint, U. (2000). Corruption: Causes, consequences and cures. Asia Pacific Development Journal, 7(2), 33-58. https://www.unescap.org/sites/default/files/apdj-7-2-2-Myint.pdf
  • Xie, J., & Zhang, Y. (2020). Anti-corruption, government intervention, and corporate cash holdings: Evidence from China. Economic Systems, 44(1), 100745. https://doi.org/10.1016/j.ecosys.2020.100745

BIG DATA AND TRANSPARENCY: THE MEDIATION EFFECT OF PROFESSIONAL JUDGMENT

Year 2023, , 12 - 18, 31.03.2023
https://doi.org/10.17261/Pressacademia.2023.1714

Abstract

Purpose- The purpose of this article is to present a model of transparency in economic, social, and cultural fields in order to create social justice and use the expert judgment of researchers, lawyers, and journalists to prevent corruption when the relevant institutions do not have effective supervision due to systematic corruption. One of the detrimental and irrefutable aspects of the growth and advancement of societies is corruption. A nation or group with the desire to end corruption needs to have a lot of fighting potential. In this regard, the professional judgment of academics, attorneys, and journalists can be based on open data in the form of a data ecosystem, which includes rules and regulations, the identities of decision makers, and data from numerous economic and social domains online. Professional judgment is a supervision tool that can be highly effective in thwarting and exposing the emergence and spread of systematic corruption in a variety of contexts. This procedure raises the likelihood that corruption will be found, prevents managers from ignoring their duties, and broadens the scope of their responsibilities.
Methodology- In this article, the conceptual model, assumptions, and measurement indicators of the research variables are presented.
Findings- Corruption flourish in countries where there is a lack of accountability, openness, and consistency, as well as institutional deficiencies in the legislative and judicial systems.
Conclusion- Corruption increases the costs of the administrative, economic, and judicial systems and makes it impossible to achieve social justice. Access to open data and big data in various fields, as well as publishing them in the data life cycle with the judgment of researchers, lawyers, and journalists, is a practical and important method for discovering, preventing, and combating corruption. In fact, omissions and systematic corruption happen in areas where there is no supervisory presence.

References

  • De Chiara, A., & Manna, E. (2022). Corruption and the case for safe-harbor regulation. Economics Letters, 216, 110546. https://doi.org/10.1016/j.econlet.2022.110546
  • Dottin, E. S. (2009). Professional judgment and dispositions in teacher education. Teaching and Teacher Education, 25(1), 83-88. https://doi.org/10.1016/j.tate.2008.06.005
  • Ferris, S. P., Hanousek, J., & Tresl, J. (2021). Corporate profitability and the global persistence of corruption. Journal of Corporate Finance, 66, 101855. https://doi.org/10.1016/j.jcorpfin.2020.101855
  • Ghasemaghaei, M., & Calic, G. (2020). Assessing the impact of big data on firm innovation performance: Big data is not always better data. Journal of Business Research, 108, 147-162. https://doi.org/10.1016/j.jbusres.2019.09.062
  • Gupta, S., Justy, T., Kamboj, S., Kumar, A., & Kristoffersen, E. (2021). Big data and firm marketing performance: Findings from knowledge-based view. Technological Forecasting and Social Change, 171, 120986. https://doi.org/10.1016/j.techfore.2021.120986
  • Hollyer, J. R., Rosendorff, B. P., & Vreeland, J. R. (2014). Measuring transparency. Political Analysis, 22(4), 413-434. https://www.jstor.org/stable/24573081
  • Jeppesen, K. K. (2019). The role of auditing in the fight against corruption. The British Accounting Review, 51(5), 100798. https://doi.org/10.1016/j.bar.2018.06.001
  • Kul’ba, V. V., Shelkov, A. B., & Avdeeva, Z. K. (2021). Analysis of anti-corruption management effectiveness based on the scenario approach (on the Example of the Construction Industry). IFAC-PapersOnLine, 54(13), 150-154. https://doi.org/10.1016/j.ifacol.2021.10.436
  • Lee, K., Choi, S. O., Kim, J., & Jung, M. (2018). A Study on the factors affecting decrease in the government corruption and mediating effects of the development of ICT and E-Government—a cross-country analysis. Journal of Open Innovation: Technology, Market, and Complexity, 4(3), 41. https://doi.org/10.3390/joitmc4030041
  • Letzring, T. D. (2008). The good judge of personality: Characteristics, behaviors, and observer accuracy. Journal of research in personality, 42(4), 914-932. https://doi.org/10.1016/j.jrp.2007.12.003
  • McCarthy, D. R., & Fluck, M. (2017). The concept of transparency in International Relations: Towards a critical approach. European Journal of International Relations, 23(2), 416-440. https://doi.org/10.1177/1354066116651688
  • Melki, M., & Pickering, A. (2020). Polarization and corruption in America. European Economic Review, 124, 103397. https://doi.org/10.1016/j.euroecorev.2020.103397
  • Myint, U. (2000). Corruption: Causes, consequences and cures. Asia Pacific Development Journal, 7(2), 33-58. https://www.unescap.org/sites/default/files/apdj-7-2-2-Myint.pdf
  • Xie, J., & Zhang, Y. (2020). Anti-corruption, government intervention, and corporate cash holdings: Evidence from China. Economic Systems, 44(1), 100745. https://doi.org/10.1016/j.ecosys.2020.100745
There are 14 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Articles
Authors

Nader Salehı 0000-0001-5424-1801

Publication Date March 31, 2023
Published in Issue Year 2023

Cite

APA Salehı, N. (2023). BIG DATA AND TRANSPARENCY: THE MEDIATION EFFECT OF PROFESSIONAL JUDGMENT. Research Journal of Business and Management, 10(1), 12-18. https://doi.org/10.17261/Pressacademia.2023.1714
AMA Salehı N. BIG DATA AND TRANSPARENCY: THE MEDIATION EFFECT OF PROFESSIONAL JUDGMENT. RJBM. March 2023;10(1):12-18. doi:10.17261/Pressacademia.2023.1714
Chicago Salehı, Nader. “BIG DATA AND TRANSPARENCY: THE MEDIATION EFFECT OF PROFESSIONAL JUDGMENT”. Research Journal of Business and Management 10, no. 1 (March 2023): 12-18. https://doi.org/10.17261/Pressacademia.2023.1714.
EndNote Salehı N (March 1, 2023) BIG DATA AND TRANSPARENCY: THE MEDIATION EFFECT OF PROFESSIONAL JUDGMENT. Research Journal of Business and Management 10 1 12–18.
IEEE N. Salehı, “BIG DATA AND TRANSPARENCY: THE MEDIATION EFFECT OF PROFESSIONAL JUDGMENT”, RJBM, vol. 10, no. 1, pp. 12–18, 2023, doi: 10.17261/Pressacademia.2023.1714.
ISNAD Salehı, Nader. “BIG DATA AND TRANSPARENCY: THE MEDIATION EFFECT OF PROFESSIONAL JUDGMENT”. Research Journal of Business and Management 10/1 (March 2023), 12-18. https://doi.org/10.17261/Pressacademia.2023.1714.
JAMA Salehı N. BIG DATA AND TRANSPARENCY: THE MEDIATION EFFECT OF PROFESSIONAL JUDGMENT. RJBM. 2023;10:12–18.
MLA Salehı, Nader. “BIG DATA AND TRANSPARENCY: THE MEDIATION EFFECT OF PROFESSIONAL JUDGMENT”. Research Journal of Business and Management, vol. 10, no. 1, 2023, pp. 12-18, doi:10.17261/Pressacademia.2023.1714.
Vancouver Salehı N. BIG DATA AND TRANSPARENCY: THE MEDIATION EFFECT OF PROFESSIONAL JUDGMENT. RJBM. 2023;10(1):12-8.

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