Marketing and Management of Innovations

ISSN (print) – 2218-4511 

ISSN (online) – 2227-6718

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Identifier in the register: R30-01179 Decision dated August 31, 2023, No. 759

The language of publication is English. 

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Oleksii Lyulyov

Sumy State University | Ukraine

Innovative Approaches to Ensuring Cybersecurity and Public Safety: The Socio-Economic Dimension

Inna Tiutiunyk 1,*, ,  , Iryna Pozovna 1,  , Wojciech Zaskorski 2, 
  1. Sumy State University, Ukraine
  2. WSB University in Dabrowa Gornicza, Poland

     * Corresponding author

Received: 10 May 2024

Revised: 15 October 2024

Accepted: 26 November 2024

Abstract

This study is devoted to the analysis of socio-economic aspects of the development of cybercrime and the involvement of society, both as victims and direct initiators, in this activity. The paper examines the impact of socio-economic development indicators, in particular gross national income, spending and remittances, literacy and unemployment, on the dynamics of cybercrime worldwide, and analyses trends in public awareness and involvement in criminal activity in the digital space. The methodological tools of the study are the methods of correlation and canonical analysis, implemented in the Statistica 12 software. The analysis of the relationship between the socio-economic conditions of society and cybercrime-related behaviour established a dual impact of these factors on vulnerability to cybercrime and participation in criminal activity as a way of income generation. On the one hand, socio-economic disparities, in particular income inequality and unemployment, contribute to the increasing vulnerability of society to cybercrime. On the other hand, a high level of poverty among the population motivates a certain part of it to participate in cybercriminal activities. The results of the study indicate that socio-economic inequality and unemployment play a critical role in managing cybercrime risks. A higher level of economic development and social security is accompanied by greater resilience to cyberthreats, while a high level of unemployment and significant economic inequality increase the vulnerability of society to such risks. The findings also revealed that the socio-economic development of the country largely depends on the level of its cybercrime. This highlights the need to integrate cybersecurity measures into national economic development strategies. The practical significance of the obtained results lies in the application of a comprehensive approach to understanding cybercrime, which considers both victimization and active participation of society in this activity. This study can serve as a basis for the development of targeted measures to prevent cybercrime and increase the resilience of society to cyberthreats. The findings highlight the importance of integrating economic and social components in the development of effective cybersecurity strategies, which will contribute to minimizing the risks associated with the use of digital space and strengthening the socio-economic stability of the country.

Keywords: cybercrime; data manipulation; economic development; fraud; information accessibility; population income; social inequality.

How to Cite: Tiutiunyk, I., Pozovna, I., & Zaskorski, W. (2024). Innovative Approaches to Ensuring Cybersecurity and Public Safety: The Socio-Economic Dimension. Marketing and Management of Innovations, 15(4), 127–140. https://doi.org/10.21272/mmi.2024.4-10

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References

  1. Achim, M. V., Văidean, V. L., Borlea, S. N., & Florescu, D. R. (2021). The Impact of the Development of Society on Economic and Financial Crime. Case Study for European Union Member States. Risks9(5), 97. [Google Scholar] [CrossRef]
  2. Afzal, M., Ansari, M. , Ahmad, N., Shahid, M., & Shoed, M. (2024). Cyberfraud, usage intention, and cybersecurity awareness among e-banking users in India: an integrated model approach. Journal of Financial Services Marketing, 29, 1503–1523. [Google Scholar] [CrossRef]
  3. Ahmead, M., El Sharif, N., & Abuiram, I. (2024). Risky online behaviors and cybercrime awareness among undergraduate students at Al Quds University: a cross sectional study. Crime Science,13, 29. [Google Scholar] [CrossRef]
  4. AlDairi, A., & Tawalbeh, L. (2017). Cyber Security Attacks on Smart Cities and Associated Mobile Technologies. Procedia Computer Science, 109, 1086–1091. [Google Scholar] [CrossRef]
  5. Alharbi, N., Alkalifah, B., Alqarawi, G., & Rassam, M. A. (2024). Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection. Future Internet16(10), 367. [Google Scholar] [CrossRef]
  6. Anderson, R., Barton, C., Böhme, R., Clayton, R., Van Eeten, M., Levi, M., Moore, T., & Savage, S. (2019). Measuring the changing cost of cybercrime. Journal of Cybersecurity, 5(1), 1–12. [Google Scholar]
  7. Bada, M., & Nurse, J. (2019). The Social and Psychological Impact of Cyber-Attacks. Emerging Cyber Threats and Cognitive Vulnerabilities, 73-92. [Google Scholar] [CrossRef]
  8. Boppre, B., Salisbury, E.J., Parker, J. (2018). Pathways to Crime. In Oxford Research Encyclopedia of Criminology and Criminal Justice. Oxford University Press: Oxford, UK. [Google Scholar] [CrossRef]
  9. Boussi, O., Gupta, H., & Hossain, S. (2024). A machine learning model for predicting phishing websites. International Journal of Electrical and Computer Engineering (IJECE), 14, 4228. [Google Scholar] [CrossRef]
  10. Brewer, R., Cale, J., Goldsmith, A., & Holt, T. (2018). Young People, the Internet, and Emerging Pathways into Criminality: A Study of Australian Adolescents. International Journal of Cyber Criminology, 12, 115–132. [Google Scholar] [CrossRef]
  11. Chen, S., Ding, F., Buil-Gil, D., Hao, M., Maystadt, J. F., Fu, J., … & Jiang, D. (2024). The impact of COVID-19 lockdown on fraud in the UK. Humanities and Social Sciences Communications11(1), 1-11. [Google Scholar] [CrossRef]
  12. Chen, S., Hao, M., Ding, F., Jiang, D., Dong, J., Zhang, S., Guo, Q., & Gao, C. (2023). Exploring the global geography of cybercrime and its driving forces. Humanities and Social Sciences Communications, 10(71). [Google Scholar] [CrossRef]
  13. Choi, K.S., Chawki, M., & Basu, S. (2024). Digital shadows: analyzing factors influencing sentencing in child sexual abuse material (CSAM) cases. Journal of Aggression, Conflict and Peace Research, 16(4), 363-381. [Google Scholar] [CrossRef]
  14. Cotrina, L., León, P., Reyes, C., Arbulú Ballesteros, M., Guzmán, M., Castillo, J., Acosta, R., & Morales, A. (2024). Cyber Crimes: A Systematic Review of Evolution, Trends, and Research Approaches.Journal of Educational and Social Research14(5), 96. [CrossRef]
  15. Creese, S., Dutton, W. H. & Esteve-González, P. (2021). The social and cultural shaping of cybersecurity capacity building: a comparative study of nations and regions. Personal and Ubiquitous Computing, 25, 941–955. [Google Scholar] [CrossRef]
  16. Dawson, M., Bacius, R., Gouveia, L. B., & Vassilakos, A. (2021). Understanding the challenge of cybersecurity in critical infrastructure sectors. Land Forces Academy Review, 26(1), 69–75. [Google Scholar] [CrossRef]
  17. De Kimpe, L., Walrave, M., Verdegm, P., & Ponnet, K. (2022). What we think we know about cybersecurity: An investigation of the relationship between perceived knowledge, internet trust, and protection motivation in a cybercrime context. Behaviour & Information Technology, 41(8), 1796–1808. [Google Scholar] [CrossRef]
  18. Dodel, M., & Gustavo M. (2019). An integrated model for assessing cyber-safety behaviors: how cognitive, socioeconomic and digital determinants affect diverse safety practices. Computers & Security, 86, 75–91. [Google Scholar] [CrossRef]
  19. Dodel, M., & Gustavo, M. (2018). Inequality in Digital Skills and the Adoption of Online Safety Behaviors. Information, Communication & Society,21(5), 712–728. [Google Scholar] [CrossRef]
  20. Dodel, M., & Mesch, G. (2018). Inequality in digital skills and the adoption of online safety behaviors. Information, Communication & Society21(5), 712–728. [Google Scholar] [CrossRef]
  21. Donner, C.M., Marcum, C.D., Jennings, W.G., Higgins, G.E., Banfield, J. (2014). Low self-control and cybercrime: Exploring the utility of the general theory of crime beyond digital piracy. Computers in Human Behavior, 34, 165–172. [Google Scholar] [CrossRef]
  22. El Asam, A., & Katz, A. (2018). Vulnerable young people and their experience of online risks. Human–Computer Interaction33, 281–304. [Google Scholar] [CrossRef]
  23. Forssell, R. C. (2020) Gender and organisational position: predicting victimisation of cyberbullying behaviour in working life. The International Journal of Human Resource Managemente, 31, 2045–2064. [Google Scholar] [CrossRef]
  24. Google Trends (2024). [Link]
  25. Havers, B., Tripathi, K., Burton, A., McManus, S., & Cooper, C. (2024). Cybercrime victimisation among older adults: A probability sample survey in England and Wales. PLoS ONE, 19(12), e0314380. [Google Scholar] [CrossRef]
  26. Hoy, M. G., & Milne, G. (2010). Gender differences in privacy-related measures for young adult Facebook users. Journal of Interactive Advertising, 10(2), 28–45. [Google Scholar] [CrossRef]
  27. Hytönen, E., Trent, A., & Ruoslahti, H. (2022). Societal Impacts of Cyber Security in Academic Literature – Systematic Literature Review. Proceedings of the 21st European Conference on Cyber Warfare and Security, 21(1), 86-93. [Google Scholar] [CrossRef]
  28. Ilievski, A., & Bernik, I. (2016). Social-economic aspects of cybercrime. Innovative Issues and Approaches in Social Sciences, 9(3), 8-22. [Google Scholar] [CrossRef]
  29. Isaia, E., Oggero, N., & Sandretto, D. (2024). Is financial literacy a protection tool from online fraud in the digital era? Journal of Behavioral and Experimental Finance, 44, 100977. [Google Scholar] [CrossRef]
  30. John, M. S. (2024). Cybersecurity Stats: Facts And Figures You Should Know. [Link]
  31. Khan, N. F., Ikram, N., & Saleem, S. (2023). Effects of socioeconomic and digital inequalities on cybersecurity in a developing country. Security Journal, 37, 214–244. [Google Scholar] [CrossRef]
  32. Kwon, D., Borrion, H., Wortley, R. (2024). Measuring Cybercrime in Calls for Police Service. Asian Journal of Criminology, 19, 329–351. [Google Scholar] [CrossRef]
  33. Lee, C. S., & Chua, Y. T. (2024). The Role of Cybersecurity Knowledge and Awareness in Cybersecurity Intention and Behavior in the United States. Crime & Delinquency70(9), 2250-2277. [Google Scholar] [CrossRef]
  34. Lee, H., & Lim, H. (2019) Awareness and Perception of Cybercrimes and Cybercriminals. International Journal of Cybersecurity Intelligence & Cybercrime, 2(1), 1-3. [Google Scholar] [CrossRef]
  35. Leukfeldt, E.R., & Yar, M. (2016). Applying Routine Activity Theory to Cybercrime: A Theoretical and Empirical Analysis. Deviant Behavior37, 263–280. [Google Scholar] [CrossRef]
  36. Luo, Q. (2024). Cybercrime as an industry: examining the organisational structure of Chinese cybercrime. Humanities and Social Sciences Communications, 11, 1554. [Google Scholar] [CrossRef]
  37. Lusthaus, J. (2012). Trust in the world of cybercrime. Global Crime, 13(2), 71–94. [Google Scholar] [CrossRef]
  38. Lusthaus, J., Kleemans, E., Leukfeldt, R., & Holt, T. (2024).Cybercriminal networks in the UK and Beyond: Network structure, criminal cooperation and external interactions. Trends in Organized Crime,27, 364–387. [Google Scholar] [CrossRef]
  39. Martineau, M., Spiridon, E., & Aiken, M. (2024). Pathways to Criminal Hacking: Connecting Lived Experiences with Theoretical Explanations. Forensic Sciences4(4), 647-668. [Google Scholar] [CrossRef]
  40. Martineau, M., Spiridon, E., Aiken, M. A (2023). Comprehensive Framework for Cyber Behavioral Analysis Based on a Systematic Review of Cyber Profiling Literature. Forensic Sciences3, 452–477. [Google Scholar] [CrossRef]
  41. Monteith, S., Bauer, M., Alda, M., Geddes, J., Whybrow, P. C., Glenn T. (2021). Increasing Cybercrime Since the Pandemic: Concerns for Psychiatry. Current Psychiatry Reports, 23(18). [Google Scholar] [CrossRef]
  42. Moubarak, H. F. A., & Afthanorhan, A. (2024). Risk assessments of virtual interactions on Saudi families. Humanities and Social Sciences Communications, 11, 281. [Google Scholar] [CrossRef]
  43. Ngo, F.T., & Paternoster, R. (2011). Cybercrime victimization: An examination of individual and situational level factors. International Journal of Cyber Criminology, 5, 773. [Google Scholar]
  44. Nguyen, T., & Luong, H. T. (2021). The structure of cybercrime networks: transnational computer fraud in Vietnam. Journal of Crime and Justice, 44(4), 419–440. [Google Scholar] [CrossRef]
  45. Odinot, G., Verhoeven, M. A., Pool, R. L. D., & De Poot, C. J. (2017). Organised cyber-crime in the Netherlands: empirical findings and implications for law enforcement. WODC, Den Haag. Cahier 2017-1. [Google Scholar]
  46. Padyab, M., Padyab, A., Rostami, A., & Ghazinour, M. (2024). Cybercrime in Nordic countries: a scoping review on demographic, socioeconomic, and technological determinants. SN Social Sciences, 4, [Google Scholar] [CrossRef]
  47. Pandian, T., & Maraimalai, N. (2024). Understanding cybercrime’s impact on women’s physical and psychological well-being. African Journal of Reproductive Health / La Revue Africaine de La Santé Reproductive28(5), 103–112. [Google Scholar] [CrossRef]
  48. Patil, R.Y., Patil, Y.H., Despande, H., & Bannore, A. (2024).Proactive cyber defense through a comprehensive forensic layer for cybercrime attribution. International Journal of Information Technology, 16, 3555–3572. [Google Scholar] [CrossRef]
  49. Phillips, K., Davidson, J. C., Farr, R. R., Burkhardt, C., Caneppele, S., & Aiken, M. P. (2022). Conceptualizing Cybercrime: Definitions, Typologies and Taxonomies. Forensic Sciences2, 379–398. [Google Scholar] [CrossRef]
  50. Rao, S., Verma, A. K., & Bhatia, T. (2021). A review on social spam detection: Challenges, open issues, and future directions. Expert Systems with Applications, 186, 115742. [Google Scholar] [CrossRef]
  51. Ratul, M. H. A., Mollajafari, S., & Wynn, M. (2024). Managing Digital Evidence in Cybercrime: Efforts Towards a Sustainable Blockchain-Based Solution. Sustainability16(24), 10885. [Google Scholar] [CrossRef]
  52. Rey-Ares, L., Fernández-López, S., & Álvarez-Espiño, M. (2024). The role of financial literacy in consumer financial fraud exposure (via email) and victimisation: evidence from Spain. International Journal of Bank Marketing, 42(6), 1388-1413. [Google Scholar] [CrossRef]
  53. Rich, M. S., & Aiken, M.P. (2024). An Interdisciplinary Approach to Enhancing Cyber Threat Prediction Utilizing Forensic Cyberpsychology and Digital Forensics. Forensic Sciences4, 110–151. [Google Scholar] [CrossRef]
  54. Shettar, I., Hadagali, G.S., Kaddipujar, M., Bulla, S.D., Agadi, K., Ganjihal, G.A., Hiremath, R., Dundannanavar, A., & Babu, B.R. (2024). Scientometric analysis of global cyber security research output based on Web of Science. Iberoamerican Journal of Science Measurement and Communication, 4(2), 1-15. [Google Scholar] [CrossRef]
  55. Steinmetz, K. F., Schaefer, B. P., McCarthy, A. L., Brewer, C. G., & Kurtz, D. L. (2024). Exploring Cybercrime Capabilities: Variations Among Cybercrime Investigative Units. Criminal Justice Policy Review35(4), 194-215. [Google Scholar] [CrossRef]
  56. Sulich, A., Rutkowska, M., Krawczyk-Jezierska, A., Jezierski, J., & Zema, T. (2021). Cybersecurity and sustainable development. Procedia Computer Science, 192, 20–28. [Google Scholar] [CrossRef]
  57. Wissink, I. B., Standaert, J.C.A., Stams, G.J.J.M., Asscher, J.J., Assink, M. (2023). Risk factors for juvenile cybercrime: A meta-analytic review. Aggression and Violent Behavior, 70, 101836. [Google Scholar] [CrossRef]
  58. World Bank (2024). World Bank Open Data [Link]
  59. Wright, D., & Kumar, R. (2023). Assessing the socio-economic impacts of cybercrime. Societal Impacts, 1(100013). [Google Scholar][CrossRef]
  60. Wright, M. F., & Wachs, S. (2020). Adolescents’ Cyber Victimization: The Influence of Technologies, Gender, and Gender Stereotype Traits. International Journal of Environmental Research and Public Health17(4), 1293. [Google Scholar] [CrossRef]
  61. Yigzaw, Y., Mekuriaw, A., & Amsalu, T. (2023). Analyzing physical and socio-economic factors for property crime incident in Addis Ababa, Ethiopia. Heliyon, 9(2), e13282. [Google Scholar] [CrossRef]
  62. Zhou, Y., Tiwari, M., Bernot, A., & Lin, K. (2024).Metacrime and Cybercrime: Exploring the Convergence and Divergence in Digital Criminality. Asian Journal of Criminology, 19, 419–439. [Google Scholar] [CrossRef]
  63. Zsila, Á., Urbán, R., Griffiths, M. D., & Gemetrovics, Z. (2019). Gender Differences in the Association Between Cyberbullying Victimization and Perpetration: The Role of Anger Rumination and Traditional Bullying Experiences. International Journal of Mental Health and Addiction, 17, 1252–1267. [Google Scholar] [CrossRef]

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