Influence of Protection Motivation Theory on Information Security Practices: The Case of Ghanaian Mobile Banking Merchants

Authors

  • Paul Danquah, Ph.D Author
  • Henry Akwetey Matey Author
  • Kenneth Asiamah Author

Keywords:

Information Security Practice, Protection Motivation Theory (PMT), Mobile Banking, Threat and Coping Appraisal, Mobile Money

Abstract

The mobile banking industry has grown to become Ghana's most popular digital financial service (DFS). Since its inception in 2018, the first interoperable system in Africa has allowed transactions between Ghana's various telecommunication companies and banks. Today, the mobile banking and financial services sectors have seen immense turmoil, including cybercrime on mobile banking transactions. It generally involves the manipulation of customers' accounts without authorization, prompts sent under the pretext of a telco promotion, fake SMS sent to indicate a deposit into a customer's account, and fraudsters posing as delivery companies instructing customers to deposit to a mobile banking account in exchange for delivering goods. This study premises to determine how applicable Protection Motivation Theory (PMT) is to explaining mobile banking-related cybercrime. Therefore, this work seeks to analyze mobile banking merchants' attitudes and intentions to secure transaction-related data in light of mobile banking services in Ghana. Finally, this study empirically tests the theoretical model with a data set representing the survey and the responses of 410 mobile banking merchants in the greater Accra region of Ghana. Partial least squares structural equation models (PLS-SEM) are used in analyzing the data since the method facilitates assessing patterns of causation of target constructs in the proposed model. The study results suggest that the perceived probability of Vulnerability was not a significant predictor of Intention to secure information or Attitude toward securing information. The perceived severity of the threat also has a positive relationship with the Attitude toward securing information but is not significant. Perceived self-efficacy has a negative relationship with the Intention to secure information but is not significant. The most vital relationship emerged where the perceived severity of the threat significantly predicted the Intention to secure information. Perceived response efficacy significantly predicted Intention to secure information and positively predicted Attitude toward securing information. Finally, the results also indicated that perceived self-efficacy significantly impacted attitudes toward securing information.

Author Biographies

  • Paul Danquah, Ph.D

    Dr. Paul Asante Danquah is an IT professional and researcher with over 20 years’ experience. He holds a BSc HONS in Computing, MSc in Information Security and a PhD in Information Technology (IT) from the University of Greenwich UK, Anglia Ruskin University UK and Open University of Malaysia respectively. He has various industry certifications, some of which are ISO 27001 Lead Implementer, Certified Ethical Hacker(CEH), Certified Security Operations Center Analyst (CSA), Data Center Infrastructure Expert(DCIE), Cisco Certified Network Professional (CCNP), Microsoft Certified Systems Engineer (MCSE) and Certified EC-Council Instructor (CEI). Dr. Danquah has worked in various capacities over the years, these range from Programmer, Network Engineer, IT Manager, Deputy Director of IT, Lecturer and Research Scientist at various prominent technology companies and Universities in Ghana. Some of these are namely Soft Company Limited (now Soft Tribe), Africa Online Ghana Limited, Net Africa Ghana Limited, Ghana Institute of Management & Public Administration (GIMPA), University of Professional Studies Accra (UPSA), the Council for Scietific and Industrial Research (CSIR) and Heritage Christian University College. He has managed several projects and provided numerous technical solutions to over fifty organizations across multiple countries such as Liberia, UK, Gambia and Ghana, he brings an invaluable experience to the table. He has performed vulnerability assessments and penetration test for over 30 organizations in various sectors ranging from government and banks to fintech and utility companies. Dr. Danquah has over 30 published articles in internationally refereed journals and over 15 published conference proceedings. He also has two books cyber security.

  • Henry Akwetey Matey

    Mr. Henry Akwetey Matey is a Lecturer at the University of Professional Studies in Accra. He holds a Master of Science degree in Information Technology, and the majority of his research interests are focused on the topic of information security practices. In the field of research and education, he has around ten years of experience.

  • Kenneth Asiamah

    Mr. Kenneth Asiamah is an ICT professional with an MSc and BSc in Information and Communication Technology Management. He's an Assistant Research Scientist at CSIR – INSTI, focusing on cutting-edge research and the development of innovative tools and systems. Kenneth's technical expertise extends to web development, computer systems administration, and fundamentals of data security. He's committed to industry knowledge, earning the Google IT Support Professional Certificate in 2023.

References

Abebe, F. (2020). DigitalCommons @ Kennesaw State University Factors Affecting Mobile Payment Adoption by Merchants in Ethiopia. 0–11.

Altmann, J. (1974). Observational study of behavior: sampling methods. Behaviour, 49(3–4), 227–266.

Anderson, C. L., & Agarwal, R. (2010). Practicing safe computing: A multimedia empirical

examination of home computer user security behavioral intentions. MIS Quarterly, 34(3),

–643.

Asani, E.O., Omotosho, A., Danquah, P.A., Ayegba, P.O. & Longe, O.B. (2021), A maximum entropy classification scheme for phishing detection using parsimonious features, Telkomnika (Telecommunication Computing Electronics and Control)this link is disabled, 19(5), pp. 1707–1714

Aspers, P., & Corte, U. (2019). What is Qualitative in Qualitative Research. 1, 139–160.

Barclay, D., Higgins, C., Thompson, R. (1995). The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration. Technology Studies, 2(2), 285.

Bhatt, V., Hiteshi Ajmera, A., & Nayak, K. (2021). An Empirical Study On Analyzing A User’s Intention Towards Using Mobile Wallets; Measuring The Mediating Effect Of Perceived Attitude And Perceived Trust. Turkish Journal of Computer and Mathematics Education, 12(10), 5332–5353.

Boerman, S. C., Kruikemeier, S., & Borgesius, F. J. Z. (2021). Exploring Motivations for Online Privacy Protection Behavior : Insights From Panel Data. https://doi.org/10.1177/0093650218800915

Brook, C. (2017), "Mobile Banking Trojan BankBot Identified, Removed From Google Play". Digital Guardian. Retrieved 3 October 2018.

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In Modern Methods for Business Research (Vol. 295, pp. 295–336). https://doi.org/10.1016/j.aap.2008.12.010

Crossler, R. E. (2009). Protection Motivation Theory : Understanding the Determinants of Individual Security Behavior Protection Motivation Theory : Understanding the Determinants of Individual Security Behavior.

Danquah, P. (2020), Security Operations Center: A Framework for Automated Triage, Containment and Escalation, Journal of Information Security Vol.11 No.4, DOI: 10.4236/jis.2020.114015

Habibi, A., Yusop, F.D., Razak, R. . (2020). The role of TPACK in affecting pre-service language teachers’ ICT Integration during teaching practices. Indonesian Context. Educ Inf. Technol, 25(3), 1929–1949.

Hair, J. F., Risher, J. J., Sarstedt, M., Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.

Hair, J F, Hult, G. T. M., Ringle, C., Sarstedt, M., Danks, N., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. In Springer.

Hair, Joseph F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202

Hair, Joseph F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hair, Joseph F, Risher, J. J., & Ringle, C. M. (2018). When to use and how to report the results of PLS-SEM. 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Heny Sidanti, Dian Citaningtyas Ari Kadi, Hari Purwanto, & Wahyu Sri Lestari. (2022). The Effect Of Easy Perception And Security Perception On The Intention Of Using Shopeepay Through Attitude As Intervening Variables In Madiun. International Journal of Science, Technology & Management, 3(1), 215–228. https://doi.org/10.46729/ijstm.v3i1.430

Herath, T., & Rao, H. R. (2009). Protection motivation and deterrence : a framework for security policy compliance in organisations. February, 106–125. https://doi.org/10.1057/ejis.2009.6

Huang, R., Wang, Z., Yuan, T., Nadarzynski, T., Qian, H., Li, P., Meng, X., Wang, G., Zhou, Y., Luo, D., Wang, Y., Cai, Y., & Zou, H. (2021). Using protection motivation theory to explain the intention to initiate human papillomavirus vaccination among men who have sex with men in China. Tumour Virus Research, 12, 200222. https://doi.org/10.1016/j.tvr.2021.200222

Ifinedo, P. (2011). Understanding information systems security policy compliance : An integration of the theory of planned behavior and the protection motivation theory. Computers & Security, 31(1), 83–95. https://doi.org/10.1016/j.cose.2011.10.007

Kester, A., Ong, S., Tri, Y., Ma, J., Salazar, L. D., Jacob, J., Erfe, C., Abella, A. A., Nayat, M., Chuenyindee, T., Nadlifatin, R., Agung, A., & Perwira, N. (2022). Investigating the acceptance of the reopening Bataan nuclear power plant : Integrating protection motivation theory and extended theory of planned behavior. Nuclear Engineering and Technology, 54(3), 1115–1125. https://doi.org/10.1016/j.net.2021.08.032

Kester, Q.A. & Danquah P.,(2012),A novel cryptographic key technique, Adaptive Science & Technology (ICAST), 2012 IEEE 4th International Conference, Pages 70 – 73

Kimpe, L. De, Walrave, M., Verdegem, P., & Ponnet, K. (2021). 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, 0(0), 1–13. https://doi.org/10.1080/0144929X.2021.1905066

Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential method. Information Systems Journal, 28(1), 227–261.

Lee, Y. Y., Gan, C. L., & Liew, T. W. (2023). Thwarting Instant Messaging Phishing Attacks : The Role of Self ‑ Efficacy and the Mediating Effect of Attitude towards Online Sharing of Personal Information.

Li, L., Xu, L., & He, W. (2022). Computers in Human Behavior Reports The effects of antecedents and mediating factors on cybersecurity protection behavior. 5(October 2021). https://doi.org/10.1016/j.chbr.2021.100165

Longe O.B.,Danquah P. & Ebem D.U. (2012), De-Individuation, Anonymity and Unethical Behaviour in Cyberspace – Explorations in the Valley of Digital Temptations, Computing Information Systems Journal, Vol. 16, Issue 1, pp.46-55

Luo, Y., Guiping Wang, Y. L., & Ye, Q. (2021). Examining Protection Motivation and Network Externality Perspective Regarding the Continued Intention to Use.

Lynn, M. R. (1986). Determination and quantification of content validity. Nurs. Res., 35(6), 382–385.

Matey, A.H., Danquah, P. & Koi-Akrofi, G.Y. (2022), Predicting Cyber-Attack using Cyber Situational

Awareness: The Case of Independent Power Producers (IPPs), International Journal of Advanced Computer Science and Applicationsthis link is disabled, 13(1), pp. 700–709

Martens, M., Wolf, R. De, & Marez, L. De. (2019). Computers in Human Behavior Investigating and comparing the predictors of the intention towards taking security measures against malware , scams and cybercrime in general. Computers in Human Behavior, 92(November 2018), 139–150. https://doi.org/10.1016/j.chb.2018.11.002

Mills, A. M., & Sahi, N. (2019). An empirical study of home user intentions towards computer security. Proceedings of the Annual Hawaii International Conference on System Sciences, 2019-January, 4834–4840. https://doi.org/10.24251/hicss.2019.583

Mohamed, N., & Ahmad, I. H. (2012). Information privacy concerns, antecedents and

privacy measure use in social networking sites: Evidence from Malaysia. Computers in

Human Behavior, 28(6), 2366–2375.

MTN Mobile Money Commission in Ghana (2023), ICT Catalogue, https://ictcatalogue.com/mtn-mobile-money-merchant-commission/, Retrieved 7 July 2023.

Muhaimin, M., Asrial, A., Habibi, A., Mukminin, A., Hadisaputra, P. (2020). Science teachers’ integration of digital resources in education: a survey in rural areas of one Indonesian province. Heliyon, 6(8), 04631.

Mumtaz A. H. & Arachchilage, N. A. G. (2019). On the Impact of Perceived Vulnerability in the Adoption of Information Systems Security Innovations. International Journal of Computer Network and Information Security, 11(4), 9–18. https://doi.org/10.5815/ijcnis.2019.04.02

Mwagwabi, F. M. (2015). A Protection Motivation Theory Approach to Improving Compliance with Password Guidelines.

Ooijen, I. Van, Segijn, C. M., & Opree, S. J. (2022). Privacy Cynicism and its Role in Privacy Decision-Making. https://doi.org/10.1177/00936502211060984

Ophoff, J., & Lakay, M. (2019). Mitigating the Ransomware Threat: A Protection Motivation Theory Approach. In Communications in Computer and Information Science (Vol. 973). Springer International Publishing. https://doi.org/10.1007/978-3-030-11407-7_12

Preacher, K.J.; Hayes, A. . (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behav. Res. Methods Instrum. Comput. [CrossRef] [PubMed], 36, 717–731.

Rajab, M., & Eydgahi, A. (2019). Evaluating the explanatory power of theoretical frameworks on intention to comply with information security policies in higher education. Computers and Security, 80, 211–223. https://doi.org/10.1016/j.cose.2018.09.016

Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. Ournal of Psychology, 91(1), 93–114. https://doi.org/doi:10.1080/00223980.1975.9915803. PMID 28136248.

Sharma, S., & Aparicio, E. (2022). Computers & Security Organizational and team culture as antecedents of protection motivation among IT employees. 120. https://doi.org/10.1016/j.cose.2022.102774

Shmueli, G., Hair, J. F., Ting, H., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM : guidelines for using PLSpredict. https://doi.org/10.1108/EJM-02-2019-0189

Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189

Syed, A. A. B., Hashim, F., & Amran, A. (2019). Determinants of Green Banking Adoption: A Theoretical Framework. KnE Social Sciences, 2019, 1–14. https://doi.org/10.18502/kss.v3i22.5041

Tang, Z., Miller, A. S., Zhou, Z., & Warkentin, M. (2021). Does government social media promote users ’ information security behavior towards COVID-19 scams ? Cultivation effects and protective motivations. Government Information Quarterly, 38(2), 101572. https://doi.org/10.1016/j.giq.2021.101572

Voica, C., Singer, F. M., & Stan, E. (2020). How are motivation and self-efficacy interacting in problem-solving and problem-posing ? 487–517

Workman, M., Bommer, W. H., & Straub, D. (2008). Security lapses and the omission of information security measures: A threat control model and empirical test. Computers in Human Behavior, 24(6), 2799–2816.

Wu, D. (2020). Computers in Human Behavior Empirical study of knowledge withholding in cyberspace : Integrating protection motivation theory and theory of reasoned behavior. Computers in Human Behavior, 105(December 2019), 106229. https://doi.org/10.1016/j.chb.2019.106229

Xiao, H., Peng, M., Yan, H., Gao, M., Li, J., Yu, B., Wu, H., & Li, S. (2018). An instrument based on protection motivation theory to predict Chinese adolescents ’ intention to engage in protective behaviors against schistosomiasis. Global Health Research and Policy, November. https://doi.org/10.1186/s41256-016-0015-6

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Published

06/13/2024

How to Cite

Danquah, P., Matey, H., & Asiamah, K. . (2024). Influence of Protection Motivation Theory on Information Security Practices: The Case of Ghanaian Mobile Banking Merchants. Journal of Applied Science and Information Technology, 1(1). https://jasit.csirgh.com/index.php/journal/article/view/2