The Factors Influence Intention to Use Parental Control Software Among Malaysian Parents: The UTAUT Model

Authors

  • Nurul Hafizul Mohamed Faculty of Human Development, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
  • Abdul Halim Masnan Faculty of Human Development, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
  • Lu Man Hong Faculty of Entrepreneurship and Business, Universiti Malaysia Kelantan, Kelantan, Malaysia

DOI:

https://doi.org/10.37134/saecj.vol13.2.6.2024

Keywords:

effort expectancy, facilitating control, intention to use parental control software, performance expectancy, social infleunce

Abstract

Parental controls are a method of controlling who has access to smart devices because individuals may regulate who else in their family members, particularly younger ones, sees inappropriate websites. Indeed, parental control software provides the ability to select which applications are permitted on online devices, and it may also be used to see content loaded as well as the purpose of device usage among parents towards their child, particularly for educational purposes. In actuality, Malaysian parents have little desire to employ parental control software to monitor their children's gadget activities, particularly for educational purposes. Hence, the primary aim of this research is to ascertain the factors that impact the intention of Malaysian parents to utilize parental control software. The study employs four key variables: performance expectancy, effort expectancy, social influence, and facilitating control. Employing a quantitative methodology, the investigation gathers 374 responses through a Google Form survey. The data is subsequently analyzed using SPSS 22.0 and SmartPLS 4.0. The results indicate that both social influence and facilitating control significantly influence the intention to use parental control software. These findings hold the potential to inform government agencies and parental control software developers about the crucial role these two factors play in the development of effective parental control software solutions.

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References

Abd Aziz, N. N., Kader, M. A. R. A., & Ab Halim, R. (2021). The impact of Technostress on student satisfaction and performance expectancy. Asian Journal of University Education, 17(4), 538-552.

Al-Rahmi, A. M., Shamsuddin, A., Alturki, U., Aldraiweesh, A., Yusof, F. M., Al-Rahmi, W. M., & Aljeraiwi, A. A. (2021). The influence of information system success and technology acceptance model on social media factors in education. Sustainability, 13(14), 7770.

Alelyani, T., Ghosh, A. K., Moralez, L., Guha, S., & Wisniewski, P. (2019). Examining parent versus child reviews of parental control apps on Google Play. Paper presented at the Social Computing and social media. Communication and Social Communities: 11th International Conference, SCSM 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26-31, 2019, Proceedings, Part II 21.

Altarturi, H. H., Saadoon, M., & Anuar, N. B. (2020). Cyber parental control: A bibliometric study. Children and Youth Services Review, 116, 105134.

Berdik, D., Otoum, S., Schmidt, N., Porter, D., & Jararweh, Y. (2021). A survey on blockchain for information systems management and security. Information Processing & Management, 58(1), 102397.

Chauhan, S., Jaiswal, M., & Kar, A. K. (2018). The acceptance of electronic voting machines in India: a UTAUT approach. Electronic Government, an International Journal, 14(3), 255-275.

Chauhan, S., Kumar, P., & Jaiswal, M. (2022). A meta-analysis of M-commerce continuance intention: moderating impact of culture and user types. Behaviour & Information Technology, 41(13), 2905-2923.

Commission, M. C. a. M. (2020). Internet users survey 2020. Retrieved from https://www.mcmc.gov.my/skmmgovmy/media/General/pdf/IUS-2020-Report.pdf

Doggui, R., Gallant, F., & Bélanger, M. (2021). Parental control and support for physical activity predict adolescents’ moderate to vigorous physical activity over five years. International Journal of Behavioral Nutrition and Physical Activity, 18, 1-10.

Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM): Sage publications.

Henseler, J., & Chin, W. W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural equation modeling, 17(1), 82-109.

Martins, M. V., Formiga, A., Santos, C., Sousa, D., Resende, C., Campos, R., . . . Ferreira, S. (2020). Adolescent internet addiction–role of parental control and adolescent behaviours. International Journal of Pediatrics and Adolescent Medicine, 7(3), 116-120.

Rachmawati, I. K., Bukhori, M., Majidah, Y., & Hidayatullah, S. (2020). Analysis of use of mobile banking with acceptance and use of technology (UTAUT). International Journal of Scientific and Technology Research, 9(8), 534-540.

Rahi, S., Ghani, M., Alnaser, F., & Ngah, A. (2018). Investigating the role of unified theory of acceptance and use of technology (UTAUT) in internet banking adoption context. Management Science Letters, 8(3), 173-186.

Ramírez-Correa, P., Grandón, E. E., Ramírez-Santana, M., Arenas-Gaitán, J., & Rondán-Cataluña, F. J. (2023). Explaining the Consumption Technology Acceptance in the Elderly Post-Pandemic: Effort Expectancy Does Not Matter. Behavioral Sciences, 13(2), 87.

Sahak, S. Z., Fauzi, M. F. M., Darus, F. M., & Muhammad, U. (2019). Assessing the Impact of Website Design on Purchase Intent: A Case Study on Go Shop. International Journal of Academic Research in Business and Social Sciences, 9(12).

Sang, G., Wang, K., Li, S., Xi, J., & Yang, D. (2023). Effort expectancy mediate the relationship between instructors’ digital competence and their work engagement: evidence from universities in China. Educational technology research and development, 1-17.

Tseng, T. H., Lin, S., Wang, Y.-S., & Liu, H.-X. (2022). Investigating teachers’ adoption of MOOCs: the perspective of UTAUT2. Interactive Learning Environments, 30(4), 635-650.

Tusyanah, T., Wahyudin, A., & Khafid, M. (2021). Analyzing factors affecting the behavioral intention to use e-wallet with the UTAUT model with experience as moderating variable. Journal of Economic Education, 10(1), 113-123.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.

Wut, E., Ng, P., Leung, K. S. W., & Lee, D. (2021). Do gamified elements affect young people’s use behaviour on consumption-related mobile applications? Young Consumers.

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Published

2024-10-31

How to Cite

Mohamed, N. H., Masnan, A. H., & Lu, M. H. (2024). The Factors Influence Intention to Use Parental Control Software Among Malaysian Parents: The UTAUT Model. Southeast Asia Early Childhood Journal, 13(2), 88–100. https://doi.org/10.37134/saecj.vol13.2.6.2024

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