Smart Home Adoption in Malaysia: Unpacking Generational Differences, Key Drivers, and Barriers

Muniandy, Vikramithan (2026) Smart Home Adoption in Malaysia: Unpacking Generational Differences, Key Drivers, and Barriers. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

The advancement of Malaysia’s digital infrastructure has accelerated the potential for smart home technologies, yet adoption remains uneven across generations. This study investigates generational differences in smart home adoption in urban and suburban Klang Valley, focusing on key drivers and barriers. A quantitative survey was conducted with 141 valid respondents, grouped into three cohorts: Young Adults (18–34), Middle-aged Adults (35–54), and Older Adults (55+). Data were analyzed using descriptive statistics, chi-square tests, and one-way ANOVA with effect sizes and Holm–Bonferroni adjustments. Findings reveal a clear generational gap. Younger adults reported the highest awareness (over 90%) and adoption rates (46.2%), driven by convenience and technological enthusiasm. Middle-aged adults emphasized safety, family practicality, and investment value. Older adults showed the lowest adoption (12%), constrained by complexity, low digital literacy, and limited awareness. Privacy and data security were key concerns for younger cohorts, while older respondents prioritized trust and usability. The alternative hypothesis (H₁) was supported, confirming that younger adults adopt more readily due to greater digital readiness and fewer psychological barriers. This study contributes by integrating the Technology Acceptance Model (TAM) with Generational Theory to offer a nuanced understanding of adoption behaviors. Recommendations include generationally tailored strategies: affordability and privacy for younger users; investment framing for middle-aged families; and onboarding and education for older users. Limitations include the cross-sectional design and Klang Valley sampling. Future research should expand to rural populations and apply longitudinal tracking to assess evolving behaviors