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This paper investigates staff acceptance of SPOT-Me, a mobile attendance management system implemented at Politeknik Tuanku Syed Sirajuddin (PTSS) in Malaysia. This study primarily examined the effects of performance expectancy, effort expectancy, social influence, and facilitating conditions on system acceptance by employing Unified Theory of Acceptance and Use of Technology (UTAUT). This study utilised a quantitative cross-sectional survey administered to 205 academic and non-academic staff. Due to the non-normal distribution, this study used descriptive statistics, Spearman’s rho correlation, and the Mann–Whitney U test for analysis. Interestingly, the findings indicated that staff acceptance of SPOT-Me is very high. In addition, performance expectancy was associated with the strongest relationship with acceptance. Analogously, effort expectancy, facilitating conditions, and social influence also indicated similar results. Moreover, statistically, no significant difference was found between academic and non-academic staff. These findings highlighted clearly that both groups perceived the system in a broadly similar way. Overall, this study found that mobile attendance systems are acceptable when users consider them useful, easy to use, and supported by the institution. In a nutshell, the findings of this study provide practical evidence from the Malaysian polytechnic context and may be beneficial for similar digital transformation initiatives in public higher education
Keywords:
SPOT-Me, UTAUT, user acceptance, attendance system, public higher education, digital adoption
Cite Article:
"Smart Attendance, Smarter Acceptance: Understanding Staff Adoption of SPOT-Me Through the UTAUT Lens", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.c160-c164, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604292.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator