VTracker: Impact of User Factors On Users Intention to Adopt Dietary Intake Monitoring System with Auto Workout Tracker

Nowadays, Malaysian are so concerned about their body health. In respond to this, this study proposed a conceptual prototype called vTracker to assist its users to have a healthier body. vTracker is a web-based mobile application which helps users to self-monitor their dietary intakes and workout activities in a few simple steps. A research framework has been proposed using the Unified Theory of Acceptance and Use of Technology (UTAUT) model to understand the level of users’ intention to adopt vTracker in Malaysia. Data was collected from 206 respondents in Malaysia using survey method. Based on the result of the analysis, it was found that respondents agree to subscribe to the system. It addition to that, it was found that the three factors have a positive impact on users’ intention to adopt vTracker. These mentioned factors are performance effort expectancy (PEE), social influence (SI) and facilitating condition (FC). These significant factors are used for designing vTracker portal.

💡 Research Summary

This paper addresses the growing problem of overweight and obesity in Malaysia by proposing a conceptual mobile‑web application called vTracker, which integrates dietary intake monitoring with an automatic workout tracker. The authors adopt the Unified Theory of Acceptance and Use of Technology (UTAUT) as a theoretical framework to investigate which user‑related factors influence the intention to adopt vTracker.

The original UTAUT model comprises four core constructs—Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Condition (FC)—and four moderators (gender, age, experience, voluntariness). Because of time constraints, the moderators are omitted, and FC is treated as a direct predictor of behavioral intention rather than of usage behavior. An exploratory factor analysis revealed that PE and EE load onto a single factor, which the authors label Performance‑Effort Expectancy (PEE). Consequently, the final model includes three independent variables (PEE, SI, FC) and one dependent variable (Behavioral Intention, BI).

Data were collected via a questionnaire adapted from prior UTAUT studies, translated into English and Malay, and distributed both in hard‑copy (52 responses) and online (164 responses). After discarding incomplete entries, 206 valid responses remained. Reliability analysis showed high internal consistency for all constructs (Cronbach’s α > 0.84, with SI the highest at 0.909). Descriptive statistics indicated that respondents generally agreed (mean ≈ 5 on a 7‑point Likert scale) that they would adopt vTracker.

Pearson correlation coefficients were positive for all three predictors (PEE r = 0.628, SI r = 0.548, FC r = 0.488) and significant at p < 0.01, supporting hypotheses H1–H4. Simple linear regressions demonstrated that PEE explained 39.4 % of the variance in intention (R² = 0.394), SI explained 30.1 % (R² = 0.301), and FC explained 23.8 % (R² = 0.238). A multiple regression model incorporating all three predictors yielded an overall R² = 0.478, indicating that 47.8 % of the variance in behavioral intention is accounted for by the model. In this model, PEE and SI were statistically significant at the 0.01 level, whereas FC reached significance only at the 0.10 level, suggesting a weaker but still positive influence. All beta coefficients were positive, confirming the directionality of the relationships.

The authors interpret these findings as evidence that the perceived usefulness and ease of use (captured by PEE) and the influence of important others (SI) are the most critical drivers for adopting vTracker. The relatively lower impact of FC is attributed to the prototype stage of the system; the authors recommend that future implementations provide clear technical support, training, and infrastructure to strengthen this factor.

Limitations of the study include the use of convenience sampling, the exclusion of moderator variables, and the reliance on intention rather than actual usage data. The authors suggest that subsequent research should employ structural equation modeling to test moderator effects, collect longitudinal usage logs to examine the intention‑behavior gap, and conduct field trials with a functional prototype.

In conclusion, the study validates the applicability of a modified UTAUT framework for predicting user adoption intentions of a health‑monitoring mobile application in the Malaysian context. Emphasizing performance‑effort expectancy and social influence in the design and promotion of vTracker is likely to enhance acceptance, thereby contributing to better self‑monitoring practices and potentially mitigating the nation’s obesity epidemic.