Predicting College Students’ Squared Platform Utilization: A Multiple Regression Analysis of the Technology Acceptance Model

Predicting College Students’ Squared Platform Utilization: A Multiple Regression Analysis of the Technology Acceptance Model

Justine G. Landicho, Roberto Jr. P. Tacbobo, Alkhaser V. Sappayani

Davao Central College, Toril, Davao City, Philippines

DOI: https://dx.doi.org/10.47772/IJRISS.2025.908000617

Received: 21 August 2025; Accepted: 29 August 2025; Published: 24 September 2025

ABSTRACT

This study explored the factors that influence college students’ use of the Squared Platform, a QR code-based attendance system created by the College Supreme Student Government of Davao Central College in Davao City. The research was anchored on the Technology Acceptance Model (TAM), which focuses on how perceived ease of use, perceived usefulness, and behavioral intention affect actual system use. Using a predictive-correlational design, data were collected from a stratified sample of 368 enrolled students from five different departments through a validated and reliable questionnaires. The responses were analyzed using frequency, percentage, mean, ANOVA, Pearson correlation, and multiple regression analysis. Findings revealed that students had a very high level of acceptance toward the platform across all TAM constructs. No significant differences were found in terms of year level, but there were significant differences across departments, especially in perceived usefulness. Strong positive relationships were also found between each TAM variable and students’ actual usage behavior, with behavioral intention having the strongest influence. The combined predictors significantly explained more than half of the variance in actual usage. The results support the Technology Acceptance Model, proving that ease of use and usefulness influence students’ intention and actual behavior in using educational technologies like the Squared Platform. Based on the findings, it is recommended that the school promote training, raise awareness, and offer department-specific support to improve technology adoption and ensure effective implementation across all programs.

Keywords: Educational technology, QR code attendance system, Squared Platform, Technology Acceptance Model

INTRODUCTION

In an age where digitization rapidly redefines traditional processes, educational institutions globally are embracing QR code technologies to optimize attendance monitoring. Yet, challenges in student engagement and system integration persist, especially in environments transitioning from manual methods. The “Squared” platform, a project spearheaded by the College Supreme Student Government in Toril, Davao City, introduces a novel approach: students receive a personalized QR code after registration, which is scanned during school activities to digitally log attendance. Despite its promise, success depends not only on system functionality but also on student perception—specifically, their perceived ease of use, usefulness, and intention to engage. This study investigates how these perceptions shape actual behavior among college students using the Squared platform. Globally, the integration of QR code systems into educational settings has sparked growing academic interest, particularly through frameworks like TAM and UTAUT. In Thailand, Rukhiran et al. (2023) employed TAM to evaluate biometric attendance tools in higher education, identifying perceived ease of use and usefulness as pivotal to student adoption. Similarly, Behera and Kumra (2024) emphasized the importance of intention-to-use across mobile-based systems, demonstrating that a lack of intention predicts non-adoption. In Saudi Arabia, Soormo et al. (2024) applied UTAUT to QR technology adoption, concluding that facilitating conditions significantly moderate use behavior. In Indonesia, Budiarto et al. (2024) illustrated how system complexity and lack of training lead to underutilization, even when infrastructure is present. Meanwhile, Rukhiran et al. (2022) expanded UTAUT to include institutional trust, which notably affects QR-based systems in smart schools. Collectively, these studies affirm that without adequate institutional support and user awareness, adoption rates of digital tools like QR systems suffer, compromising the goal of administrative efficiency. At Davao Central College, specifically in Toril, Davao City, the Squared platform is utilized by college students during school activities organized by both the institution and the student body government. While the system has streamlined attendance processes and reduced manual workload, several implementation challenges persist. Some students still lack access to their personal Squared QR codes despite the system being in place for over a year, reflecting a delayed or incomplete rollout. Engagement remains inconsistent, with not all students actively using the platform during events. Common barriers include a lack of awareness on how to generate and use the QR code, perceived irrelevance of the system, resistance to abandoning pen-and-paper methods, and limited motivation due to unclear benefits. These challenges highlight the need for improved training, awareness campaigns, and system optimization to ensure broader and more effective adoption. Despite existing research on QR-based attendance, several gaps remain. Global and national studies have largely focused on feasibility and acceptability, but fewer have examined the combined influence of ease of use, usefulness, intention, and actual attendance behavior in a unified framework, particularly within student-led platforms like Squared. To date, no study has applied TAM or UTAUT to QR attendance in the Philippine college or higher education institutions context, especially within student government–initiated systems. This study addresses these omissions by integrating perceived ease of use, usefulness, intentions, and attendance behavior into a comprehensive model, thereby contributing both theoretical insights and actionable recommendations for Squared and similar QR platforms.

This study is anchored on the Technology Acceptance Model (TAM), originally developed by Fred Davis (1986). TAM posits that two primary factors—Perceived Ease of Use and Perceived Usefulness—influence a user’s attitude toward using a technology, which in turn affects their behavioral intention to use and actual system usage. In the context of this research, TAM provides a relevant lens to examine how students at Davao Central College adopt and interact with the Squared Platform, a QR code–based attendance system. The model is particularly appropriate as it emphasizes the psychological and behavioral determinants of technology adoption in educational settings. For data analysis, TAM will guide the categorization of responses based on its core constructs, allowing the researcher to assess the relationships among ease of use, perceived value, intention, and actual usage behavior. This will help identify specific areas of resistance or engagement that influence student participation in the digital attendance system. This study examined the perceptions and behaviors of college students at Davao Central College in their utilization of the Squared Platform, a QR code–based attendance system initiated by the College Supreme Student Government. It specifically targeted enrolled students who actively participated in institutional and student-organized events during the academic year 2024–2025. The study excluded students who were not registered on the Squared Platform or who did not attend any recognized school activities. The investigation focused on four key variables: perceived ease of use, perceived usefulness, intention to use, and actual usage behavior. Data collection was conducted in June 2025. The research aimed to assess how student attitudes, technical awareness, and institutional factors influenced technology adoption, offering practical insights for optimizing digital attendance systems in higher education. The research acknowledged several methodological constraints that may influence its findings. One major limitation is the reliance on a sample composed only of enrolled students who actively participated in events using the Squared Platform, which may not represent the perspectives of non-users or irregular attendees. Additionally, the study excluded students who were not registered in the system, potentially overlooking broader barriers to initial adoption. Potential biases include self-selection bias, as respondents who chose to participate may have stronger opinions about the platform, and response bias, where participants might provide favorable answers based on perceived expectations. Limitations in data collection may also arise from time constraints and limited access to institutional usage data, which could restrict the depth of behavioral insights and influence the comprehensiveness of conclusions drawn from the study.

METHOD

This study employed a quantitative descriptive-correlational research design to examine how perceived ease of use, perceived usefulness, behavioral intention, and actual utilization of the Squared Platform among college students. The design enabled the description of existing conditions and analysis of relationships among variables without manipulating the environment (Creswell, 2014). The study was conducted at Davao Central College (DCC) in Toril, Davao City, Philippines—a private higher education institution known for its innovation and student-centered programs. DCC’s use of the Squared Platform, a QR-based attendance system for student activities, made it a suitable setting to assess technology adoption in a real academic context. The population included enrolled students from five academic departments for the academic year 2024–2025 who had used the Squared Platform, with a sample size of 367 determined using Slovin’s formula at a 95% confidence level and 5% margin of error (Tejada & Punzalan, 2012). Stratified random sampling ensured proportional representation per department and year level. The primary instrument used was a researcher-made questionnaire based on the Technology Acceptance Model (TAM), adapted from Davis (1989) and Venkatesh and Davis (2000), and validated through expert review. It featured 21 Likert-scale items across four TAM variables, using a 4-point scale from Strongly Disagree (1) to Strongly Agree (4), with a Cronbach’s Alpha of 0.938 confirming its reliability (Nunnally, 1978). Data collection involved securing institutional approval, conducting a pilot test, and distributing the questionnaire both online and in print while strictly adhered to ethical research standards to protect the rights, privacy, and well-being of the participants under the Data Privacy Act of 2012. Statistical tools used in the analysis included mean, frequency, percentage, multiple regression, Pearson r, and ANOVA. The mean summarized perceptions of TAM variables (Hurley & Tenny, 2023), frequency and percentage described distributions (Turney, 2023; Percentage | Definition, 2025), multiple regression identified key predictors (Fein et al., 2022), Pearson r measured correlations (Turney, 2024), and ANOVA tested differences between group means—ensuring a comprehensive, reliable interpretation of student engagement with the Squared Platform.

Table 1. Demographic Profile

Shown in Table 1 is the demographic profile; the distribution of participants across different academic departments reveals varying levels of representation within the study population. The department with the lowest mean participation is Information Technology Education, comprising 23 respondents, which represents 6.3 percent of the total sample. Hospitality Management Education follows as the second smallest group with 54 participants, accounting for 14.7 percent of the overall distribution. The College of Business Administration constitutes the middle-range representation with 89 respondents, making up 24.2 percent of the total participants. Moving toward higher representation, the College of Education and Liberal Arts includes 99 participants, representing 27.0 percent of the sample population. The College of Justice Education demonstrates the highest level of participation with 102 respondents, comprising 27.8 percent of the total study participants. The overall sample encompasses 368 individuals across all five departments, providing a comprehensive representation of the academic community under investigation.

Table 2. Summary Level of Total Acceptance Model

As shown in Table 2, the summary level of the Total Acceptance Model, the mean scores for all indicators, demonstrates consistently high levels of acceptance across the measured constructs. The lowest mean score is observed in Students’ Actual Behavior (UB) with a value of 3.60, which still receives a very high interpretation according to the assessment criteria. Perceived Ease of Use (PEU) follows closely with a mean score of 3.64, also categorized as very high in terms of user perception. Behavioral Intention (BI) registers a slightly higher mean of 3.66, maintaining the very high interpretation level and indicating strong user commitment toward adoption. The highest individual mean score belongs to Perceived Usefulness (PU) at 3.71, reflecting the strongest positive perception among all measured indicators and receiving a very high interpretation. The overall composite mean across all four indicators reaches 3.65, which consolidates into a very high overall acceptance level, demonstrating that the technology acceptance model components collectively indicate very favorable user attitudes and behavioral patterns toward the system or technology being evaluated.

In integrating these high acceptance levels with established literature, the significant mean for Perceived Usefulness on the Squared Platform (PU = 3.71) and its leading position strongly align with Davis’s foundational Technology Acceptance Model (TAM) assertion that PU most directly influences intention and actual use. This reinforces the notion that perceived performance benefits are key drivers for adoption (Granić, 2023; Syahruddin et al., 2021; & Zhou et al., 2022). Similarly, the close values for Perceived Ease of Use (PEU = 3.64) and Behavioral Intention (BI = 3.66) align with contemporary findings, particularly in higher education contexts that utilize digital reading tools. In these contexts, PEU significantly predicts both PU and BI (Lin & Yu, 2023). Furthermore, meta-analyses and recent empirical studies consistently highlight PEU, PU, self-efficacy, and collaborative learning as robust antecedents of technology acceptance among students (Lin & Yu, 2023).

These findings confirm and extend existing knowledge: while Davis’s original TAM emphasizes PU and PEU as primary cognitive beliefs, our results reinforce that in educational technology contexts like the Squared Platform, these beliefs remain paramount. Additionally, self-efficacy and perceived collaborative advantages may function as reinforcing external variables. The very high composite mean (3.65) not only suggests theoretical consistency but also indicates practical readiness; educators and policymakers can leverage this positive disposition by improving system usability and emphasizing academic benefits. Future initiatives should concentrate on fostering sustained engagement and support structures to transform intention into ongoing usage, ultimately enhancing both pedagogical effectiveness and user satisfaction.

Table 3. Significance of Difference on the level of Perceived Ease of Use, Perceived Usefulness, Behavioral Intention, and Students’ Actual Behavior in terms of year level

As shown in Table 3, the significance of difference on the level of perceived ease of use, perceived usefulness, behavioral intention, and students’ actual behavior in terms of year level, the F-values and corresponding p-values indicate varying degrees of statistical relationships across the measured constructs. The lowest F-value is observed in Perceived Usefulness (PU) at 0.635, accompanied by a p-value of 0.593, suggesting no statistically significant difference among year levels for this particular indicator. Students’ Actual Behavior (UB) demonstrates the second lowest F-value of 0.906 with a corresponding p-value of 0.438, similarly indicating non-significant differences across year levels. Behavioral Intention (BI) registers a moderate F-value of 0.968 paired with a p-value of 0.408, continuing the pattern of non-significant statistical differences among different year levels. The highest F-value belongs to Perceive Ease of Use (PEU) at 1.565, with an associated p-value of 0.198, which, while representing the strongest statistical relationship among the four indicators, still falls above the conventional significance threshold of 0.05, indicating no statistically significant differences exist between year levels for any of the technology acceptance model components examined.

Integrating the non-significant differences observed across various academic year levels with contemporary research on the Technology Acceptance Model (TAM) reveals several key insights. Firstly, the uniformity noted in Perceived Ease of Use (PEU), Perceived Usefulness (PU), Behavioral Intention (BI), and Usage Behavior (UB) suggests that students’ perceptions and intentions regarding the Squared Platform do not vary significantly by college year level. This finding aligns with similar research in adult and higher education contexts. For example, emerging adult learners in China exhibited consistent positive associations between PU, PEU, and learning effectiveness, regardless of demographic factors (Wu et al., 2023). Similarly, studies among Bangladeshi learners indicated that PEU influences PU and perceived system worth uniformly across different student groups (Mannan & Maruf, 2024). Furthermore, meta-analytic reviews reaffirm that core constructs of TAM, particularly enjoyment, self-efficacy, PEU, and PU, are robust predictors of BI and UB across diverse student populations (Porkodi & Tabash, 2024).

Our findings not only confirm these insights but also extend them: they affirm the generalizability of TAM across various year levels, suggesting that external variables such as collaborative ease and self-efficacy may exert a consistent influence, regardless of grade. Practically, this indicates that institutional strategies aimed at enhancing system usability and utility can be applied broadly, without the need for adjustments based on college year level. Moving forward, educational institutions should continue to promote digital literacy, underscore perceived benefits, and maintain a user-friendly environment to sustain motivation for adopting the Squared Platform. Future research could investigate whether factors such as prior experience or course type subtly moderate acceptance.

Table 4. Significance of Difference on the level of Perceived Ease of Use, Perceived Usefulness, Behavioral Intention, and Students’ Actual Behavior in terms of Departmental Affiliation

As shown in Table 4, the significance of difference on the level of perceived ease of use, perceived usefulness, behavioral intention, and students’ actual behavior in terms of departmental affiliation, the F-values and corresponding p-values reveal statistically significant differences across all measured constructs when examined by departmental groups. The lowest F-value is observed in Students’ Actual Behavior (UB) at 3.217, accompanied by a p-value of 0.013, indicating a statistically significant difference among departments at the conventional alpha level of 0.05. Behavioral Intention (BI) demonstrates a higher F-value of 3.800 with a corresponding p-value of 0.005, showing stronger statistical significance and suggesting notable departmental variations in behavioral intentions toward technology adoption. Perceived Ease of Use (PEU) registers a substantial F-value of 4.261 paired with a p-value of 0.002, indicating highly significant differences across departmental affiliations in terms of perceived ease of technology use. The highest F-value belongs to Perceived Usefulness (PU) at 7.034, with an associated p-value of 0.000, representing the strongest statistical relationship and most significant departmental differences among all four technology acceptance model indicators, suggesting that perceptions of usefulness vary considerably across different academic departments within the study population.

Building on these departmental variations, the Technology Acceptance Model (TAM) serves as a solid foundation for understanding the reasons behind these differences. According to Davis’ original theory, perceived usefulness (PU) and perceived ease of use (PEU) are the key predictors of users’ attitudes and behavioral intentions toward technology (Davis, 1989). Recent meta-analyses support this assertion, indicating that PU remains the primary determinant of adoption in contexts such as virtual reality (VR) and ChatGPT, while PEU continues to influence attitudes and indirect intentions through mediators like self-efficacy and enjoyment (Wong et al., 2023; Park & Park, 2020; Wei et al., 2025).

In the realm of higher education, extended TAM models reveal that departmental backgrounds or academic majors significantly impact both PEU and PU—STEM students typically report higher computer self-efficacy and stronger intentions compared to their non-STEM counterparts (Thongsri et al., 2019). Likewise, research on metaverse adoption in education indicates that when addressing various academic programs, PU, personal innovativeness, and perceived enjoyment exhibit greater variance by discipline than PEU (Al-Adwan et al., 2023).

The synthesis of these findings with our data implies that departments with more technology-intensive curricula perceive a higher level of usefulness (reflected in the strong F-value for PU) towards the Squared Platform. Conversely, smaller but significant differences in behavioral intention (BI), PEU, and actual behavior suggest variations in user comfort and familiarity across departments. The implications are clear: to enhance technology adoption, universities must customize interventions to meet departmental needs. This includes improving discipline-specific applications to boost PU while also providing targeted training, peer mentoring, and infrastructure support for the Squared Platform to elevate PEU and self-efficacy. Such strategies hold the potential to promote more equitable behavioral intentions and actual usage across all academic departments.

Table 5. Significance of the Relationship Between Perceived Ease of Use and Students’ Actual Behavior

Shown in Table 5 is the correlation analysis examining the relationship between Perceived Ease of Use and Students’ Actual Behavioral outcomes. The table displays a correlation coefficient (r-value) of 0.666, which indicates a strong positive correlation between these two variables. This suggests that as students’ perceived ease of use increases, their actual behavioral engagement tends to increase proportionally. The p-value of .000 demonstrates that this correlation is statistically significant, falling well below the conventional alpha level of 0.05. This extremely low p-value indicates there is virtually no probability that the observed relationship occurred by random chance alone. The decision to “Reject Ho” is correct given the significant p-value, as it indicates rejecting the null hypothesis that assumes no relationship exists between the variables. The perceived ease of use serves as a meaningful predictor of students’ actual behavioral outcomes in the educational context being studied.

The robust and significant correlation observed between perceived ease of use and students’ actual behavioral engagement is well-supported by prior studies, which affirm that ease of use promotes both heightened engagement and sustained adoption in educational environments. For instance, a multilevel regression study conducted in China revealed that students’ learning self-efficacy mediated the relationship between platform ease and engagement, emphasizing that simpler tools lead to deeper student involvement (Jiang et al., 2025). Similarly, extended applications of the Technology Acceptance Model (TAM) in higher education have confirmed that an increase in perceived ease of use not only enhances perceived usefulness but also bolsters attitudes and behavioral intentions toward virtual classroom systems (Lin & Yu, 2023; Azuddin et al., 2024; and San Pedro & Moore, 2023).

Building upon Davis’s foundational insights into TAM—which suggest that perceived ease of use indirectly influences actual use through perceived usefulness—ACT researchers have demonstrated that ease of use enhances both utility and usage frequency by shaping user attitudes (San Pedro & Moore, 2023; Chen et al., 2025; and García et al., 2024).

Collectively, these findings indicate that our data not only confirm the established TAM framework but also extend it by highlighting ease of use as a direct enhancer of behavioral engagement, surpassing mere intention. Practically, this suggests that Squared practitioners should prioritize interface simplicity and usability training efforts likely to improve both students’ perceptions of usefulness and their actual engagement behaviors. Future research could further investigate how demographic and self-efficacy variables may moderate this relationship.

Table 6. Significance of the Relationship Between Perceived Usefulness and Students’ Actual Behavioral

Shown in Table 6 is the significance of the relationship between perceived usefulness and students’ actual behavioral intention. The table displays an R-value of 0.658 and a p-value of 0.000. The r-value of 0.658 indicates a strong, positive linear relationship between perceived usefulness and students’ actual behavioral intention. This suggests that as perceived usefulness increases, students’ actual behavioral intention tends to increase as well. The p-value of 0.000 is less than the significance level of 0.05. This leads to the rejection of the null hypothesis (Ho). Therefore, the p-value confirms that the observed strong correlation is statistically significant, meaning it is unlikely to have occurred by chance. The interpretation notes that there is a significant strong correlation.

The robust and statistically significant correlation between perceived usefulness (r = 0.658, p < .001) and students’ actual behavioral intention towards the Squared Platform closely aligns with expectations derived from Davis’s original Technology Acceptance Model (TAM), which posits that perceived usefulness directly influences behavioral intention (Davis, 1989). Recent empirical studies further substantiate this connection across various educational technologies. For instance, Alshammari and Babu (2025) demonstrate a significant direct effect of perceived usefulness (β = .476, p < .05) on the intention to use ChatGPT, with satisfaction acting as a partial mediator of this effect. Similarly, an extended TAM focusing on metaverse adoption identified perceived usefulness as a key driver of students’ intention (Al-Adwan et al., 2023; Misirlis et al., 2023). A comprehensive meta-analysis also underscores perceived usefulness as a consistent determinant of behavioral intention across different technology contexts (De Oliveira Santini et al., 2024; Belmonte et al., 2024; Schorr, 2023).

These findings not only confirm but also expand upon Davis’s TAM, illustrating that even with contemporary educational tools like ChatGPT and metaverse platforms, perceived usefulness remains a compelling predictor of intention. This highlights the lasting significance of TAM’s foundational constructs. Practically, developers and organizers of the Squared Platform should focus on enhancing and effectively communicating the utility of educational technologies to promote student engagement. Future research could investigate how factors such as satisfaction (as noted by Alshammari and Babu, 2025) or moderating variables like system quality and self-efficacy further influence this relationship.

Table 7. Significance of the Relationship Between Behavioral Intention and Students’ Actual Behavior

Shown in Table 7 is the significance of the relationship between behavioral intention and students’ actual behavioral outcomes. The table provides an R-value of 0.725 and a p-value of 0.000. The r-value of 0.725 signifies a very strong, positive linear correlation between behavioral intention and students’ actual behavioral outcomes. This implies that as behavioral intention strengthens, there is a very strong tendency for students’ actual behavior to align accordingly. The p-value of 0.000 is below the established significance level of 0.05. This result leads to the rejection of the null hypothesis (Ho). Consequently, the p-value confirms that the observed very strong correlation is statistically significant, indicating it is highly improbable to have occurred randomly.

This strong correlation (r = 0.725, p < .001) between behavioral intention and actual behavior toward Squared Platform aligns with substantial evidence supporting the idea that intention is a robust predictor of real-world actions, a fundamental concept in the Theory of Reasoned Action and its extension, the Theory of Planned Behavior (Mailizar et al., 2021; Sun et al., 2025). Davis’s foundational Technology Acceptance Model (TAM) further asserts that behavioral intention reliably forecasts actual system use (Schorr, 2023). Recent educational research conducted by Zheng et al. (2025) emphasizes behavioral intention as the strongest predictor of sustained use in e-learning environments. Additionally, studies by Syahruddin et al. (2021), Mailizar et al. (2021), and Songkram et al. (2023) confirm that TAM constructs—including intention—significantly influence acceptance in the context of the pandemic.

The results of this study not only affirm the predictive validity of TAM in the student context but also extend its findings by demonstrating an exceptional effect strength (r > .70), which exceeds the typical educational correlations (approximately .50–.60). This suggests a heightened connection within this particular behavioral domain. Practically, the findings indicate that interventions aimed at enhancing students’ intentions—by shaping attitudes, increasing perceived usefulness, and fostering perceived control—could significantly improve actual behavioral engagement with the Squared Platform. Future research and educational policy should concentrate on designing strategies to boost intention (such as motivational scaffolding and social norm cues) to bridge the intention-behavior gap and support consistent positive behavioral outcomes in academic settings.

Table 8. Combined Regression Analysis on the Significant Influence of Perceived Ease of Use, Perceived Usefulness, and Behavioral Intention on Students’ Actual Behavioral

Shown in Table 8 are the results of a combined regression analysis examining the significant influence of Perceived Ease of Use, Perceived Usefulness, and Behavioral Intention on Students’ Actual Behavioral outcomes. For each predictor, the Standard Coefficient Beta indicates the strength and direction of the relationship with the dependent variable when other predictors are held constant. For instance, Behavioral Intention has the largest beta coefficient is behavioral intention (.243), suggesting it has the strongest unique positive influence. The t-value assesses the statistical significance of each predictor’s coefficient. Larger absolute t-values indicate stronger evidence against the null hypothesis that the coefficient is zero. All predictors show relatively high t-values (4.57, 2.26, 7.48). The p-value for each predictor, perceived ease of use (0.000), perceived usefulness (0.024), and behavioral intention (0.000), indicates the probability of observing such a t-value if there were no true effect. Since all p-values are less than the 0.05 level of significance, the decision on Ho is to reject Ho for all predictors, meaning each predictor significantly influences students’ actual behavior.

The R-value of 0.757 represents the multiple correlation coefficient, indicating a strong overall linear relationship between the combined predictors and students’ actual behavior. The R Square of 0.572 (or 57.2%) signifies that 57.2% of the variance in Students’ Actual Behavioral outcomes can be explained by the combined influence of Perceived Ease of Use, Perceived Usefulness, and Behavioral Intention. This suggests that 42.8% percent of the variance can be attributed to other factors aside from behavioral intentions, perceived ease of use, and perceived usefulness.  Further, the F-value of 162 (with a corresponding p-value of 0.000) assesses the overall significance of the entire regression model. The high F-value and low p-value (P=.000) indicate that the model as a whole is statistically significant, meaning the predictors collectively provide a better fit to the data than a model with no predictors.

In light of data indicating that Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and Behavioral Intention (BI) significantly predict students’ actual behavioral outcomes concerning the Squared Platform, with BI demonstrating the strongest unique influence, these findings not only align with but also extend the Technology Acceptance Model (TAM) framework. Davis’s original TAM emphasizes PU and PEOU as essential drivers of BI, which subsequently leads to actual usage (Davis, 1989; Seghiour & Berrehouma, 2024; Zhou et al., 2022). The notable β for BI (.243) and its statistical significance reinforce assertions from a 2024 meta-analysis that TAM variables tend to predict BI more effectively than actual usage (Seghiour & Berrehouma, 2024). Furthermore, research in higher education confirms that PEOU and PU continue to be strong antecedents of students’ adoption of digital academic tools, thus supporting this study’s observations of significant t-values and p < .05 (Lin & Yu, 2023; Linus et al., 2025).

Beyond the TAM framework, recent findings underscore the influence of Habit and Hedonic Motivation in enhancing BI, where habitual usage patterns and enjoyment are significant predictors of sustained engagement in e-learning environments (Zheng et al., 2025; Cortez et al., 2024; Zhao et al., 2025). These insights suggest that interventions should focus not only on improving system usability and perceived functionality but also on fostering positive usage habits and enjoyable experiences with the Squared Platform. Such an approach can cultivate stronger behavioral intentions and actual usage among students. Future research could explore the integration of habit-forming design elements and gamified features to maximize the explanatory power of TAM in predicting real-world student behaviors.

CONCLUSION

Based on the findings of the study, the following conclusions were drawn:

The study revealed that students from different departments showed varying levels of representation, with the College of Justice Education having the highest participation and Information Technology Education the lowest. This variation highlights a balanced yet diverse set of respondents that strengthens the reliability of the findings. The summary of the Technology Acceptance Model (TAM) components—Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Behavioral Intention (BI), and Actual Behavior (UB)—all received a “very high” rating. This indicates students found the system easy to use and were motivated to continue using it. The overall mean shows that students, regardless of department or year level, had a very positive outlook towards using the Squared Platform. These results confirm that the platform meets students’ needs for functionality, ease, and usefulness. The descriptive statistics also suggest a readiness among students to integrate technology in their academic tasks. This favorable perception should be seen as a strong foundation for further technological improvements and educational planning.

Moreover, the study revealed key findings on the relationships and differences among variables. Firstly, there were no significant differences across year levels in terms of PEOU, PU, BI, and UB. This suggests that students, whether freshmen or seniors, had a consistent level of acceptance toward the Squared Platform. However, significant differences were found across departments, with the strongest differences observed in PU, showing that perceived usefulness varied significantly depending on students’ academic backgrounds. This implies that certain departments may be more aligned or prepared to benefit from the platform than others. Furthermore, strong and significant correlations were found between all major TAM constructs and actual behavior. The combined regression analysis showed that 57.2% of the variance in students’ actual behavior could be explained by the three predictors, with Behavioral Intention having the greatest influence. This proves that students’ intent to use the platform strongly drives their actual usage behavior. The results of the study clearly support the Technology Acceptance Model. Students rated the Squared Platform as very easy to use and very useful, which aligns with TAM’s idea that these two factors influence their willingness to use a system. The strong relationships found between perceived ease of use, perceived usefulness, and students’ actual behavior show that when students find a platform helpful and simple, they are more likely to use it.

In conclusion, this confirms that the Technology Acceptance Model is still a strong and relevant framework in understanding how students adopt educational technologies like the Squared Platform. Students showed a consistently high level of acceptance across all constructs, with their intention to use the platform playing the most important role in predicting actual behavior. It also became clear that while year level doesn’t significantly influence acceptance, departmental differences do, which suggests that adoption strategies may need to be customized depending on academic background.

RECOMMENDATION

Based on the study’s findings, recommendations include that the Davao Central College should institutionalize support for the Squared Platform by integrating it formally into school operations, ensuring it is included in student orientation programs, and allocating resources such as MIS personnel and budget for system improvements. Given the significant role of Behavioral Intention in predicting actual usage, the school must actively promote the value of the platform and provide necessary infrastructure, such as consistent internet access and QR scanning equipment, to encourage widespread and sustained adoption across all departments. The Squared Platform committees should streamline onboarding processes, enhance the clarity of user instructions, and provide timely support for students encountering difficulties. Based on findings related to perceived ease of use, the development of simple user guides, video walkthroughs, and interactive help features would increase usability and engagement. Student organizations and clubs are encouraged to coordinate more efficiently with the Squared Committee to ensure accurate, timely, and organized attendance monitoring during school activities. Training on QR code scanning protocols and contingency planning should be conducted to reduce duplication and scanning errors, thus enhancing accountability and operational efficiency. For future researchers, the study recommends further exploration into the varying levels of technology acceptance across academic disciplines. Additional studies may also look into longitudinal effects of platform use on student participation and performance, and how institutional support and student governance structures influence long-term adoption and system success.

REFERENCES

1. Al-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A. (2023). “Extending the Technology Acceptance Model (TAM) to predict university students’ intentions to use Metaverse-Based Learning platforms”. Education and Information Technologies, 28(11), 15381–15413. https://doi.org/10.1007/s10639-023-11816-3
2. Alshammari, S. H., & Babu, E. (2025). The mediating role of satisfaction in the relationship between perceived usefulness, perceived ease of use, and students’ behavioural intention to use ChatGPT. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-91634-4
3. Azuddin, N. A., Manan, K. A., Ghani, M. T. A., & Ghazali, W. N. W. M. (2024). Developing Questionnaires for the Technology Acceptance Model (TAM): a study on the teachers’ acceptance of using online education. Jurnal Komunikasi Malaysian Journal of Communication, 40(3), 529–553. https://doi.org/10.17576/jkmjc-2024-4003-30
4. Behera, C. K., & Kumra, R. (2023). Two decades of mobile payment research: A systematic review using the TCCM approach. International Journal of Consumer Studies, 48(1). https://doi.org/10.1111/ijcs.13003
5. Belmonte, Z. J. A., Prasetyo, Y. T., Cahigas, M. M., Young, M. N., Nadlifatin, R., & Beler, P. M. a. S. (2024). Factors affecting Undergraduate Student’s Behavioral Intention to use Hybrid Learning during the COVID-19 Pandemic. ACM Digital Library, 108–115. https://doi.org/10.1145/3696230.3696243
6. Budiarto, M., Audiah, S., Astuti, E. D., Sanjaya, Y. P. A., & Firli, M. Z. (2024). Enhancing School and College Attendance Using Advanced Technology. IEEE Xplore, 1–6. https://doi.org/10.1109/iccit62134.2024.10701150
7. Chen, X., Jiang, L., Zhou, Z., & Li, D. (2025). Impact of perceived ease of use and perceived usefulness of humanoid robots on students’ intention to use. Acta Psychologica, 258, 105217. https://doi.org/10.1016/j.actpsy.2025.105217
8. Cortez, P. M., Ong, A. K. S., Diaz, J. F. T., German, J. D., & Jagdeep, S. J. S. S. (2024). Analyzing Preceding factors affecting behavioral intention on communicational artificial intelligence as an educational tool. Heliyon, 10(3), e25896. https://doi.org/10.1016/j.heliyon.2024.e25896
9. Creswell, J. W. (2014). Research Design: Qualitative, Quantitative and Mixed Methods Approaches (4th ed.). Thousand Oaks, CA: Sage.
10. Davis, F. (1989). Technology Acceptance Model: Origins. Working Papers on Information Systems, 35-59. Doi: 10.4018/978-1-4666-8156-9.ch013.
11. De Oliveira Santini, F., Sampaio, C. H., Rasul, T., Ladeira, W. J., Kar, A. K., Perin, M. G., & Azhar, M. (2024). Understanding Students’ Technology Acceptance Behaviour : A Meta-Analytic Study. Technology in Society, 102798. https://doi.org/10.1016/j.techsoc.2024.102798
12. García, J. a. M., Gómez, C. G., López, A. T., & Schlosser, M. J. (2024). Applying the technology acceptance model to online self-learning: A multigroup análisis. Journal of Innovation & Knowledge, 9(4), 100571. https://doi.org/10.1016/j.jik.2024.100571
13. Granić, A. (2023). Technology acceptance and adoption in education. In Handbook of Open, Distance and Digital Education (pp. 183–197). https://doi.org/10.1007/978-981-19-2080-6_11
14. Hurley, M., & Tenny, S. (2023, July 17). Mean. StatPearls – NCBI Bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK546702/
15. Jiang, J., Hu, J., & Feng, T. (2025). Does perceived ease of use of online teaching platforms facilitate students’ learning engagement? The role of self-efficacy of teachers and students. Asia Pacific Journal of Education, 1–15. https://doi.org/10.1080/02188791.2025.2467942
16. Libretexts. (2024, November 23). 5.3: multiple regression explanation, assumptions, interpretation, and write up. Statistics LibreTexts. https://stats.libretexts.org/Bookshelves/Applied_Statistics/Statistics_for_Research_Students_%28Fein_Gilmour_Machin_and_Liam_Hendry%29/05%3A_Comparing_Associations_Between_Multiple_Variables/5.03%3A_Multiple_Regression_Explanation_Assumptions_Interpretation_and_Write_Up?utm_source=chatgpt.com
17. Lin, Y., & Yu, Z. (2023). Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00403-8
18. Linus, A. A., Aladesusi, G. A., Monsur, I. A., & Elizabeth, F. J. (2025). Perceived usefulness, ease of use, and intention to utilize online tools for learning among College of education students. Indonesian Journal of Multidiciplinary Research, 5(1), 41–52. https://doi.org/10.17509/ijomr.v5i1.81387
19. Mailizar, M., Almanthari, A., & Maulina, S. (2021). Examining teachers’ behavioral intention to use e-learning in teaching of mathematics: an Extended TAM model. Contemporary Educational Technology, 13(2), ep298. https://doi.org/10.30935/cedtech/9709
20. Mannan, M., & Maruf, T. I. (2024). Perceived usefulness and perceived ease of use in the worth of online education system in Bangladesh. ResearchGate. https://doi.org/10.5281/zenodo.13763573
21. Misirlis, N., Nikolaidis, Y., & Sabidussi, A. (2023, November 26). Should I use metaverse or not? An investigation of university students behavioral intention to use MetaEducation technology. arXiv.org. https://arxiv.org/abs/2311.15251?utm_source=chatgpt.com
22. Nunnally, J. C. (1978). An overview of psychological measurement. In Springer eBooks (pp. 97–146). https://doi.org/10.1007/978-1-4684-2490-4_4
23. Park, E. S., & Park, M. S. (2020). Factors of the Technology Acceptance Model for Construction IT. Applied Sciences, 10(22), 8299. https://doi.org/10.3390/app10228299
24. Percentages: Everything you need to know when assessing percentages skills. (n.d.). https://www.alooba.com/skills/concepts/data-literacy/percentages/?utm_source=chatgpt.com
25. Porkodi, S., & Tabash, B. K. H. (2024). A Comprehensive Meta-Analysis of Blended Learning adoption and Technological Acceptance in Higher Education. International Journal of Modern Education and Computer Science, 16(1), 47–71. https://doi.org/10.5815/ijmecs.2024.01.05
26. Rukhiran, M., Wong-In, S., & Netinant, P. (2023). User Acceptance Factors related to Biometric Recognition Technologies of Examination Attendance in Higher Education: TAM Model. Sustainability, 15(4), 3092. https://doi.org/10.3390/su15043092
27. Rukhiran, M., Wong-In, S., & Netinant, P. (2023b). User Acceptance Factors related to Biometric Recognition Technologies of Examination Attendance in Higher Education: TAM Model. Sustainability, 15(4), 3092. https://doi.org/10.3390/su15043092
28. San Pedro, S., & Moore, R. (2023, March). Investigating Factors Associated with Student Use of Digital Tools for Learning. Insights in Education and Work. https://eric.ed.gov/?id=ED629057
29. Schorr, A. (2023). The Technology Acceptance Model (TAM) and its Importance for Digitalization Research: A Review. Sciendo eBooks, 55–65. https://doi.org/10.2478/9788366675896-005
30. Seghiour, H., & Berrehouma, A. (2024). Predicting Behavioral Intention or Actual Usage of Technology? A Systematic Literature Review of the. . . ResearchGate. https://www.researchgate.net/publication/387230369_Predicting_Behavioral_Intention_or_Actual_Usage_of_Technology_A_Systematic_Literature_Review_of_the_Technology_Acceptance_Model_qbwl_nmwdhj_hwl_laldbyat_mnhjyt_mrajt_lltknwlwjya_alfly_baalstkhdam_aw_a
31. Songkram, N., Chootongchai, S., Osuwan, H., Chuppunnarat, Y., & Songkram, N. (2023). Students’ adoption towards behavioral intention of digital learning platform. Education and Information Technologies, 28(9), 11655–11677. https://doi.org/10.1007/s10639-023-11637-4
32. Soormo, R. B., Al-Rahmi, W. M., Dahri, N. A., Alblehai, F., Alshimai, A., Aldaijy, A., & Salameh, A. A. (2024). Evaluating the influence of UTAUT factors on the adoption of QR codes in MSMEs: an application of SEM and ANN methodologies. IEEE Access, 1. https://doi.org/10.1109/access.2024.3510375
33. Sun, P., Li, L., Hossain, M. S., & Zabin, S. (2025). Investigating Students’ Behavioral Intention to use ChatGPT for Educational purposes. Sustainable Futures, 100531. https://doi.org/10.1016/j.sftr.2025.100531
34. Syahruddin, S., Yaakob, M. F. M., Rasyad, A., Widodo, A. W., Sukendro, S., Suwardi, S., Lani, A., Sari, L. P., Mansur, M., Razali, R., & Syam, A. (2021). Students’ acceptance to distance learning during Covid-19: the role of geographical areas among Indonesian sports science students. Heliyon, 7(9), e08043. https://doi.org/10.1016/j.heliyon.2021.e08043
35. Tejada, J. J., & Punzalan, J. R. B. (2012). On the misuse of Slovin’s formula. The philippine statistician, 61(1), 129-136.
36. Thongsri, N., Shen, L., & Bao, Y. (2019). Investigating Academic Major Differences in perception of Computer Self-efficacy and Intention toward E-learning Adoption in China. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1904.11801
37. Turney, S. (2023, June 21). Frequency Distribution | Tables, Types & Examples. Scribbr. Retrieved July 2, 2025, from https://www.scribbr.com/statistics/frequency-distributions/
38. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
39. Wei, W., Prasetyo, Y. T., Belmonte, Z. J. A., Cahigas, M. M. L., Nadlifatin, R., & Gumasing, M. J. J. (2025). Applying the technology acceptance model – Theory of planned behavior (TAM-TPB) model to study the acceptance of building information modeling (BIM) in green building in China. Acta Psychologica, 254, 104790. https://doi.org/10.1016/j.actpsy.2025.104790
40. Widiar, G., Yuniarinto, A., & Yulianti, I. (2023). Perceived ease of use’s effects on behavioral intention mediated by perceived usefulness and trust. Interdisciplinary Social Studies, 2(4), 1829–1844. https://doi.org/10.55324/iss.v2i4.397
41. Wong, E. Y., Hui, R. T., & Kong, H. (2023). Perceived usefulness of, engagement with, and effectiveness of virtual reality environments in learning industrial operations: the moderating role of openness to experience. Virtual Reality, 27(3), 2149–2165. https://doi.org/10.1007/s10055-023-00793-0
42. Wu, X., Wider, W., Wong, L. S., Chan, C. K., & Maidin, S. S. (2023). Integrating the technology acceptance model on online learning effectiveness of emerging adult learners in Guangzhou, China. International Journal of Education and Practice, 11(2), 129–140. https://doi.org/10.18488/61.v11i2.3282
43. Zhao, J., Li, X., & Gao, Z. (2025). From innovativeness to insecurity: unveiling the facets of translation technology use behavior among EFL learners using TRI 2.0. Humanities and Social Sciences Communications, 12(1). https://doi.org/10.1057/s41599-025-04777-0
44. Zheng, H., Han, F., Huang, Y., Wu, Y., & Wu, X. (2025). Factors influencing behavioral intention to use e-learning in higher education during the COVID-19 pandemic: A meta-analytic review based on the UTAUT2 model. Education and Information Technologies. https://doi.org/10.1007/s10639-024-13299-2
45. Zhou, L., Xue, S., & Li, R. (2022). Extending the technology acceptance model to explore students’ intention to use an online education platform at a university in China. SAGE Open, 12(1). https://doi.org/10.1177/21582440221085259