Improving Medical Education through Student Feedback: Key Factors Driving Response Rates in Online Student Evaluations of Teaching - An Extended Technology Acceptance Model

Improving Medical Education through Student Feedback: Key Factors Driving Response Rates in Online Student Evaluations of Teaching - An Extended Technology Acceptance Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Improving Medical Education through Student Feedback: Key Factors Driving Response Rates in Online Student Evaluations of Teaching - An Extended Technology Acceptance Model Leandra Fien, Peter Neumann, Martin Krebber, Ralph Berger, Mandy Petzold This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8165782/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Introduction: Student evaluation of teaching (SET) provides medical faculties with an empirical foundation for evidence-based enhancement of teaching quality for both medical educators and coordinators. However, the transition from paper-based to online evaluation has led to a marked decline in response rates, undermining the reliability and validity of the data obtained. At Charité, a newly developed online tool was implemented, after which response rates increased substantially. This study aimed to identify technical, procedural, and motivational factors associated with higher response rates in SET by applying an extended version of the Technology Acceptance Model (TAM) that integrates Actual System Use (AU). Methods: A cross-sectional survey was conducted among medical students at Charité - Universitätsmedizin Berlin (n = 834). A custom questionnaire operationalized core TAM constructs (Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Behavioral Intention (BI)) alongside Intrinsic Motivation (IM), Extrinsic Motivation (EM), and Perceived Workload (PW). The instrument was first examined using exploratory factor analysis (EFA; n = 325) and subsequently validated through confirmatory factor analysis (CFA; n = 509). Structural equation modeling (SEM) was then performed on the CFA sample to test hypothesized relationships. AU was objectively measured via student response rates in SET. Results: The classical TAM structure was confirmed. IM influenced BI indirectly via PU, whereas EM had the strongest direct effect on BI and positively reinforced IM. PW negatively influenced PEOU. PW and IM did not directly significantly influence the students’ intention to participate in SET. Moreover, BI strongly influenced AU. Conclusion: Higher response rates in SET can be achieved through a user-friendly and useful evaluation system, strategically implemented incentives, and the avoidance of adverse processes such as excessive evaluation workload. Overall, the study provides practical guidance for designing inclusive and effective evaluation systems that support continuous improvement of teaching quality in medical education. Student Evaluation of Teaching (SET) Technology Acceptance Model (TAM) Online Evaluation Systems Response Rates Intrinsic Motivation Extrinsic Motivation Medical Education Faculty Development Figures Figure 1 Figure 2 Figure 3 1 Introduction The importance of student evaluation of teaching (SET) is particularly pronounced in health professions education, given the unique demands placed on teaching and learning contexts. In these settings, the role of feedback is not just informative; it can actively shape teaching practices and student outcomes. First, unlike in other academic fields, educators in clinical teaching, for example in medicine or midwifery, must not only convey state-of-the-art knowledge and up-to-date scientific insights. Educators also need to show how these concepts translate into practical competencies in everyday clinical settings. Feedback from SET plays a pivotal role in refining these pedagogical skills. It contributes to the ongoing development of their professional identity as teachers, in addition to their roles as researchers and clinicians [1, 2]. Second, insights from SET help to better integrate unique course formats, such as laboratory-based training, simulation exercises, bedside teaching, and clinical rotations into modern curricula that foster students' competence-based medical training in interdisciplinary and interprofessional settings from the first semester onward [3]. Third, aggregated SET data across teaching sites, facilities, and hierarchies of decision-making can help to monitor the organizational and structural conditions and provide an empirical basis for allocating teaching responsibilities and financial resources [4]. By doing so, SET enable faculties to coordinate medical education effectively within the operational realities of hospital care, where shift work, patient safety, and other logistical constraints create unique challenges. To serve its purpose effectively by ensuring statistical validity and adequate interpretability, sufficient participation in SET is crucial [5]. However, with the shift from paper-based to online evaluations, SET response rates have declined markedly, increasing the risk of non-response bias, as shown by many studies [6–15]. To address this challenge at Charité, we implemented a newly developed online tool (described in section 2.1). Following implementation, response rates increased substantially, prompting an investigation into the factors that contributed to this improvement. To identify in particular which technical, procedural, and motivational factors in particular drive higher participation in online student evaluations of teaching - and to allow for potential generalization to other educational contexts - we applied an extended Technology Acceptance Model (TAM) [16–19]. Unlike many previous TAM-based studies, this research links acceptance mechanisms directly to objective response behavior, rather than self-reported intention - a rarely examined outcome in SET research [19]. 1.1 Online SET Online SET offer the advantages of cost-effectiveness, faster return of results and overall higher convenience and flexibility for students, teachers and administration. In addition, since students can fill out the surveys after the courses, they have more time to consider their answers and are more likely to fill out and give longer responses in open-ended questions [10, 12, 15]. However, one major challenge is the often significantly lower response rate compared to paper-based SET. Reasons for this include a lack of social pressure, more distractions outside the classroom, survey fatigue due to more feasible and therefore more frequent online evaluations, an increased perception of survey length, or technical difficulties [6–15, 20]. Most studies do not find a significant difference between online and paper-based for the evaluation scores themselves [7–12, 15]. Rather than the difference between online and paper-based evaluation, the risk of distorted results stems from a general difference between responders and non-responders. Responders in SET have higher grades and higher academic performance [6, 7, 21–24], differ in study major and personality type [6, 14, 24], are more likely to be female [7, 21–25] and are more likely to be white [7, 21, 26]. To reduce the non-response bias and obtain a representative sample of opinions on teaching quality, it is essential for faculties to aim for high SET response rates, since the decisions derived from this data affect all student subgroups. Therefore, we investigated which factors are associated with increased participation in SET, using TAM as a framework. In doing so, we aim to promote equitable opportunities for all students to contribute to teaching improvement, support lifelong learning among medical educators and strengthen evidence-based faculty development, thus supporting the principles of Sustainable Development Goal 4 [27]. 1.2 The Technology Acceptance Model Core Constructs TAM [16, 17] is one of the most widely used theoretical frameworks for explaining and predicting user acceptance of technology-based systems [18, 28, 29]. At its core, it posits that two central beliefs of technology users — Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) determine their Behavioral Intention to Use (BI) the system, which in turn predicts its Actual Use (AU). PU refers to the degree to which a person believes that using a particular system will enhance their performance, which directly affects the user’s BI to use the system. PEOU describes the degree to which a person believes that using the system will be free of effort. It affects BI directly and indirectly via PU, i.e. a user-friendly system design increases the system’s PU as well as the user’s BI to use it. These core relationships have been applied and validated across a wide range of contexts [18, 28, 29]. However, no study has yet applied TAM to online SET in medical education programs. Further, the majority of studies applying TAM to various technologies do not test whether BI indeed predicts AU [19]. To this end, we aim to test the hypotheses of the core TAM in the context of online SET: Hypothesis 1: Perceived usefulness has a positive effect on behavioral intention. Hypothesis 2a: Perceived ease of use has a positive effect on behavioral intention. Hypothesis 2b: Perceived ease of use has a positive effect on perceived usefulness. Hypothesis 3: Behavioral intention has a positive effect on actual system use. TAM Extension Additional factors may play a role in determining whether students participate in online SET: First, the medical profession is characterized by a wide variety of highly specialized subdisciplines. This means that in modern medicine curricula many experts share the teaching load, resulting in a high number of individual courses. In addition, small group sizes vastly increase the overall number of teaching sessions. The aim of a good evaluation system is to capture this complexity in high resolution to provide individual feedback tailored to teachers and teaching departments. Consequently, students are expected to fill out a large number of surveys, which could lead to a perception of the evaluation system as having a high workload. This could overwhelm students and thereby decrease their BI to use the evaluation system overall, as indicated by studies on survey fatigue [6, 30–32]. Alternatively, this could also be perceived as a design-flaw of the technology, negatively affecting the user-friendliness of the system. No studies so far have explicitly tested how Perceived Workload (PW), defined as the intensity or frequency by which users are expected to use the technology, influences its acceptance. In the seminal paper of TAM, Davis defines PEOU as the “ lack of effort a user has to bring up ” [17], which is independent of our variable PW, because it refers more to the friendliness or ease-of-use of the design, which also holds true for a single interaction. Thus, our mutually non-exclusive hypotheses are: Hypothesis 4a: Perceived workload has a negative effect on perceived ease of use. Hypothesis 4b: Perceived workload has a negative effect on behavioral intention. Within the TAM framework, Intrinsic Motivation (IM) and Extrinsic Motivation (EM) have been extensively studied as drivers behind technology acceptance [33]. In this context, IM is usually defined as the perceived enjoyment of using a system at its own end, whereas EM refers to using a system to achieve outcomes other than the interaction itself, for example improved performance in the task [34]. However, since our goal is to investigate the motivational drivers of participation in SET, our definitions of IM and EM are conceptually distinct from those used in the traditional TAM framework. Here, IM denotes students’ inner desire to provide meaningful feedback and contribute to teaching improvement, regardless of how enjoyable the system itself is to use. EM, in contrast, refers to participation driven by external factors such as incentives. Understanding how these two motivational mechanisms act and interact is essential for designing evaluation systems that are both engaging and educationally sustainable. Research indicates that response rates are higher in courses within students’ major field of study [6], likely due to stronger identification with and responsibility toward their academic community. Such perceptions foster IM, which in turn is associated with sustained engagement and higher-quality responses. Moreover, communicating to students the potential role of evaluations for improving teaching quality has been discussed as a means to increase participation [6, 11, 15]. In the TAM framework, IM may enhance PU, as students who are internally motivated to provide constructive feedback are more likely to recognize how the system supports educational improvement. Moreover, intrinsically motivated students should display a stronger BI to use the system, as their participation stems from self-determined engagement rather than obligation. Thus, we formulated the following hypotheses: Hypothesis 5a: Intrinsic motivation has a positive effect on perceived usefulness. Hypothesis 5b: Intrinsic motivation has a positive effect on behavioral intention. In addition to intrinsic drivers, incentives have been shown to increase students’ BI to use technological systems in the context of SET [9, 35, 36]. Examples include small grade incentives, bringing a note card to the examination, making the examination optional, or offering a cash prize or treats [9, 35, 36]. However, introducing external rewards carries the potential risk of undermining students’ IM to provide meaningful feedback, leading them to participate solely for the incentive rather than to improve teaching quality [37]. A recent meta-analysis provided evidence for this effect for rewards contingent to the task or performance [38]. It is plausible that this applies to incentives in SET, since students receive the reward upon completing the evaluation, which may reduce the quality of responses and long-term engagement with evaluation processes. To our knowledge, this trade-off between EM and IM has not yet been investigated in the context of SET. Based on these considerations, we formulated the following hypotheses: Hypothesis 6a: Extrinsic motivation has a positive effect on behavioral intention. Hypothesis 6b: Extrinsic motivation has a negative effect on intrinsic motivation. Figure 1 provides a visual overview of all hypotheses. 2 Methods 2.1 Participants and Procedure The data was collected by the Quality Assurance Department for Teaching and Learning at Charité - Universitätsmedizin Berlin, Germany. The study was approved by the Ethics Committee of Charité (approval number: EA2/064/25). All medical students from the 2nd to the 10th semester were invited to participate in the survey. The survey was distributed electronically and remained open for four weeks in July 2025. Students who had not yet participated received up to three weekly reminder emails. Participants provided informed consent prior to completing the survey. Participation was voluntary, anonymous, and could be withdrawn at any time without disadvantage. In total, 897 of 3,088 invited students accessed the questionnaire, resulting in a response rate of 29.05%. The technology tested in this study, EVABOX, is a newly developed tool (implemented in fall 2023) for online SET at Charité. It includes several improvements over previous systems, such as an improved online interface, a clearer and more transparent evaluation process, shorter and standardized questionnaires, and an incentive system designed to encourage higher participation rates. Students receive points for completing their SET. These points give them a small non-academic advantage - a higher chance of being placed in the preferred study group in the next semester. For this study, participation in the survey was linked to this system: students received points for completing the survey, thereby providing a non-monetary incentive to participate. 2.2 Measures Independent Variables: Instrument Development and Validation A custom questionnaire was developed to operationalize the core constructs of the TAM and to include additional factors hypothesized to influence participation in online SET. It was specifically designed to support the internal development of EVABOX, while also allowing the testing of study hypotheses. Item development followed a three-step process. First, items were generated based on an extensive literature review and the department’s experience in educational evaluation. The items were drafted to represent key TAM constructs (PU, PEOU, BI) as well as complementary constructs (IM, EM, PW). Second, the items underwent an expert review, during which faculty and educational researchers with longstanding experience evaluated them for content validity, clarity, and contextual relevance. Finally, the preliminary version was refined through cognitive pretesting using think-aloud interviews and structured feedback from a student pilot group (N = 7). Most items were measured on a five-point Likert scale ranging from “completely agree” (1) to “completely disagree” (5). Items on PW were measured using bipolar scales ranging from “too high” (1) to “too low” (5) or “too long” (1) to “too short” (5) (for custom questionnaire see Table 5 in Supplementary Material). Dependent Variable To objectively measure Actual System Use (AU) and prevent socially desirable self-reports, we obtained an objective measure by assigning students to twelve survey groups corresponding to their response rate in SET from the previous two semesters (0%, >0–5%, >5–10%, >10–20%, >20–30%, >30–40%, >40–50%; >50–60%; >60–70%; >70–80%, >80–90%, >90–100%). For an overview of participation rates per group, see Figure 2. Group sizes were chosen to ensure anonymity and to minimize the risk of re-identification when combined with demographic information (age, sex, year of study) collected in the survey. Depicted are the responders (blue) and non-responders (red) to our TAM evaluation survey plotted against their participation rate in student evaluation during the two-semester preceding the study. Percentages indicate the proportion of responders among all invited students within each group. 2.3 Data Preprocessing Data cleaning of the initial dataset (N = 897) followed a stepwise procedure. Item-level inspection revealed that 93% of respondents were unsure or incorrect about where to access aggregated cohort-level evaluation questionnaires and results. Given this knowledge gap, items about their usability and usefulness (PEOU6, PU6, PU10) could not be validly evaluated and were therefore excluded from further analyses. At the case level, n = 44 participants with missing data resulting from study withdrawal or lack of informed consent were removed. Additionally, participants were excluded due to non-use of the system either entirely or prior to the previous semester (n = 12), or invariable response patterns indicative of straightlining (variance < .1, n = 7), yielding a final sample of N = 834. 2.4 Data Analysis The statistical analysis of the data was performed in R, version 4.4.3 [39] and RStudio, version 2024.09.01 [40]. Packages used during the analysis included psych [41] for psychometric analyses including reliability and exploratory factor analysis, lavaan [42] for confirmatory factor analysis (CFA) and structural equation modeling (SEM) and ggplot2 [43] and flextable [44] for visualization. To examine and validate the factor structure without double-testing, the sample was stratified by year of study and randomly split into two subsamples (EFA: n₁ = 325; CFA: n₂ = 509). This stratified random sampling ensured balanced representation across study years and an adequate subject-to-item ratio for factor analysis. Stratification by study year was used because TAM questionnaire response rates varied across academic years (r(829) = –.385, p < .001). Given notable skewness across multiple items, the EFA was conducted using polychoric correlations, with minimum residual estimation and oblique rotation to allow for correlated factors. Parallel analysis informed the selection of the number of factors, loadings ≥ .30 were considered meaningful. The subsequent CFA was carried out on the second subsample, treating items as ordered and using robust weighted least squares estimation (WLSMV), based on the factor structure identified in the EFA and theoretical considerations. Finally, an SEM using ordered items and WLSMV was specified to test the hypothesized relationships between latent constructs, with factor loadings freely estimated based on the CFA-validated measurement model. Model fit was assessed using multiple indices: Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) ≥.90 indicated acceptable fit, while Root Mean Square Error of Approximation (RMSEA) ≤.08 and Standardized Root Mean Square Residual (SRMR) ≤.08 indicated good fit. Standardized factor loadings were extracted. Internal consistency of the identified factors was assessed using Cronbach’s alpha and McDonald’s omega, with values ≥.70 considered acceptable. 3 Results 3.1 Participant Characteristics A total of N = 834 participants were included in the final analysis. Of these, 554 (66.4%) identified as female, 257 (30.8%) as male, 5 (.6%) as diverse, and 18 (2.2%) preferred not to disclose their gender. Of the participants, 522 (62.6%) were aged 21–23 years, 179 (21.5%) were 18–20 years, 97 (11.6%) were 24–26 years, and 33 (4%) were over 26 years. 3 (.4%) preferred not to disclose their age. All study cohorts were represented, with 244 (29.3%) in the third year, 212 (25.4%) in the second year, 171 (20.5%) in the fourth year, 133 (15.9%) in the first year (second semester), 71 (8.5%) in the fifth year, and 3 (.4%) did not disclose their year of study. 3.2 Instrument Validation EFA and CFA EFA was conducted on polychoric correlations of 36 items using minimum residual estimation with oblimin rotation. Consistent with our theoretical expectation, a multifactorial structure of PU, PEOU, IM, EM, PW and BI was confirmed. Items that loaded on additional factors were not examined further in this study (see Table 6 in Supplementary Material). CFA was performed on the second subsample using the WLSMV estimator. The scaled fit estimates indicated an acceptable fit to the data: χ²(155) = 497.359, p < .001; CFI = .986; TLI = .982; RMSEA = .066 (90% CI [.059, .072]); SRMR = .066. All standardized factor loadings were statistically significant (p < .001), with an average loading of .799, ranging from .463 (PU9) to .983 (IM3), indicating good item-factor relationships. Factor correlations ranged from –.38 to .6 (Table 1 ), indicating moderate associations and supporting discriminant validity. <div align="left" class="colspec" Table 1 CFA latent factor correlations Factor PU PEOU BI PW IM EM PU 1.000 .406 .493 –.254 .291 .389 PEOU 1.000 .519 –.237 .104 .311 BI 1.000 –.376 .239 .597 PW 1.000 –.166 –.192 IM 1.000 .096 EM 1.000 All correlations are significant (p < .01) except PEOU-IM (p = .07) and IM-EM (p = .09). Internal consistency Internal consistency of each factor was evaluated using both Cronbach’s alpha and McDonald’s omega coefficients. While Cronbach’s alpha values for PU and PEOU were moderately low (around .65), McDonald’s omega consistently exceeded the commonly accepted threshold of .7. Given that omega does not assume tau-equivalence and accounts for varying item loadings, it provides a more accurate and robust estimate of internal consistency than alpha. Overall, these results corroborate the factorial validity of the instrument (see Table 2 ). Table 2 Reliability estimates for CFA factors Factor Cronbach’s Alpha McDonald’s Omega PU .657 .669 PEOU .656 .729 BI .928 .934 PW .689 .765 IM .927 .929 EM .690 .723 The table reports Cronbach’s alpha and McDonald’s omega coefficients for all latent constructs. 3.3 Descriptive statistics for validated factors The descriptive statistics indicate that the students perceived the evaluation tool generally positively. Participants showed relatively high scores on BI (M = 1.43, SD = .73), IM (M = 1.7, SD = .836), and PU (M = 2.047, SD = .622), reflecting favorable attitudes and perceived benefits. PEOU was also rated positively (M = 1.498, SD = .61), suggesting that the tool was relatively easy to use. PW received a higher mean score (M = 2.86, SD = .57), indicating that participants generally perceived the workload as a bit too high. EM (M = 2.046, SD = 1.031) showed the greatest variability, reflecting diverse opinions regarding the incentive system. 3.4 SEM The SEM was conducted on the same subsample used for CFA (n₂ = 509), scaled fit estimates are reported. The chi-square test was significant (χ²(179) = 660.372, p < .001) as expected with large samples. Absolute fit indices indicated acceptable fit (RMSEA = .073 (90% CI [.067, .079]), SRMR = .087), and incremental fit indices supported good fit (CFI = .980, TLI = .976). All observed variables showed standardized factor loadings > .43 on their respective latent constructs, indicating good indicator reliability. Table 3 depicts the standardized path coefficients of the measurement model. Table 4 presents whether the hypothesized relationships in the structural model were supported. Table 3 Standardized SEM loadings Factor Item β SE z p PU PU3 .779 .049 16.014 < .0001 PU7 .722 .033 21.642 < .0001 PU8 .693 .037 18.796 < .0001 PU9 .459 .044 10.522 < .0001 PEOU PEOU1 .871 .034 25.484 < .0001 PEOU2 .923 .033 27.841 < .0001 PEOU3 .430 .052 8.284 < .0001 BI BI1 .957 .007 128.042 < .0001 BI2 .973 .007 135.283 < .0001 BI3 .948 .009 106.529 < .0001 PW PW1 .711 .033 21.563 < .0001 PW2 .656 .037 17.526 < .0001 PW3 .664 .033 19.937 < .0001 PW4 .895 .033 27.149 < .0001 IM IM3 .982 .007 150.674 < .0001 IM4 .901 .010 86.383 < .0001 IM6 .955 .007 138.166 < .0001 EM EM1 .889 .036 24.861 < .0001 EM2 .631 .039 16.245 < .0001 EM3 .793 .032 24.433 < .0001 Along with the standardized factor loadings (β), the table presents the corresponding standard errors, z-values, and significance levels for all SEM items. Table 4 Hypothesis testing results for SEM paths Hypo-thesis Path Expected Direction β SE z p Sup-ported H1 PU → BI positive .227 .067 3.398 < .001 yes H2a PEOU → BI positive .386 .071 5.461 < .0001 yes H2b PEOU → PU positive .469 .049 9.490 < .0001 yes H3 BI → AU positive .561 .037 15.042 < .0001 yes H4a PW → PEOU negative –.385 .051 –7.510 < .0001 yes H4b PW → BI negative –.008 .063 –.130 .90 no H5a IM → PU positive .318 .050 6.390 < .0001 yes H5b IM → BI positive .019 .054 .352 .73 no H6a EM → BI positive .526 .052 10.118 < .0001 yes H6b EM → IM negative .213 .056 3.787 < .001 no Supported indicates whether the hypothesis was confirmed based on the significance and sign of the standardized path coefficient (β). For a visual representation of the model, including the relationships among latent constructs and the strength of each path, see Fig. 3 . 4 Discussion 4.1 Interpretation of Findings This study examined factors that drive medical students’ participation in online SET through the lens of an extended TAM. Consistent with TAM’s core propositions [ 17 , 45 ], we show that PU and PEOU directly determine the students’ BI to use the evaluation system. We further replicated the established indirect effect of PEOU on BI via PU, indicating that good usability additionally promotes participation in the evaluation system because it is perceived as more useful. Furthermore, unlike many previous studies [ 19 ], we validate our TAM by showing that users’ intention to engage with the system translates into actual usage, as indicated by the strong, positive relationship between BI and AU. These findings align with prior applications of TAM in educational evaluation contexts [ 46 ] and suggest that an intuitive, purpose-driven system design can effectively enhance response rates in SET. PW was inversely related to PEOU but did not directly affect BI. Thus, higher evaluation workload negatively affects participation in SET, not because students are discouraged by the workload per se, but because it reduces the overall acceptance of the system. Thus, while an evaluation system may meet technological requirements by being user-friendly and useful in fostering teaching quality, the underlying processes, such as completing numerous evaluation forms, may not align well with it. Our results indicate that students perceive this mismatch, which indirectly lowers engagement and, ultimately, participation rates. Our results show that the influence of IM on BI is fully mediated by PU. Students who are intrinsically motivated to improve teaching quality are inclined to participate not merely because they find evaluation meaningful in itself, but because they perceive the system as an effective tool for achieving this goal. This is in line with Self-Determination Theory [ 47 ], which posits that intrinsically motivated behavior depends on the fulfillment of autonomy, competence, and relatedness needs. The technology thus functions as a means of translating students’ IM into action by supporting their internalized values of contributing to better teaching for future cohorts. Finally, our results show that introducing an incentive that rewards higher response rates substantially increases students’ BI to use the system and is therefore its strongest predictor. This replicates previous findings on the positive influence of incentives within the TAM framework [ 9 , 35 , 36 ]. Surprisingly, instead of a negative association, we observe a positive effect of EM on IM, which provides no evidence of an undermining effect on the drive of intrinsically motivated students due to the introduction of an incentive [ 37 ]. This may be explained by recent findings that observational studies relying on self-reported IM provide only weak evidence for an undermining effect, whereas studies using behavioral data typically report stronger negative effects once rewards are withdrawn [ 38 ]. In our observational study, incentives were available, and data was collected shortly after their introduction into the SET system, which may explain why this undermining effect was not observed. 4.2 Practical Implications The results have three main practical implications for increasing response rates in online evaluation systems in medical education. First, medical faculties should reinforce students’ perception of the system’s usefulness, particularly to engage those who are intrinsically motivated to improve teaching quality. Increasing the visibility of changes implemented in response to feedback can strengthen students’ sense of efficacy and help cultivate a constructive, sustainable culture of SET within faculties. Second, increased response rates can be achieved by designing an intuitive and user-friendly SET technology. However, the processes in which the technology is embedded can be misaligned, resulting for example in increased evaluation workload, which leads to a decrease in technology acceptance and hence response rates. Thus, although adverse evaluation processes can to some extent be compensated for by a user-friendly technology as indicated by our results, designers and policy makers should aim for a better fit by adapting not only the technology’s usability features but also the underlying evaluation processes. Third, strategically implemented extrinsic incentives can substantially increase response rates, potentially motivating students who would not otherwise participate. Since they engage intrinsically motivated students as well, incentives can help faculties develop more inclusive, motivating, and sustainable feedback systems. Increasing representative participation in SET supports equitable educational outcomes, contributing to the aims of SDG 4 [ 27 ]. These findings can inform institutional strategies for faculty development workshops and evaluation policies, ensuring that feedback data is effectively translated into teaching improvement and faculty growth. 4.3 Limitations One limitation is the study’s cross-sectional design with data assessed shortly after implementing the SET system. This could introduce a novelty bias, neglecting potential temporal effects and restricting causal inference. Further, the study implemented a custom questionnaire that was adapted to specifics of the SET technology and partly resulted in factors with relatively low internal consistency, which could limit its generalizability to other evaluation technologies. 4.4 Future Research Longitudinal studies could explore temporal dynamics that may negatively affect response rates or data quality, including fatigue from continuous evaluation demands, the decreasing effectiveness of incentives, and the and the possibility that sustained extrinsic motivation gradually erodes intrinsic motivation, where EM gradually erodes IM, potentially leading to lower-quality data due to careless or nonsensical responses. Furthermore, future studies could explore the mechanisms linking IM and PU of SET by integrating self-efficacy and outcome expectancy. When students feel confident in their ability to give constructive feedback and believe that their evaluations lead to real improvements in teaching, these beliefs may reinforce IM and enhance PU, ultimately promoting more thoughtful and effective feedback. Understanding these mechanisms could inform the design of evaluation systems that enhance both participation and the quality of student feedback, thereby fostering a sustained and effective culture of SET in which feedback is valued, acted upon, and integrated into ongoing teaching improvement. 4.5 Conclusion Taken together, these findings reinforce TAM’s explanatory power in the context of SET while highlighting the importance of integrating motivational components into acceptance models that regard the purpose of the technology and not its usage as such. PU remains the central gateway linking IM to BI, whereas EM exerts the strongest influence on participation. For institutional practice, this suggests that fostering sustainable engagement requires more than technical optimization. It demands a system that students perceive as useful, impactful, and responsive. Building visible feedback loops, simplifying participation, and strategically using incentives can together enhance the acceptance of the evaluation technology and hence increase response rates in SET. Ultimately, strengthening participation in SET contributes directly to faculty development by creating a continuous feedback culture that supports reflective teaching practice, evidence-based improvement, and sustainable quality enhancement in medical education. Abbreviations AU Actual Use BI Behavioral Intention CFI Comparative Fit Index CFA Confirmatory Factor Analysis EFA Exploratory Factor Analysis EM Extrinsic Motivation IM Intrinsic Motivation Declarations Ethics approval and consent to participate Ethical approval was obtained from the Ethics Committee of Charité – Universitätsmedizin Berlin (approval number: EA2/064/25). All procedures were conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. Consent for publication All participants provided written informed consent for publication. Availability of data and materials The data used in this research are available upon individual request from the corresponding authors. The data are not publicly available due to privacy-related restrictions. Competing interests The authors declare no competing interests. Funding Leandra Fien and Peter Neumann received funding for their positions from the Senate of Berlin through the program “Berliner Qualitäts- und Innovationsoffensive II” (CH-QIO-II-01).Formularbeginn Formularende Authors' contributions L.F. and P.N. developed the questionnaire. L.F. conducted the pretest and collected data for all items except Actual Use (AU). P.N., M.K., and R.B. collected the data forming the basis for the Actual System Use (AU) variable. L.F. and P.N. performed the data analysis, M.P. assisted. L.F., and P.N. interpreted the results, M.P. assisted. P.N., L.F., and M.P. contributed to writing the main manuscript text. P.N. prepared the figures. All authors reviewed and approved the manuscript. Leandra Fien and Peter Neumann share first authorship, as they contributed equally to this work. Acknowledgement We would like to thank all study participants for their valuable contributions. We also gratefully acknowledge the excellent statistical consulting provided by Levin Wiebelt from the Statistical Consulting Service at Charité, as well as the support of Sebastian Galli, Ulrike Waschau, Hannah Tame, and Matthias Berlin, who were part of the EVABOX Development Team. References Mahajan R, Goyal PK, Singh T. Professional Identity Formation of Medical Educators: A Thematic Analysis of Enabling Factors and Competencies Needed. Int J Appl Basic Med Res. 2022;12:189–95. https://doi.org/10.4103/ijabmr.ijabmr_257_22 . Steinert Y, O’Sullivan PS, Irby DM. Strengthening Teachers’ Professional Identities Through Faculty Development. Acad Med. 2019;94:963–8. https://doi.org/10.1097/ACM.0000000000002695 . Hitzblech T, Maaz A, Rollinger T, Ludwig S, Dettmer S, Wurl W, et al. The modular curriculum of medicine at the Charité Berlin – a project report based on an across-semester student evaluation. GMS J Med Educ. 2019;36:Doc54. https://doi.org/10.3205/zma001262 . Constantinou C, Wijnen-Meijer M. Student evaluations of teaching and the development of a comprehensive measure of teaching effectiveness for medical schools. BMC Med Educ. 2022;22:113. https://doi.org/10.1186/s12909-022-03148-6 . Nulty DD. The adequacy of response rates to online and paper surveys: what can be done? Assess Eval High Educ. 2008;33:301–14. https://doi.org/10.1080/02602930701293231 . Adams MJD, Umbach PD. Nonresponse and Online Student Evaluations of Teaching: Understanding the Influence of Salience, Fatigue, and Academic Environments. Res High Educ. 2012;53:576–91. https://doi.org/10.1007/s11162-011-9240-5 . Avery RJ, Bryant WK, Mathios A, Kang H, Bell D. Electronic Course Evaluations: Does an Online Delivery System Influence Student Evaluations? J Econ Educ. 2006;37:21–37. https://doi.org/10.3200/JECE.37.1.21-37 . Benton SL, Webster R, Gross AB, Pallett WH. An analysis of IDEA student ratings of instruction using paper versus online survey methods 2002–2008 data. IDEA Tech Rep. 2010;16:1–26. Dommeyer CJ, Baum P, Hanna RW, Chapman KS. Gathering faculty teaching evaluations by in-class and online surveys: their effects on response rates and evaluations. Assess Eval High Educ. 2004;29:611–23. https://doi.org/10.1080/02602930410001689171 . Guder F, Malliaris M. Online and Paper Course Evaluations. Am J Bus Educ. 2010;3:131–8. Kucsera JV, Zimmaro DM. Electronic course instructor survey (eCIS) report. Austin TX Div Instr Innov Assess Univ Tex Austin; 2008. Layne BH, Decristoforo JR, Mcginty D, ELECTRONIC VERSUS TRADITIONAL STUDENT, RATINGS OF INSTRUCTION. Res High Educ. 1999;40:221–32. https://doi.org/10.1023/A:1018738731032 . Nowell C, Gale LR, Handley B. Assessing faculty performance using student evaluations of teaching in an uncontrolled setting. Assess Eval High Educ. 2010. https://doi.org/10.1080/02602930902862875 . Sax LJ, Gilmartin SK, Bryant AN. Assessing response rates and nonresponse bias in web and paper surveys. Res High Educ. 2003;44:409–32. Stowell JR, Addison WE, Smith JL. Comparison of online and classroom-based student evaluations of instruction. Assess Eval High Educ. 2012;37:465–73. https://doi.org/10.1080/02602938.2010.545869 . Davis FD. A technology acceptance model for empirically testing new end-user information systems: theory and results. Thesis. Massachusetts Institute of Technology; 1986. Davis FD, Perceived, Usefulness. Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989;13:319–40. https://doi.org/10.2307/249008 . Granić A, Marangunić N. Technology acceptance model in educational context: A systematic literature review. Br J Educ Technol. 2019;50:2572–93. https://doi.org/10.1111/bjet.12864 . Marikyan D, Papagiannidis S, Stewart G. Technology acceptance research: Meta-analysis. J Inf Sci. 2023;01655515231191177. https://doi.org/10.1177/01655515231191177 . Nevo D, University RY, Albany U. of. Harnessing Information Technology to Improve the Process of Students’ Evaluations of Teaching: An Exploration of Students’ Critical Success Factors of Online Evaluations. J Inf Syst Educ. 2010;21:99–110. Fidelman CG. Course evaluation surveys. In-class paper surveys versus voluntary online surveys. Boston College; 2007. McGourty J, Scoles K, Thorpe S. Web-based student evaluation of instruction: Promises and pitfalls. Toronto, CA; 2002. p. 2003. Porter SR, Umbach PD. Student Survey Response Rates across Institutions: Why Do they Vary? Res High Educ. 2006;47:229–47. https://doi.org/10.1007/s11162-005-8887-1 . Porter SR, Whitcomb ME. Non-response in student surveys: The Role of Demographics, Engagement and Personality. Res High Educ. 2005;46:127–52. https://doi.org/10.1007/s11162-004-1597-2 . Sax LJ, Gilmartin SK, Lee JJ, Hagedorn LS. Using Web Surveys to Reach Community College Students: An Analysis of Response Rates and Response Bias. Community Coll J Res Pract. 2008;32:712–29. https://doi.org/10.1080/10668920802000423 . Clarkberg M, Robertson D, Einarson M. Engagement and student surveys: Nonresponse and implications for reporting survey data. Seattle, WA; 2008. Transforming our world. the 2030 Agenda for Sustainable Development: resolution. New York: UN; 2015. Lee Y, Kozar KA, Larsen KRT. The Technology Acceptance Model: Past, Present, and Future. Commun Assoc Inf Syst. 2003;12. https://doi.org/10.17705/1CAIS.01250 . Marangunić N, Granić A. Technology acceptance model: a literature review from 1986 to 2013. Univers Access Inf Soc. 2015;14:81–95. https://doi.org/10.1007/s10209-014-0348-1 . Fass-Holmes B. Survey Fatigue–What Is Its Role in Undergraduates’ Survey Participation and Response Rates? J Interdiscip Stud Educ. 2022;11:56–73. Porter SR, Whitcomb ME, Weitzer WH. Multiple surveys of students and survey fatigue. New Dir Institutional Res. 2004;2004:63–73. https://doi.org/10.1002/ir.101 . Wu M-J, Zhao K, Fils-Aime F. Response rates of online surveys in published research: A meta-analysis. Comput Hum Behav Rep. 2022;7:100206. https://doi.org/10.1016/j.chbr.2022.100206 . Abdullah F, Ward R. Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Comput Hum Behav. 2016;56:238–56. https://doi.org/10.1016/j.chb.2015.11.036 . Davis FD, Bagozzi RP, Warshaw PR. Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. J Appl Soc Psychol. 1992;22:1111–32. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x . Ballantyne C. Online Evaluations of Teaching: An Examination of Current Practice and Considerations for the Future. New Dir Teach Learn. 2003;2003:103–12. https://doi.org/10.1002/tl.127 . Goodman J, Anson R, Belcheir M. The effect of incentives and other instructor-driven strategies to increase online student evaluation response rates. Assess Eval High Educ. 2015;40:958–70. https://doi.org/10.1080/02602938.2014.960364 . Deci EL, Koestner R, Ryan RM. A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychol Bull. 1999;125:627–68. https://doi.org/10.1037/0033-2909.125.6.627 . Lehtivuori A. When do extrinsic rewards undermine intrinsic motivation? A meta-analysis. 2023. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2025. Posit team. RStudio: Integrated Development Environment for R. Boston, MA: Posit Software, PBC; 2024. Revelle W. psych: Procedures for Psychological, Psychometric, and Personality Research. Evanston, Illinois: Northwestern University; 2025. Rosseel Y. lavaan: An R Package for Structural Equation Modeling. J Stat Softw. 2012;48:1–36. https://doi.org/10.18637/jss.v048.i02 . Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York: Springer-; 2016. Gohel D, Skintzos P. flextable: Functions for Tabular Reporting. 2025. Venkatesh V, Davis FD. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Manag Sci. 2000;46:186–204. https://doi.org/10.1287/mnsc.46.2.186.11926 . To WM, Tang MNF. Computer-based course evaluation: an extended technology acceptance model. Educ Stud. 2019;45:131–44. https://doi.org/10.1080/03055698.2018.1443797 . Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55:68–78. https://doi.org/10.1037//0003-066x.55.1.68 . Tables Table 5 and 6 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Jan, 2026 Reviews received at journal 19 Jan, 2026 Reviewers agreed at journal 19 Jan, 2026 Reviews received at journal 16 Jan, 2026 Reviewers agreed at journal 10 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 19 Dec, 2025 Reviewers invited by journal 19 Dec, 2025 Editor invited by journal 03 Dec, 2025 Editor assigned by journal 28 Nov, 2025 Submission checks completed at journal 27 Nov, 2025 First submitted to journal 27 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8165782","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":563305233,"identity":"0e390085-f759-402d-8ee5-4117175dfdf4","order_by":0,"name":"Leandra Fien","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIie3PMWrDMBSA4SceSMujWhUMzRUaAg6FNLmKi6Fd0tVLAjUEnCUH8OBzdOqgIHC2nKBDSsFdWvAUsrVSIUMHOR076AcJJPTxEEAo9A+7cBvZJZZ2a0EDoLvifsJPhAwAK38I/pW4x+QInCNi2ew/nydAAuu38eKlP1rJzR6ysZ9QPRpUTQqE/H44q5tBZRCvYHfnJyqJI9IIU6Q4esgNKxG5YoXxk/77wZJHO0UeouvcTC0RR1Z8dUwhN8VYQjxiubl1U4AVuuMvs6xX6S3Zv8S9dW1SS4Yq2aVeIsX2SX3o+SVJ06jjwtyUcvPattnES07R72NyFoRCoVCoq29/VkVpQrIy7gAAAABJRU5ErkJggg==","orcid":"","institution":"Charité - University Medicine Berlin","correspondingAuthor":true,"prefix":"","firstName":"Leandra","middleName":"","lastName":"Fien","suffix":""},{"id":563305234,"identity":"fc65b8b9-1f75-42ae-a0c3-c388840f5c08","order_by":1,"name":"Peter Neumann","email":"","orcid":"","institution":"Technical University of Berlin","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Neumann","suffix":""},{"id":563305235,"identity":"98d5d0fb-5161-4ef8-bd02-aa19577468d8","order_by":2,"name":"Martin Krebber","email":"","orcid":"","institution":"Charité - University Medicine Berlin","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Krebber","suffix":""},{"id":563305236,"identity":"ea5ed291-1740-4f56-ac66-0654af72c623","order_by":3,"name":"Ralph Berger","email":"","orcid":"","institution":"Charité - University Medicine Berlin","correspondingAuthor":false,"prefix":"","firstName":"Ralph","middleName":"","lastName":"Berger","suffix":""},{"id":563305237,"identity":"6437532d-a038-4a45-8af6-c24d7b676e28","order_by":4,"name":"Mandy Petzold","email":"","orcid":"","institution":"Charité - University Medicine Berlin","correspondingAuthor":false,"prefix":"","firstName":"Mandy","middleName":"","lastName":"Petzold","suffix":""}],"badges":[],"createdAt":"2025-11-20 14:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8165782/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8165782/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98821398,"identity":"53f10b77-848a-48ae-9e6c-6fed9eb8ca76","added_by":"auto","created_at":"2025-12-22 17:22:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5516282,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptv5.docx","url":"https://assets-eu.researchsquare.com/files/rs-8165782/v1/95d14fbe7184c8023e4e0840.docx"},{"id":98821390,"identity":"c0e12bd4-1cac-4582-b5fd-4f2746f20b83","added_by":"auto","created_at":"2025-12-22 17:22:44","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8040,"visible":true,"origin":"","legend":"","description":"","filename":"a8a8f41659ee4394a1c93f25f0ab1d0f.json","url":"https://assets-eu.researchsquare.com/files/rs-8165782/v1/a48adfd37816dbf305ff549f.json"},{"id":99307584,"identity":"9856a545-8477-4fe7-8ee1-e0db65164d8f","added_by":"auto","created_at":"2025-12-31 16:06:26","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4068072,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8165782/v1/468c24a032b79a48fad6914a.docx"},{"id":99307924,"identity":"0e6fea94-4802-41f5-9472-26e376f0d76a","added_by":"auto","created_at":"2025-12-31 16:07:06","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":171484,"visible":true,"origin":"","legend":"","description":"","filename":"a8a8f41659ee4394a1c93f25f0ab1d0f1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8165782/v1/58744c4fe1fc564119e52ab3.xml"},{"id":98821397,"identity":"2f6cbdc9-11a4-4c39-9be9-4cbc2aad2732","added_by":"auto","created_at":"2025-12-22 17:22:44","extension":"emf","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1004400,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.emf","url":"https://assets-eu.researchsquare.com/files/rs-8165782/v1/ee1170cf58a2eb5232cdd0cf.emf"},{"id":98821394,"identity":"4e4a6f64-362a-489a-a24a-e83a34f10102","added_by":"auto","created_at":"2025-12-22 17:22:44","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12315,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8165782/v1/3e2c389fef78860e4d59b003.png"},{"id":98821396,"identity":"0061f619-a9b0-4e72-b320-ac99a3d07535","added_by":"auto","created_at":"2025-12-22 17:22:44","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13201,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8165782/v1/5a64b0d08f93f7345df21247.png"},{"id":98821402,"identity":"82acc713-3bef-454b-83e0-90f1f8697d0e","added_by":"auto","created_at":"2025-12-22 17:22:44","extension":"xml","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":169436,"visible":true,"origin":"","legend":"","description":"","filename":"a8a8f41659ee4394a1c93f25f0ab1d0f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8165782/v1/569ab0ae7333ff32da4e1eba.xml"},{"id":99307547,"identity":"98819cc8-f08c-450f-aea1-22b057331b5e","added_by":"auto","created_at":"2025-12-31 16:06:23","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":185331,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8165782/v1/0adcf63cee46bf468286d7f9.html"},{"id":98821393,"identity":"8a253ff8-97af-4969-b044-5df439e2c0b5","added_by":"auto","created_at":"2025-12-22 17:22:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45701,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesis-based extended TAM.\u003c/p\u003e\n\u003cp\u003eDark blue: original TAM, light blue: extensions.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8165782/v1/cc055b34a66201d8b328343d.png"},{"id":99308007,"identity":"cdaa0a4c-1d4e-4c6f-88a8-c175f4e63835","added_by":"auto","created_at":"2025-12-31 16:07:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7267,"visible":true,"origin":"","legend":"\u003cp\u003eResponse rates to the TAM evaluation questionnaire\u003c/p\u003e\n\u003cp\u003eDepicted are the responders (blue) and non-responders (red) to our TAM evaluation survey plotted against their participation rate in student evaluation during the two-semester preceding the study. Percentages indicate the proportion of responders among all invited students within each group.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8165782/v1/048dab8c1a63b76f94d683bc.png"},{"id":98821392,"identity":"01e12274-c5d0-4aa8-a21c-3954a1704590","added_by":"auto","created_at":"2025-12-22 17:22:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49649,"visible":true,"origin":"","legend":"\u003cp\u003eSEM with standardized path coefficients\u003c/p\u003e\n\u003cp\u003eDark blue: original TAM, light blue: extensions, *** \u0026lt; .0001\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8165782/v1/49e2494a9ca8292280e0aa36.png"},{"id":99322342,"identity":"fd4bdc68-bb24-47d9-93a3-4b6e5095f246","added_by":"auto","created_at":"2025-12-31 16:43:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1028901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8165782/v1/736bb716-4d31-44f9-b755-dc00240733d7.pdf"},{"id":98821399,"identity":"da1ca4dd-b267-4da7-b709-7c5a5073b5ff","added_by":"auto","created_at":"2025-12-22 17:22:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4068072,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8165782/v1/01857fde661a2a09c4a557cc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improving Medical Education through Student Feedback: Key Factors Driving Response Rates in Online Student Evaluations of Teaching - An Extended Technology Acceptance Model","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe importance of student evaluation of teaching (SET) is particularly pronounced in health professions education, given the unique demands placed on teaching and learning contexts. In these settings, the role of feedback is not just informative; it can actively shape teaching practices and student outcomes. First, unlike in other academic fields, educators in clinical teaching, for example in medicine or midwifery, must not only convey state-of-the-art knowledge and up-to-date scientific insights. Educators also need to show how these concepts translate into practical competencies in everyday clinical settings. Feedback from SET plays a pivotal role in refining these pedagogical skills. It contributes to the ongoing development of their professional identity as teachers, in addition to their roles as researchers and clinicians [1, 2]. Second, insights from SET help to better integrate unique course formats, such as laboratory-based training, simulation exercises, bedside teaching, and clinical rotations into modern curricula that foster students\u0026apos; competence-based medical training in interdisciplinary and interprofessional settings from the first semester onward [3]. Third, aggregated SET data across teaching sites, facilities, and hierarchies of decision-making can help to monitor the organizational and structural conditions and provide an empirical basis for allocating teaching responsibilities and financial resources [4]. By doing so, SET enable faculties to coordinate medical education effectively within the operational realities of hospital care, where shift work, patient safety, and other logistical constraints create unique challenges.\u003c/p\u003e\n\u003cp\u003eTo serve its purpose effectively by ensuring statistical validity and adequate interpretability, sufficient participation in SET is crucial [5]. However, with the shift from paper-based to online evaluations, SET response rates have declined markedly, increasing the risk of non-response bias, as shown by many studies\u0026nbsp;[6\u0026ndash;15].\u003c/p\u003e\n\u003cp\u003eTo address this challenge at Charit\u0026eacute;, we implemented a newly developed online tool (described in section 2.1). Following implementation, response rates increased substantially, prompting an investigation into the factors that contributed to this improvement. To identify in particular which technical, procedural, and motivational factors in particular drive higher participation in online student evaluations of teaching - and to allow for potential generalization to other educational contexts - we applied an extended Technology Acceptance Model (TAM)\u0026nbsp;[16\u0026ndash;19]. Unlike many previous TAM-based studies, this research links acceptance mechanisms directly to objective response behavior, rather than self-reported intention - a rarely examined outcome in SET research [19].\u003c/p\u003e\n\u003ch2\u003e1.1 Online SET\u003c/h2\u003e\n\u003cp\u003eOnline SET offer the advantages of cost-effectiveness, faster return of results and overall higher convenience and flexibility for students, teachers and administration. In addition, since students can fill out the surveys after the courses, they have more time to consider their answers and are more likely to fill out and give longer responses in open-ended questions [10, 12, 15]. However, one major challenge is the often significantly lower response rate compared to paper-based SET. Reasons for this include a lack of social pressure, more distractions outside the classroom, survey fatigue due to more feasible and therefore more frequent online evaluations, an increased perception of survey length, or technical difficulties\u0026nbsp;[6\u0026ndash;15, 20]. Most studies do not find a significant difference between online and paper-based for the evaluation scores themselves\u0026nbsp;[7\u0026ndash;12, 15]. Rather than the difference between online and paper-based evaluation, the risk of distorted results stems from a general difference between responders and non-responders. Responders in SET have higher grades and higher academic performance\u0026nbsp;[6, 7, 21\u0026ndash;24], differ in study major and personality type\u0026nbsp;[6, 14, 24], are more likely to be female\u0026nbsp;[7, 21\u0026ndash;25]\u0026nbsp;and are more likely to be white\u0026nbsp;[7, 21, 26]. To reduce the non-response bias and obtain a representative sample of opinions on teaching quality, it is essential for faculties to aim for high SET response rates, since the decisions derived from this data affect all student subgroups. Therefore, we investigated which factors are associated with increased participation in SET, using TAM as a framework. In doing so, we aim to promote equitable opportunities for all students to contribute to teaching improvement, support lifelong learning among medical educators and strengthen evidence-based faculty development, thus supporting the principles of Sustainable Development Goal 4\u0026nbsp;[27].\u003c/p\u003e\n\u003ch2\u003e1.2 The Technology Acceptance Model\u003c/h2\u003e\n\u003ch3\u003eCore Constructs\u003c/h3\u003e\n\u003cp\u003eTAM [16, 17] is one of the most widely used theoretical frameworks for explaining and predicting user acceptance of technology-based systems\u0026nbsp;[18, 28, 29]. At its core, it posits that two central beliefs of technology users \u0026mdash; Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) determine their Behavioral Intention to Use (BI) the system, which in turn predicts its Actual Use (AU). PU refers to the degree to which a person believes that using a particular system will enhance their performance, which directly affects the user\u0026rsquo;s BI to use the system. PEOU describes the degree to which a person believes that using the system will be free of effort. It affects BI directly and indirectly via PU, i.e. a user-friendly system design increases the system\u0026rsquo;s PU as well as the user\u0026rsquo;s BI to use it. These core relationships have been applied and validated across a wide range of contexts\u0026nbsp;[18, 28, 29]. However, no study has yet applied TAM to online SET in medical education programs. Further, the majority of studies applying TAM to various technologies do not test whether BI indeed predicts AU\u0026nbsp;[19]. To this end, we aim to test the hypotheses of the core TAM in the context of online SET:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHypothesis 1: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Perceived usefulness has a positive effect on behavioral intention.\u003c/p\u003e\n\u003cp\u003eHypothesis 2a: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Perceived ease of use has a positive effect on behavioral intention.\u003c/p\u003e\n\u003cp\u003eHypothesis 2b:\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Perceived ease of use has a positive effect on perceived usefulness.\u003c/p\u003e\n\u003cp\u003eHypothesis 3: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Behavioral intention has a positive effect on actual system use.\u003c/p\u003e\n\u003ch3\u003eTAM Extension\u003c/h3\u003e\n\u003cp\u003eAdditional factors may play a role in determining whether students participate in online SET: First, the medical profession is characterized by a wide variety of highly specialized subdisciplines. This means that in modern medicine curricula many experts share the teaching load, resulting in a high number of individual courses. In addition, small group sizes vastly increase the overall number of teaching sessions. The aim of a good evaluation system is to capture this complexity in high resolution to provide individual feedback tailored to teachers and teaching departments. Consequently, students are expected to fill out a large number of surveys, which could lead to a perception of the evaluation system as having a high workload. This could overwhelm students and thereby decrease their BI to use the evaluation system overall, as indicated by studies on survey fatigue\u0026nbsp;[6, 30\u0026ndash;32]. Alternatively, this could also be perceived as a design-flaw of the technology, negatively affecting the user-friendliness of the system. No studies so far have explicitly tested how Perceived Workload (PW), defined as the intensity or frequency by which users are expected to use the technology, influences its acceptance. In the seminal paper of TAM, Davis defines PEOU as \u0026nbsp;the \u0026ldquo;\u003cem\u003elack of effort a user has to bring up\u003c/em\u003e\u0026rdquo; [17], which is independent of our variable PW, because it refers more to the friendliness or ease-of-use of the design, which also holds true for a single interaction. Thus, our mutually non-exclusive hypotheses are:\u003c/p\u003e\n\u003cp\u003eHypothesis 4a: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Perceived workload has a negative effect on perceived ease of use.\u003c/p\u003e\n\u003cp\u003eHypothesis 4b: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;Perceived workload has a negative effect on behavioral intention.\u003c/p\u003e\n\u003cp\u003eWithin the TAM framework, Intrinsic Motivation (IM) and Extrinsic Motivation (EM) have been extensively studied as drivers behind technology acceptance [33]. In this context, IM is usually defined as the perceived enjoyment of using a system at its own end, whereas EM refers to using a system to achieve outcomes other than the interaction itself, for example improved performance in the task [34]. However, since our goal is to investigate the motivational drivers of participation in SET, our definitions of IM and EM are conceptually distinct from those used in the traditional TAM framework. Here, IM denotes students\u0026rsquo; inner desire to provide meaningful feedback and contribute to teaching improvement, regardless of how enjoyable the system itself is to use. EM, in contrast, refers to participation driven by external factors such as incentives. Understanding how these two motivational mechanisms act and interact is essential for designing evaluation systems that are both engaging and educationally sustainable.\u003c/p\u003e\n\u003cp\u003eResearch indicates that response rates are higher in courses within students\u0026rsquo; major field of study [6], likely due to stronger identification with and responsibility toward their academic community. Such perceptions foster IM, which in turn is associated with sustained engagement and higher-quality responses. Moreover, communicating to students the potential role of evaluations for improving teaching quality has been discussed as a means to increase participation [6, 11, 15]. In the TAM framework, IM may enhance PU, as students who are internally motivated to provide constructive feedback are more likely to recognize how the system supports educational improvement. Moreover, intrinsically motivated students should display a stronger BI to use the system, as their participation stems from self-determined engagement rather than obligation. Thus, we formulated the following hypotheses:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHypothesis 5a: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intrinsic motivation has a positive effect on perceived usefulness.\u003c/p\u003e\n\u003cp\u003eHypothesis 5b: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intrinsic motivation has a positive effect on behavioral intention.\u003c/p\u003e\n\u003cp\u003eIn addition to intrinsic drivers, incentives have been shown to increase students\u0026rsquo; BI to use technological systems in the context of SET [9, 35, 36]. Examples include small grade incentives, bringing a note card to the examination, making the examination optional, or offering a cash prize or treats [9, 35, 36]. However, introducing external rewards carries the potential risk of undermining students\u0026rsquo; IM to provide meaningful feedback, leading them to participate solely for the incentive rather than to improve teaching quality [37]. A recent meta-analysis provided evidence for this effect for rewards contingent to the task or performance [38]. It is plausible that this applies to incentives in SET, since students receive the reward upon completing the evaluation, which may reduce the quality of responses and long-term engagement with evaluation processes. To our knowledge, this trade-off between EM and IM has not yet been investigated in the context of SET. Based on these considerations, we formulated the following hypotheses:\u003c/p\u003e\n\u003cp\u003eHypothesis 6a: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Extrinsic motivation has a positive effect on behavioral intention.\u003c/p\u003e\n\u003cp\u003eHypothesis 6b: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Extrinsic motivation has a negative effect on intrinsic motivation.\u003c/p\u003e\n\u003cp\u003eFigure 1 provides a visual overview of all hypotheses.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003ch2\u003e2.1 Participants and Procedure\u003c/h2\u003e\n\u003cp\u003eThe data was collected by the Quality Assurance Department for Teaching and Learning at Charit\u0026eacute; - Universit\u0026auml;tsmedizin Berlin, Germany. The study was approved by the Ethics Committee of Charit\u0026eacute; (approval number: EA2/064/25). All medical students from the 2nd to the 10th semester were invited to participate in the survey. The survey was distributed electronically and remained open for four weeks in July 2025. Students who had not yet participated received up to three weekly reminder emails. Participants provided informed consent prior to completing the survey. Participation was voluntary, anonymous, and could be withdrawn at any time without disadvantage. In total, 897 of 3,088 invited students accessed the questionnaire, resulting in a response rate of 29.05%.\u003c/p\u003e\n\u003cp\u003eThe technology tested in this study, EVABOX, is a newly developed tool (implemented in fall 2023) for online SET at Charit\u0026eacute;. It includes several improvements over previous systems, such as an improved online interface, a clearer and more transparent evaluation process, shorter and standardized questionnaires, and an incentive system designed to encourage higher participation rates. Students receive points for completing their SET. These points give them a small non-academic advantage - a higher chance of being placed in the preferred study group in the next semester. For this study, participation in the survey was linked to this system: students received points for completing the survey, thereby providing a non-monetary incentive to participate.\u003c/p\u003e\n\u003ch2\u003e2.2 Measures\u0026nbsp;\u003c/h2\u003e\n\u003ch3\u003eIndependent Variables: Instrument Development and Validation\u003c/h3\u003e\n\u003cp\u003eA custom questionnaire was developed to operationalize the core constructs of the TAM and to include additional factors hypothesized to influence participation in online SET. It was specifically designed to support the internal development of EVABOX, while also allowing the testing of study hypotheses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eItem development followed a three-step process. First, items were generated based on an extensive literature review and the department\u0026rsquo;s experience in educational evaluation. The items were drafted to represent key TAM constructs (PU, PEOU, BI) as well as complementary constructs (IM, EM, PW). Second, the items underwent an expert review, during which faculty and educational researchers with longstanding experience evaluated them for content validity, clarity, and contextual relevance. Finally, the preliminary version was refined through cognitive pretesting using think-aloud interviews and structured feedback from a student pilot group (N = 7).\u003c/p\u003e\n\u003cp\u003eMost items were measured on a five-point Likert scale ranging from \u0026ldquo;completely agree\u0026rdquo; (1) to \u0026ldquo;completely disagree\u0026rdquo; (5). Items on PW were measured using bipolar scales ranging from \u0026ldquo;too high\u0026rdquo; (1) to \u0026ldquo;too low\u0026rdquo; (5) or \u0026ldquo;too long\u0026rdquo; (1) to \u0026ldquo;too short\u0026rdquo; (5) (for custom questionnaire see Table 5 in Supplementary Material).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eDependent Variable\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eTo objectively measure Actual System Use (AU) and prevent socially desirable self-reports, we obtained an objective measure by assigning students to twelve survey groups corresponding to their response rate in SET from the previous two semesters (0%, \u0026gt;0\u0026ndash;5%, \u0026gt;5\u0026ndash;10%, \u0026gt;10\u0026ndash;20%, \u0026gt;20\u0026ndash;30%, \u0026gt;30\u0026ndash;40%, \u0026gt;40\u0026ndash;50%; \u0026gt;50\u0026ndash;60%; \u0026gt;60\u0026ndash;70%; \u0026gt;70\u0026ndash;80%, \u0026gt;80\u0026ndash;90%, \u0026gt;90\u0026ndash;100%). For an overview of participation rates per group, see Figure 2. Group sizes were chosen to ensure anonymity and to minimize the risk of re-identification when combined with demographic information (age, sex, year of study) collected in the survey.\u003c/p\u003e\n\u003cp\u003eDepicted are the responders (blue) and non-responders (red) to our TAM evaluation survey plotted against their participation rate in student evaluation during the two-semester preceding the study. Percentages indicate the proportion of responders among all invited students within each group.\u003c/p\u003e\n\u003ch2\u003e2.3 Data Preprocessing\u003c/h2\u003e\n\u003cp\u003eData cleaning of the initial dataset (N = 897) followed a stepwise procedure. Item-level inspection revealed that 93% of respondents were unsure or incorrect about where to access aggregated cohort-level evaluation questionnaires and results. Given this knowledge gap, items about their usability and usefulness (PEOU6, PU6, PU10) could not be validly evaluated and were therefore excluded from further analyses. At the case level, n = 44 participants with missing data resulting from study withdrawal or lack of informed consent were removed. Additionally, participants were excluded due to non-use of the system either entirely or prior to the previous semester (n = 12), or invariable response patterns indicative of straightlining (variance \u0026lt; .1, n = 7), yielding a final sample of N = 834.\u003c/p\u003e\n\u003ch2\u003e2.4 Data Analysis\u003c/h2\u003e\n\u003cp\u003eThe statistical analysis of the data was performed in R, version 4.4.3 [39] and RStudio, version 2024.09.01 [40]. Packages used during the analysis included psych [41] for psychometric analyses including reliability and exploratory factor analysis, lavaan [42] for confirmatory factor analysis (CFA) and structural equation modeling (SEM) and ggplot2 [43] and flextable [44] for visualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo examine and validate the factor structure without double-testing, the sample was stratified by year of study and randomly split into two subsamples (EFA: n₁ = 325; CFA: n₂ = 509). This stratified random sampling ensured balanced representation across study years and an adequate subject-to-item ratio for factor analysis. Stratification by study year was used because TAM questionnaire response rates varied across academic years (r(829) = \u0026ndash;.385, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eGiven notable skewness across multiple items, the EFA was conducted using polychoric correlations, with minimum residual estimation and oblique rotation to allow for correlated factors. Parallel analysis informed the selection of the number of factors, loadings \u0026ge; .30 were considered meaningful. The subsequent CFA was carried out on the second subsample, treating items as ordered and using robust weighted least squares estimation (WLSMV), based on the factor structure identified in the EFA and theoretical considerations. Finally, an SEM using ordered items and WLSMV was specified to test the hypothesized relationships between latent constructs, with factor loadings freely estimated based on the CFA-validated measurement model.\u003c/p\u003e\n\u003cp\u003eModel fit was assessed using multiple indices: Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) \u0026ge;.90 indicated acceptable fit, while Root Mean Square Error of Approximation (RMSEA) \u0026le;.08 and Standardized Root Mean Square Residual (SRMR) \u0026le;.08 indicated good fit. Standardized factor loadings were extracted. Internal consistency of the identified factors was assessed using Cronbach\u0026rsquo;s alpha and McDonald\u0026rsquo;s omega, with values \u0026ge;.70 considered acceptable.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Participant Characteristics\u003c/h2\u003e\n \u003cp\u003eA total of N\u0026thinsp;=\u0026thinsp;834 participants were included in the final analysis. Of these, 554 (66.4%) identified as female, 257 (30.8%) as male, 5 (.6%) as diverse, and 18 (2.2%) preferred not to disclose their gender. Of the participants, 522 (62.6%) were aged 21\u0026ndash;23 years, 179 (21.5%) were 18\u0026ndash;20 years, 97 (11.6%) were 24\u0026ndash;26 years, and 33 (4%) were over 26 years. 3 (.4%) preferred not to disclose their age. All study cohorts were represented, with 244 (29.3%) in the third year, 212 (25.4%) in the second year, 171 (20.5%) in the fourth year, 133 (15.9%) in the first year (second semester), 71 (8.5%) in the fifth year, and 3 (.4%) did not disclose their year of study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Instrument Validation\u003c/h2\u003e\n \u003cp\u003eEFA and CFA\u003c/p\u003e\n \u003cp\u003eEFA was conducted on polychoric correlations of 36 items using minimum residual estimation with oblimin rotation. Consistent with our theoretical expectation, a multifactorial structure of PU, PEOU, IM, EM, PW and BI was confirmed. Items that loaded on additional factors were not examined further in this study (see Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e in Supplementary Material).\u003c/p\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eCFA was performed on the second subsample using the WLSMV estimator. The scaled fit estimates indicated an acceptable fit to the data: \u0026chi;\u0026sup2;(155)\u0026thinsp;=\u0026thinsp;497.359, p\u0026thinsp;\u0026lt;\u0026thinsp;.001; CFI\u0026thinsp;=\u0026thinsp;.986; TLI\u0026thinsp;=\u0026thinsp;.982; RMSEA\u0026thinsp;=\u0026thinsp;.066 (90% CI [.059, .072]); SRMR\u0026thinsp;=\u0026thinsp;.066. All standardized factor loadings were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), with an average loading of .799, ranging from .463 (PU9) to .983 (IM3), indicating good item-factor relationships. Factor correlations ranged from \u0026ndash;.38 to .6 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), indicating moderate associations and supporting discriminant validity.\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003c/div\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCFA latent factor correlations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEM\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eAll correlations are significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.01) except PEOU-IM (p\u0026thinsp;=\u0026thinsp;.07) and IM-EM (p\u0026thinsp;=\u0026thinsp;.09).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eInternal consistency\u003c/p\u003e\n \u003cp\u003eInternal consistency of each factor was evaluated using both Cronbach\u0026rsquo;s alpha and McDonald\u0026rsquo;s omega coefficients. While Cronbach\u0026rsquo;s alpha values for PU and PEOU were moderately low (around .65), McDonald\u0026rsquo;s omega consistently exceeded the commonly accepted threshold of .7. Given that omega does not assume tau-equivalence and accounts for varying item loadings, it provides a more accurate and robust estimate of internal consistency than alpha. Overall, these results corroborate the factorial validity of the instrument (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eReliability estimates for CFA factors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCronbach\u0026rsquo;s Alpha\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMcDonald\u0026rsquo;s Omega\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.929\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eThe table reports Cronbach\u0026rsquo;s alpha and McDonald\u0026rsquo;s omega coefficients for all latent constructs.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\u003cbr\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Descriptive statistics for validated factors\u003c/h2\u003e\n \u003cp\u003eThe descriptive statistics indicate that the students perceived the evaluation tool generally positively. Participants showed relatively high scores on BI (M\u0026thinsp;=\u0026thinsp;1.43, SD\u0026thinsp;=\u0026thinsp;.73), IM (M\u0026thinsp;=\u0026thinsp;1.7, SD\u0026thinsp;=\u0026thinsp;.836), and PU (M\u0026thinsp;=\u0026thinsp;2.047, SD\u0026thinsp;=\u0026thinsp;.622), reflecting favorable attitudes and perceived benefits. PEOU was also rated positively (M\u0026thinsp;=\u0026thinsp;1.498, SD\u0026thinsp;=\u0026thinsp;.61), suggesting that the tool was relatively easy to use. PW received a higher mean score (M\u0026thinsp;=\u0026thinsp;2.86, SD\u0026thinsp;=\u0026thinsp;.57), indicating that participants generally perceived the workload as a bit too high. EM (M\u0026thinsp;=\u0026thinsp;2.046, SD\u0026thinsp;=\u0026thinsp;1.031) showed the greatest variability, reflecting diverse opinions regarding the incentive system.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 SEM\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe SEM was conducted on the same subsample used for CFA (n₂ = 509), scaled fit estimates are reported. The chi-square test was significant (\u0026chi;\u0026sup2;(179)\u0026thinsp;=\u0026thinsp;660.372, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) as expected with large samples. Absolute fit indices indicated acceptable fit (RMSEA\u0026thinsp;=\u0026thinsp;.073 (90% CI [.067, .079]), SRMR\u0026thinsp;=\u0026thinsp;.087), and incremental fit indices supported good fit (CFI\u0026thinsp;=\u0026thinsp;.980, TLI\u0026thinsp;=\u0026thinsp;.976). All observed variables showed standardized factor loadings\u0026thinsp;\u0026gt;\u0026thinsp;.43 on their respective latent constructs, indicating good indicator reliability. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e depicts the standardized path coefficients of the measurement model. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents whether the hypothesized relationships in the structural model were supported.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStandardized SEM loadings\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBI1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBI3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePW3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePW4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIM3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIM6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEM3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eAlong with the standardized factor loadings (\u0026beta;), the table presents the corresponding standard errors, z-values, and significance levels for all SEM items.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHypothesis testing results for SEM paths\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHypo-thesis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpected Direction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSup-ported\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU \u0026rarr; BI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU \u0026rarr; BI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU \u0026rarr; PU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBI \u0026rarr; AU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH4a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePW \u0026rarr; PEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;7.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH4b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePW \u0026rarr; BI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash;.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH5a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIM \u0026rarr; PU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH5b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIM \u0026rarr; BI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH6a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEM \u0026rarr; BI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH6b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEM \u0026rarr; IM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eSupported indicates whether the hypothesis was confirmed based on the significance and sign of the standardized path coefficient (\u0026beta;).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\u003cbr\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eFor a visual representation of the model, including the relationships among latent constructs and the strength of each path, see Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Interpretation of Findings\u003c/h2\u003e \u003cp\u003eThis study examined factors that drive medical students\u0026rsquo; participation in online SET through the lens of an extended TAM. Consistent with TAM\u0026rsquo;s core propositions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], we show that PU and PEOU directly determine the students\u0026rsquo; BI to use the evaluation system. We further replicated the established indirect effect of PEOU on BI via PU, indicating that good usability additionally promotes participation in the evaluation system because it is perceived as more useful. Furthermore, unlike many previous studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], we validate our TAM by showing that users\u0026rsquo; intention to engage with the system translates into actual usage, as indicated by the strong, positive relationship between BI and AU. These findings align with prior applications of TAM in educational evaluation contexts [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and suggest that an intuitive, purpose-driven system design can effectively enhance response rates in SET.\u003c/p\u003e \u003cp\u003ePW was inversely related to PEOU but did not directly affect BI. Thus, higher evaluation workload negatively affects participation in SET, not because students are discouraged by the workload per se, but because it reduces the overall acceptance of the system. Thus, while an evaluation system may meet technological requirements by being user-friendly and useful in fostering teaching quality, the underlying processes, such as completing numerous evaluation forms, may not align well with it. Our results indicate that students perceive this mismatch, which indirectly lowers engagement and, ultimately, participation rates.\u003c/p\u003e \u003cp\u003eOur results show that the influence of IM on BI is fully mediated by PU. Students who are intrinsically motivated to improve teaching quality are inclined to participate not merely because they find evaluation meaningful in itself, but because they perceive the system as an effective tool for achieving this goal. This is in line with Self-Determination Theory [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], which posits that intrinsically motivated behavior depends on the fulfillment of autonomy, competence, and relatedness needs. The technology thus functions as a means of translating students\u0026rsquo; IM into action by supporting their internalized values of contributing to better teaching for future cohorts.\u003c/p\u003e \u003cp\u003eFinally, our results show that introducing an incentive that rewards higher response rates substantially increases students\u0026rsquo; BI to use the system and is therefore its strongest predictor. This replicates previous findings on the positive influence of incentives within the TAM framework [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Surprisingly, instead of a negative association, we observe a positive effect of EM on IM, which provides no evidence of an undermining effect on the drive of intrinsically motivated students due to the introduction of an incentive [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This may be explained by recent findings that observational studies relying on self-reported IM provide only weak evidence for an undermining effect, whereas studies using behavioral data typically report stronger negative effects once rewards are withdrawn [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In our observational study, incentives were available, and data was collected shortly after their introduction into the SET system, which may explain why this undermining effect was not observed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Practical Implications\u003c/h2\u003e \u003cp\u003eThe results have three main practical implications for increasing response rates in online evaluation systems in medical education.\u003c/p\u003e \u003cp\u003eFirst, medical faculties should reinforce students\u0026rsquo; perception of the system\u0026rsquo;s usefulness, particularly to engage those who are intrinsically motivated to improve teaching quality. Increasing the visibility of changes implemented in response to feedback can strengthen students\u0026rsquo; sense of efficacy and help cultivate a constructive, sustainable culture of SET within faculties.\u003c/p\u003e \u003cp\u003eSecond, increased response rates can be achieved by designing an intuitive and user-friendly SET technology. However, the processes in which the technology is embedded can be misaligned, resulting for example in increased evaluation workload, which leads to a decrease in technology acceptance and hence response rates. Thus, although adverse evaluation processes can to some extent be compensated for by a user-friendly technology as indicated by our results, designers and policy makers should aim for a better fit by adapting not only the technology\u0026rsquo;s usability features but also the underlying evaluation processes.\u003c/p\u003e \u003cp\u003eThird, strategically implemented extrinsic incentives can substantially increase response rates, potentially motivating students who would not otherwise participate. Since they engage intrinsically motivated students as well, incentives can help faculties develop more inclusive, motivating, and sustainable feedback systems. Increasing representative participation in SET supports equitable educational outcomes, contributing to the aims of SDG 4 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These findings can inform institutional strategies for faculty development workshops and evaluation policies, ensuring that feedback data is effectively translated into teaching improvement and faculty growth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Limitations\u003c/h2\u003e \u003cp\u003eOne limitation is the study\u0026rsquo;s cross-sectional design with data assessed shortly after implementing the SET system. This could introduce a novelty bias, neglecting potential temporal effects and restricting causal inference. Further, the study implemented a custom questionnaire that was adapted to specifics of the SET technology and partly resulted in factors with relatively low internal consistency, which could limit its generalizability to other evaluation technologies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Future Research\u003c/h2\u003e \u003cp\u003eLongitudinal studies could explore temporal dynamics that may negatively affect response rates or data quality, including fatigue from continuous evaluation demands, the decreasing effectiveness of incentives, and the and the possibility that sustained extrinsic motivation gradually erodes intrinsic motivation, where EM gradually erodes IM, potentially leading to lower-quality data due to careless or nonsensical responses.\u003c/p\u003e \u003cp\u003eFurthermore, future studies could explore the mechanisms linking IM and PU of SET by integrating self-efficacy and outcome expectancy. When students feel confident in their ability to give constructive feedback and believe that their evaluations lead to real improvements in teaching, these beliefs may reinforce IM and enhance PU, ultimately promoting more thoughtful and effective feedback. Understanding these mechanisms could inform the design of evaluation systems that enhance both participation and the quality of student feedback, thereby fostering a sustained and effective culture of SET in which feedback is valued, acted upon, and integrated into ongoing teaching improvement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Conclusion\u003c/h2\u003e \u003cp\u003eTaken together, these findings reinforce TAM\u0026rsquo;s explanatory power in the context of SET while highlighting the importance of integrating motivational components into acceptance models that regard the purpose of the technology and not its usage as such. PU remains the central gateway linking IM to BI, whereas EM exerts the strongest influence on participation. For institutional practice, this suggests that fostering sustainable engagement requires more than technical optimization. It demands a system that students perceive as useful, impactful, and responsive. Building visible feedback loops, simplifying participation, and strategically using incentives can together enhance the acceptance of the evaluation technology and hence increase response rates in SET. Ultimately, strengthening participation in SET contributes directly to faculty development by creating a continuous feedback culture that supports reflective teaching practice, evidence-based improvement, and sustainable quality enhancement in medical education.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eActual Use\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBehavioral Intention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComparative Fit Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfirmatory Factor Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExploratory Factor Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtrinsic Motivation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntrinsic Motivation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Ethics Committee of Charit\u0026eacute; \u0026ndash; Universit\u0026auml;tsmedizin Berlin (approval number: EA2/064/25). All procedures were conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this research are available upon individual request from the corresponding authors. The data are not publicly available due to privacy-related restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLeandra Fien and Peter Neumann received funding for their positions from the Senate of Berlin through the program \u0026ldquo;Berliner Qualit\u0026auml;ts- und Innovationsoffensive II\u0026rdquo; (CH-QIO-II-01).Formularbeginn\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFormularende\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.F. and P.N. developed the questionnaire. L.F. conducted the pretest and collected data for all items except Actual Use (AU). P.N., M.K., and R.B. collected the data forming the basis for the Actual System Use (AU) variable. L.F. and P.N. performed the data analysis, M.P. assisted. L.F., and P.N. interpreted the results, M.P. assisted. P.N., L.F., and M.P. contributed to writing the main manuscript text. P.N. prepared the figures. All authors reviewed and approved the manuscript. Leandra Fien and Peter Neumann share first authorship, as they contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all study participants for their valuable contributions. We also gratefully acknowledge the excellent statistical consulting provided by Levin Wiebelt from the Statistical Consulting Service at Charit\u0026eacute;, as well as the support of Sebastian Galli, Ulrike Waschau, Hannah Tame, and Matthias Berlin, who were part of the EVABOX Development Team.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMahajan R, Goyal PK, Singh T. Professional Identity Formation of Medical Educators: A Thematic Analysis of Enabling Factors and Competencies Needed. Int J Appl Basic Med Res. 2022;12:189\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/ijabmr.ijabmr_257_22\u003c/span\u003e\u003cspan address=\"10.4103/ijabmr.ijabmr_257_22\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteinert Y, O\u0026rsquo;Sullivan PS, Irby DM. Strengthening Teachers\u0026rsquo; Professional Identities Through Faculty Development. Acad Med. 2019;94:963\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/ACM.0000000000002695\u003c/span\u003e\u003cspan address=\"10.1097/ACM.0000000000002695\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHitzblech T, Maaz A, Rollinger T, Ludwig S, Dettmer S, Wurl W, et al. The modular curriculum of medicine at the Charit\u0026eacute; Berlin \u0026ndash; a project report based on an across-semester student evaluation. GMS J Med Educ. 2019;36:Doc54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3205/zma001262\u003c/span\u003e\u003cspan address=\"10.3205/zma001262\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConstantinou C, Wijnen-Meijer M. Student evaluations of teaching and the development of a comprehensive measure of teaching effectiveness for medical schools. BMC Med Educ. 2022;22:113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12909-022-03148-6\u003c/span\u003e\u003cspan address=\"10.1186/s12909-022-03148-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNulty DD. The adequacy of response rates to online and paper surveys: what can be done? Assess Eval High Educ. 2008;33:301\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02602930701293231\u003c/span\u003e\u003cspan address=\"10.1080/02602930701293231\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdams MJD, Umbach PD. Nonresponse and Online Student Evaluations of Teaching: Understanding the Influence of Salience, Fatigue, and Academic Environments. Res High Educ. 2012;53:576\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11162-011-9240-5\u003c/span\u003e\u003cspan address=\"10.1007/s11162-011-9240-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvery RJ, Bryant WK, Mathios A, Kang H, Bell D. Electronic Course Evaluations: Does an Online Delivery System Influence Student Evaluations? J Econ Educ. 2006;37:21\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3200/JECE.37.1.21-37\u003c/span\u003e\u003cspan address=\"10.3200/JECE.37.1.21-37\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenton SL, Webster R, Gross AB, Pallett WH. An analysis of IDEA student ratings of instruction using paper versus online survey methods 2002\u0026ndash;2008 data. IDEA Tech Rep. 2010;16:1\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDommeyer CJ, Baum P, Hanna RW, Chapman KS. Gathering faculty teaching evaluations by in-class and online surveys: their effects on response rates and evaluations. Assess Eval High Educ. 2004;29:611\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02602930410001689171\u003c/span\u003e\u003cspan address=\"10.1080/02602930410001689171\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuder F, Malliaris M. Online and Paper Course Evaluations. Am J Bus Educ. 2010;3:131\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKucsera JV, Zimmaro DM. Electronic course instructor survey (eCIS) report. Austin TX Div Instr Innov Assess Univ Tex Austin; 2008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLayne BH, Decristoforo JR, Mcginty D, ELECTRONIC VERSUS TRADITIONAL STUDENT, RATINGS OF INSTRUCTION. Res High Educ. 1999;40:221\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1023/A:1018738731032\u003c/span\u003e\u003cspan address=\"10.1023/A:1018738731032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNowell C, Gale LR, Handley B. Assessing faculty performance using student evaluations of teaching in an uncontrolled setting. Assess Eval High Educ. 2010. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02602930902862875\u003c/span\u003e\u003cspan address=\"10.1080/02602930902862875\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSax LJ, Gilmartin SK, Bryant AN. Assessing response rates and nonresponse bias in web and paper surveys. Res High Educ. 2003;44:409\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStowell JR, Addison WE, Smith JL. Comparison of online and classroom-based student evaluations of instruction. Assess Eval High Educ. 2012;37:465\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02602938.2010.545869\u003c/span\u003e\u003cspan address=\"10.1080/02602938.2010.545869\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis FD. A technology acceptance model for empirically testing new end-user information systems: theory and results. Thesis. Massachusetts Institute of Technology; 1986.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis FD, Perceived, Usefulness. Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989;13:319\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/249008\u003c/span\u003e\u003cspan address=\"10.2307/249008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGranić A, Marangunić N. Technology acceptance model in educational context: A systematic literature review. Br J Educ Technol. 2019;50:2572\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/bjet.12864\u003c/span\u003e\u003cspan address=\"10.1111/bjet.12864\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarikyan D, Papagiannidis S, Stewart G. Technology acceptance research: Meta-analysis. J Inf Sci. 2023;01655515231191177. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/01655515231191177\u003c/span\u003e\u003cspan address=\"10.1177/01655515231191177\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNevo D, University RY, Albany U. of. Harnessing Information Technology to Improve the Process of Students\u0026rsquo; Evaluations of Teaching: An Exploration of Students\u0026rsquo; Critical Success Factors of Online Evaluations. J Inf Syst Educ. 2010;21:99\u0026ndash;110.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFidelman CG. Course evaluation surveys. In-class paper surveys versus voluntary online surveys. Boston College; 2007.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGourty J, Scoles K, Thorpe S. Web-based student evaluation of instruction: Promises and pitfalls. Toronto, CA; 2002. p. 2003.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePorter SR, Umbach PD. Student Survey Response Rates across Institutions: Why Do they Vary? Res High Educ. 2006;47:229\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11162-005-8887-1\u003c/span\u003e\u003cspan address=\"10.1007/s11162-005-8887-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePorter SR, Whitcomb ME. Non-response in student surveys: The Role of Demographics, Engagement and Personality. Res High Educ. 2005;46:127\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11162-004-1597-2\u003c/span\u003e\u003cspan address=\"10.1007/s11162-004-1597-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSax LJ, Gilmartin SK, Lee JJ, Hagedorn LS. Using Web Surveys to Reach Community College Students: An Analysis of Response Rates and Response Bias. Community Coll J Res Pract. 2008;32:712\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10668920802000423\u003c/span\u003e\u003cspan address=\"10.1080/10668920802000423\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClarkberg M, Robertson D, Einarson M. Engagement and student surveys: Nonresponse and implications for reporting survey data. Seattle, WA; 2008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTransforming our world. the 2030 Agenda for Sustainable Development: resolution. New York: UN; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee Y, Kozar KA, Larsen KRT. The Technology Acceptance Model: Past, Present, and Future. Commun Assoc Inf Syst. 2003;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17705/1CAIS.01250\u003c/span\u003e\u003cspan address=\"10.17705/1CAIS.01250\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarangunić N, Granić A. Technology acceptance model: a literature review from 1986 to 2013. Univers Access Inf Soc. 2015;14:81\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10209-014-0348-1\u003c/span\u003e\u003cspan address=\"10.1007/s10209-014-0348-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFass-Holmes B. Survey Fatigue\u0026ndash;What Is Its Role in Undergraduates\u0026rsquo; Survey Participation and Response Rates? J Interdiscip Stud Educ. 2022;11:56\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePorter SR, Whitcomb ME, Weitzer WH. Multiple surveys of students and survey fatigue. New Dir Institutional Res. 2004;2004:63\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ir.101\u003c/span\u003e\u003cspan address=\"10.1002/ir.101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu M-J, Zhao K, Fils-Aime F. Response rates of online surveys in published research: A meta-analysis. Comput Hum Behav Rep. 2022;7:100206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chbr.2022.100206\u003c/span\u003e\u003cspan address=\"10.1016/j.chbr.2022.100206\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdullah F, Ward R. Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Comput Hum Behav. 2016;56:238\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2015.11.036\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2015.11.036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis FD, Bagozzi RP, Warshaw PR. Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. J Appl Soc Psychol. 1992;22:1111\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1559-1816.1992.tb00945.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1559-1816.1992.tb00945.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBallantyne C. Online Evaluations of Teaching: An Examination of Current Practice and Considerations for the Future. New Dir Teach Learn. 2003;2003:103\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/tl.127\u003c/span\u003e\u003cspan address=\"10.1002/tl.127\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoodman J, Anson R, Belcheir M. The effect of incentives and other instructor-driven strategies to increase online student evaluation response rates. Assess Eval High Educ. 2015;40:958\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/02602938.2014.960364\u003c/span\u003e\u003cspan address=\"10.1080/02602938.2014.960364\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeci EL, Koestner R, Ryan RM. A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychol Bull. 1999;125:627\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0033-2909.125.6.627\u003c/span\u003e\u003cspan address=\"10.1037/0033-2909.125.6.627\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLehtivuori A. When do extrinsic rewards undermine intrinsic motivation? A meta-analysis. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePosit team. RStudio: Integrated Development Environment for R. Boston, MA: Posit Software, PBC; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRevelle W. psych: Procedures for Psychological, Psychometric, and Personality Research. Evanston, Illinois: Northwestern University; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosseel Y. lavaan: An R Package for Structural Equation Modeling. J Stat Softw. 2012;48:1\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/jss.v048.i02\u003c/span\u003e\u003cspan address=\"10.18637/jss.v048.i02\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWickham H. ggplot2: Elegant Graphics for Data Analysis. New York: Springer-; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGohel D, Skintzos P. flextable: Functions for Tabular Reporting. 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh V, Davis FD. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Manag Sci. 2000;46:186\u0026ndash;204. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1287/mnsc.46.2.186.11926\u003c/span\u003e\u003cspan address=\"10.1287/mnsc.46.2.186.11926\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTo WM, Tang MNF. Computer-based course evaluation: an extended technology acceptance model. Educ Stud. 2019;45:131\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/03055698.2018.1443797\u003c/span\u003e\u003cspan address=\"10.1080/03055698.2018.1443797\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55:68\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037//0003-066x.55.1.68\u003c/span\u003e\u003cspan address=\"10.1037//0003-066x.55.1.68\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 5 and 6 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Student Evaluation of Teaching (SET), Technology Acceptance Model (TAM), Online Evaluation Systems, Response Rates, Intrinsic Motivation, Extrinsic Motivation, Medical Education, Faculty Development","lastPublishedDoi":"10.21203/rs.3.rs-8165782/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8165782/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Student evaluation of teaching (SET) provides medical faculties with an empirical foundation for evidence-based enhancement of teaching quality for both medical educators and coordinators. However, the transition from paper-based to online evaluation has led to a marked decline in response rates, undermining the reliability and validity of the data obtained. At Charité, a newly developed online tool was implemented, after which response rates increased substantially. This study aimed to identify technical, procedural, and motivational factors associated with higher response rates in SET by applying an extended version of the Technology Acceptance Model (TAM) that integrates Actual System Use (AU).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A cross-sectional survey was conducted among medical students at Charité - Universitätsmedizin Berlin (n = 834). A custom questionnaire operationalized core TAM constructs (Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Behavioral Intention (BI)) alongside Intrinsic Motivation (IM), Extrinsic Motivation (EM), and Perceived Workload (PW). The instrument was first examined using exploratory factor analysis (EFA; n = 325) and subsequently validated through confirmatory factor analysis (CFA; n = 509). Structural equation modeling (SEM) was then performed on the CFA sample to test hypothesized relationships. AU was objectively measured via student response rates in SET.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The classical TAM structure was confirmed. IM influenced BI indirectly via PU, whereas EM had the strongest direct effect on BI and positively reinforced IM. PW negatively influenced PEOU. PW and IM did not directly significantly influence the students’ intention to participate in SET. Moreover, BI strongly influenced AU.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eHigher response rates in SET can be achieved through a user-friendly and useful evaluation system, strategically implemented incentives, and the avoidance of adverse processes such as excessive evaluation workload. Overall, the study provides practical guidance for designing inclusive and effective evaluation systems that support continuous improvement of teaching quality in medical education.\u003c/p\u003e","manuscriptTitle":"Improving Medical Education through Student Feedback: Key Factors Driving Response Rates in Online Student Evaluations of Teaching - An Extended Technology Acceptance Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 17:22:39","doi":"10.21203/rs.3.rs-8165782/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-27T05:12:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-19T22:44:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283914740455488493571617611678273377953","date":"2026-01-19T21:55:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-16T21:15:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79651776789333972512890614936106582878","date":"2026-01-10T15:33:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308319490381231760661548065697361098063","date":"2026-01-07T07:35:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281122120391402483279469522254990811086","date":"2025-12-19T13:35:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-19T13:01:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-03T08:03:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-28T06:28:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-27T15:47:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-11-27T15:39:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f3d57281-a797-417a-b061-66a3cefa1eb6","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-26T15:53:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 17:22:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8165782","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8165782","identity":"rs-8165782","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}