Previous studies have identified key factors affecting response rates, and various procedures and techniques to improve participation from the target sample are examined. Enhancing response rates will ensure that the findings more properly represent the wider community, hence reinforcing the validity and relevance of the results (Schmidt et al., 2025; Taherdoost and Madanchian, 2025). Recent literature across multiple disciplines indicates a tendency of fluctuating, and sometimes diminishing, response rates for numerous large-scale and minor survey research. This drop is apparent across many group characteristics such as patients versus professionals, instructors versus college students or male versus female (Koskey et al., 2015; Lavidas et al., 2022; McKibben et al., 2025), survey methodologies including paper, web, and mixed techniques, and contextual influences such as the impact of the epidemic (Petrovčič et al., 2016; Saleh and Bista, 2017; Daikeler et al., 2020; Ward and Edwards, 2021; Gummer et al., 2021; Lavidas et al., 2022; Schmidt et al., 2025; Taherdoost and Madanchian, 2025). Numerous extensive studies emphasize that response rates remain a vital metric of survey quality and rigor, while also indicating that expectations should be contextualized within the survey’s design and objectives for representation (Maliković and Tončić, 2021, Klingwort and Toepoel, 2025). In addition, the most recent research not only validates the best practices that have been established, but it also presents novel issues that are relevant to the virtual setting (Petrovčič et al., 2016; Lavidas et al., 2022). Scholars concur that a singular solution is inadequate; rather, a comprehensive strategy proves to be the most effective approach like the use of mixed-mode survey designs when applicable (Daikeler et al., 2020; Schmidt et al., 2025). Key recommendations include the implementation of personalized and transparent communication strategies (Maliković and Tončić, 2021; McKibben et al., 2025), the provision of unconditional pre-paid monetary incentives whenever feasible (Ward and Edwards, 2021; Gummer et al., 2021), and the careful reduction of technical and survey fatigue through optimized survey design (Saleh and Bista, 2017; Klingwort and Toepoel, 2025).

In a meta-analysis, Daikeler et al. (2020) revealed that mixed-mode surveys (e.g., mail and web) consistently outperform single-mode web surveys. Due of their potential to reach more people, including those without internet connection or digital literacy. To support this, Schmidt et al. (2025) examined mixed-mode design mechanisms. They found that supplying a supplementary medium (like paper) following a web nonresponse helps reclaim a considerable amount of the sample. Meeting participants via their preferred communication method reduces their burden.

Saleh and Bista (2017) found that two or three strategically scheduled reminders—an initial invitation and two or three follow-ups—maximized response on timeliness and substance. They also found that modifying reminder email subject lines engages ignored recipients. A survey pre-notification email prepares participants and legitimizes the offer. Compared to generic invitations, Petrovčič et al. (2016), found that personalized invitation text with recipient names enhanced survey response rates. Most crucially, their research stressed the need of transparency: clearly expressing the survey’s aim, projected time, and data use and protection increased confidence and participation. Moreover, Ward and Edwards (2021) found that monetary incentives, even small, unconditional pre-paid incentives, outperform promised or non-monetary alternatives. Increasingly important is survey salience—the recipient’s personal relevance to the topic. Gummer et al. (2021) found that respondents prefer engaging or career-relevant questionnaires. Thus, emphasizing the survey invitation’s relevance to the target group might enhance response at affordable expense.

The cognitive and time burden of questionnaires deters. A recent study by Maliković and Tončić (2021) highlights user-centered design including duration and progress indicators and mobile Optimization. Complex longitudinal studies demand lengthier surveys, while shorter ones with progress indications (e.g., “you are almost done! Or you are 80% done!”) can manage respondent expectations and reduce desertion (Maliković and Tončić, 2021). Since most emails are opened on mobile devices, a non-mobile-friendly survey design is cumbersome. Comprehensive smartphone and tablet survey testing is advised. An unpleasant mobile experience increases survey dropout rates, especially early on.

Lavidas et al. (2022), using cross-sectional study, they explored why teachers often show low participation rates in web-based surveys. The study focuses on Greek teachers (n = 263) to identify factors influencing their willingness to respond to web surveys. The study’s central goal is to determine which factors most strongly influence teachers’ intentions to participate in online surveys. The researchers examine six key dimensions derived from prior literature: Authority/Belonging (survey sponsorship), Motivation/Incentives, Survey Structure (form or design), Ethical Issues, Reminders/Advance Notice, and Survey Timing (preferred time). The study also highlights that teachers’ increased digital familiarity post-pandemic may enhance engagement with online surveys.

Various factors such as survey design, incentives, and timing are well established in the literature as significantly influence Survey Response Propensity (McKibben et al., 2025; Klingwort and Toepoel, 2025). The literature has likewise emphasized demographic characteristics and its placement in the survey. For instance, Ericson et al. (2023), looked at graduate-level medical and allied health students and emphasized factors for high response rates. According to their findings, improving response rates required being sensitive to timing (in order to minimize survey exhaustion), and employing follow-up strategies that were both innovative and persistent. Furthermore, Ziegenfuss et al. (2021) experiment assessed whether including demographic questions in a survey affected overall response rates. They found that demographic items did not negatively impact response rate and that respondents’ self-reports aligned well with administrative records.

In a 15 years study by Kolaja et al. (2023), they found that higher educational attainment, being married, female sex, older age, and non-Hispanic White race/ethnicity were positively associated with survey follow-up response among US military personnel. Similarly, in their large-scale study, Jildeh et al. (2021) found that age significantly influence response rate and response behavior in outcome questionnaires; older participants were much more likely to respond. Moreover, Saleh and Bista (2017) looked at academic researchers (students and faculty) as respondents and found that older participants and male participants had higher response rates when reminders or incentives were used. It was observed by Lavidas et al. (2022) that demographic factors such sex, age group, level of education, and research experience did not show statistically significant associations with intention to participate with a sample of Greek teachers. Likewise, McKibben et al. (2025) found no gender or academic position effect on agricultural instructors’ survey response rates. The study demonstrates that researcher-practitioner connections are worsening, emphasizing the need for meaningful collaboration and access researchers should design brief, relevant surveys, strategically target participants, and provide valuable data.

Collectively, the reviewed studies underscore that survey participation is influenced by an interplay of methodological, psychosocial, and demographic variables. However, empirical investigations within the Saudi higher education context remain scarce. There is limited understanding of how factors like authority, ethical trust, and motivation operate among faculty and graduate students in digital survey environments. This gap justifies the current study’s focus on applying Dillman’s TDM framework to identify determinants of survey response behavior in Saudi universities.

This research utilized a cross-sectional, quantitative survey methodology based on Dillman et al. (2014) TDM and Social Exchange Theory, which highlights the right balance of security, efforts, and perceived reward in fostering involvement. Structural equation modeling (SEM) employing the partial least squares method (PLS-SEM) was utilized to examine the proposed correlations between design-related variables and survey response behavior.

The requirement for inclusion was being a current student or a current faculty member at college of education. According to the recent statistics from the university’s 2024–2025 academic year, the college of education has 183 faculty members and 780 graduate students enrolled in various graduate programs (King Faisal University, n.d.). Consequently, the random sampling was stratified to ensure representation of the two groups. A sample of 125 faculty members and 258 graduate students were selected using the Raosoft sampling calculator¹ to ensure a representative sample for each population. The response rates were satisfactory for this study’s topic and goal, with n = 41 faculty and n = 71 graduates responding, 33 and 28%, respectively (Carley-Baxter et al., 2013; Price et al., 2022). Table 1 presents the demographic composition of the sample (n = 112).

Data collection was conducted during 2024–2025 academic year utilizing the Web-Survey scale by Lavidas et al. (2022). To enhance response propensity, Lavidas et al. (2022) incorporated and scientifically validated six key characteristics of online survey participation (Authority/Affiliation, Motivation/Incentives, Survey Structure, Ethical Issues, Reminders/Advance Notice, and Survey Timing). Items of the questionnaire developed from previous validated studies (e.g., Saleh and Bista, 2017) and evaluated in collaboration with educators. It pertains to teachers attitudes, timing constraints, and trust concerns, rendering it pertinent to our sample of faculty and graduate students in college of education. The questionnaire underwent expert validation, pilot testing, and test–retest reliability assessments, proving it applicable in several cultures and contexts with minor modifications. The questionnaire contained 31 closed-ended items measured on a four-point Likert scale (Strongly Disagree, Disagree, Agree, Strongly Agree). The dependent variable termed ‘Survey Response Rate’ denotes respondents’ self-reported probability of engaging in online surveys, rather than an empirically measured response rate. Consequently, it is construed as a perceptual indicator of response propensity.

To reach as much as possible of faculty and graduates, the link for the online survey was sent through WhatsApp groups as well as in person using QR codes during working days at evening time where graduate courses took place across departments. The subjects were informed about the voluntary participation, aim of the study, and the freedom to withdraw at any time.

Before carrying out the data collection, the study plan was endorsed and scrutinized by the Research Ethics Committee to prevent any infringement of ethical standards for research involving human subjects (KFU-REC-2024-MAY-ETHICS2271).

SPSS-22 was used for data coding and preliminary screening procedures, including assessments of missing data, outliers, and multicollinearity. In addition to the five-core factors included in the structural model. The sixth construct—Time Preference—was evaluated descriptively to capture respondents’ availability and preferred timing for survey participation. Subsequently, the measurement and structural models were analyzed using (PLS-SEM) via SmartPLS 3.0. The PLS-SEM was deemed appropriate for this study due to its ability to accommodate non-normal data, smaller sample sizes, and varied measurement scales, offering increased flexibility compared to the stricter requirements of covariance-based SEM (Hair et al., 2022).

Common method bias (CMB) appears to pose a significant challenge to behavioral research since the subjects have little question that all behaviors have been self-reported and unanimously related (Podsakoff et al., 2024). Bias on the validity and efficiency of the instrument can be observed by the CMB. Harmen single factor tests were applied to the data using SPSS. Results showed that the first component of the matrix showed 29.62% of the variance, however, a total of 15 factors were obtained from the data. The cumulative percentage of the variance of all factors was 63.05%. Table 2 shows the values of all 15 factors and their variance. Previous research suggests that the primary factor should account for no more than 50% of the variance. Despite the fulfillment of this criteria, Harman’s single-factor test serves as a limited diagnostic tool, and the existence of certain CBM cannot be conclusively excluded.

The internal consistency estimates, including Cronbach’s alpha and Composite Reliability, are influenced by the number of items within each scale or sub-dimension. In general, reliability coefficients between 0.60 and 0.70 may be considered acceptable for early-stage or exploratory research, whereas values above 0.80 indicate good internal consistency (Hair et al., 2022). The reliability coefficients for the main scale and its sub-dimensions are presented in Table 3.

The sampling was adequately balanced in representing the two major target groups, with 36.6% (n = 41) ascertaining as faculty members and 63.4% (n = 71) as graduate students. This distribution exists to see that both student and faculty groups are covered, which is crucial in understanding the study’s aim of investigating influences on response propensity. With respect to gender, female participants were the majority (66.1%, n = 74), whereas male participants constituted 33.9% (n = 38). Such a gender distribution may reflect population structure across those schools participating and may influence interpretations made of some variables related to responses. The age split indicated that most (76.8%, n = 86) fell in the 20–40-year range, and 23.2% (n = 26) were more than 40 years old. This predominantly youthful sample is accounted for by the high proportion of students in the sample and is mostly relevant since age has been found elsewhere to be linked with response style and motivation in survey contexts (e.g., Kolaja et al., 2023). Figure 1 shows the distribution of the sample according to the demographic variables.

For the factor Survey Timing (best time to complete a questionnaire), more than half (57.1%, n = 64) did not have a preferred time, showing flexibility. Approximately 22.3% of them were willing to complete surveys from 6:00 in the morning to 6:00 in the evening, and 20.5% from evening to midnight. These findings could possibly be helpful to optimize delivery times for future online surveys. Figure 2 shows the distribution of the sample according to best time for questionnaire completion.

Regarding the ideal deadline to return an online questionnaire, the majority (65.2%, n = 73) chose 1 week, while 20.5% desired 2 weeks or so, and 14.3% (n = 16) 1 month. This suggests that tighter deadlines—but not so tight as to be off-putting—are generally acceptable to the target market and are perhaps liable for more responsiveness rates. Figure 3 shows the distribution of the sample according questionnaire deadline.

As for the best position for demographic questions, most participants (64.3%, n = 72) wanted them placed at the start of the instrument. A smaller percentage (15.2%, n = 17) wanted them placed at the end, and 20.5% (n = 23) felt they were not necessary. This finding identifies the significance of survey layout in optimizing respondent convenience and data completeness. Figure 4 shows the distribution of the sample according to demographic information’ location.

Univariate normality was examined using skewness and kurtosis statistics as suggested by Hair et al. (2022). Results in Table 4 indicated that all variables exhibited statistically significant skewness, as the z-skew values exceeded the ±1.96 threshold (p < 0.05), suggesting departures from normal distribution. In addition, most constructs showed significant kurtosis which is an indication of data non-normality. According to the data presented in Table 5, the multivariate skewness of Mardia’s test (b = 7.75, z = 144.75, p < 0.001) and kurtosis (b = 55.44, z = 4.01, p < 0.001) were also found to be statistically significant., confirming a violation of the assumption of multivariate normality. Given these results, PLS-SEM was considered an appropriate analytical approach, as it is robust under conditions of non-normality and smaller sample sizes, and is recommended for prediction-oriented modeling (Hair et al., 2022).

With five direct hypothesized relationships and a relatively complex structure, the research aimed to estimate and explain variance in the focal constructs rather than test model fit in a confirmatory approach. Each structure was evaluated with exploratory factor analysis (EFA), by evaluating the average extracted variance (AVE), the square root of the average extracted variance, and inter-constructive associations for psychometric properties of validity, reliability and dimensionality (Figures 5, 6).

The Chi-Square test was employed to examine the statistical association between the demographic variables (such as gender, age, and academic position) and each factor of Survey Response Propensity, as follow:

The strong effect of Authority/Belonging (f² = 2.109) supports a core premise of the TDM Theory. However, because the dependent variable is perceptual and closely aligned conceptually with authority cues, this effect should be interpreted as reflecting strong relational influence rather than a precise estimate of actual behavioral impact. As proposed by Dillman et al. (2014), persons are more inclined to participate when they perceive trust, legitimacy, and reciprocal benefit in the research connection. In collectivist cultures like Saudi Arabia, institutional connection and social belonging significantly influence the propensity to engage. The significant predictive value of this factor indicates that confidence in institutional sponsorship and the perception of contributing to a collective academic mission greatly improve survey participation.

These findings corroborate other studies by Lavidas et al. (2022) and Hendra and Hill (2018), which indicated that the legitimacy of the sponsoring institution enhances both perceived value and ethical trust. In the Saudi context—where educational institutions are pivotal to national development under Vision 2030—the perceived authority of the university or ministry may possess enhanced moral and civic significance, prompting teachers and graduates to regard involvement as a professional obligation. This corresponds with the social-exchange theory, which posits that engagement is reciprocated through a sense of belonging and mutual progress (Saleh and Bista, 2017).

Reminders/Advance Notice emerged as the second-most significant predictor (f² = 1.049), highlighting the necessity of continuous and purposeful communication. This outcome aligns with empirical findings indicating that timely reminders enhance visibility, diminish memory loss, and demonstrate sustained researcher involvement (Petrovčič et al., 2016; Saleh and Bista, 2017). The discovery corroborates Dillman’s suggestion to employ many, strategically timed contact points that prioritize recognition and legitimacy over mere persistence.

In Saudi academia, characterized by email saturation and conflicting institutional expectations, reminders can act as nuanced indicators of sincerity and respect, especially when tailored to the individual. The positive correlation identified indicates that respondents view reminders not as intrusive, but as validations of their significance to the study process. This analysis underscores the need of humanized communication tactics in digital survey contexts (Hossan et al., 2023).

Ethical Issues significantly influence participation (f² = 0.934), indicating that assurances of privacy, data security, and explicit purpose statements remain crucial for encouraging involvement. This finding corroborates previous studies indicating that individuals are more inclined to engage when they trust that their information will be utilized ethically (Klingwort and Toepoel, 2025; Maliković and Tončić, 2021).

In Saudi higher education, ethical assurances are particularly crucial as institutions increasingly focus on digital ethics and privacy regulations. The findings indicate that establishing explicit connections to ethical assessment, data storage security, and anonymity not only diminishes resistance but also enhances the ethical dimension of reciprocity in Social Exchange Theory. Promoting ethical openness is an essential strategy and a societal need for enhancing response propensity.

The Survey Structure demonstrated a positive and substantial correlation with the response rate (f² = 0.815). This discovery underscores the need of a clean layout, succinct questions, logical progression, and mobile optimization, especially in online surveys. Prior research (Maliković and Tončić, 2021; Lavidas et al., 2022) indicates that user-centered survey design decreases cognitive load and attrition rates, reinforcing the notion that technical simplicity enhances perceived ease of participation—an essential component of TDM.

This study indicates that while structure had a slighter impact than authority or reminders, its importance highlights that effective design quality enhances relational and motivating factors. An organized tool conveys professionalism and respect for respondents’ time, hence enhancing confidence and reciprocity. The results indicate that Saudi institutions ought to persist in their investment in professional digital survey design technologies that improve clarity and accessibility across various devices.

Motivation/Incentives (f² = 0.735) showed the smallest effect size among the five predictors; however, it remained significantly positive. Given its lower convergent validity, this effect should be interpreted as reflecting perceived motivational influence rather than a precise estimate of a single, unified incentive dimension. This pattern corresponds with current research indicating that material or external rewards have less influence in academic or professional contexts compared to internal or social motives (Gummer et al., 2021; Ward and Edwards, 2021). In higher education, instructors and graduate students may exhibit greater responsiveness to ethical motives—such as promoting institutional quality or aiding peer research—than to external incentives.

From a Social Exchange perspective, this suggests that the symbolic worth of aiding in shared academic advancement surpasses physical advantages. Nonetheless, incorporating minor gestures of gratitude or recognition messages is advantageous, especially when paired with individualized dialogue and institutional support.

The findings regarding demographic characteristics offer more understanding of survey participation patterns within the context of Saudi higher education. Differences by gender were observed to slightly correspond with existing studies on demographic factors affecting online survey participation. Although several studies have indicated weak or nonsignificant gender effects (Lavidas et al., 2022; McKibben et al., 2025), others have detected differences in digital communication and engagement behaviors, particularly in contexts influenced by perceptions of trust, belonging, and usability (Saleh and Bista, 2017; Jildeh et al., 2021; Kolaja et al., 2023). The current findings confirm this latter viewpoint, illustrating that gender substantially affected perceptions of authority and the clarity of Survey Structure—two elements crucial to the sense of confidence and comfort in participation. The findings indicate that male and female respondents may differ in their perceptions of institutional authority cues and user experience in digital surveys, potentially mirroring broader socialization tendencies in academic communication.

The lack of gender differences in Motivation/Incentives, Ethical Issues, and Reminders/Advance Notice suggests that these elements are predominantly seen in a similar manner by all genders (Petrovčič et al., 2016; Maliković and Tončić, 2021; Klingwort and Toepoel, 2025). This trend substantiates the claim that fundamental participation mechanisms—trust, perceived mutuality, and diminished effort—are universal behavioral reactions rather than constrained by gender. According to Social Exchange Theory, this convergence indicates that both male and female participants react similarly to signals of fairness, trust, and transparency, which are fundamental motivators for voluntary involvement (Dillman et al., 2014). The findings indicate that survey design elements related to authority and structure may require gender-sensitive adjustments, although other engagement aspects seem generally relevant across subgroups (Price et al., 2022; Holtom et al., 2022).

The results indicate that age has a minimal or negligible effect on online survey participation in higher education, aligning with current research. Lavidas et al. (2022) showed no significant correlation between age and participation intention among instructors, but McKibben et al. (2025) observed analogous findings among agricultural educators. Holtom et al. (2022) similarly determined that methodological and structural factors, rather than demographic characteristics, are the principal predictors of response behavior. The present study corroborates this conclusion: when individuals possess similar academic and digital literacy contexts, as shown in Saudi graduate education, age diminishes in significance as a predictor of involvement. This indicates that the digital change in higher education may be leveling participation possibilities among different age groups.

Likewise, the academic status (faculty compared to graduate students) exhibited no notable changes in response behavior. Both groups shown comparable responsiveness to the five design criteria, suggesting that professional rank does not significantly influence perceptions of authority, ethics, or motivation in survey participation. This discovery aligns with previous research indicating negligible impacts of respondent demographic traits on response rates (Lavidas et al., 2022; McKibben et al., 2025). Recent analyses (Holtom et al., 2022; Ziegenfuss et al., 2021) have similarly underscored that the legitimacy, structure, and communication tactics of surveys are more significant than demographic factors in influencing participation. Consequently, in alignment with previous studies in educational and professional fields, the current findings indicate that efficient survey design concepts can be universally implemented across academic roles with minimal alteration.

These demographic observations collectively support the foundational premise of Social Exchange Theory: involvement in online surveys is predominantly influenced by relational and structural factors—trust, mutuality, perceived advantage, and minimized effort—rather than fixed demographic characteristics. By fostering fair design, honest communication, and institutional legitimacy, researchers can improve inclusivity and engagement among diverse gender, age, and academic subgroups in higher education.

Although Survey Timing was not included in the inferential model, it was utilized to ascertain participants’ preferred times for completing online surveys and their desired timing for receiving responses. Lavidas et al. (2022) posited that timing is a contextual variable that interacts with institutional schedules and individual obligations. Over 50 % of respondents (57.1%, or 64 individuals) did not specify a preferred time for the poll. This indicates that the majority of individuals are adaptable and willing to participate in surveys at any hour of the day. Among those expressing a willingness to complete surveys, 22.3% preferred to do so between 6 a.m. and 6 p.m., while 20.5% favored the overnight hours until midnight. This indicates that online surveys can be disseminated at various times throughout the day and remain effective, thereby accommodating individuals with diverse schedules. Similar to the findings of Lavidas et al. (2022), which indicated that teachers’ technological proficiency facilitates student participation beyond conventional hours, this trend demonstrates an increasing flexibility in online academic engagement. Moreover, a majority of respondents (65.2%, n = 73) said that the optimal timeframe for returning a questionnaire is within 1 week. Twenty-five percent said it should occur within 2 weeks, while 16 % (n = 16) believed it should occur within 1 month. Prior research (Saleh and Bista, 2017; Petrovčič et al., 2016) indicated that shorter yet realistic deadlines elicit more replies, since they maintain a sense of importance and urgency without overwhelming individuals. The Social Exchange Theory posits that these preferences represent a balance between perceived effort and reciprocal commitment. Individuals are more inclined to engage when time commitments are manageable, and the researcher demonstrates equity and regard by honoring their availability. This conduct aligns with Dillman et al. (2014) Tailored Design Method: when participants see their time is valued, they are more inclined to engage punctually.

Overall, these findings indicate that faculty members and graduate students exhibit considerable flexibility regarding the completion of surveys. Strategically designed delivery and realistic response timeframes increase the likelihood of survey completion. Incorporating daily and longitudinal time sensitivity enhances the relational dimension of the survey process by demonstrating trust, reciprocity, and respect. This underscores the principles of social interaction that enable individuals to willingly participate in academic research. This trend illustrates the impact of contextual and workload-related factors on participation behavior.

This study shows that elements such as trust, credibility, and a survey’s usability play a key role in encouraging participants engagement—even when demographic characteristics have slight influence. The strong effects of Authority/Belonging and Survey Structure on response behavior support the core ideas of Social Exchange Theory and demonstrate that Dillman’s TDM is applicable to Saudi graduate students and faculty members.

Overall, the findings add to the expanding research on digital survey design by confirming that well-designed communication strategies and user-friendly formats are especially important in higher-education settings. For educators and researchers, the results emphasize the importance of clear messaging, credible sponsorship, and ethical transparency when distributing online questionnaires. Universities can improve participation by using thoughtful outreach methods, personalized invitations, strong confidentiality assurances, and straightforward survey layouts. Additionally, fostering a culture that values research and participation can help boost response propensity and enhance the quality of data collected in future academic studies.