Higher education has normalized fully online, hybrid, and live remote education since the mid-1990s, particularly since the COVID-19 pandemic. This shift in educational systems resulted in videoconferencing platforms becoming the default infrastructure for live instruction and synchronous online teaching (Alsayer 2023; Alsayer et al. 2024; Singh and Thurman 2019). The landscape of synchronous online classes has seen a significant transformation, allowing educators to incorporate various digital tools to achieve optimal course objectives (Correia et al. 2020; Jiang et al. 2022). Within this ecosystem, interactive tools, like public and private messaging, polling, breakout rooms, collaborative whiteboards, screen sharing, and webcams, help to create real-time interaction, approximating the dynamics of face-to-face classes (Al-Samarraie 2019; Belt and Lowenthal 2023; Correia et al. 2020; Jiang et al. 2022). Nevertheless, empirical evidence suggests that students adopt these tools unevenly, with some preferring certain interactive tools over others (Shahid et al. 2024).
For example, many prefer private messaging to public chat or speaking, and webcams are frequently left off (Alawamleh et al. 2020; Lenkaitis 2020; Shahid et al. 2024). Despite the centrality of visual and auditory channels to social presence and collaborative meaning-making, university students across diverse settings exhibit a general apprehension about turning cameras on, citing concerns related to privacy, self-presentation, fatigue, and domestic visibility, alongside perceived limited usefulness for certain tasks (Andel et al. 2020; Belt and Lowenthal 2023; Händel et al. 2022; Lenkaitis 2020; Li et al. 2020; Munoz et al. 2021). Webcam reluctance is especially pronounced in conservative contexts, where cultural expectations and privacy concerns shape norms of presence and exposure (Sederevičiūtė-Pačiauskienė et al. 2022; Slimi 2020).
Research on webcam use remains in its infancy, even as higher education continues the widespread adoption of online and distance-learning technologies. Evidence on actual in-class patterns and preferences is limited, and webcam behaviors are especially under-documented (Abbasi et al. 2015; Al-Samarraie 2019; Händel et al. 2022). Existing studies often rely on small samples or didactic language-learning settings and are concentrated in Western, individualistic contexts, leaving gaps in diverse institutional and cultural environments (Al-Samarraie 2019).
This study addresses this gap by investigating webcam practices among language majors at the University of Jordan. Focusing on a conservative, collectivist setting where norms around modesty, gendered interaction, household privacy, and public self-presentation shape on-camera decisions. The paper explores why the student sample keeps cameras off during synchronous classes, despite the available interactive affordances and generally positive attitudes toward the technology. The context is pedagogically salient because language learning depends on spoken interaction, nonverbal feedback, and timely correction; at the same time, prevailing norms can raise the perceived social costs of visual exposure in mixed-gender, instructor-led spaces (Sederevičiūtė Pačiauskienė et al. 2022; Slimi 2020).
The paper situates these practices within local expectations and examines how subjective norms operate in closely knit communities where social approval and group alignment carry significant weight. In such settings, pressure to conform can deter camera use even when visual presence would support learning. By providing contextually grounded evidence from Jordan, the study extends a literature largely shaped by Western, individualistic contexts and clarifies how culture, social expectations, and platform affordances jointly shape real-time participation in video-conferenced classes (Al Samarraie 2019; Andel et al. 2020; Händel et al. 2022; Li et al. 2020; Munoz et al. 2021).
The literature on using webcams in synchronous online courses uncovers a general apprehension that may be attributed to various social and psychological factors and facilitating conditions (Bedenlier et al. 2021; Meishar-Tal and Forkosh-Baruch 2022). Social elements related to the connections between individuals and groups are thought to have a major impact on the utilization of videoconferencing tools (Karl et al. 2021). A particular aspect of these elements often investigated in scholarly research examines how forming group identity and/or individual virtual presence affects students’ use of webcams. However, the findings from these studies are, in general, conflicting. While some evidence suggests that interactive tools, including webcams, can create a sense of community among students enrolled in online classes (Lin and Gao 2020). Some students argue that videoconferencing tools do not foster a sense of community (Bedenlier et al. 2021). A gap remains in understanding how non-Western, collectivist contexts mediate the formation of virtual presence and group identity in ways that differ from those in individualistic societies.
Another important aspect of social factors in online higher education is the role of building rapport and facilitating dialogue on digital platforms. The use of webcams enhances the effectiveness of online classes (Al-Samarraie 2019; Lenkaitis 2020) and promotes collaborative opportunities (Racheva 2018). Non-verbal cues, which can be observed when students are visible during synchronous classes, have improved instructor performance and enhanced student communication (Castelli and Savary 2021; Francescucci and Rohani 2018; Racheva 2018). However, while webcam use is beneficial for establishing rapport between students and educators during initial meetings, it can feel cumbersome and intrusive from the students’ perspective as the course progresses (Kozar 2016). This paradox of pedagogical value versus perceived intrusion lacks a clear mechanism within the literature to identify the external pressures causing this shift, especially in culturally sensitive contexts.
Some studies have examined how a group’s influence affects an individual’s decision to turn on their webcams during synchronous online classes. Essentially, this involves peer pressure and power dynamics. Students are more likely to follow the prevailing social norms set by their peers or by authoritative figures in the online classroom environment. For instance, when most students choose not to use their webcams, others may also refrain (Castelli and Savary 2021; Gherhes et al. 2021). In communities that value seniority and hierarchical structures, students often turn on their webcams out of respect for educators or in response to the established power dynamics (Tobi et al. 2021). Additionally, some students have reported that a perceived lack of social support from their peers in online classes discourages them from participating with their webcams (Bedenlier et al. 2021). Although these studies document the influence of social norms on webcam behavior, they do not examine how subjective norm operates differently across cultural contexts or how cultural values amplify or diminish its effect. Nor have they tested this construct within a well-established, replicable theoretical framework. This study fills this gap by empirically testing subjective norm as a direct antecedent within a collectivist framework where social conformity carries greater weight.
In addition to facilitating conditions and social factors that affect students’ use of videoconferencing tools, psychological factors also play a significant role. These factors relate to students’ mindsets and emotional and psychological states. For instance, some students feel overwhelmed by seeing their own faces during online classes and may be uncomfortable turning on their webcams (Telles 2010). Others have reported being self-conscious about their body positioning and posture (Vallespin and Prasetyo 2022). Additionally, students might be worried about their overall appearance (Castelli and Savary 2021). Students are increasingly concerned about their environment and the background visible on their screens. Many choose not to turn on their webcams because they fear exposing their rooms, family spaces, or any areas of their homes (Castelli and Savary 2021; Tobi et al. 2021; Wut et al. 2022).
Some individuals even feel the need to designate a special spot in their homes for comfortable webcam use (Wut et al. 2022). Unsurprisingly, anxiety and fear of peer judgment also contribute to students’ reluctance to use webcams (Bedenlier et al. 2021; Tobi et al. 2021; Wut et al. 2022). These psychological and privacy barriers may reflect an intricate cultural system where webcam use carries heightened social costs due to norms around modesty, gendered interaction, household privacy, and religious considerations. However, existing literature has not theorized these culturally amplified concerns within validated frameworks, leaving unclear how cultural values transform individual psychological barriers into collective resistance to webcam adoption.
While webcam use in synchronous online courses has attracted scholarly attention across disciplines, the absence of a unified theoretical framework undermines the validity and replicability of empirical findings. Studies employing ad hoc or discipline-specific approaches yield fragmented insights that cannot be systematically compared or built upon. The Technology Acceptance Model (TAM) provides a solution: a validated, replicable framework capable of integrating multiple factors within a coherent theoretical structure. By applying TAM to webcam adoption in online learning environments, this study makes a dual contribution: advancing technology acceptance theory through cultural extension and establishing a systematic foundation for webcam-use research.
The Technology Acceptance Model (TAM) is frequently used in higher education to understand how students engage with educational technologies (Granić and Marangunić 2019). TAM explains adoption by linking beliefs to attitudes, intentions, and use, drawing on the Theory of Reasoned Action (Ajzen and Fishbein 1980; Davis 1989). Its core belief constructs are perceived usefulness (PU), defined as the extent to which using the technology enhances performance, and perceived ease of use (PEU), defined as the extent to which using it requires minimal effort (Davis 1989). These beliefs shape attitude toward the technology (AT), which, together with PU and PEU, influences intention and subsequent use (Zhang et al. 2008).
In higher education, PU and PEU consistently emerge as the strongest predictors of acceptance, with AT contributing alongside them (Alfadda and Mahdi 2021; Granić and Marangunić 2019). TAM is widely used to understand student engagement with educational technologies and has seen extensive application in language teaching and learning, particularly among educators (Arribathi et al. 2024; Cao 2024; Hamid et al. 2024; Kianinezhad 2024; Koç et al. 2021; Lorenzo et al. 2013; Luo et al. 2023; Martin et al. 2020; Ulutaş and Ölmez 2021; Yang et al. 2024; Zhang et al. 2023). Yet relatively few studies have examined students’ perceptions, attitudes, and intentions toward synchronous videoconferencing (Camilleri and Camilleri 2022; Luo et al. 2023), and fewer still have focused on the acceptability of specific interactive tools within these platforms. Namely, webcam use in language learning (Alfadda and Mahdi 2021).
To model webcam activation as a technology adoption problem among university students, this study employs TAM with contextually relevant extensions. Given the literature on webcam avoidance, social explanations are salient but often under-theorized within a broader cultural framework. Considering Hofstede’s (2011) cultural model, the study addresses this gap by operationalizing societal values as potential determinants of technology acceptance. Subjective norms (SN) refer to the perceived social pressure an individual feels to perform or refrain from performing a specific behavior (Abbasi et al. 2015; Nguyen et al. 2022). Rooted in normative beliefs, this construct captures the influence of expectations and opinions held by key social groups, such as family, peers, or colleagues (Alfadda and Mahdi 2021). Within technology acceptance models (TAM), SN is a key factor influencing an individual’s behavioral intention to use a system (Nguyen et al. 2022). This influence is particularly strong in collectivist societies, where social expectations and group norms play a substantial role in shaping individual behavior, i.e., “subjective norm is salient when adoption involves people living in collectivist societies” (Lee and Wan 2010, p. 1). SN is also essential to account for in communities where conformity to group values is prioritized over personal preference or risk assessment (Ajzen and Fishbein 1980; Huang et al. 2019).
Despite evidence that subjective norm matters more in collectivist contexts, TAM studies in online education have rarely tested this construct or examined how it interacts with cultural orientation. This study addresses this omission by positioning subjective norm as a central predictor of both perceived usefulness and ease of use, and by testing whether its influence is amplified in collectivist versus individualist orientations. This is particularly relevant in societies like Jordan, which are typically characterized as collectivist, valuing seniority, social roles, and societal hierarchies.
To capture these broader influences, Hofstede’s individualism-collectivism index was also included as a key construct in the proposed TAM. This index positions individuals on a continuum; at one end, people prioritize personal goals and the interests of their immediate family (individualism), while at the other, they emphasize loyalty and belonging to a cohesive community (collectivism) (Hofstede 2011; Jeffy et al. 2022).
Accordingly, this study extends TAM by modeling SN and IC as sociocultural antecedents that inform PU/PEU and ultimately webcam use. This integrated TAM-Hofstede framework explores why students in Jordan, where collectivist norms are strong (Alawamleh et al. 2020), may be reluctant to activate webcams despite their perceived usefulness (Sederevičiūtė Pačiauskienė et al. 2022). Empirical evidence of normative influences (Castelli and Sarvary 2021) and contextual reasons for webcam deactivation (Gherhes et al. 2021) further support this integrated TAM-Hofstede framework, which will be tested in the following hypotheses.
The present study addresses several gaps in the existing literature on webcam use in synchronous online learning. First, while prior research has documented students’ reluctance to activate cameras, most studies have been conducted in Western, individualistic contexts with limited attention to how cultural orientations shape technology acceptance. This study extends the Technology Acceptance Model by integrating subjective norm, a construct often underexplored in TAM applications, and Hofstede’s individualism-collectivism dimension to examine webcam adoption in a collectivist, conservative setting.
Second, whereas existing work has identified various barriers to camera use (privacy concerns, self-presentation anxiety, technical issues), few studies have empirically tested the mechanisms through which social influence operates or how cultural orientation moderates these effects. By employing structural equation modeling to test direct, indirect, and moderating pathways, this research clarifies the relative weight of social pressure versus individual acceptance in shaping actual behavior. The study also provides methodological rigor often lacking in exploratory studies of webcam use.
Third, the study focuses on language learners in Jordan, a context where webcam use carries heightened social costs due to norms around modesty, gendered interaction, and household privacy, yet where visual presence is pedagogically critical for developing communicative competence. This combination of theoretical extension, methodological rigor, and contextual specificity allows the study to explain the paradox of positive attitudes coexisting with low usage, i.e., a pattern observed globally but inadequately theorized. In doing so, the research contributes both to technology acceptance scholarship and to culturally responsive pedagogy in online language education.
The following hypotheses formalize the paper’s proposal, followed by a visual representation of the resulting extended TAM in Fig 1:.
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H1 Subjective norm (SN) significantly influences perceived usefulness (PU).
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H2 Subjective norm (SN) significantly influences perceived ease of use (PEU).
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H3 Individualism-collectivism (IC) significantly influences perceived usefulness (PU).
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H4 Individualism-collectivism (IC) significantly influences perceived ease of use (PEU).
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H5 Perceived usefulness (PU) directly influences attitude (AT).
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H6 Perceived ease of use (PEU) directly influences attitude (AT).
Proposed model structure *the arrow represents a direct relationship between all the variables on its left with the variables to its right.
The paper adopts a quantitative, cross-sectional survey to test a theory-driven model of webcam acceptance. Large-sample designs are well-suited to TAM-based inference because they (i) provide statistical power to estimate direct, indirect, and moderating effects; (ii) enable model comparison and robustness checks (e.g., multicollinearity diagnostics, interaction terms); and (iii) support generalizable estimates across a broad student population. This approach aligns with the objective to evaluate how social influence and cultural orientation shape perceived usefulness and ease of use in a collectivist setting. While qualitative methods are indispensable for capturing lived experience and emotion, the present study prioritizes causal-structure testing and generalizable effect estimation.
An extended version of the Technology Acceptance Model (TAM) is utilized to investigate how language majors at the University of Jordan use webcams during online synchronous classes. As part of their curriculum, all undergraduate students must enroll in various online courses delivered through Microsoft TEAMS. This platform, part of the Office 365 suite, serves as a collaborative tool designed specifically for educational institutions, incorporating a wide array of online teaching resources. TEAMS features interactive channels enabling real-time collaboration among students, leveraging built-in applications such as videoconferencing, webcam functionalities, interactive boards for sharing ideas, learning documents, and private and public messaging systems. The videoconferencing capabilities, embedded into the platform, allow participants to engage visually and verbally, facilitating lectures and interactive learning activities during online sessions. A conceptual framework based on TAM has been developed to analyze the factors influencing webcam acceptability in the sample. This framework identifies PU, PEU, and user attitudes as the central constructs driving technology acceptance. The model’s external factors include SN and IC index, and the actual usage of webcams serves as the resultant output of this model structure.
This study’s population consists of students affiliated with the faculty of foreign languages who have been enrolled at least once in online classes. The study’s respondents are a sample of 479 undergraduate students who were enrolled in Language majors at the University of Jordan during the data collection period (2022–2023). The primary criterion for selection was that participants must have taken at least one online class before participating in the study.
The decision not to collect demographic data in the survey was informed. A pilot focus group with volunteer students and faculty recommended minimizing identifiability to encourage candid responses in a culturally sensitive context. In Jordan’s collectivist setting, where social networks are closely knit and institutional communities are relatively small, participants expressed concern that demographic identifiers, particularly gender combined with academic major and enrollment year, could compromise their anonymity. Given that webcam use intersects with norms around modesty, gendered interaction, and household privacy, the research team prioritized creating conditions under which students would feel comfortable reporting their actual practices and attitudes without fear of identification or social judgment.
This methodological choice carries trade-offs. The absence of demographic data, particularly gender, limits the study’s ability to examine whether webcam practices differ systematically between male and female students. Especially that gender is a potentially important dimension in a context where gendered norms shape public self-presentation and mixed-gender interaction. Consequently, the findings reflect aggregate patterns across the sample and cannot account for possible gender-based variation in subjective norms, cultural orientation, or perceived ease of use that influence webcam adoption. Future research in less sensitive contexts or with larger, more dispersed samples may be better positioned to disaggregate results by gender and other demographic characteristics, thereby enhancing the generalizability and nuance of findings on webcam use in culturally conservative settings.
All ethical considerations were addressed, and willing participants completed an online survey distributed during the 2022–2023 academic year. A power analysis(1) , (2) was conducted to justify the sample size of 479 participants. With 45 degrees of freedom and α = 0.05, the analysis revealed strong statistical power (0.960) for detecting medium effect sizes (0.3) and perfect power (1.000) for large effect sizes (0.5). While power was lower for small effects (0.130), the sample size is adequate for detecting meaningful relationships in the structural model, as most effects in technology acceptance research tend to be medium to large (Hair et al. 2017; Sarstedt et al. 2021).
The instrument used in this study is a questionnaire designed to capture the factors outlined in the previously proposed structural model. Each construct in the model is represented by a specific set of items within the questionnaire. These items were developed based on existing surveys in the literature to ensure reliability and validity (Alfadda and Mahdi 2021; Ajzen and Fishbein 1980; Davis 1989; Granić and Marangunić 2019; Hofstede 2011; Huang et al. 2019; Marangunić and Granić 2015; Scherer et al. 2019; Zhang et al. 2008). The paper followed content reliability and validity guidelines and the acceptable item-to-construct ratio. According to Mutammimah et al. (2024), the acceptable norm for the number of items per construct is three.
The questionnaire was designed to maintain participant anonymity, and demographic information was not collected following a focus group session with volunteer students and faculty members who recommended this approach. The questionnaire was then translated into Arabic before being distributed via Microsoft Forms, as all students have active accounts associated with the University of Jordan. The first part of the questionnaire focuses on the participants’ online learning experiences. Information about facilitating conditions was collected as a precautionary measure to rule out the possibility that technological inequalities might account for students’ webcam usage patterns. This step ensures that any observed reluctance to use webcams can be more confidently attributed to social or cultural factors, rather than limitations in access to necessary technology. Respondents answered yes-or-no questions regarding their preferences for smart devices and their choices for broadband access. The second part of the questionnaire utilizes a three-point Likert scale, where participants indicated their level of agreement with items representing different constructs in the structural model. The questionnaire includes items related to the following constructs: PU (4 items), PEU (4 items), SN (5 items), IC (4 items), AT (4 items), and actual use (4 items). For reference, a Table of the questionnaire’s items with their concomitant constructs is provided in the Appendix.
Structural Equation Modeling (SEM) is a comprehensive statistical technique that enables the simultaneous analysis of multiple relationships among variables in a model, making it the predominant method of data analysis within the Technology Acceptance Model (TAM) literature (Scherer et al. 2019). SEM distinguishes between observed variables that are directly measured through survey items and unobserved (latent) variables, representing underlying theoretical constructs such as PU, PEU, and AT. These latent variables are not directly observable but are inferred from patterns in the observed data (Kirbas and Dogan 2023). By modeling observed and latent variables, SEM provides a framework fit for examining the complex and interconnected pathways that characterize technology acceptance. The advanced features of Smart PLS 4.0 further support this analysis, offering enhanced capabilities for model estimation and validation (Ringle et al. 2024). This has particularly motivated its application in this paper.
The results indicated that most respondents (87 %) preferred not to turn on their webcams during online synchronous classes. Among these, 63 % reported never using webcams in their online courses, even if explicitly requested by instructors (25 %). Nevertheless, students have also reported that being required to do so is uncommon (27 %). The data also revealed a strong preference for messaging options, with 71 % of students favoring this method of communication and building rapport with their teachers. The Smart PLS 4.0 software performs statistical analysis for variance-based structural equation modeling (SEM) utilizing the partial least squares (PLS)2 path modeling technique to estimate models at two distinct levels (Hair et al. 2023). The first level involves the assessment of the measurement model (outer model), while the second level pertains to the evaluation of the structural model (inner model) (Venturini and Mehmetoglu 2019). The measurement model illustrates the connections between latent constructs (here, the extended TAM constructs) and their defining indicators (here, the questionnaire items). The structural model demonstrates the relationships among the latent variables themselves and measures the strength and direction of such paths.
In structural equation modeling, the measurement model establishes that indicators reliably and validly capture latent constructs. The paper assesses internal consistency, convergent validity, and discriminant validity using Cronbach’s alpha, Composite Reliability (CR), and Average Variance Extracted (AVE). Following common thresholds, alpha and CR above 0.70 indicate acceptable reliability; an AVE value above 0.50 indicates adequate convergent validity. These criteria confirm psychometric soundness before proceeding to the structural model.
The measurement model analysis reveals strong evidence of internal consistency and reliability across the constructs. Table 1 demonstrates that most constructs have acceptable to high Cronbach’s Alpha values, ranging from 0.757 to 0.878. These values substantially exceed the conventional threshold of 0.7 and indicate high reliability in the measurement instrument, supporting the model’s overall validity and consistency. PU is the most reliable construct with a Cronbach’s Alpha of 0.878, followed by the IC index at 0.846, indicating strong internal consistency. SN and PEU also show reliability with values of 0.792 and 0.757, respectively. The AT construct, at 0.653, meets acceptable reliability standards in social science research, where lower thresholds for attitudinal measures are often recognized as adequate (Bonett and Wright 2014; Quintana 2023).
Reliability and validity indices for the proposed measurement model.
| Model’s constructs | Cronbach’s alpha (standardized) | Cronbach’s alpha (unstandardized) | Composite reliability (rho_c) | Average variance extracted (AVE) |
|---|---|---|---|---|
| AT | 0.878 | 0.877 | 0.875 | 0.605 |
| IC | 0.909 | 0.908 | 0.902 | 0.619 |
| PEU | 0.941 | 0.940 | 0.930 | 0.634 |
| PU | 0.972 | 0.971 | 0.957 | 0.648 |
| SN | 1.003 | 1.002 | 0.985 | 0.662 |
The overall strength of the measurement model is further evidenced by the obvious consistency between standardized and unstandardized Cronbach’s Alpha values across all constructs (Table 1). This consistency indicates measurement stability and reinforces the instrument’s reliability. The model demonstrates reliability values suitable for advanced analysis, with an average Cronbach’s Alpha of 0.785 across all constructs.
Convergent and discriminant validity are crucial for assessing construct validity in measurement models, ensuring instruments accurately capture intended constructs while distinguishing between different theoretical concepts.
Convergent validity examines how multiple indicators of the same construct share variance. Key metrics include Average Variance Extracted (AVE), which should exceed 0.5; Composite Reliability (CR), which needs to be above 0.7; and factor loadings above 0.7. In this model, AVE values range from 0.605 to 0.662, indicating strong convergent validity. CR values span from 0.875 to 0.985, demonstrating excellent internal consistency. While one loading for the AT construct is lower at 0.547, overall loadings still reflect a solid relationship between indicators and constructs.
Discriminant validity assesses whether constructs are distinct, using the Heterotrait-Monotrait (HTMT) ratio. Values should ideally remain below 0.85 to 0.90. In this study, HTMT values ranged from −0.370 to 0.092, all under the conservative threshold of 0.90, confirming adequate distinction between constructs. This consistent pattern indicates that the constructs measure unique aspects of the model.
Following the validation of the measurement model, the analysis proceeds to the structural model to illustrate the model’s predictive capabilities.
The strongest predictive relationships centered around PU and SN, with these constructs demonstrating robust direct and indirect effects on AT. However, it is noteworthy that all paths involving IC factor proved non-significant (IC → AT: β = −0.078, t = 1.861, p = 0.063; IC → PEU: β = −0.016, t = 0.277, p = 0.782; IC → PU: β = −0.001, t = 0.018, p = 0.985). The statistical significance of these coefficients, indicated by t-values and p-values, also offers a robust measure of the reliability of these effects. Table 2 presents the magnitude and the significance of the model’s constructs.
Significant, moderately significant and non-significant paths of the model.
| Path category | Path | Coefficient, T-value, P-value | Associated constructs |
|---|---|---|---|
| Significant paths | PU -> AT | 0.623, 7.929, 0.000 | Perceived usefulness (PU) -> attitude (AT) |
| SN -> PU | 1.555, 9.301, 0.000 | Subjective norm (SN) -> perceived usefulness (PU) | |
| SN -> PEU | 1.073, 7.359, 0.000 | Subjective norm (SN) -> perceived ease of use (PEU) | |
| SN -> AT | 0.513, 3.079, 0.002 | Subjective norm (SN) -> attitude (AT) | |
| Moderately significant paths | PEU -> AT | 0.216, 2.989, 0.003 | Perceived ease of use (PEU) -> attitude (AT) |
| Non-significant paths | IC -> AT | −0.078, 1.861, 0.063 | Individualism-collectivism index (IC) -> attitude (AT) |
| IC -> PEU | −0.016, 0.277, 0.782 | Individualism-collectivism index (IC) -> perceived ease of use (PEU) | |
| IC -> PU | −0.001, 0.018, 0.985 | Individualism-collectivism index (IC) -> perceived usefulness (PU) |
The relationship between PU and AT emerged as one of the most robust pathways in the model (β = 0.623, t = 7.929, p < 0.001). This aligns with TAM’s core premise that PU is a key determinant of user acceptance. It also demonstrates that users’ perceptions of the system’s utility strongly influence their attitudes toward it.
SN demonstrated particularly strong influences across multiple pathways. The most pronounced effect was observed in its relationship with PU (β = 1.555, t = 9.301, p < 0.001). The finding suggests that social influences play a crucial role in shaping users’ perceptions of webcam utility. Similarly, SN exhibited a strong positive influence on PEU (β = 1.073, t = 7.359, p < 0.001), indicating that social factors significantly contribute to users’ perceptions of webcam usability. Additionally, SN showed a direct positive effect on AT (β = 0.513, t = 3.079, p < 0.01), further emphasizing the importance of social influences in the model, and indicating that social acceptance and peer influence are crucial in forming positive attitudes towards technology.
The analysis also revealed a moderately significant relationship between PEU and AT (β = 0.216, t = 2.989, p < 0.01). While this relationship was less pronounced than the influence of PU, it nonetheless indicates that webcam perceived usability contributes meaningfully to users’ overall attitudes, albeit to a lesser extent than its PU. This suggests that while ease of use contributes to positive attitudes, its impact is secondary to usefulness, aligning with TAM’s traditional emphasis on usefulness.
However, not all hypothesized relationships in the model proved significant. Notably, all paths involving IC failed to reach statistical significance at the conventional p < 0.05 level. The relationship between IC and AT showed marginal effects (β = −0.078, t = 1.861, p = 0.063), while its relationships with both PEU (β = −0.016, t = 0.277, p = 0.782) and PU (β = −0.001, t = 0.018, p = 0.985) were notably weak and non-significant.
The final step of measuring the model’s predictive power is examining the R2 values of its endogenous variables. This focus on endogenous variables is due to their role as dependent variables within the structural equation model. They are explicitly designed to be explained by other variables in the model, making their R2 values meaningful indicators of the model’s explanatory power.(3) , (4) In other words, these R2 values quantify how well the model explains the variance in key dependent variables (AT, PU, PEU) and reflect the combined influence of all relevant predictors. Table 3 presents the R2 values for the endogenous variables.
R2 values for exogenous variables.
| Endogenous variables | Exogenous variables | R2 values |
|---|---|---|
| Attitude (AT) | Influenced by PU, PEU, SN, and IC | 0.537 |
| Perceived usefulness (PU) | Influenced by SN and IC | 0.966 |
| Perceived ease of use (PEU) | Influenced by SN and IC | 0.329 |
The model shows strong predictive power for Attitude, moderate predictive power for Perceived Usefulness, and relatively low predictive power for Perceived Ease of Use. The next step in the discussion is path analysis, which is employed to examine the relationships among the latent constructs.
Path model analysis estimates models with complex variable dependencies (Ringle et al. 2024). It is a special type of SEM that focuses on observed variables. Path coefficients quantify the strength and direction of relationships between variables, allowing for the calculation of direct, indirect, and total effects within the model. A direct path represents a straightforward relationship between two variables, while an indirect path involves the influence of one variable on another through one or more intervening variables. The statistical significance of these paths indicates whether the observed effects are likely to be genuine or due to chance. This analysis of direction, magnitude, and significance enables a deeper understanding of the model’s predictive and explanatory power, which will be discussed in the following section. To assess the significance of the path coefficients, bootstrapping with 5,000 sub-samples was conducted in SMART PLS 4.0.
The path analysis revealed complex relationships between the model’s constructs, demonstrating direct and indirect effects contributing to users’ attitudes, as shown in Table 4. In terms of direct relationships, Perceived Usefulness (PU) emerged as a particularly strong predictor of Attitude (AT), exhibiting a statistically significant direct path (β = 0.623, t = 7.929, p < 0.001). This finding emphasizes that users’ perceptions of utility are crucial in shaping their attitudes. Notably, Subjective Norm (SN) demonstrated substantial direct influences on both Perceived Usefulness (β = 1.555, t = 9.301, p < 0.001) and Perceived Ease of Use (PEU) (β = 1.073, t = 7.359, p < 0.001), highlighting the significant impact of social influences on users’ perceptions. Additionally, Perceived Ease of Use showed a moderate but significant direct effect on Attitude (β = 0.216, t = 2.989, p < 0.01), suggesting that the system’s perceived usability directly contributes to users’ attitudes.
Path analysis: Direct effects, indirect effects, and total effects in the model.
| Path | Direct effects | Indirect effects | Total effects |
|---|---|---|---|
| IC -> AT | −0.078 (1.861) | −0.004 (0.266) | −0.082 (1.362) |
| IC -> PEU | −0.016 (0.277) | N/A | −0.016 (0.277) |
| IC -> PU | −0.001 (0.018) | N/A | −0.001 (0.018) |
| PEU -> AT | 0.216 (2.989)** | N/A | 0.216 (2.989)** |
| PU -> AT | 0.623 (7.929)** | N/A | 0.623 (7.929)** |
| SN -> AT | 0.513 (3.079)** | 1.201 (6.316)*** | 1.714 (10.015)*** |
| SN -> PEU | 1.073 (7.359)*** | N/A | 1.073 (7.359)*** |
Values in parentheses represent t-values. *p < 0.05; **p < 0.01; ***p < 0.001.
The analysis also revealed important indirect relationships that further enriched our understanding of the model’s dynamics. Subjective Norm demonstrated significant indirect effects on Attitude through two distinct pathways. First, through its influence on Perceived Usefulness (SN → PU → AT: β = 0.970, t = 6.316, p < 0.001), indicating that social influences enhance users’ perceptions of the system’s utility, which in turn positively affects their attitudes. Second, through its impact on Perceived Ease of Use (SN → PEU → AT: β = 0.231, t = 3.051, p < 0.01), suggesting that social norms also contribute to users’ attitudes by enhancing their perceptions of the system’s ease of use.
The path analysis also revealed weak and non-statistically significant relationships. Individualism-Collectivism demonstrated weak and not statistically significant relationships with the model’s constructs. Specifically, the direct path from Individualism-Collectivism to Attitude was negative and negligible (β = −0.078, t = 1.861, p > 0.05). Similarly, the direct effects of Individualism-Collectivism on Perceived Ease of Use (β = −0.016, t = 0.277, p > 0.05) and Perceived Usefulness (β = −0.001, t = 0.018, p > 0.05) were small and far from statistical significance.
In terms of indirect relationships, the analysis revealed that Individualism-Collectivism had a negligible indirect effect on Attitude (IC → PEU/PU → AT: β = −0.004, t = 0.266, p > 0.05). The total effect of Individualism-Collectivism on Attitude, combining both direct and indirect pathways, remained weak and statistically non-significant (β = −0.082, t = 1.362, p > 0.05). These results suggest that, unlike other factors in the model, IC does not substantially influence students’ perceptions of ease of use, usefulness, or overall attitudes toward webcam use.
Figure 2 presents the image produced by the SMART PLM 4.0 software, illustrating the output model for the extended TAM.
SMART PLS 4.0 output model.
The results, summarized in Table 5, indicate that hypotheses H1, H2, H5, and H6 were confirmed, while the analyses did not support H3 and H4.
Confirmed and unconfirmed hypotheses.
| Hypothesis number | Path coefficient | T-value | P-value | Confirmed/not confirmed |
|---|---|---|---|---|
| H1 | 1.555 | 9.301 | 0.0 | Confirmed |
| H2 | 1.073 | 7.359 | 0.0 | Confirmed |
| H3 | −0.001 | 0.018 | 0.985 | Not confirmed |
| H4 | −0.016 | 0.277 | 0.782 | Not confirmed |
| H5 | 0.623 | 7.929 | 0.0 | Confirmed |
| H6 | 0.216 | 2.989 | 0.003 | Confirmed |
The finding that 87 % of students report pronounced reluctance to use webcams, with 63 % never activating their cameras, echoes the “Generation Invisible” pattern documented globally in synchronous learning (Bedenlier et al. 2021; Gherhes et al. 2021). While the data align with prior work showing that perceived usefulness (PU) and perceived ease of use (PEU) are positively associated with favorable attitudes toward technology (Lyu et al. 2024), these acceptance beliefs did not translate into the higher-visibility behavior of turning cameras on. This decoupling emphasizes a key limitation of relying on technology acceptance beliefs alone when the affordances of the tool (a live, bidirectional video feed from the home) collide with sociocultural and privacy norms (Majeed et al. 2022).
In Jordan’s closely knit, conservative social fabric, webcam activation is not a neutral technical choice but a public act that collapses the boundary between the private household and the classroom. Students navigate expectations around privacy (protecting the home from exposure), modesty (managing how one’s body and surroundings are seen), gendered interaction (comfort with mixed-gender visibility and gaze), and public self-presentation (concerns about being judged, recorded, or re-circulated) during live sessions. In such settings, shared norms and social approval carry significant weight. Students read cues from peers, instructors, and family members and anticipate reputational consequences in ways that elevate the perceived social costs of appearing on camera. These dynamics help explain why many continue to participate through lower-visibility channels, such as chat and private messaging, while resisting the visual and spatial exposure of the webcam feed (Händel et al. 2022; Castelli and Sarvary 2021; Gherhes et al. 2021).
Additionally, turning on a webcam publicly reveals the body, the home, and gendered identity. Students navigate expectations around modesty, household privacy, mixed-gender interaction, and public self-presentation, often opting for lower-visibility participation (e.g., chat) to align with collective norms and avoid social scrutiny.
Therefore, framing webcam reluctance around gendered visibility rather than around gender categories per se clarifies why positive attitudes toward the technology can coexist with persistent camera-off behavior. The operative constraint is the social meaning attached to being seen, judged, or recorded in mixed-gender, instructor-led spaces (Händel et al. 2022; Castelli and Sarvary 2021; Gherhes et al. 2021). This interpretation is consistent with evidence that privacy and modesty concerns, along with community expectations and the weight of social approval, shape participation choices in Middle Eastern contexts (Sederevičiūtė-Pačiauskienė et al. 2022; Slimi 2020; Alawamleh et al. 2020).
Not collecting demographics, including gender, is a limitation that constrains subgroup analysis and generalizability. This design choice was adopted following pilot feedback from students and faculty who recommended strict anonymity to enable candid reflection about webcam visibility in a culturally sensitive setting. Paradoxically, this omission accentuates, rather than diminishes, the salience of gendered dynamics in technology use. In settings where modesty, mixed-gender visibility, and household privacy are consequential, the strong preference for anonymity signals that webcam activation and discussion of it are socially sensitive. The need to protect identity serves as an indicator of the sociocultural pressures surrounding webcam visibility; reluctance to disclose gender can reflect concerns about being judged or singled out for (non)compliance with gendered visibility norms.
The study distinguishes between general technology acceptance and feature-specific usage, revealing that while students report positive attitudes toward videoconferencing technologies in language learning (Alfadda and Mahdi 2021; Bailey et al. 2022), there is a clear gap between these attitudes and actual webcam activation. This gap indicates that established predictors of technology adoption, such as perceived usefulness and ease of use (Bailey et al. 2022; Granić and Marangunić 2019), do not fully explain students’ reluctance to use webcams. Instead, factors beyond core TAM constructs, including discomfort, self-consciousness, privacy concerns, and anxiety, play a significant role (Al-Samarraie 2019; Bedenlier et al. 2021; Jimemez et al. 2020; Lenkaitis 2020). Students’ strong preference for messaging over webcam use reflects not resistance to communication, but a response to the social and privacy dynamics of being on camera. This trend is observed cross-culturally.
For example, in Jordan, students favor private messaging for its versatility in academic, social, and management contexts (Alawamleh et al. 2020). In China, it supports understanding, social connection and resolving technical issues (Zhang et al. 2022). Additionally, in privacy-conscious communities like Lithuania and Oman, cultural factors further reinforce this preference (Sederevičiūtė-Pačiauskienė et al. 2022; Slimi 2020). Thus, students’ communication needs and preferences may not align with the perceived benefits of webcams, and external factors may play a more significant role in shaping webcam use.
The current study validated that PU is a powerful predictor of Attitude toward webcam use, aligning with previous TAM-based studies (Al-Adwan et al. 2023; Alfadda and Mahdi 2021). Students who believe that webcams contribute to better learning outcomes and a more authentic classroom experience are more likely to express favorable attitudes toward their use. Both the direct effect of perceived ease of use (PEU) on attitude toward technology (AT) and its generally weaker influence compared to perceived usefulness (PU) were validated in the present study, consistent with previous findings (Al-Adwan et al. 2023; Alfadda and Mahdi 2021). When students perceive a technology as straightforward and user-friendly, they tend to form more positive attitudes toward adopting it. However, these favorable attitudes, supported by both perceived usefulness and ease of use, do not inevitably lead to actual webcam activation. Reiterating the importance of extended TAM studies to understand students’ decisions regarding webcam use.
Subjective norm (SN) emerges as a powerful predictor of webcam intentions, exerting both direct effects on use decisions and indirect effects through perceived usefulness (PU) and perceived ease of use (PEU). This pattern in our data aligns with prior work showing that social expectations from peers, instructors, and family shape attitudes and intentions, and that SN often elevates PU more strongly than PEU (Alfadda and Mahdi 2021; Bedenlier et al. 2021; Jimemez et al. 2020; Händel et al. 2022; Nguyen et al. 2022; Tarhini et al. 2017). Cross-cultural TAM syntheses similarly indicate that in more collectivist settings, group endorsement and perceived approval are decisive drivers of technology acceptance, whereas in more individualist contexts personal assessments of usefulness and ease tend to dominate (Jan et al., 2022).
Interpreting SN in Jordan requires attention to its broader sociocultural grounding. In a broadly collectivist milieu where compliance with shared expectations often guides individual behavior, students are highly attentive to what important others are doing and approving (Nguyen et al. 2022; Viberg et al. 2023). When webcam activation is not mandated, the collective choice to keep cameras off rapidly solidifies into a descriptive norm of non-use that is difficult to break (Gherheş et al. 2021). Moreover, privacy, modesty, and gendered visibility concerns amplify the social stakes of appearing on camera: students seek to avoid exposing domestic spaces, being recorded or circulated, and breaching conventions around mixed-gender visibility and public self-presentation. Evidence from culturally proximate settings (e.g., Saudi higher education) underscores how gender roles and visibility norms intensify these concerns, making the perceived social costs of webcam use especially salient (Almekhled and Petrie 2024). Fears of unauthorized image capture and domestic exposure further heighten caution in contexts with stricter social boundaries (Alier et al. 2021; Castelli and Sarvary 2021). Taken together, the strong influence of SN in our sample reflects a cultural reality: in closely knit communities like Jordan, aligning with perceived group expectations, or avoiding social judgment, often outweighs individual risk–benefit calculations, thereby sustaining camera-off practices even when students hold positive views of the technology (Bedenlier et al. 2021; Händel et al. 2022).
Individualism-collectivism, as an external factor in TAM, did not exert a statistically significant effect on the acceptability of webcam use in online classes. Given this null result, it was essential to verify whether the absence of a significant effect genuinely reflects the underlying dynamics, or if it may be attributable to methodological, procedural, or theoretical limitations. From a methodological standpoint, the study’s instrument was rigorously designed, with multiple items per construct to ensure robust measurement of all relevant variables, including IC, SN, PU, PEU, and AT. A thorough discussion of the measurement model (Consult Section 4.1) further supports the adequacy of the survey as a data collection tool and a credible source of data analysis.
Relatedly, it was important to reassess how IC relates to other constructs in the model, as these relationships, such as direct and indirect paths, mediation, moderation, and collinearity, can obscure actual IC effects. This step ensures valid theoretical conclusions about each construct’s role and the interpretation of results. In path analysis, a direct path shows an immediate effect (e.g., PU directly influencing AT), while an indirect path typically involves mediation, where the effect passes through intermediary variables (e.g., SN affecting AT via other variables). In this study, SN had significant indirect effects on AT through two pathways. Moderation occurs when the relationship between two variables changes depending on a third variable (the moderator). For example, the effect of SN on PEU may vary with a person’s cultural orientation (IC). Moderation is typically tested by including interaction terms (e.g., SN × IC) in the model.4 Collinearity arises when predictor variables are highly correlated, making it hard to separate their individual effects. For instance, if SN and IC are not clearly defined and/or operationalized (as items in the survey), their overlap can inflate standard errors and hide significant relationships, possibly leading to incorrect conclusions.
This complimentary analysis ruled out any significant direct or indirect effects of IC on AT, PEU, and PU. All effects were weak and non-significant, indicating no substantial relationship between IC and these variables. Collinearity between SN and IC was also ruled out. Strong factor loadings, HTMT values below 0.85, and VIFs under 2 confirmed that SN and IC are empirically distinct and free from problematic collinearity.
The theorized moderation effect between IC and SN was then examined. Critically, testing for moderation clarified IC’s role, i.e., including the interaction term (SN × IC), revealed a significant influence on PEU. Specifically, IC functions as a contextual moderator that strengthens the impact of SN on perceived ease of use. This aligns with cross-cultural literature showing that cultural orientation shapes how other constructs operate (Viberg et al. 2023). For individuals in collectivist-leaning contexts, the pressure exerted by SN to adopt a technology is fundamentally stronger than in individualistic societies; collective endorsement acts as a cultural validator that reduces psychological barriers to adoption. This pattern is consistent with comparative findings in collectivist cultures (Nguyen et al. 2022) and with evidence that cultural orientation can be assessed at both national and individual levels (Dorfman and Howell 1988; Hofstede 1980).
Situated in Jordan’s closely knit, collectivist milieu, these results have a clear interpretation. Students are highly attentive to shared expectations and the reputational consequences of visible participation in live online classes. When important others implicitly or explicitly disapprove of being on camera, SN does not merely shape attitudes in the abstract; it also reframes ease perceptions. That is how effortless, safe, and socially acceptable webcam use feels in practice. In this context, collective endorsement (or its absence) recalibrates PEU, making social influence a more efficacious lever of acceptance than individual cost-benefit assessments alone. Thus, IC’s non-significant direct effect alongside its significant moderating role is theoretically coherent in Jordan. Cultural orientation does not directly make webcams more or less acceptable, but it intensifies how strongly social expectations translate into the felt ease of turning cameras on.
The moderating effect of IC has been observed in contexts closely resembling the present study, such as Lebanon, in other generally collectivist societies like Pakistan and Turkey, and large-scale cross-national research, highlighting the consistency of this pattern across diverse educational and cultural environments. For instance, research with an individual-level conceptualization of IC for understanding technology acceptance in educational settings in Lebanon found that IC significantly moderated the effect of SN on behavioral intention (Tarhini et al. 2017). Another similarity was that the impact of SN on intention was strongest for students who identified as more collectivists (Tarhini et al. 2017). This highlights that in collectivist-oriented individuals, social encouragement or pressure exerts a greater influence on technology acceptance decisions compared to those with more individualistic orientations. This is why cultural homogeneity cannot be assumed, a priori, within student populations, emphasizing that even within societies broadly characterized as collectivist, significant individual differences exist.
A focused comparison with Abbasi et al. (2015), Ajzen and Fishbein (1980), Al-Adwan et al. (2023) provides valuable insight into how individualism-collectivism (IC) shapes technology acceptance. In their cross-cultural study, Abbasi et al. examined academics in Pakistan, a more collectivist society, and Turkey, a relatively more individualist context, using an individual-level approach to IC. Their findings, much like those of the current study, show that collectivist respondents experienced a stronger relationship between subjective norm (SN) and both perceived usefulness (PU) and behavioral intention. The moderating effects of IC were apparent among collectivist academics in Pakistan, where the influence of SN and management support on PU, perceived ease of use (PEU), and behavioral intention was more pronounced. In contrast, Turkish academics, who tended to be more individualist, showed a stronger effect of PU on behavioral intention. These results highlight that individuals can differ significantly in their responsiveness to social norms, and that cultural orientation plays a key role in shaping the pathways through which technology acceptance unfolds.
Jan et al. (2022) analyzed technology acceptance across 15 countries using Hofstede’s national-level index of individualism-collectivism (IC). By treating IC as a country-level cultural dimension, they demonstrated how national culture moderates the effects of social norms and perceived ease of use (PEU) on technology adoption. Their meta-analysis of 22 studies found that IC acts primarily as a moderator, amplifying the influence of social norms and PEU rather than serving as a direct predictor. This pattern aligns with the present study, which also shows that IC strengthens the relationship between social norms and users’ evaluations of technology, especially in collectivist societies like Jordan. In these contexts, group endorsement and social validation play a crucial role in technology adoption.
Jimemez et al. (2020) offer a similar perspective, conceptualizing IC as a contextual moderator at the national level within the Technology Acceptance Model (TAM). Their findings confirm that in collectivist societies, social norms have a stronger impact on perceived usefulness (PU) and PEU, driving technology adoption. In contrast, individualist societies place greater emphasis on personal assessments of usefulness and ease of use, with social norms playing a lesser role. Together, these cross-cultural studies reinforce the current study’s conclusion that IC moderates the effect of social norms on technology acceptance, highlighting the importance of cultural context in shaping adoption behaviors.
Understanding these cross-cultural patterns is essential for designing effective strategies to encourage technology adoption in educational settings. In collectivist environments, group expectations and social endorsement are powerful motivators. Interventions in these contexts should focus on building community support and leveraging group norms to foster acceptance of new technologies. For example, involving respected community members or peer leaders in technology initiatives can help create a sense of shared purpose and increase buy-in among students and educators. In contrast, individualist contexts require a different approach. Here, strategies that emphasize the personal benefits of technology, such as its usefulness and ease of use, are likely to be more successful. Providing clear, individualized information about how technology can improve learning outcomes or simplify tasks can help increase acceptance among users who prioritize personal agency and self-evaluation.
It is also important to recognize that cultural orientations are not uniform within any given society. While Jordan is often characterized as collectivist, there is considerable variation in cultural attitudes among individuals and groups. Other cultural dimensions, such as power distance or attitudes toward authority, may also shape how technology is received and adopted. This complexity highlights the need for flexible and context-sensitive approaches when applying technology acceptance models in diverse educational environments. By considering both the broader cultural context and the specific characteristics of the target population, educators and policymakers can develop more effective interventions that support meaningful and sustained technology use.
Aside from methodological considerations that may account for the non-significance of individualism-collectivism (IC) in the model, it is also important to recognize that the inherently intrusive nature of webcam use introduces additional factors. Privacy, self-presentation, and personal comfort may play a more decisive role in students’ decisions to activate their webcams, potentially overshadowing the influence of broader cultural dimensions like IC.
This study addresses a crucial issue at the intersection of technology use and cultural context in online education, where students are reluctant to use webcams despite generally positive attitudes toward the technology. Applying an extended TAM, the study examines how cultural context shapes decisions not to appear on camera, regardless of the concrete learning benefits and the pedagogical value associated with their use.
Subjective norm emerged as the principal driver of acceptance. It influenced attitudes directly and indirectly through perceived usefulness and perceived ease of use. Individualism-collectivism did not operate as a direct predictor. Instead, it acted as a contextual moderator; it amplified the influence of social expectations on perceived ease of use. This pattern reflects the weight of privacy, modesty, gendered visibility, and public self-presentation norms in Jordan. It also reflects the influence of shared expectations in closely knit communities, where social approval and group alignment matter.
Methodologically, the study offers a rigorous approach through its comprehensive survey design, robust measurement model, and detailed analysis of direct, indirect, and moderating effects. The use of multiple indicators for each construct, validation of measurement properties, and careful attention to potential biases strengthen the credibility of the findings. This approach provides a valuable template for future research on technology acceptance, especially when investigating complex social and cultural constructs.
For educators and instructional designers, the results suggest several practical strategies. Promoting positive peer behaviors and establishing explicit class norms can help normalize webcam use. Involving respected students, faculty, or community members as advocates can further reinforce these norms. To enhance perceived usefulness and ease of use, it is important to communicate the benefits of webcam use for learning and interaction, and to provide technical support and training. Recognizing cultural context is essential: students from collectivist backgrounds may respond best to strategies that emphasize community benefits and shared goals, while those in individualist settings may prefer approaches that respect personal choice and autonomy. Addressing privacy concerns and individual differences by offering flexible participation options and fostering a supportive environment is also crucial for inclusive practice.
This study has limitations. It draws on self-reported, cross-sectional data from language majors at a single institution, constraining causal inference and generalizability across disciplines and contexts. Demographic information, particularly gender, was not collected to preserve anonymity in a culturally sensitive setting, which likely enhanced candor but precluded subgroup analyses and may mask heterogeneity. Notably, the strong preference for anonymity aligns with the interpretation that on-camera visibility is socially sensitive where norms around modesty, mixed-gender interaction, and public self-presentation carry significant weight.
Future work should broaden institutional and disciplinary scope, employ longitudinal or experimental designs to assess changes in norms and behavior over time, and incorporate mixed-methods. Optional, anonymized demographic measures, complemented by interviews or focus groups, can deepen understanding of how privacy, modesty, gendered visibility, and public self-presentation are negotiated in practice, and how these negotiations mediate the power of social norms in synchronous online learning.
Power analysis is a statistical procedure used to determine the minimum sample size required to detect an effect of a given size with a specified level of confidence. It assesses the probability (power) that a study will correctly reject a false null hypothesis, thereby ensuring that the sample size is sufficient to identify meaningful relationships between variables.
Unlike traditional covariance-based SEM, PLS path modeling focuses on maximizing the explained variance of the dependent variables by estimating path coefficients that best predict the relationships specified in a model.
Unlike endogenous variables, exogenous variables are determined by factors outside the model and serve as independent predictors. therefore, calculating r2 values for exogenous variables would not provide meaningful insights into the model’s predictive capabilities, as these variables are not intended to be explained by the model’s relationships.
The decision to examine the interaction between SN and IC with PEU was guided by theory and prior research, which suggests that PEU is especially responsive to the combined effects of social and cultural influences. Therefore, this pathway was selected because it offered the strongest potential for detecting meaningful moderation effects.