Multi-lens analysis of LMS feature adoption in higher education

Introduction: This study investigates how university lecturers adopt Learning Management System (LMS) features, focusing on innovation attributes, psychological-emotional influences in the adoption process (knowledge, persuasion, decision, implementation, and confirmation), and the role of higher education as a social system.

Method: This study applies a qualitative approach by using Rogers' Diffusion of Innovations (DOI) framework, and qualitative data from focus group discussions (FGD). FGD was conducted with lecturers, staff, and students, and was analyzed through three lenses: innovation attributes (relative advantage, compatibility, complexity, trialability, and observability), emotional factors in the adoption process, and social-environmental influences (e.g., institutional policy, peer norms, student expectations). Data was collected from three focus group discussions (FGDs) with lecturers and students, and triangulated with LMS system usage logs capturing assessment feature usage over five semesters.

Result: The findings reveal that adoption is not solely determined by feature functionality but also by emotional readiness and social-cultural contexts. A total of 104 references were identified and coded. Complexity emerged as the most frequent barrier (35 codes), followed by compatibility (23) and relative advantage (24). Emotional factors were prominent: anxiety (18 mentions) and avoidance (9) shaped late adopters' hesitation, while confidence (11) distinguished early adopters. Late adopters referenced the confirmation stage almost twice as often (11 vs. 6), reflecting a stronger need for reassurance. While early adopters engage LMS features with confidence and curiosity, late adopters are usually hindered by complexity, anxiety, and a lack of support. Institutional mandates and peer modeling encourage diffusion, but departmental inconsistencies create barriers.

Conclusion: It suggests that successful LMS integration requires more than training and tools; it demands emotional scaffolding, culturally responsive leadership, and multi-level stakeholder engagement. The findings offer actionable guidance for LMS developers and higher education institutions to create and maintain sustained and inclusive digital learning environments.

1 Introduction

Learning Management Systems (LMS) have become essential tools in higher education, providing integrated platforms to support course delivery, assessment, and communication across various learning formats (Cottam, 2021; Al-Busaidi and Al-Shihi, 2010). Despite widespread adoption of LMS platforms, studies consistently show that many lecturers limit their use to basic features such as uploading materials or announcements, while more advanced tools—such as grading rubrics, automated assessments, or discussion forums, remain underutilized (Boland, 2020; Kusnadi et al., 2025; Su and Chen, 2025).

The development of LMS platforms commonly follows Agile methodologies, which emphasize iterative improvements and incremental feature releases (Taye, 2024). Whether the LMS is custom-developed or based on an open-source platform like Moodle, the Agile methodology appears to dominate the development process (Gumińskia et al., 2023). New features may be released without sufficient user awareness or support, resulting in a persistent gap between the availability of LMS functions and their actual use by educators. While this approach enables continuous updates without disrupting ongoing operations, it also presents challenges for adoption. The gradual release of features enabled by the Agile methodology may provide ample time for users to learn them. On the other hand, it may stifle the rate of adoption due to reasons such as unnoticed information and a lack of training.

This challenge is evident at a private university with over 40,000 students and approximately 1,600 lecturers. Since the initial release of its institutional LMS in late 2020 with 13 core features, the platform has expanded to 38 features. Despite extensive training and the availability of support resources, many lecturers remain unaware of, or choose not to adopt, newly introduced features. This recurring pattern raises a critical question: why do some lecturers readily adopt LMS features, while others do not?

Previous research often frames LMS adoption through the lens of The Diffusion of Innovation (DOI) theory (Rogers, 2003), which emphasizes five key attributes influencing adoption: relative advantage, compatibility, complexity, trialability, and observability. DOI has been extensively applied in technology adoption studies in education (Granić, 2023; Rehy and Tambotoh, 2022; Saleh et al., 2024), the usability of LMS technology (Alghamdi and Alzahrani, 2024; Kusnadi et al., 2025), and its impact on learning outcomes (Roa et al., 2023). However, DOI's focus on innovation attributes and linear decision stages does not fully account for the emotional and social complexities that shape adoption behaviors in real-world educational settings. In LMS adoption, lecturers do not base decisions solely on perceived advantages or ease of use; their choices are also influenced by affective responses (e.g., anxiety, confidence, avoidance) and social pressures (e.g., peer modeling, institutional mandates, cultural norms). To address this gap, we integrate DOI with Wang's emotional adoption framework and social-systems theory. This multi-lens approach allows us to explore how emotional states interact with adoption stages and how institutional and peer dynamics shape the diffusion process. By doing so, we move beyond rational-choice assumptions and provide a more holistic understanding of how and why LMS features are differentially adopted in higher education.

Strengthening this integration, prior work has shown that DOI explains only the cognitive evaluation of innovations, whereas emotional responses and social-system dynamics often determine whether users advance through or withdraw from the adoption stages (Chang et al., 2023; Rogers, 2003). Combining these perspectives provides a theoretically coherent rationale for analyzing LMS adoption as an interplay of cognitive judgments, affective experiences, and institutional influences rather than as a linear, attribute-driven process.

However, dominant technology-adoption models such as TAM, UTAUT, and readiness frameworks primarily emphasize perceived usefulness, ease of use, and user readiness. While these models are valuable for predicting adoption intentions, they do not fully capture the emotional and psychological factors that increasingly emerge in digital learning environments. Such models also offer limited insight into the broader nature of LMS as an innovation with distinct features, each carrying different levels of relative advantage, compatibility, complexity, trialability, and observability (Rogers, 2003). This narrow focus overlooks the complexity of how specific LMS features are adopted or resisted within the teaching and learning ecosystem. Accordingly, this study shifts from measuring acceptance to understanding diffusion dynamics, a perspective essential for designing more effective LMS implementation strategies and institutional policies.

This study addresses that gap by applying the Diffusion of Innovations (DOI) framework, which enables a more nuanced exploration of how innovation attributes interact with emotional, psychological, and social factors to influence adoption decisions. Previous research often frames LMS adoption through the lens of the Diffusion of Innovations (DOI) theory (Rogers, 2003), which emphasizes five key attributes influencing adoption: relative advantage, compatibility, complexity, trialability, and observability. While DOI has been extensively applied in technology adoption studies in the education field (Liu et al., 2025; Patwary and Sajib, 2025; Rauner and Stummer, 2025; Rizaldy et al., 2025). It does not fully capture the role of emotional and psychological factors that increasingly emerge as significant in digital learning environments (Menzli et al., 2022). By integrating DOI with emotional responses and the influence of the institutional social system, this study provides a richer understanding of the barriers and enablers of sustained LMS adoption in higher education. Through the lens of the Diffusion of Innovations (DOI) theory (Rogers, 2003), which emphasizes five key attributes influencing adoption: relative advantage, compatibility, complexity, trialability, and observability.

Emerging research highlights how emotions, such as anxiety, satisfaction, confidence, or avoidance, shape users' willingness to adopt new technologies (Wang, 2010). Faculty members' decisions are often influenced not only by the perceived usefulness of a feature but also by how it aligns with their sense of control, their comfort with technology, and the pressures of their institutional environment. Furthermore, adoption behaviors are deeply embedded within social systems, including institutional policies, peer norms, and student expectations (Rogers, 2003; Ali, 2024). Such understanding will enable us to determine the type of support that should accompany feature rollout and interventions that match people's needs. We suspect it's not just the feature's quality but also aspects of the institutional and social environment that hinder adoption.

Building on these perspectives, this study investigates how university lecturers adopt LMS features by applying a multi-lens framework that integrates DOI, psychological-emotional influences, and the role of higher education as a social system. This research aims to provide a deeper understanding of not only the technical and functional factors but also the emotional and institutional dynamics that drive or hinder LMS adoption. Overall, this study offers three key contributions. First, it shows how innovation attributes, emotional dynamics, and social-system forces interact to create LMS-specific adoption patterns beyond classical DOI predictions. Second, it identifies non-obvious dynamics, including information gaps arising from Agile updates, emotional barriers to progression past the Decision stage, and inequities stemming from institutional inconsistency. Third, it presents an integrated, multi-lens model of LMS adoption.

2 Materials and methods

2.1 Study design

A qualitative focus group discussion is used to examine how LMS features are adopted in higher education (refer to Label A in Figure 1). In doing so, we utilize Rogers' Diffusion of Innovation (DOI) as the underlying theory, supplemented with Wang's Emotional and Psychological framework. Label B in Figure 1 illustrates a potential emergent relationship between DOI and Wang's framework, which this research also aims to explore.

Figure 1

Figure 1. The research method framework.

Figure 1 includes an expanded legend and directional arrows to clarify the flow between DOI attributes, emotional-psychological factors, and emergent themes. The diagram has been refined to ensure terminological consistency with the text and to make explicit the relationships analyzed in this study.

2.2 Participants

In this Focus-Group Discussion (FGD) study, we gathered eight lecturers and eight students to explore their experiences, beliefs, and perspectives in a social, interactive context of higher education. At the same time, they attempted to adopt newly released LMS features.

2.3 Data collection procedure

Three FGDs were conducted via Zoom in April 2025. Each session lasted approximately 90 min. Discussions were audio-recorded and transcribed verbatim. Key discussion topics included experiences with LMS features, barriers, emotional responses, and institutional support. Data saturation was reached when no new codes or themes emerged during the final FGD session, indicating that the sample size was adequate for the scope of this study.

2.4 Ethical considerations

This research was conducted in compliance with ethical standards. Ethical approval was obtained from the Institutional Ethics Review Board, which recommended that the university's name not be disclosed to maintain anonymity and confidentiality. Information consent was obtained from all participants before their involvement in the study. Participants were fully briefed on the purpose of the research, the procedures involved, their rights to confidentiality and anonymity, and their ability to withdraw from the study at any stage without penalty.

2.5 Data analysis

We use both inductive (emerging from the data) and deductive (guided by theory) coding. A hybrid thematic analysis was employed, utilizing both deductive coding based on Rogers' five attributes of innovation and Wang's emotional framework (refer to Label A in Figure 1), as well as inductive coding to capture emergent themes (refer to Label B in Figure 1; Saldaña, 2021). Coding was performed to support AI ChatGPT tools using a 4.0 model to recognize patterns, and two researchers manually cross-checked the coding to enhance reliability.

2.6 Data triangulation

This study employed data triangulation to enhance the validity and credibility of findings. Primary data sources included three FGDs involving eight lecturers and eight students, selected to capture diverse adoption experiences and roles within the LMS ecosystem. To complement the qualitative data, LMS system usage logs from 2022 to 2025 were analyzed. These logs tracked the number of authentic assessments created independently by faculty members and those created with assistance from institutional staff. By comparing participant narratives with system-generated usage patterns, the study cross-validated themes related to perceived complexity, institutional support, and emotional readiness. The triangulation of lecturer and student perspectives with behavioral data yielded a richer, more holistic view of LMS feature adoption.

2.7 Data saturation

Saturation was achieved when no new codes or themes emerged during the third FGD. A saturation tracking matrix confirmed that themes from the first and second sessions were consistently reiterated, with little variation. This suggests that the sample size was sufficient to support both thematic depth and diverse perspectives.

2.8 Trustworthiness

To ensure credibility and rigor, triangulation was achieved through multiple coder verification, and participants were invited to review the preliminary findings (member checking). FGDs were selected as the primary data collection method because they allow participants to articulate experiences collectively, reveal shared practices, and surface emotional and social dynamics that are difficult to access through surveys or individual interviews. This approach was particularly suitable for examining LMS adoption, where peer interaction, institutional norms, and collective interpretations strongly shape behavior. To enhance analytical rigor despite relying on internal triangulation, we use external methodological triangulation from LMS system-usage logs data.

3 Findings

3.1 Lecturers' perception on the attribute of Innovation in LMS features that affects adoption

To address this question, we analyzed qualitative data from focus group discussions (FGDs) with eight university lecturers. Thematic coding was guided by Rogers' five innovation attributes: Relative Advantage, Compatibility, Complexity, Trialability, and Observability. Additionally, lecturers were classified as early adopters or late adopters based on their proactiveness, willingness to explore new LMS features, and integration into teaching practices. This dual-layered analysis highlights not only how these attributes shape LMS adoption but also how perceptions differ between adopter types. The frequency of each innovation attribute is shown in Table 1.

Table 1

Table 1. Frequency of attributes of innovation.

3.1.1 Relative advantage

(Rogers 2003) defines the relative advantage attribute as the degree of innovation to have additional benefit and value compared to previous approaches (Rogers, 2003). This study finds that relative advantage was the most frequently cited attribute (23 references), with a dominant positive tone (21 out of 23).

The early adopters frequently emphasized increased teaching efficiency, streamlined grading, and stronger student engagement as the relative advantages to consider adopting an innovation in LMS features:

“Using the LMS assignment feature saves time because I can collect and grade everything in one place.”

Late adopters, though recognizing some benefits, expressed hesitation or doubt:

“I still feel like using WhatsApp is easier than navigating through the LMS notification feature.”

Beyond functional benefits, perceptions of relative advantage were emotionally mediated. Early adopters expressed satisfaction and curiosity when features streamlined their work. In contrast, late adopters often evaluated advantages through anxiety-laden comparisons with familiar tools, leading to hesitation despite acknowledgment of potential benefits.

It can be concluded that early adopters perceive LMS features as value-enhancing tools. Late adopters, in contrast, tend to compare LMS features with traditional methods and may perceive a lesser net gain. This data shows that users tend to adopt innovation when they feel it gives them a relative advantage.

3.1.2 Complexity

The complexity attribute relates to how complicated or confusing a feature is for users. This attribute received 23 codes and was overwhelmingly associated with negative perceptions, especially among late adopters.

Late adopters repeatedly described LMS features as confusing, cluttered, or unintuitive:

“There are too many steps just to activate one feature. I get lost in the menus.”

Early adopters, while acknowledging some complexity, tended to overcome it through trial, training, or peer help:

“At first it was overwhelming, but once I learned how to navigate, it got easier.”

Feature complexity triggered strong emotional reactions. Late adopters frequently interpreted confusing interfaces as a sign of personal inadequacy, heightening anxiety and avoidance, while early adopters framed initial difficulty as a problem-solving challenge. These contrasting emotional interpretations help explain divergence in willingness to persist through early friction. It can be concluded that complexity is the most prominent barrier for late adopters, reinforcing hesitation and minimal engagement. Conversely, early adopters exhibit stronger resilience and problem-solving behavior.

3.1.3 Compatibility

A total of 21 statements reflected compatibility attributes, with most expressing positive alignment between LMS features and pedagogical needs. The compatibility attribute relates to how well a LMS feature fits the lecturer's needs or work/teaching style.

In the compatibility criteria, early adopters described how LMS tools support their blended or student-centered teaching strategies:

“The LMS forum works really well for asynchronous group discussions.”

Late adopters showed selective acceptance of adopting features only when strongly aligned with familiar teaching routines:

“For large classes, the LMS feels useful. But for my tutorials, I still prefer direct communication.”

Emotional alignment also shaped compatibility perceptions. When LMS features fit existing pedagogy, lecturers expressed comfort and confidence. When misaligned, late adopters felt pressure or discomfort, leading to avoidance. Thus, compatibility functioned not only as pedagogical fit but also as affective fit with teaching identity. It can be concluded that compatibility plays a key role across both adopter types, though early adopters proactively adjust their teaching to match available LMS tools, while late adopters expect LMS tools to fit into existing practices.

3.1.4 Trialability

The trialability attribute refers to the opportunity users have to try a feature without a high level of commitment. Both groups saw Trialability positively, but the tone and usage patterns differed

Early adopters used the LMS in a sandbox manner, testing new tools in real classes with confidence:

“I piloted the rubric grading tool for just one assignment before using it in all my classes.”

Late adopters found reassurance in low-risk experimentation:

“I only tried the quiz feature because my colleague showed me first—it was actually okay.”

Trialability reduced emotional risk by offering low-stakes experimentation. Early adopters experienced excitement and curiosity during trials, while late adopters reported reassurance and reduced anxiety when supported by peers. Trialability, therefore, served as both a cognitive and emotional safety net. It can be concluded that trialability facilitates exploratory behavior in both groups, but early adopters are more self-initiated, whereas late adopters rely on demonstration and peer assurance.

3.1.5 Observability

Observability refers to the extent to which users can see the results of, or the successful use of, an LMS feature. Although it appeared least frequently (3 references), it carried a 100% positive tone, particularly among late adopters. For these users, seeing others use the LMS effectively often served as the decisive trigger for adoption. As one lecturer noted, “After watching how another lecturer used the LMS attendance tool, I decided to give it a try.”

Student FGDs showed similar patterns. Many reported learning LMS features primarily by observing peers rather than through formal guidance: “Information usually comes from friends; lecturers often don't know” and “I asked my friend how to do it”. These behaviors reflect vicarious learning and peer modeling, where observing others reduces uncertainty and increases perceived competence.

Observability was also evident in students' comparisons of lecturers. Those who actively used LMS features were described as making learning “easier to follow,” while low LMS usage created confusion, as in: “My friend entered class and was confused about what to do”. These reactions illustrate social proof, where visible use by credible others shapes perceptions of legitimacy and usefulness.

Overall, Observability plays a motivational role primarily for late adopters, who depend on demonstrations and visible success. For early adopters, it functions more as a mechanism for diffusion—helping others adopt—than as a factor influencing their own initial use.

3.2 Emotional factors of lecturers in making decisions in adopting LMS features

To understand how lecturers make decisions about adopting LMS features, we analyzed their statements through the lens of Rogers' Innovation-Decision Process. Additionally, we consider psychological and emotional factors that influence their willingness to engage with and persist in using these features.

All eight lecturers were previously classified as either early adopters or late adopters, based on the initiative, experimentation, and autonomy they demonstrated in engaging with LMS features. The five stages of adoption were all observed in the data, but lecturers' progression through these stages varied depending on their adopter type as can be seen in Table 2.

Table 2

Table 2. Frequency of code for the adoption process.

Lecturers classified as early adopters demonstrated a more complete progression through the adoption stages. They commonly moved through the stages of Knowledge, Persuasion, Decision, Implementation, and Confirmation, often cycling back to improve or refine their use of LMS tools.

• Implementation and Confirmation were highly prevalent in this group.

• Observed benefits, trial results, and a growth mindset drove their decisions.

• Early adopters often expressed confidence, satisfaction, and curiosity in their statements.

Example Statements:

“I tried the tracking feature and now use it every week to monitor student progress.” — Early Adopter 1

“I always check for feature updates and experiment with them during the semester.” — Early Adopter 2

Late adopters' decision-making was shaped by uncertainty, fear of complexity, and external obligations. Even when they reached the decision stage, the outcome was often not followed by implementation or was quickly abandoned after initial difficulties.

Most lecturers began their adoption journey by acquiring awareness of a feature—often through institutional training, peer conversations, or LMS announcements. However, simply knowing about a feature did not guarantee further exploration or usage.

“I only learned about the rubric feature during the AoL workshop. Before that, I had no idea it existed.”

The persuasion stage was marked by the development of a positive or negative attitude toward a feature, shaped by peer input, ease of use, and perceived value. Some lecturers were intrigued and willing to try; others were skeptical or hesitant.

“It sounds useful, but I haven't tried it yet. I'm not sure I want to add more complexity to my class.”

The decision stage captured moments where lecturers made an explicit choice to adopt or reject a feature. Both internal beliefs and external pressures, such as accreditation or standardized reporting, influenced these decisions.

Lecturers who proceeded to implementation reported trying out features such as forums, rubrics, and quizzes in their actual teaching practice. However, adoption did not always persist.

“I tried the quiz feature once, but the setup was too complicated, so I went back to manual grading.”

The confirmation stage was particularly significant: lecturers reevaluated their decisions based on experience. Some reaffirmed adoption, while others decided to stop using the feature altogether.

Contrary to our initial interpretation, Table 2 shows that late adopters referred to the Confirmation stage almost twice as often as early adopters (11 vs. 6). This indicates that late adopters require more reassurance, validation, and post-adoption reflection before committing to sustained use. Rather than signaling readiness, frequent confirmation-related statements suggest continued uncertainty and the need for repeated verification, reinforcing the emotional fragility of late adopters in the latter stages of the adoption process.

Decision-making was deeply intertwined with psychological and emotional responses. These factors helped explain why some lecturers progressed smoothly while others withdrew or stalled in the early stages. To understand why lecturers proceeded or hesitated at each stage, we coded statements into psychological-emotional categories, as shown in Table 3. Five key factors emerged.

Table 3

Table 3. Frequency of psychological and emotional factors.

3.2.1 Satisfaction

The most common factor, satisfaction, reflected positive reinforcement from LMS usage. Both early and late adopters reported satisfaction, although early adopters often linked it to successful integration.

“It's satisfying to see all assignments in one place—I don't want to go back to email submissions.” (Early Adopter).

3.2.2 Anxiety

This emotion was more dominant among late adopters, often associated with uncertainty, feature complexity, or past negative experiences.

“I was afraid to try because I didn't want to mess up the grading.” (Late Adopter).

3.2.3 Confidence

Confidence facilitated progression through the adoption stages. Early adopters often expressed confidence from prior digital experience or peer support.

“Even though I didn't master it right away, I was confident I could learn.” (Early Adopter).

3.2.4 Avoidance

Avoidance emerged from overwhelming interfaces or a mismatch between LMS tools and teaching needs. It was a barrier for both adopter types.

“It was too complicated, so I just stopped using it altogether.” (Late Adopter).

A key insight is the interdependence between decision stages and emotional and psychological states. For instance:

• Anxiety and Avoidance were often linked to the Persuasion and Decision stages—leading to stagnation or rejection.

• Satisfaction and Confidence reinforced decisions at the Implementation and Confirmation stages.

• Curiosity acted as a trigger during Knowledge or early Persuasion stages.

This indicates that adoption is not purely rational but emotionally mediated, especially for late adopters who require more affective support.

Lecturers make decisions to adopt LMS features through a process shaped by awareness, evaluation, trial, and validation, in line with Rogers' Innovation-Decision Process. However, this process is deeply influenced by psychological and emotional factors such as confidence, anxiety, and satisfaction. Early adopters navigate the stages with higher autonomy, driven by curiosity and confidence, while late adopters often require reassurance and emotional safety to move forward.

3.3 Higher education as a social system to support lecturers in adopting LMS features

To understand how the social and institutional environment influences LMS feature adoption, we analyzed qualitative data from focus group discussions involving both lecturers and students. Thematic coding was conducted within the “Social and Environmental Influence” dimension, with subdimensions representing key aspects of the university as a social system: policy, peer dynamics, institutional culture, and student expectations.

From the lecturers' perspective, the social system within the university environment shaped their adoption behavior in at least five significant ways:

3.3.1 Institutional policy support

Institutional-level support, such as official mandates and training, served as a key structural enabler. Lecturers stated that institutional requirements legitimized and normalized the use of LMS features.

“If the institution requires us to use LMS for assessments, I'll make it work.” (Lecturer)

This aligns with the role of formal systems in reinforcing adoption behavior, especially among those who may not adopt voluntarily.

Another form of institutional support mentioned in the FGD is staff support in implementing the innovation. Regarding the LMS's authentic assessment features, we found that staff support for assessment entry would reduce FM's administrative burden. This effort could help minimize FM's anxiety and concerns about the additional administrative and technical tasks arising from innovation adoption. System usage logs revealed that the percentage of assessments created with staff support increased dramatically from 4.9% in 2022 (odd semester) to over 66% in 2023 and 2024. This quantitative trend confirms FGD findings that lecturers, especially late adopters, expressed greater confidence and a willingness to adopt complex LMS features when supported by institutional staff. It highlights the role of technical scaffolding in reducing perceived complexity and emotional resistance.

3.3.2 Peer influence

Peer dynamics significantly influenced LMS adoption. Lecturers often cited exposure to colleagues' success as the trigger for experimentation.

“I started using it because my colleague showed me how helpful it was.” (Lecturer)

Such vicarious learning mechanisms are especially important in fostering adoption among those hesitant or unfamiliar with the platform.

3.3.3 Cultural expectations

In some departments, the use of LMS was seen as a professional standard. This cultural push functioned as soft enforcement, nudging even reluctant users toward adoption.

“If everyone in my department uses it, I feel like I need to catch up.” (Lecturer)

This reflects the impact of organizational culture in shaping technological norms.

3.3.4 Student-driven expectations

Although students were not the focus of LMS policy implementation, their expectations exerted indirect social pressure on lecturers. Students called for greater consistency and more transparent communication via LMS.

“I hope lecturers use LMS more. It helps us track deadlines and access feedback.” (Student)

This highlights a bottom-up influence in which user needs indirectly shape adoption through perceived teaching effectiveness and accountability.

3.3.5 Structural gaps and disparity

Two additional subthemes—“Dosen Influence” and “Perbandingan Dosen”—revealed internal disparities in LMS adoption and their effects on the overall culture. When only a few lecturers adopted certain features, students and peers often found this inconsistency problematic.

“It's confusing when each lecturer uses the LMS differently.” (Student)

Such fragmentation limits the systemic potential of LMS integration, even when individual cases are successful.

This finding highlights that LMS adoption is not merely a matter of individual lecturer readiness or technological capability. It is deeply embedded in the social system of higher education, shaped by multiple actors and layers of influence—from institutional policy to peer culture and student expectations. To conclude, Table 4 synthesizes key subdimensions that emerged from the analysis, describing how each element of the higher education environment either supported or constrained lecturers in adopting LMS features:

Table 4

Table 4. Social system influence on LMS adoption.

For the “Student Expectations” row, the original label “Implied” referred to statements mentioned indirectly across four FGDs. Upon coding review, this has been assigned a derived frequency of 4 to improve clarity and consistency in reporting. This reflects bottom-up pressure identified in both lecturer and student discussions, even when it is not explicitly stated. Table 4 captures a crucial insight: adoption decisions are social decisions. When LMS use is embedded within institutional expectations, supported by peers, and reinforced by student needs, lecturers are more likely to adopt—and sustain—use of LMS features. For LMS strategies to succeed on a large scale, institutions must enforce clear policies while also offering flexibility. This is a collective effort to nurture a culture of collaboration and sharing among faculty.

Students in the FGDs also emphasized that inconsistent LMS practices among lecturers created confusion, uneven access to materials, and information gaps. Many relied on peers—not lecturers—to understand new features or troubleshoot issues, noting that “information usually comes from friends.” Including students' voices in LMS planning and standard-setting is therefore essential for improving clarity, ensuring consistency, and strengthening the overall learning experience.

Ultimately, LMS is not just a tool; it's a shared academic space. And like all shared spaces, its success depends on the people who shape and support it.

4 Discussion and conclusion

This study offers a comprehensive view of how lecturers adopt LMS features, integrating insights from Rogers' (2003) five innovation attributes—relative advantage, compatibility, complexity, trialability, and observability. Lecturers, particularly early adopters, highlighted the value of LMS features in streamlining teaching and assessment tasks, aligning with the notion of relative advantage as a key driver of adoption. Previous studies (Šumak et al., 2011; Ortiz-López et al., 2024) that perceived usefulness is a strong predictor of LMS engagement. For LMS developers, this underlines the need to demonstrate the feature's instructional value, while institutions could reinforce adoption by embedding LMS use into workload models or evaluation systems.

Compatibility was also crucial. When LMS tools aligned with existing pedagogical practices, lecturers were more willing to adopt them. This aligns with findings from Liu (2011) and Cavus (2015) who emphasized the role of instructional alignment in sustained use. Institutions should offer implementation autonomy, while developers should support customization for different teaching styles.

Differences across departments did not determine compatibility in our data, but by how lecturers actively configured LMS tools to fit their teaching models. Early adopters tended to adjust their pedagogy to the LMS, experimenting with structures that aligned with platform workflows. In contrast, late adopters expected the LMS to adapt to their existing routines, leading to frustration when features felt misaligned with those routines. This interpretive distinction clarifies why compatibility shaped adoption differently across lecturer groups.

Conversely, complexity was a barrier, particularly among late adopters. Many found LMS tools unintuitive or overwhelming, echoing Al-Azawei et al. (2016) and Mathur and Sharma (2024) on the importance of perceived ease of use. Simplified interfaces and scaffolding can help ease these concerns. Trialability emerged as an enabler—lecturers appreciated the ability to explore features with minimal risk. Prior studies (Lavidas et al., 2022, 2023) support this, showing experimentation fosters adoption. Observability, though less mentioned, encouraged late adopters by allowing them to learn from peers, supporting (Chang et al., 2023).

In addition to innovation attributes, this study found that LMS adoption is an emotionally dynamic process. Drawing on Rogers' Innovation-Decision Process, lecturers moved through knowledge, persuasion, decision, implementation, and confirmation phases—each influenced by emotional states. Anxiety and avoidance disrupted early adoption stages, while confidence and satisfaction sustained long-term use. These patterns are supported by Chang et al. (2023), and Self-Determination Theory (Chang et al., 2023), which highlight how self-efficacy and intrinsic motivation influence behavior (Zulherman et al., 2025).

The emotional patterns identified in this study can be further interpreted using established affective theories. Drawing on Control–Value Theory (Pekrun, 2006), late adopters' anxiety and avoidance reflect low perceived control when navigating complex LMS features. In contrast, early adopters' confidence and satisfaction are associated with high perceived control and clear value alignment with their teaching goals. Complementing this, Affect-as-Information Theory (Schwarz, 1983) helps explain why lecturers often treated their momentary feelings as cues for judging the LMS itself: late adopters interpreted confusion or overwhelm as signals that the system was “too difficult” or “not worth the effort,” while early adopters interpreted curiosity or small successes as indicators to continue exploring. These dynamics also mirror established technology-anxiety and technostress constructs, where cognitive overload, feature complexity, and inconsistent institutional expectations heighten stress responses and contribute to avoidance behaviors among users with lower digital self-efficacy. Taken together, these theories clarify why emotional factors played such a decisive role in LMS adoption in our findings and highlight that effective interventions must address not only technical skills but also the affective experience and perceived control that shape lecturers' willingness to engage with new digital tools.

Finally, institutional structures and social systems played a critical role. LMS adoption was shaped by top-down mandates, peer influence, departmental norms, and student expectations (Hassan et al., 2024; Laursen and Weiss, 2025). These findings align with Rogers' view of the social system. Peer modeling and cultural expectations helped diffusion, while inconsistencies and cultural fragmentation posed barriers. Integrating student feedback and promoting peer mentoring are essential to building a consistent, collaborative digital culture. The usage of log data served as triangulation to validate the qualitative theme that institutional support plays a critical role in LMS feature adoption. The rising support rate—from under 5% to over 66%—mirrors the narratives from late adopters who indicated that staff involvement lowered the emotional threshold and technical barriers to innovation uptake. This underscores the importance of human support systems in sustaining technology diffusion.

Differences in LMS practices across departments also created inequities in the learning experience. Some lecturers fully integrated LMS features, while others relied on informal tools such as WhatsApp or Google Drive, resulting in inconsistent access to materials and inconsistent communication quality. Students noted confusion when norms varied widely across courses, indicating that departmental culture strongly shapes diffusion. These disparities highlight the need for institution-wide alignment, peer support, and shared expectations to ensure a more consistent and equitable LMS experience.

Although institutional mandates ensure baseline LMS use, the FGDs showed that compulsory requirements can also create resistance and reduce lecturers' sense of autonomy. In such cases, LMS engagement becomes compliance-driven rather than pedagogically meaningful, leading to superficial use of features. Consistent with innovative research, mandated adoption accelerates uptake but not the depth of implementation. Support and peer modeling are therefore needed to transform compliance into genuine adoption.

Figure 2 synthesizes these findings by integrating the three analytical lenses used in this study—innovation attributes, emotional dynamics, and social-system influences—into a unified conceptual model of LMS feature adoption. The model illustrates how lecturers' perceptions of relative advantage, compatibility, complexity, trialability, and observability interact with emotional states such as confidence, anxiety, and satisfaction, and are further shaped by peer influence, institutional policies, and cultural expectations. These interdependent factors form the multi-layered adoption pathways revealed in our qualitative analysis.

Figure 2

Figure 2. Integrated process flow of LMS feature adoption combining innovation attributes, emotional trajectories, and social-system influences.

Together, these insights suggest that successful LMS adoption requires not only technological readiness but also alignment in terms of emotions, social dynamics, and culture. Institutions must engage stakeholders at all levels, developers must design for flexibility and clarity, and educators must be supported both technically and effectively to ensure sustained and meaningful LMS integration.

The findings point to several design priorities for LMS developers. Adaptive onboarding should tailor guidance to users' experience levels, reducing early anxiety and supporting late adopters. A simplified, consistent interface can minimize cognitive load, while a progressive feature rollout allows lecturers to explore functionalities without being overwhelmed. Incorporating emotional scaffolding, such as contextual tips, reassurance prompts, and success feedback, can further strengthen user confidence. Together, these design considerations can foster smoother adoption and more meaningful engagement with LMS features across diverse user groups.

Late adopters in this study struggled primarily with anxiety, low perceived control, and inconsistent support structures. Institutions can help reduce these barriers by implementing peer-mentoring schemes in which confident users demonstrate workflows and model successful LMS practices. Institutions should recognize that sustained LMS adoption, especially among hesitant users, may depend on ongoing staff assistance. The usage log trend shows that staff-supported assessment creation can significantly increase adoption rates, underscoring the need for embedded technical mentoring systems within LMS strategies. Psychologically safe training spaces that emphasize exploration without fear of error can also build trust and confidence. Further, emotionally scaffolded onboarding, using guided checklists, just-in-time tips, and reassurance prompts, can ease early uncertainty. Finally, micro-credentialed LMS literacy tracks can recognize gradual progress and motivate deeper, sustained engagement.

This study reveals that innovation attributes, emotional dynamics, and institutional social systems shape LMS adoption among lecturers. While relative advantages, compatibility, and trialability encourage adoption, complexity and emotional barriers hinder it—particularly for late adopters. Institutional policy, peer modeling, and student expectations further shape adoption patterns. However, as with most qualitative studies, the findings are context-specific and not statistically generalizable to all higher education settings. However, the qualitative approach offers depth and richness, enabling a nuanced exploration of the emotional, psychological, and social factors that influence LMS feature adoption, insights that would be difficult to capture through quantitative methods alone.

Although this study is situated within a single institution, the findings have potential transferability to similar higher education contexts, particularly private universities operating with flexible pedagogical models, large multi-campus institutions managing uneven digital cultures across locations, and LMS ecosystems that evolve through incremental Agile updates. These environments often face comparable challenges related to consistency, emotional readiness, and differential adoption across departments. Readers in such settings may therefore find the insights applicable when designing LMS strategies or interpreting user adoption behavior.

Future research should include longitudinal data and varied institutional contexts. Institutions and developers must co-design emotionally supportive, pedagogically aligned, and socially reinforced strategies to ensure sustainable and inclusive LMS adoption. Future studies should also examine longitudinal emotional trajectories to understand how confidence, anxiety, and motivation evolve across semesters and feature updates. A cross-campus comparison would reveal how institutional culture shapes adoption patterns in different learning environments. Additionally, feature-level adoption mapping, tracking which tools are adopted, ignored, or substituted, would offer clearer insight into how specific LMS design choices influence uptake and sustained use. Emerging AI capabilities may directly address several adoption challenges identified in this study. By personalizing guidance, reducing perceived complexity, and dynamically responding to emotional signals such as confusion or hesitation, AI-enabled LMS features can strengthen both relative advantage and trialability while offering just-in-time scaffolding for late adopters. These developments suggest that future LMS ecosystems will increasingly integrate adaptive, emotionally aware support aligned with the multifaceted adoption dynamics revealed in this research.

Looking ahead, the landscape of LMS adoption is poised to evolve further with the integration of artificial intelligence (AI)-powered capabilities. Future LMS platforms are increasingly incorporating intelligent recommendations, adaptive learning pathways, automated feedback, and predictive analytics, fundamentally reshaping how educators interact with these systems. While this study focuses on current adoption patterns grounded in traditional LMS features, it also raises important questions about how innovation attributes—such as perceived complexity, trialability, and emotional readiness—will intersect with AI-driven functionalities.

Data availability statement

The original contributions presented in the study are publicly available. This data can be found here: https://doi.org/10.5281/zenodo.17890149.

Ethics statement

The studies involving humans were approved by Research and Technology Transfer Office Binus University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

UU: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Writing – original draft, Writing – review & editing. TB: Investigation, Methodology, Resources, Writing – original draft, Writing – review & editing. HD: Investigation, Resources, Software, Writing – review & editing. AS: Data curation, Investigation, Project administration, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was used in the creation of this manuscript.

This manuscript benefited from language suggestions and draft improvements using ChatGPT (OpenAI, GPT-4 model).

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

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