Is There A Relationship Between Valence and All Factors in Online Motivation?

Is There A Relationship between Valence and All Factors in Online Motivation?

^*1Nurhafeza Mohd Akhir, ¹Wardah Ismail, ¹Nasiha Nasrudin, ¹Afina Nazira Afnizul, ⁵Nurul Atiqah Amran, ¹Noor Hanim Rahmat

¹Akademi Pengajian Bahasa, Universiti Teknologi MARA,Shah Alam, Malaysia

⁵ Department of English, Faculty of Modern Languages and Communication, Universiti Putra Malaysia, Serdang, Malaysia

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.909000314

Received: 08 September 2025; Accepted: 16 September 2025; Published: 09 October 2025

ABSTRACT

Nowadays, online learning has transformed educational engagement that affects learners’ motivation in learning from virtual environments. Due to that, it is important to understand the relationship between motivation factors to improve online learning experiences. Through Vroom’s Expectancy Theory and Fowler’s motivational components, this study investigated how students’ beliefs, values, and social support influence their motivation to learn online. This study aimed to look at the connections among these components. A quantitative study was employed through an online survey involving 189 full-time undergraduate students from Malaysian higher education institutions. The survey consisted of 41 items based on three main sections; expectancy (self-efficacy and control of learning beliefs), valence (intrinsic/extrinsic goal orientation and task value), and instrumentality (social and instructor support). The findings revealed strong positive correlations among all components. Hence this indicates that a learner’s motivation to study online is multidimensional and interdependent. These findings offer valuable implications for pedagogical practices and instructional design. Thus, it highlights the importance of developing self-belief, meaningful content, and support systems to enhance online learning engagement.

Keywords: online learning, learners’ motivation, expectancy, valence, instrumentality

INTRODUCTION

Background of the Study

Online learning has become a norm in higher education, particularly in Malaysia, following the emergence of covid-19 as it forced an immediate transition from offline to online learning. Even so, despite advantages such as flexibility and wide range of access towards materials and audiences, it also contributes to several challenges such as lacking in face-to-face interaction which can lead to limited feedback and requiring students to increase their self-regulation in learning (Yong & Thi, 2022). Hence, motivation in online learning is significant in securing success. This is attributable to learners’ engagement while learning online may influence their goals completion. By staying committed, learners will be able to attain objectives and desired results which could be earned with the assistance of motivation.

Valence, in this context can be defined as the value a learner puts on a possible outcome (Tandler & Dalbert, 2020). In other words, valence is essential in identifying learners’ motivation level as it is closely related to goal orientations. According to Ryan and Deci (2020), valence is influenced with the assistance of two components, which are intrinsic and extrinsic. Learners seek to attain personal satisfaction when intrinsic orientation is emphasized, while external factors such as grades and rewards are pursued through extrinsic orientation. Therefore, in online learning, valence plays a crucial role as learners require constant supervision, by helping in guiding learners’ judgements, making it significant.

Problem Statement

As a matter of fact, overdependence on slang can Research with regards to expectancy, valence and instrumentality in online learning were discovered in earlier studies, where several of them highlight the significant connections among these components in Malaysian higher education setting with learners’ engagement (Amir et al., 2023). Despite that, the research mainly emphasizes the key factors in isolation without incorporating all items of Vroom’s Expectancy Theory, making this study essential. Along with that, past studies underlined that, the association between valence and motivation remains contradictory suggesting the necessity of research in this area. Owing to online learning is a common practice in Malaysian education context, exploring this gap is relevant. Therefore, this study seeks to comprehensively determine the relationship among valence and all factors in online motivation.

Research Objective

This study is done to explore online motivation. Specifically, this study is done to answer the following questions;

1. How does expectancy influence online learning motivation?
2. How does valence influence online learning motivation?
3. How does instrumentality influence online learning motivation?
4. Is there a relationship between all components in online motivation?

LITERATURE REVIEW

Theoretical Framework of the Study

Vroom Expectancy Theory and Online Learning

Vroom’s Expectancy Theory (1964) provides a foundational framework for understanding motivation by identifying three key psychological mechanisms. The first is expectancy, which is the belief that effort will lead to successful performance, instrumentality, the belief that performance will result in desired outcomes, and the third is valence, the subjective value placed on those outcomes. In the context of online learning, this theory helps explain how students’ motivation is shaped by their confidence in navigating digital environments, their perception of the usefulness of course outcomes, and the external and internal rewards associated with learning (Fowler, 2018).

Expectancy plays a crucial role in online education, where self-regulated learning is essential. Students with high self-efficacy, those who are confident in their ability to complete tasks are more likely to persist in online courses, even when faced with technical challenges or limited face-to-face interaction (Aldhahi et al., 2022). Instrumentality, on the other hand, relates to learners’ belief that their efforts will lead to tangible benefits, such as academic success, career advancement, or skill acquisition (Sharif et al., 2023). Valence, the emotional and cognitive value students assign to learning outcomes, further influences motivation. For example, students who perceive online coursework as directly applicable to their future careers (high valence) exhibit greater engagement (Rahmat & Thasrabiab, 2024).

The interdependence of these components is particularly relevant in online learning environments, where isolation and lack of immediate feedback can demotivate learners (Almahasees et al., 2021). Vroom’s theory suggests that educators must foster all three elements by building students’ confidence (expectancy), clarifying the real-world benefits of coursework (instrumentality), and making content meaningful (valence) in order to sustain motivation in virtual classrooms (Fowler, 2018). Empirical studies support this view, showing that students with balanced expectancy, instrumentality, and valence demonstrate higher persistence and satisfaction in online courses (Lim et al., 2021).

Motivation to learn Online

Motivation in online learning is a complex, multidimensional construct influenced by both intrinsic and extrinsic factors. Intrinsic motivation arises when learners engage with material out of genuine interest or personal fulfilment, while extrinsic motivation is driven by external rewards such as grades, certifications, or career opportunities (Aldhahi et al., 2022). Research indicates that successful online learners often exhibit a combination of both types of motivation, with intrinsic factors fostering deeper engagement and extrinsic factors providing short-term incentives (Nguyen, 2019).

There are several key factors that influence online learning motivation. First, self-efficacy, which is a learner’s belief in their ability to succeed, is critical, as students who doubt their capacity to navigate online platforms or comprehend digital content are more likely to disengage (Moosa & Aloka, 2023). Second, goal orientation plays a significant role, as students with strong intrinsic goals such as mastering a subject and extrinsic goals like earning high grades tend to perform better in online settings (Fowler, 2018). Third, social and instructor support mitigates the isolation often experienced in virtual classrooms. Regular feedback, interactive discussions, and a sense of community enhance motivation by reinforcing instrumentality of the belief that effort leads to recognition and success (Lim et al., 2021).

In summary, Vroom’s Expectancy Theory provides a wide lens for analysing online learning motivation, emphasising the dynamic interplay between self-belief, perceived value, and supportive structures. By integrating these principles, educators can design more effective online courses that will cater to diverse motivational drivers (Fowler, 2018; Rahmat & Thasrabiab, 2024).

Past Studies

Past Studies on Motivation to Learn Online

Many studies have been done to investigate learners’ drive to acquire a language in both ESL and EFL context with regards to varying learning environments (Aldhahi et al., 2022; Lim et al., 2021; Khau & Tach, 2021). In both traditional and online classrooms, motivational factors (intrinsic and extrinsic) have secured its place as the determining factor of a success of language acquisition. Addressing these factors in the lens of online learning is essential to further understand how the construct operates when face-to-face communication is absent.

Sharif et al. (2023) examined the continuous decline of English proficiency among Malaysian graduates which consequently affect their employability. A quantitative approach was employed among 100 students in a public university in Malaysia to investigate the students’ motivation to learn English adhering to Vroom’s Expectancy Theory which entails the three components: valence, expectancy and instrumentality which are considered in this study. It was reported that in terms of expectancy, the respondents believed that they could master a language if they put high emphasis on their effort, a view supported by Moosa and Alooka (2023). For instrumentality, respondents are motivated to learn English due to the intrinsic and particularly extrinsic factors which is due to their desire to achieve good grades. For the influence of valence, the respondents reported experiencing anxiety stemming from feelings of inadequacy and the act of comparing themselves to peers which could be the drive tied to excellent outcomes.  The findings further specified that the respondents’ motivation to learn English is closely based on the value they put in the language which is grounded by how one manages learning-related anxiety.

Another study by Rahmat (2022) also explored the drive behind English language learners applying Vroom’s Expectancy Theory among 35 language learners in a Malaysian public university setting during the pandemic when the major shift to online learning amplified. Adapting Nguyen’s (2019) questionnaire adopted by Gardner’s Attitude/Motivation Test Battery (AMTB) (2004) the result revealed that learners are driven by instrumental factors as they are primarily driven by the benefits one gained by being proficient in English language.  Amali et al. (2023) on the other hand studied 108 French beginner learners’ motivation in using Vroom’s expectancy theory and Pintrich and De Groot’s motivational scale in EFL context. The findings indicated that learners’ motivation intensified when they acknowledged the language usefulness (instrumentality), believed in their capability to acquire a language through effort (expectancy) and understand the possible emotional impacts of facing failure (valence). These interrelations between each component factored in that motivation could be heightened when learners value the result, expect success and put effort.

Based on the aforementioned studies, it has been underlined that valence does not work in isolation as it functions dynamically with the remaining two components, expectancy and instrumentality. Bridging the theory into current educational practice, especially to online learning context it is significant to note that regulating emotions is more complex due to isolation, lack of motivation or time management (Almahasees et al., 2021), in an online learning environment. This could evidently heighten the possibility of learners feeling anxious (valence) and it directly impacts how one interprets their expectancy and their outcomes (instrumentality). Building on this ground, this study aims to further explore how valence relates to other motivational factors in online language learning settings with the hope to offer practical insights of emotionally responsive pedagogical practices. While numerous studies have underlined the motivation behind learning a language particularly relying on Vroom’s Expectancy Theory as a focal point, there is a notable gap in research that examines how this theory relates to motivation in online language learning settings. With the classroom shift post-pandemic and the growing execution of digital learning, it is essential to investigate if the motivational patterns underlined in Vroom’s Expectancy Theory observed in face-to-face environments remains constant for online language learning.

Conceptual Framework of the Study

Online learning has become a new norm in higher institutions. While many students are comfortable with online mode, some need more push to stay motivated. Online learning has encouraged students to be flexible in their search for knowledge (Rahmat & Thasrabiab, 2024). What keeps the students motivated to study online? According to Vroom’s (1964) theory of motivation, motivation is tied to three components and they are expectancy, instrumentality and valence.

With reference to figure 1, this study is conceptualized from Vroom’s Expectancy theory of motivation and replicated from Fowler ‘s (2018) motivational components in online learning. Valence refers to the value that the learners give to the outcomes of the learning. The concept of valance is further elaborated by Fowler’s (2018) value components through learners’ intrinsic and extrinsic goal orientations.

Next, the concept of expectancy is supported by the fact that learners are motivated if they have self-efficacy and control of their learning beliefs. Finally, the concept of instrumentality by Vroom (1965) refers to the type of motivation that is fuelled by rewards and recognitions. Recognition for success is usually through learners’ social support.

This study also explores if there is a relationship between valence and expectancy. It investigates if there is a relationship between expectancy and instrumentality as well as instrumentality and valence.

Figure 1- Conceptual Framework of the Study

The Influence of Valence for Online Motivation

METHODOLOGY

This quantitative study is done to explore online motivation from the point of view of Vroom’s theory. A convenient sample of 189 participants responded to the survey. The instrument used is a 5 Likert-scale survey.  Table 1 below shows the categories used for the Likert scale; 1 is for Never, 2 is for Seldom, 3 is for Sometimes, 4 is for Often and 5 is for Almost Always.

Table 1- Likert Scale Use

Table 2 shows the distribution of items in the survey. This study is anchored in Vroom’s (1965) theory and the instrument is replicated from Fowler (2018) to reveal the variables in the table below. Section B has 13 items on Expectancy. Section C has 16 items on Valence. Section D has 12 items on Instrumentality.

Table 2- Distribution of Items in the Survey

Table 2 also shows the reliability of the survey. The analysis shows a Cronbach alpha of ,871 for Expectancy, .903 for Valence and .870 for Instrumentality. The overall Cronbach Alpha for all 41 items is .946; thus, revealing a good reliability of the instrument chosen (Jackson, 2015). Further analysis using SPSS is done to present findings to answer the research questions for this study.

RESULTS AND DISCUSSION

Demographic Analysis

Table 3- Percentage for Demographic Profile

The demographic profile of the respondents reveals a majority of female participants (67%), with males comprising 33% of the sample. Looking at the aspect of the mode of study, an overwhelming 99% of the respondents are enrolled as full-time students, while only 1% are part-timers. Their level of study indicates that most of the respondents in this research project are pursuing a degree with 97%, with a small fraction undertaking diploma (2%) and postgraduate (1%). These results suggest that the study primarily reflects the perspectives of full-time, degree-level female students.

Descriptive Statistics

Findings for Expectancy

This section presents data to answer research question 1- How does expectancy influence online learning motivation?  In the context of this study, expectancy is measured by (i) self-efficacy, and (ii) control of learning beliefs.

Self- Efficacy (Ese)

Figure 2- Mean for Self-Efficacy                 

Figure 2 presents the mean scores (Mean) and standard deviations (SD) for eight components measuring students’ self-efficacy (ESE) in their motivation to learn context. The data reveal that overall, students demonstrate a moderate to high level of self-efficacy. Remarkably, the highest mean scores were recorded for ESEQ5 (I’m confident I can do an excellent job on assignments and tests) and ESEQ6 (I expect to do well), both at 4.1 with a standard deviation of 0.8. This finding indicates a strong self-confidence in their performance capabilities and expected academic outcomes. Conversely, the lowest mean scores of 3.2 were seen in ESEQ2 (I’m certain I can understand the most difficult material presented in the readings) and ESEQ4 (I’m confident I can understand the most complex material presented by the instructor) with standard deviations of 0.7 and 0.8 each, suggesting that students may feel uncertain in their academic conviction when confronted with particularly challenging and complex academic content. ESEQ3, which assesses students’ confidence in learning basic concepts, scored relatively high (M=3.9 and SD=0.7), pointing to their general comfort with elementary and fundamental knowledge. Overall, the results shown in Figure 2 imply students generally perceive themselves as capable learners but their confidence tends to waver slightly when faced with more challenging or complex knowledge and materials. The standard deviations which range from 0.7 to 0.8 across all eight items, show a moderate level of variability in students’ responses. This particularly exhibits variation in self-efficacy beliefs among individuals within the sample.

Control Of Learning Beliefs (Ecb)

Figure 3- Mean for Control of Learning Beliefs                  

Figure 3 demonstrates the mean scores and standard deviations for five items measuring students’ beliefs regarding their control over learning in an online environment. Overall, the data reflects a moderately high level of control of learning beliefs among respondents and the conviction that they have agency over their own learning outcomes. ECBQ3 (If I try hard enough, then I will understand the material presented online), is marked the highest mean score of 4.1 with standard deviation of 0.7, suggesting that students largely believe effort directly influences their success in understanding online content. Similarly, both ECBQ1 and ECBQ2 recorded the second highest mean score (M= 3.9, SD= 0.8), reflecting students’ confidence that using appropriate learning strategies and having personal responsibility would lead to the mastery of online materials. Compared to ECBQ4 and ECBQ5 that scored slightly lower (M= 3.6 and 3.7 respectively with similar SD= 0.9), the results reflect that while students acknowledge their role in the learning process, there is slightly less conviction when it comes to attributing failure to lack of effort or taking full responsibility. Withal, the overall trend suggests that students perceive themselves as active agents in their academic journey. The standard deviations, ranging from 0.7 to 0.9, represent a relatively consistent pattern of agreement, though with some variation, suggesting at different levels of self-directedness among the student population in this study.

Findings for Valence

This section presents data to answer research question 2- How does valence influence online learning motivation? In the context of this study, this is measured by Value components such as (i) intrinsic goal orientation, (ii) extrinsic goal orientation and (ii) task value.

Intrinsic Goal Orientation (VI)

Figure 4- Mean for Intrinsic Goal Orientation

Figure 4 illustrates the mean scores in relation to intrinsic goal orientation. VIQ5 (I am motivated to learn, even when I am working on an assignment on my own) has recorded the highest mean score of 3.9 with 0.8 standard deviation. This suggests that most students feel motivated to learn even though they have to go through it without the help of external factors. Besides that, items 3 (VIQ3: The most satisfying thing for me is trying to understand the online content as thoroughly as possible) and 4 (VIQ4 When working on assignments in online settings, I prioritise choosing topics that I can learn from, even if they may not result in the highest grade) are found to have a similar mean score of 3.6, with the standard deviations of 0.8 and 0.9, respectively. Both may indicate that students have a strong intrinsic motivation for learning. Furthermore, VIQ2 (I prefer online materials that arouse my curiosity, even if it is difficult to learn) has been ranked second from the lowest with 3.4 mean score, 0.8 standard deviation. Item 1, VIQ1 (I prefer material that really challenges me, so I can learn new things) with a mean score of 3.3 and a standard deviation of 0.8, is reported to be the lowest, which reveals that students are motivated by internal interest rather than external factors.

Extrinsic Goal Orientation (VE)

Figure 5- Mean for Extrinsic Goal Orientation                    

Figure 5 shows the mean for extrinsic goal orientation. Item 1 (mean=4.5, SD=0.7) shows the highest mean and it states for learners, getting a good grade is satisfying. Next, item 5 (mean=4.3’ SD=0.7) state that the learners are motivated to excel in their online studies to secure a well-paying job. Two items share the lowest mean. The first is item3 (mean=4.1, SD=0.9) and it reports that the learners’ main aim is to get good grades. Lastly is item 3 (mean=4.1, SD=0.9) and it states that learners want to get better grades than their peers.

Task Value (VT)    

Figure 6- Mean Task value   

Figure 6 illustrates that students regard their course content as both valuable and relevant, with the highest ratings for the material’s importance (VTQ2: M = 4.0, SD = 0.7) and practical usefulness (VTQ4: M = 4.0, SD = 0.7), reflecting an appreciation for its application beyond the classroom. Students also reported confidence in transferring their learning to other contexts (VTQ1: M=3.9, SD=0.7), suggesting they perceive meaningful connections across their academic experiences. Intrinsic interest in the course content (VTQ3, VTQ5: M = 3.7–3.8) was rated moderately, yet the narrow range of standard deviations (SD = 0.7–0.8) indicates that these views are widely shared throughout the student group. These findings align with key principles of expectancy-value theory (Eccles & Wigfield, 2020), particularly the emphasis on utility value as a critical driver of academic motivation, and suggest that students engage most meaningfully when they can clearly identify how course content serves their educational and professional goals. The results underscore the importance of designing learning experiences that highlight both the immediate and transferable value of academic content to enhance student motivation in higher education settings.

Findings for Instrumentality

This section presents data to answer research question 3- How does instrumentality influence online learning motivation? In the context of this study, this is measured by social support. Social support encompasses (i) social engagement, and (ii) instructor support.

Social Engagement (SSE)

Figure 7- Mean for Social Engagement

Based on Figure 7, it can be seen that students reported the highest agreement with “I pay attention in classes” (SSEQ2), which achieved the highest mean score of 3.4 with a standard deviation of 0.8. This suggests strong individual engagement during learning sessions. The second-highest mean, 3.3, was shared by two items: “I feel ‘disconnected’ from my teacher and fellow students in classes” (ISSEQ1, reverse-coded) and “I enjoy class discussions” (SSEQ3), which indicates that despite some feelings of disconnection, students still appreciate interactive components of the class. Next in ranking were “I feel like I can freely communicate with other students in classes” (SSEQ4) and “I have strong relationships with fellow students in this course” (SSEQ5), each scoring a mean of 3.0 with a standard deviation of 1.0 respectively. These lowest-rated items imply that while attention and participation are high, interpersonal connections and peer-to-peer communication may require further support to enhance social engagement in the online learning environment.

Instructor Support (SIS)       

Figure 8- Mean for Instructor Support

Figure 8 presents students’ perceptions of instructor support in a class based on seven items, with mean scores ranging from 3.2 to 3.9 and standard deviation value ranging from 0.7 to 0.9. Overall, students rated instructor support positively, indicating generally favourable perceptions. The highest rating (M=3.9) was given to items the instructor’s provision of guidance for success (SISQ4), presentation of relevant materials to the learners (SISQ5), and the freedom of learners’ autonomy in guiding their own learning (SISQ6), suggesting that these are the key strengths of the instructor support in the learning process. Responsiveness to questions (SISQ2) and provision of regular feedback (SISQ7) were also relatively high (M=3.8), reflecting learners’ satisfaction with the instructors’ engagement and performance monitoring. Clarity of expectations (SISQ3) received a moderate score (M=3.6), indicating some scope for improved communication between instructors and learners. The lowest rating (M=3.2), was for the ability to freely communicate with the instructor (SISQ1), which coupled with its highest variability (SD=0.9), highlights a potential area for development that could further encourage open communication channels through various possible platforms during and after the learning process. Overall, while the data highlight strong instructional support in guidance, relevance, and autonomy, they also point to the need for enhancing openness in communication and expectations.

Exploratory Statistics

Findings for Relationship between all components in online motivation

This section presents data to answer research question 4- Is there a relationship between all components in online motivation? To determine if there is a significant association in the mean scores between all components in online motivation, data is analysed using SPSS for correlations. Results are presented separately in table 4, 5 and 6 below.

Table 4- Correlation between Valence and Expectancy

**Correlation is significant at the level 0.01(2-tailed)

Table 4 shows there is an association between valence and expectancy. Correlation analysis shows that there is a high significant association between valence and expectancy (r=.674**) and (p=.000). According to Jackson (2015), coefficient is significant at the .05 level and positive correlation is measured on a 0.1 to 1.0 scale. Weak positive correlation would be in the range of 0.1 to 0.3, moderate positive correlation from 0.3 to 0.5, and strong positive correlation from 0.5 to 1.0. This means that there is also a strong positive relationship between valence and expectancy.

Table 5- Correlation between Expectancy and Instrumentality

**Correlation is significant at the level 0.01(2-tailed)

Table 5 shows there is an association between expectancy and instrumentality. Correlation analysis shows that there is a high significant association between expectancy and instrumentality (r=.671**) and (p=.000). According to Jackson (2015), coefficient is significant at the .05 level and positive correlation is measured on a 0.1 to 1.0 scale. Weak positive correlation would be in the range of 0.1 to 0.3, moderate positive correlation from 0.3 to 0.5, and strong positive correlation from 0.5 to 1.0. This means that there is also a strong positive relationship between expectancy and instrumentality.

Table 6- Correlation between Instrumentality and Valence

**Correlation is significant at the level 0.01(2-tailed)

Table 6 shows there is an association between instrumentality and valence. Correlation analysis shows that there is a high significant association between instrumentality and valence (r=.680**) and (p=.000). According to Jackson (2015), coefficient is significant at the .05 level and positive correlation is measured on a 0.1 to 1.0 scale. Weak positive correlation would be in the range of 0.1 to 0.3, moderate positive correlation from 0.3 to 0.5, and strong positive correlation from 0.5 to 1.0. This means that there is also a strong positive relationship between instrumentality and valence.

CONCLUSION

Summary of Findings and Discussions

The objective of this study was to explore the motivational factors influencing students’ engagement in online learning based on Vroom’s Expectancy Theory and Fowler’s motivational framework. Due to that, four research questions were introduced which focused on the roles of expectancy, valence, instrumentality, and their interrelations in influencing online learning motivation.

RQ1 (How does expectancy influence online learning motivation?)

The findings revealed that students believed they were capable of managing their learning outcomes. They were confident in dealing with their academic tasks and they recognised their personal responsibility for their success or failure. This aligns with Fowler (2018), who found that self-efficacy and control beliefs are important to improve the motivation in virtual classrooms. Similarly, Rahmat and Thasrabiab (2024) also highlighted the importance of learners’ self-regulation and self-motivation in online learning, reinforcing the role of expectancy in developing academic engagement. Sharif et al. (2023) revealed that in terms of expectancy, their participants believed that they could master a language if they put high emphasis on their effort, which is also a view supported by Moosa and Alooka (2023).

RQ2 (How does valence influence online learning motivation?)

The findings showed that valence has been considered as a strong motivator, specifically through extrinsic goal orientations. Students were driven by rewards such as grades, career goals, and social acceptance. This supports Vroom’s theory which states that value of outcomes plays a key role in motivation. Not to mention, Fowler’s (2018) findings also indicated that goal orientation for both intrinsic and extrinsic motivation has directly impacted the learners’ persistence. In addition Rahmat (2022) found that learners are driven by instrumental factors as they are primarily driven by the benefits one gains by being proficient in English language.

RQ3 (How does instrumentality influence online learning motivation?)

The findings found that participants acknowledged the importance of social support, particularly instructor support, regular feedback, and relevance of course material. Although peer engagement was perceived as lower, instructor support stood out as a significant contributor. To support, Fowler (2018) similarly observed that motivation increases when learners feel supported. In a similar manner, Rahmat and Thasrabiab (2024) also argued that constructive feedback and clear expectations empower students to take ownership of their learning. These findings underscore the critical role of educators in supporting motivation within online environments.

RQ4 (Is there a relationship between all components in online motivation?)

The study confirmed there were strong positive correlations between expectancy, valence, and instrumentality which confirms the interdependent nature of motivational factors as conceptualised in both Vroom’s theory and Fowler’s model. These findings align from prior research indicating that there is no single factor that acts in isolation, but motivation is sustained through a dynamic system of beliefs, values, and supportive structures. Besides that, Amali et. al (2023) found that learners’ motivation intensified when they acknowledged the language usefulness (instrumentality), believed in their capability to acquire a language through effort (expectancy) and understand the possible emotional impacts of facing failure (valence). These interrelations between each component factored in that motivation could be heightened when learners value the result, expect success and put effort.

To conclude, the findings reinforce the multidimensional nature of online learning motivation and it has been supported with existing literature. While previous studies have explored these components separately, this research emphasises their combined influence and introduces in depth understanding of motivation in online contexts.

Implications and Suggestions for Future Research

Theoretical and Conceptual Implications

The findings of this study provide strong theoretical support for Vroom’s Expectancy Theory (1964), confirming that online learning motivation is a multifaceted construct shaped by learners’ beliefs about their capabilities (expectancy), the value they assign to academic tasks (valence), and the influence of social reinforcement (instrumentality). The significant positive correlations among all three variables (expectancy, valence, and instrumentality) prove the interdependent relationships presented in the theoretical model.

Additionally, the conceptual framework from Fowler (2018) enhances the traditional theory by emphasising goal orientations and task value as meaningful components within valence. These were reflected in the findings. For instance, extrinsic goal orientation scored the highest, suggesting that learners are particularly driven by external rewards such as grades, career prospects, and social recognition. By the same token, expectancy measures like self-efficacy and control of learning beliefs demonstrated moderate to high averages, reinforcing the idea that confidence and perceived responsibility are critical to sustained motivation in online settings.

This reveals that motivation is not simply a reaction to internal belief or external stimuli, but rather a dynamic convergence of personal goals, perceived value, and supportive environments. The integration of self-directed learning perspectives with socially reinforced constructs invites future theoretical expansion, possibly by combining Vroom’s model with socio-cultural learning theories in order to better capture learners’ needs in online education.

Pedagogical Implications

The findings also reveal positive correlations among all components (expectancy, valence, and instrumentality) based on Vroom’s Expectancy Theory and Fowler’s motivational components, indicating that learners’ motivation to study online is multidimensional and interdependent. This highlights the importance of fostering self-belief, designing meaningful content, and strengthening support systems to sustain online learning engagement. To address some of the issues highlighted above, teaching practices could incorporate more structured and accessible communication channels to encourage participation from diverse learners with various learning styles. 

Limitations of the study

The limitation of this study is the sampling, which employed a convenience sample of 189 full-time undergraduate students from Malaysian higher education institutions. The demographic profile indicates that the majority of respondents were female degree-level students, with minimal representation from diploma and postgraduate students. Hence, this lack of balance may limit the generalisability of the findings, as the results primarily reflect the perspectives of a specific group of students. Therefore, the findings of this study may not adequately capture the diverse experiences of learners across different academic levels.

Suggestions for Future Research

The present study manages to collect limited information from a specific group of students, hence future research should include a more balanced mix of participants in terms of gender, academic fields, and institutions to diversify the findings. In addition, future studies shall include causal analysis using regression or structural equation modelling, as well as cross-cultural or discipline-specific studies. Future research could expand on these findings by exploring the causal relationships between expectancy, valence, and instrumentality components to further determine which elements exert the strongest influence on online learning engagement. Comparative studies across different cultural contexts, academic disciplines, or levels of study could reveal whether these motivational patterns are consistent or context-dependent. In addition, with emerging technologies such as AI-driven learning platforms and adaptive feedback systems, there is a huge potential to look into the interconnections among motivation components in virtual learning environments.

ACKNOWLEDGEMENTS

We would like to express our sincere appreciation to all participants who have contributed to the successful completion of this study and the preparation of this article. Their willingness to share their time and insights has been crucial to the findings presented in this article.

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