Individual Innovativeness, Emotional Intelligence and Self- Efficacy Towards Online Learning Readiness
Nor Aniza Ahmad1*, Zulfalilla Salim2
1 Educational Psychology, Faculty of Educational Studies
2 Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia
*Corresponding Author: [email protected] Accepted: 15 December 2021 | Published: 31 December 2021
DOI:https://doi.org/10.55057/ijares.2021.3.4.12
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Abstract: In Malaysia, the Ministry of Higher Education (MoHE) announced that all public and private universities need to conduct all classes through online learning to ensure the continuity of education. Online learning is a form of distance learning that takes place partially or entirely over the internet. Online learning can fulfil students’ academics by providing quality education, regardless of location and time, if internet access is available.
Measuring students’ readiness in online learning should also be of great concern to universities and has yet to be investigated, particularly for Universiti Putra Malaysia (UPM) students. The student's readiness determines how his or her behaviour changes. In this study, the researcher intends to find out the level of individual innovativeness, emotional intelligence, self-efficacy and online learning readiness among students at Universiti Putra Malaysia (UPM). The research design is a descriptive correlational study, as well as the data collection method is a quantitative approach. This study comprised 356 postgraduate students from the Faculty of Educational Studies and participants were asked to fill up the questionnaires. Descriptive and inferential statistics are used to analyse the research data.
Findings revealed that UPM students have a moderate level of individual innovativeness, emotional intelligence and online learning readiness, however, a high level in self-efficacy.
This study has contributed to the new dimension in students’ online learning readiness especially in the education field and the institution that is related to it. More strategies may be developed to ensure students are able to learn online effectively by identifying their level of individual innovativeness, emotional intelligence, self-efficacy, and online learning readiness.
Keywords: individual innovativeness, emotional intelligence, self-efficacy, online learning, online learning readiness
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1. Introduction
Online learning is becoming popular nowadays in educational institutions, as a result, the universities should re-examine student readiness and re-measure more comprehensively methods to help students facing all the challenges (Hung, Chou, Chen & Own, 2010). Lecturers also should design and develop new activities through online courses and guide students towards the best experiences in the process of learning. To achieve effective online learning, students should know and possess the dimensions of online learning readiness to make them better understand this new environment of learning education. Thus, the first stage in
structuring an efficient online learning system is determining student readiness for online learning (Aydin & Tasci, 2005). This is because readiness is a crucial input for the learning- teaching system and is incredibly important in the education-instruction process (Engin, 2017).
The student's readiness determines how his or her behaviour changes.
Following the government's announcement of Movement Control Order (MCO), online learning becomes crucial and debated among academicians and students. The issue of online learning readiness among students was also discussed and discovered by most researchers.
Adnan & Anwar (2020) revealed that two problems exist when practicing distance learning or virtual learning. First, little is established from the macro’s view regarding the efficacy and the effects of online education. Second, differ in the wide range of learning goals especially in the capacity to teach digitally in guiding the instructional and educational priorities. Students were found struggling to learn through online learning, which is a subcategory of distance education, and that this new norm required them to be familiar with internet and World Wide Web usage (Bates, 2005). McCarthy (2020), also revealed that underprivileged children and young adults obviously impacted by the school’s suspension, educational instability sadly can cause psychological stress at various levels. Despite all of the preparations made by the Ministry of Education (MoE), MoHE, educational institutions and educators, the issue of students’
readiness should be looked into whether they are ready or not to adopt online learning.
According to Liyu & Kaye (2016), online learning readiness refers to a student's cognitive awareness and maturity required to study well in a web-based environment and innovation related skills. Identifying self-directedness, adopting learning approaches, acquiring technological skills, adapting to digital ethics, and being ready to seek help are all needed by students to acquire online learning readiness. However, internet infrastructure problems are faced among students when it comes to online learning (Chung, Noor & Mathew, 2020). These problems are associated with weak and unstable connections, limited mobile data connection as students find it hard to acquire beyond what they can afford. Yi, Fiedler & Park (2006) admits that only relatively few studies examine the potential impacts of users’ individual innovativeness on perceived decision to accept or use technologies. It is the intention of this study to bridge this knowledge gap by investigating those relationships among all these variables.
On the other hand, a large number of frustrations are associated with individual innovativeness which relates to a person's willingness to embrace, try, buy, or accept innovations, as well as their capacity to understand, receive, socially appraise, spread, implement, and use them (Hero, Lindfors, and Taatila, 2017). According to Guglielmino (2003), a user's capacity to use technical tools may be used to determine whether or not they are ready for online learning.
Students want to get information that will be provided online, on the website, but they cannot seem to find it. Due to technological advances, students were found stressed with online learning and lack of individual innovativeness (Dhull & Sakshi, 2017). Also, during online learning, many students experience frustration such as difficulty to log in, lack of clear instructions, difficulty to find the available website and so on. Administrative procedures and unclear instructions can cause frustration that lead to computer anxiety. Computer anxiety is a distressing mental condition in which a person suffers from debilitating physical and emotional intelligence symptoms (Dhull & Sakshi, 2017). A lot of studies revealed the adoption and diffusion of innovation (Aydin & Tasci, 2005). However, only few studies have examined the relationship between individual innovativeness and one’s perceived self-efficacy (Celik, 2003).
Thus, like Celik’s (2013) study, this research also attempts to determine how students’
individual innovativeness and self-efficacy correlates with their online learning readiness.
Many studies show that an individual’s success is related with elements of emotional intelligence such as high motivation, high self-confidence and teamwork (Sulaiman & Noor, 2015). This is because emotional intelligence is an important variable in an individual's achievement of success in all aspects and very important for optimal performance (Goleman, 1995). Moreover, emotional intelligence is described by Neophytou (2013), Goleman (2009), and Bar-On (1977) as a set of emotional and social abilities that impact people's behaviour and performance. Salovey & Mayer (1990) stated that emotional intelligence is the ability of a person to deal with his or her emotions. However, while online learning, there was a lack of social interaction and it was difficult to participate in group discussions (Chung, Subramanian
& Dass, 2020). Thus, lack of interaction with instructors, response time, and social interaction among peers will affect the students’ emotional intelligence (Sari & Nayir, 2020). Although there are several studies on emotional intelligence in Malaysia (Habibullah, 2008; Mohd Shahril, 2008), there are still no studies done to evaluate the emotional intelligence that correlate with online learning readiness in the Malaysian context. Further, despite its contributions to success in other venues, there has been little investigation into the construct of emotional intelligence as a predictor for success in the online environment (Imel, 2003).
This study also explored students' self-efficacy towards online learning readiness. Students’
self-efficacy can be seen as students’ beliefs and ability to carry out a plan of action successfully in a particular situation and in a given circumstance (Bandura, 1977). Self-efficacy should not be considered as a measure of specific skill because it concerns the extent to which individuals believe they can perform by using their skills (Lee & Medlinger, 2011). When people become online learners, especially for the first time, they may feel less confident, despite being familiar with day-to-day computer and technology usage. They may still lack essential learning and technology skills for tertiary education and online learning. Tapjutorus, Hansen & Beown (2012) stated that online modules can enhance learning, improve attitudes and increase self-efficacy. However, little research has been done to examine self-efficacy of online learners’ readiness with different levels of learner control in a real online class setting.
Therefore, this study focuses on this gap in research, and uses a quantitative research design to investigate the relationship between self-efficacy and online learning readiness.
The objectives of this study as stated below:
i. To measure the level of individual innovativeness, emotional intelligence, self-efficacy and online learning readiness among students at Universiti Putra Malaysia (UPM).
ii. To determine the relationship between individual innovativeness and online learning readiness among students at Universiti Putra Malaysia (UPM).
iii. To determine the relationship between emotional intelligence and online learning readiness among students at Universiti Putra Malaysia (UPM).
iv. To determine the relationship between self-efficacy and online learning readiness among students at Universiti Putra Malaysia (UPM).
v. To determine the influence of individual innovativeness, emotional intelligence and self- efficacy on online learning readiness among students at Universiti Putra Malaysia (UPM).
2. Literature Review
Individual Innovativeness and Online Learning Readiness
In the literature, there are a number of researchers that have been done in studying the relationship between individual innovativeness and online learning readiness. Researchers that
have been done did show that individual innovativeness significantly correlates with online learning readiness. In a study that has been done by Surme et el., (2019) towards 573 students in Enciyes University Health Sciences Faculty Department of Nursing, to investigate the relationship between individual innovation and online learning readiness. The findings revealed that nursing student’s level of readiness for online learning with their individual innovation features was low; there was a positive relationship between individual innovation and online learning readiness. Students are more likely to be ready for online learning in the first year, innovations develop as class levels increase. Bubou & Job (2020) explored the role of individual innovativeness in predicting the e-learning readiness of 217 of the students of an open and distance education in institutions in an African context. The researchers discovered that a strong positive and significant relationship was observed between individual innovativeness and e-learning readiness of first and second year students of the Yenagoa Study Centre of the National Open University of Nigeria (NOUN); a statistically significant relationship was also found between individual innovativeness and e-learning readiness. The finding also revealed that male respondents had higher e-learning readiness than their female counterparts. Another descriptive study that has been done by Gayan (2020) in Sri Lanka also analyzed the relationship of individual innovation on online learning among 271 students at Sri Lanka International Schools. Pearson correlation analysis revealed that individual innovation possesses a positive relationship with online learning variables and the relationship was significant. In this study, learners’ individual innovativeness relates to demonstrable learner’s behaviour that is directed at engaging in innovative learning activities, including being actively involved in online learning environments.
In a related study, Lu, Yaob & Yu (2005) investigated the relationship between the variables of personal innovativeness, social influences and adoption of wireless internet services via mobile technology. This study involved 388 students from the Master of Business Administration in the University of Texas during the 2002 – 2003 academic session. The findings revealed that there were strong causal relationships amongst the social influence, individual innovativeness and perceptual beliefs which subsequently impacted on students’
intentions to adopt internet services via mobile technology (Lu et al., 2005). Another study done by Mahat, Mohd Ayub & Wong (2012) also revealed that students who had high levels of individual innovativeness scores were also found to like exploring and trying out new technologies. On the other hand, study done by Munoz et al., (2020) on their research title
“Videogames and Innovation: Fostering Innovators’ Skills in Online Learning Environment”
also discovered that the use of video games in online learning environment related to innovation and entrepreneurship has some potential to foster innovators’ skills.
Emotional Intelligence and Online Learning Readiness
In the literature, the relationship between emotional intelligence and online learning readiness have been studied by a variety of researchers. Studies that have been done did show that emotional intelligence significantly correlates with online learning readiness. In a research that has been done by Engine (2017), the researcher studied whether there was a significant relationship between the students’ readiness in online learning and their emotional intelligence level. The questionnaires were applied to 95 students who attend Computer II course, which is provided only through distance learning in Uludag University, Turkey. In this study, the result showed that there was a relationship between students’ online learning readiness and trait emotional intelligence level. The finding showed that there were significant positive relationships between emotional intelligence sub-dimension social skills, self-control skills, and well-being and online learning readiness sub-dimensions. Thus, individuals with a high social skills sub-dimension of emotional intelligence had high online learning readiness levels.
One of the most predictors of students’ readiness was the emotional intelligence levels of individuals in an online learning environment.
Alenezi (2020) discovered that emotional intelligence has considerable effects on learners’
preparedness for online learning. In a related study, Buzdar, Ali & Tariq (2016) investigated psychometric aspects of students’ preparedness for online learning and the purpose of this study was to examine students’ emotional intelligence as a determinant of their readiness for online learning. The 432 master level students enrolled in Allabama Iqbal Open University (AIOU) Islamabad, Pakistan in which directly involved in distance learning programs participated in the study. The findings revealed that the students’ readiness for online learning has significant and direct association with their emotional intelligence and its four indicators (self-emotions appraisal (SEA)), others-emotions appraisal (OEA), use of emotions (UOE), and regulation of emotions (ROE)). Hypothesized relationship among psychological aspects of students’
readiness for online learning and psychometric abilities of emotional intelligence provided the theoretical foundations for this research and convinced us to scientifically trace out causational association among different traits of emotional intelligence and psychological indicators of students’ readiness for online learning. Zahed-Babelan and Meonikia (2010) investigated the role of emotional intelligence to predict academic achievement of Payame Noor University.
The respondents were 328 students selected as samples via multistage sampling. The findings revealed that emotional intelligence predicts students’ academic achievement statistically significant. Further, the results showed there was a positive and significant correlation between components of emotional intelligence (interpersonal intrapersonal, adaptability and general mood). However, a study done by Arul & Deepa (2013) found that there is no significant difference between emotional intelligence and academic achievement of high school students in Kanyakumari District. In addition, Davis (2006), in the study conducted on the readiness of distance learning stakeholders, staff and students for online learning stated that readiness was significant in increasing the achievement of students and to expand lifelong learning potential.
Self-Efficacy and Online Learning Readiness
In the literature, there are a number of researchers that have been done in studying the relationship between self-efficacy and online learning readiness. Researchers that have been done did show that self-efficacy significantly correlates with online learning readiness.
Tapjutorus, Hansen & Beown (2012) investigated a relationship between learner control and self-efficacy in an online learning environment among 112 students at a tertiary institution in New Zealand. The finding showed that there was a positive relationship between learner control and online learning self-efficacy. Similar results to the above study by Negara, Nurlaelah, Wahyudin, Herman & Tamur (2020) stated that there was a positive relationship between mathematics self-efficacy and mathematics performance in online learning. The results also showed that most respondents had a high level of mathematical self-efficacy in online learning. Research on self-efficacy started before online learning occurred, between the late 1970s and the early 1990s, which was before the birth of online learning (Hodges, 2008).
In 2008, Hodges stated research on self-efficacy in the online learning environment is in its infancy. He suggested that more research is needed in the area of self-efficacy in online learning. Thus, in an online learning environment, research on self-efficacy and computers is mainly related to learners’ confidence in their capability of using computers and other types of technology. For example, Lim (2001) found that computer self-efficacy was statistically a significant predictor of students’ satisfaction, and there was a positive relationship between students’ satisfaction and future attention to take online courses. Similarly, Womble (2007) also found a significant positive relationship between computer self-efficacy and student satisfaction in online learning environments.
In a research done by Tanius, Alwani & Abdul Muein (2020) on a sample of 166 university students, revealed that online learning technology experience, learners’ attitudes, learners’
motivation, computer anxiety, and social support correlate with self-efficacy on online learning technology. According to Ismail, Abdul Rahim & Azmi (2019), they examined the impacts of students' self-efficacy (online measure) on grade point scores among 59 sport-science majors who were in their third semester and were enrolled in the subject of Exercise Psychology. The result analysis showed a positive significant relationship in that students with higher self- efficacy scores also scored higher in grade point scores. Another study done by Afridi, Jan, Ayaz, & Irfan (2021) investigated the relationship among students’ practices of self-leadership strategies, self-efficacy, and performance outcomes levels in the online learning environment.
217 students who enrolled at a university participated in this study. The result revealed that students with high self-efficacy tend to have more effective performance in uncertain or predictable situations. In addition, when students show a high level of self-leadership in their online learning, their level of self-efficacy and performance satisfaction has increased. Another study done by Ithirah, Ridwandono & Suryanto (2020) found that there was a strong influence between self-efficacy in the use of e-learning. Similarly, in the finding results by Bubou & Job (2020) also discovered a strong positive and significant relationship between self-efficacy and e-learning readiness of first and second year students at the National Open University of Nigeria (NOUN). Another empirical study done by Pellas (2014) with 305 university students who were taking online courses, found that computer self-efficacy has a positive relationship with students’ cognitive and emotional engagement factors, and negative relationship with behavioural factors. Lee & Hwang (2007) in his study discovered that computer self-efficacy is a very important and critical factor to student satisfaction with online learning or e-learning environments.
3. Methodology
For research methodology, this research used a quantitative approach, which applied descriptive correlational research design in order to answer the predetermined research questions. The researcher used a questionnaire to collect the data, then, computed and analyzed using IBM SPSS Statistics Software. A total of 356 postgraduate students from the Faculty of Educational Studies participated in this research. The sampling technique used in this study is a simple random sampling technique. In order to measure the variables, the researcher has adopted four instruments which are Individual Innovativeness Scale developed by Hurt, Joseph
& Cook (2013), Trait Emotional Intelligence Questionnaire (TEIQue-SF) developed by Petrides (2009), General Self-Efficacy Scale developed by Schwarzer & Jerusalem (1995) and Online Learning Readiness Scale (OLSR) developed by Hung et al., (2010).
4. Finding and Discussion
The summary of the findings based on the research hypotheses is illustrated in Table 1.
Table 1: Summary of The Findings Based On Research Hypotheses
Hypotheses Results
Ho1 There is no significant relationship between individual innovativeness and online learning readiness among students at UPM.
Rejected
Ho2 There is no significant relationship between emotional intelligence and online learning readiness among students at UPM.
Rejected
Ho3 There is no significant relationship between self-efficacy and online learning readiness among students at UPM.
Rejected Ho4 There is no significant influence of individual innovativeness,
emotional intelligence and self-efficacy on online learning readiness among students at UPM.
Rejected
The Level of Individual Innovativeness among Students at UPM
The level of individual innovativeness among UPM students can be summarized as shown in Table 2.
Table 2: Distribution of Mean Score and Level for Individual Innovativeness
Level Mean SD Frequency %
Overall Low Moderate
3.65 0.502
20 235
5.6 66.0
High 101 28.4
Total 356 100
According to Table 2 above, it shows that 20 respondents which indicate 5.6% have low level of individual innovativeness, 235 respondents which indicate 66.0% have moderate level of individual innovativeness and 101 respondents which indicate 28.4% have high level of individual innovativeness. The results indicate that the majority of UPM students have a moderate level of individual innovativeness. From the findings, the analysis shows that 101 students have a high level of individual innovativeness, which means they quickly adopt and adapt or modify innovations in their daily life. They have the ability to adopt, try, buy or accept innovations, or an individual’s ability to understand, receive, socially estimate, spread, implement and use innovations in their learning activities. The result showed that the majority of UPM students have a moderate level of individual innovativeness. From the findings, the analysis shows that most students have had a high level of individual innovativeness, which means they quickly adopt and adapt or modify innovations in their daily life. They have the ability to adopt, try, buy or accept innovations, or an individual’s ability to understand, receive, socially estimate, spread, implement and use innovations in their learning activities.
The Level of Emotional Intelligence among Students at UPM
The level of emotional intelligence among UPM students can be summarized as shown in Table 3.
Table 3: Distribution of Mean Score and Level for Emotional Intelligence
Level Mean SD Frequency %
Overall Low Moderate
3.48 0.434
22 234
6.2 65.7
High 100 28.1
Total 356 100
From Table 3 above, it shows that 22 respondents (6.2%) have a low level of emotional intelligence, 234 respondents (65.7%) have moderate level of emotional intelligence and 100 respondents (28.1%) have high level of emotional intelligence. It shows that the majority of UPM students have a moderate level of emotional intelligence. Students have the capacity to understand, use, and control their own emotions in constructive ways such as relieve stress, communicate successfully, empathize with others, overcome problems and so on. The result showed that the majority of UPM students have a moderate level of emotional intelligence.
Students have the capacity to understand, use, and control their own emotions in constructive ways such as relieve stress, communicate successfully, empathize with others, overcome problems and so on.
The Level of Self-Efficacy among Students at UPM
The level of self-efficacy among UPM students can be summarized as shown in Table 4.
Table 4: Distribution of Mean Score and Level for Self-Efficacy
Level Mean SD Frequency %
Overall Low Moderate
3.62 0.889
43 121
12.1 34.0
High 192 53.9
Total 356 100
From Table 4 above, it shows that 43 respondents (12.1%) have a low level of self-efficacy, 121 respondents (34.0%) have moderate level of self-efficacy and 192 respondents (53.9%) have high level of self-efficacy. The results indicate the majority of UPM students have a high level of self-efficacy. They have a collection of beliefs that determines how successfully a person can carry out a plan of action in a given circumstance. This result is supported by Negara et al., (2020), they stated that most respondents had a high level of mathematical self-efficacy in online learning among students at one of the universities in Indonesia. The result showed that the majority of UPM students have a high level of self-efficacy. They have a collection of beliefs that determines how successfully a person can carry out a plan of action in a given circumstance. This result is supported by Negara et al., (2020), they stated that most respondents had a high level of mathematical self-efficacy in online learning among students at one of the universities in Mataram City, Indonesia.
The Level of Online Learning Readiness among Students at UPM
The level of online learning readiness among UPM students can be summarized as shown in Table 5.
Table 5: Distribution of Mean Score and Level for Online Learning Readiness
Level Mean SD Frequency %
Overall Low Moderate
3.67 0.571
22 208
6.2 58.4
High 126 35.4
Total 356 100
From the Table 5 above, it shows that 22 respondents (6.2%) have low level of online learning readiness, 208 respondents (58.4%) have moderate level of online learning readiness and 126 respondents (35.4%) have high level of online learning readiness. The results indicate that the majority of UPM students have a moderate level of online learning readiness. This finding was supported by Cigdem & Yildrim (2014) in their research indicating that students at vocational colleges exhibited above-medium levels of readiness for online learning. The result showed that the majority of UPM students have a moderate level of online learning readiness. This finding was supported by Mastor, Salleh & Ibrahim (2021), they found that the level of students’ readiness for the implementation of online teaching was moderate. Cigdem & Yildrim (2014) in their research also indicated that students at vocational colleges exhibited above- medium levels of readiness for online learning.
The Relationship between Individual Innovativeness and Online Learning Readiness among Students at UPM
The result for the relationship between individual innovativeness and online learning readiness among students at UPM as demonstrated in Table 6.
Table 6: Pearson's Correlation Coefficient between Individual Innovativeness and Online Learning Readiness
Mean for Individual Innovativeness (Pearson Correlation, r)
Interpretation of the Relationship
Mean for Online
Learning Readiness 0.583** Strong Positive Relationship **. Correlation is significant at the 0.01 level (2-tailed)
Referred to Table 6 above, inferential statistic on Pearson’s Correlation analysis that has been done, there is a significant strong positive relationship between the mean for individual innovativeness and the mean for online learning readiness [N = 356, r = 0.583**; p < 0.01].
According to Cohen (1988), this relationship is classified as having a strong relationship. This showed that individual innovativeness is having a strong positive relationship with online learning readiness. So, the research hypothesis, Ho1, is rejected because there is a significant relationship between individual innovativeness and online learning readiness among students at UPM.
The Relationship between Emotional Intelligence and Online Learning Readiness among Students at UPM
The result for the relationship between emotional intelligence and online learning readiness among students at UPM as demonstrated in Table 7.
Table 7: Pearson's Correlation Coefficient between Emotional Intelligence and Online Learning Readiness
Mean for Emotional Intelligence (Pearson Correlation, r)
Interpretation of the Relationship
Mean for Online
Learning Readiness 0.296** Weak Positive Relationship
**. Correlation is significant at the 0.01 level (2-tailed)
Referred to Table 7 above, inferential statistic on Pearson’s Correlation analysis that has been done, there is a significant positive relationship between the mean for emotional intelligence and the mean for online learning readiness [N = 356, r = 0.296**; p < 0.01]. According to Cohen (1988), this relationship is classified as having a weak relationship. This showed that emotional intelligence is having a weak positive relationship with online learning readiness.
Thus, the research hypothesis, Ho2, is rejected because there is a significant relationship between emotional intelligence and online learning readiness among students at UPM.
The Relationship between Self-Efficacy and Online Learning Readiness among Students at UPM
The result for the relationship between self-efficacy and online learning readiness among students at UPM as demonstrated in Table 8.
Table 8: Pearson's Correlation Coefficient between Self-Efficacy and Online Learning Readiness Mean for Self-Efficacy (Pearson
Correlation, r)
Interpretation of the Relationship
Mean for Online
Learning Readiness 0.380** Moderate Positive
Relationship **. Correlation is significant at the 0.01 level (2-tailed)
Referred to Table 8 above, inferential statistic on Pearson’s Correlation analysis that has been done, there is a significant positive relationship between the mean for self-efficacy and the mean for online learning readiness [N = 356, r = 0.380**; p < 0.01]. According to Cohen (1988), this relationship is classified as having a moderate positive relationship. This showed that self-efficacy is having a moderate positive relationship with online learning readiness.
Hence, the research hypothesis, Ho3 is rejected because there is a significant relationship between self-efficacy and online learning readiness among students at UPM.
The Influence of Individual Innovativeness, Emotional Intelligence, Self-Efficacy on Online Learning Readiness among Students at UPM
From the finding analysis, the result shows that the combination of variables to influence online reading readiness from individual innovativeness, emotional intelligence and self-efficacy was statistically significant, [F (3, 352) = 72.189, p < .001]. Individual Innovativeness influences the model (B = .602, p < .001.). Self-Efficacy also influence significantly to the model (B = .136, p < .001.). However, emotional intelligence did not influence significantly to the model (B = -.036, p > .001). The adjusted R2 value was 0.376. This indicates that 37% of the variance in online learning readiness was explained by the model. This result is supported by the study done by Arul & Deepa (2013) found that there is no significant difference between emotional intelligence and academic achievement of high school students in Kanyakumari District.
Meanwhile, from the Coefficient analysis in Table 11, individual innovativeness and self- efficacy are significantly contributing to the equation. However, all of the variables need to be included to obtain this result, because the overall F value was computed with all the variables in the equation. Note that individual innovativeness and self-efficacy influence online learning readiness when all three variables are included. In addition, refer to the Beta analysis in Table 12, the results indicate [Beta (Individual Innovativeness) = .529]. It shows that individual innovativeness is the most influence factor for online learning readiness among UPM students.
Therefore, Ho4 is rejected since there is a significant influence of individual innovativeness and self-efficacy on online learning readiness among students at UPM. The detailed inferential statistical analysis as mentioned in Table 9, Table 10 and Table 11.
Table 9: Model Summary
R R Square Adjusted R Square Std. Error of the Estimate
.617 0.381 0.376 0.45134
Table 10: ANOVA
Sum of Squares df Mean Square F Sig.
Regression 44.117 3 14.706 72.189 .000
Residual 71.706 352 0.204
Total 115.823 355
Table 11: Coefficients Unstandardized
Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
(Constant) 1.111 .215 5.161 .000
Individual Innovativeness .602 .059 .529 10.166 .000
Emotional Intelligence -.036 .066 -.027 -.550 .583
Self-Efficacy .136 .029 .212 4.777 .000
5. Conclusion
The purpose of this study is to investigate the relationship between individual innovativeness, emotional intelligence, self-efficacy towards online learning readiness among students at Universiti Putra Malaysia (UPM). The researcher intends to measure the level of individual innovativeness, emotional intelligence, self-efficacy and online learning readiness among UPM students. Besides, the researcher intends to determine the relationship between individual innovativeness, emotional intelligence, self-efficacy between online learning readiness among UPM students. The researcher also intends to determine the most influence of these independent variables (individual innovativeness, emotional intelligence, self-efficacy) for online learning readiness among UPM students.
From the finding analysis, UPM students have a moderate level of individual innovativeness, emotional intelligence and online learning readiness, however, high level in their self-efficacy.
In addition, all these three independent variables (individual innovativeness, emotional intelligence and self-efficacy) have a positive relationship with the dependent variable (online learning readiness) among UPM students. The result showed that individual innovativeness is the most influential towards online learning readiness.
There are several recommendations are being suggested by the researcher in order to improve future research. Since only quantitative methods were applied to collect data in this study, a mix of quantitative and qualitative research methods, such as interview sessions, may be used to obtain a more in-depth understanding of the phenomenon. On the other hand, current research only used Pearson Correlation to investigate the relationship between individual innovativeness, emotional intelligence, self-efficacy towards online learning readiness among students at Universiti Putra Malaysia (UPM). Future research could use other inferential statistics to further explain the predictive relationship between variables. Furthermore, this research only looked at one university, UPM. As a result, it cannot be applied to all Malaysian universities, both public and private. To explore this phenomenon at a national level, a larger- scale research for additional states or perhaps the entire country should be done. It is important to understand students’ online readiness in Malaysia since technology is well-known and widely used in the education system nowadays. More research is needed to discover other variables or factors that may be linked to students' readiness for online learning. A longitudinal study should also be carried out to obtain more detailed information on the elements that
influenced or correlated with students' readiness for online learning. This study focuses solely on university students. Since the Ministry of Education (MoE) has also required all primary and secondary schools to conduct all classes in online learning and Pengajaran dan Pembelajaran di Rumah (PdPR), an in-depth study of their readiness for online learning can be observed in the future.
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