Journal of Technology and Science Education

Figure 1. Model proposed

3.1. Hypotheses

H1: Performance expectancy positively affects students’ actual usage of social networks sites for educational purposes in the Jordanian universities

H2: Effort expectancy positively affects students’ actual usage of social networks sites for educational purposes in the Jordanian universities

H3: Social influence positively affects students’ actual usage of social networks sites for educational purposes in the Jordanian universities

H4: Facilitating conditions positively affects students’ actual usage of social networks sites for educational purposes in the Jordanian universities

H5: Hedonic motivation positively affects students’ actual usage of social networks sites for educational purposes in the Jordanian universities

H6: Habit positively affects students’ actual usage of social networks sites for educational purposes in the Jordanian universities

H7: Lecturer’s Support positively affects students’ actual usage of social networks sites for educational purposes in the Jordanian universities

H8: Student- related-factors positively affect students’ actual usage of social networks sites for educational purposes in the Jordanian universities

4. Methodology

4.1. Instruments

In order to achieve the study objectives, data were collected using a questionnaire that was designed electronically by a sample of the students from different Jordanian universities. The population of the current study is about (211663) students from the public Jordanian universities, while the selected study sample is (411) students, who were chosen by using the sample calculator available on (Sample Size Calculator- https://www.surveysystem.com/sscalc.htm) via a multistage-sampling technique. The total number of collected questionnaires was 411. After the refinement process, (31) questionnaires were excluded since they were not completed correctly. Finally, about (380) questionnaires were analyzed. The first part of the questionnaire consisted of the demographic data, including (gander, faculty, Educational qualification, the region). The second part of the questionnaire consisted of (38) questions which aim at measuring the model’s variables (see Table 1).

The questionnaire used Likert’s 5-point scale, that ranges from strongly disagree to strongly agree. The study instrument’s validity was verified by introducing it to a number of academic arbitrators who modified, explained and deleted some items.

Variable	Items	Reference
Performance Expectancy	I find that SNs are beneficial in my educational life	Venkatesh et al., 2012
	Using SNs increases my opportunities to achieve important things in my education
	Using SNs helps me to accomplish my educational tasks quickly
	Using SNs increases my educational productivity
Effort Expectancy	Learning how to use SNs is easy for me	Venkatesh et al., 2012
	My interaction with SNs is clear and well-understood
	Using SNs is easy for me
	I can easily be skilful in using SNs
Social influence	The other important persons for me think that it is necessary to use SNs for educational purposes	Venkatesh et al., 2012
	The individuals affecting my behavior think that I should use SNs for educational purposes
	The individuals whose opinions are appreciated by me advise me to use SNs in my education
	My friends’ suggestions and recommendations affect my decision related to using SNs for educational purposes	Yang, 2010
Facilitating Conditions	I have the necessary resources that enable me to use SNs	Venkatesh et al., 2012
	I have the necessary knowledge that enables me to use SNs
	I can ask for help from others when I have difficulties in using SNs
	Using SNs is entirely within my control	Venkatesh et al., 2003
Hedonic Motivation	Using SNs for educational purposes is interesting	Venkatesh et al., 2012; Yang, 2013
	Using SNs for educational purposes is entertaining
	Using SNs for educational purposes gives me the feeling of enjoyment
	Using SNs for educational purposes is inspiring
Habit	Using SNs for educational purposes became a habit for me	Venkatesh et al., 2012
	I’m addicted to using SNs for educational purposes
	We must use SNs
	Using SNs for educational purposes became a usual thing for me
	Using SNs for educational purposes is something that I do without hesitation	Verplanken & Orbell, 2003
The Lecturer Support	I use SNs to ask for help from my teacher	Gupta & Bashir, 2018
	SNs allows me to communicate with my Lecturer’s anywhere and anytime	Malkawi & Halasa, 2016
	I can use SNs to do, accomplish and submit my educational duties without the need to go to university
	Using SNs gives me the opportunity to communicate with my Lecturer’s	AL-Rahmi & Othman, 2013
	Lecturer’s encourage using SNs for educational purposes
Student- related-factors	SNs allows constructing groups and using them for educational purposes	Malkawi & Halasa, 2016
	SNs facilitate communication and interaction between students
	I use SNs to exchange new educational ideas with my friends	Gupta & Bashir, 2018
	Using SNs gives me the opportunity to discuss with my colleagues	AL-Rahmi & Othman, 2013
	SNs provides the possibility of getting help related to educational matters from non-colleagues	Al-Mukhain et al 2014
The actual usage	I will use the SNs if I have the chance for that	Jakkaew& Hemrungrote, 2017
	I intend to continue using SNs in the future for educational purposes
	I plan to continue using SNs frequently for educational purposes
	I always try to use SNs in my education
	I will use the SNs if I have the chance to do so

Table 1. Research Questionnaire

4.2. The Study Sample

In Table 2, the frequencies and percentages for the demographic variables of the study are presented.

Variable		Frequency	Percent
Gender	Male	202	44.7%
	Female	178	55.3%
Faculty	Scientific faculty	199	62.3%
	Humanitarian faculty	181	37.7%
Educational Qualification	Diploma	17	4.3%
	Bachelor	251	80%
	Master	100	13.7%
	PhD	12	2%
The region	South	111	30.3%
	North	131	32.3%
	Middle	138	38%
Total		380	100%

Table 2. Sample description

5. Results

5.1. The Reliability of the Study Instrument:

The reliability of the current study was assessed using Cronbach alpha coefficient. Cronbach alpha coefficient is considered as one of the most common methods for testing the internal consistency of the study variables. Table 3 shows Cronbach alpha, where all the study variables are acceptable since they are more than (0.70).

Factor	Cronbach’s alpha
PE EE HM FC HA SI LS SRF Total	0.88 0.89 0.92 0.79 0.85 0.87 0.92 0.90 0.94

Table 3. Reliability of the questionnaire

5.2. Research Findings

The main question in this study is “What are the factors affecting students’ Actual Usage of Social Networks Sites for educational purposes in the Jordanian universities?” The students’ answers were collected and analyzed using the statistical package SPSS version 23 in order to calculate the means and standard deviations as illustrated Table 4.

As noticed in Table (4), all the calculated means for the study variables are with a high degree. Particularly, the factor of effort expectancy was ranked in the first place, with a mean and a standard deviation of “4.3592, .61873” respectively, while the factor of social influence was in the last rank with a mean and a standard deviation of “3.7342, .77285” respectively.

Factor	Mean	Sed	Category
PE EE SI FC HM HA LS	3.9513 4.3592 3.7342 4.3303 3.9909 4.0053 4.1933	.74232 .61873 .77285 .44574 .84395 .74305 .66813	High Very High High Very High High High High

Table 4. the means and the standard deviations

5.2.1. Testing the Study Hypotheses:

In order to answer the research question, the researcher conducted several researches using variance inflation factor and tolerance test in order to verify that there is no high correlation between the independent variables; in that the value of variance inflation factor (VIF) shouldn’t exceed (10) (Diamantopoulos & Winklhofer, 2001) and tolerance should exceed the value (0.05). Table 5 shows the test results. (cut-off point less than 10).

Table 5 shows that all the values of the independent variables for the variance inflation factor were less than (10) and that all the values of the independent variables for tolerance test were more than (0.05). Moreover, variance test was used in order to test the hypotheses of the model. Table 6 shows the results.

Table 6 shows that R value which is (.776) could explain that the eight factors proposed by our model (Effort expectancy, Performance expectancy, Social influence, Facilitating condition, Habit, Hedonic motivation, lecturer support, and student related factors), may account for (R Square= .602) of variance concerning students’ dependence on SNS for educational purposes. Table 6 also shows that the total regression model is significant as at the level (α≤0.05) which means multiple regressions indicates significant positive effect between the independent and the dependent variables at the 0.05 level. The Table 7 shows the test of multiple regressions for the independent variables.

Factor	Collinearity Statistics
	Tolerance	VIF
PE HM HA FC Lec_Sup St_RF EE IS	.563 .605 .491 .720 .640 .615 .707 .644	1.776 1.654 2.037 1.388 1.562 1.627 1.414 1.553

Table 5. VIF and Tolerance Statistics

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Sum of Squares	df	Mean Square	F	Sig
Regression Residual Total	.776	.602	.593	.42556	101.472 67.188 168.660	101.472 67.188 168.660	12.684 .181	70.039	.000

Table 6. Model summary

Model	Non-standardized Coefficients		Standardized Coefficients	T	Sig
	B	Std. Error	Beta
1 (Constant) PE HM HA FC Lec_Sup St_RF EE SI	-.274 .096 .014 .158 .099 .175 .304 .117 .116	.237 .039 .033 .042 .058 .041 .042 .042 .035	.106 .018 .177 .066 .176 .302 .108 .137	-1.155 2.438 .423 3.774 1.711 4.286 7.229 2.777 3.348	.0249 .015 .673 .000 .088 .000 .000 .006 .001	Accept Reject Accept Reject Accept Accept Accept Accept

Table 7. Coefficients

The results of multiple regression test in Table 7 shows that there are six independent variables which have a statistically significant impact on the dependent variable at the level (0.05). The results also showed that there are two factors that have no effect on the dependent variable, in that hedonic motivation has no statistically significant impact at (0.05). The results also revealed that there are two factors which have no impact on the dependent variable, where hedonic motivation has no statistically significant impact at the level (.673) and the value of Beta was (.106). The results also showed that the variable of facilitating conditions has no statistically significant impact at the level (.088), where the value of Beta was (.066).

Based on the above results, the variable of Performance expectancy is statistically significant at the level (.015), where the value of Beta was (.106). The variable of habit is statistically significant at the level (.000), where the value of Beta was (.177). The results also revealed that the variables of effort expectancy and social influence are statistically significant at the level (.006) (.001) respectively, where the value of Beta was (.108) (.137) respectively. The results also showed that the variables of lecturer’s support and student-related factors are statistically significant at the level (.000) (.000) respectively, where the value of Beta was for lecturer support and (.302) for student-related factors. (.176).

Based on the above results, the two null hypotheses were accepted as they state that “H4: Facilitating conditions (FC) positively affects students’ actual usage of social networks sites for educational purposes in the Jordanian universities”, “H5: Hedonic motivation positively affects students’ actual usage of social networks sites for educational purposes in the Jordanian universities”, while the alternative hypothesis for the other variables were accepted as it states that “the factors ‘Effort expectancy, Performance expectancy, Social influence, Habit, lecturer support, and student related factors’ positively affects students’ actual usage of social networks sites for educational purposes in the Jordanian universities”

6. Discussion

The results showed that performance expectancy (PE) impacts the actual usage of SNs for educational purposes among students in the Jordanian universities. The progress that people witnessed all over the world as well as the multiple available methods and resources of knowledge have provided students with more opportunities to increase their knowledge and experiences in a certain field. This can be interpreted by the wide dissemination for the SNs that some of them are becoming interested in science (mention some of them) and the methods of conveying it to students with the easiest and most viable ways. The results revealed that at the significance level (α=0.015), where students think that when there is more benefit from using SNs, they will use them more frequently in education. This finding agrees with (Malkawi & Halasa, 2016) in that students intend to use the applications of SNs that are more beneficial for educational purposes.

Furthermore, the factor of effort expectancy (EE) has a positive impact on students’ actual usage for SNs, where the students have a good knowledge about the way of using SNs due to their frequent use of them. The significant result of regression analysis at the significance level (α=0.015) revealed that students think that it is easy to use SNs in their education since they don’t require significant experience or effort for using them. This finding agrees with (Balakrishnan, 2017), which described the category of youth who are more brilliant in using modern technology, in that they find it easy to use SNs for their educational process.

The results also revealed that the factors of habit (HA) and social influence (SI) have a positive impact on the students’ actual usage of SNs for academic purposes in the Jordanian universities. SNs play a vital role in all the societies. This is attributed to the ease of using and dealing with these sites, which made them more acceptable and used by the different categories in the society, particularly the “young students”. Therefore, due to the students’ usual usage of social networks sites in their daily communication styles, they won’t find a difficulty in using them for educational purposes. The significance level (α=0.00), (α=0.001) respectively. Revealed that students are affected by the opinions and decisions of their families and colleagues, in that the results showed that (70%) of the students consider the experiences of others in using SNs for educational purposes. They also pay attention to the feedback introduced by their colleagues. This finding agrees with (Al-Zedjali et al., 2014), which revealed that students are affected by the experiences of their friends and the surrounding community. This means that the decisions of the students in the Jordanian universities will be affected by their friends and peers regarding the actual usage of social networks sites for the educational purposes.

Facilitating conditions (FC) are not seen as factors that have a positive impact on the actual usage of social networks sites from the perspective of students in the Jordanian universities. This finding is attributed to the fact that all the students have their own laptops and internet-service provider, in that they can use them anywhere and at any time. Therefore, students think that the services provided by university in this regard has no impact on their decision to use (SNs) for educational purposes. This finding agrees with (Balakrishnan, 2017), where the study attributed this result to the prevalence of Internet and laptops among students, and thus they can access SNs any time.

The results also revealed that the factor of hedonic motivation (HM) doesn’t have a positive impact on the actual usage of social networks sites. SNs are enjoyable and attractive in terms of usage due to the numerous services and characteristics that attract users. However, the results showed that when the students use SNs, they don’t feel comfortable and enthusiastic because they think that they are monitored by the faculty members which, in turn, urge students to avoid using them for educational purposes. This finding agrees with (Bonilla Polo & Osman, 2017) where students think that using SNs doesn’t make their study interesting. Also, some respondents suggested that using SNs in their educational process requires self-control.

The study results also revealed that the factors of lecturer support (LS) and student-related factors (SRF) have a positive impact on the actual usage of social networks sites for educational purposes by the students of the Jordanian universities. Lecturers and students are the most two important factors in the educational process, in that the absence of one of them will lead to the failure of the educational process, particularly in the time that is characterized by rapid advancement and development. According to the student’s perspective, the existence of Lecturer’s and their participation in using the SNs would help students to communicate with them anywhere and at any time without boundaries; students can save time and effort while communicating with their Lecturer’s for educational purposes via SNs. The significance level at (α=0.00) for the student-related factors implies that students tend to participate, cooperate and interact with their colleagues in discussing the issues related to their assignments and lessons, where students communicate to solve and discuss the educational assignments. According to the study results, students tend to use social networks sites more frequently in their educational process, in case they noticed accessibility and cooperation of lecturers outside the lectures’ time. This finding agrees with the results of (Malkawi & Halasa, 2016), which was conducted in the universities of Irbid governorate/ Jordan, where the study revealed that the students don’t have the sufficient awareness to use SNs due to the lack of lecturer’s guidance for the joint assignments that students can do. The results also agree with (Alhazmi et al., 2014).

7. Conclusions

Finally, this study aimed at identifying the factors affecting the students’ Actual Usage of Social Networks Sites for the educational purposes in the Jordanian universities. The results showed that all the factors of this extension were accepted, and considered as having positive effects except for the facilitating conditions and hedonic motivation, considering that the students give no attention to the extent of facilitating conditions within the university environment or the extent of fun that they will experience during the process of learning.

Universities should know the factors most affecting students, focus on them, and use them to increase and raise the educational level at the university. Accordingly, the researcher recommends:

1. 1.Universities should pay more attention to the importance of SNs by establishing an educational environment that is characterized the ability to convey knowledge to students via the best possible ways.  

2. 2.Universities should save the high cost of some applications and replace that with the services provided by SNs, which are almost free and require little cost.  

3. 3.Universities should make advantage of the prevalence of SNs among students for their interest, where universities should establish open communication channels between students and universities.  

4. 4.These factors should be taken into consideration by universities and decision makers in order to promote and increase students’ use for SNs for educational purposes. 

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

References

Abelmonaim, A. (2013). Acceptance of E-entrepreneurship: case of Morocco.

Aburagaga, I., Agoyi, M., & Elgedawy, I. (2020). Assessing Faculty’s Use of Social Network Tools in Libyan Higher Education via a Technology Acceptance Model. IEEE Access, 8, 116415-116430. https://doi.org/10.1109/ACCESS.2020.3004200

Al-Zedjali, K.H., Al-Harrasi, A.S., & Al-Badi, A.H (2014). Motivations for using social networking sites by college students for educational purposes. World Academy of Science, Engineering and Technology, International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 8(8), 2577‑2580.

Al Zboun, M.S., Al Ghammaz, S.A.D., & Al Zboun, M.S. (2018). The impact of the use of YouTube and Facebook on students’ academic achievement in geography course at the University of Jordan for the bachelor’s degree. Modern Applied Science, 12(3), 164-174. https://doi.org/10.5539/mas.v12n3p164

Alalwan, A.A., Dwivedi, Y.K., & Rana, N.P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99-110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002

Alhazmi, A.K., Rahman, A.A., & Zafar, H (2014). Conceptual model for the academic use of Social Networking Sites from student engagement perspective. 2014 IEEE Conference on e-Learning, e-Management and e-Services (IC3e). IEEE. https://doi.org/10.1109/IC3e.2014.7081232

Alkhunaizan, A., & Love, S. (2012). What drives mobile commerce? An empirical evaluation of the revised UTAUT model. International Journal of Management and Marketing Academy, 2(1), 82-99.

Balakrishnan, V. (2017). Key determinants for intention to use social media for learning in higher education institutions. Universal Access in the Information Society, 16(2), 289-301. https://doi.org/10.1007/s10209-016-0457-0

Bexheti, L.A., Ismaili, B.E., & Cico, B.H (2014). An analysis of social media usage in teaching and learning: The case of SEEU. Proceedings of the 2014 International Conference on Circuits, Systems, Signal Processing, Communications and Computers.

Bonilla Polo, P.A., & Osman, M. (2017). Is social the new smart?: Factors influencing students on their decision to use social media for academic purposes.

Boyd, D.M., & Ellison, N.B. (2007). Social network sites: Definition, history, and scholarship. Journal of computer‐mediated Communication, 13(1), 210-230. https://doi.org/10.1111/j.1083-6101.2007.00393.x

Brown, S.A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS quarterly, 399-426. https://doi.org/10.2307/25148690

Casey, T., & Wilson-Evered, E. (2012). Predicting uptake of technology innovations in online family dispute resolution services: An application and extension of the UTAUT. Computers in Human Behavior, 28(6), 2034-2045. https://doi.org/10.1016/j.chb.2012.05.022

Diamantopoulos, A., & Winklhofer, H.M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of marketing research, 38(2), 269-277.

Eid, M.I., & Al-Jabri, I.M. (2016). Social networking, knowledge sharing, and student learning: The case of university students. Computers & Education, 99, 14-27. https://doi.org/10.1016/j.compedu.2016.04.007

Erzurum, F., & Tiryakioglu,F. (2011). Use of social networks as an education tool. Contemporary Educational Technology, 2(2), 135-150. https://doi.org/10.30935/cedtech/6048

Gharrah, A., Aljaafreh, A., & Al-Ma’aitah, N. (2019). Toward a Model for Actual Usage of Social Networks Sites for Educational Purposes in Jordanian Universities.

Ho, K.K. (2014). The role of learners’ academic background on E-learning: An empirical study on the use of discussion forum. International Journal of Systems and Service-Oriented Engineering (IJSSOE), 4(4), 51-64. https://doi.org/10.4018/ijssoe.2014100104

Hutter, K., Hautz, J., Dennhardt, S. & Füller, J. (2013). The impact of user interactions in social media on brand awareness and purchase intention: the case of MINI on Facebook. Journal of Product & Brand Management, 22(5-6), 342-351. https://doi.org/10.1108/JPBM-05-2013-0299

Hujran, O., Abu-Shanab, E., & Aljaafreh, A. (2020). Predictors for the adoption of e-democracy: an empirical evaluation based on a citizen-centric approach. Transforming Government: People, Process and Policy. https://doi.org/10.1108/TG-03-2019-0016

John, G.C., & Govender, I. (2020). Implementation of mobile learning using a social network platform: Facebook. Problems of education in the 21st century, 78(1), 24-47. https://doi.org/10.33225/pec/20.78.24

Kaplan, A.M., & Haenlein, M. (2012). Social media: back to the roots and back to the future. Journal of Systems and Information Technology, 14(2), 101-104. https://doi.org/10.1108/13287261211232126

Kijsanayotin, B., Pannarunothai, S., & Speedie, S.M. (2009). Factors influencing health information technology adoption in Thailand’s community health centers: Applying the UTAUT model. International journal of medical informatics, 78(6), 404-416. https://doi.org/10.1016/j.ijmedinf.2008.12.005

Kumar, S. (2013). The moderating factors of 3G user acceptance technology in Shimla (India) using UTAUT model. International journal of computer science & engineering technology, 4(6), 670-674.

Limayem, M., & Hirt, S.G. (2003). Force of habit and information systems usage: Theory and initial validation. Journal of the Association for Information Systems, 4(1), 3. https://doi.org/10.17705/1jais.00030

Limayem, M., Hirt, S.G., & Cheung, C.M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 705-737. https://doi.org/10.2307/25148817

Lin, M.F.G., Hoffman, E.S., & Borengasser, C. (2013). Is social media too social for class? A case study of Twitter use. TechTrends, 57(2), 39-45. https://doi.org/10.1007/s11528-013-0644-2

Malkawi, N.M., & Halasa, A. (2016). Exploiting Electronic Social Networks in Educational Process: Study at Universities in Irbid State-Jordan.

Moran, E.T., & Gonyea, T. (2003). The Influence of Academically-Focused Peer Interaction on College Students’ Development.

Nawi, N.B.C., Nasir, N.A.B.M., & Al Mamun, A. (2016). Factors contributing to the acceptance of social media as a platform among student entrepreneurs: A review. Mediterranean Journal of Social Sciences, 7(2), 42.

Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2020). Acceptance of mobile phone by University students for their studies: An investigation applying UTAUT2 model. Education and Information Technologies, 1-17. https://doi.org/10.1007/s10639-020-10157-9

Sabino, F.M.R. (2010). Contributions of online social networks for e-learning.

Seely-Brown, J., & Adler, R. (2008). Open education, the long tail, and learning 2.0. Educause review, 43(1), 16-20.

Tan, M., Shao, P., & Yu, P. (2014). Factors influencing engineering students’ use of social media in learning. World Trans. Eng. Technol. Educ, 12(4), 648-654.

Umrani-Khan, F., & Iyer, S. (2009). ELAM: a Model for Acceptance and use of e-Learning by Teachers and Students. Proceedings of the International Conference on e-Learning, Institute of Technology Bombay. Mumbai, India.

Van Raaij, E.M., & Schepers, J.J. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838-852. https://doi.org/10.1016/j.compedu.2006.09.001

Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478. https://doi.org/10.2307/30036540

Venkatesh, V., Thong, J.Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 157-178. https://doi.org/10.2307/41410412

Wang, Y.S., Wu, M.C., & Wang, H.Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British journal of educational technology, 40(1), 92-118. https://doi.org/10.1111/j.1467-8535.2007.00809.x

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