Next Article in Journal
Finding the Optimal Bus-Overtaking Rules for Bus Stops with Two Tandem Berths
Previous Article in Journal
Conceptual Framework of a Simulation-Based Manpower Planning Method for Construction Enterprises
Previous Article in Special Issue
Effects of Flipped Learning Approaches on Students’ Learning Performance in Software Engineering Education
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 14
Issue 9
10.3390/su14095342
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Modeling the Impact of Massive Open Online Courses (MOOC) Implementation Factors on Continuance Intention of Students: PLS-SEM Approach

by
Al-Baraa Abdulrahman Al-Mekhlafi
1,*,
Idris Othman
2,
Ahmed Farouk Kineber
2,
Ahmad A. Mousa
3 and
Ahmad M. A. Zamil
4
1
Department of Management & Humanities, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
2
Department of Civil & Environmental Engineering, University Technology PETRONAS, Seri Iskandar 32610, Perak, Malaysia
3
Discipline of Civil Engineering, School of Engineering, Sunway Campus, Monash University, Subang Jaya 47500, Selangor, Malaysia
4
Department of Marketing, College of Business Administration, Prince Sattam bin Abdulaziz University, P.O. Box 165, Al-Kharj 11942, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(9), 5342; https://doi.org/10.3390/su14095342
Submission received: 18 February 2022 / Revised: 24 March 2022 / Accepted: 24 March 2022 / Published: 28 April 2022
(This article belongs to the Special Issue Engineering Education for Sustainable Development)
Browse Figures
Versions Notes

Abstract

:
The Engineers in Society (EIS) course is a common course unique to Universiti Teknologi PETRONAS. However, every semester, the course receives 300 to 500 students, making managing and delivering it a challenging task. The EIS course is thus in need of a suitable mode of delivery where the teaching and learning process can cater to a large number of participants from a variety of programs. The aim of this study is to address the effect of Massive Open Online Courses MOOC factors implementation on the continuance intention of students. The study employed a survey that was designed from a literature review. The survey adopted a series of questions to gather information about the problem under investigation. One hundred forty-eight responses were collected from the students in different engineering, project and operation management, quality, sustainability, and entrepreneurship programs. In addition, partial least squares regression-structural equation modelling was used to analyze data. Based on the results, there is a significant impact of MOOC implementation factors on the continuance intention of students. Nevertheless, students showed a high intention to continue studying engineering in society courses online MOOC. Therefore, the current study provides practical evidence for management and lecturers of the university to enhance MOOC factors to ensure the high quality of teaching and enhance the continuance intention of students to study in a MOOC environment.

1. Introduction

Higher education institutions, particularly in developing countries, should suggest steps to address problems linked to teaching quality, reducing costs, and educational disparities in order to maintain the system’s long-term viability [1]. In this scenario, Massive Open Online Courses MOOC, as an emerging paradigm of massive information distribution, aroused hopes about its capacity to address pedagogical, strategic, and economic challenges in higher education [2]. Many studies have suggested that MOOC would negatively impact the higher education process [3,4,5,6]; however, there is growing agreement that MOOC will be incorporated into the current higher education system [3]. MOOCs have been utilized as a new kind of online learning with face-to-face traditional university courses [4,5].
When technologies are integrated into the educational process, it is necessary to assess students’ intent to continue using them. The technology-acceptance model was created for this reason [6]. This model has been widely used by MOOC researchers [7,8,9,10]. According to the literature, there is a correlation between the intention to continue using IT and repurchasing it [11]. In addition, the literature in higher education has demonstrated a link between overall quality, value perception, and ongoing usage [12]. As a result of the quality concerns with MOOC, it is essential to analyze consumer perceptions, perceived value, and desire to continue using them.
The engineering in society EIS course is a common course that is compulsory for final year students from all engineering programs at Universiti Teknologi PETRONAS UTP. Due to this, the number of students ranges from 300 to 500 students per semester, with four to five instructors from all engineering departments. Nevertheless, several issues have arisen that call for drastic measures for the future sustainability of the course. Managing many students becomes challenging, especially when organizing lecture timetables, test and examination venues, and mass adjunct lectures. Because the course involves students from all engineering programs, finding a suitable time and venue to slot students to attend classes can be difficult. Evaluation has also been a big challenge due to the large number of students to manage. In terms of delivery, lectures are conducted the conventional way in lecture halls and leave little room for student-instructor engagement. This may cause students to feel less involved in class and may breed boredom for the course. In addition to that, instructors also find monitoring attendance in class difficult as it is done manually, and students may record attendance on behalf of absent friends. Calling out the attendance roll is not an option due to the large number. Thus, because the EIS course faces various problems, the teaching mode has been changed to deliver by MOOC to enhance the learning system. This study suggests that the course should incorporate the MOOC concept in order to address the concerns and problems faced. MOOC have been successfully implemented globally and cater to many participants [13,14]. The evaluation process is conducted online through the various available applications. Active interaction between instructors and students is also part of the MOOC structure that fits modern-day learners who are comfortable using social media for interaction [15]. This study conducts MOOC using the Open Learning online platform used by other universities in Malaysia [16].
This research aims to combine the MOOC implementation factors continuance intention of students to learn this subject on the MOOC system in a proposed model to recognize the associations among these factors and continuance intention. It also aims to provide scientific results to researchers interested in the continuance intention of students in MOOC environments. Finding the best ways to mix MOOC with face-to-face classes improves teaching effectiveness, efficiency, and engagement; hence, this study enhances MOOC-based online education development. Moreover, this approach may be used to determine which MOOC techniques produce the greatest perceived quality by learners.

2. Background

MOOC are online courses distinguished by the lack of traditional entry criteria, free involvement, content supplied fully online, and a project aimed at assisting many students [17]. There is no requirement to maintain an institutional link with universities, there is no deadline to register, there is no penalty for non-compliance [18], and there is unconstrained and asynchronous delivery [19]. MOOC was identified as a resource that can improve admission to high-quality higher education [20], “popularize education, and expand access to knowledge” [21]. Despite the numerous beneficial effects ascribed to MOOC, there has been a rapid cycle of enthusiasm and disillusionment [22], and numerous issues regarding the low instructional quality of MOOC have been raised [3]. MOOC have been chastised for poor educational methods since they do not contribute to individualized and adaptable learning. It is impossible to provide tailored feedback and engagement [17,23] because they are based on multiple-choice questions, and the assessment models do not allow the evaluation of diverse abilities and competencies [24,25,26]. They do not fulfil the standards for instructional design [24].
The term “intention to continue to use” in the context of higher education may be defined as “good conduct toward the educational institution resulting in positive recommendations, or the intention of continuing future studies in the same school” [12]. As a result, the notion of “intention to continue to use” is defined as a positive attitude toward MOOC-based online learning and a suggestion for future courses to use this resource. The evaluation of a choice’s greatness or superiority is referred to as perceived quality [25]. The perceived quality of online learning may be influenced by the quality of online resources and how well they are incorporated into courses [26]. As a result, this study evaluates perceived quality in two areas: (i) the online learning course itself, taking into account the direction of activities, the effectiveness of MOOC integration in the course, the communication process, and the assessment of activities; and (ii) the MOOC quality per se, taking into account cooperation and communication, course design, and teamwork.
System quality refers to how effectively the hardware and software interact; in other words, it reflects the efficiency with which system data is processed [27]. Information quality refers to the measurement of a system’s ability to provide high-quality data [28]. In contrast, quality of service represents the degree and way in which the information system industry or these offer services [27]. Samarasinghe [29] indicated insufficient dimensions in the DeLone and McLean [27] model for measuring system effectiveness, including all important features for online learning success. Therefore, it is suggested that other critical aspects connected to both learners and courses be included in the model as adapted by Albelbisi [30,31]. As a result, this study presents a six-variable model: attitude, course quality, information quality, service quality, system quality, and continuance intention. As illustrated in Figure 1, arrows represent potential connections among the six variables in the study model.
In this model, students’ attitude refers to learner attitudes toward MOOC and whether they are beneficial or harmful. The second factor in this study, “course quality,” relates to a learner’s perception of a MOOC material quality. Third, information quality represents the information accuracy and applicability provided by MOOC structures. In other words, learners’ perceptions of the MOOC system’s ease of use, ease of learning, integrity, and reliability are known as system quality. As a result, the purpose of this research is to investigate the following hypothesis: MOOC implementation variables have a positive impact on continuous intention.

3. Research Method

This study uses positivist analysis to perform an empirical analysis of primary data collection through a questionnaire survey. This method is well-established in current construction management literature [32,33,34]. Positivism was used to review existing research, identify relevant MOOC implementation elements on Continuance Intention, and develop a conceptual model. As a result, the connections between constructs were defined using structural equation modelling (SEM). Figure 2. depicts the overall epistemological research design.

3.1. Survey Administration

The discovered components were utilized to create a closed-ended 5-point Likert scale with 5 representing very high, 4 representing high, 3 representing average, 2 representing low, and 1 representing extremely low, as used in many previous studies [35,36,37,38,39,40,41,42,43,44,45,46]. A closed-ended questionnaire was chosen because of its inherent capacity to produce suitable replies from a big survey performed and cheap administration expenses [47]. The questionnaire was divided into two sections: (1) respondent’s demographic; and (2) 5-point Likert scale questions on the constructs as seen in Table 1. A total of one hundred forty-eight (148) questionnaires were collected out of 500 students who studied this online course mode. A total of 148 respondents provide reliable data for analysis [48]. According to Yin [49] recommended that for SEM, it is appropriate for the sample size to exceed 100. Eighty-three of the respondents were male, while 65 were female, and all of them were at the undergraduate level. The sample was from different departments such as Electrical and Electronics Engineering, Civil & Environment Engineering Department, Mechanical Engineering, Chemical Engineering Department, and Petroleum Engineering.

3.2. Data Analysis

3.2.1. Reliability Test

The internal consistency of the study’s constructs in the questionnaire was determined using Cronbach’s Alpha Reliability Test (CART). Cronbach’s alpha coefficients have a range of values from 0 to 1 [50,51] 0.90 indicates excellent dependability, 0.80 indicates moderate reliability, while 0.70 indicates low reliability [52]. The following formula in the equation is used to compute CART (3) (Cronbach, 1951):
α = n n − 1 ( 1 − ∑ i V i V t )
where n = the number of items; Vt = the variance of the total scores; Vi = the variance of the item scores
The constructions’ reliability test found a Cronbach’s alpha coefficient of 0.870, regarded as extremely trustworthy.

3.2.2. Analytical Technique

SEM uses a confirmatory technique to analyze a structural theory that is based on a set of circumstances [53]. The theory usually represents “causal processes” that result in the creation of many variables [54]. Thus, it is a vital tool for multivariate regression analysis that can handle latent variables [55]. SEM includes two critical components of the procedure: (1) a set of regression equations that imply the investigation of causal processes, and (2) a diagrammatic depiction of structural relationships for improved theory conceptualization [54]. The statistical inquiry done in this work included measurement and structural model assessment techniques using SmartPLS3.2.7 software, which are detailed in the subheadings below.

3.2.3. Measurement Model

The measurement model describes a link between the symptoms or measurements and the construct [56]. The convergent and discriminant validity evaluations can help with this. The amount of association between two measures in each construct is investigated through convergent validity [57]. Furthermore, discriminant validity emphasizes that each construct’s idea is empirically different, and it suggests that no construct recognizes the concept found in the SEM [58,59,60] Figure 3.

3.2.4. Structural Model

The structural model was used in the primary step of the suggested model assessment [61]. The structural model was created in this work using route analysis to analyze all complex connections between constructs simultaneously [62]. As a result, the suggested Partial Least Squares Structural Equation Modeling (PLS-SEM) model evaluated a total of four structural equations for MOOC Implementation constructions, illustrating the relationships between MOOC Implementation’ constructs and Continuance Intention (Figure 4).

4. Results

4.1. Measurement Model (First-Order Construct)

For this investigation, the SEM illustrated in Figure 3 reproduces the aforementioned conceptual model. (1) Cronbach alpha analysis; (2) internal consistency of composite reliability; (3) Average Variance Extracted (AVE); and (4) discriminant validity were all part of the measurement analysis for the suggested model [63].
Items with an outside load of 0.40 to 0.65 can usually only be suggested for removal from the scale if the indicator’s removal results in a significant increase in composite reliability and AVE [63,64]. This is when the construct describes half of the item’s variation and the variance reported is more than the error variance. Figure 3 illustrate the external loads for the measurement models.
As a result, all outside loads were approved. Furthermore, due to Cronbach’s Alpha limitations, which influence sensitivity to the number of items included, the internal consistency of Composite Reliability (cr) was examined [63]. As a result of these two analyses, all constructs met the Cronbach alpha and cr > 0.60 standards, indicating that they were suitable [65,66,67]. The findings also revealed that all constructions pass the AVE test with a value greater than 0.50, meaning that all constructs had convergent values Table 2 [65].
To determine if each concept contains separate phenomena that are not captured by other constructs in the proposed model, discriminant validity analysis is necessary [68]. This study used Fornell and Larcker [69] to assess discriminant validity using the principles and Cross Loading criteria. According to these principles, Table 3 confirms the discriminant validity of the measurement model, highlighting that the square root of the AVE for all constructs is larger than the correlation among the latent variables [70,71].
Despite this, much research has come to the opposite conclusion regarding the Fornell and Larcker [69] criterion of characteristic discriminatory validity. As a result, the cross-loading criteria were also employed to test discriminant validity in this study. This method ensures that the loading of their structures’ indicators (items) must be higher than that of another construct. Table 4 shows that the loading on all indicators of a particular construct is larger than the loading on other constructs, proving the preceding cross-loading concepts (by row).

4.2. Measurement Model (Second-Order Construct)

Because the MOOC Adoption construct was a latent second-order construct, the bootstrapping technique was used to determine all latent first-order constructs (i.e., Attitude, Course Quality, Information Quality, Service Quality, and System Quality). On the other hand, the MOOC Implementation concept was formative, and excessive correlations across latent first-order constructs are rarely predicted. Furthermore, the strong correlation among formative items suggests collinearity, which is a concern [68]. As a result, the value of the Variable Inflation Factor was used to investigate the collinearity between the formative latent first-order constructs (VIF). Table 5 reveals that all latent first-order notions have VIF values less than 3.5, indicating that they contribute to MOOC Adoption separately.
The results in Table 5 show that four first-order subscales for MOOC implementation, including Attitude, Course Quality, Information Quality, Service Quality, System Quality had a significant standard path coefficient β (outer weight).

4.3. Structural Model (Path Analysis)

Structural model assessment can begin after the measurement model has been fitted. First, the connections among variables are rationalized in-depth in the structural model. The connection among external and endogenous constructs is depicted in this study [72]. Next, the hypothesized parameter evaluations are used to evaluate the structural model, followed by the variables’ magnitude, direction, and significance [72].
For the research hypotheses, SEM was utilized. The influence of MOOC implementation on Continuance Intention was examined with PLS-SEM (Figure 4).
As a result, the bootstrapping technique was used to assess the relevance of the model hypothesis, data reliability, and therefore the inaccuracy of the computed route coefficients set [73]. The impact of MOOC adoption on Continuance Intention was statistically favorable and substantial, as shown in Figure 4 and Table 6.

4.4. The Prediction Analysis (R2)

The independent construct (MOOC implementation) in the dependent constructions demonstrates the value of R2 as the total of the variation. The R2 value enhances the structural model’s predictive power, the greater the number, the stronger the model. R2 values of 0.7 and above are usually regarded as excellent, with values ranging from −1 to 1 [74]. Furthermore, Ringle [75] Values among 0.02 and 0.12 are regarded as weak, 0.13 and 0.25 are considered right, and values over 0.26 are deemed significant. In this investigation, the pls algorithm concluded that R2 is identical to traditional regression and applies the same principles [75]. The corrected R2 for the dependent variable in this model was 0.617 in Continuance Intention, as shown in Table 7. According to Chin [76], the findings mean that the size described by MOOC implementation is high.

4.5. Predictive Relevance of the Structural Model

A structural model’s capacity to evaluate the model’s predictive significance is a critical function. Therefore, a blindfolding procedure was used to assess the cross-validated redundancy measures for each dependent construct in this study. The results show that the Q2 values for Continuance Intention (0.557) were more than zero, implying that the independent construct (MOOC implementation) has predictive significance for the dependent constructs Table 8 [77].

5. Discussion

This research looked into how MOOC might be better integrated into online learning. It also illustrated the pedagogical approach for incorporating MOOC within the course and how students assess quality and value. According to the findings, the design of the MOOC-based online learning includes group activities, readings, ancillary activities, diversification of evaluation techniques, meetings for feedback with students, and video courses to remark on the outcomes of the exercises. This approach reduces gaps in cooperation and interaction, instructional design, information and assistance to students, assessment, material, and course technology [78,79]. We discovered that MOOC-based online learning increases student participation and enhances the educational process, changing students into active learners in this course [80]
The study’s findings revealed that attitude had a significant effect on continuance intention. In other words, a favorable student attitude toward MOOC increases student retention, thereby impacting online learning success. One reason for this finding is that students’ favorable attitudes about MOOC activities, such as their confidence, enjoyment, and interest in utilizing MOOC, may increase their desire to continue their studies through online learning. The findings are consistent with prior research [81,82]. As a result, student attitudes affect their online learning. Furthermore, student attitudes have a crucial role in successfully adopting e-learning [83,84].
The impact of course quality on continuance intention was significant when tested. This result offer evidence that characteristics of quality content such as design, appropriateness of outputs, and simplicity of comprehension of course materials played key roles in encouraging students’ intent to continue learning in a MOOC online. The important link between course quality and student retention suggested that success in MOOCs is dependent on high-quality course design, content, and learning resources that are easy to grasp. Previous research backs up the discovery of this connection [85,86]. In addition, according to Sun [87], the material quality element directly influences learning in online settings. As a result, MOOC producers and instructors should guarantee that MOOC materials are easy to grasp and that only high-quality information is given in a high-quality manner in order to create genuine possibilities for students to be more active learners through MOOC.
Furthermore, the study found that the impact of quality of information on continuance intention was significant. Testing the relationship revealed that increasing the availability and understandability of MOOC information would increase students’ intention to continue learning online. The findings of this association testing followed those of previous research in online learning systems [88,89]. As a result, instructors must give direction and assistance in navigating the learning content, exercises, and evaluations in order to enhance students’ continued intention to study via MOOC. To improve students’ capacity to organize and manage their learning processes in MOOC, it is also best to offer highly relevant material to them that is aligned with the course’s learning objectives. Furthermore, the findings revealed that the system’s quality substantially influences students’ intentions to continue. The system’s quality demonstrates that factors such as simplicity of use, ease of learning, and main function allow the design, system functionality, and integration to impact students’ persistence [88,89].
Finally, the study found that service quality had a significant impact on continuance intention. It has been suggested that the quality of MOOC services, such as teacher assistance and institutional support, might influence students’ continuance intention to participate in MOOC and their eventual success in MOOC learning. For example, the MOOC system’s dependability in responding to students’ questions and its capacity to send lectures, resources, and comments to students in a timely manner might increase students’ chance to enhance their learning using a MOOC system, as several scholars have [88,89], and lecturer and institutional assistance should be viewed as essential elements for enhancing identity learning with MOOC students. Concerning the ethical issues of this study, first, research participants did not subject to harm in any way whatsoever, and second, respecting the dignity of research participants was prioritised, and third, full consent was obtained from the participants prior to the study. Lastly, the protection of the privacy of research participants was ensured [90].

6. Conclusions

This study shows how MOOC implementation factors affect the continuance intention of students for online learning in engineering in society courses. The findings demonstrated that improving attitude, course quality, information quality, system quality, and service quality are critical factors for boosting online learning in MOOC settings. Students showed a high intention to continue studying engineering in society courses online instead of face to face. To put it another way, developing MOOC settings should include successful interactive learning environments that take these six criteria into account. The study results should be used to assist and advise higher education institutions in their use of MOOC. This research should be beneficial to MOOC designers in developing an effective MOOC environment in which learners become more comfortable with MOOC, resulting in successful MOOC uptake. In conclusion, the impact of MOOC implantation factors on student’s continuance intention in a MOOC environment was examined in this study. As a result, this article adds to the current body of information on online learning through MOOC. An investigation of the students’ satisfaction to continue learning by MOOC mode is required for future studies.

Author Contributions

Conceptualization, A.-B.A.A.-M. and A.F.K.; data curation, A.-B.A.A.-M. and I.O.; formal analysis, A.F.K.; funding acquisition, I.O.; investigation, A.-B.A.A.-M., methodology, A.-B.A.A.-M., A.F.K. and A.A.M.; project administration, I.O.; supervision, I.O.; validation, A.A.M. and A.M.A.Z.; visualization, A.-B.A.A.-M.; writing—original draft, A.-B.A.A.-M.; writing—review & editing, I.O., A.F.K., A.A.M. and A.M.A.Z. All authors have read and agreed to the published version of the manuscript.

Funding

The authors would like to acknowledge Universiti Teknologi PETRONAS for funding this research under SoTL grant CL2020-0019, grant cost center 015LF0-047.

Institutional Review Board Statement

Ethical review and approval were waived due to there is no ethical approval in survey studies, especially non-medical studies. Therefore, we provide an introduction on the first page of the questionnaire to inform the participants about the study’s goal and let them know that participating in this survey is voluntary (not compulsory). In addition, we guarantee them the confidentiality of their information.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data are available for those who want to see it for justified reasons. Please contact the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Gulati, S. Technology-enhanced learning in developing nations: A review. Int. Rev. Res. Open Distrib. Learn. 2008, 9. [Google Scholar] [CrossRef]
  2. Albó, L.; Leo, D.H.; Riera, M.O. Are higher education students registering and participating in MOOCs? The case of MiríadaX. In Proceedings of the EMOOCs 2016 Conference, Graz, Austria, 22–24 February 2016. [Google Scholar]
  3. Lee, Y.-H. Scaffolding university students’ epistemic cognition during multimodal multiple-document reading: The effects of the epistemic prompting and the automated reflection report. Internet High. Educ. 2021, 49, 100777. [Google Scholar] [CrossRef]
  4. Albó, L.; Hernández-Leo, D. Conceptualising a visual representation model for MOOC-based blended learning designs. Australas. J. Educ. Technol. 2020, 36, 1–26. [Google Scholar] [CrossRef]
  5. Montgomery, A.P.; Hayward, D.V.; Dunn, W.; Carbonaro, M.; Amrhein, C.G. Blending for student engagement: Lessons learned for MOOCs and beyond. Australas. J. Educ. Technol. 2015, 31, 657–670. [Google Scholar] [CrossRef]
  6. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 319–340. [Google Scholar] [CrossRef] [Green Version]
  7. Hsu, J.; Chen, C.-C.; Ting, P.-F. Understanding MOOC continuance: An empirical examination of social support theory. Interact. Learn. Environ. 2018, 26, 1100–1118. [Google Scholar] [CrossRef]
  8. Joo, Y.J.; So, H.-J.; Kim, N.H. Examination of relationships among students’ self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Comput. Educ. 2018, 122, 260–272. [Google Scholar] [CrossRef]
  9. Nordin, N.; Norman, H.; Embi, M.A. Technology Acceptance of Massive Open Online Courses in Malaysia. Malays. J. Distance Educ. 2015, 17, 1–16. [Google Scholar] [CrossRef] [Green Version]
  10. Wu, B.; Chen, X. Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Comput. Hum. Behav. 2017, 67, 221–232. [Google Scholar] [CrossRef]
  11. Roca, J.C.; Chiu, C.-M.; Martínez, F.J. Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. Int. J. Hum.-Comput. Stud. 2006, 64, 683–696. [Google Scholar] [CrossRef] [Green Version]
  12. Dlačić, J.; M Arslanagić, S.K.; Raspor, S. Exploring perceived service quality, perceived value, and repurchase intention in higher education using structural equation modelling. Total Qual. Manag. Bus. Excell. 2014, 25, 141–157. [Google Scholar] [CrossRef]
  13. Rodriguez, C.O. MOOCs and the AI-Stanford Like Courses: Two Successful and Distinct Course Formats for Massive Open Online Courses. Eur. J. Open Distance E-Learn. 2012, 16, 1–13. [Google Scholar]
  14. Yuan, L.; Powell, S.; Olivier, B. Beyond MOOCs: Sustainable Online Learning in Institutions; Centre for Educational Technology, Interoperability and Standards: Bolton, UK, 2014; pp. 1–20. [Google Scholar]
  15. Masters, K. A brief guide to understanding MOOCs. Internet J. Med. Educ. 2011, 1, 2. [Google Scholar]
  16. Yuan, L.; Powell, S. Partnership model for entrepreneurial innovation in open online learning. E-Learn. Pap. 2015, 41, 1–9. [Google Scholar]
  17. Chaplot, D.S.; Rhim, E.; Kim, J. Predicting student attrition in MOOCs using sentiment analysis and neural networks. In CEUR Workshop Proceedings; Institute of Cognitive Science, Carleton University: Madrid, Spain, 2015. [Google Scholar]
  18. Alemán de la Garza, L.Y.; Sancho-Vinuesa, T.; Zermeño, M.G.G. Atypical: Analysis of a Massive Open Online Course (MOOC) with a Relatively High Rate of Program Completers. Glob. Educ. Rev. 2015, 2, 68–81. [Google Scholar]
  19. DeBoer, J.; Ho, A.D.; Stump, G.S.; Breslow, L. Changing “course” reconceptualizing educational variables for massive open online courses. Educ. Res. 2014, 43, 74–84. [Google Scholar] [CrossRef] [Green Version]
  20. Clair, R.S.; Winer, L.; Finkelstein, A.; Fuentes-Steeves, A.; Wald, S. Big hat and no cattle? The implications of MOOCs for the adult learning landscape. Can. J. Study Adult Educ. 2015, 27, 65–82. [Google Scholar]
  21. Schmid, L.; Manturuk, K.; Simpkins, I.; Goldwasser, M.; Whitfield, K.E. Fulfilling the promise: Do MOOCs reach the educationally underserved? Educ. Media Int. 2015, 52, 116–128. [Google Scholar] [CrossRef]
  22. Teixeira, A.; Mota, J.; García-Cabot, A.; García-Lopéz, E.; De-Marcos, L. A new competence-based approach for personalizing MOOCs in a mobile collaborative and networked environment. RIED. Rev. Iberoam. De Educ. A Distancia 2016, 19, 143–160. [Google Scholar] [CrossRef]
  23. Vera, S.; Urrutia, L.M.; Davis, H. Desafíos en la Creación, Desarrollo e Implementación de los MOOC: El Curso de Web Science en la Universidad de Southampton. In Challenges in the Creation, Development and Implementation of MOOCs: Web Science Course at the University of Southampton; Grupo Comunicar Ediciones: Huelva, Spain, 2015; pp. 37–44. [Google Scholar]
  24. Margaryan, A.; Bianco, M.; Littlejohn, A. Instructional quality of massive open online courses (MOOCs). Comput. Educ. 2015, 80, 77–83. [Google Scholar] [CrossRef] [Green Version]
  25. Zeithaml, V.A. Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. J. Mark. 1988, 52, 2–22. [Google Scholar] [CrossRef]
  26. Griffiths, R.; Mulhern, C.; Spies, R.; Chingos, M. Adopting MOOCS on campus: A collaborative effort to test MOOCS on campuses of the university system of Maryland. Online Learn. 2015, 19, n2. [Google Scholar] [CrossRef]
  27. DeLone, H.W.; McLean, E.R. The DeLone and McLean model of information systems success: A ten-year update. J. Manag. Inf. Syst. 2003, 19, 9–30. [Google Scholar]
  28. Petter, S.; DeLone, W.; McLean, E. Measuring information systems success: Models, dimensions, measures, and interrelationships. Eur. J. Inf. Syst. 2008, 17, 236–263. [Google Scholar] [CrossRef]
  29. Samarasinghe, S.M. E-Learning Systems Success in an Organisational Context: A Thesis Presented in Partial Fulfilment of the Requirements. Ph.D. Thesis, Management Information Systems at Massey University, Palmerston North, New Zealand, 2012. [Google Scholar]
  30. Albelbisi, A.N.; Al-Adwan, A.S.; Habibi, A. Self-regulated learning and satisfaction: A key determinants of MOOC success. Educ. Inf. Technol. 2021, 26, 3459–3481. [Google Scholar] [CrossRef]
  31. Albelbisi, N.A.; Yusop, F.D. Factors influencing learners’ self–regulated learning skills in a massive open online course (MOOC) environment. Turk. Online J. Distance Educ. 2019, 20, 1–16. [Google Scholar] [CrossRef]
  32. Aghimien, O.D.; Aigbavboa, C.; Edwards, D.J.; Mahamadu, A.-M.; Olomolaiye, P.; Nash, H.; Onyia, M. A fuzzy synthetic evaluation of the challenges of smart city development in developing countries. Smart Sustain. Built Environ. 2020. [Google Scholar] [CrossRef]
  33. Edwards, J.D.; Rillie, I.; Chileshe, N.; Lai, J.; Hosseini, M.R.; Thwala, W.D. A field survey of hand–arm vibration exposure in the UK utilities sector. Eng. Constr. Archit. Manag. 2020. [Google Scholar] [CrossRef]
  34. Baarimah, O.A.; Alaloul, W.S.; Liew, M.; Kartika, W.; Al-Sharafi, M.A.; Musarat, M.A.; Alawag, A.M.; Qureshi, A.H. A bibliometric analysis and review of building information modelling for post-disaster reconstruction. Sustainability 2021, 14, 393. [Google Scholar] [CrossRef]
  35. Kineber, F.A.; Othman, I.; Oke, A.E.; Chileshe, N.; Alsolami, B. Critical Value Management Activities in Building Projects: A Case of Egypt. Buildings 2020, 10, 239. [Google Scholar] [CrossRef]
  36. Kineber, A.F.O.; Oke, A.E.; Chileshe, N.; Buniya, M.K. Identifying and Assessing Sustainable Value Management Implementation Activities in Developing Countries: The Case of Egypt. Sustainability 2020, 12, 9143. [Google Scholar] [CrossRef]
  37. Al-Mekhlafi, A.A.-B.; Isha, A.S.N.; Chileshe, N.; Abdulrab, M.; Kineber, A.F.; Ajmal, M. Impact of Safety Culture Implementation on Driving Performance among Oil and Gas Tanker Drivers: A Partial Least Squares Structural Equation Modelling (PLS-SEM) Approach. Sustainability 2021, 13, 8886. [Google Scholar] [CrossRef]
  38. Oke, E.A.; Kineber, A.F.; Albukhari, I.; Othman, I.; Kingsley, C. Assessment of Cloud Computing Success Factors for Sustainable Construction Industry: The Case of Nigeria. Buildings 2021, 11, 36. [Google Scholar] [CrossRef]
  39. Olanrewaju, I.O.; Kineber, A.F.; Chileshe, N.; Edwards, D.J. Modelling the relationship between Building Information Modelling (BIM) implementation barriers, usage and awareness on building project lifecycle. Build. Environ. Technol. Sci. J. 2021, 9, 1–15. [Google Scholar] [CrossRef]
  40. Othman, I.; Kineber, A.; Oke, A.; Zayed, T.; Buniya, M. Barriers of value management implementation for building projects in Egyptian construction industry. Ain Shams Eng. J. 2020, 12, 21–30. [Google Scholar] [CrossRef]
  41. Kineber, F.A.; Othman, I.B.; Oke, A.E.; Chileshe, N. Modelling the relationship between value management’s activities and critical success factors for sustainable buildings. J. Eng. Des. Technol. 2021, 20, 414–435. [Google Scholar]
  42. Oke, A.E.; Kineber, A.F.; Alsolami, B.; Kingsley, C. Adoption of cloud computing tools for sustainable construction: A structural equation modelling approach. J. Facil. Manag. 2022. [Google Scholar] [CrossRef]
  43. Buniya, K.M.; Othman, I.; Sunindijo, R.Y.; Kineber, A.F.; Mussi, E.; Ahmad, H. Barriers to safety program implementation in the construction industry. Ain Shams Eng. J. 2020, 12, 65–72. [Google Scholar] [CrossRef]
  44. Oke, E.A.; Kineber, A.F.; Albukhari, I.; Dada, A.J. Modeling the robotics implementation barriers for construction projects in developing countries. Int. J. Build. Pathol. Adapt. 2021. [Google Scholar] [CrossRef]
  45. Kineber, F.A.; Othman, I.; Oke, A.E.; Chileshe, N.; Zayed, T. Value management implementation barriers for sustainable building: A bibliometric analysis and partial least square structural equation modeling. Constr. Innov. 2021. [Google Scholar] [CrossRef]
  46. Mohammed, M.; Shafiq, N.; Elmansoury, A.; Al-Mekhlafi, A.B.A.; Rached, E.F.; Zawawi, N.A.; Ibrahim, M.B. Modeling of 3R (Reduce, Reuse and Recycle) for Sustainable Construction Waste Reduction: A Partial Least Squares Structural Equation Modeling (PLS-SEM). Sustainability 2021, 13, 10660. [Google Scholar] [CrossRef]
  47. Gilham, B. Developing a Questionnaire, 2nd ed.; Bloomsbury: London, UK, 2015; ISBN 978-0-826409631-7. [Google Scholar]
  48. Collins, H. Creative Research: The Theory and Practice of Research for the Creative Industries, 2nd ed.; Bloomsbury: London, UK, 2018. [Google Scholar]
  49. Yin, R.K. Case Study Research: Design and Methods. Appl. Soc. Res. Methods Ser. 2009, 30, 5. [Google Scholar]
  50. Santos, J.R. Cronbach’s alpha: A tool for assessing the reliability of scales. J. Ext. 1999, 37, 1–5. [Google Scholar]
  51. Buniya, K.M.; Othman, I.; Durdyev, S.; Sunindijo, R.Y.; Ismail, S.; Kineber, A.F. Safety Program Elements in the Construction Industry: The Case of Iraq. Int. J. Environ. Res. Public Health 2021, 18, 411. [Google Scholar] [CrossRef] [PubMed]
  52. Adedokun, A.O.; Ibironke, O.T.; Olanipekun, A.O. Vulnerability of motivation schemes in enhancing site workers productivity for construction industry’s sustainability in Nigeria. Int. J. Sustain. Constr. Eng. Technol. 2013, 4, 21–30. [Google Scholar]
  53. Olanrewaju, I.O.; Kineber, A.F.; Chileshe, N.; Edwards, D.J. Modelling the Impact of Building Information Modelling (BIM) Implementation Drivers and Awareness on Project Lifecycle. Sustainability 2021, 13, 8887. [Google Scholar] [CrossRef]
  54. Byrne, B.M. Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming; Multivariate Applications Series; Taylor and Francis Group: New York, NY, USA, 2010; Volume 396, p. 7384. [Google Scholar]
  55. Chileshe, N.; Rameezdeen, R.; Hosseini, M.R.; Martek, I.; Li, H.X.; Panjehbashi-Aghdam, P. Factors driving the implementation of reverse logistics: A quantified model for the construction industry. Waste Manag. 2018, 79, 48–57. [Google Scholar] [CrossRef]
  56. Hulland, J. Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strateg. Manag. J. 1999, 20, 195–204. [Google Scholar] [CrossRef]
  57. Mohammed, M.; Shafiq, n.; Al-Mekhlafi, A.-B.A.; Rashed, E.F.; Khalil, M.H.; Zawawi, N.A.; Muhammad, A.; Sadis, A.M. The Mediating Role of Policy-Related Factors in the Relationship between Practice of Waste Generation and Sustainable Construction Waste Minimisation: PLS-SEM. Sustainability 2022, 14, 656. [Google Scholar] [CrossRef]
  58. Hair, F.J.; Anderson, R.E.; Babin, B.J.; Black, W.C. Multivariate Data Analysis: A Global Perspective; Pearson: Upper Saddle River, NJ, USA, 2010; Volume 7. [Google Scholar]
  59. Al-Mekhlafi, A.A.-B.; Isha, A.S.N.; Chileshe, N.; Abdulrab, M.; Saeed, A.A.H.; Kineber, A.F. Modelling the Relationship between the Nature of Work Factors and Driving Performance Mediating by Role of Fatigue. Int. J. Environ. Res. Public Health 2021, 18, 6752. [Google Scholar] [CrossRef]
  60. Al-Mekhlafi, A.A.-B.; Isha, A.S.N.; Naji, G.M.A. The Relationship between Fatigue and Driving Performance: A Review and Directions for Future Research. J. Crit. Rev. 2020, 7, 134–141. [Google Scholar]
  61. Kineber, F.A.; Othman, I.; Oke, A.E.; Chileshe, N.; Buniya, M.K. Impact of Value Management on Building Projects Success: Structural Equation Modeling Approach. J. Constr. Eng. Manag. 2021, 147, 04021011. [Google Scholar] [CrossRef]
  62. Tabachnick, G.B.; Fidell, L.S.; Ullman, J.B. Using Multivariate Statistics; Pearson: Boston, MA, USA, 2007; Volume 5. [Google Scholar]
  63. Hair, J.F., Jr.; Matthews, L.M.; Matthews, R.L.; Sarstedt, M. PLS-SEM or CB-SEM: Updated guidelines on which method to use. Int. J. Multivar. Data Anal. 2017, 1, 107–123. [Google Scholar] [CrossRef]
  64. Henseler, J.; Ringle, C.M.; Sinkovics, R.R. The Use of Partial Least Squares Path Modeling in International Marketing. In New Challenges to International Marketing; Emerald Group Publishing Limited: Amsterdam, The Netherlands, 2009. [Google Scholar]
  65. Wong, K.K.-K. Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Mark. Bull. 2013, 24, 1–32. [Google Scholar]
  66. Bagozzi, P.R.; Yi, Y. Specification, evaluation, and interpretation of structural equation models. J. Acad. Mark. Sci. 2012, 40, 8–34. [Google Scholar] [CrossRef]
  67. Perry, H.R.; Charlotte, B.; Isabella, M.; Bob, C. SPSS Explained; Routledge: London, UK, 2004. [Google Scholar]
  68. Hair, F.J.; Ringle, C.M.; Sarstedt, M. Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Plan. 2013, 46, 1–12. [Google Scholar] [CrossRef]
  69. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  70. Chin, W.W.; Marcolin, B.L.; Newsted, P.R. A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Inf. Syst. Res. 2003, 14, 189–217. [Google Scholar] [CrossRef] [Green Version]
  71. Kineber, F.A.; Othman, I.; Oke, A.E.; Chileshe, N.; Zayed, T. Exploring the value management critical success factors for sustainable residential building—A structural equation modelling approach. J. Clean. Prod. 2021, 293, 126115. [Google Scholar] [CrossRef]
  72. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E.; Tatham, R.L. Multivariate Data Analysis, 6th ed.; Pearson Prentice Hall: Upper Saddle River, NJ, USA, 2006. [Google Scholar]
  73. Chin, W.W. The partial least squares approach to structural equation modeling. Mod. Methods Bus. Res. 1998, 295, 295–336. [Google Scholar]
  74. Olanrewaju, I.O.; Oyewobi, L.O.; Idiake, J.E.; Alumbugu, P.; Babarinde, S.A.; Oke, A.E. Nexus of economic recession and building construction cost in Nigeria. J. Niger. Inst. Quant. Surv. 2019, 65, 85–98. [Google Scholar]
  75. da Silva, D.; Bido, D.; Ringle, C. Structural equation modeling with the SmartPLS. Braz. J. Mark. 2015, 13, 56–73. [Google Scholar]
  76. Chin, W.W. Commentary: Issues and Opinion on Structural Equation Modeling. JSTOR 1998, 22, vii–xvi. [Google Scholar]
  77. Hair, F.J.; Ringle, C.M.; Sarstedt, M. PLS-SEM: Indeed a silver bullet. J. Mark. Theory Pract. 2011, 19, 139–152. [Google Scholar] [CrossRef]
  78. Holotescu, C.; Grosseck, G.; CREŢU, V.; Naaji, A. Integrating MOOCs in Blended Courses. In Proceedings of the 10th eLearning and Software for Education Conference—eLSE, Bucharest, Romania, 24–25 April 2014. [Google Scholar]
  79. Morris, N.P. How Digital Technologies, Blended Learning and MOOCs Will Impact the Future of Higher Education. In Proceedings of the International Conference e-Learning 2014 Multi Conference on Computer Science and Information Systems, Lisbon, Portugal, 15–19 July 2014. [Google Scholar]
  80. Lowenthal, R.P.; Hodges, C.B. In search of quality: Using quality matters to analyze the quality of massive, open, online courses (MOOCs). Int. Rev. Res. Open Distrib. Learn. 2015, 16, 83–101. [Google Scholar] [CrossRef] [Green Version]
  81. Kramarski, B.; Gutman, M. How can self-regulated learning be supported in mathematical E-learning environments? J. Comput. Assist. Learn. 2006, 22, 24–33. [Google Scholar] [CrossRef]
  82. Zimmerman, J.B.; Schunk, D.H. Self-Regulated Learning and Academic Achievement: Theoretical Perspectives; Routledge: London, UK, 2001. [Google Scholar]
  83. Presley, A.; Presley, T. Factors influencing student acceptance and use of academic portals. J. Comput. High. Educ. 2009, 21, 167–182. [Google Scholar] [CrossRef]
  84. Hammoud, L. Factors Affecting Students’ Attitude and Performance When Using a Web-Enhanced Learning Environment; Brunel University, School of Information Systems, Computing and Mathematics: London, UK, 2010. [Google Scholar]
  85. Hassanzadeh, A.; Kanaani, F.; Elahi, S. A model for measuring e-learning systems success in universities. Expert Syst. Appl. 2012, 39, 10959–10966. [Google Scholar] [CrossRef]
  86. Owens, D.J.; Price, L. Is e-learning replacing the traditional lecture? Educ. Train. 2010, 52, 128–139. [Google Scholar] [CrossRef] [Green Version]
  87. Sun, P.-C.; Tsai, R.J.; Finger, G.; Chen, Y.-Y.; Yeh, D. What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Comput. Educ. 2008, 50, 1183–1202. [Google Scholar] [CrossRef]
  88. Liaw, S.-S.; Huang, H.-M. Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Comput. Educ. 2013, 60, 14–24. [Google Scholar] [CrossRef]
  89. Zhao, H. Factors Influencing Self-Regulation in E-learning 2.0: Confirmatory Factor Model|Facteurs qui influencent la maîtrise de soi en cyberapprentissage 2.0: Modèle de facteur confirmative. Can. J. Learn. Technol. 2016, 42, 1–21. [Google Scholar] [CrossRef]
  90. Petousi, V.; Sifaki, E. Contextualising harm in the framework of research misconduct. Findings from discourse analysis of scientific publications. Int. J. Sustain. Dev. 2020, 23, 149–174. [Google Scholar]
Sustainability 14 05342 g001 550
Figure 1. Study model.
Figure 1. Study model.
Sustainability 14 05342 g001
Sustainability 14 05342 g002 550
Figure 2. Research design.
Figure 2. Research design.
Sustainability 14 05342 g002
Sustainability 14 05342 g003 550
Figure 3. The PLS initial model with outer loading and R2.
Figure 3. The PLS initial model with outer loading and R2.
Sustainability 14 05342 g003
Sustainability 14 05342 g004 550
Figure 4. Bootstrapping analysis.
Figure 4. Bootstrapping analysis.
Sustainability 14 05342 g004
Table
Table 1. Questionnaire items.
Table 1. Questionnaire items.
ConstructItems References
Continuance intentionIf I were to take this subject again, I would like the teacher to use MOOCs as part of the content. [10]
I recommend that other course subjects use MOOCs as part of their content.
I think I made the right choice when I enrolled in an institution that uses MOOCs in its subjects.
AttitudeI feel confident in using MOOC. [30,31]
I enjoy using MOOC for my studies.
I believe that MOOC allows me to acquire new knowledge.
I believe that MOOC enhances my learning experience.
I believe that convenience is an important feature of MOOC.
I believe that MOOC increases the quality of learning because it integrates all forms of media.
I believe that adopting MOOC allows for increased student satisfaction.
I believe that studying courses that use MOOC is interesting.
In my MOOC learning experiences, the content of the course is up-to-date
Course qualityIn my MOOC learning experiences, learning outcomes are summarized in clearly written, straightforward statements.by [30,31]
In my MOOC learning experiences, courses are designed to encourage learners to utilize problem-solving activities to develop topic understanding.
In my MOOC learning experiences, the course content is communicated well.
Information qualityI believe that the MOOC system provides me with the outputs that I need. by [30,31]
I believe that information (i.e., learning materials) from the MOOC system is in a readily usable form.
I believe that the MOOC system provides information (i.e., learning materials) that is easy to understand.
I believe that information (i.e., learning materials) from the MOOC system is concise.
System qualityFor me, the MOOC system is easy to use. by [30,31]
For me, the MOOC system is easy to manage.
For me, MOOC system meets my expectations.
For me, MOOC system includes necessary features and functions for my study.
For me, all data within MOOC system is fully integrated and consistent.
Service qualityIn my MOOC learning experiences, the instructors are good to learners. by [30,31]
In my MOOC learning experiences, the instructors are friendly to learners.
In my MOOC learning experiences, the instructors are knowledgeable enough about the content.
In my MOOC learning experiences, the instructors are available via e-mail, phone.
Table
Table 2. Construct reliability and validity tests.
Table 2. Construct reliability and validity tests.
ConstructsCronbach’s AlphaComposite ReliabilityAVE
Attitude0.9570.9630.746
Continuance Intention0.9520.9690.912
Course Quality 0.9050.9400.84
Information Quality 0.950.9640.87
MOOC Implementation 0.9760.9780.639
Service Quality0.9570.9690.885
System Quality0.9540.9650.846
Table
Table 3. Discriminant validity.
Table 3. Discriminant validity.
ConstructsAttitudeContinuance IntentionCourse Quality Information Quality Service QualitySystem Quality
Attitude0.864
Continuance Intention0.7690.955
Course Quality 0.7350.6290.917
Information Quality 0.7390.620.7070.933
MOOC Implementation 0.8320.7870.840.853
Service Quality0.7190.6650.6720.6230.941
System Quality0.7220.6910.6970.7260.7430.920
Table
Table 4. Cross loadings test.
Table 4. Cross loadings test.
ItemsAttitudeContinuance IntentionCourse Quality Information Quality Service QualitySystem Quality
Att10.8200.7140.5620.6170.6120.64
Att20.8650.6890.6660.6470.6130.606
Att30.8990.6780.6880.6070.6760.584
Att40.8720.6730.6480.6070.5340.601
Att50.8020.5380.5920.6780.6580.667
Att60.8950.7030.6380.6170.5610.617
Att70.9030.70.6330.6380.6030.632
Att80.8750.6580.5850.60.630.606
Att90.8360.6220.6930.7280.6960.657
CI10.760.9520.640.5940.6750.685
CI20.6990.9480.5330.5420.5760.61
CI30.740.9650.6210.6360.6480.678
CQ10.6710.5330.8960.7080.6040.665
CQ20.6630.5730.9050.5640.6230.617
CQ30.6860.6220.9480.6690.6210.633
IQ10.7320.5760.7160.9020.5530.619
IQ20.6270.5430.6080.9270.5640.666
IQ30.6990.6060.6560.9560.5790.701
IQ40.6970.5860.6560.9460.6270.723
SEQ10.7020.6020.6480.6040.9260.699
SEQ20.6530.650.6220.5770.9510.681
SEQ30.6780.6270.6440.5880.9490.716
SEQ40.6730.6230.6120.5750.9370.7
SYQ10.6160.5970.6050.5970.670.897
SYQ20.6440.6260.6210.620.7080.932
SYQ30.6670.630.6420.6720.6540.916
SYQ40.6970.6860.6560.70.6940.947
SYQ50.6910.6320.6750.7430.6910.905
Table
Table 5. Bootstrapping analysis for formative constructs.
Table 5. Bootstrapping analysis for formative constructs.
PathβSET Valuesp-ValuesVIF
Attitude -> MOOC Implementation 0.4010.01429.33703.2
Course Quality -> MOOC Implementation 0.130.00620.56502.7
Information Quality -> MOOC Implementation 0.1790.00821.50902.8
Service Quality -> MOOC Implementation 0.1870.00727.34802.7
System Quality -> MOOC Implementation 0.2330.00926.31603.1
Table
Table 6. List of hypotheses and relative paths for the model.
Table 6. List of hypotheses and relative paths for the model.
PathβSET Valuesp-Values
MOOC Implementation -> Continuance Intention0.7870.03920.1890.000
Table
Table 7. Results of Prediction Analysis (R2).
Table 7. Results of Prediction Analysis (R2).
Endogenous Latent VariableR SquareR Square AdjustedExplained Size
Continuance Intention0.6200.617High
Table
Table 8. Results of predictive relevance (Q2).
Table 8. Results of predictive relevance (Q2).
Endogenous Latent VariableSSOSSEQ² (=1 − SSE/SSO)
Continuance Intention444196.5920.557
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Al-Mekhlafi, A.-B.A.; Othman, I.; Kineber, A.F.; Mousa, A.A.; Zamil, A.M.A. Modeling the Impact of Massive Open Online Courses (MOOC) Implementation Factors on Continuance Intention of Students: PLS-SEM Approach. Sustainability 2022, 14, 5342. https://doi.org/10.3390/su14095342

AMA Style

Al-Mekhlafi A-BA, Othman I, Kineber AF, Mousa AA, Zamil AMA. Modeling the Impact of Massive Open Online Courses (MOOC) Implementation Factors on Continuance Intention of Students: PLS-SEM Approach. Sustainability. 2022; 14(9):5342. https://doi.org/10.3390/su14095342

Chicago/Turabian Style

Al-Mekhlafi, Al-Baraa Abdulrahman, Idris Othman, Ahmed Farouk Kineber, Ahmad A. Mousa, and Ahmad M. A. Zamil. 2022. "Modeling the Impact of Massive Open Online Courses (MOOC) Implementation Factors on Continuance Intention of Students: PLS-SEM Approach" Sustainability 14, no. 9: 5342. https://doi.org/10.3390/su14095342

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop