Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 2157 2022-04-26 09:06:37 |
2 format correct Meta information modification 2157 2022-04-26 09:43:17 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Altamimi, A.; Al-Okaily, M.; Al-Bashayreh, M.; Almajali, D.; Masadeh, R. Sustainable Learning and Education. Encyclopedia. Available online: https://encyclopedia.pub/entry/22273 (accessed on 03 July 2024).
Altamimi A, Al-Okaily M, Al-Bashayreh M, Almajali D, Masadeh R. Sustainable Learning and Education. Encyclopedia. Available at: https://encyclopedia.pub/entry/22273. Accessed July 03, 2024.
Altamimi, Ahmad, Manaf Al-Okaily, Mahmood Al-Bashayreh, Dmaithan Almajali, Raed Masadeh. "Sustainable Learning and Education" Encyclopedia, https://encyclopedia.pub/entry/22273 (accessed July 03, 2024).
Altamimi, A., Al-Okaily, M., Al-Bashayreh, M., Almajali, D., & Masadeh, R. (2022, April 26). Sustainable Learning and Education. In Encyclopedia. https://encyclopedia.pub/entry/22273
Altamimi, Ahmad, et al. "Sustainable Learning and Education." Encyclopedia. Web. 26 April, 2022.
Sustainable Learning and Education
Edit

Sustainable learning and education (SLE) is a relatively new ideology based on sustainability principles and developed in response to the United Nations’ recently proclaimed Sustainable Development Goals (SDGs). Recently, the coronavirus (COVID-19) pandemic has affected educational systems globally, leading them to embrace more innovative technological methods to meet academic demands while maintaining SLE principles. Mobile learning apps (MLA) refers to using the unique capabilities of mobile apps to engage and collaborate towards establishing robust online learning.

sustainable learning mobile learning apps distance learning acceptance

1. Introduction

SLE is a relatively new educational concept that aims to develop ways for Sustainable Learning and Education. Additionally, SLE seeks to assist students in applying their knowledge [1][2][3]. The chances of achieving sustainability increase in direct proportion to public acceptance of its importance [4][5]. According to Ben-Eliyahu [6], SLE is a continual, responsive, and proactive learning process. As circumstances change, learners can successfully construct their knowledge. As such, it is lifelong learning, characterized by conscious and purposeful learning in the present despite adversity and limited potential [7][8]. With a focus on sustainability, SLE should be less structured and more flexible than traditional classrooms. A key SLE equation feature is the capacity of learning systems to swiftly adapt and transfer learning in complex and demanding situations [9][10]. Other aspects include providing students with the skills necessary for survival and the development of a sustainable future. Decentralized solutions, such as free online resources, will help students to obtain and keep the information they need. Adopting mobile learning technologies during the COVID-19 pandemic demonstrated a substantial impact on the learning process and the development of teaching techniques as a result [11][12][13][14][15].
COVID-19 swept the globe, forcing half of the world’s population to close down by April 2020 [16]. Its consequences affected many aspects of human life. The educational systems, for instance, witnessed instability that had never been experienced before. Therefore, educational institutions have adopted new technologies for processing and delivering materials anywhere and anytime [17][18][19]. These technologies have transformed education, leading to inquiries for novel technological methods. Here, one can consider the rise of distance learning as a substitute for traditional learning, where the traditional learning environments (i.e., face-to-face learning) have changed to distance learning.
Mobile technologies have been regarded as the most prominent invention in recent years [20][21][22][23]. These technologies allow learning to occur irrespective of time and place. For example, mobile devices encompassing wireless information and communications technologies provide societies with constant connectedness regardless of time or location [24]. Furthermore, individual users can also benefit from mobile devices regarding information processing and contribution [1][23][25].
Mobile learning is a novel, cutting-edge method that facilitates accessing learning content through mobile devices [26]. If they have a smart mobile device linked to the Internet, mobile users can learn whenever and wherever they want. Mobile learning possesses the potential of converting the existing state of face-to-face learning environments to remote learning. It encompasses a novel form of learning that combines universal communication technology and cutting-edge user interfaces [27][28]. This form of learning allows learners to experience individualized or remotely learning through their mobile devices [17].
Recent years have witnessed the emergence of some state-of-the-art mobile apps which combine mobile technologies with educational systems [28][29][30][31]. Meanwhile, following the outbreak of the COVID-19 pandemic, educational institutions were forced to close to enforce social distancing to limit virus spread. Accordingly, educational institutions were forced to use different teaching approaches [17][26][32]. Therefore, this has become a subject of interest among several researchers of technology adoption, as its success is determined by users’ acceptance [33][34][35]. Furthermore, because M-learning apps are new, they have yet to be thoroughly investigated, particularly in terms of how these apps affect education [35][36].
Recent studies have started incorporating the well-established acceptance theories and examining their interrelationships to develop an acceptance model for mobile learning apps (MLA). Using various theories in one model allows the acceptance of technology from a unique perspective, leading to novel knowledge [20][34][35]. Yet, somehow, a literary gap was found to exist, involving a model that focuses on the intent of users to use mobile learning [17][35].
Studying the factors that influence MLA user acceptance was the focus of this study. It was therefore decided to develop a model and empirically validate it. The proposed model involves factors adopted from the social cognitive theory (SCT), innovation diffusion theory (IDT), and technology acceptance model (TAM), which were developed by Bandura [37], Rogers [38], and Davis [39], respectively. The literature on acceptance theories such as SCT, IDT, and TAM has a long research and development history. Moreover, these theories serve as a theoretical foundation for further research into user acceptance theory. Therefore, the researchers in this study adopted the self-efficacy (SE) factor from SCT. Additionally, perceived compatibility (PCOM) was adopted from IDT. Moreover, the perceived ease of use (PEOU) and perceived usefulness (PU) factors were adopted from TAM. In addition to these factors, the model also adopted the perceived convenience (PCV) factor from a study of Yoon and Kim [40], and the factor of perceived enjoyment (PE) was adopted from [41]. Thus, the effect of PU, PEOU, PCOM, SE, PCV, and PE on behavior intention to use MLA is examined here.

2. Literature Review and Hypotheses Development

SCT started as the social learning theory (SLT) by Bandura [37]. When it comes to SCT, individuals, environments, and behaviors are all assumed to be involved in a dynamic and mutually engaging process [37]. SCT is a learning model highlighting how individuals change their behavior in response to various environmental variables. Bandura [37] identified six factors: expectations, observational learning, reciprocal determinism, reinforcements, behavioral capability, and self-efficacy. The SLT was used to establish the first five factors. When the theory evolved into SCT, the element of self-efficacy was added. Later on, Compeau and Higgins [42] adopted the self-efficacy factor into their technology acceptance study. Accordingly, the researchers in this study adopted the self-efficacy factor from Compeau and Higgins [42].
IDT describes the diffusion of the innovation process, which begins with innovation advancement and progresses to the attitudes of users and their ultimate judgment of acceptance or refusal [38]. The factors examined in IDT concentrate only on technology-related factors [43]. Rogers [38] specified five essential factors related to the possible user’s viewpoint: observability of the innovation, compatibility, relative advantage, trialability, and complexity. IDT’s compatibility factor was incorporated into this study.
TAM originated from the theory of reasoned action (TRA) in order to anticipate and justify users’ adoption and refusal of technology [39]. Using TAM as a foundation, researchers can examine the effects of external factors on user behavior and identify key determinants of technology acceptance. Technology acceptance behaviors are defined by TAM as a combination of PU and PEOU, and these two factors are influenced by external factors. Users’ attitude (ATT) is influenced by factors such as PU and PEOU, according to the TAM’s claim. As a result, the actual system use is affected by ATT and PU, which influence the behavioral intention (BI) [39]. This study developed a theoretical model to investigate the factors impacting MLA user acceptance, as depicted in Figure 1.
Sustainability 14 04325 g001 550
Figure 1. The research model.
The vitality of SCT, SCT, and TAM has been evaluated in the MLA. As a result, many previous studies adapted these acceptance theories to take into account newer aspects dependent on the technology under investigation [28][44][45][46][47][48][49][50][51][52][53][54][55][56]. The following parts provide in-depth explanations of each of the factors that have been adopted in this study.

2.1. Perceived Usefulness and Perceived Ease of Use

Numerous researchers have explored TAM empirically. Most of them proved that PU impacts BI [28][32][47][50][51][52][53][54][56][57][58], while other previous studies related to TAM found no significant association between PU and BI [23]. In addition, the outcomes of TAM studies confirmed that PEOU affects PU [47][50][51][52][54], while some of the prior studies also found no significant relationship between PEOU and PU [50]. Moreover, TAM studies confirmed that PEOU affects BI [23][28][47][53][54][56], while in [50] the researchers found no significant correlation between PEOU and BI. As a consequence, the following hypotheses were established in this study:
Hypothesis 1 (H1).
Perceived usefulness has a positive direct effect on Jordanian students’ intention to use mobile learning apps.
Hypothesis 2 (H2).
Perceived ease of use has a positive direct effect on perceived usefulness.
Hypothesis 3 (H3).
Perceived ease of use has a positive direct effect on Jordanian students’ intention to use mobile learning apps.

2.2. Perceived Convenience

PCV was acquired from Yoon and Kim [40]. PCV has been used in multiple technology acceptance studies as a predictor of PU in a wide range of fields, such as MLA. For example, Taiwanese studies [59][60][61] found that TAM, when improved with other factors, could be a comprehensive model for evaluating MLA’s user acceptance. Using PCV, they found that TAM was improved and that PCV was a reliable indicator of PU. Consequently, the subsequent hypothesis is presented:
Hypothesis 4 (H4).
Perceived convenience has a positive direct effect on perceived usefulness.

2.3. Self-Efficacy

As discussed in the theoretical background, the SE factor originated from SCT [37]. Later on, Compeau and Higgins [42] adopted the SE factor into their technology acceptance study. Some researchers added the SE factor as the predictor of PU and PEOU. Another study was performed to explore university students’ acceptance of MLA in South Korea [62]. The findings specified no obvious correlation between SE and PU. In a study in Bangladesh, TAM was improved to determine the university students’ acceptance of MLA [63]. The findings demonstrate that SE was a major predictor of PU and PEOU. In additional research in Malaysia, TAM was improved to examine the university students’ acceptance of MLA [50]. They confirmed that SE was a significant predictor of PEOU, and no significant correlation was found between SE and PU. In another study in Malaysia that asserted the robustness of TAM, a model was presented to explore the factors influencing the adoption of MLA [51]. The study found that SE was a major predictor of PEOU. Additionally, the adoption of MLA was examined among university students in Ghana [52]. They confirmed that SE was a significant predictor of PEOU. Moreover, the acceptance of MLA was studied among university students in Cambodia [54]. The findings confirm that SE was a significant predictor of PEOU, and no significant correlation was found between SE and PU. The present study proposes the following hypothesis:
Hypothesis 5 (H5).
Self-efficacy has a positive direct effect on perceived usefulness.
Hypothesis 6 (H6).
Self-efficacy has a positive direct effect on perceived ease of use.

2.4. Perceived Enjoyment

PE was adopted from Davis, Bagozzi, and Warshaw [41]. Some researchers include the PE factor as the predictor of BI. Some studies were performed to examine the acceptance of MLA in Taiwan [59][60][64]. The findings confirmed that PE was a major predictor of BI. In a study in China, TAM was enhanced to determine the university students’ adoption of MLA [55]. The findings confirm that PE was a major predictor of BI. In a study in Pakistan, TAM was enhanced to explore the university students’ acceptance of MLA [65]. They found no significant correlation between PE and BI. In other studies in Malaysia, TAM was enhanced to explore the university students’ acceptance of MLA [51][53]. They confirmed that PE was a major predictor of BI. In contrast, research found no significant correlation between PE and BI [66]. Furthermore, the adoption of MLA was examined among school students in Indonesia. The findings confirm that PE was a significant predictor of BI [48][56]. Because of this, the following theory is put forth:
Hypothesis 7 (H7).
Perceived enjoyment has a positive direct effect on Jordanian students’ intention to use mobile learning apps.

2.5. Perceived Compatibility

As discussed in the theoretical background, the PCOM factor emerged from IDT [38]. The researchers added the PCOM factor as the predictor of PEOU, PU, PE, and BI. Some studies were conducted to explore mobile phone users’ acceptance of MLA in Taiwan [60][64]. The findings confirm that PCOM was a major predictor of PU, PEOU, PE, and BI. In another study, an extended TAM was implemented to explore university students’ acceptance of MLA in Jordan. They confirmed that PCOM was a major predictor of BI [49]. Thus, the following hypotheses are suggested:
Hypothesis 8 (H8).
Perceived compatibility has a positive direct effect on perceived usefulness.
Hypothesis 9 (H9).
Perceived compatibility has a positive direct effect on perceived ease of use.
Hypothesis 10 (H10).
Perceived compatibility has a positive direct effect on perceived enjoyment.
Hypothesis 11 (H11).
Perceived compatibility has a positive direct effect on Jordanian students’ intention to use mobile learning apps.

2.6. Mediating Factors between PCOM and BI

According to [67], a full mediator is one whose indirect influence exceeds the direct effect. If the indirect influence is less than the direct effect, however, it is not regarded as a mediator. Thus, identifying the mediators (PEOU and PE) between PCOM and BI leads us to consider if PCOM can be used as a method of adjusting PEOU and PE. Improving PEOU and PE among the MLA users can suggest appropriate PCOM to encourage increased BI to use MLA. Accordingly, this study proposed a related hypothesis as below:
Hypothesis 12 (H12).
Perceived ease of use mediates the relationship between perceived compatibility and on Jordanian students’ intention to use mobile learning apps.
Hypothesis 13 (H13).
Perceived enjoyment mediates the relationship between perceived compatibility and on Jordanian students’ intention to use mobile learning apps.

References

  1. Al-Adwan, A.S.; Albelbisi, N.A.; Hujran, O.; Al-Rahmi, W.M.; Alkhalifah, A. Developing a holistic success model for sustainable e-learning: A structural equation modeling approach. Sustainability 2021, 13, 9453.
  2. Alghazi, S.S.; Kamsin, A.; Almaiah, M.A.; Wong, S.Y.; Shuib, L. For sustainable application of mobile learning: An extended utaut model to examine the effect of technical factors on the usage of mobile devices as a learning tool. Sustainability 2021, 13, 1856.
  3. Hays, J.; Reinders, H. Sustainable learning and education: A curriculum for the future. Int. Rev. Educ. 2020, 66, 29–52.
  4. McCullough, B.P.; Orr, M.; Watanabe, N.M. Measuring externalities: The imperative next step to sustainability assessment in sport. J. Sport Manag. 2020, 34, 393–402.
  5. Altamimi, A.; Al-Bashayreh, M.; AL-Oudat, M.; Almajali, D. Blockchain technology adoption for sustainable learning. Int. J. Data Netw. Sci. 2022, 6.
  6. Ben-Eliyahu, A. Sustainable learning in education. Sustainability 2021, 13, 4250.
  7. Tchamyou, V.S. Education, lifelong learning, inequality and financial access: Evidence from African countries. Contemp. Soc. Sci. 2020, 15, 7–25.
  8. Sebastián-López, M.; De, R.; González, M. Mobile Learning for Sustainable Development and Environmental Teacher Education. Sustainability 2020, 12, 9757.
  9. Chen, F.H. Sustainable education through e-learning: The case study of ilearn2.0. Sustainability 2021, 13, 10186.
  10. Ahmad, N.; Hoda, N.; Alahmari, F. Developing a Cloud-Based Mobile Learning Adoption Model to Promote Sustainable Education. Sustainability 2020, 12, 3126.
  11. Alkhwaldi, A.F.; Abdulmuhsin, A.A. Crisis-centric distance learning model in Jordanian higher education sector: Factors influencing the continuous use of distance learning platforms during COVID-19 pandemic. J. Int. Educ. Bus. 2021; ahead of print.
  12. Coskun-Setirek, A.; Tanrikulu, Z. M-Universities: Critical Sustainability Factors. SAGE Open 2021, 11, 2158244021999388.
  13. Jeong, K.-O. Facilitating Sustainable Self-Directed Learning Experience with the Use of Mobile-Assisted Language Learning. Sustainability 2022, 14, 2894.
  14. Lee, C.-J.; Hsu, Y. Sustainable Education Using Augmented Reality in Vocational Certification Courses. Sustainability 2021, 13, 6434.
  15. Al-Adwan, A.S.; Yaseen, H.; Alsoud, A.; Abousweilem, F.; Al-Rahmi, W.M. Novel extension of the UTAUT model to understand continued usage intention of learning management systems: The role of learning tradition. Educ. Inf. Technol. 2021.
  16. Alsaad, A.; Al-Okaily, M. Acceptance of protection technology in a time of fear: The case of COVID-19 exposure detection apps. Inf. Technol. People, 2021; ahead of print.
  17. Al Majali, D.; Masadeh, R.; Almajali, D.A.A.; Masa’deh, R.R.; Al Majali, D.; Masadeh, R. Antecedents of students’ perceptions of online learning through COVID-19 pandemic in Jordan. Int. J. Data Netw. Sci. 2021, 5, 587–592.
  18. Taamneh, A.; Alsaad, A.; Elrehail, H.; Al-Okaily, M.; Lutfi, A.; Sergio, R.P. University lecturers acceptance of moodle platform in the context of the COVID-19 pandemic. Glob. Knowl. Mem. Commun. 2022; ahead of print.
  19. Taamneh, A.M.; Taamneh, M.; Alsaad, A.; Al-Okaily, M. Talent management and academic context: A comparative study of public and private universities. EuroMed J. Bus. 2021; ahead of print.
  20. Al-Husamiyah, A.; Al-Bashayreh, M. A comprehensive acceptance model for smart home services. Int. J. Data Netw. Sci. 2022, 6, 45–58.
  21. Al-Bashayreh, M.G. Domain Model Validation of Context-Aware Mobile Patient Monitoring Systems. Procedia Comput. Sci. J. 2015, 62, 539–546.
  22. Al-Okaily, M.; Al-Okaily, A. An empirical assessment of enterprise information systems success in a developing country: The Jordanian experience. TQM J. 2022; ahead of print.
  23. Al-Emran, M. Mobile learning during the era of COVID-19. Rev. Virtual Univ. Catól. Norte 2020, 1–2.
  24. Zaidi, S.F.H.; Osmanaj, V.; Ali, O.; Zaidi, S.A.H. Adoption of mobile technology for mobile learning by university students during COVID-19. Int. J. Inf. Learn. Technol. 2021, 38, 329–343.
  25. Al-Adwan, A.S.; Albelbisi, N.A.; Aladwan, S.H.; Al Horani, O.M.; Al-Madadha, A.; Al Khasawneh, M.H. Investigating the Impact of Social Media Use on Student’s Perception of Academic Performance in Higher Education: Evidence from Jordan. J. Inf. Technol. Educ. Res. 2020, 19, 953–975.
  26. Naciri, A.; Baba, M.A.; Achbani, A.; Kharbach, A. Mobile Learning in Higher Education: Unavoidable Alternative during COVID-19. Aquademia 2020, 4, ep20016.
  27. Sharma, S.K.; Kitchens, F.L. Web Services Architecture for M-Learning. Electron. J. e-Learn. 2004, 2, 203–216.
  28. Al-Hamad, M.Q.; Mbaidin, H.O.; AlHamad, A.Q.M.; Alshurideh, M.T.; Kurdi, B.H.; Al-Hamad, N.Q. Investigating students’ behavioral intention to use mobile learning in higher education in UAE during Coronavirus-19 pandemic. Int. J. Data Netw. Sci. 2021, 5, 321–330.
  29. Al-Qudah, A.A.; Al-Okaily, M.; Alqudah, H. The relationship between social entrepreneurship and sustainable development from economic growth perspective: 15 ‘RCEP’ countries. J. Sustain. Financ. Invest. 2021, 12, 44–61.
  30. Al-Okaily, A.; Al-Okaily, M.; Teoh, A.P. Evaluating ERP systems success: Evidence from Jordanian firms in the age of the digital business. VINE J. Inf. Knowl. Manag. Syst. 2021; ahead of print.
  31. Gao, S.; Krogstie, J.; Siau, K. Adoption of mobile information services: An empirical study. Mob. Inf. Syst. 2014, 10, 147–171.
  32. Al-Okaily, M.; Al Natour, A.R.; Shishan, F.; Al-Dmour, A.; Alghazzawi, R.; Alsharairi, M. Sustainable FinTech Innovation Orientation: A Moderated Model. Sustainability 2021, 13, 13591.
  33. Bao, H.; Chong, A.Y.L.; Ooi, K.B.; Lin, B. Are Chinese consumers ready to adopt mobile smart home? An empirical analysis. Int. J. Mob. Commun. 2014, 12, 496–511.
  34. Nikou, S. Factors driving the adoption of smart home technology: An empirical assessment. Telemat. Inform. 2019, 45, 101–283.
  35. Nikou, S.A. Web-based videoconferencing for teaching online: Continuance intention to use in the post-COVID-19 period. Interact. Des. Archit. 2021, 47, 123–143.
  36. Althunibat, A. Determining the factors influencing students’ intention to use m-learning in Jordan higher education. Comput. Hum. Behav. 2015, 52, 65–71.
  37. Bandura, A. Social Foundations of Thought and Action: A Social Cognitive Theory/Albert Bandura; Prentice-Hall: Hoboken, NJ, USA, 1986; Volume 16.
  38. Rogers, E.M. Diffusion of Innovations, 5th ed.; Free Press: New York, NY, USA, 2003; ISBN 9780743222099.
  39. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989, 13, 319–340.
  40. Yoon, C.; Kim, S. Convenience and TAM in a ubiquitous computing environment: The case of wireless LAN. Electron. Commer. Res. Appl. 2007, 6, 102–112.
  41. Davis, F.D.; Bagozzi, R.P.; Warshaw, P.R. Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. J. Appl. Soc. Psychol. 1992, 22, 1111–1132.
  42. Compeau, D.; Higgins, C. Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Q. 1995, 19, 189–211.
  43. Hubert, M.; Blut, M.; Brock, C.; Zhang, R.W.; Koch, V.; Riedl, R.; Wenjiao Zhang, R.; Koch, V.; Riedl, R.; Zhang, R.W.; et al. The influence of acceptance and adoption drivers on smart home usage. Eur. J. Mark. 2019, 53, 1073–1098.
  44. Buabeng-Andoh, C. New technology in health education: Nursing students’ application of mobile technology in the classroom in Ghana. Interact. Technol. Smart Educ. 2018, 15, 46–58.
  45. Böhm, S.; Constantine, G.P. Impact of contextuality on mobile learning acceptance. Interact. Technol. Smart Educ. 2016, 13, 107–122.
  46. Al-Emran, M.; Arpaci, I.; Salloum, S.A. An empirical examination of continuous intention to use m-learning: An integrated model. Educ. Inf. Technol. 2020, 25, 2899–2918.
  47. Alshurideh, M.; Al Kurdi, B.; Salloum, S.A. Examining the Main Mobile Learning System Drivers’ Effects: A Mix Empirical Examination of Both the Expectation-Confirmation Model (ECM) and the Technology Acceptance Model (TAM); Hassanien, A.E., Shaalan, K., Tolba, M.F., Eds.; Springer: Cham, Switzerland, 2020; Volume 1058, pp. 406–417.
  48. Sukmaningsih, D.W. Assessing High School Students Readiness for Mobile Learning. In Proceedings of the 2019 International Conference on Information Management and Technology, ICIMTech 2019, Jakarta/Bali, Indonesia, 19–20 August 2019.
  49. Almaiah, M.A.; Al Mulhem, A. Analysis of the essential factors affecting of intention to use of mobile learning applications: A comparison between universities adopters and non-adopters. Educ. Inf. Technol. 2019, 24, 1433–1468.
  50. Kumar, J.A.; Bervell, B.; Annamalai, N.; Osman, S. Behavioral intention to use mobile learning: Evaluating the role of self-efficacy, subjective norm, and whatsapp use habit. IEEE Access 2020, 8, 208058–208074.
  51. Nabipour Sanjebad, N.; Shrestha, A.; Shahid, P. The Impact of Personality Traits Towards the Intention to Adopt Mobile Learning. In IFIP Advances in Information and Communication Technology; Springer: Cham, Switzerland, 2020; Volume 618.
  52. Buabeng-Andoh, C. Exploring University students’ intention to use mobile learning: A research model approach. Educ. Inf. Technol. 2021, 26, 241–256.
  53. Al-rahmi, A.M.M.; Al-rahmi, W.M.M.; Alturki, U.; Aldraiweesh, A.; Almutairy, S.; Al-adwan, A.S.S.; Al-Rahmi, A.M.M.; Al-Rahmi, W.M.M.; Alturki, U.; Aldraiweesh, A.; et al. Exploring the factors affecting mobile learning for sustainability in higher education. Sustainability 2021, 13, 7893.
  54. Sophea, D.; Sungsuwan, T.; Viriyasuebphong, P. Factors Influencing Students’ Behavioral Intention on Using Mobile Learning (M-Learning) in Tourism and Hospitality Major in Phnom Penh, Cambodia. Ph.D. Thesis, Burapha University, Saen Suk, Thailand, 2022.
  55. Zhonggen, Y.; Xiaozhi, Y. An extended technology acceptance model of a mobile learning technology. Comput. Appl. Eng. Educ. 2019, 27, 721–732.
  56. Pratama, A.R. Fun first, useful later: Mobile learning acceptance among secondary school students in Indonesia. Educ. Inf. Technol. 2021, 26, 1737–1753.
  57. Al-Adwan, A.S.; Khdour, N. Exploring student readiness to moocs in Jordan: A structural equation modelling approach. J. Inf. Technol. Educ. Res. 2020, 19, 223–242.
  58. Al-Adwan, A.S. Investigating the drivers and barriers to MOOCs adoption: The perspective of TAM. Educ. Inf. Technol. 2020, 25, 5771–5795.
  59. Chang, C.-C.; Liang, C.; Yan, C.-F.; Tseng, J.-S. The Impact of College Students’ Intrinsic and Extrinsic Motivation on Continuance Intention to Use English Mobile Learning Systems. Asia-Pac. Educ. Res. 2013, 22, 181–192.
  60. Cheng, Y.-M.M. Towards an understanding of the factors affecting m-learning acceptance: Roles of technological characteristics and compatibility. Asia Pac. Manag. Rev. 2015, 20, 109–119.
  61. Chang, C.C.; Yan, C.F.; Tseng, J.S. Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australas. J. Educ. Technol. 2012, 28, 809–826.
  62. Park, S.Y.; Nam, M.-W.; Cha, S.-B. University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. Br. J. Educ. Technol. 2012, 43, 592–605.
  63. Fatima, J.K.; Ghandforoush, P.; Khan, M.; Masico, R. Di Role of innovativeness and self-efficacy in tourism m-learning. Tour. Rev. 2017, 72, 344–355.
  64. Cheng, Y.-M. Exploring the intention to use mobile learning: The moderating role of personal innovativeness. J. Syst. Inf. Technol. 2014, 16, 40–61.
  65. Iqbal, S.; Qureshi, I.A. M-Learning Adoption: A Perspective from a Developing Country. Int. Rev. Res. Open Distance Learn. 2012, 13, 147–164.
  66. Suki, N.M.; Suki, N.M. Users’ Behavior Towards Ubiquitous M-Learning. Turkish Online J. Distance Educ. 2011, 12, 118–129.
  67. Hair, J.F.; Babin, B.J.; Anderson, R.E.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis, 8th ed.; Cengage: Andover, UK, 2019; ISBN 9781473756540.
More
Information
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , , , ,
View Times: 765
Entry Collection: COVID-19
Revisions: 2 times (View History)
Update Date: 26 Apr 2022
1000/1000
Video Production Service