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Computer Science, Information Systems
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]. Our 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.
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 sections 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.
This entry is adapted from the peer-reviewed paper 10.3390/su14074325
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