International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923 – Vol  17 No  16 (2023) 30 International Journal of Interactive Mobile Technologies (iJIM) iJIM | Vol. 17 No. 16 (2023) iJIM | eISSN: 1865-7923 | Vol. 17 No. 16 (2023) | JIM International Journal of Interactive Mobile Technologies Abu-AlSondos, I.A., Salameh, A.A., Alkhwaldi, A.F., Mushtaha, A.S., Shehadeh, M., Al-Junaidi, A. (2023). Evaluating Mobile E-Learning Systems Acceptance: An Integrated Model. International Journal of Interactive Mobile Technologies (iJIM), 17(16), pp. 30–47. https://doi.org/10.3991/ijim.v17i16.42679 Article submitted 2023-05-29. Resubmitted 2023-07-03. Final acceptance 2023-07-03. Final version published as submitted by the authors. © 2023 by the authors of this article. Published under CC-BY. Online-Journals.org PAPER Evaluating Mobile E-Learning Systems Acceptance: An Integrated Model ABSTRACT Previous studies have confirmed that managers need to develop influencing strategies to encourage employees to accept mobile information systems. Despite the recognition in past research that external variables can influence employee’s perceptions, the explanation of how their approval of the framework is affected by these external variables’ pathways or procedures remains limited. Participants were selected from Malaysian institutions that have previously rolled out mobile e-learning technologies into their operations. Empirical findings disclose that source credibility is positively associated with playfulness, perceived ease of use, and perceived usefulness. Similarly, organizational support and task equivocality are signifi- cantly related to perceived ease of use and perceived usefulness. Additionally, perceived ease of use positively affects playfulness, perceived usefulness, and employee attitude. Finally, an employee’s attitude is positively and significantly related to behavioral intentions (BI). The findings of this study provide insight for firms considering implementing a mobile informa- tion system at all levels of their institutions. Furthermore, it offers employees valuable infor- mation about the system, its value, benefits, and advantages. KEYWORDS information systems, distance learning acceptance, mobile e-learning systems, Technology Acceptance Model (TAM), structural equation modeling 1 INTRODUCTION Information technology (IT) is integral to the execution of many different mana- gerial, analytical, and systemic procedures within organizations [1, 2]. It provides a valuable new opportunity for workers to improve their expertise and abilities through advanced technology [3]. It is not uncommon for firms to invest millions of dollars in the introduction of new systems to accomplish long-term benefits [38]. It is essential to note that system adoption is not exclusively determined by technology or systems, but also by the willingness of employees to adopt such systems [4, 8, 76]. Ibrahim A. Abu-AlSondos1, Anas A. Salameh2(), Abeer F. Alkhwaldi3, Alaa S. Mushtaha4, Maha Shehadeh5, Ala’a Al-Junaidi6 1American University in the Emirates (AUE), Dubai, United Arab Emirates (UAE) 2College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia 3Department of Management Information Systems (MIS), College of Business, Mutah University, Karak, Jordan 4College of Business Administration, American University in the Emirates (AUE), Dubai, United Arab Emirates (UAE) 5Department of Finance and Banking Sciences, Faculty of Business, Applied Science Private University, Amman, Jordan 6College of Business, Universiti Utara Malaysia, Malaysia a.salameh@psau.edu.sa https://doi.org/10.3991/ijim.v17i16.42679 https://online-journals.org/index.php/i-jim https://online-journals.org/index.php/i-jim https://doi.org/10.3991/ijim.v17i16.42679 https://online-journals.org/ https://online-journals.org/ mailto:a.salameh@psau.edu.sa https://doi.org/10.3991/ijim.v17i16.42679 iJIM | Vol. 17 No. 16 (2023) International Journal of Interactive Mobile Technologies (iJIM) 31 Evaluating Mobile E-Learning Systems Acceptance: An Integrated Model This study makes several research contributions. The focus of this research is to use Technology Acceptance Model (TAM) as the baseline approach to determine how employees will use mobile e-learning systems within their organizations. Firstly, this study uses TAM to predict how employees use mobile e-learning systems within organizations. The hypothesized relationships between employees’ perceptions and attitudes toward using mobile e-learning systems were examined and validated by SEM [7]. An essential addition to this study was the use of attitude as a dependent variable to predict the intention to use TAM. Moreover, this research expanded TAM by considering other variables related to the user, including source credibility rating (SCR), task-related factors, and organizational variables. The following are the con- tents of the study: The second part of this paper is a literature review that focuses on the theoretical underpinnings and hypothesis development. We detail the data collection processes and sample design used to formulate the study questions in Section 3. Section 4 of this paper examines the results of the empirical research, while Section 5 discusses the potential management implications, future research prospects for practitioners, and the paper’s limitations. 2 RESEARCH MODEL AND HYPOTHESES FORMULATION 2.1 Technology acceptance model The Technology Acceptance Model (TAM) is grounded in the theory of reasoned action [42]. According to the theory of reasoned action (TRA), behavior is a result of a person’s beliefs about the outcome of behavior, and outcomes are assessed according to their value. The TAM philosophy is comparable to TRA, where indi- vidual beliefs influence attitudes towards using a system, resulting in the intent to exploit it. It is attitudes and actual usage intentions that derive from perceived usefulness (PU) and perceived ease of use (PEU). PU is the extent to which an employee anticipates technology to improve their output, while PEU is the extent to which they feel utilizing the technology would be convenient and straightfor- ward [49]. This is one of the most popular models and is frequently used in study- ing technology acceptance, mainly due to its efficiency and flexibility of usage [14, 15, 16, 26]. The purpose of this study is to scrutinize employees’ adoption behavior of mobile e-learning system changes using the TAM model as the theo- retical and the proposed research model is represented in Figure 1. 2.2 Task equivocality, perceived ease of use, and perceived usefulness Numerous researchers have studied the characteristics of tasks and how they affect the use of information systems [50, 51, 70, 72, 73]. However, the constructs of the TAM are not fully applicable to the different task environments in which users work. A lack of task focus has resulted in mixed results when evaluating IT and its acceptance, use, and performance [52, 53, 54, 63]. It is necessary to explicitly include task characteristics in TAM’s usefulness concept to improve your insight into the use of IT [9, 13, 14, 15, 16, 29, 67, 71]. A study conducted by [17] found a positive connection between task characteris- tics and PEU and PU. In task characteristics, equivocality refers to how much uncer- tainty or confusion is present during the performance of the task [34]. According to [34], it appears that task equivalency (TE) has a positive impact on individuals’ https://online-journals.org/index.php/i-jim 32 International Journal of Interactive Mobile Technologies (iJIM) iJIM | Vol. 17 No. 16 (2023) Abu-AlSondos et al. PU of an e-learning system. Research has also suggested that TE and interconnected tasks influence a person’s willingness to use online learning programs. Nevertheless, only TE significantly affected PU in these studies [53, 54, 63]. Moreover, this study contends that when employees are required to take on demanding, novel, or dif- ficult duties, they perceive that a digital training platform is stable and somewhat user-friendly [44, 48]. Consequently, this study developed e-learning technology approaches related to task equivalency. H1a: Task equivalency has a positive effect on perceived ease of use H1b: Task equivalency has a positive effect on perceived usefulness 2.3 Source credibility, playfulness, perceived ease of use, and perceived usefulness According to [61], SCR is a person’s skill to convey an independent opinion about the subject matter of the advertisement. It has been shown that SCR influences deci- sion-making by shifting or enhancing the way that messages are processed [75]. According to [80, 84], individuals who focus on external cues are more likely to exhibit strong emotional responses than those who elaborate on them. As employ- ees rely on the credibility and trustworthiness of sources, they respond to external cues and evaluate messages accordingly Bhattacherjee and Sanford (2006) suggest that supplementary cues, such as the SC, may affect human behavior. When employ- ees feel confident in the credibility of the source, they are more inclined to seek out information, arouse imaginations, and engage in independent awareness [6]. It is more enjoyable to acceptance of a system when responses from authentic sources are combined. Consequently, the following hypothesis is suggested: H2a: Source credibility has a positive effect on playfulness Individuals who receive information from credible sources have a positive per- ception of the system [59]. A source’s credibility may have a positive impact on employee cognitive evaluations, such as their perceptions of PU and PEU. In the presence of associated remarks from experts, workers are more willing to evaluate the information seriously and come up with better ideas or proposals [75]. If busi- nesses use trustworthy experts to highlight the simplicity and usefulness of new technology, workers may nevertheless replace their intellectual processes with the researcher’s advice [27, 30]. Researchers found that consultants perceive the useful- ness of an information system as more valuable when the SCR is high [77]. A firm that uses reliable expert recommendations to establish SCR motivates understand- ing among workers of the value of technology. Therefore, owing to the argument above, the following hypotheses are developed: H2b: Source credibility has a positive effect on perceived ease of use H2c: Source credibility has a positive effect on perceived usefulness 2.4 Organizational support, perceived ease of use, and perceived usefulness As defined in the study, organizational support (OS) denotes the degree to which executives and middle managers allocate resources to support coworkers in achiev- ing managerial objectives [9, 13, 16]. In previous literature, positive relationships https://online-journals.org/index.php/i-jim iJIM | Vol. 17 No. 16 (2023) International Journal of Interactive Mobile Technologies (iJIM) 33 Evaluating Mobile E-Learning Systems Acceptance: An Integrated Model have been documented between OS and the use of computer systems [49]. Several studies have demonstrated that OS affects beliefs and behaviors about computer uti- lization [32, 58, 80]. Besides, the lack of an OS adversely affected the effectiveness of computer use [49]. Additionally, [32] articulates that the level of OS is related to both PU and PEU. Furthermore, top management support lends itself to promoting the usage of digital technology programs through the provision of necessary resources and upgrades to computer systems. The support of top management enhances atti- tudes that are favorable toward computers and changes perceptions about their PU and PEU [81]. In this regard, organizational and managerial support strengthens employees’ trust in the organization, leading them to take risks in implementing digital technology [11, 18–21]. Based on the above discussion, this study formulates the following hypothesis: H3a: Organizational support has a positive effect on perceived ease of use H3b: Organizwational support a positive effect on perceived usefulness 2.5 Perceived ease of use, the playfulness of the e-learning system and perceived usefulness It is important to recognize that individuals are not always logical or rational, and sentiment plays a significant role in their acceptance of new technologies [52]. As part of the technology acceptance research, three different approaches have been proposed: perceived enjoyment, flow, and perceived level of fit (PLF). In e-learning systems, PLF has been stated as “the degree of cognitive spontaneity in user interac- tions with the online system” [6, 10]. Furthermore, [37] contends that the PEU of the information system is directly related to PLF. As a consequence, employees feel more interested in and in control of information system implementation when they spend time working on it and experience its benefits. H4: Perceived ease of use has a positive effect on the playfulness of the e-learning system In addition, PEU and PU have been studied [49]. According to [83], PEU exerts a positive influence on the PU of online hotel systems. Several prior investigations have shown the relationship between PEU, PU, and attitude has been empirically validated across a variety of management settings [69]. As a result, they concluded that PU and PEU were the two most significant components for system usage. The PEU refers to the individual’s attitudes toward how often brain effort is needed to perform a particular task [35, 36, 37]. There is a direct correlation between PEU and PU, which influences the user’s original purpose for using online systems [62]. H5: Perceived ease of use has a positive impact on perceived usefulness 2.6 Perceived usefulness, perceived ease of use, playfulness, and attitude of e-learning system According to [68], an individual’s attitude toward an e-learning system can be determined by their perception of positive or negative emotions related to it. A per- son’s attitude toward using the electronic learning platform, along with PU and PEU, will influence both how technology is actually used and how well it works [52]. It is believed that both PU and PEU significantly affect a user’s attitude toward using https://online-journals.org/index.php/i-jim 34 International Journal of Interactive Mobile Technologies (iJIM) iJIM | Vol. 17 No. 16 (2023) Abu-AlSondos et al. an online learning system. In the present study, PU and PEU are well related to AT across a range of information systems, which supports earlier findings [81, 85]. The empirical results from [54] also confirm that PEU directly affects attitude. The rela- tionship between PEU, PU, and attitude has been empirically validated across a vari- ety of organizational contexts, as established by [69]. In their study, they concluded that PU and PEU are the two key drivers of people’s attitudes. H6a: Perceived usefulness has a positive effect on the attitude of e-learning system H6b: perceived ease of use has a positive effect on the attitude of e-learning system The concept of PLF refers to a user’s intrinsic motivation for engaging in an activ- ity solely for the sake of enjoyment of that activity [6]. It is more likely that employee participation in the information system will be sustained by those who experience more PLF [36]. [41] argue that individuals will have a greater likelihood of having an optimistic attitude and outcomes if the environment is computer-based and support- ive. In a study by [49], it was found that PLF is one of the biggest factors driving atti- tudes toward e-learning among individuals. A study conducted by [82] revealed that PLF has a positive effect on attitudes regarding the use of portal websites. Therefore, based on the above argument, this study formulates the following hypothesis: H6c: playfulness has a positive effect on the attitude of the e-learning system 2.7 Playfulness, perceived usefulness, and behavioral intentions The studies of E. Park and [64] inspired Moon and Kim to include the concept of Perceived PLF in TAM. It is the belief that an individual has about the system that deter- mines their perceived PLF. In past studies of information technology (IT), PLF of prod- ucts or services significantly impacted the satisfaction of users [53, 54]. PLF relates to a person’s intrinsic motivation to act for the sake of pleasure [8]. A playful workplace is more inclined to endure employee involvement using an information management system [63]. [41] argues that individuals’ attitudes and outcomes will improve if they are exposed to a technology-based environment that encourages collaboration. According to [36, 37], PLF plays a key role in driving BI to adopt e-learning systems. [82] also found that PLF affects attitudes and intentions regarding portal use. The study by [84] found that perceived PLF was the most significant antecedent to BI. In some studies, PU was found to be a major component of the probability of users adopting the online system [64]. Scholars are of the opinion that PU plays a significant role in promoting psycho-cognitive shifts in consumers’ decision-making processes and influencing their decisions [66, 22]. As a result, the following hypothesis is proposed: H7: Playfulness has a positive effect on behavioral intentions H8: Playfulness has a positive effect on behavioral intentions 2.8 Employee’s attitude and behavioral intentions A scholar defines attitude toward technology as the overall emotional reactions induced by using innovative technology. An individual’s BI to use new technologies is defined as their willingness to continue to use them [12]. In contrast, recent IT adoption studies have argued that understanding BI requires consideration of an individual’s attitude [42]. It is said that “when all things are in balance, people make https://online-journals.org/index.php/i-jim iJIM | Vol. 17 No. 16 (2023) International Journal of Interactive Mobile Technologies (iJIM) 35 Evaluating Mobile E-Learning Systems Acceptance: An Integrated Model intentions to perform behavior toward which they are positive” [42, 52] concluded that individuals who interact with technology tend to encounter both pleasant and unpleasant situations at the same time. The assessment of behavior by [56] suggests that individual beliefs are one of the variables that define attitudes toward behavior. There has been extensive research conducted in the past on attitudes toward infor- mation systems. Similarly, [74] proposed that ATTs toward IT adoption would affect IT adoption intentions. As a result, this study proposes the following proposal in light of the aforementioned reasoning: H9: Employee’s attitude has a positive effect on behavioral intentions Fig. 1. Research model 3 RESEARCH METHOD 3.1 Data and sample collection This investigation used an online survey and a mail survey to gather relevant data for the empirical examination of the descriptive studies. It is not possible to collect a random sample of all end-users of mobile e-learning systems in Malaysian organizations because a trustworthy sampling frame is missing. Thus, this study used non-probability sampling (e.g., convenience sampling) for data collection rather than random sampling. This study collected sample data from four industries in Malaysia where e-learning is widely used [78], such as manufacturing, marketing and service, information technology, and telecommunications, to improve the like- lihood that the results can be applied broadly. Our study examined 16 companies with mobile e-learning systems (four per industry). Several companies in Malaysia provide e-learning training systems. Two hundred online surveys and four hundred paper surveys were sent out, with an estimated 380 completed and returned. 23 questionnaires were uncompleted https://online-journals.org/index.php/i-jim 36 International Journal of Interactive Mobile Technologies (iJIM) iJIM | Vol. 17 No. 16 (2023) Abu-AlSondos et al. and not used, resulting in 357 valid questionnaires. This study analyzed 357 com- pleted questionnaires and found that 59.5% of respondents answered. The research- ers interviewed 357 employees, 35.3% of whom were between 23 and 27 years old and 55.7% between 28 and 30. It was estimated that 73.4% of respondents were male and 26.6% were female. A total of 47.3% of respondents had a master’s from a college or university; 9% had completed a PhD degree. 43.7% of respondents worked in the IT sector. 3.2 Measures This study adapted an existing questionnaire for its survey from other studies and authors to better suit the needs of the current investigation. A study by [30] pro- vided the basis for SCR. A sample item is, “The person providing the information sys- tem training was trustworthy.” Items of OS have been taken from the study of [39]. A sample item is, “My organization provides opportunities to obtain information through an e-learning platform.” According to [52, 55, 63], this study assessed employees’ TE by utilizing two items. For instance, “To accomplish my work effec- tively, I often have to adapt new techniques or processes. The following items are taken from [57, 58, 59] to study PLF, PEU, and PU. Some examples of responses include, “I think e-learning enables me to expand my creativity by collecting knowl- edge,” “I believe e-learning materials are educational,” and “I find the e-learning sys- tem to be straightforward to use.” [63] Study found that BI consists of three factors, and attitude consists of three factors. Both “I think that working with computers is extremely tough” and “I will highly suggest people to utilize it” fall into this category. 4 RESEARCH RESULTS 4.1 Verification of the measuring model This research used correlation analysis to look at the connections between the variables. A very substantial correlation between the two variables was found via statistical testing. When evaluating concept validity, this research employed the AVE square root. Evidence for discriminant validity is provided by the data since the square root of AVE is larger than its correlation with other variables [5]. It is also possible to evaluate discriminant validity by comparing AVE to its MSV value across all components. It is considered to have discriminatory validity if AVE is greater than MSV. According to [43], Discriminant validity model selection suggests that the AVE’s square root is more predictive than the AVE itself. [43] further demonstrates that the composite dependability (CR) values for all variables are greater than 0.70, ranging from 0.842% to 0.899 [45, 46, 47]. We then used AVE and item loadings to perform a convergent validity test, which looked at the likely relationship between these items [47]. All AVE values are greater than 0.5, showing that the variables are both accept- able and much more variable than the minimum required by the criterion. 4.2 Reliability analysis Cronbach’s alpha was utilized to examine internal consistency across all com- ponents in this research. According to the findings, the Cronbach alpha for all con- structs was greater than 0.70, which is commendable according to [46, 47], proving https://online-journals.org/index.php/i-jim iJIM | Vol. 17 No. 16 (2023) International Journal of Interactive Mobile Technologies (iJIM) 37 Evaluating Mobile E-Learning Systems Acceptance: An Integrated Model the validity of the data. A CR estimate was run to check the consistency across all of the variables. The research shows that the CR values are higher than the threshold of 0.70 [47]. 4.3 Common method variance The common method variance (CMV) has been measured using a wide range of statistical and methodological techniques. The items were first created with simplic- ity, clarity, and precision in mind, and then a pilot study was conducted to determine which instruments would work best [79]. Second, if a single component accounts for at least half of the total variation, as shown by Harman’s model, then CMV impacts [79]. This research confirmed that the data did not include CMV since the most significant component explained just 26.89% of the variance (less than the 50% cutoff). To exam- ine the CMV, [28, 36] thirdly looked into the association between latent variables. There is no pairwise correlation above 0.90 between any of the variables. As a result, our statistical evaluations rule out the presence of common method variance. 4.4 Multicollinearity To check for multicollinearity concerns and determine the values of the thresh- old and variance inflation factor (VIF), a regression test is performed. The VIF value must not be more than 0.3 [40]. Based on the findings, this model does not exhibit multicollinearity problems since the scores of VIF and threshold are within each variable’s recommended ranges [23, 24, 25]. 4.5 The model’s predictive ability (Q2) The Stone and Geisser test in SmartPLS was used to evaluate the structural valid- ity of our model. For a particular conceptual model, its predictive capacity is assessed by whether or not its Q2 value is larger than zero (>0) [47]. Because of this, we know the route model is correct because Q2 for every path’s dependent variable is larger than zero (see Table 1). Table 1. General model blindsight statistics Construct SSO SSE (Q2 = 1-SSE/SSO) Behavioral Intention  800 635.121 0.206 Employee’s Attitude  800 689.25 0.138 Organizational Support  800 611.58 0.235 Perceived Ease of Use 1000 947.225 0.052 Perceived Usefulness  800 694.772 0.132 Playfulness 1000 850.359 0.150 Source Credibility 1000 648.514 0.189 Task Equivocality 1000 658.455 0.116 https://online-journals.org/index.php/i-jim 38 International Journal of Interactive Mobile Technologies (iJIM) iJIM | Vol. 17 No. 16 (2023) Abu-AlSondos et al. 4.6 Structural model and hypothesis outcomes The purpose of this research was to examine the validity and reliability of our reliable measures as well as the hypothesized connections between them and the provided model. Figure 2’s R2 value of 0.603 confirms the existence of a relevant explanation as it is higher than the minimal threshold of 0.35 [33]. The SEM tech- nique and a covariance-based regression analysis were also used to verify the hypothesized connection between the variables in the model. The findings show that the f-value for linearity between all linkages is very high. To further prove the suitability of the suggested structural model to our data (i.e., Chi-Square = 564.637, NFI = 0.805, and SRMR = 0.061), this research further conducted a battery of fitness tests [60]. Analysis of the findings revealed that TE had a considerable favorable effect on PEU. (H1a–βTE → PEU = 0.384, p < 0.05) and PU (H1b–βTE → PU = 0.368, p < 0.05). Furthermore, SCR has a positive and significant association with PLF (H2a–βSCR → PLF = 0.225, p < 0.05), PEU (H2b–βSCR → PEU = 0.217, p < 0.05), and PU (H2c–βSCR → PU = 0.250, p < 0.05). So, it agrees with both the first and second hypothesis. Furthermore, the direct effect of the third hypothesis showed that OS had no discernible association with PEU. (H3a–βOS → PEU = 0.299, p < 0.05) and insignificantly related to PU (H3b–βOS → PU = 0.009, p > 0.05). In addition, PEU has a positive and substantial impact on PLF (H4–βCPEU → PLF = 0.574, p < 0.05) and PU (H5–βCPEU → PU = 0.261, p < 0.05). Furthermore, findings indicated that PU (H6a–βPU → ATT = 0.114, p < 0.05), PEU (H6b–βPU → PEU = 0.313, p < 0.05), and PLF (H6c–βPLF → ATT = 0.279, p < 0.05) have a positive influence on ATT, therefore, our 6th hypothesis supported the study. In hypothesizing H7 & H8, this study finds that the PLF (H7–βPLF → BI = 0.271, p < 0.05) and PU (H8–βPU → BI = 0.426, p < 0.05) have a significant effect on BI. In last, employees’ attitudes were significantly and pos- itively related to BI (H9–βATT → BI = 0.186, p < 0.05). As a result, our study’s hypotheses H9 were supported. Table 2 represents the testing results of all Hypotheses. Fig. 2. Results of hypotheses https://online-journals.org/index.php/i-jim iJIM | Vol. 17 No. 16 (2023) International Journal of Interactive Mobile Technologies (iJIM) 39 Evaluating Mobile E-Learning Systems Acceptance: An Integrated Model Table 2. Hypotheses testing Hypotheses Beta S.D t-Values p-Values H1a Task Equivocality -> Perceived Ease of Use 0.384 0.086 4.472 0.000 H1b Task Equivocality -> Perceived Usefulness 0.368 0.099 3.726 0.000 H2a Source Credibility -> Playfulness 0.225 0.089 2.528 0.012 H2b Source Credibility -> Perceived Ease of Use 0.217 0.095 2.285 0.023 H2c Source Credibility -> Perceived Usefulness 0.250 0.091 2.733 0.006 H3a Organizational Support -> Perceived Ease of Use 0.299 0.079 3.772 0.000 H3b Organizational Support -> Perceived Usefulness 0.009 0.101 0.089 0.929 H4 Perceived Ease of Use -> Playfulness 0.574 0.084 6.814 0.000 H5 Perceived Ease of Use -> Perceived Usefulness 0.261 0.080 3.282 0.001 H6a Perceived Usefulness -> Employee’s Attitude 0.114 0.130 0.879 0.380 H6b Perceived Ease of Use -> Employee’s Attitude 0.313 0.064 4.913 0.000 H6c Playfulness -> Employee’s Attitude 0.279 0.123 2.270 0.024 H7 Playfulness -> Behavioral Intention 0.271 0.088 3.088 0.002 H8 Perceived Usefulness -> Behavioral Intention 0.426 0.080 5.329 0.000 H9 Employee’s Attitude -> Behavioral Intention 0.186 0.079 2.356 0.019 5 RESEARCH DISCUSSION The TAM model was especially used in this research because it explains why a given scenario can result in different outcomes for the system’s acceptance [65]. According to the research findings, it is possible to infer that SCR plays a crucial role in influencing the PLF, PEU, and PU of persuasive messages. Consistent with the previous findings, [30] claims that supplementary cues, such as the credibility of the source, may affect human behavior. The employees are more likely to seek out information, and their imagination is more likely to be positive when they are con- fident in the credibility of the source [6]. It is more enjoyable for a person to accept a system when it is accompanied by information that comes from credible sources. A source’s credibility may have a positive impact on employee cognitive evaluations, such as their perceptions of PU and ease of use. Moreover, the findings of this research reveal that OS and TE significantly affect PEU and PU, which in turn influence intentions to use. This study confirms previ- ous findings [66] that workers are more likely to assume that online learning plat- forms are helpful and easy to use when they are provided with assistance and other resources from top management. As reported in previous research, these findings also suggest that OS influences both individual and organizational performance. It is therefore important that managers provide employees with OS to improve their perception of ease of use regarding e-learning platforms [31]. Similarly, our findings reveal that TE has a favorable impact on PEU and PU. Our results support prior studies indicating a favorable connection between task attributes and PEU and perceived usefulness. https://online-journals.org/index.php/i-jim 40 International Journal of Interactive Mobile Technologies (iJIM) iJIM | Vol. 17 No. 16 (2023) Abu-AlSondos et al. It can be concluded from the research findings that the TAM provides scholars with an appropriate, theoretically sound framework for predicting workers’ will- ingness to use mobile e-learning systems in their workplaces. The outcomes sug- gest that PEU and PU have an impact on employees’ intentions to adopt e-learning platforms. The findings of this experiment confirm previous findings that both PU and PEU play an important role in influencing the adoption of e-learning systems adoption in organizations [35]. 6 RESEARCH IMPLICATIONS As a result of the above discussion, several managerial implications can be drawn. The present study argued that employees’ perceptions of PU and PEU are not the primary elements that influence  system acceptance; emotional responses are also important. In the authors’ opinion and to the best of their understanding, this study provides the first scientific claim of how persuasive messages influence functional responses toward the implementation of a new system. Given these consequences, it can therefore be anticipated that SCR is a crucial factor in creating effective messages as well as generating different types of responses. Those employees who are more likely to elaborate on the system should be provided with information regarding its value, benefits, and advantages. It is possible to influence employees’ perceptions of a new system’s PU and PEU by conveying a clear message and communicating use- ful arguments at the same time. In the course of implementing technology, it is also significant to consider the effect of influencing routes on employee PLF, PEU, and PU attitudes and BI. 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Abu-AlSondos is an Assistant Professor in Management Information Systems (MIS) at American University in the Emirates (AUE), Dubai, United Arab Emirates (UAE). His current research interests include Business Intelligence (BI), Digital Transformation, Knowledge Management (KM), Enterprise Systems, E-Business, E-Services, and FinTech (E-mail: ibrahim.abual- sondos@aue.ae). Anas A. Salameh is an Associate Professor in Management Information Systems (MIS) at Prince Sattam Bin Abdulaziz University, 165 Al-Kharj 11942, Saudi Arabia. His major research interests include E-Commerce (M-Commerce), E-Business, E-Marketing, Technology Acceptance/Adoption, E-Learning, E-CRM, and Service Quality (E-mail: a.salameh@psau.edu.sa). Abeer F. Alkhwaldi is an Assistant Professor in Management Information Systems (MIS) at Mutah University, Al-Karak, Jordan. Her research interests include HCI, Technology Acceptance/Adoption, Digital Marketing, E-Government, E-Services, E-Learning, HRIS, Digital Transformation, Digital Accounting, Perceived Security, Blockchain, E-Payment, E-Wallet, and FinTech (E-mail: abeerkh@mutah.edu.jo). Alaa S. Mushtaha is an Assistant Professor in Healthcare Management at American University in the Emirates (AUE), Dubai, United Arab Emirates (UAE). His current research interests include BSC implementation, Critical Success Factors (CSFs), Organizational Performance, TQM, CSR, Innovation, and Strategic Management (E-mail: alaa.mushtaah@aue.ae; alaa.mushtaha@aue.ae). https://online-journals.org/index.php/i-jim https://doi.org/10.20547/jms.2014.1704202 https://doi.org/10.1016/j.ijhm.2015.01.009 https://doi.org/10.1080/17538157.2017.1363761 https://doi.org/10.1080/17538157.2017.1363761 https://doi.org/10.2307/25148660 https://doi.org/10.1080/08961530.2020.1712293 https://doi.org/10.1108/09604521211219007 https://doi.org/10.3991/ijim.v14i17.16599 mailto:ibrahim.abualsondos@aue.ae mailto:ibrahim.abualsondos@aue.ae mailto:a.salameh@psau.edu.sa mailto:abeerkh@mutah.edu.jo mailto:alaa.mushtaah@aue.ae mailto:alaa.mushtaha@aue.ae iJIM | Vol. 17 No. 16 (2023) International Journal of Interactive Mobile Technologies (iJIM) 47 Evaluating Mobile E-Learning Systems Acceptance: An Integrated Model Maha Shehadeh is an Assistant Professor in Financial Technology at Applied Science Private University, Amman, Jordan. His current research interests include organizational behavior, financial technology, modern financial digital technolo- gies such as: Artificial Intelligence, Cloud Computing, Mobile Phones, and Robotics (E-mail: mahashehadeh88@gmail.com). Ala’a M. Al-Junaidi is an Assistant Professor in E-Business Administrator. His current research interests include E-Business, Business Intelligence (BI), Digital Transformation, E-Commerce, E-Services, E-Government, E-Learning, E-Payment, Mobile Commerce, CRMS, and HRMS (E-mail: alaaaljunaidi@yahoo.com). https://online-journals.org/index.php/i-jim mailto:mahashehadeh88@gmail.com mailto:alaaaljunaidi@yahoo.com