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Journal of Computer Information Systems
ISSN: 0887-4417 (Print) 2380-2057 (Online) Journal homepage: http://www.tandfonline.com/loi/ucis20
Determinants of Mobile Learning Adoption: An Empirical Analysis
Garry Wei-Han Tan, Keng-Boon Ooi, Jia-Jia Sim & Kongkiti Phusavat
To cite this article: Garry Wei-Han Tan, Keng-Boon Ooi, Jia-Jia Sim & Kongkiti Phusavat (2012) Determinants of Mobile Learning Adoption: An Empirical Analysis, Journal of Computer Information Systems, 52:3, 82-91
To link to this article: https://doi.org/10.1080/08874417.2012.11645561
Published online: 11 Dec 2015.
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82 Journal of Computer Information Systems Spring 2012
Received: June 29, 2011 Revised: September 6, 2011 Accepted: October 11, 2011
AbSTRACT
The purpose of this study is to explore how related factors influence the adoption of mobile learning in Malaysia by incorporating subjective norms and individual differences with Technology Acceptance Model (TAM). This study deploys a self-administered questionnaire and was tested on 401 respondents. Multiple regression analysis was conducted to test the hypotheses in this study. Overall, the results indicated that perceived usefulness, perceived ease of use and subjective norms is positively associated with the intention to adopt mobile learning. The findings for individual differences require further investigations as gender factor did not show significant effect on intention towards mobile learning usage. The study provides valuable managerial implications on how to speed up the process of adopting mobile learning in emerging markets by improving the assessment of mobile learning. Keywords: Mobile learning, Technology Acceptance Model, subjective norms, and individual differences
INTRODUCTION
The proliferation of information networks and advances of technology have embraced the growth of mobile learning (m- learning). According to eLearn Magazine [26], mobile-based education is one of the major predictions in the latest learning trend [26]. With the worldwide increased in the adoption of portable devices such as laptop, mobile computing and smart phones, they have altered how traditional courseware are being delivered in higher education [79]. The advancement of technology in mobile devices and applications have enhanced m-learning as a powerful tool for informal learning. M-learning allows students to learn independently regardless of time and place facilitated by the arrays of mobile devices and wireless Internet [25], [79]. Thus, students can interact with education resources although physically away from the classroom [15]. Students can catch live classes or download reading materials without the constraints of location. It also allows colleges to attract more working or adult students [56]. The popularity of mobile technology and Internet usage are making mobile devices an important tool for m-learning [43]. However, the benefits of m-learning will not be realized if learners are reluctant to deploy this new innovation. Suc-
cessful technology-mediated learning is measured by the active participation from the students’ ground [12]. Therefore, in this study, we attempt to discover the factors that might encourage more students to engage in m-learning. According to previous studies, cognitive style will affect one’s decision making and behavior [35], [50], [84]. Different individuals have different styles and such differences may affect the individual’s decision to accept or reject a new technology [13]. This has eventually called for further investigations in the studying of the relation- ships between individual differences and the acceptance of new technology. Thus, in our research model, we include the different individual factors in order to explain the individuals’ acceptance of m-learning. Technology Acceptance Model (TAM) has been a popular model in explaining the acceptance of new technology [30], [44]. In order to have a wider view of ICT adoption, there is a need to include other variables within the TAM, as usefulness and ease of use only focus on functional perspective [45]. In the context of students’ acceptance of m-learning, we believe the motivator is also grounded from the influence of environment. Therefore, human and social change factors should also be taken into consideration [45], [49]. Hence, in this study, we try to incorporate TAM with social influence. Through a clearer understanding of social factors motivating the adoption of m-learning, this will help to devise strategy to harness online education, thereby increasing the success of m-learning. The success of new technology acceptance is highly dependable on the individuals’ willingness in adopting to a particular technology. Therefore, different individual factors are tested with perceived usefulness (PU), perceived ease of use (PEOU), and social influence. As the basis of our argument, we rely on the theory of reasoned action (TRA) in linking our model with subjective norms (SN) and intention to use and as well as TAM and to synthesize this with the individual differences factors. These factors are believed to provide better explication on the m-learning process. We begin the paper by exploring the background of m-learning. This is followed by examining the theoretical usage thus leading to our hypothesis. Next, the description on the methodology and analysis used are provided. Then, we present our results and discussions. While in the final section, we will include the conclusion, implications, limitations and suggestions for future research.
DETERMINANTS OF MObILE LEARNING ADOPTION: AN EMPIRICAL ANALYSIS
GARRY WEI-HAN TAN JIA-JIA SIM Universiti Tunku Abdul Rahman Universiti Tunku Abdul Rahman 31900 Kampar, Perak, Malaysia 31900 Kampar, Perak, Malaysia
KENG-bOON OOI KONGKITI PHUSAvAT Universiti Tunku Abdul Rahman Kasetsart University 31900 Kampar, Perak, Malaysia Bangkok 10900, Thailand
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Mobile learning
According to past researchers, there are two approaches to learning process, namely objectivism and constructivism [10], [74]. Objectivism is a conventional teaching process which is instructor- centric focus. Constructivism however allows knowledge to be generated by learners through the interaction of experiences and ideas. Scholars have concluded that constructivism has become one of the recent paradigms in educational teaching and learning process [9], [81]. The usage of mobile devices is making online education an integral part of constructivist learning. Wang et al. [79] commented that constructivism is accomplishable through extended classroom as well as individualized learning [79]. Noteworthy individuals today rarely leave homes without their mobile phones, thus education institutions can take opportunity by bringing in new modes of learning to remote students [2]. Past findings indicated that students are more interested in technology- mediated learning as compared to traditional learning methods. Thornton and Houser [73] for example conducted a survey among students taking English as Foreign Language (EFL). Exercises were conducted via mobile messages for one group while another via printed method. The results showed that mobile learners perform better in their exercise. In China, full time working students appeared to enjoy the flexi and student-centric approach provided by college. They tune in to their classes through live broadcast or watch archived video through their mobile devices. By the end of 2007, the number of college’s students increased from 200 to a total of 17,000 students [79]. This indicated that students are interested in participating in learning processes that are not bound to classroom with constructivist learning and interaction approach [79].
TAM and Mobile learning
In order to understand the reasons why consumers accept or reject an information system (IS), the study adopts the TAM by Davis et al. [23]. The model predicts an individual’s acceptance of a particular system through a brief interaction [72]. The presence of TAM is grounded in the Theory of Reasoned Action (TRA) which explains the users’ intention when performing a behavior, by predicting the attitude and measuring the weight of other views when they perform the behavior [28]. Two technology- related antecedents have been added in TAM. They are PU and PEOU [21]. These two constructs are linked to attitude towards use and followed by the intention to use (IU). IU is the central outcome that researchers want to know at the end of any study. It
is the likelihood or willingness of a person to employ a particular system or applications [52]. Attitude towards use on the other hand is the user’s own evaluation on how much desire he or she is to deploy a system. The attitude of user, in turn, can be explained by the two beliefs of PU and PEOU. Although TAM is the leading model for most of IS researchers, some researchers claimed that there is still lack of studies in explaining how multiple variables are transformed into the decision act, thus leaving to some critical gaps [44]. As such, the research on TAM is still ongoing to bridge the gap between existing and new variables. The determinant of actual usage is not included in the research model. Our study deliberately focuses on the usage intention which in turn predicts the actual usage of m-learning. According to Agarwal and Prasad [1], in order to access actual usage, the data should be collected from time to time. Our research design however is grounded from contemporaneous bases. Therefore, we include usage intention as a more appropriate measurement for this study. Davis et al. [23] in their study hypothesized and confirmed that actual usage in TAM can be predicted by using both subjective and objective measures. Hill et al. [36] and DeSanctis [24], also indicated that usage intention is a major predictor of user behavior. In summary, it is reasonable for this study to adopt behavioral intention as the determinant factor to predict future actual usage of m-learning.
RESEARCH MODEL AND HYPOTHESES
As shown in Figure 1, intention to adopt m-learning is determined by PU, PEOU, SN and individual differences. Definitions and relationships between these constructs are presented in the following section.
Individual Differences and the Acceptance of Mobile learning
Individuals’ perceptions are posited to influence the adoption behaviors [54], [67], whereby the IS acceptance is driven by different backgrounds and personality’s factor. The individual differences mean dissimilarities among people [1]. Previous literature classified the differences into demographic, personality, cognitive style and situational variables [4]. Personality refers to one’s organized characters in response to various situations [31]. Cognitive style on the other hand is the manner on how individuals acquire knowledge and process the information. Lastly, situational variable is the characteristics of situation [4], [35]. Numerous researches have been conducted on the effect of individual differences on new technologies implementations
FIGURE 1: The Research Model
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[57]. For example, gender, age, experience and personality have been investigated from the context of end-user computing [35]. Kwon and Zmud [42] concluded that individual’s differences in relation to users’ motivation are critical in the adoption of new technology. Sim et al. [71] in their studies found that age, gender, innovativeness, and education influenced the decision to adopt broadband. In the context of m-learning acceptance, we attempt to test individual differences particularly from the aspect of gender, age and past experience. We choose to focus on these variables in view that m-learning is still at its infancy stage and the basis of demographic and previous experience will serve as useful information for m-learning acceptance.
The relationships between gender and perceived usefulness, perceived ease of use and subjective norms
Ong and Lai [59] in their finding revealed that the usefulness in content is important to attract male users to adopt e-learning. The study was conducted among employees from six international companies in Taiwan. The study suggested that men consider productivity-related factors more important when compared to women in accepting new technology. In the same research however, the direct effect of PEOU between genders was not significant. Moon and Kim [53] obtained an opposite results indicating that men view ease of use of e-learning higher than women. Gefen and Straub [29] also examined the effect of individual difference on IS adoption. A total of 392 respondents were collected from employees working in airline industry. Their findings suggested that gender should be included in the IT diffusion framework along with other cultural factors. From the perspective of social presence, different gender will perceive differently in considering whether to adopt a new technology [76]. Therefore, the following hypotheses have been proposed:
H1: There is a positive relationship between gender and PU.
H4: There is a positive relationship between gender and PEOU.
H7: There is a positive relationship between gender and SN.
The relationships between age and perceived usefulness, perceived ease of use and subjective norms
The effect of age difference is closely related to SN. Different age’s group tends to have different responses towards IS [55]. When comparing to younger employees, older employees tend to have lower needs for autonomy [18], [27]. Researchers argued that older workers conform to majority opinions and with the purpose to please others such as peers and superiors [34], [65]. Zajicek and Hall [83] experimented by dividing the elderly into two groups with personal support and without personal support in performing a set internet tasks. PU was found lower in older adults by weighting longer time to learn and operate certain systems. Arning and Ziefle [5] on the other hand re- vealed that age influence the extent of PU and PEOU. Although Agarwal and Prasad [1] conceptualized age as one of the im- portant factor in workforce, but in a later study, they found that there was no relationship towards attitude and behavioral intention. As the knowledge on the influence of age factor on the estimation of PU, PEOU and SN is limited, the following hypotheses are proposed:
H2: There is a positive relationship between age and PU. H5: There is a positive relationship between age and
PEOU. H8: There is a positive relationship between age and SN.
The relationships between past experience and perceived usefulness, perceived ease of use and subjective norms
Users’ past experience is useful when the likelihood of a new system is similar to the old system [38]. Jackson et al. [38] in their study among 139 employees revealed that with past experience, users will find familiarity, thus leading to higher confidence in trying a new technology [38]. Similar sentiment was echoed by Benbasat et al. [8] in which the familiarity features designed in a system will tend to effect one’s perceptions about the system. When considering the adoption of a new system, the influence from referents plays an important role. Individuals will comply with the opinions of others in the decision to adopt or not to adopt a certain system [80]. Given previous findings, we argued that the relationships between past experience and PU and PEOU are stronger among users that have adopted similar system previously. Further, we also proposed that the relationship between past experience and subjective norms will be stronger for users that are more exposed to referents influence. Therefore, we propose the following hypotheses:
H3: There is a positive relationship between past experience and PU.
H6: There is a positive relationship between past experience and PEOU.
H9: There is a positive relationship between past experience and SN.
Perceived Usefulness
PU explains the utility value in a technology usage [43]. It is the extent to which individuals perceived the usage to be useful for them. In this context of study, PU is the degree to which the students perceive using m-learning will enhance their course’s performance [16], [68]. M-learning offers bene- fits in several ways. It allows students a greater control over the learning environment [39]. Students can choose their learn- ing environment according to their own convenience and preference [43]. Thus, m-learning enables the learning process to take place in ways that cater to different individual’s needs [21]. In terms of performance support, m-learning has created just-in-time mobile learning environment. It is a quick refer- ence tool to access information and on-the-spot delivering of knowledge in a more accessible manner [18], [26], [49]. Given these advantages, mobile technologies have enabled m-learning to become more robust. In a study by Chong et al. [14], the results indicated that PU has a significant positive impact on the intention to use 3G in Malaysia. The results in Hong Kong on 544 first-year undergraduate students posited that PU has significant direct effects on attitude in using Internet-base medium [43]. If individuals perceive m-learning usage will increase their efficiency of learning process, they are more likely to adopt m-learning. Hence, the following hypothesis has been proposed:
H10: PU has significant relationship towards m-learning adoption.
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The influence of Subjective Norms
Theory of Reasoned Action (TRA) was pioneered by Fishbein and Ajzen [28] and has been widely deployed to explore human behavior in the social psychology disciplines [11], [17]. Using TRA as a foundation, Theory of Planned Behavior (TPB) was introduced. The added perceived behavioral control is the belief that behavioral intention can be controlled by individuals [3]. There are three pathways in TPB namely, attitudinal, normative, and controls. The beliefs of normative in both TRA and TPB are similar. Although SN has been removed in TAM, it calls for detailed exploration in the later researches due to its role and importance of SN [23]. Given its famous used in IT research, SN is a significant predictor in predicting one’s intention to use a system [60], [69]. SN, which is also conceptualized as social norms [37], normative beliefs [77] or social influence [40], is the perceived social pressure from others who are important to an individual, whether or not to engage a certain behavior [28]. The rationale for the effect of SN is that a person may choose to engage a certain behavior, even though it is not a favorable one at first. If their important referents think they should, they will comply with the particular behavior [75]. There is a high degree of social interdependence between individuals and social actors when they carry out certain activities [13]. The processes of social exchange, coalition formation, and resource allocation will in turn, increase the likelihood to influence one’s behavior within a group [6], [61], [62]. Using longitudinal data collected from four organizations, Venkatesh and Davis [75] explained on the effect of SN on behavioral intention. Potential users may choose to adopt to new technology, although not favorable at first when influenced by someone who is important to them [75]. Schepers and Wetzels [69] examined 51 published articles have proved that intention to use new technology tends to be higher when social environment is encouragable. Ooi et al. [60] in their investigations on 175 undergraduates discovered that primary influence (family and friends) has stronger impact when compared to secondary influence (e.g. information from media). Therefore, we develop the following hypothesis:
H11: SN has significant relationship towards m-learning adoption.
Perceived Ease of Use
PEOU is the perceptions on how easy to use a particular technology [14]. Since the introduction of m-learning, this new tool encounters several challenges. Corbeil and Valdes- Corbeil [19] claimed that there are many students who are not familiar with technologies where m-learning requires differ- ent types of applications to enable video viewing or audio listening. Other challenges are such as connectivity and facili- tating resources capability [79]. Ha et al. [33] explored the adoption of mobile games under wireless access environment. The results imply that potential gamers perceived ease of use as a critical driver in future wireless gaming environment. Thus, the technical and format design are important as it will allow users to use the applications in an easier way [46]. In the past TAM research, PEOU was found to significantly affect PU. However, Jackson et al. [38] sampled 139 employees from diverse system development projects revealed that the relationship between PU and PEOU was not significant. Therefore, the
PEOU need to be redefined taking into consideration the as- pect of learning perspective. In this study, the PEOU is extended to include the belief that the easier the technology, students would be more willing to adopt m-learning. Hence, the following hypotheses have been proposed:
H12: PEOU has significant relationship towards m-learning adoption.
H13: PEOU has significant relationship towards PU.
RESEARCH METHODOLOGY
Sampling and data collection
Since m-learning renders benefits beyond the boundary of traditional e-learning, the study should therefore focus on what factors to attract potential students to use m-learning. Therefore, it is natural that the respondents of this study are students with mobile devices. In this case, university students with mobile devices were chosen as we believed that they are more likely to adopt m-learning for educational purposes due to their income and education level background [82]. To access our research model, our study focuses at one of the largest private university in the state of Perak region, Malaysia. The university has a population of more than 12,000 students. As such, the sample would represent a fair balance of representation from all over Malaysia. The data was collected through a self-administered questionnaire during a two-week data collection period in 2011. Out of the 432 questionnaires, 31 samples were discarded due to being only partially completed thus securing an effective data return rate of 92.82 percent. The sample size of this study is comparable with studies of other scholars using students as sample such as Davis [21], Mathieson [51] and Sim et al. [71].
Survey Instruments
In order to examine the hypothesized relationships, the independent variables were adopted from exiting literatures wherever possible [82]. Slight modifications of the surveys instruments were made to ensure that the questionnaires are appropriate from the perspective of Malaysia. A total of 11 questions were constructed in order to capture the 3 adoption factors (see Appendix A). As in most past studies, all the questions were measured using the Likert-type scale. Responses were in the form of agreement ranging from “1” denoted as strongly disagree to “5” denoted as strongly agree. The study adopted two items from Davis [21], [22], in the measurement of user’s behavioral intention towards the adoption of m-learning (see Appendix A). Likewise, the question was measure again by a five-point Likert- type scales ranging from 1 to 5.
DATA ANALYSES AND RESULTS
Profile of Respondents
The demographic profiles of the target respondents are shown in Table 1. Of all the participants, 47.13% were male and 52.87% were female. Over 77.31% of the respondents are above the age of 21 while only 22.69% are below the age of 20 years old. In terms of the education level, 20.95% consists of pre-university holders, 14.96% are diploma holders, 54.36% with bachelor degree while 9.73% consists of postgraduate holders.
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Factor Analysis and Reliability
The Principle Component Analysis (PCA) and varimax rotation were conducted with Exploratory Factor Analysis (EFA). Kaiser-Meyer-Olkin (KMO) and Bartlett’s test as in Table 2 indicates good sampling adequacy of 0.842 and a significant Bartlett’s sphericity (χ2 = 1885.557, p<0.001) hence permitting further factor analysis to be carried on [7]. The Cronbach’s alpha coefficient ranges from 0.721 to 0.816, in which all these values are greater than 0.70 and thus the data are considered good reliability and good internal consistency [58]. The factor loadings of less than 0.5 were suppressed while the Kaiser’s criterion of using Eigenvalues > 1.0 is utilized. As a result of the data purification process, 6 items were deleted from the survey. As recommended by Raubenheimer [66], PEOU3, SN1, SN2, SN3, IU1 and IU3 were deleted in view of the poor factor loadings (<0.5). 4 factors with a 66.162% total variance explained have been successfully extracted using EFA. Table 2 shows the factor loadings of these factors.
Multiple Regression Analysis
The research model as shown in Figure 1 was tested using the Multiple Regression Analysis (MRA). The initial findings revealed that TAM was able to explain the user’s ultimate decision in the adoption of m-learning services. Please see Table 3 for more information. Furthermore, the results of this study were segregated into two sections. The first section, H1 to H9 represents the improved TAM in the relationship between individual characteristics, PU and PEOU. As for the impact of demographical factors (H1 to H9) on the adoption of m-learning services in which consists of gender (H1, H4, H7), age (H2, H5, H8), and past experience (H3, H6, H9), age was found to predict PEOU and SN and thus the hypothesis for H5 and H8 were supported. Similarly, past experience was able to predict PU and SN but not PEOU, thus H3 and H9 were supported while H6 was not supported in this study. The factors within TAM namely PEOU, PU and IU generated from H10 to H13 were discussed in the second section. There is a positive association
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between PEOU, SN, PU and IU. As such, H10 to H13 were fully supported by our empirical findings. In Model 4, in explaining the consumers’ intention towards IU, PU, PEOU and SN the variance is accounted for at 50.5 percent. The findings also indicated a positive relationship between PU, PEOU, SN and IU; that is the greater the consumer PU (β = 0.250, p < 0.01), SN (β = 0.314, p < 0.01) and PEOU (β = 0.109, p < 0.05), the greater their intention towards m-learning usage. In regards to the associations of PU and PEOU, the variance at 58.7 percent in consumers’ PEOU of m-learning services (β = 0.387, p < 0.01) are accounted by PU. The findings support past TAM studies that examine the strong consistent relationship between PEOU, PU and mobile learning adoption. Thus, the hypotheses from H10 to H13 were supported. The relationship between variables that provides the β values for every hypothesis studied is shown in Table 3.
DISCUSSION
Relationships between Individual Differences and SN, PEOU and PU
The results from this study reveal that age has significant relationship with SN. In view that 88.53% of the respondents are below the ages of 26 years old, their actions and thoughts are likely to be shaped by the opinions of peer groups, relatives and friends [47], [48]. Findings also indicate that age is signifi- cant with PEOU and is consistent with the study of broadband adoption in Malaysia [71]. The findings however contradict
with past scholars such as Pijpers et al. [63] and Porter and Donthu [64]. The result was not surprising, as age does influence the ability to process information [70]. Since the majority of the respondents are young, they are capable of navigating the mobile devices easily, thus they tend to perceive that the adop- tion of m-learning will be free from effort. Interestingly age has no significant relationship with PU. One possible explana- tion is due to the suitability of the mobile devices for edu- cation purposes. Although m-learning enables a student to learn on the fly, the mobile devices factors such as the screen size, lower display resolution, shorter battery life span and CPU speed [20], [32], [41] may hamper the suitability of the devices for m-learning. This limitation may diminish the perceived benefits of adopting mobile devices for learning purposes. In the adoption of new technologies, Venkatesh and Morris [76] concluded that female generally is affected by social influence factors. However, we were unable to determine the relationship between gender and SN. One plausible explanation is due to the sample size of which the size of male and female are equally similar in number. Thus the size is not likely large enough to suggest the occurrence of any significance. Similarly we could not determine the relationship between gender and PEOU and PU. Again, the reason might be attributed to the sample size of our study. Our results contradict with findings of different settings such as Yang [82] in the area of mobile commerce and Sim et al. [71] in the adoption of broadband. Finally, past experience has a significant effect on PU and SN. However, not all prior experience has significant effect
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on the acceptance of new IT [1]. In this case, there is no significant relationship between past experience and PEOU. Due to the flow of experience, the switching costs to different technologies may offset the individuals’ positive gain towards a new technology [1]. This study thus indicated that positive transfer effects will be noted only when individuals share compatible and common experiences in new technology.
Relationships between PU, PEOU, SN and IU
The results of this study support the notion that PU, PEOU, and SN are important variables in the acceptance of m-learning. Of these, PU has positive impact towards the intention to use. This shows that users attach greater importance to the valuable functions of m-learning system. According to Wang et al. [78], m-learning contents should be compatible with different variety of mobile devices. According to our analysis, PEOU has significant effects towards both PU and IU. Service providers therefore should stress on the concept of simplicity. User-friendliness interfaces should be taken into consideration. If using m-learning requires high efforts, users may be discouraged from adopting the system. Therefore, enhancing the feeling of ease-of-use will alter the perceptions towards m-learning usage. SN was found to have the highest significant effect on IU. This shows that individuals rely greater importance to others’ opinions, such as family, friends, and colleagues. If the surrounded environment is encouraging, individual will feel more positive in trying out new technology.
THEORETICAL IMPLICATIONS
This study has contributed to an overall conceptual understanding of m-learning by using components of intention factors. The first contribution is towards user acceptance, which incorporates the individual difference factors. The effects of demographic factors have been analyzed through empirical analysis. The demographic characteristics comprised of gender, age, and past experience as the subjects to give further validation to the research model. The empirical results support our research model on how individual differences drive the belief towards m-learning acceptance. These demo- graphic variables have found to be important in this study which helped to understand individual behavioral in accepting new technology. Joining together, the model has served as the foundation for better understanding on individuals’ decision to adopt m-learning. Second, TAM has again been proven robust with the salience of PU and PEOU beliefs. In addition, we extended the model by incorporating the presence of social factor, which in turn, was found to be an influential factor as well. Taking together, the research model has provided a com- plementary foundation in IS literature by demonstrating the formation process of individuals’ beliefs’ towards new technol- ogy acceptance in the context of learning environment.
MANAGERIAL IMPLICATIONS
The findings of present study, therefore, provide service providers, mobile phone manufacturers, educational institutions or even governmental agencies important guidelines on the design and implementations of m-learning innovation. Subjective norms, PU and PEOU were found to be the key drivers for m- learning adoption. Here, we offer four guidelines design and implementations of m-learning:
• Creating awareness about the usefulness of m-learning. Educational institutions should take the opportunity to promote the usefulness of m-learning services, especially in the areas of life-long learning. For example, m-learning provides convenience to accessing learning materials anytime and anywhere, the improvement of quality of interaction between students and lecturers and learning on the go.
• Developing coursework or programs that are relevant to suit the context of m-learning. Educational institutions should develop programs that might be perceived as beneficial for students. Take for example the ability to check the examination results and course registration through the mobile devices.
• Providing and designing tools or applications for m- learning content. Mobile phone manufacturers should ensure that the phones are well suited for m-learning purposes. The consideration should also be given when designing the mobile phone size, text input, screen display, battery and CPU speed.
• Emphasizing promotions to foster m-learning usage. Mobile service providers, government and even educational institution should cast their advertisements on popular social networking. As age has significant relationship with SN and PEOU, therefore they should not be neglected. Since young consumers are easily influenced by SN, education institutions agencies should cast their advertisements by adopting opinion leaders in their marketing strategies.
LIMITATION AND FUTURE RESEARCH
Although the findings of this study are encouraging, we acknowledge some limitations that should be considered for future studies. Firstly, this study was based on self-report data. The samples were collected from a private university in Malaysia. Those characteristics could cause some limitations in externalizing the results of this study. In future studies, there are necessities to develop more strategic sampling and data collection method from wider geographical scope in order to make generalizations from the data. Secondly, the constructs of this study were measured at a single point of time. The study served as preliminary tendencies in m-learning accep- tance as it is still very much in its infancy stage and indi- viduals’ perceptions may change over time. Individuals’ inten- tion to adopt m-learning is still in an ongoing process. For instant, Davis et al. [23] found that PEOU was not significant towards intention to use when a technology was newly intro- duced. The results turned out to be significant fourteen weeks later. Furthermore, the examination of behavioral intention to continue adopting may be more appropriate after users have gained some experience. Therefore, the timely issue needs to be acknowledged in future research. We encourage researchers to explore this phenomenon through a longitudinal approach, where variables constructs are access at multiple points during the decision adoption process. This will provide practitioners useful knowledge in evaluating beliefs and be- havior over time. Third, of particular to IS researchers is the fact that the existing model should include economy-related constructs. While this study focused on adoption factors and individual differences characteristics, future research should explore the determinant in using technology from the economy
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perspectives. For instant, cost may play a critical role in deter- mining adoption or non-adoption decision. Consumers not acquire the new technology if the cost is high. These percep- tions are believed to provide researcher a clearer understanding towards behavioral intention to adopt m-learning from the economic point of view. The input will be important to extend the existing model.
CONCLUSION
The paper’s objective is to investigate the factors that could contribute to the adoption of m-learning. In order to achieve this objective, the TAM was utilized with individual differences and subjective norms. In a nutshell, the findings for the individual differences construct remains mixed. First of all, gender has no significant relationship with PU, SN and PEOU. Secondly, age is only significant with SN and PEOU and not PU. Thirdly, there is no significant relationship between past experience behavior and PEOU apart from PU and SN. Finally, like most past studies, PU, PEOU and SN, remain as one of the important predictors in the intention to adopt m-learning. The findings here will stimulate service providers, mobile phone manufacturers, educational institutions and government in advancing their marketing and corporate strategies.
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