International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923 – vol. 13, No. 8, 2019 Paper—Diagnosing Adoption to Mobile Learning Diagnosing Adoption to Mobile Learning https://doi.org/10.3991/ijim.v13i08.10083 Eliza B. Ayo (*), Marcial L. Anacio, Lani E. Sakay, Rosemarievic A. Bustamante, Teresita S. Mijares Centro Escolar University, Manila, Philippines ebayo@ceu.edu.ph Abstract—This study was undertaken to address the need to modernize the modes of teaching and learning pedagogy by taking advantage of the prolifera- tion of electronic gadgets and mobile devices since, as counted there are more computers than the people in the world [1]. This research found out how learn- ing in the new platform could be adopted by teachers and students. In addition, it tested the different variables involved in the use of mobile in learning by fac- ulty and students of Centro Escolar University (CEU) to determine factors that made implementing technology a success or a failure. To attain the set objectives, this study made use of descriptive and inferential method to the data gathered from respondents via stratified random sampling. Specifically, it employed frequency, mean, standard deviation, t-test, analysis of variance and multiple linear regressions for the treatment of data derived from the survey questionnaires. To complete the triangulation approach and for con- sistency of findings, observation and interview were also conducted. Subsequently, in order to determine adoption to mobile learning the modi- fied Unified Theory of Acceptance and Use of Technology (UTAUT) model was employed. This model shows relationship of moderating variables which are age, gender, voluntariness of use and experience from independent variables like performance expectancy, effort expectancy, social influence, facilitating conditions, anxiety and self-efficacy. After all the statistical treatments were applied, Anxiety, Self-Efficacy, Ef- fort Expectancy, Performance Expectancy and Facilitating Condition contribute to the success of the introduction of technology to the organization, while social influence was excluded as the reasons why users adopt it. Keywords—Mobile learning, technology adoption, 1 Introduction A new breed of era started when mobile devices landed on peoples’ hands. These technology savvy individuals find ways on how to use these gadget to their advantage. With the recent studies [2], [3] it revealed that by 2020 the number of things connect- ed to the internet will reach 50 billion and by the year 2019 there are 5 billion smart phone and tablet users. 124 http://www.i-jim.org https://doi.org/10.3991/ijim.v13i08.10083 https://doi.org/10.3991/ijim.v13i08.10083 Paper—Diagnosing Adoption to Mobile Learning Looking at the spread of these gadgets, this will make an impact in all fields. From industrial to manufacturing to agriculture, this phenomenon could be an enabler to more advanced things or could disrupt existing traditions and processes. Education will not be spared from it. In fact Daniel Burrus, chief executive officer of Burrus Research Associates Inc., predicted that mobile learning represents an amazing dis- ruption and opportunity [4]. The use of innovative technology in the classroom has a positive impact on stu- dents learning [5]. As a way of embracing changes, it is interesting to note that schools and universities are coping up by searching and introducing new platforms. They are working towards improvement and innovation, knowing that classroom- based lessons and other approaches may not suffice to the growing mobile users and fast paced development in technology. The availability of these gadgets combined with creativity of teachers, this tool can be used to deliver quality teaching and amaz- ing learning experiences among students. However, implementing mobile learning is still not as easy as abc, aside from learning the technicality to use this state the of the art technology, human factors in order to adopt it, needs to be studied as well. What are the factors that will make teachers and students adopt mobile learning? If there are studies that show people reject new technologies when they replace humanity while embraces them when they support human desire for purpose, challenge, meaning and alignment with nature even if these technologies are unwieldy, expensive, time-consuming to use, and constantly break down [6]. In fact, when smart phones and tablet landed on the hand of teachers and students and a number of website offers on-line education at no cost, there was an assumption that adopting to it will take a leapfrog. However when Edx was launched even Har- vard faculty argued on its impact to its current system [7]. Moreover, since technology gadgets can influence the socio emotions of students, balancing and controlling the use of it are needed [8]. This is one of the evidences of human’s resistance to adopt to changes. An illustration that even technological advances offers a modern way on how to do things there are barriers for its implementation. Since financial investment is at stake in implementing mobile learning, identifying variables on user adoption to it is deemed necessary. This study gives light on which factors to address and the challenges administrators are facing in enhancing teaching and learning through technology. When all these where identified and considered, adoption will no longer an issue if in the future new technology for improvement and sustainability are introduced. 2 Background of the Study Currently, Centro Escolar University is considering a new learning management system (LMS) as an enhancement to its various learning modalities for its diverse learners that can be accessed in mobile. In the context of this study, mobile learning includes the use of LMS in mobile devices. Since both technologies offers features that when adopted could provide a brand new way to conduct classes as well as in- iJIM ‒ Vol. 13, No. 8, 2019 125 Paper—Diagnosing Adoption to Mobile Learning crease teachers’ productivity and enrich classroom experiences to the students, this study was conducted. This study determined the adoption of mobile learning using Unified Theory of Acceptance and Use of Technology (UTAUT). Using this model, it could determine teachers’ and students’ attitude, preparedness and concerns with regard to mobile learning adoption [9]. 3 Statement of the Problem 1. What is the profile of the respondents in terms of: (a) age; (b) gender; (c) experience; (d) voluntariness of use and (e) mobile devices ownership? 2. How do the respondents assess mobile learning based on the following determi- nants of user intention? (a) Performance Expectancy (PE); (b) Effort Expectancy (EE); (c) Social Influence (SI); (d) Facilitating Conditions (FC); (e) Anxiety(AX) ; (f) Self-Efficacy(SE); 3. How do the respondents assessments of mobile learning in terms of performance expectancy, effort expectancy, social influence and facilitating conditions compare when grouped according to age, gender, voluntariness of use and experience? 4. Among the determinants of behavioral intention, which are the factors that will make the teachers and students adopt mobile learning? 4 Hypothesis The factors that will make the teachers and students adopt mobile learning will not be determined by the use of UTAUT. 5 Methods and Procedures The Sloven’s Formula was used to identify the number of respondents needed in this study. The questionnaire was composed of two parts. The first part is for demo- graphic profiling that served as moderating variables which includes the age, gender, voluntariness of use and experience. Part II is patterned to the UTAUT model that made use of determinants of behavioral intention namely performance expectancy, effort expectancy, social influence, facilitating conditions anxiety and self-efficacy. A marked of .957 or excellent verbal interpretation when the questionnaire was checked for internal consistency using Chronbach’s Alpha. 461 questionnaires were distributed 126 http://www.i-jim.org Paper—Diagnosing Adoption to Mobile Learning among the students and employees in CEU. These were tested and treated using Sta- tistical Packages for Social Sciences (SPSS) application. The following statistical method, frequency distribution, percentage, mean, standard deviation, T-test, Analysis of Variance (ANOVA) to come up with the needed answers to the question posted. 5.1 Theoretical framework This study is anchored at The Unified Theory of Adoption and Use of Technology. This theory explores the different factors that make adoption to the technology a suc- cess. UTAUT is consists of moderating variables such as age, gender, experience and voluntariness of use. These variables when relate to dependent variables performance expectancy, effort expectancy, social influence and facilitating condition factors on the use and adoption can be identified. These identified factors are useful to make necessary adjustment to ensure success in implementation. In the case of this study, all moderating variables were tested to the different dependent variables and on the additional factors, anxiety, self-efficacy and attitude towards using technology thereby increasing the scope on what to consider. Fig. 1. Modified Unified Theory of Acceptance and Use of Technology (UTAUT) 5.2 Profile of the respondents Out of 461 respondents, majority of it is within the age ranges from 19 to be- low or 65.9% percent. The respondents were mostly female or 63.4%, which is in the third year level and consider using mobile in learning as voluntary. A number of re- spondents answered smartphones as the device they use for mobile learning which is 76.8 percent while tablets and netbooks scored 34.5% and 9.3 percent respectively. iJIM ‒ Vol. 13, No. 8, 2019 127 Paper—Diagnosing Adoption to Mobile Learning 6 Results and Discussions In assessing the different determinants of user intention a five-point Likert scale was used. The Assessments of Mobile Learning in terms of Performance Expectancy is pre- sented on table1. The respondents agreed that learning from mobile is useful to his/her work or study (x=4.25). It enables them to accomplish the task quickly (x=4.18), increases productivity (x=4.13), and increases the chance of getting good grades or good performance in their job (x= 4.02). The overall mean (x=4.48) strongly suggest that learning from mobile help them attain gain in study/job performance. Perfor- mance expectancy has direct effect on the adoption of web based training [10] [11]. To take advantage of the results presented, teachers could use mobile devices in en- riching the teaching and learning experience of the students within or outside the campus by creating activities because students will surely take interest on it. Table 1. Assessments of Mobile Learning in terms of Performance Expectancy Performance Expectancy N Mean Std. Devia- tion Verbal Interpretation Learning from mobile is useful to my work/study. 460 4.2478 1.3331 Agree Using mobile devices enables me to accomplish task more quickly. 460 4.1826 1.3008 Agree Learning from mobile increases my productivity. 458 4.1332 1.2715 Agree Learning from mobile increases my chances of getting a good grade/good performance rating. 459 4.0261 1.2466 Agree Performance Expectancy 461 4.478 1.2094 Agree As seen on Table 2, on the Assessments of Mobile Learning In Terms of Effort Expectancy. The result on effort expectancy connotes that both students and faculty do not have a hard time to use mobile devices in teaching and learning. The overall mean (X=3.65) suggests that respondents exerted less effort to use the system to achieve his/her goals in using mobile to learn/teach. Based on the rating, respondent’s claimed that it is easier for them to use mobile in the learning/teaching(x=3.59) as well as to become skillful (x=3.87). On the other hand, the rating on the use of mobile devices (x=3.62) and connecting to internet (x=3.51) using their gadgets were easy for them as noted on the results. Effort expectancy confirms that when a technology is easy to use and require less effort is one of the reasons why user adopts a system [12]. Social Influence as defined by Venkatesh is the degree to which an individual per- ceives that people who are important to him/her believe that he/she should use the new technology which the overall mean of (x=3.92) implies as reflected on table 3. People who influence respondent’s behavior (x=3.87) and those that are important to them (x=3.82) think that they should teach/learn through mobile. The support to use mobile devices is also seen in their classmates and colleagues, in fact respondents claimed they have been helpful in the use mobile in the teaching/learning (x=3.92). However, the results minimally agree was yielded when the respondents were ask on 128 http://www.i-jim.org Paper—Diagnosing Adoption to Mobile Learning the university’s support on the use of mobile in teaching/learning (x=3.09). On the interview with the respondents in investigating on why the aforementioned results were derived, the policy on mobile use inside the classroom as well as the low wi-fi signal in the campus are the two common reasons. People adopt technology to blend or connect to other people [13]. However, after the user used the technology social influence is no longer significant [14]. Table 2. Assessments of Mobile Learning In Terms of Effort Expectancy Effort Expectancy N Mean Std. Deviation Verbal Interpretation Using mobile to learn/teach is easy for me. 461 3.5900 1.19920 Agree It is easy for me to become skillful through mobile learning/teaching 461 3.8698 1.12113 Agree Mobile devices in teaching/learning is easy for me. 461 3.6291 1.20467 Agree Connecting to internet for mobile learning is easy for me. 460 3.5087 1.20182 Agree Effort Expectancy 461 3.6497 1.05943 Agree Table 3. Assessments of Mobile Learning in terms of Social Influence Social Influence N Mean Std. Deviation Verbal Interpretation People who influence my behavior think that I should teach/learn through mobile 461 3.8655 1.07102 Agree People who are important to me think that I should use mobile devices in teaching/learning 461 3.8265 1.09358 Agree My classmates/colleagues have been helpful in the use mobile in teaching/learning 460 3.9174 1.15363 Agree In general, the university has supported the use of mobile in learn- ing. 461 3.0954 1.18119 Minimally Agree Social Influence 461 3.9262 .99850 Agree On facilitating condition as seen on table 4, the overall mean score of 4.14 shows the respondents’ belief that an organizational and technical infrastructure exists to support the use of mobile in the teaching and learning. The ratings on the table re- flected that availability of resources exists in CEU (x=4.23) and respondents have the necessary knowledge (x=4.34) to use mobile devices in teaching and learning. The respondents also agreed that their mobile device is always compatible with other sys- tems they use (x=3.95) and in case they need assistance there is a specific person or group is available (x=4.04). Financing, skills, capacity and infrastructure are examples of challenges encountered when implementing technologies [15]. In the case of CEU, the Teaching and Learning Technology Department (TLTD) and Information Communication Technology (ICT) are the departments responsible in iJIM ‒ Vol. 13, No. 8, 2019 129 Paper—Diagnosing Adoption to Mobile Learning facilitating the use of modern technology. The former is responsible in implementing technological advances in the teaching and learning process while the latter is on implementing such infrastructure needed to modernize CEU. Table 4. Assessments of Mobile Learning in terms of Facilitating Condition Facilitating Condition N Mean Std. Deviation Verbal Interpretation I have the resources necessary to learn from mobile 461 4.2321 1.13842 Agree I have the knowledge necessary to use mobile in learning 460 4.3413 1.12354 Agree My mobile device used in learning is always compatible with other systems I use. 460 3.9565 1.18370 Agree A specific person (or group) is available for assistance with my mobile device 459 4.0414 1.15805 Agree Facilitating Condition 461 4.1421 .98192 Agree Anxiety and Self efficacy are the additional variables tested in this study, this test that aside from perceived usefulness other factors are being considered by users be- fore adapting to new technology [16]. Anxiety questions measure the degree of an individual apprehension or even fear when he/she is faced with the possibility of using mobile devices in teaching and learning is reflected on table 5. All indicators of anxie- ty level were rated minimially agree, from respondents apprehension (x=2.91), securi- ty issues (x=2.76), fear of making mistakes (x=2.59), down to the feeling of being intimidated (x=2.61). The overall rating of (x=2.72) suggests that anxiety in using mobile devices is not a matter to consider when in using mobile in the teaching and learning. Table 5. Assessments of Mobile Learning in terms of Anxiety Anxiety N Mean Std. Deviation Verbal Interpretation I feel apprehensive about using mobile in learning 459 2.9107 1.07303 Minimally Agree It scares me to think that mobile learning is not safe 460 2.7630 1.09780 Minimally Agree I hesitate to use mobile in learning of fear of making mistakes I cannot correct. 458 2.5939 1.09750 Minimally Agree Mobile learning is somewhat intimidating to me. 459 2.6144 1.08054 Minimally Agree Anxiety 461 2.7178 .96465 Minimally Agree Table 6 shows the result on self-efficacy of the respondents. Self-efficacy refers to an individual's belief in his or her capacity to execute behaviors necessary to produce specific performance attainments [17]. The capacity of the respondents to use mobile in the teaching and learning is high as seen on the different indicators of self-efficacy. This means that respondents believe in their own ability to succeed in accomplishing 130 http://www.i-jim.org Paper—Diagnosing Adoption to Mobile Learning task. Students will use technology when it is capable of increasing their efficiency [18]. The overall rating of 3.29 translated to as minimally agree when ask whether the respondents could perform the task when there is someone around to tell them what to do while working (x=4.30), calling help desk for oral instruction(x=3.24), plenty of time and resources are provided (x=3.35) and instruction readily available (x=3.26) are not necessary for them to complete their job or task. Table 6. Assessments of Mobile Learning in terms of Self Efficacy Self-Efficacy N Mean Std. Deviation Verbal Interpretation I can complete a job or task using mobile: If there is someone around to tell me what to do as I work. 461 4.3080 1.16120 Minimally Agree If I can call to help desk for oral instruction 460 3.2391 1.04130 Minimally Agree If I have a lot of time to complete the job and resources are provided. 461 3.3536 1.09084 Minimally Agree If I have the instruction readily available 461 3.2646 1.09912 Minimally Agree Self- Efficacy 461 3.2914 .98283 Minimally Agree Table 7 presents the comparison of respondents’ assessments of mobile learning technology in terms of performance expectancy, effort expectancy, social influence and facilitating conditions when grouped according to age bracket. Looking at the table age of a person yielded a very significant variable in the results of performance expectancy, effort expectancy, social influence and facilitating conditions when ana- lyzing the adoption of an individual to technology. An F value of .949 with a mark of 001with a verbal interpretation of very significant was observed on pair 19-below VS 30-39, connotes that in terms of performance expectancy, older respondents tend to adopt the technology more compared to younger respondents. This means that tech- nology will be adopted more by older individuals if this will help them excel in their performance or help them in their task or job compared to younger individuals. There- fore, performance expectancy has positive impact to older individuals when compared to younger individuals on mobile learning. On the other hand, effort expectancy has positive impact to younger individuals when compared to older individuals. An F value of 9.117 with a mark of .001 with a verbal interpretation of very significant was observed to pairs 19-below VS 20 – 29, 19- below VS 30 – 39, 19- below VS 40 -49 and 19- below VS 50-above in terms of effort expectancy. This data connotes that young ones exerted less effort in mobile learning compared to older people. Influencing younger individuals on the adoption to mobile learning is much easier compared to older individuals. The data prove this because an F value of 5.823 with a mark of .000 with a verbal interpretation of very significant was observed to pairs 19- below VS 20 – 29, 19- below VS 30 – 39, 19- below VS 40 -49 and 19- below VS 50- above in terms of social influence. Opinions of the people who are important to them iJIM ‒ Vol. 13, No. 8, 2019 131 Paper—Diagnosing Adoption to Mobile Learning are much regard and by young ones compared to older people. Therefore social influ- ence in mobile learning has positive impact to younger compared to older individuals. When it comes to facilitating condition, there is a positive impact on older individ- uals on the adoption to mobile learning compared to younger individuals. An F value of 5.9775 with a mark of .000 with a verbal interpretation of very significant was observed to pairs 19- below VS 30 – 39, 19- below VS 40 -49 and 19- below VS 50- above was the basis of this analysis. Older persons have more positive views that organizational and technical infrastructure exist to support mobile learning compared to younger individuals. This shows that older persons are more aware that in the or- ganization there is a unit that will assist them on the use of mobile learning. This dif- ference suggest that adults put emphasis on facilitating condition compared to young- er person [19]. Using this finding older people will look for someone to assist them on their technology needs. An F value of 2.963 with a mark of .016 and a verbal interpretation of significant was observed to pairs 19 - below VS 50 above, 20 - 29 VS 50 above, 30 - 39 VS 50 above and 40 - 49 VS 50 above yielded from Anxiety. The data connotes that younger individuals exhibit a positive emotional reaction towards mobile learning compared to older persons. It is safe to say that self-efficacy of the respondents when related to age is not sig- nificant in the adoption of mobile learning, an F value of 1.504 with a mark of .200 as seen on table. Understanding age difference is beneficial on the use of technology [20]. Among the determinants of user intention, facilitating condition and self-efficacy were found to have a significant difference when the respondents are grouped accord- ing to gender. The scores for facilitating condition in female respondents(x=3.0234, SD=.98717) and male respondents (x=3.3159, SD=.94511) with a value of =3.096, p=.003) suggests that male respondents have higher belief that organizational and technical infrastructure such as hardware, software and people resources exists in CEU for mobile learning compared to female respondents. In terms of self-efficacy, the results show that female respondents(x=3.1750, SD=1.02900) and male respondents (x=3.4835, SD=.86999) with a value of =3.259 p=.001). Male users’ belief in his capacity towards mobile learning is greater when compared to female respondents. This suggests that male users are more independent in doing task related to mobile learning and that male are more users of technology compared to female. This findings relates to that male students has higher self- efficacy in computing than the male students.[21] However in table 8, there is no significant relationship when respondents’ assess- ments of mobile learning in terms of performance expectancy, effort expectancy, social influence and anxiety when grouped according to gender. 132 http://www.i-jim.org Paper—Diagnosing Adoption to Mobile Learning Table 7. Comparison of Respondents’ Assessments of Mobile Learning Technology In Terms Of Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Con- ditions When Grouped According To Age Mean SD F Sig. V.I. Pair Performance Expec- tancy 19 below 4.01 1.19 .949 .001 Very Signifi- cant 19- below VS 30-39 20 - 29 4.24 1.21 30 - 39 4.80 1.05 40 -49 4.5 1.30 50-above 4.91 1.14 Total 4.14 1.22 Effort Expectancy 19 below 3.53 0.99 9.117 .001 Very Signifi- cant 19- below VS 20 - 29 19- below VS 30 - 39 19- below VS 40 -49 19- below VS 50-above 20 - 29 3.60 1.00 30 - 39 4.19 1.13 40 -49 4.39 1.48 50-above 4.84 1.16 Total 3.65 1.06 Social Influence 19 below 3.83 0.98 5.823 .000 Very Signifi- cant 19- below VS 20 - 29 19- below VS 30 - 39 19- blow VS 40 -49 19- below VS 50-above 20 - 29 3.88 0.99 30 - 39 4.45 0.83 40 -49 4.39 1.07 50-above 4.75 1.18 Total 3.92 0.99 Facilitating Condition 19 below 4.02 0.95 5.977 .000 Very Signifi- cant 19- below VS 30 - 39 19- below VS 40 -49 19- below VS 50-above 20 - 29 4.22 0.91 30 - 39 4.54 1.04 40 -49 4.56 1.16 50-above 4.98 1.16 Total 4.14 0.98 Anxiety 19 below 3.7803 0.94302 2.963 .016 Signifi-cant 19 - below VS 50above 20 - 29 VS 50 above 30 - 39 VS 50 above 40 - 49 VS 50 above 20-29 3.7323 0.94102 30-39 3.5081 1.02161 40-49 3.625 1.07626 50 above 2.8409 1.10834 Total 3.7178 0.96465 Self-Efficacy 19 below 3.2281 0.9947 1.504 .200 Not Signifi- cant 20-29 3.4066 0.91584 30-39 3.5806 0.86936 40-49 3.375 1.03682 50 above 3.0682 1.33272 Total 3.2914 0.98283 iJIM ‒ Vol. 13, No. 8, 2019 133 Paper—Diagnosing Adoption to Mobile Learning Table 8. Comparison of the Determinants of User Intention When grouped according to Gen- der Group Statistics Gender Mean Std. Deviation t value Verbal Interpretation Performance Expectancy Male 3.1921 1.24308 .759 p=.448 Not Significant Female 3.1024 1.19989 Effort Expectancy Male 2.7605 1.13387 1.842 p=.066 Not Significant Female 2.5718 1.00489 Social Influence Male 2.9686 1.02718 .815 p=.415 Not Significant Female 2.8893 .98506 Facilitating Condition Male 3.3159 .94511 3.096 p=.003 Very Significant Female 3.0234 .98717 Anxiety Male 2.8009 1.01366 1.480 p=.139 Not Significant Female 2.6623 .93250 Self -Efficacy Male 3.4835 .86999 3.259 p=.001 Very Significant Female 3.1750 1.02900 Table 9 presents the results on the comparison of the respondents’ assessments of mobile learning in terms of performance expectancy, effort expectancy, social influ- ence and facilitating conditions when grouped according to experience. Among the determinants of user behavior, experience when related to self-efficacy has no signifi- cance. This was based on the yielded data 1st Year (x=3.30, SD=0.86), 2nd Year (x=3.54, SD=0.99), 3rd Year (x=3.17, SD=1.037), 4th Year (x=3.47, SD=1.093) and Employee (x=3.48, SD=0.98) with a value of =1.601 p=.158). Significant findings were found between pair 1st Years and faculty On the assess- ment on performance expectancy, the mark of 1st Year (x=3.05, SD=1.11) and faculty (x=3.95, SD=1.19) suggests that employees adoption to mobile learning is higher because they believe that this will help them in their work compared to students’ rat- ing. The same findings are on effort expectancy 1st Year (x=2.55, SD=0.94) vs Facul- ty (x=3.51, SD=1.00) , social influence 1st Year (x=2.88, SD=0.97) vs Faculty (x=3.43, SD=1.06) and anxiety 1st Year (x=2.87, SD=0.90) vs Faculty (x=2.13, SD=0.98). These data suggest that there is a higher tendency for the faculty compared to 1st Year students to adopt mobile learning if the use of such technology is easy and effortless, influenced by their peers to use and a worry free adoption. Table 9. Comparison Of The Respondents’ Assessments Of Mobile learning In Terms Of Performance Expectancy, Effort Expectancy, Social Influence And Facilitating Condi- tions When Grouped According To Experience Mean SD F Sig. V.I. Pair Performance Expectancy 1st Year 3.0525 1.11944 3.294 .011 Very Significant 1 VS 5 2nd Year 3.4645 1.24428 3rd Year 3.0127 1.20667 4th Year 2.9265 1.31626 134 http://www.i-jim.org Paper—Diagnosing Adoption to Mobile Learning Employee 3.9464 1.13162 Total 3.1085 1.19753 Effort Expectancy 1st Year 2.5543 .94596 4.152 .003 Very Significant 1 VS 5 2nd Year 2.6968 1.01341 3rd Year 2.4552 .98122 4th Year 2.7500 1.12152 Employee 3.5179 1.31728 Total 2.5675 1.00756 Social Influence 1st Year 2.8804 .97094 2.564 .038 Significant 1 VS 5 2nd Year 3.1330 .95104 3rd Year 2.7690 .94394 4th Year 2.9265 1.07079 Employee 3.4286 1.0620 Total 2.8800 .97122 Anxiety 1st Year 2.87 0.90 4.927 .000 Very Significant 1VS5 2VS 5 2nd Year 3.03 1.00 3rd Year 2.69 0.93 4th Year 2.41 0.78 Employee 2.13 0.98 Total 2.72 0.95 Self-Efficacy 1st Year 3.30 0.86 1.601 .158 Not Significant 2nd Year 3.54 0.99 3rd Year 3.17 1.037 4th Year 3.47 1.093 Employee 3.48 1.096 Total 3.29 0.98 Comparison Of The Respondents’ Assessments Of Mobile Learning In Terms Of Performance Expectancy, Effort Expectancy, Social Influence And Facilitat- ing Conditions When Grouped According To Voluntariness Of Use. In terms of respondents’ assessments of WI-FI technology when grouped according to voluntari- ness of use compared with the different determinants of user intention no significant finding were yielded. Free will is not a factor to consider in adopting mobile learning. Table 10. Predictor of the User Intention to adopt mobile learning Determinants R square (Coefficient of Determination) β (Beta Coefficient) Facilitating Condition 36 % Performance Expectancy 44% Effort Expectancy 48% Self-Efficacy 51% Anxiety 53% Excluded Determinants R square (Coefficient of Determination) β (Beta Coefficient) Social Influence iJIM ‒ Vol. 13, No. 8, 2019 135 Paper—Diagnosing Adoption to Mobile Learning Between determinants of user to adopt mobile learning, anxiety top the rank (53%), followed by self-efficacy (51%), effort expectancy (48%), performance expectancy (44%) and facilitating condition (36%) as the last factor. Excluded from the factor is social influence. This explains that among the determinants of user intention respond- ents' anxiety is a good predictor in adopting mobile learning while influences from their peers will not contribute its success. 7 Conclusion Anxiety, self-efficacy and effort expectancy are the top three identified factors in the use and adoption of mobile learning using UTAUT model. There is a need to mitigate faculty and students findings of apprehension, fear, hesitation and intimida- tion. Increase the confidence of the faculty and students by providing training work- shops and demonstration on the use of mobile learning is necessary. A comprehensive program to implement to develop skills needed in shifting on the use of technology in the teaching and learning process. Since self-efficacy was also identified as one of the factors inclusion of criteria in their performance ranking and on grading system on mobile learning can be a scheme to make adoption a success. Moreover recognizing the effort on the use of technology could also be a strategy. Hence forth in the future anxiety, self-efficacy and effort expectancy will no longer be a factor why adopting to new technology will fail. 8 Recommendation As an initial action to address anxiety on the adoption to mobile learning, training of users, provision of technical assistance to faculty and students and readily available manuals and guidelines should be drafted to ensure success in mobile learning imple- mentation. 9 References [1] Boren, Z. D. (2014, October 7). Independent. Retrieved from Indendent: more-mobile- devices-than-people-in-the-world-9780518.html [2] Nordrum, A. (2016, August 18). IEEE Spectrum. Retrieved December 13, 2017, from IEEE Spectrum: https://spectrum.ieee.org/tech-talk/telecom/internet/popular-internet-of-thi ngs-forecast-of-50-billion-devices-by-2020-is-outdated. https://doi.org/10.1109/mspec.201 6.7572524. [3] Statistica. (2016). The Statistical Portal. 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