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Using Mobile Learning:Determinates ImpactingBehavioral IntentionJeffrey N. Lowenthal aa Northeastern State UniversityPublished online: 01 Dec 2010.

To cite this article: Jeffrey N. Lowenthal (2010) Using Mobile Learning: DeterminatesImpacting Behavioral Intention, American Journal of Distance Education, 24:4,195-206, DOI: 10.1080/08923647.2010.519947

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The Amer. Jrnl. of Distance Education, 24:195–206, 2010Copyright © Taylor & Francis Group, LLCISSN 0892-3647 print / 1538-9286 onlineDOI: 10.1080/08923647.2010.519947

Using Mobile Learning: Determinates ImpactingBehavioral Intention

Jeffrey N. LowenthalNortheastern State University

Abstract: This study examined the factors or determinates that impact the behavioralintention of students to use mobile learning (m-learning) technology. These deter-minates include performance expectancy, effort expectancy, and self-management oflearning, all mediated by age, gender, or both. Regression coefficients showed strongand significant relationships between performance expectancy and effort expectancyand the behavioral intention of using an m-learning strategy, whereas age and genderwere determined to have no mediating impact.

Mobile learning (m-learning) is a rapidly growing alternative educationalstrategy. Today, entire conferences are dedicated to this technology. These con-ferences feature a plethora of sessions on topics ranging from technology tocontent-specific courses. Very few, if any, of these sessions explore presentationstyles or modalities or the behavioral aspects of learners.

With m-learning the limitation of learning location is eliminated by theuse of portable devices such as cell phones and iPods. The learner can conve-niently access content from virtually anywhere. M-learning can be considered“any sort of learning that happens when the learner is not at a fixed, prede-termined location, or learning that happens when the learner takes advantageof the learning opportunities offered by mobile technologies” (O’Malley et al.2003, 6).

M-learning is still developing in terms of both its technologies and itspedagogies. It was not until recently that Kukulska-Hulme and Traxler (2005)published the first comprehensive handbook of m-learning.

The purpose of this article is twofold: first, to explore the issue of readi-ness of the potential learners—that is, explore the determinates of a successful

Correspondence should be sent to Jeffrey N. Lowenthal, Northeastern StateUniversity, College of Business and Technology, 700 North Grand Avenue, HH-218,Tahlequah, OK 74464. E-mail: lowentha@nsuok.edu

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m-learning intervention; and second, to examine if age or gender has anymediating impact of behavioral intention.

DETERMINANTS, AGE, AND GENDER DIFFERENCES

As with any new technology, general acceptance is one of the key issuesconfronting e-learning and, more directly, m-learning. Only recently havestudies explored the topic of m-learning acceptance. A foundational studyfor this research was the published work of Wang, Wu, and Wang (2009).These authors examined the various determinates in the use of m-learning.Specifically, they used five identified determinants of behavioral intention touse m-learning: performance expectancy, effort expectancy, social influence,perceived playfulness, and self-management of learning. Their research sug-gested that age differences can moderate the effects of effort expectancy andsocial influence on m-learning use intention and that gender differences canmoderate the effects of social influence and self-management of learning onm-learning use intention. Wang, Wu, and Wang expanded on the works ofCzaja and Lee (2001); Davis, Bagozzi, and Warshaw (1989); and Govindasamy(2002).

One of the potential limitations of the Wang, Wu, and Wang (2009) studywas that it examined a particular technology and targeted a specific user groupin Taiwan (i.e., business and industry). Can these results be generalized to par-ticipants in the United States? Second, their study measured perceptions andintentions at a single point in time, and perceptions change over time as indi-viduals gain experience (Mathieson, Peacock, and Chin 2001; Venkatesh andDavis 1996; Venkatesh et al. 2003). The current study explores whether theseidentified determinants to behavioral intentions can be generalized to Americanparticipants and specifically university students.

LEARNING THEORY TO DESIGN INSTRUCTION

As noted earlier, a critical determinate to the success of an m-learning projectwas effort expectancy or the ease of use of the courseware. This elementcontains two components: technology or presentation modality and the instruc-tional design of the content. Craik and Lockhart (1972) and Ausubel (1974)saw learning as an internal process and suggested that the amount learneddepends on the processing capacity of the learner, the amount of effortexpended during the learning process, the depth of the processing, and thelearner’s existing knowledge structure. Learners learn best when they canattach personal meaning to the content or when they can see some immedi-ate application of the knowledge. M-learning facilitates personalized learning

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because learning (and collaboration) from any place and at any time allows thelearning to be contextualized (Hewson et al. 2003).

Given that mobile devices have small screen displays, the content pre-sented should compensate for the smaller size. Ideally, the interface shouldbe graphical and present no more than five to nine blocks of information onthe screen to prevent information overload in short-term memory. Since mostlearning courseware is information intense, the instructional design shouldpresent fewer concepts on one screen to prevent informational overload. In theMarch 2009 issue of The Chronicle of Higher Education, the term micro-lectures was presented (Shieth 2009). A micro-lecture is the presentation ofcontent in one- to five-minute blocks with information organized in the formof concept maps to give the overall structure. One of the key advantages of themicro-lecture structure is that it allows a course or lesson to be comprised of anumber of learning objects (i.e., micro-lectures) that are sequenced to form aninstructional event for a lesson or learning session (Ally 2004).

This instructional strategy broadens the acquisition of knowledge by thecreation of various learning objects to accommodate different learning stylesand characteristics. These objects would then be placed in an electronic repos-itory for just-in-time access from anywhere using mobile devices. The use oflearning objects allows for instant assembly of learning materials by learners,intelligent agents, and instructors, which facilitates just-in-time learning andtraining.

RESEARCH MODEL AND STUDY QUESTIONS

The key questions this study explored are as follows:

● Which of the following determinates—performance expectancy, effortexpectancy, and self-management of learning—impacted the behavioralintention to use m-learning?

● Does age, gender, or both have any influence on these determinates?● Which m-learning presentation format best promotes the transference of

knowledge?

The research model used in this study is shown in Figure 1.

Determinates Defined

Performance expectancy is defined as the extent to which an individual believesthat using m-learning will be helpful in acquiring knowledge and/or getting abetter class grade (Morris and Venkatesh 2000; Venkatesh et al. 2003; Wang,

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Figure 1. Research Model of m-Learning Determinates

Wu, and Wang 2009). Effort expectancy is defined as the extent to whichan individual believes that the m-learning system is easy to use (Venkatesh,Morris, and Ackerman 2000; Venkatesh et al. 2003; Wang, Wu, and Wang2009). Self-management of learning is defined as the extent to which an indi-vidual believes he or she is self-disciplined and can engage in autonomouslearning (Smith, Murphy, and Mahoney 2003). The need for self-managementor self-direction in the success of distance learning can be seen throughout theliterature (Evans 2000; Smith, Murphy, and Mahoney 2003).

METHOD

Measures

This study looked at two components: m-learning determinants and presen-tation modalities. Regarding the former, the items used to measure perfor-mance expectancy, effort expectancy, and behavioral intention were adaptedfrom Wang, Wu, and Wang (2009) and Venkatesh et al. (2003). Four itemsselected from Smith, Murphy, and Mahoney (2003) were used to measureself-management of learning. The wording was modified so as to direct the

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Table 1. Characteristics of the Respondents

Characteristics Number %

Male 51 45Female 62 5520–29 61 5430–39 29 2640–49 15 1350–60 8 7L-Undergrad 30 27U-Undergrad 46 41Grad 37 33

questions to university students instead of corporate participants. A seven-pointLikert scale was used for all construct items.

Specifically, lessons were developed on the three distinct Microsoft Exceltopics of (1) how to print a worksheet, (2) how to use the Excel ribbon tomanipulate (e.g., change cell type, size, and reference) data, and (3) how toformat cells. For each topic, a lesson was produced using each of the differentpresentation styles. Study participants were provided three lessons for a singletopic and given a period of time to view the different formats. They were nextasked to force-rank the formats that they felt would promote the learning frombest to least. The Appendix lists the original items used in this study.

Participants

This study adopted a convenience sampling technique (i.e., nonrandom sam-pling) to collect the study data. University students were identified as studyparticipants. Three general classifications of students were identified: lower-level undergraduates (freshman and sophomores), upper-level undergraduates(juniors and seniors), and graduate students. To make the results applicable toa broad population, an attempt was made to ensure that there was an equaldistribution among the three groups (refer to Table 1 for numbers within eachgrouping and the total).

Potential participants were invited to participate in the study and were bro-ken into two groups: individuals who had a mobile device that could play videofiles and those who did not have this capability. The latter group was presentedwith a one-page survey (refer to Appendix) and introduced to the definitionof m-learning. This allowed these participants to understand fully the meaningof the term m-learning in the questionnaire. The former group of respondentswho qualified and agreed to participate were provided a disc with various Excellessons included. They were asked to view the various files on their mobiledevice, and two weeks later they were given the same instructions to circle

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the response that best described their level of agreement with the determinatestatements. On this basis, a sample of 113 usable responses (out of 115 com-pleted surveys) was obtained from a variety of respondents. The two droppedsurveys represented participants who withdrew from the course and could notbe located.

DATA ANALYSIS AND RESULTS

A multianalysis approach was utilized. Statistical tests included a VarimaxRotation Factor Analysis, Pearson product-moment correlation coefficient, anda regression analysis.

Determinates

The first step was to understand or reduce the data dimensions by analyzing thedata covariance structure. Specifically, a Varimax Rotation Factor Analysis wasconducted in an attempt to reduce the data into a smaller number of componentsand to describe the covariance among variables in terms of a few underly-ing factors. Four distinct factor groupings are identified as noted in Table 2;this was confirmed using a cluster variable analysis that assessed correlationcoefficient distance, with single linkage with amalgamation steps. Refer toTable 2.

Table 2. Varimax Rotation Factor Analysis

Variable Factor1 Factor2 Factor3 Factor4 Factor5 Communality

PE1 0.869 0.352 −0.109 0.024 −0.054 0.894PE2 0.907 0.228 −0.138 −0.104 0.132 0.922PE3 0.891 0.238 −0.026 0.049 0.235 0.909PE4 0.847 0.242 −0.073 0.153 0.338 0.919EE1 0.379 0.863 −0.083 0.025 −0.038 0.898EE2 0.399 0.887 −0.063 0.048 0.023 0.953EE3 0.365 0.893 −0.052 0.055 0.005 0.937SL1 0.039 −0.042 0.287 −0.934 −0.031 0.958SL2 −0.145 −0.159 0.742 −0.510 0.121 0.871SL3 −0.138 −0.089 0.914 −0.162 0.031 0.890SL4 −0.030 0.016 0.942 −0.028 −0.078 0.895BI1 −0.004 0.362 −0.034 0.867 −0.209 0.928BI2 0.010 0.351 −0.102 0.849 −0.300 0.945BI3 −0.034 0.312 −0.111 0.887 −0.253 0.961

Variance 5.8266 3.0079 2.4291 1.2032 0.4119 12.8788% Var 0.416 0.215 0.174 0.086 0.029 0.920

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Table 3. Correlation Analysis

PE EE SL BI

Performance expectancy 1.000 .637∗ −.230∗∗ .889∗

Sig. .000 .014 .000Effort expectancy .637∗ 1.000 −.209∗∗ .679∗

Sig. .000 .026 .000Self-management of learning −.230∗∗ −.209∗∗ 1.000 −.223∗∗

Sig. .014 .026 .018Behavioral intention .889∗ .679∗ −.223∗∗ 1.000Sig. .000 .000 .018

∗Correlation is significant at the .01 level.∗∗Correlation is significant at the .05 level.

To determine if there is a relationship between determinates and behaviorintention, a correlative analysis was conducted. Several variables were deter-mined to have a weak relationship whereas others were determined to have astrong relationship, as seen in Table 3.

Next, a regression equation was estimated. The independent variablesincluded the clusters of performance expectancy, effort expectancy, and self-management of learning. The results of the analysis are presented in Table 4.The columns in Table 4 represent the regression estimated with the coeffi-cient on the performance expectancy, effort expectancy, and self-managementof learning. The t statistic and the number of observations are also reported.

The author expected the coefficient on the behavioral intention to be posi-tive in the regression, indicating that an increase in the behavioral intention ofthe respondent is associated with an increase of performance expectancy andeffort expectancy.

The first model regresses performance expectancy on behavioral intentionto use m-learning. The coefficient on performance expectancy is 0.78876 andis significant at the 5% level, given the t statistic of 14.08. This means that, as

Table 4. Relationship Between Measures of Behavioral Intention and Determinates

Independent variables

Performanceexpectancy

Effortexpectancy

Self-managementof learning

Coefficient on 0.78876 0.21182 −0.01153behavioral intention (14.08)∗ (3.50)∗ (−0.16)

Observations 113 113 113

Note: t statistics in parentheses.∗Significant at the 5% level.

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Table 5. Relationship Between Measures of Behavioral Intention andDeterminates With Age and Gender as Mediating Factors

Mediating factors

Age Gender

Coefficient on behavioral intention 0.007263 0.12712(0.79/.429) (0.79/.430)

Observations 113 113

t statistics/sig in parentheses.∗Significant at the 5% level.

the respondent feels that the m-learning system would help increase his or herability to do better in a learning environment, he or she is much more likely touse this modality as a learning strategy.

The second model regresses effort expectancy on behavioral intention touse m-learning. The coefficient is 0.21182 and is significant at the 5% levelsince the t statistic is 3.50. The coefficient of 0.21182 means that even if therespondent views the m-learning system as easy to use, this will only slightlyincrease his or her probability to use this modality as a learning strategy.The final model regresses self-management of learning on behavioral inten-tion. Since the coefficient is –0.01153 and the t statistic was a minus number(–0.16), the author was unable to establish any significance and this was alsosupported in the correlation analysis.

Age and Gender

Study Questions 4 and 5 examined the relationship between measures of behav-ioral intention and determinates with age and gender as mediating factors.A regression equation was estimated. The independent variables included theclusters of performance expectancy, effort expectancy, and self-management oflearning with the mediation of age and gender. The results of the analysis arepresented in Table 5. The columns in the table represent the regression esti-mated with the coefficient on the age and gender; the t statistic and the numberof observations are also reported.

These models regress age and gender as mediating factors and the coef-ficients are .007263 and 0.12712, respectfully. The t statistics in both modelswere 0.79, and the author was unable to establish any significance.

DISCUSSION, CONCLUSIONS, AND IMPLICATIONS

As noted in a prior section, the author was able to determine a positiverelationship between the determinate of performance and effort expectancy

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and the behavioral intent of the user. This supported the findings of Wang(2003); Wang, Wu, and Wang (2009); and Venkatesh et al. (2003). However,the burgeoning literature—Attewell and Savill-Smith (2003); Gayeski (2002);Davis, Bagozzi, and Warshaw (1989); and many others—seems to analyzemany aspects of m-learning ranging from collaborative learning (Lundin andMagnusson 2003) to content-specific areas such as nursing and MBA studies(Frohberg 2004), but there seems to be little research on how best to encour-age users to use the learning strategy. These authors focused on technologyand content, not on the behavior or psychological aspects of this learningstrategy.

Implications for Researchers

This study looked at those factors or determinates that would promote thebehavioral intentions of potential uses of m-learning. A strong attempt wasmade to ensure that thorough procedures were implemented.

There are, however, some limitations to this study. First, this studyfocused only on university students. Although there was a broad age rangecovered (20- to 60-year-olds), this study did not look at m-learning withina business/industry setting as did Wang, Wu, and Wang (2009). Are thedeterminates different for corporate training in the United States?

Second, the study sample was limited to students within a college/schoolof business. The results might not be generalized to all fields of study within auniversity. To eliminate this potential sampling bias, future studies should takea stratified random sample from across a university, cover multiple campusesat various locations in a broader geographic area, or both.

Third, the use of self-report scales to measure study variables suggests thepossibility of a common method bias for some of the results. Future researchshould employ both objective and subjective measures.

Finally, this study did not actually examine those factors that impact theuse of m-learning. Venkatesh and Davis (2000) and Venkatesh, Morris, andAckerman (2000) empirically demonstrated causal link between intention andbehavior. This is not a major issue and can easily be eliminated in the proposedsecond phase of this study—assessing the learning outcomes of the m-learningstrategy.

Practical Implications

There is a rapid growth in m-learning as technology continually changes andeducational institutions adjust to the changing demand of students. However,no evidence suggests that simply shifting content from on-ground classesto mobile devices would be sufficient. The diversity of student populations

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requires that institutions carefully develop programs that would satisfy a broadrange of learning requirements. This challenge is intensified by changes tothe competitive environment. These changes include lifelong learning wheretraditional schools are competing with corporate and virtual universities forreturning and mature student populations.

To properly implement m-learning, institutions are positioned to helpstudents succeed by preparing them for this new learning strategy, mak-ing the courseware user friendly, and making apparent to the students thebenefit of m-learning technology. Potentially, the critical factors identifiedwithin this study may either propel a program forward or, if implementedimproperly, demolish it. However, more research on this topic is required andsuggested.

M-learning potentially offers universities benefits, such as increasedenrollment and broader student populations (demographically and geograph-ically), as learners are able to participate from a widened expanse of locations.Additionally, educational institutions can significantly increase learning trans-fer and gain a much-needed competitive edge in today’s marketplace.

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APPENDIX

Determinates Survey Items Used in the Study1

Performance expectancyPE1: I would find m-learning useful in my learning.PE2: Using m-learning would enable me to accomplish learning activities more

quickly.PE3: Using m-learning would increase my learning productivity.PE4: If I use m-learning, I will increase my chances of getting a better grade in

class.

Effort expectancyEE1: It would be easy for me to become skillful at using m-learning.EE2: I would find m-learning easy to use.EE3: Learning to operate m-learning is easy for me.

Self-management of learningSL1: When it comes to learning and studying, I am a self-directed person.SL2: In my studies, I am self-disciplined and find it easy to set aside reading

and homework time.SL3: I am able to manage my study time effectively and easily complete

assignments on time.SL4: In my studies, I set goals and have a high degree of initiative.

Behavioral intention to use m-learningBI1: I intend to use m-learning in the future.BI2: I predict I would use m-learning in the future.BI3: If available, I plan to use m-learning in the future.

1Adapted from the surveys of Wang, Wu, and Wang (2009); Venkatesh et al. (2003);and Smith, Murphy, and Mahoney (2003).

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