The purpose of this empirical study was to test specific factors of behavioral intention to use m-learning in a community college setting using a modified technology acceptance model and antecedent factors suggested by the researcher’s review of the literature. In addition, the study’s purpose was to expand understanding of behavioral intention to use m-learning and to contribute to the growing body of research. This research model was based on relevant technology acceptance literature. The study examines the significance of “prior use of e-learning” and correlation with the behavioral intention to use m-learning. Existing models have looked at prior use of e-learning in other domains, but not specifically m-learning. Other models and studies have primarily looked at the prior use of e-learning variable as a moderating variable and not one that is directly related to attitude and behavioral intention. The study found that there is a relationship between prior use of e-learning and behavioral intention to use m-learning. This research direction was proposed by Lu and Viehland.

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DOI: 10.1177/2158244015621114
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Article
The growth of the Internet and mobile wireless technologies,
and the acceptance of electronic learning (e-learning) have
provided a foundation for the growth of mobile learning
(m-learning) and formed a context in which it can co-exist
and expand educational opportunities (Hoppe, Joiner, Milrad,
& Sharples, 2003; Massey, Ramesh, & Khatri, 2006). This
introduction examines the concept of m-learning and how
the technology is viewed and framed in higher education and
how the individual looks at this technology. Little research
has been done on m-learning adoption factors, although
many studies have been completed on wireless service adop-
tion and other areas, which offer some insight into the study,
as the mobile wireless industry is related to m-learning from
a technological perspective. Such studies have proven useful
in understanding adoption factors and intentions that make
the technology more useful and user friendly. These studies
have been driven by new research into the context of m-learn-
ing as mobile devices have become the main source of com-
munication device for college students and other groups (Lu
& Viehland, 2008; Walker & Jorn, 2009). The studies sug-
gest that the Technology Acceptance Model (TAM) devel-
oped by Davis in 1989 is useful in determining the correlation
and effects of antecedent variables on behavioral intentions
(BI) to use wireless devices in many organizational contexts
for a variety of purposes. To support this point, it is estimated
that more than 500 million smartphones were purchased
worldwide in 2011 (Weintraub, 2010) and that they will rep-
resent the majority of purchased cellular devices by 2011
(Entner, 2010). Smartphone sales are also expected to be
greater than that of personal computers in 2012 (Brownlow,
2012). The number of smartphone users worldwide is
expected to surpass 2 billion in 2016 (Curtis, 2014). These
facts have made studying mobile devices critical for many
areas both inside and outside of education.
The task–technology fit needs to be understood at a more
comprehensive level than it currently is; universities and
schools need empirical research of m-learning to make deci-
sions on its use and implementation, and most importantly,
621114SGOXXX10.1177/2158244015621114SAGE OpenAbramson et al
research-article2015
1
Post University, Waterbury, CT, USA
2
University of Missouri–St. Louis, USA
3
McNeese State University, Lake Charles, LA, USA
Corresponding Author:
Maurice Dawson, Assistant Professor, Department of Information
Systems, College of Business Administration, University of Missouri–St.
Louis, 228 Express Scripts Hall, 1 University Blvd., St. Louis, MO 63121,
USA.
Email: Maurice.e.dawson@gmail.com
An Examination of the Prior Use
of E-Learning Within an Extended
Technology Acceptance Model and the
Factors That Influence the Behavioral
Intention of Users to Use M-Learning
Jonathan Abramson1
, Maurice Dawson2
, and Jeffery Stevens3
Abstract
The purpose of this empirical study was to test specific factors of behavioral intention to use m-learning in a community
college setting using a modified technology acceptance model and antecedent factors suggested by the researcher’s review
of the literature. In addition, the study’s purpose was to expand understanding of behavioral intention to use m-learning
and to contribute to the growing body of research. This research model was based on relevant technology acceptance
literature. The study examines the significance of “prior use of e-learning” and correlation with the behavioral intention
to use m-learning. Existing models have looked at prior use of e-learning in other domains, but not specifically m-learning.
Other models and studies have primarily looked at the prior use of e-learning variable as a moderating variable and not one
that is directly related to attitude and behavioral intention. The study found that there is a relationship between prior use of
e-learning and behavioral intention to use m-learning. This research direction was proposed by Lu and Viehland.
Keywords
m-learning, technology acceptance, behavioral intention
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2. 2 SAGE Open
its effectiveness. Just because a technology is widely avail-
able, does not mean that it will be used or adopted (Liu, Han,
& Li, 2010). This idea has been the premise of adoption
research in all systems research: A system is only good if it is
used.
Mobile devices are used in high numbers; and the ques-
tion is one of how, why, and what. What makes the students
want to use mobile devices, and how can this best be facili-
tated by organizations such as universities? This study and
other related studies are attempting to address the new reality
of ubiquitous computing devices at the university level.
Yordanova (2007) states that wireless technologies have high
acceptance among the younger generation. M-learning has
the attributes of being both mobile and ubiquitous (Alexander,
2006; Yordanova, 2007). M-learning is an extension of
e-learning and has been tested in the literature as a stand-
alone educational platform, but more importantly, as add-on
platform and channel for existing hybrid, face to face, and
e-learning classes. Seong states that “mobile learning pre-
sumes the use of mobile Internet technology to facilitate the
learning process” (Seong, 2006, p. 1). This presumption is
founded on the rapid growth of wireless and mobile comput-
ing devices (Seong, 2006). Mobile devices are already being
used by a majority of the students for other purposes; the
literature illustrated that there were many studies regarding
learning and mobile devices. Sharples (2007) stated there is
a need to re-conceptualize learning for the mobile age, point-
ing out that there are many existing roles of mobility and
communication in the learning process. Many of these
changes are found within e-learning with its collaborative
advantages and constructivist nature. This was shown in the
review of the literature, as numerous applications of m-learn-
ing were provided. Therefore, a logical next step is to deter-
mine the effective ways to use these devices within the
contemporary classroom, whether brick and mortar, e-
learning, or a hybrid learning environment.There is a lack of
empirical research concerning m-learning adoption factors.
A continuing issue in information systems research is the
identification and determination of the factors that are related
to the cause and then acceptance of a technology (King &
He, 2006). Shengquan, Xianmin, Gang, and Minjuan (2015)
indicate that not much research has been conducted in the
discipline of m-learning as this is fairly new and is just gain-
ing acceptance as a research object within the literature.
M-learning is supplemental and aids the student by provid-
ing ubiquitous access to both the online and hybrid class-
room. Because these types of learning are collaboration
intensive and constructivist in nature, the smartphone has
been adopted by many for the purpose of extending as it is an
ideally suited technology for expanding the classroom.
SEM Model Variables
Self-efficacy (SE) is the individual’s comfort level with
using technology (Tweed, 2013). The facilitating conditions
are the second component, which is the availability of
resources needed to use the technology. Subjective norms
(SN) are social pressures that make an individual perform a
particular behavior (Ajzen, 1991). The individual’s social
groups may have different opinions regarding the adoption
of a technology. Prior use of e-learning (PRIORE) is the indi-
vidual’s prior exposure and use of e-learning technologies. In
this study, we are using a learning management system as the
standard for including the individual in the study.
Perceived usefulness (PU) is the degree to which the indi-
vidual believes that a technology would improve his or her
job performance (Davis, 1989). Perceived ease of use
(PEOU) is the degree to which an innovation is easy to
understand (Rogers, 2003) or the degree to which the tech-
nology is free of effort (Davis, 1989). Innovations that are
perceived to be less complex to use and have a higher possi-
bility of adoption/acceptance by potential users
BIs are correlated with actual behavior. BIs are “the single
best predictor of actual usage” (Venkatesh & Davis, 1996,
p. 20). In addition, “the intention-behavior linkage is proba-
bly the most uncritically accepted assumption in social sci-
ence research” (Bagozzi, 2007, p. 245). Ajzen (1991) found
that an individual’s attitude toward a particular behavior is
equivalent to that person’s overall assessment of performing
the behavior.
Data Analysis
Many of the relevant variables in this study had been prede-
termined by the application of previous research models.
Therefore, selection of relevant variables was predetermined
for examination and possible inclusion or exclusion in the
study based on their respective strength in the related
studies.
The survey data were entered into Warp Partial Least
Squares (PLS) 3.0. PLS is a second generation statistical
technique for conducting Structural Equation Modeling
(SEM)-based analysis. The utility of PLS is detailed else-
where (Falk & Miller, 1992). With respect to technology
acceptance, a number of recent studies utilized PLS
(Al-Gahtani, 2001; Venkatesh, Morris, Davis, & Davis,
2003). PLS allows for the evaluation of psychometric prop-
erties of indicators used to measure a variable, and the esti-
mation of direction and strength of the relationships among
the model variables. PLS includes two sets of equations: the
measurement model, or outer model, composed of equations
representing the relationships between indicators and the
variable they measure, and the structural model composed of
equations representing the paths among the study’s variables.
PLS calculates weights and loading factors for each item in
relation to the construct. The weights, calculated by PLS, are
used to calculate latent variable scores for the constructs,
which reflect the contribution of each variable to its con-
struct. Factor loading, as with other studies of this nature,
were high (Cocosila & Archer, 2010), which are typical for
TAM studies.
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3. Abramson et al 3
Model fit indices are provided by the software after run-
ning the PLS analysis. Three model fit indices and associated
p values for the average path coefficient (APC) and average
R-squared (ARS). Model fit indices are a useful set of mea-
sures related to model quality (Kock, 2011). P values for the
APC andARS should be less than .05 (Kock, 2011); this con-
dition is met by both of the measures. Figure 1 below dis-
plays the research model with correlational coefficients and
associated metrics.
The measurement, or structural model, consists of latent
variables and relationships that represent the conceptual fac-
tors of interest. The path coefficients and p values are pre-
sented for the latent variables. Coefficients of determination
indicate the strength and direction of the relationship between
the latent variable pathways. The latent variables are the
results of the loadings, which are the values from the specific
questions in the study’s survey after treatment, including re-
sampling by the Warp PLS 3.0 program. Path coefficient, or
beta, is the equivalent of Spearman’s Rho correlational coef-
ficient, which, in the structural model, is used to describe the
strength of the linear relationships between the latent con-
structs. A corresponding measure that is shown with the beta
is the significance level, which is the t statistic for that coef-
ficient to its standard error. Shown on all but the outside
latent variables or antecedent variables are the coefficients of
determination or R square, which is calculated by squaring
the path coefficients. The result is used to determine the vari-
ance of the independent variable. SN in the research model
are significantly related to BI through PEOU but not through
PU. The identification of this relationship is going to be
dependent on many factors, such as causal relations.
Results
Attitude (ATT) was left in the research model even though
some TAM studies have removed this construct (Heerink,
Krose, Evers, & Wielinga, 2009; Holden & Karsh, 2010).
Attitude is quite significant in the model, as can be seen by
observing the research model with path coefficients. Specific
to the key external factor of interest in the study, we can
observe that the stronger significant relationship is through
PEOU, rather than directly to BI. The effect of ATT is defi-
nitely a strong determinant of BI and one that effectively
mediates many antecedent variables in the TAM model. This
research model is using ATT as an antecedent to BI.
Djamasbi, Siegel, Tullis, and Dai (2010) found that ATT was
an important factor and antecedent. Also, the use of affect as
a variable, which is the user’s global feelings, moods, and
emotions (Djamasbi et al., 2010), was significant and posi-
tively related to attitude. It was also found that affect can also
negatively influence attitude, although this point is beyond
the scope of this research. What is not beyond the scope of
Figure 1. Research model with correlational coefficients and associated metrics.
Note. SE = self-efficacy; PU = perceived usefulness; SN = subjective norms; ATT = attitude; BI = behavioral intention; PEOU = perceived ease of use;
PRIORE = prior use of e-learning.
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4. 4 SAGE Open
this research is the strong effect of attitude on BI and the
effectiveness of attitude as a relevant and critical mediator in
this model and others. Other studies have used Exploratory
Factor Analysis (EFA) and Confirmatory Factor Analysis
(CFA) to establish this when just the latent variables were
examined purely on correlation of the latent variables, with
no user intention model. Attitude’s inclusion as an anteced-
ent is based on the strong support in the literature for Attitude
Toward Behavior (ATB), which has been highly correlated
with user intention. ATB is defined as “an individual’s posi-
tive or negative feelings (evaluative affect) about performing
the target behavior” (Fishbein & Ajzen, 1975). M-learning
involves objects, but it is a behavior; therefore, attitude is
going to be a more relevant variable and antecedent to BIs.
Zhang, Aikman, and Sun (2008) tested attitude’s predictive
capability on intentions and usage of information communi-
cations technology (ICT) and devices and found that Attitude
Toward Objects (ATO) and ATB had significant predictive
capabilities for initial use and continued use. Zhang et al.
(2008) noted that we should not make assumptions on atti-
tude regarding related technologies, as attitudes change as a
user’s ICT use increases. Therefore, attitude is regarded as a
highly relevant antecedent to BIs and a critical part of
answering the research questions in this study.
Convergent and Discriminant Validities
According to Geffen and Straub, convergent validity is demon-
strated when a measurement item loads with a significant t
value, with a corresponding p value at less than .05 (Geffen &
Straub, 2005). Reliability tests for the reflective nature of the
model are exhibited by the high Cronbach alpha scores.
Composite reliability is an indicator of how well constructs in
the measurement model are described by indicators. W. H.
Chin (1998) states that the recommended threshold is .7; there-
fore, values above this number imply that constructs are well
described by indicators. Convergent validity is demonstrated in
this study’s data by examination of the models’loads and cross
loadings, which should all be in the −1 to 1 range. Kock (2012)
states that the two criteria recommended as the basis for con-
cluding that a measurement model demonstrates convergent
validity are that p values associated with the loadings be lower
than .05, and the loadings be equal to or greater than .5. The
study’s analysis results demonstrated convergent validity.
Reliability Tests
A Cronbach’s alpha value of at least .7 is commonly seen as
acceptable (Churchill & Brown, 2006). Individual construct
reliability tests need reported values above .7 to suggest that
all constructs could be considered reliable (see Table 1 to
review these values). Testing using Cronbach’s alpha values
shows that the data exhibit high levels of reliability (Adams,
Nelson, & Todd, 1992).
TheAverage Variances Extracted (AVE) are used to assess
discriminant validity and convergent validity. Average vari-
ances that demonstrate acceptable validity should be 0.5 or
greater (Fornell & Larcker, 1981), and all of the latent vari-
ables were at, or exceeded, this value (see Table 2).
Full collinearity Variance Inflation Factors (VIFs) aid in
determining collinearity. There are multiple accepted tests
for multicollinearity in the literature. Hair, Anderson,
Tatham, and Black (1998) stated that VIFs should be lower
than 10. VIFs in the study ranged from 1.496 to 5.722 for all
of the latent variables. The Warp PLS 3.0 program calculates
VIFs on a full collinearity test enabling vertical and lateral
collinearity (Kock, 2012). In Table 3, the full collinearity
VIFs are displayed, while in Table 4, the research model met-
rics are displayed.
Geffen and Straub (2005) defined what measurements are
needed for factorial validity in PLS analysis studies. Many of
these measurements have been adopted in the research that
uses PLS in technology adoption and user intention studies.
This study has used some of the indicators identified in this
research document, and has demonstrated that PLS can be
proven to possess factorial validity. Factorial validity is the
Table 1. Model Cronbach’s Alpha Values.
Cronbach’s alpha
PU .934
PEOU .973
BI .916
ATT .966
SN .931
SE .86
PRIORE .842
Note. PU = perceived usefulness; PEOU = perceived ease of use; BI =
behavioral intention; ATT = attitude; SN = subjective norms; SE = self-
efficacy; PRIORE = prior use of e-learning.
Table 2. Average Variances Extracted.
PU PEOU BI ATT SN SE PRIORE
0.791 0.902 0.923 0.937 0.830 0.787 0.86
Note. PU = perceived usefulness; PEOU = perceived ease of use; BI =
behavioral intention; ATT = attitude; SN = subjective norms; SE = self-
efficacy; PRIORE = prior use of e-learning.
Table 3. Full Collinearity VIFs.
PU PEOU BI ATT SN SE PRIORE
5.722 5.973 5.030 4.757 1.885 1.545 1.496
Note. VIF = variance inflation factor; PU = perceived usefulness; PEOU =
perceived ease of use; BI = behavioral intention; ATT = attitude;
SN = subjective norms; SE = self-efficacy; PRIORE = prior use of e-learning.
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5. Abramson et al 5
establishment of validity for latent constructs or latent vari-
ables (Geffen & Straub, 2005) and are “research abstractions
that cannot be measured directly; variables such as beliefs
and perceptions” (Geffen & Straub, 2005, p. 91). The pri-
mary variables of interest are beliefs and perceptions and are
not a directly measured variable, such as age and gender.
Reliability and Internal Consistency
Loadings for all of the latent variables, which are the correla-
tional coefficients between the indicator variables or ques-
tions from the survey and the latent variables, were within
acceptable ranges. W. W. Chin and Gopal (1995) suggest that
the suggested threshold value for loadings (coefficients) is .8.
The AVE is a measure of internal consistency in the model.
All measures were above the .5 value, which is commonly
used as a threshold (W. H. Chin, 1998). As shown, all scores
were well above the accepted threshold. As shown in Table 1,
PLS Factorial Validity Measurements, all of the AVE esti-
mates are well above the .5 value and will therefore be
accepted (Dillon & Goldstein, 1984). Composite reliability is
an indicator of how well each of the constructs is described by
the indicators in the measurement model. All indicators for
the model of the latent variables of PU, PEOU, ATT, and BI
demonstrate high scores and will therefore be used in the
analysis. Indicators were judged according to the .7 threshold
(W. W. Chin & Gopal, 1995). Communality is a measurement
of the squared correlation between an indicator and its latent
construct (W. H. Chin, 1998). It is a measurement of capacity
for describing the related latent constructs that meet the estab-
lished threshold for communality of .5 (W. H. Chin, 1998).
Table 5 displays the effect sizes for path coefficients.
Warp PLS 3.0 provides path coefficients and effect sizes
after the analysis. The effect sizes are Cohen’s (1988)
f-squared coefficients (Kock, 2012). Standard errors and
effect sizes are presented in the same manner that the path
coefficients are. This makes visualization easier as they are
in the same order. Effect sizes are the most relevant to this
analysis and discussion as they offer insight into the indi-
vidual contributions of the predictor latent variables to the
R-square coefficients of the criterion latent variable of each
latent variable (Kock, 2012). Effect sizes aid in determining
whether the effects indicated by path coefficients are small,
medium, or large (Kock, 2012). Recommended values are
0.02, 0.15, and 0.35, respectively (Cohen, 1988). Therefore,
all significant relationships identified by the correlation
coefficients were determined to have adequate effects for
consideration and inclusion in the analysis. Non-significant
values were seen as lacking effect values that would indicate
a smaller or greater effect.
Discussion
Research hypotheses represent if/then logic statements
(Creswell, 2008). This study used demographic or exoge-
nous variables, independent or endogenous variables, and
dependent variables. The TAM was introduced early in the
discussion, as it is a “rationale for the connections among the
Table 4. Research Model Quality Metrics.
R2
Composite
reliability
Cronbach’s
alpha
Average variance
extracted
Full collinearity
extracted—VIF
Q squared
coefficients
PU .781 .95 .934 .791 5.722 .778
PEOU .503 .979 .973 .902 5.973 .503
BI .785 .96 .916 .923 5.03 .786
ATT .767 .978 .966 .937 4.757 .765
SN .931 .83 1.885
SE .86 .787 1.545
PRIORE .842 .864 1.496
Note. VIF = variance inflation factor; PU = perceived usefulness; PEOU = perceived ease of use; BI = behavioral intention; ATT = attitude; SN = subjective
norms; SE = self-efficacy; PRIORE = prior use of e-learning.
Table 5. Effect Sizes for Path Coefficients.
Effect sizes for path coefficients
PU PEOU BI ATT SN SE PRIORE
PU 0.738 0.054 0.002 0.009
PEOU 0.106 0.248 0.149
BI 0.393 0.296 0.097
ATT 0.3 0.467
Note. PU = perceived usefulness; PEOU = perceived ease of use; BI = behavioral intention; ATT = attitude; SN = subjective norms; SE = self-efficacy;
PRIORE = prior use of e-learning.
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6. 6 SAGE Open
variables” (Creswell, 2008). There should be a positive rela-
tionship between the PRIORE and the BIs to use m-learning.
The antecedent variables will also have an effect on the stu-
dents’ BIs to use m-learning. This study uses hypotheses set
forth by TAM (Davis, 1989) and those of the antecedent vari-
ables that are used to extend the research model.
Hypothesis 1: The PU of m-learning will have a positive
effect on the users’ BIs to adopt m-learning as mediated
through attitude. PU has a significant positive relationship
with BIs, β = .46 at (p < .01). PU is also positively corre-
lated with attitude (ATT), β = .36 at (p < .01). The hypoth-
esis is accepted.
Hypothesis 2: PEOU of m-learning will have a positive
effect on the users’ BIs to adopt m-learning as mediated
through attitude. PEOU is mediated by attitude and also
PU in the research model and subsequently in the PLS
research analysis model. PEOU is “the degree to which a
person believes that using a particular system would be
free of effort” (Davis, 1989, p. 320). PEOU is signifi-
cantly and positively related to PU, β = .84 at (p < .01) and
also to ATT, β = .54 at (p < .01). With such strong positive
correlations to PU and ATT, which is the direct antecedent
to BI, the hypothesis is accepted.
Hypothesis 3: SN will have a positive effect on the users’
BIs to adopt m-learning as mediated through PU and
PEOU as mediated through attitude. SNs have a positive
relationship with PEOU, β = .22 at (p < .01); its relation-
ship to PU is not supported, β = .10 at (p = .43). Therefore,
the hypothesis is accepted, as there is a significant rela-
tionship with PEOU. Normative behavior is represented
by SNs, and this is expressed as the individuals’ perceived
view of referent others and the individual may approve of
m-learning use if others view this as a positive activity for
the individual. However, they can refuse or reject the inno-
vation based on the opinions of others as well. This is also
contingent on the relationship between normative behav-
ior and attitude. External factors would include reference
groups, demographics, and the individual’s personality.
Hypothesis 4: SE will have a positive effect on the users’
BIs to adopt m-learning though PU and PEOU as medi-
ated by attitude. SE has a significant positive relationship
with PEOU, β = .41 at (p < .01). The second path from SE
to PU is not supported, β = .00 at (p = .47). The hypothesis
is accepted as SE is significantly correlated with BI
through the PEOU→ATT→BI pathway. SE is the per-
son’s judgment on his or her capability to perform the
task. SSE is strongly influenced by a person’s motivation,
perseverance, and effort to perform a task (Wood &
Bandura, 1989). Therefore, if this is true, it would stand to
reason that the prior use of a related technology would be
related to SE. A direct connection was made in Warp PLS
3.0, and the model was re-run for the purposes of answer-
ing this question related to this hypothesis (see Figure 2).
As can be seen, there is a strong relationship between
PRIORE and SE. Therefore, it is possible to further explain SE
and PRIORE. SE was not significantly correlated with PU.
Figure 2. Research model with correlational coefficients and associated metrics, with added PRIORE → SE.
Note. PRIORE = prior use of e-learning; SE = self-efficacy; PU = perceived usefulness; SN = subjective norms; ATT = attitude; BI = behavioral intention;
PEOU = perceived ease of use.
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7. Abramson et al 7
Hypothesis 5: PRIORE will have a positive effect on the
users’ BIs to adopt m-learning directly, and as mediated
through PEOU and PU to ATT. PRIORE has a positive
and significant relationship with BI, β = .17 at (p < .01).
PEOU also has a significant positive relationship with
PEOU, β = .29 at (p < .01). The third pathway, which
begins with PU, was not supported, b = −.02 at (p = .40).
Two of the three pathways, including the direct first order
path to BI, had a significant and positive correlation;
therefore, the hypothesis is supported. Prior use of a tech-
nology or related technology has been correlated with
intention to use and actual use in numerous information
system studies.
Findings
The study found that there is a relationship between PRIORE
and BI to use m-learning. Similar questions have been asked
of other types of learning, and of previous uses of m-learn-
ing as a predictor of m-learning (Akour, 2010). Similar
research questions have been examined in the literature
regarding e-learning, but not as it relates to the BI to use
m-learning. There is a pattern in the e-learning research that
follows a similar path. Haverila (2011) found that prior
e-learning experience influenced or affected perceived
learning outcomes in an undergraduate environment.
Therefore, this study and others helped establish new ques-
tions to be answered in future research. This study repre-
sents an addition to the body of knowledge for the BI to use
m-learning.
Implications
Prior experience with e-learning had a significant and posi-
tive effect on PU and BI to use m-learning. This is additive
to the work of Akour (2010), who found that previous use
of e-learning had a significant and positive effect on the
users’ BI to use m-learning. Akour’s final research model
uses attitude as an antecedent and moderator to BI.
Researchers and practitioners should be aware of this strong
connection as it may be critical to designing m-learning
programs at community colleges or universities.
Researchers should see that experience in the m-learning
use intention models plays a key role and may explain vari-
ance in their models.
Future Research
The addition of time and financial resources could have
made a more comprehensive study and one that would have
benefited from a mixed method and a longitudinal approach
that may have included actual usage. Actual usage could be
measured by launching the programs to be used in m-learn-
ing tasks from a special group of programs within a menu
of programs. By categorizing the programs, it would be
possible to track them easier and gather meaningful usage
statistics. In addition, it would be interesting to gather
more demographic information in a more homogeneous
group to gather a more in-depth picture of the users and
additional factors that may have an effect on the BI to use
m-learning.
As has been discussed, there has been massive and expo-
nential growth in the use of mobile wireless computing plat-
forms. This study has documented this growth and some of
the use that is seen in the contemporary university and com-
munity college. Many studies have examined and are exam-
ining the potential uses for these technologies and how and
where they are most effective. M-learning has been driven
from disruptive innovation generated by advances in mobile
computing and wireless communication technology. How
this is used in the university and what are the factors that
influence intentions to use m-learning were some of the key
questions that were examined.
Conclusion
Development of a working definition of m-learning was sim-
plified and refined to the use of a smartphone or other mobile
computing device that is connected to the Internet that can be
used for ad hoc tasks to aid the student, which includes log-
ging on to a learning management system that either has or
has not been optimized for mobile users. The definition of
m-learning was left open, as the focus of the study was to see
the intentions toward m-learning and how the past e-learning
experiences may or may not contribute to the user’s intention
to use m-learning. It was found that there was a significant
correlation between previous e-learning and the intention to
use m-learning. In addition to addressing the research ques-
tions of the study through analysis of the hypotheses, it was
learned that PU and PEOU played a large role in determining
the BI to use m-learning among students. Whether the
research model used attitude or not, the results normally
explain a large part of the variance. It is also possible, as seen
in the literature and demonstrated in this study, to decompose
constructs, by adding latent variables that aid in explaining
the variance in the research model. Previous learning experi-
ences within user intention studies may be highly relevant for
inclusion and study, as this study demonstrated. As experi-
ences change, new experiences and their potential effects on
BI should be examined as they may aid in explaining inten-
tions to use.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research and/or
authorship of this article.
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8. 8 SAGE Open
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Author Biographies
Jonathan Abramson holds a master’s degree in organizational man-
agement and a doctoral degree in computer science. He has worked in
a variety of technology and management positions in the public and
private sector. In addition, he started and ran a systems integration
and database analysis and programming business for 8 years. He is
currently the academic program manager at Post University in
Computer Information Systems, in Waterbury, Connecticut.
Maurice Dawson serves as an assistant professor of information
systems at the University of Missouri–St. Louis, former assistant
professor (honorary) of industrial and systems engineering at the
University of Tennessee Space Institute, and Fulbright Scholar
Specialist. Dawson is recognized as an information assurance sys-
tem architect and engineer by the U.S. Department of Defense.
Research focus area is cyber security, systems security engineer-
ing, open source software, mobile security, and engineering
management.
Jeffery Stevens holds two master’s degrees, one in human
resources (HR) and the other in general management. He has
more than 16 years in the areas of HR, management, and process
engineering within the private sector. Currently, he is the presi-
dent and CEO of an international consulting company that aids
small and mid-size companies in growth and process refinement.
Within the academic realm, he has conducted research and pub-
lished several articles within the areas of HR, statistics, educa-
tion, research methodology, and homeland security as well as
ground breaking research within the area of virtual education. He
has taught a wide array of courses in both the campus and online
settings and is now working with the American Council on
Education and the Higher Learning Commission in accrediting
academic programs.
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