IAFOR Journal of Education: Technology in Education

Volume 8 – Issue 2 – 2020

Learning Management System Adoption in Higher Education Using the Extended
Technology Acceptance Model
Marissa R. Fearnley
De La Salle-College of Saint Benilde Manila
Philippines
Johnny T. Amora
De La Salle-College of Saint Benilde Manila
Philippines

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Abstract
A learning management system is capable of enriching instruction and acceptance of thislearning technology by users is crucial to its successful application in higher education. This
study investigated factors that influenced adoption of a learning management system by higher
education teachers using the technology acceptance model which incorporates three external
constructs: system quality, perceived self-efficacy and facilitating conditions. Data collectedfrom faculty respondents (n=127) through an online survey were examined by employing
partial least squares-structural equation modeling. While several relationships were confirmed,
others were not supported by this research. Results showed that both system quality andperceived self-efficacy strongly influenced perceived usefulness, which in turn indirectly
affected attitudes towards the technology and behavioral intention. Additionally, system
quality directly affected perceived ease of use and attitudes toward technology use. The strong
and direct influence of perceived self-efficacy on perceived usefulness and perceived ease ofuse suggests that faculty with positive beliefs about their ability to use the learning management
system will regard it as both useful and easy to use. Facilitating conditions, on the other hand,affected neither perceived ease of use nor attitudes. Implications for practice, policy andpotential research directions are likewise presented.
Keywords: higher education, learning management system, technology acceptance model,
facilitating conditions, perceived self-efficacy, system quality

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Emerging technologies in education are driving colleges and universities to progressively
infuse their use in higher education (Rodriguez & Anicete, 2010; Alharbi & Drew, 2014;
Bermúdez-Hernández et al., 2017). Given their incontestable importance (Revythi & Tselios,2019), instructors are challenged to incorporate them as a means to complement conventional
learning environments (Lee et al., 2009; Rhode et al., 2017), enhance learners’ experiences,and improve academic outcomes (Parkman et al., 2018).
A learning management system (LMS) is a web-based application capable of transforming
face-to-face sessions by offering students a space for online learning (Wichadee, 2015). Usinga LMS is an effective way of delivering instruction to students by offering 24/7 access to course
content, while enabling convenient course creation and management for teachers (Bousbahi &Alrazgan, 2015). Despite the perceived benefits of using LMSs, many faculty members remain
hesitant to adopt them as a teaching tool (Wichadee, 2015; Zanjani et al., 2016). Moreover,teachers tend to underutilize this educational technology despite its widespread availability inhigher education settings (Fathema & Sutton, 2013; Bousbahi & Alrazgan, 2015). The
variables that affect faculty adoption of the technology include teachers’ perceptions, selfefficacy beliefs and instructional goals, as well as the availability of resources, support services,and time (Baturay et al., 2017; Siyam, 2019).
Teachers play an important role in carrying out any innovation in the classroom (Alharbi &Drew, 2014). This is why determining the variables that motivate teachers to provide atechnology-supported learning environment to their students is essential (Teo et al., 2012). This
paper aims to assess the factors that affect how teachers accept and adopt a learning
management system. Additionally, it seeks to confirm whether the technology acceptancemodel (TAM) in its extended version is applicable in a local higher education context.
Literature Review
Learning Management Systems
Learning management systems are platforms that offer a variety of integrated tools fordelivering and managing online instruction. Whether open source (e.g., Moodle, Sakai) orcommercial (e.g., Blackboard, Brightspace D2L), most LMSs are flexible, easy to use,
accessible and user-friendly (Kasim & Khalid, 2016).
With a LMS, an instructor can create online course content and subsequently manage thatcourse to enhance critical thinking abilities and promote collaboration among university
students (Zanjani et al., 2016). LMSs offer many tools such as online group chats, discussion
threads, video conferencing, lecture materials, learning modules, grading and course
evaluations, all of which may be customized to suit specific instructional needs (Fathema etal., 2015; Walker et al., 2016). According to Anshari et al. (2017), non-traditional forms oflearning supported by online approaches to instruction positively affect both teachers andlearners. Some of the benefits provided by a LMS include organized course content, enhanced
student engagement, improved autonomy among learners, convenient submission ofrequirements and immediate feedback (Adzharuddin et al., 2013; Cavus, 2015; Alenezi, 2018).
The utilization of LMSs to aid in educational initiatives has become widespread amongcolleges and universities over the years (Walker et al., 2016). Higher education institutions use
them to supplement face-to-face learning sessions, as well as support blended instruction anddistance learning (Klobas & McGill, 2010). Their significant contribution to instructionaldelivery notwithstanding, financial investment and technical demands are critical factors in the91

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selection of a particular LMS (Kasim & Khalid, 2016). In addition, hindrances to LMS
utilization reported by teachers include: (1) deficient computer units and inadequate computercompetence; (2) problematic integration of technology into the curriculum; and (3) a lack ofsupervisory and technical personnel (Li, 2007; Baturay et al., 2017). Other concerns that
impede the delivery of technology-supported instruction in higher education are systeminfrastructure, effort from faculty, and satisfaction with the system (Surry et al., 2005).
Technology Acceptance Model
Before an innovation, such as a new LMS, can be implemented, it has to be accepted (Rogers,
2003). Investigating technology acceptance helps determine the purpose of teachers’
technology use (Scherer et al., 2019). Of the many theories and models that attempt to describe
how users accept and adopt a given technology (Ahmad et al., 2010; Shih-Chih et al., 2011),
one of the most prominent is the Technology Acceptance Model (TAM) (Park, 2009; Scherer
et al., 2019). Introduced by Davis (1989), the TAM has been implemented in different domains
of information technology and information systems (Shih-Chih et al., 2011), and has becomea useful model in exploring adoption behavior of a particular technology in various contexts(Fathema & Sutton, 2013).
The model (Figure 1) shows how perceived usefulness and perceived ease of use are strong
determinants of attitudes towards technology use (Siyam, 2019). Perceived usefulness
represents users’ views on the degree to which a technology facilitates improvement in job
performance, while perceived ease of use indicates users’ expectations that use of a technology
is easy and effortless (Davis, 1989; Lai & Savage, 2013). Both determinants influence users’
attitudes toward technology use (Baturay et al., 2017), and are themselves influenced byexternal variables (Shih-Chih et al., 2011). These external variables include subjective norm,
job relevance, output quality, and result demonstrability (Venkatesh & Davis, 2000), among
others. Users’ attitudes configure their behavioral intention, which in turn defines actualtechnology use (Alharbi & Drew, 2014; Revythi & Tselios, 2019).

Figure 1: Original technology acceptance model by Davis et al. (1989)
Over the years, different versions of the TAM have been employed in many empirical studies(Scherer et al., 2019). Other parameters that have been linked to the TAM are system quality,
computer self-efficacy and facilitating conditions (Fathema et al., 2015; Salloum et al., 2019),
as well as personal innovativeness (Mazman Akar, 2019) and access to technology andplanning time (Siyam, 2019).
The model proposed by Siyam (2019) consisted of the following external variables: job
relevance, access to technology, self-efficacy and planning time. Of the four factors studied,92

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only self-efficacy and planning time were found to be significant and positive determinants oftechnology acceptance among special education teachers. A related study conducted byParkman et al. (2018) in the United Arab Emirates sought to determine how 82 female preservice teachers accepted environments that were adequately supported by technology. This
investigation utilized the original TAM constructs and two external variables, namely
perceived user resources and computer self-efficacy, as predictors of behavioral intention.
Results showed that behavioral intention was strongly correlated with perceived usefulness,
perceived ease of use and computer self-efficacy. Between the two variables used to extend theTAM, computer self-efficacy was found to have a greater predictive power than perceived user
resources. In Turkey, 476 pre-service university teachers were surveyed by Baturay et al.
(2017) to investigate the relationship among competence in computer use, attitude towards
computer-assisted instruction and intention to use technology, and whether these external
constructs may be determined by four factors: gender; computer ownership; access to theInternet; and daily computer usage. Findings revealed that the correlations among three of theconstructs were statistically significant. Of the four factors examined, however, only daily
computer use was found to positively affect computer competence. The studies describedabove highlight the importance of competence in computer use or computer self-efficacy in the
adoption of a particular technology. In the context of this paper, the same construct is expressedas perceived self-efficacy.
In Serbia, knowledge of mathematics, subjective norm and facilitating conditions were usedby Teo and Milutinovic (2015) in their version of the TAM to assess factors that affecttechnology use in mathematics teaching among 313 pre-service teachers. Their findings
revealed that attitude towards technology use was the only variable with a direct effect on
behavioral intention. The TAM was also used by Fathema et al. (2015) to explore thedeterminants of technology acceptance among 560 university teachers and teaching assistantsin the United States whose LMS use was non-mandatory. Structural equation modelingrevealed that system quality, perceived self-efficacy and facilitating conditions were usefuldeterminants of attitudes toward use. Another study among 487 Turkish pre-service teachers
revealed that perceived usefulness, attitudes toward use and computer self-efficacy directly
affect behavioral intention.Perceived ease of use, facilitating conditions and technologicalcomplexity indirectly influence technology acceptance and perceived usefulness is a powerfuldeterminant of intention to use a particular technology (Teo et al., 2012).
While previous studies conducted by Teo and Milutinovic (2015), Fathema, et al. (2015), andTeo et al. (2012), incorporated facilitating conditions as an external variable of the TAM, only
the work of Fathema et al. (2015) explained how system quality affects technology acceptance.
A more recent study by Salloum et al. (2019) revealed that among university students, system
quality is a significant predictor of perceived ease of use but not perceived usefulness. Thus,
the inclusion of system quality as an external variable was deemed an important factor in thecontext of using a commercial LMS.
Research Framework and Hypotheses
The current study adopted the extended technology acceptance model proposed by Fathema etal. (2015) which integrated system quality, perceived self-efficacy, and facilitating conditions
as factors that influence adoption and implementation of a LMS by faculty. Additionally, theeffect of system quality on perceived ease of use was included in this investigation. A total of
fourteen (14) hypotheses were formulated to test this study’s model (Figure 2).

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Figure 2: Extended technology acceptance model for learning management systems by
Fathema et al. (2015)
System quality (SQ) characterizes desirability of the system (i.e., the LMS) in terms offunctions, speed, features, content, and interaction capability (Delone & Mclean, 2003;
Fathema et al., 2015). If the LMS is desirable, teachers will find it useful, view it positively,
and use it intentionally. Another significant factor in adopting an information system isperceived self-efficacy (SE), or self-assessment of one’s capability to carry out the tasks neededto accomplish a set of outcomes (Bandura, 1993; Gong et al., 2004; Nikou & Economides,2019). In this study, SE indicates how confident a faculty member is in his or her ability tooperate, navigate, and work with the LMS. Lastly, facilitating conditions (FC) are factors thatcan positively or negatively affect how easy or difficult it is for an individual to accomplish aparticular job (Teo, 2010). In terms of LMS use, FC is measured by the availability of technicalsupport, capacity building opportunities, and related resources. If FC are present, faculty
members are expected to show high perceived ease of use and positive attitudes towards LMSuse.
The first eight (8) hypotheses address the effects of the external variables SQ, SE and FC on
the original TAM constructs. These are:
H1: System quality directly affects PU.
H2: System quality directly affects PE.
H3: System quality directly affects AT.
H4: System quality directly affects BI.
H5: Perceived self-efficacy directly affects PU.
H6: Perceived self-efficacy directly affects PE.
H7: Facilitating conditions directly affect PE.
H8: Facilitating conditions directly affect AT.
The other six (6) hypotheses examine the relationships among the original TAM variables
namely, PE, PU, AT and BI.

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H9: Perceived ease of use directly affects PU.
H10: Perceived ease of use directly affects AT.
H11: Perceived usefulness directly affects AT.
H12: Perceived usefulness directly affects BI.
H13: Attitude toward LMS use directly affects BI.
H14: Behavioral intention directly affects AU.
As educational technologies are being integrated into teaching and learning practices, theexamination of factors that are vital to user adoption becomes increasingly important (Rhodeet al., 2017). This investigation is aimed at confirming the applicability of the extended TAM(Fathema et al., 2015), with a focus on the effects of system quality, perceived self-efficacy
and facilitating conditions.
Methods
Participants of the Study
This research explored the acceptance and adoption of Brightspace, a learning management
system being utilized by faculty members at a private college in Manila, Philippines.
Brightspace is a commercial LMS designed by the Desire2Learn (D2L) Corporation (Moseley& Ajani, 2015). It is a cloud-based platform with a suite of tools for delivering flexible
instruction and facilitating engaged learning.
A total of 127 out of 250 (51%) served as the study sample. These teachers had experience
using the LMS for at least one academic term (i.e., 14 weeks) for purposes beyond downloadingclass lists and uploading course syllabi. Hair et al. (2012) recommended that the minimum
sample for a partial least squares-path model be equal to the number of structural paths for theconstruct with the greatest complexity multiplied by 10, which, in this case, would require aminimum sample of 40 participants. Thus, the study’s actual sample of 127, which is more than
three times the minimum requirement, is deemed adequate for the purposes of thisinvestigation. Table 1 presents demographic data related to the sample group.
The respondents in this study comprised of 51% male and 49% female teachers who use some,or all, of the available features of the LMS. A majority (75%) of the respondents were parttime faculty, and 98% (125 out of 127) were current users of the LMS at the time the data werecollected. Three of the respondents were teachers with administrative positions (i.e., otherfaculty) who were given teaching loads. A great majority (73%) of the faculty had completed
graduate education, and about 44% (56 out of 127) had a minimum of four years of teachingexperience in the institution.

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Table 1: Demographic profile of respondents (n = 127)
Variables
Sex
FemaleMale
Education
BA/BS
Doctorate/Post-doctorate
Master’s
Classification
Full-Time Teaching FacultyPart-Time Teaching Faculty
Other Faculty
Current LMS users
Yes
No
No. of years of teaching in the institution
Below 55 to 910 to 14
15 to 19
20 & above
Total

n

%
62
65

48.82
51.18

34
18
75

26.77
14.17
59.06

29
95
3

22.83
74.80
2.36

125
2

98.43
1.57

56
24
24
19
4
127

44.09
18.90
18.90
14.96
3.15
100.00Research Instrument
The research instrument used for data collection was an online survey that consisted of threeparts: the first part described the objectives of the study and sought informed consent from therespondents; the second part asked about the respondents’ gender, education, facultyclassification, current LMS use, and number of years of teaching at the institution; the thirdpart consisted of survey items related to LMS adoption. The TAM-related survey items wereadopted from Fathema et al. (2015) and were re-worded to align with the context of thisresearch. A total of 28 statements were used to assess the eight constructs: SQ (4 items), SE (3items), FC (3 items), PE (4 items), PU (4 items), AT (4 items), BI (3 items), and AU (3 items).
All items were measured on a four-point Likert scale with 1 = strongly disagree, 2 = disagree,
3 = agree, and 4 = strongly agree.
Data Gathering Procedure
A web-based survey created using Google Forms was sent to faculty members through theirofficial email addresses in the College. Data collection was done in the middle of the academic
year, approximately one year after the college shifted to Brightspace from an open source LMS.
Conducting this study was supported and monitored by the institutional research office.
Compliance with prescribed ethical guidelines was ensured through informed consent from therespondents by requiring them to signify understanding of the nature and purposes of the study,offering no rewards for participation, and providing an option to be excluded from the study incase they change their mind. Personal identifiable information of the respondents (i.e., email
address) were accessible only to the researcher who analyzed the data.

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Data Analysis
This is a quantitative study that applied partial least squares-structural equation modeling (PLSSEM) to determine the association among eight constructs related to TAM (PU, PE, AT, BI,
AU, SQ, FC, and SE). This technique enables estimation of complex models with several
constructs (Revythi & Tselios, 2019) but requires less stringent assumptions on sample size,
distribution and normality (Hair et al., 2019).
Findings
This section follows the stages of PLS-SEM model analysis and interpretation suggested by
Hulland (1999). The first part presents the results generated from the tests of reliability andvalidity, and the second part shows the tests of hypotheses related to the structural model.
Reliability and Validity
Construct reliability evaluates the degree of consistency between the reflective item and theintended measure (Roldan & Sanchez-Franco, 2012; Kock, 2015; Amora et al., 2016).
Construct reliability is deemed adequate when composite reliability (CR) and Cronbach’s alpha(CA) are at least 0.70 (Fornell & Larcker, 1981; Nunnaly & Bernstein, 1994; Kock, 2015).
Convergent validity assesses whether the level of understanding by the respondents on theitems associated with each variable matches the intention of the designer of the instrument. In
PLS-SEM, there are two methods used to test convergent validity. First, item loadings related
to the variable should be at least 0.50 in value, and statistically significant (p < 0.5). Itemloading indicates the correlation between the item and the variable. Next, average varianceextracted (AVE) should be at least 0.50 (Hair et al., 2012; Kock, 2015; Amora et al., 2016).
AVE measures the overall dispersion attributed to the construct against that of observational
error (Fornell & Larcker, 1981).
The values in Table 2 indicate that all eight constructs were above the recommended 0.70
guideline for construct validity. Likewise, convergent validity of the constructs in the structuralmodel were deemed sufficient with item loadings and AVE values recorded well above 0.50.
Table 2: Item loadings, AVE, and reliability of the constructs
Constructs

No. of
items
3

Item
loadings***.77-.96

AVE

CA

CR

.78

.86

.92

2. Behavioral Intention (BI)3.91-94

.85

.91

.95

3. Perceived Usefulness (PU)

4.91-.95

.88

.95

.97

4. Perceived Ease of Use (PE)4.81-.91

.73

.88

.92

5. Attitude Towards Using (AT)4.87-.91

.81

.92

.95

6. Facilitating Conditions (FC)3.86-.92

.81

.88

.93

7. Perceived Self-Efficacy (SE)3.90-.94

.85

.91

.95

8. System Quality (SQ)

4.73-.85

.68

.84

.90

1. Actual Use (AU)***p < .001.

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Discriminant validity is present when respondents do not confuse the items of a particularvariable with those of other variables in the instrument, particularly in terms of meaning (Kock,2015). If the √AVE of a variable (i.e., any of the diagonal values) is greater than the coefficients(i.e., off-diagonal values) for any combination of this variable with another, then the items inthat variable have a strong correlation (Teo, 2010). In this case, all diagonal values are higher
than the related off-diagonal values which shows acceptable discriminant validity for all
constructs (see Table 3).
Table 3: Correlation coefficients and AVE of the constructs
Constructs

1

2

3

4

5

6

7

1. Actual Use (AU)0.89

2. Behavioral Intention (BI)

0.62

0.92

3. Perceived Usefulness (PU)

0.60

0.79

0.94

4. Perceived Ease of Use (PE)

0.49

0.64

0.72

0.86

5. Attitude Towards Using (AT)

0.61

0.87

0.84

0.72

0.90

6. Facilitating Conditions (FC)

0.30

0.55

0.57

0.65

0.56

0.90

7. Perceived Self-Efficacy (SE)

0.55

0.67

0.68

0.81

0.72

0.64

0.92

8. System Quality (SQ)

0.39

0.66

0.72

0.79

0.74

0.69

0.73

8

0.83

Note. Diagonal values represent the square root of AVE of constructs (√AVE), while the offdiagonal values are the correlation among the constructs.
Tests of convergent validity, discriminant validity and reliability satisfied the conditions for
estimating the structural model: average path coefficient (APC) = 0.304 (p < .001), average Rsquared (ARS) = 0.656, p < .001; average adjusted R-squared (AARS) = 0.649, p < .001;
average block VIF (AVIF) = 3.073 (acceptable if ≤5, ideally ≤ 3.3); average full collinearity
VIF (AFVIF) = 3.773 (acceptable if ≤ 5, ideally ≤ 3.3); and Tenenhaus goodness of fit (GoF)= 0.726 (large ≥ 0.36) (Kock, 2015). Overall, the fit and quality indices of the structural modelin the present study fell within acceptable limits.
Test of Structural Model
Table 4 presents the parameter estimates and outcomes for proposed relationships in theextended TAM. Of the fourteen (14) hypotheses, only 10 were supported by the data.

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Table 4: Parameter estimates of the extended technology acceptance model for LMS
HypothesesPathβp-valuef2
Results
H1
SQ àPU
.340
.000
.082
Supported**
H2
SQ à PE
.390
.000
.081
Supported**
H3
SQ à AT
.237
.003
.084
Supported*
H4
SQ à BI
.016
.428
.088
Not supported
H5SE à PU
.226
.004
.159
Supported*
H6
SE à PE
.470
.000
.380
Supported**
H7
FC à PE
.081
.177
.053
Not supported
H8FC à AT
.005
.476
.003
Not supported
H9PE à PU
.278
.000
.205
Supported**
H10
PEà AT
.108
.108
.080
Not supported
H11
PU à AT
.590
.000
.499
Supported**
H12
PU à BI
.219
.005
.173
Supported*
H13
AT à BI
.666
.000
.574
Supported**
H14
BI à AU
.633
.000
.401
Supported**2
Note. f is Cohen’s effect size: .02=small, .15=moderate, .35=large; β=path coefficient*p < .05, **p < .01
In terms of system quality, results of the structural model revealed that SQ directly affected
PU, PE, and AT. Positive β values (i.e., path coefficients) indicated that high SQ led to highPU, PE and AT. Additionally, SQ significantly affected BI. For perceived self-efficacy, it canbe noted that SE significantly affected PU and PE. Moreover, the extent of the effect of SE canbe described as large on perceived ease of use (f2 = .380) and moderate on perceived usefulness(f2 = .159). FC did not have a significant effect on PE and AT.
PE had a positive and significant effect on PU, but no statistically significant effect on AT.
The positive β value of PE on PU suggested that when teachers reported ease of LMS usage,
they also perceived the LMS to be useful. Moreover, PU had a more dominant effect on AT (f2= .499) than BI (f2 = .173). The data also offered evidence that faculty who found the LMS
useful would have positive attitudes and strong intentions to use the LMS.
AT positively and significantly affected BI, and in turn, BI directly affected AU. This meansthat teachers with favorable attitudes toward LMS usage were likely to have stronger
behavioral intentions which led to actual technology use. Similar to the effect of AT on BI (f2= .574), behavioral intention influenced AU to a large extent (f2 = .401).
Discussion
This research sought to confirm the applicability of the extended TAM (Fathema et al., 2015)
in a local higher education context. While it confirmed several relationships from the model,
four hypotheses were not supported by this research. More specifically, the effects of SQ on
BI, FC on PE and AT, and PE on AT were all not statistically significant. Findings from thisstudy expand the existing body of knowledge on TAM by reinforcing a new dimension to therelationships that exist between SQ and PE.

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Effects of External Variables
System quality is a significant external variable that affects users’ attitudes towards technology
usage (Fathema & Sutton, 2013). Aligned with the work of Fathema et al. (2015) and Salloum
et al. (2019), this study revealed that SQ positively and significantly affects PU and PE. Thismeans that a high-performing LMS in terms of quality will be perceived by teachers as both
useful and easy to use. Consistent with the finding of Fathema et al. (2015), SQ has a significant
effect on AT but not BI. This means that the characteristics of a LMS, although desirable, do
not assure its consequent utilization by faculty.
Teachers’ perceived self-efficacy has a significant influence on PU and PE, which confirms
previous findings that self-efficacy is a critical factor in assessing whether technology use will
be successful or not (Ahmad et al., 2010; Teo et al., 2012; Fathema et al., 2015). This result
suggests that teachers with high SE also have high levels of comfort and confidence that using
the LMS will help them achieve their goals at work. Based on previous research, high SE
among teachers translates to improved perceptions and stronger intentions to use the LMS(Baturay et al., 2017; Parkman et al., 2018).
The provision of technical support for technology use is rated by faculty as an enabler (Siyam,2019), and its absence is a barrier to technology integration in the classroom (Lim & Khine,2006). According to Fathema et al. (2015), teachers are likely to develop more approving
attitudes toward the technology when facilitating conditions (i.e., training, tutorials, support,etc.) are present. In terms of FC, the results of this study were not in agreement with earlier
work by Teo (2010) and Fathema et al. (2015) which reported FC to have a significant effecton PE. It could be possible that adequate facilitating conditions were not fully in place at thetime of data collection, which made the effects of FC on PE weak and insignificant.
Alternatively, SQ and SE may be quite high such that teachers do not feel the need for FC, andthe lack of facilitating conditions presented no significant effect on PE. Consistent with one of
the findings of this research, however, are related studies that found no significant association
between FC and AT (Teo et al., 2012; Teo & Milutinovic, 2015).
Effects of Original TAM Constructs
The findings of this research are generally consistent with previous TAM-related work thatreported strong relationships between PE and PU, and PU and AT (Teo, 2010; Teo et al., 2012;
Fathema et al., 2015, Teo & Milutinovic, 2015; Stockless, 2018; Siyam, 2019). Between PU
and PE, PU is a stronger predictor of AT compared to PE. Contrary to the researchers’
expectation, this investigation showed that PE does not significantly predict AT. This suggeststhat faculty who perceive the LMS to be easy to use do not necessarily possess positive attitudes
towards the LMS. This may be explained by the high perceived self-efficacy reported byteachers which could strongly affect how easy it is for them to work with the LMS. Whenteachers feel confident in their abilities to use a technology and find no difficulty in its
utilization, their attitudes towards it may likely be unaffected. Thus, PE had no significant
effect on AT. As noted earlier, SE has the largest influence on PE compared to SQ and FC,
thus, lending further support to the above explanation. The positive effect of AT on behavioral
intention, and the similar effect of BI on actual use are all in agreement with previous work by
Fathema et al. (2015), Teo (2010), and Ahmad et al. (2010).
Conclusion and Recommendation
The technology acceptance model has been modified numerous times based on emerging
technologies (Benbasat & Barki, 2007), and these various modifications may provide some100

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explanation as to why there is no single model that is unanimously accepted by the researchcommunity (Stockless, 2018). This may be attributed to differences in educational context,
characteristics of respondents, and emphasis as regards to the external variables utilized in theinvestigation (Wu & Liu, 2015). While the study results provide an acceptable level of support
for the TAM and its extended version, the relationships among constructs that were notconfirmed by this research (i.e., the effects of SQ on BI, FC on PE and AT, and PE on AT)
merit further examination.
Among the three external variables included in this investigation, perceived self-efficacy has
the strongest influence on PU and PE. As such, there is further evidence that teachers withpositive beliefs about their capacity for technology will find a LMS both useful and easy touse. This research also confirms the desirability of the learning management system being
utilized in the college, because system quality positively and significantly affects PU, PE andAT.
The current study provides an empirical validation of the extended TAM proposed by Fathema
et al. (2015). It offers practical implications in terms of practice, policy and future related work.
For practitioners, self-confidence in their ability (i.e., perceived self-efficacy) to use atechnology is an essential element of acceptance and adoption. Moreover, faculty with high SE
may be recruited as partners in promoting LMS use or engaged as mentors for other teachers
who are at the early stages of LMS integration. For policy makers who make decisions on the
procurement of educational technologies by their respective institutions, system quality should
be considered as an important selection criterion. By assessing system quality, administrators
can prioritize the kind of institutional support to be put in place for users of the technology(Rhode et al., 2017). For researchers, succeeding studies on the TAM may seek to further
explore the effects of other external variables including personal innovativeness andtechnology access (Mazman Akar, 2019), job relevance and planning time (Siyam, 2019),
management support (Ayele & Birhanie, 2018), subjective norm (Teo, 2010) and computer
anxiety (Venkatesh & Bala, 2008), among others. While the single method of data collectionused in this study limits generalizing the findings to other populations, this research provides
a better understanding of how higher education teachers accept, adopt, and use technology in
the Philippine context. Replication studies may also consider a mixed-method approach togenerate qualitative data that can enrich the explanatory power of the different TAM constructs.

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Corresponding author: Marissa R. Fearnley
Contact email: marissa.fearnley@benilde.edu.ph106