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Expanding The Technology Acceptance Model (TAM) to Examine Faculty Use of Learning Management Systems (LMSs) In Higher Education Institutions

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Expanding The Technology Acceptance Model (TAM) to Examine Faculty Use of Learning Management Systems (LMSs) In Higher Education Institutions

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Universities have made a considerable investment in the use of Learning Management Systems (LMSs) to facilitate their teaching learning processes; however these systems are not used by the faculty members to their fullest capabilities. To address this issue, this study investigated factors that affect faculty members’ LMSs usage behavior, focusing on user related variables and their pivotal role in determining faculty attitudes toward LMSs. This study offers an empirical evaluation of an extension of Davis’s (1989)’s Technology Acceptance Model (TAM) to investigate how faculty members’ beliefs and attitudes influence their intention and actual use of LMSs under conditions of non-mandatory use of LMSs in higher education institutions. Data were obtained from 560 faculty members (from two universities) and analyzed using Structural Equation Modeling. The study results revealed that the three proposed external variables: system quality; perceived self-efficacy and facilitations conditions were significant predictors of faculty attitude towards LMSs. Similar to prior research findings, the study results further confirmed the validity of the extended TAM in determining users’ technology acceptance behavior. The study also addressed the implications of the findings for researchers and practitioners.
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Expanding The Technology Acceptance Model (TAM) to Examine Faculty Use of
Learning Management Systems (LMSs) In Higher Education Institutions
Nafsaniath Fathema
Post-Doctoral Fellow
Wisconsin Center for Educational Research:WCER
University of Wisconsin- Madison
Madison, WI 53706, USA
fathema@wisc.edu
David Shannon
Professor
Department of Educational Foundations Leadership and Technology
Auburn University
Auburn, AL, 36830, USA
shanndm@auburn.edu
Margaret Ross
Professor
Department of Educational Foundations Leadership and Technology
Auburn University
Auburn, AL, 36830, USA
rossma1@auburn.edu
Abstract
Universities have made a considerable investment in the use of Learning Management
Systems (LMSs) to facilitate their teaching learning processes; however these systems
are not used by the faculty members to their fullest capabilities. To address this issue,
this study investigated factors that affect faculty members’ LMSs usage behavior,
focusing on user related variables and their pivotal role in determining faculty attitudes
toward LMSs. This study offers an empirical evaluation of an extension of Davis’s
(1989)’s Technology Acceptance Model (TAM) to investigate how faculty members’
beliefs and attitudes influence their intention and actual use of LMSs under conditions of
non-mandatory use of LMSs in higher education institutions. Data were obtained from
560 faculty members (from two universities) and analyzed using Structural Equation
Modeling. The study results revealed that the three proposed external variables: system
quality; perceived self-efficacy and facilitations conditions were significant predictors of
faculty attitude towards LMSs. Similar to prior research findings, the study results further
confirmed the validity of the extended TAM in determining users’ technology acceptance
behavior. The study also addressed the implications of the findings for researchers and
practitioners.
Keywords: learning management systems (LMSs), technology acceptance model
(TAM), attitude, usage, Canvas, structural equation modeling
Introduction
Internet based Learning Management Systems (LMSs) (i.e. Moodle, Blackboard, WebCT, Desire2Learn)
are popular Internet technologies that have been supporting distance, face-to-face and hybrid/blended
teaching-learning processes. (Dahlstrom, Brooks, & Bichsel, 2014; McGill & Hobbs, 2008; Connolly,
MacArthur, Stansfield, & McLellan, 2007; El Mansour & Mupinga 2007; DeNeui & Dodge 2006). A LMS
can be defined as “a self-contained webpage with embedded instructional tools that permit faculty to
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organize academic content and engage students in their learning” (Gautreau, 2011, p.2). By including
computer and Internet technologies in the learning processes and by offering multiple teaching learning
tools, LMSs provide virtual way of increased and faster communications among students and teachers
and offer speed and effectiveness in educational processes. While LMSs offer various supporting
features for teaching learning processes, and though universities make considerable investment on
LMSs, these are not used by faculty members to their fullest capabilities (Jaschik and Lederman , 2014;
Dahlstrom, et. al., 2014; Allen & Seaman, 2010). Pajo and Wallace (2001) stressed that successful
integration of technology in teaching depends not only on availability of technology but also on how
instructors embrace and use it. Hustad and Arntzen (2013) reported that faculty members mostly use
LMSs as supplements to their lectures; synchronous functionalities of LMSs (i.e. Chat, Online
discussions) were seldom used by faculty members with no direct contact with the participants. In a
survey on faculty attitudes on technology conducted by Jaschik and Lederman (2014), majority of the
faculty reported using a LMS, but using limited features: posting course syllabus (78%), recording grades
(58%), communicate with students (52%). Only 20% of faculty reported using the LMS to record lecture
content. While approximately 99% of higher education institutions have a LMS in place, approximately
one-half of faculty report using such systems on a regular basis and the majority of the faculty do not take
advantage of advanced LMS capabilities that have potential to improve the student outcomes (Dahlstrom,
et. al., 2014). All these findings indicate that to ensure increased use of LMSs by faculty members, more
research is required to gain better understanding of the factors that affect faculty members LMSs usage.
The focus of the current study was on faculty perspective of LMS usage. Two major purposes of the study
were: (i) to identify the factors that influence faculty members LMS usage behavior and (ii)) to determine
the underlying causal relationships among the factors. The core expectation was that understanding the
factors that affect faculty members’ LMS usage behavior can shed light on the development, selection,
training, maintenance and investments on such systems. To this end, the current study utilized Davis’s
(1989) Technology Acceptance Model (TAM) as a baseline model to predict faculty intention and usage of
LMS in higher education institutions. Also, this study proposed an extension of the original TAM by
including three external variables: system quality, perceived self-efficacy and facilitating conditions in it
and examined its validity in explaining faculty members’ LMS usage behavior. By conducting an empirical
study among university faculty members, this study presented important findings pertaining to faculty
attitude under conditions of non-mandatory use of LMSs. Based on the findings; the significant
determinants of LMS usage are discussed.
Literature Review
Learning Management Systems (LMSs) provide tools and functions like course management tools, online
group chats and discussions, documents (lecture materials, homework and assignments etc.), power
points, video clips uploading, grading and course evaluations to support teaching and learning. Since,
LMSs have evolved in a complex way in terms of educational contents, technological resources and
interaction possibilities; there is an increasing concern in regard to the quality of the interface and the
ways in which tasks are completed in these systems (Freire, Arezes, Campos, Jacobs & Soares, 2012).
Freire et.al. (2012) stated that, the definition of the term “usability” varies according to the area in which it
is being studied. In the view point of ergonomics, the term “usability” can be defined as “the capacity a
system has to offer to the user in carrying out of his tasks, in an effective efficient and satisfactory
manner”(Freire et al., 2012, p.1039). They stated that, to evaluate the LMSs’ usability: “the users’
perspective”, not anymore “the systems perspective”, is the main point to look at (Freire et al., 2012).
Many of the prior LMSs studies found that, not all the functions of LMSs were equally used by the users,
some functions are used more frequently than the other functions (Jaschik & Lederman, 2014, Weaver,
Spratt & Nair 2008, Panda & Mishra, 2007, Akpinar, Bal & Simsek, 2004; Woods, Baker & Hopper, 2004).
Fathema and Sutton (2013) found document uploading; grade posting and assignments were the most
frequently used features of Blackboard learning management systems by faculty members. They reported
that according to faculty members specific challenges including system problems and design flaws
reduce the overall utilization of the LMS by faculty. Holden and Rada (2011) indicated that, k-12
teachers’ technology self-efficacy has effect on teachers’ use of technology. Panda and Mishra (2007)
found that the significant barriers for e-learning adoption as perceived by faculty members were: poor
internet access, lack of training, followed by institutional policy on and instructional design for e-learning.
They found personal interest to use technology; intellectual challenge and sufficient provision for
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technology infrastructure were the important motivators in e-learning adoption by faculty members. Pajo
and Wallace (2001) identified personal barriers (lack of knowledge, skills, training, role models and time),
attitudinal barriers (no faith in technology, unwillingness to work with technology, concern about student
access) and organizational barriers (inadequate technical support, hardware, software, instructional
design, no recognition of the value of online teaching) that impeded that implementation of web-based
teaching by university teachers. Moreover, a significant number of prior studies examined students’
acceptance of various technologies including LMSs which showed similarities among their findings. For
example, Pituch and Lee (2006) found that usefulness and ease of use to be good determinants of the
student acceptance and distance learning. Lee, Cheung, and Chen (2005) found that perceived
usefulness and perceived enjoyment had an impact on both students’ attitude toward and students’
intention to use Internet-based learning medium. Pituch and Lee (2006) reported that system
characteristics were important determinants of college students’ perceived usefulness and perceived
ease of use of an e-learning system as well as of their e-learning usage behavior. Saadé, Nebebe, and
Tan (2007) found that perceived usefulness had significant effect on university students’ attitude toward
Multimedia learning Environments (MMLS) and revealed that students’ attitudes affect their behavioral
Intention to use MMLS. Weaver, et. al., (2008) reported that in using LMS, system quality is important to
both the students and faculty. Park (2009) revealed that e-learning self-efficacy and subjective norm play
an important role in affecting attitude (students) towards e-learning and behavioral intention to use e-
learning.
Technology Acceptance Model
Technology Acceptance Model (TAM) (Figure 1) is based on Ajzen and Fishbein’s (1980) Theory of
Reasoned Action (TRA). According to TRA, an individual’s intention to perform a behavior is a function of
his/her attitude toward the act or behavior and social norms. An individual’s attitude predicts his/her
intention and intention shapes the actual behavior.
Figure 1. Technology Acceptance Model (TAM) (Davis, Bagozzi & Warshaw, 1989, p.985).
TAM (Davis, 1989) claims that, Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) are the
two fundamental determinants of user acceptance of technology (Davis, 1989). PEOU is defined as “the
degree to which a person believes that using a particular technology would be free from effort” (Davis
1989, p.320). PU is defined as “the degree to which a person believes that using a particular system
would enhance his or her job performance” (Davis, 1989, p.320). TAM claims that PU will be influenced
by PEOU: when users’ find a technology “easy to use”, then they perceive the technology as a “useful
one”. TAM offers the causal relationships of these two fundamental constructs (PEOU and PU) with three
other constructs “attitude toward using (ATT)”, “behavioral intention to use (BI)” and “actual use (AU)”.
ATT is defined as “an individual's positive or negative feeling about performing the target behavior (e.g.,
using a system)” (Fishbein & Ajzen 1975, p.216). According to TAM, both PEOU and PU influence the
users’ attitude toward using a technology. It claims that if users find a technology useful and easy to use
than they develop a positive attitude toward this technology. The fourth construct,Behavioral Intention
(BI)”, is defined as the degree to which a person has formulated conscious plans to perform or not
perform some specified future behavior (Davis, 1989). TAM claims, PU and ATT directly influences BI. If
users find a specific technology as a useful one (PU) then they develop a positive intention of using it.
Similarly users’ positive attitude toward a specific technology leads them developing an intention to use
this technology. TAM suggests usersbehavioral intention (BI) shapes their actual use of the technology
(AU). If users have intention to use a specific technology then they use it.
TAM is chosen to use in this study because prior research has found TAM as the most influential,
commonly employed, and highly predictive model of IT adoption (Adams, Nelson & Todd, 1992; Davis, et
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al., 1989; Venkatesh & Davis, 2000 ; Lee, Kozar, & Larsen, 2003; Venkatesh & Bala, 2008). Though TAM
was designed to study technology acceptance decisions across different organizational settings and
users’ population, research on TAM’s application in education was limited in past (Teo, Lee & Chai,
2008). Recently, adopting TAM as an explanatory tool in investigating e-learning processes has become
a trend (Park, 2009). This study delved more deeply to the TAM research by applying it in the education
sector. Also, it contributed to the TAM literature by proposing an extension of the original TAM framework.
The study examined the effect of three external variables on the five original TAM constructs. A
discussion of the research model and hypotheses follows.
Research Model and Research Hypotheses
In order to provide a better understanding to the exploration of LMS acceptance amongst faculty
members three factors “System quality”, “perceived self-efficacy” and “facilitating conditions” were
incorporated as external variables in the original TAM. The proposed model (as depicted in Figure 2) was
used to explore the effects of the proposed external variables on faculty members LMS usage behavior.
In the next section, brief definitions and the inferences of the proposed three factors as antecedents of
LMS usage and related hypotheses are presented.
Figure 2. Proposed research model for faculty acceptance of LMSs.
Note: SQ= System Quality, PSE= Perceived Self-efficacy, FC= Facilitating Conditions, PEOU= Perceived
Ease of Use, PU= Perceived Usefulness, ATT= Attitude Toward Using, BI= Behavioral Intention to use,
AU= Actual Use
System Quality (SQ)
System Quality (SQ) in the Internet environment measures the desired characteristics (usability,
availability, reliability, adaptability, and response time) of an e-commerce system (i.e. LMS) (Delone &
Mclean, 2003). In this study, SQ is defined as the quality related to the functions, speed, features,
contents, interaction capability of LMS. Prior research found ‘System Quality had significant effect on
perceived usefulness (PU) of a wide variety of information systems including various e-learning systems
including LMS and Information and Communication Technology (ICT) (Fathema & Sutton, 2013, Park,
Nam, & Cha ,2012; Condie & Livingston, 2007; Pituch & Lee 2006; Russell, Bebell & O’Connor,
2003).Furthermore, studies have reported SQ’s significant positive effect on users’ attitudes (ATT) toward
using different types of technologies (i.e. LMS, Internet protocol television) (Fathema & Sutton, 2013,
Dong Hee, 2009), and on users’ behavioral intentions (BI) to use technologies, specifically in the context
of LMS, mobile learning and various e-commerce systems(Fathema & Sutton, 2013, Park et.
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al.,2012;Delone & Mclean, 2003). Based on prior literature, three hypotheses were formulated about the
relationship of SQ with perceived usefulness (PU), attitude toward using (ATT) and behavioral intention to
use (BI). The theme behind these hypotheses is, if a system has all the expected characteristics in it, then
users will (i) find it as a useful system (ii) develop a positive attitude toward the system and (iii) develop a
positive intention to use the system.
H
1
: System Quality (SQ) of LMS has a significant positive effect on the perceived usefulness
(PU) of LMS
H
2
: SQ of LMS has a significant positive effect on faculty members’ attitudes (ATT) toward using
LMS
H
3
: SQ of LMS has a significant positive effect on faculty members’ behavioral intention (BI) of
using LMS
Perceived Self-efficacy (PSE)
Perceived Self-efficacy (PSE) is defined as “an individual's judgment of his or her capability to organize
and execute the courses of action required to attain designated types of performances. It is not
concerned with the skills one has, but with the judgments of what one can do with whatever skills one
possesses” (Bandura, 1986, p.391). In the LMS usage context, PSE indicates a faculty member’s
judgment or the confidence of his/her own capability of operating/ navigating/ working with LMS. In
general, users with higher perceived self-efficacy develop stronger perceptions of perceived ease of use
(PEOU) and perceived usefulness (PU) of a system. In contrast, if an individual perceives himself/
herself as less capable of using a system (i.e. LMS) than he/she will find the system as ‘less useful’ and
‘difficult to use’. Prior research has reported that users’ PSE has significant positive effect on the PEOU of
LMS, e-learning or mobile learning systems (Fathema & Sutton, 2013, Park et. al.,2012; Yuen & Ma,
2008, Ong & Lai, 2006; Roca, Chiu & Martinez, 2006; Pituch & Lee, 2006; Grandon, Alshare, & Kwan,
2005; Ong, Lai, &Wang, 2004). In addition, research findings have supported PSE’s significant positive
effect on PU of different types of information systems including various computing technologies and e-
learning systems (Compeau, Higgins & Huff., 1999; Ong, et.al., 2004; Ong & Lai, 2006). Drawings from
these findings two hypotheses were examined:
H
4
: Faculty members’ perceived self-efficacies (PSE) have significant positive effects on their
perceived ease of use (PEOU) of LMS
H
5
: Faculty members’ PSEs have significant positive effects on their perceived usefulness (PU) of
LMS
Facilitating Conditions (FC)
Facilitating conditions (FCs) are the factors (Ngai, Poon & Chan, 2007) that can be stated as “perceived
enablers or barriers in the environment that influence a person’s perception of ease or difficulty of
performing a task” (Teo, 2010). Venkatesh and Bala (2008) elaborated it as “FCs are related to
individuals’ control beliefs regarding the availability of organizational resources and support structures to
facilitate the use of a system”. Here in LMS context, FCs indicates the availability of the related resources
i.e technical help, internet infrastructure, hardware, software, training, online help to work with Canvas.
Previous studies on teachers’ acceptance of various technologies (Teo, 2010, Teo et. al., 2008, Panda &
Mishra, 2007, Pajo & Wallace, 2001) have reported that FC is a key belief that influences user adoption of
technology. Teo et. al (2008) and Teo (2010) revealed FC’s significant effect on perceived ease of use
(PEOU), in terms of pre-service teachers’ computing technology acceptance behavior. Furthermore, Teo
(2010) reported FC’s significant effect on pre-service teachersattitude (ATT) towards using computer
technology. Ngai et.al, (2007) studied students’ attitude and reported that facilitating conditions (FC)
significantly affect university students’ LMS acceptance behavior by influencing on the perceived ease of
use (PEOU) and students’ attitude (ATT) toward using LMS. Therefore, the current study proposed two
hypotheses to examine the effect of facilitating conditions (FC) on the PEOU and ATT in the context of
faculty attitudes toward LMS.
H
6
: FC has a significant positive effect on faculty members’ PEOU of LMS
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H
7
: FC of LMS has a significant positive effect on faculty members’ attitude (ATT) toward using
LMS.
Hypotheses related to five original TAM constructs
Applying the arguments claimed by TAM (Davis, 1989) regarding the technology adoption behavior and
considering the prior TAM based research findings; the following hypotheses for LMS usage by faculty
members were examined in the current study.
H
8
: Faculty members’ PEOU of LMS has a significant positive effect on their perceived
usefulness (PU) of LMS
H
9
: Faculty members’ PEOU of LMS has a significant positive effect on their attitudes toward
using LMS (ATT).
H
10
: Faculty members’ PU of LMS has a significant positive effect on their ATT toward LMS use
H
11
: PU has a significant positive effect on faculty members’ behavioral intention (BI) to use LMS
H
12
: Faculty members’ ATT toward using LMS have a significant positive effect on their BI of
using LMS
H
13
: Faculty members’ BI toward using LMS will have a significant positive effect on their actual
use (AU) of LMS.
Method
This study, approved by the Institutional Review Board (IRB), was focused on Canvas: a newly
introduced LMS which was launched to post-secondary institutions in February 2011. Together with the
standard features of LMSs, Canvas provides advanced options like learning outcomes, peer review,
migration tools, e-portfolios, screen sharing and video chat etc. Canvas is currently used by more than
300 colleges, universities and school districts (www.instructure.com ).
Data were collected from individuals with teaching responsibilities (faculty members and graduate
teaching assistants) from two universities in the United States. Using a purposive sampling method, two
universities were selected on the basis of their similarity in the institutional characteristics and LMS
adoption background. Both of the universities were public, land grant, research universities and have had
Blackboard as their LMS before they adopted Canvas. In both of the universities, faculty members have
the flexibility to use none, some, or all of the available features of Canvas and they are allowed to use any
other software over and beyond Canvas in facilitating their teaching-learning activities.
Procedures
Using a web-based survey, data were collected from the two universities from January- April, 2013. The
email addresses of the faculty members were collected from the university websites. An email invitation
including the survey link was directly sent to the faculty members and Graduate Teaching Assistants
(GTAs) irrespective of whether they used Canvas or not in January 2013. Later, two reminder emails (one
in February 2013 and another in March 2013) were sent to fill-in the survey. Survey participation was
voluntary and no incentive was offered to the participants. Data collection was anonymous and no
identifiable information was collected. There were three parts in the survey. The first part included the
survey information letter and consent agreement, second part included questions related to Canvas
usage and the third part covered the demographic information. The survey items in the second part were
randomized to avoid potential order effects. The survey items were reviewed by two content experts and
the survey was pilot tested. The final survey questionnaire was composed of 28 Likert scale items on
eight constructs (SQ, PSE, FC, PEOU, PU, ATT, BI, and AU). All constructs (except the self-developed
construct ATT) were adapted from prior studies (Table 1). However, the items were re-worded to make
them relevant to the specific context of the study. All internal consistency reliabilities (based on Cronbach’
alphas) for all eight scales ranged from .870 to .963 (Table 1) and were considered to be good (Hair,
Anderson,Tatham, & Black, 1998).
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Table 1.
Measurement Scales (Items, Reliability and Sources)
Scale
Total
Items
Reliability
(α)
Adapted from
*System Quality (SQ)
4
0.870
Liaw(2008)
*Perceived *Self Efficacy (PSE)
3
0.930
Liaw(2008)
*Facilitating Conditions(FC)
3
0.883
Teo ( 2010)
*Perceived Ease of Use ( PEOU)
4
0.934
Venkatesh and Davis (2000)
*Perceived Usefulness (PU)
4
0.963
Venkatesh and Davis (2000)
*Attitude toward Using (ATT)
4
0.963
Self-developed
Behavioral Intention (BI)
3
0.898
Liaw(2008)
**Actual Use (AU)
3
0.875
Malhotra and Galletta, (1999)
Note.
*All Items for SQ, PSE, FC, PEOU, PU, ATT and BI were measured on a 7-point Likert scale from 1
being strongly disagree to 7 being ‘ strongly agree.’
**Items for AU were measured on a 7-point Likert scale from 1 being Not at all to 7 being to a great extent
Data collection
In total, 560 individuals completed the survey with an average response rate of 24%. The response rate
was low, because it was the percentage of the 2330 faculty members and GTAs (both Canvas users and
non-users) to whom the survey invitations were sent. Out of the 560 respondents 298 (53.21%) were
male and 262 (46.79%) were female. Most of the respondents (30.18%) were at the age range of 51-60.
The descriptive statistics of the respondents’ demographics are presented in Table 2.
Table 2.
Demographics (Gender, Age, Academic Rank)
Variable
%
Gender
Male
53.21%
Female
46.79%
Age Range
30 or less
15.36%
31-40
22.32%
41-50
19.29%
51-60
30.18%
61-70
11.96%
70 and up
0.89%
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Academic Rank
Graduate Teaching Assistant
13.75%
Instructor
7.14%
Lecturer
7.68%
Assistant Professor
19.29%
Associate Professor
27.32%
Professor
18.57%
Other
6.25%
Data Analysis
Following Anderson and Gerbing’s (1988) recommendations, a two-step approach for Structural Equation
Modeling (SEM) has been used for data analysis. At the first step, a Confirmatory Factor Analysis (CFA)
was conducted to develop the measurement model. To examine the causal relationships among all
constructs, the proposed structural model was tested using SEM. The software program Analysis of
Moment Structures (AMOS) and part of the Statistical Package for the Social Sciences (SPSS) software
(Arbuckle, 2007) were used to conduct the CFA and SEM. SEM was chosen to use because it
simultaneously analyses the paths in the model and tests the goodness of fit of the model. CFA was
employed to measure the construct validity of the instrument used in the study. SEM techniques using
AMOS graphics were employed to evaluate the fit of both the measurement and structural components of
the proposed model.
Data Screening and Normality test
No missing data were found since the survey software (Qualtrics.com) prevented to record any partially
completed survey. Since the data did not meet the univariate and multivariate normality assumptions, a
Bollen-Stine bootstrap method was used for inference of exact measurement and structural model
(Byrne, 2009). The overall LMS usage was measured using eight constructs and 28 variables. The
respondents were asked to rate their responses on 7 point Likert scales with 1 being the lowest rating
and 7 being the highest. As shown in Table 3, the mean scores of all the items ranged from 4.23 to 5.56
(neutral to agree) and the standard deviations of the scores ranged from 1.36 to 2.23, indicating that on
average faculty members are neutral or agreed on the statements.
CFA
CFA was used to test the factorial structure of the hypothesized eight factor measurement model (Figure
3). All these factors were allowed to correlate. Each of the 28 measures was allowed to load only on the
main factor of interest not on any other factors.
Table 3.
Mean and Standard Deviation of the measurement Constructs and Items
Constructs and Items
Mean
SD
Constructs and Items
Mean
SD
System Quality (SQ)
4.93
1.48
Perceived Usefulness (PU)
4.74
1.68
Perceived Self Efficacy (PSE)
4.98
1.51
Behavioral Intention (BI)
5.25
1.48
Facilitating Conditions(FC)
5.27
1.51
Attitude toward Using (ATT)
4.92
1.70
Perceived Ease of Use( PEOU)
4.59
1.61
Actual Use (AU)
4.94
2.10
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Figure 3 The hypothesized eight factor CFA model for faculty attitude toward LMSs.
Note: SQ= System Quality, PSE= Perceived Self-efficacy, FC= Facilitating Conditions, PEOU= Perceived
Ease of Use, PU= Perceived Usefulness, ATT= Attitude Toward Using, BI= Behavioral Intention to use,
AU= Actual Use
Bivariate Correlations and factor loadings
To investigate the linearity between the observed variable, bivariate Pearson correlation coefficient was
computed. All of the inter-item correlation values of the indicators of each of the eight constructs were
significant and in medium to high levels ranging from (.42 to .92) (Cohen, 1988), indicating that the items
and constructs were interrelated to each other and the linearity assumption between indicator and latent
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variables were met. All indicators significantly loaded onto the respective factors and the loadings were
fair to excellent ranging from 0.473 to .962 (Tabachnick & Fidell, 2007). Also, the unstandardized
parameter estimates and the critical ratios for all 28 items were found significant which supported the
items and their relationships with their relative latent constructs. Model fit was assessed. Except for χ², all
fit indices reached recommended level of fit: (χ ² = 1076.694, df = 322, p <.001, CMIN/DF= 3.344, RMSEA
= 0.06, SRMR = 0.0431, CFI= 0.958, IFI=. 958, NFI= 0.941, TLI= .95, AIC= 1244.694). Since χ² is
sensitive to large sample size, with a large sample of 560 participants, it was not unusual to get a
significant value. Also, for sample size greater than 250, significant χ²value is acceptable (Hair, Black,
Bablin, & Anderson, 2006). Therefore, the significant χ² value is acceptable for this study. Since the fit
indices met the recommended level of fit, the CFA results provided strong support for the reliability and
the original eight factors structure of the measurement items (28 items measuring eight latent constructs)
in evaluating the faculty attitude toward LMS use.
SEM
This study was intended to simultaneously examine the direct and indirect relationships among the
constructs of the proposed model and to test the fit between the proposed model and the obtained data
(Figure 4). For its ease and wide applicability in modeling multivariate relations (Byrne, 2009), SEM with
AMOS 18(Arbuckle, 2007) was chosen to do the analyses.
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Figure 4. Hypothesized structural model of Faculty attitude toward LMSs (Canvas).
Note: SQ= System Quality, PSE= Perceived Self-efficacy, FC= Facilitating Conditions, PEOU= Perceived
Ease of Use, PU= Perceived Usefulness, ATT= Attitude Toward Using, BI= Behavioral Intention to use,
AU= Actual Use
Relationships among the latent constructs (Bivariate Correlations)
The bivariate relationships indicated that all of the variables were significantly correlated with each other
at the 0.01 level. The correlations among the latent constructs ranged from .191 to .885 and no multi-
collinearity was found among the latent variables (Table 4).
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The results indicated the fit indices for the research model as: χ² = 1436.851, df= 334, p <.001,
CMIN/DF= 4.302, SRMR= 0.077, CFI= 0.938, IFI= .938, NFI= 0.921, TLI= .93, RMSEA =0.077, AIC=
1636.851. Except for the χ² and RMSEA, all the fit indices met the recommended level of acceptable fit.
Though all the path coefficients demonstrated significance (p<.05), the SEM results showed the effect of
SQ on BI was in contrast to what was hypothesized. Therefore, the path was removed from the model
and the model was revised and tested again. It showed a good fit comparative to the proposed model, but
not at an acceptable level (χ² = 1436.823, df= 335, p <.001, CMIN/DF= 4.34, SRMR= 0.713, CFI= 0.937,
IFI= .937, NFI= 0.92, TLI= .929, RMSEA =0.077, AIC= 1636.851). The modification indices indicated
adding a path from SQ to PEOU would notably improve the values of the fit indices. In practical, it makes
sense that if LMS maintains a high quality than it will be easier to use. Therefore, if the quality of LMS
goes up than faculty members will perceive it as an easier system to use. So the suggested change was
made by adding a path from SQ to PEOU. The fit indices (χ² = 1205.409, df= 334, p <.001, CMIN/DF=
3.609, SRMR= 0.0593, CFI= 0.951, IFI= .951, NFI= 0.934, TLI= .945, RMSEA =0.068, AIC= 1405.409)
except for χ² and RMSEA indicated a good model fit. After this modification was made, the path from FC
to PEOU became statistically insignificant (p >.05). One possible reason for this insignificant path could
be the operational definition of the term ‘facilitating conditions’ which explained the concept in terms of
technical help and support in general for all sorts of technology use not specific to LMS use. Also, some
prior studies reported facilitating conditions did not affect the ease of use of technology. (i.e. Karahanna
& Straub 1999, Thompson, Higgins & Howell, 1991). Therefore, this insignificant path was removed from
the model and the model was tested again. After the third modification, the SEM results showed the fit
indices (except for χ²) of the model met the acceptable cut-off values (χ² = 1205.745, df= 335, p <.001,
CMIN/DF= 3.599, SRMR= .0595, CFI= 0.951, IFI= .951, NFI= 0.934, TLI= .945, RMSEA =0.068, AIC=
1403.745 Also the results indicated that the structural model fits the data fairly well. The χ²value showed
statistically significant value; however it is acceptable with a large data set of 560 samples (Hair et al,
2006). So, the third revised model was chosen to be the final model (Figure 5). The fit indices considered
to test the models are depicted in Table 5. Overall, the model fitted the data well and showed a high
predictive power in determining the faculty attitudes (ATT) toward LMS, the behavioral intention (BI) of
faculty members to use LMS and the actual use (AU) of LMS by faculty members.
Table 4
Correlations among the eight latent constructs
SQ
PSE
FC
PEOU
PU
ATT
BI
AU
System Quality (SQ)
1
Perceived Self Efficacy (PSE)
.625
**
1
Facilitating Conditions (FC)
.417
**
.404
**
1
Perceived Ease of use
(PEOU)
.776
**
.772
**
.407
**
1
Perceived Usefulness (PU)
.691
**
.657
**
.440
**
.709
**
1
Attitude Toward Technology
(ATT)
.758
**
.678
**
.467
**
.768
**
.885
**
1
Behavioral Intentions (BI)
.573
**
.611
**
.515
**
.589
**
.758
**
.783
**
1
Actual use (AU)
.191
**
.373
**
.319
**
.266
**
.398
**
.368
**
.479
**
1
**. Correlation is significant at the 0.01 level (2-tailed).
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Figure 5. The structural model for f faculty attitudes toward LMSs.
Note: SQ= System Quality, PSE= Perceived Self-efficacy, FC= Facilitating Conditions, PEOU= Perceived
Ease of Use, PU= Perceived Usefulness, ATT= Attitude Toward Using, BI= Behavioral Intention to use,
AU= Actual Use
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Table 5
Fit Indices of the Proposed Measurement Model
Recommended
Level of Fit
Proposed
Model
Modification: 1
(Removed
insignificant
Path : SQ!BI)
Modification:2
(Added significant
path :SQ !PEOU)
Modification:3
(Removed
insignificant path:
FC ! PEOU)
Absolute fit indices
Chi-Square
Significant at p<0.05
1436.851
df=334,
p=0.000
14533.823
df=335
p=.000
1205.409
df=334,
p=.000
1205.745
df=335
p =.000
Relative Chi-Square (CMIN/DF)
2~5 , <5,
(Bentler,1990)
4.302
4.34
3.609
3.599
RMSEA (Root Mean Square of Error
Estimation)
<=0.06 (Joreskog
&Sorbom,1993)
0.077
0.077
0.068
0.068
SRMR (Standardized Root Mean
Residual)
<=.80 (Teo, 2012)
0.0719
0.713
0.0593
0.0595
Incremental fit indices
CFI (Comparative Fit Index)
>=.90 (Browne &
Cudeck,1992)
0.938
0.937
0.951
0.951
IFI (Incremental Fit Index)
>=.90 (Bentler,1990)
0.938
0.937
0.951
0.951
NFI (Normed Fit Index)
>=.95 good, .90 to .95
acceptable,(Bentler,1990)
0.921
0.92
0.934
0.934
TLI (Tucker Lewis Index)
>=.95 Or >=.90 ((Marsh,
Hau, & Wen, 2004)
0.93
0.929
0.945
0.945
Parsimonious fit Index
AIC (Akaike Information Criterion)
Smaller value better fit
1636.851
1651.823
1405.409
1403.745
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Hypotheses testing results
The SEM results revealed that all of the three proposed external variables (SQ, PSE and FC) have
significant effect on faculty attitudes toward LMS use. Out of the proposed 13 hypotheses, 11 were
supported. The results indicated that, the first external construct SQ significantly affects PU and ATT.
Therefore hypotheses H
1
and
H
2
were supported. However, no significant effect of SQ on BI was found, so
hypothesis H
3
was rejected. Also, the results revealed a new significant path from SQ to PEOU with a
regression weight of .567 indicating that SQ significantly affects PEOU. As expected, the second external
construct PSE was found to be significant determinant of PEOU and PU. Thus, both of the proposed
hypotheses H
4
and
H
5
were supported. No significant of FC on PEOU was found. Therefore hypothesis H
6
was rejected. FC was found to be significant determinant of ATT, supporting hypotheses H
7
. Also, all the
proposed hypotheses (H
8,
H
9,
H
10,
H
11,
H
12,
and H
13
) were supported by the SEM results indicating the
relationships among the original TAM constructs (as proposed) were significant. The influences of each of
the exogenous variables on the endogenous variables were assessed as well. To do so, the standardized
total effects, direct and indirect effects associated with each of the eight variables were tested. Table 6
shows the results of the hypotheses tests including the regression weights of each of the 11 significant
paths as well as the regression weight of the new significant path from SQ to PEOU. Each of these
regression weights represents the determinant’s direct effect on the respective endogenous variable. All
these regression weights (ranging from .184 to .567) of the significant paths are considered to be medium
to large as recommended by Cohen (1988).
Table 6.
Hypotheses Testing Results
Hypotheses
Path
Support
Regression weight
H
1:
SQ!PU
Yes
0.432**
H
2:
SQ!ATT
Yes
0.263**
H
3:
SQ!BI
No
_
New path
SQ!PEOU
Yes
0.567**
H
4:
PSE!PEOU
Yes
0.435**
H
5:
PSE!PU
Yes
0.239**
H
6:
FC!PEOU
No
_
H
7
FC!ATT
Yes
0.062**
H
8:
PEOU!PU
Yes
0.184*
H
9:
PEOU!ATT
Yes
0.20**
H
10:
PU!ATT
Yes
0.53**
H
11:
PU!BI
Yes
0.31**
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H
12:
ATT!BI
Yes
0.72**
H
13:
BI!AU
Yes
0.47**
*P<.05, ** P<.001
Note: SQ= System Quality, PSE= Perceived Self-efficacy, FC= Facilitating Conditions, PEOU= Perceived
Ease of Use, PU= Perceived Usefulness, ATT= Attitude Toward Using, BI= Behavioral Intention to use,
AU= Actual Use
All three exogenous variables (SQ, PSE and FC) were found statistically significant determinants of the
five endogenous variables (PEOU, PU, ATT, BI and AU) (Figure 6).
Figure 6. Results of the structural model for faculty attitudes toward LMS.
Note: SQ= System Quality, PSE= Perceived Self-efficacy, FC= Facilitating Conditions, PEOU= Perceived
Ease of Use, PU= Perceived Usefulness, ATT= Attitude Toward Using, BI= Behavioral Intention to use,
AU= Actual Use
The endogenous variable PU was found to be significantly determined by three variables SQ (β = .432, p
<.001), PSE (β = .239, p <.001) and PEOU (β = .184, p <.05), resulting in an R
2
of .62, which means that
the SQ, PSE and PEOU jointly accounted for 62% of the variance in PU. Similarly, PEOU was
significantly determined by PSE (β = .435, p <.001) and SQ (β = .567, p <.001) resulting in an R
2
of .84,
indicating 84% of the variance of PEOU is explained by FC and PSE. ATT was significantly determined
by SQ (β = .263, p <.001), FC (β = .062, p <.05), PU (β = .53, p <.001) and PEOU (β = .20, p <.001)
resulting in an R
2
of .704 indicating 70.4% of the variance in ATT is explained by these four (SQ, FC, PU
and PEOU) variables. BI was found to be significantly determined by PU (β = .31, p <.001) and ATT (β =
.72, p <.001), resulting in an R
2
of .66, which means that PU and ATT accounted for 66% of the variance
in BI. Finally AU was significantly determined by BI (β = .47, p <.001), resulting in an R
2
of .23 which
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indicates that 23% of the variance in AU is accounted by BI ( Figure 6).Therefore, the results indicated
that the extended technology acceptance model had high predictive power in determining the faculty
member’s LMS usage behavior.
Discussion and Conclusion
This study identified the factors that influence faculty members’ attitudes toward LMS and determine the
underlying causal relationships among the factors using the proposed extension of original TAM
framework. By collecting data from a sample of 560 faculty members from two universities in the US, the
study results generally supported the proposed model with minor revisions and confirmed the significant
influence of perceived self-efficacy (PSE), systems quality (SQ) and facilitating conditions (FC) on the use
of LMS by faculty members in higher educational institutions.
The results of this study revealed that system quality (SQ) had significant positive effect on perceived
ease of use (PEOU) and perceived usefulness (PU) of LMS. This indicates that faculty members place
emphasis on the quality issues (i.e., functions, contents, navigation speed, and interaction capability) of
LMS. Also the results indicated SQ had significant positive effect on faculty members’ attitude towards
LMS. These findings are in line with recent LMS studies that reported several system issues like:
suitability of design in screen and system, easiness of course procedure, interoperability of system,
easiness of instruction management and appropriateness of multimedia use, flexibility of interaction and
test, learner control, variety of communication and test types and user accessibility as important LMS
features that directly or indirectly benefit LMS or e-learning users and influence their attitudes towards
LMS (Fathema & Sutton, 2013; Kim & Leet, 2008; Weaver et. al,2008; Panda & Mishra, 2007; Pituch &
Lee, 2006; Russell, et.al.,2003).
Consistent with the findings of previous studies, faculty perceived self-efficacy (PSE) was also found to
be a significant factor in determining their usage of technology (Holden & Rada, 2011, Panda & Mishra,
2007, Pajo & Wallace, 2001). In addition, PSE was found to be a significant determinant of perceived
ease of use (PEOU) (Yuen & Ma, 2008; Roca, et.al., 2006; Pituch & Lee, 2006; Ong & Lai, 2006;
;Grandon et.al., 2005; Ong et. al., 2004) and perceived usefulness (PU) (Ong & Lai, 2006; Ong, et.al.,
2004). These findings indicated that faculty members with higher self-efficacy find LMS useful and easy to
use comparative to faculty members with lower self-efficacy. In other words, faculty members who are
confident about their LMS skills (i.e. operating basic features, LMS functions, online learning contents)
perceive LMS as a useful technology to use and experience lower complexity using it. Consequently,
confident faculty members use LMS more than the less confident ones.
The study also revealed a weak positive effect of facilitating conditions (FC) on attitudes (ATT) toward
using technology and perceived ease of use (PEOU). It could be possible that faculty members develop
positive attitudes toward LMS if adequate facilitating conditions (i.e., adequate guidance on LMS use,
personal/ group assistance, specialized instructions concerning LMS use)) are available. Another
possible explanation of finding a weak relationship can be, if LMS quality is really high and faculty
members have high self-efficacy than they do not care as much about or have a need for the availability
of facilitating conditions (facilities, training etc.) for using LMS. This finding contradicts McGill, Klobas, and
Renzi, (2011), who reported no effect on facilitating conditions on LMS utilization by instructors. However,
the current findings partially support Teo (2010) f where he reported that facilitating conditions had
significant positive effects on ATT and PEOU. Also the current findings are alignment with Panda and
Mishra’s (2007) findings that indicated inadequate FC is one of the most important barriers of LMS usage
by faculty members.
Findings from the current study also support research pertaining to the strong relationships among PU,
PEOU and ATT in the context of teachers’ technology usage. In line with prior findings, perceived
usefulness (PU) of LMS was significantly determined by the perceived ease of use (PEOU) of LMS and
faculty members behavioral intention (BI) to use LMS was significantly determined by the perceived
usefulness (PU) of the LMS (Hu, Clark & Ma, 2003). In addition, this study revealed significant effects of
perceived usefulness (PU) (Holden & Rada, 2011) and perceived ease of use (PEOU) (Lee, Hsieh and
Chen 2013) on faculty attitudes (ATT) toward LMS. The positive effect of ATT on BI (Farahat, 2012) and
positive effect of BI on AU (Wang & Wang 2009) are also supported. These findings further validated
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Davis’s (1989) claim. In the context of LMS usage, Davis’s (1989)’s claims can be restated as: first faculty
members evaluate how easy or difficult it is to work with the LMS, then they look at the usefulness of it for
them. If they find it as an ‘easy to use’ and ‘useful’ technology for them then they develop a positive
attitude towards it. The positive attitudes lead them to develop a positive intention to use it. Finally their
positive intention influences their actual use of LMS. Hence, all original TAM constructs significantly
predicted intention to use LMS and actual use of LMS. Also, the three external constructs: system
quality, perceived self-efficacy and facilitating conditions directly or indirectly influenced faculty members’
attitude towards LMS, behavioral intention to use LMS and their actual use of LMS. These provide
support for the validity of the proposed extension of original TAM in explaining faculty attitude toward
LMSs.
Implications
The results provide important issues to be considered to ensure increased use of LMS by faculty
members in higher education. Based on the study results, we would offer the following recommendations:
The study found that system quality is a strong salient factor that shapes faculty member’s LMS use.
Therefore, LMS designers and university policy makers should concentrate more efforts on the quality
improvement of LMS to make it more usable to the faculty members. User-friendliness, easy accessibility
and reliability are important areas to focus on. The interface, features, functions, contents, navigation
speed, interaction capability etc., of the LMS should be periodically monitored and improved according to
the faculty members need. To maintain better quality, a continuous quality improvement process should
be conducted which will collect feedback from the LMS users about the quality issues, problems and
recommendation for improvement and will plan for LMS improvement actions accordingly. It is important
to ensure that universities periodically collect information from LMS users (i.e. faculty members’ and
students) about their experiences with LMS usage, problems they are facing and their recommendations
about improvement of LMS. Based on the information collected, universities should improve and update
LMS so that it can support the users more efficiently.
The study results revealed that self-efficacy was significant and salient factor in determining users’
acceptance of LMS. Therefore, once a new LMS is adopted, it is important to inform the faculty members
about the features, usefulness, and technical issues of it so that they can gain an in-depth understanding
of the features of the LMS and feel confident using it. Fathema and Sutton (2013) reported that faculty
members would like universities to offer extensive training, workshops and awareness programs on LMS
features, usage and benefits to help increase the faculty use of LMSs. Moreover, in a recent national
survey, 57% of faculty indicated that they would be more effective if they were better skilled in using LMS
technology in their courses (Dahlstrom, et. al., 2014). In the same study, faculty members also indicated
that they would be motivated to learn and use LMS more if they are aware that there is clear evidence of
the positive impact of such technology on student learning. Therefore, to increase faculty self-efficacy
and to ensure increased use of LMS by faculty, universities should offer periodic training programs and
extended online help for LMS use. These would help faculty members get more hands-on experiences,
gain improved skills and become more competent in using LMS which in turn, will increase their LMS use.
Our results indicated that, though not extensive, facilitating conditions had a weak influence on faculty
attitudes toward LMS. Therefore, universities need to pay attention to ensure availability of reliable
network access and technological support to ensure smooth running of LMS. Also universities should
provide extensive online and face-to-face support and guidance for faculty members to ensure faculty
members positive attitudes toward LMS which will in turn ensure extended use of LMS by them (Panda &
Mishra, 2007, Hustad & Arntzen, 2013).
Limitations and future directions
The study has some limitations. The study is based on a single LMS example. Using a purposive
sampling approach, data were collected only from two universities; therefore results of the study may be
restricted to the particular settings. Replication of this study in other settings and sample groups would
help understanding the implications of this extended TAM. Future researchers should strongly consider
evaluating the impact of the three significant external variables (system quality, perceived self-efficacy
and facilitating conditions) on acceptance and usage behavior of different populations and different LMSs.
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A follow-up qualitative study to know more about the faculty members’ perspectives about LMS would be
an important future research direction.
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Appendix
Measurement items in the Survey Questionnaire
System Quality (SQ)
SQ1
I am satisfied with the CANVAS functions
SQ2
I am satisfied with the Internet Speed
SQ3
I am satisfied with the CANVAS content
SQ4
I am satisfied with CANVAS interaction
Perceived Self-Efficacy (PSE)
PSE1
I feel confident using CANVAS features
PSE2
I feel confident operating CANVAS functions
PSE3
I feel confident using Online learning content in CANVAS
Facilitating conditions (FC)
FC1
When I need help to use CANVAS guidance is available to me
FC2
A specific person / group is available for assistance with any difficulties related with
CANVAS use
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FC3
Specialized instruction concerning CANVAS use is available to me
Perceived Ease of Use (PEOU)
PEOU1
My interaction with CANVAS is clear and understandable
PEOU2
Interacting with CANVAS does not require a lot of my mental effort
PEOU3
I find CANVAS to be easy to use
PEOU4
I find it easy to get CANVAS to do what I want it to do
Perceived Usefulness (PU)
PU1
Using CANVAS improves my performance as a faculty member
PU2
Using CANVAS in my job increases my productivity
PU3
Using CANVAS enhances my effectiveness in my job
PU4
I find CANVAS to be useful in my job
Attitude toward Using (ATT)
ATT1
I think it is worthwhile to use CANVAS
ATT2
I like using CANVAS
ATT3
In my opinion, it is very desirable to use CANVAS for academic and related purposes
ATT4
I have a generally favorable attitude toward using CANVAS
Behavioral Intention to Use (BI)
BI1
I intend to use the functions and content of CANVAS to assist my academic activities
BI2
I intend to use the functions and content of CANVAS as often as possible
BI3
I intend to use the functions and content of CANVAS in the future
Actual Use ( AU)
AU1
Overall to what extent do you use CANVAS?
AU2
To what extent did you use CANVAS last month?
AU3
To what extent did you use CANVAS last week?
Source: Fathema, N. (2013). Structural Equation Modeling (SEM) of an extended Technology Acceptance
Model (TAM) to report web technology adoption behavior in higher education institutions (Ph.D
thesis). Auburn University, Auburn, AL, United States.
This work is published under a Creative Commons Attribution-Non-Commercial-Share-Alike License
For details please go to: http://creativecommons.org/licenses/by-nc-sa/3.0/us/
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... Actual use system` Actual system use is described as "a favorable or unfavorable emotion of an individual regarding the conduct of the target (e.g., using a system) (Fathema et al., 2015). Potential users form motivational tendencies reasonably quickly after exposure to a new regime and are well informed about the observable behavioral consequences of such tendencies. ...
Research
The Covid-19 pandemic has spread across the globe affecting various aspects of human life, including education. Higher education is facing an unprecedented crisis all over the world. According to the United Nations, Educational, Scientific and Cultural Organization "UNESCO" more than 1.5 billion students in 165 countries have dropped out of academies and universities due to the Covid-19 outbreak. The pandemic has forced academic bodies to discover new learning and teaching forms, including E-learning. Therefore, this study aims to identify the factors influencing the Effectiveness of the E-learning system during Covid-19 in worldwide universities, including Malaysia. A systematic literature review (SLR) has been conducted as the method. It is started by a mapping process extracted from the current guidelines for classifying and structuring research guides published in the field of E-learning in Malaysian universities and worldwide. A total of 220 papers from three online databases were analyzed and underwent three filtering steps. A total of 24 articles were finally accepted for this study to answer the research question. Seven factors likely influencing E-learning efficacies during the pandemic and post-pandemic period were identified. Twenty-four research papers have been acquired, and through these papers, factors apperceived, which substantiated the results (SLR) of seven factors that can affect the adoption of E-learning systems in universities worldwide during the Covid-19 pandemic: 1) Perceived usefulness 2) Perceived ease of use 3) System Quality 4) Actual use system 5) Behavioral intention 6) Attitude toward using 7) Information Quality. The SLR results will be beneficial for understanding the needs of universities to improve the E-learning systems, especially during the period of the pandemic, post-pandemic, or any crises in the future, by looking at the factors that may be a measure of the environment to adapt. The study's results contribute to forming a clear vision of the Effectiveness of E-learning systems during Covid-19 by considering the factors and their future consequences.
... As concerns, the additional constructs, the meaning of Perceived Self Efficacy (PSE) is the degree of self-confidence in the ability to complete a task. Many studies have proven that a higher Self Efficacy enhances productivity using an information system (Chao C. M., 2019;Fokides, 2017;Fathema, Shannon, Ross, 2015;Moghavvemi, 2014). Thus, the meaning of Perceived Learnability (PL) would be how someone understands, learns and remembers using an application. ...
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We are very happy to publish this issue of the International Journal of Learning, Teaching and Educational Research. The International Journal of Learning, Teaching and Educational Research is a peer-reviewed open-access journal committed to publishing high-quality articles in the field of education. Submissions may include full-length articles, case studies and innovative solutions to problems faced by students, educators and directors of educational organisations. To learn more about this journal, please visit the website http://www.ijlter.org. We are grateful to the editor-in-chief, members of the Editorial Board and the reviewers for accepting only high quality articles in this issue. We seize this opportunity to thank them for their great collaboration. The Editorial Board is composed of renowned people from across the world. Each paper is reviewed by at least two blind reviewers. We will endeavour to ensure the reputation and quality of this journal with this issue.
Thesis
Les institutions de médiation culturelle, notamment les musées, utilisent de plus en plus les nouvelles technologies afin d'attirer les visiteurs. D'un côté, la Réalité Mixte permet aux visiteurs d'explorer des reconstitutions de lieux passés ou inaccessibles, mais aussi de naviguer spatialement et temporellement dans ces reconstitutions. D'un autre côté, les interfaces tangibles sont utilisées pour proposer des expériences interactives innovantes et engageantes.Dans cette thèse nous émettons l'hypothèse que l'utilisation d'interfaces tangibles faciliterait la navigation spatio-temporelle sur plusieurs échelles au sein d'Environnements Virtuels. Nos travaux ont deux objectifs : 1) proposer un modèle permettant de représenter l'espace et le temps sur plusieurs échelles ; 2) proposer une interface tangible permettant de naviguer sur ces différentes échelles.En réponse au premier objectif, notre proposition de représentation du temps et de l'espace s'appuie sur des notions utilisées en Histoire des Sciences & Techniques et propose quatre échelles. Nous nous appuyons sur notre modèle pour répondre au second objectif pour lequel nous avons mis en place une démarche de co-conception impliquant des experts en médiation culturelle. Le résultat de cette démarche est SABLIER, un interacteur tangible permettant de naviguer spatio-temporellement au sein d'un Environnement Virtuel.
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The today's scenario of changing word due to the pandemic situation created by covid 19 is affecting the all activities in human life, educational system as a part of our life, too affected with this, to overcome this situation and continue with education we can implement the Learning Management System (LMS), as it is driven online it has taken rapid uptake of campus-wide learning LMS is changing the character of the on-campus learning experience. In this paper will discuss about the basic concept of LMS, importance and usability of LMS, evaluation factors for LMS and the detailed study of MOODLE. It discusses in particular the possible effects of LMS on teaching practices, on student engagement, on the nature of academic work and on the control over academic knowledge.
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The purpose of this research project was to measure the effects of the perceptions that students hold of the functionality of LMS and students’ self-efficacy specific to using LMS in their studies on student LMS acceptance and use. The theoretical framework of the study is based on the Technology Acceptance Model (TAM), into which perceived functionality and LMS self-efficacy were incorporated as external variables. A web-based questionnaire was administered to students in a private higher education institution in Auckland, New Zealand. These responses were analyzed using Pearson’s correlation and linear regression. The results indicated that perceived functionality significantly influenced perceived usefulness. Similarly, it was found that LMS self-efficacy significantly influenced perceived ease of use. However, no evidence was found that attitudes towards using LMS predicted behavioural intention to use.
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Goodness-of-fit (GOF) indexes provide "rules of thumb"—recommended cutoff values for assessing fit in structural equation modeling. Hu and Bentler (1999) proposed a more rigorous approach to evaluating decision rules based on GOF indexes and, on this basis, proposed new and more stringent cutoff values for many indexes. This article discusses potential problems underlying the hypothesis-testing rationale of their research, which is more appropriate to testing statistical significance than evaluating GOF. Many of their misspecified models resulted in a fit that should have been deemed acceptable according to even their new, more demanding criteria. Hence, rejection of these acceptable-misspecified models should have constituted a Type 1 error (incorrect rejection of an "acceptable" model), leading to the seemingly paradoxical results whereby the probability of correctly rejecting misspecified models decreased substantially with increasing N. In contrast to the application of cutoff values to evaluate each solution in isolation, all the GOF indexes were more effective at identifying differences in misspecification based on nested models. Whereas Hu and Bentler (1999) offered cautions about the use of GOF indexes, current practice seems to have incorporated their new guidelines without sufficient attention to the limitations noted by Hu and Bentler (1999).