S P E C I A L I S S U E A R T I C L E
Comparing technology acceptance of K-12 teachers with and
without prior experience of learning management systems: A
Covid-19 pandemic study
Muhterem Dindar1 | Anna Suorsa2 | Jan Hermes3,4 | Pasi Karppinen5 |
Piia Näykki6
1Learning and Educational Technology
Research Unit, Faculty of Education,
University of Oulu, Oulu, Finland
2History, Culture and Communication Studies,
Faculty of Humanities, University of Oulu,
Oulu, Finland
3Department of Marketing, Management and
International Studies, Oulu Business School,
University of Oulu, Oulu, Finland
4Department of Organisational Behaviour and
Marketing, Faculty of Economic Sciences and
Management, Nicolaus Copernicus University,
Toru
n, Poland
5Oulu Advanced Research on Service and
Information Systems, Faculty of Information
Technology and Electrical Engineering,
University of Oulu, Oulu, Finland
6Faculty of Education and Psychology,
University of Jyväskylä, Jyväskylä, Finland
Correspondence
Muhterem Dindar, Learning and Educational
Technology Research Unit, Faculty of
Education, University of Oulu, FI-90014 Oulu,
Finland.
Email: muhterem.dindar@oulu.fi
Funding information
University of Oulu; Academy of Finland
Abstract
Covid-19 pandemic has caused a massive transformation in K-12 settings towards
online education. It is important to explore the factors that facilitate online teaching
technology adoption of teachers during the pandemic. The aim of this study was to
compare Learning Management System (LMS) acceptance of Finnish K-12 teachers
who have been using a specific LMS as part of their regular teaching before the
Covid-19 pandemic (experienced group) and teachers who started using it for emer-
gency remote teaching during the pandemic (inexperienced group). Based on the Uni-
fied Theory of Acceptance and Use of Technology framework, a self-report
questionnaire was administered to 196 teachers (nexperienced = 127; ninexperienced = 69).
Our findings showed no difference between the two groups of teachers in terms
of performance expectancy, effort expectancy, LMS self-efficacy and satisfaction.
However, the experienced group had higher behavioural intention to use LMS in
the future, reported receiving higher online teaching support and displayed higher
online teaching self-efficacy in terms of student engagement, classroom management,
instructional strategies and ICT skills. For the experienced group, the most significant
predictor of satisfaction with LMS was performance expectancy whereas for the inex-
perienced group, it was the effort expectancy. In terms of behavioural intention to
use LMS in the future, the most significant predictor was the performance expectancy
for both groups. Further, support was also a significant predictor of behavioural inten-
tion for the inexperienced group. Overall, our findings indicate that teachers should
not be regarded as a unified profile when managing technology adoption in schools.
K E YWORD S
Covid-19, K-12 education, online education, technology acceptance, UTAUT
1 | INTRODUCTION
Worldwide, there has been a growing emphasis on developing digital
competencies in school settings (Siddiq et al., 2016). It is argued that
education systems should develop digital skills of individuals to help
them cope with the demands of an increasingly digital world (Voogt &
McKenney, 2017). Thus, schools continually invest in Information and
Communication Technologies (ICT), and teachers are encouraged to
Received: 8 October 2020 Revised: 8 February 2021 Accepted: 28 March 2021
DOI: 10.1111/jcal.12552
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2021 The Authors. Journal of Computer Assisted Learning published by John Wiley & Sons Ltd.
J Comput Assist Learn. 2021;37:1553–1565. wileyonlinelibrary.com/journal/jcal 1553
take advantage of technology in their teaching practices (Valtonen
et al., 2017). However, using technology to facilitate meaningful learn-
ing has been challenging for many teachers, which can be seen in the
limited use of technology in their classrooms (Fraillon et al., 2014;
OECD, 2015). Therefore, developing knowledge and skills of teachers
in utilizing technology effectively in their classrooms has been an
important agenda for governments across the globe (Chen &
Jang, 2014).
A significant number of studies has explored the promise and
challenges of teachers' ICT usage in school settings through a variety
of frameworks (Scherer et al., 2019). For example, the technological
pedagogical content knowledge (TPACK) framework has been intro-
duced to conceptualize how technology can be blended with peda-
gogical practices in the classrooms (Mishra & Koehler, 2006). In
addition, several technology acceptance frameworks have been uti-
lized to explain the factors that contribute to teachers' technology
acceptance. Among those, Unified Theory of Acceptance and Use of
Technology (UTAUT) has been a prominent framework in studying
teachers' behavioural intention to use technology for teaching and
learning (Chao, 2019). UTAUT originally comprises four key factors
(performance expectancy, effort expectancy, social influence, facilitat-
ing conditions) and several mediators (gender, age, experience and
voluntariness of use) that impact the behavioural intention to use a
specific technology (Venkatesh et al., 2003). UTAUT was able account
for 70% of the variance in intention to use, which has cemented the
model as a gold standard for measuring ICT acceptance and usage
varying from, for example, mobile learning (Chao, 2019) to tele-
presence robot in an educational setting (Han & Conti, 2020). UTAUT
has been extensively applied in university settings specifically among
pre-service teachers and university lecturers (Baydas & Goktas, 2017;
Garone et al., 2019). However, it has been applied to a limited
extend in studying technology acceptance of in-service teachers in
K-12 education (Wong, 2016).
The ongoing Covid-19 pandemic has caused unforeseen interrup-
tions in education worldwide. Many countries have responded to the
pandemic crisis by switching to online distance education. In general,
K-12 schooling systems are designed for face-to-face education. Thus,
the urgent switch from face-to-face to online distance education has
created a state of chaos in many schools. School administrators,
teachers and parents have been struggling to facilitate meaningful and
effective learning experiences throughout the chaos (Richmond
et al., 2020). In a short period of time, schools have had to adopt a
variety of technologies [e.g., online teaching tools and Learning Man-
agement Systems (LMS) to continue their education]. During the tran-
sition to online remote education, teachers have faced multiple
challenges such as lack of technological infrastructure and support,
inexperience with digital technologies and lack of online teaching
skills (Khlaif et al., 2020). This has caused immense amounts of
workload and stress on teachers (Marek et al., 2020). Conse-
quently, students have expressed several concerns about the qual-
ity of online teaching during the pandemic (Patricia Aguilera-
Hermida, 2020; Perrotta, 2020). Further, studies have found signif-
icant differences between the schools in terms of the quality of
the pandemic-time education (Maity et al., 2020). Considering
these findings, it is important to investigate the factors that facili-
tate effective technology integration in schools during this
extraordinary time. Drawing on this, the current study compared
technology acceptance of two groups of Finnish K-12 teachers.
The first group has been using a specific LMS (i.e., Qridi) before
the pandemic as a support to their classroom teaching, and contin-
ued using the LMS when the schools transformed to mostly online
teaching due to pandemic. This group is named as experienced
group in this study. The second group started using the Qridi LMS
for emergency online teaching due to the pandemic. This group is
named as inexperienced group in this study. Based on the UTAUT
framework (Venkatesh et al., 2003), the study explores whether
the two groups differ from each in terms of performance expec-
tancy, effort expectancy, Qridi LMS self-efficacy, support, satisfac-
tion, online teaching self-efficacy and behavioural intention to use
LMS in the future. Further, the study investigates the factors that
predict satisfaction and behavioural intention to use LMS for each
group. The existing literature on technology acceptance mostly
deals with teachers' use of technology in the classroom as a peda-
gogical support rather than being the actual teaching medium
(Scherer & Teo, 2019). Thus, there is dearth of research on K-12
in-service teachers' technology use in solely online teaching condi-
tion. The current study addresses this gap through studying Finn-
ish K-12 teachers' technology acceptance for online teaching.
Further, the current study contributes to the literature by explor-
ing how previous experience with LMS affects technology accep-
tance for emergency online teaching.
2 | LITERATURE REVIEW
2.1 | Teachers' technology acceptance and UTAUT
A prominent framework in studying teachers' technology acceptance
has been Technology Acceptance Model (TAM) (Davis et al., 1989).
TAM asserts that behavioural intention to use a specific technology is
influenced by two core beliefs: perceived usefulness and perceived
ease of use. Perceived usefulness refers to the belief that using a spe-
cific technology will improve work performance (Davis, 1989). Per-
ceived ease of use refers to the belief whether using a particular
technology is free of effort (Davis et al., 1989). Venkatesh
et al. (2003) extended the TAM with new constructs from other tech-
nology acceptance frameworks (i.e., Theory of Reasoned Action, The-
ory of Planned Behaviour, Motivational Model, Model of PC
utilization, Socio-cognitive theory, Diffusion of Innovations) and
developed UTAUT. According to UTAUT, performance expectancy,
effort expectancy, facilitating conditions and social influence are the
main predictors of technology acceptance that is operationalized as
behavioural intention to use technology. Performance expectancy
encompasses perceived usefulness in TAM, and is the belief about the
extend the technology will improve the job performance (Venkatesh
et al., 2003). Effort expectancy is based on the perceived ease of use in
TAM, and refers to the perceptions about how easy it is to use a tech-
nology (Venkatesh et al., 2012). Facilitating conditions refers to the
1554 DINDAR ET AL.
beliefs regarding whether technical infrastructure, relevant training
and support are provided to the individuals (Venkatesh et al., 2003).
Social influence comprises beliefs about the degree which important
others value or suggest the use of a specific technology (Venkatesh
et al., 2012).
To date, UTAUT model has been tested in diverse educational
contexts and extended with new factors. Specifically, self-efficacy
have emerged as significant variable that influence technology adop-
tion (Venkatesh et al., 2012). In technology acceptance literature, self-
efficacy has been seen as a general or a targeted concept. As a general
concept, self-efficacy refers to teachers' self-perceptions about their
competency in employing effective pedagogical activities with the
support of technology (Tondeur et al., 2020). According to this per-
spective, self-efficacy does not imply a specific technology, but it is
rather teachers' overall ICT competencies. For example, research on
TPACK framework underlines the importance of general ICT compe-
tencies of teachers' in their educational practices (Mishra &
Koehler, 2006). From a targeted perspective, self-efficacy refers to
the teachers' beliefs about their capability in using a specific educa-
tional technology (Kemp et al., 2019). Regardless of general or
targeted, teachers' ICT self-efficacy have been generally found to
influence their technology acceptance and use (Barton &
Dexter, 2020; Chiu, 2017; Hong et al., 2020; Long et al., 2018). How-
ever, studies on the interplay of self-efficacy and technology accep-
tance in schools either solely focus on general self-efficacy or
targeted self-efficacy. Therefore, a current gap in the literature is to
explore how general and targeted self-efficacy together impact tech-
nology acceptance in K-12 settings.
Recent advancements in UTAUT research have also shown that
affective factors such as pleasure, enjoyment and satisfaction influ-
ence technology acceptance (Ching-Ter et al., 2017; Kemp
et al., 2019; Venkatesh et al., 2012). For example, satisfaction has
been found to predict future intention to use mobile learning
(Chao, 2019). Further, satisfaction has been found to predict actual
use of e-learning environments along with behavioural intention
(Mohammadi, 2015). Perceived enjoyment was a significant predictor
of intention to use technology among pre-service teachers in their
prospective teaching (Teo & Noyes, 2011). Enjoyment and interest
were further related with LMS and e-learning environment usage
among higher education students (Ching-Ter et al., 2017; Khechine
et al., 2020). Despite such findings, there is dearth of research on how
affective factors, specifically satisfaction, influence technology accep-
tance among in-service teachers.
Technology acceptance is not a one-time process, and occurs
over time (Blau & Shamir-Inbal, 2017). Thus, it is important to study
technology acceptance by focusing on users who are at different of
their technology experience and acceptance. This is because actual
experience with a technology might change individuals' beliefs and
perceptions about the necessity the technology over time (Johnson
et al., 2014). Nevertheless, only few studies investigated technology
acceptance among different teacher groups in terms of their adoption
stage. For example, Šumak and Šorgo (2016) have found significant
differences between teachers who were either pre- or post-adopters
of interactive whiteboards. For pre-adopters, the social influence had
a stronger impact on behavioural intention, and performance expec-
tancy had stronger impact on attitude to use whiteboards compared
to the post- adopters. For post-adopters, facilitating conditions had
stronger impact on actual use of interactive whiteboards than pre-
adopters. In another study, Šumak et al. (2017) investigated teachers'
technology acceptance among the prospective, existing and former
users of interactive whiteboards. Their findings revealed that existing
users displayed higher scores in performance expectancy, effort
expectancy and management support. No difference was observed
between former and prospective users. Ursavas¸ et al. (2019) com-
pared technology acceptance of pre- and in-service teachers. For pre-
service teachers, attitude towards using technology was the strongest
predictor of behavioural intention followed by subjective norms, per-
ceived ease of use and perceived usefulness. For the in-service
teachers, the strongest predictor of behavioural intention was attitude
followed by perceived usefulness, perceived ease of use and subjec-
tive norms. In their longitudinal study, Pynoo et al. (2011) have
explored secondary school teachers' acceptance of digital learning
environments at three different time points. It has been found that
performance expectancy was the primary predictor of behavioural
intention at the beginning, but it was marginally significant at the end.
Effort expectancy was not a significant predictor of behavioural inten-
tion at the beginning of the study, but it was the strongest predictor
of behavioural intention at the end. No direct effect of facilitating
conditions were found on technology acceptance. Overall, the limited
number of findings show that teachers' perceptions at the different
technology acceptance dimensions might shift in later stages of tech-
nology adoption depending on their experience with the system. Nev-
ertheless, the current literature offers little understanding about
teachers' technology acceptance in extraordinary circumstances such
as Corona pandemic. Further, studies comparing technology accep-
tance of teachers who are at different stages of adoption process
mostly measured technology acceptance in terms of behavioural
intention (Scherer & Teo, 2019). However, the affective outcomes of
technology acceptance (e.g., satisfaction) across different teacher pro-
files is yet to be explored.
The ongoing Covid-19 pandemic has triggered a new phase of
technology use in educational settings. On a very short notice, schools
across the world had to adopt online tools and environments to con-
tinue their education. Regardless of whether they have the relevant
skills or not, many K-12 teachers have found themselves teaching
online as an emergency response to the pandemic. Such abrupt
switching to online education is worthy of investigation from multiple
aspects in terms of technology acceptance. For example, it would be
interesting to know to what extend technology acceptance in emer-
gency teaching conditions differ from technology acceptance in regu-
lar teaching conditions (e.g., before the Covid-19 pandemic). Further,
comparing the factors that influence technology acceptance for both
teaching conditions deserves attention. It is known for long that past
experiences with a technology influences individuals' intention to use
it (Fishbein & Ajcen, 1975). However, as summarized previously, only
few studies have dealt with comparing technology acceptance of K-
12 teachers in terms of their previous experiences. There is dearth of
research on the relationship between technology acceptance
DINDAR ET AL. 1555
constructs among the teachers with differing technology experience.
Considering this, the current study compares technology acceptance
of Finnish K-12 teachers who have been using Qridi LMS before the
Covid-19 pandemic (experienced group) and who started Qridi LMS
due to Covid-19 pandemic (inexperienced group).
3 | RESEARCH QUESTIONS
RQ1. Are there differences between the experienced and inexperi-
enced teacher groups in terms of their LMS acceptance:
(a) performance expectancy, (b) effort expectancy, (c) support,
(d) Qridi LMS self-efficacy, (e) satisfaction, (f) behavioural intention
and (g) online teaching self-efficacy?
RQ2. What are the predictors of satisfaction and behavioural
intention to use LMS for the experienced group and the inexperi-
enced group of teachers?
4 | METHODOLOGY
4.1 | Procedure and participants
Finnish K-12 schools transformed from face-to-face education to
mostly online teaching on 17 March 2020. Online teaching period
ended on 14 May 2020. During this period, face-to-face teaching con-
tinued with a small minority of students with special education needs
and students between first and third grade, who could not get support
for online learning at home. Data collection in the current study
started on 10 May 2020 and ended on 5 June 2020. In collaboration
with Qridi Company, Finnish K-12 teachers who have been using the
Qridi LMS were contacted through their e-mail addresses.
Volunteering teachers participated in the study by clicking on the
online survey link provided in the e-mail. Among the participants, five
teachers were randomly chosen, and each was given a 20 EUR gift
card from a bookstore. In total, 196 teachers completed the online
survey in full. The mean age of the participants was 44 (SD = 9.6). A
total of 25 of them were male, and 169 of them were female. A total
of 130 participants were primary school teachers and 65 of them
were subject teachers. Participants represented 19 provinces of Fin-
land. Majority of them were from North-Ostrobothnia (n = 85;
% = 43) followed by Uusimaa (n = 36; % = 18), Pirkanmaa (n = 15;
% = 8), North Savo (n = 12; % = 6), Southwest (n = 9; % = 5) and Lap-
land (n = 7; % = 4). A total of 127 of the participants have been using
the Qridi platform before the Covid-19 pandemic, and 69 of them
have started using the platform after the Covid-19 pandemic.
4.2 | Measures and instruments
In UTAUT literature, main variables of interest have been behav-
ioural intention, satisfaction, performance expectancy, effort
expectancy, self-efficacy, social influence and facilitating condi-
tions (Garone et al., 2019; Venkatesh et al., 2012). In the current
study, we employed questionnaires to measure behavioural inten-
tion, satisfaction, performance expectancy and effort expectancy.
Further, we used two different self-efficacy questionnaire that
measure teachers both targeted (i.e., Qridi LMS) and general
(i.e., online teaching) self-efficacy. Participants of the current study
had no other option than online teaching due to the pandemic.
Social influence (i.e., beliefs of significant others on using a tech-
nology) is a redundant factor in such an obligatory online teaching
condition. Therefore, social influence was left out in this study.
Facilitating conditions factor in UTAUT underlines two issues in
terms of technology acceptance: existing infrastructure and sup-
port (Venkatesh et al., 2003). All participants in this study were
provided Qridi LMS. Therefore, we only measured the support
aspect of facilitating conditions.
4.3 | LMS technology acceptance
Teachers' LMS acceptance was measured with a questionnaire
adapted from Chao (2019). The Likert type questionnaire measured
teachers' technology acceptance in the following dimensions: Effort
Expectancy (five items; e.g., ‘I find Qridi easy to use’), Performance
Expectancy (four items; e.g., ‘Using Qridi has enhanced my effectiveness
in online teaching’), Satisfaction (five items; e.g., ‘I have been pleased
with Qridi’), Qridi LMS self-efficacy (three items, e.g., ‘I am confident of
using Qridi even if there is no one around to show me how to do it’) and
Behavioural Intention (three items; e.g., ‘I plan to use Qridi in the
future’). The questionnaire displayed good internal consistency across
its dimensions according to Cronbach's Alpha scores (Effort expec-
tancy = 0.88; Performance expectancy = 0.82; Satisfaction = 0.90;
Qridi self-efficacy = 0.81; Behavioural Intention = 0.88).
A three-item questionnaire was developed to measure the extend
of support provided to the teachers for their online teaching. The
items of the questionnaire were ‘I am provided support when solving
problems in online teaching’, ‘Resources (e.g., tools, software, guidelines)
about online teaching are made available to me’, and ‘I am provided
opportunities for personal development in online teaching’. Internal con-
sistency of the questionnaire was 0.676. A Principal Component Anal-
ysis was conducted to examine the factor structure of the
questionnaire. The single factor structure (Kaiser Meyer Olkin Mea-
sure of Sampling Adequacy = 0.621; Bartlett's test of spheric-
ity = 102.1; p < 0.001)) explained 61% of the total variance. The
component matrix scores of the items were between 0.677 and
0.816. The item communality scores ranged between 0.458
and 0.712.
In total there were 23 items in the LMS technology acceptance
questionnaire sheltering all the constructs summarized above. Ques-
tionnaire items were answered on a five-point scale. Answers varied
between ‘Strongly disagree’ to ‘Strongly agree’. The whole question-
naire had a Cronbach's Alpha value of 0.92.
1556 DINDAR ET AL.
5 | ONLINE TEACHING SELF-EFFICACY
QUESTIONNAIRE
The questionnaire was originally developed by Robinia (2008), and
measures teachers' online teaching self-efficacy under four dimen-
sions with 32 items. These dimensions are Student engagement
(e.g., ‘How much can you do to motivate students who show low interest
in online work?’), Instructional strategies (e.g., ‘How much can you do to
use a variety of assessment strategies for an online course?’), Classroom
management (e.g., ‘How much can you do to get students to follow the
established rules for assignments and deadlines during an online class?’)
and ICT efficacy (e.g., ‘How well can you navigate the technical infra-
structure at your institution to successfully create an online course?’).
Each dimension sheltered eight items. In the current sample, the ques-
tionnaire dimensions displayed good internal consistency in terms of
Cronbach's alpha scores (Student Engagement = 0.79; Instructional
strategies = 0.82; Classroommanagement = 0.73; ICT efficacy = 0.804).
The Cronbach's Alpha score for the whole questionnaire was 0.92.
5.1 | Data analysis
Each variable of interest in the current study was measured with mul-
tiple items. An initial step prior to the statistical analyses was to calcu-
late mean scores of items belonging to each variable. Based on the
mean scores, normal distribution of the variables across the two
teacher groups (i.e., experienced and inexperienced) were checked.
Table 1 presents Skewness and Kurtosis results.
Based on the results presented in Table 1, it can be claimed that
all variables displayed normal distribution. In order to answer the first
research question, is there differences between the experienced and
inexperienced groups of teachers in terms of their LMS acceptance,
independent samples t tests were conducted for each LMS accep-
tance dimension (i.e., Effort expectancy, Performance expectancy,
Qridi self-efficacy, Support, Satisfaction, Behavioural Intention and
online teaching self-efficacy (i.e., Student engagement, Classroom
management, Instructional strategies and ICT self-efficacy). The analy-
sis proceeded to explore the predictors of satisfaction and behavioural
intention to use LMS for the experienced and inexperienced teacher
groups (i.e., second research question). Two separate regression ana-
lyses were conducted for each teacher group. Satisfaction with Qridi
LMS use was the dependent variable for the first regression and
behavioural intention to Use Qridi in the future was the dependent
variable for the second regression.
6 | RESULTS
RQ1. Are there differences between the experienced and inexperi-
enced teacher groups in terms of their LMS acceptance?
According to the independent sample t test results, no difference
was observed between experienced (Exp) and inexperienced (inExp)
teachers in terms of Effort Expectancy (MExp = 4.08; SDExp = 0.60;
MinExp = 4.03; SDinExp = 0.66), Performance Expectancy (MExp = 4.19;
SDExp = 0.71; MinExp = 4.20; SDinExp = 0.62), Qridi self-efficacy
(MExp = 4.19; SDExp = 0.66; MinExp = 4.05; SDinExp = 0.73) or
TABLE 1 Distribution of variables
across the comparison groups
Experienced group Inexperienced group
Skewness Kurtosis Skewness Kurtosis
Effort expectancy −0.823 1.446 −1.001 1.057
Performance expectancy −0.568 −0.475 −0.656 0.072
Qridi self-efficacy −0.521 0.139 −1.134 2.476
Satisfaction −0.409 −0.334 −0.836 0.612
Behavioural intention −1.603 1.929 −1.303 2.426
Support −1.254 1.64 −0.643 0.197
Student engagement self-efficacy 0.287 0.037 0.895 1.668
Classroom management self-efficacy 0.09 −0.283 0.586 0.244
Instructional strategies self-efficacy 0.012 −0.311 −0.29 1.282
ICT self-efficacy −0.325 −0.117 −0.534 0.843
TABLE 2 Independent samples t test
results for effort expectancy,
performance expectancy, Qridi LMS self-
efficacy, satisfaction and behavioural
intention
t df p Mean difference Partial eta-squared
Effort expectancy 0.463 194 0.644 0.04311 0.001
Performance expectancy −0.107 194 0.915 −0.01096 0
Qridi self-efficacy 1.347 194 0.180 0.13804 0.009
Support 2252 194 0.025 0.24444 0.025
Satisfaction 1.040 194 0.300 0.10217 0.006
Behavioural intention 3.580 105.568 0.001 0.33735 0.075
DINDAR ET AL. 1557
satisfaction (MExp = 4.18; SDExp = 0.64; MinExp = 4.08; SDinExp = 0.68).
However, the independent samples t test showed that experienced
teachers (MExp = 4.16; SDExp = 0.76) had higher perceived support
compared to the inexperienced teachers (MinExp = 3.91; SDinExp = 0.67).
Further, experienced teachers reported higher Behavioural Intention
to use Qridi compared with the inexperienced teachers (MExp = 4.70;
SDExp = 0.48; MinExp = 4.37; SDinExp = 0.69). t test results are pres-
ented in Table 2, and the group averages are presented in Figure 1.
Independent samples t tests results further showed that experi-
enced group had higher online teaching self-efficacy in Student
engagement (MExp = 3.42; SDExp = 0.50; MinExp = 3.20; SDinExp = 0.46),
Classroom management (MExp = 3.62; SDExp = 3.37; MinExp = 3.38;
SDinExp = 0.45), Instructional Strategies (MExp = 3.76; SDExp = 0.53;
MinExp = 3.52; SDinExp = 0.46) and ICT skills (MExp = 3.86; SDExp = 0.58;
MinExp = 3.47; SDinExp = 0.59). Table 3 displays the t test results.
Figure 2 displays group means.
RQ2. What are the predictors of LMS acceptance for the experi-
enced and the inexperienced teacher groups?
In the current study technology acceptance was measured with
two separate dependent variables. The affective acceptance to the
LMS was measured with satisfaction and the behavioural attachment
was measured with behavioural intention. Prior to the regression ana-
lyses, Pearson's correlation analysis was conducted to see the
bivariate relationships among the variables. Results are presented in
Table 4.
For both teacher groups, a stepwise regression was run with per-
formance expectancy, effort expectancy, Qridi self-efficacy, support,
student engagement self-efficacy, instructional strategy self-efficacy,
classroom management self-efficacy and ICT self-efficacy as the inde-
pendent variables, and the satisfaction as the dependent variable.
Results are presented in Table 5. For the experienced teachers, the
significant predictors of satisfaction were performance expectancy
(ΔR2 = 59.2%), effort expectancy (ΔR2 = 10.3%), Student engagement
self-efficacy (ΔR2 = 1.2%) and Qridi self-efficacy (ΔR2 = 1%). For the
inexperienced teachers, the significant predictors of satisfaction were
Effort expectancy (ΔR2 = 51.1%) and Performance expec-
tancy (ΔR2 = 5.7).
A stepwise regression was run to explore the predictors of behav-
ioural intention to use Qridi LMS in the future. Performance expec-
tancy, effort expectancy, Qridi self-efficacy, support, student
engagement self-efficacy, instructional strategy self-efficacy, class-
room management self-efficacy and ICT self-efficacy were included as
independent variables in the analysis. Results are displayed in Table 6.
For the experienced teachers, the only significant predictors of behav-
ioural intention were performance expectancy (ΔR2 = 36%) and Class-
room management self-efficacy (ΔR2 = 2.6%). For the inexperienced
teachers, the significant predictors of behavioural intention were per-
formance expectancy (ΔR2 = 20.5%), Instructional strategies self-
efficacy (ΔR2 = 16.4%) and support (ΔR2 = 4.1%).
7 | DISCUSSION
The aim of this study was to investigate and compare technology
acceptance of two groups of Finnish K-12 teachers (i.e., experienced
and inexperienced Qridi LMS users) in the context of online teaching
during Covid-19 pandemic. The experienced group had prior experi-
ence in teaching with a specific LMS before the pandemic. The inex-
perienced group had no prior experience with the LMS, but they
started using Qridi during the pandemic. Both groups used the LMS
for online teaching during the pandemic. Based on the UTAUT frame-
work (Venkatesh et al., 2003), a questionnaire (Chao, 2019) was
administered to participants to explore their perceptions about the
LMS use in terms of performance expectancy, effort expectancy, Qridi
LMS self-efficacy, support, satisfaction and behavioural intention. In
addition, participants' general self-efficacy beliefs about their online
teaching were measured in terms of their beliefs how they can
F IGURE 1 Group scores across the UTAUT dimensions. BI,
behavioural intention; E, experienced group; EE, effort expectancy; I,
inexperienced group; PE, performance expectancy; QSE, Qridi self-
efficacy; SAT, satisfaction; SUP, support [Colour figure can be viewed
at wileyonlinelibrary.com]
TABLE 3 Independent samples t test results for the online teaching self-efficacy dimensions
t df p Mean difference Partial eta-squared
Student engagement self-efficacy 2.934 193 0.004 0.21488 0.043
Classroom management self-efficacy 3.399 193 0.001 0.23885 0.056
Instructional strategies self-efficacy 3.112 193 0.002 0.23761 0.048
ICT self-efficacy 4.399 191 <0.001 0.38984 0.092
1558 DINDAR ET AL.
provide support for student engagement, conduct classroom manage-
ment and use instructional strategies and use ICT in teaching.
No difference was evidenced between the experienced and inex-
perienced teachers in terms of their performance expectancy, effort
expectancy, Qridi LMS self-efficacy and satisfaction. These findings
indicate that inexperienced teachers' perceptions about the LMS's
performance benefits for teaching activities, its ease of use and their
self-efficacy in using it has reached to the level of experienced
teachers in a rather short time period. In addition, inexperienced
teachers were equally satisfied with the LMS as experienced teachers.
These findings can be explained from several aspects. From an infor-
mation systems design perspective, studies have shown that the qual-
ity of system design (i.e., technical infrastructure and user experience)
have a positive effect for performance expectancy, effort expectancy
and satisfaction in LMS use (Alsabawy et al., 2013; Kintu et al., 2017;
Mohammadi, 2015). Based on this, it can be argued that the system
design of LMS was not a barrier for inexperienced teachers during
their teaching in times of Covid-19 pandemic. From a contextual/his-
torical perspective, the Covid-19 pandemic have triggered extraordi-
nary circumstances in education landscape. The remote/online
teaching during the pandemic did not leave much option for teachers
other than using LMSs or other digital platforms for teaching.
According to the common view, top-down or forced technology adop-
tion processes might face resistance by the adopters (Rogers, 2003).
Therefore, many of the schools are embracing bottom-up technology
adoption processes by including teachers, and in some cases also stu-
dents, in planning and decision-making processes of the technological
affordances in use (Hauge & Norenes, 2015). Even though bottom-up
approach is involving teachers to the development work, it seems that
in general it has not largely affected on teachers' proactive develop-
ment. For example, Petko et al. (2015) found no difference between
teachers' participation in professional development activities among
the Swiss schools that employed top-down or bottom-up technology
adoption. Further, no difference was observed between the schools
with top-down and bottom-up technology adoption in terms of ICT
use in the classrooms (Petko et al., 2015). Complimenting such find-
ings, the current study showed that contextual/historical circum-
stances might impact teachers' technology acceptance in forced
adoption conditions or in emergency remote teaching. That is,
teachers might be willing to accept a specific teaching technology
more easily in times of unexpected events (Barbour et al., 2020). This
conclusion is supported by Hong et al. (2020)'s study that reported a
F IGURE 2 Group scores across online teaching self-efficacy
dimensions. CMAN, classroom management self-efficacy; E,
experienced group; I, inexperienced group; ICT, information and
communication technology; INST, instructional strategies self-
efficacy; SENG, student engagement self-efficacy [Colour figure can
be viewed at wileyonlinelibrary.com]
TABLE 4 Correlations between the study variables for the experienced and inexperienced groups
BI SAT EE PE QSE SUP SENG CMAN INST ICT
BI 0.599** 0.431** 0.466** 0.379** 0.318** 0.208 0.210 0.453** 0.283*
SAT 0.607** 0.720** 0.585** 0.553** 0.254* 0.371** 0.203 0.383** 0.224
EE 0.361** 0.703** 0.515** 0.573** 0.231 0.365** 0.291* 0.436** 0.144
PE 0.604** 0.772** 0.564** 0.567** 0.153 0.293* 0.209 0.452** 0.391**
QSE 0.318** 0.617** 0.645** 0.532** 0.066 0.393** 0.273* 0.393** 0.386**
SUP 0.131 0.113 0.124 0.038 0.115 0.297* 0.233 0.159 0.171
SENG 0.312** 0.397** 0.377** 0.257** 0.177* 0.206* 0.697** 0.649** 0.417**
CMAN 0.278** 0.313** 0.301** 0.168 0.162 0.257** 0.776** 0.701** 0.572**
INST 0.156 0.213* 0.336** 0.081 0.226* 0.231** 0.683** 0.712** 0.659**
ICT 0.153 0.143 0.303** 0.040 0.232** 0.239** 0.447** 0.562** 0.626**
Note: Upper part of the diagonal displays results for the inexperienced group. Lower part of the diagonal displays results for the experienced group.
Abbreviations: BI, behavioural intention; CMAN, classroom management self-efficacy; EE, effort expectancy; ICT, ICT self-efficacy; INST, instructional
strategies self-efficacy; PE, performance expectancy; QSE, Qridi LMS self-efficacy; SAT, satisfaction; SENG, student engagement self-efficacy; SUP,
support.
**Correlation is significant at the 0.01 level (2-tailed);
*Correlation is significant at the 0.05 level (2-tailed).
DINDAR ET AL. 1559
TABLE 5 Regression results for satisfaction with Qridi LMS
Experienced group Inexperienced group
Variable B SE B β Variable B SE B β
Step 1 (Constant) 1.271 0.219 (Constant) 1.078 0.363
Performance expectancy 0.695 0.051 0.772*** Effort expectancy 0.744 0.089 0.720***
Adjusted R2 59.2 Adjusted R2 51.1
F 182.71*** F 70.092***
Step 2 (Constant) 0.400 0.231 (Constant) 0.359 0.414
Performance expectancy 0.496 0.054 0.551*** Effort expectancy 0.589 0.098 0.570***
Effort expectancy 0.418 0.064 0.392*** Performance expectancy 0.320 0.104 0.291**
Adjusted R2 69.5 Adjusted R2 56.8
F 143.736*** F 44.404***
Step 3 (Constant) 0.064 0.266
Performance expectancy 0.489 0.053 0.543***
Effort expectancy 0.373 0.065 0.349***
Student engagement 0.161 0.067 0.126*
Adjusted R2 70.700
F 101.451***
Step 4 (Constant) −0.112 0.273
Performance expectancy 0.455 0.054 0.505***
Effort expectancy 0.287 0.074 0.268***
Student engagement 0.179 0.067 0.139**
Qridi self-efficacy 0.146 0.063 0.150*
Adjusted R2 71.700
F 80.106***
Abbreviations: B, unstandardized regression coefficient; β, standardized regression coefficient.
*p < 0.05; **p < 0.01; ***p < 0.0001.
TABLE 6 Regression results for behavioural intention to use Qridi LMS
Experienced group Inexperienced group
Variable B SE B β Variable B SE B β
Step 1 (Constant) 2.986 0.209 (Constant) 2.176 0.522
Performance expectancy 0.411 0.049 0.604*** Performance expectancy 0.523 0.123 0.466***
Adjusted R2 36.000 Adjusted R2 20.5
F 70.186*** F 18.067***
Step 2 (Constant) 2.403 0.307 (Constant) 1.208 0.625
Performance expectancy 0.391 0.049 0.574*** Performance expectancy 0.368 0.132 0.328
Classroom management 0.185 0.073 0.182* Instructional strategies 0.459 0.178 0.305
Adjusted R2 38.600 Adjusted R2 26.9
F 39.878*** F 13.160***
Step 3 (Constant) 0.526 0.683
Performance expectancy 0.342 0.29 0.305*
Instructional strategies 0.420 0.174 0.279*
Support 0.237 0.108 0.227*
Adjusted R2 31.000
F 10.884***
Abbreviations: B, unstandardized regression coefficient; β, standardized regression coefficient.
*p < 0.05; ***p < 0.0001.
1560 DINDAR ET AL.
positive relationship between high value to students and acceptance
of ICT policy among the Taiwanese teachers.
Inexperienced teacher group reported receiving less support for
their online teaching than the experienced group did. A plethora of
studies have underlined the importance of support in effective integra-
tion of technology in schools (Ertmer et al., 2012; Liu et al., 2017). Sup-
port can be in the form of technical assistance about using specific ICT
tools or in terms of pedagogical guidance about how to implement ICT
to facilitate meaningful learning experiences (Vanderlinde & van
Braak, 2010). Considering that teaching in K-12 settings is traditionally
organized in a face-to-face format, the literature mostly discusses about
means to support ICT integration in actual classrooms. However, Covid-
19 pandemic have moved the K-12 education to a new territory. It can
be assumed that fully online teaching was a new phenomenon for both
teacher groups examined in the current study as well as for their stu-
dents. However, our findings showed that the experienced teacher
group in the current study might have benefited from the pre-existing
support structures in their schools. The pre-existing support structures
in the schools of experienced teachers could be the school administra-
tion who has been familiar with the LMS, other teachers who have
varying knowledge and experience with the LMS or face-to-face train-
ing activities that took place before the pandemic. The experienced
group might have received higher support for their online teaching dur-
ing the pandemic from such resources when they faced technical or
pedagogical challenges in teaching with the LMS. However, the inexpe-
rienced teachers did not have much of these opportunities. For exam-
ple, school administration and other teachers in their schools were not
familiar with the LMS either before the pandemic hit. No face-to-face
training opportunities were provided to the inexperienced teachers.
Thus, it can be assumed that dealing with the technical and pedagogical
issues about the LMS was not as straightforward for the inexperienced
teachers compared to the experienced teachers.
Overall, the current study is in line with the aforementioned
previous research that highlights the importance of building sus-
tainable support to facilitate effective ICT integration in schools
(Barbour et al., 2020; Beaunoyer & Dup, 2020; Klapproth
et al., 2020). Prior research has evidenced that one of the major
challenges for the teachers during the COVID19 remote teaching
was the lack of sufficient technical and social support (Klapproth
et al., 2020). Since the lack of support potentially increases feel-
ings of stress (e.g., Kyriacou, 2001; Punch & Tuetteman, 1996), it is
fundamentally important to explore in more details of the possible
characteristics of sustainable support in remote teaching. This
would provide information what are the support mechanisms that
facilitate teachers to cope with the challenging remote teaching
times and from where and how teachers seek support.
The teachers who have been using the LMS before the pandemic
had displayed higher levels of online teaching self-efficacy across student
engagement, classroom management strategies, instructional strategies
and ICT dimensions. It has been argued that teachers' digital competency
development occurs gradually based on their use of ICT for everyday
teaching purposes (Blau & Shamir-Inbal, 2017; Vanderlinde & van
Braak, 2010). Supporting this view, several empirical studies have found
that teachers' daily experiences with the technology develops their confi-
dence in technology use in their classrooms (Liu et al., 2017; Miranda &
Russell, 2012). In accordance with the previous research, the current
study shows that providing opportunities for teachers to use ICT in their
teaching can improve their online teaching self-efficacy in the long term.
Further, it can be argued that Covid-19 pandemic might have a positive
impact on improving self-efficacy of teachers in using ICT for pedagogical
purposes. The pandemic has pushed teachers to embrace LMSs or other
online teaching platforms regardless of whether they would like to use
them or not. The pandemic does not seem to be eradicated in the short-
term. This means that teachers across the world will keep using teaching
online technologies for some time. Thus, it is possible that the continuous
online teaching during the pandemic might improve teachers' self-efficacy
beliefs about online teaching and facilitate a faster uptake of technology
enhanced learning in K-12 schools. Future research should test this
assumption.
For both teacher groups, experienced and inexperienced, perfor-
mance expectancy and effort expectancy together explained the most
variance on satisfaction with the LMS. However, performance expectancy
was the strongest predictor of satisfaction for the experienced group,
whereas it was effort expectancy for the inexperienced group. In general,
positive beliefs regarding the effectiveness of a learning technology
(i.e., perceived usefulness or performance expectancy) has been found to
facilitate higher satisfaction with technology enhanced learning environ-
ments than effort expectancy (i.e., ease of use) (Al-Samarraie et al., 2018;
Hung et al., 2011; Jin, 2014; Joo et al., 2018; Tawafak et al., 2018). Sev-
eral studies have also shown that ease of use could only have an indirect
effect on satisfaction through the mediation of perceived usefulness
(Barrio-García et al., 2015; Joo et al., 2016; Liao et al., 2009; Poz
on-L
opez
et al., 2020). It should be noted that past studies have mostly dealt with a
single user profile. Extending previous work, the current study showed
that the impact of performance expectancy and effort expectancy on sat-
isfaction might be different on different teacher groups. That is, inexperi-
enced teachers base their LMS satisfaction mostly on the effort that is
necessary to use the system, whereas experienced teachers base their
satisfaction beliefs on the performance advantage offered by the system.
The current findings have implications for LMS design. In order to attract
inexperienced users, LMSs should focus on building simple systems that
enable users to complete essential tasks with little effort. For this pur-
pose, some complex features of the system can be, for example, hidden
from the inexperienced users. Users could be allowed to activate complex
features later on as they improve their skills in using the LMS. To our
knowledge, no research has been conducted on designing customizable
LMSs that allows for gradual increment of system complexity and perfor-
mance. Future research should explore this opportunity. Our findings fur-
ther showed that student engagement self-efficacy and LMS self-efficacy
together explained around 2% variance on satisfaction in the experienced
group. Considering the low variance, their practical impact on satisfaction
is arguable. Thus, we are cautious in claiming a relationship between self-
efficacy and satisfaction.
Several studies have shown that both performance expectancy and
effort expectancy have direct influence on teachers' technology accep-
tance (Chen & Tseng, 2012; Mazman Akar, 2019; Scherer & Teo, 2019).
DINDAR ET AL. 1561
However, in some studies effort expectancy had an indirect effect on
behavioural intention either through the mediation of perceived useful-
ness (Hu et al., 2003; Teo et al., 2019) or attitude (Scherer et al., 2019).
There are also studies that reported no direct or indirect relationship
between effort expectancy and behavioural intention (Šumak &
Šorgo, 2016). In the current study, performance expectancy was the
strongest predictor of behavioural intention for both the experienced and
inexperienced teachers. However, no direct effect of effort expectancy
was found on behavioural intention. Earlier studies have argued that indi-
viduals would not accept a technology simply because it requires little
effort to use it (Hu et al., 2003; Keil et al., 1995). Supporting this, the cur-
rent findings imply that both experienced and inexperienced teachers are
willing to use the LMS if they believe that it offers significant advantage
in terms of their teaching practice. Therefore, technology integration
efforts in education should involve developing positive beliefs among the
teachers about the usefulness of instructional technologies. This can be
achieved by, for example, providing demonstrations, models and exam-
ples to teachers about how to use a specific technology most effectively
in teaching.
Online teaching self-efficacy was another significant predictor of
behavioural intention to use LMS although the dimensions that predicted
behavioural intention was different for the experienced (i.e., classroom
management) and inexperienced teachers (i.e., instructional strategies).
The current findings are in accordance with the previous work that
underlined the importance of teachers' digital competencies in tak-
ing advantage of technology enhanced learning (Kirschner &
Selinger, 2003; Straub, 2009). Teachers' technology acceptance
does not solely rely on the affordances of a specific technology.
Rather, it is a complex process that also includes teachers' self-
efficacy beliefs, and motivational attributes (Scherer & Teo, 2019).
In the current study, the targeted self-efficacy, that is the efficacy in
using a specific technology (i.e., Qridi LMS), did not predict behav-
ioural intention in any of the teacher groups. Therefore, it can be
argued that technology integration initiatives in educational contexts
should go beyond developing competencies of teachers in using specific
technologies. Rather, the initiatives should include more holistic
approaches and develop teachers' digital competencies in seamless inte-
gration of learning technologies aligned with effective pedagogical
approaches (Häkkinen et al., 2017; Valtonen et al., 2021).
Finally, support was a significant predictor of behavioural intention
only for the inexperienced teachers. The significance of support on
behavioural intention disappeared among the teachers who have expe-
rience in using the LMS for some time. Previous studies highlight that
providing support to teachers is crucial for successful technology inte-
gration (Ertmer, 1999; Liu et al., 2017; Vanderlinde & van Braak, 2010).
Contributing to such findings, the current study showed that providing
support to teachers specifically at the early stages of technology adop-
tion is important for successful technology integration in education. This
support can be in the form of offering training activities to teachers,
providing them resources (e.g., software, tool and guidelines) or provid-
ing them technical support. In time of the pandemic, providing techno-
logical support to teachers is not only important for effectiveness of
online remote teaching. Studies have reported significant associations
between teaching efficacy and well-being of teachers (Molero Jurado
et al., 2019; Putwain & von der Embse, 2019). With the start of the
pandemic, many K-12 teachers have found themselves in the online
teaching territory they are not much familiar with. The challenges they
face during adaptation to online remote teaching imposes high stress
and emotional burden on teachers (Marek et al., 2020). It has been
found that lack of online teaching skills is one of the main contributors
of stress among the teachers (Khlaif et al., 2020). Therefore, providing
effective technology integration support to teachers can be considered
as crucial for also supporting their well-being. This can be viewed also
as a collaborative act, where teachers in a stressful working situation
would gain from the peer/collegial support.
8 | LIMITATIONS
The current study explores the direct relationships between the vari-
ables of interest. Due to sample size, it was not possible to investigate
the indirect relationships among the variables across the groups. Future
research should address this limitation by collecting data from a bigger
sample of teachers and testing structural equation models for both
groups. This is a cross-sectional study. Thus, the current findings inform
about two groups of teachers' technology acceptance beliefs at a spe-
cific time. A longitudinal approach that measures teachers' beliefs multi-
ple times over time would have been a better approach. This would
provide a fine-grained look in how teachers' beliefs change as they
experience specific educational technologies. The current study is based
on a modest sample size. Although the sample size allows us to run rele-
vant parametric tests to answer our research questions, the current
findings might not be generalized to teacher population in Finland or
elsewhere. Further, majority of the participants were female in the cur-
rent study, which characterize gender distribution within the teaching
profession in Finland where the great majority of primary school
teachers are females. Due to small male sample, we could not include
gender as a distinct variable in the statistical analyses. However, gender
has been reported as a significant construct in teachers' technology
adoption process (Teo, 2014). Therefore, future studies should address
this limitation by recruiting higher samples from both males and
females. Another limitation of this study is that it employed conve-
nience sampling of Qridi LMS users. Eventually, majority of the partici-
pants who responded to the survey appeared to be the teachers who
had previous experience in Qridi LMS before the pandemic. It would
have been better if the sample size of the inexperience teachers were
proportionate to the experience teachers. In the current study, the fre-
quency of Qridi LMS usage was not measured. Exploring behavioural
usage patterns of LMS could provide valuable insights about under-
standing the technology acceptance among teachers with and without
prior LMS experience when the pandemic hit the education landscape.
9 | CONCLUSION
The ongoing Covid-19 pandemic has caused an unprecedented shift
towards online remote teaching. Hence, it is important to understand
the impact of such abrupt shift on teachers' online teaching
1562 DINDAR ET AL.
technology acceptance. Drawing on this, the current study explored
the LMS acceptance of two groups of Finnish K-12 based on their
prior experiences with a specific LMS. The first group had been using
the LMS before the pandemic (i.e., experienced group). The second
group started using it during the pandemic (i.e., inexperienced group).
The current study raises several key conclusions. First, inexperi-
enced teachers displayed similar levels of technology acceptance
with the experienced teachers although they had to adopt the LMS
in a forced manner due to the pandemic. These findings imply that if
teachers are convinced about the necessity of using a specific tech-
nology in a specific condition (e.g., pandemic) they will accept it
regardless of whether it is imposed on them or not. Thus, we suggest
school administrators and policy makers to put effort in developing
open communication with their teachers about the importance of
using technology in their practice rather than solely focusing on
developing facilitating conditions for technology use (i.e., the tech-
nological infrastructure and training). From a practical perspective,
the existing study addresses an important implication about LMS
design. We have found that ease of use is a significant factor for
inexperienced teachers' satisfaction with the LMS. This calls for
future research on user experience design in LMSs that specifically
focuses on facilitating easy-to-use interfaces for inexperienced
teachers. Another key contribution of this study is that technology
integration in education is a developmental process that is impacted
by multiple factors. These factors might be related to the technology
in use, teachers' general or targeted digital competencies in teaching
with technology, and organizational support provided to teachers.
Further, teachers' prior experiences with the technology can be
interrelated with these factors. Thus, we suggest that future
research should study teachers' technology acceptance by looking at
how technology acceptance constructs vary among teachers with
different prior experience rather than studying them as a unified
profile.
ACKNOWLEDGEMENTS
This research is connected to the GenZ project, a strategic profiling
project in human sciences at the University of Oulu. The project is
supported by the Academy of Finland (project number: 318930) and
the University of Oulu.
CONFLICT OF INTEREST
The authors declare no conflicts of interest.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the
corresponding author upon reasonable request.
ORCID
Muhterem Dindar https://orcid.org/0000-0002-4094-5274
Jan Hermes https://orcid.org/0000-0002-6822-8269
Pasi Karppinen https://orcid.org/0000-0002-7800-8243
Piia Näykki https://orcid.org/0000-0002-2931-704X
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Karppinen P, Näykki P. Comparing technology acceptance of
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DINDAR ET AL. 1565