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Anuratha Kanniah 

AsiaCALL Online Journal 

Vol. 9, 2014 | http://asiacall.info/acoj | ISSN 1936-9859 A27 

Teachers’ acceptance level in using technology in the c lassroom 

Anuratha Kanniah 
Asia Pacific University of Technology and Innovation, Malaysia 

anuratha412@gmail.com  

 
Submitted 17 February, 2014; accepted in final form 24 June, 2014 

 

 
 
Abstract 

The success rate on implementation of ICT in the education system is pre-determined by the school 

teachers’ acceptance in applying technology in the teaching and learning process. It is important 

to find out the readiness of the drivers of ICT usage at schools to ensure successful transformation 

in implementing the ICT policy in the Malaysian education system.  Thus, the aim of the present 

research was to find out the readiness level of the government schoo l teachers in integrating 

technology in teaching. A quantitative research design was employed in responding to the 

research questions. Data were randomly collected from 50 student teachers who teach at schools 

in Kuala Lumpur and Klang Valley, Malaysia. A questionnaire survey was used to collect data 

pertinent to the Technology Acceptance Model (TAM) containing questions related to 

demographic profile and Likert scale measuring variables on Behavior Intention (BI) and 

Behavior Usage (BU) and Perceived Ease of Use (PEOU). The SPSS findings for this research 

demonstrated a significant relationship between PU, BU and PEOU. Recommendations such as 

trainings to be provided and digitised materials to be designed have been emphasised. This 

research contributes to e-learning literature, as well as to content developers. Furthermore, the 

research also has shed light onto many opportunities for future research. 

 

Keywords: Technology Acceptance Model (TAM), Behavior Intention (BI), Behavior Usage (BU) , 

Perceived Ease of Use (PEOU) 

 

 

Introduction  

With the advent of technology, change is so conspicuous and abrupt in the realm of education. The 

teaching and learning processes have been redefined due to the influence and value of technology which 

has become immense in andragogy. The reciprocal relationship that technology has with education has led 

educators to comprehend and leverage it in the classroom. Many policy makers embed elements of 

technology in curriculum planning, intentionally to connect students with outside world. Education 

conserves the potential energy for a radical transformation to bridge the demands of the outside world. 

Educators have long ago started embedding technological tools in formal education based on how the 

world approaches socialization, interaction and connectivity. However, the undeniable fact is that 

technology-supported teaching differs from one country to another.  

 

In Malaysia, the introduction of computers in primary and secondary education differs in terms of 

location. Malaysia’s effort to provide quality education is a multifaceted process. As part of efforts to 

introduce e-learning, in 1997, the Malaysian Smart School project was launched with the intention to 

produce productivity-driven citizens who are technologically literate. This project was set as the catalyst 

for massive transformation in Malaysia’s national curriculum. The concept of Smart School involves 

appropriate combinations of learning styles to foster interactive classroom environment. Classrooms are 

equipped with multimedia courseware, presentation facilities and groupware to enhance collaborative 

learning skills. By 2003, the government spent approximately USD285,000 to create 1,494 courseware 

titles (Bismillah Khatoon, 2007). In terms of learning resources, the learners have access to a database 

centre to explore various multimedia courseware to facilitate learning. The Smart Schools are also 

mailto:anuratha412@gmail.com


Teachers’ acceptance level in using technology in the classroom 
Anuratha Kanniah 

AsiaCALL Online Journal 

Vol. 9, 2014 | http://asiacall.info/acoj | ISSN 1936-9859 A28 

equipped with advanced computer laboratories which have readily accessible multimedia and audiovisual 

equipment. Generally, the Smart School subscribes to the constructivist learning approach that encourages 

the learners to actively construct knowledge; providing the teachers formulate the right learning 

environment.  

 

Teachers often face challenges due to continual changes in policies. As technology grows in the realm of 

education, teachers’ role also changes to suit the new climate. Integration of technology in the curriculum 

does not have a linear relationship with the outcome (Florian-Gaviria, Glahn & Fabregat, 2013). In other 

words, the benefit of the new idea can be attained only if the teachers know how to approach the 

innovative idea technically and pedagogically. This imperatively spells out that technology is not 

automatic, but requires the facilitation of an instructor. Many educational institutions have invested a lot 

of money with predetermined positive outcomes that learning quality will ascend with the introduction of 

sophisticated technology. However, there is a great misconception as teachers are the driving force behind 

any innovative ideas related to curriculum. The absence of teachers’ involvement at the preamble stage of 

any fresh attempts to renew the curriculum may result in failure (Oloruntegbe, 2011). An understanding of 

teachers’ acceptance toward usage of technology is important to determine usage behaviors in order to 

proactively advocate technology in classroom. 

 

The Technology Acceptance Model (TAM) developed by Davis, 1989, is widely used to predict users’ 

acceptance behaviour. The purpose behind its wide usage is due to its simplicity and strength (Venkatesh 

& Bala, 2008). TAM believes that the success of a system can be predicted by user acceptance of the 

proposed system and this can be measured by three factors: Perceived Usefulness (PU), Perceived Ease of 

Use (PEOU), and attitude towards usage (ATU) of the system (Davis, 1989). Besides TAM, the Theory of 

Reasoned Action (TRA) (Fishbein, 2008), the Theory of Planned Behaviour (TPB) (George, 2004) and 

Innovation Diffusion Theory (Ritu Agarwal & Jayesh Prasad, 1997) are also widely used in studying user 

acceptance. The mentioned theories will be dealt with in detail at a later stage in the present study. 

Previous research indicates that elements of technology have been hindered despite large investments due 

to limited user acceptance.  

 

Purpose of the study 

The general purpose of this study was to find out the Malaysian school teachers’ acceptance level of the 

integration of technology in the classroom. The following encapsulates the specific aims of the present 

research. 

1. To find out the government school teachers’ intention in using technology in classroom 

(Behavior Intention, BI) 

2. To investigate to what extent the government school teachers believe that technology can 

improve their performance (Perceived Usefulness, PU) 

3. To study if technology is seen as a factor that may reduce the effort of school teachers 

(Perceived Ease of Use) 

 

The significance of this research is to enlighten the policy makers on the implementation of ICT policy in 

Malaysia. The findings of the research can shed light into the next course of action to be considered in 

effectively implementing the ICT policy. Furthermore, course coordinators from Education faculties can 

also gain some insights in terms of module designing. Courses offered at the teachers’ training colleges 

and Education faculties should meet the demand of today’s education industry.  

 



Teachers’ acceptance level in using technology in the classroom 
Anuratha Kanniah 

AsiaCALL Online Journal 

Vol. 9, 2014 | http://asiacall.info/acoj | ISSN 1936-9859 A29 

Literature Review 

ICT Policy in Malaysian Education 

Malaysia as a rapidly developing country has considered education as an important element in the growth 

of the country. In line with the priority given to the education sector, the Ministry of Education (MOE) 

has decided to instill ICT elements in the school curriculum. The ICT policy started off with the 

introduction of The Smart School. As mentioned earlier, the Smart School is a learning institution that is 

governed by the use of technology. The implementation of the Smart School has been divided into four 

phases which are known as Wave 1, Wave 2, Wave 3 and Wave 4.  

 

Wave 1 (1999-2002), constitutes the pilot test stage whereby 88 schools were selected to be transformed 

as Smart Schools. After that, was the post-pilot phase, which represented Wave 2 (2003-2005). At this 

phase, the concentration was on the building of computer laboratories, teaching of science and 

Mathemathics in English and development of courseware. Under Wave 3 (2006-2010), great effort was 

made in turning all schools into Smart Schools. Lastly, under Wave 4 (2010-2020), the Smart School leapt 

into a stage where the application of ICT elements in education becomes insidious. At this phase, 

technology becomes an integral part of the pedagogical implications. 

 

Now, the national curriculum focuses on digital age proficiency and technology awareness that intends to 

produce productive workforce. Currently, the workforce has infused elements of ICT widely that even 

general workers are expected to possess some knowledge of ICT. Hence, the pedagogy at school is 

expected to include ICT tools at an optimum level. Delivery of knowledge with the aid of ICT tools 

requires IT savvy workforce at schools. Failure in training the teachers on how to use the ICT tools or 

understanding teachers’ acceptance towards the implementation of the ICT policy can result in failure of 

achieving the tangible outcomes.  

 

Theoretical Models in Technology Acceptance and Usage; Theory of Reasoned Action (TRA) 

Theory of Reasoned Action (TRA) is a widely studied model by Ajzen and Fishbein from social 

psychology perspective to examine individuals’ consciously intended behavior (Fishbein, 2008). Three 

core elements of TRA are attitude (A), behavioral intention (BI) and subjective norm (SN). Attitude 

submits to an individual’s belief about the impact of intention to perform a behavior, whereas BI is the 

individual’s strength and voluntariness to perform the behavior (Fishbein & Ajzen, 1975). SN on the other 

hand refers to the individual’s elaborative thought on others’ perception (Hsu, 2012). This social influence 

includes expectations of friends, family and other members in the society who controls the performance of 

the recommended planned behaviour. TRA believes that an individual’s attitude to perform a behavior is 

volatious and influenced by other referents that determine the acceptance and rejection in terms of 

intention (BI = A + SN). To summarise the definition into simple logic: if a person intends to perform a 

behavior, then there is a high probability that the individual will do it. However, it can be inferred that 

attitude and norm may not be weighted equally as both the variables can have different effects in an 

individual; (A≠SN). This equation is sensible because an individual who is less attenuated by others’ 

perception will implicate reduced weight for SN.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   

 

 

 

Attitude 

Subjective Norm 

Behavioral 

Intention 

Volitional 

Behavior 



Teachers’ acceptance level in using technology in the classroom 
Anuratha Kanniah 

AsiaCALL Online Journal 

Vol. 9, 2014 | http://asiacall.info/acoj | ISSN 1936-9859 A30 

Figure 1: Causal Diagram of Basic Components of the Theory of Reasoned Action, Source: Madden et al. 

(1992, p.4) 

 

Figure 1 translates the equation into a causal model that posits volitional behaviors as the direct 

influencing factor of behavioral intentions that modify both attitudes and subjective norms. The target 

points suggest that performance of volitional behavior could be embraced by aiming at intentions, 

attitudes or subjective norms. For example, a persuasive message could aim to prevent people from 

smoking, influence people’s attitude towards smoking and shed lights about how others perceive smokers, 

or some combinations of these three elements.  

 

In a nutshell, TRA suggests that usage behavior is determined by the individual’s intention and also 

concerned with the likelihood of others. The predictor could also be a cross product of both the individual 

and society as TRA does not specify the beliefs that are operative for a particular behavior. It is also 

important to understand that TRA succumbs to prediction, rather than outcome of behaviors. Intention can 

be seen as the cognitive representation of an individual’s preparedness to perform the expected behavior. 

A common understanding in TRA is, the greater the attitude and the subjective norm are, the stronger 

should be the individual’s preference in performing the behavior. For accuracy in making the prediction, 

the interval between the measure of behavior and observation of behavior should be quite short as 

intentions change over time. 

 

The contention of TRA is the ambiguity between norms and attitudes because attitudes can also be related 

to norms and vice versa. Besides that, intention cannot be seen as the sole variable that influences 

performance of behavior as it is governed by limitations like ability, time, and organisational limit, 

environmental and unconscious habit. In other words, TRA is only successful in a condition where 

volitional control is high. To resolve this limitation, the Theory of Planned Behavior (TPB) was 

introduced. 

 

Theory of Planned Behaviour (TPB) 

TPB was proposed by Icek Ajzen in 1985 as an extension of TRA which was conceptualized by Ajzen 

and his collaborator, Martin Fishbein. TPB is a widely studied model from a social psychology 

perspective to evaluate a newly introduced component which is “perceived behavioral control” that 

consorts non-volitional behaviors in the prediction of behavioral intention and actual behavior. In brief, 

TRA consolidates three elements: beliefs about the significance of the behavior (behavioral beliefs), 

beliefs about the normative expectations of others (normative beliefs) and beliefs about the presence of 

variables that may motivate or impede performance of the behavior (control beliefs). In their respective 

components, behavioral beliefs produce a supportive or unsupportive attitude toward the behavior; 

normative beliefs influence perceived social pressure (subjective norm); and control beliefs give rise to 

perceived behavioral control (Hsu, 2012). In its simplest form, TPB can be represented by the following 

equation.  

 

BI = (Wı)AB[(b)+(e)] + (W₂)SN[(n)+(m)] + (W₃)PBC[(c) + (p)] 

 

 

 

 

 

 

 

BI: Behavioral intention; AB: Attitude toward behavior; (b): the strength of each belief; (e): the 
evaluation of the outcome or attribute; SN: Subjective norms; (n): the strength of each 
normative belief; (m): the motivation to comply with the referent; PBC: Perceived Behavioral 
Control; (c): the strength of each control belief; (p): the perceived power of the control factor; 
W: empirically derived weight/coefficient 
 



Teachers’ acceptance level in using technology in the classroom 
Anuratha Kanniah 

AsiaCALL Online Journal 

Vol. 9, 2014 | http://asiacall.info/acoj | ISSN 1936-9859 A31 

The equation clearly delineates that BI is a joint function of several correlated conditions; the greater the 

intentions and subjective norm, the higher the probability of performing the intended behavior. In addition 

to it, the presence of actual behavioral control that refers to support and opportunities available in 

performing the behavior will also dictate the likelihood of behavioral achievement. Perceived behavioral 

control posits as a pivotal component in TPB. According to Montano & Kasprzyk (2002), perceived 

behavioral control is related to Bandura’s self efficacy concept. 

 

Self-efficacy Theory (SET) 

Bandura proposed SET in 1977, which has similarity with perceived behavioral control. Self-efficacy 

explains that an individual is able to accomplish the targeted result if there is adequate outcome 

expectancy (Bandura, 1993). Self refers to the individual characteristics while efficacy is defined as the 

power to generate an impact (Merriam-Webster’s Online Dictionary). The combined meaning of these 

two words implies human’s consciousness in producing productivity. On the other hand, outcome 

expectancy refers to an individual’s confidence in attaining the outcome if adequate support and 

motivation are provided (Bandura, 1997). In short, SET believes that an individual’s expected 

performance is influenced by internal forces such as self decision making and efforts, and at the same 

time, controlled by other variables such as fate and luck. Utilisation of cognitive and affective attributes is 

important in achieving the desired goal, which is often associated with internal locus of control.  

 

Individuals with high level of efficacy tend to ambitiously set higher goals to face challenges, thus 

Bandura argues that human behavior is governed by cognized goals (Bandura, 1989). Potential negative 

repercussions are often disregarded as the aim is to achieve the desired outcome. This implies that 

individuals with high self-efficacy will endeavor to accomplish those goals. In terms of affective factor, 

the emotional reactions determine the predicted performance of behavior. Every individual exhibits 

different degrees of ability in handling stress in a challenging situation. Individuals who believe in being 

able to manage stress can lower their anxiety and exercise control over behavior. On the other hand, the 

internal locus of control refers to an individual’s belief in being able to perform a task. According to 

Bandura (1997), a high level of self-efficacy in an individual will lead to utilization of cognitive and 

affective processes. Besides that, social experiences too determine self-efficacy. For example, an 

individual who has gained adequate skills and experience relevant to the expected performing task will 

produce a higher level of efficacy. In contrast, an individual who has experienced failure in retrospective 

with the accomplishment of the task will exhibit a low level of self-efficacy. In short, confidence can be 

perceived as a prerequisite to try a new task.  

   

 

 
 

Figure 2: Theory of Planned Behavior Model, Source: Ajzen (1991), p. 182 



Teachers’ acceptance level in using technology in the classroom 
Anuratha Kanniah 

AsiaCALL Online Journal 

Vol. 9, 2014 | http://asiacall.info/acoj | ISSN 1936-9859 A32 

 

Even Figure 2 encapsulates that Intention is the nucleus in TPB. Intention portrays an individual’s 

motivational force to perform the intended behavior. Motivational actions are self-determined and can be 

wholly volitionally as it is one’s sense of self (Ryan, Richard & Deci, 2000), whereas actions can be 

influenced by various factors such as persuasion by some interpersonal force. In other words, a person can 

perceive the locus of causality as internal where behavior is self-determined, or it can also be considered 

external, whereby behavior is controlled. The important point here is that either it is self-determined 

behavior or controlled behavior, both attributes postulate energisation issues that address motivation to 

perform a given behavior. The general rule is, the stronger the intention, the more likely that an individual 

will perform the behavior. Besides Intention, behavioral attitude too determines one’s motivation or 

ability to perform a task (Khlood, 2010). As mentioned earlier, behavioral attitude is concerned with one’s 

perception of the level of difficulty of performing a behavior which is influenced by some facilitating 

factors. The facilitating factors are being referred to as opportunity that has an effect on the performing 

behavior of an individual.  

 

In addition to that, subjective norm which considers the customary codes of behavior of other people too 

manipulates the behavior of an individual. It simply means that approval from significant people of the 

behavior will likely ensure that an individual will  perform the behavior.  

 

Technology Acceptance Model (TAM) 

The Technology Acceptance Model (TAM) is one of the most widely used models to investigate an 

individual’s attitude towards technology. TAM was adapted by Davis from the TRA model (see Figure 1) 

in 1986. TRA’s two significant tenets are subjective norm and attitude towards that behavior, whereas 

TAM posits two key areas which are perceived usefulness (PU) and perceived ease of usefulness (PEOU). 

PU is defined as an individual’s perception of the significance of the technology in improving job 

performance. PEOU on the other hand explains an individual’s belief in the ability of the technology to 

reduce effort or workload. Figure 3 illustrates the TAM designed by Davis in 1989. 

 
 

The initial stage of TAM, similar to TRA, BI (prediction of behavior performance) was considered. 

Beliefs of the behavior performance were deduced from the target users for a specific context, and then 

the belief is weighed by the value determined by the attributed behavior. This idea is criticized by many 

researchers because TAM could not go beyond measuring perceived ease of use or perceived usefulness 

variables, and the focus was primarily on volatile attitude. In addition, TAM is also unable to explain the 

reasons for which an individual would prefer to perform the behavior. At the initial stage, many 

researchers have agreed that TAM is suitable to study intended behavior in the usage of technology for 

various contexts. Consequently, many researchers have tried incorporating external factors to the original 

model and Davis himself has collaborated with Vengkatesh to enhance the model by adding additional 

External 

variables 

Perceived 

usefulness 

Perceived 

ease of use 

Attitude 

towards using 

Behavioural 

intention to use 
Actual system 

use 

Figure 3: Technology Acceptance Model (Davis et al., 1989, p.985) 



Teachers’ acceptance level in using technology in the classroom 
Anuratha Kanniah 

AsiaCALL Online Journal 

Vol. 9, 2014 | http://asiacall.info/acoj | ISSN 1936-9859 A33 

characteristics. This can be seen as an attempt to overcome the limitation of the original model as it did 

not consider social influences on usage behavior. The following encapsulates the revised model, which is 

known as TMA 2. 

 
Figure 4: Technology Acceptance Model 2 (Venkatesh & Davis, 2000: 188) 

 

The model above was published in 2000. The additional values in the above revised models include social 

influence processes (subjective norm, volunteerism and image). In addition to that, aspects pertinent to 

cognitive instrumental processes such as job relevance, output quality, result demonstrability, and 

perceived ease of use have been embedded in TMA 2 to enlighten deeper understanding on user 

acceptance.  

 

As mentioned earlier in the predecessor TAM, the revised model contains Subjective Norm (SN) and plays 

a similar role which is to find out the perception of an individual on the consequences of performing the 

behavior. Voluntariness (V) too inherits the same explanation which is to measure an individual’s 

participation in a non-mandatory situation. Innovations in the new model encompass Image (IMG), 

Experience (EXP), Job Relevance (JR), Output Quality (OQ) and Result Demonstrability (RD). 

Experience (EXP) represents the previous knowledge seized by an individual toward the new system. 

EXP is proven to be correlated (Vengkatesh et al. 2000) as it has impact on SN, PU and BI. For instance, 

possession of experience acquired from previous knowledge can foster one’s confidence level and 

increase the quality of being independent. JR explains an individual’s perception of the extent to which 

the target technology is applicable to his or her job. OQ examines the impact of using the target 

technology in terms of output. RD reflects the formulation of positive perception upon significant 

outcome achieved when compared between usage and targeted outcome.  

 

Generally, the successor model includes the social factors that were absent in TAM, but, however, did not 

fill up gaps such as time, organizational, ability and environmental limitations. To overcome the 

mentioned limitations, Venkatesh and collaborators further expanded the model by developing The 

Unified Theory of Acceptance and Usage Theory (UTAUT) which has considered 32 variables. The 

current research however has considered TMA2 as it meets the aims of the research.  

 

Methodology 

Given the nature of the question, the researcher analysed the responses collected from the questionnaire 

using the quantitative strand to synthesise the result.  

 

Sample 

The respondents in the current research were undergraduates teaching in public primary schools in 

Malaysia. The Teacher Graduate Programme (known as Program Pensiswazahan Guru-PPG in Malay 



Teachers’ acceptance level in using technology in the classroom 
Anuratha Kanniah 

AsiaCALL Online Journal 

Vol. 9, 2014 | http://asiacall.info/acoj | ISSN 1936-9859 A34 

language) was launched under the 10th Malaysia Plan. The Malaysia Plan is the economic developmental 

plan launched every 5 years by the government to promote the welfare of citizens and especially living 

conditions in rural areas. In line with that, scholarships were given to public school teachers with Diploma 

qualifications to elevate their status as Degree holders. The teachers are to enrol as distance learning 

candidates and participate in face-to-face learning sessions for 10 hours in a semester. This study 

evaluates 50 valid questionnaire responses from data collected over the period of six months.  

 

Instrument 

The quantitative research used an online questionnaire as its data collection method. The questionnaire 

was derived from the Technology Acceptance Model. The dependant variables (DV) used in the 

instrument are Perceived Use (PU), Perceived Ease of Use (PEOU) and Self Efficacy (SE). The 

questionnaire was designed using Google Drive and e-mailed to all the participants of the PPG 

programme. The acceptance of technology aspects were ranked using a 7-point Likert scale where 

responses ranged from 1 (least important) to 5 (most important). Out of 67 respondents, valid replies were 

obtained from only 50 students. The limitations of using questionnaires include honesty of respondents in 

replying to the questions and the researcher’s control over respondents in completing the questionnaire.  

 

Research Design 

Creswell (2009) explains quantitative research in its simplest way as analysis of numbers. It simply means 

collecting numerical data to explain a particular phenomenon that can be answered using quantitative 

methods. The flowchart (Figure 5.1) provided the guidance for the researcher to conduct the quantitative 

research. As indicated in the flowchart, the researcher began with the construction of research questions to 

reflect the need of the quantitative mode of research. Then, permission was obtained from the University 

to extract a sample from the Faculty of Education. To shape the quantitative strand of the research, a 

questionnaire was designed as the instrument for data collection and to further analyze the results using 

inferential statistics. As for steps 2 and 3, results were interpreted based on group differences. The 

researcher started with tabulating the statistical data obtained by conducting the questionnaire-survey then  

interpreted the numerical data.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Design and Implement the Quantitative Strand 

 State quantitative research questions and determine the quantitative 

approach 

 Obtain permissions 

 Identify the quantitative sample 

 Collect close-ended data with instruments 

 Analyze the quantitative data using descriptive statistics, inferential 

statistics and effect sizes to answer the quantitative research questions 

and facilitate the selection of participants for the second phase 

Use Strategies to Follow from the Quantitative Results 

 Determine which results will be explained, such as 

- Significant results, 

- Nonsignificant results 

- Outliers, or 

- Group differences 

 

S
T

E
P

 1
 

S
T

E
P

 2
 



Teachers’ acceptance level in using technology in the classroom 
Anuratha Kanniah 

AsiaCALL Online Journal 

Vol. 9, 2014 | http://asiacall.info/acoj | ISSN 1936-9859 A35 

 

 

 

 

 

 

 

Figure 5: Flowchart of the Basic Procedures in Implementing a Quantitative Research 

 

Results 

This section is divided into parts; first it presents the ANOVA statistical findings of the quantitative 

research and latter discusses data gained from qualitative research design. The intent of this analysis was 

to specify a variance model of the acceptance of the school teachers towards using technology. The results 

of the analysis of variance also identify the parameters of interest by ensuring that the ANOVA statistics 

is congruent with the normal linear regression. To ensure mutual orthogonality, analysis of variance was 

run multiple times in progression to determine if an additional covariate contributed more to the explained 

variance. If the additional variate did not reduce the unexplained variance, then there was no necessity to 

add an additional covariate in the existing model. The table below encapsulates the statistical findings of 

the acceptance level of the teachers in using technology in the classroom. 

 

Table 1: Factor Analysis of Acceptance on Technology Usage Based on Technology Acceptance Model 

(TAM) 

Strands in TAM User Acceptance Criterion Average  Factor Loadings  

Behavioral 

Intention (BI) 

 

Q1 I intend to use technology in the delivery of 

my lessons 

5.02 0.484 

Q2 I intend to use technology to enhance my 

subject matter knowledge  

5.44 0.888 

Q3 I intend to use technology to enhance my 

teaching skills 

5.4 0.899 

Q4 I intend to contact my students via e-mail 5.32 0.922 

Q5 I intend to use the Internet for downloading 

research material for my own research 

knowlege 

5.2 0.883 

Q6 Assuming I have access to the Internet, I 

intend to use it in my homework preparation for 

class 

6.36 0.735 

Q7 Assuming I have access to the Internet, I 

intend to use it in my non-academic tasks with 

students 

4 0.743 

Q8 Whenever it will be possible to me, I plan to 

use the Internet in assessment 

2.9 0.18 

Q9 I intend to provide online consultation to my 

students 

4.28 0.828 

Q10 I intend to request my students to submit 

homework online 

3.84 0.198 

Q 11 I predict that learning will be more 

interactive via usage of technology 

6.34 0.495 

Interpret the Connected Results 

 Summarize and interpret the quantitative results 

 Discuss to what extent and in what ways the quantitative results explain 

the phenomena S
T

E
P

 3
 



Teachers’ acceptance level in using technology in the classroom 
Anuratha Kanniah 

AsiaCALL Online Journal 

Vol. 9, 2014 | http://asiacall.info/acoj | ISSN 1936-9859 A36 

Q 12 I intend to foster interaction via online 

forum 

5.96 0.788 

Perceived 

Usefulness (PU) 

Q1 Using technology enables me to accomplish 

my teaching quickly. 

5.36 0.855 

Q2 Using technology improves the quality of 

my research. 

5.5 0.892 

Q3 Using technology enriches my teaching 

resources 

5.56 0.901 

Q4 Using technology enhances my research 

effectiveness for classroom preparation  

5.48 0.886 

Q5 Using technology gives me greater control 

over my delivery in class 

5.8 0.78 

Q6 Use of technology will generally increase 

my productivity as a teacher 

6.6 0.551 

Q7 Use of technology will allow more time for 

revision  before examination 

5.08 0.56 

Q8 Use of technology has the potential to 

enhance students’ performances 

5.78 0.552 

Q9 Instructions can be understood easily with 

the presence of technology 

6.54 0.836 

Q 10 Abstract concepts can be explained clearly 

with the aid of technology 

5.54 0.156 

Q 11 Interactive lessons using technology can 

make my lessons interesting 

6.42 0.493 

Perceived Ease 

of Use (PEOU) 

Q1 Usage of technology to support my teaching 

is clear 

4.98 0.718 

Q2 Obtaining teaching materials can be easy 

when using technology to support my teaching 

5.1 0.823 

Q3 Time spent on marking homework scripts 

can be reduced with the intervention of 

technology  

4.8 0.660 

Q4 Time spent on marking exam scripts can be 

reduced with the intervention of technology 

5.16 0.632 

Q5 Dissemination of homework is easier with 

the presence of the Internet 

5.3 0.872 

Q6 Time spent on face to face feedback can be 

reduced with the introduction of online 

feedback systems 

5.28 0.868 

Q7 Online practices can reduce my effort to 

prepare additional teaching materials 

5.56 0.548 

Q8 Monitoring students’ performance will be 

easier if technology is introduced 

5.14 0.762 

Q9 Remedial classes conducted online can 

reduce effort 

5.06 0.785 

Q10 Virtual classrooms can reduce my effort  5.16 0.825 

Q11 Online homework submission is easier 

than the conventional method 

5.3 0.825 



Teachers’ acceptance level in using technology in the classroom 
Anuratha Kanniah 

AsiaCALL Online Journal 

Vol. 9, 2014 | http://asiacall.info/acoj | ISSN 1936-9859 A37 

Q 12 Conducting online assessment is easier 

than the conventional method 

4.5 0.015 

Note: Rotated loadings of 0.400 and greater are in boldface and underlined. Ratings of importance were 

based on a seven-point scale where 7=most accepted and 1=least accepted.  

 

Table 1 encompasses the average and variance scores of the preferences of the teachers in using 

technology in classroom for the three main strands which are Behavioral Intention (BI), Perceived 

Usefulness (PU) and Perceived Ease of Use (PEOU). For achieving the objectives of the research, a 

hypothetical chain relation was proposed: 

 

{H1a:  BI --> PU; H1b:  BI --> PEOU} {H2a:  PEOU --> BI; H2b:  PEOU --> PU} 

In order to test the acceptance of behavior in using technology, a Rotated Factor Matrix was used to 

examine if each variable had a substantial loading in a factor. The current study considered substantial 

loading which is 0.50 or higher as the sample is below than 100. According to Johnson, et al. (2003), if 

the sample size is below 100, loading above a significance of 0.5 is sufficient. Substantial loading from 

each component was selected based on its highest value. The outcome of the factor analysis for the 

present study did not exemplify any complex variables, so there was was no need to eliminate any factor.  

 

The table provides empirical evidence that all the three variables (BI, PU and PEOU) establish a 

significant relationship. In accordance with previous literature (Bandura, 1992), it is found that the 

findings are similar whereby the total effect of BI, PU and PEOU has a significant relationship. These 

findings are relevant to the current context of investigation, which exhibit that an individual will only 

perceive the performing behavior as useful and believe that it can reduce effort if there is intention to 

perform the behavior. A possible explanation for these significant results is the TAM itself which suggests 

that an increased intention to accept a behavior will result in strong direct significance of PU and PEOU.  

 

Using factor loadings as the yardstick, generally for BI the intention to contact students via e-mail had the 

largest effect on intent to use technology (0.922), followed by preference to use the Internet to augment 

teaching skills (0.899) and enhance subject matter knowledge (0.888). Besides that, intention to use the 

Internet for downloading research materials is quite significant (0.883). The respondents are also driven to 

conduct online consultation to the students (0.828). It also appears that the teachers intend to initiate 

online forum (0.788), access the Internet to communicate with the students to discuss on non-academic 

tasks (0.743) and for homework preparation for class (0.735). The significance of predicting that learning 

will be more interactive with the use of technology also obtained a significant factor loading (0.495) 

followed by intending to use technology to deliver lessons in the classroom (0.484). The significance of 

using the Internet for homework submission and assessment received less attention with factor loadings 

0.198 and 1.8 respectively.  

 

As for PU, Table 1 presents that 11 out of 12 of the factor loadings have eigen values greater than 4. 

These findings primarily indicate a high probability of usage intention on the basis of its usefulness 

among the potential adopters. “Using technology enriches my teaching resources” accounted for 0.901 

loading value, then entailed by intention to use technology to improve the quality of research (0.892). The 

third factor that was extracted corresponds to using technology to enhance effectiveness of research for 

classroom preparation (0.886). The other two loadings above 0.8 are for performance expectancy to 

accomplish teaching quickly (0.855) and positive effect of giving instructions via technology (0.836).  

The only loading above eigenvalue of 0.7 is for the belief that technology could provide greater control in 

classroom for teachers (0.78). Three components were detected to have an eigenvalue greater than 0.5; 

belief that more time for revision sessions can be allotted before examination (0.56), overall perception 



Teachers’ acceptance level in using technology in the classroom 
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AsiaCALL Online Journal 

Vol. 9, 2014 | http://asiacall.info/acoj | ISSN 1936-9859 A38 

that technology can enhance performance of students (0.552) and lastly confidence that technology can 

increase productivity of teachers (0.551). The only factor above the eigenvalue of 0.4 is the certainty that 

technology can inspire interactive lessons which can excite students (0.493). The only factor with a weak 

loading was “the belief” that abstract concepts can be explained clearly with the aid of technology” 

(0.156). 

 

Finally, for PEOU, the findings resulted in 5 out of 12 components with eigenvalue greater than 0.8. The 

highest factor represents a value of 0.872, suggests that dissemination of homework can reduce teachers’ 

effort. Consistent with this belief, the respondents also predict that online feedback systems can reduce 

teachers’ effort (0.868). Two components with similar factor loadings (0.825) are perceived: effort 

expectancy on virtual classroom and online homework submission which have great potential in adopting 

the usage behavior. The respondents also strongly believe that technology can ease their burden in 

searching for teaching materials (0.823). The three loadings with eigenvalue of above 0.7 are components 

related to reduction of effort in terms of remedial classes (0.785), monitoring of students performance 

(0.762) and finally general support obtained in teaching (0.718). Components pertinent to relief from 

marking homework and feeling of great distress from assessing exam scripts have respectively attained 

factor loadings of 0.660 and 0.632. The empirical statistics also reveal that the prospective adoptive users 

believe that online practices can reduce required effort in the preparation of additional teaching materials 

(0.548). The weakest score for the PEOU strand is the belief that technology can reduce teachers’ effort if 

online assessment is introduced (0.015).  

 

Table 2: ANOVA Output for Behavioral Intention (BI) 

Source of Variation SS df MS F P-value F-crit 

Between Groups 151.8026 11 13.80024 38.58051 5.14E-45 1.833178 

Within Groups 77.26316 216 0.3577    

Total 229.0658 227     

 

Table 3: ANOVA Output for Perceived Usefulness (PU) 

Source of Variation SS df MS F P-value F-crit 

Between Groups 48.36364 10 4.836364 11.66308 1.09E-15 1.878767 

Within Groups 82.10526  198 0.414673    

Total 130.4689 208     

 

Table 4: ANOVA Output for Perceived Ease of Use (PEOU) 

Source of Variation SS df MS F P-value F-crit 

Between Groups 33.05263 11 3.004785 4.870315 1.01E-06 1.833178 

Within Groups 133.2632 216 0.616959    

Total 166.3158 227     

 

To ensure the results of calculations is trustworthy, 25 subjects were randomly selected. The overall F 

ratio for the ANOVA output for BI, PU and PEOU are insignificant as the F-ratio for BI is F(11, 216) = 

38.58 (F) > 1.83 (FCV). Similarly, the hypothesis-testing for PU too does not exhibit a normal distribution 

as the output is F(10, 198) = 11.66, 38.58 (F) > 1.83 (FCV) . Finally, the F ratio for PEOU is F(11, 216) = 

4.87 (F) > 1.83 (FCV). The null hypotheses for all the three strands are rejected since every group has a 

mean score of p ≤ α, which explains that the overall mean score is significantly different from each other. 

Thus, the alternative hypothesis is: 

 



Teachers’ acceptance level in using technology in the classroom 
Anuratha Kanniah 

AsiaCALL Online Journal 

Vol. 9, 2014 | http://asiacall.info/acoj | ISSN 1936-9859 A39 

Ha: At least one mean pressure is not statistically equal 

 

For BI, the variation between groups is relatively larger than within group. This explains that the mean of 

the samples is not equally distributed, probably due to its heterogenous sample consisting of females and 

males from various age groups. As revealed in previous studies (Oloruntegbe, 2011), some females are 

quite hesitant towards the acceptance of e-learning. On the other hand, males think that e-learning is 

valuable as it allows more time to perform other tasks to meet life demands. Besides that, a wide range of 

age group could have contributed to the disparity in the variance. Gen-Y has the capability to understand 

and apply complicated technical knowledge pertinent to technology. Such ability to cope with new change 

foreshadows samples from this particular age group to become early adopters of technology. In other 

words, their perception towards the usage of technology could be higher as compared to respondents who 

are older. The older generation is proven to be among the last adopters of technology due to high 

resistance towards changes. 

 

Discussion 

Generally, the factor loadings exhibit a consistent varimax rotation score for all the three strands; BI, PU 

and PEOU. In terms of the average value, each strand represented 5.05 (BI), 5.79 (PU) and 5.11 (PEOU). 

The average value for PU is the highest then entailed by PEOU and BI. This briefly explains that the 

expectation of rewards out of performance is greater than the influence of performance on intention or the 

belief that usage behavior could free one from effort. The variance between the average scores is however 

steady, indicating significant relationship among the variables. This is contradicting some of the previous 

studies (Abbasi, Irani & Chandio, 2010). The current study generally hypothesised that BI, PU and PEOU 

share a quasi-similar total effect on performance of behavior; however to a certain extent, BI and PEOU 

could be evoked by PU, as an increased perception of usefulness is noticeable.  

 

Behavioral Intention (BI) 

In average, respondents scored 5.05 on a 7-point Likert scale. The significance of this score is in 

contradiction with TAM (Pai & Huang, 2011; Venkatesh, Morris & Ackerman, 2000) that the total effect 

of BI is greater than PU and PEOU. This is not something unusual as previous studies have obtained 

similar results (McCoy, Everard & Jones, 2005). Intention explains the disposition to respond positively 

or negatively towards the usage behavior. In the present study, the respondents appeared to be interested 

in providing online consultation to students. Providing online consultation to students is not formalized in 

the education system. The positive response for this aspect could be due to the respondents’ acquired 

experience as candidates in distance learning. Distance learning by its nature operates most the processes 

virtually, which includes consultation. Thus, the intention of the respondents who are from a traditional 

background (public school) to provide online consultation to students can be interpreted as a complex 

amalgam derived from their experience. This socially or cognitively acquired disposition can be seen as a 

subconcious process that is open to change. In short, the meaningful engagement of the respondents with 

online consultation could have stimulated the surface attitude towards online consultation to be introduced 

in the current conservative school setting.  

 

Besides that, the intention to adopt the usage behavior is also noticeable and it could be due to awareness 

of IT skills. The Ministry of Education in Malaysia has recognized the importance of ICT in schools, thus 

empowerment of technology in education began in 1999. The integration of technology in curriculum has 

provided opportunity for teachers at school to acquire ICT skills. It is undeniable that the young 

generation is demanding it, hence some teachers do take the effort to provide the desired exposure by 

integrating elements of technology to deliver an interesting lesson.  The inspiration from the ICT policy, 

somewhat has provided school teachers with sufficient ICT knowledge that includes the usage of the 



Teachers’ acceptance level in using technology in the classroom 
Anuratha Kanniah 

AsiaCALL Online Journal 

Vol. 9, 2014 | http://asiacall.info/acoj | ISSN 1936-9859 A40 

Internet. This could be a significant predictor for adopting technology as the statistics do reflect the 

teachers’ intention to use the Internet to disseminate homework, initiate e-forums and vastly use it as a 

teaching tool.  

 

The lowest factor loading for BI is the aspect related to assessment. The respondents’ degree of conscious 

intention to perform e-assessment in classroom is generally low. The weak specified future behavior could 

be due to the current challenges faced by the school teachers with the introduction of School-based 

assessment. The School-based assessment aspires to evaluate the students throughout the learning process 

instead of evaluating the students at the end of the year. In this system, teachers will conduct formative 

assessments in order to make necessary improvements or changes in teaching approach to bridge the gaps 

among students. Upon conducting the assessments, the teachers are required to key in the marks in the 

system and this is reported to be a challenging task for the teachers. The Star newspaper highlighted that 

school teachers are facing great difficulties to key in the data (Zuhrin Azam, 2014) in the system. 

Frequently mentioned complaints are congestion of the system and server issues. The teachers are 

frequently unable to meet the deadline, so they have to work late at night to key in the marks in the system 

to escape from the congested system.  

 

Perceived Usefulness (PU) 

The average score for PU is 5.79, which is the highest scoring strand. A similar finding was attained by 

(Khlood, 2010) when investigating the adoption of e-learning by students of Saudi Arabia. The findings of 

the present study delineate that the respondents perceive the achievement of the usage behavior as 

extrinsic motivation to integrate technology in classroom. Technology is perceived as a tool to achieve the 

learning outcomes and make learning become meaningful. The statistical findings exhibit that teachers 

believe that technology will improve their performance in terms of enriched teaching resources and 

increased productivity in classroom delivery. Besides that, the teachers also believe that integration of 

technology will enhance the performance of students.  

 

The majority of the respondents believe that technology can elevate their performance in the classroom. 

Technology is seen as a mass power that enables the teachers to foster the students’ engagement in class. 

Interactive lessons via technology are seen as an enabler to provide meaningful learning opportunities to 

students. Most of the young generation are skilled and exposed to variety of technology applications, thus 

they are able to handle the technological aspect independently. The strong scores attained on the belief 

that students’ performance can be harnessed mirrors the respondents’ ready acceptance of technological 

innovations. Confidence in the ability to achieve learning outcomes can be interpreted to provide the 

impetus for teachers to plan and prepare teaching materials using technology which includes the Internet. 

Therefore, it is not surprising that the teachers predict that their performance and productivity can be 

increased with the presence of technology.  

 

Perceived Ease of Use (PEOU) 

Concretely, the statistics reported a significantly positive response that technology can reduce the effort of 

the prospective behavior performers. There is a remarkable degree of agreement that introduction of 

technology would reduce teachers’ effort for marking homework and exam scripts. This emphasizes a 

positive attitude towards the introduction of online practices where technology can act as a supplementary 

tool to enhance the learning process. With the presence of mobile technology especially, students have a 

great amount of flexibility in terms of time, pace and place of study (Anuratha, 20). Upon completion of 

the practices, the system will be able to automatically produce the outcome. This concept of autonomous 

learning is not only helping the students, but also teachers’ required effort to assess the assignments. Thus, 

in the present study, PEOU has strong direct significance with BI and PU as all the three traits somewhat 



Teachers’ acceptance level in using technology in the classroom 
Anuratha Kanniah 

AsiaCALL Online Journal 

Vol. 9, 2014 | http://asiacall.info/acoj | ISSN 1936-9859 A41 

lead to the connotation that technology can function as a layer of support to the students’ learning 

experience. In addition, e-assessments could also provide similar impact in terms of assessing the end 

product. This will definitely be a great relief for the teachers as at most of the public schools, classroom 

sizes are huge  with classes sometimes exceeding 40 students.  

 

Moreover, the respondents too prefer providing online consultation to students as it is expected to reduce 

time spent on face to face sessions. Again this conception could have been formulated from their personal 

experience as a blended learning candidate. Online feedback systems can be operated personally or 

collectively. Online chat forums can also be used as a supportive system for feedback to be given. This 

can be perceived as scaffolding rather than online tutoring and all the participants of the chat forum can 

gain from the interaction. A system such as this can also lead to the construction of collaborative learning 

spaces where other members of the forum can contribute to the scaffolding process.  

 

Limitations and directions for future research 

The methodology employed in the present study needs to be enhanced. It is important to consider mixed-

mode research design as it has the potential to provide greater insights from the respondents’ point of 

view especially on low factorial loadings. Therefore, the research should be replicated by involving a 

larger sample size for the generalization of the findings. Besides that, future research can also consider 

conducting a comparative study between student-teachers who are involved in blended learning and 

practicing teachers to further understand the underlying variables that influence behavior factors. 

Furthermore, more independent variables should be included for more dynamic findings in gaining 

insights from the prospective users. A discrepancy was identified in the F-test, but had minimal impact 

with data as the factor loadings were mostly consistent. To further investigate the weak significance in the 

F-Test, future research should consider conducting a Manova analysis by studying the correlations 

between gender and age against preferences.  

 

Conclusion 

Despite the limited technological tools in public schools, the potential users of technology have shown a 

positive interest towards usage behavior. This is a positive sign for transforming education as high 

significance is identified among BI, PU and PEOU among the respondents. If the teachers who are the 

enablers of ICT in education themselves are ready, then there is a great potential for enhancing the 

education system to significantly elevate student outcomes. Besides that, the study also alerts to the need 

for effort to be taken in manipulating the teachers’ perception about e-assessment. Generally, the 

respondents are hesitant with e-assessment and this has been inferred to be related to the introduction of 

School-based assessment; however further investigation is required to validate this assumption. In a 

nutshell, the present study highlights the intention and belief that behavior usage is rewarding and has 

great value in dealing with an inflexible situation. Thus, the government should consider providing 

adequate training to enable the outstanding performance of educators.  

 

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