Explaining the intention to usetechnology among student

teachersAn application of the Theory of Planned

Behavior (TPB)

Timothy Teo and Chwee Beng LeeLearning Sciences and Technologies Academic Group,

National Institute of Education, Nanyang Technological University, Singapore

Abstract

Purpose – This paper aims to examine pre-service teachers’ self-reported intention to use technologyby employing the Theory of Planned Behavior (TPB) as the research framework.

Design/methodology/approach – In total, 157 student teachers completed a survey questionnairemeasuring their responses to four constructs in the TPB. These were administered at the beginning ofthe course in which technology was taught and used. Structural equation modeling (SEM) was used asthe technique for data analysis.

Findings – The results of this study showed that attitude toward usage and subjective norms weresignificant predictors of behavioral intention to use technology while perceived behavioral control wasnot. Overall, this study found that the three explanatory variables in the TPB explained about 40percent of the variance in behavioral intention to use technology.

Originality/value – This study contributes to the growing interests among researchers in usingmodels to explain users’ intention to use technology. While prior research have use the theory ofplanned in explaining variables of interest in psychology, this study attempts to test the explanatoryability of the TPB on the intention to use technology in an educational setting. By doing so, the paperhopes to obtain greater insights into the applicability of TPB to explain issues of educational interests.

Keywords User studies, Communication technologies, Students, Teachers, Behaviour, Singapore

Paper type Research paper

IntroductionFor many educational systems, the integration of technology has been recognized asone of the key drivers to the improvement of teaching and learning, leadinggovernments to launch major initiatives and made considerable capital investments tobuild and maintain support information communication technology (ICT)infrastructures in the schools. Acceptance of technology has been a topic that hasoccupied researchers for the last two decades. From the literature, researchers wereinterested in identifying the conditions or factors that influence technology adoptionand usage (Legris et al., 2003). Arising from this motivation, several models weredeveloped to help in predicting technology acceptance. Among these models, theTheory of Planned Behavior (TPB) (Ajzen, 1991) is a widely-used and validated model.

Theoretical frameworkTPB was proposed by Ajzen in 1991. An extension of Theory of Reasoned Action(Ajzen and Fishbein, 1980), the TPB has been used by researchers over the past

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20 years and shown to be able to predict a variety of intentions and behaviors.According to Ajzen (1991), a person’s action is determined by behavioral intentions,which in turn are influenced by an attitude toward the behavior and subjective norms.In addition to attitude toward the behavior and the subjective norm in the TPB,perceived behavioral control (PBC) can influence intention as well. PBC influences theindividual’s decision through behavioral intention.

Behavioral intentions are factors that capture how hard people are willing to try toperform a behavior (Azjen, 1991). In the TPB, behavioral intention is the mostinfluential predictor of behavior. This was supported by Armitage and Conner (2001),who examined empirical 185 studies that had been published up to the end of 1997. Theauthors found that the TPB accounted for 27 percent and 39 percent of the variance inbehavior and intention, respectively. Among the TPB constructs, intention was a betterpredictor of behavior. In the TPB, attitude toward the behavior is defined as one’spositive or negative feelings about performing a behavior (e.g. using technology). It isdetermined through an assessment of one’s beliefs regarding the consequences arisingfrom one’s behavior and an evaluation of the desirability of these consequences.

Attitude toward use (ATU) guides behavior and is defined as the way individualsrespond to and are disposed towards an object. This disposition may be negative orpositive. The success of any initiatives to implement technology in an educationalprogram depends strongly on the support and attitudes of teachers involved. It hasbeen suggested that if teachers believed or perceived computers not to be fulfillingtheir own or their students’ needs, they are less likely to introduce technology into theirteaching-learning process. In other words, attitudes toward computer use, whetherpositive or negative, are shaped by how teachers perceive the usefulness of technologyin the instructional and learning environment.

Subjective norm (SN) is defined as one’s perception of whether people important tothe individual think the behavior should be performed. The contribution of the opinionof any given referent (e.g. person) is weighted by the motivation that an individual hasto comply with the wishes of that referent (e.g. person). For example, a teacher may feelthe need to use technology because the mandate was given by the school management.Perceived behavioral control is defined as a person’s perception of how easy or difficultit would be to carry out a behavior (Ajzen, 1991). Subjective norm refers to a person’sperception that most people who are important to him or her think he should or shouldnot perform the behavior in question (Fishbein and Ajzen, 1975). In this study,subjective norm is the degree to which a person perceives the demands of the“importance” of others on that individual to use technology. Subjective norm wasproposed to have a direct effect on perceived usefulness. This point was stressed byVenkatesh and Davis (2000), who argued that when a co-worker thought that thesystem was useful, a person tended to have the same idea, through internalization.Within the school environment, Bellone and Czerniak (2001) reported students’ positiveopinions of the instructor increased in proportion to the use of the computers in theclassroom.

PBC refers to the perceived ease or difficulty of performing the behavior and theamount of control one has over the attainment of the goals from said behavior. Actualand perceived personal inadequacies and external obstacles can interfere with theability to perform a given behavior, and consequently with the perception of controlthat one has over the action and outcomes of the behavior. The TPB was introduced to

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be applied in situations where people may lack volitional control over the behavior inquestion (Ajzen, 1991). In the context of technology-based behaviors, PBC has beenfound to correlate well with perceived ease of use or difficulty related to a particulartechnology, which have been shown to be major factors predicting intention to use thattechnology (Compeau and Higgins, 1995). It was postulated that the easier a system isto use, the greater the belief that the system will support information needs.

This aim of this study is to examine the efficacy of the TPB to explain pre-serviceteachers’ intention to use technology. It is hypothesized that the three independentvariables in TPB (attitude toward use, subjective norm, and perceived behavioralcontrol) will exert significant influence on the behavioral intention to sue technology.Figure 1 shows the TPB.

MethodologyResearch participants and data collectionParticipation in this study was voluntary and 157 pre-service teachers who wereenrolled at the National Institute of Education (NIE) in Singapore agreed to take part inthis study. An invitation to participate in this study was made to students enrolled inthe four-year Bachelor of Arts (with Education) program. The participants in thisstudy form about 40 percent of the student population in this program. Among them,72.0 percent were female. The mean age of all participants was 22.4 (SD ¼ 3:30).Participants were briefed on the purpose of this study and informed that they coulddecline to participate in the study before or after they had completed the questionnaire.On average, each participant took less than 20 minutes to complete the questionnaire.

MeasuresA survey questionnaire comprising previously validated items was used. Participantswere asked to provide their demographic information and respond to 11 statements onthe six constructs in his study. They are: attitudes towards usage (ATU) (four items),subjective norm (SN) (two items), perceived behavioral control (three items), andbehavioral intention to use (BIU) (two items). Each statement was measured on afive-point Likert scale with 1 ¼ strongly disagree to 5 ¼ strongly agree. Table I showsthe items and the sources where the items were adapted.

Figure 1.TPB

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ResultsThe statistical analyses in this section include examining the descriptive statistics ofthe measurement items and assessed the reliability and validity of the measure used inthis study. This is followed by testing of the hypotheses by assessing the model fitusing various fit indices and evaluating the research model.

Descriptive statisticsThe mean values of all variables are above the midpoint of 3.00. The standarddeviations range from 0.66 to 0.75 and this indicates a narrow spread around the mean.The skew index ranges from 20.32 to 20.15 and kurtosis index ranges from 20.19 to0.54, which meets Kline’s (2005) recommendations for the purposes of structuralequation modeling.

Convergent validityFornell and Larcker (1981) proposed three procedures to assess for convergent validityof the measurement items:

(1) item reliability of each measure;

(2) composite reliability of each construct; and

(3) the average variance extracted.

The item reliability of an item was assessed by its factor loading onto the underlyingconstruct. Hair et al. (2006) recommended a factor loading of 7.0 to be acceptableindicative of validity at the item level. In this study, construct reliability was measuredusing Cronbach’s alpha, with a value of 0.70 or higher being recommended (Nunnallyand Bernstein, 1994). The third indicator of convergent validity, average varianceextracted, measures the overall amount of variance that is attributed to the construct inrelation to the amount of variance attributable to measurement error. Convergentvalidity is judged to be adequate when average variance extracted equals or exceeds

Construct Item

Attitudes toward usage (adapted fromCompeau and Higgins, 1995)

ATU1 Computers make work more interesting

ATU2 Working with computers is funATU3 I like using the computerATU4 I look forward to those aspects of my job that

require me to use computersSubjective norm (adapted from Ajzen, 1991;Davis et al., 1989)

SN1 People whose opinions I value will encourageme to use computers

SN2 People who are important to me will supportme to use computers

Perceived behavioral control (adapted fromDavis, 1989)

PBC1 My interaction with computers is clear andunderstandable

PBC2 I find it easy to get computers to do what Iwant it to do

PBC3 I find computers easy to useBehavioral intention to use (Davis et al., 1989) BIU1 I will use computers in future

BIU2 I plan to use the computer often

Table I.List of items and their

sources

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0.50, when the variance captured by the construct exceeds the variance due tomeasurement error. In Table II, all factor loadings, except PBC2 were high and met therecommended guidelines, indicating that the convergent validity for the proposedconstructs of the measurement model is adequate.

Discriminant validityDiscriminant validity is present when the variance shared between a construct and anyother construct in the model is less than the variance that construct shares with itsindicators (Fornell et al., 1982). Discriminant validity was assessed by comparing thesquare root of the average variance extracted for a given construct with thecorrelations between that construct and all other constructs. If the square roots of theAVEs are greater than the off-diagonal elements in the corresponding rows andcolumns exceed the correlations between a given construct and others in the model,this suggests that a construct is more strongly correlated with its indicators than withthe other constructs in the model. In Table III, the diagonal elements in the correlationmatrix have been replaced by the square roots of the average variance extracted.Discriminant validity appears satisfactory at the construct level in the case of allconstructs.

Test of the measurement modelThe research model in this study was tested using the structural equation modelapproach, using the computer software program AMOS 7.0. A variety of indices wasused in this study. These are absolute fit indices that measure how well the proposedmodel reproduces the observed data. In other word, the fit indices evaluate the overall

Latent variable/item

Standardized factor loading(. 0.70)a

Average variance extractedb

(. 0.50)aCronbach alpha (.

0.70)a

Attitude toward usage 0.69 0.89ATU1 0.85ATU2 0.88ATU3 0.82ATU4 0.76

Subjective norm 0.84 0.91SN1 0.88SN2 0.95

Perceived behavioral control 0.65 0.83PBC1 0.83PBC2 0.68PBC3 0.90

Behavioral intention to use 0.88 0.93BI1 0.92BI2 0.96

Notes: aIndicates an acceptable level of reliability or validity; bAVE: average variance extracted. Thisis computed by adding the squared factor loadings divided by number of factors of the underlyingconstruct

Table II.Results of themeasurement model

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discrepancy between the implied and observed covariance matrices. They include thex 2 statistic, and the standardized root mean residual (SRMR). The next category of fitindices, parsimonious indices is similar to the absolute fit indices except that it takesinto account the model’s complexity. The root mean square error of approximation(RMSEA) is widely used for this purpose. Finally, the incremental fit indices assesshow well a specified model fit relative to an alternative baseline model. Examples ofincremental fit indices are the comparative fit index (CFI) and Tucker-Lewis Index(TLI). Results of the model test revealed an acceptable fit. Except for the x 2, all valuessatisfied the recommended level of acceptable fit (x2 ¼ 65:147, p , 0:004;x 2=df ¼ 1:714; TLI ¼ 0:967; CFI ¼ 0:977; RMSEA ¼ 0:068; SRMR ¼ 0:038).

Test of the structural modelFigure 2 shows the path coefficients of the TPB model. From the figure, the paths fromATU (b ¼ 0:52) and SN (b ¼ 0:16) to BIU are significant at p , 0:05. The path fromPBC (b ¼ 0:02) is not significant. A total of 39.2 percent of the variance of theendogenous variable, BIU was explained by the three exogenous variables (ATU, SNand PBC).

Discussion and conclusionThis paper aims to examine the key predictors underlying pre-service teachers’intention to use technology. Using the TPB as a research model, the results of thisstudy showed that attitude toward computer use and subjective norm have significanteffect on behavioral intention to use technology, while PBC does not. Overall, the three

Figure 2.Results from the structural

model test

Construct ATU SN PBC BIU

ATU (0.75)SN 0.42 * (0.91)PBC 0.56 * 0.38 * (0.79)BIU 0.61 * 0.39 * 0.38 * (0.87)

Notes: *p , 0:01; diagonal in parentheses: square root of average variance extracted from observedvariables (items); off-diagonal: correlations between constructs

Table III.Discriminant validity

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variables contributed about 40 percent of the variance in behavioral intention to use.This suggests that the TPB is fairly efficient as a model to predict the behavioralintention to use technology among pre-service teachers in Singapore. It is possible thatwhen pre-service teachers have positive attitude, they would be inclined to usetechnology. This finding supports current research, which found a close relationshipbetween a positive attitude and technology use of technology (e.g. Teo, 2006, 2008,2009). Also, when pre-service teachers think that others whose opinions they(pre-service teachers) value, they are motivated to use technology.

Of the exogenous variables, behavioral intention was not significantly predicted byPBC, as hypothesized in the TPB. It was possible that PBC alone was not enough tomotivate pre-service teachers to use technology. For example, pre-service teachers maynot use technology simply because the conditions were favorable (e.g., technicalsupport was provided), a facet of perceived behavioral control. However, the results inthis study showed that perceived behavioral control is significantly correlated withattitude and subjective norm. An examination of the interplay of the three exogenousvariables (ATU, SN, and PBC) on behavioral intention to use is warranted to acquiregreater insights into the TPB’s propensity to explain technology usage intention.

This study is limited on several grounds. First, the data were collected throughself-reports and a single method of data collection. This may have lead to the commonmethod variance, a situation where the associations between variables tend to becomeinflated. To address the issue of common method variance arising from a singlemethod of data collection, future research could employ the multi-trait multi-method(MTMM). Second, the sample in this study employed pre-service teachers asparticipants. The fact that the participants are teachers-in-training may haveinfluenced their responses. For example, the items in the questionnaire may have beeninterpreted with a professional frame of mind. As such, the findings in this study maybe limited in terms of it generalizability to other populations. Future research mayinclude additional variables to assess their impact on the TPB to explain the behavioralintention use technology. In addition, attempts could be made to unpack and clarify therole and properties of perceived behavioral control as a variable in the TPB.

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Corresponding authorTimothy Teo can be contacted at: timothy.teo@nie.edu.sg

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