Teachers and technology: development of an extended theory of … and... · 2016. 9. 26. ·...

1 23

Educational Technology Researchand DevelopmentA bi-monthly publication ofthe Association for EducationalCommunications & Technology ISSN 1042-1629 Education Tech Research DevDOI 10.1007/s11423-016-9446-5

Teachers and technology: development ofan extended theory of planned behavior

Timothy Teo, Mingming Zhou & JanNoyes

1 23

Your article is protected by copyright and allrights are held exclusively by Associationfor Educational Communications andTechnology. This e-offprint is for personaluse only and shall not be self-archived inelectronic repositories. If you wish to self-archive your article, please use the acceptedmanuscript version for posting on your ownwebsite. You may further deposit the acceptedmanuscript version in any repository,provided it is only made publicly available 12months after official publication or later andprovided acknowledgement is given to theoriginal source of publication and a link isinserted to the published article on Springer'swebsite. The link must be accompanied bythe following text: "The final publication isavailable at link.springer.com”.

RESEARCH ARTICLE

Teachers and technology: development of an extendedtheory of planned behavior

Timothy Teo1 • Mingming Zhou1 • Jan Noyes2

! Association for Educational Communications and Technology 2016

Abstract This study tests the validity of an extended theory of planned behaviour (TPB) toexplain teachers’ intention to use technology for teaching and learning. Five hundred andninety two participants completed a survey questionnaire measuring their responses toeight constructs which form an extended TPB. Using structural equation modelling, theresults showed that the constructs in the extended TPB were significant in explainingteachers’ intention to use technology in their work. Among the constructs in the researchmodel, attitude towards computer use had the largest positive influence on technologyusage intention, followed by perceived behavioral control. However, subjective norm had anegative impact on intention. The inclusion of the antecedent variables had alsostrengthened the ability of the extended TPB model to explain intention. This studycontributes to the growing discussions in applying psychological theories to explainbehavioral intention in educational contexts.

Keywords Theory of planned behavior ! Technology in education ! Teachers !Technology usage intention

Teachers are key to the success of integrating technology into teaching and learning (Zhaoet al. 2001). However, users’ intentions to use technology are influenced by various factorsfrom personal characteristics to work environments. More specifically, these factorsinclude attitudes toward computer use (Rainbow and Sadler-Smith 2003), perceivedcomplexity (Teo 2009), subjective norms (Teo and Tan 2011) and facilitating conditions(Teo 2011, 2014). In attempting to measure and to understand the factors that affect aperson’s intention to use technology, various models and theories have been developed.

& Timothy [email protected]

1 Faculty of Education, University of Macau, Avenida da Universidade, Taipa, Macau SAR, China

2 School of Experimental Psychology, University of Bristol, 12A Priory Road, Bristol BS8 1TU, UK

123

Education Tech Research DevDOI 10.1007/s11423-016-9446-5

Author's personal copy

Among these, the theory of planned behavior (TPB) has received wide support as a robustand parsimonious model to explain individuals’ intentions to engage with technology(George 2004; Ndubisi 2006).

Theory of planned behavior

The TPB was proposed by Ajzen (1985) as an extension of the theory of reasoned action(TRA; Fishbein and Ajzen 1975). In brief, the TPB states that if individuals perceive aplanned behavior as positive, they will be more motivated to perform the behavior. Byadding perceived behavioral control as the third construct in the TRA model, the TPBexplains the intention to perform the behavior. Intention is concerned with how hard peopleare willing to try to perform a behavior and it has been shown to be influenced by attitude,subjective norms and perceived control of behavior (Ajzen 1991).

The TPB has been widely applied in research where the main interest was in under-standing the role of intention in changing people’s behavior (Ajzen and Manstead 2007). Intheir meta-analysis of TPB, Sheppard et al. (1988) found an average correlation of .53between intention and behavior. In a further meta-analysis, Downs and Hausenblas (2005)observed that the three constructs (attitude, subjective norms and perceived behavioralcontrol) were significant predictors of intention. Hagger et al. (2002) found these threeconstructs together explained 45 % of the variance for intention, with attitude and per-ceived behavioral control being stronger predictors of intention. This fitted with Armitageand Conner’s (2001) meta-analysis of 185 studies, where attitudes, subjective norms, andperceived behavioral control accounted for 24, 12, and 18 % of the variance in intention,respectively.

Besides TPB, several other models have also postulated that behavior is predicated byintention, including the Technology Acceptance Model (TAM; Davis et al. 1989) and theUnified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al. 2003).The TAM is widely accepted as a framework to understand the determinants of users’intention to adopt a given type of technology. It uses perceived usefulness, instead ofsubjective norm, as the second determinant of behavioral intention. Mathieson (1991)compared the TAM and TPB when predicting university students’ intention to use aninformation system. Both models were found to be effective, but TPB provided morespecific guidance to developers. Compared to TAM (with a focus on perceived usefulnessand perceived ease of use), the TPB with its emphasis on intention has the capability toprovide educators and researchers with a more comprehensive view of belief systems thatcan help with issues relating to the use of technology (Smarkola 2008; Teo et al. 2011).

In contrast, the UTAUT is a synthesis of eight distinct theoretical models based onsociological and psychological theories explaining human behavior. It posits four ante-cedents of behavioral intention, including performance expectancy, effort expectancy,social influence, and facilitating conditions. Although UTAUT has been found to predictsuccessfully a large proportion of variance in users’ IT intentions and behavior acrosscontexts and cultures (Gruzd et al. 2012; Im et al. 2011; Wu et al. 2013) the multifacetedconceptualization of the model has been previously criticized as overly complex andunlikely to measure individual variables (van Raaij and Schepers 2008). This was evidentin a recent review by Williams et al. (2011), who observed that the majority of researchpublications with citations of UTAUT did not really use the model for their actualinvestigations, rather, they simply briefly described the theory within the broad discussion

T. Teo et al.

123


on the evolution of theories in this field. Further, the reliance on moderating variables toobtain the high correlations reported in Venkatesh et al. (2003) adds to the complicatednature of this particular framework (van Raaij and Schepers 2008). Bagozzi (2007) went onto criticize the model, describing it as ‘‘a patchwork of many largely unintegrated anduncoordinated abridgements’’ (p. 252). This is one of the reasons why we chose the TPB inthis study as the basic framework to investigate teachers’ intentions concerning the use oftechnology—it has a relatively simple model structure, whilst maintaining the core factorsaffecting intention.

The TPB has been applied in many commercial settings: for example, predicting the useof technology-based self service outlets (Bobbitt and Dabholkar 2001); the adoption ofhousehold technologies (Brown and Venkatesh 2005), telemedicine by healthcare pro-fessionals (Chau and Hu 2002) and web-based e-commerce amongst small businesses(Riemenschneider and McKinney 2001); Internet purchasing (George 2004) and banking(Shih and Fang 2004). In contrast, relatively few studies in the educational arena haveemployed the TPB to understand teachers’ intention to use technology. Examples includeLee et al. (2010) study wherein they surveyed 137 middle and high school teachers inKorea and found that their decisions about using computers for teaching purposes wereinfluenced strongly by their positive attitudes, moderately by the opinions of significantothers, and weakly by their perceived ability to do so. Similarly, Lumpe et al. (1998) alsofound science teachers’ attitudes, subjective norms and perceived behavioral control allsignificantly predicted their intention to implement technology in classroom. Yet Sugaret al. (2004) found teachers’ individual attitudes towards technology adoption to be theonly factor that affected their decision to use that technology. In contrast, Shiue (2007)analyzed the factors that influenced intention to use technology among 242 secondaryscience teachers and found that, contrary to other studies, the TPB did not adequatelyexplain the multifaceted influences on intention. Using semi-structured interviews,Smarkola (2008) examined contributing factors to teachers’ intentions to use computerapplications in their lessons and showed the efficacy of using TPB for predicting intentionto use computers. However, data from Smarkola’s study was collected with a small sampleof 19 teachers and this may limit the generalizability of the findings to other educationalusers.

In sum, the above inconsistent findings raise the possibility that (1) the TPB may not beefficient as a model to explain in-service teachers’ intention to use technology as it is inother contexts, (2) it is necessary to conduct more empirical studies with teacher samples toreplicate, review or interrogate past conclusions, and (3) there is a need to expand thetheory by considering other relevant factors in order to account for the multifaceted natureof teachers’ intention to use technology.

Extended theory of planned behavior model

Despite the strong empirical support for the TPB, there remains a proportion of unac-counted variance. Ajzen (1991) argued for the inclusion of additional predictors as longas there is a strong theoretical justification and they capture a significant portion ofunique variance in intentions or behavior. There may be specific drivers and inhibitors ofintention to use technology within the educational context. Thus, rather than assumingthat the influences affecting teachers’ intention to use technology can be applied toeveryone in that broad category, it would be useful to investigate variables perceived to

Teachers and technology: development of an extended theory…

123


have an impact on teachers’ decision making. Based on past literature, four additionalvariables were identified that were directly related to the core constructs in the originalmodel: perceived ease of use, perceived usefulness, management expectation, andtechnical support.

Perceived ease of use

Perceived ease of use is operationally defined as the degree to which a user believes thatusing technology is relatively free of effort (Teo and Zhou in press). Past research hasshown that when the use of technology is perceived to be relatively easy to use (Venkateshet al. 2003) individuals are likely to develop a positive attitude toward its use (AlQudah2014; Pynoo and van Braak 2014).

Perceived usefulness

Perceived usefulness refers to the extent to which a person believes that using technologywould enhance his or her job productivity (Davis et al. 1989). It acts as an antecedent toattitudes toward technology use, which indicates that users typically develop a positiveattitude toward computer use when they perceive technology to be useful (Davis 1989).This relationship has been well supported by recent studies showing that perceived use-fulness influences attitudes and further intention to use technology (Teo 2011; Pynoo andvan Braak 2014; Wong et al. 2013).

Management expectation

Researchers have repeatedly argued that subjective norms do not capture the impact ofsocial influence on behavior (Terry and Hogg 1996). For a teacher, the social influencegenerally originates from the school management, colleagues, parents, and students(Sugar et al. 2004). Among these, meeting the expectations of school leaders and theministry has become one of the important factors teachers consider when makingdecisions in using technology. In our context, as the policy in Singapore increasinglydirects teachers to make greater use of technology in their classroom practices (Ministryof Education [MOE] Singapore, 2008), it is assumed that this would enhance theirintention to use technology. Thus, expectations from the Ministry/school managementwere included in our extended TRB model as an antecedent of subjective norm to testthis assumption.

Technical support

Although management influence may encourage or inhibit teachers’ intentions to usetechnology, researchers have noted that the level of technical support can also influence thedecision to use technology. Ragu-Nathan et al. (2008) showed, for example, that technicalsupport provision reduces technostress. This is especially applicable for school teachers,who usually develop an apprehension of using technology with technical complexity(Hsiao 2011). However, Wang et al. (2011) noted that availability of support was not asignificant factor which influenced older adults’ technology intention, although it wasdeemed very important. Hence, it was decided to include this factor in the extended TPBmodel to examine further its contribution to behavioral intention.

T. Teo et al.

123


Aims and contributions of this study

The aims of this study are twofold: (1) to develop and assess the validity of an extendedTPB in explaining teachers’ intention to use technology and, (2) to determine the factors inthe extended model that have significant influences on this intention. This study willcontribute to the continuing work on the TPB as a framework to explain the intentionbehavior within educational contexts. In particular, it will provide insights into the directand indirect influences that affect teachers’ usage intentions in the workplace. Finally, byextending the original TPB model, empirical evidence will be obtained to decide if theextended model is valid and parsimonious in explaining teachers’ intentions to use tech-nology. Hence, the study was guided by the following research questions:

1. To what extent is the extended TPB a valid model in explaining teachers’ intentions touse technology?

2. Which factors in the extended TPB model are significant in explaining teachers’intentions to use technology?

3. Which factors act as mediators that influence teachers’ intentions to use technology?

Guided by the above research questions, nine hypotheses were developed to examinethe validity of the proposed extended TPB model.

Research model and hypotheses

Behavioral intention (BI)

The TPB works on the premise that intention has a close link to actual behavior (e.g., Huet al. 2003; Kiraz and Ozdemir 2006; Ma et al. 2005). In this study, data on intention iscollected rather than actual usage of technology. There are a number of reasons for this: (1)access to information on the use of technology in schools is sensitive; (2) asking teachers toreport their actual use (e.g., number of hours or number of lessons in the computer lab)may result in socially-desirable data, that is, a situation where participants respond in whatthey perceive is a desired way by reporting a higher level of technology use; (3) intentionhas been viewed as being more progressive and predictive compared to actual use, which ismore static and retrospective (e.g., Roca et al. 2006; Spiller et al. 2007), and (4) Intention isan indication of an individual’s readiness to perform a given behavior, and is considered tobe the immediate antecedent of behavior and there is a strong correlation between intentionand actual usage (Ajzen 2005; Fishbein and Ajzen 1975).

Attitudes toward use (ATU)

Attitude represents a person’s disposition toward performing a certain behavior (Daviset al. 1989). A teacher’s attitude toward technology use is central to successful use oftechnology in education (Huang and Liaw 2005). Based on the literature, attitude has beenfound to be significantly influenced by perceived usefulness and ease of use (Teo 2009,2011; Pynoo and van Braak 2014). Using path analysis, Teo et al. (2008) found that thesetwo factors significantly influenced pre-service teachers’ attitude toward computer use(b = 0.46, p\ 0.01 and b = 0.20, p\ .01 respectively). In this study, the followinghypotheses were proposed:


123


H1 Attitude toward technology use has a significant and positive influence on teachers’usage intention.

H2 Perceived usefulness has a significant and positive influence on teachers’ attitudetoward technology use.

H3 Perceived ease of use has a significant and positive influence on teachers’ attitudetoward technology use.

Subjective norm (SN)

Fishbein and Ajzen (1975) referred to subjective norm as the perceived pressures on aperson to perform a given behavior and their motivation to comply with these pressures. Inthe case of technology use, subjective norm refers to the degree to which a person per-ceives the demands of referent others (e.g. the school management, colleagues, parents,and students [Sugar et al. 2004]) to use a particular technology. In other words, if a teacherbelieves that the use of technology is a management expectation, their intention to use it islikely to be strong. Besides having an influence on intention, subjective norm is closelyrelated to perceived usefulness. Venkatesh and Davis (2000) found that when a person’sco-workers perceived a system to be useful, that individual tended to share the same view.A series of recent studies (e.g., Azam and Lubna 2013; Hopp 2013; Hu et al. 2013; Teo andZhou 2014; Zolait 2014) have provided empirical support for this. Therefore, the followinghypotheses were proposed:

H4 Subjective norm has a significant and positive influence on teachers’ intention to usetechnology.

H5 Management expectation has a significant and positive influence on teachers’ sub-jective norm.

H6 Perceived usefulness has a significant and positive influence on teachers’ subjectivenorm.

Perceived behavioral control (PBC)

Perceived behavioral control (PBC) is defined as a person’s perceived ease or difficulty inperforming a task given the resources and opportunities that are available to them (Ajzen1991). In this study, PBC refers to the teachers’ perception of the extent to which tech-nology is difficult to use or technological complex, akin to perceived ease of use (Ajzen2002). Generally, technological complexity penalizes a person’s perceived ease of use oftechnology and places a limit on his or her information processing capacity. Thus, a controlbelief (such as PBC) is a perception of the availability of skills, resources, and opportu-nities necessary for performing the behavior under discussion, such as perceived ease ofuse and technical support (Teo and van Schaik 2009). Hence, the following hypotheseswere proposed:

H7 Perceived behavioral control has a significant and positive influence on teachers’intention to use technology.

H8 Perceived ease of use has a significant and positive influence on teachers’ perceivedbehavioral control.

T. Teo et al.

123


H9 Technical support has a significant and positive influence on teachers’ perceivedbehavioral control.

Method

Participants

Participation in this study was voluntary and 592 teachers from 31 primary and secondaryschools in Singapore were recruited. They responded to an invitation issued by the authorsand those who agreed to take part in this study were given a website address to access theonline questionnaire. Of these participants, 452 (76.4 %) were females and the mean age ofall participants was 35.03 years (SD = 8.83). On average, participants had 9.26 years(SD = 8.29) of teaching service and had used the computer for 14.79 years (SD = 5.03).A large majority of the participants had at least an undergraduate degree (75.9 %) and allparticipants were briefed on the purpose of this study through the online instructions andinformed of their rights to withdraw their participation at any time. No reward was givenand, on average, participants completed the questionnaire in 20 min or less.

Measures

A survey questionnaire was developed by adapting items that had been validated in pre-vious studies (Ajzen 1991; Burnkrant and Page 1988; Compeau and Higgins 1995; Daviset al. 1989; Taylor and Todd 1995; Thompson et al. 1991). A total of 29 items were used tomeasure eight constructs in the extended TPB model: intention, attitudes toward use,subjective norm, perceived behavioral control, perceived usefulness, perceived ease of use,management expectation, and technical support. From previous studies, these items havebeen found to be reliable, with reliability coefficients ranging from .70 to .95. Participantswere asked to provide demographic information and respond to the 29 items. Each itemwas measured on a 7-point Likert-type scale from ‘1 = strongly disagree’ to ‘7 = stronglyagree’. The items are shown in the appendix.

Data analysis

Data in the study were analysed using structural equation modelling (SEM). This mod-elling was employed in this study for its ability to analyse the relationships between thelatent and observed variables, and to estimate random errors in the observed variablesdirectly, giving rise to more precise measurements of the items and constructs in thesurvey. In this study, we wish to test the influence of each construct in the TPB model.Each construct was measured by several items (observed variables). Because of the shiftfrom the items to the constructs, SEM has an added advantage over traditional dataanalysis techniques in its ability to model the relationships among constructs (latentvariables) and becoming more aligned with how hypotheses are expressed conceptuallyand statistically (Hoyle 2011).

Using the two-step approach to SEM (Schumacker and Lomax 2010) the analysis beganby estimating the measurement model (confirmatory factor analysis model) for all latentvariables, which describes how well the observed indicators (survey items) measure theunobserved (latent) constructs. In the second step, the structural part of the SEM is


123


estimated. This part specifies the relationships among the exogenous and endogenous latentvariables. To obtain reliable results in SEM, researchers recommend a sample size of100–150 cases (e.g., Kline 2010). It is also recommended that the Hoelter’s critical N(Hoelter 1983), which refers to the sample size for which the hypothesis for the proposedresearch model is correct at the .05 level of significance, is consulted to assess the suit-ability of a sample size in a study. The Hoelter’s critical N for the model in this study is226 and, given that the sample size of this study is 592, SEM was regarded as anappropriate technique for analysing the data.

Results

Descriptive statistics

The descriptive statistics for the 29 items were computed and examined for data normality.The mean values of all items ranged from 4.57 to 5.98, above the midpoint of 4.00. Thestandard deviations ranged from 0.97 to 1.41. Assessment of univariate normality in thedata was carried out by examining skewness and kurtosis indices and these ranged from-1.72 to -0.34 and -0.47 to 4.57, respectively. Following Kline’s (2010) recommen-dation that the skewness and kurtosis indices should not be more than |3| and |10|respectively, the data for this study were regarded as normal and suitable for furtheranalyses.

Test of the measurement model

In testing the measurement model, a confirmatory factor analysis was conducted withAMOS 21.0 using the maximum likelihood estimation (MLE) procedure. Although theMLE is a popular and robust procedure for use in SEM (Schumacker and Lomax 2010),this procedure assumes multivariate normality of the observed variables. On this account,the data in this study were examined using the Mardia’s normalized multivariate kurtosisvalue (Mardia 1970). The Mardia’s coefficient for the data in this study was 605.89, whichis lower than the value of 899 computed based on the formula, p(p ? 2) where p equals thenumber of observed variables in the model (Raykov and Marcoulides 2008). On this basis,multivariate normality of the data in this study was assumed.

The overall model fit was assessed using the v2 test and, because it is highly sensitive tosample size, the ratio of v2 to its degree of freedom was also computed (v2/df), with a valueof not more than 3.0 being indicative of an acceptable fit between the hypothetical modeland the sample data (Carmines and Mclver 1981). In addition, other fit indices were alsoconsidered: the Tucker–Lewis Index (TLI); the Comparative Fit Index (CFI); the rootmean square error of approximation (RMSEA); the standardized root mean square residual(SRMR). Hu and Bentler (1999) proposed that TLI and CFI statistics greater than 0.95 andRMSEA and SRMR values less than 0.06 and 0.08, respectively, represent a good modelfit. An initial test revealed a lack of acceptable fit for the research model (v2 = 1196.35;v2/df = 3.37; TL1 = 0.96; CFI = 0.96; RMSEA = 0.063 (0.059, 0.067); SRMR =0.078). On examination of the modification indices, possible relationships were found forTS ? ATU and ME ? UI. Thus, when users perceive that they are supported, they maydevelop a positive attitude toward use. It was also plausible that management expectationscould have a direct influence on usage intentions without the former being mediated by

T. Teo et al.

123


other variables. In sum, when users know that their superiors expect them to use tech-nology, they would use it whether they thought it was useful or easy to use, and whetherthey felt happy using it or not. On this basis, the model was re-specified and found topossess an acceptable fit (v2 = 1019.19; v2/df = 2.96; TL1 = 0.96; CFI = 0.97;RMSEA = 0.058 (0.054, 0.062); SRMR = 0.038). Figure 2 shows the re-specifiedextended TPB model.

The reliability and validity of the items purported to measure each variable weremeasured using the composite reliability (CR) (Raykov 1997), and the average varianceextracted (AVE) (Fornell and Larcker 1981). Cronbach’s alpha was not reported because itis prone to violate key assumptions when used with a multidimensional and multi-itemscale such as the one used in this study (Teo and Fan 2013).

In assessing the validity of the items, the direction, magnitude, and statistical significantof each parameter (t-value) were examined (Schumacker and Lomax 2010). An itemexplains its latent variable well if its standardized estimate is greater than 0.50 (Hair et al.2010). Using a more conservative indicator of validity, the AVE for each construct, wascomputed. This measures the amount of variance captured by the construct in relation tothe amount of variance attributable to measurement error. Both the CR and AVE arejudged to be adequate when they equal or exceed 0.50 (i.e., when the amount of variancecaptured by the construct exceeds the variance due to measurement error) (Fornell andLarcker 1981). Table 1 shows the results of the confirmatory factor analysis for themeasurement model. As shown, the t-values, standardized estimates, CR, and AVE of allitems and variables met the recommended guidelines.

Convergent validity is judged to be adequate when average variance extracted equals orexceeds 0.50 (Nunnally and Bernstein 1994). Discriminant validity was assessed bycomparing the square root of the AVE for a given construct with the correlations betweenthat construct and all other constructs. If the square roots of the AVEs are greater than theoff-diagonal elements in the corresponding rows and columns in a correlation matrix,which suggests that a construct is more strongly correlated with its indicators than with theother constructs in the model, discriminant validity is achieved (Fornell et al. 1982). In thisstudy, the diagonal elements in the correlation matrix were greater than the off-diagonalelements, indicating that discriminant validity was satisfactory at the construct level (seeTable 2). This table also shows the values of the AVE to be adequate representations ofconvergent validity.

Test of the structural model

A test of the structural model revealed a marginal fit (v2 = 1246.63, v2/df = 3.50;TL1 = 0.95; CFI = 0.96; RMSEA = 0.065 [0.061, 0.069]; SRMR = 0.071, see Fig. 1).An inspection of the modification indices suggested a re-specification of the model toinclude two additional paths (H10: TS ? ATU and H11: ME ? UI) and one correlatederror (from PBC to ATU) to improve the fit (see Fig. 2). This led to a significantimprovement in the model fit (v2 = 1084.36, v2/df = 3.07; TL1 = 0.96; CFI = 0.97;RMSEA = 0.059 [0.055, 0.063]; SRMR = 0.047). With the model re-specification, twomore hypotheses were tested (H10 and H11), bringing the total number of hypotheses to 11.

Tests of hypotheses

The results showed that nine out of 11 hypotheses were supported by the data. Allhypotheses pertaining to the original TPB, except for H4 (Subjective norm has a significant


123


and positive influence on teachers’ intention to use technology), were supported. Thoserelating to the external variables, except for H6 (Perceived usefulness has a significant andpositive influence on teachers’ subjective norm), were also supported. Four endogenousvariables (UI, ATU, SN, and PBC) were tested in the research model. Overall, the variancein UI was highest with an R2 = 0.717. This means that together, ATU, SN, and PBCaccounted for 71.7 % of the variance in UI. The variance of each of the variables in theTPB (ATU, SN, and PBC) was explained by their antecedents at amounts of 59.2, 66.2, and33.1 %, respectively. A summary of the hypotheses testing results is shown in Table 3.

Table 1 Results of the confirmatory factor analysis for the measurement model

Item UE t value* SE CRb AVEc

Usage intention (UI) UI1 1.00 –a .94 .96 .94

UI2 .97 48.72 .96

UI3 .95 44.41 .94

Attitude towards use (ATU) ATU1 1.00 –a .74 .95 .88

ATU2 1.03 26.79 .82

ATU3 1.19 25.42 .97

ATU4 1.20 24.98 .96

ATU5 1.19 25.23 .97

Subjective norm (SN) SN1 1.00 –a .96 .91 .91

SN2 .936 18.34 .87

Perceived behavioral control (PBC) PBC1 1.00 –a .94 .93 .90

PBC2 1.02 40.67 .93

PBC3 .968 31.80 .85

Perceived usefulness (PU) PU1 1.00 –a .84 .96 .91

PU2 1.27 34.50 .97

PU3 1.26 32.07 .93

PU4 1.21 30.87 .92

PU5 1.26 30.32 .92

Perceived ease of use (PEU) PEU1 1.00 –a .95 .98 .95

PEU2 .96 48.54 .94

PEU3 1.03 52.17 .96

PEU4 1.03 53.67 .96

PEU5 .99 51.18 .95

Management expectation (ME) ME1 1.00 –a .83 .79 .77

ME2 .86 17.56 .79

Technical support (TS) TS1 1.00 –a .87 .93 .85

TS2 .95 32.34 .94

TS3 1.02 31.04 .90

TS4 .79 22.37 .77

UE unstandardized estimate, SE standardised estimate

* p\ .01a This value was fixed at 1.00 for model identification purposesb CR = (

Pk)2/(

Pk)2 ? (

P(1-k2))

c VE = (P

k2)/(P

k2) ? (P

(1-k2))

T. Teo et al.

123


Discussion

This study examined the extent to which an extended TPB is a valid model to explainteachers’ intention to use technology. Generally, the data provided empirical support forthe eight variables included in the extended model, explaining a great amount of variance

Fig. 1 Research model of an extended theory of planned behavior (UI usage intention, ATU attitude; SNsubjective norm, PBC perceived behavioural control, PU perceived usefulness, PEU perceived ease of use,ME management expectation, TS technical support)

Table 2 Discriminant validity for the measurement model

Construct UI ATU SN PBC PU PEU ME TS

UI (.97)

ATU .75** (.94)

SN .20** .22** (.95)

PBC .64** .71** .19** (.95)

PU .64** .63** .20** .56** (.95)

PEU .59** .67** .11** .75** .61** (.97)

ME .51** .44** .45** .40** .44** .27** (.88)

TS .51** .52** .14** .51** .43** .42** .42** (.92)

Diagonal in parentheses: square root of average variance extracted from observed variables (items); off-diagonal: correlations between constructs

UI usage intention, ATU Attitude, SN subjective norm, PBC perceived behavioural control, PU perceivedusefulness, PEU perceived ease of use, ME management expectation, TS technical support

** p\ .01


123


in teachers’ intention to use technology. Teachers’ attitudes as well as their perceivedbehavioral control plays an important role in explaining their intention to use technology(H1 and H7). This finding was supported by previous studies in predicting teachers’intention to use different technologies (Ma et al. 2005; Sadaf et al. 2012; Smarkola 2008;Teo 2009, 2010). However, the negative path between subjective norm and intention wasnot supported (H4). Some researchers (e.g., Davis et al. 1989; Mathieson 1991; Sugar et al.

Fig. 2 Re-specifiedresearch model of anextended theory of plannedbehavior (UI usageintention; ATU attitude, SNsubjective norm, PBCperceived behaviouralcontrol, PU perceivedusefulness, PEU perceivedease of use, MEmanagement expectation,TS technical support)

Table 3 Hypothesis testing results

Hypotheses Path Path coefficientb t-value Results

H1 ATU ? UI .52** 10.90 Supported

H2 PU ? ATU .24** 6.50 Supported

H3 PEU ? ATU .45** 11.30 Supported

H4 SN ? UI -.16* -3.76 Not supported

H5 ME ? SN .66** 7.00 Supported

H6 PU ? SN -.18* -2.76 Not supported

H7 PBC ? UI .26** 8.38 Supported

H8 PEU ? PBC .67** 20.99 Supported

H9 TS ? PBC .24** 6.98 Supported

H10a TS ? ATU .43** 6.50 Supported

H11a ME ? UI .14* 3.32 Supported

UI usage intention, ATU attitude, SN subjective norm, PBC perceived behavioural control, PU perceivedusefulness, PEU perceived ease of use, ME management expectation, TS technical support

* p\ .05; ** p\ .01a Hypothesis added into the re-specified modelb These are standardised paths

T. Teo et al.

123


2004) have found no significant effect of subjective norm on intention, whereas others(e.g., Huang et al. 2011; Taylor and Todd 1995; Trafimow and Finlay 1996) have shown apositive direct effect. The negative effect in this study suggested that the relationshipbetween these two constructs is complex.

Researchers have explained the complicated predictive power of subjective norm inseveral ways. Hartwick and Barki’s (1994) contingency perspective stated that subjectivenorm only had a significant effect on intention when system use was perceived to bemandatory (see Venkatesh and Davis 2000). Another perspective on the missing rela-tionship between subjective norm and intention was based on teaching experience. It waspossible that experienced teachers did not have to rely on institutional mandate (e.g.,school principal or department head) to decide whether or not to use technology (Robertand Henderson 2000; Teo 2011). Other researchers have noted that people who were moreinfluenced by attitude were less likely to be influenced than people who were controlled bynormative influences (Sheeran et al. 1999; Trafimow and Finlay 1996). Hence, subjectivenorm would be weaker with data samples containing more ‘attitudinal’ than ‘normative’participants (Lee et al. 2009).

The antecedents of subjective norm are important in shaping influences on intention. Inthe model, the two antecedents could have affected the subjective norm in different ways.Management expectation, for example, could represent a top-down influence on behavior,whereas perceived usefulness is likely to be more aligned with personal aspects. As aleading nation in Asia and the world, Singapore has made great efforts to develop anefficient education system, for example, the recent masterplan for ICT in education(2009–2014) aimed at enriching and transforming the learning environments in school(Ministry of Education (MOE) Singapore 2008). In order to fulfill their job requirementsand in line with their beliefs of the management’s expectations, the teachers in this studymay have exhibited a higher level of technology usage intention. This was confirmed bythe positive path from management expectation to subjective norm (H5) and intention(H11) in the model.

On the other hand, the study showed a negative link between perceived usefulness andsubjective norm (H6). This suggested that even if teachers were demanded to use tech-nology, for various reasons, they might not perceive the use of technology to be useful.This relationship was also reflected in the study by Salleh (2003) with Singaporean primaryschool teachers that the teachers did not behave in a way consistent with what they wereexpected to do, especially when technology integration has to compete for time in analready tight curriculum. In this situation, despite the required use of technology, teachers’perceived usefulness of technology tended to be low, perhaps due to unresolved issuesbetween policy stipulation and its translation into practice.

The inclusion of several antecedents had strengthened the ability of the extended TPBmodel to explain intention. All the antecedent variables had indirect influences on tech-nology usage intention. Three antecedents (PU, PEU, TS) were found to predict attitudestoward using technology, resulting in the largest portion of behavioral intention beingexplained. When teachers believed the use of technology to be useful (H2), found thetechnology easy to use (H3), and perceived to have adequate technical support (H10), theywere more likely to develop positive attitudes toward using it (e.g., Porter and Donthu2006; Teo 2011). In turn, attitude had strengthened their intention for technology use.Further, when teachers believed adequate technical support to be available (H9), they hadcontrol over their behavior in terms of their interaction with technology, thus strengtheningtheir technology usage intention (Teo 2011).


123


Implications

Overall, these findings made several theoretical contributions. First, the original TPBmodel has been extended by adding (a) perceived ease of use and perceived usefulnessabout technology as two belief variables and (b) management expectation and technicalsupport as two facilitating environmental factors. Second, the addition of perceived use-fulness and management expectation in the model allowed us to discover how subjectivenorm could be differentiated in predicting teachers’ intentions. This offered an alternativeperspective in explaining the inconsistent results of subjective norm and intention. Third,this study provided a fuller picture of the factors which had influenced teachers’ intentionsto use technology. As noted by Chen (2010), the process teachers go through in adoptingtechnology is multidimensional, and it is important to delve into their belief systems andvalues in order to understand better their decision-making.

This study could also inform practice. The results showed that teachers would be willingto use technology if they perceived it to be part of their job requirement, and easy andenjoyable to use. The caveat being that their other concerns, such as the striving for bettergrades and the rush to complete the syllabus, need to be mitigated. It is possible that whenteachers are persuaded that technology could assist to resolve these concerns, they wouldexhibit a higher level of behavioral intention to use it. At the teacher preparation andcontinuing education stages, focus should be given to developing positive attitudes amongteachers and ensuring a positive environment to facilitate their technology use. Finally,support should be provided for the progression from teachers’ intentions to actual tech-nology use by resolving the issues that transverse policy and classroom practice.

Limitations

There were three primary limitations to this study. First, the variables in this study werelimited to those included in the model that we deemed critical to explain teachers’ tech-nology behavior in Singapore. The social, contextual and cultural factors such as com-munication, organizational climate (Amoako-Gyampah 2007; Georgina and Olson 2008)and social environments (Venkatesh and Davis 2000) could also account for one’sintention. Hence, these variables could be focal points in future studies. Second, the cross-sectional design of this study did not involve experimental manipulation of theoreticalconstructs. Experimental replications of these findings would enhance causal interpreta-tions of observed relationships. Third, the teachers who volunteered to take part in thisstudy could have different attitudes from those who opted out. In future studies, a morerepresentative sample would help improve the generalizability of the findings.

Conclusion

The adoption of technology in schools remains a complex, elusive, yet extremely importantissue. This study confirms the theoretical importance of teachers’ perceptions (i.e., per-ceived ease of use, perceived usefulness, perceived behavioral control) and attitudestowards technology in predicting their future intention of technology use and alsodemonstrates that environmental factors (i.e., management expectation and technicalsupport) also play a significant role in influencing their technology intention. Thesefindings are important given the role of teachers, as stated by Bakkenes et al. (2010), in

T. Teo et al.

123


providing the impetus to the implementation of teaching innovations, the introduction ofnew teaching initiatives, and the integration of new technology into teaching and learning.Only after we identify key factors in teachers’ decision-making process of adopting acertain type of technology can we be ready to intervene so as to improve the efficiency andeffectiveness of technology use in school and classroom.

Appendix

See Table 4.

Table 4 List of constructs and their corresponding items

Construct Item

User intention (UI, adapted from Davis et al.1989)

UI1 I intend to continue to use technology in thefuture

UI2 I expect that I would use technology in the future

UI3 I plan to use technology in the future

Attitudes toward use (ATU, adapted fromCompeau and Higgins 1995)

ATU1 Once I start using technology, I find it hard tostop

ATU2 I look forward to those aspects of my job thatrequire the use of technology

ATU3 I have fun using technology

ATU4 Using technology is pleasant

ATU5 I find using technology to be enjoyable

Subjective Norm (SN, adapted from Ajzen1991; Davis et al. 1989)

SN1 People who influence my behavior think that Ishould use technology

SN2 People who are important to me think that Ishould use technology

Perceived behavioral control (PBC, adaptedfrom Thompson et al. 1991)

PBC1 I have control over technology

PBC2 I have the resources necessary to use technology

PBC3 I have the knowledge necessary to usetechnology

Perceived usefulness (PU, adapted from Daviset al. 1989)

PU1 Using technology enables me to accomplish tasksmore quickly

PU2 Using technology improves my performance

PU3 Using technology increases my productivity

PU4 Using technology enhances my effectiveness

PU5 Technology is useful to my job

Perceived ease of use (PEU, adapted fromDavis et al. 1989)

PEU1 Learning to use technology is easy for me

PEU2 I find it easy to use technology to do what I wantto do

PEU3 My interaction with technology does not requiremuch effort

PEU4 It is easy for me to become skillful at usingtechnology

PEU5 I find technology easy to use

Management expectation (ME, adapted fromBurnkrant and Page 1988)

ME1 The management in my school supports the useof technology in my job

ME2 The people whose views I respect support the useof technology


123


References

Ajzen, I. (1985). From intention to action: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.),Action control: From cognition to behavior (pp. 11–40). New York: Springer.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes,50, 179–211.

Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of plannedbehavior. Journal of Applied Social Psychology, 32, 665–683.

Ajzen, I. (2005). Attitudes, personality and behaviour (2nd ed.). Maidenhead, Berkshire, England: OpenUniversity Press.

Ajzen, I., & Manstead, A. S. (2007). Changing health-related behaviors: An approach based on the theory ofplanned behavior. In M. Hewstone, H. A. W. Schut, J. B. F. De Wit, K. Van Den Bos, & M. S. Stroebe(Eds.), The scope of social psychology: Theory and applications (pp. 43–63). New York: PsychologyPress.

AlQudah, A. A. (2014). Accepting Moodle by academic staff at the University of Jordan: Applying andextending TAM in technical support factors. European Scientific Journal, 10, 183–201.

Amoako-Gyampah, K. (2007). Perceived usefulness, user involvement and behavioral intention: Anempirical study of ERP implementation. Computers in Human Behavior, 23, 1232–1248.

Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behavior: A meta-analytic review.British Journal of Social Psychology, 40, 471–499.

Azam, S., & Lubna, N. (2013). Mobile phone usage in Bangladesh: The effects of attitude towards beha-viour and subjective norm. Annamalai International Journal of Business Studies & Research, 5, 25–34.

Bagozzi, R. P. (2007). The legacy of the Technology Acceptance Model and a proposal for a paradigm shift.Journal of the Association for Information Systems, 8, 244–254.

Bakkenes, I., Vermunt, J. D., & Wubbels, T. (2010). Teacher learning in the context of educational inno-vation: Learning activities and learning outcomes of experienced teachers. Learning and Instruction,20, 533–548.

Bobbitt, L. M., & Dabholkar, P. A. (2001). Integrating attitudinal theories to understand and predict use oftechnology-based self-service: The internet as an illustration. International Journal of Service IndustryManagement, 12, 423–450.

Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline modeltest and extension incorporating household life cycle. MIS Quarterly, 29, 399–426.

Burnkrant, R. E., & Page, T. J. (1988). The structure and antecedents of the normative and attitudinalcomponents of Fishbein’s theory of reasoned action. Journal of Experimental Social Psychology,24(1), 66–87.

Carmines, E., & Mclver, J. (1981). Analyzing models with unobservable variables: Analysis of covariancestructures. In G. Bohmstedt & E. Borgatta (Eds.), Social measurement: Current issues (pp. 65–115).Beverly Hills, CA: Sage.

Chau, P. Y. K., & Hu, P. J. (2002). Investigating healthcare professionals’ decisions to accept telemedicinetechnology: An empirical test of competing theories. Information & Management, 39, 297–311.

Chen, R. J. (2010). Investigating models for preservice teachers’ use of technology to support student-centered learning. Computers & Education, 55, 32–42.

Table 4 continued

Construct Item

Technical support (TS, adapted fromTaylor and Todd 1995)

TS1 Specialized instruction concerning technology isavailable to me

TS2 When I encounter difficulties in usingtechnology, a specific person is available toprovide assistance

TS3 When I encounter difficulties in usingtechnology, I know where to seek assistance

TS4 When I encounter difficulties in usingtechnology, I am given timely assistance

T. Teo et al.

123


Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initialtest. MIS Quarterly, 19, 189–211.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of informationtechnology. MIS Quarterly, 13, 319–340.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: Acomparison of two theoretical models. Management Science, 35, 982–1003.

Downs, D. S., & Hausenblas, H. A. (2005). The theories of reasoned action and planned behavior applied toexercise: A meta-analytic update. Journal of Physical Activity & Health, 2, 76–97.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intension and behavior: An introduction to theory andresearch. Reading, MA: Addison Wesley.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables andmeasurement error. Journal of Marketing Research, 48, 39–50.

Fornell, C., Tellis, G. J., & Zinkhan, G. M. (1982). Validity assessment: A structural equations approachusing partial least squares. In Proceedings of American Marketing Association Educators’ Conference(pp. 405–409). Chicago: AMA.

George, J. F. (2004). The theory of planned behavior and internet purchasing. Internet Research, 14,198–212.

Georgina, D. A., & Olson, M. R. (2008). Integration of technology in higher education: A review of facultyself-perceptions. The Internet and Higher Education, 11, 1–8.

Gruzd, A., Staves, K., & Wilk, A. (2012). Connected scholars: Examining the role of social media inresearch practices of faculty using the UTAUT model. Computers in Human Behavior, 28, 2340–2350.

Hagger, M. S., Chatzisarantis, N. L. D., & Biddle, S. J. H. (2002). A meta-analytic review of the theories ofreasoned action and planned behavior in physical activity: Predictive validity and the contribution ofadditional variables. Journal of Sport and Exercise Psychology, 24, 3–32.

Hair, J. F, Jr, Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate dataanalysis: A global perspective. New Jersey: Prentice-Hall International.

Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use.Management Science, 40, 440–465.

Hoelter, J. W. (1983). The analysis of covariance structures goodness-of-fit indices. Sociological Methods &Research, 11(3), 325–344.

Hopp, T. M. (2013). Subjective norms as a driver of mass communication students: Intentions to adopt newmedia production technologies. Journalism and Mass Communication Educator, 68, 348–364.

Hoyle, R. H. (2011). Structural equation modeling for social and personality psychology. London, UK:Sage.

Hsiao, W. (2011). Technology integration: Preparing in-service teachers for teaching a digital generation.International Journal of Technology, Knowledge & Society, 7, 17–28.

Hu, H. F., Al-Gahtani, S. S., & Hu, P. J. H. (2013). Examining the moderating role of gender in Arabianworkers’ acceptance of computer technology. Communications of the Association for InformationSystems, 33, 47–66.

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Con-ventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.

Hu, P. J., Clark, T. H. K., & Ma, W. W. (2003). Examining technology acceptance by school teachers: Alongitudinal study. Information & Management, 41, 227–241.

Huang, R. T., Deggs, D., Machtmes, K., & Rouge, B. (2011). Faculty online technology adoption: The roleof management support and organizational climate. Online Journal of Distance Learning Adminis-tration, 14. Retrieved http://www.westga.edu/*distance/ojdla/summer142/huang_142.html

Huang, H. M., & Liaw, S. S. (2005). Exploring user’s attitudes and intentions toward the web as a surveytool. Computers in Human Behavior, 21, 729–743.

Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption. Testing theUTAUT model. Information & Management, 48, 1–8.

Kiraz, E., & Ozdemir, D. (2006). The relationship between educational ideologies and technology accep-tance in pre-service teachers. Educational Technology & Society, 9, 152–165.

Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd ed.). New York: GuilfordPress.

Lee, J., Cerreto, F. A., & Lee, J. (2010). Theory of planned behavior and teachers’ decisions regarding use ofeducational technology. Educational Technology & Society, 13, 152–164.

Lee, R., Murphy, J., & Swilley, E. (2009). The moderating influence of hedonic consumption in an extendedtheory of planned behavior. Service Industries Journal, 29, 539–555.

Lumpe, A. T., Haney, J. J., & Czerniak, C. M. (1998). Science teacher beliefs and intentions to implementscience-technology-society (STS) in the classroom. Journal of Science Teacher Education, 9(1), 1–24.


123


Ma, W. W. K., Andersson, R., & Streith, K. O. (2005). Examining user acceptance of computer technology:An empirical study of student teachers. Journal of Computer Assisted learning, 21, 387–395.

Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57,519–530.

Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with thetheory of planned behavior. Information Systems Research, 2, 173–191.

Ministry of Education (MOE) Singapore (2008). Third masterplan for ICT in education. Retrieved June 23,2014, from http://www.moe.gov.sg/media/press/2008/08/moe-launches-third-masterplan.php

Ndubisi, N. (2006). Factors of online learning adoption: A comparative juxtaposition of the theory ofplanned behaviour and the technology acceptance model. International Journal on E-Learning, 5,571–591.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw-Hill.Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes

determine Internet usage: The role of perceived access barriers and demographics. Journal of BusinessResearch, 59, 999–1007.

Pynoo, B., & van Braak, J. (2014). Predicting teachers’ generative and receptive use of an educational portalby intention, attitude and self-reported use. Computers in Human Behavior, 34, 315–322.

Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of technostressfor end users in organizations: Conceptual development and empirical validation. Information SystemsResearch, 19, 417–433.

Rainbow, S. W., & Sadler-Smith, E. (2003). Attitudes to computer-assisted learning amongst business andmanagement students. British Journal of Educational Technology, 34, 615–624.

Raykov, T. (1997). Estimation of composite reliability for congeneric measures. Applied PsychologicalMeasurement, 21(2), 173–184.

Raykov, T., & Marcoulides, G. A. (2008). An introduction to applied multivariate analysis. New York:Routledge.

Riemenschneider, C. K., & McKinney, V. R. (2001). Assessing belief differences in small business adoptersand non-adopters of web-based e-commerce. The Journal of Computer Information Systems, 42,101–107.

Robert, P., & Henderson, R. (2000). Information technology acceptance in a sample of governmentemployees: A test of the technology acceptance model. Interacting with Computers, 12, 427–443.

Roca, J. C., Chiu, C. M., & Martinez, F. J. (2006). Understanding e-learning continuance intention: Anextension of the Technology Acceptance Model. International Journal of Human-Computer Studies,64, 683–696.

Sadaf, A., Newby, T. J., & Ertmer, P. A. (2012). Exploring factors that predict preservice teachers’intentions to use Web 2.0 technologies using decomposed theory of planned behavior. Journal ofResearch on Technology in Education, 45, 171–195.

Salleh, H. (2003). A qualitative study of Singapore primary school teachers’ conceptions. Teaching andLearning, 24, 105–115.

Schumacker, R. E., & Lomax, R. G. (2010). A beginner’s guide to structural equation modeling. New York:Routledge.

Sheeran, P., Norman, P., & Orbell, S. (1999). Evidence that intentions based on attitudes better predictbehavior than intentions based on subjective norms. European Journal of Social Psychology, 29,403–406.

Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis ofpast research with recommendations for modifications and future research. Journal of ConsumerResearch, 15, 325–343.

Shih, Y. Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study Internetbanking in Taiwan. Internet Research, 14, 213–223.

Shiue, Y. M. (2007). Investigating the sources of teachers’ instructional technology use through thedecomposed theory of planned behavior. Journal of Educational Computing Research, 36, 425–453.

Smarkola, C. (2008). Efficacy of a planned behavior model: Beliefs that contribute to computer usageintentions of student teachers and experienced teachers. Computers in Human Behavior, 24,1196–1215.

Spiller, J., Vlasic, A., & Yetton, P. (2007). Post-adoption behavior of users of internet service providers.Information & Management, 44, 513–523.

Sugar, W., Crawley, F., & Fine, B. (2004). Examining teachers’ decisions to adopt new technology.Educational Technology and Society, 7, 201–213.

Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19,561–570.

T. Teo et al.

123


Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers& Education, 52(1), 302–312.

Teo, T. (2010). A path analysis of pre-service teachers’ attitudes to computer use: applying and extendingthe technology acceptance model in an educational context. Interactive Learning Environments, 18(1),65–79.

Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test.Computers and Education, 57, 2432–2440.

Teo, T. (2014). Unpacking teachers’ acceptance of technology: Tests of measurement invariance and latentmean differences. Computers & Education, 75, 127–135.

Teo, T., & Fan, X. (2013). Coefficient alpha and beyond: Issues and alternatives for educational research.The Asia-Pacific Education Researcher, 22(2), 209–213.

Teo, T., & van Schaik, P. (2009). Understanding technology acceptance in pre-service teachers: A struc-tural-equation modeling approach. Asia-Pacific Education Researcher (De La Salle University Man-ila), 18(1), 47–66.

Teo, T., & Tan, L. (2011). The Theory of Planned Behaviour (Theory of Planned Behavior) and pre-serviceteachers’ technology acceptance: A validation study using structural equation modelling. Journal ofTechnology and Teacher Education, 20(1), 89–104.

Teo, T., & Zhou, M. (2014). Explaining the intention to use technology among university students: Astructural equation modeling approach. Journal of Computing in Higher Education, 26(2), 124–142.

Teo, T., & Zhou, M. (In Press). The influence of teachers’ conceptions of teaching and learning on theirtechnology acceptance. Interactive Learning Environments.

Teo, T., Koh, N. K., & Lee, C. B. (2011). Teachers’ intention to teach financial literacy in Singapore: A pathanalysis of an extended Theory of Planned Behaviour (TPB). Asia-Pacific Education Researcher,20(2), 410–419.

Teo, T., Lee, C. B., & Chai, C. S. (2008). Understanding pre-service teachers’ computer attitudes: Applyingand extending the Technology Acceptance Model (TAM). Journal of Computer Assisted Learning,24(2), 128–143.

Terry, D. J., & Hogg, M. A. (1996). Group norms and the attitude-behavior relationship: A role for groupidentification. Personality and Social Psychology Bulletin, 22, 776–793.

Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual modelof utilization. MIS Quarterly, 15, 124–143.

Trafimow, D., & Finlay, K. A. (1996). The importance of subjective norms for a minority of people:Between subjects and within-subjects analyses. Personality and Social Psychology Bulletin, 22,820–828.

van Raaij, E., & Schepers, J. (2008). The acceptance and use of a virtual learning environment in China.Computers & Education, 50, 838–852.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Fourlongitudinal field studies. Management Science, 46, 186–204.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of informationtechnology: Toward a unified view. MIS Quarterly, 27, 425–478.

Wang, L., Rau, P. P., & Salvendy, G. (2011). Older adults’ acceptance of information technology. Edu-cational Gerontology, 37, 1081–1099.

Williams, M. D., Rana, N. P., Dwivedi, Y. K., & Lal, B. (2011). Is UTAUT really used or just cited for thesake of it? A systematic review of citations of UTAUT’s originating article. Paper presented atEuropean Council of International Schools.

Wong, K., Osman, R. B., Goh, P. C., & Rahmat, M. K. (2013). Understanding student teachers’ behaviouralintention to use technology: Technology Acceptance Model (TAM) validation and testing. Interna-tional Journal of Instruction, 6, 89–104.

Wu, C., Li, C., & Tsai, C. (2013). Factors determining of effects of teachers’ web-based teaching platformusage: Using UTAUT to explore. Journal of Internet Technology, 14, 919–928.

Zhao, Y., Tan, S. H., & Mishra, P. (2001). Technology, teaching and learning: Whose computer is it?Journal of Adolescent and Adult Literacy, 44, 348–381.

Zolait, A. S. (2014). The nature and components of perceived behavioural control as an element of theory ofplanned behaviour. Behaviour & Information Technology, 33, 65–84.

Timothy Teo is Professor of Education at the University of Macau. His research interests includeEducational Psychology, ICT in Education and Quantitative Methods.


123


Mingming Zhou is Assistant Professor of Education at the University of Macau. Her research interestsinclude, self-regulated learning, motivation, and emotion in a computer-based learning environment.

Jan Noyes is Professor of Human Factors Psychology at the University of Bristol. Her research involves arange of phenomena relating to Human Factors and applied cognitive psychology.

T. Teo et al.

123


Teachers and technology: development of an extended theory of … and... · 2016. 9. 26. ·...

Documents

Transcript of Teachers and technology: development of an extended theory of … and... · 2016. 9. 26. ·...