Investigating success factors of e-governance adoption by Greek teachers Vrana Vasiliki Department of Business Administration Technological Education Institute of Serres Terma Magnesias, 62124 Serres, Greece Email: v_vrana@otenet.gr Karavasilis Ioannis, Vagianos Dimitrios and Zafiropoulos Kostas Department of International and European Studies University of Macedonia Egnatias 156, 54006 Thessaloniki, Greece Email: {karavasil, vagianos, kz}@uom.gr Abstract: Governments around the world invest enormous amounts and resources in e-governance. The investments are based on their understanding of what citizens need and without measuring what increases citizens’ willingness to adopt e-governance services. For successful implementation of e-governance, governments need to define priorities within the framework of their national policy goals, vision and strategic objectives, understand the variables that influence citizens’ adoption of e-governance services and take them into consideration when delivering services online. The study uses the Technology Acceptance Model, the Diffusion of innovation model and constructs of trust and risk, in order to build a model for teachers’ adoption of e-governance in Greece. Primary and secondary education teachers responded to an online survey resulting to 230 questionnaires. A SEM validation of the proposed model reveals that operational factors are better predictors of intention to e-governance use, compared to attitudinal factors. Keywords: e-governance adoption, TAM, DOI, trust, risk, LISREL, education, Greece 1. Introduction “Governance” of a nation is defined as the manner in which power is exercised in the management of a country (World Bank 1994). e-Governance refers to the use of information and communication technologies to transform and support the processes and structures of a governance system (Godse & Garg, 2007). Misuraca (2006, p.210) defined e-governance as “the use of the electronic medium to facilitate an efficient, speedy and transparent process of disseminating information to the public and other agencies, and for performing government administration activities”. Nowadays, the importance of implementing e-governance is being recognized in many countries around the world and governments are moving forward in e-governance development (Panagis et al., 2008). However the introduction of e-governance and its adoption by citizens are not only technical issues but also social and many factors are involved (Luo, 2009). A gap exists between potential usage and actual usage of electronic public services. Deursen et al. (2006) investigated the factors influencing this gap. The findings have shown that population doesn’t have sufficient motivation for using computers and the Internet, insufficient digital skills produce serious problems, but the striking fact is that government doesn’t know what citizens want, how they use ICT en what the consequences for citizens are. Governments’

are driving the development agenda of e-governance and their investment in electronic services is based on their understanding of what citizens need and without measuring what increases citizens’ willingness to adopt e-governance services. For successful implementation of e-governance, governments need to define priorities within the framework of their national policy goals, vision and strategic objectives (Misuraca, 2006), understand the variables that influence citizens’ adoption of e-governance services and take them into consideration when delivering services online (Mofleh & Wanous, 2008). Trust issues (Belanger & Carter, 2008; Carter & Weerakkody, 2008; Colesca, 2009) risk issues (Belanger & Carter, 2008), personal characteristics of adopters (Colesca & Dobrica, 2008; Sang et al., 2009) along with perceived usefulness (Colesca and Dobrica, 2008; Sang et al., 2009; Shih, 2004; Teo et al., 2009; Wangpipatwong et al., 2008 ) and perceived ease of use (Carter and Belanger, 2004; Colesca and Dobrica, 2008; Sang et al., 2009; Shih, 2004; Teo et al., 2009) can impact on adoption e-government services. Given the above context and taking into consideration that a percentage of 40.69% of permanent civil servants and 16.38% of all civil servants in Greece (Ministry of Interior, http://www.ypes.gr) are teachers, it is interesting to investigate factors that affect adoption of educational e-governance websites by them. The term “educational e-Governance websites” refers to the webpages of Greek School Network, the Ministry of Education, Lifelong Learning and Religious affairs and websites of Regional and Local Primary and Secondary Education Administrations. This study uses an online survey to record teachers; opinions and attitudes. Colesca (2009, p32) wrote: “nonusers haven’t favorable attitudes towards the use of electronic services in relation with the governmental agencies”. Therefore, the research does not investigate people who are electronically incapable of accessing services and takes into consideration the intention to continue using e-governance websites. It analyzes the data using a refinement procedure, controlling reliability and validity, and validates the proposed model using Structural Equation Modeling. 2. Trust and risk Trust is defined as “an individual's (trustor, here is citizen) belief or expectation that another party (trustee, here e-government) will perform a particular action important to trustor in the absence of trustor's control over trustee's performance” (Alsaghier et al., 2009 p.298). Trust is the foundation of any transaction that takes place between two parties (Schaupp & Carter, 2008), is central to daily interactions, transactions, and practices and is considered as a crucial enabler in e-governance adoption (Al-adawi, et al., 2005; Belanger &Carter, 2008; Colesca, 2009; Huang et al., 2006). Trust is a dynamic concept with different developmental stages each with specific characteristics. This dynamic view of trust “has led to the development of different trust models that identify different relationships and actors” (Tassabehji and Elliman, 2006 p.3). Teo et al. (2008) examined the role of trust in e-government success. In their model there are two dimensions of citizen’s trusting beliefs toward an e-government Web site—trust in government and trust in technology. Colesca (2009) identified age, perceived usefulness, perceived quality, perceived concerns, perceived organizational trustworthiness, trust in technology, propensity to trust and years of internet experience as the factors that affect citizens’ trust in e-governance. Five constructs, namely, disposition to trust, familiarity, institution-based trust, perceived website quality, perceived usefulness and perceived ease of use were used by Alsaghier (2009) to conceptualize trust in e-government. The trust problem could be due to many critical factors such as the impersonal nature of the online environment, the use of technology, security issues and the uncertainty and risk of using open infrastructures (AlAwadhi & Morris, 2009; Browne, 2001). Perceived risk is defined as

“the citizen’s subjective expectation of suffering a loss in pursuit of a desired outcome” (Warkentin, 2002 p. 160). According to Jarvenpaa et al. (1999) and Pavlou (2003) perceived risk is an essential construct when uncertainty is present (Mayer et al., 1995) and affects consumer behavior in B2C e-commerce. Belanger and Carter (2008) claimed that the same happens for e-government. 3. Technology Acceptance Model The Technology Acceptance Model (TAM) was developed by Davis (1980), Davis et al. (1989) and Bagozzi et al. (1992). TAM intents “to provide an explanation of the determinants of computer acceptance that is general, capable of explaining user behavior across a broad range of end-user computing technologies and user populations, while at the same time being both parsimonious and theoretically justified (Davis et al, 1989, p.985). It has strong behavioral elements (Bagozzi et al., 1992). A person’s actual system usage is mostly influenced by his or her behavioral intentions toward usage and are influenced by the perceived usefulness and perceived ease of use of the system. Attitudes can be used to predict behavior with considerable success under appropriate conditions (Petty & Cacioppo, 1981). The major advantage of TAM is that it can be extended when new technologies are introduced by using domain-specific constructs. Previous studies used TAM in order to investigate factor that affect citizens' adoption of e-governance services (Colesca and Dobrica, 2008; Al-adawi et al., 2005; Sang et al., 2009). TAM alone was not able to explain issues of technology adoption related to e-Government so additional factors, may considered as well (Jaeger and Matteson, 2009). Previous researches have also tried to found the connection between trust and TAM (Gefen et al., 2003a, 2003b; Pavlou, 2003; Wu & Chen, 2005). Gefen et al. (2003a) found out that trust is an antecedent of perceived ease of use and perceived usefulness and influences behavioral intention to use. Pavlou (2003) claimed that trust is one of the factors that influence perceived usefulness and Wu & Chen, (2005) that trust is considered as an important antecedent of intention to use and attitude. Thus, trust and risk are significant notions that should be investigated to understand citizens’ adoption of e-governance and are integrated with TAM in the proposed model. 4. Diffusion of Innovations The Diffusion of Innovations (DOI) theory was developed intending to explain how an innovation diffuses through a society. Five attributes affect the rate of diffusion : relative advantage, compatibility, complexity, triability and observability (Rogers, 1995). E-governance services can be considered as an innovation as they are perceived to be novel by citizen, and are delivered via the Internet (communication channel) to citizens, a community of potential adopters (Singh, 2008). Previous studies considered DOI for investigation of e-governance acceptance and integrated it with other models (Carter & Bélanger, 2005; Patel & Jacobson, 2008; Sang et al., 2009). Sang et al. (2009) based of previous researches (Agarwal & Prasad, 1998; Carter & Bélanger, 2005) claimed that relative advantage, compatibility and complexity are more important than others in predicting intention to use a technology. Moreover complexity construct in the DOI is often considered as perceived ease of use construct in the TAM and triability and observability have no strong correlations between them and users’ attitude toward IT adoption. Therefore they included only relative advantage and compatibility constructs in their research model. Sang’s et al. (2009) views were adopted in this paper.

5. Hypotheses Consequently in the unified model, the following hypotheses are tested (Figure 1): H1. Trust in government websites has a direct effect on perceived risk. H2. Perceived ease of use has a positive direct effect of perceived usefulness. H3. Trust to e-government websites has a direct effect on intention to use. H4. Perceived ease of use has a positive effect on Intention to use. H5. Perceived risk has a direct effect on Intention to use. H6.Rerceived usefulness has a direct positive effect on intention to use. H7. Compatibility has a positive direct effect on Intention to use. H8. Relative advantage has a positive direct effect on Intention to use.

Figure 1. The research model after validation.

6. Methodology An empirical research study was conducted using an online survey. Internet users have been chosen to be surveyed. The reason is that lack of e-Government usage focus primarily on the ‘‘digital divide’’ (Mofleh and Wanous, 2008). Colesca (2009, p32) wrote: “nonusers haven’t favourable attitudes towards the use of electronic services in relation with the governmental agencies”. Therefore, the research does not investigate people who are electronically incapable of accessing services.

A link to the main webpage of the Greek School Network (http://www.sch.gr) notified users of the website about the online questionnaire website. Users willing to participate visited a

tailor made web site and responded to the questionnaire. The data were recorded to a database. The Greek School Network offers e-mail accounts (username@sch.gr form) and fully personalized access to education staff. In order to ensure that the responder was a teacher, the e-mail of the responder was recorded. From all questionnaires that were received only those of username@sch.gr form were admitted. Finally, 230 completed and usable questionnaires were received.

The questionnaire used in this study was adopted from previous studies. Five point Likert scales are used ranging from strongly disagree to strongly agree. The questionnaire consists of six parts: 1) Trust in e-governance websites, 2) Perceived Ease of Use, 3) Perceived Usefulness, 4) Perceived risk, 5) Compatibility, 6) Relative Advantage, 7) Intention to use.

A pilot study using an extended questionnaire containing all the scales proposed in the literature review was conducted by administering the questionnaire to 50 primary and secondary education teachers. Finally, for each construct the scale presenting the largest Cronbach’s alpha was decided to be included in the final questionnaire, since Cronbach’s alpha provides the lower-bound estimate for the Composite Score Reliability, which is eventually used in the analysis of the resulting questionnaire (Table 1).

Table 1. Items used.

Scales and items Trust in e-governance websites adopted by Teo et al. (2009). Constructs also tested in pilot study: Colesca and Dobrica’s (2008), Sang’s et al.(2009). 1 e-government Web sites are trustworthy 2 e-government Web sites are seem to be honest and truthful to me 3 e-government Web sites can be trusted Perceived Risk adopted from Belanger and Carter (2008) 1 The decision of whether to use a state e-government service is risky 2 In general, I believe using state government services over the Internet is risky Compatibility Adopted from Sang et al. (2009). Constructs also tested in pilot study: Carter and Belanger (2005) 1 I think using e-Government systems would fit well with the way that I like to gather information from government agencies. 2 I think using e-Government systems would fit well with the way that I like to interact with government agencies. 3 Using e-Government systems to interact with government agencies would fit into my lifestyle. 4 Using e-Government systems to interact with government agencies would be compatible with how I like to do things. Relative advantage adopted from Sang et al. (2009). Constructs also tested in pilot study: Carter and Belanger (2005) 1 Using e-Government systems would enhance my efficiency in gathering information from government agencies. 2 Using e-Government systems would enhance my efficiency in interacting with government agencies. 3 Using e-Government systems would make it easier to interact with government agencies. 4 Using e-Government systems would give me greater control over my interaction with government agencies. Perceived Ease of Use adopted from Carter and Belanger (2004) . Constructs also tested in pilot study: Teo’s et al. (2009), Colesca and Dobrica’s (2008), Sang’s et al.(2009), Shih’s (2004).

1 Learning to interact with a state government Website would be easy for me. 2 I believe interacting with a state government Website would be a clear and understandable process. 3 I would find most state government Websites to be flexible to interact with. 4 It would be easy for me to become skilful at using a state government Website. Perceived Usefulness adopted from Wangpipatwong et al. (2008). Constructs also tested in pilot study: Teo’s et al. (2009), Colesca and Dobrica’s (2008), Sang’s et al.(2009) and Shih’s (2004). 1 Using e-Government websites enables me to do business with the government anytime not limited to regular business hours. 2 Using e-Government websites enables me to accomplish tasks more quickly. 3 The results of using e-Government websites are apparent to me. 4 Using e-Government websites can cut travelling expense. 5 Using e-Government websites can lower travelling and queuing time. Intention to use adopted from Carter and Belanger (2004). Constructs also tested in pilot study: Al-adawi’s et al. (2005), Belanger and Carter’s (2008), Hung’s et al. (2006) and Sang’s et al.(2009). 1 I would use the Web for gathering state government information. 2 I would use state government services provided over the Web. 3 Interacting with the state government over the Web is something that I would do. 4 I would use the Web to inquire about state government services.

7. Findings LISREL 8.8 was used to analyse the data. Model estimation was done using the maximum likelihood approach, with the item covariance matrix used as input.

The measurement model was first examined for validating and refining the research constructs, followed by an analysis of the structural equation model for testing the research hypotheses in the research model.

Confirmatory Factor Analysis was used for model refinement. Testing the measurement model involves examining the convergent validity, discriminant validity, and internal consistency of the constructs. Reliability and convergent validity of the measurements are estimated by the item factor loadings, Composite Reliability, and Average Variance Extracted (Fornell and Larcker 1981).

Convergent validity refers to the extent to which the items under each construct are actually measuring the same construct. Two methods were applied to assess convergent validity. First, item reliability was examined for each item, which suggested that the factor loading of each item on its corresponding construct must be higher than 0.55 (Teo et al. 2009). As shown in Table 2, all items had a loading above the suggested threshold. Convergent validity was assessed by examining the average variance extracted (AVE) for each construct. The AVE for a construct reflects the ratio of the construct’s variance to the total variances among the items of the construct. The average extracted variances are all above the recommended 0.50 level (Hair et al. 1998, Teo et al. 2009).

Discriminant validity refers to the extent to which a given construct differs from other constructs. As all items loaded more heavily on their corresponding constructs rather than on other constructs, discriminant validity was satisfied. Further, the square roots of all AVEs were larger than correlations among constructs, thereby satisfying discriminant validity (Table 4). Table 5 shows that all the inter-construct correlations are below 0.9. Also the estimated

correlation between all construct pairs is below the suggested cutoff of 0.9 and this implies distinctness in construct content or discriminant validity (Gold et al. 2001, Teo et al. 2009).

Compared to Cronbach’s alpha, which assumes equal weights of all the items of a construct and is influenced by the number of items, Composite Reliability relies on actual loadings to compute the factor scores and thus provides a better indicator for measuring internal consistency (Teo et al. 2009). As shown in Table 4, Composite Reliabilities are above the threshold of 0.7. Overall, the measures in this study are reliable and valid.

Table 2. Factor Loadings 1 2 3 4 5 6 7 1 e-government Web sites are trustworthy .852 .213 .113 .075 .176 .136 .224 2 e-government Web sites are seem to be honest and truthful to me

.874 .087 .126 .174 .173 .125 .089

3 e-government Web sites can be trusted .885 .198 .105 .145 .160 .153 .103 1 The decision of whether to use a state e-government service is risky

-.230

-.890

-.079

-.111

-.149

-.037

-.141

2 In general, I believe using state government services over the Internet is risky

-.194

-.896

-.071

-.156

-.110

-.126

-.124

1 Learning to interact with a state government Website would be easy for me.

.076 .098 .736 .206 .278 .021 .251

2 I believe interacting with a state government Website would be a clear and understandable process.

.123 .038 .824 .203 .136 .280 .155

3 I would find most state government Websites to be flexible to interact with.

.153 -.013

.765 .191 .098 .344 .049

4 It would be easy for me to become skilful at using a state government Website.

.067 .188 .616 .407 .274 .026 .175

1 Using e-Government websites enables me to do business with the government anytime not limited to regular business hours.

.152 .087 .245 .794 .180 .146 .215

2 Using e-Government websites enables me to accomplish tasks more quickly.

.161 .067 .175 .817 .159 .147 .242

3 The results of using e-Government websites are apparent to me.

.178 .019 .401 .618 .093 .272 .182

4 Using e-Government websites can cut travelling expense.

.070 .129 .144 .853 .154 .126 .205

5 Using e-Government websites can lower travelling and queuing time.

.057 .111 .147 .856 .194 .154 .202

1 I think using e-Government systems would fit well with the way that I like to gather information from government agencies.

.103 .173 .249 .187 .696 .199 .232

2 I think using e-Government systems would fit well with the way that I like to interact with government agencies.

.220 .080 .196 .212 .742 .308 .247

3 Using e-Government systems to interact with government agencies would fit into my lifestyle.

.163 .051 .166 .182 .796 .200 .215

4 Using e-Government systems to interact with government agencies would be compatible with how I like to do things.

.219 .122 .137 .186 .801 .205 .223

1 Using e-Government systems would enhance my efficiency in gathering

.054 .155 .171 .238 .345 .504 .337

information from government agencies. 2 Using e-Government systems would enhance my efficiency in interacting with government agencies.

.119 .067 .202 .181 .305 .776 .228

3 Using e-Government systems would make it easier to interact with government agencies.

.114 .079 .248 .185 .271 .789 .192

4 Using e-Government systems would give me greater control over my interaction with government agencies.

.215 .041 .119 .165 .130 .810 .173

1 I would use the Web for gathering state government information.

.078 .232 .149 .229 .249 .221 .752

2 I would use state government services provided over the Web.

.120 .111 .123 .360 .231 .191 .762

3 Interacting with the state government over the Web is something that I would do.

.190 .075 .161 .247 .234 .236 .761

4 I would use the Web to inquire about state government services.

.184 .039 .220 .258 .240 .179 .774

Table 3. Inter-Construct Correlations.

Trust to e-

government website

Perceived risk

Perceived ease of use

Perceived usefulness

Compatibility Relative advantage

Perceived risk

-0.49

Perceived ease of use

0.40 -0.29

Perceived usefulness

0.39 -0.36 0.61

Compatibility 0.51 -0.41 0.59 0.56 Relative advantage

0.45 -0.32 0.63 0.55 0.71

Intention to use

0.46 -0.41 0.57 0.67 0.70 0.66

Table 4. Composite Reliability (CR), and Average Variance Extracted (AVE). AVE CR

Trust to e-government website 0.84 0.94

Perceived risk 0.84 0.91

Perceived ease of use 0.64 0.87

Perceived usefulness 0.75 0.94

Compatibility 0.74 0.92

Relative advantage 0.69 0.90

Intention to use 0.61 0.93

Table 5: Analysis of the goodness-of-fit of the overall model

Fit index compared to Recommended value

Research model

Chi-square/d.f. ≤3.0 2.49 GFI ≥0.80 0.85 AGFI ≥0.80 0.81 NFI ≥0.90 0.96 NNFI ≥0.90 0.97 RMSEA ≤0.08 0.08 CFI ≥0.90 0.97

8. The model testing The first step in model testing is to estimate the goodness-of-fit of the research model. Recommended fits are suggested from previous studies (Bagozzi and Yi 1988, Hair et al. 1988): goodness-of-fit index (GFI), normed fit index (NFI), non-normed fit index (NNFI), comparative fit index (CFI), and the root-mean-square error of approximation (RMSEA) as the indices for evaluating the overall model fitness. The chi-square test provides a statistical test for the null hypothesis that the model fits the data, but it is too sensitive to sample size differences, especially where the sample sizes exceed 200 respondents (Fornell and Larcker 1981). Bagozzi and Yi (1988) suggested a chi-square per degrees of freedom instead. All of the fit indexes indicate that the structural model have a good fit (Table 5).

The second step in model estimation is to examine the path significance of each hypothesized association in the research model and variance explained (R2) by each path. The standardized path coefficients, and explained variances of the structure model are shown in Figure 1.

Figure 1 presents the research model after validation along with the hypotheses. Non significant estimates (p>0.05) are presented with dotted lines and arrows. From Figure 1 it is obvious that:

Hypothesis H1 is supported since the effect of Trust to e-government websites to Perceived risk is significant, and equals -0.49. Negative value implies that risk is perceived to be low by users who trust e-government websites. R2 equals 0.24, thus Perceived risk is at large explained by trust. H2 is also supported since perceived ease of use affects significantly and positively Perceived usefulness (β=0.65). Totally, 42% of Perceived usefulness is explained by Perceived ease of use.

Perceived risk has not a significant direct effect to Intention to use (H5 not supported). Trust to e-government websites has significant no direct thus H3 is also not supported or indirect (via Perceived risk) effect to Intention to use. Finally, Perceived ease of use has not a significant direct effect on Intention to use, but it does have an indirect effect through Perceived usefulness (γ=0.221), thus H4 is partially supported.

Hypotheses H6, H7 and H8 are supported. Perceived usefulness has a direct effect (β=0.34) on Intention to use, Compatibility has a little smaller effect (β=0.32) and Relative advantage comes last with β=0.22. Overall a large portion of the variance of Intention to use is explained by the supposed predictors (R2=0.60).

Overall, Intention to use is affected by Perceived usefulness (β=0.34), Compatibility (β=0.32), Perceived ease of use (γ=0.221), Relative advantage (β=0.22). On the other hand, Trust and Risk, have no effect directly or indirectly on intention to use.

9. Conclusion

Previous research on the same field has provided evidence of the effects that Trust has on Intention to use e-government services (Zafiropoulos et al 2010). It seems that in the presence of both attitudinal variables (trust, perceived risk, etc) and operational variables (compatibility, relative advantage) it is the second set that has an effect on intention to use. The present paper modifies and enriches previous findings but considering the importance of operational variables. This analysis suggests that in the presence of operational factors, it is the operational factors that influence intention to use e-government websites, compared to trust and perceived risk. Practical implications and suggestions should not avoid enhancing trust of citizens on the one hand and diminishing their perception of risk using e-government on the other. What the

findings of this paper suggest is that along with emphasizing and augmenting social benefits of using e-governance, the primary effort should be made in providing efficient and effective e-government practices. These could satisfy the citizens and engage them to use e-government services further. Raising trust and diminishing perceived risk are important steps, but providing results and efficient solutions to the citizens through e-governance services is more important to raise intention to use them.

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