The influence of e-Participation on e-Filing Participation ... 3/Issue 5/IJESIT201405_32.pdf · developed through the review and mapping of the eight dominant theories and models

ISSN: 2319-5967

ISO 9001:2008 Certified International Journal of Engineering Science and Innovative Technology (IJESIT)

Volume 3, Issue 5, September 2014

251

The influence of e-Participation on e-Filing

Participation: A Study of Citizen Adoption on

e-Government Services Lim Ai Ling, Maslin Masrom, Sabariyah Din

UTM Razak School of Engineering and Advanced Technology

Universiti Teknologi Malaysia

Kuala Lumpur, Malaysia

Abstract -It is a global trend that many governments used web-based technologies to keep pace with the various

changes arising from the socio-political and economic environment of the times. This paper asserts that the concept of e-

government which was originally aimed to improve efficiency of the government management system and more

importantly to expand government revenue would eventually recast towards a participatory government, without which

the survival of any institution cannot be guaranteed. The paper briefly reviewed major issues that might explain the e-

Participation behavior. (They include: Information Quality, Systems Quality, User Satisfaction, Facilitating Condition,

Performance Expectancy and Effort Expectancy) To this effect, the Malaysia’s e-Filing Taxation System (e-FTS) was

studied due to the facts that despite incredible efforts put by the Malaysian government the actual citizen participation in

the e-FTS is lagging. The Unified Theory of Acceptance and Use of Technology (UTAUT) Model was utilised to study the

gap that exists between the expected and the actual citizen participation in the Malaysia’s e-Filing Taxation Systems (e-

FTS). The paper examines (i) the relationships between Information Quality and Systems Quality on User Satisfaction of

the e-FTS; ii) the relationships between User Satisfaction on Performance and on Effort Expectancies of the e-FTS; iii)

the effects of Performance and Effort Expectancies and Facilitating Condition (FC) on Behaviour Intention to Use (BIU)

of the e-FTS ; and finally (iv) effects of e-Participation on FC of the e-FTS.The results had indicated that e-Participation,

Information Quality and Systems Quality are significant indicators for taxpayers’ intention to use the e-FTS.A

confirmatory factor analysis (CFA) using AMOS was conducted to test the measurement model. In conclusion,

Malaysian tax payers were found to be rather pessimistic in using the new technology and e-Government services. Policy

measures were forwarded which could be considered to close the gap between the high expectation and the reality of

citizen adoption one Government services.

Index Terms - e-Participation, e-Filing, e-Government.

I. INTRODUCTION

The term electronic-Government (e-Government) is not a mere technology innovation but it is a process of e-

Governance, which demands the application ofvarious Information CommunicationTechnology (ICT) in the

process of delivering government services. E-Government should also facilitate citizens‟ participation (e-

Participation) for better decision-making, building democratic and effective governance [1]. As defined by

Sanford and Rose [2, p.406], e-Participation is „the extension and transformation of participation in societal

democratic and consultative processes, mediated by information and communication technologies (ICTs)‟.This

paper explained how participatory research could be applied in decision-making to enhance e-Government

services, focusing on Malaysia‟s tax e-Filing System. In the following Section 2.0 literature on e- Participation

was discussed in the light of broad societal use of the ICT and social networking. The Paper was based on the

Research Model and Hypotheses presented in Section 3. The Research Design and Methods were summarised in

Section 4, prior to Data Analyses conducted and Results explained in Section 5. Research contributions and

limitations were discussed in Section 6. In the concluding Section 7, some guidelines for online services that

might enhance the use of the e-Filing System were listed.

II. LITERATURE REVIEW

A. E-participation

Recently, the Internet has become a valuable means of enable governments to decrease administrative costs,

improve service delivery, and enable citizens‟ participation anywhere and anytime it is convenient for them.

Thus, using e-participation, governments have attempted to create citizen-based online discussion forums, group

support and exchange ideas. Broad societal use of social networking, chat technologies, discussion forums,

group decision support systems and blogs provide a positive environment for e-Participation facilitation [3]. The

combination of motivated citizens and improvements in technological infrastructure has resulted in a global

proliferation of e-Participation projects that facilitate exchange information [4; 5]. It was found that increases in

ISSN: 2319-5967



252

access to social networking over the years did not lead to increased community-government involvement,

suggesting that present day technologies were still ineffective in facilitating new forms of public participation in

governance [6]. In addition, e-Participation tools remain unnoticed and unused on many government websites

[7].

B. Facilitating Condition

Facilitating Condition is the degree to which an individual believes that an organizational and technical

infrastructure exists to support use of the system [8]. Facilitating conditions was comprised of three root

constructs: perceived behavioural control, facilitating conditions, and compatibility [8]. Each of these root

constructs is operationalized to include aspects of the technological and/or organizational environment that are

meant to remove barriers to use [8].

C. Performance Expectancy

Performance expectancy is defined in this study as the degree to which a citizen believes that using government

online services in e-Filing is helpful, useful and practical more than the traditional government services.

Performance expectancy was measured by the perceptions of using e-government services in terms of benefits,

such as saving time, money and effort, facilitating communication with government, improving the quality of

government services and by providing citizens with an equal basis on which to carry out their business with

government [9]. Hence, individuals expect the technology such as information system would improve

performance and more likely the technology introduced been adopted [8]. With performance expectancy,

citizens would be able to accomplish task quickly such as saving time, enhance effectiveness with e-

Government and intention to use e-Filing technology.

D. Effort Expectancy

Effort Expectancy is the degree of ease associate with the use of the system [8]. The UTAUT model identifies

three constructs, from the eight models, which make up the concept of effort expectancy: perceived ease of use,

complexity, and ease of use [8].

E. Information Quality

Information quality is defined as the degree to which quality information is provided to the users to fulfil their

needs of getting the appropriate information [10]. The content of the website is the most important element for

success and to give rise to user satisfaction either to attract or driven away citizens‟ from the website [11]. Good

website design must fulfil users‟ needs for information [12].

F. System Quality

System quality is the measure of the actual system of the electronic information system by using the constructs

of functionality, reliability, usability and efficiency [27] as the determinant of overall user satisfaction and

significant predictor of system use [13]. Hence, the measure of the system quality is on the features and

performance characteristic of the e-Government website to enhance user‟s satisfaction of the user with the

system. A better system quality and a better service are further related to user satisfaction [14].

G. User Satisfaction

User satisfaction is the measurement of success or effectiveness of the user experience related to Information

System satisfaction and System quality satisfaction [10;15]. Hence, information provided by the system and the

quality of the systems are decisive in determining the level of the web-based information system satisfaction.

Information quality and system quality were positively related to online satisfaction [16].

I. RESEARCH MODEL AND HYPOTHESES

The Unified Theory of Acceptance and Use of Technology (UTAUT) was proposed and validated in order to

provide a unified theoretical basis to facilitate research on Information System (IS), Information Technology

(IT) adoption and diffusion [8]. The Unified Theory of Acceptance and Used of Technology (UTAUT) was

developed through the review and mapping of the eight dominant theories and models such as Theory of

Reasoned Action (TRA) [55], the Theory of Acceptance Model (TAM) [56], the Motivational Model (MM)

[57], the Theory of Planned Behaviour (TPB) [58], a combined Theory of Planned behaviour and Technology

Acceptance Model (C-TPB-TAM) [59], the Model of PC Utilization (MPCU) [60], the Innovation Diffusion

ISSN: 2319-5967



253

Theory (IDT) [61] and lastly the Social Cognitive Theory (SCT) [62]. A number of factors had been considered

in building the theoretical framework before it was presented as an integrated model shown in Figure 1.

Fig.1 Research model

A. Information Quality

Information quality is the key measurement of user satisfaction. Satisfaction reflects user positive perception on

the quality of web-based information and quality relevance of information to user needs [17]. In the e-Filing

system, the reliability content dimension include information accuracy, information relevance and information

completeness in order to achieve the intended level of information satisfaction [18].

B. System Quality

System quality is the measure of the actual electronic information system by using the constructs of

functionality, reliability, usability and efficiency [10] as the determinant of overall user satisfaction. A better

system quality and a better service are further related to user satisfaction [19]. According to Ambali [20, p152],

in line with the era of fast moving world, a better system from respective government to citizens are highly

needed.

C. User Satisfaction

Internet experience plays an important role in influencing people‟s use of government online services [8]. Thus,

Internet experience needed to be considered in order to explain users‟ effort and performance expectancy [21].

Hence, Anna and Yusniza [22] suggested that a system that is useful and easy to use is important for taxpayers

to voluntarily e-File their tax returns electronically. According to Zaherawati[23], the tax payers‟ perception

towards e-Filing is influenced by their evaluation of the usefulness of the e-filing system. Thus, the government

should increase its efforts to promote the usefulness and user-friendliness of the e-Filing system of high output

quality with greater result for demonstrability of satisfaction.Ambali [20] suggested a need for improvement in

the implementation of the online e-Filing to ensure that the system conform to the public e-filers‟ satisfaction as

the result for users‟ retention. As a matter of fact, the design of the tasks and context of the technology could

influence the performance and effort expectancy which in turn could influence the adoption and use of the

system due to user satisfaction [24].

D. Performance Expectancy

Several studies on UTAUT indicated that Performance Expectancy has a significant influence on the intention to

use the system [25;26;27;28]. Internet experience also plays an important role in influencing people‟s use of

online government services [8]. Performance expectancy is defined in this study as the degree to which a citizen

believes that using government online services in e-Filing is more helpful, useful and practical compared to

traditional government services. Hence, users who expect technology such as information systems would

improve performance would be more likely to adopt the technology introduced [8].

ISSN: 2319-5967



254

E. Effort Expectancy

Effort expectancy has been validated as a direct determinant of user‟s behavioural intentions to use e-Filing

technology if does not require much effort then it would improve to participation to adopt the system [29].

Likewise, the design of an appropriate tasks and context of the technology would eventually influence the

performance and effort expectancy that would influence the adoption of the use of the system. Some studies

under UTAUT indicated that there is a positive effect of the Effort Expectancy variable on the Behaviour

Intention to use the system [25,27,30,31,32]. Therefore, effort expectancy could have result in a positive

relationship with behavioural intention in accepting e-Filing among tax in Malaysia.

F. Facilitating Condition

The main aim of facilitating conditions is to remove barriers of use. In other words, facilitating conditions are

measured by the perception of being able to access required resources to obtain knowledge and skills and the

necessary support needed to use the online e-Filing system [33,34]. They are also influenced by the perception

of the fit between technology and the lifestyle of the user [9]. The findings from several researchers concluded

that there is a strong causal relationship between wireless trust and facilitating conditions [35] whereas

facilitating conditions were found to have a direct relationship with infusion and adoption of a number of new

information system innovations [36].

G. E-Participation

E-participation is often regarded as an opportunity to handle legitimacy crisis, fostering transparency and

facilitating the inclusion of citizens in the political process [37]. Whereas to compare the traditional methods of

participation of the citizens the rate declined as today people favour in e-Participation in the form of social

networking, chat technologies, discussion forums, group decision support systems and blogs [3]. The

combination of motivated actors and improvements in technological infrastructure has resulted in a proliferation

of e-Participation projects [4; 38].

IV. RESEARCH DESIGN AND METHOD

A set of questionnaire survey was designed and piloted before the actual set was utilised to collect data and

information that could be analysed to test the above hypotheses. Since the Likert scale was most frequently used

scale in information system research [39] the scales were utilised as measures of the construct for this study.

They were treated as interval scales (shown in Figure 2) arranged from „Strongly Positive‟ to Strongly Negative‟

to categorise the response levels from e-Filing participants.

Strongly Strongly

Disagree Disagree Neutral Agree Agree

1 2 3 4 5

Fig. 2 Measurement scale

A convenience sampling techniques was adopted the non-probability sampling was utilised to select e Filing

users since member of the population does not have an equal chance of selection. In this study quantitative

methodology was used and self-administered questionnaires have been adopted to collect data.A total of 600

users of the e-Filing System in Sarawak, Malaysia were selected from randomly chosen business organisations

in areas such Kuching, Miri, Sibu and Bintulu. Questionnaires were mailed or personally distributed to business

organisations such as Shell Company, Shin Yang Company, libraries, malls, employees of Sarawak University.

Two statistical software packages are used to analyse the data. One was IBM SPSS version 20, and the second is

Structural Equation Modelling (SEM) software, namely AMOS was applied to test the relative adequacy of the

model‟s fit which is essential for this study. After testing the model fit, this work examined the individual path

coefficients corresponding to the research hypotheses. SPSS was used to analyse the preliminary data and

descriptive analyses on the thesis sample such means and frequencies.

Study samples were asked to circle the response which best described their level of agreement with the

statement provided in the Questionnaire Set. From 600 questionnaires collected, 93 were discarded due to

missing response items. As such a total number of completed questionnaires were 507.

ISSN: 2319-5967



255

V. DATA ANALYSIS AND RESULTS

A. Measurement model

A variety of measures were proposed in previous research to assess the relative fit of the data to the model

[43,44,45]The results for this study are shown in Table 1. The recommended values for GFI and CFI should be

0.90 or above [40], while values for the AGFI should be 0.80 or above [41]. Finally RMSEA should be below

0.10 [42] but has also been suggested to represent a very good fit should RMSEA were below 0.08 [58]. Table 1.Goodness-of-Fit Indices of the initial and refined overall measurement models

Goodness of fit indices Benchmark Overall measurement model

CMIN/DF 1 to 3 2.680

GFI >.90 .841

AGFI >.80 .818

CFI >.90 .967

RMSEA <.08 .062

As shown in Table 1, the value of df is around 2.680, which is below the recommended cut off value of 3.0. The

GFI should be at or above 0.90. AGFI is a variant of GFI which adjusts GFI for degree of freedom. The

recommended value of AGFI should be 0.80 or above. As shown in Table 1, the GFI value is 0.841 which is

below the recommended value. Likewise, AGFI can also fall between 0to1 and it is generally accepted value of

0.90 or above [46]. Some researchers had indicated that AGFI should beat or above 0.80 or above [41]. Due to

the sensitivity of downward bias of degree of freedom in comparison to small sample size and causes upward

bias with large samples [47] the index GFI has become less popular in recent years and it has been

recommended that this index should not be used [48] as a measure of goodness-of-fit. In several studies such as

in Chang et al. [17]; Hu et al. [49]; Segars and Grover [50] the value for GFI was found to be below 0.80.

Reliability and convergent validity of the factors were estimated by the composite reliability and discriminant

validity (see Table 2). Discriminant validity was examined by comparing the shared variances between factors

with the average variance extracted of the individual factors [51]. In this study, the shared variance between

factors was lower than the average variance extracted of the individual factors, which confirms discriminant

validity.

Table 2.Reliability and discriminant validity

B. Convergent Validity

As for convergent validity, evidence has been found in which all factor loadings for items measuring the same

construct are statistically significant [52,53]. As indicated in Table 3, all factors included high loadings (greater

than .50) and were statistically significant (P<0.001). Bagozzi and Yi (1988) [54] recommended that CR should

be equal to or greater than .60, and AVE should be equal to or greater than .50.The results of AVE presented in

Table 3. Table 3.Measurement model evaluation

Construct Items Standardized

Loading

Cronbach‟s

alpha

CR AVE

EP EP13

EP14

EP15

EP16

EP17

EP18

EP19

.918

.925

.919

.903

.915

.884

.884

.979 .970 0.823

Factor CR 1 2 3 4 5 6

User satisfaction 0.70 1.00

Performance

Expectancy

0.68 0.04 0.95

Behaviour Intention

to Use

0.89 0.02 0.79 0.89

System Quality 0.90 0.01 0.86 0.95 0.95

Facilitating Condition 0.70 0.05 0.10 0.15 0.13 1.00

Information Quality 0.91 0.02 0.72 0.82 0.82 0.10 1.00

ISSN: 2319-5967



256

SQ SQ31

SQ35

SQ36

SQ37

SQ38

SQ39

.824

.823

.856

.845

.821

.792

.968 .928 .684

EE EE49

EE50

EE52

EE53

.953

.936

.935

.938

.985 .968 .885

US US25

US26

US27

US28

.933

.929

.930

.925

.968 .962 .864

FC FC40

FC41

FC43

FC44

.839

.925

.838

.924

.941 .934 .779

BITU BITU54

BITU55

BITU56

BITU57

.950

.957

.870

.887

.940 .955 .841

IQ IQ21

IQ22

IQ24

.951

.925

.947

.956 .959 .886

PE PE46

PE47

PE48

.954

.923

.944

.945 .958 .884

C. Structural model

In this study, a comparison of the fit indices with their corresponding recommended values provided evidence of

the good model fit. After this test the next path is to examine the path coefficients of the structural model.

Properties of the causal paths, including standardized path coefficients, t-values and variance explained, for each

equation in the hypothesized model are presented in Table 4.

The effect of information quality on user satisfaction was significant at 0.05 level (β1=-.159), thus H1 was not

supported. In this aspect, user satisfaction reflects user negative perception satisfaction pertaining to the website

information. Therefore, the information at the website did not fulfil users‟ needs. H2 was supported as

(β1=0.449). This indicated that a better system quality would leads user for their satisfaction on the system. The

results support H3(β1=0.066) and H4(β1=0.166) of this study for user satisfaction towards performance

expectancy and effort expectancy. This indicated that the system were useful to the users. Whereas,H5(β1=0.14)

on performance expectancy was supported for behaviour intention to use but not H6 (β1=-0.005) for Effort

expectancy. Effort expectancy was not supported due to lack of support on the context and task on the system.

H7 (β1=-.039) also not supported for facilitating condition. The users found their perception towards the new

technology is still not convincing and therefore it is necessary to give technical support to the users in order to

change the user mind set from using e-filing rather than using manual filing of the income tax form even it is

found useful. Hence, H8(β1=0.229) was supported as users are keen to facilitate the exchange of information at

the social networking as it is a new tool of communication.

Table 4. Hypotheses

Hypotheses Path Beta Value S.E t-value Sig.

H1 IQ → US -.159 .091 -1.748 Not supported

H2 SQ → US .449 .057 7.864 Supported

H3 US → PE .066 .023 2.900 Supported

H4 US → EE .166 .040 4.200 Supported

H5 PE → BITU .149 .045 3.303 Supported

ISSN: 2319-5967



257

H6 EE → BITU -.005 .025 -.210 Not supported

H7 FC → BITU -.039 .027 -1.454 Not supported

H8 EP → FC .229 .042 5.398 supported

Note: NS

Non-significant; *Significant at 5% level, **Significant at 1% level

VI. DISCUSSION AND CONCLUSION

This research provided several important contributions to the theory. First, the e-Participation literature is

expanded through this research, which investigated e-Participation within the facilitation condition context in a

non-western country, namely Malaysia. Although studies regarding the outcomes of e-Participation are

emerging in the literature, it should not be assumed that findings derived using the western data could be

generalised to other regions of the world such as Asia, particularly Malaysia. The findings for this study found

that citizens‟ e-Participation application with the quality of government responsiveness is positively associated

with their decision making, especially exchanging information, feedback and comments through e-Participation.

E-Participation application has been advocated as a crucial tool for e-government to facilitate citizens‟

participation by offering easier and more effective access to policy information and involvement in

administrative decision-making procedures.

Secondly, is the examination of information quality with user satisfaction of the citizens in the context of

Malaysia.This study highlighted the phenomena of cumulative experience in the context of Malaysia as a whole

and adoption involved citizens of the region as a subjects. Therefore in this study website features are

highlighted in accordance with the cumulative experience.

Lastly, the results of the study show that tax payers are pessimistic on new technology and technology readiness

level especially in Malaysia. It appears that technology readiness is the adoption of the system and low readiness

levels leads to low actual usage of e-Filing system.

As far as the information and system quality is concerned, the findings implied that there is a key influential on

user satisfaction. Such findings indicated that quality of the technological devices provided in order to enhance

the service interface of the e-filing website were found complicated and not useful. As compared to western

context are different.

There are several limitations to this study. Studies conducted in the form of a survey research in a natural setting

are normally accompanied by limitations. One of the limitations is the reliance of the research on self-reported

measure in the form of a questionnaire survey as the main source of data. This study depends on the users‟

openness and sincerity when completing to the questionnaire.

Secondly, the sample of this study was restricted to individual tax payers. Other tax payers such as companies,

employers and tax agents did not take part in this study. Therefore, future research is needed to determine if the

results apply to other groups of tax payers. Caution may be needed before generalizing the findings to the whole

country in Malaysia.

Thirdly, this study only focuses on e-filing adopters. Non adopters‟ perspectives were not collected as it would

prove better understanding of the factors contributing to the e-Filing system.

This study assimilates construct from UTAUT with the new construct of e-Participation. The results indicated

that e-Participation, Information quality and System quality are the significant indicators for taxpayers‟ intention

to use the e-Filing system. In order to promote the use of e-Filing, IRBM should provide a user friendly

interface and design a suitable information system that are compatible with users‟ work style and the

government should keep improving the quality of government web sites especially those that provide online

services to their citizens. The findings can provide useful recommendations for the development of practice.

This study would also enable IRBM to provide guideline for the online services that will meet the needs of their

taxpayers in the future in order to enhance the use of the e-Filing system.

REFERENCES

[1] L. Kim, Zailani, S.T. Ramayah, Y. Fernando, “Factors Influencing Intentions to Use e-Government services among

citizens in Malaysia,” International Journal of Information Management, vol.29, no.6, pp.458-475, 2010.

ISSN: 2319-5967



258

[2] C. Sanford, J. Rose, “Characterizing e Participation,” International Journal of Information Management , vol.27, no.6,

pp.405-421, 2007.

[3] S.J. Pharr, R.D. Putnam, Disaffected democracies: what troubling the trilateral countries, Princeton University Press,

New Jersey, 2000.

[4] S.J. Best, B.S. Krueger, “Analysing the representativeness of Internet political participation,” Political Behaviour, vol.

27, no.2, pp. 183-216, 2005.

[5] R,Ghose, “The complexities of citizen participation through collaborative governance,” Space and Polity, vol. 9, no.1,

pp. 61-75, 2005.

[6] A, Macintosh, A. Whyte, “Towards an evaluation framework for e Participation,” Emerald Group Publishing Limited,

vol.2, no.1, pp.16-30, 2008.

[7] A.M. Kanstrup, J. Rose, L. Tarpe, “A multi-perspective approach to e-Participation,” European Research Workshop,7th

Mediteranean Conference on Information System.Venice, 2008.

[8] V. Venkatesh, M. Morris, G. Davis, F. Davis, “User Acceptance of Information Technology: Toward a Unified View,”

MIS Quarterly, vol. 27, no.3, pp. 425-478, 2003.

[9] S. AlAwadhi, A. Morris, “The use of the UTAUT Model in adoption of e-government services in Kuwait,” Proceedings

of the 41th Hawaii International Conference on System Science, pp.288-291, 2008.

[10] W.H. DeLone, E.R. McLean, “Measuring E-Commerce Success: Applying the DeLone& McLean Information Systems

Success Model,” International Journal of Electronic Commerce, vol.12004, pp. 31-47, 2004.

[11] E. Turban, D.Gehrke, “Determinants of e-commerce web site,” Human System Management, vol. 19, no.2, pp.111-120,

2000.

[12] F.Heldal, E, Sjovold, E., A.F. Heldal, “Success on the Internet-optimizing relationships through the corporate site,”

International Journal of Information Management, vol.24, no.2, pp. 115-129, 2004.

[13] J. Livari, “An Empirical Test of the DeLone- McLean Model of Information System Succes,”The Data Base for

Advances in Information System vol.36, no.2, 2005, pp. 8-27, 2005.

[14] T. Teo, S. Srivastava, L. Jiang, “Trust and Electronic Government Success: An Empirical Study,”Journal of

Management Information Systems, vol.25, no.3, pp.99-131, 2008.

[15] J. Zhang, J. Huang, “Empirical Research on User Acceptance of Mobile Searches,”Tsinghua Science & Technology,

vol.15, no.1, pp. 235-245, 2005.

[16] W.H. DeLone, E.R. McLean, “Information Systems Success: The Quest for the Dependent Variable,” Information

Systems Research, vol.3, no.1, pp. 60-95, 1992.

[17] I. Chang, Y. Li, W. Hung, H. Hung, “An empirical study on the impact of quality antecedents on taxpayers acceptance

of Internet tax filing systems,” Government Information Quarterly, vol.22, no.3, pp. 389-410, 2005.

[18] Y.J. Kim, R. Kishore, G.L. Sanders, “From DQ to EQ: Understanding data quality in the context of e-business system,”

Communications of the ACM, vol.48, no.10, pp.75-81, 2005.

[19] T. Teo, S. Srivastava, L. Jiang, “Trust and Electronic Government Success: An Empirical Study,” Journal of

Management Information Systems, vol. 25, no.3, pp. 99-131, 2008.

[20] A,R. Ambali, “E-Government Policy: Ground Issues in E-Filing System,” European Journal of Social Sciences, vol.11,

no.2, pp. 249-266, 2009.

[21] J. Lu, C.S. Yu, C. Liu, “Mobile Data Service Demographics in Urban China,” Journal of Computer Information

Systems, vol.2, no.1, pp.117-126, 2009.

[22] C. A. Anna, K. Yusniza, K, “Adoption of tax e-filing: A conceptual paper,” African Journal of Business Management,

vol.4, no.5, pp. 599-603, 2010.

[23] Z. Zaherawati, H. Zaliha Z. Zuriawati, “E-filing in Malaysia,” Cross Cultural Communication, vol.6, no.2), pp. 84-92,

2009.

[24] T. Brown, “The company and the product: corporate associations and consumer product responses,” Journal of

Business Research, vol. 2, no.1, pp. 61-69, 2010.

[25] S. S. Al-Gahtani, G.S. Hubona, J. Wang, “Information technology (IT) in Saudi Arabia: Culture and the acceptance and

use of IT,” Information and Management, vol. 44, no.8, pp. 681-691. 2007.

ISSN: 2319-5967



259

[26] I.C. Chang, H.G. Hwang, W.F. Hung, Y.C. Li, “Physicians acceptance of pharmacokinetics based clinical decision

support system,” Expert Systems with Application, vol. 33, no.2, pp. 296-303, 2007.

[27] B. Sapio, T. Turk, M. Cornacchia, F. Papa, S. Livi, “Building scenarios of digital television adoption,” Technology

Analysis and Strategies Management, vol. 22, no.1, pp.43-63, 2010.

[28] V. Venkatesh, X. Zhang, “Unified Theory of Acceptance and Use of Technology: US vs China,” Journal of Global

Information Technology Management, vol. 13, no.1, pp. 5-27, 2010.

[29] V. Venkatesh, F.D. Davis, “A theoretical extension of the technology acceptance model: four longitudinal field

studies,” Management Science, vol.46, no.2, pp.186-204, 2000.

[30] B. Gupta, S. Dasgupta, A. Gupta, “Adoption of ICT in government organization in a developing country: An empirical

study,” Journal of Strategic Information Systems, vol.17, no.2, pp.140-154, 2008.

[31] C.P. Lin, B. Anol, “Learning Online Social Support: An Investigation of Network Information Technology Based on

UTAUT,” Cyber Psychology and Behaviour, vol.11, no.3, pp.268-272, 2008.

[32] B. Kijsanayotin, S. Pannarunothai, S.M. Speedie, “Factor influencing health information technology adoption in

Thailand community health centre Applying the UTAUT model,” International Journal of Medical Informatics, vol. 78,

no.6, pp.404-416, 2009.

[33] L.C. Schaupp, L. Carter, M.E McBride, “E-file adoption: A study of U.S. Tax payers‟ intentions,” Computer in Human

Behavior, vol.26, no.1, pp. 636-644, 2010.

[34] J. R. Fu, C.K. Farn, W.P. Chao, “Acceptance of electronic tax filing: a study of taxpayer intentions,” Information &

Management, vol. 43, no.2, pp. 109-126, 2006.

[35] S.S. Yu,J. Lu, J, C. Lui,“Beyond concern-a privacy trust behavioural intention model of electronic commerce,”

Information and Management, vol. 42, no.1, pp. 289-304, 2005.

[36] S. Sundaram, A. Schwarz, E. Jones, W.W. Chin, “Technology use on the front line: how information technology

enhances individual performance,” Journal of the Academy of Marketing Science, vol. 35, no.1, pp. 101-112, 2007.

[37] A. Macintosh, S. Coleman, “Promise and Problems of E-Democracy, Challenges of online citizen engagement,” OECD,

2009.

[38] S. Curwell, M. Deakin, I. Cooper, K. Paskaleva-Shapira, J. Ravetz, D. Babicki, “Citizen expectations of information

cities: Implication for urban planning and design,” Building Research and Information, vol.33, no.1, pp. 55-66, 2005.

[39] U. Sekaran, Research Method for Business: A Skill Building Approach, John Wiley and Sons, New York, 2005.

[40] R.H. Hoyle, The Structural Equation Modelling Approach: Basic Concepts and Fundamental Issues, Canada: Sage,

Thousand Oaks, 1995.

[41] W. Chin, A. Todd, “On the use, usefulness and ease of use of structural equation modelling in MIS research: A note of

caution,” MIS Quaterly, vol. 19, no.2, pp. 237-246, 1995.

[42] M. W. Briwne, R. Cudeck, Alternative ways of assessing model fit, CA: Sage Mewbury, 1993.

[43] C.A. Anna, L.B Ng, “The Acceptance of the e-Filing system by Malaysian Taxpayers,” Electronic Journal of e-

Government, vol. 8, no.1, pp.13-22, 2010.

[44] C.S. Ludwig, C. Lemuria, E.M. Megan, “E-File adoption: A Study of US Taxpayers‟ Intentions,” Computers in Human

Behaviour, vol. 26, no.1, pp.636-644, 2010.

[45] S.C. Anna, K. Yusniza, A. Nor Haida, “Perceived Risk and the Adoption of Tax e-Filing,” World Applied Sciences

Journal, vol. 20, no.4, pp.532-53, 2012.

[46] H. Daire, J. Coughlan, M. Mullen, “Structural Equation Modelling: Guidelines for Determining Model Fit,” Electronic

Journal of Business Research Methods, vol.6, no.1, pp. 53-60, 2008.

[47] J. Miles, M. Shevlin, M, “Effects of sample size, model specification and factor loadings on the GFI in confirmatory

factor analysis,” Personality and Individual Differences, vol. 25, no.1, pp. 85-90, 1998.

[48] S. Sharma, S. Mukherjee, A. Kumar, W.R. Dillon, “A simulation study to investigate the use of cut off values for

assessing model fit in covariance structure models,” Journal of Business Research, vol. 58, no.1, pp. 935-43., 2005.

[49] P. J. Hu, P.Y.K. Chau, O.R.L. Sheng, K.Y. Tam, “Examining the Technology Acceptance Model Using Physician

Acceptance of Telemedicine Technology,” Journal of Management Information Systems, vol. 16, no.2, pp. 91-112,

1999.

ISSN: 2319-5967



260

[50] A.H. Segars, V. Gover, “Re-Examining Perceived Ease of Use and Usefulness: A Confirmatory Factor Analysis,” MIS

Quarterly, vol. 17, no.4, pp. 517-522, 1993.

[51] C. Fornell, C, D.F. Larcker, “Evaluating Structural Equation models with unobserved variables and measurement

error,” Journal of Marketing Research, vol. 18, no.1, pp. 39-50, 1981.

[52] J.C. Anderson, D.W. Gerbing, “Structural equation modelling in practice: A review and recommended two-step

approach,” Psychological Bulletin, vol.103, no.3, pp. 411-423. 1998.

[53] P. Holmes-Smith, Introduction to structural equation modelling: Using AMOS 4.0. Melbourne: Swinburne, 2002.

[54] R.P Bagozzi, “Evaluating Structural equation models with unobservable variables and measurement error: A

comment,”Journal of Marketing Research, vol.2, no.1, pp. 375-381, 1988.

[55] M. Fishbein, I. Ajzen, Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research, Addison-

Wesley., Reading. MA, 1975.

[56] F.D. Davis, “Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology,” MIS

Quarterly, vol.13, no.3, pp. 319-339, 1989.

[57] F.D. Davis, R.P. Bagozzi, P.R Warshaw, “Extrinsic and Intrasic Motivation to Use computers in the Workplace,”

Journal of Applied Social Psychology, vol. 22, no.14, pp. 1111-1132, 1992.

[58] I. Ajzen, “The theory of planned behaviour,” Organizational Behavior and Human. Decision Processes, vol. 50, no.1,

pp.179–211, 1991.

[59] S. Taylor, P. Todd, “Understanding Information Technology Usage: A

Test of Competing Models,” Information Systems Research, vol.6, no.2), pp. 144-176, 1995.

[60] R.L.Thompson, C.A. Higgins, J.M. Howell, “Personal computing: toward a conceptual model of utilization,” MIS

Quarterly, vol. 15, no.1, 1991, pp.124-143, 1991.

[61] G.C. Moore, I. Benbasat, “Development of an instrument to measure the perception of adopting an information

technology innovation,” Information Systems Research, vol.2, no.3, pp.192-223, 1991.

[62] D.R. Compeau, C.A. Higgins, “Application of social cognitive theory to training for computer skills,” Information

Systems Research, vol. 6, no.2, pp. 118-143, 1995.

Documents

The influence of e-Participation on e-Filing Participation ... 3/Issue 5/IJESIT201405_32.pdf · developed through the review and mapping of the eight dominant theories and models