Factors Influencing eBidding (electronic reverse auctions eRAs) Adoption in Malaysian Public Sector

8/10/2019 Factors Influencing eBidding (electronic reverse auctions eRAs) Adoption in Malaysian Public Sector

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CHAPTER I

INTRODUCTION

1.1 Background of the Study

This chapter presents the current research in e-government development and online

reverse auctions system, the problem and goals of the study and the significance of the

research. An overview of Malaysias e-government initiatives and Multimedia Super

Corridor MSC! applications, particularly e"erolehan is provided in the chapter together

with an overview of reverse auctions system and e#idding benefits. A description of the

status of e#idding usage and the transactional value are also included.

1. !"o#a" e$!o%ern&ent U'e

e-$overnment facilitates governments to provide services to business, government

agencies and citi%ens by leveraging on information communication technology &CT!

and the &nternet. e-$overnment is referred to as public service delivery to the public,

citi%ens and private sector via the internet Ahmad and 'thman, ())*!. e-$overnment

is defined as the use of &nternet as venue for more efficient administration and

governance '+C, ())!. Moon ())! argues governments employ the system as a

strategy to response to public e/pectations for enhanced and better public service

delivery. The government is facing increasing e/pectations by the public for fast and

efficient services similar to the 0uality offered by the private sector. The citi%ens who

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The global governments effectiveness in delivering online services is trac1ed in a few

studies, for e/ample, e-$overnment initiatives in (( nations were e/amined in the

Accenture Study in ()33. The study finds that leading world governments are modifying

the traditional models of service delivery to a higher level to strengthen their

relationships with citi%ens Accenture, ()33!. e-$overnment relationships with the

sta1eholders can be briefly categori%ed as $overnment-to-Citi%ens $(C!, $overnment-

to-#usinesses $(#! and $overnment-to-$overnment $($! 7eong, ())*!.

&n a $overnment-to-Citi%ens $(C! relationship, user-friendly one-stop services centers

are employed to facilitate citi%ens interactions with the government. &n a $overnment-

to-#usinesses $(#! relationship, government and the private sector communication

are improved to facilitate business transactions between parties. &n a $overnment-to-

$overnment $($! category, collaborations between governmental agencies are

enhanced to increase data sharing and electronic transactions. MAM"2 ()3)!

contends that in $($, these relationships cover more integrated agencies in terms of

collaboration between officials, departments, ministries and foreign countries.

1.(. e$!o%ern&ent De%e"o)&ent

e-$overnment evolves through a series of stages, from basic information,

communication feedbac1 to conducting transactions and finally the interactive web

presence Siau and 9ong, ()):!. e-$overnment development stages can be depicted in

stages of development model as shown as ;igure 3. The 2nited 6ations ;ive-Stage

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Model 26, ())! summari%es the developmental stages any government may

e/perience as the e-government evolves. The first three stages cover automation and

digiti%ation while the following stages cover government transformation, in terms of the

internal operations and citi%ens participation in policy-formulation and decision-ma1ing.

*+gure 1. UN,' -$Stage ode" of e$!o%ern&ent De%e"o)&ent

Source < 26, ())!

=arious government researchers have developed numerous stage models for e-

government developments, for e/ample, 9ayne and 9ee, ())3! ;our-Stage Model and

Moon ())(! ;ive-Stage Model, 9ayne and 9ees ())3! ;our-Stage Model and Moons

())(! ;ive-Stage Model. These models are based on a combination of technical,

organi%ation and managerial factors. M ;our-Stage Model comprises automate,

enhance, integrate and on demand stage M #usiness Consulting Services, ())!.

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i

ii

iii

iv

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#elanger and >iller ())?! ;ive-Stage Model considers political participation by citi%ens

in the highest stage by offering services such as online voting, online registration, or

posting comments on line. 9ee ()3)! study compares twelve different e-government

stage models and classify e-government stage models based on two themes< operation

and technology theme on one side and citi%en services on the other. According to the

author, five distinct correlated metaphors, presenting, assimilating, reforming, morphing

and e-governance will dictate the relationship between each themes. These models

embrace the concepts of interaction, transaction, participation and involvement of the

citi%en with integration, transformation and process management.

Ta#"e 1. Co&)ar+'on' of Stage ode"' +n e$!o%ern&ent De%e"o)&ent

ode" Stage'

26 ())! -Stage Model i. emerging web presence,ii. enhanced web presence,

iii. interactive web presence,iv. transactional web presence@

v. seamless integrated web presence

9ayne and 9ees -StageModel())3!

i. catalog@ii. transaction@iii. vertical integration@iv. hori%ontal integration

Moons -Stage Model())(!

i. simple information dissemination@ii. two-way communication@

iii. service and technical transaction@iv. vertical and hori%ontal integration @v. political participation

M Study ())! i. automate @ii. integrate @iii. enhance @iv. on demand

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ode" Stage'

#elanger and >iller ())?! i. web presence@ii. interaction@iii. transaction@iv. transformation @

v. political participation9ee, ()3)! i.presenting@

ii. assimilating@iii. [email protected] @v. e-governance

As seen in Table 3, there are Bleaps between each of the stages. The development

stages of e-government follow several phases from Baccess level that allows citi%ens

and business access to government information@ Binteraction level, which allows

interactions with government through email or download forms@ Btransaction level that

allows users to conduct transactions online@ and Bintegration level, which integrates all

services in different e-government organi%ations and governance. #ased on the models

developed by the researchers, e-government development occurs in stages from

access level to transaction and integration level and finally e-governance. $overnment-

to-#usiness $(#! systems are grouped in the Bintegration stages of development due

to the capabilities and services offered to the business community and citi%ens.

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government and leverage the industrial capabilities of Multimedia Super Corridor MSC!

by addressing the following areas Siddi0uee, ())4!owever,

reverse auctions do not allow the e/pression of non-price attributes such as 0uality,

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service and reliability, thus hindering collaboration in buyer-supplier relations +miliani

and Stec, ())!. As such, one of the possible reasons of low usage is that the sourcing

officials feel they are no longer contributing to the bidding process as negotiator and

service provider while losing the supplier-buyer relationships normally present in manual

transactions.

1.17 eB+dd+ng Ado)t+on I''ue'

&t has been si/ years since e#idding was rolled-out in ())? and based on the statistics

from M';s e"erolehan team pro8ect report, the e#idding system adoption is

e/periencing a low adoption problem www.home.e"erolehan.gov.my, ()3)!. There are

only ?,))) suppliers who are active users out of the appro/imately ),))) suppliers that

are capable of conducting e"erolehan transactions The Star, ( April ()3)!. 2ntil April

()3(, there are a total of 3:,?44 transacting procurement units involving all ministries

and agencies in the "eninsular of Malaysia. >owever the number of transacting

procurement units via e#idding is only 3 transactions out of 3:,?44 transactions of

procurement units. The balance amount was transacted using the manual system

www.home.e"erolehan.gov.my, ()3(!.

The governments approach to address the problem of low adoption of the system are

by using infrastructure and system perspectives increasing &nternet band-with, updating

system and hardware re0uirements!, rather than loo1ing at user acceptance of the

system. The e"erolehan support team only addresses issues related to training,

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enablement of government responsible centers, site readiness, user complaints and

revenue generation from the use of the system. The team does not e/amine the factors

that influence e#idding adoption. There has been no empirical study underta1en by the

e"erolehan "ro8ect Team or Commerce ot Com Sdn. #hd. the concessionary! on

e#idding to address the issue of low adoption by the government users. &mplementation

issues were previously mitigated by coordination efforts by the concessionary company

and respective agencies monitored by MAM"2.

The non-adoption of e#idding by government users and their suppliers pose serious

implications for the continuous usage and development of the innovative system. The

suppliers could not participate in e#idding if the system is not chosen by the officials for

transactions. The low rate of adoption by the users indicates inherent officials problem

with e#idding rather than with the suppliers. There is an e/ample where e#idding has

been pointed as the cause for failure in goods and services procurement in a

government agency, due to a mista1e in the transaction caused by ine/perienced and

untrained officials. As a result, the procurement was canceled and re-tendered via

manual transaction 9aporan Audit ()3), page ()3!. This e/ample is one of the possible

causes why officials are not employing e#idding, preferring the alternative manual

procurement methods, although e#idding has been touted as the best tool for cost-

saving for the government.

The losses from the low adoption is in terms of the investment poured into the planning

and development of e#idding which would be going to waste if there is low acceptance

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by the supplier community. &t is a fact that huge investment has been spent on the

development, implementation and maintenance of the system and this problem would

defeat the governments aspirations of increasing efficiency, productivity and

transparency in its operations. The losses are also in terms of the cost to purchase and

develop the technology including the recurring costs to maintain and conduct the

reverse auctions. CommerceotCom Sdn. #hd. CCS#! has invested DM34) million

to develop the e"erolehan system including the e#idding module. ue to the low usage

of the system, the contract between the government and CCS# has been e/tended

until ()3(. A high level of usage by suppliers is important as the business model is fee-

based and depends on the number of transactions and it is important for CCS# to

maintain a high number of transactions for the continuity of their business.

6otwithstanding, the government also loses in terms of e/pected cost saving benefits

from e#idding use if the system is not adopted by the users. The $overnment would be

able to en8oy () to ) percent savings from the operational budget #+D6AMA, ()):!.

;rom the saving, the funding can be channeled to other beneficial development

pro8ects. ;urthermore, the sourcing officials would have to revert to conventional

procurement processes that re0uire the tender documents to be downloaded, printed

and deliberated at tender meetings, which will consume more time and administrative

costs. These processes would have incurred higher operating costs which could be

channeled to other activities in delivering services to the public.

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1.11 Pro#"e& State&ent

The low usage reflected the possible inclination of the sourcing officials to procure

goods and services via conventional methods direct purchase or manual 0uotation

system! rather than using e#idding. The use of e#idding by the government users is

voluntary and the sourcing officials have the options, either to conduct a manual

transaction or use e#idding "e1eliling "erbendaharaan, ()):!. The e#idding system

offers compelling benefits to improve administrative efficiency and 0uality of service

delivery. >owever, these benefits may be wasted by officials unwillingness to use the &S

for many reasons that is worth investigating.

The focus of previous e-government studies has always been on the &S adoption by

suppliers rather from the sourcing officials perspective. $eorge ())*! argues despite

increased trainings and promotions, low e-government adoption remains a continuous

problem for the e-government sta1eholders. The system is designed to lessen officials

burden and improve efficiency in public service, as such the 0uestions regarding

government users adoption of e#idding still remain unanswered. Guestions are still

being as1ed by the sta1eholders whether the officials are using e#idding and reasons

the system is not being used. &t is unclear how far is the e#idding system being used by

the officials and what are the users perspectives about the information system. As

such, the research aims are to investigate the constructs that influence officials

adoption of e#idding.

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1.1 O#0ect+%e' of the Study

The ob8ectives of this study are to e/amine the factors influencing e#idding adoption by

procuring officials in the public sector and to understand the users behavior in adopting

e#idding. The specific purposes of this study are as followsowever, TAM( only e/plores the basis

of perceived usefulness "2! and ignores perceived ease of use "+'2! construct

which is generalisable. The model is e/tended to TAM which integrates TAM( with "2

determinants to e/plain "+'2 =en1atesh and #ala, ())4!. The additional factors to

TAM are computer self-efficacy, perception of e/ternal control, computer an/iety and

computer playfulness. Computer self-efficacy attribute refers to as an individual

perception whether the user could complete the tas1. "erception of e/ternal control is

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user perception about the e/istence of support for the system. Computer an/iety is

defined as the fear related to using a new technology while computer playfulness is

defined as internal motivation to use a new technology. "erceived en8oyment describes

the feeling when using a system becomes satisfactory. 'b8ective usability is the effort

needed to perform a tas1 =en1atesh and #ala, ())4!.

Although the TAM is more comprehensive in that it increases the "+'2 and "2, the

effects are focused only on the user and not in a much wider perspective. #roader

organisational issues, e.g. the influence of peers and user involvement in decision-

ma1ing may also influence the adoption of a new technology. >owever, many studies

find that TAM is not comprehensive enough in e/plaining users decisions to use the &S.

TAM is used as a base model and additional variables are added according to the

conditions and nature of &S being investigated. ;or e/ample, in a study by

5amarul%aman ())*!, TAM was used as base model and adds other variables such as,

personal cognitive influence. TAM was combined with other adoption models as well, for

e/ample, '& was integrated with TAM in a study on online ban1ing by >ernande% and

Ma%%on ())*!.

&n summary, TAM has been successfully employed in various studies to e/plain

individual acceptance and usage behavior in a wor1ing environment. >owever, there is

a need to e/tend the model by adding additional variables depending on the types of

technologies and environment.

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.-. D+ffu'+on of Inno%at+on' 6DOI9

&nnovation iffusion Theory Dogers, 3::! is another user acceptance theory which

has been praised by many researchers as an effective model in e/plaining user

behavior in technology acceptance. '& theory as shown in ;igure , focuses on the

individual characteristics that relate to technology adoption behavior. The '& is a

widely used model in behavioral sciences to investigate adoption of innovations by

individuals 'liveira et al., ()33!. The aim of the '& theory is to comprehend how and

why users either embrace or re8ect innovations Dogers, 3::!. &n 3::, he studied the

characteristics of individuals in terms of openness to innovations, and he developed

'&, which proposes that individuals react differently to change based on a stable

predisposition.

'& focuses on diffusion of innovation process when the new technology is transmitted

through the channels in the social system Dogers, ())!. iffusion of innovations

refers to how new ideas are employed and employed by the users within a specific

setting. According to '&, an innovation will be diffused at an increased rate if it could

be tested before adoption trialability!. &n addition, an innovation offers observable

results observability! and new technology has an advantage relative to other

innovations relative advantage!. Another innovation attributes include comple/ity the

technology is not overly comple/! and whether the innovation is compatible with e/isting

practices and values compatibility!.;ichman, ()))! defines innovation diffusion as the

process when a technology permeates and is adopted across a population.

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*+gure /.Perce+%ed Attr+#ute' Inf"uenc+ng Ind+%+dua" Ado)t+on of Inno%at+on

"erceived Attributes of &nnovations

Source< Dogers, ())!

As depicted in ;igure , these attributes are empirically inter-related, however each

attributes are different from each other and these attributes are based on previous

literature. escription of the attributes is given belowowever, 2TA2T does not address the issues relevant to technical and system 0uality

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factors such as, system, service and information 0uality dimensions. The model

limitation is that it cannot provide e/planation as to why the same application system

can be adopted in different ways in various settings Tsi1na1is and 5ouroubali, ())4!.

>ence, it is posited that, by combining all these independent factors it could better

represent factors that determine intention to use or behavioural intention of e#idding.

The effect of users personal innovativeness in the modified 2TA2T framewor1 will also

be investigated by including "&&T construct as a moderating variable. 2TA2T model is a

comprehensive model, but it is unable to include the measure to e/amine individual

traits, such as innovativeness and ris1 ta1ing that contributes towards technology

acceptance Dosen, ())!. #y including "&&T to the integrative 2TA2T and &S Success

Model, it is e/pected to boost the amount of variance e/plained in the adoption behavior

that allows a much wider perspective of user adoption of an &S. The proposed

framewor1 for this research is as shown in ;igure 4.

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*+gure 4. Pro)o'ed Re'earch ode"

68

"erformance+/pectancy

+ffort+/ ectanc

Social &nfluence

;acilitatingconditions

+/perience"ersonal

&nnovativeness in&T "&&T!

e#iddingAdoption

System Guality

ServiceGualit

&nformationGuality

Satisfaction

H1

H3

H5a

H6a

H2

H4

H1aH2a

H2b

H3a

H4a

H7a

H6

H5

H7


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A research framewor1 is proposed based on the combinations of construct drawn from

both models. The proposed integrative model would provide a more realistic picture of

individual level e#idding adoption by the procuring officials. &n the proposed framewor1,

e#idding adoption is the dependent variable with performance e/pectancy "+!, effort

e/pectancy ++!, social influence S&! and facilitating conditions ;C!, information

0uality &G!, system 0uality SG! and service 0uality S=G! as independent variables.

2ser satisfaction is proposed as the mediator while "&&T is added as the moderator

between "+ and ++ with e#idding adoption.

The adapted 2TA2T model will be tested without two moderator variables, age and

gender. $ender variable is e/cluded due to the environment in the government setting

that did not discriminate the wor1 functions and responsibilities between male or female

officials. #oth genders receive similar training, e/posure and rulings on the use of any

e-government applications. Similarly, age variable is also eliminated from the study due

to the government setting that did not differentiate the wor1 functions and

responsibilities with regards to officials age. =oluntariness variable is e/cluded since

e#idding usage is optional and totally voluntary. A variable for moderating role namely

e/perience, is drawn from 2TA2T. "rior research suggests that increased ine/perience

will lower the influence of effort e/pectancy ++! and social influence S&! on the

adoption of the system =en1atesh et al., ())!. The "&&T is added to e/amine the

mediator effects between "+ and ++ with e#idding adoption. "&&T is posited to have a

moderator role on users perceptions about an innovation Agarwal and "rasad, 3::4!.

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Thus, this study assumes the possibility that e/perience and "&&T have moderating

effects on e#idding adoption by government users.

;or the &S Success Model, the two &S success categories, namely the organi%ational

impact and net benefits variables are e/cluded because it is not the intention of the

study to investigate these two categories, but the behavioural intention category to

adopt e#idding without the various impacts such as societal, individual, and

organi%ational impact of the system. =ariable satisfaction is posited as a mediating

factor between system factors, namely the information 0uality &G!, system 0uality SG!

and service 0uality S=G! and e#idding adoption. The research model put forward in

this study would be tested through the following hypotheses.

(.- Hy)othe'e' De%e"o)&ent

(.-.1 De)endent


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adoption of the e#idding is operationalised based on the wor1s of =en1atesh et al.,

())!, namely planning, intention, and predicting the use and actual use. The level of

system use is also 0uantitatively assessed using criteria, for e/ample planning,

intention, actual use, use fre0uency and amount of use of specific features 6elson et

al., ())!.

The study will focus solely on actual adoption because it is more practical to measure

the use of technology instead of intention to use. There is a strong correlation between

intentions to use with actual adoption. TAM e/plains the relationship between intention

to use and actual usage avis, 3::!. #ehavioural intention is found to be a valid

predictor of usage and adoption Sun and Jhang, ())!. As such it is ade0uate to test

only one of the variables because of the positive significant relationship between these

variables to each other. &t is posited that adoption is influenced by performance

e/pectancy "+!, effort e/pectancy ++!, facilitating conditions ;C!, social influence

S&! and system 0uality factors with three moderators, "&&T, voluntariness and

e/perience mediated by satisfaction.

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.-. Inde)endent


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(.-.. Effort e?)ectancy

+ffort e/pectancy refers to the degree of ease of system usage. The construct is similar

to TAMs perceived ease of use which is confirmed by 2TA2T authors. &n TAM and

TAM(, the effort e/pectancy is represented as perceived ease of use in '&. The TAM

factors include ease of use, ease of achieving level of e/pertise and easily

understandable =en1atesh et al., ())!.

+ffort e/pectancy construct is posited to be an antecedent of behaviour adoption and

use. "ar1 et al., ())*! find that effort e/pectancy significantly affects user adoption of

mobile service. &n Carlsson et al., ())?! study, the authors support the relationship

between effort e/pectancy and adoption. The authors contend that effort e/pectancy

has significant influence on the behavioural acceptance of the service. "revious

research also supports the contention that lesser effort is re0uired to learn and use the

system will ultimately influence its acceptance $efen and Straud, ()))!.

&n the study, effort e/pectancy is the belief that using e#idding would assist the

procuring officials to gain certain advantages for e/ample, improve 8ob performance and

increase productivity in their wor1 environment. As officials increasingly become used to

a new technology, the effort needed to use e#idding will decline. The more the officials

believes that the system is easy to use and help them become very s1illful in using the

system the more the inclination to use e#idding. &t is posited that the officials will use

e#idding if the system is easy to use, easy to operate and learn and re0uire less effort

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to understand. As such in the research model, effort e/pectancy is posited to have

significant relationship with the adoption of e#idding. >ence, it is posited as the

followingence, it is posited as the followingair et al.,

())?!. The respondent designations and mailing list of the DC agencies were sourced

from the e"erolehan 2nit, Ministry of ;inance Mo;! across different ministries in

"utra8aya, 5uala 9umpur and Shah Alam.

The main method of data collection for the study is by using mail survey in which the

0uestionnaires were administered by postal service. The advantages of the method are

the respondents can provide their responses to the 0uestionnaire at their own leisure. At

the same time this method can draw a sample from wider and larger geographical area

Se1aran, ())!. Although better response rate could be gained by distributing the

0uesionnaire by hand, time and cost limitations were the ma8or constraints since the

government agencies in the sample si%e are located all over 5lang =alley and

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"utra8aya.A random number generator called Desearch Dandomi%er 2rbania1 "lous,

3::*! was employed to randomly select respondents for the study. #efore the data

collection e/ercise, permission was obtained from the Secretary $enerals of the (4

Ministries located in "utra8aya.

The process of determining the right and conducive sample si%e was based on the

sample si%e table of ade0uate sample si%e for the si%e of a population. This was done

by using the guidelines by #artlett, 5otrli1 and >iggins ())3!. According to Table , a

population of 3,)* re0uire a minimum sample si%e of 33( responses. The sample si%e

plays an important role in calculation of efficiency of coefficients of internal consistency.

'%damar 3:::! argued that for reliability coefficient calculation, the sample si%e should

be more than ). Ni ()3(! proposed that the sample si%e should be above 3)) for a

study that employ Structural +0uation Modelling S+M!. A sample si%e of ()) is

recommended but less than )) because a higher number will render the S+M analysis

to be too sensitive >air, et al., ())?!.

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Ta#"e -. Ta#"e for Deter&+n+ng Returned Sa&)"e S+:e for !+%en Po)u"at+on S+:e for Cont+nuou' and Categor+ca" Data #y Bart"ett5 >otr"+k5 H+gg+n' 67719

Source< #artlett, 5otrli1, and >iggins ())3!

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/.(. Data Co""ect+on

The participation of DCs in the survey was solicited by mail survey. Their contact

information email, postal addresses, telephone numbers! were sourced from

e"erolehan 2nit, Ministry of ;inance and invitations to participate were sent to the DCs

starting from the first wee1 of ;ebruary ()3. After approvals were obtained from the

Ministries, the 0uestionnaires were sent to the >ead of epartmentsF2ndersecretary of

"rocurementF;inanceFevelopment ivisions which supervise the DCs and underta1e

procurement transactions based on the list provided by the e"erolehan 2nit. The

00uestionnaires were sent through postal services, emails and followed by phone calls

to the DCs. ;urther assistance and phone calls to clarify the 0uestionnaires to the

respondents were done as and when re0uested by the respondents. The face-to-face

engagements will enable the respondents to clarify any doubts in the 0uestionnaires

with the researcher Se1aran, ())!.

The survey respondents were informed of the privacy of the data provided in the survey.

The respondents returned the completed 0uestionnaire in one month from the date the

0uestionnaires were being delivered to them. #y the end of the period, follow-ups were

conducted via phone calls, and email reminder. 'fficials from e"erolehan 2nit also

provide assistance in terms of reminding the procuring officials in the list to complete the

0uestionnaire in their emails to the DCs. 'ne hundred and ten responses 33)! were

returned within the specified time. Gueries on the low return of the survey

0uestionnaires revealed that e#idding module has not been employed at the time

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considered as an early part of the year and the respondents were busy with the

scheduled annual budget planning wor1 and did not have time to complete the

0uestionnaire. After the cut-off date, another forty )! responses were returned after

follow-ups and personal visits to the DCs. This brought the accumulated returned

0uestionnaires to 3) valid survey forms, about 3)L of the sampling population. This

response e/ceeded the re0uired minimum responded sample si%e of 33( for a

population of 3,)) according to #artlett, 5otrli1 and >iggins ())3!.

/./ @ue't+onna+re De'+gn

The 0uestionnaire in the study is divided into three parts as in Appendi/ 3. The first part

of the survey is to collect the data of factors influencing the procurement officials

adoption of the e#idding. This part is divided into seven sections, corresponding to the

number of independent variables. The measurements were adapted from the studies of

2TA2T and &nformation System Success Model. The second part of the instrument is to

ac0uire respondents profiles position, department, years of service, age, and gender!@

and hisFher years of service, department si%e and location!. This section consists of

closed 0uestion formats and open-ended format, using nominal scale. The third part of

the 0uestionnaire was to obtain the data of officials actual use of the e#idding.

Guestions are on the actual use, level of usage in transactions, fre0uency of use in

years and period of utili%ation. This section is based on closed 0uestion format, using a

9i1ert scale.

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/.- ea'ure&ent of Satisfaction * &nterval Hi/om and Todd,())!

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NoSect+

on


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/.-.1 Actua" U'e

This variable measures actual usage of the &S. The construct is measured from

planning, intention, a visit to the &S site, navigation, information 0uery and actual

transaction =en1atesh et al., ())!. Despondents are as1ed to choose their preference

based on the statements using a seven-point 9i1ert scale from 3! Be/tremely

infre0uent to *! Be/tremely fre0uent@ and one 3! statement using ordinal data scale

with a rating scale attached ranging from3 ! Bless than 3 year @ ( ! B3-( years@! B-

years@ ! Bmore than years .

/.-. Perfor&ance E?)ectancy 6PE9

"erformance e/pectancy measures how users believe that using the &S will increase

their 8ob performance =en1atesh et al., ())!. Seven *! statements were used to

measure this construct that the respondents could choose using a seven-point 9i1ert

scale.

/.-.( Effort E?)ectancy 6EE9

+ffort e/pectancy refers to ease of use of the information systems =en1atesh et al.,

())!. Seven *! statements are used to measure this construct that the respondents

could choose using a seven-point 9i1ert scale.

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/.-./ Soc+a" Inf"uence 6SI9

Social influence refers to how a user belief that important others believe he or she

should use the &S =en1atesh et al., ())!. Seven *! statements are used to measure

this construct that the respondents could choose based on the statements using a

seven-point 9i1ert scale.

/.-.- *ac+"+tat+ng Cond+t+on' 6*C9

;acilitating conditions construct refers to how a user believes the e/istence of

supporting infrastructure e/ists to facilitate the use of the &S. Seven *! statements are

used to measure this construct that the respondents could choose using a seven-point

9i1ert scale.

/.-.2 Sy'te& @ua"+ty 6S@9

System 0uality measures the technical characteristics of the system, namely the

adaptability, availability, reliability, response time and usability e9one and Mc9ean,

())!. Seven *! statements are used to measure this construct that the respondents

could choose using a seven-point 9i1ert scale.

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/.-.3 Infor&at+on @ua"+ty 6I@9

&nformation 0uality is defined as the attributes of the &S, for e/ample, completeness,

personalisation, accuracy and relevance e9one and Mc9ean, ())!. Seven *!

statements are used to measure this construct that the respondents could choose using

a seven-point 9i1ert scale.

/.-.4 Ser%+ce @ua"+ty 6S


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/.-.17 E?)er+ence

+/perience refers to individuals e/perience whether he or she is affected since the last

time heFshe has used the system =en1atesh, et al., ())!. ;ive ! statements are

used to measure this construct that the respondents could choose using a seven-point

9i1ert scale.

/.-.11 Per'ona" Inno%at+%ene'' +n the Do&a+n of Infor&at+on Techno"ogy 6PIIT9

"&&T refers to a users propensity to ta1e the ris1 and try an innovative technology

Agarwal and 5arahanna, ()))!. ;ive ! statements are used to measure this construct

that respondents could select using a seven-point 9i1ert scale.

/.2 P+"ot Study

=erbal and written feedbac1 regarding the 0uestionnaire construction was obtained from

fifty )! participants and pilot-tested in 2"M. The respondents were chosen based on

convenience sampling, however they were not included as respondents in this study.

"ilot testing is to improve the reliability and validity of the proposed research

instruments Se1aran, ())!.The pilot test enables removal of vague and confusing

0uestions, determine the time ta1en to complete the survey and to chec1 for proper

se0uencing of the 0uestions. The feedbac1 is to be employed to finali%e the content of

the 0uestionnaire. Decommendations from the respondents were incorporated and

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modifications were made. Several items were re-sorted in order to ma1e them more

convenient for the respondents. Several sentences were rephrased and reworded to

ma1e them more appropriate with the ob8ectives of the study.

Analysis of data from the trial determines the reliability of the survey 0uestions.

Cronbachs alpha reliability coefficient was employed to e/amine the reliability of the

research instruments. Coefficient r! from ) and 3 with coefficient closer to one indicates

higher reliability $eorge and Mallery, ())!. Deliability coefficients should be at least .

*) or higher to be considered reliable for an effective research instrument Hallen and

;raen1el, ())3!.

CronbachIs alpha of each of the instrument was obtained. ;or nP), all the items for

each variable posed a CronbachIs alpha value of ).4 to ).: in terms of reliability >air et

al., ())?!. All CronbachIs alpha coefficient of the scale from the pilot test is above the

acceptance value of ).*. >ence, the survey 0uestions are valid and reliable and can be

used at the DCs for the purpose of measuring the outcome of e#idding adoption.

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/.3 Data Ana"y'+' A))roache'

The data analysis approaches in the research are based on descriptive and inferential

statistics. The data will be tested for goodness of fit using factor analysis and reliability

of measures using Cronbach Alpha. &n addition, proposed hypotheses will be e/amined

using relevant statistical tests. ata analysis as depicted in ;igure :, involves several

steps before hypothesis testing in ensuring good 0uality data for further analysis by data

preparation@ feel for the data@ test the goodness of data@ e/amine the hypothesis using

structural e0uation modelling @ and e/amine the model fit Se1aran, ())!.

*+gure 8 *"o D+agra& for Data Ana"y'+' Proce''

Source< Se1aran, ())!

102

;eel for data-6ormality-Correlations

$oodness ofdata-Deliability- =alidity

>ypothesistesting-AppropriatestatisticalmanipulationsS+M,>ierarchical,Degression!

Testing modelfit-DMS+A-T9C etc

Answer for

research0uestions

ataCollection

ata Analysis

&nterpretationof Desults

iscussion


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/.3.1 De'cr+)t+%e Stat+'t+c'

escriptive statistics is employed in order to organise the results which covers the tests

of fre0uencies, mean and standard deviation. ;re0uency distribution enables

researchers to view the entire responses to the 0uestionnaire. Mean is used to measure

average response by adding all the numbers and dividing by the number of cases.

Standard deviation shows the distribution scores from the mean value ;in1, ())4!.

/.3. Inferent+a" Stat+'t+c'

&n the study, inferential statistics is used to enable the results obtained from samples to

be generalised. Structural +0uation Modelling S+M! and AM'S software are used to

e/amine the measurement model ade0uacy and structural model goodness-of-fit $o;!

including the hypotheses testings.

/.3.( Structura" Euat+on ode"+ng 6SE9

The study employs the Structural +0uation Modeling S+M! techni0ue to test the

relationships among the variables in the model. The method involves multiple

regression analysis of factors among a single measured dependent variable and a

group of predictors 2llman ())*!. >air et al., ())?! argued that S+M is able to

e/amine two types of models, i.e. the measurement model that represents the theory

and the model which represent the latent factors. A structural e0uation modeling S+M!

employs multivariate analyses and will show relationships between constructs and the

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causal dependencies between endogenous and e/ogenous variables >air et al., ())?!.

The S+M also allows two groups comparisons which ma1es it suitable for testing the

hypotheses of the research. Therefore, S+M is selected in this study to maintain

parsimony in the proposed model while benefiting from its strength in testing the

research hypotheses.

The study uses AM'S version ? to e/amine the relationships between the independent

variables "+, ++, S&, ;C, SG, &G, S=G!, moderator variables "&&T and e/perience!

and one mediator satisfaction! and behavioural actual use of e#idding service. There

are two main reasons behind the decision to adopt this software. ;irst, the software is

available in the graduate school resource centre, as such it is accessible for the

analysis of data. Second, AM'S is rarely employed in previous empirical and

conceptual research of user acceptance Tong, ())*!.

Structural +0uation Modeling S+M! in AM'S involves drawing a circle and arrow path

diagram. AM'S is easy to learn and use and it is capable of analysing many goodness-

of-fit measures. AM'S also offers fle/ibility and ability to analyse numerous linear

models $arson, ()):!. The data will be analysed with descriptive and inferential

statistics tests. The following statistical methods are used in this study to organi%e the

datae argued that by reporting all goodness-of-fit

measures, it implies that the researcher is on a fishing e/pedition. >owever,

5line ())! recommended the four most commonly reported goodness-of-fit

tests are chi-s0uare@ $oodness-of-;it &nde/ $;&!, 6ormed ;it &nde/ 6;&!,

Comparative ;it &nde/ C;&!, and Doot Mean S0uare +rror of Appro/imation

DMS+A!.

Chi-s0uare Q(! is the most popular goodness-of-fit test used for S+M $arson,

()):!. AM'S outputs list chi-s0uare as chiPRCM&6. The model chi-s0uare

values tend to decrease better fit! when more paths are inserted or created in a

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model. 'n the other hand, significant Q( value shows poor model fit $arson,

()):@ 5assim, ())3@ Tong, ())*!. Delative chi-s0uare Q(Fdf! is used in S+M so

that the Q( value will be less influenced by the si%e of the sample 2llman,

())*!. Delative E( is listed in AM'S as DAT&' P CminFdf, and a relative E(

value of or less is considered as acceptable Tong, ())*!.

Comparative ;it &nde/ C;&! is another measurement for $oodness-of-;it test.

C;& is used to measure the model fit improvement compared to a null model

$arson, ()):@ Tong, ())*!. AM'S lists C;& as C;&PRcfi Tong, ())*!. C;& value

which is close to 3 indicates an e/cellent model fit $arson, ()):@ Tong, ())*!.

6ormally, C;& has to be .:) for a model to be accepted. C;& has to be .:) for

a model to be accepted Tong, ())*!.

6ormed ;it &nde/ 6;&! is an alternative inde/ of C;& Tong, ())*!. 6;& ranges

from ) bad fit! to 3 good fit! 2llman, ())*!. 6;& value of ).) means the S+M

model improves by fifty per cent in comparison to null model Tong, ())*!. 6;&

values above .: are considered outstanding, 6;& values from ).:) to ).: are

desirable, but the researcher has to re-specify the model if 6;& value is below

).:) $arson, ()):@ Tong, ())*!.

Tuc1er 9ewis ;it &nde/ T9&! is another incremental fit inde/ similar to 6;&

$arson, ()):!. T9& is relatively independent of sample si%e. T9& is e/pressed

as the fit per degree of freedom Tong, ())*!. AM'S lists T9& as T9& PRT9&

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Tong, ())*!. T9& value close to 3 or T9& value ).:) shows a good model fit,

while T9& value below ).:) shows the need to re-specify the model Tong,

())*!.

Another important indicator is $oodness of ;it &nde/ $;&! which analyses the

per cent in the model co-variances $arson, ()):!. AM'S lists $;& as $;& PRgfi

Tong, ())*!. $;& should be e0ual to ).: or higher for a parsimonious model

$arson, ()):! whereas, Schumac1er and 9oma/ ())! recommended a $;&

value of ).: or higher.

Another criterion for $oodness-of-;it test is Doot Mean S0uare +rror of

Appro/imation DMS+A!, which is the difference per degree of freedom Tong,

())*!. DMS+A is an effective measure of model fit as there is no need to

compare a S+M model with a null model Tong, ())*!. AM'S lists DMS+A as

D+S+A PRrmsea. DMS+A value of U ).4 as good model fit Tong, ())*!.

g9 Stage 3 od+fy+ng the ode"

The model was e/amined for potential model modifications after an initial S+M

model was established. A re-specification process was done by trimming the

model by adding path arrows and removing parameters to achieve a model with

good fit $arson, ()):!.

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CHAPTER -

DATA ANA;SIS AND DISCUSSION

-.7 Introduct+on

The chapter outlines the findings of the study with analysis of the data gathered in the

study. The hypotheses and the proposed framewor1 are e/amined to analyse the

behavioural and technological factors associated with the adoption of an e-government

system within the government setting.

The chapter outlines the descriptive and inferential analysis of the data including

reliability and normality tests. The reliability and validity of the constructs were tested

using conformatory factor analysis. S+M was conducted to test associations between

the constructs in proposed framewor1. S+M was also used for the statistical analysis in

relation to the ob8ectives of the study and the proposed hypotheses.

-.1 *ee" for Data

&n the study, feel for data was done by e/amining the central tendency and dispersion.

As suggested by Se1aran, ())!, the analysis is organi%ed into the followingair et al., ())?!.

Ta#"e 11. Re"+a#+"+ty and


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NoCon'truct

*actor"oad+ng

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-.(. D+'cr+&+nant


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Tuc1er 9ewis &nde/ T9&!. Table 3 shows the criteria for the $o; indices and the

desirable range.

Ta#"e 1(. !oodne''$of *+t 6!O*9 Ind+ce'

Deter&+ner Syo" S)ec+f+cat+on

Absolute fit indices Desirable Criteria

CminCmin p .) means significant

6ormed Cmin CminFdf3.)XcminFdfX.)

Doot Mean S0uare +rror ofAppro/imation

DMS+A DMS+AX.)4

Comparative ;it &nde/ C;&

6;& or e0ual to .: indicatessatisfactory fit

.4X6;&X .: indicatesacceptable fit

Tuc1er-9ewis inde/ T9& T9& .: indicates acceptable fit

6ormed ;it &nde/ 6;&6;& P .: indicates acceptable fit

).4X6;&X .: indicates acceptable fit

$oodness-of-;it &nde/ $;&

$;& P .: indicates acceptable fit

).4X$;&X .: indicates acceptable fit

Source< >air et al. ())?!

The ma/imum li1elihood M9! estimates to measure parameters of the measurement

models of the uni-dimensional construct in the study are shown in ;igure 3) to ;igure

3*. The values of standard errors related to the estimates and the goodness-of-fit

indices for each measurement models are shown in Table 3( to Table (3.

*+gure 17. ea'ure&ent ode" for Perfor&ance E?)ectancy

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Ta#"e 1-. !oodne''$of$f+t for Effort E?)ectancy

!O*

+nd+ce'


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Ta#"e 12. !oodne''$of$f+t for Soc+a" Inf"uence

!O*+nd+ce'


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-.- Data Ref+ne&ent and


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+. Perfor&ance E?)ectancy

There are seven *! items under the "+. As shown in ;igure 34, after re-specification,

three ! items have to be removed, as the loading is less than )., which is considered

to be wea1. This leaves four items for the ne/t stage of analysis..

The goodness-of-fit indices from Table (( show the E(value produces non-significant

results which show that the model fits the data after modification. DMS+A value is less

than ).)?, which suggests a good fit. $;&, C;&, 6;&, and T9& values are 3, indicating a

perfect fit. "+( produces the highest factor loading of ).:, followed by "+3 ).4:!,

"+ ).4! and "+ ).4)!.

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*+gure 14. Re%+'ed ea'ure&ent ode" for Perfor&ance E?)ectancy

Source < eveloped for the study

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ii. Effort E?)ectancy

There are seven *! items under the ++. After re-specification, three ! items have to

be removed as the loading is less than ). which indicates wea1 relationship ;igure

3:!. This leaves four ! items remaining for the ne/t stage of analysis. &tem ee

produces the highest factor loading ).:*!, followed by ee ).:!, ee? ).:)! and ee*

).*)!. Desults from Table ( illustrate the E(value produced a non-significant result,

which indicates that the model fits well. DMS+A value is lower than ).)4 which

indicates a good fit. $;&, C;&, 6;& and T9& values are 3, indicating a perfect model fit.

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+++. Soc+a" Inf"uence

There are seven *! items under the construct S&. After re-specification, three items !

are omitted as factor loadings are less than ). ;igure ()!. As shown in Table (, test

of the fitness model yields E(value of .*:, degree of freedom of 3 with CM&6F; is

reported to be .*:. This value produces significant results pX).)! indicating the data

does not fit the model well.

>owever, other indices C;& ).:4:!, $;& ).:4:!, 6;& ).:4*! and T9& ).:(! are higher

than ).: indicating the data fits the model well.

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*+gure 7. Re%+'ed ea'ure&ent ode" for Soc+a" Inf"uence 6SI9

Source< eveloped for the Study

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+%. *ac+"+tat+ng Cond+t+on' 6*C9

There are seven *! items under the ;C construct. After re-specification, three ! have

to be omitted with four ! items remained for the ne/t stage of analysis ;igure (3!. As

shown in Table (, the E( value generates non-significant results, indicating the data fit

the model very well. DMS+A value was ).)) indicating a perfect fit. 'ther indices $;&

3.))!, C;& 3.))!, 6;& 3.))! and T9& 3.))! indicate perfect model fit.

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*+gure 1. Re%+'ed ea'ure&ent ode" for *ac+"+tat+ng Cond+t+on' 6*C9

Source < eveloped for the Study

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%. Infor&at+on @ua"+ty 6I@9

There are seven *! items placed under &G. After re-specification, three ! items are

removed and four ! items remain for the ne/t stage of analysis ;igure ((!. As in

Table (?, the analysis reveals that E(value of (.?3@ degree of freedom ( with CM&6F;

value of 3.3 p).)!, indicating model fit. DMS+A is ).)? @ confidence interval ).))

to ).3*?!.

'ther indices, namely $;& ).::3!, C;& ).::4!, 6;& ).::! and T9& ).::! indicating a

good model fit. 'verall factor loadings for each item are higher than the ). limit

indicating model fit.

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*+gure . Re%+'ed ea'ure&ent for Infor&at+on @ua"+ty 6I@9

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%+. Sy'te& @ua"+ty 6S@9

There are seven *! items under the SG construct. Three ! items are removed after

re-specification with four ! items remaining for the ne/t stage ;igure (!. The results

in Table (*, indicate the E(value is ).:: @ degree of freedom (, CM&6F; value of

).*. DMS+A value of ).))) is lower than ).)? that indicate it is a model. The

confidence interval for DMS+A is ).)))-).3).

'ther indices such as, $;& ).::*! and 6;& ).::4! values are close to 3 indicating that

the data is compatible with the model. Similarly, C;& and T9& produced the value of 3

indicating perfect fit. &n addition, all factor loadings are above ). as recommended by

>air et al, ())?!.

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*+gure (. Re%+'ed ea'ure&ent ode" for Sy'te& @ua"+ty

Source< eveloped from Study

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%++. Ser%+ce @ua"+ty

There are seven *! items placed under the S=G construct. After re-specification, three

! items are removed with four ! items remaining for the ne/t stage ;igure (!. As

shown in Table (4, the E(value of was .? @ degree of freedom of (@ CM&6F; is

(.*. The non-significant results showed that the model was well fitting.

'n the other hand, the DMS+A value is ).)*: with confidence interval ).)))-).3.

'ther indices such as, T9& ).:?*!, 6;& ).:4! and $;& ).:4(! further highlight

satisfactory values which are close to 3. The findings show that the data fit well with the

model.

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*+gure /. Re%+'ed ea'ure&entode" for Ser%+ce @ua"+ty

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+?. Sat+'fact+on

There are seven *! items placed under the satisfaction construct. After re-specification

process, three ! items are removed with four ! items remaining for the ne/t steps of

analysis ;igure (!. As shown in Table (:, E(value is 3.( with degree of freedom of

( and CM&6F; value of ).**3. The non-significant results show the model fits with the

data.

Meanwhile, DMS+A value is ).)) with confidence interval ).)))-).3). Desults from

$;& ).::! and 6;& ).::(! further prove satisfactory values which are close to 3. C;&

and T9& produce the value of 3 indicating good fit. These findings indicate the fitness of

data with the model.

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*+gure -. Re%+'ed ea'ure&ent ode" for Sat+'fact+on

Source < eveloped for Study

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-.2 Structura" Euat+on ode""+ng 6SE9

-.2.1 Hy)othe'+:ed *+r't Order C*A ode"

The S+M statistical techni0ue was used to e/amine the relationships between

behavioural factors with e#idding adoption, the role of satisfaction as mediating well as

the moderating effects of e/perience and "&&T between behaviour factors and e#idding

adoption by government procurement officials. C;A was underta1en to measure

parameters of the measurement models.A $oodness-of-;it test was used to assess the

proposed structural model to decide either to accept or re8ect the model. The overall fit

statistics results obtained from testing the model is presented in Table ).

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A summary of the results indicates the E( statistics are )3., $;& at ).? and C;& at

).?), DMS+A ).):!, T9& ).*:! and 6;& ).?3!. #ased on the guidelines given by >air

et.al, ())?!, the proposed model does not have a good overall fit and any parameter

estimates in S+M with a poor fit are not generali%able. Model re-specification is

underta1en to improve and develop a model that is generali%able.

-.2. Re%+'ed ea'ure&ent ode"

Confirmatory factor analysis C;A! was used to improve the $'; indices of the model.

After re-specification, the overall fit for the revised model were e/amined based on the

output obtained as ;igure (?. A summary of results is presented in Table 3. The test of

fitness of the model used on the whole sample produces a QY value of *44.4 while the

CM&6F; is reported to be 3.:3:. Deferring to the E(value, the model does not seem

to be compatible. 'ther indices are also used as indicators to determine the goodness

of fit of the study model.

The T9& ).:(! and $;& ).4! values are within desirable range, which suggest the

model can fit the data. C;& and 6;& show reasonable values which ).:, and ).4*

close to 3!, which suggest that the model and the data are harmonious with one

another. Additionally, the DMS+A value is ).)4 within the desirable range for models fit.

&n other words, the re-specification process had therefore improved the models fit.

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*+gure 2. Re%+'ed ea'ure&ent ode" of the Study

Source De%e"o)ed fro& 'tudy

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The revised measurement model shows a good fit, as such the model can be used to

e/amine the hypotheses proposed in this research. The relationship between the

variables e/amined and e#idding adoption are analysed by e/amining the significance

of the path coefficients in the model. &n the ne/t section, the data will be analysed in

terms of mediating and moderating effects including the forwarded hypotheses in the

model. The reports of the findings will be compared with published studies reported in

the literature including possible e/planations for the results.

-.3 Te't' for ed+at+ng Effect' 6Sat+'fact+on9

A mediating effect is present when the variable e/plains the relationships between the

predictor and the outcome variable of the study. >air et al., ())?! argued that

mediating effect is not supported if the relationships between the predictor and the

outcome variable is unchanged when the mediating varibale is added to the model. As

depicted by ;igure (*, Mac5innon ()))! contended a mediating variable is an

asymmetric relations among the variables in the model.

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+. Te't for ed+at+ng Effect' of Sat+'fact+on on 6Sy'te& @ua"+ty Ado)t+on9

The measurement model is shown as ;igure (4. As shown in Table (, chi-s0uare value

produces significant results which show that the data do not fit the model. DMS+A value

).)4! suggests not a good fit. >owever, other indices such as C;& ).:4!, 6;& ).:!,

$;& ).:(! and T9& ).:?! indicate a model fit.

*+gure 4. ea'ure&ent ode" ed+at+ng Effect of Sat+'fact+on 6Sy'te& @ua"+tyAdo)t+on9


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Ta#"e (.Te't of ed+at+ng Effect' of Sat+'fact+on on Sy'te& @ua"+tyAdo)t+on

Re"at+on'h+)

!O*Ind+ce' owever,

the relationship between SG and satisfaction show significant results

ZP).?4,C.D.P?.), pX).)!. ;urthermore, satisfaction has a significant effect on

adoption ZP3.?, C.D.P:.**3 pX).)!. irect effect is ).): versus indirect effect

).(. &n conclusion, satisfaction is considered as full mediator between system 0uality

and adoption.

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++. Te't for ed+at+ng Effect' of Sat+'fact+on on 6Infor&at+on @ua"+ty Ado)t+on

Re"at+on'h+)9

The measurement model is shown as in ;igure (:. As shown in Table , the E ( value

produces significant results being less than !, which indicate that the data do not fit

the model well. >owever, DMS+A being less than ).)4, suggests a good fit supported

by other indices such as $;& ).:!, C;& ).::!, 6;&).:?:! and T9& ).::! indicating a

reasonable model fit.

*+gure 8. ea'ure&ent ode" of ed+at+ng Effect of Sat+'fact+on on 6Infor&at+on

@ua"+ty Ado)t+on9

Source< eveloped for study

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Ta#"e (/. Te't' of ed+at+ng Effect' of Sat+'fact+on on the Infor&at+on @ua"+ty$Ado)t+on Re"at+on'h+)

!O*Ind+ce'


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Ta#"e (-. Hy)othe'e' Te't+ng 6ed+at+ng Effect' of Sat+'fact+on on the Infor&at+on@ua"+ty$Ado)t+on Re"at+on'h+)9

Ste) E't+&ate SC C.R.t Re'u"t'

3 Adoption X--- &G .* . *.?4 S+gn+f+cant( Satisfaction X--- &G .??? .?? *.):: S+gn+f+cant

Adoption X--- Satisfaction .: .* .?( S+gn+f+cant

irect effect .

&ndirecteffect

.(:

The findings as in Table ! show a significant relationship between information 0uality

and adoption ZP).*, C.DP*.?4, pX).)!. &t is confirmed that information 0uality

significantly influence satisfaction ZP).???, C.DP*.)::,pX).)!. There is also

significant influence of satisfaction on adoption ZP).:, C.DP.?(, pX).)!. irect

effect is ). versus indirect effect ).(:. >ence, satisfaction is partial mediator

between information 0uality and adoption.

+++. Te't for ed+at+ng effect' of Sat+'fact+on on 6Ser%+ce @ua"+ty Ado)t+on

re"at+on'h+)9

The measurement model is shown as in ;igure ). Desults as Table ? shows E( value

with significant results indicating poor model fit. >owever, DMS+A value ).)?! less

-.)4 which indicate good fit. 'ther indices such as $;& ).:!, C;& ).:44!, 6;& ).:?!,

T9& ).:*?! also show a reasonable model fit.

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*+gure (7. ea'ure&ent ode" to Te't ed+at+ng Effect of Sat+'fact+on on 6Ser%+ce@ua"+ty and Ado)t+on9


Ta#"e (2.Te't' of ed+at+ng Effect of Sat+'fact+on on the Ser%+ce @ua"+ty$Ado)t+onRe"at+on

!O*Ind+ce'


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Ta#"e (3.Hy)othe'e' Te't+ng 6ed+at+ng Effect' ofSat+'fact+on on the Ser%+ce@ua"+ty$Ado)t+on Re"at+on9

Ste) E't+&ate SC C.R.t Re'u"t'

3 Adoption X--- S=G .?( .(*( .)( S+gn+f+cant( Satisfaction X--- S=G .: .* *.: S+gn+f+cant

Adoption X--- Satisfaction .:( .*3: ?.(?? S+gn+f+cant

irect effect .(*(

&ndirecteffect

.(4

The findings as in Table * show a significant relationship between S=G and adoption

ZP).?(, C.DP.)(, pX).)!. The relationship between S=G and satisfaction also

produced significant findings ZP).:, C.D.P *.:, pX).)!. There is a significant

relationship between satisfaction and adoption ZP).:(, C.DP?.(??, pX).)!. #ased on

the findings, satisfaction becomes partial mediator between S=G and adoption with

direct effect is ).(*( while indirect effect is ).(4. ;rom the findings, satisfaction show

partial mediating effect between S=G and e#idding adoption.

.

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-.4Te't' for oderat+ng Effect' 6PIIT and E?)er+ence9

A moderating variable is the construct which has a strong contingent effect on the

relationships between the predictor and the outcome variables. A contingent effect

indicates an e/istence of another variable that influences the relationships between the

predictor and the outcome Se1aran ())!. The moderating effect can be tested using

multiple-group analysis in AM'S which is able to estimate two or more groups

simultaneously Arbuc1le, ())!.

According to ;ra%ier et al., ())! moderating variable is present between predictor and

criterion variable. &t modifies the correlation in two ways by influencing the changes in

the correlation strength or the changes in the causality direction either negative or

positive!. The moderating hypothesis will e/amine the paths between the predictor and

the outcome variables, in terms of magnitude andFor directions. ;or e/ample, any

difference across the groups indicates that the predictor influence toward outcome

variable is moderated by that construct.

&n the case of the study, two moderators "&&T and e/perience! were tested to see

whether they will affect the influence of predictor variables toward the outcome variable

e#idding adoption!. As first moderator, "&&T was tested on the relationship between

performance e/pectancy "+! and effort e/pectancy ++! with e#idding adoption.

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+/perience second moderator! was tested on the relationships between effort

e/pectancy ++!, social influence S&! and facilitating conditions ;C!.

+. oderat+ng owever, there is an insignificant relationship between "&&T

and adoption p).)!. &n the second model it is proven that "&&T does not become

significant predictor between "+ and adoption p).)!.

Ta#"e (4. Te't for oderat+ng effect of PIIT 6Perfor&ance E?)ectancy$$Ado)t+on9

ode"

Un'tandard+:edCoeff+c+ent'

Standard+:ed

Coeff+c+ent' t S+g.

BStd.Error

Beta

Constant! 3.?*3 .44 .3) ).)))

"+ .? .)? .?( 3).3 ).)))"&&T .): .)43 .)3 .** ).?

Constant! (.?*? .44) .)3 ).))

"+ .): .(): .? 3.* ).3

"&&T -.3*4 .34: -.33 -.: ).*

"+W "&&T .) .)( .44 3.(*( ).()

a. ependent =ariable< Adoption

D(P).

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++. oderat+ng %ar+a#"e PIIT 6Effort E?)ectancyAdo)t+on9

The results of the study in Table : show ++ does not influence adoption ;

(,3*P3)*.3, pX).)!. The interaction between ++ and "&TT do not influence adoption

of e#idding adoption. This indicates "&&T does not become moderator variable of

relationship between ++ and adoption of e#idding p).)!.

Ta#"e (8. Te't for oderat+ng Effect of PIIT 6Effort E?)ectancy$ Ado)t+on9

ode"


Standard+:edCoeff+c+ent' t S+g.

BStd.Error

Beta

Constant! 3.( .( .:: ).)))

++ .*(: .)( .*?4 3.3 ).)))

"&&T .)3( .)?: .)): .3?4 ).4??

Constant! 3.*: .43 (.3( ).)

++ .:: .((3 .?3 (.*3( ).))*

"&&T -.)4 .3*( -.)?* -.4: ).?(?

++W "&&T .)(? .) .3*? .?)? ).a. ependent =ariable< Adoption

D(P).: ,

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+++. oderat+ng %ar+a#"e E?)er+ence 6Effort E?)ectancyAdo)t+on9

The results in Table ) show ++ and e/perience! have significant relationship with

e#idding adoption ; (,3*P33.4(,pX).)!. 2nder the second model only ++ has

significant relationship with adoption. The interaction between ++ and e/perience also

produces an insignificant result. The result shows that e/perience is an insignificant

moderator between ++ and e#idding adoption p).)!.

Ta#"e /7. Te't for oderat+ng Effect of E?)er+ence 6Effort E?)ectancy$ado)t+on9

ode"


Standard+:edCoeff+c+ent'

t S+g.

BStd.Error

Beta

Constant! .44* .(:) .)?3 ).))

++ .?:? .)3 .* 3.3? ).)))

+/perience .3 .)?* .3( (.(*) ).)(

Constant! .*4 .*34 .(* ).::

++ .4(* .3** .4*3 .?*3 ).)))

+/perience .(* .3?: .(() 3.?3 ).3):++W+/perience -.)) .)4 -.3: -.** ).)


D(P).?): .

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+%. oderat+ng %ar+a#"e E?)er+ence 6Soc+a" Inf"uence

Ado)t+on9

The results in Table 3 disclose S& and e/perience! have significant association with

adoption of using e#idding ; (,3*P3.34, pX).)!. 2nder the second model, only S&

remains significant. The interaction between S& and e/perience does not yield

significant results. The results indicate e/perience is not a significant moderator

between S& and adoption.

Ta#"e /1.Te't for oderat+ng Effect of E?)er+ence 6Soc+a" Inf"uence$E?)er+ence9

ode"



t S+g.

BStd.Error

Beta

Constant! -.3(4 .(4: -. .?:

S& .4?? .) .**3 3.:(4 ).)))

+/perience .3:( .)?) .3 .()( ).))(

Constant! .?? .*: .4* ).4S& .*3: .3:3 .?) .*?: ).)))

+/perience .)3 .3:4 .) .()4 ).4?

S&W+/perience .)* .) .() .4) ).(


D(P).?*:

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%. oderat+ng %ar+a#"e E?)er+ence 6*ac+"+tat+ng Cond+t+on' JK Ado)t+on9

The findings in Table ( show significant relationships between ;C and e/perience and

e#idding adoption ; (,3*P(4.):4, pX).)!. 2nder the second model ;C and

e/perience still yield significant results. >owever, the interaction between ;C and

e/perience does not influence adoption. This result indicates e/perience is not a

significant moderator between ;C and e#idding adoption.

Ta#"e /. Te't for oderat+ng Effect of E?)er+ence 6*CJE?)er+ence9

ode"



t S+g.B

Std.Error

Beta

Constant! .4) .) .* ).(

;C .:? .)44 .:? .?) ).)))

+/perience .3? .)4* . .** ).)))

Constant! -.:(4 3.3 -.? ).(3;C .4)* . .? (.(? ).)3*

+/perience .*(* . .4? (.34 ).)3

;CW+/perience -.)* .)*? -.? -.:?: ).

a. ependent =ariable(

+ffort e/pectancy is significantly related to officials adoption of

e#idding Su))orted

>Social influence is significantly related to officials adoption ofe#idding Su))orted

>;acilitating conditions is significantly related to officials adoptionof e#idding Su))orted

>&nformation 0uality is significantly related to e#idding adoption

Su))orted

>?

System 0uality is significantly related to e#idding adoption

. 6ot Supported

>*Service 0uality is significantly related to e#idding adoption

6ot Supported

>aSatisfaction significantly mediates relationship betweeninformation 0uality and e#idding Adoption

Su))orted

>?a Satisfaction significantly mediates relationship between system0uality and e#idding Adoption

Su))orted

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Hy)othe'e' Re'u"t'

>*aSatisfaction significantly mediates relationship between service0uality and e#idding Adoption

Su))orted

>3a"&&T positively moderates the relationship between performancee/pectancy and e#idding adoption

6ot Supported

>(a"&&T positively moderates the relationships between efforte/pectancy and e#idding adoption

6ot Supported

>(b+/perience negatively moderates the relationship between efforte/pectancy and e#idding adoption

6ot Supported

>a+/perience negatively moderates the relationship betweensocial influences and e#idding adoption

6ot Supported

>a+/perience positively moderates the relationship betweenfacilitating conditions and e#idding adoption 6ot Supported

Source < eveloped for the Study

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-.8 Hy)othe'e' Te't+ng

-.8.1 Hy)othe'+' Te't+ng of Perfor&ance E?)ectancy

H1 Perfor&ance e?)ectancy +' '+gn+f+cant"y re"ated to off+c+a"', ado)t+on ofeB+dd+ng

As in Table , the research findings in this study indicate that performance e/pectancy

positively influences e#idding adoption ZP).3)?, C.D.P(.?, pP).))4 X).)!. >ence,

null hypothesis has been re8ected at [P).). &n support of hypothesis >3, performance

e/pectancy is found to have a significant and positive relationship with e#idding

adoption.

This result is in conformance with =en1atesh et al., ())! wor1s which confirmed that

performance e/pectancy has a significant and positive effect on system acceptance.

The implication of the result indicates that the greater the procuring officials perceives

that by employing e#idding they enhance the wor1 performance, there will be more

willingness to use e#idding. The result is also consistent with recent studies related to

performance e/pectancy on e-government systems 9ouho et al., ())? @ Al-Geisi ()):!.

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-.8. Hy)othe'+' Te't+ng of Effort E?)ectancy

H Effort e?)ectancy +' '+gn+f+cant"y re"ated to off+c+a"', ado)t+on of eB+dd+ng

The results in Table shows that the hypothesis on effort e/pectancy is supported,

hypothesis null has been re8ected at [P).). #ased on the coefficient ZP).)*,

C.DP.(3(, pP).))X).)!. &T is determined that effort e/pectancy significantly

correlated with the dependent variable. Cody-Allen and 5ishore ())?! argued that

effort e/pectancy is defined as how a user believes the level of difficulty to use the

system. Such perception will influence the decision whether to adopt or not to adopt the

&S. 'ther studies have found similar findings that effort e/pectancy significantly affects

the use intention. Therefore in the case of government procuring officials, the result is in

conformance with other findings that effort e/pectancy was significantly related to &S

usage >elaiel, ()):@ Dosen, ())@ =en1atesh et al., ())!.

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-.8.( Hy)othe'+' Te't+ng of Soc+a" Inf"uence

H( Soc+a" +nf"uence +' '+gn+f+cant"y re"ated to off+c+a"' eB+dd+ng ado)t+on

The results in Table show that the hypotheses on social influence are supported.

#ased on the coefficient ZP).)*, C.D.P.*, pP).)))X).)!, social influence is

determined to have a significant correlation with the dependent variable. The findings

reveal that social influence positively influenced adoption of e#idding Thus, the null

hypothesis has been re8ected at [P).).

;rom the results, social influence is confirmed to be related to the e#idding adoption.

The result shows the higher social influence, the higher the officials propensity to adopt

e#idding. Therefore in the case of government procuring officials, it can be concluded

that S& has significant bearing on the adoption of e#idding. The result is also consistent

with several studies that shows e-government systems usage is affected by social

influence 5arahanna and Straub, 3:::@ Dosen, ())@ =en1atesh et al., ())!.

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-.8./ Hy)othe'+' Te't+ng of *ac+"+tat+ng Cond+t+on'

H/ *ac+"+tat+ng cond+t+on' +' '+gn+f+cant"y re"ated to off+c+a"', ado)t+on ofeB+dd+ng

The results in Table show that the hypothesis on facilitating conditions is supported.

#ased on the coefficient ZP-).?)?, CDP-.*3, pP).)))X).)!, facilitating conditions is

proven to be significantly correlated to the e#idding adoption. Thus, the hypothesis null

is re8ected at [P).).

&n the case of government procuring officials, it can be concluded that there is

significant relationship between facilitating conditions and e#idding use. The finding is

consistent with the results of >ung et al., ())?! wor1 on e-government systems usage

that found the influence of supporting conditions on e-government adoption.

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-.8.- Hy)othe'+' Te't+ng of Infor&at+on @ua"+ty

H- Infor&at+on ua"+ty +' '+gn+f+cant"y re"ated to eB+dd+ng ado)t+on

The results in Table show that the hypothesis on information 0uality is supported.

#ased on the coefficient ZP3.?):, C.DP.(3*, p).))3X).)!, information 0uality is

determined to have a significant correlation with the dependent variable hence, the null

hypothesis is re8ected at [P).).

&n the case of government procuring officials, we were able to conclude that information

0uality has an influence on the adoption of e#idding. The result is consistent with the

findings o

Documents

Factors Influencing eBidding (electronic reverse auctions eRAs) Adoption in Malaysian Public Sector