Antecedents of continuance intention of using Internet ...

DOI: 10.1002/jsc.2197

RESEARCH ARTICLE

Antecedents of continuance intention of using Internet banking in Saudi Arabia: A new integrated model* Ahmed Alghamdi1 | Ibrahim Elbeltagi1 | Ahmed Elsetouhi2 | Mohamed Yacine Haddoud1

1 Plymouth Business School, Plymouth University, United Kingdom

Abstract

Faculty of Commerce, Mansoura University, Plymouth, Egypt

Along with cognitive perceptions, customers’ psychological traits are key determinants of Inter‐

Correspondence Mohamed Yacine Haddoud, Plymouth Business School, Plymouth University, Mast House Campus, Devon PL4 0HJ, UK.

models strengthens their explanatory power and boosts our understanding of online behavior.

2

Email: [email protected]

net banking continuance to use. Incorporating psychological traits into technology acceptance Users’ technology readiness, uncertainty avoidance, and satisfaction are key determinants of Internet banking continuous use. Bank managers should take users’ personal preferences into consideration by offering flexible online systems with diversified designs and choices.

1 | INTRODUCTION

Despite their wide acceptance and popularity, TAMs are attributed

The majority of modern banks are deploying Internet banking in order

et al., 2005). While the initial acceptance of IS was found to be a cru‐

to increase efficiency, improve customer service and enhance loyalty

cial element, continuance usage of such systems is considered to be

(Chang, 2002; Hitt & Frei, 2002). However, despite its recognized ben‐

more important. This is mainly because gaining new customers is very

efits for both, banks and customers, the adoption of Internet banking

costly and more difficult than retaining existing customers (Hsieh,

have been limited and falling below expectations (Xue et al., 2011).

Hsieh, Chiu, & Feng, 2012; Zhou, 2011). IS continuance use emerges

In this respect, the empirical literature on information systems (IS)

in the implementation stage as a key indicator of continuance success.

in general and Internet banking in particular [see e.g., Lee, Kozar, &

Similarly, satisfaction perceptions of actual use in the implementation

Larsen (2003), Lichtenstein & Williamson (2006), Kesharwani & Bisht

phase would also affect the likelihood that users will continue to use

(2012), Martins, Oliveira, & Popovič, (2014)] have used a number of

the system in the long term.

as being models designed mainly for users’ initial IS acceptance (Lee

the so‐called technology acceptance models (TAMs) to explain—and

Contrastingly, the IS literature has mainly focused on the adoption

therefore improve—people’s perceptions, attitudes, and usage of new

phase while neglecting the implementation phase (Saeed & Abdin‐

technologies. These include models such as technology readiness

nour, 2013; White, Jones, & Beynon‐Davies, 2015; Zhou, 2011; Zhu

(TR) (Fishbein & Ajzen, 1975), diffusion of innovation theory (Rogers,

& Kraemer, 2005). In the Internet banking literature, most previous

1995), TAM (Davis, 1989), theory of planned behavior (TPB) (Ajzen,

studies concentrated on consumers’ adoption or acceptance of online

1991) and the unified theory of acceptance and use of technology

systems and to some extent ignored their continued use (Adapa &

(UTAUT) (Venkatesh et al., 2003).

Cooksey, 2013). Additionally, the few postadoption existing stud‐

Generally, the TAMs are intention‐oriented theories. They have

ies have focused on organizational and work‐related behaviors (e.g.,

been widely accepted and confirmed as a reliable predictor of IS users’

employees’ IT adoption) yet minimal research was conducted on a

behaviors (Jones, Muir, & Beynon‐Davies, 2006). They start from

business‐to‐consumers context. Consequently, the IS literature has

the presumption that there are key antecedents of IS users’ behav‐

currently limited relevance for understanding online consumer behav‐

ioral intention and behavioral use. According to TAMs, the main con‐

ior (Kim & Son, 2009).

structs influencing intentions are users’ perceived usefulness, ease

In an effort to understand IS postadoption behaviors, IS research

of use, social influence, facilitating conditions and subjective norms.

has simulated the explanations of conventional repurchasing behavior, theorizing that a user’s satisfaction and continuance intention is simi‐

*

JEL Classification codes: O32, O33.

Strategic Change. 2018;27(3):231–243.

lar to the conventional satisfaction and repurchasing behaviors found

wileyonlinelibrary.com/journal/jsc

© 2018 John Wiley & Sons, Ltd.

231

232

ALGHAMDI

et al.

in the shopping literature (Chen, Meservy, & Gillenson, 2012). One of

to some extent, inclusive view of technology acceptance and readi‐

the earliest pieces of research to theoretically validate a model of IS

ness. Therefore, representing them by single variables is justifiable and

continuance was the expectation confirmation model of IS continu‐

may provide a more inclusive understanding.

ance (ECM‐IS) of Bhattacherjee (2001). The model was based on the

This article is structured as follows. First, a conceptual model and

expectation confirmation theory (ECT) of Oliver (1980) who identi‐

theoretical background are presented, alongside the definitions of the

fied performance, expectations, confirmation, and satisfaction as

key research construct involved in this study. Second, the research

antecedents of repurchasing behavior. Bhattacherjee adapted the ECT

methods including the data collection procedure are explained. Third,

framework and replaced performance with usefulness, and repurchas‐

the results are succinctly reported and finally the discussion and both

ing with continuance. His work gained wide acceptance and was the

theoretical and practical implications are drawn.

basis of many IS studies that followed. However, the customer’s deci‐ sion‐making process in the IS context is psychologically more complex than in conventional shopping (Parasuraman, 2000), and the ECM‐IS does not consider that. Technology‐based services including Internet banking involve making judgments and using more resources (e.g., connection, device) as well as revealing sensitive personal information with every payment or access without the assurance of human contact (e.g., credit/debit card details, date of birth). Such limitations are highly important factors in societies such as Saudi Arabia’s with high human presence preferences (Feghali, 1997).

2 | CONCEPTUAL MODEL AND THEORETICAL BACKGROUND A summary of the research constructs and their sources is provided in Table 1. In addition, the relationships between the constructs and the hypotheses are presented in Figure 1. The following sections review the theoretical and empirical evidence underpinning the proposed conceptual model (as shown in Figure 1).

To measure the effects of psychological tendencies on the use of IS technology‐based services, Chen et al. (2009) obtained novelty by integrating TR into the ECM‐IS framework. TR is a well‐established

2.1 | Uncertainty avoidance

theory and measures individuals’ readiness to use technology by

Lee, Choi, Kim, and Hong (2007) illustrated that culture at the individ‐

accounting for their psychological tendencies and the extent of their

ual level affects behavior in the IS postadoption context. In this regard,

computer knowledge and experience (Parasuraman, 2000). Chen’s

Hofstede’s cultural model (Hofstede, Hofstede, & Minkov, 2010) has a

framework was built by incorporating constructs from various theo‐

dimension related to measuring UA. This dimension is relevant to this

ries. These include perceived usefulness, ease of use, subjective norms

study because uncertainty has been underlined as being important in

and behavioral control, as well as optimism, innovativeness, discom‐

having a significant influence on online financial behavior (Grabner‐

fort and insecurity. This framework, however, did not include the

Krouter & Kaluscha, 2003) and as being relevant to most IS research

ECM‐IS construct of confirmation. Chen and Li (2010) investigated

studies based in Arabic countries as it has a considerable influence

the adoption of e‐services by integrating TR into the TPB. The authors

on the perceptions and attitudes of Arab people toward technology

found that TR significantly influences attitude, subjective norm and

(Shoib & Jones, 2003). This research proposes that UA influences

perceived behavioral control. In turn, attitude and perceived behav‐

UTAUT perceptions and continuance intention positively. The Inter‐

ioral control influence continuance intentions. “Confirmation” was

net banking system is a kind of captive IS for people who have already

also not included.

adopted it, and people with high UA (such as in Saudi Arabia) were

In reviewing the literature, it is noticeable that the studies that

found to be proactive in handing unavoidable changes and adaptive

considered duplicating the ECM‐IS framework have contributed to

when facing inevitable risks (Baker & Carson, 2011). While uncertainty

knowledge by incorporating more cognitive beliefs, such as ease of

might have undermined the Internet banking adoption process, it may

use, and/or by including psychological effects such as TR constructs

benefit the postadopters who are expected to attach themselves to

or culture. However, this research contributes to the previous stud‐

the service. Schneider and De Meyer (1991) explained that while UA

ies in different ways. First, it considers the full structure of ECM‐IS

scores remain the same, cultural preferences in responding to uncer‐

by including “confirmation” and incorporating the effects of both

tainty are different. Individuals may reduce feelings of uncertainty by

uncertainty avoidance (UA) and TR. Second, it comprises the UTAUT

adapting to their environment, which implies having an achievement

cognitive constructs and examines the influence of TR and culture

orientation. Such people realize opportunities for meaningful change

on its constructs, including “social influence.” Third, it examines the

(Bateman & Crant, 1993).

extended model within the Saudi Arabian context, in which none of the TR, ECM‐IS, UTAUT, and UA have been tested before. Fourth, this research adopts a more comprehensive approach by encapsulating the

2.2 | Technology readiness

TR and UTAUT models to represent them by single variables called

TR is related to the degree of preparedness that customers display in

“UTAUT” and “TR.” Variables underpinning these two models will be

adopting and using new technology to achieve their goals in life (Para‐

positioned as “second order.” This enables the full use of structural

suraman, 2000). The TR segments the views of customers about tech‐

equation modeling facilities and is a novel way of representing these

nology into four dimensions: “optimism,” “innovativeness” (positive

research influences. TR and UTAUT represent a comprehensive and,

views), “discomfort,” and “insecurity” (negative views). Positive and

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ALGHAMDI et al.

TA BL E 1 Research constructs

Variable ECM‐IS

UTAUT

TR

Source

Definition

Satisfaction (SAT)

Oliver (1980), Bhattacherjee et al. (2008)

Overall psychological state that emerges when the emotions surrounding confirmed expectations are coupled with the consumer’s prior feelings about the consumption experience (Oliver, 1981)

Continuance intention (CI)

Bhattacherjee et al. (2008)

People’s intention to continued use of the IS

Confirmation (CON)

Bhattacherjee (2001)

Users’ perception of the congruence between expectation of IS use and its actual performance

Performance Expectancy (PE)

Venkatesh et al. (2003)

The degree to which the user expects that using a technology will make it easier to carry out certain activities

Effort Expectancy (EE)


The user’s perceptions of how easy it will be to use a technology; social influence

Social Influence (SI)


How strongly a user perceives that important others encourage the use of a technology

Facilitating Conditions (FC)


The environmental factors enhancing individuals’ likelihood of using technology

Optimism (OPT)

Parasuraman (2000)

Optimistic view of technology and a conviction that technology provides people with increased flexibility, organization, control and effectiveness

Innovativeness (INN)

Parasuraman (2000)

A tendency to be a technology explorer and leading user of technology; individu‐ als with high scores for innovativeness tend to be technology pioneers

Discomfort (DIS)

Parasuraman (2000)

A perception of a lack of control over technology and feelings of being over‐ whelmed by it

Insecurity (INS)

Parasuraman (2000)

Not trusting technology and having doubts about its ability to work correctly

Srite & Karahanna (2006)

A society’s acceptance of ambiguity and uncertainty, showing the degree to which a culture’s members are comfortable or uncomfortable in situations that are unstructured

Uncertainty Avoidance (UA)

FIGU RE 1 The research model

negative beliefs about technology may coexist. People can be placed

2011). Positive views of technology increase the usefulness and ease

at different levels on the scale of TR beliefs, from a highly positive to

of use perceptions (equivalent to performance expectancy and effort

a highly negative attitude. The connection between people’s TR and

expectancy in the UTAUT), while negative views undermine those (Lin

their preference to use technology has been empirically confirmed.

et al., 2007). Negative beliefs about TR in this research were reverse

Positive TR has an impact on consumers’ online usage intentions and

coded in accordance with Lin and Hsieh (2007). Thus, all TR constructs

online behaviors in general. TR is the result of an in‐depth analysis of

in this study represent positive influences. TR influences have not

the literature and extensive qualitative research on customer reactions

been yet tested on social influences and facilitating conditions. Facili‐

to technology. It influences satisfaction and continuance intentions

tating conditions is similar to perceived behavioral control and social

for self‐service technologies (Lin, Shih, & Sher, 2007). It also influences

influence is similar to subjective norms. Chen and Li (2010) found that

UTAUT perceptions positively (Venkatesh, Thong, Chan, Hu, & Brown,

TR associates positively with both.

234

ALGHAMDI

2.2.1 | UTAUT

et al.

employs 35 items of the item scale established by Parasuraman (2000)

UTAUT consolidates eight models of technology acceptance into a unified view. The model explains the perceptions that lead to users’ behavioral intentions and usage behavior. In the postadoption con‐ text Venkatesh et al. (2011) illustrated that the model’s cognitive constructs, namely performance expectancy, effort expectancy, social influence, and facilitating conditions, influence IS satisfaction and continuance intentions positively. This study will retest these evalu‐ ations within a new context, which is the Saudi Arabian banking‐cus‐ tomer context.

to assess the four dimensions of TR: (1) OPT, (2) INN, (3) DIS, and (4) INS. For UA, this research follows Srite and Karahanna (2006) in using the scale proposed by Dorfman and Howell (1988). Dorfman and Howell (1988) criticized Hofstede’s measurement for not being sound at the micro unit of analysis. They refined the scale to facilitate the use of Hofstede’s dimensions to systematically reflect culture at an individual level. The research questionnaire was originally written in English, and was then translated into Arabic by two certified translators. Later, back translation was conducted through another indepen‐ dent certified translator to ensure that no meaning had been lost

2.2.2 | ECM‐IS

during the translation process (Zikmund, 2003). The research team

ECM‐IS explains the cognitive beliefs and affect that lead to IS satis‐

analyzed the translations and resolved the differences between

faction and usage continuance intentions. The model identified con‐

the versions until it was agreed that both versions were virtually

firmation, perceived usefulness, and satisfaction as antecedents of

identical. A final check was conducted by an IS specialist of Arab

continuance intentions. As stated earlier, the model has gained wide

origin who conducts research in both languages; he confirmed

acceptance from IS researchers and has been extended to account for

that no further modifications were needed as the translation was

a wider range of predictions of satisfaction and continuance inten‐

adequate.

tions. This research presents a new customization of the model and extends its application in a new context. From the above discussions, and according to Figure 1, the follow‐

3.2 | Data collection The empirical data for this study were collected using a cross‐sectional

ing hypotheses are proposed:

field survey of Internet banking users in Saudi Arabia. The respondents

H1:

Positively influences

CI

H2:


SAT

Saudi bank. The bank offers its customers a range of personal banking

H3:


UTAUT perceptions

products, including online deposit accounts, credit cards, bill payment,

H4:


SAT

money transfers, and stockbroking through the opening of a personal

H5:


CI

equity portfolio. The bank introduced its Internet services more than

H6:


SAT

H7:


CI (indirectly) through the influence on SAT

H8:


UTAUT

H9:


SAT (indirectly) through its influence on UTAUT

H10:


CI

H11:


UTAUT

3 | RESEARCH METHODS

were customers of the Internet banking section of a middle‐sized

10 years ago and updates its system regularly. Customer access to an Internet account involves obtaining a user name and password. When they are entered in the system, the customer receives a confirmation SMS via mobile phone, with a special code that allows them entry to the main account page. First, the questionnaire was piloted. Out of 110 questionnaires delivered to the bank’s customers, 50 were returned and used to test the reliability of the draft questionnaire. For all constructs, Cronbach’s α was greater than .70, and three items were omit‐ ted as they had item‐total correlations below .30. Overall, these results indicated that the questionnaire had good reliability (see Table 2). Next, 600 copies of the questionnaire were distributed

The following sections present the instrument development as well as

randomly to customers at the doors of eleven branches of the bank

the data collection protocol.

in the same city. Of these, 261 valid responses were received, of which 41% were from women; 52% of the respondents were aged

3.1 | Instrument development This research uses previously validated scales. The focal constructs were taken from Bhattacherjee’s (2001) ECM‐IS. These constructs include confirmation, satisfaction, continuance intention, and useful‐ ness (PE). UTAUT constructs were modified in line with Bhattacher‐

25–34 years.

4 | DATA ANALYSIS AND FINDINGS 4.1 | Exploratory factor analysis

jee’s to suit the research context. These are EE, FC, and SI (Venkatesh

Exploratory and confirmatory factor analyses (EFA and CFA) were used

et al., 2003). Finally, to account for psychological traits, this research

in this study. Exploratory methods were used to measure the reliability

235

ALGHAMDI et al.

TA BL E 2 Results of the reliability tests from the pilot study

Model

UTAUT

UA

TR

ECM‐IS

Construct

PE

EE

SI

FC

UA

OPT

INN

DIS

INS

CON

SAT

CI

No. items

4

4

5

5

6

10

7

9

9

3

4

4

Cronbach’s α

.70

.77

.75

.72

.85

.85

.74

.70

.81

.73

.75

.82

Deleted items

PE4

ID7

DIS1

along with the dimensionality of the constructs. The principal compo‐

degrees of freedom (DF) = 494, (χ2/DF) = 1.177, goodness of fit index

nents method was employed; factor extraction with Varimax rotation

(GFI) = .892, adjusted goodness of fit index (AGFI) = .862, Tucker Lewis

was carried out using the SPSS 20 software package. Table 3 shows

index (TLI) = .976, comparative fit index (CFI) = .980, and root mean

that the factor loadings of all items exceed .5 while Cronbach’s α val‐

square error of approximation (RMSEA) = .026. Then, second‐order

ues are all above .7.

CFA was performed to confirm that these subdimensions were cor‐ related with the overall dimensions. The measurement model was found to have a suitable fit with the following indices: χ2 = 339.650,

4.2 | Confirmatory factor analysis

DF = 279, χ2/DF = 1.217, GFI = .91, AGFI = .89, TLI = .98, CFI = .983, and

To make a stronger assessment, CFA was performed on each dimen‐

RMSEA = .029.

sion using AMOS 20. In this process, this research examines the mea‐

Once the fit of the final measurement model had been found

surement model to check that each item only loads on its expected

acceptable, its constructs were evaluated to assess unidimensionality,

latent variable (Thompson, 2004). This research CFA consisted of two

convergent validity, average variance extracted (AVE), and discrimi‐

parts, first‐order CFA and second‐order CFA. The first‐order CFA was

nant validity. As Table 4 shows, the standardized loading estimates (λ)

employed to analyze the validity of each dimension. Many items were

of the items are all .60 or higher, which is acceptable as Hair (1998)

deleted as they had weak loading coefficients and cross‐loading items.

recommended that these values should exceed .5. All items are signifi‐

After removing these items, this research had a final measurement

cant according to the results, as the critical ratios associated with the

model that fitted the indices within a suitable range: χ2 = 581.216,

factor loadings and their standard errors all exceed 1.96, the threshold

TA BL E 3 Factor analysis and reliability analysis

UTAUT (UT) PE Cronbach’s α: .73

EE: .835

PE1

PE2

PE3

EE2

EE3

EE4

.720

.770

.600

.749

.819

.803

SI: .784

FC: .861

SI1

SI2

SI5

FC4

FC5

.871

.871

.758

.813

.862

Culture UA: .959 UA1

UA2

UA3

UA4

UA5

UA6

.789

.741

.845

.857

.835

.844

Technology readiness (TR) OPT: .825

INN: .875

OPT1

OPT2

OPT3

OPT6

OPT8

.698

.702

.688

.742

.750

DIS: .744

INN1

INN3

INN4

INN5

INN6

.658

.813

.800

.773

.826

INS: .762

DIS3

DIS4

DIS5

DIS6

INS5

INS6

INS7

INS8

.655

.752

.750

.742

.771

.651

.800

.723

ECM‐IS CON: .807

SAT: .778

IC: .921

CON1

CON2

CON3

SAT2

SAT3

CI2

CI3

CI4

.712

.829

.619

.740

.663

.767

.781

.821

236

ALGHAMDI

TA BL E 4 Standardized loadings, reliability, and validity for the measurement model

Items and constructs

Standardized loading

Error variance

Loading

Error

Critical ratio

ES

PE1

.786

e45

.10

7.5

PE2

.782

11.67

e46

.07

7.6

PE3

.670

10.15

e90

.37

9.5

e40

.13

7.9

EE EE3

.791

EE2

.777

12.32

e41

.15

8.3

EE4

.817

12.82

e43

.10

7.29

SI SI1

.902

e48

.16

5.7

SI2

.941

20.12

e36

.09

3.52

SI5

.676

12.84

e39

.40

10.6

e91

.091

1.17

e92

.39

6.80

e78

.19

6.5

FC .960

FC5

.788

11.42

UA UA6

.878

UA3

.786

14.8

e81

.27

9.1

UA5

.834

16.1

e79

.25

8.02

UA1

.687

12.3

e83

.30

10.14

e2

.33

9.47

OPT OPT2

.640

OPT6

.785

9.2

e6

.13

6.89

OPT8

.752

9.12

e8

.15

7.7

e31

.43

.8.99

INN INN3

.775

INN4

.751

12.2

e32

.46

9.37

INN5

.790

13

e33

.35

8.7

INN6

.849

13.8

e34

.08

3.5

e95

.74

8.2

DIS DIS6

.643

DIS4

.715

7

e94

.49

6.4

DIS3

.561

6

e93

.74

9.36

INS INS8

.741

e21

.547

7.40

INS7

.900

9.7

e22

.20

2.6

INS5

.513

7.7

e24

1.15

10.6

e54

.30

8.9

e55

.26

6.18

e58

.211

8.08

e59

.169

6.40

e61

.08

8.91

CON CON3

.713

CON2

.825

11.12

SAT SAT3

.746

SAT2

.864

12.14

AVE

.791

.559

.838

.632

.883

.719

.870

.771

.875

.639

.771

.530

.870

.627

.676

.413*

.772

.542

.744

.593

.779

.638

.922

.798

Critical ratio

PE

FC4

CR

CI CI4

.862

CI3

.867

18.7

e62

.08

8.8

CI2

.949

21.4

e63

.03

4.41

Note: λ = standardized loading, ES = estimate, CR = composite reliability, AVE = average variance extracted.

et al.

237

ALGHAMDI et al.

4.3 | Structural model

for a .05 significance level in samples using critical values. Moreover, all of the error variances are positive, indicating there is no identifica‐

Structural equation modeling (SEM) was used to test the research

tion problem related to negative variances.

hypotheses. The indices of model fit are all within suitable bounds:

Table 4 also shows that composite reliability (CR) is higher than

χ2 = 290.856, DF = 175, χ2/DF = 1.66, GFI = .91, AGFI = .88, TLI = .95,

the approved limit (.7) for all constructs, expect for DIS (for which

CFI = .96, and RMSEA = .05. Table 6 presents the path coefficients of

CR = .676). In addition, the AVE is above the minimum threshold of .5

the research model. All the error variances are positive, so there is no

for all constructs apart from DIS (AVE = .413). These results confirmed

identification problem related to negative variances. The critical ratios

the convergent validity of these constructs. Next, the square roots of

associated with the paths and their standard errors all exceed 1.96,

the AVEs were employed to examine the discriminant validity (Fornell

indicating that they are significant.

& Larcker, 1981). These values (shown in Table 5) should be higher

From Table 6, the results can be summarized as follows: the path

than the correlations between the dimension in question and each

coefficients between SAT and CI (β = .348, p < .003); CON and SAT

of the other dimensions in the measurement model. All constructs

(.624, p < .017); CON and UTAUT (β = .318, p < .005), UTAUT and SAT

were found to have discriminant validity, except for CON and SAT.

(β = .537, p = .012), TR and CI (β = .605, p < .002); UA and CI (β = .251,

Although only two constructs were found to have no discriminant

p = .000); TR and UTAUT (β = .730, p < .000) were all found to be posi‐

validity, it was decided to keep them in the model as these constructs

tive and significant. In contrast, the relationships between TR and SAT

do not affect the fit of the measurement model (Wu, Choi, & Rungtu‐

(β = −.279, p = .142) and UTAUT and UA (β = 153, p = .077) were found to

sanatham, 2010).

TA BL E 5 Factor correlation matrix with square roots of AVE

FC FC

OPT

INN

SI

EE

PE

CON

SAT

CI

INSE

UA

DIS

.878

OPT

.076

.728

INN

–.037

.519

.792

SI

.282

.308

.298

.848

EE

.289

.239

.269

.411

.795

PE

.253

.384

.224

.431

.597

.747

CON

.573

.385

.221

.356

.393

.357

.770

SAT

.530

.362

.164

.345

.395

.506

.816

.799

CI

.282

.443

.228

.358

.520

.623

.489

.505

.893

INSE

.024

−.119

−.012

.069

.171

.153

.015

.145

.141

.736

UA

.089

.006

−.157

−.006

.111

.214

.210

.218

.270

.209

.799

DIS

.121

−.186

−.187

−.111

.117

.022

−.133

.173

.025

.417

.057

TA BL E 6 Regression weights and p values

Paths coefficients

Standardized estimate

p

Result

H1 SAT CI

.348

.003

Supported

H2 CON SAT →

.624

.017

Supported

H3 CON UTAUT

.318

.005

Supported

H4 UTAUT SAT →

.537

.012

Supported

H6 TR SAT

–.279

.142

Rejected

H7 TR CI →

.605

.002

Supported

H8 TR UTAUT

.730

.000

Supported

H10 UA CI →

.251

.000

Supported

H11 UA UTAUT

.153

.077

Rejected

H5 UTAUT SAT CI →

.187

.019

Supported

H9 TR UTAUT SAT →

.392

.000

Supported

Direct effects

Indirect effects

Significant p < .05.

.643

238

ALGHAMDI

be statistically nonsignificant. Furthermore, the coefficient of determi‐

et al.

H3 proposed that confirmation is positively associated with the

nation (R ) for UTAUT (endogenous variable) was .66, which means that

UTAUT constructs. Confirmation represents the amount of postus‐

66% of the variance in UTAUT is explained by CON and TR while 34% is

age realized expectations, while postusage expectations represent ex

related to other factors. Similarly, UTAUT, TR, and CON explained 75%

postperceptions or beliefs regarding PE, EE, and SI (Chen et al., 2012).

of the variance in SAT (R2 = .75). Therefore, hypotheses 1, 2, 3, 4, 7, 8,

The UTAUT cognitive constructs are some of the most salient beliefs

and 10 are accepted, while hypotheses 6 and 11 are rejected.

determining acceptance behaviors across a wide range of end‐user IS

2

In terms of the indirect effect, due to the nonsignificant direct

technologies. In the ECM‐IS, confirmation of the UTAUT constructs

effect of TR on SAT, the researcher proposes that UTAUT mediates

work as the evaluation standard; customers use them to assess per‐

the relationship between the two. The results show that UTAUT plays

formance and to build a satisfaction or dissatisfaction judgment. The

an important role in supporting the impact of TR on SAT (β = .392,

result of the SEM validated the role of confirmation as a key significant

p