USER ACCEPTANCE OF SELF-SERVICE TECHNOLOGIES

An Integration of the Technology Acceptance Model and the

Theory of Planned Behavior

Chiao-Chen Chang, Wei-Lun Chang

Department of Business Administration, Tamkang University, 151 Ying-Chuan Road

Tamsui, Taipei County, Taiwan, R.O.C., Taiwan

Yang-Chieh Chin

Department of Management Science, National Chiao Tung University

1001 Ta-Hsueh Road, Hshinchu, Taiwan, R.O.C., Taiwan

Keywords: Self-service technology, Theory of planned behaviour, Technology acceptance model.

Abstract: This study examines what may affect consumers’ intention to use a self-service technology (SST). The

objective of this study is to advance our understanding on the intention to use SSTs by comparing and

integrating the theory of planned behaviour (TPB) and the technology acceptance model (TAM) as they

relate to this issue. Data was collected from 280 adult consumers, and a structural equation modelling

approach was employed to test the hypotheses. Although attitude, subjective norm, perceived usefulness

have direct positive relationships to behavioural intention to use a SST, perceived behavioural control plays

the most important role in explaining the intention to use SSTs. We conclude with managerial implications

and directions for future research.

1 INTRODUCTION

With the growth of online services, consumers are

replacing traditional face-to-face communications

with e-services. The development of technology-

based self-service formats (Dabholkar, 1994) allows

consumers to perform services themselves quickly

and conveniently. Self-service technologies (SSTs),

such as online banking transactions, checking out of

hotel rooms through interactive television screens,

and using self-scanning systems at retail stores

(Bobbitt and Dabholkar, 2001) are increasingly

widespread and becoming an important component

of marketing. From the perspective of customers,

SSTs are extremely flexible and provide various

benefits for consumers including increased

consumer convenience, saving of money and time,

increased perceived consumer control, and

customization. On the other hand, encouraging the

SST intention can save companies a lot of money in

human resources and can free human resources for

other kinds of services. Given these benefits,

practitioners and researchers have attempted to

understand what may lead to more intentions of

using SSTs.

Although past research has discussed SST usage

(van Dijk, Minoch and Laing, 2007), less work has

been done on applying a strong unifying theory to

this form of service. This study first reviews well

known theories and empirically tests three models:

the theory of planned behavior (TPB) (Ajzen, 1985,

1991), the technology acceptance model (TAM)

(Davis, 1986) and an integrated TPB/TAM model to

explain consumers’ acceptance of SST.

2 LITERATURE REVIEW

2.1 Self-Service Technology (SST)

Past consumer research has focused on discussing

how the internet is used as a SST, and the adoption

of e-commerce, or e-services, has been investigated

in the context of the adoption of other SSTs, such as

cash machines, voice-mail systems and airline

161

Chang C., Chang W. and Chin Y. (2009).

USER ACCEPTANCE OF SELF-SERVICE TECHNOLOGIES - An Integration of the Technology Acceptance Model and the Theory of Planned Behavior.

In Proceedings of the 11th International Conference on Enterprise Information Systems - Human-Computer Interaction, pages 161-164

DOI: 10.5220/0001853601610164

 SciTePress

ticketing machines (van Dijk, Minoch and Laing,

2007). The literature on this topic explains that

people are willing to use a SST if it matches their

attitudes toward technology in general and towards

technology-based self-service options in particular

(Bobbitt and Dabholkar, 2001).

2.2 The Theory of Planned Behaviour

(TPB)

According to the TPB, an individual’s behaviour can

be explained by his or her behavioural intention,

which is influenced by attitude, the subjective norm

and perceived behavioural control (Ajzen, 1991). In

our context, the TPB states that a consumer’s

intention to use a SST is simultaneously determined

by such factors as a positive or negative evaluative

effect about using the technology.

2.3 The Technology Acceptance Model

(TAM)

Based on Fishbein and Ajzen’s (1975) theory of

reasoned action (TRA), the TAM focuses on the role

of ease of use and usefulness in predicting the

attitude toward using a new technology (Davis,

1986). Following prior research findings and using

the TPB as a basic structure, attitude is decomposed

by incorporating perceived usefulness and perceived

ease of use. The integrated model proposed here

combines the TPB and the TAM and explains the

adoption of SSTs for online services.

We then infer that the casual factors of the TPB,

the TAM, and an integrated TPB/TAM model will

have positive relationships with the behavioural

intention. H1, H2 and H3 are initiated underlying

TPB, while H4, H5, H6, and H7 are proposed based

on the TAM. These hypotheses were further verified

for their validity by empirical data.

H1: Attitude toward using a SST will have a

positive impact on behavioural intention.

H2: The subjective norm will have a positive

impact on behavioural intention.

H3: Perceived behavioural control will have a

positive impact on behavioural intention.

H4: The perceived usefulness of SSTs will have

a positive impact on attitude toward using a SST.

H5: The perceived usefulness of SSTs will have

a positive impact on behavioural intention.

H6: The perceived ease of use of SSTs will have

a positive impact on attitude toward using a SST.

H7: The perceived ease of use of SST will have

a positive impact on perceived usefulness of a

SST.

3 METHODS

3.1 Sample and Procedure

Questionnaires were distributed to a sample of 280

adult consumers in Taiwan. Participation in the

survey was completely voluntary and anonymous.

The definition and classification of SSTs used in this

survey were based on the typology of Meuter, et al.

(2000), which is one of the few comprehensive and

empirically based SST classification schemes.

Respondents were asked to evaluate their

experiences with SSTs that they had used most

frequently, as well as their resulting behavioural

intentions. Respondents’ evaluations covered a wide

range of SST providers, including post offices,

banks, stock exchanges, cinemas, railways, airlines,

bookstores, and internet services.

The sample was comprised of 48.2% males and

51.8% females, and the age range of the participants

was 18-45. Most respondents were university-

educated with a monthly personal income level of

less than NT $ 20,000.

3.2 Measures

Twenty-one measure variables were used to reflect

the components of an integrated TPB/TAM model.

Subjects responded using a five-point scale (1 =

completely disagree; 5 = completely agree) to

questions about the intention to use SSTs. The scales

of behavioural intention (BI) to use a SST are

borrowed from Venkatesh and Davis (1996, 2000).

Attitude (ATT) was adapted from the measurement

defined by Bhattacherjee (2000). For measuring

subjective norm (SN) and perceived behavioural

control (PBC), the items are adopted from Taylor

and Todd (1995) and Bhattacherjee (2000). The

perceived usefulness (PU) and the perceived ease of

use (PEOU) scales are adopted from Venkatesh and

Davis (1996, 2000). In addition, some demographic

variables, such as sex, age and college year, were

collected.

4 RESULTS

First, we performed empirical exploratory factor

analysis (EFA) and conducted reliability analysis to

confirm the reliability of the variables adopted in the

study. The reliability of all instruments assessed by

the Cronbach’s reliability coefficients ranged from

0.71 to 0.87 and exceeded the .60 lower limit of

acceptability (Hair, et al., 1998). Next, confirmatory

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factor analysis (CFA) in the AMOS 17.0 statistical

program was used to analyze latent variables. To

ensure data validity, we examined content validity,

convergent validity, and discriminant validity. We

determined that the content validity should be

acceptable since the parts of questionnaire were all

adapted from the literature and had been reviewed

by practitioners. The assessment of convergent

validity requires assessing the loading of each

observed indicator on its latent construct (Anderson

and Gerbing, 1988) and the CFA results indicated

that all loadings were significant (

001.0<P ), so the

evidence revealed satisfactory convergent validity.

Finally, discriminant validity was assessed by

ensuring that the average variance extracted (AVE)

of each construct was larger than its square

correlation with other constructs (Fornell and

Larcker, 1981), and the test of discriminant validity

revealed good discriminant validity.

A structural model should be assessed for

goodness-of-fit. Results show that the goodness-of-

fit index (GFI) and the Bentler Bonnet Normed fit

index (NFI) were above 0.90 in the TPB, the TAM

and the integrated TPB/TAM model, the

comparative fit index (CFI) was above the

recommended value of 0.95 for both models, the

standardized root mean square residual (RMR) was

less than 0.05 and the root mean square error of

approximation (RMSEA) was below 0.08 for both,

indicating a good fit (Hair, et al., 1988; Jöreskog and

Sörbom, 1993). For the integrated TPB/TAM model,

the results of the CFA indicated that the structural

model provided a very good fit to the data:

df/

1.87,

001.0<P , GFI = 0.92, NFI = 0.93, CFI =

0.97, RMR = 0.01, and RMSEA = 0.04. All of the

model-fit indices exceeded their common respective

acceptance levels.

4.1 Hypothesis Testing

An integrated TPB/TAM model appears to be

superior to the TPB and the TAM in explaining

behavioural intention because

= 0.82,

ATT

= 0.79 and

= 0.68 for the integrated

model, whereas

= 0.66 for the TPB, and

= 0.80,

ATT

= 0.78 and

= 0.67 for

the TAM. The path significance was consistent with

all the investigated models under high statistical

significance levels (

001.0<P

). In the integrated

model, ATT (

001.0,39.0 <

) and SN

(

001.0,26.0

) are positively associated with

the intention of using a SST, thereby supporting H1

and H2. Furthermore, among these three models,

PBC (

001.0,63.0

) has the strongest effect on

the intention of using a SST, thus supporting H3.

The impact of PU (

49.0=

) and PEOU

(

45.0

) on ATT are significant at

001.0<P

Consequently, H4 and H6 can be supported.

Meanwhile, PU (

001.0,51.0 <

) has significant

impact on the BI, so supporting H5. Moreover,

PEOU has strong effect on PU

(

001.0,82.0

), validating H7 and allowing

the inference that PEOU fosters a user’s PU toward

using a SST. As a result, all hypotheses were

supported.

5 DISCUSSION AND

IMPLICATIONS

The results of this study offer encouraging evidence

that the TPB, the TAM and integrated models can

help explain the intention to use a SST. The TPB

explains approximately 66% of the variance in BI,

the TAM explains approximately 80% of the

variance in BI, and the integrated model explains

approximately 82% in BI. Therefore, an integrated

TPB/TAM model represents an improvement in

explanatory powers over the other two models.

PBC appeared to be the most significant factor

affecting the SST intention, so a service provider

should develop a well established skill test to form a

complete SST and reinforce the intention to use. A

plausible explanation for the significant but modest

effect is that the operations of SSTs in general may

not be particularly complicated, especially when

considering consumers’ general competence,

learning capability, and the staff support commonly

available from customers and technologists.

Our study extends the existing literature on SST

into the online context, and its findings can improve

understanding about how to predict the intention to

use a SST. Recognizing the underlying factors that

affect the intention to use a SST has important

managerial implications for customer-service

provider relationships. Although self-service

applications are well known for saving companies

money, a few companies are finding out that, when

done right, they can bring in revenue as well. Hence,

service providers should make efforts to develop

well constructed SSTs and promote their usage. For

USER ACCEPTANCE OF SELF-SERVICE TECHNOLOGIES - An Integration of the Technology Acceptance Model and

the Theory of Planned Behavior

163

example, practitioners can prepare a guide and a

users’ manual for SSTs and can present SSTs as

useful and easy-to-use platforms that provide a rich

variety of new applications and useful information.

ACKNOWLEDGEMENTS

We would like to thank the National Science

Council, Taiwan for financial support.

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