Delineating the effects of general and system-specific computer

self-efficacy beliefs on IS acceptance

Bassam Hasan *

College of Business Administration, The University of Toledo, 2801 W. Bancroft St., Toledo, OH 43606, United States

Received 23 September 2004; received in revised form 11 December 2004; accepted 11 November 2005

Available online 3 April 2006

Abstract

This paper discusses extensions to previous research on computer self-efficacy (CSE) and systems acceptance by examining the

impact of multilevel CSE on IS acceptance. Based on the technology acceptance model (TAM), we examined the effects of general

and system-specific CSE on perceived ease of use, perceived usefulness, and behavioral intention to use a system. The results of a

field experiment indicated that system-specific CSE represented a stronger predictor of perceived usefulness and behavioral

intention than general CSE. In contrast, general CSE had a stronger effect on perceived ease of use. The research and practical

implications of these findings are discussed.

# 2006 Elsevier B.V. All rights reserved.

Keywords: General computer self-efficacy; System-specific computer self-efficacy; Ease of use; Usefulness; Behavioral intention; IS acceptance

www.elsevier.com/locate/im

Information & Management 43 (2006) 565–571

1. Introduction

The positive effects of IS on job performance and

organizational effectiveness have motivated organiza-

tions to increase their investment in IS technologies

[25]. However, lack of system acceptance and utiliza-

tion by intended users has proved to be an obstacle to

achieving the benefits of IS. This has been termed the

productivity paradox [27] and has underscored the

importance of IS acceptance as a precondition for

achieving any returns from the investment that

organizations make in IS [30]. Accordingly, under-

standing factors that influence a user’s decision to

accept or reject a system has become an important issue.

TAM [10,11] is recognized as a simple and robust

model for studying systems acceptance and utilization.

It has been used in various settings to explain system

* Tel.: +1 419 530 2431; fax: +1 419 530 2290.

E-mail address: bhasan@utnet.utoledo.edo.

0378-7206/$ – see front matter # 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.im.2005.11.005

acceptance across a wide range of technologies and user

groups, e.g. [12,26,38]. TAM models IS acceptance as a

function of users’ perceptions of usefulness and ease of

use of a target system.

Computer self-efficacy (CSE), confidence in one’s

ability to use computer skills to execute a task, has been

found to be a reliable determinant of acceptance intention

and usage behavior. For example, high CSE beliefs

reduced individual resistance to technological innovation

and facilitated IS acceptance [14]. Likewise, CSE

demonstrated significant effects on other determinants

of systems acceptance such as playfulness and computer

anxiety [16] and had a positive effect on intention to use

Internet-based applications [31,39]. The effects of CSE

on perceptions of ease of use, usefulness, behavioral

intention to use a system, and actual system usage have

been confirmed across many studies, e.g. [20,22,43].

A review of studies of CSE demonstrated that it was

a multilevel construct with general and system-specific

components [33]. While general CSE refers to a

generalized and system-independent individual trait,

B. Hasan / Information & Management 43 (2006) 565–571566

system-specific CSE pertains to judgments of self-

efficacy toward a specific system or software package.

Several studies have considered the influence of system-

specific CSE on learning performance in computer

training [24] and computer task performance [44].

Little is known, however, about the effect of system-

specific CSE on acceptance behavior. Furthermore, few

studies have utilized CSE as an external factor affecting

TAM’s key variables and most have focused on CSE as

a general, system-independent variable [37]. Therefore,

our study drew a distinction between general and

system-specific CSE and examined the role of each

level of CSE on IS acceptance.

2. Theoretical background

2.1. TAM

TAM achieved widespread acceptance as a model for

explaining and predicting IS acceptance. It also

provides a basis for understanding the impact of

external factors on acceptance behavior. However,

studies have generally restricted their focus to the core

variables and little attention has been given to the role of

external factors [28,32].

2.2. Computer self-efficacy

Self-efficacy was first introduced as a core concept in

the social cognitive theory (SCT) [5]. It refers to

‘‘people’s judgments of their capabilities to organize and

execute courses of action required to attain designated

types of performance.’’ This clearly indicates that self-

efficacy does not refer to assessments of the actual skills

that people posses but with evaluations of what people

believe they can accomplish. Self-efficacy regulates

human behavior by influencing people’s motivation,

perseverance, and effort to surmount difficulties and

perform successfully [15,42]. Individuals with stronger

efficacy beliefs are expected to exert more effort and tend

to be more persistent in their efforts.

Since SCT maintains that self-efficacy is a task-

specific and dynamic variable that varies across tasks

and domains, the concept has been applied to specific

tasks, including computer and other IS-related activ-

ities [23]. The differentiation between general and

system-specific CSE beliefs is important for several

reasons. First, general CSE is considered a trait-

oriented belief, whereas system-specific CSE is

considered a state-oriented belief that is easier to

influence or manipulate. Second, this differentiation is

more closely aligned with self-efficacy and personal

behavior. Third, system-specific beliefs represent a

better representation of an individual’s cognition in a

particular context, providing greater explanation and

prediction of the target behavior [7]. Finally, it allows

assessments of the two constructs to exclude evalua-

tions of cross-domain cognitions or skills that may

change the performance of a behavior.

3. Literature review and research hypotheses

3.1. General CSE

General CSE (GCSE) refers to ‘‘an individual’s

judgment of efficacy across multiple computer

domains.’’ It thus refers to perception of ability to

use a computer in general (without regard to a particular

computing task, application, or environment).

The relationship between GCSE and IS acceptance

has been the subject of much research [36,41] but the

influence of GCSE on perceived ease of use, and

perceived usefulness has been meager [21,29] and fewer

studies have investigated the direct effect of GCSE on

IS acceptance and utilization.

Past research reported mixed results on the impact of

GCSE on TAM variables; some studies found that

GCSE had a significant effect on ease of use [1], but

others reported non-significant effects. Results pertain-

ing to the impact of perceived usefulness have followed

a similar pattern, with some studies reporting significant

positive relationships and other studies reporting non-

significant, negative relationships. Finally, based on

empirical results, some authors suggested that CSE can

be used to predict an individual’s intention to use an IS

[17] and ultimate system acceptance. Therefore:

Hypothesis 1. GCSE will have positive effects on

perceived ease of use, perceived usefulness, and beha-

vioral intention.

3.2. System-specific CSE

System-specific computer self-efficacy (SCSE)

refers to an ‘‘individual’s perception of self-efficacy

in performing specific computer related tasks within the

domain of general computing.’’ It pertains to judgments

of efficacy in performing a defined computing task

using a specific computer application. This is consistent

with Bandura’s [3,4] suggestion that self-efficacy

beliefs can be specified at the task or domain level.

In the IS literature, very few studies have reported

attempts to examine the role of SCSE in IS acceptance

and, as a result, little is known about this relationship.

One study examined the impact of GCSE and two SCSE

B. Hasan / Information & Management 43 (2006) 565–571 567

(Lotus 123 and Windows 95) beliefs on perceived ease

of use while another, examined SCSE as an antecedent

to the acceptance of Blackboard technology, which

showed that Blackboard CSE had a significant effect on

ease of use and system usage. Others have demonstrated

that web-specific CSE had a positive effect on usage

intention and actual usage of an e-shopping application

[18] and Internet-specific CSE had significant effects on

usefulness, ease of use, and intention to use an

electronic medical application. Therefore:

Hypothesis 2. SCSE will have positive effects on

perceived ease of use, perceived usefulness, and beha-

vioral intention.

3.3. The relationship between GCSE and SCSE

Bandura suggests that perceptions of self-efficacy in

one domain may transfer to similar tasks or behaviors

within the same domain. In the realm of computing,

studies have shown that GCSE had a significant effect

on SCSE; e.g. Agarwal et al. [2] found that GCSE had a

significant effect on Windows 95 CSE; however, they

also showed that GCSE had a non-significant effect on

Lotus 123 efficacy. More recently, Hsu and Chiu found

that general Internet CSE had a significant effect on

web-specific CSE. Thus:

Hypothesis 3. GCSE will have a positive effect on

SCSE.

3.4. TAM variables

Refinements of TAM indicated that perceived ease of

use (PEOU) had a strong influence on perceived

usefulness (PU) and that usage attitude added little

predictive value to the model. Several authors suggested

that TAM should include only PEOU, PU, and behavioral

intention; Thus, two hypotheses were also tested:

Hypothesis 4. Perceived ease of use will have positive

effects on perceived usefulness and behavioral intention.

Hypothesis 5. Perceived usefulness will have a posi-

tive effect on behavioral intention.

4. Research methodology

4.1. Participants and procedure

The participants were 83 undergraduate students

enrolled in three sections of an elective course at a

university in the Midwestern U.S. Although students

had to use a text editor to complete their work, they were

not required to use any specific one. Twenty-nine of the

participants were females (35%). The mean age of

participants was 25.0 years (S.D. = 5.7).

Participants were given an introductory training on the

use of pico, which is a Unix-based text editing application

being relatively easy to learn and use. However, it

remains a command-based application running in a Unix

command-based environment and thus using it effec-

tively requires learning and mastering some Unix skills.

Data were collected using a two-part survey ques-

tionnaire. The first part of the survey was conducted

before training; it included items to assess participants’

general CSE, system-specific (pico) CSE, and demo-

graphic information. The second part was administered

after training and contained items to assess participants’

perceptions of ease of use, usefulness, and behavioral

intention to use pico in current and future courses.

4.2. Measures

General CSE beliefs were measured by nine items

from the widely used CSE instrument [10]. These asked

participants to indicate their ability to perform an

unspecified computing task using unfamiliar software.

Consistent with the original measure, responses were

recorded on a 10-point Likert scale ranging from 1 (not

at all confident) to 10 (totally confident). A sample

statement was: ‘‘I could use a software to complete a

computing job if I had seen someone else using it before

trying it myself’’.

System-specific CSE was measured by nine items

from the work of Marakas et al. These asked

participants to indicate their agreement or disagreement

with statements related to their ability to use pico. A

sample statement is: ‘‘I believe I have the ability to

rename a file in pico’’. Consistent with prior studies

examining system-specific beliefs, responses to these

statements were recorded on a 7-point Likert-type scale

ranging from 1 (strongly disagree) to 7 (strongly agree).

PEOU and PU were measured by three and four

items, respectively, from the instruments developed by

Davis. Behavioral intention refers to the degree to

which a person has formulated conscious plans to

perform or not perform a future behavior [40]; it was

measured by three items adapted from [1]. Responses

were recorded on a 7-point Likert-type scale ranging

from 1 (strongly disagree) to 7 (strongly agree).

5. Results

Prior to analyzing the data, reliability and factor

analyses were performed on the variables. All items

B. Hasan / Information & Management 43 (2006) 565–571568

Table 1

Means, standard deviations, alpha, and correlation among study variables

Variable Mean S.D. a 1 2 3 4 5

1 General CSE (GCSE) 63.35 17.57 0.94 1.00 0.65*** 0.64*** 0.08 0.21

2 System-specific CSE (SCSE) 32.70 13.83 0.85 1.00 0.63*** 0.35*** 0.49***

3 Perceived ease of use (PEOU) 19.31 3.91 0.96 1.00 0.40*** 0.67***

4 Perceived usefulness (PU) 19.88 6.44 0.85 1.00 0.73***

5 Behavioral Intention (BI) 14.57 6.03 0.92 1.00

*** P < 0.001.

Table 3

Results of hierarchal regression analysis for PEOU

GCSE SCSE R2 DR2 Sig.

Step 1 0.64*** 0.41 0.41 0.000

Step 2 0.40*** 0.37*** 0.48 0.07 0.001

*** P < 0.001.

Table 4

Results of hierarchal regression analysis for PU

loaded on their intended constructs and demonstrated

high reliability as can be seen in Table 1, which also

shows the descriptive statistics of the variables. The

mean score of GCSE was higher than that of SCSE.

Additionally, the table shows that GCSE and SCSE have

a significant positive correlation with most of the other

variables and that all correlations were below the 0.80

threshold [8], indicating that multicollineraity was not

present in the data.

Hierarchical regression analysis was used to test the

research hypotheses and examine the unique impact of

each level of CSE on the dependent variables. In the this

procedure, predictor variables were entered into the

regression model in stages, based on empirical or

theoretical considerations. This approach allowed the

assessment of the unique contribution that each predictor

variable introduced in the regression model. Consistent

with previous studies, the most exogenous variables in

the research model were entered first, resulting in four

separate hierarchical regression models.

The first hierarchical regression analysis was

performed to evaluate the influence of GCSE on SCSE.

The results are presented in Table 2, which indicates

that GCSE had a significant impact on SCSE and

explained a significant amount of variance in SCSE

(DR2 = 0.43, P < 0.001).

The second hierarchical regression analysis (shown

in Table 3) was conducted to assess the unique effects of

GCSE and SCSE on PEOU. In this model, GCSE was

entered first, followed by SCSE. GCSE had a significant

effect on PEOU and explained about 41% of the

variability in PEOU (DR2 = 0.40, P < 0.001). Similarly,

SCSE had a significant effect on PEOU and explained

about an additional 7% of the variance in PEOU

(DR2 = 0.07, P < 0.001). The combination of GCSE

Table 2

Results of hierarchal regression analysis for SCSE

GCSE R2 DR2 Sig.

Step 1 0.65*** 0.43 0.43 0.000

*** P < 0.001.

and SCSE explained about 48% of the variability in

PEOU.

The results of the third hierarchical regression

analysis which was conducted to assess the effects of

GCSE, SCSE, and PEOU on PU are presented in

Table 4. As it shows, GCSE had a non-significant effect

on PU and explained about 1% of the variability in PU

(DR2 = 0.01, P = 0.444). By contrast, SCSE demon-

strated a significant effect on PU and explained about an

additional 13% of the variance in PEOU (DR2 = 0.13,

P < 0.001). Together, GCSE and SCSE explained about

14% of the variability in PU. Finally, PEOU demon-

strated a significant effect on PU and explained about

11% of the variance in PU.

The final hierarchical regression analysis was

conducted to assess the effects of GCSE, SCSE, PEOU,

and PU on BI. The regression results are presented in

Table 5. As can be seen, GCSE demonstrated a non-

significant impact on BI and explained about 4% of the

variability in BI (DR2 = 0.04, P = 0.070). However,

SCSE was a stronger predictor of BI and explained an

additional 21% of the variance in PEOU (DR2 = 0.21,

P < 0.001). PEOU and PU both had significant effects

GCSE SCSE PEOU R2 DR2 Sig.

Step 1 0.09 0.01 0.01 0.444

Step 2 �0.20 0.48*** 0.14 0.13 0.001

Step 3 �0.413** 0.31* 0.463*** 0.25 0.11 0.001

* P < 0.05.** P < 0.01.

*** P < 0.001.

B. Hasan / Information & Management 43 (2006) 565–571 569

Table 5

Results of hierarchal regression analysis for BI

GCSE SCSE PEOU PU R2 DR2 Sig.

Step 1 0.20 0.04 0.04 0.070

Step 2 �0.19 0.61*** 0.25 0.21 0.000

Step 3 �0.51*** 0.31** 0.81*** 0.59 0.34 0.000

Step 4 �0.32*** 0.17 0.60*** 0.46*** 0.75 0.16 0.000

** P < 0.01.*** P < 0.001.

Table 6

Summary of hypotheses testing

Hypothesis Result

Hypothesis 1. GCSE will have positive effects on perceived ease of use, perceived usefulness and

behavioral intention

Partially supported

Hypothesis 2. SCSE will have positive effects on perceived ease of use, perceived usefulness and

behavioral intention

Supported

Hypothesis 3. GCSE will have a positive effect on SCSE Supported

Hypothesis 4. Perceived ease of use will have positive effects on perceived usefulness and

behavioral intention

Supported

Hypothesis 5. Perceived usefulness will have a positive effect on behavioral intention Supported

on BI and explained additional 34% and 16%

(respectively) of the variance in PU.

Table 6 presents a summary hypotheses testing. As

Table 6 shows, the results of hierarchal regression tests

provided support for all of the research hypotheses

except for Hypothesis 1, which was only partially

supported.

6. Discussion

The primary objective of the present study was to

examine the role of multilevel CSE in IS acceptance.

The results provided adequate support for the hypothe-

sized relationships and clearly demonstrated the

important roles that GCSE and SCSE play in IS

acceptance. More importantly, the results confirmed the

significant effect of SCSE on systems acceptance over

and above that of GCSE.

As shown in past research, GCSE was shown to be a

strong predictor of perceived ease of use. However,

some studies have reported a non-significant relation-

ship between GCSE and perceived ease of use [3,9].

The technologies and participants’ prior experience

may offer an explanation for this disagreement. Most

studies have examined common applications such as

Microsoft Word and Excel. In contrast, studies that

found a significant relationship between the two

variables have used less familiar technologies.

Overall, the results indicated that SCSE had stronger

effects on perceived usefulness and behavioral intention

and explained greater amounts of variability in these

variables than GCSE. These results are consistent with

findings reported in other studies and further underscore

the importance of SCSE beliefs in IS acceptance. For

instance, Ma and Liu found that Internet self-efficacy

was a stronger predictor of behavioral intention to use

an Internet-based medical application. In addition, Yi

and Hwang found that SCSE was a strong predictor of

actual usage of a Blackboard application. From a

theoretical perspective, TRA [13] suggested that the

prediction of a given behavior can be greatly improved

if the behavior and its antecedents were associated with

the same task or object. Thus, the results of this study

are theoretically sound and provide empirical evidence

for domain-specificity of computer efficacy beliefs in IS

settings.

The results also revealed that GCSE had a positive

impact on SCSE (b = 0.65, P < 0.001) and explained

about 43% of the variance in SCSE. This suggested that

even though GCSE demonstrated non-significant direct

effect on perceived usefulness and behavioral intention,

it still affected IS acceptance indirectly through its

direct effects on SCSE and perceived ease of use.

The study also showed that perceived ease of use was a

significant predictor of perceived usefulness (DR2 = 0.11,

P < 0.001) and behavioral intention (DR2 = 0.34,

P < 0.000). Conversely, the contribution of perceived

usefulness in explaining behavioral intention (DR2 =

0.16, P < 0.001) was weaker than that of perceived ease

of use.

B. Hasan / Information & Management 43 (2006) 565–571570

7. Implications for research and practice

Possible limitations of the study should be first

considered when interpreting the results: the model was

not meant to be comprehensive and to include all

possible factors affecting IS acceptance. Certainly,

other factors were not examined.

Furthermore, the study was conducted in an

educational setting and used students as the sample.

Examinations of more diverse and heterogeneous

samples are needed to improve the generalizability of

the results to other user populations. Another limitation

pertains to the examined dependent variable. Consistent

with past studies [19,35], we examined intention to use

rather than actual system usage.

The study distinguished between general and

system-specific CSE. The results showed that the two

levels of CSE exerted varying effects on IS beliefs and

acceptance behavior. In addition, Bandura maintained

that more specific efficacy beliefs are more relevant if

the purpose were to explain and predict performance in

a given situation. The results of this study are consistent

with this assertion. System-specific CSE was found to

be a better predictor of IS acceptance than general CSE,

providing empirical support for the multidimensionality

of the CSE construct.

The study examined two levels of CSE as external

variables affecting perceived ease of use, perceived

usefulness, and behavioral intention; by doing so, it

responded to calls to examine CSE at the general and

system-specific levels. Furthermore, while numerous

studies of CSE have demonstrated a significant

relationship between CSE and systems acceptance,

most past studies have not examined the concurrent

effects of CSE on PEOU and PU.

For practice, the present study extended prior

research by integrating two levels of CSE as external

factor affecting PEOU, PU, and behavioral intention to

use a system and provided further insights into the

relationships among these variables. Undoubtedly,

better understanding of factors affecting the determi-

nants of IS acceptance allows managers and organiza-

tions to devise more effective plans and interventions to

improve users’ perceptions of a target system and

thereby boost subsequent acceptance of the system.

The results suggested that SCSE beliefs would be

more influential than GCSE in affecting users’ PEOU,

PU, and usage intentions: training and other organiza-

tional interventions aimed at enhancing systems

acceptance therefore should focus on improving

system-specific CSE beliefs. The results also seem to

indicate that for unfamiliar applications or technologies

that will be used for low-level tasks [6], PEOU exerts

stronger effect on usage intentions than PU. Thus,

emphasizing the ease of use of less familiar applications

may enhance user beliefs and improve user acceptance.

The availability of user support mechanisms such as

individual help resources [34] has been found to

enhance PEOU.

Acknowledgments

The author would like to acknowledge the Editor and

other anonymous reviewers for their helpful comments

and suggestions.

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Bassam Hasan is an Assistant Professor of

Management Information Systems at The

University of Toledo. He holds a PhD in

MIS from The University of Mississippi.

His research interests include end-user

computer training and management of

information systems. His work has been

published in several IS journals and pre-

sented at various regional and national IS

conferences.