ART_2009_Chatzoglou, Sarigiannidis, Vraimaki e Diamantidis_Investigating Greek Employees Intention...

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Investigating Greek employees’ intention to use web-based training

Prodromos D. Chatzoglou *, Lazaros Sarigiannidis, Eftichia Vraimaki, Anastasios Diamantidis

Production and Management Engineering Department, Democritus University of Thrace, Library Building, Kimmeria, 67100 Xanthi, Greece

a r t i c l e i n f o

Article history:

Received 30 July 2008

Received in revised form 29 April 2009

Accepted 1 May 2009

Keywords:

Adult learning

Interactive learning environments

Country-specific developments

a b s t r a c t

In the last few decades, the implementation of information technology has given rise to several organi-

zational training needs that have to be satisfied, in order to empower organizational IT performance. The

users of new technologies have to be trained quickly and efficiently, and since they are usually distrib-

uted to different remote locations, web-based training is the preferred, and sometimes the only, process

for employee training. This study deals with the prognosis of employees’ intention to use a web-based

training process, by extending the technology acceptance model using some other related factors, such

as learning goal orientation, management support, enjoyment, self-efficacy and computer anxiety. Two

hundred and eighty seven employees participated in this study to test the validity of the research model.

The findings of the structural equation modeling indicate that enjoyment, perceived usefulness and per-

ceived ease of use directly affect employees’ intention to use web-based training, while learning goal ori-

entation has the strongest indirect impact on employees’ intention. Finally, three new causal relations are

proposed for further research.

2009 Elsevier Ltd. All rights reserved.

1. Introduction

The combined effect of reduced cost and the improved capabilities of information technology has inevitable led to significant increasedin computer delivered training, such as computer- and web-based training, e-learning and multimedia learning environments ( Brown,

2001). Welsh, Wanberg, Brown, and Simmering (2003: 246) define e-learning as ‘‘the use of computer network technology, primarily over

an intranet or through the internet, to deliver information and instruction to individuals [employees]”, while Sun, Tsai, Finger, Chen, and

Yeh (2008) describe it as a web-based system that makes information or knowledge available to people for education and training purposes

in a modern society. Moreover Galagan (2000), stressed the increased use of internet technologies to deliver training, introducing the ‘e-

learning Revolution’ period. For DeRouin, Fritzsche, and Salas (2005) and Burgess and Russell (2003), e-learning is a powerful tool which

helps firms to deliver ‘‘many and varied instructional technologies and methods” to employees ( DeRouin et al., 2005: 921).

One of the major benefits of such systems is the allowance of individuals to control the pace of the training and to tailor learning accord-

ing to their personal needs (Ely, Sitzmann, & Falkiewicz, 2009). As training is regarded as a one of the most pervasive means for productivity

and job performance enhancement in the work environment ( Gupta & Bostrom, 2006), organizations of all sizes should capitalize on the

advantages of such technologies to provide employees with the continually increasing demand for new skills acquiring. That is particularly

evident in relation to new technology usage, where evidence from the US conveys that one third of the employee-sponsored programs in

2004 targeted for computer skills improvement (Dolezalek, 2004). The enhancement of skills and abilities of employees in relation to infor-

mation and telecommunication technologies (ICT) is of vital importance for European organizations, as well. Support for the importance of ICT skills, namely professionalskills, user skills and e-business skills, thecompetitiveness andgrowth of theEuropean economy canbe found

in various European Commission documents and initiatives (European Communities, 2008: The European e-Business Report 2008).

This issue is especially significant for small and medium sized enterprises (SMEs) whose lack of ICT skilled human resources and inabil-

ity to keep up with current market demands may jeopardize their viability ( Duan et al., 2002). Realizing the importance of SMEs for na-

tional economies, since in the European Union (EU) 99.8% of the firms are SMEs, contributing to two-thirds of all employment (Carayannis,

Popescu, Sipp, & Stewart, 2006), several EU funded projects, such as the Leonardo da Vinci were set up to assess training needs and

provide SMEs with a web-based training system that is flexible, low cost and easily accessible ( Duan et al., 2002). Statistics regarding

the penetration of new technology in Greek business environment looks promising. According to The European Innovation Scoreboard

0360-1315/$ - see front matter 2009 Elsevier Ltd. All rights reserved.doi:10.1016/j.compedu.2009.05.007

* Corresponding author. Tel./fax: +30 25410 79344.

E-mail addresses: [email protected] (P.D. Chatzoglou), [email protected] (L. Sarigiannidis), [email protected] (E. Vraimaki), [email protected]

(A. Diamantidis).

Computers & Education 53 (2009) 877–889

Contents lists available at ScienceDirect

Computers & Education

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / c o m p e d u

mailto:[email protected]:[email protected]:[email protected]:[email protected]://www.sciencedirect.com/science/journal/03601315http://www.elsevier.com/locate/compeduhttp://www.elsevier.com/locate/compeduhttp://www.sciencedirect.com/science/journal/03601315mailto:[email protected]:[email protected]:[email protected]:[email protected]


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report for 2008 (EIS, 2009) broadband access by Greek firms has increased by 51.6%, over the past 5 years. Yet, the European Information

Technology Observatory (EITO) reported that in 2004, IT expenditure in Greece was still among the lowest in EU countries. These steps

forward can be largely attributed to several projects that have been developed to help SMEs and very small enterprises keep up with e-

business and e-commerce technologies, such as the ‘‘Go-digital project”, funded by the Greek Ministry of Development and approved in

2000 by the European Commission as a part of the eEurope action plan. An example of actions undertaken in line with this project include

a model for the design of a web-based electronic train systems (ETS) for Greek agribusiness SMEs proposed by Costopoulou, Vlachos, and

Tsiligiris (2002). The basic aims of this system include building awareness on ICT and e-business practices, teaching of necessary skills in

using the Internet for e-commerce, provision of necessary education material and provide on-line training and consultancy. Despite thosesignificant developments, little is known regarding the acceptance of such system by users in the organizational context. This is because

most of prior studies on user acceptance have focused on specific information systems in Management Information Systems fields, other

than education (Lau & Woods, 2008). Moreover, investigating the acceptance and use of such systems by employees is as important as

building web-training systems, specifically designed to address the needs of Greek enterprises.

Stemming from the importance, diversity of form, effectiveness and impact of e-learning, this study concentrates, primarily, on employ-

ees’ training approach and, more specific, their training through the web (web-based training). The significant growth of the World Wide

Web has enabled it to emerge as a powerful new tool, which provides organizations the necessary facilities to strengthen and improve their

operational and managerial processes. Web-based training indicates the ‘‘planned efforts to increase job-related knowledge and skill”

(Welsh et al., 2003: 246) through the web. With the intention of achieving a successful and useful web-based training in organizations,

managers have to take into account different views, such as the field of design and development, different departments, like marketing,

human resources, research and development (R&D) and information technology (IT), and different employee needs, roles and capabilities

(Chan et al., 2002). Web-based training can be seen as a vehicle that may increase the speed, decrease the barriers, disperse the geograph-

ical range and reduce the costs of knowledge sharing within an organization and improve and accommodate the communication between

users. Nevertheless, there are also a few inhibitors that limit its adoption and implementation by organizations, such as software and hard-

ware constraints or psychological factors (Chan et al., 2002).

Web-based training has generated the noteworthy interest of scholars from social psychology and information system fields who have

identified specific constructs which influence the intention of employees to use it for their educational purposes ( Sun et al., 2008; Yi &

Hwang, 2003). The technology acceptance model (TAM), introduced by Davis (1986) is one of the most frequent used models for predicting

and explaining user behavior and IT usage. According to Davis, Bagozzi, and Warshaw (1989: 985), the main goal of TAM is to ‘‘provide an

explanation of the determinants of computer acceptance that is general, capable of explaining user behavior across a broad range of end-

user computing technologies and user populations”.

However, many scholars (Venkatesh, 2000) underlined the parsimony of the initial TAM and proposed several extension constructs from

related theories, such as economic, psychology and marketing (Xu & Yu, 2004). Based on another similar research (Yi & Hwang, 2003), this

study makes an effort to extend original TAM and its constructs (perceived usefulness and perceived ease of use), by incorporating addi-

tional ones, namely, management support, enjoyment, self efficacy, computer anxiety and learning goal orientation, for predicting the

intention of Greek employees to adopt web-based training. Although similar models have been widely utilized in the past to examine user

intentions, it is significant to further test the applicability of the model and the generalizability of results produced from Western-Euro-

pean, Northern-American and Asian samples in the Greek context.

This paper is organized as follows. In Section 2, a theoretical framework and the research hypotheses are presented. Section 3 providesan overview of the methodological approach adopted concerning the data collection instrument and process. The results of the data anal-

ysis are discussed in Section 4, while some concluding remarks, managerial implications, limitations and directions for future research are

provided in Section 5.

2. Theoretical framework and hypotheses

2.1. Technology acceptance model

In the original TAM, the variables of ‘intention’ and ‘attitude’ mediate the ‘perceived usefulness’ and ‘ease of use’ to influence technology

acceptance and use (Kim, Park, & Lee, 2007). Davis (1989: 320) defines perceived usefulness as ‘‘the degree to which a person believes that

using a particular system would enhance his or her job performance” and perceived ease of use as ‘‘the degree to which a person believes

that using a particular system would be free of effort”. In addition, according to the TAM’s predecessor Theory of Reasoned Action (TRA)

(Fishbein & Azjen, 1975; Verhoef & Langerak, 2001), ‘‘behavioral intention refers to a person’s subjective probability that he will performsome behavior”, while ‘‘an attitude represents a person’s general feeling of a favorableness or unfavorableness toward some stimulus

object” (Fishbein & Azjen, 1975: 288; 216). However, in consistency with other empirical studies ( Adams, Nelson, & Todd, 1992; Davis

et al., 1989; Igbaria, Zinatelli, Cragg, & Cavaye, 1997; Lee, Kim, & Lee, 2006; Szajna, 1996; Venkatesh & Davis, 2000) that exclude attitude

from their models, considering it as a weak mediator ( Yi & Hwang, 2003), the proposed research model of this study (see Fig. 1) also ex-

cludes it. Therefore, based on the literature (Chin & Gopal, 1995; Davis et al., 1989; Gefen & Straub, 2000; Liu & Wei, 2003; Moon & Kim,

2001; Venkatesh, 1999), the following hypotheses are first proposed:

H1. Perceived Ease of Use has a positive effect on Behavioral Intention.

H2. Perceived Usefulness has a positive effect on Behavioral Intention.

Moreover, TAM posits that perceived usefulness is determined by perceived ease of use, advocating that a system would be perceived to

be more useful if it is easier to use ( van der Heijden, Verhagen, & Creemers, 2001; Vijayasarathy, 2004). Thus, the following hypothesis is

also proposed:

H3. Perceived Ease of Use has a positive impact on Perceived Usefulness.

878 P.D. Chatzoglou et al. / Computers & Education 53 (2009) 877–889


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2.2. Management support

Although TAM has emerged as an effective model for predicting and explaining user behavior and IT usage, IS researchers have proposed

many extensions to the original TAM model, incorporating exogenous constructs (Lu, Hsu, & Hsu, 2005; Vijayasarathy, 2004; Yu, Ha, Choi, &

Rho 2005). One of these constructs is management support. This study is focused on this construct, examining its influence on the accep-

tance of web-based training in the workplace by employees (Kim et al., 2007). For Igbaria, Guimaraes, and Davis (1995) and Igbaria, Zina-

telli, Cragg, and Cavaye (1997), management support includes, among others, top management encouragement, information center support

and allocation of resources.

According to TAM, management support, being an external variable to the model, influences perceived usefulness and perceived ease of

use; there is also evidence in the literature supporting the positive relationship among these variables (Igbaria et al.,1995, 1997; Kim et al.,

2007; Trevino & Webster, 1992). The following hypotheses are therefore, proposed:

H4. Management Support has a positive effect on Perceived Usefulness.

H5. Management Support has a positive effect on Perceived Ease of Use.

2.3. Enjoyment

Davis, Bagozzi, and Warshaw (1992) and Igbaria, Schiffman, and Wieckowshi (1994) introduced perceived enjoyment and placed it in

parallel to the main belief constructs of TAM as a cognitive response ( Al-Gahtani & King, 1999). Perceived enjoyment is defined as ‘‘the

extent to which the activity of using the technology is perceived to be enjoyable in its own right, apart from any performance consequences

that may be anticipated” (Davis et al., 1992: 1113). Scholars argue that the perceived enjoyment of using a system positively influences the

perceived ease of use (Koufaris & Hampton-Sosa, 2002; Moon & Kim, 2001; Venkatesh, 1999, 2000; Yi & Hwang, 2003), the perceived use-

fulness (Agarwal & Karahanna, 2000; Koufaris, 2002; Yi & Hwang, 2003 ) and the behavioral intention to use a system (Davis et al., 1992;

Venkatesh, Speier, & Morris, 2002). Consequently, the following hypotheses are proposed:

H6. Perceived Enjoyment has a positive effect on Perceived Ease of Use.

H7. Perceived Enjoyment has a positive effect on Perceived Usefulness.

H8. Perceived Enjoyment has a positive effect on Behavioral Intention.

2.4. Computer anxiety

In a web-based environment, computers are the key tools for carrying out the learning task, therefore, anxiety, which may stem from its

usage, would probably obstruct the intention to use such a system. Computer anxiety can be defined as ‘‘the tendency of individuals to be

uneasy, apprehensive, or fearful about current or future use of computers” ( Igbaria & Parasuraman 1989: 375).

Many researchers have attempted to document the significance of computer anxiety on original TAM constructs, such as perceived use-

fulness and perceived ease of use. First of all Igbaria and Chakrabarti (1990), Igbaria et al. (1994) and Igbaria and Iivari (1995) presented

empirical support for the presumption that computer anxiety is negatively related to perceived usefulness. In addition Venkatesh (2000)

and Igbaria and Parasuraman (1989) supported that computer anxiety has a negative influence on the perceived ease of use of a system.Thus, the following hypotheses are proposed:

Management

Support

Self Efficacy

Learning GoalOrientation

Enjoyment

Computer

Anxiety

Intention

Perceived

Ease of Use

Perceived

Usefulness

H1

H4

H3

H2

H5

H6

H7

H9

H8

H10

H11

H14

H15H12

H17

H16

H13

Fig. 1. Employees’ acceptance of web-based training.

P.D. Chatzoglou et al./ Computers & Education 53 (2009) 877–889 879


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H9. Computer Anxiety has a negative effect on Perceived Ease of Use.

H10. Computer Anxiety has a negative effect on Perceived Usefulness.

2.5. Self efficacy

Computer self-efficacy is studied regularly from the scope of social cognitive theory (Venkatesh, 2000) as a main predecessor to tech-

nology use and acceptance (Compeau, Higgins, & Huff, 1999). According to Wood and Bandura (1989: 408), ‘‘self-efficacy refers to beliefs inone’s capabilities to mobilize the motivation, cognitive resources and courses of action needed to meet given situational demands”, while

Bandura (1986: 391) explained that it is not related ‘‘with the skills one has but with judgments of what one can do with whatever skills

one possesses”.

There are many theoretical and empirical studies in the IT literature which support that people with high self-efficacy will have a po-

sitive perception regarding how easy and useful a system is (Gong, Xu, & Yu, 2004; Venkatesh, 2000; Venkatesh & Davis, 1996; Yi & Hwang,

2003), because of its impact on ‘‘the degree of effort, the persistence and the level of learning which takes place” (Igbaria & Iivari, 1995). The

following hypotheses are proposed:

H11. Computer Self efficacy has a positive effect on Perceived Ease of Use.

H12. Computer Self efficacy has a positive effect on Perceived Usefulness.

Furthermore, many scholars applied Bandura (1977) theory of self-efficacy to computer-based learning and argued that there is a neg-

ative influence of self-efficacy on computer anxiety (Brosnan, 1998; Compeau et al., 1999; Igbaria & Iivari, 1995; Meier, 1985). The follow-

ing hypothesis is therefore, proposed:

H13. Computer Self efficacy has a negative effect on Computer Anxiety.

Moreover, people with low self-efficacy tend not only to be very anxious but also to have a low perception about their capabilities, con-

cerning the tasks that they have to carry out (Xu & Yu, 2004). This fact, normally, leads to vulnerability, low performance and dysfunction.

On the other hand, the sense of enjoyment in using a given system is emerging as a critical factor, being able to efficiently reduce anxiety

and raise people confidence about their skillfulness to successfully carry out the requisite actions (Yi & Hwang, 2003). Consequently, relat-

ing computer self-efficacy with perceived enjoyment, the following hypothesis is suggested:

H14. Perceived Enjoyment has a positive effect on Computer Self efficacy.

2.6. Learning goal orientation

Goal orientation is a theory that conceptualizes a personality dimension from the broader goals pursued by individuals ( Peloso & Gal-

liford, 2003) and attempts to explain the reasons for setting goals and motivations for achieving or failing to achieve those goals ( Code,

MacAllister, Gress, & Nesbit, 2006). It was initially divided into two different classes: learning goal or mastery orientation, and performance

goal orientation, also called ego orientation and ability-goal orientation (Dweck, 1986; Dweck & Leggett, 1988; Elliot & Dweck, 1988; Nich-

olls, 1984). This research focuses on the learning goal orientation, which is concentrated on the learning process itself, through understand-

ing the task and task strategies (Carson, Mosley, & Boyar, 2004). It is related to an incremental theory about their skills development

(through effort and experience), motivating individuals to improve their level of competence in order to facilitate task performance

improvements (Carson et al., 2004; Hwang & Yi, 2002; Printrich, 2000). It assesses their competence comparing it with previous levels

of competence, engages them in solution-oriented self-instruction and chooses, persists and enjoys on a challenging task that foster learn-

ing (Egan, 2005; Hwang & Yi, 2002; Steele-Johnson, Beauregard, Hoover, & Schmidt, 2000). Learning goal orientation was found to have a

positive and statistical significant effect on self-efficacy, indicating that individuals with a learning goal orientation are more likely to de-

velop a higher sense of confidence (Bandura, 1986; Hwang & Yi, 2002; Peloso, 2003; Phillips & Gully, 1997; Yi & Hwang, 2003). Thus, the

following hypothesis is proposed:

H15. Learning Goal Orientation has a positive effect on Self efficacy.

In addition, learning goal orientation is found to have a positive impact on ease of use perception via its effects on self-efficacy (Hwang &

Yi, 2002). In this study, the direct relation between the two constructs will also be examined. Therefore, the next hypothesis is also sug-

gested:

H16. Learning Goal Orientation has a positive effect on Perceived Ease of Use.

Moreover, considering the adoption of a new system, it has been found that learning goal orientation enhances the enjoyment which

individuals are expected to have from the challenge of learning new features of the technology. Yi and Hwang (2003) tested this positive

relationship, but they did not find any significant effect on it. Thus, the following final hypothesis is proposed:

H17. Learning Goal Orientation has a positive effect on Perceived Enjoyment.



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3. Research methodology

3.1. Sampling and data collection

A structured questionnaire was designed and used for collecting data. This study measures eight (8) constructs and the questionnaire

was divided into nine sections. The first section refers to the general characteristics of the correspondent and the firm, while each one of the

following sections refer to (includes questions that measures) each of the constructs used in the research model: correspondents’ perceived

ease of use, perceived usefulness, firm’s management support for web-based training usage, correspondent’s enjoyment, computer anxiety,self efficacy, learning goal orientation and, finally, the last section measures correspondents’ intention to use web-based training. All items

(totally 44) were measured using a seven point Likert scale ranging from 1 (totally disagree) to 7 (totally agree) ( Appendix A). Table 1

shows the questionnaire constructs, their operational definition, the number of items used to measure each construct and the related

literature.

3.2. Instrument validation

Sample’s content validity was established through questionnaire pre-testing process (Zikmund, 2003). Ten employees (pre-test partic-

ipants) were asked to make remarks regarding the research questionnaire instructions and to point out any drawbacks or lack of clarity of

the items examined. Further, a number of modifications (wording) are made in order to ensure that the original text was clearly interpreted

in the target language (Greek). The back translation method, which refers to the fact that the questionnaire is translated back to the original

language to secure correspondence with the original version, was used to validate the translated questionnaire (Francis et al., 2004). More-

over, the wording of the questions was again slightly modified before the final draft was established, based on the pre-test process partic-

ipants’ remarks and instructions.Totally, five hundred (500) employees from a hundred (100) firms were initially contacted and 414 of them accepted to participate in

this research. Finally, only 287 employees (response rate 69.3%) from 72 firms have successfully completed and returned the questionnaire.

More of these firms have not implemented web-based or other e-learning training programs. Table 2 presents in brief the profile of the

research participants. The research sample consists of medium to large sized competitive firms, balanced as far as the sector they belong

to is concerned, while the respondents are well educated with many years of professional experience. Data analysis has been performed

using AMOS software and the Structural Equation Modeling (SEM) Approach.

4. Data analysis and results

4.1. Confirmatory factor analysis

In this study confirmatory factor analysis (CFA) was used to assess each factor’s construct validity. Four fit measures were used to eval-

uate the model fit: chi-square/degree of freedom (v2/d.f.), goodness-of-fit index (GFI), comparative fit index (CFI), and root mean squareresidual (RMR).

Table 3 presents the model fit results for all (eight) research constructs. As can be seen, all loadings are above 0.6 (threshold 0.5, Ber-

geron, Raymond, & Rivard, 2004), chi-square/degree of freedom (v2/d.f.) scores are close to the accepted threshold score 5 (Harrison & Rain-

er, 1996) for most of the constructs, GFI scores are above the 0.92 threshold (Bollen & Long, 1993), CFI scores are also above the 0.9

threshold (Smith & McMillan, 2001), while RMR values are below the 0.1 threshold ( Bollen, 1989; Hair, Anderson, Tatham, & Black,

1992). It must be stressed here, that as far as the self-efficacy construct is concerned, CFA indicated that six items (SEF 5, SEF 6, SEF 7,

SEF 8, SEF 9 and SEF 10) should be excluded from the construct due to statistically insignificant loadings.

Table 1

The questionnaire constructs and operational definitions.

Constructs Operational definition Items References

Intention to use

web-based

training

A person’s subjective probability that he will perform some behavior (use Web-based

training).

5 Fishbein and Azjen (1975) and Hsu, Lu, and

Hsu (2007)

Management

support

Perceived level of general support offered by top management, including encouragement

and resource support (technical and managerial).

6 Igbaria et al. (1997)

Perceived enjoyment The extent to which the activity of using the technology is perceived to be enjoyable in its

own right, apart from any performance consequences that may be anticipated.

3 Davis et al. (1992) and Yi and Hwang

(2003)

Perceived usefulness The degree to which a person believes that using a computer would enhance his/her job

performance.

4 Arbaugh (2000); Davis (1989); Davis et al.

(1989); Sun, Ke, and Cheng (2007)

Perceived ease of use The degree to which a person believes that using a computer will be free of effort (with

the minimum effort possible).

4 Arbaugh (2000); Davis (1989); Davis et al.

(1989); Sun et al. (2007)

Learning goal

orientation

The motivation of individuals to improve their level of competence in order to facilitate

task performance improvements.

8 Carson et al. (2004)); Hwang and Yi

(2002)); Printrich (2000))

Self-efficacy The beliefs in one’s capabilities to mobilize the motivation, cognitive resources, and

courses of action needed to meet given situational demands.

10 Compeau and Higgins (1995); Compeau

et al. (1999); Wood and Bandura (1989)

Computer anxiety The tendency of an individual to be uneasy, apprehensive and phobic towards current or

future use of computers in general.

4 Compeau et al. (1999); Igbaria (1993);

Raub (1981)

P.D. Chatzoglou et al. / Computers & Education 53 (2009) 877–889 881


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Table 2

Respondents profile.

Measure Items Percentage

Employees Gender Male 59.8%

Female 40.2%

Age Mean: 37.24 years

Std. 9.816

Education High school 31.4%

Undergraduate studies 59.3%Postgraduate studies 9.3%

Work experience Mean: 12.39 years

Std. 9.583

Computer experience Mean: 10.54 years

Std. 5.745

Occupation Full time 95.1%

Part time 4.9%

Position Management 50.8%

Production 22.8%

Support (Technical & clerical) 26.4%

Firms Sector Manufacturing 28.9%

Trade 25.1%

Construction 6.6%

Banks 21.3%

Services 18.1%

Competitive position Leader 28.6%

Big player 27.5%Competitive 33.1%

Small player 10.5%

Follower 0.3%

Sample size, N = 287.

Table 3

Confirmatory factor analysis.

Construct Items Mean St. deviation Loadings CMIN/DF GFI CFI RMR

Ease of use EOU1 5.55 1.403 0.904 7.545 0.967 0.986 0.029

EOU2 5.58 1.359 0.922

EOU3 5.45 1.454 0.902

EOU4 5.43 1.456 0.917

Usefulness US1 5.87 1.237 0.860 5.764 0.976 0.986 0.025

US2 5.67 1.354 0.902

US3 5.77 1.253 0.863

US4 5.70 1.367 0.916

Management support MS1 5.46 1.570 0.709 1.624 0.985 0.997 0.035

MS2 5.18 1.721 0.932

MS3 5.03 1.770 0.901

MS4 5.05 1.680 0.866

MS5 5.25 1.660 0.854

MS6 5.22 1.658 0.830

Enjoyment ENJ1 5.56 1.375 0.910 1.452 0.926 0.931 0.061

ENJ2 5.55 1.318 0.977

ENJ3 5.39 1.404 0.834

Computer anxiety CA1 1.90 1.608 0.854 8.500 0.957 0.980 0.042

CA2 2.10 1.666 0.887

CA3 1.94 1.603 0.943

CA4 1.71 1.506 0.934

Self efficacy SEF1 4.64 1.896 0.909 3.795 0.987 0.994 0.042

SEF2 4.87 1.756 0.911

SEF3 5.02 1.735 0.869

SEF4 5.21 1.599 0.756

Learning goal orientation LGO1 6.05 1.149 0.673 3.250 0.945 0.965 0.037

LGO2 6.31 1.149 0.761

LGO3 5.90 1.231 0.631

LGO4 6.27 1.012 0.822

LGO5 6.27 1.021 0.790

LGO6 6.20 1.116 0.758

LGO7 6.31 9.935 0.840

LGO8 6.10 1.127 0.700

Intention INT1 5.88 1.482 0.725 4.203 0.964 0.977 0.057

INT2 5.88 1.324 0.884

INT3 5.27 1.545 0.785

INT4 5.82 1.379 0.822

INT5 5.97 1.303 0.776



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4.2. The metric model

The overall metric model was tested using the Structural Equation Modeling Approach. The overall model fit was appraised using again

the five common fit measures: (v2/d.f.), (GFI), (CFI), (RMR) and root mean square error of approximation (RMSEA). Table 4 summarizes the

overall fit values of the CFA model, where all the extracted fit values are within the acceptable levels.

Table 4

Overall fit of the CFA model.

Model-fit index Scores

Chi-square/degree of freedom (v2/d.f.) 2.944

Goodness-of-fit index (GFI) 0.974

Comparative fit index (CFI) 0.974

Root mean square residual (RMR) 0.079

Root mean square error of approximation (RMSEA) 0.082

Intention

(R2= .52)

PerceivedUsefulness

(R2= .43)

PerceivedEase of Use

(R2= .55)

Enjoyment

(R2= .27)

Self

Efficacy(R2= .19)

Computer

Anxiety

(R2= .07)

Management

Support

(R2= .09)

LearningGoal

Orientation

.31***

.16**

.39***

- .23**

.41***

.21***

- .23***

.28***

.11**

.27***

.44***

.24***

- .18***

.26***

.31***

.20***

.23***

Notes: *** Significant at the p < 0.01 level, ** Significant at the p < 0.05 level

________: Originally Proposed Causal Paths

- - - - - - - : Additional Relationships Proposed by Modification Indexes

Fig. 2. Research structural model.

Table 5

Hypotheses testing results.

Hypothesis Path Path coefficient Remarks

H1 Perceived ease of use? intention 0.16** Accepted

H2 Perceived usefulness? intention 0.31*** Accepted

H3 Perceived ease of use? perceived usefulness 0.26*** Accepted

H4 Management support? perceived usefulness 0.11** Accepted

H5 Management support? perceived ease of use – Dropped

H6 Perceived enjoyment? perceived ease of use 0.24***

AcceptedH7 Perceived enjoyment? perceived usefulness 0.28*** Accepted

H8 Perceived enjoyment? intention 0.39*** Accepted

H9 Computer anxiety? perceived ease of use 0.18*** Accepted

H10 Computer anxiety? perceived usefulness – Dropped

H11 Self-efficacy? perceived ease of use 0.41*** Accepted

H12 Self-efficacy? perceived usefulness – Dropped

H13 Self-efficacy? computer anxiety 0.23** Accepted

H14 Perceived enjoyment? self efficacy 0.21*** Accepted

H15 Learning goal orientation? self efficacy 0.27*** Accepted

H16 Learning goal orientation? perceived ease of use 0.20*** Accepted

H17 Learning goal orientation? perceived enjoyment 0.44*** Accepted

Proposed causal relationships

Computer anxiety? perceived enjoyment 0.23*** Accepted

Learning goal orientation? perceived usefulness 0.23*** Accepted

Learning goal orientation?management support 0.31*** Accepted

**

p < 0.05 level.*** p < 0.01 level.



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4.3. The structural model: Results and discussion

Fig. 2 demonstrates the structural model with the extracted path coefficients and the adjusted R2 scores while Table 5 presents the over-

all findings as far as the hypotheses tested are concerned.

First, the relationships between (i) management support and perceived ease of use (H5), (ii) computer anxiety and perceived usefulness

(H10), as well as, (iii) self-efficacy and perceived usefulness (H12), are not supported by the results of the statistical analysis thus, they

must be removed from the model. The remaining 14 relationships originally proposed are acceptable and statistically significant. Moreover,

modification indexes indicated that three new additional relationships appeared to be statistically significant and therefore, should be in-cluded in the model (they are presented in Fig. 2 with dotted lines). These relationships are between: (i) learning goal orientation and man-

agement support, (ii) enjoyment and computer anxiety and (iii) learning goal orientation and perceived usefulness. Table 6 summarizes the

total, direct and indirect effects between all model constructs.

As regards to the relationship between management support and perceived usefulness and ease of use, only the former is accepted. The

relative effect of management support on ease of use and usefulness perceptions has been found to vary studies in the literature. For exam-

ple Igbaria et al. (1995) found that organizational support only affects ease of use, while results of Igbaria et al. (1997) and Kim et al. (2007)

studies indicate that management support has a stronger effect on usefulness than ease of use. The variance in results can be partially

attributed to the different dependent variables used (Igbaria et al., 1997). In this case; however, the fact that the pre-implementation

acceptance is examined should be taken into consideration. This means that management efforts, that can take many forms ( Igbaria

et al., 1997), may be primarily focused on enhancing web-based training usefulness, in order to encourage employee acceptance. Moreover,

employee perceptions of ease of use could be rather associated with application specific training, which in this case was not examined.

Nonetheless, the relationship between management support and perceived usefulness, although quite low, is statistically significant, indi-

cating the necessity of management support in firms’ web training processes and, especially, in supporting a trainee’s perception of web

training usefulness.

As far as learning goal orientation is concerned, the initial three hypotheses (H15, H16 and H17) are confirmed, while the stron-

gest relationship (0.44***, H17) appears to be between learning goal orientation and perceived enjoyment. Results regarding the

relationship between LGO and self-efficacy have been also confirmed by past studies (Hwang & Yi, 2002; Yi & Hwang, 2003). How-

ever, in this case the proposed direct effect of LGO on enjoyment and ease of use is statistically significant, as well. By definition,

individuals with high learning goal orientation react to challenging work with positive affect and intrinsic motivation (Dweck &

Leggett, 1988). They would therefore, enjoy exploring the features of the specific application per se, along with any learning oppor-

tunities this would offer them. Moreover, since these individuals are expected to relish challenging work and are willing to try

hard when working on a task, it would be reasonable to assume that they are likely to view the features of web-based training

as easy to use.

Two new causal paths linking learning orientation with management support (0.31 ***) and perceived usefulness (0.23***) have also

been suggested by modification indexes. As regards to the relationship between LGO and usefulness, it would be safe to say that the

learning opportunities offered by a web-based training program could only be viewed as appealing to individuals with high learning

goal orientation. In general Kozlowski et al. (2001) explain that individuals with a learning goal orientation have adaptive responses

to new and/or challenging situations. As a result, these situations are treated as opportunities for self-improvement through learning

(Loraas & Diaz, 2009). In effect, except for the positive influence on perceptions of ease of use, or even regardless of the appraisalregarding the ease of use, potential users with a learning goal orientation are expected to have more positive evaluations regarding

the usefulness of a web-based training tool. Linderbaum (2006), investigating feedback seeking behavior, proposed that learning goal

orientation is positively related to perceptions of utility, which ‘‘. . .refers to an individual’s tendency to believe that feedback is useful

in achieving goals and obtaining desired outcomes” (p. 29). Correspondingly, employees with higher LGO may develop more positive

Table 6

Direct and indirect standardized effects of the model constructs.

LGO ENJ SEF CA MS EOU US

ENJ D 0.436

I 0.019

T 0.455

SEF D 0.269 0.212I 0.096 0.002

T 0.372 0.214

CA D 0.230

I 0.084 0.049 0.003

T 0.084 0.049 0.233

MS D 0.308

I

T 0.308

EOU D 0.198 0.241 0.410 0.179

I 0.274 0.099 0.059 0.077

T 0.472 0.340 0.469 0.256

US D 0.227 0.280 0.107 0.262

I 0.283 0.092 0.137 0.031

T 0.511 0.372 0.137 0.031 0.107 0.262

Intention D 0.393 0.163 0.306

I 0.411 0.186 0.104 0.172 0.033 0.080

T 0.411 0.565 0.104 0.172 0.033 0.243 0.306

Note: ENJ, enjoyment; SEF, self efficacy; CA, computer anxiety; MS, management support; EOU, ease of use; US, usefulness; D, direct effect; I, indirect effect; T, total effect.



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perceptions regarding the utility of a web-based training application. On the other hand Saadé (2007), proposed that LGO comprises,

along with extrinsic (outcome expectations and performance goal orientation) and intrinsic motivation (enjoyment), the three sub-

dimensions of perceived usefulness. The author developed an extended TAM model to study students’ intention to use an Inter-

net-based learning tool, in which perceived usefulness was replaced by the three aforementioned dimensions. Saadé (2007: 296) pos-

tulates that ‘‘. . .LGO can be considered equivalent to perceiving the system that is being used to be useful, but for reasons of self-

improvement”.

The suggested relationship between LGO and perceptions of management support, to the best of the authors’ knowledge, has not

indicated before in the relevant literature, although the impact of various environment features has been widely examined in train-ing-relevant studies. For example, evidence of a positive impact of organization climate (which includes management support) on

valence, i.e. according to expectancy theory, ‘‘individuals’ beliefs regarding the desirability of outcomes obtained from training . . .” (Col-

quitt & LePine, 2000: 680). Moreover, past literature has advocated that the extent to which environmental features as a whole are per-

ceived by learners as barriers or enablers has an influence on their motivation to learn; these perceptions are not necessarily based on

actual events or conditions (Klein, Noe, & Wang, 2006). As Klein et al. (2006) explains, learner characteristics, including learning goal

orientation, influence the likelihood that certain features are perceived as enablers or barriers; accordingly, the impact of LGO on moti-

vation to learn is proposed to be, at least partially, mediated by those perceptions. Seen from this angle, individuals with high LGO could

view management support as an enabling environmental feature, being more sensitive to realizing the efforts on behalf of top

management for promoting and supporting the utilization of such systems and viewing it as a means to achieve further personal

development.

Alternatively, there is evidence in the literature that LGO may be influenced by environmental factors (e.g. Button, Mathieu, & Zajac,

1996; Dweck, 1986; Sample, 2004; VandeWalle, Cron, & Slocum, 2001). VandeWalle (2001) postulates that the dispositional goal orienta-

tion that an employee brings to the workplace can be supported or discouraged by the organizational culture. Although goal orientation is

mainly regarded as a dispositional factor, and is conceptualized as such in this study, literature has also been focusing on situational goal

orientation (also categorized as learning and performance) that can be induced via normative instruction (Loraas & Diaz, 2009). This has

given rise to a number of studies concerned with the design of goal orientation-based training interventions, which seem to yield consis-

tent findings with those of trait-based research (Kozlowski et al., 2001). Sample (2004), proposes a series of strategies that practitioners

may use in order to effectively combine learning a performance, among which are the assessment of employees goal orientation and

the design of human resource management strategies that emphasize and enhance learning for long-term results. However, further qual-

itative research is granted to study the dynamics between these constructs, as well as more empirical investigation to examine whether the

aforementioned relationship can be replicated.

Similarly to other studies (Koufaris, 2002; Venkatesh et al., 2002; Yi & Hwang, 2003), all the hypotheses for the perceived enjoyment

construct have been confirmed by the results of this analysis (H7, H6, H8 and H14). The strongest direct relationship is between perceived

enjoyment and a trainee’s intention to use web training (0.39 ***, H8), indicating that the level of the trainee’s perceived enjoyment mainly

affects the intention construct. Focusing on this relationship, it can be said that an employee, possibly, intends to use a web-training plat-

form when he/she believes that the training process will be an interesting, helpful and enjoyable one. In other words, if the employee

thinks that the training program will be boring and without any real value for him/her, he/she will not be excited enough to participate

in the training process.

Moreover, if the training program is enjoyable for and considered as interesting by the trainee, its on-the-job usefulness (H7) and ease of use (H8) will be realized more easily and faster. The relationship between perceived enjoyment and self efficacy confirms the fact that an

interesting and joyful training program may lead trainees to develop new initiatives, to overcome difficult and anxious on-the-job situa-

tions and to improve their personal job-esteem (H14).

Further, there is also a proposed negative relationship (0.23***) between perceived enjoyment and computer anxiety. This relationship

possibly indicates that, when the trainee enjoys his participation to a web-training program, his computer anxiety level is low. On the other

hand, if the trainee’s perceived enjoyment is low then he is unsure of his computer usage capabilities, he is not confident to use the com-

puter for the training program and he may possibly put relatively limited effort into the training process.

Further, self-efficacy positively affects a trainee’s perceived ease of use (H11), as Venkatesh (2000) also found, because of the fact that

trainees’ perceived ease of use is possibly based on their perception of their capabilities. In other words, if trainees have a strong confidence

in their computer usage capabilities (judgment, skills and abilities) then they more easily realize the easiness of the training program.

As Brosnan (1998) also found, there is a negative relationship (0.23**, H13) between self-efficacy and computer anxiety, probably be-

cause, when trainees are confident of their computer usage capabilities (judgment, skills and abilities) then they are not fearful and anxious

about using a web-training platform.

As far as the computer anxiety construct is concerned, only one of the two causal relationships is valid. Firstly, there is a negative rela-tionship (0.18***, H9) between computer anxiety and perceived ease of use as Igbaria and Iivari (1995) also found. This relationship is

possibly extracted because when trainees are anxious and fearful about their computer use, then it is more difficult for them to realize

or understand the easiness of a web training learning method.

As far as the perceived ease of use is concerned, its positive relationships with intention and usefulness (H1, H3) have been confirmed,

as many other studies also found (Liu & Wei, 2003; Moon & Kim, 2001; Vijayasarathy, 2004). These findings imply that if a trainee has

realized the easiness of a training program then he or she also realizes its on-the-job usefulness and intent to use it. Finally, a positive

relationship between perceived usefulness and intention (H2) also appears, as Moon and Kim (2001) and Liu and Wei (2003) also found,

suggesting that when a trainee understands the usefulness of the web-training program then the lack of his/her intention to participate

increases.

Overall, all these direct relationships between enjoyment, ease of use, usefulness with intention have been found to be statistically sig-

nificant (Table 6). Interestingly, the relationship with the highest magnitude is the one between enjoyment and intention (0.393 ***) sug-

gesting that, apart from anything else, web-training program designers must pay particular attention to make the whole process more

joyful for the trainees. The second more important relationship seems to be between usefulness and intention (0.306***) and the third be-

tween ease of use and intention (0.163**

). Surprisingly, though, when taking into consideration the indirect impact as well, learning orien-tation arises as the relationship with the second highest total importance for intention (0.411***). This is a very significant result indicating



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that training programs must be directly related to trainees’ real task-execution problems and that managers must explain to trainees the

usefulness of these programs to each one of them.

As regards to the three relationships of the originally proposed model, they are not supported by the statistical analysis; therefore, they

must be removed from the model. In detail, the relationship between management support and perceived ease of use (H5) found to be

insignificant (as Wu, Chen, & Lin, 2007 also found). This finding is in contrast with other previous researches (Igbaria & Iivari, 1995; Igbaria

et al., 1995, 1997) where there is, indeed, a significant relationship between management support and perceived ease of use. However, it

must be stressed here that in these previous researches the relationship coefficients are too weak (0.09 in Igbaria & Iivari, 1995; 0.14 in

Igbaria et al., 1995; 0.07 in Igbaria et al., 1997) and as Igbaria and Iivari (1995: 601) stated ‘‘managers . . .

are most likely to have a greatimpact on the user’s belief in the system’s usefulness and benefits” implying that management support influences mostly perceived use-

fulness than perceived ease of use.

The finding that management support is not related to perceived ease of use probably indicates the fact that managers stress out mainly

the benefits for the employees derived from using a web-based training system and thinking that their employees will eventually use the

training platform regardless its degree of easiness. This finding is in line with the aspect that ‘‘perceived usefulness is more influential than

perceived ease-of-use in determining usage (Igbaria & Iivari, 1995: 602)”.

The second relationship that has to be removed from the model is between computer anxiety and perceived usefulness (H10). The find-

ings indicate no direct influence to perceived usefulness, but a weak negative (0.031) indirect influence to perceived usefulness through

perceived ease of use. As Igbaria and Iivari (1995) also pointed the effects of computer anxiety to perceived usefulness are mainly indirect

and channelled through perceived ease of use. Since this form of anxiety is a dynamic feature of the trainees, it can be changed by improv-

ing trainees’ abilities via training. This finding probably indicates that as trainees use the training platform systematically their anxiety is

reduced and their confidence is increased, thinking now that the training platform is an ease one to handle and eventually try to utilize it in

the most useful way for them.

Furthermore, as Igbaria and Iivari (1995) and Wu et al. (2007) also found, the relationship between self-efficacy and perceived useful-

ness (H12) has also to be removed from the model as the findings indicate that their relationship is not a significant one. However, again

there is a weak (0.137) indirect influence to perceived usefulness through perceived ease of use. According to Venkatesh (2000: 347) ‘‘there

is experimental evidence supporting the causal flow from computer self-efficacy to system-specific perceived ease of use ( Venkatesh &

Davis, 1996).” and their relationship can be ‘‘. . . justified on the basis that in the absence of direct system experience, the confidence in one’s

computer related abilities and knowledge can be expected to serve as the basis for an individual’s judgment about how easy or difficult a

new system will be to use” Venkatesh (2000: 347).

5. Conclusions and research limitations

5.1. Conclusions

This study has examined the employee’s intention to accept a web-based training program. The results that were extracted

from the analysis of data from 287 employees indicated that trainees’ enjoyment, perceived usefulness and perceived ease of use directly affects their intention to use a web-training platform. Furthermore, learning goal orientation significantly affects all

models’ constructs and has the second strongest total effect on usage intention. These results provide further support for the

need to assess individual differences of employees before designing a training program, as fitting a training program to each em-

ployee has been found to have a positive impact on training effectiveness (Hertenstein, 2001; Ingham, 1991). Individuals with

different levels of goal orientation are believed to respond differently to different training designs; for example people with high

LGO would be better suited to a mass training program (Hertenstein, 2001). It is therefore, suggested that employees could be

first assessed and then assigned to different types of training so as to fit their level of learning orientation, as Hertenstein

(2001) also proposes.

From a managerial perspective, after the organizational training needs identification, managers should customize the firm’s tech-

nical and managerial support on the web-training program needs. Successful employee training needs an identification process that

may help managers to outline the correct set of learning goals that have to be achieved by a web-based training program. Man-

agers ought to focus on employees on-the-job learning needs in order to design a training program that will satisfy employees’

learning needs. Further, managers need to design trainee-centered programs taking into account the trainees’ knowledge level

and style, in order to increase their interest and motivation for the training program. The program’s participants should feel com-

fortable and joyful during the training process, in order to quickly realize the usefulness and ease of use of the training programme.

For a successful web-training program, managers should design and create an environment where the trained employees will be

convinced of their personal knowledge and abilities, they will feel free to overcome challenging on-the-job problems and they will

learn how to use their mistakes in order to improve their job capabilities. Conclusively, a well designed web-training program must

ensure and improve trainees’ enjoyment, self efficacy, reduce their anxiety in order for them to successfully accept and use the

training program.

5.2. Research limitations

A potential limitation of this research relates to the sample size, which is considered a small one (287). This study does not include any

dynamic changes that may appear after trainees test a web-training program and detail the changes that may occur in trainees’ self efficacy

after testing a specific web-training platform. With the exception of learning goal orientation, all other results do not deviate from past

research. Learning goal orientation should be further examined so that its role can be explained. Also, further research may be useful

for the examination of the direction of causality for the proposed relationships of the model. Finally, further research on employee’s char-

acteristics should receive more attention.



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Appendix A. Questionnaire items

Intention

1 = Extremely Unlikely to 5 = Extremely Likely (for all 5 items)INT1 I intent to use web-based training when it will be implemented.

INT2 I intent to use web-based training in order to improve my performance.

INT3 I intent to use web-based training on a regular basis.

INT4 Given the circumstances, in would use web-based training.INT5 I would strongly recommend my colleagues to use web-based training.

Perceived Ease of Use

1 = Extremely Disagree to 5 = Extremely Agree (for all 4 items)

EOU1 It would be easy for me to become skilful at using web-based training.

EOU2 Learning to operate web-based training would be easy for me.EOU3 I would find it easy to get web-based training to do what I want it to do.

EOU4 I would find web-based training easy to use.

Perceived Usefulness


US1 Using web-based training would enhance my job effectiveness.

US2 Using web-based training would improve my performance.

US3 I would find web-based training useful in my job.

US4 Using web-based training would enhance my productivity.

Computer Anxiety


CA1 I feel apprehensive about using computers

CA2 It scares me to think that I could cause the computer to destroy a large amount of information by hitting the wrong key.

CA3 I hesitate to use a computer for fear of making mistakes I cannot correct.

CA4 Computers are somewhat intimidating to me.

Self Efficacy


I could use web-based training if...

SEF1 . . .there was no one around to tell me what to do as I go.SEF2 . . .I had never used anything like it before.

SEF3 . . .I had only the software manuals for reference.

SEF4 . . .I had seen someone else using it before trying it myself.

SEF5 . . .

I could call someone for help if I got stuck.SEF6 . . .someone else had helped me get started.

SEF7 . . .I had a lot of time to complete the job for which the software was provided.

SEF8 . . .I had just the built-in help facility for assistance.SEF9 . . .if someone showed me how to do it first.

SEF10 . . .I had used similar packages before this one to do the same job.

Enjoyment

1 = Not at all to 5 = Very much (for all 3 items)

ENJ1 I would have fun using web-based training.

ENJ2 Using web-based training would be pleasant.

ENJ3 I would find enjoyable to use web-based training.

Learning Goal Orientation


LGO1 The opportunity to do challenging work is important to me.

LGO2 When I fail to complete a difficult task, I plan to try harder the next time I work on it.LGO3 I prefer to work on tasks that force me to learn new things.LGO4 The opportunity to learn new things is important to me.

LGO5 I do my best when I’m working on a fairly difficult task.

LGO6 I try hard to improve my past performance.

LGO7 The opportunity to extend the range of my abilities is important to me.LGO8 When I have difficulty solving a problem, I enjoy trying different approaches to see which one will work.

Management Support

1 = Extremely Disagree to 5 = Extremely Agree (for all 6 items)MS1 Management is aware of the benefits that can be achieved with the use of web-based training.

MS2 Management would always support and encourages the use of web-based training for every-day job related work.

MS3 Management would provide most of the necessary help and resources to enable people to use web-based training.

MS4 Management would be really keen to see that people are happy with using web-based training.

MS5 Management provides good access to hardware resources when people need them.

MS6 Management provides good access to various types of software when people need them.



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