Re-Sit Test

DR.KONSTANTINOS Z.VASILEIOUUniversity of Ioannina, Greece

DR.GEORGE S.SPAISUniversify of Péloponnèse, Greece

A revision of Technology Acceptance Modelfor the measurement of business schoolstudents* intention to increase PC and

Internet use for academic purposes

The main aim of the present inquiry is to identify and measure the factors thatinfluence business school students' intention to increase PC and internet use toimprove their academic performance. In this inquiry we empiricaify build andinvestigate a differentiation of the well known Technology Acceptance Model(TAM) (Davis et al, 1989), to study the adoption of Information TechnologyApplications by Business School students for academic purposes. The revisedmodel was structured in the logic that the actual computers and internet use, as wellas possessing PC and accessing internet at home may contribute to predictingstudents' intention to increase PC and internet use to enhance their academicknowledge. The developed model was empirically tested using a sample ofBusiness School students of two Greek Universities.

Introduction

THE TRANSFORMATIONS THAT HAVE TAKEN PLACE in the structure andthe character of modern university education have rendered technology a

particularly important factor in the organisational transformation of educationalstrategy and educational processes (Fusilier and Durlabhji, 2005; Govindasamy,2002). Universities are supposed to prepare their students to deal with anincreasingly competitive working environment, where digital literacy (sophisticateduse of IT and the Internet) is emerging as a new key competency required byworkers and citizens for the Knowledge Society (European Commission, 2005;Padilla-Meléndez and Garrido-Moreno, 2007). This is particularly the case forBusiness School students who will, in their future, work in an environment whereadvanced knowledge of Information Technology applications seems to be a keyprerequisite for a successful career (Padilla-Meléndez and Garrido-Moreno, 2007).However, the only published research regarding the adoption of Information

Vasileiou, Spais] A REVISION OF TECHNOLOGY ACCEPTANCE MODEL 191

Technologies in Greece showed that Greek citizens still lack behind comparing toother Europeans, although young people use PC and Internet considerably morethan other age groups (EDET, 2006). Other factors contributing to IT adoption aresex, income level, occupation and education level.

Literature review revealed that none research so far has dealt with the factors thatrelate to business school students' intention to increase computers (PC) and internetuse for academic purposes.

The basic aim of this inquiry is identify and measure the importance of thefactors that relate to students' intention to PC and internet to improve theiracademic performance.

According to the number of citations, the two frequently investigated models inthis area are the theory of planned behavior (TPB) (Ajzen 1991) and the technologyacceptance model (TAM) (Davis 1989; Davis et al. 1989). The theory of plannedbehavior posits that behavioral intention to perform an activity is determined by:attitude, perceived behavioral control, defined as the perception of how easy ordifficult it is to perform a behavior; and subjective norm, defined as one's beliefsabout whether significant others think that one should engage in the activity. TAMstates that behavioural intention to use a technology derives from two beliefs: (1)perceived usefulness, defined as the expectation that the technology will enhanceone's job performance; and (2) perceived ease of use, defined as the belief thatusing the technology will be free of effort (Adams et al., 1992; Davis et al., 1989;Mathieson, 1991; Shivers - Blackwell and Charles, 2006; Stoel and Lee, 2003).This formulation of TAM has been developed as a result of extensive testing andrefinement (Venkatesh and Davis 1996; Venkatesh 1999).

The other two parts of TAM are Experience from previous use of Technology andBehavioral Intention to Use. The Experience from previous use of Technologyrefers to individual's perceived experience on the particular application(s) of theTechnology. Behavioral Intention to Use is a measure of the probability that aindividual will use a particular application. Finally, the dependent variable of TAMis the Actual Use of the application, which is measured by the time of duration orthe frequency the application is used.

In this inquiry we empirically build and investigate a differentiation of the wellknown Technology Acceptance Model (TAM) (Davis et al, 1989), to study theadoption information technology applications by business school students foracademic purposes. The revised model was structured in the logic that the actualcomputers and internet use, as well as possessing PC and accessing internet at homemay contribute to predicting students' intention to increase PC and internet use toenhance their academic knowledge. Thus, it is the first time that the actual use'sinfluence on perceived experience, self-efficacy and usefulness is examined,compared to previous studies on the intention to PC and/or internet use for academicpurposes (Pituch and Lee, 2006; Ong and Lai, 2006; Roca et al., 2006; King, 2006;Elwood et al, 2006; Lee, 2006), or in the context of the application of TAM tomanagement education (Martins & Kellermans, 2004). The developed model wasempirically tested using a sample of business school students of two universities.

Research Hypotheses & Research ModelWe adopt the following new definition for students' intention to increase PC and

192 JOURNAL OF BUSINESS AND SOCIETY [20, 2007

internet use for academic purposes: "the measure of probability that a student willincrease a PC or Internet application (word processing, data processing(spreadsheets), internet and e-mail)"

The research model is based on the study of Liaw (2007). The theoreticalbackground of research derives from the work of Levine & Donitsa - Schmidt(1998), and Zhang & Espinoza (1998).

The research model was used to test the following research hypotheses:(Hi): Owning a PC or accessing Internet at home increases their use(H2): Owning a PC or accessing Internet at home increases perceived self-efficacy(H3): Increased PC and Internet actual use implies higher perceived experience

with PC and Internet use.(H4): Increased PC and Internet actual use implies higher perceived self-efficacy

with PC and Internet use(H5): Increased PC and Internet actual use implies higher perceived usefulness of

PC and Internet use(He): Increased PC and Internet actual use implies higher intention to increase

PC and Internet use.(H7): Higher perceived experience with PC and Internet use implies higher

perceived self-efficacy with PC and Internet use.(Hg): Higher perceived self-efficacy with PC and Internet use implies higher

intention to increase PC and Internet use.(H9): Higher perceived usefulness of PC and Internet use implies higher intention

to increase PC and Internet use

Figure 1The revised Technology Acceptance Model TAM to explore students'

intention to increase PC and Internet use to improve academicperformance

Own PC & Internetaccess at home

Hi

H2Perceived

Experience

Perceived Seif-Efficacy

PC & Internet ActualUse

Perceived Usefuiness

H,

Intention to increase PC &Intemet use for academic

purposes


Table 1Definitions of research model constructs

Constructs

1. Perceivedexperience withPC and Internetuse

2. Perceived Self-efficacy with PCand Internet use

3. Perceivedusefulness of PCand Internet use

Definition

Defined in two ways:i) perceived usage, the amount of timespent interacting witb a microcomputer andfrequency of use.ii) variety of use, the importance of useand the collection of software packageuse.

Beliefs in one's capabilities to organizeand execute the courses of action requiredto produce given attainments, orAn individual's confidence in his/herability that may impact the performance oftasks.

The degree to which an individualbelieved that using a particular systemwould enhance his or her job

References

Mitra (1998);Igabaria et al(1995); üaw(2007)

Bandura (1997);Kinzieetal (1994)(Liaw & Huang,2003).

RESEARCH METHODParticipants, Procedure & Data Collection

The research model was tested by conducting questionnaire survey to a sample of827 students from two Greek Universities, the University of Athens and theUniversity of Ioannina. The response rate was 93.5%. The number of male andfemale students was almost equal (410 and 417, respectively). About forty (40.1%)aged 18-20, forty-nine (48.7%) aged 21-23, about nine (8.89%) aged 24-26 andaround two (2.3%) aged more than 26.

MeasuresThe questionnaire used to test the revised TAM research model consisted of six

parts:(a) Owning a PC and accessing Internet at home,(b) Actual PC and Internet use,(c) Perceived experience construct measured with 5 variables (7-points Likert

scale ranging from "l=no experience" to "7=very high experience"),(d) perceived self-efficacy consisting of 5 variables (7-points Likert scale ranging

from "l=totally disagree" to "7=totally agree"),(e) perceived usefulness (6 variables (7-points Likert scale ranging from

"l=totally disagree" to "7=totally agree").


(f) intention to increase PC and Internet use in order to improve academicperformance (5 variables (7-points Likert scale ranging from "1= totally disagree" to"7=totally agree") and

(g) demographic information, including sex, age, family income and Englishlanguage knowledge level.

With establishing content validity, the questionnaire was refined through rigorouspre-testing. The pre-testing was focused on instrument clarity, question wordingand validity. During the pre-testing, three doctoral students and three professors(from the University of Ioannina) were invited to comment on the questions andwordings. The comments of these six (6) individuals then provided a basis forrevisions to the construct measures.

AnalysesMean and standard deviation: Descriptive statistics will allow describing the

basic features of the data in our study. The mean or average is probably the mostcommonly used method of describing central tendency. The standard deviation is amore accurate and detailed estimate of dispersion because an outlier can greatlyexaggerate the range. The Standard Deviation will allow showing the relation thatset of scores has to the mean of the sample.

Cronbach-a: The internal consistency was measured calculating the Cronbach-acoefficient, based on the average inter-item correlation.

Spearman Rho's correlation analysis: The research model hypotheses wereinitially tested carrying out correlation analysis among the variables.

Multiple Regression analysis based on Durbin-Watson statistic andmultiple R̂ coefficient: Path analysis is the most commonly used methodologyto examine the relationships among variables in the form of linear causal models. Ingeneral, the value of the path coefficient (R )̂ associated with each path representsthe strength of each linear influence. The Durbin-Watson statistic examines thedegree of auto-correlation and multicolinearity was checked through correlationanalysis among dependent variables, which in every case was less than 0.8.

RESEARCH RESULTSSample Characteristics

The response rate, as mentioned before, was 93.5%. The participants in the studywere 827 undergraduate Greek University students, almost half of them male andhalf female (49.6% and 50.4%, respectively). The number of male and femalestudents was almost equal (410 and 417, respectively). About forty (40.1%) aged18-20, forty-nine (48.77o) aged 21-23, about nine (8.89%) aged 24-26 and aroundtwo (2.37o) aged more than 26. About ninety-three (92.6%) had a personalcomputer in their home; about seven (7.4%) had not. About fifty-five (55.3%) hadinternet access in their home and about forty-five (44.7%) had not. About sixty-three percent (63.1%) used computers less than 15 hours/week, about twenty-three(22.7%) used computers 15.01 - 30 hours/week and about fourteen (14.1%) usedcomputers more than 30 hours/week. About sixty-six (65.9%) used internet lessthan 6 hours/week, about seventeen (17.0%) used internet 6.01-12 hours/week andseventeen (17.0%) used internet more than 12 hours.


Table 3Actual students' PC and Internet use (hours/week) (n=827)

HA'

No use0,1-55,1 - 1010,1 -1515,1-2020,1 - 2525,1 - 30>30Total

N1620320499915245117827

%1,924,524,712,011,06,35,414,1100,0

Internet

No use0,1-22,1-44,1-66,1-88,1 -1010,1-12> 12Total

N4928810999388815141827

7o5,934,813,212,04,610,61,817,0100,0

Descriptive StatisticsTable 2 presents the mean, standard deviation values for the variables that define

perceived experience, perceived self-efficacy, perceived usefulness and intention toincrease PC and Internet use constructs. The Cronbach-a and item-total correlationfor the variables of each construct have also been calculated.

Table 2Descriptive statistics, Item-total correlations and internal consistency

reliability (n=827)

1. Experience using PC & Internet:Operating systems (Windows, Linux)Word processing packages (Word, Wordpad)Database packages (Spreadsheets - Excel)InternetE-mail

2. Perceived self-efficacy using PC & Internet:PC generallyWord processing packagesDatabase packagesE-mailInternet to find information

Mean

4.7475.0464.4094.7424.341

5.0005.0064.4914.4175.146

1.5601.5551.7331.7041.982

1.5801.5281.6981.9281.677

Item-totalcorre-lation

0.71010.80360.66130.73890.6867

0.80110.82560.6810.71170.7136

Cron-bach a

.8847

.8984


3. Perceived usefulness of PC&'lnternet use:PC ¡s helpful for data processingPC is helpful for text processingIncreasing PC use can improve myacademic performanceIncreasing Internet use can improve my

academic performanceE-mail is useful to contact with classmatesand professorsInternet is useful to find information foracademic purposes

4. Intention to increase PC & Internet useto improve academic performance:InternetPC in generalWord processing packagesDatabase packagesE-mail

Mean

6.3286.334

5.461

5.490

5.412

6.156

5.2745.6435.3565.1034.521

0.9780.954

1.448

1.435

1.576

1.205

1.5501.3681.5391.6251.701

Item-total

corre-lation

0.53620.5655

0.6653

0.6685

0.5431

0.6073

0.60830.709

0.76890.69770.651

Cron-bach a

.8294

.8659

Participants in the survey claim to have more experience (rather high) with wordprocessing packages, followed by operating systems and internet use, and to lessextent database packages and email (relatively high). Consequently, it is notsurprising that students mention to feel more comfortable (rather agree) using PCgenerally, word processing packages and internet than email and databasepackages (neither disagree not agree). Although students find that PC is very helpfulfor data and text processing and Internet is also very useful to find information foracademic purposes, they hold, to less extent, positive stance regarding theimprovement they can achieve in their academic pertormance by increasing PCand Internet use. The last finding may be explained by the fact that few modules inthe faculties studied require from their students coursework where intense PC andInternet use is necessary.

Regarding their intention to increase PC and Internet use to improve academicperformance students, being consistent with their other perceptions, hold positivestance to do so. Word processing packages, internet and database packages seemto capture more the future interest of students that email use.

Cronbach-a values are rather high (from 0.83 to 0.90) for all constructs. Item-totalcorrelations range from 0.66 and 0.81 for perceived experience variables, from0.71 to 0.83 for perceived self-efficacy, from 0.54 to 0.67 for perceived usefulnessand from 0.61 to 0.77 for intention to increase PC and Internet use, which can bedeemed acceptable for further data analyses of constructs.


Research model correlation and path analysesThe Spearman's Rho correlation-coefficients among the research model variables

indicate that all variables are significantly correlated (bi-variate relationships) witheach other and the correlation was all less than 0.75.

Table 4Correlation analyses (n = 827)

Variables

1. Actual PC use

2. Actual Internet use

3. Perceived experience usingOperational Systems

4. Perceived experience withtext processing packages

5. Perceived experience withdata processing packages

6. Perceived experienceusing Internet

7. Perceived experienceusing e-mail

8. Perceived self-efficacyusing PC & Internet

9. Perceived usefuinessof PC & Intemet use

10. Intention to inaease PC& Intemet use to improveacademic performance

2.

0.727(»')

1.000

0.404(**)

0.358(")

0.322(**)

0.512(")

0.498(**)

0.498(")

0.379(")

0.219(")

3.

0.460(*')

0.404(**)

1.000

0.584(**)

0.559(")

0.569('*)

0.547(")

0.679(")

0.435(**)

0.294(")

4.

0.386(**)

0.358(")

0.684(**)

1.000

0.721(")

0.583C')

0.580(»*)

0.737(**)

0.455(")

0.299(")

5.

0.367(**)

0.322(")

0.559(**)

0.721(")

1.000

0.497(»»)

0.459C*)

0.584(**)

0.351(")

0.269(")

6.

0.428(*»)

0.512(**)

0.569(*»)

0.583(*»)

0.497(**)

1.000

0.750(**)

0.730(**)

0.485(")

0.266(")

7.

0.413(**)

0.498(")

0.547(")

0.580(**)

0.459(»*)

0.750(**)

1.000

0.752(**)

0.589(")

0.261(*»)

8.

0.487(")

0.498(")

0.679(**)

0.737(**)

0.684(*')

0.730(")

0.752(")

1.000

0.582(")

0.365(")

9.

0.334(**)

0.379(")

0.435(**)

0.455(")

0.351(**)

0.485(»*)

0.589(")

0.582(**)

1.000

0.544(**)

10

0.226(")

0.219(»*)

0.294(**)

0.299('**)

0.269(")

0.266(")

0.251(")

0.365(")

0.544(")

1.000

** Spearman's rho, Correlation is significant at the .01 level (2-tailed)

Path analysis was carried out to test the research hypotheses, since it is the mostcommonly used multivariate analytical methodology for empirically examining setsof relationships in the form of linear causal models. Multicollinearity on multiplelinear regression can be controlled in two ways: (1) correlation betweenindependent variables should all be less than 0.8; (2) variance inflation factors (VIF)should be less than 10. In our study multicollinearity was not an issue because thecorrelations between independent variables were all less than 0.8 and the VIFs wereall less than 10. Autocorrelation on multiple linear regression can be controlled byDurbin-Watson statistic, where a values close to 2 indicates non-autocorrelation, avalue toward 0 indicates positive autocorrelation, and a value toward 4 indicatesnegative autocorrelation. In almost all path relations testes Darbin-Watson statisticranged between 1.62 and 1.96 indicating that autocorrelation should not beconsidered as an issue.


Regression resultsDependentvariable

Hi:. Actual PC useActual Intemetuse

H2: Perceived self-efficacyPerceived self-efficacy

H3: Perceivedexperience

using OperationalSystemPerceivedexperience withtext processingpackagesPerceivedexperience withdata processingpackagesPerceivedexperience usingInternet

H4: Perceived self-efficacyPerceived self-efficacy

H5: PerceivedusefulnessPerceivedusefulness

Hi: Intention toincrease PCand Internet useIntention toincrease PC andInternet use

H7: Perceived self-efficacy

Perceived self-efficacy

Perceived self-efficacyPerceived self-efficacy

Perceived self-efficacyPerceived self-efficacy

H8: Intention toincrease PCand Internet use

H9: Intention toincrease PC andInternet

Independentvariables

Own PCInternet accessat home oniTiOwn PC

Intemet accessat home onftiActual use PC

Actual use PC

Actual use PC

Actual useInternet

Actual use PC

Actual useInternetActual use PC

Actual use Intemet

Actual use PC

Actual useInternet

Perceivedexperience Op.SystemsPerceivedexperienceWORDPerceivedexperience ExcelPerceivedexperienceInternetPerceivedexperience emailAll above

Perceived self-efficacy

Perceivedusefulness

Table .of predicted path relationships

t

-7.269-13.102

-10.026

-10.868

13.591

11.457

10.546

15.636

15.244

15.353

9.462

10.712

6.305

5.686

27.155

34.060

27.867

31.635

32.607

5.529 -12.62511.193

19.314

6

-1.844-1.945

-1.653

-1.007

.315

.272

.282

.350

.313

.286

.133

.136

.127

.105

.623

.696

.569

.615

.536

.123-

.227

.322

.lis

DurbinWatson

1.3211.510

1.800

1.745

1.976

1.802

1.676

1.945

1.878

1.867

1.692

1.673

1.648

1.617

1.852

1.956

1.827

1.798

1.874

1.896

1.640

1.789

.060

.172

.109

.125

.183

.137

.119

.229

.220

.222

.098

.122

.046

.038

.472

.584

.485

.548

.563

.781

.132

.311

i ( n =

P

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

827)Comment(support)

LowLow

Low

Low

Low

Low

Low

Medium

Medium

Medium

Low

Low

Low

Low

Strong

Strong

Strong

Strong

Strong

Very strong

Low

Medium

Vasileiou, Spais^ A REVISION OF TECHNOLOGY ACCEPTANCE MODEL 199

Regression results support, from low to high extent, the predicted pathrelationships. More precisely:

a. Possessing PC at home influences the students' actual PC use, but inparticularly low degree. Accessing Internet at home influences students' actual usein relatively important degree.

b. Both possessing PC and accessing Internet at home influence students'perceived self-efficacy with PC and Internet use. Therefore, it seems that it would beuseful for students to possess PC and have Internet access at home to improve theirperceived self-efficacy.

c. The actual PC and internet use constitute important factors in building students'perceived experience and, to more extent, perceived self-efficacy. However, itseems that even students with relatively low actual use perceive that they haveimportant experience and self-efficacy with PC and internet applications.

d. Actual PC and internet use influence, but to relatively small extent, perceivedusefulness of PC and internet applications, probably because of the same reasonsreported before. Consequently, it does not surprising that actual use can influenceonly to very small extent the intention of increase PC and internet use.

e. Perceived self-efficacy depends highly on students' perceptions with regard totheir experience. Perceived experience with the various PC and internetapplications can additively influence up to 78% perceived self-efficacy. Therefore,in such a case, adding actual PC and internet use variables in the multipleregression model can improve very little, if not at all, perceived self-efficacyprediction.

f. Perceived self-efficacy can influence, but to low extent, students' intention toincrease PC and internet use in order to improve their academic performance.However, perceived usefulness seems to play a rather important role in promptingstudents to increase PC and internet use for academic purposes.

DISCUSSIONAccording to research results, none of the nine research hypotheses was rejected.

However, the path relationships intensity varied from low to high degreeMore precisely, possession of PC at house influences students' actual PC use,

however, in particularly low degree. Internet access at home also influences actualuse in relatively important manner. PC possession and internet access at home alsoinfluence students' perceived self-efficacy with PC and internet use. the actual PCand internet use constitutes an important factor in the formation of perceivedexperience and perceived self-efficacy, this does not deter students with relativelylow actual use to perceive that they have significant experience and self-efficacywith PC and internet applications. This may be attributed to either some particularstudents overestimate their experience and self-efficacy with regard to PC andinternet applications or because of the major developments on InformationTechnology sector these students can gain high experience and self-efflcacy eventhough they spend relatively litfle time with PC and internet. According to ourexperience, the first interpretation is more trustworthy to explain this phenomenon.

Actual PC and internet use influence, but to relatively small extent students'perceived usefulness of PC and internet applications, for the same reasons reportedbefore. Consequently, it is surprising that the actual use can influence only in very


small degree students' intention to increase PC and internet use. One of the moreimportant findings of this study is that perceived self-efficacy depends highly onstudents' perceptions with regard to their experience.

Another important finding of this study is that perceived self-efficacy can predictstudents' intention to increase PC and internet use, but to low degree. It is theperceived usefulness construct that appears to play quite an important role toprompt students to increase PC and internet use to improve their academicperformance.

Practical ImplicationsDuring the personal interviews with students it was revealed that some particular

factors could enhance the perceived usefulness of PC and internet use. Such factorsare: (a) the course modules, (b) the course requirements and in general (c) thestimuli and motives the academic environment may offer

Universities' Information Technology laboratories (PC Rooms) improvement,extension and upgrade could, also, contribute to raise students' intention toincrease PC and internet. In this way, business school students' access to PC andInternet would improve, the degree of collaboration between them would beincreased preparing group coursework and they would spend their time onUniversity campuses more efficiently and effectively.

Research Implications and DirectionsThe most important contribution of this study to management studies research is

that it develops and validates a model linking a set of factors and a set ofperceptions that influence business school students' intention to increase PC andinternet use to improve their academic performance. Though prior empiricalresearch has demonstrated a set of influences to be an important determinant ofuser behavior, the reason behind this association was not clearly understood. Theproposed model postulates some factors and a set of perceptions as importantcomponents of business schools' students' intention to increase IT usage (in orderto improve their academic performance).

Further research might focus on the connection between academic informationand communication technologies use and effective, students' performancepertaining to internet and other information technologies-related activities. It hasbeen assumed that information and communication technologies use in universitiesprepares individuals to work in a wired world. Future studies could also incorporatemore psychological and sociological factors in different circumstances or stages ofeducational circle educated in order to understand more of this research theme.

Study's LimitationsAs with all empirical research, this study has a few limitations. First, this study

refers to business school students in Greece who may have different perceptions oftechnology from students in other parts of the world. Second, the respondents inthis study were full-time undergraduate students. The generalization of the study'sfindings should be done with care. The lifestyles, educational backgrounds andexperiences of these students may differ from those of part-time students orpostgraduate students.


CONCLUSIONS

Technology adoption is one of the most widely researched topics in informationsystems research. It has been studied at the individual (Venkatesh et al., 2003), group(e.g., Sambamurthy and Chin, 1994), and organizational (e.g., Fichman and Kemerer,1997) levels. This article focuses on the identification and the evaluation of the factorsthat influence business school students' intention to increase PC and internet use foracademic purposes. Stating that research on individual-level technology adoption ismature is an understatement (see Venkatesh et al., 2003, for a review and synthesis).Much of this work was sparked by the seminal articles by Fred Davis on the technologyacceptance model (TAM; Davis, 1989; Davis et al., 1989). The impact of Davis'original work on the TAM and follow-up research has been substantial, as evidencedby well over 1,000 cites to Davis' original two articles.

We believe that students' awareness of PC and internet usefulness in preparingthemselves for the future business environment will play probably the most importantrole in increasing the adoption of new developments of Information Technology andCommunications sciences. Nowadays, managers' and financial consultants' survival inthe workplace arena depends to great extent on their abilities to deal with the rapidlychanging market requirements. Handling the Information Technology andCommunications applications (e.g. PC and the internet) is crucial for future managersto survive in the global competitive economy. Achieving the above requires businessschool students to continuously upgrading their knowledge and become familiar withthe developments Information Technology and Communications sciences This alsomeans, in terms of management education, that higher education institutions shouldadapt their educational strategy to the rapidly changing Information Technology andCommunications environment and focus exclusively in the use of modem educationalmeans.

References Cited

Adams, D., Nelson, R.R. and Todd, P. (1992) 'Perceived usefulness, ease of use andusage of information technology: A replication', MIS Quarterly, 16: 227-248

Ajzen, I. (1991), The Theory of Planned Behaviour', Organizational Behavior andHuman Decision Processes, 50: 179-211

Bandura, A. (1997). Self-Efficacy: the Exercise of Control, USA

Davis, F.D. (1989), 'Perceived usefulness, perceived ease of use, and useracceptance of information technology', MIS Quarterly, 13(3): 319-339

Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989), 'User acceptance ofcomputer technology: a comparison of two theoretical models' ManagementSc/ence, 35(8): 982-1003

EDET (2006) 'National Research on New Technologies and Information Society,2006', [www document] http:// www.ebusinessforum.gr (accessed 13February 2007)

Elwood S, Changchit C and R Cutshall (2006). 'Investigating students' perceptionson laptop initiative in higher education. An extension of the technologyacceptance model', Campus-Wide Information Systems, 23 (5): 336-349


European Commission (2005). Elearning Conference conclusions. Brussels,Belgium: European Commission

Fichman, R. G., & Kemerer, C. F. (1997). The assimilation of software processinnovations: An organizational learning perspective. Management Science,43, 1345-1363

Fusilier, M and Durlabhji, S (2005) 'An exploration of student internet use in India:the technology acceptance model and the theory of planned behaviour',Campus-Wide Information Systems 22(4); 233-246

Govindasamy, T. (2002) 'Successful implementation of E-learning pedagogicalconsiderations', Internet and Higher Education, 4: 287-299

King W and J He (2006) 'A meta-analysis of the technology acceptance model'.Information & Management, 43: 740-755

Lee Y-C (2006) 'An empirical investigation into factors influencing the adoption ofan e-learning system', Online Information Review, 30(5): 517-541

Levine, T. and Donitsa-Schmidt, S. (1998) 'Computer use, confidence, attitudes,and knowledge: a causal analysis'. Computers in Human Behavior, 14 (1):125-146

Liaw, S. S., & Huang, H. M. (2003). 'An investigation of user attitudes towardsearch engines as an information retrieval tool'. Computers in HumanBehavior, 19(6), 751-765

Liaw, S-S (2007). 'Computers and the Internet as a job assisted tool: based on thethree-tier use model approach'. Computers in Human Behavior , 23 (1):399-414

Martins, L. and Kellermanns, F W (2004). A model of business school students'acceptance of a Web-based course management system. /4cadefny ofManagement Learning and Education, 3 (1): 7-26

Mathieson, K. (1991) 'Predicting user intentions: Comparing the technologyacceptance model with the theory of planned behavior' Information Si^stemsResearch 2: 173-191

Ong, C.S. and Lai, J.Y. (2006) 'Gender differences in perceptions and relationshipsamong dominants of e-learning acceptance'. Computers in Human Behavior,22: 816-829

Padilla-Meléndez, A.; Garrido-Moreno, A. (forthcoming, 2007): 'Use of e-coUaboration technologies among students of Management'. In Kock, N. (ed.):Encyclopedia of e-Collaboration. Idea Group. In press

Pituch, K. and Lee, Y.K. (2006) 'The influence of system characteristics on e-learning use. Computers & Education, 47: 222-244

Roca, J.C, Chiu, CM. and Martinez F.J. (2006). 'Understanding e-learningcontinuance intention: An extension of the Technology Acceptance Model',International journal Human-Computer Studies, 64: 683-696

Sambamurthy, V., & Chin, W. (1994). The effects of group attitudes toward GDSSdesigns on the decision-making performance of computer-supported groups.Decision Sciences, 25, 215-242


Shivers-Blackwell, S. and Charles, A. (2006) 'Ready, set, go: examining studentreadiness to use ERP technology', Journal of Management Development,25(8): 795-805

Stoel, L and Lee, K.H. (2003) 'Modeling the effect of experience on studentacceptance of Web-based courseware', Internet Research: ElectronicNetworking Applications and Policy, 13(5): 364-374

Venkatesh, V. (1999) 'Creation of favorable user perceptions: exploring the role ofintrinsic motivation', MIS Quarterly, 23 (2): 239-260

Venkatesh, V. and Davis, F.D. (1996), 'A model of the antecedents of perceivedease of use: development and test' Decision Sciences, 27(3): 451-481

Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). Useracceptance of information technology: Toward a unified view. MIS Quarterly,27, 425-478

Zhang, Y. and Espinoza, S. (1998) 'Relationships among computer self-efficacy,attitudes toward computers, and desirability of learning computing skills',Researcb on Computing in Education, 30(4): 421^36

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