Impacts of Personal Characteristics of Students on Their Acceptance

of ERP Solutions in Learning Process

Ruben Picek

1 a

, Samo Bobek

2 b

and Simona Sternad Zabukovšek

2 c

Faculty of Organization and Informatics, University of Zagreb, Pavlinska 2, 42000 Varaždin, Croatia

Faculty of Economics and Business, University of Maribor, Razlagova ul. 14, 2000 Maribor, Slovenia

Keywords: ERP Solutions, TAM, Graduates of Informatics, Acceptance Model, Personal Characteristics.

Abstract: Enterprise Resource Planning (ERP) solutions are the most frequently used software tool in companies in all

industries. The growing body of scientific literature about the acceptance of ERP solutions by users in

companies reflects the growing perceived importance of ERP solutions for business management as well. The

labour market requires the knowledge and skills for usage of ERP solutions from graduates – future employees.

The main objective of our paper is therefore the identification of important factors that contribute to the

acceptance of ERP solutions by students in economics and business and that shape their intentions to use this

knowledge in the future. The conceptual model of our research is based on the Technology Acceptance Model

(TAM), extended by previously identified important multidimensional external factors that refer to students’

personal characteristics and information literacy. The conceptual model formed was tested using the structural

equation modelling. Research results revealed that only two of external factors play an important role in

shaping the attitudes towards acceptance of ERP solutions by students. Results of the study have important

implications for higher education institutions, reforming and updating their study programs, as well as for

educators in the field of information science.

1 INTRODUCTION

Backbone of every successful business today is

integrated information system known as ERP

(Enterprise Resource Planning) system. Because of

the large number of ERP implementations worldwide,

number of the ERP users within organizations is

growing very fast as well. Each company strives to

have very good ERP based skill worker, and we could

say that that become highly demandable prerequisite.

But today’s business is going much further of just

perform every day activities on ERP system.

The turbulent changes that have occurred in the

last few years with the emergence of new coins such

as Digital Transformation, Internet of

Things/Everything, Cloud Computing, Machine

Learning, Natural Language Processing, Block

Chain, Augmented Reality, Virtual Reality have a

strong influence on the operations of each company.

The direct consequence of this is the change in

https://orcid.org/0000-0002-6784-7500

https://orcid.org/0000-0001-6927-6820

https://orcid.org/0000-0002-7651-7706

knowledge and skills in the labour market. This

means that the knowledge and skills required by

employers are directed towards the application of

these new digital technologies. That reflex to the ERP

market where some radical changes in the way that

ERP solutions support business are done and

currently new technologies are trying to be embedded

in new versions of ERP systems.

The gap between industry and the academic is

certainly decreasing and these higher education

institutions are trying to produce a competitive

student for the labour market. Therefore, new

curricula are created where courses include models

that have just been developed for this purpose like

Standards of Profession, Skills Framework for the

Information Age, Croatian Qualification Framework

and so on.

On the other hand, all leading ERP vendors such

as SAP, Microsoft, Oracle etc. have university

academic alliances such as SAP University Alliances

460

Picek, R., Bobek, S. and Zabukovšek, S.

Impacts of Personal Characteristics of Students on Their Acceptance of ERP Solutions in Learning Process.

DOI: 10.5220/0007745304600467

In Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS 2019), pages 460-467

ISBN: 978-989-758-372-8

(SAP, 2018), Microsoft Dynamics Academic

Alliance (Microsoft, 2018), Oracle University

(Oracle, 2018) etc. which help higher education

institutions to use their ERP solutions in their

curriculum and thus preparing students with hands-on

experience in using modern business applications.

All the above, leads to the conclusion that

acquiring a set of skills and knowledge of ERP

solutions usage are among important competences of

graduates in the field of information systems and

economics and business, for achieving a competitive

position in the labour market. Also, because students

have different starting points in knowledge of

business and information-comunicaton technology

skills and as digital technologies are emerging in

business it is necessarily to define a student's profile

suitable for the labour market.

The question that arise is: How to teach student

this extensive segment of the whole business of a

company that is supported through the information

system (ERP system)?

Despite the recognized importance of the ERP

solutions as a business management tool within

companies and the importance of this knowledge for

graduates, researches aimed at identification of

factors shaping students’ attitudes towards the

acceptance of ERP solutions, are rather scarce (Davis

andComeau, 2004; Shivers-Blackwell and Charles,

2006; Scott and Walczak, 2009; Iriberri, 2015).

The main objective of our paper is therefore the

identification of important factors of personal

characteristics and information literacy (PCIL) that

contribute to the acceptance of ERP solutions by

students in informatics and that shape their intentions

to use this knowledge in the future. The conceptual

model of our research is based on TAM.

2 LITERATURE REVIEW ON

ACCEPTANCE OF ERP

SOLUTIONS

Several theoretical models have been used to

investigate the determinants of acceptance and the use

of new information technology (IT), such as the

theory of reasoned action (TRA; Fishbein and Ajzen,

1975), the theory of planned behaviour (TPB; Ajzen,

1991), the theory of the technology acceptance model

(TAM; Davis et al., 1989), innovation diffusion

theory (IDT; Rogers, 2003), stage model (SM; Poon

and Swatman, 1999), technology-environment-

organization (T-O-E; Tornatzky and Fleisher, 1990);

and others. Compared to competing models, TAM is

believed to be more parsimonious, predicative, and

robust (Venkatesh and Davis, 2000; Lu et al., 2003;

Liu and Ma, 2006), and so among the theoretical

models is most widely used by IS/IT researchers

(Davis, 1989; Davis et al., 1989; Amoako-Gyampah

and Salam, 2004; Lee et al., 2010; Costa et al., 2016)

and therefore numerous IS researchers apply this

method to ERP research.

Even though TAM can be applied to a variety of

technologies, the constructs of TAM need to be

extended by customizing factors for specific

information systems (Calisir et al., 2009). Few

studies, have investigated ERP user acceptance and

usage utilizing TAM, and most of them investigate a

small number of external factors (for latest researches

see Calisir et al., 2009; Shih and Huang, 2009; Sun et

al., 2009; Youngberg et al., 2009; Lee et al., 2010;

Sternad et al. 2011; Sternad and Bobek, 2013, 2014;

Mayeh et al., 2016; Costa et al., 2016). Shivers-

Blackwell and Charles (2006) and Scott and Walczak

(2009) researched students ERP acceptance through

TAM model. But both authors used small numbers of

external factors. Shivers-Blackwell and Charles

(2006) also researched student readiness to use ERP

technology through model TAM, but they researched

ERP acceptance after students read an online

newsletter provided by the ERP communication,

education, and training team entitled “What is ERP”.

Participants were then requested by their professors

to complete the survey. So, they did not have practical

experience with use of ERP solution. Their research

shows that gender and perceived ERP benefits are

related to students’ readiness for change, and

readiness for change is a significant predictor of

students’ attitude toward usage of the ERP system.

Scott and Walczak (2009) examined cognitive

engagement, prior experience, computer anxiety, and

organizational support as determinants of computer

self-efficacy in the use of a multimedia ERP system’s

training tool. They also examined the impact of

computer self-efficacy on its acceptance. Their

sample consisted of students taking an ERP course

elective in the information systems undergraduate

and graduate programs.

3 CONCEPTUAL MODEL AND

RESEARCH DESIGN

The main objective of our research is to identify the

factors, included into the extended TAM as external

factors, that are significantly shaping the antecedents

Impacts of Personal Characteristics of Students on Their Acceptance of ERP Solutions in Learning Process

461

of students’ attitudes and future intentions of students

to use the ERP solutions.

As already mentioned, the TAM introduced by

Davis (1989) and Davis et al. (1989), suggests the

following relationships (this original TAM is

presented by grey rectangle in Figure 1) among the

factors, that are perceived ease of use (PEOU),

perceived usefulness (PU), attitude toward using ERP

system (AT), behaviour intention (BI) and actual use

(Use) of IT/IS (hypotheses H1 to H5). In the case of

our research refer to the ERP solutions:

 H1: Perceived ERP ease of use (PEOU) has

positive and direct effect on perceived ERP

usefulness (PU).

 H2: Perceived ERP ease of use (PEOU) has

positive and direct effect on attitude toward ERP

system (AT).

 H3: Perceived ERP usefulness (PU) has positive

and direct effect on attitude toward ERP system

(AT).

 H4: Attitude toward ERP system (AT) has

positive and direct effect on behaviour intention

(BI).

 H5: Behavior intention (BI) has positive and

direct effect on actual use (Use).

Even though TAM can be applied to a variety of

technologies, it must be extended and modified for

analysis of specific information systems (Calisir et

al., 2009), as we already pointed out. The literature

review revealed that the external factors in general

can be divided into more groups of factors (Sternad et

al., 2011, Sternad and Bobek, 2013, 2014).

One of exposed groups could be factors of

personal characteristics and information literacy,

which including personal characteristics that can

influence individuals’ perceptions of ERP system

acceptance and usage.

We were exposed five factors:

 Personal Innovativeness toward IT (PI) from

the IT view-point (Yi et al., 2006; Thompson et

al., 2006),

 Computer Anxiety (CA) (Venkatesh et al.,

2003; Scott and Walczak, 2009)

 Computer Self-Efficiency (CS) (Venkatesh and

Davis, 2000; Venkatesh et al., 2003; Shih and

Huang, 2009)

 Individual Benefits (IB) (Hsu et al., 2015;

Rienzo andHan, 2011)

 Computer Playfulness (CP) (Venkatesh

andBala 2008)

Above mentioned authors expose in their studies,

that external factors of PI, CA, CS, BI and CP have

impact on perceived ease of use (PEOU) and/or

perceived usefulness (PU) in different IT/IS

environment (mostly voluntary use). In the case of

our research refer to the ERP solutions, Therefore the

following two hypotheses were formed:

 H6: External factors PI, CA, CS, BI and CP have

positive and direct effect on perceived ERP

usefulness (PU).

 H7: External factors PI, CA, CS, BI and CP have

positive and direct effect on perceived ERP ease

of use (PEOU).

The questionnaire was developed in three phases.

In the first phase, we clarified the relationships

between the constructs and the measurement scales

for individual constructs, we reviewed the literature

and resources. A questionnaire was employed. All

items in the questionnaire were scored on a 7-point

Figure 1: Conceptual Model.

Use

PEOU

Personal Characteristics and

Information Literacy (PCIL):

 Personal Innovativeness

toward IT (PI)

 Computer Anxiety (CA)

 Computer Self-Efficiency

(CS)

 Individual Benefits (IB)

 Computer Playfulness (CP)

TAM

ICEIS 2019 - 21st International Conference on Enterprise Information Systems

462

Likert scale ranging from 1 (strongly disagree) to 7

(strongly agree). The research design consisted of

five constructs arising from the TAM model (PEOU,

PU, AT, BI and Use) and five external factors (PI,

CA, CS, IB and CP), that we formed and included into

the expanded TAM model. Our conceptual model

includes ten first-order factors.

In the second phase the instrument was pilot

tested with a small group of students, who head ERP

solution (20 students) as elective subject. Based on

the results of the pilot testing, small revisions and

additions regarding word order were made to the

instrument.

In the third phase the survey was conducted. Our

sample included a total of 85 Croatian students in the

second (4

semester) year of graduate study programs

"Information and Software Engineering",

"Organization of Business Systems" and "Databases

and Knowledge Base". The survey was carried out at

the beginning of semester after students’ have

introduction with Microsoft Dynamics NAV ERP

solution (after 2 lecture hours), within the course that

includes all together 30 teaching hours of lectures of

ERP topics with focus on selecting and implementing

integral information systems in methodological way

and 30 hours in computer labs where students adopt

the knowledge of the business processes functions in

Microsoft Dynamics NAV (introduction, basic in

finance and accounting process, purchasing process,

sales process and some advance functionality

simulating every day activities). The Microsoft

Dynamics NAV 2016 (NAV) was used. On the

second lecture in the semester (October 2018) 85

questionnaires were properly filled out by

respondents and used for the purpose of analysis.

Respondents were 16.47 % (14) male and 83.53 %

(71) female. The average age of students was 20.70

years.

Demographic data was analysed by SPSS. All

other empirical data was analysed in two steps

analysis using partial least squares (PLS) technique,

with Smart PLS 3.2.1 (Ringle et al., 2015). PLS path

modelling is a variance-based structural equation

modelling (SEM) technique which is widely used in

education, business and social sciences in past two

decades (Henseler et al., 2016; Garson 2016). We

utilized this approach because of the relatively small

sample size. In the first step, measurement model was

assessed, and in the second step, structural model.

Path significance has been estimated using

bootstrapping resampling technique with 5000 sub-

samples as suggested by Ringle et al. (2015). While

analysing data, we followed the guidelines specified

by Henseler et al. (2016) and Garson (2016).

4 ANALYSIS AND RESULTS

All measurement scales were examined for their

psychometric properties (reliability, convergent

validity, and discriminant validity) prior to testing

hypotheses (bootstrapping with 5000 subsamples)

(Henseler et al., 2016). While all items did not meet

assessment requirements of the measurement model,

it was excluded from further analysis. The final

version of the model is present. Detailed analysis can

be obtained from the authors.

Table 1: Descriptive statistics and psychometric properties

of measures (n=85).

Factor

Items

Loadings

AVE

Personal

Innovativeness

toward IT (PI)

PI1

5.11

1.18

0.85

0.89

0.73

PI2

4.24

1.38

0.85

PI3

5.17

1.21

0.86

Computer

Anxiety (CA)

CA1*

2.17

1.57

0.89

0.90

0.74

CA2*

1.59

1.27

0.84

CA3*

1.51

1.08

0.86

Computer

Self-Efficiency

(CS)

CS1

5.10

1.24

0.84

0.81

0.52

CS2

5.01

1.33

0.75

CS3

5.50

1.21

0.65

CS4

5.63

1.16

0.62

Individual

Benefits (IB)

BI1

6.61

1.18

0.60

0.75

0.61

BI2

5.15

1.26

0.93

Computer

Playfulness

(CP)

CP1

4.85

1.44

0.91

0.96

0.86

CP2

4.70

1.41

0.85

CP33

4.67

1.38

0.95

CP4

5.03

1.42

0.90

Perceived ERP

usefulness

(PU)

PU1

5.08

1.03

0.85

0.89

0.67

PU2

5.10

1.13

0.80

PU3

4.61

1.18

0.85

PU4

4.41

1.16

0.77

Perceived ERP

ease of use

(PEOU)

PEOU1

4.51

0.89

0.82

0.89

0.66

PEOU2

4.55

0.87

0.81

PEOU3

4.33

1.05

0.72

PEOU4

4.48

0.93

0.90

Attitude

toward using

ERP (AT)

AT1

5.22

1.27

0.81

0.86

0.68

AT2

4.91

1.27

0.83

AT3

4.61

1.24

0.83

Behaviour

intention (BI)

BI1

4.50

1.25

0.95

0.96

0.89

BI2

4.41

1.34

0.95

BI3

4.27

1.34

0.94

Use

Use1

5.03

1.57

0.46

0.86

0.61

Use2

4.95

0.99

0.85

Use3

4.65

0.98

0.86

Use4

4.53

1.07

0.89

Note: * Intverted scale

Impacts of Personal Characteristics of Students on Their Acceptance of ERP Solutions in Learning Process

463

Internal consistency reliability was examined by

composite reliability (CR), where value should be

more than 0.6. For assessment of validity, two

validity subtypes are usually used: the convergent

validity and the discriminant validity. For convergent

validity Fornell and Larcker’s assessment criteria has

been used: the average variance extracted (AVE) for

each construct should exceed 0.50. As shown in Table

1 each of our ten factors had value CR above 0.6 and

value AVE above 0.50. All factors loadings are

significant at p0.01 and almost all (except one)

exceed 0.60.

Our measurement scales meet the criteria for

convergent validity. AVE is also used to establish

discriminant validity by the Fornell and Larcker

criterion. For our model values of the square root of

AVE are higher than correlations between factors,

which appear below it. The value of standardized root

mean square residual (SRMS) measures the

difference between the observed correlation matrix

and the model. Model has good fit when SRMS is less

than 0.10 (Garson, 2016). SRMR of our model is

0.082, which means that model is acceptable.

The structural model was examined to test

hypotheses. Paths are interpreted as standardised beta

weights in a regression analysis. The relationships

testing results are based on bootstrapping (with 5000

subsamples) to test the statistical significance of each

path coefficient using t-tests, as recommended by

Chin (1998).

Our research confirms results of original TAM.

All relationships in original TAM are statistically

significant as shown in Table 2 and Figure 2.

Perceived ERP ease of use (PEOU) has significant

effect on perceived ERP usefulness (PU) (β = 0.442,

p<0.01) and significant effect on attitude toward

using ERP system (AT) (β = 0.318; p<0.01).

Perceived ERP usefulness (PU) has significant effect

on attitude toward using ERP system (AT) (β = 0.395;

p<0.01). Attitude toward using ERP system (AT) has

significant effect on behaviour intention (BI) (β =

0.678; p<0.01) and behaviour intention (BI) has

significant effect on actual use (Use) (β = 0.492;

p<0.01).

We researched the impact of external factors of

personal characteristics and information literacy

(PCIL) through factors personal innovativeness

toward IT (PI), computer anxiety (CA), computer

self-efficiency (CS), individual benefits (IB) and

computer playfulness (CP). We cannot confirm, that

any of five factors have impact on PU at the beginning

of using ERP solution, but two of them computer

playfulness (CP) (β = 0.207, p<0.01) and computer

self-efficiency (CS) (β = 0.264, p<0.01) have

significant positive effect on perceived ERP ease of

use (PEOU) (see Table 2 and Figure 2).

The R

indicates the exploratory power or

variance explained of the latent endogenous variable

and it is the most common effect size measure in path

models (Garson, 2016). The PCIL factors (namely CP

and CS) could explain 41.1 percent variance in PU

= 0.411) and 20.4 percent variance in PEOU (R

= 0.204). PU and PEOU together explain 39.3 percent

of the variance in AT (R

= 0.393). The AT explain

45.9 percent of variance in BI (R

= 0.459) and BI

explain 24.2 percent of Use (R

= 0.242) (Figure 2).

Figure 2: Results of structural model analysis.

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464

Table 2: The structural model was examined to test the

hypotheses.

Relationship

β-coefficient

t-statistics

PI  PEOU

0.11

1.14

n.s.

CA  PEOU

0.13

1.40

n.s.

CS  PEOU

0.26

3.29**

IB  PU

0.20

1.48

n.s.

CP  PU

0.20

1.64

n.s.

CP  PEOU

0.21

2.50**

PU  AT

0.40

3.85**

PEOU  PU

0.44

5.17**

PEOU  AT

0.32

3.24**

AT  BI

0.68

9.59**

Note: ** 0.01 of significance; n.s. not significant.

5 DISSCUSSION

Results of the present study regarding the hypotheses

of original TAM model are consistent with several

other research results regarding the IT/IS acceptance

(Davis, 1989; Davis et al., 1989; etc.). Both, PEOU

and PU have strong positive effect on ERP usage,

with the relationship of PU being a bit stronger.

Therefore, hypothesis H2 and H3 were confirmed.

Also, PEOU has statistical effect on PU. Hypothesis

H1 was also confirmed. The findings about the

importance of PEOU and PU in the literature are

vague; Davis (1989), Davis et al. (1989) and Simon

and Paper (2007) exposed that PU has stronger

positive effect on IT/IS usage as PEOU, while PEOU

has weaker or even no statistical effect on IT/IS usage

after some time of usage. Since students were

surveyed at the beginning of semester, where they did

not know the ERP solution, this could be the reason

for the results obtained.

Hypotheses H4 and H5 were confirmed. Factor

AT is vital in the TAM model and has very strong

positive effect on BI and through it also indirect

strong positive effect on Use, which is consistent with

other researches (Pijpers and Montfort, 2006; Simon

and Paper, 2007).

The main result of this research is the

identification of external factors which influence

students’ ERP acceptance and have an impact on the

antecedents of PU and PEOU at the beginning of ERP

course.

None of observed five external factors have

significant impact on the PU (see Figure 2).

Therefore, hypothesis H6 was not confirmed. Only

two factors exposed in group PCIL – namely

computer self-efficiency (CS) and computer

playfulness (CP) - had significant impact on PEOU.

Hypotheses H7 is supported.

Factor computer anxiety (CA) is not statistically

significant – this can be explained by the fact that the

computer anxiety is probably a state of fear that is not

known any more to the young population who grew

up with the computers included in all (or at least

many) aspects of the everyday’ s life. Factor Personal

Innovativeness toward IT (PI) captures

characteristics of students regarding using new

software tools and applications in general. From

Table 1 we can see that mean values are a little above

average (4). We can speculate that this generation of

students are not keen on new software tools and

applications. Individual Benefits (IB) includes claims

how ERP knowledge can increase student’s

effectiveness and productivity and have impact on

regarding future job. We think that because of

complexity of ERP solutions students did not

recognize individual benefits of ERP knowledge after

two hours of lectures.

We suggest teachers to put an important effort into

the preparation of ERP lectures and that try to explain

ERP topics related content to students using simple

routines, with the real business environment

characteristics. To understand the ERP solutions is

challenging for students, because they do not have

practical experience of how ERP solutions are used in

enterprises.

6 CONCLUSION

The aim of this research was to identify which

external factors have impact on students‘ acceptance

of ERP at the beginning of study programme, while

they are exposed to ERP solution (in our case

Microsoft Dynamics NAV). We want to know how to

motivate students to take course dealing with the ERP

solution Microsoft Dynamics NAV, with all due

seriousness and importance. That is why we studied

five personal characteristics and information literacy

(PCIL) external factors which might have an impact

on students’ ERP acceptance. Studying the influence

of the system of external factors on constructs not

only contributes to the theory development, but also

helps in designing teachers’ curriculum. Our research

shows that most important researched external factors

are especially two: computer self-efficiency (CS) and

computer playfulness (CP). That factors will be in

focus in future work.

Several implications for researchers and

practitioners arise from the results of the extended

version of TAM. Findings indicate that students have

Impacts of Personal Characteristics of Students on Their Acceptance of ERP Solutions in Learning Process

465

positive perception on the PU, PEOU, AT, BI and

Use and that they understand the usefulness of ERP

systems and their relevance as the support, important

for their current or future jobs. These findings can

help business schools assess students’ engagement as

they develop ERP software skills desired by

employers. By many organizations, a big concern is

whether students understand business processes (also

process flows, sub processes, etc.) behind ERP

system. ERP system is very complex system and no

single factor alone influences student’s use of ERP.

Our research showed that most important external

personal factors are computer playfulness and

computer self-efficiency. For students are important

to understand the functionality of the system, its

usefulness, and ease of use.

This study has certain limitations which are at the

same time the opportunities for further research

within this important and comprehensive topic. Since

the respondents were limited to one group of students

in Croatia, the study could be extended to other

countries. Further research is needed to explore the

importance of external factors included in different

time frames (after introduction of course, at the end

of course) as well as inclusion of additional external

factors. Another limitation is also that research was

conducted for one ERP solution only – namely for

Microsoft Dynamics NAV; the importance of

external factors may be different, when other ERP

solutions are taking place (SAP, Infor ERP etc.).

REFERENCES

Ajzen, I. 1991. The theory of planned behaviour.

Organizational Behaviour and Human Decision

Processes, 50, 179–211. DOI: 10.1016/0749-

5978(91)90020-T.

Amoako-Gyampah, K., Salam, A. F. 2004. An extension of

the technology acceptance model in an ERP

implementation environment. Information and

Management, 41, 731–745. DOI:10.1016/j.im.2003.

08.010.

Calisir, F., Gumussoy, C. A., Bayram, A. 2009. Predicting

the behavioural intention to use enterprise resource

planning systems—An exploratory extension of the

technology acceptance model. Management Research

News, 32(7), 597–613. DOI: 10.1108/014091709109

65215.

Chin, W. W. 1998. Issues and opinion on structural

equation modelling. MIS Quarterly, 22(1), 7–16.

Costa, C., Ferreira, E., Bento, F., Aparicio, A. 2016.

Enterprise resource planning adoption and satisfaction

determinants. Computers in Human Behaviour, 63,

659–671. DOI: 10.1016/j.chb.2016.05.090.

Davis, C. H., Comeau, J. 2004. Enterprise Integration in

Business Education: Design and Outcomes of a

Capstone ERP – based Undergraduate e-Business

Management Course. Journal of Inforamation Systems

Education, 15 (3), 287-300.

Davis, F. D. 1989. Perceived usefulness, perceived ease of

use, and user acceptance of information technology.

MIS Quarterly, 13(3), 319–340. DOI: 10.2307/249008.

Davis, F. D., Bagozzi, R. P., Warshaw, P. R. 1989. User

acceptance of computer technology: A comparison of

two theoretical models. Management Science, 35(8),

982–1003. DOI: 10.1287/mnsc.35.8.982.

Fishbein, M., Ajzen, I., 1975. Belief, attitude, intention, and

behaviour: An introduction to theory and research.

Addison-Wesley, Reading, MA.

Garson, G. D., 2016. Partial Least Squares: Regression and

Structural Equation Models. Statistical Associates

Publishers, Asheboro, NC.

Henseler, J., Hubona, G., Ray, P. A., 2016. Using PLS Path

Modeling in New Technology Research: Updated

Guidelines. Industrial Management and Data Systems,

116(1), 2–20. DOI: 10.1108/IMDS-09-2015-0382.

Hsu, P.-F., Yen, H. R., Chung, J.-C., 2015. Assessing ERP

post-implementation success at the individual level:

Revisiting the role of service quality. Information and

Management, 52(8), 925–942. DOI: 10.1016/j.im.

2015.06.009.

Iriberri, A., 2015. Integrating an ERP into the curriculum at

a busienss school: the students' perceptions of SAP.

Academy of Ecucation Leadership Journal, 19(2), 99-

108.

Lee, D. H., Lee, S. M., Olson, d. L., Chung, S. H., 2010.

The effect of organizational support on ERP

implementation. Industrial Management and Data

Systems, 110(2), 269–283. DOI: 10.1108/0263557101

1020340.

Liu, L., Ma, Q., 2006. Perceived system performance: A

test of an extended technology acceptance model.

Journal of Organizational and End User Computing,

18(3), 1–24. DOI: 10.1145/1161345.1161354.

Lu, J., Chun-Sheng, Y., Liu, C., Yao, J. E., 2003.

Technology acceptance model for wireless internet.

Internet Research: Electronic Networking Applications

and Policy, 13(3), 206–222. DOI: 10.1108/106622

40310478222.

Mayeh, M., Ramayah, T., Mishra, A., 2016. The role of

absorptive capacity, communication and trust in ERP

adoption. The Journal of Systems and Software, 119,

58–69. DOI: 10.1016/j.jss.2016.05.025.

Microsoft, 2018. Microsoft Dynamics Academic Alliance.

Retrieved May 20, 2018, from https://dynamics.

microsoft.com/en-us/academic/

Oracle, 2018. Oracle Univrstiy. Retrived May 20, 2018,

from http://education.oracle.com/pls/web_prod-plq-

dad/db_pages.getpage?page_id=3.

Pijpers, G. G. M., Montfort, K., 2006. An investigation of

factors that influence senior executives to accept

innovations in information technology. International

journal of management, 23(1), 11–23.

ICEIS 2019 - 21st International Conference on Enterprise Information Systems

466

Poon, S., Swatman, P., 1999. An exploratory study of small

business internet commerce issues. Information and

Management, 35(1), 9–18. DOI:10.1016/S0378-

7206(98)00079-2.

Rienzo, T., Han, B., 2011. Does ERP Hands-On Experience

Help Student Learning Business Process Concepts?

Decision Sciences Journal of Innovative Education

9(2), 177-207.

Ringle, C. M., Wende, S., Becker, J.-M., 2015. SmartPLS

3. Boenningstedt: SmartPLS GmbH. Retrieved

February 8, 2016, from http://www.smartpls.com.

Rogers, E., 2003. Diffusion of Innovations. The Free Press,

New York, 4

edition.

SAP, 2018. SAP Universty Alliances. Retrieved May 20,

2018, from https://www.sap.com/training-certification/

university-alliances.html.

Scott, J. E., Walczak, S., 2009. Cognitive engagement with

a multimedia ERP training tool: Assessing computer

self-efficacy and technology acceptance. Information

and Management, 46, 221–232. DOI: 10.1016/j.im.

2008.10.003.

Shih, Y. Y., Huang, S. S., 2009. The actual usage of ERP

systems: An extended technology acceptance

perspective. Journal of Research and Practice in

Information Technology, 41(3), 263–276.

Shivers-Blackwell, S. L., Charles, A. C., 2006. Ready, set,

go: Examining student readiness to use ERP

technology. Journal of Management Development,

25(8), 795–805. DOI: 10.1108/02621710610684268.

Simon, S. J., Paper, D. 2007. User acceptance of voice

recognition technology: an empirical extension of the

technology acceptance model. Journal of

organizational and end user computing, 19(1), 24–50.

Sternad Zabukovšek, S., Gradišar, M., Bobek, S. (2011).

The influence of external factors on routine ERP usage.

Industrial management + data systems, 111(9), 1511–

1530. DOI: 10.1108/02635571111182818.

Sternad, S., Bobek, S., 2013. TAM-based external factors

related to ERP solutions acceptance in organizations.

International Journal of Information Systems and

Project Management, 1(4), 25–38. DOI:

10.1016/j.protcy.2013.12.004.

Sternad, S., Bobek, S., 2014. Comparative analysis of

acceptance factors for SAP and Microsoft Dynamics

NAV ERP solutions in their maturity use phase:

enterprise 2.0 issues. In M.M. Cruz-Cunha, F. Moreira,

and J. Varajao (Eds.), Handbook of research on

enterprise 2.0: technological, social, and

organizational dimensions (pp. 389–415). IGI Global,

Hershey, New York: Business Science Reference.

Sun, Y., Bhattacherjee, A., Ma, Q., 2009. Extending

technology usage to work settings: The role of

perceived work compatibility in ERP implementation.

Information and Management, 46(6), 351–356.

DOI:10.1016/j.im.2009.06.003.

Thompson, R., Compeau, D., Higgins, C., 2006. Intentions

to use information technologies: an integrative model.

Journal of organizational and end user computing,

18(3), 25–46. DOI: 10.4018/joeuc.2006070102.

Tornatzky, L., Fleisher, M., 1990. The process of

technology innovation. Lexington Books, Lexington.

Venkatesh, V., Bala, H., 2008. Technology acceptance

model 3 and a research agenda on interventions.

Decision sciences, 39 (2), 273–315. DOI:

10.1111/j.1540-5915.2008.00192.x.

Venkatesh, V., Davis, F. D., 2000. A theoretical extension

of the technology acceptance model: Four longitudinal

field studies. Management Science, 46(2), 186–205.

DOI: 10.1287/mnsc.46.2.186.11926.

Venkatesh, V., Morris, M. G., Davis, F. D., Davis, G. B.,

2003. User acceptance of information technology:

Toward a unified view. MIS Quarterly, 27(3), 425–479.

Yi, Y. M., Fiedler, K. D., Park, J. S., 2006. Understanding

the role of individual innovativeness in the acceptance

of IT-based innovativeness: comparative analyses of

models and measures. Decision Sciences, 37(3), 393–

426. DOI: 10.1111/j.1540-5414.2006.00132.x.

Youngberg, E., Olsen, D., Hauser, K., 2009. Determinants

of professionally autonomous end user acceptance in an

enterprise resource planning system environment.

International Journal of Information Management,

29(2), 138–144. DOI:10.1016/j.ijinfomgt.2008.06.001.

Impacts of Personal Characteristics of Students on Their Acceptance of ERP Solutions in Learning Process

467