This document summarizes a research paper presented at INTED 2014 that examined factors driving e-learning usage. It discusses 3 hypotheses about perceived usefulness, ease of use, and their relationship. An empirical study was conducted of 344 students, measuring perceptions and acceptance of an e-learning system. Results provided support for Hypothesis 1 about usefulness and partial support for Hypothesis 2 about ease of use. Perceived usefulness seemed to have a stronger influence on acceptance than ease of use. More analysis is needed to fully understand the relationships.

1. Hannes Rothe
Department Business Information Systems
Which factors drive
e-learning usage?
Anne-Marie Horn, Hannes Rothe,
Martin Gersch
Department Business Information Systems
Presentation of a research paper at
INTED 2014, March 10th 2014

2. 2
I. Background
(Educational Technology Acceptance)
II. Discussion
(Three hypotheses for e-learning usage)
III. Empirical Study
(Sample and empirical Results)
IV. Conclusion
Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe
Agenda
Educational Technology Acceptance of Web-based Trainings

3. 3
I. Background
What is E-Learning?
Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe
Ref. Illustration is based on Ebner et al. (2013) and Oliver Tomann | L3T
Research Question:
What affects students’ acceptance of information systems for
e-learning in higher education?

4. 4
II. Discussion
Educational Technology Acceptance
Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe
Hypothesis1: The perceived usefulnessis positively related to acceptances of e-learningsystems
Hypothesis2: The perceived ease-of-useis positively related to acceptance ofe-learningsystems
Hypothesis3: The perceived ease-of-useis positively associated with the perceivedusefulness
Ref. Davis, Bagozzi & Warshaw (1989), Venkatesh and Davis (2000), Venkatesh and Bala (2008)

5. 5
II. Discussion
Influencing factors for ETA following a literature review
Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe
Ref. Müller-Böling and Müller (1986),
Stone (2005),Magoulas (2006),Chu
(2010),Arenas-Gaitan et al. (2011),
Sumak (2011),Kreidl (2011), Tarhini
et al. (2013)
I. Socio-
demographic
factors
II. Personality
traits
III. Experience
and prior
knowledge
IV. Attitude and
interests
- Age
- Gender
- Origin and Place of
Study
- Socio economical status
- …
- Cognition
- Learning style and
competence
- Learning motivation
- Locus of control
- Self Efficacy
- Computer anxiety
- Computer Playfulness
- „Big-Five“
- Achievement motivation
- …
- Domain knowledge
- IT competence
- Experiences with
computers and the web
- Experiences with
educational technology
- System-related
- System knowledge
- Usage success
- Fun
- Flow experience
- …
- Learning task
- Learning satisfaction
- IT affinity
- Attitude towards e-
learning
- System-related
- Task fit
- Perceived value
- Simplicity
- System performance
- Result demonstrability
- Subjective norm
- Trust
- …
Ref. Garff (2003),Jackson et al.
(2005),Lang and Fries (2006),
Magoulas (2006),Eom (2006),
Rentroia-Bonito etal. (2006),
Goodyear and Ellis (2007), Blackler et
al. (2007),Aikman (2007), Gravill and
Compeau (2008),Venkatesh (2008),
Huber et al. (2008), Larsen etal.
(2009),Sitzmann (2009),Bekele
(2010),Fisher (2010), Orivs (2011),
Hassanzadeh etal.(2012)
Ref. Davis et al. (1982),Nielsen (1993),
Csikszentmihalyi (1997),Goodhue
(1995),Utz and Sassenberg (2001),
Keramati et al. (2001),Jung (2002),
Martins and Kelleramanns (2004),
Kleimann etal. (2005),Mohs et al.
(2006),Magoulas (2006),Wan et al.
(2008),Sengpiel and Wandke (2008),
Sengpiel and Dittberner (2008),Hessel
(2009),Lee (2010), Sengpiel (2011),
Traxler (2011)
Ref. Davis et al (1989), Goodhue and
Thompson (1995),Larsen etal. (2009),
Venkatesh and Davis (2000),Ausburn
(2004),Martins & Kelleramanns (2004),
Schmidt(2005), Eom (2006),Bliue,
Goodyear and Ellis (2007),Park and
Wentling (2007),Hochholdinger and
Schaper (2008),Sitzmann et al. (2009),
Lee (2010), Bekele (2010), Traxler
(2011),Hassanzadeh (2012),Tarhini et
al. (2013)

6. 6
III. Empirical Study
Information Management
Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe
WBT
Wiki
Online
discussion
content communication
with multiple mediaelements
Web-based
following designand
educational patterns.
Ref. Piccoli et al. (2001), Gabriel et al. (2009)

7. 7
III. Empirical Study
Sample
Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe
Pseudonymized
Data Collection
Complete evaluations
(n=344)
155
106
40 43
2009 2010 2011 2012
Gender distribution
71
52
17 21
70
40
21 20
2009 2010 2011 2012
male female
Descriptives
Age distribution
23,65
23,2
23,76
24,05
2009 2010 2011 2012
Key data
Ref. Gabriel et al. (2006)
Feedbackform

8. 8
III. Empirical Study
Measurement
Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe
Perceived ease-of-use of an e-
learning system
Ref. Davis et al. (1989), Šumak et al. (2011)
Perceived usefulness of an
e-learning system
Acceptanceof an e-learning system
Other factors
a) Learning satisfaction
(4 items; α=0,78, n=248)
b) Behavioural intention
a-priori (5 items; α=0,82, n=271)
a-posteriori (1 item)
(7 items; α=0,88, n=220)a) Unweighed
(2 items; α=0,87, n=282)
b) Weighed by importance of
accessibility and simplicity
(4 items)
Scale: 1 (low) to 6 (high) Scale: 1 (high) to 6 (low)
Scale: 1 (high) to 6 (low)

9. 9
III. Empirical Study
Results of parameter-free rang correlation analysis
Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe
General Perceived
Usefulness
(a-priori)
General Perceived
Ease-of-Use
(a-priori)
General Perceived
Ease-of-Use
(a-priori; weighed)
τb p τb p τb p
General Intention to
Use (a-priori)
.57** .000 -.28** .000 -.15** .000
Learning Satisfaction .27** .000 -.47** .000 -.10* .036
General Intention to
Use (a-posteriori)
.32** .000 -.18** .000 n.s.
General Perceived
Usefulness (a-priori)
- -.26** .000 -.14* .001
* significant with p<.05; ** significant with p <.01; n.s. non-significant
Perceived Ease of Use inversely polarized:Lowvalues equal high perceived ease-of-use ofe-learning.
H1 H2
H3

10. 10
III. Empirical Study
Results of exploratory analysis
Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe
General
Perceived
Ease of Use
(a-priori)
General
Perceived
Ease of Use
(a-priori;
weighed)
General
Perceived
Usefulness
(a-priori)
General
Intention to Use
(a-priori)
General
Intention to Use
(a-posteriori)
Learning
Satisfaction
Correlation τb p τb p τb p τb p τb p τb p
Drop-out rate (key data) .09* .038
Drop-out rate (feedback
forms)
-.11* .021 -.10* .042
Time required for the WBT -.12** .008
I. Age -.09* .044 -.15** .000 -.12* .023
II. Intensity of Internet
Usage
.10* .027 .10* .027 -.10* .021 -.17** .000
III. Computer Skills .18** .000 .19** .000 -.17** .000
III. Diversity of E-Learning
Usage
.10* .047 .12* .019
III. E-Learning Knowledge -.20** .000 .19** .000 -.09* .038 -.19** .000
IV. Interest in E-Learning -.34** .000 .17** .000 -.37** .000 -.44** .000 -.33** .000 -.22** .000
ANOVA (cat. Variables) Value p Value p Value p Value p Value p Value p
I. Year (F-Test) 4,352 .005
I. Sex (T-Test)
6,961,
000
.001 2,152 .032
I. Studies (F-Test) 4,629 .008
III. E-Learning Usage –
private vs. studies (U-Test)
77,500 .032 166,000 . 030

11. 11
III. Conclusion
Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe
H3: positive correlation
H1: positive correlationH2: indecisive
Perceived ease-of-use
of an e-learning system
Perceived usefulness
of an e-learning system
Acceptance of an e-
learning system
Summed up: Perceived usefulness is
likely to have a more important
influence on ETA
But: There is a need for
multivariate Analysis and inclusion
of usage data

12. Hannes Rothe
Department Business Information Systems
Thank you for your
attention.