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

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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

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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?

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II. Discussion Educational Technology Acceptance

Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe

Hypothesis 1: The perceived usefulness is positively related to acceptances of e-learning systems

Hypothesis 2: The perceived ease-of-use is positively related to acceptance of e-learning systems

Hypothesis 3: The perceived ease-of-use is positively associated with the perceived usefulness

Ref. Davis, Bagozzi & Warshaw (1989), Venkatesh and Davis (2000), Venkatesh and Bala (2008)

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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 et al. (2006), Goodyear and Ellis (2007), Blackler et

al. (2007), Aikman (2007), Gravill and Compeau (2008), Venkatesh (2008), Huber et al. (2008), Larsen et al.

(2009), Sitzmann (2009), Bekele (2010), Fisher (2010), Orivs (2011),

Hassanzadeh et al. (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 et al. (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 et al. (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)

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III. Empirical Study Information Management

Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe

WBT

Wiki

Online discussion

content communication

with multiple media elements

Web-based

following design and

educational patterns. Ref. Piccoli et al. (2001), Gabriel et al. (2009)

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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)

Feedback form

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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

Acceptance of 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)

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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: Low values equal high perceived ease-of-use of e-learning.

H1 H2

H3

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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

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III. Conclusion

Which factors drive e-learning usage?, March 10th 2014, Hannes Rothe

H3: positive correlation

H1: positive correlation H2: 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

Hannes Rothe

Department Business Information Systems

Thank you for your

attention.