"Technology Acceptance of Augmented Reality and Wearable Technologies" #TAM at #iLRN2017 by Fridolin Wild, Roland Klemke, Paul Lefrere, Mikhail Fominykh and Timo Kuula Paper presented at the 3rd Immersive Learning Research Network Conference in Coimbra, Portugal on 28 June 2017 Publication: https://link.springer.com/chapter/10.1007/978-3-319-60633-0_11

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Technology Acceptance of
Augmented Reality and
Wearable Technologies
presented by Mikhail Fominykh, Europlan UK ltd
MIKHAIL.FOMINYKH@EUROPLAN-UK.EU

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Team
Fridolin Wild
Performance Augmentation Lab, Oxford Brookes University, UK
wild@brookes.ac.uk
Roland Klemke
Open University of the Netherlands
Roland.Klemke@ou.nl
Paul Lefrere
CCA Ltd, UK
paul.lefrere@cca-research.co.uk
Mikhail Fominykh
Europlan Ltd, UK
mikhail.fominykh@europlan-uk.eu
Timo Kuula
VTT, Finland
Timo.Kuula@vtt.fi

3. Motivation:
Immersive Learning
Immersive Learning is a dynamic area:
Societal needs evolve to require new learning goals
& methods
Enabling technologies include new mixes (eg AR +
wearables)
R&D into immersive learning finds promising ways
to address long-standing needs (eg faster learning,
from peers, at lower cost)
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4. Motivation: Technology
Acceptance Models
Technology Acceptance Models (TAMs) can predict
which aspects of which R&D innovations fit which
Stakeholder needs.
The technology acceptance aspects of each
generation of learning technology require re-
evaluation, to assess user satisfaction with new
affordances, experiences & possibilities.
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5. Technology Acceptance
Models & Immersive Learning
Stakeholder acceptance of new generations of Immersive-Learning
technology depends on ‘fit’ between Technology, Needs, Wants:
1. Technology-push: eg immersive learning exploiting advances in
entertainment-focused input & output technologies in mobile
devices (such as sensors for eye-tracking in augmented reality)
2. Demand-pull: eg learners who judge immersive learning solutions
in terms of their use of the latest phone’s new features
3. TAMs: broaden to include possible changes in eg our capacity to re-
experience & reflect upon our own experiences; our ability to share
actual experiences; our capacity to synthesize partly-false
memories that combine fragments of our direct experiences &
synthesized third-party experiences; etc
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WEKIT
Funding: EU Horizon 2020, ICT-20 2015: Technologies
for better human learning and teaching
Budget: EUR 2.753.143,75
WEKIT Community https://wekit-community.org/
WEKIT project website http://wekit.eu/

7. Experience and knowledge
Learning =
= converting experience to knowledge
immediate experience
(aka ‘practice’)
information for a master
level
(aka ‘theory’)
separated
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Mikhail Fominykh, Fridolin Wild, Carl Smith, Victor Alvarez and Mikhail Morozov: "An Overview of Capturing
Live Experience with Virtual and Augmented Reality”, DOI: 10.3233/978-1-61499-530-2-298.

8. Experience and knowledge
Learning =
= converting experience to knowledge
immediate experience
(aka ‘practice’)
information for a master
level
(aka ‘theory’)
Experiencedlearner
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9. Methodology
What is Wearable Experience?
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The Wearable Experience training methodology aims to provide an
innovative learning method that is based on the idea of capturing
the experience of an expert and enabling trainees to wear it while
re-enacting, thus giving the trainee access to the tacit knowledge of
the expert and enabling master-apprentice knowledge sharing.
Capture Re-enact Evaluate
Bibeg Limbu, Mikhail Fominykh, Roland Klemke, Marcus Specht, and Fridolin Wild: "Supporting Training of
Expertise with Wearable Technologies”, Springer, in press.

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Hardware
design

11. TAMs for benchmarking
acceptance of AR/WT solutions
AR/WT solutions have widely-varying
form factors & user interfaces. These set
constraints on AR/WT acceptance,
limiting how we construct acceptance
scales & then benchmark solutions.
Comments on scale-construction &
benchmarking in the application areas
of WEKIT:
1. AR/WT in Aviation
2. AR/WT in Medicine
3. AR/WT in Space
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Structural Equation Model of the Unified
Theory of Acceptance and Use of Technology
(Venkatesh et al., 2003)

12. Methodology
1. Collecting items from existing TAMs (91 items)
2. Testing reliability and measuring internal validity (15 subjects)
◦ Measuring the correlation (Pearson’s r) across the responses with
the sum scores of all items
◦ Calculating the item-to-item correlations to identify further those
items loading onto the same construct
◦ Measuring Cronbach’s α to estimate interrater reliability,
comparing the reliability for the full pool as well as the final subset
selection
3. Running the resultant questionnaire with experts (33 subjects)
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13. Item pool generation and
reduction
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0.3 0.4 0.5 0.6 0.7 0.8
Number of items for different Pearson's
correlation coefficient thresholds
020406080
69
64
45
36
12
2

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−1
−0.8
−0.6
−0.4
−0.2
0
0.2
0.4
0.6
0.8
1
LR
N
1
IO
P1
FC
1
FC
2
IO
P2
BI2
PV5
SI1
SI2
SI3
PE3
PE4
BI3
IM
G
1
IM
G
2
PE10
R
EG
3IO
P3
H
T2
IS6
H
M
2
PE7
PE8
PE2
PE9
EE2
PE5
H
M
1
H
M
3
H
M
5
H
T3
C
SE4IM
G
4
H
M
2bATU
4
BI1
LRN1
IOP1
FC1
FC2
IOP2
BI2
PV5
SI1
SI2
SI3
PE3
PE4
BI3
IMG1
IMG2
PE10
REG3
IOP3
HT2
IS6
HM2
PE7
PE8
PE2
PE9
EE2
PE5
HM1
HM3
HM5
HT3
CSE4
IMG4
HM2b
ATU4
BI1
Item to item correlations
(selected 36 items)

15. Excluded items
▪ Anxiety (group CANX) does not correlate with the sum scores
(probably not relevant for work context)
▪ Questions about management support are too early to ask (lack
of exposure)
▪ Questions about integration with legacy systems do not work
(need the right users)
▪ Appeal of the workplace to younger people is out of place
(respondents may not know this)
▪ Content and content experience could not be answered (maybe
end-users do not see this separation between content and
system)
▪ Privacy (may be a result of lack of exposure)
▪ Value for money (need the right users)
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16. Item grouping
Analysis of the 36 included items and their item
groups provides the following:
Several items correlate highly within their group
and a choice can be made for the phrasing with
more clarity or for the aesthetically more
pleasing formulation
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17. Final questionnaire
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CODE STATEMENT
ATU4 I look forward to those aspects of my job that require me to use AR & WT.
CSE4 I could complete a job, if I had used similar technologies before this one to do the same job.
EE2 My interaction with AR & WT is clear and understandable.
FC1 I have the resources necessary to use AR & WT.
HM2b I like working with AR & WT.
HT2 I am addicted to using AR & WT.
IMG1 People in my organization who use AR & WT have more prestige than those who do not.
IMG4 I use AR & WT solutions, because I want to be a forerunner in technology exploitation.
IOP1 Interoperability is important for AR & WT.
IOP2 I am worried about vendor lock in with AR & WT.
IOP3 Integration costs of AR & WT with other software systems in use are high.
IS6 I would find it useful if my friends knew where I am and what I am doing.
LRN1 Learning curve for AR & WT is too high compared with the value they would offer.
PE10 With AR & WT, I immediately know when a task is finished.
PE4 Using AR & WT increases my productivity.
PE8 AR & WT increase precision of tasks.
SI1 People who are important to me think that I should use AR & WT.
BI2 I will always try to use AR & WT in my daily life.
UF1 Please choose your usage frequency of AR/WT
Freq.

18. Current level of
Technology Acceptance
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Technology Acceptance and Use
Strongly
Disagree
Disagree
Somewhat
Disagree
Neither agree
or disagree
Somewhat
agree
Agree
Strongly
Agree
ATU4 BI2 CSE4EE2 FC1HM2bHT2 IMG1IMG4IOP1IOP2IOP3 IS6 LRN1PE10 PE4 PE8 SI1
Inverse item

19. Conclusions (1)
A metric scale to assess technology acceptance with constructs
and items beyond existing models:
Interoperability
Learnability
Privacy
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20. Conclusions (2)
Action required:
Equip workers with devices
Match high expectations
Issues:
Device management
Legacy integration
Lack of social influence
Lack of concern about privacy
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24. Disclaimer
This project has received funding from the European Union’s
Horizon 2020 research and innovation programme under
grant agreement No 687669. http://wekit.eu/
Q & A
Presented by Mikhail Fominykh
mikhail.fominykh@europlan-uk.eu
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