1. © 2013 HYVE AG
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Augmented Reality
Scientific Study on Foresight and Consumer Acceptance
a first overview of the research questions, methodology and preliminary findings
conducted by HYVE Science Labs
in cooperation with our technology partner Insius
Munich, 2013

2. © 2013 HYVE AG
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Contents
Aims and Objectives
Methodology - Advantages
Methodology – Social Media Network Audit
First Findings – Quantitative Assessment
First Findings – Demographic Data based on Alexa Traffic
First Findings – Topic Analysis
2
6
5
4
3
1
7
Project Overview

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Project Overview

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Project Overview
A Project of HYVE and INSIUS
Data Collection
Data will be collected
through quantitative web
monitoring using Insius
software InMap; social
network analysis, text
mining and linguistics will
be combined in order to
analyze community content
Theoretical Touch
Points
Literature on Augmented
Reality, Foresight Methods,
Web Monitoring
Study Design
Longitudinal study
measuring the change of
consumers„ perception and
acceptance
Foresight and Consumer Acceptance – AR

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Aims and Objectives

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Aims and Objectives
Investigating AR from a Consumer Perspective
Investigating Augmented Reality from a Consumer Perspective
Time
• How has AR changed over time from an end
user perspective?
• Are there temporal changes regarding the
usage of AR?
• How has the consumer acceptance/
sentiments regarding AR changed over time?
• Are there significant jumps? In connection to
what (launch of new technology/
application)? Which concepts existed before/
afterwards? Is there a different focus?
Sentiments
• How is AR perceived and accepted by
consumers?
• Which kinds of sentiments exist towards AR?
Application
• What topics are related to AR?
• What are the main fields of utilization?
• What is the actual use of AR?
Quantity
• How many comments/ entries exist about
AR?
• How intensive is AR discussed by
consumers?
Outlook
• How is AR expected to develop in the future?

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Methodology

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Insius Web Monitoring
Advantages
► HYVE with its long-lasting record in netnographic research is teaming up with INSIUS, a
technology specialist from Cologne, to provide social media based research on an
advanced automated level
► Their joint efforts provide the following benefits
1) Data crawling is based on simulated search-behavior and provides thereby an
orientation towards relevant social media posts from the beginning
2) A special identification of „real“ user generated content is applied, not a collection
of mere „technical“ UGC (efficient machine learning algorithms as well as domain-
specific sentiment-assessment are used to ensure this)
3) The analysis of posts explore relevant topics and subtopics and is thereby
„discovering“ unknown dependencies - not only coding already known categories
4) The identified topics are mapped onto an association-network, dependencies and
structures across all discussions can be seen here in a clear overview

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Analyzing Social Media Content at „Big Data Scale“
Social Media Network Audit
To identify brand and product related social media content on
a „big data“ scale, a process with 4 distinct steps is
conducted. Effective computer algorithms as well as
experience netnographic researchers are hereby interacting
in each step:
► Crawl: data collection by simulating human online search
behavior
► Extract: cleaning of data and extraction of user generated
content by specialized machine learning algorithms
► Analyze: identification of topics and sentiments
► Map: construction of a topic network – automatized
mapping of topics, subtopics – their sentiments and
drivers
Crawl1
Clean &
Extract2 Analyse3 Map4

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Social Media Network Audit
Crawl – Clean&Extract
► keyword strings that are representing the research topic at hand, are
formulated by experienced netnographers
► crawling of possible relevant UGC by queries against all relevant
search engines
► content is extracted from all (!) search engine result pages and stored
in a data base
Due to the simulation of human online search behavior, the data crawling is
already focused on relevant UGC.
Cleaning of automated data is necessary due to the following facts:
► Spam and Code has to be excluded
► only real user generated content should be taken into account (no
editorial content, no advertisement and PR)
Our technology is using two technologies for this
► rendering and deconstruction of a website into its pieces
► machine learning algorithms that differentiate Spam, Code, editorial
and PR content from UGC
Crawl
Clean&Extract
1
2

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Social Media Network Audit
Analyse - Map
► using specialized computer linguistic models, the crawled data is
analyzed to identify topics, subtopics and their drivers
► a sentiment analysis gives a first impression of the polarity of
products, brands and topics (positive, negative, neutral)
► an identification of positive and negative drivers provides a deeper
understanding of topics and their relations in terms of the actual
content of the discussions
Analyse
Map
► for every analyzed brand or product the relations between discussed
topics are evaluated by using network analysis
► in the center of the network the major research topic can be found –
grouped around are the negative and positive associations (topics)
HYVE„s Social Media Network Audit provides hereby a quick understanding
of the relations and dependencies between discussions on brands,
products and technology. It also serves as an ideal starting point for further
netnographic research.
3
4

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First Findings
Quantitative Assessment

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0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
5,500
6,000
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Quantitative assessment
Total volume timeline
Average growth of
47 % p.a. since 2007
amount of English UGC-posts related to the term “augmented reality” (per year)

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0
100
200
300
400
500
600
700
800
900
1,000
1,100
1,200
1,300
1,400
1,500
1,600
1,700
1,800
1,900
2,000
2,100
2,200
2,300
2,400
2,500
Quantitative assessment
Total volume timeline
amount of English UGC-posts related to the term “augmented reality” (per quarter)
Google Glass at
Google I/O
HomeSpotter at
NAR
CES 2013;
Oculus Rift etc.

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2,321
1,820
1,414
1,024
695
648
626
582
580
514
507
486
455
453
439
365
348
267
257
250
245
240
229
225
224
219
208
199
196
195
182
171
167
165
164
163
163
162
158
156
153
150
150
150
148
147
144
143
139
www.mtbs3d.com
stackoverflow.com
seminarprojects.com
forums.macrumors.com
forum.unity3d.com
www.ign.com
www.xtremesystems.org
www.defencetalk.com
www.gamespot.com
answers.yahoo.com
forum.rpg.net
3dsforums.com
forum.xda-developers.com
forums.penny-arcade.com
www.escapistmagazine.com
forums.toucharcade.com
gamrconnect.vgchartz.com
www.neogaf.com
www.meetup.com
developer.vuforia.com
warships1discussionboards.yuku.com
www.futuretimeline.net
androidforums.com
www.serebiiforums.com
talk.maemo.org
www.abovetopsecret.com
www.nintendolife.com
forums.ubi.com
www.avforums.com
forums.cgsociety.org
forums.whirlpool.net.au
forum.lowyat.net
www.quora.com
www.gamedev.net
www.pojo.biz
forum.beyond3d.com
forums.gametrailers.com
mybroadband.co.za
forum.magicball.net
www.neoseeker.com
forums.steampowered.com
hardforum.com
forums.eidosgames.com
forum.mobilism.org
www.giantitp.com
www.psu.com
forums.sjgames.com
forums.comicbookresources.com
eclipsephase.com
Quantitative assessment
Communities
24%
15%
14%
14%
8%
6%
6%
5%
5%
3%Gamer Community
3D-Game-Technology-
Community
Computer and Technology
Community
Q&A-Site
Smartphone-Community
Smartphone-Developer-
Community
Military Community
Home Entertainment
Community
Game Developer Community
Sonstige

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Quantitative assessment
Total Volume - Summary
► Augmented reality sees a massive leap in user generated content after 2006
► Since then the amount of UGC grows nearly 50% from year to year
Growing interest since 2006
► Google Glasses showed a significant impact on the discussion around AR, but on a very high level
► Other products like the Oculus Rift (VR) were also able to stimulate the discourse on AR
All about Google Glasses?
► The major areas of discussion in the social media landscape are game-development and gaming related
► Smartphone-Communities and App-developer communities (like XDA-Developers) follow with wide margin.
Is it a gamers thing?

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Demographic data based on Alexa traffic
Age and education
age education
47
20
-15
-31
-37
-31
18 to 24
25 to 34
35 to 44
44 to 45
55 to 64
65 plus
0
16
5
-8
no college
some college
college
graduate school
► Age-focus lies beneath 35 years, correlating to the major “gaming” topic that is prevalent in the discussion-
sites
► The relatively low educational level could also be a phenomenon of the gaming focus: the majority
of users is simply too young for a college degree
Age and education correspond to the impression of “gamers”

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Demographic data based on Alexa traffic
Gender, children and income
gender and children income
► The most used forums and communities are frequented by males without children.
► The low income of community members could again be a mere age-phenomenon.
gender, children and income
60
-39
male
female
-25
34
has children
has no children
28
-2
-7
-20
income 0 to 30k
income 30k to 60k
income 60k to 100k
income 100k plus

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Demographic data based on Alexa traffic
Place of internet usage
place of internet usage
► The place of internet usage is maybe the most significant
evidence that “gamers”, still going to school are dominating
the online discussions: Internet usage is above average –
but not at “work”
No internet-usage at work
21
24
-14
internet at home
internet at school
internet at work

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Topic analysis
Overview

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Network overview
Introduction
48.742 posts
3.236 sites
6.264 concepts
Augmented Reality

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Network overview
Introduction
Augmented Reality
each node in the
network represents a
language concept
these appear in
sentences and have
a specific polarity
negative
neutral
positive
the more central a
concept is, the more
frequent it occurred in
the crawled discussions
the numbers inside the
concepts give a relative
impression of the
frequency of a concept
in this specific network

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Network overview
Topics overall

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Network overview
Topics - development
0
50
100
150
200
250
1970-2007 2008-2010 2011-2013
information&knowledge
advertising&marketing
shopping
navigation
education
450
950
1,450
gaming

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Network overview
Example: gaming
► the network to the isolated
gaming-related concepts
► all concepts that co-occur in
posts with gaming related topics
are taken into account
► it serves as a first basis for
deeper topic understanding and
can already show significant
phenomena
Isolation of the gaming network

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Network overview
Time shift
2005 and before after 2005

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The entire study including the final outcomes will
be available soon!

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Oliver Gluth
oliver.gluth@hyve.de
fon +49.89.189 081-458
fax +49.89.189 081-400
HYVE Innovation Research GmbH
HYVE Science Labs
Schellingstraße 45
80799 München
www.hyve.de
Thank you for your attention
Your personal contact
HYVE Innovation Research GmbH
HYVE Science Labs
Schellingstraße 45
80799 München
www.hyve.de
Hanna Stockinger
hanna.stockinger@hyve.de
fon +49.89.189 081-479
fax +49.89.189 081-400
Marc Egger
marc.egger@insius.com
fon +49.221.4558026-0
fax +49.221.4558026-9
Insius UG
Technologiepark Köln
Eupener Str. 165
50933 Köln
http://insius.com