Presentation to WordCamp 2015 in Birmingham.

1. Crowd-funding
WordPress
Plugins
Jonathan Bishop
Wordcamp 2015, Birmingham

2. Jargon, jargon and more neologisms
KEY THEMES RO BE EXPLORED
 Geo-demographic factors
 Online advertising
 Intellectual property
 Project management
SPECIALIST TERMINOLOGY
 WordPress Plugins
 Agile development
 Big Data
 Contingent working
 Crowd-funding

3. Format of presentation
PART 1 – INTROUDCTION TO CROWD-FUNDING
 Extent and nature of crowd-
funding
 PledgeBank (failure)
 IndieGoGo (successful)
 WordPress + PayPal (successful)
 Person-to-Person (successful)
PART 2 – THE ROLE OF ONLINE ADVERTISING
 Geo-demographic factors
 Crowd-funding supported
through online advertising
 Equality issues
 Big Data
PART 3 – PROJECT MANAGENENT ISSUES
 Agile Development
 Contingent-working
 User testing

4. Archeology (Bonacchi, 2015)
Renovation and Sustainable
development (Kunkel, 2015)
The Built and Human
Environment
Bitcoins and currency exchange
Anonymity issues
Election campaigning
Marketable via plugin website
Code must be open source
Subscriptions possible
Limited financial return
WordPress plugins
PART 1
The extent
of crowd-
funding

5. • Difficult to follow up if target has
not reached
• Do people trust you?
• How many actually use it?
PledgeBank
• Purpose-built
• Secure funding
• Brand awareness
• Social proof
IndieGoGo + AdWords
• Completely customisable
• Uses trusted payment platform
• Dedicated websites can be
linked to from many sources
and search engine optimised.
WordPress + PayPal
PART 1

6. Conclusions from Part 1
END OF PART 1
 Extent and nature of crowd-funding
 Can be used for many projects, but be mindful of the law
 PledgeBank
 Did not work because of trust and unsuitable design
 IndieGoGo
 Secured users because it was purpose built and trustworthy
 WordPress + PayPal
 Secured funding due to detail and trust
 Person-to-Person
 People do business with people, who they know and trust.

7. Using Big Data to identify geo-demographic
factors in advertising crowd-funding projects
Variable N Wales Scotland South
East
F p-value SE
Trolling Incidents
(Adj)
- 237 365 10201 - - -
Productivity - 164 212 320 - - -
Education Level 161 2 3 3 6.63 <0.003 0.11
Intelligence 150 92 102 105 4.27 <0.003 0.62
Quality of Life 150 33.11 32.01 30.05 3.11 <0.017 0.5
Rooms in House 161 5.96 5.05 5.09 4.45 <0.014 0.01
PART 2
Source: J. Bishop (2014).

8. This is an example of an advert on Google and its
partner website in order to entice people into
supporting the development of QPress through
crowd-funding.
For the data the CV was 1.786 ,
which was good. The data
showed the most salient factors
affecting success are
impressions and average
position. Clicks are also a
factor, but the costs to the
advertiser are not.
Factors affecting
success of advertising
crowd-funded projects
Advertising your crowd-funding project
Factor df Meansquare F p Null
Clicks 12 0.016 2.271 0.007 Reject
Impressions 12 5437.158 6.330 0.000 Reject
Costs 12 0.009 1.229 0.256 Keep
AveragePosition 12 0.000 18.745 0.000 Reject
PART 2

9. Equality issues in crowd-funding project
advertising
Comparing Portugal’s (lowest) online ad expenditure with the United States’ (highest)using
Big Data metrics
Metric Portugal (M) United States (M) t-score p-value
Impressions 14.37 3.25 -6.624 0.000
Clicks 0.02 0.06 1.879 0.077
Average Position 2.79 4.41 6.533 0.000
Cost 0.02 0.00 0.002 0.002
Comparing Spain’s (highest) percentage of NEETs
with Japan’s (lowest) using Big Data metrics
Metric Mexico (M) Switzerland (M) t-score p-value
Impressions 18.73 4.89 -1.985 0.032
Clicks 0.04 0.00 -1.389 0.004
Average Position 2.35 4.01 3.829 0.000
Cost 0.01 0.00 -1.044 0.036
PART 2

10. Equality and diversity issues in crowd-
funding project advertising
Comparing Mexico’s (lowest) rooms in house and education outcomes with the United States’
(highest( using Big Data metrics
Metric Spain (M) Japan (M) t-score p-value
Impressions 12.38 6.67 1.737 0.025
Clicks 0.11 0.03 2.581 0.000
Average Position 2.96 2.32 3.164 3.164
Cost 0.01 0.00 1.255 1.255
Comparing Spain’s (highest) percentage of NEETs
with Japan’s (lowest) using Big Data metrics
Metric Mexico (M) United States (M) t-score p-value
Impressions 18.73 3.25 -9.887 0.000
Clicks 0.04 0.06 0.630 0.209
Average Position 2.35 4.41 6.003 0.000
Cost 0.01 0.00 -1.269 0.014
PART 2

11. Conclusions from Part 2
END OF PART 2
 Crowd-funding supported through online advertising
 Increase reach through targeted marketing
 Geo-demographic factors
 Ensure appropriate markets are tapped into, but avoid those who
just want ad income
 Equality issues
 Ensure all are considered and included
 Big Data
 Measuring performance and finding trends

12. Agile Development
Stage 1 - Q/Qu (data collection)
 Q-sort data collection
 Questionnaire data collection
Stage 2 - QPress 0.5 (activities)
 Administrator Panel
 Create surveys
Stage 3 – QPress 1 (data exporting)
 Export data to standardised packages
(e.g. SPSS, PQMethodl PCQ, Excel)
 Export data in bundled formats (i.e. UK
Data Archive; RTF, Excel, SPSS)
Stage 4 – QPress 2 (data analysis)
 Administrator Panel
 Create surveys
PART 3

13. Contingent Working
Employee-based Model
 Employees have to work set
hours
 Can be moved from project to
project
 Have to be paid for any work
done, including overtime, even if
they didn’t do it correctly
 Are employed even when there
is no work to do
 Must do things the way their
manager tells them, even if they
would prefer otherwise
Sub-contractor based model
 Can work at a time and place
most suited to them
 Only work on the projects their
skills are required for
 Are paid a fixed rate to do a
particular task regardless of how
long it takes
 Are only engaged for a specific
task as and when needed
 Must do things the way they
want, so long as the outcomes
are achieved
PART 3

14. User Testing
 Scenario-based
 Can identify expectations
 Can highly problems to solve
 Software testing
 Can identify problems early
 Can provide suggestions for improvement
 Marketing and community
 Can build a loyal user base
 Can assist ‘buy-in’
PART 3

15. Conclusions from Part 3
END OF PART 3
 Agile Development
 Means plugins can be put together as separate embodiments as
money comes in, not after
 Contingent-working
 Means workers are taken on when the money is there to pay
them, rather than having to pay an ongoing salary
 User testing
 Testing on users can cut out many of the costs of redeveloping
plugins as a result of design flaws

16. A conceptual framework
BEGINNING OF THE END

17. Questions and feedback
ANY QUESTIONS?
 Agile development
 Contingent working
 Crowd-funding
 Geo-demographics
NEXT SPEECH
I will be speaking at the
12th International
Conference on Web
Based Communities and
Social Media 2015, 22 –
24 July, Las Palmas de
Gran Canaria, Spain.
Ask for a discount form for my book: “Gamification for Human
Factors Integration: Social, Educational and Psychological Issues”
MIDDLE OF THE END

18. References and further reading
 Bishop, J. (2014). Digital Teens and the ‘Antisocial Network':
Prevalence of Troublesome Online Youth Groups and Internet
trolling in Great Britain. International Journal of E-Politics (IJEP),
5(3), 1-15.
 Bishop, J. (2011). Mum’s the WordPress: A Comparative Analysis of
Political and Mommy Bloggers. In Hamid R. Arabnia; Victor A.
Clincy & Ashu M. G. Solo (Eds.) Proceedings of The 2011 Internet
Conference on Internet Computing (ICOMP’2011). July 18-21, 2011.
Las Vegas Nevada, USA.
 Bonacchi, C., Bevan, A., Pett, D., & Keinan-Schoonbaert, A. (2015).
Developing Crowd-and Community-fuelled Archaeological
Research. Early results from the MicroPasts project.
 Kunkel, S. (2015). Green Crowdfunding: A Future-Proof Tool to
Reach Scale and Deep Renovation?. In World Sustainable Energy
Days Next 2014 (pp. 79-85). Springer Fachmedien Wiesbaden.
THE END