Crowd-funding WordPress

Plugins

Jonathan Bishop

Wordcamp 2015, Birmingham

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

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

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

• 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

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.

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).

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

Equality issues in crowd-funding project

advertisingComparing Portugal’s (lowest) online ad expenditure with the United States’ (highest)using

Big Data metrics

Metric Portugal(M) UnitedStates(M) t-score p-value

Impressions 14.37 3.25 -6.624 0.000

Clicks 0.02 0.06 1.879 0.077AveragePosition 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

AveragePosition 2.35 4.01 3.829 0.000

Cost 0.01 0.00 -1.044 0.036

PART 2

Equality and diversity issues in crowd-

funding project advertisingComparing 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

AveragePosition 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) UnitedStates(M) t-score p-value

Impressions 18.73 3.25 -9.887 0.000

Clicks 0.04 0.06 0.630 0.209

AveragePosition 2.35 4.41 6.003 0.000

Cost 0.01 0.00 -1.269 0.014

PART 2

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

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

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

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

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

A conceptual framework

BEGINNING OF THE END

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

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