ICT Strategies

Margarita Quihuis, from Persuasive Technology Lab at Stanford University, presents the EPIC initiative at the Social Entrepreneurship panel of Relief 2.0 in Haiti @ Stanford.

Barack Obama's campaign promises on technology. Are his government actions living up to these promises?

The first step would be to understand the local ecosystem and its stakeholders and to become familiar with existing and similar experiences, their successes and failures, both at a local level and in other places. This would keep us from duplicating efforts, repeating mistakes and would allow us to learn from such experiences and to identify local partners and others with relevant experience.

Given enabling environments for social entrepreneurship, both individuals and institutions will have similar incentives (beyond their social motivation) to innovate, explore, pursue and develop solutions, goods and services for their local communities and global markets.

Last week my work of the last eight months for United Nations Economic Commission of Latin America and the Caribbean doing a Public ICT and Information Society assessment of the Caribbean region was presented in a regional Policy Makers Seminar at Barbados.

It has been a shock to read a note published in a local Barbados newspaper highlighting only negative parts of the extensive 12 page Public ICT Profile I put together for Barbados.

How to promote entrepreneurial initiatives and spirit at the local level by engaging relevant stakeholders and providing adequate support with ICT, beyond microcredit or financing. Money is not enough to promote and support entrepreneurial activity. Know-how, experience, administrative skills, marketing skills are all part of the picture and can only be attained by engaging multiple stakeholders.

How is CODE3 different from a Conventional Development Process?

CODE3 differs from a conventional development in that we are taking the traditional approach…

• From discrete to continuous.
• From intermediated to direct.
• From top-down / bottom-up to horizontal.
• From anonymous contribution to value recognition.
• From unknown decision process to documented and trackable evolution.
• From partial (individual) observation to complete (social) monitoring.
• From making up profiles to real users needs and preferences.
• From standardization to customization.

Artificial Biases

A diagnose, design or research group may not be representative of the public being served, introducing biases in the observations and conclusions. The bias can come in many different aspects, some often unnoticed...

The end user is not looking for the perfect application, neither the simplest one, nor the fastest.

Actually no one can tell what the community is looking for until the community is actively engaged, not just probed or sampled the application, and the behavior trends start to form.

The very introduction of a product or service can actually trigger trends among a community of target audience. Often, this behavior trends have nothing to do with usability or features, but just as it happens in financial markets, the end user decision and preferences come out of “noise”, word of mouth, even plain copy-others behavior and the definite impact of early adopters and influential members of the community.

More often than not, what determines the success of an Information Technologies project is not how well it addresses the needs of the its users, but how well it helps the end user to be perceived by others and how well the user can fit in within a larger group and thrive within it.

There are countless of well documented cases of excellent designs that do poorly among the consumers they are meant to serve and are perfectly design for.

Think about the Mac computer, which for all the well deserved praise, usability features, gorgeous design, ease of use, remains with less than a 5% of the market share.