Sunday, April 29, 2007
Wednesday, April 25, 2007
Genetic code - Wikipedia, the free encyclopedia
Genetic code - Wikipedia, the free encyclopedia: "The purine bases adenine (A) and guanine (G) are larger and consist of two aromatic rings. The pyrimidine bases cytosine (C) and thymine (T) are smaller and consist of only one aromatic ring. In the double-helix configuration, two strands of DNA are joined to each other by hydrogen bonds in an arrangement known as base pairing"
Don Dodge on The Next Big Thing: 1st, 2nd, or 3rd is great, everything else is irrelevant
great application of Geoff Moore's Inside the Tornado.
There is a message for us in the Tornado(scaling) and network effects - 1 2and 3 take all, everyone else losesDon Dodge on The Next Big Thing: 1st, 2nd, or 3rd is great, everything else is irrelevant
There is a message for us in the Tornado(scaling) and network effects - 1 2and 3 take all, everyone else losesDon Dodge on The Next Big Thing: 1st, 2nd, or 3rd is great, everything else is irrelevant
The first screen is the cellphone -
relative to iPhone usage and my idea for Al's IT as content on the new iphone this article shows that at least for a microsoftie he gets the idea of the mobile being everybody's AORTA. We need a BEBT machine for we oldsters.
Don Dodge on The Next Big Thing: Cell phones are the first screen in China
Don Dodge on The Next Big Thing: Cell phones are the first screen in China
Tuesday, April 24, 2007
DeVenCI (Defense Venture Catalyst Initiative)
DeVenCI (Defense Venture Catalyst Initiative): "based on an open and competitive solicitation process, are asked to help the DoD find and assess relevant"
Monday, April 23, 2007
Sunday, April 22, 2007
Thursday, April 19, 2007
Wednesday, April 18, 2007
Diffusion of Innovations
Diffusion of Innovations DOI theory sees innovations as being communicated through certain channels over time and within a particular social system (Rogers, 1995). Individuals are seen as possessing different degrees of willingness to adopt innovations and thus it is generally observed that the portion of the population adopting an innovation is approximately normally distributed over time (Rogers, 1995). Breaking this normal distribution into segments leads to the segregation of individuals into the following five categories of individual innovativeness (from earliest to latest adopters): innovators, early adopters, early majority, late majority, laggards (Rogers, 1995). Members of each category typically possess certain distinguishing characteristics as shown below:
* innovators - venturesome, educated, multiple info sources
* early adopters - social leaders, popular, educated
* early majority - deliberate, many informal social contacts
* late majority - skeptical, traditional, lower socio-economic status
* laggards - neighbours and friends are main info sources, fear of debt
When the adoption curve is converted to a cumulative percent curve a characteristic S curve (as shown in the first figure below) is generated that represents the rate of adoption of the innovation within the population (Rogers, 1995). The rate of adoption of innovations is impacted by five factors: relative advantage, compatibility, trialability, observability, and complexity (Rogers, 1995). The first four factors are generally positively correlated with rate of adoption while the last factor, complexity, is generally negatively correlated with rate of adoption (Rogers, 1995). The actual rate of adoption is governed by both the rate at which an innovation takes off and the rate of later growth. Low cost innovations may have a rapid take-off while innovations whose value increases with widespread adoption (network effects) may have faster late stage growth. Innovation adoption rates can, however, be impacted by other phenomena. For instance, the adaptation of technology to individual needs can change the nature of the innovation over time. In addition, a new innovation can impact the adoption rate of an existing innovation and path dependence may lock potentially inferior technologies in place.
Diffusion of innovations theory was formalized by Everett Rogers in a 1962 book called Diffusion of Innovations. Rogers stated that adopters of any new innovation or idea could be categorized as innovators (2.5%), early adopters (13.5%), early majority (34%), late majority (34%) and laggards (16%), based on a bell curve. Each adopter's willingness and ability to adopt an innovation would depend on their awareness, interest, evaluation, trial, and adoption. Some of the characteristics of each category of adopter include:
* innovators - venturesome, educated, multiple info sources, greater propensity to take risk
* early adopters - social leaders, popular, educated
* early majority - deliberate, many informal social contacts
* late majority - skeptical, traditional, lower socio-economic status
* laggards - neighbours and friends are main info sources, fear of debt
Rogers also proposed a five stage model for the diffusion of innovation:
1. Knowledge - learning about the existence and function of the innovation
2. Persuasion - becoming convinced of the value of the innovation
3. Decision - committing to the adoption of the innovation
4. Implementation - putting it to use
5. Confirmation - the ultimate acceptance (or rejection) of the innovation
* innovators - venturesome, educated, multiple info sources
* early adopters - social leaders, popular, educated
* early majority - deliberate, many informal social contacts
* late majority - skeptical, traditional, lower socio-economic status
* laggards - neighbours and friends are main info sources, fear of debt
When the adoption curve is converted to a cumulative percent curve a characteristic S curve (as shown in the first figure below) is generated that represents the rate of adoption of the innovation within the population (Rogers, 1995). The rate of adoption of innovations is impacted by five factors: relative advantage, compatibility, trialability, observability, and complexity (Rogers, 1995). The first four factors are generally positively correlated with rate of adoption while the last factor, complexity, is generally negatively correlated with rate of adoption (Rogers, 1995). The actual rate of adoption is governed by both the rate at which an innovation takes off and the rate of later growth. Low cost innovations may have a rapid take-off while innovations whose value increases with widespread adoption (network effects) may have faster late stage growth. Innovation adoption rates can, however, be impacted by other phenomena. For instance, the adaptation of technology to individual needs can change the nature of the innovation over time. In addition, a new innovation can impact the adoption rate of an existing innovation and path dependence may lock potentially inferior technologies in place.
Diffusion of innovations theory was formalized by Everett Rogers in a 1962 book called Diffusion of Innovations. Rogers stated that adopters of any new innovation or idea could be categorized as innovators (2.5%), early adopters (13.5%), early majority (34%), late majority (34%) and laggards (16%), based on a bell curve. Each adopter's willingness and ability to adopt an innovation would depend on their awareness, interest, evaluation, trial, and adoption. Some of the characteristics of each category of adopter include:
* innovators - venturesome, educated, multiple info sources, greater propensity to take risk
* early adopters - social leaders, popular, educated
* early majority - deliberate, many informal social contacts
* late majority - skeptical, traditional, lower socio-economic status
* laggards - neighbours and friends are main info sources, fear of debt
Rogers also proposed a five stage model for the diffusion of innovation:
1. Knowledge - learning about the existence and function of the innovation
2. Persuasion - becoming convinced of the value of the innovation
3. Decision - committing to the adoption of the innovation
4. Implementation - putting it to use
5. Confirmation - the ultimate acceptance (or rejection) of the innovation
Tuesday, April 17, 2007
Monday, April 16, 2007
Thursday, April 12, 2007
Wednesday, April 11, 2007
Tuesday, April 10, 2007
Monday, April 09, 2007
Saturday, April 07, 2007
Friday, April 06, 2007
Thursday, April 05, 2007
Wednesday, April 04, 2007
Tuesday, April 03, 2007
Monday, April 02, 2007
the adventure of strategy
http://www.robmillard.com/archives/strategic-people-issues-jerks-in-the-workplace.html
good review of an article on dealing with jerks ina networked world.
good review of an article on dealing with jerks ina networked world.
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