The Social Software Learnability Prediction (SSLP) Tool

There are many social software�s in the market, but not all of them are being utilized probably due to complicated user interface features, memorability of the software is difficult or due to very difficult language used in the application. The main purpose of this paper is to design the mobile social software learnability prediction tool, to assist the prediction of the learnability of the social software before it is released to the market. A sample of 361 respondents was selected, with 345 respondents returning feedback. Primary data was collected through the use of questionnaires targeting mobile social users in Central Rift valley of Kenya. Social networks were targeted include, WhatsApp, Facebook and Twitter. Data analysis was done using descriptive statistics. Principal component analysis was used to selected the most significant variables that were uses in the design of the tool, the variables that were considered include Customization, Satisfaction, Software consistency, User interface features, Language complexity and Memorability. Results from the tool were used in the prediction of social software learnability.