Voices on the World Stage: Processing the Power of Individual Experience in Recommendations for Action

摘要

"Women Weave the Web" was the 2014 digital action campaign sponsored by World Pulse, "… the leading network using the power of digital media to connect women worldwide and bring them a global voice" (World Pulse 2014). In three ten-week phases, women were encouraged to submit entries using on-line journals about their experiences with digital access, digital literacy and digital empowerment. During the seven-month campaign, 590 journal entries from women in 71 countries were analyzed to make recommendations not only concerning digital inclusion, but also library development, internet affordability, internet rights, and technology related violence against women. Because women were responding to campaign prompts, they contributed to solutions within the on-line community, as well as informed the larger goals of the organization and its advocacy partners. A knowledge management framework was used alongside a social constructionist approach to analyze the entries. This approach emphasizes the construction of knowledge from the collective commentary by the community. Data visualization was used to address issues related to this large qualitative data set in a timely manner consistent with organizational goals. Recommendations produced through the data analysis highlight real solutions to global problems, while maintaining the integrity of individual experiences. Included in campaign advocacy packages, graphic summaries of recommendations, representative quotes and links to original entries present the information in layers that increase the utility of this powerful and unique knowledge collection. As qualitative material becomes more accessible online, organizations need trustworthy, timely methods that will allow them to utilize these rich data sets for knowledge creation that can inform organizational goals. Illustrations of the type of analysis used in the "Women Weave the Web" campaign can be important resources for building an analysis framework that meets the demands of big qualitative data.