Social Media Mining Methods Used to Improve Business Intelligence at Safaricom Public Limited Company

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dc.contributor.author Kendi, Caroline
dc.contributor.author Oduor, Collins
dc.date.accessioned 2020-08-24T13:22:41Z
dc.date.available 2020-08-24T13:22:41Z
dc.date.issued 2016
dc.identifier.uri http://erepo.usiu.ac.ke/11732/5967
dc.description Journal Article en_US
dc.description.abstract Thestudy sought to establishsocial media mining techniques that were used at Safaricom PLC. This study was anchored on the tenets of the General Systems Theory and the Technology Acceptance Model (TAM) and took on a descriptive survey design. The target population in the study comprised of 150 employees of Safaricom PLC in the social media and business development departments. It used simple random sampling, which saw 72employees of Safaricom PLC being sampled. A questionnaire was used as the main research instrument. The reliability of the study was tested using the Cronbach Alpha test and the validity test was ensured by consulting experts and supervisors of the study. The collected data was sorted, cleanedand coded into SPSS 23 for subsequent descriptive and inferential statistics. The analyzed data was presented using charts, figures and tables. The study found that text mining was used for social media mining at Safaricom PLC as well as clustering and visualization. The study concluded that social media mining methods improved business intelligence and significantly predicted business intelligence at Safaricom PLC. The study recommends that the organization can share the information mined to other departments which can be used to show where it is not performing well and therefore improve its performance. en_US
dc.publisher International Journal of Science and Research (IJSR) en_US
dc.subject Business intelligence, social media mining, Safaricom Public Limited Company,social media mining techniques, social media en_US
dc.title Social Media Mining Methods Used to Improve Business Intelligence at Safaricom Public Limited Company en_US
dc.type Article en_US

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