Browsing by Author "Kibe, Lucy Wachera"

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Cyber-bullying research in Kenya: a meta-analysis
(Emerald Publishing Limited, 2021-03-01) Kwanya, Tom; Kogos, Angella C.; Kibe, Lucy Wachera; Ogolla, Erick Odhiambo; Onsare, Claudia
Purpose – Cyber-bullying is a form of harassment that is perpetrated using electronic media. The practice has become increasingly common especially with the growing ubiquity of social media platforms. Most cyberbullying cases inevitably occur on Facebook because it is the most preferred social media platform. However, little is known about cyber-bullying research in Kenya. This paper aims to analyse the quantity, quality, visibility and authorship trends of scholarly publications on cyber-bullying from and/or about Kenya. Design/methodology/approach – This study was conducted as a systematic literature review. A meta-analysis approach was used. Bibliometrics approaches were used to conduct the analysis. Data on the publications was collected from Google Scholar using Harzing’s “Publish or Perish” software and then analysed and presented using Microsoft Excel, Notepad and VOSviewer. Findings – The study yielded 359 research publications on cyber-bullying in Kenya. There was a gradual increment in the number of publications, peaking in 2018. Nearly half of the publications have not been cited indicating low uptake of research on cyber-bullying in Kenya. It also emerged that most of the research has been published on subscription channels thereby restricting their visibility, access and use. Minimal collaboration in research on cyberbullying in Kenya was also observed since 67.4% of the publications were written by a single (one) author. The authors conclude that the quantity, quality and visibility of research on cyber-bullying in Kenya is low. Originality/value – This is an empirical study. The findings can be used to promote and mainstream research on cyber-bullying in Kenya.
Relationship between big data analytics and organisational performance of the Technical University of Kenya and Strathmore University in Kenya
(Emerald Publishing Limited, 2020-04-12) Kwanya, Tom; Kibe, Lucy Wachera; Owano, Ashah
Abstract Purpose – Big data analytics is a set of procedures and technologies that entails new forms of integration to uncover large unknown values from large data sets that are various, complex and of an immense scale. The use of big data analytics is generally considered to improve organisational performance. However, this depends on capabilities of different organisations to provide the resources required for big data analytics. This study aims to investigate the influence of big data analytics on organisational performance of Technical University of Kenya (TUK) and Strathmore University (SU). Design/methodology/approach – This study was conducted as a mixed method research to enable a deep understanding of the concept. Primary data was collected through structured questionnaires and interviews with clientele and information communication technology staff from the TUK and SU, both in Nairobi, Kenya. Secondary data was collected through interviews and questionnaires. Data was analysed and presented using descriptive statistics. Findings – The findings revealed that most of the variables of organisational performance such as innovativeness, creativeness, effectiveness, productiveness and efficiency are affected positively by conducting big data analytics in both institutions. The results demonstrate that the TUK showed a negative relationship between big data analytics and competiveness and profitability while SU showed a positive relationship between the two variables. In terms of regression analysis, the findings revealed that SU showed a good relationship between independent and dependant variables while the TUK had a weak influence.
Techniques and Tools of Big Data Analytics at the Technical University of Kenya and Strathmore University
(2019-07-17) Kibe, Lucy Wachera; Kwanya, Tom; Owano, Ashah
Big data is the term used to refer to any group of datasets so huge and composite that it is difficult to process the same using traditional data processing applications. Big data analytics is a set of procedures and technologies that entail new forms of integration to uncover large unknown values from large datasets that are various, complex, and of an immense scale. Analysing big data is a challenging task as it contains huge dispersed file systems which should be fault-tolerant, flexible and scalable. There is an immense need of constructions, platforms, tools, techniques and algorithms to handle big data. Some of the tools used to anlyse big data are Hadoop, Map Reduce, Apache Hive, and No SQL, among others. Techniques for big data analytics include descriptive, diagnostic and prescriptive analytics. This chapter compares the techniques and tools used for big data analytics by the Technical University of Kenya with those used by Strathmore University. The study on which this chapter is based was conducted as a mixed method research to enable deep understanding of the concept. Primary data was collected through structured questionnaires and interviews with clientele and information communication technology staff from the two institutions in Nairobi, Kenya. Secondary data was collected through document analysis. Data was analysed and presented using descriptive statistics. The findings revealed that the tools used frequently for big data analytics were SQL and Java. The two academic institutions mostly used descriptive big data analytics techniques. There was variance in the use of some techniques where SU applied predictive and TUK diagnostic techniques, SU used rules and algorithms to detect the patterns. They also employed statistical analysis, data mining and machine learning to get meaning from data. On the other hand, TUK employed diagnostic analytics to examine their big data.