Browsing by Author "Ronoh, Richard Kipkemoi"

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Impact of Predictive Analytics of Big Data in Supply Chain Management on Decision-Making
(Copyright: © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non- commercial use, distribution, and reproduction in any medium, provided the original work is properly cited International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022-07-26) Wafula, M. Patrick; Anselemo, Peters Ikoha; Ronoh, Richard Kipkemoi; Mbugua, Samuel Mungai
The beginning of information technology has led to a burst of data in every sector of operation. Handling huge volume of data to mine useful information to support decision making is one of the current sources of competitive advantage for organizations. However, preceding research literature on predictive analytics has attributed a lack of direct causal influence on predictive analytics in a manner that support Supply Chain Management in utility companies’ performance. This is as a result of huge data posing great challenges to practitioners when incorporating it into their complex decision making which adds business value. The purpose of this study was to introduce predictive analytics in supply chain management framework that enhances decision making in Kenya Power and lighting Company in Kenya. The study was guided by the following research objectives; to assess the existing predictive analytics in Supply Chain Management, to analyse existing supply chain management systems in utility companies in Kenya and to develop an integrated predictive analytics framework for big data in supply chain management for decision making in Kenya Power and lighting Company in Kenya. This research employed the Design Science research design because one of the key outcomes of the research was framework development. The study was carried out in Kenya Power & Lighting Company in Western Region in the republic of Kenya. The target population was 10 regional finance officers, 10 regional procurement officers, 47 county stores in-charges, 47 county project supervisors and 47 county business managers totalling to 161 as the sample size. The main tools for data collection were questionnaires, interview schedules and documentary review
Modelling hardware technologies and socio-technological features as indicators of mobile phone usage patterns
(KIBU, 2018-11) Ronoh, Richard Kipkemoi
Today’s youth are growing up in an environment of constant connectivity, instant information and dynamic technological advancements. Advances in technology and the competitive market driven demand has led to addition of new services and features on mobile phones. To the majority of mobile phone users, these innovations of mobile phone features are rarely used. Consequently, many of the mobile phone features are never used even though they significantly contribute to power consumption, cost of the mobile phone and memory space. This study sought to investigate mobile phone features and their usage patterns among university students in Kenya. The study was carried out by investigation of the behavioral characteristics that influence the usage of mobile phones, the mobile phone features that influence mobile phone selection and usage, and the relationship between the usage of mobile phone features and the motivational needs among university students. The study was guided by the Usage and Gratification Theory (UGT), the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Mixed research design was used to carry out this study whereby survey design was used to gather data and experimental design was used in the development modeling of mobile phone features and their usage patterns. Data collection tools included interview schedule and a questionnaire for survey while the incremental development tool was used in the simulation of Mobile Phone Usage Assessment Tool development (MPUAT). The study found that technological development, uncertainty avoidance and individualism as well as collectivism were the behavioural dimensional characteristics that influenced university students’ usage of mobile phones. The study further established that mobile phone selection amongst university students was influenced by its appearance, number of technological features, accessibility and connectivity. In addition, if found out that though the number of features of a mobile phone did influence the selection the usage of the very features was not of much essence. It was also established that the university students’’ motivational needs were based on information gathering, relationship, entertainment and organization. Based on the findings of the study a Mobile Phone Feature Usage Model (MPFUM) was developed. MPFUM is handy in aiding university students to determine and express their mobile phone usage needs in nontechnical terms. The model would also guide marketers and manufacturers/designers to easily convert the expressed user needs into the features required hence meeting better the needs of the users without wastage. This is critical in conserving power and better memory utilization hence giving users better service. The findings alongside contribution to the academia world will be handy in enabling policy makers to make informed decision on matters regarding mobile phone features and services.