Artificial Intelligence Inclusion and Performance of Sensor Management System in Nairobi-City Water and Sewerage Company, Kenya
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Date
2023-06
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Kenyatta University
Abstract
Performance has always been the most important and pressing problem for any firm worldwide. The sensor management system often experiences poor performance in terms of there is no proper fault detection, poor information inference, and poor data mining and unfavorable pattern recognition .The research aimed at determining the inclusion of Artificial Intelligence such as fault detection, data mining, information inference and
pattern recognition on the performance of sensor management system in Nairobi City Water and Sewerage Company. The research related on Technology Acceptance Model, Adaptive Structuration Theory, Diffusion of Innovation and Technology Organization Environment theory. Descriptive and exploratory research design was adopted in the study and the target population included Nairobi City Water and Sewerage Company top and middle level manager to acquire in dept analysis of the research. The unit of analysis was the target population with a total of 360 managers . The unit of observation was the sample size of 108 managers selected which represented the 30% of the target population.
A simple random sampling was used to choose the responders for each stratum. To gather information, both closed and open-ended questions was employed as primary data and a pilot test was conducted by 5 expert respondents in which they checked of any absence of unclear instructions, wrong phrasing of questions and short spaces to write -
responses. Collection of these data lead to testing of validity and reliability of the
research instrument and the collected data was analyzed using a software tool, SPSS for
descriptive analysis and rational analysis. Multiple regression model was adopted for inferential statistics. Tables and prose discussion was used to display the data collection’s findings and the validity of questionnaire was conducted by use of expert opinions while
reliability was determined by the use of Cronbach’s alpha test. The researcher personally administered the questionnaire to the respondents, drop and pick method later was
adopted in a case where respondents were busy or were unable to fill the questionnaire at that very moment. The results showed that fault detection significantly affects sensor management system performance (p0.05, $1=0.915). In addition, data mining
significantly affects sensor management system performance (§,=0.558; p0.05). Also,
data inference significantly affects the performance of the sensor management system
(P3=-0.052 p0.05) and pattern recognition significantly affects the performance of digital
enterprises (B4=0.425). It can be argued that the inclusion of artificial intelligence enables
fault detection to be completed quickly, increasing user efficiency. For effective data
analysts to make decisions in real time, data mining is crucial. Since pattern recognition
generates more value for a business, it is widely known that the sensor management
system's effectiveness partially relies on data inferencing. According to the survey,
Nairobi City Water and Sewerage Company should regularly train their employees on the
newest addition of artificial intelligence trends. To increase effectiveness, the sensor
management system should be integrated. Create new competitive strategies and increase
technology investments in pattern recognition.
Description
A Research Project Submitted to the School of Business, Economics and Tourism in Partial Fulfillment of the Requirements for the Award of the Degree of Master in Business Administration (Mba)-Management Information System (Mis) Of Kenyatta University June, 2023