Harnessing tacit knowledge to improve employee performance using AI Voice detection - a case of Kenya Railways Corporation
Date
2023
Authors
Maina, D. A.
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Publisher
Strathmore University
Abstract
Harnessing knowledge in organizations is important in improving employee performance. Explicit knowledge is widely shared because of its descriptive nature and easy documentation. Tacit knowledge is under-utilized due to its intangible nature. It is knowledge based on experience embedded in a person. Tacit knowledge is gained from the continuous practice of organizational tasks, which helps build valuable experience, intuition, innovation, and better ways of handling situations. Experienced employees in an organization have more tacit knowledge compared to younger employees. When faced with a challenging situation at the workplace, younger employees need to consult experienced employees on the best way to tackle; if there is no one to consult they would have to try out their way or make mistakes and learn from them. When these older employees leave the organization they leave with a wealth of tacit knowledge embedded in them. Due to the lack of an efficient channel to share and store tacit knowledge, Kenya Railways loses loads of information that could help smoothen business processes save time and money, and improve the performance of its employees. Transfer of Tacit knowledge is crucial to Kenya Railways Nairobi Central Workshop because of the unique nature of its operations. To fill these gaps, this study explored the use of AI Voice detection to harness tacit knowledge. AI voice detection system was used to capture tacit knowledge in audio form and stored it in the knowledge base. Upon a user’s request, the system base is queried to give the required feedback.
The development of the AI voice detection system adopted Agile Software Development Methodology. This methodology is an iterative and incremental approach to software development. The data collected targeted engineers and technicians in the Nairobi Central Workshop working on the repair of Locomotives and DMU. The data included sources of tacit knowledge, challenges in sharing it, areas that require the tacit knowledge, and users’ functional requirements of the bot. From the challenges identified tacit knowledge was gathered and fed into the bot. This information was used to constantly train the model to increase its efficiency in delivering tacit knowledge to users without human intervention. The data collected was classified into three broad categories: Training set, Test set, and Validation set. The training used supervised learning where the bot learned from labeled datasets.
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Citation
Maina, D. A. (2023). Harnessing tacit knowledge to improve employee performance using AI Voice detection—A case of Kenya Railways Corporation [Strathmore University]. http://hdl.handle.net/11071/15385