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**Thesis Overview: Designing a system for automated employee feedback collection**

Employees are a vital asset to any organization, and their feedback is crucial for the continuous improvement of work processes and overall organizational performance. Traditional methods of collecting employee feedback, such as surveys and face-to-face interviews, are often time-consuming and may not provide real-time insights into employee sentiments.

This thesis aims to address this challenge by proposing a system for automated employee feedback collection. The system will leverage technology to streamline the feedback collection process, making it more efficient and effective. Through the use of advanced data analytics and machine learning algorithms, the system will be able to analyze and interpret employee feedback in real-time, providing valuable insights to organizational leaders.

Chapter 1 provides an introduction to the topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. Chapter 2 presents a literature review on employee feedback collection, highlighting the importance of feedback in organizational performance and discussing existing methods and technologies for feedback collection.

Chapter 3 outlines the research methodology, including the research design, data collection methods, sampling techniques, and data analysis procedures. Chapter 4 presents the findings of the study, discussing the implementation of the automated feedback collection system and its impact on organizational performance. Finally, Chapter 5 concludes the thesis with a summary of key findings, implications for practice, and recommendations for future research.

Overall, this thesis aims to contribute to the growing body of knowledge on employee feedback collection and provide practical insights for organizations looking to improve their feedback processes. By leveraging technology to automate the feedback collection process, organizations can gain valuable insights into employee sentiments and drive continuous improvement in the workplace.

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