The Impact of Performance Appraisal and Appropriate Reward on Employees' Performance

<p>The goals of the organization are yardstick for measuring organization performance; therefore, HRM practitioners adopt the use of performance appraisals in the evaluation of their employee performance in conformity with the organization's set goals since employee performance is fundamental to the organization's success. The application of human inclination to judge without a structured appraisal system does not guarantee the decision made is accurate, lawful, and defensible. Despite all the research that has been conducted in the area of staff appraisal, there has been little done on the impact of performance appraisal on employee performance as it relates to staff reward in tangent with the appraisal exercise. Therefore, this research is aimed at determining if a relationship exists between an employee's performance appraisal and their productivity and also determine the impact of a well-structured appraisal process on employee satisfaction, with a view to establishing the impact of staff appraisal and appropriate reward on staff motivation. The research uses questionnaire to obtain information from the sampled respondents. Furthermore, the researchers reviewed published articles on the subject. The research instrument was subjected to descriptive and inferential statistics. The research hypotheses were tested using regression analysis tools. Regression is used to test the relationship that exists between a dependent variable and two or more independent variables. The research established that the significance of a well-structured appraisal system cannot be overemphasized in an organization. Hence, the university management should ensure appropriate rewards are put in place in conformity with the appraisal result, with a view to creating a formidable workforce, enhancing cordiality between staff and students, and further enhancing the productivity of the workers. <br> </p&gt

From Twitter to Aso-Rock: A sentiment analysis framework for understanding Nigeria 2023 presidential election

Introduction: Social media platforms such as Facebook, LinkedIn, Twitter, among others have been used as tools for staging protests, opinion polls, campaign strategy, medium of agitation and a place of interest expression especially during elections. Aim: In this work, a Natural Language Processing framework is designed to understand Nigeria 2023 presidential election based on public opinion using Twitter dataset. Methods: Two million tweets with 18 features were collected from Twitter containing public and personal tweets of the three top contestants – Atiku Abubakar, Peter Obi and Bola Tinubu – in the forthcoming 2023 Presidential election. Sentiment analysis was performed on the preprocessed dataset using three machine learning models namely: Long Short-Term Memory (LSTM) Recurrent Neural Network, Bidirectional Encoder Representations from Transformers (BERT) and Linear Support Vector Classifier (LSVC) models. This study spanned ten weeks starting from the candidates’ declaration of intent to run for Presidency. Results: The sentiment models gave an accuracy, precision, recall, AUC and f-measure of 88%, 82.7%, 87.2%, 87.6% and 82.9% respectively for LSTM; 94%, 88.5%, 92.5%, 94.7% and 91.7% respectively for BERT and 73%, 81.4%, 76.4%, 81.2% and 79.2% respectively for LSVC. Result also showed that Peter Obi has the highest total impressions the highest positive sentiments, Tinubu has the highest network of active friends while Atiku has the highest number of followers. Conclusion: Sentiment analysis and other Natural Language Understanding tasks can aid in the understanding of the social media space in terms of public opinion mining. We conclude that opinion mining from Twitter can form a general basis for generating insights for election as well as modeling election outcomes