Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization / Mohd Norhisham Razali ... [et al.]

Employee working productivity prediction is vital for effective resource allocation, increased productivity, and upholding a high-performance culture in organizations. However, predicting employee productivity and understanding the root factors influencing working performance pose significant challenges. Traditional human resource management practices often lack data-driven insights, resulting in poor resource allocation and productivity enhancement strategies. To address these challenges, we developed a predictive model using machine learning techniques to determine employee productivity within organizations. Data from an academic institution were collected and pre-processed by encoding relevant features before applying various machine learning predictive models. Experimental results revealed that the linear regression model achieved the best performance in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE), with values of 0.4878 and 0.4682, respectively. The research findings also highlighted attributes that are imperative in predicting employee performance. Attributes such as "Department," "Actual Productive hours," "Internet Speed," and "COVID-19 adoption month" emerged as highly influential factors across multiple ranking techniques. The data visualization provided valuable insights into various aspects of employee performance, such as productivity trends before and after the pandemic, departmental performance, internet connectivity's impact on productivity, age-related trends, overtime distribution, and promotion rates. Organizations can use this data to inform workforce planning, address specific challenges in departments, and cultivate an inclusive work environment. By regularly assessing productivity data and implementing recommended strategies, organizations can enhance productivity, create a conducive work environment, and support employee well-being and growth. Future research can explore more advanced machine learning algorithms, incorporate time-series analysis for temporal dependencies, and expand data collection from diverse organizational settings to improve the generalizability of predictive models.