[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125316-en":3,"doc-seo-125316-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125316,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Exploring Employee Working Productivity - Initial Insights from Machine Learning Predictive Analytics and Visualization","Employee working productivity prediction is essential for resource allocation, productivity improvement, and maintaining a high-performance culture in organizations. Yet forecasting productivity and identifying root drivers of employee performance remains challenging, as traditional HR practices often lack data-driven understanding and lead to ineffective strategies. A predictive machine learning model was developed using institution-collected data with encoded features and multiple predictive approaches. Linear regression delivered the strongest results by MAE and MSE, while key influential attributes and visualization-based correlations supported actionable workforce insights.","Exploring Employee Working Productivity: Initial Insights from Machine Learning Predictive Analytics and Visualization  \nMohd Norhisham Razali1*, Norizuandi Ibrahim2, Rozita Hanapi3, Norfarahzila Mohd Zamri4, Syaifulnizam  \nAbdul Manaf5  \n1,3,4Faculty of Business and Management, Universiti Teknologi Mara, Cawangan Sarawak, Kampus Samarahan,  \nKota Samarahan, Sarawak Malaysia  \n1Creative Advanced Machine Intelligence (CAMI) Research Centre, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia  \n2College of Computing, Informatics and Mathematics, Universiti Teknologi Mara, Cawangan Sarawak, Kampus  \nSamarahan, Kota Samarahan, Sarawak Malaysia  \n5Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor Malaysia  \nCorresponding author: * [hishamrazali@uitm.edu.my](hishamrazali@uitm.edu.my)  \nReceived Date: 6 June 2023  \nAccepted Date: 5 July 2023  \nRevised Date: 10 August 2023  \nPublished Date: 1 September 2023  \nHIGHLIGHTS  \n• Developed a predictive analytical model using machine learning to explore and predict employee working productivity in an organization.  \n• Employed ranker algorithms to identify and assess the significance of attributes influencing employee working performance in the organizational context.  \n• Utilized data visualization techniques for descriptive analytics, providing initial insights and correlations among various attributes of employee working patterns.  \nABSTRACT  \nEmployee 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,  \novertime 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 ofpredictive models.  \nKeywords: Employee Productivity; machine learning; prediction; visualization  \nINTRODUCTION  \nRising costs like meeting minimum wages influence employee management. When workers did not match company culture, they are often encouraged to leave voluntarily. Hence, effective performance evaluation is vital for productivity and res","cbCaidJZYAnC1hlz","https://ap.wps.com/l/cbCaidJZYAnC1hlz","pdf",690717,1,11,"English","en",105,"# Highlights\n# Abstract\n# Keywords\n# Introduction\n## Motivation and challenges in HR evaluation\n## Machine learning and data visualization rationale\n## Research objective and approach","[{\"question\":\"Why is employee working productivity prediction important in organizations?\",\"answer\":\"It supports effective resource allocation, helps improve productivity, and strengthens a high-performance culture. It also enables better employment-related decision making.\"},{\"question\":\"How was the predictive model developed in the research?\",\"answer\":\"Data from an academic institution were collected, pre-processed with feature encoding, and used to train multiple machine learning predictive models. Ranker algorithms were then applied to evaluate influential attributes.\"},{\"question\":\"Which factors were identified as highly influential for employee productivity?\",\"answer\":\"Attributes such as Department, Actual productive hours, Internet speed, and COVID-19 adoption month were reported as highly influential across multiple ranking techniques. Visualization results also highlighted trends and correlations around productivity before and after the pandemic.\"}]","Exploring Employee Working Productivity - Initial Insights from Machine Learning Predictive Analytics and Visualization | PDF",1785898136,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"exploring-employee-working-productivity-initial-insights-from-machine-learning-predictive-analytics-and-visualization","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/exploring-employee-working-productivity-initial-insights-from-machine-learning-predictive-analytics-and-visualization/125316/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is employee working productivity prediction important in organizations?","Question",{"text":75,"@type":76},"It supports effective resource allocation, helps improve productivity, and strengthens a high-performance culture. It also enables better employment-related decision making.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the predictive model developed in the research?",{"text":80,"@type":76},"Data from an academic institution were collected, pre-processed with feature encoding, and used to train multiple machine learning predictive models. Ranker algorithms were then applied to evaluate influential attributes.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors were identified as highly influential for employee productivity?",{"text":84,"@type":76},"Attributes such as Department, Actual productive hours, Internet speed, and COVID-19 adoption month were reported as highly influential across multiple ranking techniques. 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