{"id":15471,"date":"2021-07-20T10:09:39","date_gmt":"2021-07-20T07:09:39","guid":{"rendered":"https:\/\/modecon.mnau.edu.ua\/?p=15471"},"modified":"2021-11-24T12:21:07","modified_gmt":"2021-11-24T10:21:07","slug":"vikoristannya-statistichnih-metodiv-u-hr-analitici","status":"publish","type":"post","link":"https:\/\/modecon.mnau.edu.ua\/en\/vikoristannya-statistichnih-metodiv-u-hr-analitici\/","title":{"rendered":"Prokopovych-Pavlyuk I., Marets O., Panchyshyn T. Statistical Methods in HR-Analytics"},"content":{"rendered":"

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JEL Classification<\/strong>: C12; C14; C18.<\/i>
\n<\/span><\/td>\n		DOI<\/b>: https:\/\/doi.org\/10.31521\/modecon.V27(2021)-18<\/a><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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Prokopovych-Pavlyuk Iryna<\/b>, Ph.D (Economics), Associate Professor, Associate Professor of Statistics Ivan Franko National University of L\u2019viv, Lviv, Ukraine<\/span><\/p>\n

ORCID ID:<\/strong> 0000-0002-8316-0127<\/span><\/a>
\ne-mail:<\/strong> iryna.prokopovych-pavlyuk@lnu.edu.ua<\/span><\/a><\/p>\n

Marets Oksana<\/b>, Ph.D (Economics), Associate Professor, Associate Professor of Statistics Ivan Franko National University of L\u2019viv, Lviv, Ukraine<\/span><\/p>\n

ORCID ID:<\/strong> 0000-0002-4044-7443<\/span><\/a><\/p>\n

Panchyshyn Taras<\/b>, Ph.D (Economics), Associate Professor, Associate Professor of Statistics Ivan Franko National University of L\u2019viv, Lviv, Ukraine<\/span><\/p>\n

ORCID ID:<\/strong> 0000-0003-3419-4635<\/span><\/a>
\ne-mail:<\/strong> taras.panchyshyn@lnu.edu.ua<\/span><\/a><\/p>\n

 <\/p>\n

Statistical Methods in HR-Analytics<\/h2>\n

 <\/p>\n

Abstract. Introduction.<\/strong> The article substantiates the feasibility of using machine learning for more effective management of company personnel, analyzes the factors that reduce or increase staff turnover. The directions of the research are the search for key indicators, the collection and thorough analysis of which will allow to identify the causes of the outflow of qualified personnel in a timely manner, as well as to detect the risks associated with the recruitment of unskilled personnel.<\/p>\n

Purpose.<\/strong> The aim of the study is to summarize effective HR metrics and successful practices of applying machine learning techniques in HR management of the company, justify the feasibility of their use in decision-making on staff motivation, and forecasting the outflow of employees to reduce staff turnover.<\/p>\n

Results.<\/strong> The results of the study allowed us to conclude that with the help of machine learning methods it is possible to make decisions on personnel management more quickly compared to traditional methods of HR departments, which is especially relevant for companies with a large number of employees. For effective HR management, finding more effective ways to motivate employees to work more productively, to career growth, ways to reduce the outflow, it is advisable to use methods of assessing relationships. Thus, the assessment of relationships revealed that seniority, monthly salary, the position held and the length of stay in it, the level of satisfaction with working conditions, the age of the employee are the key indicators that reduce the outflow of qualified personnel. Instead, overtime work, difficult working conditions, and marital status are key to employees’ desire to find a better job.<\/p>\n

Conclusions.<\/strong> The application of the HR metrics discussed in the article in combination with machine learning technologies will allow to more quickly assess the threats associated with a decrease in labor productivity and low employee motivation and to avoid the outflow of qualified personnel in advance. By processing millions of data units and analyzing information about staff, it is possible to reveal the true potential of the employee and by creating the appropriate working conditions to increase its productivity and, consequently, the growth of the company as a whole.<\/p>\n

Keywords: <\/b>HR analytics; HR metrics; personnel management of employees\u2019 outflow; machine learning methods; relationship analysis; intellectual recruitment.
\n<\/b><\/p>\n


\nReferences:<\/strong><\/p>\n

    \n
  1. 10 HR metrics that every CEO wants to see (2016). Retrieved from : <\/span>https:\/\/neohr.ru\/hr\/article_post\/10-hr-metrik-kotoryye-khochet-videt-kazhdyy-ceo<\/span> [in Ukrainian].<\/span><\/li>\n
  2. 6 key metrics in HR. (2019). Retrieved from : <\/span>https:\/\/l-a-b-a.com\/blog\/show\/481<\/span> [in Ukrainian].<\/span><\/li>\n
  3. A Beginner’s Guide to Machine Learning for HR Practitioners (2020). Retrieved from :\u00a0 <\/span>https:\/\/www.analyticsinhr.com\/blog\/machine-learning-hr<\/span> [in English].<\/span><\/li>\n
  4. Matyunina J. How Machine Learning is Changing HR Industry (2020). Retrieved from : <\/span>https:\/\/codetiburon.com\/machine-learning-changing-hr-industry<\/span> [in English].<\/span><\/li>\n
  5. HR-analytics using methods Data Analytics & Machine Learning, razbor kejsov (2017). Retrieved from : http:\/\/www.hrmedia.ru\/sites\/default\/files\/cis_dai_hr_analytics_deloitte.pdf [In Russian].<\/span><\/li>\n
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  7. Pritula, M.<\/span> The most important HR metrics (51) (2020). Retrieved from : <\/span>https:\/\/pritula.academy\/tpost\/vhlabu6itk-naibolee-vazhnie-hr-metriki-51<\/span> [in Russ.].<\/span><\/li>\n
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  10. IBM Artificial Intelligence can predict layoffs with 95% accuracy (2019). Retrieved from : <\/span>https:\/\/edwvb.blogspot.com\/2019\/04\/iskusstvennyj-intellekt-ibm-mozhet-prognozirovat-uvolnenie-rabotnikov-s-tochnostyu-95.html<\/span> [in Ukrainian].<\/span><\/li>\n<\/ol>\n

    [\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]Received: <\/strong>25\u00a0May 2021<\/span><\/strong><\/p>\n

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    Prokopovych-Pavlyuk I., Marets O., Panchyshyn T. (2021). Vikoristannya-statistichnih-metodiv-u-hr-analitici. Modern Economics<\/em>, 27(2021), 133-139. DOI: https:\/\/doi.org\/10.31521\/modecon.V27(2021)-18.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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    [vc_row][vc_column][vc_column_text] JEL Classification: C12; C14; C18. DOI: https:\/\/doi.org\/10.31521\/modecon.V27(2021)-18 [\/vc_column_text][vc_column_text] Prokopovych-Pavlyuk Iryna, Ph.D (Economics), Associate Professor, Associate Professor of Statistics Ivan Franko National University of L\u2019viv, Lviv, Ukraine ORCID ID: 0000-0002-8316-0127 e-mail: iryna.prokopovych-pavlyuk@lnu.edu.ua Marets Oksana, Ph.D (Economics), Associate Professor, Associate Professor of Statistics Ivan Franko National University of L\u2019viv, Lviv, Ukraine ORCID ID: 0000-0002-4044-7443 Panchyshyn Taras,
    Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":13475,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[153],"tags":[152],"_links":{"self":[{"href":"https:\/\/modecon.mnau.edu.ua\/en\/wp-json\/wp\/v2\/posts\/15471"}],"collection":[{"href":"https:\/\/modecon.mnau.edu.ua\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/modecon.mnau.edu.ua\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/modecon.mnau.edu.ua\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/modecon.mnau.edu.ua\/en\/wp-json\/wp\/v2\/comments?post=15471"}],"version-history":[{"count":0,"href":"https:\/\/modecon.mnau.edu.ua\/en\/wp-json\/wp\/v2\/posts\/15471\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/modecon.mnau.edu.ua\/en\/wp-json\/wp\/v2\/media\/13475"}],"wp:attachment":[{"href":"https:\/\/modecon.mnau.edu.ua\/en\/wp-json\/wp\/v2\/media?parent=15471"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/modecon.mnau.edu.ua\/en\/wp-json\/wp\/v2\/categories?post=15471"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/modecon.mnau.edu.ua\/en\/wp-json\/wp\/v2\/tags?post=15471"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}