Discovering Trends and Journeys in Knowledge-Based Human Resource Management: Big Data Smart Literature Review Based on Machine Learning Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24310%2F23%3A00011275" target="_blank" >RIV/46747885:24310/23:00011275 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10185055/keywords#keywords" target="_blank" >https://ieeexplore.ieee.org/document/10185055/keywords#keywords</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3296140" target="_blank" >10.1109/ACCESS.2023.3296140</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Discovering Trends and Journeys in Knowledge-Based Human Resource Management: Big Data Smart Literature Review Based on Machine Learning Approach

Popis výsledku v původním jazyce

The research interest of knowledge in human resource management (HRM) is significant. Bibliometric or systematic literature review studies capture the main areas and trends in the field of HRM. However, many HRM studies work only with a limited number of analyzed documents (systematic literature review) or go more in breadth than in depth of researched topics (bibliometric reviews). This smart literature review study is based on processing metadata to get results from 7,318 documents related to human resource management and knowledge, published between 1960 and 2021, retrieved from the Scopus database with research directions linked to knowledge and human resources and the current topic Covid-19 pandemic. Such a broad study has not yet been published in the field of knowledge and HRM. The study answers three research questions related to trends and innovative journeys in knowledge-based HRM. Descriptive and inferential statistics was used to capture basic trends in knowledge and HRM themes. Latent Dirichlet Allocation (LDA) was used for topic modelling with Gibbs sampling, which we use on a corpus of abstracts. Used method allowed us to identify latent topics which describe a more in-depth relationship between HRM and knowledge. We identified 13 topics related to HRM and knowledge research as the most relevant and showed directions and trends among the authors of HRM and knowledge management. The last part is devoted to the current topic of Covid-19, key areas identified in the literature and their impact on knowledge management from the perspective of HRM. During the analyzed period, the highest increase of research interest and research impact was recorded by two topics - Employee performance and Risk management. Our study maps the development of these and other topics and more deeply characterizes research in knowledge-based HRM during the pandemic. The results thus offer an up-to-date scientific map of this rapidly developing field and can be the basis for a broader discussion on the future direction of HRM.

Název v anglickém jazyce

Discovering Trends and Journeys in Knowledge-Based Human Resource Management: Big Data Smart Literature Review Based on Machine Learning Approach

Popis výsledku anglicky

The research interest of knowledge in human resource management (HRM) is significant. Bibliometric or systematic literature review studies capture the main areas and trends in the field of HRM. However, many HRM studies work only with a limited number of analyzed documents (systematic literature review) or go more in breadth than in depth of researched topics (bibliometric reviews). This smart literature review study is based on processing metadata to get results from 7,318 documents related to human resource management and knowledge, published between 1960 and 2021, retrieved from the Scopus database with research directions linked to knowledge and human resources and the current topic Covid-19 pandemic. Such a broad study has not yet been published in the field of knowledge and HRM. The study answers three research questions related to trends and innovative journeys in knowledge-based HRM. Descriptive and inferential statistics was used to capture basic trends in knowledge and HRM themes. Latent Dirichlet Allocation (LDA) was used for topic modelling with Gibbs sampling, which we use on a corpus of abstracts. Used method allowed us to identify latent topics which describe a more in-depth relationship between HRM and knowledge. We identified 13 topics related to HRM and knowledge research as the most relevant and showed directions and trends among the authors of HRM and knowledge management. The last part is devoted to the current topic of Covid-19, key areas identified in the literature and their impact on knowledge management from the perspective of HRM. During the analyzed period, the highest increase of research interest and research impact was recorded by two topics - Employee performance and Risk management. Our study maps the development of these and other topics and more deeply characterizes research in knowledge-based HRM during the pandemic. The results thus offer an up-to-date scientific map of this rapidly developing field and can be the basis for a broader discussion on the future direction of HRM.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

—

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

95567-95583

Kód UT WoS článku

001064484000001

EID výsledku v databázi Scopus

2-s2.0-85165252766