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Abstract(s)
A retenção de pessoas nas organizações é uma área crucial para a Gestão de Recursos Humanos, estimulando os níveis de produtividade e desempenho, diminuição dos níveis de rotatividade e diminuição dos custos com a saída de colaboradores, gerando maior rentabilidade.
O People Analytics surge no sentido de aumentar a contribuição dos Recursos Humanos (RH), nomeadamente, de auxiliar as organizações a tomar decisões críticas informadas em torno da aquisição, desenvolvimento e retenção de talento.
Com o objetivo de mapear as contribuições da ciência para o People Analytics na retenção de talento, este estudo apresenta uma revisão sistemática da literatura sobre o tema. Na metodologia utilizada, implementaram-se critérios de seleção que direcionaram a recolha da amostra. A amostra integra 16 documentos científicos escritos por 31 autores, publicados entre 2008 e 2020. Os resultados obtidos inferem sobre as vantagens, o impacto, os desafios e limitações e as Técnicas de Data Mining mais utilizadas no People Analytics na retenção.
Este estudo indica que apesar da crescente produção científica do People Analytics, esta ainda é escassa quando aplicada ao domínio da retenção. Identificam-se as seguintes vantagens do People Analytics na retenção: melhorar as práticas de gestão de talento; aumentar o desenvolvimento da tecnologia; a diminuição da taxa de rotatividade e o aumento a competitividade. O estudo indica um impacto positivo da implementação do People Analytics na retenção e aponta como principais desafios, as limitações de sistemas de GRH, a indisponibilidade de dados e a necessidade de formação dos profissionais de RH para aquisição de competências de análise de dados. Quanto às técnicas de data Mining mais utilizadas na retenção, foram identificadas as redes neuronais e as árvores de decisão. Por fim, são discutidas as principais implicações e algumas limitações do estudo, bem como são apresentadas sugestões para pesquisas futuras.
Nowadays, effectively managing human capital is fundamental for organizations, which implies the creation of strategies for organizations to gain competitive advantage. People retention in organizations is a crucial area for Human Resources Management, stimulating productivity and performance levels, reducing turnover levels and reducing costs with the departure of employees, generating greater profitability. People Analytics (PA) arises to increase its contribution to HR and has the potential to help organizations make informed critical decisions around the acquisition, development and retention of people. With the objective of mapping the scientific contributions towards People Analytics in people retention, this study presents a systematic literature review on this subject. In the methodology applied, selection criteria were implemented that guided the collection of the sample. The sample includes 16 scientific documents written by 31 authors, published between 2008 and 2020. The results obtained infer about: Advantages of People Analytics in retention; Impact of People Analytics on retention; Challenges and Limitations of People Analytics in retention and Data Mining Techniques most used in retention. This study indicates that despite the increasing scientific production of People Analytics, this is still scarce when applied to retention. The following advantages of People Analytics in retention are identified: improving talent management practices; increase the development of technology; reduce the attrition rate and increase competitiveness. The study indicates a positive impact of the implementation of People Analytics in retention and points out as main challenges, the limitations of HRM systems, the unavailability of data and the need to train HR professionals to acquire data analysis skills. As for the data mining techniques most used in retention, neuronal networks and decision trees were identified. Finally, the main implications and some limitations of the study are discussed, as well as suggestions for future research.
Nowadays, effectively managing human capital is fundamental for organizations, which implies the creation of strategies for organizations to gain competitive advantage. People retention in organizations is a crucial area for Human Resources Management, stimulating productivity and performance levels, reducing turnover levels and reducing costs with the departure of employees, generating greater profitability. People Analytics (PA) arises to increase its contribution to HR and has the potential to help organizations make informed critical decisions around the acquisition, development and retention of people. With the objective of mapping the scientific contributions towards People Analytics in people retention, this study presents a systematic literature review on this subject. In the methodology applied, selection criteria were implemented that guided the collection of the sample. The sample includes 16 scientific documents written by 31 authors, published between 2008 and 2020. The results obtained infer about: Advantages of People Analytics in retention; Impact of People Analytics on retention; Challenges and Limitations of People Analytics in retention and Data Mining Techniques most used in retention. This study indicates that despite the increasing scientific production of People Analytics, this is still scarce when applied to retention. The following advantages of People Analytics in retention are identified: improving talent management practices; increase the development of technology; reduce the attrition rate and increase competitiveness. The study indicates a positive impact of the implementation of People Analytics in retention and points out as main challenges, the limitations of HRM systems, the unavailability of data and the need to train HR professionals to acquire data analysis skills. As for the data mining techniques most used in retention, neuronal networks and decision trees were identified. Finally, the main implications and some limitations of the study are discussed, as well as suggestions for future research.
Description
Keywords
People analytics Big data Retenção Data mining Retention