People analytics na avaliação por pares de competências dos colaboradores no contexto empresarial: um mapeamento sistemático da literatura

André Zanette; Avanilde Kemczinski

doi:10.5380/atoz.v15.97816

Authors

André Zanette Universidade do Estado de Santa Catariana - UDESC https://orcid.org/0009-0007-0946-6623
Avanilde Kemczinski Universidade do Estado de Santa Catarina https://orcid.org/0000-0001-7671-5457

DOI:

https://doi.org/10.5380/atoz.v15.97816

Keywords:

People Analytics, Avaliação de Competências, Mapeamento Sistemático da Literatura

Abstract

Introduction: Identifying and allocating resources in software development projects can be a complex task for the project manager, especially in large companies. The use of People Analytics techniques to collect, store and make available data on individuals' competencies can save time in this process. The objective of this article was to perform a systematic mapping of the literature with a view to systematizing information related to human resource competencies for use in planning software projects and developing efficient teams, using individual data for selection and allocation of employees in software projects. Method: The methodology used was Systematic Literature Mapping (SLM). Results: The articles analyzed used PA techniques to represent feelings, promote, retain and predict burnout, stress and turnover of human resources by generating insights for human resource management. Conclusion: This SLM provides a mapping of the PA techniques that were used in primary articles and to which problems they were applied as well as their contributions to improving human resource management.

Author Biographies

André Zanette, Universidade do Estado de Santa Catariana - UDESC

Possui graduação pela Universidade do Planalto Catarinense (2005). Tem experiência na área de Ciência da Computação, com ênfase em Sistemas de Computação

Atualmente cursa o Programa de Mestrado em Computação Aplicada na Universidade do Estado de Santa Catarina - UDESC.

Avanilde Kemczinski, Universidade do Estado de Santa Catarina

Professora Associada do Departamento de Ciência da Computação da Universidade do Estado de Santa Catarina (UDESC). Possui doutorado (2005) e mestrado (2000) em Engenharia de Produção e Sistemas pela Universidade Federal de Santa Catarina (UFSC), graduação em Terapia Ocupacional pela Associação Catarinense de Ensino - Faculdade de Ciências da Saúde de Joinville (1992) e pós-doutorado em Educação pela Universidade de Málaga/Espanha (2016). Professora do Programa de Pós-graduação em Ensino de Ciências, Matemática e Tecnologia (PPGECMT), também atua no Programa de Pós-graduação em Computação Aplicada (PPGCA), e nos cursos de graduação Bacharelado em Ciência da Computação (BCC) e Tecnologia em Análise e Desenvolvimento de Sistemas (TADS). É professora pesquisadora do Grupo de Pesquisa em Informática na Educação - GPIE pelo CNPQ/UDESC. Tem interesse nas áreas correlatas à Informática na Educação, notadamente tecnologia educacional, objetos de aprendizagem, aprendizagem colaborativa (CSCL), interface humano-computador, qualidade no processo e no produto de software educacional, jogos educacionais e metodologias e/ou modelos de ensino-aprendizagem.

References

Buchinger, D., Siqueira Cavalcanti, G. A. S., & Silva Hounsell, M. da S. (2014). Mecanismos de busca acadêmica: Uma análise quantitativa. Revista Brasileira de Computação Aplicada, 6(1), 108–120. https://doi.org/10.5335/rbca.2014.3497

Cho, W., Choi, S., & Choi, H. (2023). Human Resources Analytics for Public Personnel

Management: Concepts, Cases, and Caveats. Administrative Sciences, 13(2), 41.

https://doi.org/10.3390/admsci13020041

Gelbard, R., Ramon-Gonen, R., Carmeli, A., Bittmann, R. M., & Talyansky, R. (2018). Sentiment analysis in organizational work: Towards an ontology of people analytics. Expert Systems, 35(5), Article e12289. https://doi.org/10.1111/exsy.12289

Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U., & Redzepi, A. (2022). The dark sides of people analytics: Reviewing the perils for organisations and employees. European Journal of Information Systems, 31(3), 410–435. https://doi.org/10.1080/0960085X.2021.1927213

Gryncewicz, W., Zygała, R., & Pilch, A. (2023). AI in HRM: Case study analysis. Preliminary research. Procedia Computer Science, 225, 2351–2360. https://doi.org/10.1016/j.procs.2023.10.226

Isson, J. P., & Harriott, J. S. (2016). People analytics in the era of big data: Changing the way you attract, acquire, develop, and retain talent. John Wiley & Sons.

Kitchenham, B. (2004). Procedures for performing systematic reviews (Technical Report TR/SE-0401). Keele University. https://www.inf.ufsc.br/~aldo.vw/kitchenham.pdf

Klöpper, M., & Köhne, S. (2022). People analytics and the promise of the good life: A critical transformative perspective. In Wirtschaftsinformatik 2022 Proceedings (Vol. 4, pp. 1–17). https://aisel.aisnet.org/wi2022/societal_impact/4

Madanchian, M., Taherdoost, H., & Mohamed, N. (2023). AI-based human resource management tools and techniques: A systematic literature review. Procedia Computer Science, 229, 367–377. https://doi.org/10.1016/j.procs.2023.12.236

Maurya, A., Akoglu, L., Krishnan, R., & Bay, D. (2018). A lens into employee peer reviews via sentiment-aspect modeling. In Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 670–677). IEEE. https://doi.org/10.1109/ASONAM.2018.8508512

Minocha, A., Gupta, P., & Gopal, S. (2023). Application of intelligent computing (IC) in human resources (HR) analytics: Towards better tomorrow. In Proceedings of the 2023 4th International Conference on Intelligent Engineering and Management (ICIEM) (pp. 1–4). IEEE. https://doi.org/10.1109/ICIEM59379.2023.10166838

Pandey, S., & Mehta, J. (2022). HRPA: Human resource prediction analytics. In Proceedings of the 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) (pp. 721–726). IEEE. https://doi.org/10.1109/ICICICT54557.2022.9917929

Papoutsoglou, M., Mittas, N., & Angelis, L. (2017). Mining people analytics from StackOverflow job advertisements. In Proceedings of the 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA) (pp. 108–115). IEEE. https://doi.org/10.1109/SEAA.2017.50

Saxena, M., Bagga, T., & Gupta, S. (2021). HR during COVID-19 era: Study on recent HR transformations through technological tools and trends. In Proceedings of the 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 110–113). IEEE. https://doi.org/10.1109/Confluence51648.2021.9377177

Sezer, İ. İ. C., & Sorkun, M. M. F. (2024). An investigation on logistics firms’ human resources qualifications in transition to Industry 4.0: An insight from Türkiye. Procedia Computer Science, 232, 2580–2587. https://doi.org/10.1016/j.procs.2024.02.076

Soares, V. G., Alcazar, J. J. P., & Ludena, M. E. (2022). Analytical approaches in human resources: A systematic review. JISTEM - Journal of Information Systems and Technology Management, 19, e202219014. https://doi.org/10.4301/S1807-17752022190014

Tang, A., Lu, T., Lynch, Z., Schaer, O., & Adams, S. (2020). Enhancing promotion decisions using classification and network-based methods. In Proceedings of the 2020 Systems and Information Engineering Design Symposium (SIEDS) (pp. 1–6). IEEE. https://doi.org/10.1109/SIEDS49339.2020.9106685

Tursunbayeva, A., Di Lauro, S., & Pagliari, C. (2018). People analytics: A scoping review of conceptual boundaries and value propositions. International Journal of Information Management, 43, 224–247. https://doi.org/10.1016/j.ijinfomgt.2018.08.002

Wei, D., Varshney, K. R., & Wagman, M. (2015). OptiGrow: People analytics for job transfers. In Proceedings of the 2015 IEEE International Congress on Big Data (pp. 82–89). IEEE. https://doi.org/10.1109/BigDataCongress.2015.84

Xu, H., Yu, Z., Yang, J., Xiong, H., & Zhu, H. (2019). Dynamic talent flow analysis with deep sequence prediction modeling. IEEE Transactions on Knowledge and Data Engineering, 31(10), 1926–1939. https://doi.org/10.1109/TKDE.2018.2873341

Yahia, N. B., Hlel, J., & Colomo-Palacios, R. (2021). From big data to deep data to support people analytics for employee attrition prediction. IEEE Access, 9, 60447–60458. https://doi.org/10.1109/ACCESS.2021.3074550

Zander, P., & Zieglmeier, V. (2023). Data owner benefit-driven design of people analytics. Proceedings of the ACM on Human-Computer Interaction, 7(EICS), Article 173, 1–38. https://doi.org/10.1145/3593225