Human Resources AnalyticsA systematic Review from a Sustainable Management Approach

Referencias bibliográficas

• Aguado, D., Andrés, J. C., García-Izquierdo, A. L., & Rodríguez, J. (2019). LinkedIn “Big Four”: Job performance validation in the ICT sector. Journal of Work and Organizational Psychology, 35(2), 53-64. https://doi.org/10.5093/jwop2019a7
• Aguinis, H., Hill, N. S., & Bailey, J. R. (2021). Best practices in data collection and preparation: Recommendations for reviewers, editors, and authors. Organizational Research Methods, 24(4), 678-693. https://doi.org/10.1177/1094428119836485
• *Al-Ayed, S. I. (2019). The impact of strategic human resource management on organizational resilience: An empirical study on hospitals. Business: Theory and Practice, 20(2), 179-186. https://doi.org/10.3846/BTP.2019.17
• Andersen, M. K. (2017). Human capital analytics: The winding road. Journal of Organizational Effectiveness: People and Performance, 4(2), 133-136. https://doi.org/10.1108/JOEPP-03-2017-0024
• Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1-11. https://doi.org/10.1111/1748-8583.12090
• *Aral, S., Brynjolfsson, E., & Wu, L. (2012). Three-way complementarities: Performance pay, human resource analytics, and information technology. Management Science, 58(5), 913-931. https://doi.org/10.1287/mnsc.1110.1460
• Aust, I., Matthews, B., & Muller-Camen, M. (2020). Common good HRM: A paradigm shift in sustainable HRM? Human Resource Management Review, 30(3), 100705. https://doi.org/10.1016/j.hrmr.2019.100705
• Barrena-Martínez, J., López-Fernández, M., & Romero-Fernández, P. M. (2019). Towards a configuration of socially responsible human resource management policies and practices: Findings from an academic consensus. The International Journal of Human Resource Management, 30(17), 2544-2580. https://doi.org/10.1080/09585192.2017.1332669
• Bassi, L., Carpenter, R., & McMurrer, D. (Eds.). (2012). HR analytics handbook. Reed Business.
• Booth, A. (2006). Clear and present questions: Formulating questions for evidence based practice. Library Hi Tech, 24(3), 355-368. https://doi.org/10.1108/07378830610692127
• Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662-679. https://doi.org/10.1080/1369118X.2012.678878
• *Brandt, P. M., & Herzberg, P. Y. (2020). Is a cover letter still needed? Using LIWC to predict application success. International Journal of Selection and Assessment, 28(4), 417-429. https://doi.org/10.1111/ijsa.12299
• Calude, C. S., & Longo, G. (2017). The deluge of spurious correlations in big data. Foundations of Science, 22(3), 595-612. https://doi.org/10.1007/s10699-016-9489-4
• Cooke, A., Smith, D., & Booth, A. (2012). Beyond PICO: The SPIDER tool for qualitative evidence synthesis. Qualitative Health Research, 22(10), 1435-1443. https://doi.org/10.1177/1049732312452938
• Cohen, E., Taylor, S., & Muller- Camen, M. (2012). HRM’s role in corporate social and environmental sustainability. Society for Human Resource Management Foundation.
• Dattner, B., Chamorro-Premuzic, T., Buchband, R., & Schettler, L. (2019, April 25). The legal and ethical implications of using AI in hiring. Harvard Business Review. https://hbr.org/2019/04/the-legal-and-ethical-implications-of-using-ai-in-hiring
• Davenport, T. H., Harris, J., & Shapiro, J. (2010, October 1). Competing on talent analytics. Harvard Business Review. https://hbr.org/2010/10/competing-on-talent-analytics
• Davenport, T., & Ronanki, R. (2018 January-February). Artificial intelligence for the real world: Don’t start with moon shots. Harvard Business Review.
• Deloitte. (2018). Global Human Capital Trends. https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/human-capital/deloitte-uk-human-capital-trends-2018.pdf
• Dulebohn, J. H., & Johnson, R. D. (2013). Human resource metrics and decision support: A classification framework. Human Resource Management Review, 23(1), 71-83. https://doi.org/10.1016/j.hrmr.2012.06.005
• Edwards, M. R., Charlwood, A., Guenole, N., & Marler, J. (2022). HR analytics: An emerging field finding its place in the world alongside simmering ethical challenges. Human Resource Management Journal. Advance online publication. https://doi.org/10.1111/1748-8583.12435
• Fan, J., Han, F., & Liu, H. (2014). Challenges of big data analysis. National Science Review, 1(2), 293-314. https://doi.org/10.48550/arXiv.1308.1479
• Fernandez, V., & Gallardo-Gallardo, E. (2020). Tackling the HR digitalization challenge: Key factors and barriers to HR analytics adoption. Competitiveness Review: An International Business Journal, 31(1), 162-187. https://doi.org/10.1108/CR-12-2019-0163
• Fitz-Enz, J. (1984). How to measure human resource management. McGraw-Hill.
• *Frederiksen, A. (2017). Job satisfaction and employee turnover: A firm-level perspective. German Journal of Human Resource Management, 31(2), 132-161. https://doi.org/10.1177/2397002216683885
• García-Izquierdo, A. L., Aguado, D., & Ponsoda- Gil, V. (2019). New Insights on Technology and Assessment: Introduction to JWOP Special Issue. Journal of Work and Organizational Psychology, 35(2), 49-52. https://doi.org/10.5093/jwop2019a6
• García-Izquierdo, A. L., Aguinis, H., & Ramos-Villagrasa, P. J. (2010). Science–practice gap in e-recruitment. International Journal of Selection and Assessment, 18(4), 432-438. https://doi.org/10.1111/j.1468-2389.2010.00525.x
• García-Izquierdo, A. L., Moreno, B., & García-Izquierdo, M. (2010). Applying information theory to small groups assessment: Emotions and well-being at work. The Spanish Journal of Psychology, 13(1), 309-328. https://doi.org/10.1017/S1138741600003887
• García-Izquierdo, A. L., Ramos-Villagrasa, P. J., & Lubiano, M. A. (2020). Developing biodata for public manager selection purposes: A comparison between fuzzy logic and traditional methods. Journal of Work and Organizational Psychology, 36(3), 231-242. https://doi.org/10.5093/jwop2020a22
• *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
• Gonzalez, M. F., Capman, J. F., Oswald, F. L., Theys, E. R., & Tomczak, D. L. (2019). Where’s the IO?” Artificial intelligence and machine learning in talent management systems. Personnel Assessment and Decisions, 5(3), 33-44. https://doi.org/10.25035/pad.2019.03.005
• Guastello, S. J. (2013). Chaos, catastrophe, and human affairs: Applications of nonlinear dynamics to work, organizations, and social evolution. Psychology Press.
• Haider, S., Fatima, N., & Pablos-Heredero, C. D. (2020). A three-wave longitudinal study of moderated mediation between perceptions of politics and employee turnover intentions: The role of job anxiety and political skills. Journal of Work and Organizational Psychology, 36(1), 1-14. https://doi.org/10.5093/jwop2020a1
• Harley, B. (2015). The one best way? “Scientific” research on HRM and the threat to critical scholarship. Human Resource Management Journal, 25(4), 399-407. https://doi.org/10.1111/1748-8583.12082
• *Hazarika, I., Albeshr, M., Cho, B., & Jumde, A. (2019). Role of HR metrics in enhancing firm performance of selected uae airline companies. Academy of Strategic Management Journal, 18(6), 1-8.
• *Hicks, C. (2018). Predicting knowledge workers’ participation in voluntary learning with employee characteristics and online learning tools. Journal of Workplace Learning, 30(2), 78-88. https://doi.org/10.1108/JWL-04-2017-0030
• *Hoang, P., Mahoney, T., Javed, F., & McNair, M. (2018). Large-scale occupational skills normalization for online recruitment. AI Magazine, 39(1), 5-14. https://doi.org/10.1609/aimag.v39i1.2775
• Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision Support Systems, 64, 130-141. https://doi.org/10.1016/j.dss.2014.05.013
• *Jabir, B., Falih, N., & Rahmani, K. (2019). HR analytics a roadmap for decision making: Case study. Indonesian Journal of Electrical Engineering and Computer Science, 15(2), 979-990. https://doi.org/10.11591/ijeecs.v15.i2.pp979-990
• Jac, F., & Fitz-enz, J. (1984). How to measure human resources management. McGraw-Hill.
• Johnson, R. D., & Stone, D. L. (2019). Advantages and unintended consequences of using electronic human resource management (eHRM) processes. In R. N. Landers (Ed.), The Cambridge handbook of technology and employee behavior. Cambridge University Press.
• Johnson, R. D., Stone, D. L., & Lukaszewski, K. M. (2021). The future is now! Promise and pitfalls of artificial intelligence in human resource management. Manuscript submitted for publication. Department of Management, Information Systems, and Entrepreneurship. Washington State University, Pullman, WA.
• *Kalvakolanu, S., Madhavaiah, C., & Hanumantharao, S. (2019). Applying fuzzy logic to measure analytical competencies of HR professionals. Journal of Advanced Research in Dynamical and Control Systems, 11(6), 219-224.
• *Khan, S. A., & Tang, J. (2016). The paradox of human resource analytics: Being mindful of employees. Journal of General Management, 42(2), 57-66. https://doi.org/10.1177/030630701704200205
• King, K. G. (2016). Data analytics in human resources: A case study and critical review. Human Resource Development Review, 15(4), 487-495. https://doi.org/10.1177/1534484316675818
• Kitchin, R. (2014). The Data revolution: Big data, open data, data infrastructure, and their consequences. Sage.
• *Köchling, A., Riazy, S., Wehner, M. C., & Simbeck, K. (2020). Highly accurate, but still discriminatory: A fairness evaluation of algorithmic video analysis in the recruitment context. Business and Information Systems Engineering, 63, 39-54. https://doi.org/10.1007/s12599-020-00673-w
• *Koriat, N., & Gelbard, R. (2019). Knowledge sharing analytics: The case of IT workers. Journal of Computer Information Systems, 59(4), 308-318. https://doi.org/10.1080/08874417.2017.1360163
• *Kryscynski, D., Reeves, C., Stice-Lusvardi, R., Ulrich, M., & Russell, G. (2018). Analytical abilities and the performance of HR professionals. Human Resource Management, 57(3), 715-738. https://doi.org/10.1002/hrm.21854
• Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data & Society, 5(1), 1-16. https://doi.org/10.1177%2F2053951718756684
• Leong, C. W., Roohr, K., Ramanarayanan, V., Martin-Raugh, M. P., Kell, H., Ubale, R., Qian, Y., Mladineo, Z., & McCulla, L. (2019). To trust, or not to trust? A study of human bias in automated video interview assessments. Paper presented at the ICCV Worksop on Interpreting and Explaining Visual Artificial Intelligence Models, Seoul.
• Levenson, A. (2018). Using workforce analytics to improve strategy execution. Human Resource Management, 57(3), 685-700. https://doi.org/10.1002/hrm.21850
• Levenson, A., & Fink, A. (2017). Human capital analytics: Too much data and analysis, not enough models and business insights. Journal of Organizational Effectiveness, 4(2), 145-156. https://doi.org/10.1108/JOEPP-03-2017-0029
• Leventhal, G. S. (1980). What should be done with equity theory? In K. J. Gergen, M. S. Greenberg, & R. H. Willis (Eds.), Social exchange (pp. 27-55). Springer. https://doi.org/10.1007/978-1-4613-3087-5_2
• *Lismont, J., Vanthienen, J., Baesens, B., & Lemahieu, W. (2017). Defining analytics maturity indicators: A survey approach. International Journal of Information Management, 37(3), 114-124. https://doi.org/10.1016/j.ijinfomgt.2016.12.003
• *Luo, Z., Liu, L., Yin, J., Li, Y., & Wu, Z. (2019). Latent ability model: A generative probabilistic learning framework for workforce analytics. IEEE Transactions on Knowledge and Data Engineering, 31(5), 923-937. https://doi.org/10.1109/TKDE.2018.2848658
• Margherita, A. (2022). Human resources analytics: A systematization of research topics and directions for future research. Human Resource Management Review, 32(2), Article 100795. https://doi.org/10.1016/j.hrmr.2020.100795
• Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. International Journal of Human Resource Management, 28(1), 3-26. https://doi.org/10.1080/09585192.2016.1244699
• Mazzocchi, F. (2015). Could big data be the end of theory in science: A few remarks on the epistemology of data driven science. European Molecular Biology Organization (EMBO) Reports, 16(10), 1250-1255. https://doi.org/10.15252/embr.201541001
• *McCartney, S., Murphy, C., & Mccarthy, J. (2020). 21st century HR: A competency model for the emerging role of HR analysts. Personnel Review, 50(6), 1495-1513. https://doi.org/10.1108/PR-12-2019-0670
• McIver, D., Lengnick-Hall, M. L., & Lengnick-Hall, C. A. (2018). A strategic approach to workforce analytics: Integrating science and agility. Business Horizons, 61(3), 397-407. https://doi.org/10.1016/j.bushor.2018.01.005
• Minbaeva, D. B. (2018). Building credible human capital analytics for organizational competitive advantage. Human Resource Management, 57(3), 701-713. https://doi.org/10.1002/hrm.21848
• Mobley, W. H., Griffeth, R. W., Hand, H., & Meglino, B. M. (1979). Review and conceptual analysis of the employee turnover process. Psychological Bulletin, 86(3), 493-522. https://psycnet.apa.org/doi/10.1037/0033-2909.86.3.493
• Moscoso, S., & Salgado, J. F. (2021). Meta-analytic examination of a suppressor effect on subjective well-being and job performance relationship. Journal of Work and Organizational Psychology, 37(2), 119-131. https://doi.org/10.5093/jwop2021a13
• *Nandialath, A. M., David, E., Das, D., & Mohan, R. (2018). Modeling the determinants of turnover intentions: A Bayesian approach. Evidence-based HRM, 6(1), 2-24. https://doi.org/10.1108/EBHRM-10-2016-0025
• Navarro, J., Rueff-Lopes, R., & Laurenceau, J. (2022). Studying within-person changes in work motivation in the short and medium term: You will likely need more measurement points than you think! Journal of Work and Organizational Psychology, 38(1), 1-17. https://doi.org/10.5093/jwop2022a1
• *Necula, S.-C., & Strîmbei, C. (2019). People analytics of semantic web human resource résumés for sustainable talent acquisition. Sustainability, 11(13), Article 3520. https://doi.org/10.3390/su11133520
• *Newman, D. T., Fast, N. J., & Harmon, D. J. (2020). When eliminating bias isn’t fair: Algorithmic reductionism and procedural justice in human resource decisions. Organizational Behavior and Human Decision Processes, 160, 149-167. https://doi.org/10.1016/j.obhdp.2020.03.008
• Oh, I. S. (2020). Beyond meta-analysis: Secondary uses of meta-analytic data. Annual Review of Organizational Psychology and Organizational Behavior, 7, 125-153.
• Osoba, O. A., & Welser, W. I. (2017). An intelligence in our image: The risks of bias and errors in artificial intelligence. RAND Corporation. https://www.rand.org/pubs/research_reports/RR1744.html
• *Ozgur, E., Dexter, F., & Ledolter, J. (2017). Mixed effects logistic regression modeling of daily evaluations of nurse anesthetists’ work habits adjusting for leniency of the rating anesthesiologists. Perioperative Care and Operating Room Management, 6, 14-19. https://doi.org/10.1016/j.pcorm.2017.02.001
• Peeters, T., Paauwe, J., & Van De Voorde, K. (2020). People analytics effectiveness: Developing a framework. Journal of Organizational Effectiveness, 7(2), 203-219. https://doi.org/10.1108/JOEPP-04-2020-0071
• *Pessach, D., Singer, G., Avrahami, D., Chalutz Ben-Gal, H., Shmueli, E., & Ben-Gal, I. (2020). Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134, Article 113290. https://doi.org/10.1016/j.dss.2020.113290
• Pew Research, & Elon University (2012). The future of big data. Pew Research Center. https://www.pewresearch.org/internet/2012/07/20/the-future-of-big-data/
• PRISMA-P Group, Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., & Stewart, L. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1), Article 1. https://doi.org/10.1186/2046-4053-4-1
• Pujol-Cols, L., & Lazzaro-Salazar, M. (2021). Ten years of research on psychosocial risks, health, and performance in Latin America: A comprehensive systematic review and research agenda. Journal of Work and Organizational Psychology, 37(3), 187-202. https://doi.org/10.5093/jwop2021a18
• Ramos- Villagrasa, P. J., & García- Izquierdo, A. L. (2011). Técnicas de análisis de patrones caóticos: revisión de estudios empíricos en Psicología. Anales de Psicología/Annals of Psychology, 27(1), 239-248. https://revistas.um.es/analesps/article/view/113681
• Rasmussen, T., & Ulrich, D. (2015). Learning from practice: How HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236-242. https://doi.org/10.1016/j.orgdyn.2015.05.008
• Raub, M. (2018). Bots, bias and big data: artificial intelligence, algorithmic bias and disparate impact liability in hiring practices. Arkansas Law Review, 71(2), 529-570.
• *Rombaut, E., & Guerry, M.-A. (2018). Predicting voluntary turnover through human resources database analysis. Management Research Review, 41(1), 96-112. https://doi.org/10.1108/MRR-04-2017-0098
• *Rombaut, E., & Guerry, M.-A. (2020). The effectiveness of employee retention through an uplift modeling approach. International Journal of Manpower, 41(8), 1199-1220. https://doi.org/10.1108/IJM-04-2019-0184
• *Ryan, J. C. (2020). Retaining, resigning and firing: Bibliometrics as a people analytics tool for examining research performance outcomes and faculty turnover. Personnel Review, 50(5), 1316-1335. https://doi.org/10.1108/PR-12-2019-0676
• Ryan, A. M., & Derous, E. (2019). The unrealized potential of technology in selection assessment. Journal of Work and Organizational Psychology, 35(2), 85-92. https://doi.org/10.5093/jwop2019a10
• Rubio-Aparicio, M., Sánchez-Meca, J., Marín-Martínez, F., & López-López, J. A. (2018). Guidelines for reporting systematic reviews and meta-analyses. Anales de Psicología/Annals of Psychology, 34(2), 412-420. https://doi.org/10.6018/analesps.34.2.320131
• Salgado, J. F., Blanco, S., & Moscoso, S. (2019). Subjective Well-being and Job Performance: Testing of a Suppressor Effect. Journal of Work and Organizational Psychology, 35(2), 93-102. https://doi.org/10.5093/jwop2019a9
• Salgado, J. F., & Moscoso, S. (2022). Cross-cultural evidence of the relationship between subjective well-being and job performance: A meta-analysis. Journal of Work and Organizational Psychology, 38(1), 27-42. https://doi.org/10.5093/jwop2022a3
• Savitz, A. (2013). The triple bottom line: How today’s best-run companies are achieving economic, social and environmental success – and how you can too. John Wiley & Sons.
• Savitz, A. W., & Weber, K., (2006). The triple bottom line. Jossey-Bass.
• *Schiemann, W. A., Seibert, J. H., & Blankenship, M. H. (2018). Putting human capital analytics to work: Predicting and driving business success. Human Resource Management, 57(3), 795-807. https://doi.org/10.1002/hrm.21843
• *Simón, C., & Ferreiro, E. (2018). Workforce analytics: A case study of scholar-practitioner collaboration. Human Resource Management, 57(3), 781-793. https://doi.org/10.1002/hrm.21853
• Smith, G. (2018). The AI delusion. Oxford University Press.
• *Sri Harsha, B., Jithendra Varaprasad, A., & Pavan Sai Sujith, L. V. N. (2020). Early prediction of employee attrition. International Journal of Scientific and Technology Research, 9(3), 3374-3379.
• *Sripathi, K., & Madhavaiah, C. (2018). Are HR professionals ready to adopt HR analytics? A study on analytical skills of HR professionals. Journal of Advanced Research in Dynamical and Control Systems, 10(8 Special Issue), 303-308.
• 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
• Ulrich, D., & Dulebohn, J. H. (2015). Are we there yet? What’s next for HR? Human Resource Management Review, 25(2), 188-204. https://doi.org/10.1016/j.hrmr.2015.01.004
• Van Den Heuvel, S., & Bondarouk, T. (2016). The rise (and fall?) of HR analytics: The future application, value, structure, and system support. Journal of Organizational Effectiveness, 4(2), 127-148. https://doi.org/10.5465/ambpp.2016.10908abstract
• *Vargas, R., Yurova, Y. V., Ruppel, C. P., Tworoger, L. C., & Greenwood, R. (2018). Individual adoption of HR analytics: A fine grained view of the early stages leading to adoption. International Journal of Human Resource Management, 29(22), 3046-3067. https://doi.org/10.1080/09585192.2018.1446181
• *Wang, L., & Cotton, R. (2018). Beyond Moneyball to social capital inside and out: The value of differentiated workforce experience ties to performance. Human Resource Management, 57(3), 761-780. https://doi.org/10.1002/hrm.21856
• *Wang, N., & Katsamakas, E. (2019). A network data science approach to people analytics. Information Resources Management Journal, 32(2), 28-51. https://doi.org/10.4018/IRMJ.2019040102
• Weizenbaum, J. (1976). Computer power and human reason: From judgment to calculation. W. H. Freeman and Company.
• *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
• Zerilli, J., Knott, A., Maclaurin, J., & Gavaghan, C. (2019). Transparency in algorithmic and human decision making: Is there a double standard? Philosophy and Technology, 32(4), 661-683. https://doi.org/10.1007/s13347-018-0330-6
• *Zuo, Z., & Zhao, K. (2021). Understanding and predicting future research impact at different career stages—a social network perspective. Journal of the Association for Information Science and Technology, 72(4), 454-472. https://doi.org/10.1002/asi.24415