Advanced Search
Article Contents
Article Contents

Human resources optimization with MARS and ANN: Innovation geolocation model for generation Z

*ORCID IDs: Graczyk-Kucharska [0000-0002-4241-8216]; Robert Olszewski [0000-0003-1697- 9367]; Marek Golinski [0000-0002-0170-2835]; Ma lgorzata Spycha la [0000-0003-1471-5536]; Maciej Szafranski [0000-0002-4281-9845]; Gerhard Wilhelm Weber [0000-0003-0849-7771].

Abstract Full Text(HTML) Figure(4) / Table(1) Related Papers Cited by
  • Human resources (HR) have a key impact on the creation and implementation of modern products, solutions and concepts. Relatively new and rarely undertaken research challenge in enterprise is optimization of HR in the context of their location and requirements for working conditions. A great challenge here is the transparency and reliability of the collected data. In the article, we present a modern approach to knowledge extraction based on Artificial Intelligence (AI) and Multivariate Adaptive Regression Splines optimizing the availability of HR with a high innovation rate, taking into account their availability time and location. This study was conducted on a group of 5095 young people from the Z generation. A total of 11 variables were analyzed in the context of innovation and presented in this article. The effect of research using machine learning methods is the analysis of the characteristics of generation Z representatives, whose desire is to work in innovative companies. Research results indicate that some regions offer candidates with a higher level and commitment to innovation, and thus make HR more available for the development of innovative products. Chosen models designed by using AI and Operational Research Analytics were presented in the graphic visualization, which is a novelty in the presentation of similar issues in relation to HR.


    \begin{equation} \\ \end{equation}
  • 加载中
  • Figure 1.  Distance zones from large cities (red circles – 30 km, pink circles – 60 km)

    Figure 2.  Model MLP for students graduating in 2018-2022

    Figure 3.  Model MLP for students graduating in 2018-2019

    Figure 4.  Model MLP for students graduating in 2020-2022

    Table 1.  Dependent variable and independent variables selected for the innovation geolocation model

    Willingness of representatives of the Z generation to work in an innovative company $Y$
    Gender: $X_1$
    Year of graduation: $X_2$
    Place of residence (close, average, far away from a large city): $X_3$
    Unemployment in the region: $X_4$
    Balance of migration in the region: $X_5$
    Expected salary (high, medium, low): $X_6$
    Willingness to continue university education: $X_7$
    Willingness to leave the place of residence: $X_8$
    Individual work vs. group work: $X_9$
    Remote work vs. traditional work in the company: $X_{10}$
    Work with passion vs. just work: $X_{11}$
     | Show Table
    DownLoad: CSV
  • [1] Association for the Advancement of Artificial Intelligence, available at: http://www.aaai.org/.
    [2] COM/2016/0361 Annexes to the Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions Delivering the Single market Agenda for Jobs, Growth and Investment, available at: https://secure.ipex.eu.
    [3] A. AbbasA. AvdicP. XiaobaoM. M. Hasan and Ming W, University-government collaboration for the generation and commercialization of new knowledge for use in industry, Journal of Innovation and Knowledge, 4 (2018), 23-31. 
    [4] T. Bondarouk and C. Brewster, Conceptualising the future of HRM and technology research, The International Journal of Human Resource Management, 27 (2016), 2652-2671.  doi: 10.1080/09585192.2016.1232296.
    [5] M. J. BeynonP. Jones and D. Pickernell, The role of entrepreneurship, innovation, and urbanity-diversity on growth, unemployment, and income: US state-level evidence and an fsQCA elucidation, Journal of Business Research, 101 (2019), 675-687. 
    [6] S. Breschi, F. Lissoni and C. N. Temgoua, Migration and innovation: A survey of recent studies, In Handbook on the Geographies of Innovation, (eds. R. Shearmur, C. Carrincazeaux, D. Doloreux), Edward Elgar Publishing, Cheltenham, UK, Northampton, USA, (2016), 382–398.
    [7] T. S. Calvard and D. Jeske, Developing human resource data risk management in the age of big data, International Journal of Information Management, 43 (2018), 159-164. 
    [8] J. CanedoG. B. GraenM. Grace and R. D. Johnson, Navigating the new workplace: Technology, millennials, and accelerating HR innovation, Transactions on Human-Computer Interaction, 9 (2017), 243-260. 
    [9] C. F. Chien and L. F. Chen, Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry, Expert Systems with Applications, 34 (2008), 280-290. 
    [10] P. CelikM. StormeA. Davila and N. Myszkowski, Work-related curiosity positively predicts worker innovation, Journal of Management Development, 35 (2016), 1184-1194. 
    [11] D. F. CampbellE. G. Carayannis and S. S. Rehman, Quadruple helix structures of quality of democracy in innovation systems: The USA, OECD countries, and EU member countries in global comparison, Journal of the Knowledge Economy, 6 (2015), 467-493.  doi: 10.1007/s13132-015-0246-7.
    [12] A. ÇevikG. W. WeberB. M. Eyüboğlu and K. K. Oğuz, Voxel-MARS: A method for early detection of Alzheimer's disease by classification of structural brain MRI, Ann. Oper. Res., 258 (2017), 31-57.  doi: 10.1007/s10479-017-2405-7.
    [13] R. S. Dubey and V. Tiwari, Operationalisation of soft skill attributes and determining the existing gap in novice ICT professionals, International Journal of Information Management, 50 (2020), 375-386. 
    [14] S. DoltsinisP. Ferreira and N. Lohse, A symbiotic human-machine learning approach for production ramp-up, IEEE Transactions on Human-Machine Systems, 48 (2017), 229-240. 
    [15] G. Feola and A. Butt, The diffusion of grassroots innovations for sustainability in italy and g reat b ritain: An exploratory spatial data analysis, The Geographical Journal, 183 (2017), 16-33. 
    [16] C. Fernandes, J. J. Ferreira, P. M. Veiga and M. Peris-Ortiz, Knowledge, innovation and sustainability: Past literature and future trends, in Knowledge, Innovation and Sustainable Development in Organizations, (2019), 11–22.
    [17] J. H. Friedman, Multivariate adaptive regression splines, Ann. Statist., (1991), 1–141. doi: 10.1214/aos/1176347963.
    [18] I. GoodfellowY. Bengio and A. Courville, Machine learning basics, Deep Learning, 1 (2016), 98-164. 
    [19] R. GarudP. Tuertscher and A. H. Van de Ven, Perspectives on innovation processes, Academy of Management Annals, 7 (2013), 775-819. 
    [20] B. Hmoud and V. Laszlo, Will artificial intelligence take over human resources recruitment and selection?, Network Intelligence Studies, 13 (2019), 21-30. 
    [21] J. H. HardyE. A. Day and W. Arthur, Exploration-exploitation tradeoffs and information-knowledge gaps in self-regulated learning: Implications for learner-controlled training and development, Human Resource Management Review, 29 (2019), 196-217.  doi: 10.1016/j.hrmr.2018.07.004.
    [22] L. C. HuangP. WuR. J. Kuo and H. C. Huang, A neural network modelling on human resource talent selection, International Journal of Human Resources Development and Management, 1 (2001), 206-219. 
    [23] Q. Jia, Y. Guo, R. Li, Y. Li and Y. Chen, A conceptual artificial intelligence application framework in human resource management, in Proceedings of The 18th of The International Conference on Electronic Business, (ICEB), Guilin, China, (2018), 106–114.
    [24] S. KuterG. W. WeberZ. Akyürek and A. Özmen, Inversion of top of atmospheric reflectance values by conic multivariate adaptive regression splines, Inverse Problems in Science and Engineering, 23 (2015), 651-669.  doi: 10.1080/17415977.2014.933828.
    [25] A. KiantoJ. Sáenz and N. Aramburu, Knowledge-based human resource management practices, intellectual capital and innovation, Journal of Business Research, 81 (2017), 11-20.  doi: 10.1016/j.jbusres.2017.07.018.
    [26] R. K. R. Kummitha, Why distance matters: The relatedness between technology development and its appropriation in smart cities, Technological Forecasting and Social Change, 9 (2020), 255-287. 
    [27] E. KropatR. A. Tikidji-Hamburyan and G.-W. Weber, Operations research in neuroscience, Ann. Oper. Res., 258 (2017), 1-4.  doi: 10.1007/s10479-017-2633-x.
    [28] E. KropatM. Türkay and G.-W. Weber, Fuzzy analytics and stochastic methods in neurosciences, IEEE Transactions on Fuzzy Systems, 28 (2020), 1-121. 
    [29] C. Kanchibhotla, P. Venkatesh, D. Somayajulu and P. Radha krishna, An efficient cloud-based framework for digital media knowledge extraction, in IEEE International Conference on Big Data, (2019), 1841–1850. doi: 10.1109/BigData47090.2019.9005480.
    [30] X. Li, New thinking of human resource management in the age of artificial intelligence, In: 2nd International Proceedings on International Conference on Systems, Computing, and Applications, Francis Academic Press, UK., (2018), 133–136.
    [31] M. R. Mohamad and N. M. Zin, Knowledge management and the competitiveness of small construction firms, Competitiveness Review: An International Business Journal, 29 (2019), 534-550. 
    [32] A. K. M. MasumL. S. BehM. A. K. Azad and K. Hoque, Intelligent human resource information system (i-HRIS): A holistic decision support framework for HR excellence, International Arab Journal of Information Technology, 15 (2018), 121-130. 
    [33] F. Malerba and M. McKelvey, Knowledge-intensive innovative entrepreneurship integrating schumpeter, evolutionary economics, and innovation systems, Small Business Economics, 54 (2020), 503-522.  doi: 10.1007/s11187-018-0060-2.
    [34] P. Matsa and K. Gullamajji, To study impact of artificial intelligence on human resource management, International Research Journal of Engineering and Technology, (IRJET), 6 (2019), 1229–1238.
    [35] A. K. M. MasumL. S. BehM. A. K. Azad and K. Hoque, Intelligent human resource information system (i-HRIS): A holistic decision support framework for HR excellence, Int. Arab J. Inf. Technol., 15 (2018), 121-130. 
    [36] A. MaithiliR. V. KumariS. Rajamanickam and P. J. Paul, Neural network towards business forecasting, IOSR Journal of Engineering, 2 (2012), 831-836.  doi: 10.9790/3021-0204831836.
    [37] R. Molina, N. P. De la Blanca and C. C. Taylor, Modern statistical techniques, Machine Learning, Neural and Statistical Classification, (1994), 29–49.
    [38] P. A. MerollaJ. V. ArthurR. Alvarez-IcazaA. S. CassidyJ. SawadaF. AkopyanB. L. JacksonN. ImamC. GuoY. NakamuraB. BrezzoI. VoS. K. EsserR. AppuswamyB. TabaA. AmirM. D. FlicknerW. P. RiskR. Manohar and D. S. Modha, A million spiking-neuron integrated circuit with a scalable communication network and interface, Science, 345 (2014), 668-673.  doi: 10.1126/science.1254642.
    [39] S. S. NicolaescuA. FloreaC. V. KiforU. FioreN. CocanI. Receu and P. Zanetti, Human capital evaluation in knowledge-based organizations based on big data analytics, Future Generation Computer Systems, 111 (2020), 654-667. 
    [40] A. ÖzmenG. W. WeberZ. Çavu\c{s}oğlu and Ö. Defterli, The new robust conic GPLM method with an application to finance: Prediction of credit default, J. Global Optim., 56 (2013), 233-249.  doi: 10.1007/s10898-012-9902-7.
    [41] A. Özmen and G. W. Weber, Robust conic generalized partial linear models using RCMARS method-A robustification of CGPLM, in Proceedings of the Sixth Global Conference on Power Control and Optimization, (eds. N. N. Barsoum, D. Fairman and P. Vasant), American Institute of Physics, USA, 1499 (2012), 337–343
    [42] A. Özmen and G. W. Weber, RMARS: Robustification of multivariate adaptive regression spline under polyhedral uncertainty, J. Comput. Appl. Math., 259 (2014), 914-924.  doi: 10.1016/j.cam.2013.09.055.
    [43] A. ÖzmenG. W. Weberİ. Batmaz and E. Kropat, RCMARS: Robustification of CMARS with different scenarios under polyhedral uncertainty set, Commun. Nonlinear Sci. Numer. Simul., 16 (2011), 4780-4787.  doi: 10.1016/j.cnsns.2011.04.001.
    [44] O. ParkJ. Bae and W. Hong, High-commitment HRM system, HR capability, and ambidextrous technological innovation, The International Journal of Human Resource Management, 30 (2019), 1526-1548.  doi: 10.1080/09585192.2017.1296880.
    [45] K. Pettersson and M. Lindberg, Paradoxical spaces of feminist resistance: Mapping the margin to the masculinist innovation discourse, International Journal of Gender and Entrepreneurship, 5 (2013), 323-41. 
    [46] S. Poutanen and A. Kovalainen, Gendering innovation process in an industrial plant. Revisiting tokenism, gender and innovation, International Journal of Gender and Entrepreneurship, 5 (2013), 257-74. 
    [47] L. Pecis, Doing and undoing gender in innovation: Femininities and masculinities in innovation processes, Human Relations, 69 (2016), 2117-2140. 
    [48] J. PaschenJ. Kietzmann and T. C. Kietzmann, Artificial intelligence (AI) and its implications for market knowledge in B2B marketing, Journal of Business & Industrial Marketing, 34 (2019), 1410-1419.  doi: 10.1108/JBIM-10-2018-0295.
    [49] T. PakizeF. Yerlikaya-Özkurt and G.-W. Weber, An approach to the mean shift outlier model by Tikhonov regularization and conic programming, Intelligent Data Analysis, 18 (2014), 79-94.  doi: 10.3233/IDA-130629.
    [50] S. Pandely and P. Khaskel, Application of AI in human resource management and gen Y's reaction, International Journal of Recent Technology and Engineering, 8 (2019), 2277-3878. 
    [51] G. Stoet and D. C. Geary, The gender-equality paradox in science, technology, engineering, and mathematics education, Psychological Science, 29 (2018), 581-593. 
    [52] M. Sriram and L. Gandhi, Exploring the dynamica virtus of Machine Learning (ML) in human resource management - A critical analysis of IT industry, International Journal of Computer Sciences and Engineering, 5 (2017), 173-180. 
    [53] T. M. Scholz, Big Data in Organizations and the Role of Human Resource Management: A Complex Systems Theory-Based Conceptualization, Personalmanagement und Organisation, Peter Lang GmbH, New York, 2017.
    [54] R. V. Sunita, An optimizing preprocessing algorithm for enhanced web content, in soft computing: Theories and applications, Advances in Intelligent Systems and Computing, (eds. M. Pant, T. Sharma, O. Verma, R. Singla and A. Sikander), Springer, 1053 (2020), 63–71.
    [55] N. SamaniM. Gohari-Moghadam and A. A. Safavi, A simple neural network model for the determination of aquifer parameters, Journal of Hydrology, 340 (2007), 1-11.  doi: 10.1016/j.jhydrol.2007.03.017.
    [56] V. Sharma, Artificial neural network applicability in business forecasting, International Journal of Emerging Research in Management & Technology, 1 (2012), 62-65. 
    [57] E. Savku and G. W. Weber, A stochastic maximum principle for a markov regime-switching jump-diffusion model with delay and an application to finance, J. Optim. Theory Appl., 179 (2018), 696-721.  doi: 10.1007/s10957-017-1159-3.
    [58] P. TambeP. Cappelli and V. Yakubovich, Artificial intelligence in human resources management: Challenges and a path forward, California Management Review, 61 (2019), 15-42.  doi: 10.1177/0008125619867910.
    [59] C. Y. Tung, An assessment of China's Taiwan policy under the third generation leadership, Asian Survey, 45 (2005), 343-361. 
    [60] V. TandonG. Ertug and G. Carnabuci, How do prior ties affect learning by hiring?, Journal of Management, 46 (2018), 287-320. 
    [61] R. A. Tikidji-HamburyanE. Kropat and G.-W. Weber, Preface operations research in neuroscience II, Ann. Oper. Res., 289 (2020), 1-4.  doi: 10.1007/s10479-020-03574-z.
    [62] P. Taylan, F. Yerlikaya-Özkurt, B. Bilgiç Uçak and G.-W. Weber, A new outlier detection method based on convex optimization: Application to diagnosis of Parkinson's disease, Journal of Applied Statistics, (2020), 1–20. doi: 10.1080/02664763.2020.1864815.
    [63] P. Vasant, I. Zelinka and G.-W. Weber, Intelligent computing and optimization, proceedings of the 3rd international conference on intelligent computing and optimization 2020, Advances in Intelligent Systems and Computing, Lecture Notes in Networks and Systems, Springer, 1324 (2020), 1–1327. doi: 10.1007/978-3-030-68154-8.
    [64] G. W. Weberİ. BatmazG. KöksalP. Taylan and F. Yerlikaya-Özkurt, CMARS: A new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization, Inverse Probl. Sci. Eng., 20 (2012), 371-400.  doi: 10.1080/17415977.2011.624770.
    [65] C. ZouW. Zhao and K. Siau, COVID-19 calls for remote reskilling and retraining, Cutter Business Technology Journal, 33 (2020), 21-25. 
    [66] J. ZhangH. JiangR. Wu and J. Li, Reconciling the dilemma of knowledge sharing: A network pluralism framework of firms' R & D alliance network and innovation performance, Journal of Management, 45 (2018), 2635-2665.  doi: 10.1177/0149206318761575.
  • 加载中




Article Metrics

HTML views(670) PDF downloads(738) Cited by(0)

Access History



    DownLoad:  Full-Size Img  PowerPoint