Research Article
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Artificial Intelligence in Project Management: An Application in The Banking Sector

Year 2022, Volume: 14 Issue: 27, 323 - 334, 29.11.2022

Yavuz Selim Balcıoğlu , Melike Artar , Prof. Dr. Oya Erdil

https://doi.org/10.20990/kilisiibfakademik.1159862

Abstract

Purpose: The purpose of this paper is to a new approach has been introduced to academic studies on the use of artificial intelligence in human resources functions. In personnel selection/placement and team-building processes, finding the right person for the right job will be accomplished with the support of artificial intelligence.
Design/Methodology: Artificial neural networks (ANNs) are one of the programming-based methods that provide effective solutions to problems where multiple inputs and multiple outputs are obtained. Although ANN was first used to measure the content analysis of numerical data and mathematical problems, it was later applied to measure the activities of social problems and projects. In this study, the verbal variables determined were converted into numerical expressions. In the next step, the ANN model created for analysis, using the transformed numerical expressions as input, the variable ids with the highest score were determined as output. The study was completed with the data visualizations made in the last stage.
Findings: With this study, a new approach has been introduced to academic studies on artificial intelligence in human resources functions. For example, in personnel selection/placement and team-building processes, processes will accelerate with artificial intelligence.
Limitations: There is no certain regulation for determining the general shape of artificial neural networks. The right network shape is accomplished through experience and case and error. However, ANNs can only work with numerical information. Therefore, variables must be converted to numeric data.
Originality/Value: Since the most crucial factor in the success of the projects is humans, selecting the human element with the help of artificial intelligence in the projects is examined.

Keywords

artificial intelligence project management banking sector employee competencies artificial neural network personnel selection human resources

References

  • Ahmed, O. (2018). Artificial intelligence in HR. International Journal of Research and Analytical Reviews, 5(4), 971-978. https://doi.org/10.31221/osf.io/cfwvm
  • Ally, S., Karpinski, A. C., & Israeli, A. A. (2020). Customer behavioural analysis: The impact of internet addiction, interpersonal competencies and service orientation on customers’ online complaint behaviour. Research in Hospitality Management, 10(2), 97-105. https://doi.org/10.1080/22243534.2020.1869468
  • Bjorvatn, T., & Wald, A. (2018). Project complexity and team-level absorptive capacity as drivers of project management performance. International Journal of Project Management, 36(6), 876-888. https://doi.org/10.1016/j.ijproman.2018.05.003
  • Boyatzis, R. E. (1982). The competent manager: A model for effective performance. John Wiley & Sons.
  • Collis, D. J., & Montgomery, C. A. (1995). Competing on Resources : Strategy in the 1990s Competing on Resources : Harvard Business Review.
  • Cowgill, B. (2018). Bias and Productivity in Humans and Algorithms: Theory and Evidence from Résumé Screening. Columbia Business School.
  • Demirkesen, S., & Ozorhon, B. (2017). Impact of integration management on construction project management performance. International Journal of Project Management, 35(8), 1639-1654. https://doi.org/10.1016/j.ijproman.2017.09.008
  • Denrell, J., Fang, C., & Liu, C. (2015). Perspective-chance explanations in the management sciences. Organization Science. https://doi.org/10.1287/orsc.2014.0946
  • Díaz-Fernández, M., López-Cabrales, A., & Valle-Cabrera, R. (2014). A contingent approach to the role of human capital and competencies on firm strategy. BRQ Business Research Quarterly. https://doi.org/10.1016/j.brq.2014.01.002
  • Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Overcoming Algorithm Aversion: People Will Use Algorithms If They Can (Even Slightly) Modify Them. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2616787
  • Foody, G. M. (1995). Land cover classification by an artificial neural network with ancillary information. International Journal of Geographical Information Systems. https://doi.org/10.1080/02693799508902054
  • Goldfeld, S., O’Connor, M., O’Connor, E., Chong, S., Badland, H., Woolfenden, S., … Mensah, F. (2018). More than a snapshot in time: Pathways of disadvantage over childhood. International Journal of Epidemiology. https://doi.org/10.1093/ije/dyy086
  • Guarnieri, R. A., Pereira, E. B., & Chou, S. C. (2006). Solar radiation forecast using artificial neural networks in South Brazil. Proceedings of the 8th ICSHMO, 24-28.
  • Hillson, D. (2002). Extending the risk process to manage opportunities. International Journal of Project Management. https://doi.org/10.1016/S0263-7863(01)00074-6
  • Huemann, M., Keegan, A., & Turner, J. R. (2007). Human resource management in the project-oriented company: A review. International Journal of Project Management. https://doi.org/10.1016/j.ijproman.2006.10.001
  • Ibrahim, R., Boerhannoeddin, A., & Kayode, B. K. (2017). Organizational culture and development: Testing the structural path of factors affecting employees’ work performance in an organization. Asia Pacific Management Review, 22(2), 104-111. https://doi.org/10.1016/j.apmrv.2016.10.002
  • Kubat, M. (1999). Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7. The Knowledge Engineering Review, 13(4), 409-412. https://doi.org/10.1017/s0269888998214044
  • Lee, Y. T. (2010). Exploring high-performers’ required competencies. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2009.05.064
  • Ley, T., & Albert, D. (2003). Identifying employee competencies in dynamic work domains: Methodological considerations and a case study. In Journal of Universal Computer Science. https://doi.org/10.3217/jucs-009-12-1500
  • Lind, E. A., & Van Den Bos, K. (2002). When fairness works: Toward a general theory of uncertainty management. Research in Organizational Behavior. https://doi.org/10.1016/s0191-3085(02)24006-x
  • Marković, M. R. (2008). Managing the organizational change and culture in the age of globalization. Journal of Business Economics and Management. https://doi.org/10.3846/1611-1699.2008.9.3-11
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics. https://doi.org/10.1007/BF02478259
  • Naquin, S. S., & Holton, E. F. (2006). Leadership and Managerial Competency Models: A Simplified Process and Resulting Model. Advances in Developing Human Resources. https://doi.org/10.1177/1523422305286152
  • Newton, C. (1983). The competent manager: A model for effective performance. Long Range Planning. https://doi.org/10.1016/0024-6301(83)90170-x
  • Guide, A. (2001). Project management body of knowledge (pmbok® guide). In Project Management Institute.
  • Potnuru, R. K. G., & Sahoo, C. K. (2016). HRD interventions, employee competencies and organizational effectiveness: an empirical study. European Journal of Training and Development. https://doi.org/10.1108/EJTD-02-2016-0008
  • Radujković, M., & Sjekavica, M. (2017). Project management success factors. Procedia engineering, 196, 607-615. https://doi.org/10.1016/j.proeng.2017.08.048
  • Ravichandran, T. (2018). Exploring the relationships between IT competence, innovation capacity and organizational agility. Journal of Strategic Information Systems. https://doi.org/10.1016/j.jsis.2017.07.002
  • Salman, M., Ganie, S. A., & Saleem, I. (2020). Employee Competencies as Predictors of Organizational Performance: A Study of Public and Private Sector Banks. Management and Labour Studies, 45(4), 416-432. https://doi.org/10.1177/0258042X20939014
  • Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons.
  • Silvius, A. J. G., & Schipper, R. P. J. (2014). Sustainability in project management: A literature review and impact analysis. Social Business. https://doi.org/10.1362/204440814x13948909253866
  • Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and A path forward. California Management Review. https://doi.org/10.1177/0008125619867910
  • Tabassi, A. A., Abdullah, A., & Bryde, D. J. (2019). Conflict management, team coordination, and performance within multicultural temporary projects: Evidence from the construction industry. Project Management Journal, 50(1), 101-114. https://doi.org/10.1177/8756972818818257
  • Youndt, M. A., Subramaniam, M., & Snell, S. A. (2004). Intellectual Capital Profiles: An Examination of Investments and Returns. Journal of Management Studies. https://doi.org/10.1111/j.1467-6486.2004.00435.x
  • White, R. W. (1959). Motivation reconsidered: the concept of competence. Psychol. Rev. 66:297. https://doi.org/10.1037/h0040934
  • Whitfield, G., & Farrell, D. (2010). Diversity In Supply Chains: What Really Matters? Journal of Diversity Management (JDM). https://doi.org/10.19030/jdm.v5i4.341

Proje Yönetiminde Yapay Zeka: Bankacılık Sektöründe Bir Uygulama

Year 2022, Volume: 14 Issue: 27, 323 - 334, 29.11.2022

Yavuz Selim Balcıoğlu , Melike Artar , Prof. Dr. Oya Erdil

https://doi.org/10.20990/kilisiibfakademik.1159862

Abstract

Amaç: Bu makalenin amacı, yapay zekanın insan kaynakları fonksiyonlarında kullanımına ilişkin akademik çalışmalara yeni bir yaklaşım getirmektir. Personel seçme/yerleştirme ve ekip oluşturma süreçlerinde doğru işe doğru kişinin bulunması yapay zeka desteği ile gerçekleştirilecektir.
Tasarım/Yöntem: Yapay sinir ağları (YSA), birden fazla girdi ve birden fazla çıktının elde edildiği, problemlere etkili çözümler getiren programlama tabanlı yöntemlerden biridir. YSA, ilk olarak sayısal verilerin ve matematiksel problemlerin içerik analizlerini ölçmek için kullanılsa da sonraları daha çok sosyal sorunların ve projelerin etkinliklerin ölçümlerinde uygulanmıştır. Bu çalışma ile belirlenen sözel değişkenlerin sayısal ifadelere dönüştürülmesi sağlanmıştır. Sonraki aşamada, analiz için oluşturulan YSA modeli, dönüştürülen sayısal ifadelerin girdi olarak kullanılması sayesinde, çıktı olarak en yüksek puana sahip değişken id’lerin belirlenmesi sağlanmıştır. Son aşamada yapılan veri görselleştirmeler ile çalışma tamamlanmıştır.
Bulgular: Bu çalışma ile insan kaynakları fonksiyonlarında yapay zeka ile ilgili akademik çalışmalara yeni bir yaklaşım getirilmiştir. Örneğin personel seçme/yerleştirme ve ekip oluşturma süreçlerinde yapay zeka ile süreçler hızlanacaktır.
Sınırlılıklar: Yapay sinir ağlarının genel yapısının belirlenmesi için belirli bir kural yoktur. Doğru ağ yapısı, deneyim ve deneme yanılma yoluyla elde edilmektedir. Bununla birlikte, YSA’lar sadece sayısal bilgiler ile çalışabilmektedir. Bundan dolayı, değişkenler sayısal değerlere dönüştürülmelidir.
Özgünlük/Değer: Projelerin başarısında en önemli faktör insan olduğu için projelerde insan unsurunun yapay zeka yardımıyla seçilmesi incelenmiştir.

Keywords

yapay zeka proje yönetimi bankacılık çalışan yeterliliği çalışan seçimi sinir ağları

References

  • Ahmed, O. (2018). Artificial intelligence in HR. International Journal of Research and Analytical Reviews, 5(4), 971-978. https://doi.org/10.31221/osf.io/cfwvm
  • Ally, S., Karpinski, A. C., & Israeli, A. A. (2020). Customer behavioural analysis: The impact of internet addiction, interpersonal competencies and service orientation on customers’ online complaint behaviour. Research in Hospitality Management, 10(2), 97-105. https://doi.org/10.1080/22243534.2020.1869468
  • Bjorvatn, T., & Wald, A. (2018). Project complexity and team-level absorptive capacity as drivers of project management performance. International Journal of Project Management, 36(6), 876-888. https://doi.org/10.1016/j.ijproman.2018.05.003
  • Boyatzis, R. E. (1982). The competent manager: A model for effective performance. John Wiley & Sons.
  • Collis, D. J., & Montgomery, C. A. (1995). Competing on Resources : Strategy in the 1990s Competing on Resources : Harvard Business Review.
  • Cowgill, B. (2018). Bias and Productivity in Humans and Algorithms: Theory and Evidence from Résumé Screening. Columbia Business School.
  • Demirkesen, S., & Ozorhon, B. (2017). Impact of integration management on construction project management performance. International Journal of Project Management, 35(8), 1639-1654. https://doi.org/10.1016/j.ijproman.2017.09.008
  • Denrell, J., Fang, C., & Liu, C. (2015). Perspective-chance explanations in the management sciences. Organization Science. https://doi.org/10.1287/orsc.2014.0946
  • Díaz-Fernández, M., López-Cabrales, A., & Valle-Cabrera, R. (2014). A contingent approach to the role of human capital and competencies on firm strategy. BRQ Business Research Quarterly. https://doi.org/10.1016/j.brq.2014.01.002
  • Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Overcoming Algorithm Aversion: People Will Use Algorithms If They Can (Even Slightly) Modify Them. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2616787
  • Foody, G. M. (1995). Land cover classification by an artificial neural network with ancillary information. International Journal of Geographical Information Systems. https://doi.org/10.1080/02693799508902054
  • Goldfeld, S., O’Connor, M., O’Connor, E., Chong, S., Badland, H., Woolfenden, S., … Mensah, F. (2018). More than a snapshot in time: Pathways of disadvantage over childhood. International Journal of Epidemiology. https://doi.org/10.1093/ije/dyy086
  • Guarnieri, R. A., Pereira, E. B., & Chou, S. C. (2006). Solar radiation forecast using artificial neural networks in South Brazil. Proceedings of the 8th ICSHMO, 24-28.
  • Hillson, D. (2002). Extending the risk process to manage opportunities. International Journal of Project Management. https://doi.org/10.1016/S0263-7863(01)00074-6
  • Huemann, M., Keegan, A., & Turner, J. R. (2007). Human resource management in the project-oriented company: A review. International Journal of Project Management. https://doi.org/10.1016/j.ijproman.2006.10.001
  • Ibrahim, R., Boerhannoeddin, A., & Kayode, B. K. (2017). Organizational culture and development: Testing the structural path of factors affecting employees’ work performance in an organization. Asia Pacific Management Review, 22(2), 104-111. https://doi.org/10.1016/j.apmrv.2016.10.002
  • Kubat, M. (1999). Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7. The Knowledge Engineering Review, 13(4), 409-412. https://doi.org/10.1017/s0269888998214044
  • Lee, Y. T. (2010). Exploring high-performers’ required competencies. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2009.05.064
  • Ley, T., & Albert, D. (2003). Identifying employee competencies in dynamic work domains: Methodological considerations and a case study. In Journal of Universal Computer Science. https://doi.org/10.3217/jucs-009-12-1500
  • Lind, E. A., & Van Den Bos, K. (2002). When fairness works: Toward a general theory of uncertainty management. Research in Organizational Behavior. https://doi.org/10.1016/s0191-3085(02)24006-x
  • Marković, M. R. (2008). Managing the organizational change and culture in the age of globalization. Journal of Business Economics and Management. https://doi.org/10.3846/1611-1699.2008.9.3-11
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics. https://doi.org/10.1007/BF02478259
  • Naquin, S. S., & Holton, E. F. (2006). Leadership and Managerial Competency Models: A Simplified Process and Resulting Model. Advances in Developing Human Resources. https://doi.org/10.1177/1523422305286152
  • Newton, C. (1983). The competent manager: A model for effective performance. Long Range Planning. https://doi.org/10.1016/0024-6301(83)90170-x
  • Guide, A. (2001). Project management body of knowledge (pmbok® guide). In Project Management Institute.
  • Potnuru, R. K. G., & Sahoo, C. K. (2016). HRD interventions, employee competencies and organizational effectiveness: an empirical study. European Journal of Training and Development. https://doi.org/10.1108/EJTD-02-2016-0008
  • Radujković, M., & Sjekavica, M. (2017). Project management success factors. Procedia engineering, 196, 607-615. https://doi.org/10.1016/j.proeng.2017.08.048
  • Ravichandran, T. (2018). Exploring the relationships between IT competence, innovation capacity and organizational agility. Journal of Strategic Information Systems. https://doi.org/10.1016/j.jsis.2017.07.002
  • Salman, M., Ganie, S. A., & Saleem, I. (2020). Employee Competencies as Predictors of Organizational Performance: A Study of Public and Private Sector Banks. Management and Labour Studies, 45(4), 416-432. https://doi.org/10.1177/0258042X20939014
  • Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons.
  • Silvius, A. J. G., & Schipper, R. P. J. (2014). Sustainability in project management: A literature review and impact analysis. Social Business. https://doi.org/10.1362/204440814x13948909253866
  • Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and A path forward. California Management Review. https://doi.org/10.1177/0008125619867910
  • Tabassi, A. A., Abdullah, A., & Bryde, D. J. (2019). Conflict management, team coordination, and performance within multicultural temporary projects: Evidence from the construction industry. Project Management Journal, 50(1), 101-114. https://doi.org/10.1177/8756972818818257
  • Youndt, M. A., Subramaniam, M., & Snell, S. A. (2004). Intellectual Capital Profiles: An Examination of Investments and Returns. Journal of Management Studies. https://doi.org/10.1111/j.1467-6486.2004.00435.x
  • White, R. W. (1959). Motivation reconsidered: the concept of competence. Psychol. Rev. 66:297. https://doi.org/10.1037/h0040934
  • Whitfield, G., & Farrell, D. (2010). Diversity In Supply Chains: What Really Matters? Journal of Diversity Management (JDM). https://doi.org/10.19030/jdm.v5i4.341
There are 36 citations in total.

Details

Primary Language English
Subjects Finance
Journal Section RESEARCH PAPERS
Authors

Yavuz Selim Balcıoğlu 0000-0001-7138-2972

Melike Artar 0000-0001-7714-748X

Prof. Dr. Oya Erdil 0000-0003-3793-001X

Publication Date November 29, 2022
Published in Issue Year 2022 Volume: 14 Issue: 27

Cite

APA Balcıoğlu, Y. S., Artar, M., & Erdil, P. D. O. (2022). Artificial Intelligence in Project Management: An Application in The Banking Sector. Akademik Araştırmalar Ve Çalışmalar Dergisi (AKAD), 14(27), 323-334. https://doi.org/10.20990/kilisiibfakademik.1159862
AMA Balcıoğlu YS, Artar M, Erdil PDO. Artificial Intelligence in Project Management: An Application in The Banking Sector. Akademik Araştırmalar ve Çalışmalar Dergisi (AKAD). November 2022;14(27):323-334. doi:10.20990/kilisiibfakademik.1159862
Chicago Balcıoğlu, Yavuz Selim, Melike Artar, and Prof. Dr. Oya Erdil. “Artificial Intelligence in Project Management: An Application in The Banking Sector”. Akademik Araştırmalar Ve Çalışmalar Dergisi (AKAD) 14, no. 27 (November 2022): 323-34. https://doi.org/10.20990/kilisiibfakademik.1159862.
EndNote Balcıoğlu YS, Artar M, Erdil PDO (November 1, 2022) Artificial Intelligence in Project Management: An Application in The Banking Sector. Akademik Araştırmalar ve Çalışmalar Dergisi (AKAD) 14 27 323–334.
IEEE Y. S. Balcıoğlu, M. Artar, and P. D. O. Erdil, “Artificial Intelligence in Project Management: An Application in The Banking Sector”, Akademik Araştırmalar ve Çalışmalar Dergisi (AKAD), vol. 14, no. 27, pp. 323–334, 2022, doi: 10.20990/kilisiibfakademik.1159862.
ISNAD Balcıoğlu, Yavuz Selim et al. “Artificial Intelligence in Project Management: An Application in The Banking Sector”. Akademik Araştırmalar ve Çalışmalar Dergisi (AKAD) 14/27 (November 2022), 323-334. https://doi.org/10.20990/kilisiibfakademik.1159862.
JAMA Balcıoğlu YS, Artar M, Erdil PDO. Artificial Intelligence in Project Management: An Application in The Banking Sector. Akademik Araştırmalar ve Çalışmalar Dergisi (AKAD). 2022;14:323–334.
MLA Balcıoğlu, Yavuz Selim et al. “Artificial Intelligence in Project Management: An Application in The Banking Sector”. Akademik Araştırmalar Ve Çalışmalar Dergisi (AKAD), vol. 14, no. 27, 2022, pp. 323-34, doi:10.20990/kilisiibfakademik.1159862.
Vancouver Balcıoğlu YS, Artar M, Erdil PDO. Artificial Intelligence in Project Management: An Application in The Banking Sector. Akademik Araştırmalar ve Çalışmalar Dergisi (AKAD). 2022;14(27):323-34.
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