BUSINESS EDUCATION AND TRAINING SYSTEMS IN FINANCIAL INDUSTRIES WITH A FOCUS ON OUTCOME ASSESSMENT
DOI:
https://doi.org/10.18623/rvd.v22.n5.3608Keywords:
Business Education, Intellectual Property Rights, Brand Building and Advertising, Innovation, Financial Industry TrainingAbstract
The aim of this research is to examine the effectiveness of business education and training systems in the financial industry, with a specific focus on outcome assessment in key areas such as intellectual property rights (IPR) registration and monitoring, marketing, brand building, advertising, software development, and data analysis. The theoretical background of the study emphasizes the growing importance of employee education in enhancing organizational competitiveness, managing intangible assets, and responding to rapid technological and market changes. The research employs a mixed-methods approach, combining both qualitative and quantitative methodologies. Quantitative analysis was conducted using correlation and regression analyses in SPSS based on secondary data, while the qualitative component involved structured interviews with managers and experts responsible for training and development in financial institutions. The results indicate that training related to intellectual property management has the strongest positive impact on employee participation in education and training programs, although this influence was not statistically significant. Conversely, marketing-related training showed a weaker connection, and innovation-active enterprises even displayed a slight negative relationship, possibly due to the recruitment of already highly skilled personnel. The study concludes that targeted and strategically implemented training initiatives especially those focused on intellectual property and technological skills contribute to greater organizational adaptability, innovation, and long-term competitiveness. It is recommended that financial institutions continue investing in structured, interdisciplinary education programs to strengthen internal capabilities and maintain strategic advantage in a dynamic business environment
References
Alharbi, S. A., Abdoon, M. A., Saadeh, R., Alsemiry, R. D., Allogmany, R., Berir, M., & EL Guma, F. (2024). Modeling and analysis of visceral leishmaniasis dynamics using fractional‐order operators: A comparative study. Mathematical Methods in the Applied Sciences, 47(12), 9918–9937. doi:10.1002/mma.10101
Ali, M., Alzahrani, S. M., Saadeh, R., Abdoon, M. A., Qazza, A., Al-kuleab, N., & EL Guma, F. (2024a). Modeling COVID-19 spread and non-pharmaceutical interventions in South Africa: A stochastic approach. Scientific African, 24, e02155. doi:10.1016/j.sciaf.2024.e02155
Ali, M., Guma, F. E., Qazza, A., Saadeh, R., Alsubaie, N. E., Althubyani, M., & Abdoon, M. A. (2024b). Stochastic modeling of influenza transmission: Insights into disease dynamics and epidemic management. Partial Differential Equations in Applied Mathematics, 100886.
Aljandali, A. (2017). The Box-Jenkins methodology. In Multivariate Methods and Forecasting with IBM® SPSS® Statistics. Cham, Switzerland: Springer.
Almutairi, D. K., Abdoon, M. A., Salih, S. Y. M., Elsamani, S. A., Guma, F. E., & Berir, M. (2023). Modeling and analysis of a fractional visceral leishmaniosis with Caputo and Caputo–Fabrizio derivatives. Journal of the Nigerian Society of Physical Sciences, 1453-1453. doi:10.46481/jnsps.2023.1453
Alsobhi, A. (2022). Prediction of COVID-19 disease by ARIMA model and tuning hyperparameter through GridSearchCV. Emerging Technologies in Data Mining and Information Security, 543–551. doi:10.1007/979814051_54
Alsubaie, N., EL Guma, F., Boulehmi, K., Al-kuleab, N., & Abdoon, M. A. (2024). Improving influenza epidemiological models under Caputo fractional-order calculus. Symmetry, 16(7), 929. doi:10.3390/sym16070929
Alzahrani, S. M., Saadeh, R., Abdoon, M. A., Qazza, A., Guma, F. E., & Berir, M. (2024). Numerical simulation of an influenza epidemic: Prediction with fractional SEIR and the ARIMA model. Applied Mathematics & Information Sciences, 18(1), 1-12. doi:10.18576/amis/180101
Anderson, O. D. (1977). The Box-Jenkins approach to time series analysis. RAIRO-Operations Research, 11(1), 29.
ArunKumar, K. E., Kalaga, D. V., Kumar, C. M. S., Chilkoor, G., Kawaji, M., & Brenza, T. M. (2021). Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto- Regressive Integrated Moving Average (SARIMA). Applied Soft Computing, 103, 107161.
Arwaekaji, M., Sillabutra, J., Viwatwongkasem, C., & Soontornpipit, P. (2022). Forecasting influenza incidence in public health region 8 Udonthani, Thailand by SARIMA model. Current Applied Science and Technology, 22(4). doi:10.55003/cast.2022.04.22.015
Badar, N., Ikram, A., Salman, M., Saeed, S., Mirza, H. A., Ahad, A., . . . Farooq, U. (2024). Evolutionary analysis of seasonal influenza A viruses in Pakistan 2020–2023. Influenza and Other Respiratory Viruses, 18(2). doi:10.1111/irv.13262
Bezerra, A. K. L., & Santos, É. M. C. (2020). Prediction of the daily number of confirmed cases of COVID-19 in Sudan with ARIMA and Holt-Winters exponential smoothing. International Journal of Development Research, 10(08), 394039413.
Box, G. (2013). Box and Jenkins: Time series analysis, forecasting and control. In A Very British Affair: Six Britons and the Development of Time Series Analysis During the 20th Century (p. 16215). London, UK: Palgrave Macmillan.
Chen, Q., Zheng, X., Shi, H., Zhou, Q., Hu, H., Sun, M., . . . Zhang, X. (2024). Prediction of influenza outbreaks in Fuzhou, China: Comparative analysis of forecasting models. BMC Public Health, 24(1). doi:10.1186/s1288021858x
Chen, Y., Leng, K., Lu, Y., Wen, L., Qi, Y., Gao, W., ... & Dong, J. (2020). Epidemiological features and time-series analysis of influenza incidence in urban and rural areas of Shenyang, China, 2010–2018. Epidemiology & Infection, 148, e29. doi:10.1017/S0950268820000151
Dancer, D., & Tremayne, A. (2005). R-squared and prediction in regression with ordered quantitative response.
Journal of Applied Statistics, 32(5), 483–493. doi:10.1080/02664760500079423
Dandachi, I., Alrezaihi, A., Amin, D., AlRagi, N., Alhatlani, B., Binjomah, A., . . . Aljabr, W. (2024). Molecular surveillance of influenza A virus in Saudi Arabia: Whole-genome sequencing and metagenomic approaches.
Microbiology Spectrum, 12(8). doi:10.1128/spectrum.006624
Devlin, R. K. (2008). The influenza virus. In J. K. Silver (Ed.), Influenza (pp. 1–20). doi:10.5040/9798400670053
EL Guma, F. (2024). Comparative analysis of time series prediction models for visceral leishmaniasis: based on SARIMA and LSTM. Applied Mathematics & Information Sciences, 18(1), 125-132. doi:10.18576/amis/180113
EL Guma, F., Abdoon, M. A., Qazza, A., Saadeh, R., Arishi, M. A., & Degoot, A. M. (2024). Analyzing the impact of control strategies on visceral leishmaniasis: A mathematical modeling perspective. European Journal of Pure and Applied Mathematics, 17(2), 1213–1227. doi:10.29020/nybg.ejpam.v17i2.5121
EL Guma, F., Musa, A. G. M., Alkhathami, F. D., Saadeh, R., & Qazza, A. (2023). Prediction of visceral leishmaniasis incidences utilizing machine learning techniques. In 2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) (pp. 1-6). Zarqa, Jordan: IEEE.
Hoque, K. E., & Aljamaan, H. (2021). Impact of hyperparameter tuning on machine learning models in stock price forecasting. IEEE Access, 9, 163815–163830. doi:10.1109/access.2021.3134138
Kaur, J., Parmar, K. S., & Singh, S. (2023). Autoregressive models in environmental forecasting time series: A theoretical and application review. Environmental Science and Pollution Research, 30(8), 19617–19641. doi:10.1007/s11350225149
Khan, D. R., Patankar, A. B., & Khan, A. (2024). An experimental comparison of classic statistical techniques on univariate time series forecasting. Procedia Computer Science, 235, 2730–2740. doi:10.1016/j.procs.2024.04.257
Kumar, D. S., Thiruvarangan, B. C., Vishnu, A., Devi, A. S., & Kavitha, D. (2022). Analysis and prediction of stock price using hybridization of SARIMA and XGBoost. In 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT) (pp. 1-4). Chennai, India: IEEE.
Kuran, F., Tanırcan, G., & Pashaei, E. (2023). Performance evaluation of machine learning techniques in predicting cumulative absolute velocity. Soil Dynamics and Earthquake Engineering, 174, 108175. doi:10.1016/j.soildyn.2023.108175
Li, W., Yin, Y., Quan, X., & Zhang, H. (2019). Gene expression value prediction based on XGBoost algorithm.
Frontiers in Genetics, 10, 1077. doi:10.3389/fgene.2019.01077
Luo, J., Zhang, Z., Fu, Y., & Rao, F. (2021). Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms. Results in Physics, 27, 104462. doi:10.1016/j.rinp.2021.104462
Lv, C. X., An, S. Y., Qiao, B. J., & Wu, W. (2021). Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model. BMC Infectious Diseases, 21(1). doi:10.1186/s1287020650y
Man, H., Huang, H., Qin, Z., & Li, Z. (2023). Analysis of a SARIMA-XGBoost model for hand, foot, and mouth disease in Xinjiang, China. Epidemiology and Infection, 151. doi:10.1017/s0950268823001905
Mills, T. C. (2019). ARIMA models for nonstationary time series. In Applied Time Series Analysis (pp. 57–69). doi:10.1016/b970-1813116.00001
Nelson, B. K. (1998). Time series analysis using autoregressive integrated moving average (ARIMA) models.
Academic Emergency Medicine, 5(7), 739–744. doi:10.1111/j.1552712.1998.tb02493.x
Nelson, M. I., & Holmes, E. C. (2007). The evolution of epidemic influenza. Nature Reviews Genetics, 8(3), 196–
205. doi:10.1038/nrg2053
Peixeiro, M. (2022). Time series forecasting in Python. Shelter Island, NY: Simon and Schuster.
Song, H. (2017, May 21). Review of Time Series Analysis and Its Applications With R Examples (3rd Edition) [Review of the book Time Series Analysis and Its Applications With R Examples (3rd Edition), by R. H. Shumway & D. S. Stoffer]. Structural Equation Modeling: A Multidisciplinary Journal, 24(5), 800–802. doi:10.1080/10705511.2017.1299578
Sroka, Ł. (2024). Simulation analysis of artificial neural network and XGBoost algorithms in time series forecasting, Scientific Papers of Silesian University of Technology Organization and Management Series, 2024(195). doi:10.29119/1643466.2024.195.34
Tenepalli, D., & TM, N. (2024). A systematic review on IoT and machine learning algorithms in e-healthcare.
International Journal of Computing and Digital Systems, 16(1), 27294.
World Health Organization. (2023). Global Influenza Surveillance and Response System (GISRS). Retrieved from
https://www.who.int/initiatives/global-influenza-surveillance-and-response-system
Yasmin, S., & Moniruzzaman, M. (2024). Forecasting of area, production, and yield of jute in Bangladesh using Box-Jenkins ARIMA model. Journal of Agriculture and Food Research, 16, 101203.
Yenilmez, İ., & Mugenzi, F. (2023). Estimation of conventional and innovative models for Rwanda's GDP per capita: A comparative analysis of artificial neural networks and Box–Jenkins methodologies. Scientific African, 22, e01902.
Zhang, L., Bian, W., Qu, W., Tuo, L., & Wang, Y. (2021). Time series forecast of sales volume based on XGBoost.
Journal of Physics: Conference Series, 1873(1), 012067. doi:10.1088/1746596/1873/1/012067
Zhao, Z., Zhai, M., Li, G., Gao, X., Song, W., Wang, X., . . . Qiu, L. (2023). Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China. BMC Infectious Diseases, 23(1), 71.
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