Dynamic modelling of intellectual capital efficiency and financial performance of Islamic banks: A panel and control-process approach

Shaliza  Alwi; Mohamad Nizam  Jaafar; Wan Shah  Shahdila-Shahar; Amirul Afif  Muhamat

doi:10.59400/adecp4145

Dynamic modelling of intellectual capital efficiency and financial performance of Islamic banks: A panel and control-process approach

Shaliza Alwi
Arshad Ayub Graduate Business School, Universiti Teknologi MARA, Shah Alam 40450, Malaysia
Mohamad Nizam Jaafar
Arshad Ayub Graduate Business School, Universiti Teknologi MARA, Shah Alam 40450, Malaysia
Wan Shah Shahdila-Shahar
Faculty of Economics, Accountancy and Management, Universiti Islam Selangor (UIS), Kajang 43000, Malaysia
Amirul Afif Muhamat
Faculty of Business and Management, Universiti Teknologi MARA, Puncak Alam 42300, Malaysia

Article ID: 4145

Keywords: knowledge-based resources; differential equation modelling; panel data analysis; banking profitability; human capital efficiency; financial system dynamics; Shariah-compliant finance

Abstract

This study investigates the relationship between intellectual capital efficiency and the financial performance of Islamic banks in Malaysia by employing dynamic modeling and empirical panel analysis. In knowledge-driven financial systems, intellectual capital, which includes human capital, structural capital, and capital usage efficiency, has a significant impact on creating a long-term competitive edge. This study sets up a system of differential equations to anticipate how bank performance would change over time based on the parts of intellectual capital and how they are invested. Therefore, this captures both theoretical dynamics and real-world consequences. The theoretical model delineates equilibrium conditions and demonstrates the local asymptotic stability of the intellectual capital system. The study employs the Value-Added Intellectual Coefficient (VAIC) approach, examining an unbalanced panel dataset of 11 comprehensive Islamic banks in Malaysia from 2012 to 2023, resulting in 132 bank-year observations. To find out how intellectual capital affects financial success, we use fixed-effects panel regression with robust standard errors. We look at return on assets (ROA) and return on equity (ROE). The empirical findings demonstrate that the overall efficiency of intellectual capital exerts a positive and significant influence on bank profitability. When dispersed, human capital efficiency is found to be the most important factor affecting financial performance, followed by capital employed efficiency. Structural capital efficiency, on the other hand, has no statistically significant effect. The results are similar across different model setups and diagnostic tests. These results highlight the importance of knowledge resources and efficient capital deployment in enhancing the competitiveness and sustainability of Islamic banking institutions. This study contributes to the field by integrating dynamic system modeling with empirical banking analysis, providing policy implications for strategic investment in intellectual capital.

Published

2026-06-08

How to Cite

Alwi, S., Jaafar, M. N., Shahdila-Shahar, W. S., & Muhamat, A. A. (2026). Dynamic modelling of intellectual capital efficiency and financial performance of Islamic banks: A panel and control-process approach. Advances in Differential Equations and Control Processes, 33(2). https://doi.org/10.59400/adecp4145

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Issue

Vol. 33 No. 2 (2026)

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Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

[1]Asutay M, Ubaidillah. Examining the Impact of Intellectual Capital Performance on Financial Performance in Islamic Banks. Journal of the Knowledge Economy. 2024; 15(1): 1231–1263. doi: 10.1007/s13132-023-01114-1

[2]Ahmed ZN, Hussin MRA. The Effect of Intellectual Capital Efficiency on Malaysian Firm Performance: A Dynamic Panel Estimation. Management and Accounting Review. 2024; 23(1): 58–84.

[3]Akkas E, Asutay M. Intellectual capital disclosure and financial performance nexus in Islamic and conventional banks in the GCC countries. International Journal of Islamic and Middle Eastern Finance and Management. 2022; 15(5): 943–966. doi: 10.1108/IMEFM-01-2021-0015

[4]Prasojo P, Hadinata S. Intellectual Capital and Firm Performance: Empirical Evidence from the Jakarta Islamic Index. Journal of Accounting Auditing and Business. 2020; 3(2): 64–75. doi: 10.24198/jaab.v3i2.27249

[5]Khalil HK. Nonlinear Systems, 3rd ed. Prentice Hall; 2002.

[6]Lenhart S, Workman JT. Optimal Control Applied to Biological Models. Chapman and Hall/CRC; 2007. doi: 10.1201/9781420011418

[7]Pulic A. VAICTM an accounting tool for IC management. International Journal of Technology Management. 2000; 20(5–8): 702–714. doi: 10.1504/IJTM.2000.002891

[8]Hasan R, Miah MD. Intellectual capital and firm performance: evidence from the financial sector in Bangladesh. International Journal of Accounting and Finance. 2018; 8(2): 133. doi: 10.1504/IJAF.2018.093261

[9]Becker GS. Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. University of Chicago Press; 1993. doi: 10.7208/chicago/9780226041223.001.0001

[10]Kyurkchiev N, Zaevski T, Iliev A, et al. Chaos in a Generalized Perturbed Lotka–Volterra Model. Mathematics. 2026; 14(2): 247. doi: 10.3390/math14020247

[11]Xu J, Liu F. The Impact of Intellectual Capital on Firm Performance: A Modified and Extended VAIC Model. Journal of Competitiveness. 2020; 12(1): 161–176. doi: 10.7441/joc.2010.01.10

[12]Baltagi BH. Econometric Analysis of Panel Data, 5th ed. Wiley; 2013.

[13]Wooldridge JM. Econometric Analysis of Cross Section and Panel Data, 2nd ed. MIT Press; 2010.

[14]Haris M, Yao H, Tariq G, et al. Intellectual Capital Performance and Profitability of Banks: Evidence from Pakistan. Journal of Risk and Financial Management. 2019; 12(2): 56. doi: 10.3390/jrfm12020056

[15]Hair Jr. JF, Black WC, Babin BJ, et al. Multivariate Data Analysis, 8th ed. Cengage Learning; 2019.

[16]Gujarati DN, Porter DC. Basic Econometrics, 5th ed. McGraw-Hill; 2009.

[17]Hausman JA. Specification Tests in Econometrics. Econometrica. 1978; 46(6): 1251–1271. doi: 10.2307/1913827

[18]Mohapatra S, Jena SK, Mitra A, et al. Intellectual capital and firm performance: evidence from Indian banking sector. Applied Economics. 2019; 51(57): 6054–6067. doi: 10.1080/00036846.2019.1645283

[19]Kenedy P. A Guide to Econometrics, 6th ed. Blackwell Publishing; 2008.

[20]Nadeem M, Gan C, Nguyen C. The Importance of Intellectual Capital for Firm Performance: Evidence from Australia. Australian Accounting Review. 2018; 28(3): 334–344. doi: 10.1111/auar.12184

[21]Dalwai T, Salehi M. Business strategy, intellectual capital, firm performance, and bankruptcy risk: evidence from Oman’s non-financial sector companies. Asian Review of Accounting. 2021; 29(3): 474–504. doi: 10.1108/ARA-01-2021-0008

[22]Arellano M, Bond S. Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. The Review of Economic Studies. 1991; 58(2): 277. doi: 10.2307/2297968

[23]Blundell R, Bond S. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics. 1998; 87(1): 115–143. doi: 10.1016/S0304-4076(98)00009-8

[24]Mai XTT, Nguyen HTN, Ngo T, et al. Efficiency of the Islamic Banking Sector: Evidence from Two-Stage DEA Double Frontiers Analysis. International Journal of Financial Studies. 2023; 11(1): 32. doi: 10.3390/ijfs11010032

[25]Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. Springer; 2009.

[26]Zafar MB, Yasin T. Intellectual capital and financial performance of Islamic banks: a meta-analysis. Accounting Research Journal. 2025; 38(2): 245–262. doi: 10.1108/ARJ-07-2024-0245

[27]Wahyudi T, Mahsina M, Nawawi M. Corporate governance and intellectual capital: its impact on Islamic banking performance in OIC countries. Journal of Islamic Accounting and Business Research. 2026; 1–29. doi: 10.1108/JIABR-01-2025-0054

[28]Sadiq R, Arshed N, Ahmad HK. Determinants of Cost Efficiency of Islamic Banks of Pakistan. The Journal of Muamalat and Islamic Finance Research. 2017; 14(2): 111–128.

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