Beyond the Direct Effect: Cybersecurity Readiness as a Boundary Condition of the FinTech–Operational Efficiency Relationship in Algerian Commercial Banking

Authors	Year	Context	Method	Main finding	Relevance to this study
Wang et al.	2021	China	DEA + GMM	FinTech raises productivity and cuts operating costs	Baseline direct effect
Lee et al.	2023	China, 74 banks	DEA-Malmquist + GMM	FinTech reduces overall efficiency through funding costs	Contradictory evidence
Khan et al.	2024	59 developing countries	Non-linear panel	U-shaped FinTech–efficiency relationship	Central motivation for a boundary condition
Juma Ally et al.	2025	Tanzania	Panel	Gains for large banks, losses for small and medium	Heterogeneity by capacity
Huang and Said	2025	Cross-country	Systematic review	FinTech raises efficiency, with unexplained variation in magnitude	Frames the boundary-condition question
Uddin et al.	2020	Cross-country	Panel	Cyber spending volume alone does not determine stability	Separates spending from capability
Hasan et al.	2021	Cross-sector	PLS-SEM	Cyber readiness improves security and organisational performance	Source of the readiness construct
Türegün	2025	International	Regression	Strategic integration matters more than spending volume	Supports the capability framing
Al Hammadi et al.	2024	UAE Islamic banks	Moderation analysis	IT security capability moderates the FinTech effect	Closest moderation test, different outcome
Murungu et al.	2026	Zimbabwe	Survey regression	Cybersecurity strategies enable digital transformation	Cybersecurity as enabler
Hanifan et al.	2026	Indonesia	Panel with interaction	Cyber risk management maturity raises the FinTech payoff	Closest direct moderation test
Al-Omoush	2026	Jordan, 247 respondents	PLS-SEM	AI and cybersecurity drive FinTech innovation and operational excellence	Mediated rather than moderated specification

[i] Source: own processing

***Conceptual model: Cybersecurity readiness as a boundary condition of the FinTech–operational efficiency relationship***
Source: own processing

Table 2.

Respondent profile

Characteristic	Category	Frequency	Percentage
Gender	Male	37	67.3
	Female	18	32.7
	Total	55	100
Age	25 years or under	2	3.6
	26 to 35 years	30	54.5
	36 to 45 years	21	38.2
	Over 45 years	2	3.6
	Total	55	100
Education	Bachelor's degree	16	29.1
	Master's degree	29	52.7
	Doctorate	5	9.1
	Other qualification	5	9.1
	Total	55	100

[i] Source: own processing based on SPSS 27 output

Table 3.

Convergent validity, reliability, and multicollinearity assessment

Variable	Factors	Items	Loadings(FL)	Cronbach's Alpha	RHO-A	Composite Reliability (CR)	Average Variance Extracted (AVE)	Variance Inflation Factor (VIF)
FinTech adoption		Item 1	0.916	0.944	0.945	0.955	0.782	4.794
		Item 2	0.902					4.779
		Item 3	0.848					2.823
		Item 4	0.848					2.779
		Item 5	0.896					3.537
		Item 6	0.893					4.165
Cybersecurity readiness	Technical readiness	Item 7	0.874	0.918	0.920	0.939	0.754	2.910
		Item 8	0.840					2.506
		Item 9	0.900					3.388
		Item 10	0.820					2.208
		Item 11	0.905					3.570
	Human readiness	Item 12	0.807	0.907	0.914	0.935	0.783	2.080
		Item 13	0.915					3.893
		Item 14	0.914					3.457
		Item 15	0.898					3.377
Operational efficiency	Process acceleration	Item 16	0.941	0.918	0.918	0.948	0.859	3.851
		Item 17	0.918					3.042
		Item 18	0.922					3.201
	Cost reduction	Item 19	0.889	0.889	0.901	0.924	0.752	2.902
		Item 20	0.882					2.685
		Item 21	0.928					3.846
		Item 22	0.762					1.678
	Financial inclusion	Item 23	0.950	0.920	0.822	0.949	0.862	4.677
		Item 24	0.899					2.676
		Item 25	0.935					3.984

[i] Source: own processing based on SmartPLS 4 output

***Measurement model with standardised factor loadings***
Source: own processing based on SmartPLS 4 output

Table 4.

Discriminant validity, Fornell-Larcker criterion

Variables	Human readiness	Technical readiness	Financial inclusion	Cost reduction	Process acceleration
Human readiness	0.917
Technical readiness	0.885	0.868
Financial inclusion	0.080	0.164	0.971
Cost reduction	0.003	0.049	0.891	0.883
Process acceleration	0.080	0.166	0.928	0.867	0.927

[i] Notes: Diagonal values in bold are square roots of AVE. Off-diagonal values are construct correlations.

[ii] Source: own processing based on SmartPLS 4 output

Table 5.

Discriminant validity, Heterotrait-Monotrait ratio (HTMT)

Variables	Human readiness	Technical readiness	Financial inclusion	Cost reduction	Process acceleration
Human readiness	–
Technical readiness	0.725	–
Financial inclusion	0.123	0.181	–
Cost reduction	0.137	0.143	0.779	–
Process acceleration	0.116	0.188	0.557	0.771	–

[i] Note: Values below 0.85 indicate discriminant validity.

[ii] Source: own processing based on SmartPLS 4 output

Table 6.

Coefficient of determination

Endogenous construct	R²	Adjusted R²
Operational efficiency	0.780	0.767

[i] Source: own processing based on SmartPLS 4 output

Table 7.

Effect size for structural paths

Path	f²
FinTech adoption → Operational efficiency	3.430
Cybersecurity readiness → Operational efficiency	0.220
Cybersecurity readiness × FinTech adoption → Operational efficiency	0.467

[i] Source: own processing based on SmartPLS 4 output

Table 8.

Model fit indices

Model	SRMR
Saturated model	0.088
Estimated model	0.091

[i] Source: own processing based on SmartPLS 4 output

Table 9.

Path coefficients and hypothesis testing results

Path	O	M	STDEV	T	P	Decision
H1: FinTech adoption → Operational efficiency	0.938	0.942	0.060	15.512	0.000	Supported
H2: Cybersecurity readiness → Operational efficiency	0.243	0.240	0.086	2.808	0.005	Supported
H3: Cybersecurity readiness × FinTech adoption → Operational efficiency	0.330	0.330	0.067	4.922	0.000	Supported

[i] Source: own processing based on SmartPLS 4 output

***Structural model with path coefficients and p-values***
Source: own processing based on SmartPLS 4 output

Table 10.

Conditional effects of FinTech adoption at three levels of cybersecurity readiness

Level of cybersecurity readiness	O	M	STDEV	T
High (+1 SD)	1.268	1.272	0.112	11.285
Mean	0.938	0.942	0.060	15.512
Low (−1 SD)	0.608	0.612	0.061	10.004

[i] Source: own processing based on SmartPLS 4 output

***Simple slope plot of the moderating effect of cybersecurity readiness***
Source: own processing based on SmartPLS 4 output

References

Authors

Metrics

Articles in this issue

DOI: https://doi.org/10.2478/mdke-2026-0019 | Journal eISSN: 2392-8042

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Language: English

Page range: 344 - 365

Submitted on: Jul 30, 2026

Accepted on: Sep 1, 2026

Published on: Sep 29, 2026

Published by: Scoala Nationala de Studii Politice si Administrative

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

FinTech adoption,

cybersecurity readiness,

operational efficiency,

digital transformation,

bank performance,

Algeria

Related subjects:

Business and economics,

Political economics,

Macroeconomics,

Business management,

Business management, other,

Social sciences,

Sociology,

Labor, trade unions,

Sociology of economy, organizations, and industry

© 2026 Hassina SAAD BEKHOUCHE, Hassiba ALMI, Aboubaker KHOUALED, Mouloud KHOUALED, published by Scoala Nationala de Studii Politice si Administrative
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 14 (2026): Issue 3 (September 2026)

Beyond the Direct Effect: Cybersecurity Readiness as a Boundary Condition of the FinTech–Operational Efficiency Relationship in Algerian Commercial Banking

Figures & Tables

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Paradigm

My account