The Impact of Blockchain Technology and Dynamic Capabilities on Banks’ Performance
Abstract
1. Introduction
2. A Literature Review
2.1. Blockchain Technology (BC)
- Immutability: After a transaction is validated, it cannot be changed by any party;
- Traceability: The transaction history is fully and transparently audited;
- Consensus: To avoid conflicts, all participants must agree on a single dataset;
- Automation: Under specific circumstances, commands and transactions can be performed automatically.
- 1.
- Cost Savings Leading to Higher Profit Margins:
- The reduction in operating costs directly improves banks’ profit margins. For example, by cutting trade finance costs by 50–80% and KYC compliance costs by up to 50%, banks can reallocate these savings to other areas, such as innovation or customer acquisition [48,50]. For a Spanish bank with annual operating costs of EUR 1 billion, a 50% reduction in trade finance and KYC expenses could save EUR 50–100 million annually, significantly boosting profitability;
- 2.
- Revenue Growth Through Customer Loyalty:
- Increased customer loyalty drives revenue growth by reducing churn and attracting new customers. Loyal customers are more likely to use additional services, such as loans or investment products, increasing the bank’s revenue per customer. The PwC survey finds that 84% of consumers value transparency and suggests that blockchain’s ability to enhance trust can lead to higher customer retention [56]. If a bank retains an additional 5% of its customer base annually, and each customer generates EUR 500 in revenue, a bank with 1 million customers could experience an additional EUR 25 million in annual revenue;
- 3.
- Scalability and Market Competitiveness Through Process Efficiency:
- Improved process efficiency allows banks to scale operations and serve more customers without proportional increases in costs. Faster transaction settlements and streamlined KYC processes enable banks to handle higher transaction volumes, attracting business clients who value speed and efficiency. This scalability enhances market competitiveness, potentially increasing market share. For instance, a bank that reduces cross-border payment times from 3 days to 4 sec using blockchain can capture a larger share of the EUR 150 billion global remittance market [53], directly boosting revenue.
2.2. Dynamic Capabilities (DC)
2.2.1. Integration of Blockchain with Dynamic Capabilities Theory (DCT)
Absorptive Capacity
Innovation Capacity
Detection Capability
2.3. Regulation of Blockchain in the European Union and Spain
2.3.1. Markets in Crypto-Assets Regulation (MiCA)
2.3.2. Digital Operational Resilience Act (DORA)
2.3.3. European Blockchain Services Infrastructure (EBSI)
2.3.4. Distributed Ledger Technology (DLT) Pilot Regime
2.4. Regulation of Blockchain in Spain
2.4.1. Anti-Money Laundering (AML) and Know Your Customer (KYC) Compliance
2.4.2. Regulatory Sandbox for Innovation
2.4.3. Data Protection Challenges with GDPR
Key Differences
- Sector-Specific Challenges:
- Performance Outcomes:
- Adoption Barriers:
2.5. Development of the Conceptual Model and Hypotheses
3. Methods
- -
- Development and Review of the Dynamic Capabilities (DC) Questionnaire: The research team devised a questionnaire intended for validation, which underwent a review process resulting in the inclusion of eight items—two corresponding to each dynamic capability. These items were subsequently subjected to evaluation via the Delphi method in the latter half of June 2024.
- -
- Preparation of the Questionnaire for the Initial Round of the Delphi: In this iteration, experts were solicited for their assessments regarding the clarity and appropriateness of the items associated with each dynamic capability and their respective measurement scales;
- -
- Selection of the Expert Panel: The selection of experts is a critical factor influencing the validity of the Delphi results. The criteria for expert selection and the number of experts chosen were contingent upon the subject matter and the objectives intended to be achieved through the application of the Delphi method. In this instance, the issue was addressed, and the scope of the application was notably specific.
- -
- First Round: The questionnaire developed by the research team was disseminated to eleven experts, who were invited to evaluate the appropriateness of the selected items for measuring the dynamic capabilities. The questionnaire was organized into three sections, each corresponding to one of the three dynamic capabilities. Experts were asked to indicate whether they believed the questions effectively measured the intended aspects. If they deemed any question inadequate, they were encouraged to propose alternative questions and/or provide additional suggestions or comments. After the questionnaire, experts were also asked to identify any other dynamic capabilities not addressed in this study based on their professional experience. This initial round was conducted during the week of 18–24 June 2024;
- -
- Second Round: Following the processing of responses and analysis of the overall results from the first round, a report summarizing the findings was prepared. Based on the feedback and suggestions from the experts, a revised questionnaire for the second round was drafted, which included information regarding the level of agreement on each question and addressed most of the suggestions related to terminology. This second round took place during the week of 25–31 July 2024. In this round, experts were asked to reassess their previous responses, considering the new information obtained from the first round, to reach a consensus.
- (1)
- They reflect the target population for the final survey, and they are IT specialists in the banking industry with significant expertise in the deployment and implementation of blockchain technology.
- (2)
- In the academic sector, specialists whose research efforts are connected in some form to dynamic capabilities, banking performance, and/or economics were chosen.
4. Results
4.1. Indirect Effects
4.2. Validating Higher Order Construct
5. Discussions and Implications
6. Conclusions
- Sample limitations: This study’s sample focused on banks, many of which may still be in the early stages of blockchain adoption. Banks with limited blockchain experience may not yet be fully capitalizing on their potential, leading to less dramatic performance improvements.
- The novelty of technology: Blockchain is still an emerging technology, and its widespread application across all banking operations remains limited.
- Many banks may still be in the pilot phase of blockchain projects, which limits observable performance improvements. The full benefits of blockchain adoption may only become apparent after further maturity and integration.
- Data processing tools: The tools used for data collection and analysis might not fully capture the complexity of the connections between Blockchain technology effects and performance. Future studies could benefit from more advanced data analytics tools to assess blockchain’s impact more comprehensively.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
BC | Blockchain Technology Effects |
BP | Banks’ Performance |
DC | Dynamic Capabilities |
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Study | Focus Area | Key Findings | Results |
---|---|---|---|
Kshetri, 2022 [8] | Blockchain and strategic flexibility. | Smart contracts automate processes, reducing friction in dynamic environments. | Increased operational efficiency and faster response to market changes. |
Tapscott and Tapscott, 2023 [16] | Trust and governance in blockchain ecosystems. | Blockchain reduces transaction costs and enhances trust in decentralized networks. | Improved governance structures and stakeholder collaboration. |
Kouhizadeh et al., 2021 [20] | Blockchain in supply chain agility. | Blockchain improves transparency, traceability, and real-time decision-making. | Enhanced supply chain resilience and dynamic capability development. |
Wong et al., 2022 [91] | Blockchain for innovation capabilities. | Decentralized systems foster collaborative innovation and knowledge sharing. | Firms leveraging blockchain exhibit higher innovation performance. |
H.Treiblmaier, 2023 [92] | Dynamic capabilities in blockchain adoption. | Blockchain enables rapid reconfiguration of resources in response to disruptions. | Organizations achieve higher adaptability and competitive positioning. |
Aspect | General Research Findings (Previous Summary) | Our focus—Banking-sector-specific |
Scope | Cross-industry (supply chain, innovation, governance) | Exclusive focus on banking sector performance |
Key Findings on Blockchain | - Supports dynamic resource allocation [8,92] - Enhances transparency, automation, and trust [16,20] | - Reduces fraud and intermediary costs - Improves transaction security, compliance, and operational efficiency in banks |
Performance Metrics | - Strategic flexibility [8] - Supply chain resilience [20] - Innovation output [91] | - Customer satisfaction and regulatory compliance - Financial performance (ROA, cost efficiency) |
Dynamic Capabilities (DCs) | - dynamic capabilities strengthened through agility, innovation, and collaboration [93,94] | - dynamic capabilities in banks rely on regulatory adaptation, customer trust, and rapid fintech integration |
ID | Professional Profile | Years of Experience | Academic Qualification |
---|---|---|---|
1. | Professor of Economy | +15 years | PhD in Business Org. |
2. | Professor of Economy | +12 years | PhD in Economics. |
3. | Professor of Economy | +10 years | PhD in Business Statistics |
4. | Professor of Business Administration | +20 years | PhD in Business Org. |
5. | ITC Manager | +15 years | Graduate in Technology |
6. | Network Analyst | +10 years | Degree in ICT |
7. | Group Head, Operations | +20 years | Graduate in Compliance |
8. | Head of IT | +15 years | Master in Cyber Security |
9. | Group Head, IT | +20 years | Graduate in Technology |
10. | Blockchain Specialist | +10 years | Degree in IT |
11. | Technologist | +10 years | Graduate in Technology |
Question | Brief Explanation for Including the Question | Reference |
---|---|---|
Do you find it easy to use blockchain in your bank? | This question was to figure out whether banks are using blockchain technology or not | [79] |
If you were able to use blockchain, would this help you plan your activities better? | This question was to figure out whether banks are using blockchain technology or not | [109] |
How do you currently use blockchain technology? Select as many as you see possible | This question was to determine how blockchain is being used in banks | [27] |
Do you find it hard to use blockchain that are appropriate for your products? | This question was to determine how blockchain is being used in banks | [27] |
What information would you like to receive regarding blockchain efficiency? | This question was to determine blockchain efficiency | [110] |
How would you rate the following functions of blockchain? | This question was to determine blockchain functionality | [31] |
How should the system work? Perhaps other features you would like to see? | This question was to determine blockchain features | [36] |
What blockchain technology do you use most at your bank? | This question was to determine blockchain usage | [111] |
Which of these blockchain technology do you use less often? | This question was to determine blockchain usage | [111] |
What information should be included in a blockchain? | This question was to determine blockchain features | [36] |
What are the potentials of blockchain technology? | This question was to determine blockchain features | [36] |
What are the dangers of blockchain technology? | This question was to determine blockchain security and safety | [112] |
How do you mitigate against these dangers of using blockchain technology? | This question was to determine blockchain security and safety | [112] |
How should blockchain work? Perhaps other features you would like to see? | This question was to determine blockchain functionality | [31] |
What stops your bank from using blockchain? | This question was to determine blockchain adaptability | [115] |
Does blockchain technology improve banks’ performance? | This question was to determine blockchain efficiency | [110] |
Does blockchain technology speed up your transactions? | This question was to determine blockchain efficiency | [110] |
Does blockchain technology provide more security for your transactions? | This question was to determine blockchain security and safety | [115] |
Does blockchain technology improve customer satisfaction? | This question was to determine blockchain efficiency | [110] |
Does blockchain technology reduce operational costs? | This question was to determine blockchain efficiency | [110] |
Does the usage of blockchain technology comply with regulatory directives? | This question was to determine blockchain compliance with regulations | [116] |
Does blockchain technology improve your banks’ efficiency? | This question was to determine blockchain efficiency | [110] |
Does blockchain technology improve your bank’s financial performance? | This question was to determine blockchain efficiency | [110] |
Does blockchain technology increase your banks’ market share? | This question was to determine blockchain performance | [117] |
Does blockchain technology increase your banks’ ranking in the industry? | This question was to determine blockchain performance | [117] |
Do you believe blockchain enhances your bank Absorption capacity? | This question was to determine bank’s blockchain capabilities | [118] |
Do you believe blockchain improves your bank Adoption capacity? | This question was to determine bank’s blockchain capabilities | [118] |
Do you believe blockchain boosts your bank detection capacity? | This question was to determine bank’s blockchain capabilities | [118] |
Do you believe blockchain augments your bank Innovation capacity? | This question was to determine bank’s blockchain capabilities | [118] |
Please state any other competitive advantages your bank enjoys from the use of blockchain technology. | This question was to determine bank’s competitive advantage using blockchain | [118] |
Variables | Categories | Frequency | Response Rate (%) |
---|---|---|---|
Gender | Female | 67 | 39.4% |
Male | 103 | 60.6% | |
Age | 25–35 | 79 | 46.65% |
35–50 | 75 | 43.83% | |
51 and above | 16 | 9.25% |
Path coefficient | |||||
Original sample (o) | Sample mean (M) | Standard deviation (STDEV) | T-statistics ([O/STDEV]) | p-values | |
BC → BP | 0.259 | 0.274 | 0.083 | 3.143 | 0.002 |
BC → DC | 0.611 | 0.621 | 0.047 | 13.055 | 0.000 |
DC → BP | 0.305 | 0.312 | 0.080 | 3.825 | 0.000 |
DC x BC → BP | 0.341 | 0.326 | 0.061 | 5.565 | 0.000 |
R-square | |||||
Original sample (o) | Sample mean (M) | Standard deviation (STDEV) | T-statistics ([O/STDEV]) | p-values | |
BP | 0.553 | 0.563 | 0.050 | 11.000 | 0.000 |
DC | 0.373 | 0.387 | 0.057 | 6.494 | 0.000 |
R-square adjusted | |||||
Original sample (o) | Sample mean (M) | Standard deviation (STDEV) | T-statistics ([O/STDEV]) | p-values | |
BP | 0.545 | 0.555 | 0.051 | 10.647 | 0.000 |
DC | 0.369 | 0.384 | 0.058 | 6.391 | 0.000 |
f-square | |||||
Original sample (o) | Sample mean (M) | Standard deviation (STDEV) | T-statistics ([O/STDEV]) | p-values | |
BC → BP | 0.085 | 0.106 | 0.065 | 1.311 | 0.190 |
BC → DC | 0.595 | 0.647 | 0.157 | 3.783 | 0.000 |
DC → BP | 0.126 | 0.142 | 0.075 | 1.681 | 0.093 |
DC x BC → BP | 0.191 | 0.185 | 0.069 | 2.769 | 0.000 |
Average Variance Extracted (AVE) | |||||
Original sample (o) | Sample mean (M) | Standard deviation (STDEV) | T-statistics ([O/STDEV]) | p-values | |
BC | 0.312 | 0.314 | 0.026 | 11.855 | 0.000 |
BP | 0.385 | 0.387 | 0.029 | 13.464 | 0.000 |
DC | 0.414 | 0.416 | 0.027 | 15.432 | 0.000 |
Composite Reliability (rho_c) | |||||
Original sample (o) | Sample mean (M) | Standard deviation (STDEV) | T-statistics ([O/STDEV]) | p-values | |
BC | 0.830 | 0.828 | 0.018 | 45.121 | 0.000 |
BP | 0.756 | 0.754 | 0.024 | 31.791 | 0.000 |
DC | 0.848 | 0.848 | 0.015 | 58.062 | 0.000 |
Composite Reliability (rho_a) | |||||
Original sample (o) | Sample mean (M) | Standard deviation (STDEV) | T-statistics ([O/STDEV]) | p-values | |
BC | 0.787 | 0.787 | 0.028 | 28.566 | 0.000 |
BP | 0.617 | 0.618 | 0.050 | 12.314 | 0.000 |
DC | 0.803 | 0.805 | 0.022 | 36.464 | 0.000 |
DC x BC | 1.000 | 1.000 | 0.000 | n/a | n/a |
Cronbach’s alpha | |||||
Original sample (o) | Sample mean (M) | Standard deviation (STDEV) | T-statistics ([O/STDEV]) | p-values | |
BC | 0.775 | 0.773 | 0.028 | 27.725 | 0.000 |
BP | 0.607 | 0.604 | 0.047 | 13.011 | 0.000 |
DC | 0.795 | 0.974 | 0.023 | 34.447 | 0.000 |
Heterotrait-monotrait ratio (HTMT) | |||||
Confidence intervals | |||||
Original sample (o) | Sample mean (M) | 25% | 97.5% | ||
BP ↔ BC | 0.868 | 0.870 | 0.725 | 1.012 | |
DC ↔ BC | 0.761 | 0.763 | 0.654 | 0.863 | |
DC ↔ BP | 0.819 | 0.825 | 0.686 | 0.960 |
Fornell-Larker Criterion | |||
BC | BP | DC | |
BC | 0.559 | ||
BP | 0.614 | 0.621 | |
DC | 0.611 | 0.613 | 0.643 |
Total Indirect effects | |||||
Original sample (o) | Sample mean (M) | Standard deviation (STDEV) | T-statistics ([O/STDEV]) | p-values | |
BC → BP | 0.187 | 0.194 | 0.054 | 3.473 | 0.001 |
Specific Indirect effects | |||||
Original sample (o) | Sample mean (M) | Standard deviation (STDEV) | T-statistics ([O/STDEV]) | p-values | |
BC → DC → BP | 0.187 | 0.194 | 0.054 | 3.473 | 0.001 |
Total effects | |||||
Original sample (o) | Sample mean (M) | Standard deviation (STDEV) | T-statistics ([O/STDEV]) | p-values | |
BC → BP | 0.446 | 0.467 | 0.070 | 6.390 | 0.000 |
BC → DC | 0.611 | 0.621 | 0.047 | 13.055 | 0.000 |
DC → BP | 0.305 | 0.312 | 0.080 | 3.825 | 0.000 |
DC x BC → BP | 0.341 | 0.326 | 0.061 | 5.565 | 0.000 |
Original Sample (o) | Sample Mean (M) | Standard Deviation (STDEV) | T-Statistics ([O/STDEV]) | p Values | |
---|---|---|---|---|---|
BP ↔ BC | 0.614 | 0.624 | 0.049 | 12.512 | 0.000 |
DC ↔ BC | 0.611 | 0.621 | 0.047 | 13.055 | 0.000 |
DC ↔ BP | 0.613 | 0.621 | 0.050 | 12.333 | 0.000 |
DC x BC ↔ BC | 0.491 | 0.478 | 0.067 | 7.295 | 0.000 |
DC x BC ↔ BP | 0.603 | 0.587 | 0.046 | 13.015 | 0.000 |
DC x BC ↔ DC | 0.437 | 0.427 | 0.072 | 6.091 | 0.000 |
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Ogunrinde, A.; De-Pablos-Heredero, C.; Montes-Botella, J.-L.; Fernández-Sanz, L. The Impact of Blockchain Technology and Dynamic Capabilities on Banks’ Performance. Big Data Cogn. Comput. 2025, 9, 144. https://doi.org/10.3390/bdcc9060144
Ogunrinde A, De-Pablos-Heredero C, Montes-Botella J-L, Fernández-Sanz L. The Impact of Blockchain Technology and Dynamic Capabilities on Banks’ Performance. Big Data and Cognitive Computing. 2025; 9(6):144. https://doi.org/10.3390/bdcc9060144
Chicago/Turabian StyleOgunrinde, Abayomi, Carmen De-Pablos-Heredero, José-Luis Montes-Botella, and Luis Fernández-Sanz. 2025. "The Impact of Blockchain Technology and Dynamic Capabilities on Banks’ Performance" Big Data and Cognitive Computing 9, no. 6: 144. https://doi.org/10.3390/bdcc9060144
APA StyleOgunrinde, A., De-Pablos-Heredero, C., Montes-Botella, J.-L., & Fernández-Sanz, L. (2025). The Impact of Blockchain Technology and Dynamic Capabilities on Banks’ Performance. Big Data and Cognitive Computing, 9(6), 144. https://doi.org/10.3390/bdcc9060144