The Impact of Blockchain Technology and Dynamic Capabilities on Banks’ Performance

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

To improve the quality of the article, authors should consider the following aspects: Consider whether it would be possible to expand the research sample; the number of respondents should be increased to make the results more representative of the banking sector. You may consider comparing results from different countries or regions to gain a more comprehensive perspective. Consideration of the level of blockchain implementation and it is recommended to divide the surveyed banks into groups according to the degree of blockchain implementation (e.g. early adoption, advanced implementation) and check whether the differences in the results are statistically significant. I propose to expand the regulatory analysis and, if they agree, the authors should discuss what regulations on blockchain in banking exist in Spain and Europe and how they may affect the dynamic capabilities and performance of banks. A comparative overview of regulations with other countries could be added to consider the broader context. Better substantiation of the impact of blockchain on financial performance because the article should include a more thorough analysis of intermediate factors such as reduction of operating costs, increased customer loyalty or improved process efficiency. More advanced methods of analysis, e.g. moderation or mediation, can be used to better explain the relationships.

Author Response

Dear reviewer,

Thank you for your feedback and recommendations. We are grateful for you sharing your knowledge and time with us.

According to your suggestions, we have included changes using blue color in the new manuscript.

 

Regarding your comment: “To improve the quality of the article, authors should consider the following aspects: Consider whether it would be possible to expand the research sample; the number of respondents should be increased to make the results more representative of the banking sector. You may consider comparing results from different countries or regions to gain a more comprehensive perspective”

 

From the population, the five biggest banks in Spain have been included. These big banks, Caixa Bank, BBVA, Santander, Sabadell, and Interbank, account for 69.5% of the banking industry in Spain [Statistical, 2024], so considering that the focus of the paper is the Spanish banking industry, and we have included the most outstanding banks, we thank you for your suggestion, and we will include this extension as a further line of research

 

Regarding your comment: “Consideration of the level of blockchain implementation and it is recommended to divide the surveyed banks into groups according to the degree of blockchain implementation (e.g. early adoption, advanced implementation) and check whether the differences in the results are statistically significant”,

 

The five banks chosen were all early adopters therefore, there will be no change in their SEM analysis

 

Table of Adoption Level

Name	Level of Adoption	Implementation Rate	Group
Santander, Caixa, Sabadell, BBVA, InterBank	Early	Advance (30% & above)	1
Others	Intermediary	Medium (10%-30%)	2
Others	Late	Initial (0% - 10%)	3

 

 

Regarding your comment: “I propose to expand the regulatory analysis and, if they agree, the authors should discuss what regulations on blockchain in banking exist in Spain and Europe and how they may affect the dynamic capabilities and performance of banks. A comparative overview of regulations with other countries could be added to consider the broader context.”

 

 

Blockchain Regulations in the Banking Sector: Spain, Europe, and a Global Perspective

1. Anti-Money Laundering (AML) and Know Your Customer (KYC) Compliance
   Spain adheres to the EU’s 5th Anti-Money Laundering Directive (5AMLD), effective since January 2020, which extends to virtual assets and blockchain-based financial services. Banks engaging in crypto-related activities, such as issuing tokenized assets, must register with the Bank of Spain under Royal Decree 7/2021, overseen by both the Bank of Spain and the National Securities Market Commission (CNMV) [1]. This registration process ensures transparency but can be slow, with additional information requests often delaying approvals [2]. For banks, this creates a compliance burden that may hinder the speed of blockchain adoption, particularly for smaller institutions.
2. Regulatory Sandbox for Innovation
   Through Law 7/2020 for the Digital Transformation of the Financial System, Spain established a regulatory sandbox in 2020, enabling banks to test blockchain innovations like smart contracts and tokenized securities in a controlled environment [3]. This initiative encourages experimentation while maintaining oversight, but the bureaucratic approval process can pose challenges for resource-constrained banks, potentially limiting their ability to innovate.
3. Data Protection Challenges with GDPR
   The EU’s General Data Protection Regulation (GDPR) presents a significant hurdle for blockchain adoption in Spain. Blockchain’s immutable nature conflicts with GDPR’s “right to be forgotten,” requiring banks to adopt hybrid solutions—such as storing sensitive data off-chain—to comply with data deletion mandates [4]. This added complexity can reduce the efficiency gains blockchain offers, impacting banks’ operational performance.

European Union

The EU has developed a cohesive regulatory framework for blockchain, aiming to lead globally in digital finance while ensuring stability and consumer trust.

1. Markets in Crypto-Assets Regulation (MiCA)
   MiCA, finalized in 2023 and fully implemented by late 2024, regulates crypto-assets, including stablecoins and tokenized securities used in banking. It imposes strict requirements on issuers and service providers, such as transparency and capital adequacy, to prevent market abuse and protect consumers [5]. For banks, MiCA’s rules on “significant” stablecoins—requiring 60% of reserves to be held in bank deposits—could lead to sectoral concentration risks if banks specialize in stablecoin relationships [6]. While MiCA provides clarity, its stringent standards may favor larger banks, potentially marginalizing smaller ones in the crypto-asset space.
2. Digital Operational Resilience Act (DORA)
   Effective from 2025, DORA focuses on cybersecurity and operational resilience for financial entities using blockchain. It mandates robust risk management for third-party tech providers and ensures the security of DLT systems [7]. This enhances trust in blockchain applications but increases compliance costs, which could strain banks’ financial resources, particularly for those already investing heavily in digital transformation.
3. European Blockchain Services Infrastructure (EBSI)
   The EU’s EBSI, part of the European Blockchain Partnership, supports cross-border use cases like KYC and digital identity, offering banks opportunities to reduce costs and improve efficiency [8]. However, aligning with EBSI’s technical and legal standards requires significant investment, which may exclude smaller banks unable to meet these demands.
4. DLT Pilot Regime
   Since 2023, the DLT Pilot Regime has allowed banks to test DLT-based market infrastructures for trading and settling tokenized securities, offering exemptions from existing rules to foster innovation [9]. While this regime encourages experimentation, banks must still comply with reporting standards, which can be resource-intensive.

 

Regarding your comment: “Better substantiation of the impact of blockchain on financial performance because the article should include a more thorough analysis of intermediate factors such as reduction of operating costs, increased customer loyalty or improved process efficiency.”

 

 

Intermediate Factors and Their Contribution to Process Efficiency

1. Reduction of Operating Costs

Blockchain can significantly lower operating costs for banks by automating processes, reducing intermediaries, and minimizing fraud.

 

Automation and Disintermediation: Blockchain enables the use of smart contracts—self-executing agreements with predefined rules coded on the blockchain. In trade finance, smart contracts can automate payment releases once conditions (e.g., delivery confirmation) are met, eliminating intermediaries like escrow agents or clearinghouses.

2. Increased Customer Loyalty

Blockchain can enhance customer loyalty by improving transparency, security, and service delivery, which in turn supports process efficiency.

• Transparency and Trust: Blockchain’s transparent ledger allows customers to track transactions in real time, fostering trust.
• Contribution to Process Efficiency: Transparent processes reduce the need for customer service interventions, as customers can independently verify transaction statuses. This decreases the workload on customer support teams, allowing banks to allocate resources more efficiently and streamline operations.
• Enhanced Security: Blockchain’s cryptographic security protects customer data, reducing the risk of breaches. In an era where data privacy is a top concern, this can strengthen customer trust.
• Contribution to Process Efficiency: Secure data management reduces the time and resources spent on handling data breaches or compliance violations. By minimizing these disruptions, banks can maintain smooth operations, further improving process efficiency.

3. Improved Process Efficiency

Process efficiency itself is a critical intermediate factor, as blockchain directly enhances the speed, accuracy, and scalability of banking operations.

• Faster Transaction Settlement:
• Direct Impact on Process Efficiency: Faster settlements mean banks can process more transactions in less time, increasing throughput and reducing operational bottlenecks.
• Streamlined KYC and Compliance: Blockchain can create a shared KYC database, allowing banks to verify customer identities quickly and securely.
• Direct Impact on Process Efficiency: By reducing onboarding times, blockchain minimizes delays in customer acquisition, allowing banks to scale operations more effectively. This also frees up resources previously spent on manual verification, enabling banks to focus on core activities.

How These Factors Improve Financial Performance

The intermediate factors—reduction of operating costs, increased customer loyalty, and improved process efficiency—collectively enhance financial performance through the following mechanisms:

1. Cost Savings Leading to Higher Profit Margins:
   
   
2. Revenue Growth Through Customer Loyalty:
   
   
3. Scalability and Market Competitiveness Through Process Efficiency:
   
   

 

 

Regarding your comment: “More advanced methods of analysis, e.g. moderation or mediation, can be used to better explain the relationships.”

 

The decision to estimate the proposed model using PLS-SEM (Partial Least Squares Structural Equation Modeling) was based on the possibility of incorporating latent variables into the model as is the case. Additionally, PLS-SEM can effectively handle small sample sizes and does not require the normality of the data assumption and complex theoretical frameworks. Therefore, it is appropriate for exploratory research, where the objective is to identify key constructs and develop theories.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The study revisits well-established discussions about blockchain’s benefits in banking, without introducing a truly novel approach or perspective.  The gap in literature is not well-defined. The authors claim that no prior studies have examined blockchain's effect on dynamic capabilities in banking, yet several cited works already address similar concerns. The discussion lacks a critical comparison with existing studies, which is essential to justify why this work adds something new.

- The Structural Equation Modeling (SEM) approach is inadequately justified. While SEM is widely used in business research, the paper fails to explain why it was chosen over alternative methods.

- The Delphi method is mentioned in the methodology, but there is no clear explanation of how it was implemented. How were experts selected? How many rounds were conducted? What consensus threshold was used? There is no discussion on construct validity and reliability (beyond Cronbach’s Alpha).

- The conceptual model lacks a strong theoretical foundation. While the authors reference dynamic capability theory, there is no clear integration of how blockchain specifically aligns with key constructs like absorptive capacity, innovation capacity, and detection capability. "Blockchain-Based National Digital Identity Framework – Case of Palestine"  discusses blockchain-based identity management, referencing the digital identity framework as a real-world use case demonstrating blockchain’s role in securing digital transactions.and  "BlockASP: A Framework for AOP-based Model Checking Blockchain System"  supports discussions on blockchain security, particularly in ensuring transaction integrity and reducing vulnerabilities.
"AI-Powered AOP: Enhancing Runtime Monitoring with Large Language Models and Statistical Learning" discusses blockchain’s role in banking, but AI and machine learning’s role in fraud detection, risk assessment, and predictive banking analytics are missing.
"Integrating Data-Driven Security, Model Checking, and Self-Adaptation for IoT Systems using BIP Components" introduces a secure architecture, which could be referenced in discussions about blockchain’s role in financial IoT applications (e.g., real-time transaction validation). "OSM: Leveraging Model Checking for Observing Dynamic Behaviors in Aspect-Oriented Applications"  adds depth to discussions about verifying and monitoring contract behavior.

- The language is often repetitive and unclear. Several sentences lack precision

Author Response

Dear reviewer,

 

Thank you for your feedback and recommendations. We are grateful for you sharing your knowledge and time with us.

 

According to your suggestions, we have included changes using blue color font in the new manuscript.

 

Regarding your comment: “The study revisits well-established discussions about blockchain’s benefits in banking, without introducing a truly novel approach or perspective”

 

Thanks!

 

Regarding your comment: “The gap in the literature is not well-defined. The authors claim that no prior studies have examined blockchain's effect on dynamic capabilities in banking, yet several cited works already address similar concerns.”

 

This has been rectified

 

 

Regarding your comment: “The discussion lacks a critical comparison with existing studies, which is essential to justify why this work adds something new.”

 

This has been rectified

 

Regarding your comment: “The Structural Equation Modeling (SEM) approach is inadequately justified. While SEM is widely used in business research, the paper fails to explain why it was chosen over alternative methods”

 

Structural Equation Modeling (SEM) surpasses traditional statistical models by enabling the simultaneous analysis of complex, theory-driven relationships among multiple observed and latent variables while accounting for measurement error. It offers robust estimation, comprehensive fit indices, and flexibility for testing mediation, moderation, and multi-group comparisons, making it ideal for validating theoretical frameworks in disciplines such as psychology and social sciences.

The decision to estimate the proposed model using PLS-SEM (Partial Least Squares Structural Equation Modeling) was based on the possibility of incorporating latent variables into the model as it is the case. Additionally, PLS-SEM can effectively handle small sample sizes, does not require the normality of the data assumption, and complex theoretical frameworks. Therefore, it is appropriate for exploratory research, where the objective is to identify key constructs and develop theories.

 

Regarding your comment: “The Delphi method is mentioned in the methodology, but there is no clear explanation of how it was implemented. How were experts selected? How many rounds were conducted? What consensus threshold was used? There is no discussion on construct validity and reliability (beyond Cronbach’s Alpha).”

The Delphi method was utilized in this study as a systematic way to gather expert opinions and frame questionnaires on the factors influencing the adoption of blockchain technology among Spanish firms. Here is a detailed breakdown of the methodology implementation:

The Delphi method was employed to achieve a consensus among experts regarding the relevant factors impacting blockchain adoption. This approach typically involves two rounds of questionnaires sent to a panel of experts, allowing for anonymity and iterative feedback.

Experts were selected based on specific criteria to ensure relevant knowledge and experience in blockchain technology and its applications. Individuals with a substantial understanding of the intersection of dynamic capabilities, banking, and blockchain were prioritized, which included experts in blockchain technology (2 experts from two universities), three experts in dynamic capabilities (from 2 universities), and 15 experts in implementing blockchain technology in banks). 3 rounds were conducted. A consensus threshold of 80% agreement was utilized.

Also, the question about validity and reliability is already adequately addressed in the paper. kindly check lines 508-538 in the manuscript sent to us.

 

Concerning to this comment: “The conceptual model lacks a strong theoretical foundation. While the authors reference dynamic capability theory, there is no clear integration of how blockchain specifically aligns with key constructs like absorptive capacity, innovation capacity, and detection capability. "Blockchain-Based National Digital Identity Framework – Case of Palestine” discusses blockchain-based identity management, referencing the digital identity framework as a real-world use case demonstrating blockchain’s role in securing digital transactions. And  "BlockASP: A Framework for AOP-based Model Checking Blockchain System"  supports discussions on blockchain security, particularly in ensuring transaction integrity and reducing vulnerabilities. “AI-Powered AOP: Enhancing Runtime Monitoring with Large Language Models and Statistical Learning" discusses blockchain’s role in banking, but AI and machine learning’s role in fraud detection, risk assessment, and predictive banking analytics are missing.
"Integrating Data-Driven Security, Model Checking, and Self-Adaptation for IoT Systems using BIP Components" introduces a secure architecture, which could be referenced in discussions about blockchain’s role in financial IoT applications (e.g., real-time transaction validation). "OSM: Leveraging Model Checking for Observing Dynamic Behaviors in Aspect-Oriented Applications” adds depth to discussions about verifying and monitoring contract behavior.”

This has been addressed in the new manuscript

Integration of Blockchain with Dynamic Capability Theory

1. Absorptive Capacity: Acquiring and Assimilating External Knowledge

• How Blockchain Aligns with Absorptive Capacity:

2. Innovation Capacity: Developing and Implementing New Ideas

• How Blockchain Aligns with Innovation Capacity:

3. Detection Capability: Sensing Opportunities and Threats

• How Blockchain Aligns with Detection Capability:

Theoretical Integration: A Holistic View

Blockchain integrates with DCT by enhancing all three phases of dynamic capabilities—sensing, seizing, and transforming:

• Sensing (Detection Capability)
• Seizing (Innovation and Absorptive Capacity)
• Transforming

•  

1. Absorptive Capacity

Integration with Blockchain:

• Information Flow
• Collaboration and Sharing
• Real-Time Data Access

2. Innovation Capacity

Integration with Blockchain:

• Facilitating New Business Models
• Enhanced Trust and Security
• Rapid Prototyping.

3. Detection Capability

Definition: Detection capability refers to an organization’s ability to identify opportunities and threats in its environment.

Integration with Blockchain:

• Transparency and Traceability
• Data Analytics
• Risk Management

 

Regarding the comment: The language is often repetitive and unclear. Several sentences lack precision

Language has been checked and corrected

 

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

It is clear that the authors have made some improvements in response to my original major revision request. However, some critical weaknesses remain unresolved and need further revision.  Although the authors claim they clarified the literature gap, the novelty is still not sufficiently compelling. The updated integration of blockchain with dynamic capabilities lacks fresh insights and does not critically differentiate the study from existing work. Authors should explicitly explain how their model is different from prior blockchain + dynamic capability models with specific, comparative citations. A table comparing their study with prior works would help. 

The integration between blockchain and dynamic capabilities remains surface-level. Listing absorptive/innovation/detection capacities and adding blockchain examples is helpful, but it’s still descriptive rather than theoretically rigorous. They should develop hypotheses based on theoretical mechanisms, not just state alignments. Why and how blockchain enhances each capability should be supported by theoretical reasoning, not just examples.

Delphi Method (Partially Addressed), there is some details about Delphi (experts, rounds, threshold), but no validation or evidence (e.g., panelist credentials, survey design, consensus statistics) is shown.

I see the authors explained why they used PLS-SEM, but no model fit indices (like SRMR, AVE, Composite Reliability) or validity checks are shown clearly in the manuscript. What are measurement model results (convergent validity, discriminant validity, reliability metrics) and structural model assessment following PLS-SEM standards.

Strengthen the theoretical framework, detail Delphi process more transparently, and complete SEM reporting properly. Therefore, why I asked the author to revisit the previous feedback. 

remove duplicated text

 

Comments on the Quality of English Language

Minor English editing

Author Response

Dear reviewer, we do appreciate the evaluation of our paper, and the valuable feedback provided by you. Following your recommendations, we have implemented all your suggestions. The changes made are indicated with another color (blue). We believe the paper is now enhanced and substantially improved, for which we are very grateful.

We remain entirely at your disposal.

Following there are the answers to your questions:

Comments and Suggestions for Authors

It is clear that the authors have made some improvements in response to my original major revision request. However, some critical weaknesses remain unresolved and need further revision.  Although the authors claim they clarified the literature gap, the novelty is still not sufficiently compelling. The updated integration of blockchain with dynamic capabilities lacks fresh insights and does not critically differentiate the study from existing work. Authors should explicitly explain how their model is different from prior blockchain + dynamic capability models with specific, comparative citations. A table comparing their study with prior works would help. 

Response from authors:

Tables with prior works approaches, key findings and results, comparison of results and key differences have been included to evidence the interest and the existing gap recognized:

Table: Summary of Research Findings

Study	Focus Area	Key Findings	Results
(Kouhizadeh et al., 2021) [1]	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) [2]	Blockchain for innovation capabilities	Decentralized systems foster collaborative innovation and knowledge sharing.	Firms leveraging blockchain exhibit higher innovation performance.
(Treiblmaier, 2023) [3]	Dynamic capabilities in blockchain adoption	Blockchain enables rapid reconfiguration of resources in response to disruptions.	Organizations achieve higher adaptability and competitive positioning.
(Kshetri, 2022) [4]	Blockchain and strategic flexibility	Smart contracts automate processes, reducing friction in dynamic environments.	Increased operational efficiency and faster response to market changes.
(Tapscott & Tapscott, 2023) [5]	Trust and governance in blockchain ecosystems	Blockchain reduces transaction costs and enhances trust in decentralized networks.	Improved governance structures and stakeholder collaboration.

 

 

 

Table: Comparative Analysis

Aspect	General Research Findings (Previous Summary)	Ogunrinde et al. (2025) – Banking Sector Focus
Scope	Cross-industry (supply chain, innovation, governance)	Exclusive focus on banking sector performance
Key Findings on Blockchain	- Enhances transparency, automation, and trust [1][5] - Supports dynamic resource allocation [3][4]	- Improves transaction security, compliance, and operational efficiency in banks - Reduces fraud and intermediary costs
Dynamic Capabilities (DCs)	- DCs strengthened through agility, innovation, and collaboration [2][3]	- DCs in banks rely on regulatory adaptation, customer trust, and rapid fintech integration
Performance Metrics	- Supply chain resilience [1] - Innovation output [2] - Strategic flexibility [4]	- Financial performance (ROA, cost efficiency) - Customer satisfaction and regulatory compliance

 

Key Differences

1. Sector-Specific Challenges:
• Banks: Face stringent regulations, making DCs dependent on risk management and compliance (Ogunrinde et al.).
• Other Industries: Focus on supply chain resilience [1] or open innovation [2].
2. Performance Outcomes:
• Banks: Measure success via financial metrics (ROA, cost-income ratio).
• General Studies: Track non-financial outcomes (e.g., innovation speed [2], agility [3]).
3. Adoption Barriers:
• We highlighted legacy systems and regulatory hurdles in banking, whereas broader research discusses technical scalability [3] or collaborative resistance [5].

Comments and suggestions from reviewer:

“The integration between blockchain and dynamic capabilities remains surface-level. Listing absorptive/innovation/detection capacities and adding blockchain examples is helpful, but it’s still descriptive rather than theoretically rigorous. They should develop hypotheses based on theoretical mechanisms, not just state alignments. Why and how blockchain enhances each capability should be supported by theoretical reasoning, not just examples.”

• Response from authors:

Hypotheses have been developed accordingly:

• Hypothesis 1: Organizations that adopt blockchain technology will exhibit a higher level of absorptive capacity compared to those that do not, as evidenced by their ability to identify, assimilate, and apply valuable external information more effectively.
• Hypothesis 2: The adoption of blockchain technology will positively impact an organization's innovation capacity, leading to the development and introduction of a greater number of new products, services, or processes.
• Hypothesis 3: Organizations utilizing blockchain technology will demonstrate a higher detection capacity, enabling them to identify and understand changes, opportunities, and threats in their external environment more effectively than organizations without blockchain adoption.
• Hypothesis 4: Dynamic capabilities moderate the relationship between Blockchain technology implementation and bank performance, enhancing the effectiveness of blockchain in driving performance outcomes.

 

Comments and suggestions from reviewer:

Delphi Method (Partially Addressed), there is some details about Delphi (experts, rounds, threshold), but no validation or evidence (e.g., panelist credentials, survey design, consensus statistics) is shown.

Response from authors:

A table has been included to show the panelist credentials and the survey design with consensus statistics has been enumerated appropriately. Paragraph 707 - 790

Table 1. Panel of experts who participated in the study.

ID              Professional Profile           Years of Experience      Academic Qualifcation

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 ADE                              +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 Cybersecurity 9.              Group Head, IT                                   +20 years                  Graduate in Technology 10.           Blockchain Specialist                     +10 years                 Degree in IT 11.           Technologist                                       +10 years                  Graduate in Technology

 

 

Comments and suggestions from reviewer:

I see the authors explained why they used PLS-SEM, but no model fit indices (like SRMR, AVE, Composite Reliability) or validity checks are shown clearly in the manuscript. What are measurement model results (convergent validity, discriminant validity, reliability metrics) and structural model assessment following PLS-SEM standards?

Response from authors:

Thank you for your suggestions. Please note that the measurement model results are already in the manuscript from paragraphs 994-1172, some of which are listed below:

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

The PLS-SEM model does not allow for the use of SRMR; therefore, there is no result for SRMR.

Comments and suggestions from reviewer:

Strengthen the theoretical framework, detail the Delphi process more transparently, and complete SEM reporting properly. Therefore, why I asked the author to revisit the previous feedback. 

Response from authors:

Response: The theoretical framework has been strengthened, the Delphi process has been enumerated and SEM reporting done appropriately.

For instance, on the Delphi: Preliminary Assessment of the Questionnaire for the Initial Round of the Delphi Study:

A preliminary consultation was conducted with two experts not affiliated with the expert panel, one possessing a financial background and the other an academic background. This assessment identified several areas for enhancement concerning the measurement scales and terminology typically utilized within the financial sector. The proposed modifications were subsequently reviewed and approved by the research team.

Preparation of the Final Questionnaire for the Initial Round of the Delphi Study:

The aforementioned recommendations were integrated into the questionnaire, which was ultimately distributed to the expert panel.

Exploratory Phase: This phase involved two rounds of expert consultations aimed at achieving consensus regarding the relevance and validity of the Delphi Process (DP) items and their corresponding measurement scales.

- 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 DPs. The questionnaire was organized into three sections, each corresponding to one of the three DPs. 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 DPs not addressed in this study based on their professional experience. This initial round was conducted during the week of June 18–24, 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 July 25–31, 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.

Final Phase: After processing the responses and analyzing the overall results from the second round, the research team compiled a report detailing the findings. Following a thorough examination of the experts' comments and suggestions, consensus was achieved on all items and measurement scales, thereby concluding the Delphi process. Consequently, the definitive and validated DP items were generated and incorporated into the DP questionnaire.

The experts selected were based on specific criteria to ensure relevant knowledge and experience in blockchain technology and its applications. Individuals with a substantial understanding of the intersection of dynamic capabilities, banking, and blockchain were prioritized, which included experts in blockchain technology (3 experts from two universities), three experts in dynamic capabilities (from 2 universities), and 7 experts in implementing blockchain technology in banks. A consensus threshold of 80% agreement was utilized. The original sample included 800 respondents. However, only respondents who answered ̈Yes ̈, meaning that they knew what blockchain is and had knowledge about its applications, were included in the study. This ensures the accuracy of the results of the Delphi

Comments and suggestions from reviewers:

Remove duplicated text

Response from authors:

• Duplicated text has been removed

 

Author Response File: Author Response.pdf

Round 3

Reviewer 2 Report

Comments and Suggestions for Authors

The resubmitted article demonstrates considerable improvement from the last review feedback. Specifically, the justification of PLS-SEM has been bettered, and the Delphi technique has also been more properly explained. The integration of the Dynamic Capability Theory (DCT) constructs—absorptive capacity, innovation capacity, and detection capability—into blockchain concepts is an enticing addition and supports the theoretical underpinning more.

- It continues to not show clearly how it is making a distinctive contribution to the literature. The "novel contribution" must be explicitly stated—what is the question that has not been addressed before, and how does this research address it? The review of earlier research is still more descriptive than critical. The paper must incorporate a critical comparison of its findings with at least 3–5 recent and directly comparable studies in order to substantiate its significance.

 

Author Response

Dear reviewer, we do appreciate the evaluation of our paper, and the valuable feedback provided by you. Following your recommendations, we have implemented all your suggestions. The changes made are indicated with another color. We believe the paper is now enhanced and substantially improved, for which we are very grateful.

We remain entirely at your disposal.

Following there are the answers to your questions:

Reviewer Q: The "novel contribution" must be explicitly stated

 

Authors Response:

The novel contributions of the authors to the field of blockchain technology and dynamic capabilities are as follows:

The study explores the relationship between blockchain technology adoption and banks' performance, revealing those dynamic capabilities, such as adaptability, innovation, and market responsiveness, acting as critical mediators. The authors challenge the traditional linear technology adoption model and emphasize the importance of developing these capabilities alongside technology integration. The research provides empirical evidence from banks in Spain, demonstrating that blockchain adoption positively affects dynamic capabilities, leading to enhanced operational efficiency, cost reductions, and competitive advantage. The authors propose a conceptual model that integrates blockchain technology effects, dynamic capabilities, and bank performance, offering practical insights for banking managers. This idea is novel as we are not aware of any existing literature that has dealt with this. Future research should focus on larger, diverse samples and longitudinal studies.

 

Reviewer Q: The question that had not been addressed before, and how does this research address it?

Authors Response:

To our knowledge, no author has adequately answered this question:

RQ4: Do Dynamic capabilities moderate the relationship between Blockchain technology implementation and bank performance, enhancing the effectiveness of blockchain in driving performance outcomes?

 

The question that had not been addressed before in the existing literature is the connection between blockchain technology and the development of dynamic capabilities in the banking sector. While many studies have explored the effects of blockchain and dynamic capabilities on firms in various industries, no prior research had explicitly linked blockchain technology with the generation and enhancement of dynamic capabilities in the banking sector.

This research addresses this gap by investigating how blockchain technology enables dynamic capabilities such as adaptability, innovation, and market responsiveness within banks. It empirically examines the mediating role of dynamic capabilities in the relationship between blockchain technology adoption and banks’ performance. By doing so, the study moves beyond the traditional view that blockchain directly impacts performance, instead revealing a more complex interplay where dynamic capabilities are essential mediators that enhance the benefits of blockchain adoption.

Thus, the study provides a novel theoretical and empirical framework that clarifies how blockchain technology contributes to building dynamic capabilities, which in turn improve operational efficiency, cost reduction, and competitive advantage in banks. This approach offers new insights into the strategic value of blockchain in fostering organizational capabilities necessary for thriving in rapidly changing financial environments.

 

 

Reviewer Q: The paper must incorporate a critical comparison of its findings with at least 3–5 recent and directly comparable studies in order to substantiate its significance.

Authors Response:

Ref [8] Kshetri, N. (2021)

[8]provides a comprehensive examination of blockchain technology's potential to enhance transparency and foster trust among citizens, proposing that such technological frameworks can effectively mitigate corruption and improve governance. However, while the study highlights promising applications, it somewhat overlooks the inherent challenges of implementing blockchain systems in diverse socio-political contexts where digital literacy and infrastructure may vary significantly. [8] tends to idealize the expected outcome of increased trust without critically addressing the possible disillusionment that could arise if blockchain solutions fail to deliver as anticipated. Overall, while [8] analysis contributes substantially to the discourse on blockchain’s societal impacts, it necessitates a more nuanced exploration of the interplay between technology and the complexities of human behavior and institutional dynamics.

Ref [16]: Tapscott

[16] argue that blockchain technology has the potential to revolutionize organizational structures by fostering greater transparency and collaboration, thereby dismantling traditional hierarchies. However, the authors tend to downplay the significant barriers to adoption, such as resistance from established stakeholders who may view decentralized models as threats to their power and profitability. While the envisioned shift towards more democratic organizational frameworks is compelling, the analysis lacks an exploration of the socio-economic disparities that could exacerbate inequities in access to blockchain technologies. Ultimately, while their insights provide a foundational understanding of blockchain's transformative potential, a more critical examination of the interconnection between technological advancement and existing organizational paradigms is necessary for a balanced perspective.

[95] establishes an early framework connecting blockchain technology to sustainable supply chain management, identifying both potential benefits (transparency, traceability, security) and implementation barriers (technical, organizational, supply chain-related, external). The authors employ a comprehensive literature review and conceptual analysis rather than empirical methods, which somewhat limits the practical validation of their proposed frameworks. While groundbreaking in positioning blockchain as a transformative technology for sustainable supply chains, the research primarily offers theoretical insights without fully addressing cost-benefit considerations or specific industry applications, nor the challenges and limitations of implementing blockchain for sustainability in real-world scenarios (Sharabati & Jreisat, 2024). Despite these limitations, the article has significantly influenced subsequent research by establishing foundational knowledge at the intersection of blockchain and sustainable supply chain management, evidenced by its extensive citation record in more recent empirical studies.

 

Ref [96] Wong

[96] methodically analyzes and comprehensively reviews blockchain adoption frameworks in supply chains, highlighting key adoption drivers (transparency, decentralization, security) while acknowledging significant barriers (technical limitations, regulatory uncertainty, resource requirements). [96] contributes valuable insights through their novel theoretical framework that categorizes adoption factors into technological, organizational, and environmental dimensions, providing both scholars and practitioners with a structured approach to understanding blockchain implementation challenges. Their evaluation reveals a concerning gap between theoretical benefits and practical applications, noting that despite the technology's potential, real-world implementation remains limited by scalability issues, integration challenges with legacy systems, and industry-specific adoption barriers. This timely synthesis advances the field by consolidating fragmented research streams and proposing a research agenda that emphasizes the need for more empirical studies on blockchain's actual sustainability impacts, economic feasibility, and industry-specific implementation strategies. Our study has taken one one fo these challenges by [96] in examining the effect of blockchain technology on banks’ performance.

Ref [97]: Treiblmaier

[97] explores blockchain technology as a transformative paradigm for B2C relationship management, conceptualizing a novel framework where decentralized loyalty programs empower consumers while potentially reducing operational costs for businesses. [97] skillfully integrate technology adoption theories with customer relationship management principles to propose that blockchain-based loyalty systems can fundamentally alter power dynamics between companies and customers, shifting from organization-controlled to consumer-centric models. While the theoretical foundation is robust, the article's reliance on conceptual arguments rather than empirical evidence limits its practical validation, though the authors acknowledge this constraint by positioning their work as an agenda for future research. Our paper saw this gap and provides an excellent framework for understanding the real-life impact of blockchain technology on banks’ performance empirically.

Though considered a challenge, such as the complexities of integrating blockchain with existing systems, our study has been able to examine this process across banks in Spain, which is a novel contribution to existing literature. When compared to other relevant literature, thus; this research presents a distinctive theoretical and empirical framework that elucidates the mediating function of dynamic capabilities in harnessing blockchain technology to improve bank performance, thus contributing significantly to both scholarly understanding and practical strategies within the financial industry.