To analyze the role of technologies in digital transformation (DT) and innovative business models (IBMs) in the financial industry (FI) from the perspective of strategic stakeholders.
A systematic literature review (SLR) was conducted with articles written between 2008 and 2023, utilizing Scopus, Web of Science, ScienceDirect and Wiley databases.
The adoption of innovative technologies, digital platforms and automation was identified as the key outcome. The connection between stakeholder involvement and technologies in DT is emphasized. A theoretical framework integrating DT into organizational objectives is proposed.
The study presents limitations in the selection of databases and suggests a deeper exploration of stakeholder involvement.
This systematization resulted in the framework presented in figure 6, comprising strategic elements that can be used as a managerial tool by managers seeking to internalize the principles of DT.
1. Introduction
The development of the financial system requires transformations within companies, government, and society. Actions in this regard include: (1) regulating and standardizing the system to ensure liquidity for institutions; (2) introducing new currencies; (3) implementing economic adjustments to mitigate financial crises; and (4) strengthening institutions and economic sectors in response to the impacts of the financial system (Basel Committee on Banking Supervision, 2010; Cavalcanti, Gutierrez, & Figueiredo, 2021).
Oliveira, Bomfin, and Franklin-franklin (2019) underscore that the financial ecosystem is becoming increasingly demanding owing to escalating complexity, the emergence of new competitors, and the continual fusion of the digital realm. This rapid evolution necessitates the cultivation of distinct skills (Andrade & Gonçalo, 2021). Companies such as Meta, Google, and Amazon are already participating in this ecosystem, bringing their own clientele (Di Castri & Plaitakis, 2018). Additionally, the relationship between banks, fintechs, and big techs is in constant evolution, albeit collaboration among them being challenging due to intensified competition spurred by technological advances in DT (Verhoef et al., 2021).
In Brazil, there is a positive movement in the FI, even in a hostile economic context. Taking advantage of more flexible regulatory opportunities (Lee & Shin, 2018), many actions related to controls can be observed. Some examples are (1) greater control over data usage as per Law No. 13,709/2018, distributing obligations between controllers and operators (Brazil, 2018); (2) regulation of CMN – Resolutions 4,656 and 4,657 – for fintech operations (Central Bank of Brazil (BACEN), 2018a, b); and (3) approval of guidelines to implement the Open Banking System in the country, through Communication No. 33,455 (Central Bank of Brazil (BACEN), 2019).
Therefore, companies need to evolve from merely being digital assets to constructing a more collaborative business model (Fenwick, McCahery, & Vermeulen, 2019), aiming to provide resources geared towards interaction with stakeholders, government, and society in the digital realm. As Hie (2019) suggests, studies indicate that financial institutions are being challenged by fintechs, which offer more attractive proposals to consumers, effective services, and reduced costs tracing to their business models and associated technology.
In this regard, several research gaps stand out, which converge with the proposed framework of this research project: (1) examining the financial ecosystem and disruptive innovations, particularly the emerging benchmark companies in the sector (Palmié, Wincent, Parida, & Caglar, 2020); and (2) considering managers’ proactivity in creating new business models (Gfrerer, Hutter, Füller, & Ströhle, 2020).
With the aim of addressing this gap, a systematic literature review was conducted to answer the following research question: What is the role of adopted DT technologies in IBMs in the financial industry (FI), from the perspective of engaging strategic stakeholders?
As a contribution, this research offers an overview of significant factors in financial industry DT, benefiting the advancement of the theme and generating knowledge for scientific production opportunities in Brazil. Moreover, it is essential for understanding how stakeholder engagement drives DT in the FI, providing valuable insights from a theoretical framework to promote innovation and sustainable growth. Thus, it is evident that the study holds great potential to aid the advancement of disruption and technology in adapting innovation to business models in the financial system.
In addition to this introduction, this article is divided into four more distinct sections. Section 2 provides the theoretical basis of the research, focusing on the previous literature on DT and IBM. Then, in Section 3, the chosen method for conducting the research is demonstrated. In Section 4, the results of the investigation are outlined and analyzed in line with the purpose of this study, and finally, in Section 5, the researchers' considerations on the study are presented, accompanied by recommendations for future investigations.
2. Theoretical framework
2.1 Digital transformation (DT)
The term “DT” can be contextualized in several ways: (1) the integration of digital technologies and connectivity to enhance performance and competitiveness (Ismail, Khater, & Zaki, 2017); (2) the adoption of business rules and techniques to maintain digital competitiveness (Kane, 2017); and (3) the organizational transformation through communication, computing, connectivity, and information technologies (Vial, 2021).
According to Venkatesh, Mathew, and Singhal (2019), DT emerges as a catalyst for innovation and the creation of new business models. By integrating technology and management, DT, as Venkatesh et al. (2019) argue, transforms processes and overcomes traditional limitations. For IBMs, DT represents a means of creating new operational and delivery methods, with strategic decisions being a decisive factor for the success of this transformation.
DT drives the obsolescence of traditional business models, as argued by Tongur and Engwall (2014) and Kiel, Arnold, and Voigt (2017). In response, IBMs, characterized by changes in key elements and architecture (Foss & Saebi, 2017), become crucial. This renewal enables value capture through interconnected activity systems (Zott & Amit, 2010). Digitalization also redefines the role of management, introducing new tools and concepts, as highlighted by Gustafsson, Kristensson, and Witell (2012) and Bresciani, Ferraris, and Del Giudice (2018).
The DT, an entrepreneurial and disruptive process (Henfridsson & Yoo, 2014), radically redefines how organizations create and deliver value. By integrating it into strategy, companies can drive innovation, as noted by McAfee and Brynjolfsson (2018). According to Westerman, Bonnet, and McAfee (2014), this transformation requires a reorientation of operations, customer experience, and processes, with a focus on digital aspects.
2.2 Innovative business models (IBMs)
Ramdani, Binsaif, Boukrami, and Guermat (2020) assert that innovating within a business model can induce changes in processes, personnel, partnerships, and the company itself. Financial institutions, such as traditional banks and fintechs, are adapting to the digitization of services, deemed a disruptive innovation (Das, Verburg, Verbraeck, & Bonebakker, 2018; Forcadell, Aracil, & Ubeda, 2020; Gradillas & Thomas, 2023).
The rapid advancement of digitization poses challenges for traditional institutions and fintechs to position themselves (Kutnjak, 2021). Business models are evolving towards greater adoption of platforms/ecosystems (Dhar & Stein, 2017). Ferreira (2018) suggests that fintechs have an advantage in this scenario, pressuring traditional institutions by meeting customer needs and developing their own partnerships or platforms.
Hays (2022) highlights several changes in financial business models, including banking transactions via applications or voice commands, digital personalized assistance, development of financial APIs (Application Programming Interfaces), and blockchain authentication. Furthermore, disruptive innovations have enhanced financial institutions with technologies such as artificial intelligence, big data, RPA (Robotic Process Automation), machine and deep learning, multi-cloud, biometric identification, and facial recognition, which increase the security and efficiency of the financial system.
2.3 Stakeholder engagement
Stakeholder engagement is an interactive process that requires active participation from interested parties, both from the organization side and among the stakeholder community (Viglia, Pera, & Bigné, 2018). It encompasses the practices an organization adopts to positively involve stakeholders in its activities (Noland & Phillips, 2010; Phillips, 1997). According to Agrifoglio, Cannavale, Laurenza, and Metallo (2017), such enhanced engagement magnifies the outcomes and impact of these interactions.
Organizations aim for stakeholder engagement to improve functions like idea development (Sorensen, 2021), brand promotion (Frempong, Chai, Ampaw, Amofah, & Ansong, 2019), problem-solving (Sashi, 2021), and training for product usage (Peltier, Dahl, & Swan, 2020; Van Klyton, Tavera-Mesias, & Castaño-Muñoz, 2021). Additionally, engagement helps align needs and solutions (Allataifeh & Moghavvemi, 2021), providing mutual benefits for both the organization and its stakeholders (Greenwood, 2007).
In the B2B context, DT necessitates that managers reassess organizational structures and strategies to create value for customers, while handling vast amounts of data requiring analysis and knowledge extraction (Leone, Schiavone, Appio, & Chiao, 2021).
3. Methodology
To address the research question, a SLR was conducted following Richardson (2017) guidelines. Primary data from the SLR were analyzed to identify researchers' understandings and approaches to the topic. Secondary data were derived from an in-depth analysis of the selected articles. The process comprised: (1) planning, (2) review execution, and (3) elaboration and dissemination, aimed at categorizing the studies according to the defined scope.
- (1)
Planning: This stage involved establishing the components of the review procedure, including the theoretical framework, search criteria, language, publication categories, temporal scope, and databases. Terms such as “Digital Transformation”, “Digital”, “Innovative Business Model”, “Business Model”, “Stakeholder”, “Innov*”, “Startups”, “Digital Innovation” e “Technological Innovation” The review encompassed articles from 2008 to 2023, sourced from Scopus, Web of Science, ScienceDirect, and Wiley databases.
- (2)
Content analysis: The systematic review was conducted on July 27, 2023, with an initial sampling of 1,950 articles from the four databases. Following specific filters, 563 duplicate documents were eliminated, resulting in 1,387 articles. Out of these, 51 were selected for detailed analysis, with nine excluded due to inaccessibility, leaving 40 articles. Studies with empirical data related to DT and IBMs were prioritized, culminating in 29 studies coded from T1 to T29.
- (3)
Data description and presentation: This phase involved descriptive and content analysis. The information was organized using Parsifal software (Freitas, 2020) and graphically visualized in MS Excel. The content analysis followed the methodology outlined by Bardin (2016).
The research delineation is presented in Figure 1.
In the third stage, the analysis categories were transferred to an MS Excel® file, where the analysis and discussion of the results presented in the following section took place.
3.1 Descriptive analysis
The 29 articles obtained during the selection process are listed in Table 1, highlighting the diversity of authors, publication sources, and publication years.
List of selected articles
| # | Authors | Journal | Year | # | Authors | Journal | Year |
|---|---|---|---|---|---|---|---|
| T1 | Cavallo et al. | Journal of Small Business Management | 2023 | T16 | Still et al. | Proceedings – 2017 IEEE 19th Conference on Business Informatics, CBI 2017 | 2017 |
| T2 | Danarahmanto et al. | Business: Theory and Practice | 2020 | T17 | Mattsson & Andersson | Journal of Business and Industrial Marketing | 2019 |
| T3 | Moschko et al. | Journal of Product Innovation Management | 2023 | T18 | Kozma & Teker | Society and Economy | 2022 |
| T4 | Brunetti et al. | TQM Journal | 2020 | T19 | Tohanean & Weiss | Quality – Access to Success | 2019 |
| T5 | Broekhuizen et al. | Journal of Business Research | 2021 | T20 | Matzler et al. | Journal of Business Strategy | 2018 |
| T6 | Troise et al. | Technology Analysis and Strategic Management | 2023 | T21 | Sushandoyo et al. | International Journal Of Innovation Management | 2022 |
| T7 | Gularso et al. | Pertanika Journal of Social Sciences and Humanities | 2020 | T22 | Guo et al. | IEEE Transactions on Engineering Management | 2021 |
| T8 | Bretschneider et al. | International Journal of Innovation and Technology Management | 2021 | T23 | Rodrigues, J. C. | International Journal of Entrepreneurial Behavior & Research | 2022 |
| T9 | Rambow-Hoeschele et al. | 2018 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2018 – Proceedings | 2018 | T24 | Curry et al. | Springer | 2021 |
| T10 | Agarwal et al. | Research Policy | 2021 | T25 | Zillner, S. | Springer | 2021 |
| T11 | Alberti, & Varon Garrido | Journal of Business Strategy | 2017 | T26 | Frare et al. | Brazilian Business Review | 2023 |
| T12 | Rambow-Hoeschele et al. | Advances in Science, Technology and Engineering Systems | 2019 | T27 | Li, F. | Technovation | 2020 |
| T13 | Dressler & Paunovic | European Journal of Innovation Management | 2020 | T28 | Trischler & Li-Ying | Review of Managerial Science | 2022 |
| T14 | Lin et al. | Industrial Marketing Management | 2020 | T29 | Teece, D. | Long Range Planning | 2010 |
| T15 | Przybilla et al. | IEEE Transactions on Engineering Management | 2022 |
Source(s): The authors (2024)
DT has solidified itself as one of the key drivers of substantial global changes (World Economic Forum, 2016). This prominence justifies the increase in the quantity of publications on the subject in recent years. As shown on Figure 2, the analyzed studies primarily concentrate on the period from 2020 to 2022.
Twenty-five distinct journals were identified, among which the IEEE Transactions on Engineering Management and the Journal of Business Strategy stand out, each with two publications. Table 2 lists the primary journals in the sample.
Key journals in the sample
| Articles | Number of publications |
|---|---|
| IEEE Transactions on Engineering Management | 2 |
| Journal of Business Strategy | 2 |
| Journal of Product Innovation Management | 1 |
| Journal of Small Business Management | 1 |
| Research Policy | 1 |
| Journal of Business Research | 1 |
| Technovation | 1 |
| Other | 20 |
| Total | 29 |
Source(s): The authors (2024)
The descriptive analysis reveals that the literature analyzed on DT and IBMs does not highlight a single author, as well as that journals from different areas dedicate to this research field. This corroborates the potential of DT and IBMs.
3.2 Content analysis
The categorization of the selected articles reveals that the five blocks delineating the DT pyramid are mentioned in the majority of cases. There is a consistent focus of analysis, primarily concentrated on the aspect of Technology, as indicated in Table 3:
Evidence of the DT base composition
| Corporate layers | Corporate building blocks | Context |
|---|---|---|
| Business strategy | Business Model | T1; T2; T3; T4; T6; T7; T8; T10; T11; T13; T14; T16; T17; T18; T19; T20; T21; T22; T23; T26; T27 |
| Corporate execution | Operating Model | T2; T3; T4; T6; T8; T10; T11; T13; T14; T15; T19; T20; T25 |
| Operations | T4; T8; T10; T13; T14; T19; T20; T25 | |
| Go-to-Market | T1; T2; T3; T8; T10; T13; T14; T17; T18; T23; T26; T27 | |
| Enabling technologies | Technologies | T1; T2; T3; T4; T5; T6; T7; T8; T9; T10; T11; T12; T13; T14; T15; T16; T17; T18; T19; T20; T21; T22; T23; T24; T25; T26; T27; T28; 29 |
Source(s): The authors (2024)
Notably, the “Technologies” category has gained prominence as it serves as the technical infrastructure for the operationalization of IBMs. This category synergizes with the “Business Model” category, given that companies that harness technologies to enhance a new paradigm (Verhoef et al., 2021) and manage operational matrices (Lee, Wang, Desouza, & Evans, 2021) generate greater business value.
4. Discussion of results
The following section addresses the Corporate Layers through an in-depth exploration of three key research questions.
4.1 Enabling technologies
Which technologies are adopted in DT for the transition to IBM?
Digital Transformation (DT) represents a series of social, economic, and cultural changes underpinned by technologies that enable adaptation and business success in the digital era (T4, T5, T19). The analysis of 29 articles highlights the following practices:
- (1)
Artificial intelligence (AI): This broadly applicable technology facilitates decision-making (Mansell, 2021; Gama & Magistretti, 2022), automates processes, personalizes products and services, analyzes data, and enables the provision of more intelligent services (T2, T3, T4, T10–T13, T20–T22, T24, T26);
- (2)
Big data: This allows for in-depth analyses to understand customers, identify trends, and support decision-making, proving itself critical for detecting needs and opportunities (T3, T13, T20, T22, T24, T25);
- (3)
Digital channels: Applications, websites, social media, and e-commerce platforms facilitate customer acquisition, interaction, and product promotion. Organizations resort to digital platforms to deliver services, connect with stakeholders, and manage business operations (T1–T8, T11, T13, T15–T18, T20–T23, T29);
- (4)
Digitalization: This refers to the replacement of analogical operations with digital processes, fostering automation, analysis, and efficiency by converting workflows into digital formats (T3, T4, T6, T12, T16, T19, T23, T26).
Some listed technologies – such as 5G, which has the potential to generate new revenue streams and enhance consumer engagement (T18), and omnichannel systems, which integrate various channels to improve customer experience and digital positioning (T5, p. 3) – stand out:
Companies can respond to digital change in different ways. Consequently, companies' responses also vary in the degree of (a) digital transformation, (b) data-driven decision-making, (c) platform openness, and (d) omnichannel integration between supply and demand.
Several studies make extensive use of the term Digital Technologies, although Digital Transformation (DT) typically encompasses information and communication technologies aimed at creating value and exploring new opportunities (T1, T2, T6, T7, T10, T17, T21, T27, T28 and T29). Strategically integrating these technologies is crucial in DT, as it transforms business operations and reshapes organizations' relationships with their customers.
4.2 Corporate execution
Which are the processes and tools used, as well as the market concentration of these IBMs?
In the analysis of the articles, relevant tools and processes were identified within IBMs, such as the use of data analytics and data mining technologies (T1, T2, T7). In terms of processes, key practices included the personalization of digital technologies in manufacturing, such as sensor automation and predictive maintenance (T2, T4), as well as the redesign of internal processes to enhance efficiency and drive new revenue models (T6, T10, T11, T13, T20). The digitalization of processes was also adopted to align operations with digital demands (T20, p. 15).
Companies need to grapple more intensely with the question of which new business models are enabled by digitalization. In the end, it's about linking digital products to digital processes and adding new revenue streams.
It is crucial to consider the intersection between innovation, market strategies, and competitive dynamics when addressing the processes, tools, and market concentration within an IBM.
4.3 Business strategy
What are IBMs?
Which stakeholders are strategic in DT for the transition into IBM?
Most of the articles do not address the sectors in which startups operate with a particular focus on IBMs. Some identify segments such as FinTechs (T1, T26), EdTechs (T17), Sustainability (T2), and IT Services (T14), while traditional businesses in transition encompass manufacturing, education, public sector (T3, T4, T10), wineries (T13), football clubs (T18), and cultural sectors (T23, T27). Investors (T1, T2, T3, T9, T16, T28, T29) and business partners (T1, T2, T7, T12, T16, T21, T22, T28, T29) are crucial in funding and supporting Digital Transformation (DT). Regulators and government entities (T1, T6, T10, T12, T16, T28) play a significant role, especially in regulated sectors. The community and society are also highlighted (T4, T11, T14, T19, T20, T22, T28) in supporting IBMs.
Sectors like FinTechs are more likely to adopt an IBM, involving stakeholders such as entrepreneurs, clients, investors, and regulators. The transition to IBM is critical, with strategic and operational implications. The literature focuses on FinTechs, with less attention given to insurance or credit unions. Although Brazil is mentioned, there is a global bias in the studies, suggesting the need for more local research. The interaction between DT, IBM, and stakeholders is often treated in a linear fashion, and it would be valuable to explore more complex dynamics.
Most studies employ cross-sectional approaches, neglecting the long-term impacts on stakeholder engagement. The contribution of this study lies in proposing an integrated framework, advancing the literature on DT and innovation, and providing managerial insights into the strategic role of stakeholders in adapting to new technologies.
4.4 Theoretical framework
The literature review and findings highlighted key stages in the DT process during the transition to IBM, including the adoption of technologies, processes, tools, strategies, and stakeholder engagement. These elements culminated in the framework presented in Figure 3, which serves as a valuable managerial tool.
The principles of Digital Transformation (DT) are essential for transitioning to the New Market Infrastructure, promoting interconnected stages with stakeholder engagement. This transition requires structured planning, encompassing the identification of technologies, selection of tools, and optimization of processes within the IBM. The study adopts a comprehensive approach, considering technologies, processes, tools, business strategies, and stakeholder engagement for effective transformation.
Stakeholder involvement is an interactive process that requires active participation to generate innovative solutions, promote the brand, address issues, and align organizational objectives to market demands. Stakeholders include clients, investors, business partners, regulators, governments, and society, each playing a crucial role in the adoption of technologies and the development of solutions. Clients and partners contribute to the co-creation of value, while investors and regulators facilitate the adoption of disruptive technologies.
The findings highlight the synergy among technologies such as AI, big data, and IoT, which enhance understanding of stakeholder behavior, facilitate interactions, and support decision-making. The study also identifies challenges, such as cultural, technological, and regulatory barriers, but emphasizes that the strategic engagement of stakeholders is critical for business model innovation and creation of competitive advantage in DT within the financial sector.
5. Conclusions
The study examined how technologies drive Digital Transformation (DT) within the financial industry, emphasizing the importance of stakeholder involvement. It concludes that, despite gaps such as the role of technologies and the assessment of the impact of disruptive innovations, stakeholder engagement is fundamental. The literature review addressed the intersection between DT and Business Model Innovation (BMI) in the financial industry (FI), with 29 selected and analyzed studies.
The study proposes a theoretical framework to help managers understand DT and its connections, promoting innovative practice, particularly in financial institutions with a focus on strategic stakeholders. It also highlights that the strategic integration of technologies and alignment with business objectives are crucial for new business models in the digital age. The results underscore the theoretical importance of discussing the role of technologies in DT and the transition to BMI in financial institutions (Chiu, 2016; Kujala, Sachs, Leinonen, Heikkinen, & Laude, 2022), suggesting that DT is essential for companies seeking innovative growth.
Effective stakeholder engagement is vital for the success of both DT and BMI, with strategies including stakeholder mapping, clear communication, and the co-creation of value. Education and training through workshops and seminars and participatory governance are also essential. Monitoring and recognizing stakeholder contributions ensure continuous alignment with organizational objectives.
The success of a business strategy relies on the involvement of various stakeholders. Senior management is crucial in defining the vision, setting goals, and prioritizing DT initiatives, aligning resources and directing the organization. Investors and boards provide financial resources and exert pressure for results, while business partners contribute with skills, technologies, and access to markets. Regulators and government bodies establish the legal and regulatory frameworks necessary for the implementation of innovative strategies.
Corporate execution and technological enablement are fundamental elements of DT. As outlined in the document, the following strategies are emphasized:
- (1)
Corporate execution:
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Structured governance: Develop a clear action plan with well-defined tactical and operational objectives.
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Internal engagement: Mobilize employees through transparent communication and incentives aligned with organizational goals.
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Culture of innovation: Foster interdepartmental collaboration and promote a result-oriented mindset.
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- (2)
Technological qualification:
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Infrastructure adaptation: Implement technologies such as AI, big data, IoT, and cloud computing to support strategic objectives.
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System integration: Promote interoperability between new and legacy systems.
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Digital empowerment: Train internal teams to adopt and optimize the use of enabling technologies.
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Stakeholders involved in corporate execution and technological enablement include internal employees, such as project managers and operational teams – who ensure alignment with strategic objectives – and technology partners – who provide specific solutions and technical expertise. Executive leadership oversees execution and makes decisions regarding resource allocation and technology prioritization. Regulators and auditors ensure compliance with standards, particularly in regulated sectors. The study demonstrates that effective engagement requires structured approaches, with senior management leading the strategy and aligning stakeholders to achieve DT objectives and promote innovation and competitiveness.
The framework can assist managers in implementing emerging technologies (such as AI and big data), optimizing operations and enhancing customer experience. It supports the creation of Business Model Innovation (BMI), increasing competitiveness in the financial sector. FinTechs can enhance strategic partnerships and co-create value with stakeholders. This study is beneficial for courses in management, IT, and innovation, exploring emerging technologies and stakeholder engagement, and serves as a foundation for case studies in strategy and innovation. It provides insights into policies such as Open Banking and data protection, assisting policymakers in creating innovative environments.
Digitalization, driven by stakeholder engagement, promotes financial inclusion by improving access to financial services in underserved communities. Tools such as AI and big data help personalize financial products, facilitating access to credit and digital financial services. Digitalization economically empowers vulnerable populations, fostering entrepreneurship and income generation. Microcredit programs and FinTechs targeting micro-entrepreneurs illustrate this impact.
Challenges include financial and digital literacy, as well as data security and privacy concerns. DT also contributes to sustainability by aligning with the Sustainable Development Goals (SDGs), reducing the use of physical resources, and promoting practices such as the circular economy. FinTechs adopting ESG practices encourage sustainable investments. Technologies like IoT and blockchain improve traceability and promote responsible practices. However, the lack of technological infrastructure may hinder the spread of these practices and exacerbate social and environmental inequalities.
The study highlights the need to involve all stakeholders and develop policies that integrate objectives across all areas of the company, including other stakeholders. These insights enhance innovative practices in implementing the foundations of DT, offering practical guidance for managers to focus on crucial and strategic elements to achieve positive results.
The study acknowledges limitations, such as the restriction to four databases and the use of filters that may have excluded relevant studies. It recommends expanding the search to include other data sources, such as grey literature, and emphasizes the importance of thoroughly exploring stakeholder involvement, considering various perspectives in decision-making, and driving strategic, tactical, and operational changes in each business.
Funding: Our thanks to Cnpq and Instituto Anima for the research grant.
