Future Studies Research Journal: Trends and Strategies https://www.futurejournal.org/FSRJ <p><img 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 Future Studies Research Journal: Trends and Strategies en-US Future Studies Research Journal: Trends and Strategies 2175-5825 <p><strong>Authors who publish with this journal agree to the following terms:</strong></p><p>1. Authors who publish in this journal agree to the following terms: the author(s) authorize(s) the publication of the text in the journal;</p><p>2. The author(s) ensure(s) that the contribution is original and unpublished and that it is not in the process of evaluation by another journal;</p><p>3. The journal is not responsible for the views, ideas and concepts presented in articles, and these are the sole responsibility of the author(s);</p><p>4. The publishers reserve the right to make textual adjustments and adapt texts to meet with publication standards.</p><p>5. Authors retain copyright and grant the journal the right to first publication, with the work simultaneously licensed under the <strong>Creative Commons Atribuição NãoComercial 4.0 internacional</strong>, which allows the work to be shared with recognized authorship and initial publication in this journal.</p><p>6. 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An ORCID identifier is unique to an individual and acts as a persistent digital identifier to ensure that authors (particularly those with relatively common names) can be distinguished and their work properly attributed. The use of artificial intelligence in scientific research with integrity and ethics https://www.futurejournal.org/FSRJ/article/view/845 <p>This paper addresses the evolution of Artificial Intelligence (AI) in scientific research and the ethical and integrity challenges that arise with its integration. AI has become an indispensable tool for researchers, accelerating discoveries and optimizing processes. However, using these algorithms raises concerns about bias, transparency, and accountability. The ability of machines to learn and create knowledge challenges the paradigms of authorship and credibility, putting integrity and ethics under new scrutiny. The discussion emphasizes robust ethical governance, collaboration among stakeholders, ongoing education, and the creation of transparent and auditable algorithms. It further highlights the importance of maintaining ethics and integrity at the heart of AI research to ensure its advancement benefits humanity fairly and responsibly, emphasizing the need for a holistic approach involving education, transparency, accountability, and active participation of multiple stakeholders. Finally, it reiterates that as we embark on this new era of AI-driven discovery, we must embrace both the opportunities and the ethical challenges it presents, ensuring that the use of AI in scientific research continues to benefit humanity by promoting knowledge and well-being.</p> Ricardo Limongi Copyright (c) 2024 Ricardo Limongi https://creativecommons.org/licenses/by-nc/4.0 2024-04-15 2024-04-15 16 1 e845 e845 10.24023/FutureJournal/2175-5825/2024.v16i1.845 Blockchain technology impact perception in food sector companies: a multicriteria analysis https://www.futurejournal.org/FSRJ/article/view/763 <p><strong>Purpose:</strong> Analysis of food company managers’ perceptions of the need for a technological change and the managerial aspects considered most important.</p> <p><strong>Originality/value:</strong> The study highlighted the need to apply technology for food supply integration chain to improve internal and external processes and identified the aspects that contribute the most to business growth.</p> <p><strong>Methods:</strong> The research is exploratory with a quantitative approach. A structured questionnaire was used for data collection. To analyze possible decision making about implementing blockchain technology by supply chain managers, we adopted Analytic Hierarchy Process (AHP) – a multicriteria analysis method.</p> <p><strong>Results:</strong> It was observed that the best decision making would be an implementation of the blockchain (50.08%) or other technologies (34.94%) to achieve a better corporate performance. In addition, among the evaluated criteria, operational efficiency was considered the most important to managers, followed by technology, level of logistics service, quality, sustainability, and cost.</p> <p><strong>Conclusion:</strong> It was found that regardless of a company’s size, there was a consensus among managers about the need for technological changes in their companies to keep up with market trends.</p> Sivanilza Teixeira Machado Roberto Giro Moori Copyright (c) 2024 Sivanilza Teixeira Machado, Roberto Giro Moori https://creativecommons.org/licenses/by-nc/4.0 2023-08-18 2023-08-18 16 1 e763 e763 10.24023/FutureJournal/2175-5825/2024.v16i1.763 Determinants of success in the application of agile methods in project management https://www.futurejournal.org/FSRJ/article/view/836 <p><strong>Purpose:</strong> To identify the critical factors that influence the success of agile project management.</p> <p><strong>Originality/Relevance:</strong> The outcome was the development of a research model with high technical quality, incorporating observable items derived from existing literature and logically grouped based on empirical data affinity.</p> <p><strong>Methodology:</strong> Data collection was carried out using electronic <em>survey</em>. The analysis included 475 projects managed using various agile methodologies, encompassing software products, non-software products, and services across organizations of diverse sizes, sectors, and industries.</p> <p><strong>Results:</strong> It was found that factors inherent to the characteristics of the project and the organizational environment are more critical. The factors “Participants’ Dedication”, “Participants’ Experience” and “Project and Organizational Environment” are positively associated with the Success of Agile Projects.</p> <p><strong>Conclusion:</strong> Overall, the study provided evidence that the human factor plays a critical role in achieving desired performance levels within organizations.</p> Sérgio Pinto Zacarias Roberto Sbragia João Silva Copyright (c) 2024 Sérgio Pinto Zacarias, Roberto Sbragia, João Silva https://creativecommons.org/licenses/by-nc/4.0 2024-03-18 2024-03-18 16 1 e836 e836 10.24023/FutureJournal/2175-5825/2024.v16i1.836 Innovation Culture in Public Organizations: A Systematic Literature Review https://www.futurejournal.org/FSRJ/article/view/821 <p><strong>Purpose</strong>: This study sought to identify elements and factors that contribute to the development of an innovation-focused organizational culture, examining its construction and the challenges faced in its implementation.</p> <p><strong>Originality/Value</strong>: When discussing the culture of innovation, it is widely accepted that the topic is relevant, especially for addressing inefficiencies that managerialism, a post-bureaucratic movement, has not yet addressed. Therefore, this study systematically reviews the literature on innovation culture in the public sector, a topic that is still little explored in qualified literature.</p> <p><strong>Methods</strong>: The systematic review method utilized the Parsifal platform to investigate studies published between January 2018 and April 2023.</p> <p><strong>Results</strong>: The results indicate that behavioral factors such as leadership, creativity, knowledge sharing, and organizational climate are unanimous. The implementation process also acknowledges the multiplicity of challenges, emphasizing that managers cannot ignore the specificities of their employees in order to achieve institutional maturity.</p> <p><strong>Conclusions</strong>: In terms of future research agenda, use these findings to develop a more suitable instrument to measure the level of innovation culture developed in public institutions.</p> Ana Raquel Cypriano Pinto Sena Fernando Thiago Alexandre Meira de Vasconcelos Copyright (c) 2024 Ana Raquel Cypriano Pinto Sena, Fernando Thiago, Alexandre Meira de Vasconcelos https://creativecommons.org/licenses/by-nc/4.0 2024-03-15 2024-03-15 16 1 e821 e821 10.24023/FutureJournal/2175-5825/2024.v16i1.821 Characteristics, research focuses and trends on leadership in project management: a bibliometric analysis https://www.futurejournal.org/FSRJ/article/view/768 <p><strong>Objective:</strong> The scientific literature highlights the fundamental role that leadership plays in project management. Bearing in mind the relevance of leadership in project management, this study aims to map the scientific production on the subject of leadership, within the scope of project management.</p> <p><strong>Method:</strong> The study was conducted through a bibliometric analysis of 789 articles obtained from the Web of Science database. presents a series of indicators and trends predominant in research on leadership in projects. The studies were analyzed with the aid of the Bibliometrix R-tool and VosViewer software, through bibliometric indicators.</p> <p><strong>Main Results:</strong> The study highlights a growing number of publications, especially focused on leadership competencies. The United States is the leading country in terms of production, and the International Journal of Project Management is the most widely used publication on this topic. The thematic analysis identified some thematic groupings, with emphasis on basic/traditional themes, such as innovation, change management and project team and; driving themes such as sustainability, teamwork and more recent ones, such as continuous improvement in projects.</p> <p><strong>Relevance/Originality:</strong> The survey presents an overview of research on leadership in project management, offering important insights into these two areas of knowledge: leadership and project management.</p> <p><strong>Theoretical Contributions:</strong> This document offers managers and those interested in project leadership a useful basis for the development of leadership programs and future research on the subject, within the scope of project leadership.</p> Ítalo Paula Casemiro Maxwel De Azevedo-Ferreira Copyright (c) 2023 Ítalo Paula Casemiro, Maxwel De Azevedo-Ferreira https://creativecommons.org/licenses/by-nc/4.0 2023-08-11 2023-08-11 16 1 e768 e768 10.24023/FutureJournal/2175-5825/2024.v16i1.768 Interorganizational cooperation in strategic artificial intelligence plans: a comparative analysis. https://www.futurejournal.org/FSRJ/article/view/838 <p><strong>Objective:</strong> To investigate the types of interorganizational cooperation present in the Artificial Intelligence (AI) strategies implemented by Brazil, the United States, and the European Union.</p> <p><strong>Originality/Value:</strong> The study highlighted a multidisciplinary approach to understanding AI, focusing on interorganizational cooperation, an area underexplored in the literature dominated by computer science.</p> <p><strong>Methods:</strong> This is an explanatory, descriptive, qualitative, and documentary research, utilizing content analysis to explore the strategies.</p> <p><strong>Results:</strong> A predominance of multi-actor cooperation in AI strategies, with less frequent mentions of exclusive collaborations among members of the same sector. The public sector's role in adopting and promoting AI is emphasized.</p> <p><strong>Conclusions:</strong> The research underscores the importance of multi-actor partnerships and the active role of the public sector in AI strategies. Methodological challenges indicate the need for more objective methods of content analysis in future studies and expanded analysis of national AI strategies.</p> Saulo De Oliveira Nonato Shalon da Silva de Souza Figueiredo Carlos Andre de Melo Alves Cleidson Nogueira Dias Copyright (c) 2024 Saulo De Oliveira Nonato, Shalon da Silva de Souza Figueiredo, Carlos Andre de Melo Alves, Cleidson Nogueira Dias https://creativecommons.org/licenses/by-nc/4.0 2024-05-06 2024-05-06 16 1 e838 e838 10.24023/FutureJournal/2175-5825/2024.v16i1.838 Artificial intelligence adoption in public organizations: a case study https://www.futurejournal.org/FSRJ/article/view/860 <p><strong>Purpose</strong>: The study explores the key factors influencing AI adoption by public organizations, and sought to understand the dynamics of AI adoption, aiming to identify the potential challenges of integrating AI with ESG considerations.</p> <p><strong>Originality/value</strong>: This research addresses the gap in understanding AI adoption in the public sector at the firm level, emphasizing the challenges and risks of technology integration. The study discuss how AI can be used effectively, contributing to societal appropriation of technological progress.</p> <p><strong>Methods</strong>: Methodology employs a multi-stage analysis of literature, followed by ten interviews and a case study on Brazil's Federal Revenue Service. Empirical data was probed through rigorous coding and thematic analysis, selecting the most impactful factors influencing AI adoption.</p> <p><strong>Results</strong>: The conclusions highlight the role of AI in elevating public services performance and reach. However, the deployment of AI calls for vigilant oversight to mitigate adverse effects and inequalities and demands a multidisciplinary strategy addressing an interplay of challenges.</p> <p><strong>Conclusion</strong>: The study provides a framework for effective AI adoption, offering insights for decision-makers on strategizing AI adoption, emphasizing the importance of factoring ESG concerns into de decision to adopt this technology.</p> Luis Guedes Moacir Oliveira Júnior Copyright (c) 2024 Luis Guedes, Moacir Oliveira Júnior https://creativecommons.org/licenses/by-nc/4.0 2024-03-07 2024-03-07 16 1 e860 e860 10.24023/FutureJournal/2175-5825/2024.v16i1.860 Evaluation of digitalisation impacts in the public sector: a case study in the state legislative branch https://www.futurejournal.org/FSRJ/article/view/855 <p><strong>Objective: </strong>To analyse the impacts of implementing the Electronic Information System as an innovation in a Legislative Branch.</p> <p><strong>Originality/Value: </strong>There is a necessity for studies on the influence of specific innovation outcomes on public productions. Moreover, the focus on local governments and the healthcare sector leaves other areas, such as the Legislative Branch, underexplored.</p> <p><strong>Method: </strong>This is a case study with data collected through semi-structured interviews, documentary research, and participant observation, analysed through qualitative content analysis and data triangulation.</p> <p><strong>Results: </strong>Impacts on efficiency, effectiveness, information quality, improvements in intra/intersectoral coordination and cooperation, organizational control, relationships with citizens, businesses, and other public bodies, and increased domain and job satisfaction were highlighted.</p> <p><strong>Conclusion: </strong>The study demonstrates significant impacts generated by SEI implementation beyond internal realms, underscoring the importance of digital transformation in public administration. Thus, it contributes to consolidating the national policy on electronic process management and guides governance actions towards better performance.</p> Adriana Back Koerich Clarissa Carneiro Mussi José Baltazar S. O. de Andrade Guerra Jacir Leonir Casagrande Copyright (c) 2024 Adriana Back Koerich, Clarissa Carneiro Mussi, José Baltazar S. O. de Andrade Guerra, Jacir Leonir Casagrande https://creativecommons.org/licenses/by-nc/4.0 2024-05-02 2024-05-02 16 1 e855 e855 10.24023/FutureJournal/2175-5825/2024.v16i1.855 Ethical consumption values and ecological clothing: a study in brazil https://www.futurejournal.org/FSRJ/article/view/775 <p><strong>Objective: </strong>The research aims to investigate the Brazilian consumer in their intention to purchase ecological clothing.</p> <p><strong>Method: </strong>To carry out the study, field research was carried out, of an applied nature, with an exploratory and descriptive quantitative, cross-sectional objective, with non-probabilistic sampling, and research had for. The model proposed and validated by Echeverría (2017) was used as a basis, consisting of 11 factors and 42 psychographic observable variables, in addition to demographic variables (gender, age, education, and family income).</p> <p><strong>Originality/Relevance:</strong> As the concern for sustainability becomes a universal phenomenon, the profile of the ecologically conscious consumer evolves. The ecological fashion movement gained strength and several companies began to realize the importance of sustainability and ethical conduct as indispensable factors in fashion. Brazil is the largest complete textile chain in the West, with more than 9.5 million jobs.</p> <p><strong>Results:</strong> The predominance of the sample was female, education, there was a balance between the number of respondents between undergraduate and secondary education, with an average age of respondents of 36.74 years. An interesting point of the study, which proved to be quite significant, was the positive influence of the attitude toward the purchase of ecological products on the ecological purchase intention. The increase in ecological awareness greatly interferes with consumer behavior. The theory of planned behavior can explain this phenomenon, as an individual's attitude, their beliefs concerning a certain aspect, can influence their consumption intention.</p> <p><strong>Theoretical/methodological contributions:</strong> The contribution of this study is to provide the profile of the Brazilian consumer in their intention to purchase ecological clothing. The positive influence of the attitude towards the purchase of ecological products remained evident. Increased awareness of consumer behavior. The great contribution of this study lies in the proposition of a theoretical framework, which enables an understanding of the structure of green consumer behavior.</p> <p><strong>Social contributions:</strong> Assuming that human behavior is goal-oriented, based on individual beliefs, the goals TCP applied to consumption proposes that the way people select, process information, and act about them depends on the relevance and strength of its own general goals.</p> Daiane Johann Gabriela Almeida Marcon Nora Eliana Andrea Severo Copyright (c) 2024 Daiane Johann, Gabriela Almeida Marcon Nora, Eliana Andrea Severo https://creativecommons.org/licenses/by-nc/4.0 2023-09-04 2023-09-04 16 1 e775 e775 10.24023/FutureJournal/2175-5825/2024.v16i1.775 Peer-to-peer platforms in the sharing economy: current status and future research agenda https://www.futurejournal.org/FSRJ/article/view/781 <p><strong>Purpose:</strong> To map the studies on Peer-to-Peer (P2P) platforms, presenting important considerations in a future research agenda.</p> <p><strong>Originality/Value:</strong> There is great interest in better understanding platforms based on interaction between people, as many are pointed out as ecological, democratic and at lower prices (Wirtz et al., 2019). However, there is a gap in the systematization of publications regarding the business model of P2P platforms in the broader context of the sharing economy, as the studies are fragmented.</p> <p><strong>Methods: </strong>Systematic Literature Network Analysis (SLNA) was adopted, which combines the Systematic Literature Review with Network Citation Analysis (Colicchia &amp; Strozzi, 2012). For the systematic review the research criteria were emphasized, and in the network analysis the software VOSviewer®.</p> <p><strong>Results:</strong> Through the built networks and their connections, an agenda was proposed with four future directions: i) effects of sharing by P2P platforms; ii) market structure of P2P platforms; iii) the P2P accommodation sector; and iv) emerging themes.</p> <p><strong>Conclusions:</strong> The research went beyond the description and deepened directing research themes. It was possible to contribute stimulating research and discussion in the theoretical and practical field, since, for Chen et al. (2020) digital platforms continue to flourish in the business world.</p> Lilian Carolina Viana Christiano França da Cunha Copyright (c) 2024 Lilian Carolina Viana, Christiano França da Cunha https://creativecommons.org/licenses/by-nc/4.0 2023-09-04 2023-09-04 16 1 e781 e781 10.24023/FutureJournal/2175-5825/2024.v16i1.781 Economic feasibility of activated carbon with brazilian chestnut hedgehog (bertholletia excelsa) https://www.futurejournal.org/FSRJ/article/view/765 <p><strong>Introduction: </strong>Activated carbon is a porous substance of organic material, usually woody and fibrous material. The raw materials used to obtain charcoal are almost exclusively of vegetable origin, possessing high carbon power. The study consists of considering the use of Brazil nut hedgehogs as a raw material in the production of activated carbon.</p> <p><strong>Originality/Relevance:</strong> In this study, the results of the analysis of the economic viability of the use of activated carbon produced with residues of Brazil nuts from the Amazon region, aims to generate economic benefits and reduce possible environmental damage. </p> <p><strong>Methodology:</strong> The study was developed from a simulation of the implementation of a Manufacturing Unit at Aruanã Farm, a case study site, near of 215 km from the city of Manaus-AM, based on its data and internet sources. </p> <p><strong>Results:</strong> The financial indexes, resulting from the simulations of three scenarios of activated carbon production with residues obtained from the hedgehog, indicate that it would be unfeasible to implement it to use only the waste from hedgehogs from the current productive chestnut trees on the Farm, but that would be feasible from 540,890 productive chestnut trees on the Farm and from other sources.</p> <p><strong>Conclusion: </strong>It is economically feasible to implement a Manufacturing Unit on the Farm with a minimum possible size and to keep it producing the whole year it would be necessary to seek the supply of hedgehogs from other sources and in quantity well above the production capacity of theFarm.</p> Adenes Teixeira Alves Ires Paula de Andrade Miranda Dimas José Lasmar André Costa Copyright (c) 2023 Adenes Teixeira Alves, Ires Paula de Andrade Miranda, Dimas José Lasmar, André Costa https://creativecommons.org/licenses/by-nc/4.0 2023-08-17 2023-08-17 16 1 e765 e765 10.24023/FutureJournal/2175-5825/2024.v16i1.765 Student satisfaction with the academic experience: the pandemic versus the return to presential https://www.futurejournal.org/FSRJ/article/view/832 <p><strong>Purpose:</strong> To analyze the perception of satisfaction with the academic experience of students who participated in remote pedagogical activities as a result of the Covid-19 pandemic, comparing it with the return to face-to-face activities.</p> <p><strong>Originality/value:</strong> The research is relevant as the results indicate the need for change in the Higher Education Institution's (HEI) teaching offering strategy, which can result in student loyalty.</p> <p><strong>Method:</strong> The research conducted is characterized as descriptive, transversal, with a quantitative approach, with participants being 81 students from an HEI in the State of São Paulo. The Perceived Academic Satisfaction Scale by Schleich et al. (2006), composed of three dimensions: satisfaction with the course, development opportunity and satisfaction with the HEI. The analysis was carried out using the Kolmogorov-Smirnov normality test and the Wilcoxon test for the difference between means of matched pairs.</p> <p><strong>Results:</strong> The study demonstrates that there is no relevant statistical difference in the perception of satisfaction with the academic experience by students when comparing online teaching, which took place during the COVID-19 pandemic, and in-person teaching, after the end of the global health crisis. However, the majority of students (77.8%) want a change in the teaching format currently offered by the HEI, after experiencing a digital transformation (DT), albeit incipient, in teaching.</p> <p><strong>Conclusion:</strong> The present study contributes to the theory in that it reports a driving factor for the adoption of DT that was not included in the literature review on the topic. The research improves understanding of the metrics to be used by organizations, in line with the theoretical lens on which it is based, which proposes the use of indicators capable of capturing customers' feelings regarding a DT.</p> Júlio César Teixeira Emerson Antonio Maccari André Torres Urdan Leonardo Vils Copyright (c) 2024 Júlio César Teixeira, Emerson Antonio Maccari, André Torres Urdan, Leonardo Vils https://creativecommons.org/licenses/by-nc/4.0 2024-03-01 2024-03-01 16 1 e832 e832 10.24023/FutureJournal/2175-5825/2024.v16i1.832 Critical factors for the adoption of social impact bonds in addressing hiv https://www.futurejournal.org/FSRJ/article/view/796 <p><strong>Objective:</strong> To identify the critical positive and negative factors for the adoption of the Social Impact Contract (CIS) model in actions to combat HIV in the State of Amazonas, Brazil.</p> <p><strong>Method:</strong> The research is qualitative, exploratory and descriptive. 17 semi-structured interviews were carried out with representatives of actors usually involved in a CIS: government, investors, intermediaries, Civil Society Organizations and control bodies.</p> <p><strong>Main results:</strong> The0020critical positive factors for the adoption of CIS are: testing innovative solutions to social problems with lower risk for the government; customization of solutions for specific audiences; and problem solving together with the public sector. Negative factors included lack of knowledge of the model, lack of qualifications of public managers to work with new contracting models, distrust and conflict of interests between actors, and the uncertainty of return on investment.</p> <p><strong>Relevance/originality:</strong> In Brazil, there is a lack of sources of financing for public services in the health sector and, despite the history of relations between the public and private sectors in the country, there is no Social Impact Contract (CIS) in execution.</p> <p><strong>Theoretical/methodological contributions:</strong> The study describes the perception of interest groups about CIS; demonstrates the critical positive and negative factors for the adoption of this contracting model by the health sector, especially for the treatment of HIV/AIDS.</p> Carlos Henrique Soares Carvalho Graziella Maria Comini Ana Luiza Terra Costa Mathias Copyright (c) 2024 Carlos Henrique Soares Carvalho, Graziella Maria Comini, Ana Luiza Terra Costa Mathias https://creativecommons.org/licenses/by-nc/4.0 2024-02-29 2024-02-29 16 1 e796 e796 10.24023/FutureJournal/2175-5825/2024.v16i1.796 Artisanal mining and environmental frameworks in the amazon: case study of eldorado do juma for the proposal of public policies https://www.futurejournal.org/FSRJ/article/view/782 <p><strong>Purpose</strong>: Proposal of public policies for gold mining in the Amazon. </p> <p><strong>Methodology</strong>: This is a case study of artisanal gold mining in Eldorado do Juma. A document analysis was carried out of existing legislation, considering the proliferation of similar cases in the Amazon Rainforest and the environmental and social harm caused by irregular and illegal mining. Data observed by field research are also presented, including semi-structured interviews with those involved in artisanal mining.</p> <p><strong>Main results</strong>: The proposal of six public policies emerging from the case study and the discussion about legislation presented throughout the text. These proposals aim to mitigate the damage caused by gold mining in similar areas to Eldorado do Juma. Furthermore, the research seeks to identify possible gaps and failures of legislation that could be alleviated by generating public policy capable of minimizing or curbing harmful practices.</p> <p><strong>Relevance/originality</strong>: This is a little reported situation in academic work, especially with this level of specificity and depth, involving field research in a difficult to reach area, and with interviews with actors directly involved in artisanal mining.</p> <p><strong>Methodological and theoretical contributions</strong>: Contribution to the study of legislation governing mining in the Amazon and the development of a case study method based on semi-structured interviews.</p> Rejane Viana Jacques Marcovitch Carolina Fernandes Copyright (c) 2024 Rejane Viana, Jacques Marcovitch, Carolina Fernandes https://creativecommons.org/licenses/by-nc/4.0 2024-03-05 2024-03-05 16 1 e782 e782 10.24023/FutureJournal/2175-5825/2024.v16i1.782 Moral harassment: a portrait of northern brazil based on lawsuits https://www.futurejournal.org/FSRJ/article/view/783 <p><strong>Purpose: </strong>To investigate moral harassment on northern Brazil through the lens of lawsuits.</p> <p><strong>Design/methodology: </strong>Statistical analyses on lawsuits from 2012 to 2018 issued at Labor Court, 11<sup>th</sup> Region, which includes states of Amazonas and Roraima. It also provided a qualitative discussion, based on semi-structured interviews with main actors (judges, prosecutors, and lawyers).</p> <p><strong>Findings:</strong> A representative volume of legal actions was found, signaling the presence of moral harassment in the region. There was a higher proportion of vertical harassment (management over employees), a large concentration in industry and banks, a predominance of men as harassers and great difficulty in proving the harassment.</p> <p><strong>Originality/value: </strong>This article addresses the topic of moral harassment from a lens that is still little explored, lawsuits, considering a geographic delimitation without many studies – the northern region of Brazil<strong>.</strong></p> <p><strong>Theoretical/methodological contributions: </strong>The northern Brazil presents similar characteristics regarding moral harassment to those found in other regions of the country, reinforcing the understanding of a social phenomenon under the influence of the institutional environment.</p> <p><strong>Practical implications: </strong>The findings reinforce the need to debate and review how reporting channels about moral harassment are structured in the country, as it is a phenomenon that is difficult to delimit and prove but has a profound impact on the lives of its victims.</p> Marcia Medina Wilson Amorim Michele Ruzon Amyra Sarsur Copyright (c) 2024 Marcia Medina, Wilson Amorim, Michele Ruzon, Amyra Sarsur https://creativecommons.org/licenses/by-nc/4.0 2024-04-16 2024-04-16 16 1 e783 e783 10.24023/FutureJournal/2175-5825/2024.v16i1.783