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This paper aims to explore the impacts of a blockchain network implementation to support purchasing processes of a Brazilian public organization.

The Grumbach method was used to build the scenarios. Five experts with knowledge in blockchain and experience in public procurement were consulted on 20 possible preliminary events, defining their probability of occurrence and relevance. The data obtained were processed in Puma software, which returned a selection of ten definitive events, based on probability, relevance and standard deviation indicators, generating a map of prospective scenarios.

Three following scenarios are shown, the ideal scenario, the one with greater implantation benefits and fewer complications; the trend scenario, more likely to occur under current conditions; and the most likely scenario of occurrence, according to experts. The results indicated which simulated events are drivers (motives), and which are influenced (dependent). They were categorized as opportunities or threats to the deployment of the technology.

Although public procurement processes are standardized by Brazilian legislation, new events may arise from the replication of the model in different organizations. The research revealed the need for practical testing in a simulated public procurement environment.

The article explores the interaction between disruptive network technology and processes linked to public sector efficiency. Studies on electronic government point to the future of public management.

This study aims to explore the relationship between the innovation capacity and performance of manufacturing firms in the Ecuadorian pharmaceutical and chemical sectors using strategic foresight analysis.

From an extensive literature review, the fundamental variables related to manufacturing firms’ innovation for better performance were identified. Six hypotheses and actions were proposed related to financing, economic resources, capacities and research and development. Several scenarios were tested through foresight methodology to determine the more appropriate to be implemented by manufacturing firms over the next five years.

Scenario 01, where all the hypotheses resulted positive, has a 15.3% probability of occurrence. The results offer a relevant understanding of the behavior of the variables proposed as strategic actions for the sector.

To the best of the authors’ knowledge, foresight methodology is applied for the first time to analyze the manufacturing sector in Ecuador. The authors propose a plan of action from the strategic scenario identified in this study, supporting the development of the industrial sectors under study.

The purpose of this study is to explore the process of initial sensemaking that organizational members activate when they reflect on AI adoption in their work settings, and how the perceived features of AI technologies trigger sensemaking processes which in turn have the potential to influence workplace learning modes and trajectories.

We adopted an explorative qualitative and interactive approach to capture free fantasies and imaginative ideas of AI among people within the industry. We adopt a conceptual perspective that combines theories on initial sensemaking and workplace learning as a theoretical lens to analyze data collected during 23 focus groups held at four large Swedish manufacturing companies. The data were analyzed using the Gioia method.

Two aggregated dimensions were defined and led to the development of an integrated conceptualization of the initial sensemaking of AI technology adoption. Specifically, sensemaking triggered by abstract features of AI technology mainly pointed to an exploitative learning path. Sensemaking triggered by concrete features of the technology mainly pointed to explorative paths, where socio-technical processes appear to be crucial in the process of AI adoption.

This is one of the first studies that attempts to explore and conceptualize how organizations make sense of prospective workplace learning in the context of AI adoption.

The aim of this research paper is to present a new model of innovation management under the approach of anticipatory collective intelligence strategies to improve the innovation capacity of products and business processes. The paper presents a case study of Café Galavis, one of the oldest family-owned companies in the Colombian coffee sector.

A descriptive mixed-methods research design was used. Ten experts and the general manager of the organization were interviewed using a semi-structured format. Additionally, a creativity and innovation workshop were conducted with the participation of 140 young people between the ages of 21 and 28. The traditional product of the company was evaluated using the SCAMPER technique. On the other hand, a strategic approach was taken to address the complex problem. This involved conducting a bibliometric analysis using Vosviewer software, prioritizing key variables and actors’ game through MICMAC and MACTOR software, seeking expert judgment, conducting morphological analysis, generating scenarios under the Peter Schwartz axes and designing a strategic plan.

The paper presents a new approach to developing innovation processes by connecting foresight and the SCAMPER technique as a strategy for anticipatory collective intelligence. Furthermore, it was found that the use of these methods enhances the innovation capacity of both products and business processes. Organizations can implement this model to create plans with disruptive and sustainable horizons.

The paper presents methods to enhance the adoption and performance of innovation in companies through collective anticipatory intelligence and the use of digital tools.

This paper proposes a novel approach to combining two future methods to promote innovation. This study makes a theoretical and practical contribution to the field of foresight.

Pregnant employees experience considerable interpersonal discrimination. This study explores the range of possible reactions of observers to pregnancy self-disclosure, interpersonal discrimination and various allyship interventions, and the attentional processes that lead to those reactions. Consequently, it uncovers socio-cognitive processes underlying support for and backlash toward pregnancy in the workplace.

This study used a thought-listing technique to explore observers’ spontaneous thoughts related to pregnancy. Working adults were randomly assigned to read through one of the six scenarios depicting pregnancy self-disclosure, interpersonal discrimination and male allyship interventions (i.e. stating the organization’s anti-discrimination policy, confronting the transgressor by calling out sexism, pivoting the conversation to highlight the strengths of the pregnant employee and a hybrid intervention combining highlighting strengths and confrontation) after which participants listed the top three thoughts that came to their mind (1,668 responses). Responses were thematically analyzed to explore spontaneous reactions toward the pregnant employee, transgressor and ally in the scenario.

Surprisingly, across all scenarios, the most sexist thoughts emerged during pregnancy self-disclosure, even in the absence of any transgression. After a transgression occurred, any allyship intervention was better than none in eliciting lesser sexist backlash against the pregnant employee. Stating the organization’s anti-discrimination policy was most beneficial for the pregnant employee in eliciting the least sexist backlash but at the cost of generating unfavorable impressions of the ally. Calling out the transgressor’s bias elicited the most sexist backlash toward the pregnant employee, yet it created favorable impressions of the ally. In contrast, highlighting the strengths of the pregnant employee created the most favorable impression of the ally while eliciting a few sexist thoughts about the pregnant employee. Overall, the hybrid intervention was the most effective at balancing the competing goals of generating support for the pregnant employee, creating favorable impressions of the ally, as well as holding the transgressor accountable.

This study demonstrates that the type of allyship intervention critically redirects the attentional focus of observers to certain aspects of a discrimination episode and relevant schemas which can generate support or backlash toward targets, transgressors and allies, thereby advancing or obstructing equity and inclusion in organizations.

Bank–fintech associations are significant globally, establishing purposeful eco-systems towards extending and complementing capabilities, reach and customer experiences. This paper aims to explore 39 leading fintechs in India catering across payments, lending, wealth management, regulation, neo-banks and other banking functions. Alongside fintechs, the research studies 19 leading banks (public and private) to understand the nature of bank–fintech associations in the Indian context.

The research focuses on narratives from leading banks and top fintechs in India, captured from public disclosures and leadership interviews. The study leverages qualitative research techniques, including grounded theory approaches of inductive analysis, to codify interview and narrative observations to discover relevant objectives, scenarios, challenges and outcomes in India-centric bank–fintech associations.

Bank–fintech associations in India are increasingly focusing on financial services portfolio diversification and improvement in customer experience. Simultaneously, both banks and fintechs, differentiate with innovations and extend offerings to target underserved customer segments. The associations are beneficial for both banks and fintechs in transforming offerings and improving efficiency, scale across channels. Through codification of observations, review of existing literature and evaluation of best practices, alongside subject matter expertise, the study evolves a generalized “Association Model”. The model can steer meaningful bank–fintech associations in India and globally. The association model relates to observables like objectives, enablers of bank–fintech associations, challenges and association-driven value outcomes. Built from study of practices, the proposed model is relevant for strategic orientation in bank–fintech associations.

The findings reveal practices in bank–fintech associations in India with significant learning opportunity for organizational leaders globally. Understanding the nature of association is relevant for strategic interventions, particularly in scenarios of inter-organization collaborations. Central banks, policymakers, governments, investors, banks and fintechs can use the derived association model to establish, govern and steer purposeful value-driven associations.

As financial firms incorporate impact strategies more extensively into their operations, they are asked to sustain their impact claims and thus face increased risks of regulatory scrutiny and lawsuits from private and public parties. The lack of reliable frameworks to measure impact gives rise to phenomena like impact washing, leading to litigations. This article aims to explore the main factors contributing to the impact litigation risk and the mechanisms employed by practitioners in the impact investing field to navigate and address this challenge.

We conducted semi-structured interviews involving three impact investors and three impact lawyers with specific knowledge of ESG and impact controversies, adopting the Gioia Methodology for the analysis. We triangulated such information with the analysis of secondary data.

The “great noise” around the impact investing world and the rise of impact washing, the lack of shared standards for measuring impacts and the misalignment of interests among actors involved in the initiatives constitute a potential “litigation bomb”. Such a scenario is detrimental to an investment strategy, which has the potential to tackle societal issues.

This study represents an initial effort to connect the academic debate on impact litigation with the expert’s active “on-field” standpoints. The identified and validated drivers of impact litigations provide valuable insight to enhance the governance and accountability of impact investing. Implementing Impact Measurement and Management (IMM) tools, participatory governance models, clear impact-focused contracts and a proactive approach could serve as prospective solutions to mitigate the risk of disputes.

In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of innovative project. This paper aims to prospect the promising opportunities coming from the application of Machine Learning (ML) algorithms to project risk management for organizational innovation, where a large amount of data supports the decision-making process within the companies and the organizations.

Moving from a structured literature review (SLR), a final sample of 42 papers has been analyzed through a descriptive, content and bibliographic analysis. Moreover, metrics for measuring the impact of the citation index approach and the CPY (Citations per year) have been defined. The descriptive and cluster analysis has been realized with VOSviewer, a tool for constructing and visualizing bibliometric networks and clusters.

Prospective future developments and forthcoming challenges of ML applications for managing risks in projects have been identified in the following research context: software development projects; construction industry projects; climate and environmental issues and Health and Safety projects. Insights about the impact of ML for improving organizational innovation through the project risks management are defined.

The study have some limitations regarding the choice of keywords and as well the database chosen for selecting the final sample. Another limitation regards the number of the analyzed papers.

The analysis demonstrated how much the use of ML techniques for project risk management is still new and has many unexplored areas, given the increasing trend in annual scientific publications. This evidence represents an opportunities for supporting the organizational innovation in companies engaged into complex projects whose risk management become strategic.

The purpose of this empirical study is to examine prospective teachers' international mindedness and intercultural competence, grounded in sociocultural theory. The research aims to understand the factors that foster these traits in prospective teachers and their significance in promoting competence in culturally diverse environments.

The study utilized structured equation modeling with maximum likelihood estimation to analyze data from 425 participants of two higher education institutions. The participants were selected through simple random probability sampling. This methodology allowed the researchers to explore the relationship between international mindedness and intercultural competence in prospective teachers.

The results of the study demonstrated a positive correlation between international mindedness and intercultural competence in prospective teachers. The findings suggest that by cultivating international mindedness, educators can enhance their intercultural competence, thereby fostering more effective interactions in diverse settings.

The present research holds practical implications for teacher education programs and higher education institutions. By understanding how international mindedness impacts intercultural competence, educators can implement targeted interventions and training to nurture these traits among prospective teachers. This can lead to the creation of culturally inclusive learning environments, promoting mutual respect and appreciation of diversity among students.

The originality of this study lies in its investigation of the link between international mindedness and intercultural competence in the context of prospective teachers enrolled in B.Ed. program in higher education institution. By establishing a causal relationship between these traits, the research adds to the understanding of how teachers can be prepared to engage effectively in culturally diverse classrooms. The findings hold value for educators and policymakers seeking to improve teaching practices and foster global citizenship among future educators and their students.

This works provides a thorough understanding of the challenges and opportunities associated with Additive Manufacturing (AM) adoption in the medical sector. Through this analysis, we aim to better understand when to adopt AM, how to do so, and how such adoption might change in the future.

This research first conducted a systematic literature review (SLR) to identify AM challenges and opportunities in the medical sector, which were then validated through a Delphi study. The 18 Delphi study participants were also asked to suggest countermeasures for the challenges and help identify future AM adoption scenarios. Finally, these findings were analyzed according to the ecosystem pie model to design an ecosystem model for AM in the medical sector.

Among the 13 challenges and 13 opportunities identified, the lack of a skilled workforce and the responsiveness achievable via AM were by far the most relevant challenge and opportunity. Moreover, the participants identified countermeasures for 10 challenges, as well as three future AM adoption scenarios. Finally, leveraging these findings, an ecosystem model was developed.

This work contributes to the limited understanding of the AM challenges and opportunities in the medical sector. It helps medical practitioners to better understand the challenges and opportunities associated with AM and AM manufacturers to better identify where to focus their R&D efforts and how this would impact future AM adoption levels. Furthermore, this work extends current theory supporting the design of an ecosystem model for AM in the medical sector following the ecosystem pie model.