Nelson Alfonso Gómez-Cruz

As artificial intelligence (AI) becomes increasingly integrated into organizational processes to enhance efficiency, decision-making, and innovation, aligning AI systems with human teams remains a major challenge to realizing their full potential. Although academic interest is growing, the conceptual landscape of human–AI relationships remains fragmented. This study employs a bibliometric co-word analysis of 4093 peer-reviewed documents indexed in Scopus to map the intellectual structure of the field. Using a strategic diagram, we assess the relevance and maturity of five major thematic clusters identified in the field. Results highlight the structural dominance of Human–AI Interactions (Centrality: 1595), Human–AI Collaboration (1150), and Teaming and Augmentation (1131) as foundational themes, while Conversational AI (655), and Ethics and Responsibility (431) emerge as specialized domains. Based on the analysis, we propose a conceptual framework that classifies human–AI relationships into four categories—symbiotic, augmented, assisted, and substituted intelligence—according to the level of AI autonomy and human involvement. Rather than providing prescriptive guidance for practitioners, this framework is intended primarily as a scholarly contribution that clarifies the conceptual landscape and supports future theoretical and empirical work. While potential implications for organizational contexts can be inferred, these are secondary to the study’s main goal of offering a research-based synthesis of the field. Ultimately, our work contributes to academic consolidation by offering conceptual clarity and highlighting opportunities for future research, while underscoring the critical need for ethical alignment and interdisciplinary dialogue to guide future AI adoption.

This study proposes a conceptual model that explains the influence of leadership on the relationship between human intelligence (HI) and artificial intelligence (AI). A qualitative, non-systematic literature review was conducted in Scopus and Web of Science of the literature published in the last 5 years, using Boolean combinations of the terms “leadership,” “artificial intelligence,” and “human intelligence.” The thematic analysis allowed the identification of conceptual patterns and research gaps; the model elaborated from the review shows that leadership has an ethical and strategic mediation in the HI-AI relationship in a hybrid space of cooperation, in which automated decisions are put in real context through human judgment and reasoning; ethical governance mechanisms emerge for systems supported by artificial intelligence; and finally, a balancing mechanism to algorithmic efficiency is established through cognitive adaptability. The proposed framework offers organizations some guidelines for human supervision processes for AI-supported systems that integrate ethical evaluations into automated processes. It proposes elements—leadership tools that enhance the relationship between human intelligence and artificial intelligence. This article contributes to the management of organizations by proposing a model that recognizes leadership as a dynamic facilitator between HI and AI, integrating transdisciplinary knowledge of management, technological ethics, and cognitive science, and proposing an ethical interrelationship in the decision-making architectures between HI and AI. The proposed model establishes leadership mediation of human–AI interaction through four axes showing how leadership acts as the axis that brings together human and technological systems to work together. Hierarchical interaction creates a hybrid interaction that is highly flexible, efficient, and has ethical oversight. Finally, the proposed model is an open system that interacts with the environment and is understood as a flexible tool to support strategic decision-making in complex environments.

Technology’s impact on communities, economies, and the world has received considerable attention from scholars, particularly regarding its role in driving economic development. However, technological advancements have also influenced and, in some cases, exacerbated economic and social phenomena, such as informality. Despite this, the intricate relationship between informality and technology remains unexplored. Thus, this chapter aims to address two key questions: (1) What has been the historical and contemporary connection between technology and informality? And (2) how has technology been utilized and adopted within the complex landscape of informality? A co-word analysis of 445 peer-reviewed documents obtained from Scopus reveals seven clusters that elucidate the relationship between technology and informality. The findings highlight the transformative influence of smartphones and geographic information systems as pivotal technologies that have reshaped the living, communication, transportation, and business practices of informal workers. Furthermore, a domain analysis of 137 case studies reveals that technology is primarily benefiting populations beyond informal workers or residents of informal settlements. Finally, three main challenges arise from the use of technology: 1) addressing public policy concerns, 2) developing the necessary skills to harness technology’s potential, and 3) leveraging technology as a means for formalization.

Purpose: This study aims to explore the strategic decisions at innovation level implemented by firms to thrive and transform themselves during crises. This study also aims to provide insights to answer the question: Why do some firms decide to implement certain types of innovation during a crisis? Design/methodology/approach: This research was carried out through a multiple case study involving 22 firms. The methods were implemented in three steps to increase rigor and the replication of the study: identification and selection of cases, data collection through interviews triangulated with online information and analysis based on aggregating themes and finding patterns. Findings: In the face of the COVID-19 pandemic, the companies analyzed focused their activities mainly on developing new features or functionalities for their products or services. Most of the firms implemented innovations across nearly all ten categories outlined by Keeley et al. (2013). Many of the implemented innovations involved personalized and superior service enhancements, process efficiency optimizations, channel diversification initiatives and new ways to collaborate to generate value. In general, the main drivers that led firms to decide to implement these innovations include reducing costs, enhancing operational efficiency, generating new revenue streams, augmenting sales and enhancing client relationships. Practical implications: This research significantly advances the convergence of innovation, strategy and crisis in three impactful ways. First, it constructs a pragmatic and evidence-based framework, consolidating the primary catalysts, innovation categories and strategies adopted by firms in response to the challenges posed by the COVID-19 crisis. Second, it offers insights for guiding decision-making processes related to innovation, presenting actionable recommendations derived from the study’s findings. Thirdly, this study highlights critical perspectives that can guide governmental intervention, facilitating the formulation of more tailored and effective policies to assist companies during crisis periods. Originality/value: This study centers on developing countries, specifically examining Colombian firms, considering their unique characteristics and priorities. Surprisingly, there is a scarcity of studies delving into the innovation and transformation of firms during the COVID-19 crisis in nations sharing cultural, economic and political similarities with Colombia.

Recent literature suggests there is a natural connection between innovation and digital transformation, two key topics of interest in management and organization that have spawned large, independent and well-defined areas of study. While this connection might be analytically straightforward and notable examples are not hard to find, in everyday life, it materializes in a multiplicity of ways. A need emerges, then, to better understand the interaction between innovation and digital transformation so that it can be explored and exploited by actors in academia and the public, private and third sectors. In this article, we use a co-word analysis, a text mining technique that permits to systematically map the intellectual structure of a research field, to characterize the most notable dynamics of ‘innovation-driven digitalization’ and ‘digitalized innovation’— the two major dimensions of interaction between innovation and digital transformation. The text identifies the relevant themes, subthemes and concepts that appear in the literature, as well as their relationship and level of development. It, then, aggregates them in a taxonomy that, on the one hand, readily displays their connection and, on the other hand, (i) informs about current or potential controversies, (ii) gaps, (iii) lines for novel and further research, and (iv) alternatives to bridge to other areas of study.

Los sistemas vivos procesan información; esto es lo que hacen para vivir. La capacidad de procesar información es, de hecho, uno de los rasgos más sobresalientes de los sistemas complejos adaptativos en general. Se suele aceptar, desde la biología y las ciencias de la computación, que el modelo estándar de computación algorítmica, representado por la máquina de Turing, establece los límites teóricos de lo que la vida puede computar y lo que no. Pese a ello, el tipo de computaciones que realizan los sistemas vivos difiere en maneras fundamentales de las soportadas por la máquina de Turing. En este artículo se demuestra que el sistema de navegación que emplean individualmente las hormigas y las estrategias colectivas para la recolección óptima de alimentos que usa la colonia no se pueden reducir a formas de computación algorítmica. Esta idea justifica la necesidad de desarrollar nuevos modelos de computación que nos permitan desentrañar la lógica computacional y la complejidad de la vida.

This article discusses the meaning and scope of biological hypercomputation (BH) that is to be considered as new research problem within the sciences of complexity. The framework here is computational, setting out that life is not a standard Turing Machine. Living systems, we claim, hypercompute, and we aim at understanding life not by what it is, but rather by what it does. The distinction is made between classical and nonclassical hypercomputation. We argue that living processes are nonclassical hypercomputation. BH implies then new computational models. Finally, we sketch out the possibilities, stances, and reach of BH. © 2014 Wiley Periodicals, Inc. Complexity 20: 8–18, 2014