Las funciones de generación, difusión y uso de conocimiento y tecnología en los sistemas de innovación están asociadas a las capacidades tecnológicas para la innovación (TICs, por su sigla en inglés), las cuales juegan un papel fundamental en el desempeño innovador de los sistemas de conocimiento e innovación agrícola (AKIS, por su sigla en inglés). No obstante, las metodologías actuales no exhiben perspectivas que faciliten la comprensión dinámica y organizacional de las TICs, lo que dificulta la comprensión de dichos sistemas. Este artículo presenta una propuesta metodológica para medir el nivel de las TICs al asociarlas a un modelo de comportamiento organizacional para aplicarlo en una muestra de 256 organizaciones del AKIS del café y 74 organizaciones del AKIS del aguacate en el departamento de Antioquia (Colombia). Los resultados confirman que la medición del nivel de las TICs y de sus variaciones dinámicas en el tiempo permite mejorar la comprensión de los procesos de innovación y evaluar e identificar las brechas existentes entre las TICs y las funciones de innovación de estos sistemas.

The learning approach, understood as the process through which agribusiness creates knowledge and develops capabilities, is key to understanding the voluntary effort made by the firm to acquire the capabilities necessary to compete in an agricultural innovation system (AIS) and improve their transition to sustainability. In this framework, learning is understood as a complex phenomenon emerging alongside specialization. Agent-based modelling (ABM) has proven to be an appropriate method of analysis for such phenomena; however, existing models have limitations related to the bounded rationality of agents, their relational proximity, and market forces. In order to help overcome these limitations, we propose this model representing the local dynamics of competing and collaborating innovation agents, and the complementarity of their capabilities. The model makes it possible to study the dynamics of local learning and how patterns of specialization emerge, and to improve the transfer and adoption of technologies (smart farming), increasing their productivity and sustainability, and reducing their environmental impact in an agricultural innovation system. It also provides a point of reference to guide policies, programs, and strategies aiming to improve the system’s economic and innovative performance. To achieve this objective, we use a case study of the banana production chain to build an agent-based model.

The aim of this article was to carry out a study of the life cycles of three products of Colombian companies; based on a logistic model of population growth as a life cycles measurement tool. We found that the products life cycles have a similar behavior to the population growth, according to an S curve. The inflection points of the curves were obtained by a nonlinear regression. These points might be used as a tool for strategic decision making in products, in terms of identifying key instants for launching technological innovations, investments and execute marketing strategies

The technology transfer (TT) process has been studied from different approaches to improve productivity and competitiveness in agricultural chains. However, the process is not always presented successfully due to heterogeneity and inequality in the technological capacities (TC) of the agents that are part of the transfer process, in addition to the geographical context, the interaction networks and decision rules, which are key factors to understand the TT phenomenon. In this context and as a case study, the Colombian National Federation of rice growers promoted the development and adoption of technology that increased crop competitiveness and sustainability by implementing a technology transfer program known as the Massive Technology Adoption Program (AMTEC—Adopción Masiva de Tecnología) on rice. With the AMTEC program, average production costs were reduced by 26% (USD 119 per hectare), and it increased average yields by 23% (1.27 tonnes per hectare), which shows the importance of introducing technologies in productive chains in developing countries. This research provides a better understanding of the TT processes, based on the analysis of the interaction dynamics and behaviour patterns between the agents (i.e., generators, intermediaries, or users) in the TT processes. As an analysis tool, the agent-based modelling paradigm (ABM) was proposed to study the emergence at the macro-level of behaviour patterns of a system from the interactions of semi-intelligent agents at the micro-level, using experiments.

La Teoría de restricciones permite identificar dificultades en los procesos productivos a través de lo que se ha denominado cuellos de botella, y bajo una perspectiva rigurosa y sistemática busca la solución óptima para lograr el objetivo empresarial, el cual es generar dinero. El objetivo primordial de este trabajo fue evidenciar cómo el proceso sistemático de la teoría de restricciones, puede ser replicado de manera simple bajo la perspectiva de la modelación y simulación de los comportamientos que circunscriben dicha teoría. La simulación, en este caso, se utiliza como una metodología para ejemplificar y demostrar el pensamiento contra intuitivo de esta teoría, que emerge debido a que las empresas aún consideran los inventarios y su acumulación, como parte fundamental para la generación de beneficios en las organizaciones. En términos generales, con el modelo de simulación se logró demostrar que, reduciendo los inventarios en proceso de una cadena productiva, el comportamiento de los gastos operativos disminuye, generando así un mayor flujo de dinero a través de la empresa.

In this paper, social capital was analyzed through the lens of the resources and capabilities theory, understanding it as a capability that has a role in the performance of the Smart City. Social capital relates to the technological capabilities and the technological learning process. The objective of this investigation was to build a representation model of the social capital resources and technological learning, taking social capital as a capability of the Smart City. First, on a methodological level, a preliminary exercise that shows the relation and the gaps between the concepts and the process to build the model are presented. Later, the Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to build the conceptual and measured model. Based on the configuration of the tangible variables, the model was tested with the information provided by the final users of the public urban bicycle system of the Medellín–EnCicla technological system. The impact of technological learning is interpreted by relating it to the sources of social capital, to its resources (trust, social interaction, and shared vision), and to the technological system chosen. As a result, the construction of an exploratory model is presented that interprets the resources of social capital and their relation to technological learning in a smart city. The investigation aims to contribute to the decision-making exercise in the public policies regarding the sustainable mobility of the smart city that was the object of study.