Search Results (5)

The increasing complexity of networked research demands approaches that combine rigor, efficiency, and collaboration. In this context, artificial intelligence (AI) emerges as a strategic ally in the analysis and organization of scientific literature, facilitating the construction of a robust state-of-the-art framework to support decisions. The present study focuses on evaluating a model for the use of AI that facilitates collaborative literature review by integrating AI tools. The present study employed a descriptive, non-experimental, cross-sectional design. Participants (N = 10) completed a purpose-built questionnaire comprising twenty-five indicators on specific aspects of the model’s use. The participants indicated a high level of knowledge regarding ICT use (M = 8.3; SD = 1.25). The results showed that the System Usability Scale for the tools demonstrated variability; Google Drive scored the highest (M = 77.75; SD = 11.45), while Rayyan.AI scored the lowest (M = 66.00; SD = 20.69). While the findings indicated that AI enhances the efficiency of documentary research and the development of ethical and digital competencies, the participants expressed a need for further training in AI tools to optimize the usability of those integrated into the model. The proposed model CAIM-REL proves to be replicable and holds potential for collaborative research. Full article

Transformability is one of the essential attributes of social systems. To improve transformability, one should create the preconditions for strategic intervention on the underlying social structures. This paper proposes a design-driven conflict (DDC) methodology in response to the limitation of the systemic design approach by aggregating a network of allies essential for the paradigmatic shifts. The proposed methodology has more strategic implications. It starts with unfolding the actors and shared resources (phase one context mapping). It continues with defining the power relations between them, drivers, and spillovers that cause conflicts and disagreements (phase two analysis). After this, it shows how one can synthesize the commonalities and the core narratives of actors in the form of boundary objects (phase three synthesis). By using the existing narratives and the commonalities between actors as inputs for the translation phase, DDC creates the preconditions for a network of allies construction. Next, the methodology uses translation as a method, in relation to the four moments of a ‘sociology of translation’, problematization, interessement, enrolment, and mobilization, in order to gradually change the learning paradigm of the system. In the scaling-up phase (phase five), DDC proposes ways of creating a narrative platform, shedding light on how to mobilize the results of translation from the community level onto a broader social scale. The framework for the design methodology has been evaluated based on a method content analysis and by a group of experts from diverse backgrounds and disciplines. The results show, except for the efficiency of the method, which requires additional investigation in a real-life context, the efficacy and effectiveness of the method have been elaborated in a sufficient way. Full article

This paper analyses the impact of relational capital and relationship networks on business internationalisation, in particular in the technology sector in relation to internationalised Andalusian university spin-offs. First, a literature review based on these theories is carried out, from which a series of hypotheses are established in a pioneering way. These allow us to design a model of relationships that is empirically tested through a quantitative analysis. The study constructs were measured using five-point Likert scales. Once the data had been collected through the survey developed, they were coded for statistical analysis using the SPSS Statistics V28.0 programme. It is shown that the output of the international activity of these companies depends on their capacity to develop and sustain relationships with each of the different actors involved. Along this line, university spin-offs obtain diverse and strategically valuable external information and resources, significantly reducing business failure chances. The elements that influence the internationalisation process of the companies under study are relationships with customers, allies/collaborators, suppliers, and the company’s reputation and image. Similarly, those referring to relations with competitors, public bodies, and organisations have less impact. Nevertheless, it is evident that companies need to commit more time and resources to create, maintain, and develop relationships with different actors. Therefore, a strategic tool focused on international management is provided, based on the relationships of the spin-offs studied, which may constitute a starting point for its applicability to other industrial sectors and geographical areas. Full article

Controversies are an inseparable part of social systems which, if constructed properly, can create a unique condition for higher-order learning. In addition, design inquiry, as a process of thought and planning, is also a constructive process. This provokes the question of how to construct controversies from a designerly perspective in order to steer higher-order learning. This paper presents a theoretical contribution to the field of social system design by providing the first insights into design intervention to facilitate a network of allied construction. Through a systematic review of the concept of conflict and disagreement, the link between controversies and knowledge transmission is examined in order to highlight the benefit of controversies in a constructive way. Next to that, the essential steps for constructing a network of allies are proposed. These steps are compared with specific aspects of design in order to unfold the advantages of design for network construction. Finally, the paper wraps up with concluding remarks about the necessity of having a bridging step from theory to action in order to facilitate the construction of controversies in a real-life context. Full article

A wide range of applications based on sequential data, named time series, have become increasingly popular in recent years, mainly those based on the Internet of Things (IoT). Several different machine learning algorithms exploit the patterns extracted from sequential data to support multiple tasks. However, this data can suffer from unreliable readings that can lead to low accuracy models due to the low-quality training sets available. Detecting the change point between high representative segments is an important ally to find and thread biased subsequences. By constructing a framework based on the Augmented Dickey-Fuller (ADF) test for data stationarity, two proposals to automatically segment subsequences in a time series were developed. The former proposal, called Change Detector segmentation, relies on change detection methods of data stream mining. The latter, called ADF-based segmentation, is constructed on a new change detector derived from the ADF test only. Experiments over real-file IoT databases and benchmarks showed the improvement provided by our proposals for prediction tasks with traditional Autoregressive integrated moving average (ARIMA) and Deep Learning (Long short-term memory and Temporal Convolutional Networks) methods. Results obtained by the Long short-term memory predictive model reduced the relative prediction error from 1 to 0.67, compared to time series without segmentation. Full article