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Information and communication technologies (ICT) are widely used in supply chain (SC) due to their effects on both economic performance and operational agility. This paper proposes a structural equation model integrating 17 items into four latent variables: ICT, SC agility, operating performance, and economic performance. Data analysed in the model were gathered through a questionnaire administered to 306 managers of Mexican maquiladoras. Likewise, we used statistical software WarpPLS 5®, which is based on partial least squares algorithms, to assess the six hypotheses established in the model. Such hypotheses were validated with a 95 % confidence level, and values were standardized to avoid problems regarding the measurement scale. Findings demonstrate that ICT have a positive direct impact on the other three analysed latent variables, which together account for 63 % of the variability of SC economic performance. Similarly, we found that ICT can explain up to 40 % of the variability of SC agility.

Medical diagnosis can be performed in an automatic way with the use of computer-based systems or algorithms. Such systems are usually called diagnostic decision support systems (DDSSs) or medical diagnosis systems (MDSs). An evaluation of the performance of a DDSS called ML-DDSS has been performed in this paper. The methodology is based on clinical case resolution performed by physicians which is then used to evaluate the behavior of ML-DDSS. This methodology allows the calculation of values for several well-known metrics such as precision, recall, accuracy, specificity, and Matthews correlation coefficient (MCC). Analysis of the behavior of ML-DDSS reveals interesting results about the behavior of the system and of the physicians who took part in the evaluation process. Global results show how the ML-DDSS system would have significant utility if used in medical practice. The MCC metric reveals an improvement of about 30% in comparison with the experts, and with respect to sensitivity the system returns better results than the experts.

This paper aims to describe a public procurement information platform which provides a unified pan-European system that exploits the aggregation of tender notices using linking open data and semantic web technologies. This platform requires a step-based method to deal with the requirements of the public procurement sector and the open government data initiative: (1) modeling the unstructured information included in public procurement notices (contracting authorities, organizations, contracts awarded, etc.); (2) enriching that information with the existing product classification systems and the linked data vocabularies; (3) publishing relevant information extracted out of the notices following the linking open data approach; (4) implementing enhanced services based on advanced algorithms and techniques like query expansion methods to exploit the information in a semantic way. Taking into account that public procurement notices contain different kinds of data like types of contract, region, duration, total amount, target enterprise, etc., various methods can be applied to expand user queries easing the access to the information and providing a more accurate information retrieval system. Nevertheless expanded user queries can involve an extra-time in the process of retrieving notices. That is why a performance evaluation is outlined to tune up the semantic methods and the generated queries providing a scalable and time-efficient system. Moreover, this platform is supposed to be especially relevant for SMEs that want to tender in the European Union (EU), easing their access to the information of the notices and fostering their participation in cross-border public procurement processes across Europe. Finally an example of use is provided to evaluate and compare the goodness and the improvement of the proposed platform with regard to the existing ones.

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