Fatores que afetam a adoção de análises de Big Data em empresas

With the total quantity of data doubling every two years, the low price of computing and data storage, make Big Data analytics (BDA) adoption desirable for companies, as a tool to get competitive advantage. Given the availability of free software, why have some companies failed to adopt these techniques? To answer this question, we extend the unified theory of technology adoption and use of technology model (UTAUT) adapted for the BDA context, adding two variables: resistance to use and perceived risk. We used the level of implementation of these techniques to divide companies into users and non-users of BDA. The structural models were evaluated by partial least squares (PLS). The results show the importance of good infrastructure exceeds the difficulties companies face in implementing it. While companies planning to use Big Data expect strong results, current users are more skeptical about its performance.Con la cantidad total de datos duplicándose cada dos años, el bajo precio de la informática y del almacenamiento de datos, la adopción del análisis Big Data (BDA) es altamente deseable para las empresas, como un instrumento para conseguir una ventaja competitiva. Dada la disponibilidad de software libre, ¿por qué algunas empresas no han adoptado estas técnicas? Para responder a esta pregunta, ampliamos la teoría unificada de la adopción y uso de tecnología (UTAUT) adaptado para el contexto BDA, agregando dos variables: resistencia al uso y riesgo percibido. Utilizamos el grado de implantación de estas técnicas para dividir las empresas entre: usuarias y no usuarias de BDA. Los modelos estructurales fueron evaluados con partial least squres (PLS). Los resultados muestran que la importancia de una buena infraestructura excede las dificultades que enfrentan las empresas para implementarla. Mientras que las compañías que planean usar BDA esperan muy buenos resultados, las usuarias actuales son más escépticos sobre su rendimiento.Com a quantidade total de dados duplicando a cada dois anos, o baixo preço da computação e do armazenamento de dados tornam a adoção de análises de Big Data (BDA) desejável para as empresas, como aquelas que obterão uma vantagem competitiva. Dada a disponibilidade de software livre, por que algumas empresas não adotaram essas técnicas? Para responder a essa pergunta, estendemos a teoria unificada de adoção e uso de tecnologia (UTAUT) adaptado para o contexto do BDA, adicionando duas variáveis: resistência ao uso e risco percebido. Usamos a nível da implementação da tecnologia para dividir as empresas em usuários e não usuários de técnicas de BDA. Os modelos estruturais foram avaliados por partial least squares (PLS). Os resultados mostram que a importância de uma boa infraestrutura excede as dificuldades que as empresas enfrentam para implementá-la. Enquanto as empresas que planejam usar Big Data esperam resultados fortes, os usuários atuais são mais céticos em relação ao seu desempenho

Acceptance and use of big data techniques in services companies

Companies able to take advantage of the information coming from the use of Big Data will have a competitive advantage by being able to make decisions based on greater knowledge of customers and competition. Besides, the access to the software for the treatment of this great amount of data is free. So, the objective of this paper is to study the level of acceptance and use of these technologies, Big Data techniques, by services companies. To analyse the intention and use it extends the acceptance technologies model- Unified Theory of Acceptance and Use of Technology (UTAUT) - to the context of Big Data techniques, incorporating the effect on it of three new variables: resistance to use, perceived risk and opportunity cost. The structural model was evaluated using partial least squares structural equation modelling (PLS-SEM) with an adequate global fit. The verification is carried out with a sample of 199 Spanish services companies, and its main results are the strong effect of the facilitating conditions on the intention and use of Big Data, as well as the direct effect of the opportunity cost and the resistance to use on the intention, and the indirect inhibiting effect of the perceived risk through the resistance to use on intention behaviou

Cryptocurrencies as a financial tool: acceptance factors

Cryptocurrencies are a new form of digital asset that operate through blockchain technology and whose purpose is to be used as a means of exchange. Some, such as bitcoin, have become globally recognized in recent years, but the uncertainty surrounding cryptocurrencies raises questions about their intended use. This study has the task of investigating the different factors that affect the intention behind the use of cryptocurrencies by developing a new research model and using Partial Least Squares (PLS) to assess it. The results show that all the constructs proposed have significative influence, either directly or indirectly, on the intention behind the use of cryptocurrencies. The findings provide value and utility for companies’ and cryptocurrencies’ intermediaries to formulate their business strategies

Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model

The purpose of this paper is to identify the factors that affect the intention to use Big Data Applications in companies. Research into Big Data usage intention and adoption is scarce and much less from the perspective of the use of these techniques in companies. That is why this research focuses on analyzing the adoption of Big Data Applications by companies. Further to a review of the literature, it is proposed to use a UTAUT model as a starting model with the update and incorporation of other variables such as resistance to use and perceived risk, and then to perform a neural network to predict this adoption. With respect to this non-parametric technique, we found that the multilayer perceptron model (MLP) for the use of Big Data Applications in companies obtains higher AUC values, and a better confusion matrix. This paper is a pioneering study using this hybrid methodology on the intention to use Big Data Applications. The result of this research has important implications for the theory and practice of adopting Big Data Applications

Intention to take COVID-19 vaccine as a precondition for international travel: application of extended norm-activation model

The COVID-19 pandemic is a serious threat to human health, the global economy, and the social fabrics of contemporary societies as many aspects of modern everyday life, including travel and leisure, have been shattered to pieces. Hence, a COVID-19 mandatory vaccination as a precondition for international travel is being debated in many countries. Thus, the present research aimed to study the intention to take the COVID-19 vaccine as a precondition for international travel using an extended Norm-Activation Model. The study model integrates a new construct, namely mass media coverage on COVID-19 vaccination as additional predictor of intention to take the COVID-19 vaccine. The survey data were collected from 1221 international travelers. Structural equation modelling shows a very good fit of the final model to the data; the conceptual model based on extended Norm-Activation Model was strongly supported. Awareness of consequences related to the COVID- 19 pandemic on individuals’ health has shown a positive effect on individuals’ ascribed responsibility to adopt emotionally driven (anticipated pride and anticipated guilt) pro-social behaviors that activate a personal norm towards altruistic and pro-mandatory vaccination-friendly behavior. Theoretical and practical implications are discussed

New approach for the prediction of the electric field distribution in multimode microwave-heating applicators with mode stirrers

We present a new approach for inferring the electric field distribution inside materials in multimode cavities with mode stirrers.We calculate the electric field in the dielectric material by a two-dimensional modeling of a typical multimode microwave applicator with some mobile metallic sheets. We compare simulated results with classical approaches, such as Lambert’s law or a constant electric field distribution. The proposed method allows for a better understanding of how these structures can be applied for heating materials when computing the microwave energy absorption in the dielectric. Finally, we perform experimental tests in a microwave multimode oven for validating purposes.This work was supported in part by the Spanish Science and Technology Ministry under Project TIC2001-2778-CO2-02

Effect of mode-stirrer configurations on dielectric heating performance in multimode microwave applicators

In this paper, several mode-stirrer configurations are compared in order to establish their influence on the electric-field uniformity within an irradiated dielectric sample inserted in a microwave- heating applicator. Two different scenarios are evaluated with metallic sheets moving inside the multimode applicator. The different stirrer configurations are tested and compared for low-, medium-, and high-loss dielectric sample materials. Additionally, a straightforward procedure based on a generalized plane-wave approach is proposed and evaluated as a computationally efficient alternative for calculating the electric-field distribution inside materials processed in these microwave applicators with mode stirrers. Although very different electric patterns are achieved depending on stirrer geometry and sample permittivity, the plane-wave approach has been shown to provide a very good approximation for medium and high lossy dielectric materials.This work was supported in part by the Spanish Science and Technology Ministry under Project TIC2001-2778-CO2-02

Factores que afectan a la adopción del big data como instrumento de marketing en empresas españolas

El uso masivo de los smartphones, la gran cantidad de aparatos y sensores conectados a internet y el abaratamiento de la computación y almacenamiento de datos han permitido que se generalice el Big Data. Hoy día, las empresas que sean capaces de obtener nuevos datos sobre su negocio y clientes tendrán una ventaja competitiva sobre las demás y justo por eso, las empresas de éxito están tomando sus decisiones más importantes basadas en datos y no en la jerarquía empresarial. Además, casi todo el software asociado a tecnologías Big Data es software libre por lo que nos preguntamos: “si es libre y aporta mucho valor, ¿por qué no lo usan?”. Así, en este estudio pretendemos evaluar los factores que afectan a la aceptación de esta nueva tecnología por parte de las empresas. Con ese fin, adaptamos el modelo de aceptación de tecnologías UTAUT al contexto del Big Data al que añadimos un inhibidor; la resistencia al uso de las nuevas tecnologías. El modelo estructural fue valorado mediante PLS con un adecuado ajuste global. Entre los resultados destacamos que tiene más relevancia una buena infraestructura para poder usar Big Data que la dificultad de su implantación aceptando por necesario hacer un esfuerzo en su implementación. Aportamos beneficios académicos por lo innovador y para la gestión, el beneficio que reportará a las empresas españolas el desarrollo de esta tecnología para una mejor gestión de la relación con los clientes.Massive use of smartphones, great quantity of connected apparels and sensors to internet and low prices of computing and data storage have allowed the Big Data adoption. Nowadays, companies that can get new data about their business and customers will have a competitive advantage over other companies and because of that, successful companies are taking their decisions data-driven and not management-driven. Moreover, almost all this software is free and because of that, we question ourselves: “if it is free and very valuable, why is not being used by all companies?”. That is why in this study, we intend to find the factors that drive this new technology acceptance from companies. We will use an adaptation of the UTAUT to the Big Data context with a new inhibitor such as the use resistance to new technologies. The structural model was evaluated by PLS with an adequate global fit. Among the most remarkable results is the fact that is more important to have a good infrastructure than the difficulties to implement it. So the effort to use it is taken for granted. This research will be useful for scholars and practitioners because of its innovativeness and to have an approach of this technology development influencing on CRM program in Spanish companies

Dynamic Permittivity Measurement of Ground-Tire Rubber (GTR) during Microwave-Assisted Devulcanization

[EN] Many efforts are being made to find innovative ways of recycling rubber from end-of-life tires (ELTs), also called ground tire Rubber (GTR). Recycling through devulcanization allows the reintroduction of rubber back into the manufacturing industry. Such a process requires providing enough energy to break the sulfur links, while preventing damage to the polymeric chain. Microwave heating is controllable, efficient, and it does not rely on conventional heating mechanisms (conduction, convection) which may involve high heating losses, but rather on direct dielectric heating. However, to adequately control the microwave-assisted devulcanization performance, a thorough knowledge of the GTR permittivity versus temperature is required. In this work, GTR permittivity was monitored during its devulcanization. A resonant technique based on a dual-mode cylindrical cavity was used to simultaneously heat rubber and measure its permittivity at around 2 GHz. The results show sharp changes in the GTR permittivity at 160 and 190 degrees C. After the GTR cooled down, a shifted permittivity evidences a change in the GTR structure caused by the devulcanization process. Microwave-assisted devulcanization effectiveness is proven through time-domain nuclear magnetic resonance (NMR) measurements, by verifying the decrease in the cross-link density of processed GTR samples compared to the original sample.This research project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement number 870,000. More information at https://cordis.europa.eu/project/id/870000 (accessed on 27 August 2022) and https://valuerubber.eu/(accessed on 27 August 2022). JLV and FMS also thanks the funding from Ministerio de Ciencia e Innovacion (PID2020-119047RB-I00 y PLEC2021-007793), Gobierno de Aragon (EC-22-2021) and CSIC (201860E045).Pérez-Campos, R.; Fayos-Fernández, J.; Monzó-Cabrera, J.; Martín Salamanca, F.; López Valentín, J.; Catalá Civera, JM.; Plaza González, PJ.... (2022). Dynamic Permittivity Measurement of Ground-Tire Rubber (GTR) during Microwave-Assisted Devulcanization. Polymers. 14(17):1-21. https://doi.org/10.3390/polym14173543121141