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

Análisis de clases latentes en la relación entre calidad de servicio, satisfacción y confianza con la intención de recompra

In this paper the relationship that perceived service quality, satisfaction and trust have on the purchase intention is analyzed. Concretely, three latent class segments that imply different behaviours from these relationships in different groups of consumers have been found. The latent class regression analysis is the statistical tool applied that has allowed us to identify different groups of clients of mobile telephony. In those groups significant differences are appreciated in that relationship. It has been proven that to predict the purchase intention through quality, satisfaction and trust, it is necessary to keep in mind that not for all consumers the variables of the model have the same strength, not even the same sign. For that reason, the identification of those groups of clients is fundamental in order to adapt properly marketing policiesEn este trabajo analizamos si en la relación entre calidad de servicio percibida, satisfacción y confianza y la intención de recompra, podemos distinguir clases latentes que impliquen comportamientos diferentes de estas relaciones en los distintos grupos de consumidores. El análisis de regresión de clases latentes es la herramienta estadística utilizada que permite identificar distintos grupos de clientes entre los que se aprecian diferencias significativas. Con ello comprobamos que para predecir la intención de compra a través de la calidad percibida, la satisfacción y la confianza, hay que tener en cuenta que para todos los consumidores las variables del modelo no ejercen la misma influencia, ni siquiera del mismo sentido; es decir, identificar los grupos de clientes, es fundamental si queremos adaptar a ellos políticas de marketing adecua

Antecedentes de la notoriedad del nombre en la determinación de la imagen de marca: una visión desde un producto de gran consumo

En el presente trabajo queremos establecer la relación entre el grado de notoriedad alcanzado por una marca y la conformación de su imagen. Partiendo de una revisión teórica previa, planteamos un modelo de efectos directos e indirectos de los antecedentes de marketing de la notoriedad del nombre de marca y de la imagen de marca. La base empírica de esta propuesta la realizamos sobre una muestra de consumidores de zumos naturales de los que, mediante un cuestionario, con escalas validadas para los diferentes modelos de medida, pretendemos conocer cómo afectan sobre la determinación de la imagen de marca, ciertos esfuerzos de marketing que realizan las empresas sobre sus marcas y cómo influye la consideración del nivel de notoriedad de la marca sobre las asociaciones vinculadas a la misma y que configuran su imagen

Tipología de compradores online mayores de 55 años

La edad se considera una de las variables determinantes del riesgo de exclusión social de la población, especialmente con relación al uso de las nuevas tecnologías en general y de Internet en particular. Esto ha incidido en una creciente preocupación por el estudio del colectivo de mayores de 55 años y sus peculiaridades en lo relativo al acceso, uso y aprovechamiento de las TIC (Tecnologías de la Información y la Comunicación). Sin embargo, los esfuerzos se han centrado, mayoritariamente, en el análisis del grupo de mayores como algo homogéneo, lo que ha llevado a establecer sus perfiles de comportamiento en comparación con el de otros grupos de edad. En esta investigación, con una muestra útil de 595 mayores, partimos de la consideración del colectivo como grupo heterogéneo, con distintas características y condiciones de uso de Internet, y proponemos un modelo de clases latentes que nos permite establecer tres perfiles de mayores internautas en función de la utilización de un servicio avanzado de Internet, la compra online

Online recommendation systems: Factors influencing use in E-commerce

The increasing use of artificial intelligence (AI) to understand purchasing behavior has led to the development of recommendation systems in e-commerce platforms used as an influential element in the purchase decision process. This paper intends to ascertain what factors affect consumers adoption and use of online purchases recommendation systems. In order to achieve this objective, the Unified Theory of Adoption and Use of Technology (UTAUT 2) is extended with two variables that act as an inhibiting or positive influence on intention to use: technology fear and trust. The structural model was assessed using partial least squares (PLS) with an adequate global adjustment on a sample of 448 users of online recommendation systems. Among the results, it's highlighted the importance of the inhibiting role of technology fear and the importance that users attach to the level of perceived trust in the recommendation system are highlighted. The performance expectancy and hedonic motivations have the greatest influence on intention to use these systems. Based on the results, this work provides a relevant recommendation to companies for the design of their e-commerce platforms and the implementation of online purchase recommendation systems

Identifying relevant segments of AI applications adopters : Expanding the UTAUT2’s variables

Artificial intelligence (AI) is a future-defining technology, and AI applications are becoming mainstream in the developed world. Many consumers are adopting and using AI-based apps, devices, and services in their everyday lives. However, research examining consumer behavior in using AI apps is scant. We examine critical factors in AI app adoption by extending and validating a well-established unified theory of adoption and use of technology, UTAUT2. We also explore the possibility of unobserved heterogeneity in consumers’ behavior, including potentially relevant segments of AI app adopters. To augment the knowledge of end users’ engagement and relevant segments, we have added two new antecedent variables into UTAUT2: technology fear and consumer trust. Prediction-orientated segmentation was used on 740 valid responses collected using a pre-tested survey instrument. The results show five segments with different behaviors that were influenced by the variables of the proposed model. Once known, the profiles were used to propose apps to AI developers to improve consumer engagement. The moderating effects of the added variables—technology fear and consumer trust—are also shown. Finally, we discuss the theoretical and managerial implications of our findings and propose priorities for future research.peerReviewe