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The purpose of the research is to analyze the factors that determine the intention of small- and medium-sized enterprises (SMEs) to adopt the Metaverse. For this purpose, the analysis of the effort expectancy and performance expectancy of the constructs in relation to business satisfaction is proposed.

The analysis was performed on a sample of 182 Spanish SMEs in the technology sector, using a PLS-SEM approach for development. For the confirmation of the model and its results, an analysis with PLSpredict was performed, obtaining a high predictive capacity of the model.

After the analysis of the model proposed in this research, it is recorded that the valuation of the effort to be made and the possible performance expected by the companies does not directly determine the intention to use immersive technology in their strategic behavior. Instead, the results obtained indicate that business satisfaction will involve obtaining information, reducing uncertainty and analyzing the competition necessary for approaching this new virtual environment.

The study represents one of the first approaches to the intention of business behavior in the development of performance strategies within Metaverse systems. So far, the literature has approached immersive systems from perspectives close to consumer behavior, but the study of strategic business behavior has been left aside due to the high degree of experimentalism of this field of study and its scientific approach. The present study aims to contribute to the knowledge of the factors involved in the intention to use the Metaverse by SMEs interested in this field.

The purpose of this paper is to examine the influence of perceived information and entertainment value, perceived credibility and perceived value on Generation Y consumers’ usage frequency of online consumer reviews.

The paper proposes and tests, with structural equation modelling analysis of moment structures, a research model using data from a large sample of Generation Y consumers.

The results confirm that Generation Y consumers perceive online reviews to be informative, entertaining, credible and valuable, and that they frequently consult such reviews. More specifically, the empirical analysis confirms that perceived information value, perceived entertainment value and perceived credibility significantly influenced the perceived value that Generation Y attach to online consumer reviews, which, in turn, was a significant predictor of their usage frequency of such reviews.

The results highlight the strategic importance of integrating online consumer reviews into the marketing communication mix when targeting Generation Y, together with the necessity of having filtering mechanisms to ensure that only authentic reviews are published and the need to implement tactics to ensure that such reviews are informative and entertaining and, consequently, of value.

This study contributes to marketers’ comprehension of strategically using online consumer reviews when targeting the Generation Y segment.

El objetivo de esta investigación es examinar la influencia del valor de la información, del valor del entretenimiento, la credibilidad y el valor percibido en la frecuencia de uso de las revisiones online de los consumidores por parte de los consumidores de la Generación Y.

Este documento propone y contrasta, a través de los modelos de ecuaciones estructurales (AMOS), un modelo de investigación que utiliza datos de una gran muestra de consumidores de la Generación Y.

Los resultados ponen de relieve la importancia estratégica de integrar las reseñas de consumidores en línea en la combinación de comunicaciones de comercialización al dirigirse a la Generación Y, junto con la necesidad de disponer de mecanismos de filtrado para garantizar que sólo se publiquen reseñas auténticas y la necesidad de aplicar tácticas para asegurar que dichas reseñas sean informativas y entretenidas y, por consiguiente, de valor.

Los resultados destacan la importancia estratégica de integrar las revisiones de los consumidores en línea en el mix de comunicación de marketing cuando se dirigen a la Generación Y, junto con la necesidad de contar con mecanismos de filtrado para garantizar que solo se publiquen revisiones auténticas y también se implementen tácticas para garantizar que las reseñas sean informativas y entretenidas y, en consecuencia, de gran valor.

Este trabajo ayuda a los especialistas de marketing a comprender como pueden utilizar estratégicamente las opiniones de los consumidores online para dirigirse a los consumidores de la Generación Y.

There is a gap in knowledge about the Gulf Cooperation Council (GCC) because most studies are undertaken in countries outside the Gulf region – such as China, India, the US and Taiwan. The stock market contains rich, valuable and considerable data, and these data need careful analysis for good decisions to be made that can lead to increases in the efficiency of a business. Data mining techniques offer data processing tools and applications used to enhance decision-maker decisions. This study aims to predict the Kuwait stock market by applying big data mining.

The methodology used is quantitative techniques, which are mathematical and statistical models that describe a various array of the relationships of variables. Quantitative methods used to predict the direction of the stock market returns by using four techniques were implemented: logistic regression, decision trees, support vector machine and random forest.

The results are all variables statistically significant at the 5% level except gold price and oil price. Also, the variables that do not have an influence on the direction of the rate of return of Boursa Kuwait are money supply and gold price, unlike the Kuwait index, which has the highest coefficient. Furthermore, the height score of the variable that affects the direction of the rate of return is the firms, and the accuracy of the overall performance of the four models is nearly 50%.

Some of the limitations identified for this study are as follows: (1) location limitation: Kuwait Stock Exchange; (2) time limitation: the amount of time available to accomplish the study, where the period was completed within the academic year 2019-2020 and the academic year 2020-2021. During 2020, the coronavirus pandemic (COVID-19), which was a major obstacle, occurred during data collection and analysis; (3) data limitation: The Kuwait Stock Exchange data were collected from May 2019 to March 2020, while the factors affecting the stock exchange data were collected in July 2020 due to the corona pandemic.

The study used new titles, variables and techniques such as using data mining to predict the Kuwait stock market. There are no adequate studies that predict the stock market by data mining in the GCC, especially in Kuwait. There is a gap in knowledge in the GCC as most studies are in foreign countries, such as China, India, the US and Taiwan.