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The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students’ online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement.

A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide.

The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy.

A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed.

Singapore has had a dramatic spike in coronavirus infections in early 2020, with thousands of new cases linked to clusters in migrant workers (MWs) dormitories. To control the spread, the Government attempted to isolate the dormitories, test workers and move symptomatic patients into quarantine facilities. But those measures have left thousands of them trapped in their dormitories, living in cramped conditions that make social distancing near impossible. This paper investigates how COVID-19 has impacted the lives of these workers in varied ways and highlights the migrant workers' belief if Singapore’s effort has been enough for them during the COVID-19 pandemic? The focus is mainly on the low-skilled workers from India and Bangladesh, who are prone to be affected in various ways by COVID-19. My collected data show that migrant workers are grateful to the Singapore state for the support extended during COVID-19. I used the concept of subcultures to explain the condition of the workers in the state of Singapore. Because they expect so little social protection from the state, they are genuinely grateful for its support during the pandemic.

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 study is to unearth the factors that influence tourists’ revisit intention. The proposed model of the study is grounded on using the theory of planned behaviour (TPB) and extending it with additional variables, i.e. satisfaction, destination image, perceived risk, service quality and perceived value.

This study adopted a cross-sectional approach to collect data. The data were collected by conducting a field survey questionnaire on 330 respondents and were analysed using partial least squares version 3.2.9.

The results show that perceived behavioural control, perceived value, destination image and satisfaction significantly affect visitors’ revisit intention. The influence of perceived value, perceived service quality and destination image on satisfaction is also confirmed. On the other hand, satisfaction is found to be a significant mediator between perceived service quality, destination image and perceived value.

The extended TPB model that includes perceived service quality, perceived value, perceived risk and satisfaction provided a model with a theoretical basis to explain tourist revisit intentions to a tourist destination.

El objetivo principal del estudio es descubrir los factores que influyen en la intención de revisita de los turistas. El modelo propuesto para el estudio se basa en el uso de la teoría del comportamiento planificado y se amplía con variables como la satisfacción, la imagen del destino, el riesgo percibido, la calidad del servicio y el valor percibido.

Este estudio adoptó un enfoque transversal para la recogida de datos. Los datos se recopilaron mediante un cuestionario de campo en el que participaron 330 encuestados. Los datos se analizaron utilizando la versión 3.2.9 de PLS.

Los resultados muestran que el control conductual percibido, el valor percibido, la imagen del destino y la satisfacción afectan significativamente a la intención de revisita. También se confirma la influencia del valor percibido, la calidad de servicio percibida y la imagen del destino sobre la satisfacción. Por otra parte, la satisfacción resulta ser un mediador significativo entre la calidad de servicio percibida, la imagen del destino y el valor percibido.

El modelo TPB ampliado que incluye la calidad de servicio percibida, el valor percibido, el riesgo percibido y la satisfacción proporcionó un modelo con una base teórica para explicar las intenciones de revisita de los turistas a un destino turístico.

本研究的目的是揭示影响游客重访意向的因素。本研究提出的模型以计划行为理论（TPB）为基础, 并以额外的变量（即满意度、目的地形象、感知风险、服务质量和感知价值）进行扩展。

本研究采用了横断面的方法来收集数据。通过对330名受访者进行实地调查问卷来收集数据, 并使用偏最小二乘法3.2.9版进行分析。

结果显示, 感知行为控制、感知价值、目的地形象和满意度对游客的再访意向有显著影响。感知价值、感知服务质量和目的地形象对满意度的影响也被证实。另一方面, 满意度被发现是感知服务质量、目的地形象和感知价值之间的一个重要中介因素。

包括感知服务质量、感知价值、感知风险和满意度在内的扩展TPB模型为解释游客对旅游目的地的再访意向提供了理论基础。

关键词: 满意度; 旅游; 计划行为理论; PLS-SEM; 目的地形象