Technologies have driven big data collection across many fields, such as genomics and business intelligence. This results in a significant increase in variables and data points (observations) collected and stored. Although this presents opportunities to better model the relationship between predictors and the response variables, this also causes serious problems during data analysis, one of which is the multicollinearity problem. The two main approaches used to mitigate multicollinearity are variable selection methods and modified estimator methods. However, variable selection methods may negate efforts to collect more data as new data may eventually be dropped from modeling, while recent studies suggest that optimization approaches via machine learning handle data with multicollinearity better than statistical estimators. Therefore, this study details the chronological developments to mitigate the effects of multicollinearity and up-to-date recommendations to better mitigate multicollinearity.

The development of IoT technologies and the massive admiration and acceptance of social media tools and applications, new doors of opportunity have been opened for using data analytics in gaining meaningful insights from unstructured information. The application of opinion mining and sentiment analysis (OMSA) in the era of big data have been used a useful way in categorize the opinion into different sentiment and in general evaluating the mood of the public. Moreover, different techniques of OMSA have been developed over the years in different datasets and applied to various experimental settings. In this regard, this study presents a comprehensive systematic literature review, aims to discuss both technical aspect of OMSA (techniques, types) and non-technical aspect in the form of application areas are discussed. Furthermore, the study also highlighted both technical aspect of OMSA in the form of challenges in the development of its technique and non-technical challenges mainly based on its application. These challenges are presented as a future direction for research.

Many tourism-related activities have been suspended due to the nationally enforced lockdown to combat the Coronavirus pandemic. The tourism industry suffered immensely from the lockdown, and as a result of this, digital tourism began gaining traction and attracted public attention. This study analyses the impact of digitalization on the social and economic sustainability of the tourism industry via systematic literature network analysis. The findings indicated that digitalization impacts economic sustainability, encompassing economic benefits in tourism product development, tourism consumption, and industrial development. Moreover, digitalization fosters social development, cultural awareness, and tourism participation in digital technology and cultural heritage. This study identified publication trends and research hotspots using bibliometric analysis, and it was confirmed that Sustainability was the top journal in published digital and tourism sustainability-related articles, followed by the International Journal of Tourism Research, Tourism Management, and Current Issues in Tourism. This study resulted in two implications: identifying the knowledge gap and evidence-based decision-making based on the (previous) literature. Recommendation for future research is also discussed in this study, which is helpful to policymakers, tourism planners, and researchers to develop strategies grounded in research.

A finite mixture model is well-known in statistics due to its versatility and is being actively researched. This paper reviews finite mixture model literature via bibliometric analysis, focusing on the trend and link between finite mixture model studies. The bibliometric analysis consists of four main phases; formulating research questions, locating research, research selection, evaluation, and analyzing and synthesizing selected papers. There are 667 journal articles extracted from the Web of Science (WoS) database from publications within 1988 to 2020. The Biblioshiny with R packages and VOSViewer were used as analytical tools. The findings show that there is an increasing trend of annual publication on the finite mixture model study. The results also outline key journals and the highest cited articles. Network analysis was also conducted and explored in scientific cooperation in the finite mixture model study. This study proposed a research agenda in the finite mixture model by identifying its current state and population trends.