Abstract-Objectives: Doing business through online social network is influenced by factors that might be differed compared with doing business through normal ecommerce channel. Although previous studies have been conducted to determine some of these factors which are affecting online purchase intention in social media website, little research exists with respect to the study regarding trust and risk in online social network. This research is one of the studies that use a focus group study among working adult (ages 25 to 34), to explore preliminary research model and hypotheses that had been gathered from the literature reviews regarding trust and risk that influence their online purchase decision through online social network, particularly Facebook. Results: The findings of factors and their attributes from this study are in line with the findings in the literature. The differences mainly come from details of the descriptions and expressions of each attribute.Index Terms-Social network, subjective norm, trust, risk, past online purchase experience.

In recent years, Bitcoin and other cryptocurrencies have been increasingly considered an investment option for emerging markets. However, its erratic behavior has discouraged some potential investors. To get insights into its behavior and price fluctuation, past studies have discovered the correlation between Twitter sentiments and Bitcoin behavior. Most of them have focused exclusively on their relationships, instead of the Twitter sentiment analysis itself. Finding the most suitable classification algorithms for sentiment analysis for this kind of data is challenging. For enormous data of Twitter, unlabeled data can be time-consuming and expensive for the supervised sentiment analysis approach, which has been studied to be superior to unsupervised ones. As such, we propose HyVADRF: Hybrid VADER -Random Forest and Grey Wolf Optimizer Model. Semantic and rule-based VADER was used to calculate polarity scores and classify sentiments, which overcame the weakness of manual labeling, while Random Forest was utilized as its supervised classifier. Furthermore, considering Twitter's massive size, we collected over 3.6 million tweets and analyzed various dataset sizes as these are related to the model's learning process. Lastly, Grey Wolf Optimizer parameter tuning was conducted to optimize the classifier's performance. The results show that 1) HyVADRF Model returned an accuracy of 75.29 %, precision of 70.22%, recall of 87.70%, and F1-score of 78%. 2) The most ideal percentage of dataset size is 90% of the total collected tweets (n=1,249,060). 3) With standard deviations of 0.0008 for accuracy and F1-score and 0.0011 for precision and recall. Hence, HyVADRF Model consistently delivers stable results.

The purpose of this study is to explore educational usage activities and motivations that influence the continuance intention to use Facebook for student engagement. An attitudinal model was developed and empirically tested in this study. Data were collected from 449 undergraduate university students in Thailand. Findings from partial least squares structural equation modelling (PLS-SEM) suggested that (a) communication activity has the most significant influence on utilitarian motivation; (b) resource/materials sharing activity has the most significant influence on hedonic motivation; (c) collaboration has the most significant influence on social motivation; (d) hedonic motivation has the most significant influence on satisfaction but no influence on continuance intention; and (e) continuance intention is influenced by satisfaction, social motivation and utilitarian motivation. Accordingly, the implications and recommendations for future research are suggested.

The purpose of this study is to explore educational usage activities and motivations that influence the continuance intention to use Facebook for student engagement. An attitudinal model was developed and empirically tested in this study. Data were collected from 449 undergraduate university students in Thailand. Findings from partial least squares structural equation modelling (PLS-SEM) suggested that (a) communication activity has the most significant influence on utilitarian motivation; (b) resource/materials sharing activity has the most significant influence on hedonic motivation; (c) collaboration has the most significant influence on social motivation; (d) hedonic motivation has the most significant influence on satisfaction but no influence on continuance intention; and (e) continuance intention is influenced by satisfaction, social motivation and utilitarian motivation. Accordingly, the implications and recommendations for future research are suggested.

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