This study compares the performance of Pytorch-based Deep Learning, Multiple Perceptron Neural Networks with Multiple Linear Regression in terms of software effort estimations based on function point analysis. This study investigates Adjusted Function Points, Function Point Categories, Industry Sector, and Relative Size. The ISBSG dataset (version 2020/R1) is used as the historical dataset. The effort estimation performance is compared among multiple models by evaluating a prediction level of 0.30 and standardized accuracy. According to the findings, the Multiple Perceptron Neural Network based on Adjusted Function Points combined with Industry Sector predictors yielded 53% and 61% in terms of standardized accuracy and a prediction level of 0.30, respectively. The findings of Pytorch-based Deep Learning are similar to Multiple Perceptron Neural Networks, with even better results than that, with standardized accuracy and a prediction level of 0.30, 72% and 72%, respectively. The results reveal that both the Pytorch-based Deep Learning and Multiple Perceptron model outperformed Multiple Linear Regression and baseline models using the experimental dataset. Furthermore, in the studied dataset, Adjusted Function Points may not contribute to higher accuracy than Function Point Categories.

Due to the ongoing global debate regarding the relationship between fintech and banks, including developing countries, this study aims to investigate this relationship in the case of Vietnam, an emerging nation. The study analyzes the relationship between fintech search and bank stock returns, which are measures of fintech and banks, respectively. The time series data for fintech and bank stock returns were obtained from Google Trends and Vietstock, respectively. Exploratory factor analysis was utilized to derive the fintech variables, while the bank stock return variable was calculated using a basket of eight listed banks from 2017w46 to 2021w46. The results were estimated using the vector autoregression and Granger causality method and validated with the copula method. A key finding of this study is the presence of a simultaneous negative change and bidirectional causality between bank stock returns and fintech lending. Furthermore, several other interesting findings were discovered: (1) the causal relationship from fintech to bank stock returns is weaker compared with the opposite direction; (2) unidirectional causality exists between different types of fintech, such as influence from FinFintech to FinLending, from FinPayment to FinLending and FinWallet, from FinMoney to FinFintech, from FinWallet to FinLending, and from FinProduct to FinFintech; and (3) there is an equal occurrence of simultaneous increase or decrease between bank stock returns and certain types of fintech, specifically between BankReturn and FinPayment, BankReturn and FinLending, as well as BankReturn and FinWallet. These findings shed light on the complex relationship between fintech and banks, offering insights that contribute to our understanding of this dynamic interplay in the context of Vietnam’s emerging fintech landscape.

Objective: The advancement of Internet technology brought up the tourism industry towards new development and opportunities. With application of the Internet technology tourism industry comprises a vast range of virtual communities such as Trip Advisor, Agoda, Travelocity and so on. Existing research concentrated on evaluating the factors influencing virtual communities’ behaviour with limited evaluation of tourist perception. This paper focused on examining the tourists' perception of a virtual tour through the sentimental analysis model based on eWOM for sustainable development. Method: The developed model comprises the group average Bayesian network with the computation of the polarity of the tourist perception. A Bayesian network is a data-driven method involved in estimating dependence among the variable with probabilistic computation. Results: The analysis is based on data collected from sample population in Vietnam with consideration of the 11 variables. Participation intensity, social identity, functional value, emotional value, sharing, interaction, and user satisfaction are among eleven primary variables that have been chosen. Conclusions: The analysis of the results expressed that the user satisfaction level is based on the user's experience and functional value. Additionally, the analysis stated that social value comprises the intermediary role in virtual tourism. This research adds to research methodologies of user engagement methods as well as serves as a reference for theoretical research and management practise in the virtual tourist community.