No abstract

The world started the 4th industrial revolution based on technology in the IT field, and it is continuously developing. New technologies such as big data, Internet of Things, and health care have been applied to various fields. In particular, the fintech industry where new technologies are applied is undergoing rapid change. With the start of the 4th Industrial Revolution, new technologies began to be applied to various fields. Among them, face recognition technology was applied in fields such as customs, attendance, and payment, and the convenience was greatly improved. Today, the financial industry uses face recognition technology to start the era of face recognition payment services. This paper conducts an empirical analysis was conducted on how the characteristics of the face recognition payment system and innovation resistance affect the intention to use. Based on previous studies, face recognition payment system has the characteristics of convenience, reliability, security and non-contact. The collected data were analyzed by Smart PLS 2.0 using structural equations. The results of the study are as follows. First, among the features of the face recognition payment system, convenience, reliability and security have a negative effect on user innovation resistance. Second, among the features of the face recognition payment system, non-contact has a positive impact on user innovation resistance. Third, user innovation resistance has a negative effect on the intention to use.

The combination of big data and various technologies has caused tremendous changes in the financial industry. Face recognition payment as a new payment method has attracted much attention. China's investment in face recognition technology reached its peak in 2017-2018, and after 2019, it has entered a period of rational development. The reasons include the higher cost caused by higher technical and software and hardware requirements. This paper aims to study the influence of customer characteristics on innovation resistance and to explore the influence of innovation resistance on intention to use, which will help face recognition payment technology and products to better meet the needs of users. This paper chooses the attitude towards existing products, motivation, self-efficacy, innovation, and perceived risk customer characteristics. 123 Chinese users were surveyed by questionnaires, and the collected data were analyzed through Smart PLS 2.0. In the hypothesis about the innovation resistance of customer characteristics to face recognition payment, hypotheses 1-4 that customer innovation has a negative impact on innovation resistance has not been adopted. In addition, all assumptions have been adopted. In other words, customers' attitudes towards previous products and their perceived risks have a positive impact on innovation resistance, while customers' motivation and self-efficacy have a negative impact on innovation resistance. And research shows that customer resistance to innovation will have a negative impact on usage intentions.

Smartphone enables its users to download and install software, games and other programmes provided by third-party providers. The third-party software, that is installed on most Smartphones, are also known as a mobile application. These existing mobile applications run on mobile operations systems.The purpose of this study is to explain the factors affecting the user acceptance of Telegram app in Uzbekistan. In order to comprehend user acceptance of this application, it is necessary to examine the determinants of behavioral intention to use the application. Thus, this study developed a research model, based on the Unified Theory of Acceptance and Use of Technology (UTAUT). This study is aimed to identify the factors that affect the user behavior, proposing a model based on a UTAUT model. Therefore, the result is considered to be valuable in finding the other application in Uzbekistan, as this study focused on Telegram users in Uzbekistan. SPSS 22.0 was used for basic statistics and Smart PLS 2.0 was used for hypothesis testing. The empirical results of this study are summarized as follows. First, performance expectancy, effort expectancy, social influence and facilitating conditions significantly affect behavioral intention. Second, behavioral intention significantly affect use behavior.

In this study, a survey was held of m-learning educators in order to find out about the influence given to motivation by the characteristics of m-learning based on the parameters of the learning outcome.As a result of the analysis, it was revealed that the characteristics of m-learning provide a significant effect on the learning outcome and it was shown that the learning outcome provides a significant effect on the learning flow as well.This study aims to provide direction and guidelines for support service for learners for the improvement of the quality of m-learning to bring about enhancement in results and the fulfillment of successful learning by students.