A Study of Influence Factors for Advertising on Messaging Applications Towards Mobile Buyer's Decision Making

The advancement of information technology leads to development of a new business paradigm which is focused on innovative technology. M-commerce seems to be a crucial tool to develop a country rapidly. The combination of messaging application features and business model can build start-up business driven by these technologies. However, the appropriate accessing target group of each business issues to be a main issue in the messaging application usage. This research aims to investigate the influence factors related to advertising on messaging applications. The Mixed method; quantitative and qualitative methods were implemented to investigate such factors. The findings are that three main factors, demographic factors, m-commerce factors, and behavioral factors, affected the buying decision making. Whereas, the demographic factor such as marital status showed no differences in this study. The products such as information technology accessories, beauty products and fashion goods are an important business area for customers focused on m-commerce. In addition, it was found that education had a significant effect towards advertising on messaging applications. Furthermore, the derived influence factors and criteria for advertising on messaging applications were confirmed with online merchants in the focused group method. The main advantage of messaging applications is the ability to interact with merchants and get quick responses. The results can be a guidance for start-up businesses for sustainable development

Using Keystroke Dynamics and Location Verification Method for Mobile Banking Authentication.

With the rise of security attacks on mobile phones, traditional methods to authentication such as Personal Identification Numbers (PIN) and Passwords are becoming ineffective due to their limitations such as being easily forgettable, discloser, lost or stolen. Keystroke dynamics is a form of behavioral biometric based authentication where an analysis of how users type is monitored and used in authenticating users into a system. The use of location data provides a verification mechanism based on user’s location which can be obtained via their phones Global Positioning System (GPS) facility. This study evaluated existing authentication methods and their performance summarized. To address the limitations of traditional authentication methods this paper proposed an alternative authentication method that uses Keystroke dynamics and location data. To evaluate the proposed authentication method experiments were done through use of a prototype android mobile banking application that captured the typing behavior while logging in and location data from 60 users. The experiment results were lower compared to the previous studies provided in this paper with a False Rejection Rate (FRR) of 5.33% which is the percentage of access attempts by legitimate users that have been rejected by the system and a False Acceptance Rate (FAR) of 3.33% which is the percentage of access attempts by imposters that have been accepted by the system incorrectly, giving an Equal Error Rate (EER) of 4.3%.The outcome of this study demonstrated keystroke dynamics and location verification on PINs as an alternative authentication of mobile banking transactions building on current smartphones features with less implementation costs with no additional hardware compared to other biometric methods. Keywords: smartphones, biometric, mobile banking, keystroke dynamics, location verification, securit

Modeling user interactions for conversion rate prediction in M-Commerce

Recent developments such as the introduction of new mobile banking and mobile payment services represent both an opportunity and a challenge for banks. While there is great potential to increase revenue by providing new services to customers, this goes together with the need to improve the understanding of customer data through deeper analysis, and to react quickly to changes in customer demands. It becomes increasingly important to maintain and update mobile apps with rapid release cycles. However, evaluating the results of changes in data analysis tools and their applications, such as recommender systems, sometimes requires live experiments on deployed systems. In this paper, a model based on stochastic process algebra is described for the interaction between a user and a recommending engine through a mobile app, and quantitative analysis is performed to show how changing features and parameters at the engine side may have an effect on user experience. This activity can be replicated on models representing an existing system, as a way to assess possible impacts before experimenting with live changes