Analyzing online consumer behavior in mobile and PC devices: A novel web usage mining approach

Online Payment, Individual Characteristics, and Digital Financial Inclusion in Oic Countries

“…Using UTAUT2, we explain how online transactions drive the use of mobile money services. A person who makes an online transaction, either for bill payment or to buy or pay for something, can be driven by hedonic motivations, utility and/ or social influences (Venkatesh et al, 2012;Raphaeli et al, 2017;Tang, 2019;David et al, 2019). Integrating e-commerce and mobile money accounts facilitates people to make online peer-to-peer transactions everywhere and at any time.…”

Analysing the Effect of Platform and Operating System Features on Predicting Consumers’ Purchase Intent using Machine Learning Algorithms

“…Experiments conducted on a real dataset showed that customer preference for particular product features plays a key role in decision-making and that COREL greatly outperforms the baseline methods. While in (Raphaeli et al, 2017) authors used clickstream to compare browsing behaviour in mobile and PC sessions. Analysis has been conducted to identify the differences in site usage characteristics across different channels, in which the analysis showed that user engagement in pc session is higher than mobile sessions.…”

“…Furthermore, identifying consumers' purchase intent play an important role in recommender systems when determining which users will be receptive to specific product recommendations (Korpusik et al, 2016), (Esmeli et al, 2019). The most common approach taken by many recent studies is to identify the next buyer or the buyer intent using machine learning algorithms such as deep learning (Salehinejad and Rahnamayan, 2016), (Korpusik et al, 2016), other works such as (Kim et al, 2003) used multiple classifier while authors in (Qiu et al, 2015) and (Raphaeli et al, 2017) used unsupervised learning such as association rules. Moreover, consumers buying behaviour is analysed using different methods such as statistical methods (Gupta and Pathak, 2014) and hidden Markov model (Norouzi and Alizadeh, 2016) to identify and recommend a given product or service (Martínez et al, 2020), (Raphaeli et al, 2017), (Bang et al, 2013) (Wang et al, 2015), (Kaatz et al, 2019) and (Liu et al, 2019).…”

A Qualitative Analysis to Evaluate Key Characteristics of Web Mining based e-Commerce Applications

“…Behavior of online consumer is often distinguished by both measuring user engagement and the detection of common sequences of navigation patterns, using an innovative new technique that combines footstep graph visualization with sequential association rule mining. It is also observed that sessions taken by using mobile devices are usually of taskoriented behavior on the other hand sessions conducted through PC devices are classified as exploration-oriented browsing behavior [14].…”