Volume 21, Issue 4 (3-2025)
JSDP 2025, 21(4): 67-84 |
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ghorbani M, esmaeili L. Application of web usage mining to investigate online shopping behavior via PC versus mobile devices: evidence from click-stream data. JSDP 2025; 21 (4) : 5
URL: http://jsdp.rcisp.ac.ir/article-1-1366-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1366-en.html
Islamic Azad University, Qom & PHD student of Islamic Azad University, Qom, Iran
Abstract: (390 Views)
In recent years, the widespread use of smartphones and the rapid growth of mobile technologies have significantly transformed e-commerce, leading to the rise of mobile commerce (m-commerce). Mobile commerce services such as mobile banking, mobile payments, and mobile shopping have gained substantial traction. According to Statista, mobile retail sales in the United States exceeded $360 billion in 2021 and are projected to nearly double to approximately $710 billion by 2025. Furthermore, by the end of 2021, nearly one-third of U.S. internet users reported making weekly purchases online via their mobile devices.
This unprecedented growth underscores the need to explore user behavior in online shopping, particularly through mobile platforms. Several factors influence online shopping behavior, with the device used for browsing and purchasing playing a critical role. As smartphones become the dominant means of internet access, understanding the behavioral differences between mobile and desktop users becomes increasingly important. Although mobile commerce is a subset of e-commerce and shares similarities such as convenience and speed, notable differences exist due to device characteristics. These include the constant availability of smartphones, their lower computational power compared to desktops, and their smaller screen sizes, which can negatively impact the user experience during complex transactions.
Research has shown that mobile-specific features, including screen size, speed, security, and website optimization for mobile users, influence browsing and shopping behaviors. Despite the growing recognition of these differences, limited studies have compared user behavior between mobile and desktop platforms in e-commerce settings.
This study addresses this gap by analyzing user behavior on the Basalam platform, a prominent Iranian social e-commerce marketplace that supports both desktop and mobile shopping. The primary objective is to empirically examine whether and how user browsing behaviors differ between mobile and desktop platforms. The analysis adopts a novel approach inspired by the work of Orit Raphaeli et al., utilizing sequential association rule mining to uncover frequent navigation patterns and their implications for user interaction and purchase likelihood. Unlike Raphaeli’s dataset, which focuses on specific web page content, this study employs server-side event logs from Basalam to generalize findings and enhance applicability across e-commerce platforms.
The Basalam dataset represents user interactions captured through server logs, documenting user activities on the platform. The preprocessing steps differ from those in Raphaeli’s study due to variations in data structure, features, and timeframes. The proposed methodology creatively applies sequential association rule mining to episodes of user activity rather than specific web pages, identifying patterns that influence purchase outcomes without focusing on content-specific details.
The findings reveal distinct behavioral trends between desktop and mobile users. Desktop sessions are task-oriented, resulting in higher conversion rates, while mobile users demonstrate exploratory browsing patterns. Notably, certain navigation sequences were associated with higher purchase probabilities across both platforms.
This research contributes to the field in several ways:
This unprecedented growth underscores the need to explore user behavior in online shopping, particularly through mobile platforms. Several factors influence online shopping behavior, with the device used for browsing and purchasing playing a critical role. As smartphones become the dominant means of internet access, understanding the behavioral differences between mobile and desktop users becomes increasingly important. Although mobile commerce is a subset of e-commerce and shares similarities such as convenience and speed, notable differences exist due to device characteristics. These include the constant availability of smartphones, their lower computational power compared to desktops, and their smaller screen sizes, which can negatively impact the user experience during complex transactions.
Research has shown that mobile-specific features, including screen size, speed, security, and website optimization for mobile users, influence browsing and shopping behaviors. Despite the growing recognition of these differences, limited studies have compared user behavior between mobile and desktop platforms in e-commerce settings.
This study addresses this gap by analyzing user behavior on the Basalam platform, a prominent Iranian social e-commerce marketplace that supports both desktop and mobile shopping. The primary objective is to empirically examine whether and how user browsing behaviors differ between mobile and desktop platforms. The analysis adopts a novel approach inspired by the work of Orit Raphaeli et al., utilizing sequential association rule mining to uncover frequent navigation patterns and their implications for user interaction and purchase likelihood. Unlike Raphaeli’s dataset, which focuses on specific web page content, this study employs server-side event logs from Basalam to generalize findings and enhance applicability across e-commerce platforms.
The Basalam dataset represents user interactions captured through server logs, documenting user activities on the platform. The preprocessing steps differ from those in Raphaeli’s study due to variations in data structure, features, and timeframes. The proposed methodology creatively applies sequential association rule mining to episodes of user activity rather than specific web pages, identifying patterns that influence purchase outcomes without focusing on content-specific details.
The findings reveal distinct behavioral trends between desktop and mobile users. Desktop sessions are task-oriented, resulting in higher conversion rates, while mobile users demonstrate exploratory browsing patterns. Notably, certain navigation sequences were associated with higher purchase probabilities across both platforms.
This research contributes to the field in several ways:
- It is the first study of its kind focusing on the browsing behavior of Iranian e-commerce platforms, comparing mobile and desktop interactions.
- The methodology adapts and extends prior approaches to accommodate differences in dataset characteristics, providing a scalable framework for behavioral analysis.
The results hold significant implications for e-commerce strategies, offering insights for enhancing user experience, optimizing platform design, and improving conversion rates across devices. By analyzing Basalam’s event logs, this study provides a comprehensive understanding of user behavior in mobile and desktop contexts, highlighting the strategic importance of platform-specific design in the evolving landscape of digital commerce.
Article number: 5
Keywords: M-commerce, E-commerce, Online browsing behavior, Navigation patterns, Footstep graph, Web usage mining, Sequential association rule mining
Type of Study: Applicable |
Subject:
Paper
Received: 2023/03/24 | Accepted: 2024/12/4 | Published: 2025/04/2 | ePublished: 2025/04/2
Received: 2023/03/24 | Accepted: 2024/12/4 | Published: 2025/04/2 | ePublished: 2025/04/2
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