Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior
In this study, we aim to investigate the influence of the time spent reading product information on consumer behavior in e-commerce. Given the rapid growth of e-commerce and the increasing importance of understanding online consumer behavior, our research focuses on gaining a deeper understanding of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Behavioral Sciences |
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| Online Access: | https://www.mdpi.com/2076-328X/13/6/439 |
| _version_ | 1797596070027460608 |
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| author | Sabina-Cristiana Necula |
| author_facet | Sabina-Cristiana Necula |
| author_sort | Sabina-Cristiana Necula |
| collection | DOAJ |
| description | In this study, we aim to investigate the influence of the time spent reading product information on consumer behavior in e-commerce. Given the rapid growth of e-commerce and the increasing importance of understanding online consumer behavior, our research focuses on gaining a deeper understanding of customer navigation on e-commerce websites and its effects on purchasing decisions. Recognizing the multidimensional and dynamic nature of consumer behavior, we utilize machine learning techniques, which offer the capacity to handle complex data structures and reveal hidden patterns within the data, thereby augmenting our comprehension of underlying consumer behavior mechanisms. By analyzing clickstream data using Machine Learning (ML) algorithms, we provide new insights into the internal structure of customer clusters and propose a methodology for analyzing non-linear relationships in datasets. Our results reveal that the time spent reading product-related information, combined with other factors such as bounce rates, exit rates, and customer type, significantly influences a customer’s purchasing decision. This study contributes to the existing literature on e-commerce research and offers practical implications for e-commerce website design and marketing strategies. |
| first_indexed | 2024-03-11T02:45:20Z |
| format | Article |
| id | doaj.art-014b070b770c40ed8671c6199c01648b |
| institution | Directory Open Access Journal |
| issn | 2076-328X |
| language | English |
| last_indexed | 2024-03-11T02:45:20Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Behavioral Sciences |
| spelling | doaj.art-014b070b770c40ed8671c6199c01648b2023-11-18T09:19:03ZengMDPI AGBehavioral Sciences2076-328X2023-05-0113643910.3390/bs13060439Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer BehaviorSabina-Cristiana Necula0Department of Accounting, Business Information Systems and Statistics, Faculty of Economics and Business Administration, Alexandru Ioan Cuza University of Iasi, 700505 Iasi, RomaniaIn this study, we aim to investigate the influence of the time spent reading product information on consumer behavior in e-commerce. Given the rapid growth of e-commerce and the increasing importance of understanding online consumer behavior, our research focuses on gaining a deeper understanding of customer navigation on e-commerce websites and its effects on purchasing decisions. Recognizing the multidimensional and dynamic nature of consumer behavior, we utilize machine learning techniques, which offer the capacity to handle complex data structures and reveal hidden patterns within the data, thereby augmenting our comprehension of underlying consumer behavior mechanisms. By analyzing clickstream data using Machine Learning (ML) algorithms, we provide new insights into the internal structure of customer clusters and propose a methodology for analyzing non-linear relationships in datasets. Our results reveal that the time spent reading product-related information, combined with other factors such as bounce rates, exit rates, and customer type, significantly influences a customer’s purchasing decision. This study contributes to the existing literature on e-commerce research and offers practical implications for e-commerce website design and marketing strategies.https://www.mdpi.com/2076-328X/13/6/439e-commerceclickstream dataMachine LearningPrincipal Component Analysisclustering |
| spellingShingle | Sabina-Cristiana Necula Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior Behavioral Sciences e-commerce clickstream data Machine Learning Principal Component Analysis clustering |
| title | Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior |
| title_full | Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior |
| title_fullStr | Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior |
| title_full_unstemmed | Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior |
| title_short | Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior |
| title_sort | exploring the impact of time spent reading product information on e commerce websites a machine learning approach to analyze consumer behavior |
| topic | e-commerce clickstream data Machine Learning Principal Component Analysis clustering |
| url | https://www.mdpi.com/2076-328X/13/6/439 |
| work_keys_str_mv | AT sabinacristiananecula exploringtheimpactoftimespentreadingproductinformationonecommercewebsitesamachinelearningapproachtoanalyzeconsumerbehavior |