Analysis Online Shopping Behavior of Consumer Using Decision Tree

2011 ◽  
Vol 271-273 ◽  
pp. 891-894 ◽  
Author(s):  
Lei Yue Yao ◽  
Jian Ying Xiong
Keyword(s):  
Decision Tree ◽  
Online Shopping ◽  
Shopping Behavior ◽  
Purchase Behavior ◽  
Monetary Model ◽  
Decision Variables ◽  
Demonstration Experiment ◽  
Online Shopping Behavior ◽  
Decision Making Model ◽  
C5.0 Decision Tree

Trading failure is the main reason for a dispute of C2C e-commerce. So predict the behavior of transactions can assist buyers and sellers negotiated transactions, helps to reduce transaction disputes. Separate the success and failure purchase record, then establish decision-making model through the C5.0 decision tree and RFM(Recency, Frequency, Monetary) model on consumer purchase behavior data, quantify the importance of the decision variables, the demonstration experiment shows the prediction accuracy is more than 80%.

Trust in Online Shopping Behavior

2012 ◽  
pp. 456-465
Author(s):  
Yongqiang Sun ◽  
Nan Wang
Keyword(s):  
Information Disclosure ◽  
Online Shopping ◽  
Shopping Behavior ◽  
Purchase Behavior ◽  
Future Research ◽  
Privacy Concern ◽  
Big Picture ◽  
Research Findings ◽  
Online Shopping Behavior ◽  
Post Purchase

Trust, which dominates the research on online shopping behavior, is relevant to various consumer behaviors across different online shopping stages. To provide a big picture of the research on trust in the online shopping context, this chapter reviews the literature on this topic and summarizes the major research findings. Specifically, trust-related behaviors are identified according to the three online shopping stages: information adoption and information disclosure behavior at the pre-purchase stage, product purchase behavior at the purchase stage, and relational behavior such as electronic word-of-mouth (eWOM) and re-purchase at the post-purchase stage. The research topics relevant to these behaviors, including recommendation agent, information credibility, privacy concern, trust building and transfer process, and relationship marketing in the online shopping context are detailed. The future research directions such as location-based services, trust and distrust, and trust repair are also highlighted.

scholarly journals Consumer perceived risk in online shopping environment via Facebook as medium

2018 ◽  
Vol 7 (4) ◽  
pp. 2485 ◽  
Author(s):  
Charu Panwar
Keyword(s):  
Perceived Risk ◽  
Financial Risk ◽  
Online Shopping ◽  
Shopping Behavior ◽  
Purchase Behavior ◽  
Primary Study ◽  
Product Risk ◽  
Considerable Impact ◽  
And Gender ◽  
Online Shopping Behavior

The study aims to understand the consumer buying behavior while shopping online. The study unveils the multidimensional perceived risk in online shopping that will be helpful for the marketers in mitigating the perceived risk. The study used universally accepted determinants of consumers’ perceived risk namely financial risk, product risk, delivery risk, time risk and privacy risk. This multi-pronged perceived risk has significant impact on the online shopping behavior of the customer and adversely affects their purchase behavior. The total number of 180 respondent has been selected for the primary study. The convenient sampling method of non-probability sampling has been used for selection of respondents. The study found that the demographics have a major role to play on consumers’ perception towards online shopping. Income and gender are the two important factors identified that may have considerable impact on consumers’ perception towards online shopping. T-test, ANOVA and regression analysis has been used for data analysis purpose. 

scholarly journals Machine learning-based e-commerce platform repurchase customer prediction model

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243105
Author(s):  
Cheng-Ju Liu ◽  
Tien-Shou Huang ◽  
Ping-Tsan Ho ◽  
Jui-Chan Huang ◽  
Ching-Tang Hsieh
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Linear Model ◽  
High Speed ◽  
Online Shopping ◽  
Shopping Behavior ◽  
Single Model ◽  
Fusion Algorithm ◽  
Tree Model ◽  
Online Shopping Behavior

In recent years, China's e-commerce industry has developed at a high speed, and the scale of various industries has continued to expand. Service-oriented enterprises such as e-commerce transactions and information technology came into being. This paper analyzes the shortcomings and challenges of traditional online shopping behavior prediction methods, and proposes an online shopping behavior analysis and prediction system. The paper chooses linear model logistic regression and decision tree based XGBoost model. After optimizing the model, it is found that the nonlinear model can make better use of these features and get better prediction results. In this paper, we first combine the single model, and then use the model fusion algorithm to fuse the prediction results of the single model. The purpose is to avoid the accuracy of the linear model easy to fit and the decision tree model over-fitting. The results show that the model constructed by the article has further improvement than the single model. Finally, through two sets of contrast experiments, it is proved that the algorithm selected in this paper can effectively filter the features, which simplifies the complexity of the model to a certain extent and improves the classification accuracy of machine learning. The XGBoost hybrid model based on p/n samples is simpler than a single model. Machine learning models are not easily over-fitting and therefore more robust.

Explaining online shopping behavior with fsQCA: The role of cognitive and affective perceptions

2016 ◽  
Vol 69 (2) ◽  
pp. 794-803 ◽  
Author(s):  
Ilias O. Pappas ◽  
Panos E. Kourouthanassis ◽  
Michail N. Giannakos ◽  
Vassilios Chrissikopoulos
Keyword(s):  
Online Shopping ◽  
Shopping Behavior ◽  
Online Shopping Behavior

Examining digital information literacy as a determinant of women's online shopping behavior

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Munazza Mahmood ◽  
Syeda Hina Batool ◽  
Muhammad Rafiq ◽  
Muhammad Safdar
Keyword(s):  
Information Literacy ◽  
Online Shopping ◽  
Literacy Skills ◽  
Shopping Behavior ◽  
Digital Information ◽  
Content Type ◽  
Information Literacy Skills ◽  
Information Professionals ◽  
Online Shopping Behavior

PurposeThe present study aims to examine the current digital information literacy (DIL) skills of female online shoppers in Lahore city of Pakistan. Data were gathered from a purposive sampling of women, aged between 20–50 years who were buying products online, not from the traditional retail stores. Out of 309 received questionnaires, 269 responses were useable and were utilized for data analysis. Descriptive and inferential statistics were used to deduce inferences.Design/methodology/approachQuantitative research approach was employed for this study, and a survey was conducted to collect the data from the study's respondents. For data analysis, descriptive and inferential statistics were used.FindingsResults revealed that the digital information literacy skills of women were good to a moderate level. However, they were not confident in applying advanced searching options. In accordance with what was hypothesized in a directional hypothesis, DIL was found to be a strong predictor of online shopping behavior of women, consequently highlighting the importance of such competencies in modern life. Other findings illustrate that participating women rarely engaged in online shopping and felt hesitation in using credit/debit card for online transactions.Research limitations/implicationsThese observations highlight the important role of information professionals in creating digital literacy among different population groups, specifically women, by planning digital information instruction through courses, workshops and trainings. This could eventually be possible with the dynamic role of librarians or information professionals in the society.Originality/valueThe present study adopts the unique approach of measuring online shopping behavior of female shoppers in connection with their digital information literacy skills.

Factors Influencing Online Shopping Behavior of Urban Consumers in India

2015 ◽  
Vol 5 (1) ◽  
pp. 38-50 ◽  
Author(s):  
Rajyalakshmi Nittala
Keyword(s):  
Perceived Risk ◽  
Financial Risk ◽  
Online Shopping ◽  
Shopping Behavior ◽  
Factors Influencing ◽  
Internet Users ◽  
Online Retailers ◽  
Product Risk ◽  
Electronic Marketing ◽  
Online Shopping Behavior

This study examines the factors influencing online shopping behavior of urban consumers in the State of Andhra Pradesh, India and provides a better understanding of the potential of electronic marketing for both researchers and online retailers. Data from a sample of 1500 Internet users (distributed evenly among six selected major cities) was collected by a structured questionnaire covering demographic profile and the factors influencing online shopping. Factor analysis and multiple regression analysis are used to establish relationship between the factors influencing online shopping and online shopping behavior. The study identified that perceived risk and price positively influenced online shopping behavior. Results also indicated that positive attitude, product risk and financial risk affect negatively the online shopping behavior.

scholarly journals Probability Model: Malaysian Consumer Online Shopping Behavior towards Online Shopping Scam

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mudiana Mokhsin ◽  
Azhar Abdul Aziz ◽  
Amer Shakir Zainol ◽  
Norshima Humaidi ◽  
Nur Ain Adnin Zaini
Keyword(s):  
Online Shopping ◽  
Probability Model ◽  
Shopping Behavior ◽  
Malaysian Consumer ◽  
Online Shopping Behavior

scholarly journals The Importance of Online Shopping Behavior in the Strategic Management of E-Commerce Competitiveness

2020 ◽  
Vol 12 (4) ◽  
pp. 143-160
Author(s):  
Veronika Svatosova
Keyword(s):  
Strategic Management ◽  
Online Shopping ◽  
Secondary Data ◽  
Shopping Behavior ◽  
Management Process ◽  
Main Research ◽  
Online Shopping Behavior ◽  
The Impact ◽  
Strategic Management Process

In this article a total of fifteen determinants of online shopping behavior have been identified that could have an impact on the strategic management process in e-commerce competitiveness. The main objective of the paper is to evaluate the impact of determinants of online shopping behavior on the strategic management process in e-commerce. The main research methods used in the research are as follows: analysis of secondary data, a questionnaire survey among a selected group of e-commerce companies, a critical analysis and a quality comparison of the actually applied determinants of online shopping behavior. The verification of hypotheses is realized using selected methods of statistical induction and descriptive statistics. In summary, the research has shown there is no relationship between evaluating the quality of determinants companies in e-commerce and evaluating the importance of determinants of online shopping behavior. Determinants have an important impact in the process of creating and realizing an e-commerce strategy, with all e-commerce companies regardless of their size being aware of their practical impact and importance. It can be concluded the importance and quality of determinants of online shopping behavior correspond to the type of strategy and strategic management process in terms of e-commerce competitiveness.

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