Effective Factors on Iranian Consumers Behavior in Internet Shopping: A Soft Computing Approach (original) (raw)
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Study of factors affecting internet shopping of Iranian customers and their ranking
AFRICAN JOURNAL OF BUSINESS MANAGEMENT, 2012
Internet users have significantly increased worldwide in recent years. In the meantime, Iran, between 2000 and 2007, Iran had the highest users' growth rate in the world (internet world stats, 2009), which indicates the need for more attention to e-commerce in Iran. However, successful development of ecommerce, especially internet shopping, will be possible only if factors affecting customers' web-based shopping are properly identified and used. This research has its objective identifying and prioritizing factors affecting internet shopping by Iranian customers. To this end, based on the results of the study of previous researches conducted in different countries, and taking into account Iran's conditions, the operational model of the research was introduced. Having collected data using questionnaire, hypotheses were tested on a sample consisting of 400 Iranian customers using Spearman's correlation test. Findings suggest that among factors affecting internet shopping of Iranian customers, attitude towards internet shopping is the top priority.
Prediction of Buying Intention: Factors Affecting Online Shopping
Recent times have witnessed a very significant increase in the use of electronic commerce all over the world. The fact that sales have been steadily climbing demonstrates that there is massive untapped market potential for online shopping. The process of analysing data to determine and categorise the intentions of online shoppers contributes to the accumulation of store earnings. The application of machine learning classification models to the data collected from e-commerce websites is the main goal of our endeavours. Specifically, We investigate whether machine learning is a reliable method for predicting the possibility that a consumer who explores a retailer's website will actually make a purchase. Exploratory data analysis was carried out so that we could visually examine our information and search for any patterns or trends that may have been there. In this article, we present a method that is capable of predicting the purpose of consumers who visit an e-commerce site to make a purchase based on data obtained while these users are browsing the websites in question. The Online Consumer Buying Sentiment Dataset that can be found in the UCI Machine Learning Repository is one that we use.
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