Start Date
16-8-2018 12:00 AM
Description
In this study, we analyzed consumer Internet behavior in India since there were several unique cultural dimensions of interest. After reviewing the literature, we tested hypotheses that demographic and psychological factors such as happiness, excitement, satisfaction, positive feelings, pleasant feelings, gender, age, and income level could predict consumer Internet purchase behavior. We used Spearman correlation, binary logistic regression, and discriminant analysis techniques, which resulted in effect sizes ranging from 9.5% to 59.5%. Spearman correlation confirmed that gender, age, and income level were related to consumer Internet purchase behavior. Several binary logistic regression models with goodness-of-fit-tests revealed that all satisfaction, happiness, positive feelings and pleasant feelings, but not excitement, could predict consumer Internet purchase intention. A Discriminant Analysis model was able to correctly classify 87.3% of the sample respondents using two factors, and a second model with only one factor correctly categorized 90.5% of the consumers as willing to purchase on the Internet.
Recommended Citation
Vajjhala, Narasimha Rao and Strang, Kenneth David, "Examining Internet Behavior of Young Technology-Literate Consumers in India" (2018). AMCIS 2018 Proceedings. 4.
https://aisel.aisnet.org/amcis2018/CultureIS/Presentations/4
Examining Internet Behavior of Young Technology-Literate Consumers in India
In this study, we analyzed consumer Internet behavior in India since there were several unique cultural dimensions of interest. After reviewing the literature, we tested hypotheses that demographic and psychological factors such as happiness, excitement, satisfaction, positive feelings, pleasant feelings, gender, age, and income level could predict consumer Internet purchase behavior. We used Spearman correlation, binary logistic regression, and discriminant analysis techniques, which resulted in effect sizes ranging from 9.5% to 59.5%. Spearman correlation confirmed that gender, age, and income level were related to consumer Internet purchase behavior. Several binary logistic regression models with goodness-of-fit-tests revealed that all satisfaction, happiness, positive feelings and pleasant feelings, but not excitement, could predict consumer Internet purchase intention. A Discriminant Analysis model was able to correctly classify 87.3% of the sample respondents using two factors, and a second model with only one factor correctly categorized 90.5% of the consumers as willing to purchase on the Internet.