Consumers often make unplanned online purchase spontaneously and intuitively after being exposed to stimulating cues, like price promotion, advertisement of limited offer and attractive product appearance. Prior studies suggested that 30–50% of all retail sales are from impulse purchase while almost 90% of consumers make purchase on impulse occasionally. In particular, approximately 40% of all the money spent on e-commerce sites is attributed to impulse purchase. In this regard, the present research project investigates how the website attributes interact with consumers’ personalities to entice impulse purchase online.
In our work we were interested in investigating impulsive purchases in group shopping. Online group shopping offers products and services at significantly reduced prices (typically 50–90% off retail prices) on the condition that a minimum number of buyers would make the purchase during a particular period of selling time. It is estimated that 60% (420 million) of Chinese Internet users will be online group shopping customers by 2015.
The questions we wanted to address in terms of group shopping were:
- How often does group buying involve impulse purchases?
- What are the website attributes that entice consumers’ online impulse purchase when group shopping?
- To what degree do these website attributes affect consumers’ personalities to trigger impulse purchase?
Through employing structural equation modeling analysis, we found that when a consumer feels that an online store exhibits a diversity of various interesting offers and is easy to use, the store will be perceived to be more visually appealing. Shopping in such a visually appealing site, consumers will have more pleasure of making purchases and have a positive evaluation on making unplanned purchase, resulting in a stronger temptation to make impulsive purchases. Please read our paper linked below to find out more about our results.
|Liu, Y., Li, H., & Hu, F. (2013). Website attributes in urging online impulse purchase: An empirical investigation on consumer perceptions. Decision Support Systems, 55(3), 829-837. [Impact factor: 2.201]|