Start Date
11-12-2016 12:00 AM
Description
In this paper, we empirically investigate the value of live chatting tools on purchase. We use a clickstream data from a leading online marketplace where live chats between e-tailers and customers are documented. We follow the classic two-stage choice model proposed by Moe (2006), and incorporate the choice of chatting as a new stage. In our model, consumers make a sequence of three stages of choices. In the first stage, they choose a product to view; In the second stage, they decide on whether to initiate a live chat; In the third stage, they decide on whether to purchase. This process iterate until consumers decide to stop. We find that consumers choose products with low price and high reputation in the first stage. They request live chat when e-tellers' reputation is low and the price is low. Finally, live chat has positive impact over purchase, especially for low-reputation e-tailers.
Recommended Citation
Tan, Xue; Wang, Youwei; and Tan, Yong, "The Value of Live Chat on Online Purchase" (2016). ICIS 2016 Proceedings. 16.
https://aisel.aisnet.org/icis2016/DataScience/Presentations/16
The Value of Live Chat on Online Purchase
In this paper, we empirically investigate the value of live chatting tools on purchase. We use a clickstream data from a leading online marketplace where live chats between e-tailers and customers are documented. We follow the classic two-stage choice model proposed by Moe (2006), and incorporate the choice of chatting as a new stage. In our model, consumers make a sequence of three stages of choices. In the first stage, they choose a product to view; In the second stage, they decide on whether to initiate a live chat; In the third stage, they decide on whether to purchase. This process iterate until consumers decide to stop. We find that consumers choose products with low price and high reputation in the first stage. They request live chat when e-tellers' reputation is low and the price is low. Finally, live chat has positive impact over purchase, especially for low-reputation e-tailers.