ESSAYS ON NUDGING CUSTOMERS’ BEHAVIORS: EVIDENCE FROM ONLINE GROCERY SHOPPING AND CROWDFUNDING

The dissertation consists of three essays that employ predictive analytics, structural modeling techniques and field experiments to understand and nudge customers’ behaviors in two types of online engagement platforms. The first one is customers’ purchase behaviors in an online grocery store and the other is customer’ contribution behaviors in a reward-based crowdfunding platform. In both contexts, we study how to actively nudge their behaviors. In Chapter 2, we investigates how, when dealing with products that are available in limited quantities, customers may be nudged to purchase them. Specifically, our main problem is to identify targeted customers to receive the limited number of coupons. We develop a Support Vector Machines (SVM) based approach to rank order customers. We conduct a field experiment in an online grocery store to evaluate how well the identified customers are nudged through information and/or couponing. We find that, in terms of the successful nudges, our SVM-based approach performed better than other approaches.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Management

Year

2015

Author dc:creator

• Zhang, Na

Contributors dc:contributor

• Karthik Kannan
• George Shanthikumar
• Sumon Datta
• Kemal Altinkemer

• Crowdfunding
• Incentive Design
• Predictive Analytics
• Recommender Systems
• Supply Chain

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/1440

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-2656