A Bayesian Network Model of the Consumer Complaint Process

By Jeffrey Blodgett, Ronald D. Anderson. 2000

210 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Organizational Behavior and Human Resource Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/ca8bd31717224576d77ca18c098ff3a6561a3796

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Moshe Davidow
2. Hon-Kwong Lui
3. Geng Cui
4. Man Leung Wong

In Organizational Behavior and Human Resource Management

1. Moshe Davidow
2. Hon-Kwong Lui
3. Geng Cui
4. Man Leung Wong
5. Katja Gelbrich
6. Alex M. Susskind
7. Holger Roschk
8. S. Kaiser

In Sociology and Political Science

1. Moshe Davidow
2. Hon-Kwong Lui
3. Geng Cui
4. Man Leung Wong
5. Katja Gelbrich
6. Alex M. Susskind
7. Holger Roschk
8. S. Kaiser

Citations in the corpus

• Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming (2006, methodology): “Learning Bayesian Networks Using Evolutionary Programming A BN can be constructed based on previous research, as in Blodgett and Anderson (2000), or by eliciting knowledge from domain experts, as in Cooper (2000).”
• Organizational Responses to Customer Complaints: What Works and What Doesn’t (2003, background): “Some researchers have called this variable “likelihood of success,” and it has a positive impact on the decision to actually complain (see, for example, Blodgett and Anderson 2000).”