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Leveraging the Strengths of Choice Models and Neural Networks: A Multiproduct Comparative Analysis
- Source :
- Decision Sciences. 33:433-461
- Publication Year :
- 2002
- Publisher :
- Wiley, 2002.
-
Abstract
- Choice models and neural networks are two approaches used in modeling selection decisions. Defining model performance as the out-of-sample prediction power of a model, we test two hypotheses: (i) choice models and neural network models are equal in performance, and (ii) hybrid models consisting of a combination of choice and neural network models perform better than each stand-alone model. We perform statistical tests for two classes of linear and nonlinear hybrid models and compute the empirical integrated rank (EIR) indices to compare the overall performances of the models. We test the above hypotheses by using data for various brand and store choices for three consumer products. Extensive jackknifing and out-of-sample tests for four different model specifications are applied for increasing the external validity of the results. Our results show that using neural networks has a higher probability of resulting in a better performance. Our findings also indicate that hybrid models outperform stand-alone models, in that using hybrid models guarantee overall results equal or better than the two stand-alone models. The improvement is particularly significant in cases where neither of the two stand-alone models is very accurate in prediction, indicating that the proposed hybrid models may capture aspects of predictive accuracy that neither stand-alone model is capable of on their own. Our results are particularly important in brand management and customer relationship management, indicating that multiple technologies and mixture of technologies may yield more accurate and reliable outcomes than individual ones.
- Subjects :
- Artificial Intelligence
Brand Choice
Choice Models
d-delete Jackknifing
Empirical Integrated Rand (EIR)
Hybrid Models
Logit
Model Selection
Neural Network Models
Store Choice
Information Systems and Management
Artificial neural network
business.industry
Computer science
Strategy and Management
Model selection
Rank (computer programming)
Machine learning
computer.software_genre
General Business, Management and Accounting
Variable-order Bayesian network
External validity
Management of Technology and Innovation
Jackknifing
Artificial intelligence
Data mining
business
computer
Statistical hypothesis testing
Subjects
Details
- ISSN :
- 15405915 and 00117315
- Volume :
- 33
- Database :
- OpenAIRE
- Journal :
- Decision Sciences
- Accession number :
- edsair.doi.dedup.....93417b5dd10d0573ccc61781872e7d3e
- Full Text :
- https://doi.org/10.1111/j.1540-5915.2002.tb01651.x