Expanding competence sets for the consumer decision problem

The framework of competence set analysis provides a new approach to complement the existing models for the consumer decision problem. Since it is essential for the firm to improve the competence set of its product or service to fully address the consumer's truly needed benefits, the effective expansion of competence sets plays an important role in marketing reality. The previous studies regarding competence set expansion have thrown light on the tree expansion processes, but forest learning is more suitable for the acquisition of product benefits than tree learning. Thus, this study looses the assumption of tree learning and conducts forest learning to design an effective expansion program. In addition, this study explores a more general problem involving intermediate attributes, compound benefits, and experiential effects. An algorithm is also provided for effective expansion of competence sets in consumer decision analysis.