Contribution of individual to collective brands

Traditionally, literature estimates the equity of a brand or its extension but it pays little attention to collective brand equity even though collective branding is increasingly used to differentiate the homogenous products of different firms or organizations. We propose an approach that estimates the incremental effect of individual brands (or the contribution of individual brands) on collective brand equity through the various stages of a consumer hierarchical buying choice process in which decisions are nested: “whether to buy”, “what collective brand to buy” and “what individual brand to buy”. This proposal follows the approach of the Random Utility Theory, and it is theoretically argued through the Associative Networks Theory and the cybernetic model of decision making. The empirical analysis carried out in the area of collective brands in Spanish tourism finds a three-stage hierarchical sequence, and estimates the contribution of individual brands to the equity of the collective brands of “Sun, Sea and Sand” and of “World Heritage Cities”.

Detecting Free Riders in Collective Brands through a Hierarchical Choice Process

Free-riding behaviors exist in tourism and they should be analyzed from a comprehensive perspective; while the literature has mainly focused on free riders operating in a destination, the destinations themselves might also free ride when they are under the umbrella of a collective brand. The objective of this article is to detect potential free-riding destinations by estimating the contribution of the different individual destinations to their collective brands, from the point of view of consumer perception. We argue that these individual contributions can be better understood by reflecting the various stages that tourists follow to reach their final decision. A hierarchical choice process is proposed in which the following choices are nested (not independent): “whether to buy,” “what collective brand to buy,” and “what individual brand to buy.” A Mixed Logit model confirms this sequence, which permits estimation of individual contributions and detection of free riders.