It is well known about today's knowledge-based economy that knowledge has become its key resource, and therefore new knowledge and innovation have become of central importance. However, we should not forget that knowledge creation is not the only engine of this economy. For regions, enterprises, and universities, it is not the creation of knowledge that signifies distinctive competitive advantage but the way they can apply this knowledge. As the social application of innovation has founded new approaches in recent years and the Triple Helix (Leydesdorff and Etzkowitz Science and Public Policy 23: [279][280][281][282][283][284][285][286] 1996) and then the Quadruple Helix (Carayannis and Campbell International Journal of Technology Management 46(3/4):201-234, 2009) models have emerged, the related marketing tools have also had to change inevitably. Our article aims to review the connection points of innovation and marketing in the course of changes in the models of knowledge production and innovation on the one hand and provide an answer to the latest innovation-marketing challenges with an extended marketing mix model on the other. In our paper, we examined how marketing can support the involvement of the affected segment of society into today's changed innovational context.

The idea of symbolic consumption is based on the assumption that consumption is more than just functional problem solving: products and brands have significant meanings; therefore, they can be utilized as symbols in the cultural ecosystem. However, grasping the meaning of a specific brand can be confusing because it would presume knowledge about the brand as a symbol shared by the customers. We review the contradicting findings in the literature about the symbolic meaning of brands, and we initiate a new reference point in order to dissolve the above mentioned conflict. According to our understanding, the symbolic meaning of a brand shall be examined in the context of specific brand communities and not in general. We suggest that limiting the scope of research to brands with brand communities resolves several limitations of symbolic consumption studies focusing on general issues. Our theoretical model distinguishes the different types of brand communities based on their main cohesive force. In the model, at one end we find image based brand communities where the brand image is the main cohesive force, while at the other end we find brand-subcultures where the members are more committed to each other than to the brand.

The goal of the present study is to examine the cognitive/physiological correlates of passenger travel experience in autonomously driven transportation systems. We investigated the social acceptance and cognitive aspects of self-driving technology by measuring physiological responses in real-world experimental settings using eye-tracking and EEG measures simultaneously on 17 volunteers. A typical test run included human-driven and autonomous conditions in the same vehicle, in a safe environment. In the spectrum analysis of the eye-tracking data we found significant differences in the complex patterns of eye movements: the structure of movements of different magnitudes were less variable in the autonomous drive condition. EEG data revealed less positive affectivity and slightly higher arousal in the autonomous condition compared to the human-driven condition. Correlates with personality traits are also discussed. These preliminary findings reinforced our initial hypothesis that passenger experience in human and machine navigated conditions entail different physiological and psychological correlates, and those differences are accessible using state of the art in-world measurements. These useful dimensions of passenger experience may serve as a source of information both for the improvement and design of self-navigating technology and for market-related concerns.

The goal of the present study is to examine the cognitive/affective physiological correlates of passenger travel experience in autonomously driven transportation systems. We investigated the social acceptance and cognitive aspects of self-driving technology by measuring physiological responses in real-world experimental settings using eye-tracking and EEG measures simultaneously on 38 volunteers. A typical test run included human-driven (Human) and Autonomous conditions in the same vehicle, in a safe environment. In the spectrum analysis of the eye-tracking data we found significant differences in the complex patterns of eye movements: the structure of movements of different magnitudes were less variable in the Autonomous drive condition. EEG data revealed less positive affectivity in the Autonomous condition compared to the human-driven condition while arousal did not differ between the two conditions. These preliminary findings reinforced our initial hypothesis that passenger experience in human and machine navigated conditions entail different physiological and psychological correlates, and those differences are accessible using state of the art in-world measurements. These useful dimensions of passenger experience may serve as a source of information both for the improvement and design of self-navigating technology and for market-related concerns.

No abstract