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We were able to identify a set of specific capabilities corporations need to develop in order to enhance brand love. Furthermore, the effects of most dynamic capabilities on brand love have a strong correlation to the degree of customer orientation. Other results are relevant concerning the proposed moderation and mediation hypotheses. Firstly, the impact of customer orientation on brand love is varied under specific market conditions, supporting our central moderation hypothesis (β = .259, p = .001). To be precise, the impact of customer orientation is strongest in markets that have low competitive differentiation in products and services. Other control variables like age, gender, or market form (B2B versus B2C) lead to no significant heterogeneity in the data set. Finally, mediation analyses show no significant “direct effect” of the existing DC constructs on brand love, supporting the mediating role of customer orientation.
Electronic word-of-mouth (eWoM) communication has received a lot of attention from the academic community. As multiple research papers focus on specific facets of eWoM, there is a need to integrate current research results systematically. Thus, this paper presents a scientific literature analysis in order to determine the current state-of-the-art in the field of eWoM.
Pokémon Go was the first mobile augmented reality (AR) game to reach the top of the download charts of mobile applications. However, little is known about this new generation of mobile online AR games. Existing theories provide limited applicability for user understanding. Against this background, this research provides a comprehensive framework based on uses and gratification theory, technology risk research, and flow theory. The proposed framework aims to explain the drivers of attitudinal and intentional reactions, such as continuance in gaming or willingness to invest money in in-app purchases. A survey among 642 Pokémon Go players provides insights into the psychological drivers of mobile AR games. The results show that hedonic, emotional, and social benefits and social norms drive consumer reactions while physical risks (but not data privacy risks) hinder consumer reactions. However, the importance of these drivers differs depending on the form of user behavior.