Investigating the influence of network effects on disruptive technologies in social networks
The main purpose of this doctoral thesis is to investigate the probability of technological disruption when network effects are present. It also investigates how consumer network structures, the social networks connecting consumers to each other, influence the potential for such disruption. The manner in which consumer network structures influence such technological disruption forms the subject of this investigation. The model and simulated influence of network effects assumes the inter-connectedness of all consumers within the population and the existence of a complete network. This assumption is tempered by consideration of the different ways in which consumers connect to each other within social networks, i.e. regular, lattice, small-world, and random networks characterized by clustering and path length properties. Even within the case of strong network effects, a consumer network characterized by high clustering and long path length provides favorable conditions for new potentially disruptive technology to survive in niche groups. Regular and small-world networks with few shortcuts fall within this category, whereas random networks that exhibit low clustering and short path-length properties favor established technology. Even under the favorable conditions of consumer network structures, a firm introduces a new, potentially disruptive technology requiring the creation of a critical mass if it is to enter the mainstream sector. The empirical section of this doctoral thesis identifies determinants of technology acceptance characterized by disruptiveness and network effects. The Theory of Planned Behavior and the Technology Acceptance Model provide a basis for the proposed model. An online survey was administered, while crowdsourcing together with social media were utilized to collect primary data on German and Indonesian users acceptance of technologies or applications in long-distance calls. The expected current and future number of users, representative of the technologys installed base, were found to be positively and significantly (albeit indirectly) related to their intention to use the technology. Perceptions of affordability and ease of use were also positively and significantly related to the attitude toward using which subsequently had an important relationship with an individuals intention to employ the technology.
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