Paper Type
Complete Research Paper
Description
As the success of new products is essential for firm performance and economic growth, a broad research stream focuses on consumer decision making for new products. While the majority of existing research concentrates on the reasons why individuals adopt technologies, a growing number of scholars realize the need to look at factors that explain why individuals resist innovations to fully understand consumer behaviour. The existing literature provides a general model of consumer resistance but fails to empirically validate antecedents of resistance with regard to different forms of resistance. This study differentiates between rejection and opposition as the strongest form of resistance in the context of mobile health applications. The quantitative analysis of survey data derived from 752 respondents reveals that the antecedents of rejection are different from the antecedents of opposition. While the usage barrier, the valu barrier and performance risk predominantly predict the rejection of mobile health applications, physical risk and performance risk cause active opposition. The results also differ for two subgroups of mobile health applications, indicating contextual factors that need to be taken into account. This study contributes to the understanding of individual resistance behaviour and assists product developers of mobile health applications to design their products.
MODELLING CONSUMER RESISTANCE TO MOBILE HEALTH APPLICATIONS
As the success of new products is essential for firm performance and economic growth, a broad research stream focuses on consumer decision making for new products. While the majority of existing research concentrates on the reasons why individuals adopt technologies, a growing number of scholars realize the need to look at factors that explain why individuals resist innovations to fully understand consumer behaviour. The existing literature provides a general model of consumer resistance but fails to empirically validate antecedents of resistance with regard to different forms of resistance. This study differentiates between rejection and opposition as the strongest form of resistance in the context of mobile health applications. The quantitative analysis of survey data derived from 752 respondents reveals that the antecedents of rejection are different from the antecedents of opposition. While the usage barrier, the valu barrier and performance risk predominantly predict the rejection of mobile health applications, physical risk and performance risk cause active opposition. The results also differ for two subgroups of mobile health applications, indicating contextual factors that need to be taken into account. This study contributes to the understanding of individual resistance behaviour and assists product developers of mobile health applications to design their products.