Home » Post-adoption switching of personal information technologies: A push-pull-mooring-habit model. by Chen Ye
Post-adoption switching of personal information technologies: A push-pull-mooring-habit model. Chen Ye

Post-adoption switching of personal information technologies: A push-pull-mooring-habit model.

Chen Ye

Published
ISBN : 9781109412918
NOOKstudy eTextbook
113 pages
Enter the sum

 About the Book 

Unlike technology users in business organizations, users of personal information technologies are usually not bound to specific products, and have the freedom to switch from one product to another that offers similar functionalities and satisfies theMoreUnlike technology users in business organizations, users of personal information technologies are usually not bound to specific products, and have the freedom to switch from one product to another that offers similar functionalities and satisfies the same need. As a unique and widespread product level post-adoption behavior, IT user switching has not garnered sufficient attention in the current literature. In this study, we propose and empirically verify a comprehensive model that explains user switching between alternative IT products. We apply the push-pull-mooring paradigm from human migration and customer switching literature, and incorporate three sources of reasoned influences---push, pull, and mooring factors---that determine users intention to switch. In addition, as post-adoption IT use is typically routinized, we also argue that reasoned influences alone do not fully explain post-adoption user switching. Drawing from research on habit in social psychology and post-adoption user behavior literatures, we posit that users counter-intentional habit of using the incumbent product impacts switching independent of reasoned influences.-The Push-Pull-Mooring-Habit (PPMH) model was tested on a sample of 414 users presented with a choice of switching their web browsers. The PPMH model explains 52 percent of total variance in users intention to switch and 23 percent of total variance in users actual switching. The findings suggest users are pushed to switch by low satisfaction with the incumbent, and pulled by relative advantage, perceived relative ease of use, and perceived relative security of the alternative. Intention to switch is also influenced by mooring factors of subjective norm and perceived switching costs. In addition, potential switchers habit exercises direct influence on switching intention and moderates the effect of switching intention on actual switching. This study advances the theoretical and empirical understanding of post-adoption technology switching, valuable to both researchers and practitioners.