While the diffusion of information technology (IT) has been largely discussed in the previous literature, little is known about the interventions that can drive individual adoption and usage behavior spanning over the IT implementation process. In my thesis, I developed two studies that aim to reveal the role of management interventions on technological diffusion.

In the first study, I manipulated the cue of crowd’s adoption by a field experiment in the context of implementing Microsoft Office 365. I investigated how this intervention can affect individual decision on timing of adoption. Drawing on the theory of herd behavior, I propose that the herding cue will motivate following individuals to adopt a new IT earlier. The impact exists beyond the personal beliefs of technology...[ Read more ]

While the diffusion of information technology (IT) has been largely discussed in the previous literature, little is known about the interventions that can drive individual adoption and usage behavior spanning over the IT implementation process. In my thesis, I developed two studies that aim to reveal the role of management interventions on technological diffusion.

In the first study, I manipulated the cue of crowd’s adoption by a field experiment in the context of implementing Microsoft Office 365. I investigated how this intervention can affect individual decision on timing of adoption. Drawing on the theory of herd behavior, I propose that the herding cue will motivate following individuals to adopt a new IT earlier. The impact exists beyond the personal beliefs of technology. Moreover, this cue will discount the impact of perceived usefulness about technology on early adoption, and meanwhile, enhance the normative influence from explicit social networks. I empirically evaluated the relationships using the Cox proportional-hazard models. This study contributes to the literature by understanding the complex role of herd behavior in IT adoption, and provides the guideline on facilitating technological diffusion by the intervention of herding cue.

In my second study, I developed a longitudinal model to evaluate management interventions in two IT implementation contexts. Particularly, I theorized the chain of four management interventions, beliefs formation, usage behavior and job outcome from the pre-implementation to the rollout phase, and empirically assessed the relationships using survey data from two contexts, where users have opposite degrees of volitional control over system usage. Empirical findings suggest the different roles of management interventions and exhibit a consistent pattern in the two contexts. This study sheds light on technological diffusion and expands our understanding of how to shape user perceptions and further reduce performance dip through managerial interventions in different stages of IT implementation.