Impacts of iPad attributes, Lifestyles and media dependency on adoption of iPad and intensity of iPad usage in Mainland China

The goal of this exploratory research is to identify attributes that can distinctly characterize iPad and examine the predictive power of iPad attributes, lifestyles, media dependency, and demographics on adoption of iPad, iPad usage patterns and intensity of iPad usage. Using a snowballing sample, an online survey was conducted with 623 university students in Mainland China, among which 217 were iPad users and 406 were non-users. Results of regression analyses show that application affordances is one of the few important attributes influencing the likelihood of iPad adoption, iPad usage patterns and intensity of iPad usage. Regarding lifestyles, strivers were found to be associated with higher likelihood of buying iPad; experiencers were more engaged and active when using iPad; innovators tended to use iPad for utilities, information-seeking and communication more often than other users. Interestingly, owning other Apple products has a positive impact on purchasing iPad. Furthermore, among iPad usage patterns, in particular, utilities and information-seeking are significant predictors for Intensity of iPad usage, which proves to be the most important functionalities for iPad

Functional central limit theorem with mean-uncertainty under sublinear expectation

In this paper, we introduce a fundamental model for independent and identically distributed sequence with model uncertainty on the canonical space $(\mathbb{R}^\mathbb{N},\mathcal{B}(\mathbb{R}^\mathbb{N}))$ via probability kernels. Thanks to the well-defined upper and lower variances, we obtain a new functional central limit theorem with mean-uncertainty on the canonical space by the method based on the martingale central limit theorem and stability of stochastic integral in the classical probability theory. Then we extend it to the general sublinear expectation space through a new representation theorem. Our results generalize Peng's central limit theorem with zero-mean to the case of mean-uncertainty and provides a purely probabilistic proof instead of the existing nonlinear partial differential equation approach. As an application, we consider the two-armed bandit problem and generalize the corresponding central limit theorem from the case of mean-certainty to mean-uncertainty.Comment: 31 pages. arXiv admin note: substantial text overlap with arXiv:2203.0017