Does state ownership benefit or impede firm innovation? Extant studies offer contradicting perspectives and inconsistent empirical findings regarding this issue. This study develops an integrated view of institutional and efficiency logics by proposing that whereas state ownership enables a firm to obtain R&D resources, it makes the firm less efficient in utilizing the resources to generate innovation. The results of two longitudinal panel datasets of Chinese manufacturing firms show that state ownership positively affects R&D input; however, it also weakens the effect of R&D input on innovation output, making a minority state ownership optimal for innovation development. Moreover, the efficiency problem of state ownership of transforming R&D input into innovation output decreases when industrial competition is high, as well as for start-ups. We discuss the implications of these findings for research of state ownership and firm innovation in emerging economies.

Technology abandonment may have serious repercussions for individuals with disabilities and for society. The purpose of this study was to determine how technology users decide to accept or reject assistive devices. Two hundred twenty-seven adults with various disabilities responded to a survey on device selection, acquisition, performance, and use. Results showed that 29.3% of all devices were completely abandoned. Mobility aids were more frequently abandoned than other categories of devices, and abandonment rates were highest during the first year and after 5 years of use. Four factors were significantly related to abandonment--lack of consideration of user opinion in selection, easy device procurement, poor device performance, and change in user needs or priorities. These findings suggest that technology-related policies and services need to emphasize consumer involvement and long-term needs of consumers to reduce device abandonment and enhance consumer satisfaction.

We review some of the work of the past ten years that applied the multilevel logit model. We attempt to provide a brief description of the hypothesis tested, the hierarchical data structure analyzed, and the multilevel data source for each piece of work we have reviewed. We have also reviewed the technical literature and worked out two examples on multilevel models for binary outcomes. The review and examples serve two purposes: First, they are designed to assist in all aspects of working with multilevel models for binary outcomes, including model conceptualization, model description for a research report, understanding of the structure of required multilevel data, estimation of the model via a generally available statistical package, and interpretation of the results. Second, our examples contribute to the evaluation of the approximation procedures for binary multilevel models that have been implemented for general public use.