PurposeGreen human resource management (GHRM) is critical to enhancing the ability of the companies' green innovation, but this link is rarely explored or empirically tested in the literature. Drawing upon human capital theory, the study examines a conceptual model that incorporates the effects of green human capital and management environment concern.Design/methodology/approachData were collected from 143 firms in China, and the regression analysis and bootstrapping test were used to assess the hypothesis.FindingsOur findings indicate that GHRM can positively influence green innovation, and green human capital mediated the link between GHRM and green innovation. In addition, management environment concern moderates the effect of GHRM on green human capital. The results further explore that the indirect effect of GHRM on green innovation through green human capital is significant for the firms with a high management environment concern, but not for this relationship with a low management environment concern.Originality/valueThe findings further extend the scope of GHRM research, and theoretical and practical implications of GHRM are presented to enhance environment sustainability.

Android applications (apps) grow dramatically in recent years. Apps are user interface (UI) centric typically. Rapid UI responsiveness is key consideration to app developers. However, we still lack a handy tool for profiling app performance so as to diagnose performance problems. This paper presents PersisDroid, a tool specifically designed for this task. The key notion of PersisDroid is that the UI-triggered asynchronous executions also contribute to the UI performance, and hence its performance should be properly captured to facilitate performance diagnosis. However, Android allows tremendous ways to start the asynchronous executions, posing a great challenge to profiling such execution. This paper finds that they can be grouped into six categories. As a result, they can be tracked and profiled according to the specifics of each category with a dynamic instrumentation approach carefully tailored for Android. PersisDroid can then properly profile the asynchronous executions in task granularity, which equips it with low-overhead and high compatibility merits. Most importantly, the profiling data can greatly help the developers in detecting and locating performance anomalies. We code and open-source release PersisDroid. The tool is applied in diagnosing 30 open-source apps, and we find 20 of them contain potential performance problems, which shows its effectiveness in performance diagnosis for Android apps.

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