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As social networking is becoming more popular, social software has achieved an important position in the internet business industry. For social software to be successful, it is crucial to understand how users form their continuous usage intentions toward social software. This paper aims to discuss these issues.

Drawing upon socio-technical and social cognitive perspectives, this study proposes a theory-based model that investigates the interaction effects between social (i.e. perceived user base and relationship commitment) and technical (i.e. perceived system quality) factors of social software, in addition to their direct effects on continuous usage intentions. To empirically validate the proposed research model, a structural equation modelling technique was used.

The results of our model test indicate that all relevant social and technical factors are significant determinants of continuous usage intention. Moreover relationship commitment exhibits a positive interaction effect with perceived system quality on continuous usage intention, while perceived user base does not.

Service designers or providers of social software should make an effort to nurture social relationships among users, expand users' social networks, and reinforce users' relationship commitment to their friends.

Given the lack of investigations into socio-technical interactions in prior social software studies, the theoretical perspectives and empirical findings of this study are useful to both academics and practitioners. The findings also raise new implications regarding the various types of interactions (e.g. enhancing or suppressing) between the social and technical factors around social software.

The purpose of this paper is to identify the root cause of low yield problems in the semiconductor manufacturing process using sensor data continuously collected from manufacturing equipment and describe the process environment in the equipment.

This paper proposes a sensor data mining process based on the sequential modeling of random forests for low yield diagnosis. The process consists of sequential steps: problem definition, data preparation, excursion time and critical sensor identification, data visualization and root cause identification.

A case study is conducted using real-world data collected from a semiconductor manufacturer in South Korea to demonstrate the effectiveness of the diagnosis process. The proposed model successfully identified the excursion time and critical sensors previously identified by domain engineers using costly manual examination.

The proposed procedure helps domain engineers narrow down the excursion time and critical sensors from the massive sensor data. The procedure's outcome is highly interpretable, informative and easy to visualize.