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The purpose of this study is to reveal the similarities and differences between the manufacturing and service industries in their prioritization of technologies and public research and development (R&D) roles, along with the complementation of properties of technology and public R&D role in the context of Fourth Industrial Revolution.

Two rounds of Delphi surveys were designed to meet the purpose of this study, which used rigorous triangulation techniques. The Delphi method was combined with the brainstorming method in the first-round Delphi survey, while the second-round Delphi survey focused on experts’ judgments. Finally, language network analysis was performed on the properties of technology and public R&D roles to complement the data analyses regarding prioritization.

This study identifies different prioritizations of five similar key technologies in each industry, so that it can note different technological impacts to the two industries in the Fourth Industrial Revolution. Smart factory technology is the first priority in the manufacturing industry, whereas artificial intelligence is the first priority in the service industry. The properties of the three common technologies: artificial intelligence, big data and Internet of things in both industries are summarized in hyper-intelligence on hyper-connectivity. Moreover, it is found that different technological priorities in the service and manufacturing industries require different approaches to public R&D roles, while public R&D roles cover market failure, system failure and government failure. The highest priority public R&D role for the service industry is the emphasis of non-R&D roles. Public R&D role to solve dy-functions, focus basic technologies and support challenging areas of R&D is prioritized at the highest for the manufacturing industry.

This study of the different prioritizations of technologies in the manufacturing and service industries offers practical lessons for executive officers, managers and policy-makers. They, by noting the different technological impacts in the manufacturing and service industries, can prepare for current actions and establish the priority of technology for R&D influencing the future paths of their industries in the context of the Fourth Industrial Revolution. While managers in the service industry should pay greater attention to the technological content of hyper-intelligence and hyper-connectivity, managers in the manufacturing industry should consider smart factory and robot technology.

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.

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.