A Network Analysis Model for Selecting Sustainable Technology

Technological Convergence Assessment of the Smart Factory Using Patent Data and Network Analysis

The smart factory has evolved as a key and distinctive idea for Industry 4.0. These industries impart a significant influence on sustainable production because of their consistent industrial evolution/development. Recently, their technological advancements are deemed inevitable to survive in this competitive industry due to increasing market needs. Therefore, technological convergence analysis can provide deep insight into industrial progress and convergence. Consequently, contemporary research trends are centered on evaluating technological convergence. Although various studies are already available on the technological development of the smart factory concerning Industry 4.0, however far less significant work is available on the technological convergence assessment of the smart factory by employing data networks and patents. Therefore, this work is focused on the investigation of reliable data analysis of the smart factory’s technologies and its technological convergence. This said methodology assisted in examining the network’s hidden linkages using network analysis. A relevant case study of a smart factory is also discussed to evaluate its technological convergence. Thus, data-driven technologies have diverted focus from International Patent Classification (IPC) visual networks using convergence assessment tools. The findings of this study are intended to aid companies and government officials in forecasting future sustainable technological developments and decision making.

Technological Opportunity Analysis: Assistive Technology for Blind and Visually Impaired People

As life expectancy increases, the number of people who suffer from blind and visual impairment due to presbyopia is gradually increasing. Assistive device systems have been used to overcome various physical, social, infrastructure, and accessibility barriers. As technology has advanced, the scope of assistive technologies has been expanded. Therefore, we explored technological opportunities in assistive technology for the blind and visually impaired to establish a strategy for the technology competition in the near future. Firstly, the patent vacuum is detected by generating the patent map based on generative topographic mapping (GTM). Secondly, social network analysis is applied to identify the relationship between patent vacuums and occupied grid points in the patent map. Finally, the technology activity index and technology impact index are considered at quantitative and qualitative levels. Consequently, it was identified that wearable devices, including the road situation signal acquisition module and data acquisition process control module, could be occupied in the future. This study can provide practical ideas for research and development (R&D) in the field of assistive devices for the blind and visually impaired. In addition, this study can be an ample source for decision/policy makers to project new contents.

Exploring Technology Influencers from Patent Data Using Association Rule Mining and Social Network Analysis

A patent is an important document issued by the government to protect inventions or product design. Inventions consist of mechanical structures, production processes, quality improvements of products, and so on. Generally, goods or appliances in everyday life are a result of an invention or product design that has been published in patent documents. A new invention contributes to the standard of living, improves productivity and quality, reduces production costs for industry, or delivers products with higher added value. Patent documents are considered to be excellent sources of knowledge in a particular field of technology, leading to inventions. Technology trend forecasting from patent documents depends on the subjective experience of experts. However, accumulated patent documents consist of a huge amount of text data, making it more difficult for those experts to gain knowledge precisely and promptly. Therefore, technology trend forecasting using objective methods is more feasible. There are many statistical methods applied to patent analysis, for example, technology overview, investment volume, and the technology life cycle. There are also data mining methods by which patent documents can be classified, such as by technical characteristics, to support business decision-making. The main contribution of this study is to apply data mining methods and social network analysis to gain knowledge in emerging technologies and find informative technology trends from patent data. We experimented with our techniques on data retrieved from the European Patent Office (EPO) website. The technique includes K-means clustering, text mining, and association rule mining methods. The patent data analyzed include the International Patent Classification (IPC) code and patent titles. Association rule mining was applied to find associative relationships among patent data, then combined with social network analysis (SNA) to further analyze technology trends. SNA provided metric measurements to explore the most influential technology as well as visualize data in various network layouts. The results showed emerging technology clusters, their meaningful patterns, and a network structure, and suggested information for the development of technologies and inventions.

Bayesian Structural Time Series and Regression Modeling for Sustainable Technology Management

Many companies take the sustainability of their technologies very seriously, because companies with sustainable technologies are better able to survive in the market. Thus, sustainable technology analysis is important issue in management of technology (MOT). In this paper, we study the management of sustainable technology (MOST). This focuses on the sustainable technology in various MOT fields. In the MOST, sustainable technology analysis is dependent on time periods. We propose a method of sustainable technology analysis using a Bayesian structural time series (BSTS) model based on time series data. In addition, we use the Bayesian regression to find the relational structure between technologies. To show the performance of our method and how the method can be applied to practical works, we carry out a case study using the patent data related to artificial intelligence technologies.

Sustainable Technology Analysis Using Data Envelopment Analysis and State Space Models

To find sustainable technology in various areas, we propose an analytical methodology based on data envelopment analysis (DEA) and the state space model (SSM). DEA is an analytical method used to compare the efficiencies and performances of several items. In DEA, for sustainable technology analysis, the items of DEA can be the technological keywords or international patent classification (IPC) codes in patent documents. In this paper, the proposed method is used to find the relative performance of different patent keywords using comparison and evaluation. We apply this methodology to compare the technological efficiencies between patent keywords for sustainable technology analysis. We apply the additive model and directional distance function of DEA to develop the proposed methodology for building the technological structure of target technology. In addition, we forecast the future trend of target technology using the SSM and find the area of sustainable technology by its result. The SSM is well suited for time series forecasting on technology analysis. We extract the IPC codes from patent documents for the SSM. In our research, we combine the results of DEA and the SSM to find the area of technological sustainability. To illustrate the validity and performance of our research, we conduct a case study using the patent documents used and registered by Apple.

Social Network Analysis of Sustainable Human Resource Management from the Employee Training’s Perspective

Employee training is not only important for the continuous growth of human resources but also guarantees sustainable human resource management in enterprises. It is very important to understand corporate behaviour related to employee training not only from the perspective of a single enterprise but also from that of multiple enterprises. The purpose of this study is to explore multiple enterprises’ employee training behaviours by conducting a content analysis of corporate social responsibility (sustainability) reports and a social network analysis. This study also seeks to find a way to achieve sustainable employee training by analysing the similarities in the different types of corporate training behaviours. Our analysis shows that, in 2017, 108 types of training activities were implemented by 53 enterprises; the key employee trainings (e.g., security training and skills training) and enterprises (e.g., bank of communication) are identified. The training behaviours of some of the enterprises are similar to some extent, and eight groups of firms that are very similar are identified. The results of this study show that social network analysis performs well for studying corporate employee training behaviours. Some suggestions to minimize the investment costs of training and to improve the sustainability of human resource management from the employee training perspective are provided.

Social Network Analysis of Factors Influencing Green Building Development in China

Green buildings have been viewed as one of the most effective solutions to the negative environmental impacts of construction activities. For the sustainable development of the economy and the environment, many governments in the world have launched a variety of policies to encourage the development of green buildings. However, green targets achieved during the operational stage of green buildings are far below the expectations from the design stage. In addition, the development of green buildings is unevenly distributed in different cities. To help resolve these issues, this paper identifies 28 green building influencing factors from two perspectives, the life cycle and stakeholders. Then, a social network analysis is used to analyse their interactions and identify the critical factors. Our results show that government supervision, incremental cost, property management experience, and the awareness of environmental protection in green buildings are the critical influencing factors in promoting green building development. However, some factors related to contractors, designers and suppliers are not as important as perceived. Finally, some policy recommendations are proposed to promote green buildings in China.

Evaluating the Interconnectedness of the Sustainable Development Goals Based on the Causality Analysis of Sustainability Indicators

Policymaking requires an in-depth understanding of the cause-and-effect relationships between the sustainable development goals. However, due to the complex nature of socio-economic and environmental systems, this is still a challenging task. In the present article, the interconnectedness of the United Nations (UN) sustainability goals is measured using the Granger causality analysis of their indicators. The applicability of the causality analysis is validated through the predictions of the World3 model. The causal relationships are represented as a network of sustainability indicators providing the opportunity for the application of network analysis techniques. Based on the analysis of 801 UN indicator types in 283 geographical regions, approximately 4000 causal relationships were identified and the most important global connections were represented in a causal loop network. The results highlight the drastic deficiency of the analysed datasets, the strong interconnectedness of the sustainability targets and the applicability of the extracted causal loop network. The analysis of the causal loop networks emphasised the problems of poverty, proper sanitation and economic support in sustainable development.

Bayesian Count Data Modeling for Finding Technological Sustainability

Technology developments change society, and society demands new and innovative technology developments. We analyze technology to understand society and technology itself. Much research related to technology analysis has been introduced in various fields. Most of it has been on patent analysis. This is because detailed and accurate results of research and development are patented. In this paper, we study a new patent analysis method based on the count data model and Bayesian regression analysis. Using the count data model, we analyzed the technological keywords extracted from the collected patent documents. We used the prior distribution of Bayesian statistics to reflect the experience and knowledge of the relevant technological experts in the analysis model. Moreover, we applied the proposed model to find sustainable technologies. Finding and developing sustainable technologies is an important activity for companies and research institutes to maintain their technological competitiveness. To illustrate how our modeling could be applied to real domains, we carried out a case study using the patent documents related to artificial intelligence.

Technical and Humanities Students’ Perspectives on the Development and Sustainability of Artificial Intelligence (AI)

This study investigates how the development of artificial intelligence (AI) is perceived by the students enrolled in technical and humanistic specializations at two universities in Timisoara. It has an emphasis on identifying their attitudes towards the phenomenon, on the connotations associated with it, and on the possible impact of artificial intelligence on certain areas of the social life. Moreover, the present study reveals the students’ perceptions on the sustainability of these changes and developments, and therefore aims to reduce the possible negative impact on consumers, and at anticipate the changes that AI will produce in the future. In order to collect the data, the authors have used a quantitative research method. A questionnaire-based sociological survey was completed by 928 students, with a representation error of only ±3%. The analysis has shown that a great number of respondents have a positive attitude towards the emergence of AI, who believe it will influence society for the better. The results have also underscored underlying differences based on the respondents’ type of specialization (humanistic or technical), and their gender.