In collaboration with their international research partners, the authors conducted questionnaire and interview surveys of university students on attitudes towards Edward Snowden’s revelations of the NSA’s indiscriminate mass surveillance programmes as part of their SIGINT activities, which were started in June 2013, in eight countries including Japan in October and November 2014. The survey results demonstrate that Japanese youngsters are the outliers amongst those studied internationally in terms of social attitudes towards state surveillance. In this research, the authors show the characteristics of those attitudes in Japan, where a highly networked information society has been built, based on the survey outcomes, and examine their meaning for privacy protection, individual freedom and autonomy and democracy in Japanese society taking the Japanese socio-cultural and political environment surrounding privacy and state surveillance into account.

This research aims to analyze the evaluation of the stock market for the promotion of information technology such as FinTech and Cloud in the banking industry. By looking at the market valuation you can confirm the difference between the banking industry and the shareholders. For the analysis, we use the method of event study. We also analyze the impact of promoting FinTech at city banks on the stock price of the banking industry as a whole.

In this research, deep learning is applied to the prediction of corporate bankruptcy. We collect financial statements over four fiscal years for 102 companies delisted from Japanese stock markets due to de facto bankruptcy and 2062 continuing companies. In addition, the number of samples is increased by interpolating and extrapolating the financial statements of arbitrary two fiscal years. The key point in our method is to transform a set of financial ratios calculated from the financial statements of one sample into a gray-scale image, and treat it as one image sample. Eventually, we generate 7520 samples for each class and use them as learning data of a convolutional neural network based on GoogLeNet. Obtained network indicates higher accuracy in bankruptcy prediction than the traditional methods using CART, linear discriminant analysis, SVM, and AdaBoost.

In organizations that contain multiple types of human resources, there are mutual interference as well as individual abilities, which affect performance organizations. Prior to this research, I tried to verify “Morutke’s law.” This means studying what kind of personnel type is desirable for the organization. However, in the previous research, the point of mutual interference was insufficient. This research complements the mutual interference.

When planning strategies, organizations need not only to understand levels of their own management resources but also to forecast future trajectories of the resources. While money and materials are relatively easy resources to count and recognize their capacities, human resources and other production and service provision capacities are still challenging for stakeholders. This leads to lack of sufficient expression of capacities in simulation models. In order to provide general information for building capacity resource simulation models, this paper shows generic system dynamics model structures which express dynamics behaviors of capacity resources.

In the next general policies regarding curriculum formulation by MEXT, high school students are expected to foster information utilization abilities based on the scientific understanding of information. Simulation is a good teaching material that fosters abilities of modeling, finding problems, evaluating models, and solving problems. However, knowledge about teaching materials and educational methods that can be practiced effectively is not sufficient. We introduce a practice using pedestrian simulation in a high school class and report preliminary results on educational effect.

Design thinking implies design processes and mindsets in which participants define a problem based on empathy with users, and iterate ideation, prototyping and user test. The process of problem definition is supposed to function as an anchor that sets a rough goal and prevents digressing from the main subject. We explore the impacts of the role of verbal representation as an anchor on the participants’ ideation process, conducting qualitative research in practices of workshop.

Knowledge sharing is a fundamental practice in organizations and communities in the recent knowledge-intensive society. Online communities, which are enabled by recent technology advancements such as the Internet and digital platforms, have often been used to amplify knowledge sharing. Although online communities have received much attention both in research and practice, their operating principles are not well understood. Especially, little is known about how to identify who becomes a high performing leader in the online community, which plays a critical role to grow and nurture online communities. In this paper, we adopt a relational perspective and examine whether we can identify online community leaders based on the ego and alters’ initial activities. By analyzing a software quality assurance & testing community in the Stack Exchange (online question and answer forum), this paper shows that we can identify who has the potential to become a high performing leader in the community based on her/his first-month activities. The results show that both individual expertise and social relationship (communicative actions and interactions) are important to become online community leaders. The analysis also suggests that the path dependence of user’s activities can explain the dynamics of online community leadership. This study sheds light on an under-investigated mechanism of online community leadership and opens fruitful research directions for future online community studies.

The development in information technology enables to store various kinds of data e.g.) purchase history data and evaluation history data for purchased items and so on. To make use of these data, the recommender system that recommends items that match customer preferences is widely used. Generally, the recommender system recommends items that a customer has not purchased and seems to purchase with high possibility. However, those items are likely purchase without recommendation, therefore, it needs to be recommended the item whose serendipity is high in order to improve customer satisfaction. From the above discussions, we propose the unexpected index that balances estimated purchase probabilities and predicted evaluation values, and recommender system using those indices. When constructing the recommender system, we use the Aspect Model, which is widely known as one of probabilistic latent class model. To verify the effectiveness of our proposed method, we conduct simulation experiments using benchmark data set of recommender system.