We propose a method to build a corporate investment network based on the investment relationship between unicorns and venture capital (VC) and to analyze the characteristics of the startup ecosystem. Unicorns, unlisted startup companies with valuation of over $ 1 billion, are the leading drivers in innovation. For startup companies to grow into unicorns, it is important for them to be supported in startup ecosystem, where VCs play a very important role. In this research, we propose a method to build corporate investment networks of unicorns and VCs around the world, and to quantitatively analyze the characteristics of the relationship between the unicorns and the VCs and the diffusion of startup activities from inter-industry and international viewpoints, and the factors that affect the valuation of unicorns and performance of VCs. The proposed method is able to quantitatively measure the characteristics of the startup ecosystem and effective to promote produce of unicorns.

The purpose of this research is to explore the factors that influence consumers' purchasing intentions and their relationships in online live distribution, which has become popular in recent years. Therefore, we set a structural model and a measurement model that combined technology acceptance model and flow experience, and conducted an empirical analysis by questionnaire survey using Taobao Live as a case. As a result of analyzing valid answers 226 using statistical software SPSS and AMOS, opinion leaders and atmospheric cues cannot influence purchasing intention only by perceived usefulness,is necessary to go through flow experience. Visibility affordance have been found to affect flow experience only through perceived ease of use. Therefore, it is important to provide a comfortable online live flow experience environment

In the case of business development by learning information about properties, such as recognizing scratches on the floor or wall, it is difficult to find a certain pattern in the teacher data, which makes it difficult to prepare teacher data of sufficient quality and quantity. Therefore, in this study, we investigate how to improve the accuracy of the classification model by generating pseudo-images from a small number of samples to improve the quality and quantity of the teacher data.

Nowadays, many companies publish an integrated reports (annual reports) that include not only legally required financial information such as sales and assets, but also non-financial information such as corporate governance, social responsibility (CSR), and intellectual property. In addition to companies, the number of universities issuing integrated reports is also on the rise. The integrated report describes how the organization is trying to create its value to various stakeholders, and expresses the focus items and issues of each organization. In this paper, focusing on the integrated reports of one company and several universities, the transition and characteristics of the integrated reports are analyzed based on information such as words and their occurrence frequency, and how the organizational values and issues have changed due to changes in social circumstances and how the characteristics are expressed.

This study focuses on the relationship between ESG investment and corporate gover- nance.In addition, this study will examine the effect of outside directors in particular, targeting approximately 340 domestic listed companies.The results of the analysis suggest that it is not the outside directors but the companies themselves that are promoting ESG, that the social significance of ESG has changed, and that ESG is no longer a cost to be paid by companies but a management issue that companies themselves should actively address.The results also suggest that the quality of the outside director's effect is important. Specifically, it was shown that there is an effect of whether or not outside directors hold stock. In addition to the incentive effect, it is possible that stock ownership is an indicator of the level of interest of outside directors in the company.

We conduct an empirical analysis of the relationship between innovation activity and mergers and acquisitions (M&A) by public companies in the pharmaceutical and materials industries in Japan, the U.S., and Germany. The analysis uses large-scale patent data to measure indicators of innovation activity. In addition, we attempt to quantitatively measure the technological distance between firms by analyzing patent document data using natural language processing, and apply it to the evaluation of M&A. As a result, we find a trend in the relationship between the technological distance of the acquirer and the target firm and the post-merger innovation activity. One of the novelties of this study is the application of unstructured data and information technology methods to the research field of M&A and innovation activities. This research contributes to the quantitative evaluation of intangible assets in companies.

The purpose of this research is to evaluate the environmental load reduction effect of recycling in anticipation of the timing of mass disposal of solar panels, which have been rapidly introduced in recent years. As a result of analysis using government information and life cycle assessment, which is an index for quantifying environmental load, it was found that the amount of waste in 2034 can be reduced by up to about 530,000 tons and the environmental load can be reduced by up to about 2.9 million tons-CO2. In addition, the internal cost directly involved in recycling is 194 yen / kg, the external cost that costs the environmental impact during recycling is 54 yen / kg, and the recoverable profit is 196 yen / kg. As a result of sensitivity analysis considering the demand trend of recycled resources, it was suggested that price fluctuations of polysilicon and aluminum have a significant influence on the recovery profit.

This study is part of the Chiba University of Commerce Special Lecture "Data Science, " in which students take the initiative in conducting analyses to solve the university's problems using data science methods. This study focused on gazing and eye movement as quantitative data in the usability evaluation of a web page for university entrance examinations. We analyzed the factors influencing information reaching by combining eye movement measurement data and users' subjective evaluations. We experimented with 22 subjects (21 valid data) and chose five web pages for university entrance examinations for stimuli. The analysis results revealed no difference in the time and the distance of eye movement for each Web page, but there was a difference in the variance of fixation points. In addition, the comparison of the variance with the questionnaire to the experiments revealed that the minor variance of fixation points results in lower subjective difficulty in reaching the information. Furthermore, setting a certain number of scrolls on the Web page decreases the variance and the difficulty in reaching the information.

Business cases include a lot of information on corporate activities, including informa- tion contained in securities reports. In this study, we analyze the relationship between the orga- nizational structure of Japanese IT companies and financial performance using business cases. .. The organizational structure of Japanese IT companies can be broadly divided into pyramid-type organizations and flat-type organizations. Recently, IT companies have been called for flatten- ing, and many companies are moving to flat-type organizations. However, Japan is originally a pyramid-type organization, and it is thought that there are not many companies that are emerging as flat-type organizations. The purpose of this paper is to show a concept for exploring one of the factors.

While the negative effects of selective exposure to information on digital platforms and the importance of the diversity of information are well understood, the relationship between the diversity of information offered to users and user engagement has not been well analyzed. In this study, we analyzed user behavior logs in a popular news application to clarify the relationship between diversity and engagement. We found that users who viewed articles homogeneously tended to have significantly lower retention rates both before and after the great change in the application 's recommendation system. On the other hand, we found that users with extremely high article diversity do not necessarily have high retention rates, and that the relationship between diversity and retention rates differed in part before and after the change.

In companies, individual optimization of organizations, silos, isolation of individuals and division of organizations often become issues. In this study, we quantitatively grasp connections between people from data of a company's transmission and reception logs through an internal currency with the theme of "thanks". We also verified the relationship with the team's performance to examine how al-truistic behavior fills the division between organizations. The analysis results showed that brokers who fill structural gaps have an important influence on team performance. And the more broker younger than the average age of the team have connections with key players of other teams, which have the higher the team performance. This study proposed an innovative way to diagnose the state of the organization by analyzing the log data of the company internal currency to the management of the divided organization.

In this study, we analyzed the impact of news related to information security incidentson the stock prices of companies listed on the NASDAQ market in the United States. Theanalysis data was based on news that was announced to the market participants by ReutersNews. For the analysis of news related to information security incidents, we used the BERTmodel of text analysis. In this study, we used the event study method to compare and analyzethe impact on stock prices in multiple periods.

With the spread of COVID-19, medical disinformation has been actively disseminated on social media. Medical disinformation is dangerous because it may cause people to do actions directly related to human life and it is difficult to judge whether it is right or wrong. Therefore, Social media is required to correspond to this situation. In this paper, we focused on blog posts. We classified and analyzed blog posts containing medical false contents under COVID-19. As a result, we found that it is possible to classify blog posts with high accuracy by using browsing information, that false blogs contain contents that incite anxiety, and that they tend to lead people to information products and quote social media as evidence.

Securities analysts are professionals who provide investment advice and investment management services by analyzing various types of information and evaluating investment values through the application of advanced expertise and analytical techniques in the field of securities investment. This study aims to support securities analysts by analyzing supply chain data, which is objective information, and analyst reports, which is subjective information, to discover companies that should be focused on in the evaluation of investment value. The network is created from the supply chain data, and the influence maximization problem is solved to discover firms to focus on. The extracted firms are evaluated by comparing them with co-occurrence networks created from analyst reports.

This paper proposes a model that visualizes the risk of contact infection to family members when going outspreads to various items at home. Behaviour data after returning home are extracted from the behaviour survey, such as location and contact objects. Then data tables are created, including a behaviour history table, behaviour probability table, contact probability table, number of visits table, contact circulation table, etc. The material transmission rate table is created by measuring the virus transmission rate after contact with droplets in a virus experiment laboratory,

Human location information is highly private, and it is becoming increasingly difficult to obtain human flow information based on an individual's movement history. In this paper, we propose a method for estimating human flows using point crowd density data that can be obtained on a large scale. The proposed method uses an inverse simulation method based on human movement simulation to estimate the movement trend between locations. Through verification experiments using real data, we show that the proposed method can estimate the human flow more accurately than the extended method of previous studies. In addition, through the application to real data and analysis, the practicality of the proposed method is demonstrated.