How people get multiple jobs is the focus of this study. Identification of factors that influence the choice of multiple job acquisition channel and their impact on multiple job satisfaction. When the propensity score was used to estimate the difference in satisfaction by taking into account the factor of choosing personal connection or formal means, there was no difference in satisfaction by channel. By separating the motives for multiple jobs, we also estimated the impact of the channel on satisfaction. The estimation results show: People with only non-money motives are more satisfied when they get multiple jobs through personal connections. Depending on the multiple job motive, the effect of the multiple job acquisition channel on multiple job satisfaction varies.

The runners' population is on the rise due to increased health awareness caused by the outbreak of COVID-19 and the recent popularity of running. In this study, the author conducted a questionnaire survey of citizen runners, not professional runners, in their 20s to 60s living in Japan with the aim of analyzing their behavior regarding running and other daily life. The data have been analyzed using a combination of causal inference and machine learning to determine the impact of runners' behavioral transformation regarding running events on their purchase of wearable devices. Such self-behavioral transformation can be viewed as an intervention in causal inference. This will enable us to propose effective sports marketing measures for the products.

Explainable AI, which can explain the basis of AI decisions, has been attracting attention in recent years. One type of explainable AI is rule-based machine learning. One advantage of this method is that the model itself is a white-box type that is represented by a large number of rules. In actual business settings, this method can lower the hurdle to adoption because the prediction trends can be known in advance. However, the number of rules in the model is increasing due to the complexity of recent data, and it is difficult to check each rule. In this paper, we propose a method to present a summary of rules by grouping similar rules and visualizing the relationships among the groups.

This study discusses a statistical modeling approach to simulate shopping around behavior agents using two types of macro-aggregate partial information: the number of visitors by zone and the number of passers-by by street link. Parameter estimation is described for two district cases, Osu and Nishi-Shinjuku, and simulation performance is examined. In the Nishi-Shinjuku case, we also present the results of policy effect estimation. This agent model introduces the shortest path principle into the Markovian shopping around behavior model and uses the inverse transformation of the binomial logit to perform probabilistic selection of routes. This allows for the possibility of extending the model to handle not only objective distances but also subjective distances. Future work is needed to improve the deviation rate, which indicates the degree of fitting.

In labor-intensive back-office operations, increasing productivity is a challenge. In back-office operations, the allocation of work to individuals based on their abilities is said to affect productivity. In addition, since services are produced and consumed at the same time, interrelationships among customer satisfaction, employee satisfaction, and productivity are likely to arise. In this study, we propose a work allocation planning method that uses combinatorial auctions to improve employee satisfaction, minimize total work hours, and equalize utilization rates. In a computer experiment, we verify the effectiveness of the proposed method by planning work assignments that consider individual abilities and satisfaction levels, using work records from actual workplaces and questionnaires.

This study aims to understand the effective way to manage diverse human resources in an organization. Companies pursue "Talented employee", but there are "Associated Talented Employee" who need individual support to continue working due to child-rearing, etc., and they are a minority due to the organization's structure. This study focuses on the person with "Allyship" who belongs to the majority but raises questions and takes specific actions toward the minority's detriment and barrier in the organization. In reality, verifying " Allyship " is impossible; therefore, this study develops a gaming simulation to recognize "Allyship" and how it has affected the orga- nization. The analysis shows that the number of occurrences of "Allyship" is rare but significant; furthermore, the examinee's personality and gaming environment influences the occurrence. These results suggest that Allyship's generation and persistence are crucial for diverse human resource management.

In this study, we analyzed the relationship between the technological distance calculated from the patent text data as an explanatory variable and the number of occurrences of outliers in new patents as an explained variable. This study aims to explore the technological distance that brings about innovation. We vectorized the patent text data using natural language processing called SCDV and calculated the technological distance of companies. PCA and Local Outlier Factor (LOF) were applied to calculate outliers. As a result of comparing the groups divided according to technological distance, we confirmed that the group with the shortest technological distance had a higher mean number of outliers than the other groups. Furthermore, similar results were obtained when the same comparison was made after classifying by company size. These results suggest that M&As between companies with similar technologies produce more innovation as outliers.

This paper examines the current situation and issues of the teaching profession based on the employment rate data of the teacher recruitment examination. From the result of analytics, we reconfirmed that the recruitment ratio continues to decline nationwide. In addition, we were able to confirm a significant difference between regions with high and low hiring ratios and that there are three patterns in the change in the hiring ratio. We also found the provincial governments that said, "The hiring rate dropped sharply," "The hiring rate dropped below 1.0 at the time of taking the exam," and " Only the number of successful applicants was far below the hiring plan."

Since the Covid-19 Pandemic, Chiba University of Commerce has introduced remote lecture classes. The class performance data has shown that, compared to face-to-face classes or online remote classes, "indirect instruction-type" classes with on-demand styles have tended to polarize the performance of students. The purpose of this study is to analyze the causes of the polarization focusing on the initiatives and the study time, which represent spontaneous learning attitudes. Based on the results of class evaluation questionnaires and grade data, we have conducted independence tests and residual analysis of the combinations of independence by class type, grades by initiatives, study time by class type, and grades by study time. We have found a decrease in initiatives and an increase in study time in the on-demand "indirect instruction type" lectures. The results of this analysis suggest that (1) the "indirect instruction type" results in lower student motivation, which leads to the increase in the study time required for comprehension and the decrease in learning efficiency, and (2) it is necessary to design the method to encourage the student initiatives for the better on-demand classes.

We verified through experiments whether it is possible to detect changes in the brain waves of subjects when they are given incentives for the results of a new sport game by measuring their brain waves using a simple EEG measurement device. In addition, we verified whether it is possible to classify the EEG of subjects when they receive the incentive compared to the normal EEG. The results of the experiment showed that EEG measurements detected subjects' excitement and classified their responses to incentives with approximately 65% accuracy.

In response to the recent labor shortage, this study proposes a method to train employees not only by increasing the number of employees, but also by sharing human resources within a company, thereby distributing the burden to each department. The novelty of this research is the development of multi- skilled human resources that will be useful to the company in the future by sharing human resources not between companies but between departments of the company. Training is also provided in the form of practical training by having employees who are not busy in other departments help. The possibility of long- term multi-skilling through in-house personnel sharing will be verified through a multi-agent simulation using NetLogo, in which employees, departments, and operations are regarded as agents.

The purpose of this study is to conduct a analysis of independent contractor and on the transition from independent contractor to full-time employment. Specifically, using variables included in the Basic Employment status survey, a key statistic conducted by the Ministry of Internal Affairs and Communications, we will conduct an analysis using causal inference and other methods to identify the factors that lead people to choose independent contractor work, and the factors that make it difficult for them to transition to their desired work style after they choose independent contractor work. We will clarify the factors that lead people to choose independent contractor work.

In this paper, we analyze the funding process of seed stage startups that participated in domestic Accelerator program, especially ecosystem-type Accelerator program, in order to clarify the process by which Accelerator program in Japan influence startups' fundraising. The M-GTA method, a qualitative research method, was employed as the analysis method. The theme of the analysis is "the process of successful fundraising by seed-stage startup executives through domestic ecosystem-based APs," and the target of the analysis is defined as "seed-stage startup CEOs who have raised funds through participation in domestic APs. An explanatory model was hypothetically derived based on interviews with three startup CEOs who participated in the Accelerator program in Japan. As a result of the analysis, a result diagram and storyline consisting of 16 concepts, 3 core categories, and 3 subcategories were extracted and discussed in terms of both novelty and critique in practice. The core category of novelty is the "effective VC networking," while the practical critique is suggestive from the perspective of the development of the startup ecosystem in Japan.

The government of Japan decided to educate data science for all undergraduate students from 2025. However, data science education requires basic IT skills, such as using PCs, office software, and the web prior to programming or mathematical modeling. Furthermore, basic IT skills course requires a large amount of human and time resources due to the differences of students' skill acquisition status and the fact that it is a practice-based education. Therefore, this study verified the educational effects and HR cost for each of methodologies, such as flipped classrooms and e-learning using various questionnaire data. In conclusion, we found the following results. Online classes are highly effective in education, but they reduce students' satisfaction. Reducing teachers' workload by introducing flipped classrooms does not decrease the educational effect. In addition, the increase in the number of students per class does not affect educational efficiency.

In recent years, pharmaceutical companies have been taking strategies to expand their business into areas beyond the conventional framework of treatment, such as business expansion into the areas of prevention and nursing care, and treatment using digital devices. Meanwhile, in the startup industry, the government has designated 2022 as the first year of startup creation, with a view to creating an ecosystem to create and nurture startups with a five-year, 10-fold increase in the number of startups. Thus, as demand on the part of pharmaceutical companies and supply on the part of startups increase, the number of cases of collaboration between the two sides is expected to increase in the future. However, the pharmaceutical industry is a conservative industry due to various regulations, while the startup industry is an industry where innovation and speed are important. Therefore, in addition to the size and technological gap between the two industries, there is also a time gap between the two. Therefore, the purpose of this study is to grasp the overall picture by organizing the diversified business activities of startups, and to clarify the relationship between pharmaceutical companies and startups by comparing the two using press releases on the part of pharmaceutical companies and business details on the part of startups.

In recent years, the speed of social change has increased further, and proactive behavior that anticipates the change and leads to action is attracting attention. The purpose of this study is to clarify the causes of proactive behavior using network analysis. As a result of the analysis, it was clarified that when the betweenness centrality in the business consultation network is high between individuals and others, the proactive behavior characteristics become high. In addition, it was clarified that the betweenness centrality of the business communication network with weak connections is a factor that promotes proactive behavior under certain conditions. Through this research, it became clear that proactive behavior is promoted by becoming a mediator under certain conditions.

The purpose of this study is to investigate the factors influencing user satisfaction of smart city services (SCS) and the relationships among the factors. Therefore, a hypothesis model was proposed based on Technology Acceptance Model, Theory of Planned Behavior, and Technology Readiness Index theories, and an empirical study was conducted using a questionnaire survey with SCS in Haikou, Hainan Province, China. The results showed that users' trust, users' innovativeness, and control belief affected perceived usefulness and perceived ease of use of SCS. At the same time, the results of multi-group analyses based on age, education, and income showed that users with different demographic characteristics have different priorities when using SCS. Therefore, in order to improve SCS performance, it is necessary to consider perspectives in addition to service functions, and to provide differentiated services for users with different characteristics.

In this study, we analyzed the characteristics of hit songs from 1970 to 2022 in Japan in order to clarify how popular music has changed over time. The characteristics of the data were classified into seven categories: year, decade, BPM (Beat Per Minute), key, gender, number of transpositions, and the number of notes in the entire song. The results of principal component analysis and clustering are as follows. Principal component analysis and clustering revealed that the characteristics of hit songs were divided into four groups: sad songs, high-tempo women, mid-tempo men, and ballads & bands.

Each building has its own features. To find the impact on attracting customers and tenant demand, from each feature of buildings such as facade, image recognition technology is useful to determine the number of features from image of buildings. We used convolutional neural networks of AI to learn and separate the correct and incorrect images for the three categories of commercial facilities, office buildings, and hotels predicted and conducted a comparative study for the correct and incorrect images. We found that there are unique characteristics of buildings by use, such as the ratio of glass area, the number of colors, the shape of the building, and the shape of the windows.

The purpose of this study is to predict the innovation diffusion effect of the introduction of solar and wind hybrid power generation system and to quantify the factors. Therefore, we built a model using Vensim with the system dynamics method, performed simulation experiments for each scenario. The results show that, first, the factors influencing innovation diffusion are higher in order of policy effect, cost of installation and selling price of electricity, and the expansionary effect of each factor on innovation diffusion was found to be larger than the contractionary effect. Second, based on the selling price of electricity that has been decreasing year by year, the penetration will increase due to reduced installation costs and improved policy effects. It was suggested that the pace of decline in the selling price of electricity exceeds the pace of decline in the introduction cost will have a contractionary effect on diffusion. Third, if the selling price of electricity is more than 25 yen, the introduction cost is reduced by more than 50 percent, and the amount of support is more than 17.661 billion yen, the diffusion of innovation could progress rapidly.

In the development and growth of urban areas, land price indicators such as roadside land prices are important factors. However, in recent years, it has become easier to obtain actual pedestrian movement data from mobile phone location data, and the complexity of the factor structure has increased.In this study, a covariance structure analysis was conducted for the Kanayama area of Nagoya City, using pedestrian movement volume from mobile phone location data in addition to distance from the nearest station as an accessibility, agglomeration of commercial and business uses in the neighborhood as an amount of facilities, and an integration value for the entire area as an urban area form. By comparing multiple structural models, we examined the better fit model as factors for land price indicator.

The purpose of this research is to explore the factors that influence Chinese consumers' willingness to purchase Chinese brand electric vehicles on social media (SNS) and their relationships. Therefore, we set up a structural model and measurement model that combine the S-O-R model and consumer ethnocentrism and conducted an empirical analysis through a questionnaire survey of comparative content of Chinese and foreign brand cars announced by opinion leaders. As a result of analyzing a sample of 331 valid responses using SPSS and AMOS, we found that Informational content (IN), entertainment content (EN), and emotional content (EM) influence Perceived Relative Benefit (ME) Perceived Ease of Use (EA), Perceived Risk (RI) and consumer ethnocentrism (CET), and that ME, EA, RI, and CET affect purchase intention. Multi-population analysis results show that consumers with different demographic characteristics have different focus on social media and different points of focus on Chinese brand electric vehicles. Therefore, Chinese electric vehicle manufacturers need to focus on SNS promotion based on target consumers while emphasizing technology development.

In recent years, GPS data obtained from smartphones, such as Agoop's flow population data, has become readily available. Shop around behavior has traditionally been measured using methods such as questionnaires or gate-counting surveys, but with GPS data in recent years, it has become possible to obtain large amounts of accurate data, and research has been conducted to explore aspects of migration behavior using mesh-type location data. On the other hand, there has been no analysis that clarifies shop around behavior using point-type GPS data obtained via smart phones. Therefore, in this study, we used point-based location data to clarify the characteristics of the shop around behavior of visitors to Naka and Nakamura wards in Nagoya City.

The purpose of this study is to examine the influence of the country-of-origin image of Japan and smart cockpit features on Chinese consumers' willingness to purchase a Japanese-owned electric vehicle. Based on the Rational Behavior Theory and the Technology Acceptance Model, it is formulated hypotheses and a structural equation model, and empirical research was conducted through a questionnaire survey. In this study, the author analyzed 356 Chinese consumer's data by using SPSS and AMOS to reveal that perceived ease of use and country-of-origin image have a significant impact on Chinese consumers' willingness to purchase Japanese electric vehicles. Furthermore, it was also revealed that males were significantly influenced by the country-of-origin image in terms of gender. On the other hand, middle-aged and older consumers were significantly influenced by the country-of-origin image in terms of age. Therefore, the results suggest that Japanese automakers who propose to progress in the Chinese market need to improve the country-of-origin image of the target consumers by middle-aged and older males.