This study aims to analyze the leadership tendencies exhibited within the DX (Digital Transformation) divisions of business companies. Through interviews with leaders responsible for these divisions and subsequent qualitative analysis, it was demonstrated that leadership styles vary depending on the skills and diversity of the team members. In organizations where both skills and diversity are high, delegative leadership and shared leadership were prominent, whereas in organizations with low skills and diversity, participative leadership was more commonly observed. Additionally, in organizations where skills are low, but diversity is high, inclusive leadership and directive leadership were found to be prevalent.

In today's uncertain society, understanding how organizational structure influences collective decision-making is essential. This study utilizes a simulation model based on the garbage can model to analyze decision-making in ambiguous situations. The simulation, designed with a four-layer hierarchical structure, mirrors the complexity of organizational realities. Three Results indicate. First, lower-middle-level members bear the highest decision-making burden. Second, higher-level members are more engaged in problem-solving and less likely to delegate decisions upward, Third, lower-level members tend to both solve problems and pass decisions up the hierarchy.

This study has the research question of what factors cause markets to decline because they are not suitable for their environment. The objective is to demonstrate the impact of the nature of information flowing into a market on its performance by focusing on the linkages between markets. We analyzed 1,568 time-series data from 224 markets in seven periods for markets in which Japanese firms in the chemical industry. The results of the analysis indicate that the more closed a market is, the poorer the market performance. The practical contribution of this study is that it has demonstrated the importance of having a market strategy that looks to the future of the market.

This study examines the dissemination of managerial intent throughout the process of implementing and utilizing Sales Force Automation (SFA) as a solution to address the sales challenges faced by a termite control company. The focus is placed on how the company's executive initiates and guides the adoption of this sales support tool to resolve operational issues. The paper first outlines the specific sales-related problems encountered by the termite control company and then evaluates the effectiveness of SFA in addressing these challenges. Furthermore, it investigates the process through which the executive's strategic intent is conveyed to and embraced by the employees, offering insight into one critical aspect of this diffusion process.

In recent years, while AI technology has advanced rapidly, labor shortages due to a declining population have become increasingly severe, particularly in the sales sector. To address this challenge, AI could potentially enable effective sales approaches tailored to the user's personality, thus realizing AI-driven sales activities. Using the bridal industry as an empirical field, this study explores a method for optimizing the speech behavior of dialogue-generating AI based on users' personality traits (sales AI method). As a first step, by utilizing the GPTs provided by OpenAI and referencing innovative orientation assessment questions that incorporate various elements, including the psychological personality test MBTI, we were able to construct questions that achieve a highly accurate orientation assessment tailored to the bridal industry.

Nowadays, the amount of data is rapidly increasing, and various analyses are being conducted to predict consumer behavior. Clustering exists as a means of finding useful information from the users' data. Clustering is often based on distances between data, but most studies do not regard the size of concepts. This study proposes a clustering method that takes into account the size of concepts (granularity). Clustering data with different granularity enables us to find people's thinking, such as paradigm shifts.

This study proposes a method combining graph convolutional networks (GCN) and contrastive learning to construct a context-dependent recipe recommendation system. The proposed method focuses on recipes and integrates nutritional content, ingredient co-occurrence relationships, and visual features of dishes to achieve recommendations tailored to the user's context. In the evaluation experiment, we compared the proposed method with conventional popularity-based recommendations, demonstrating the superiority of the proposed method in specific contexts. This research aims to contribute to supporting users' healthy dietary habits through context-dependent recipe recommendations.

The technology of recognizing human posture and behavior through AI and image recognition is rapidly advancing and is recognized as a crucial innovation with extensive applications, including autonomous driving. Recently, efforts to apply this technology to animal posture and behavior recognition have gained momentum. Accurately interpreting the posture and actions of animals, who cannot express themselves verbally, has significant implications beyond simply monitoring their health or detecting dangerous behaviors for pet owners. It also has industrial applications, such as assisting in diagnostics at veterinary clinics and managing livestock health and predicting births in farming facilities. This study reports on the development of tools aimed at accurately interpreting the posture and movements of cats from video footage, with these broader goals in mind.

This study explored the competencies required for digital human resources and the process of improving them.

We interviewed engineers working for business companies and SI firms to extract the characteristics of the skills required of digital human resources at each level. The competencies required for digital human resources at business companies were characterized by two key features: actively involving the others and contributing to the business based on business structure and business knowledge. The competencies required for digital human resources at SI companies were characterized by two key features: the ability to communicate while taking into consideration the other person's position and role, and to make use of a variety of experiences and connections outside the companies.

We developed a method to predict the success probability of innovation in unknown cases by analyzing known business cases using machine learning. This study updates the original method by employing generative AI to automatically convert cases into multidimensional feature vectors, nearly automating the process of analyzing extensive case documents and finding similar cases. By designing an appropriate list of F questions based on innovation factors, case documents are automatically transformed into F-dimensional feature vectors. This approach enables the automatic data conversion of numerous existing business cases, significantly accelerating machine learning analyses such as clustering, classification, similarity search, and outlier detection. We report on the details of this automatic generation technique and the substantial improvement in analysis speed achieved.

This study aims to clarify how generative AI, represented by OpenAI's ChatGPT, affects U.S. tech companies. Specifically, we quantitatively analyze the impact of events such as the launch of ChatGPT on the stock prices of tech companies like Alphabet and Microsoft using the event study method. The results of this study show that neither the launch of ChatGPT nor announcements of investments in OpenAI by other companies had a significant impact on the stock prices of these tech companies. However, a positive impact was observed for Microsoft following the announcement of the integration of OpenAI's technology into its services. This suggests that service-level partnerships play a crucial role in determining the impact of generative AI on tech companies.

As a method for the early observation of emerging businesses that are in a highly novel and unstable stage, this paper presents a framework for extracting relevant businesses from the reward-based crowdfunding market using LLMs (Large Language Models). Furthermore, by quantifying the novelty of emerging businesses, this study demonstrates that recent trends in highly novel businesses include space, metaverse, robotics, and sustainability. The findings presented in this paper will contribute to the advancement of emerging business research and can be utilized to predict the emergence of emerging industries.

The Nested Logit Model is characterized by its assumption of correlation between alternatives and the hierarchical structuring of choices within the model. Using the Nested Logit Model allows for the measurement of utilities for choice sets that are difficult to observe directly, but it also presents challenges due to its multimodal likelihood function. As a result, the parameter estimates obtained using traditional maximum likelihood estimation methods may not always accurately reflect actual consumer choice behavior. This study proposes a method to improve the accuracy of parameter estimation by using the MCMC (Markov Chain Monte Carlo) method to estimate the parameters of the Nested Logit Model.

By utilizing data and digital technology and conducting management that encourages power-saving behavior based on electricity deregulation policies such as dynamic pricing and demand response, to manage both stable electricity supply and decarbonization during times of power shortage (i.e., Green Transformation: GX). In this paper, we use data sets of electricity supply and demand, weather and wholesale electricity market in the TEPCO area from FY2016 to 2023, and we constructed and analyzed the impact assessment model of the wholesale electricity market on maximum electricity demand. Then, we assessed the impact of electricity deregulation policies on GX and verified its effectiveness.

The study aimed to construct an evaluation model of class start time that considers commuting conditions and urban traffic problems through a simulation approach to maximize educational effectiveness. As an evaluation, it attempted to solve the optimal solution in the trade-off relationship between maximizing the educational effect and minimizing the tardiness rate. Three models were set up for this goal, and the results of estimating the average behavior were presented through analysis.

The goal of this study is to clarify the strategic content and strategy formation process in the business evolution of a spin-out firm that received support from “Osaka Top Runner Project," a policy to promote innovation in SMEs and ventures by local government Osaka city. To this end, we first confirm the “discovery of business seeds" and focus on “business review" in the process of business evolution.

Interest in agile development is growing among government agencies, but various institutional and organizational issues must be resolved before it can actually be introduced. It may be desirable for these issues to be resolved by a cross-departmental promotion organization, rather than on an individual project basis. This study derives the general process of full-scale agile development and divide it into activities for individual projects and cross-departmental activities, based a case study at the Tokyo metropolitan government. By clarifying the role of cross-departmental agile development promotion organization, this paper provides suggestions for government agencies when they fully engage in agile development.

A data marketplace is an online transaction hub that facilitates the buying and selling of data. Representative examples of data marketplaces include Snowflake Marketplace and AWS Data Exchange. These are private businesses, but in Japan, with the Basic Act on the Promotion of Public and Private Data Utilization coming into force in 2016, public institutions are also making progress in disclosing and distributing data, and there are also cases of public institutions setting up data marketplaces. This study discusses the current state and challenges of data marketplaces based on examples of those established by public administrations.

This study aims to identify the conditions under which high psychological safety does not hinder the performance of routine tasks. By conducting interviews with managers of university sports teams, the relationship between psychological safety and the performance of routine tasks will be analyzed, providing concrete guidelines for organizational management.

Promoting digital transformation (DX) is crucial for Japanese companies, and numerous discussions have addressed what needs to be done to achieve it. However, human resources issues remain a major problem for many companies in advancing DX. This study focuses on DX Stocks and Noteworthy DX Companies, that are actively promoting DX. The purpose of this study is to reveal the characteristics of human resources initiatives of these companies. The contents of corporate reports were analyzed using topic modeling. The results revealed that DX Stocks and Noteworthy DX Companies mentioned topics related to “utilization of digital technologies" and “adoption of the digital era" in related to human resources more frequently than other companies.

This study is a research plan to demonstrate the impact of visual productions such as Japanese movies and animations and the advanced overseas expansion of Japanese restaurants on the overseas expansion of Japanese food manufacturers. The data set will consist of Japanese food manufacturers that have expanded overseas, and time series data from 2014 to 2021 will be used. As a result, we will demonstrate the mechanisms that enable Japanese food manufacturers to successfully expand overseas.

In this study, we estimated production efficiency, including generation capacity and generation volume, from 2013 to 2022 for 72 U.S. utilities using the DEA intermediate approach. The companies were classified into three groups based on the ratio of electricity generation from renewable energy sources. A meta-frontier analysis was then conducted to examine disparities in production efficiency relative to the ratio of renewable energy generation.

Disruptive innovation is a technological innovation in which a new product is introduced to the market that is inferior to the existing product in terms of conventional value criteria such as performance, but has some superior aspect in terms of new value criteria such as price, in a market where the performance of the existing product exceeds customer demand, resulting in excess performance. Since the process and pace of disruptive innovation vary from industry to industry and case to case, this study creates a simulation model that expresses the characteristics of the market and analyzes the factors that generate diversity in innovation and effective countermeasures against disruptive innovation in response to that diversity. This study creates a simulation model that represents the characteristics of the market.

The Innovator's Dilemma is a phenomenon in which existing firms that can afford to engage in disruptive innovation pursue sustained innovation aimed at existing customers due to their superior management capabilities and eventually lose market share. As a countermeasure to this phenomenon, a spin-out strategy has been proposed, in which an organization completely independent from existing firms is created to develop new businesses, but its effectiveness has been questioned due to the problem of ineffective utilization of resources possessed by existing firms. In recent years, a new strategy, Ambidexterity, which can allocate resources appropriately, has been considered effective. In this study, we examine effective measures to secure market share under the Innovator's Dilemma.

Various studies have analyzed the knowledge transfer triggered by the interfirm mobility of inventors. Existing studies have suggested that when inventors move between firms due to job change, not only does knowledge flow from the source firm to the destination firm, but knowledge also flows from the destination firm to the source firm, referred to as “reverse knowledge flow." In this study, we focus on the relationship between the mobile inventor and other inventors in the source firm as well as the performance of the mobile inventor, and propose a framework for analyzing the impact of these characteristics on the amount of knowledge transferred by “reverse knowledge flow."

This study focuses on the phenomenon in which established consumer-generated media (CGM) platforms, which had large numbers of users and content, are losing their competitive advantage due to the entry of a latecomer CGM platform. We aim to explore why contributors choose to post on a latecomer CGM platform rather than a first-mover one. Based on concerns that changes in technology and user's sense of values may cause the previously advantageous accumulation of content to negatively affect CGM evaluation, this study formulates hypotheses regarding contributors' decision-making when selecting a CGM platform in the recipe site market. Furthermore, it aims to develop a framework to test these hypotheses.

While there has been considerable research on brand extension, brand extension within economic zone has not been sufficiently discussed. Thus, this study focuses on the QR code payment apps, PayPay and Rakuten-Pay, to compare how the factors influencing the evaluation of extension service differ between users and non-users in each economic zone. We constructed an extension evaluation model, conducted a questionnaire survey and performed multigroup structural equation modeling. The analysis revealed differences in the factors influencing the evaluation of brand extension between users and non-users in each economic zone. These findings provide new insights into brand extension within economic zone.

In flea market apps, sellers cannot list products exclusively for specific groups of buyers with certain characteristics. However, previous studies have pointed out that sales prices in the secondhand market are influenced by the seller's attachment to the product and the buyer's usage intention. Additionally, some users may prefer to sell to buyers with whom they feel a social connection. Therefore, this study proposes a framework to examine the utility sellers derive from buyers with specific characteristics, and to analyze how the introduction of a feature allowing sellers to list products exclusively for certain buyer groups would impact sellers' utility toward the app. Furthermore, the framework explores whether this impact varies depending on the seller's attachment to the product.

Companies need to transform their business models for sustainable development and explain their long-term value creation processes. This article examines integrated reports on long-term transformation and value creation, focusing on a method for describing these processes. We extracted 430 Key Performance Indicators (KPIs ) from general construction industry reports and categorized them into 22 types, and analyzed the value creation process by using the proposed method. The results showed that the processes related to the return on investment after enhancing human, social and relationship, and natural capital were not clearly described. In addition, natural capital was either omitted or confused with impact, resulting in an unclear value creation story.

This study examined the relationship between delivery strategies and customer satisfaction in the context of heterogeneous customer time sensitivity. The analysis revealed that excessive prioritization of certain customers may compromise overall economic efficiency. These findings contribute valuable insights to the study of hybrid UAV-vehicle delivery systems.

This study aimed to determine the impact of organizational capability on organizational efficiency. The objective is to demonstrate the effects of organizational sensing capability on organizational efficiency and its inhibiting factors. To this end, multiple regression analysis will be conducted using data from Japanese airports over the five-year period from 2014 to 2018. The novelty of measuring sensing capability using archival data provides a theoretical contribution to the literature on sensing capability.

In this presentation, we used data from the Ministry of Economy, Trade and Industry's Health Management. Survey to extract factors and conduct a factor evaluation on questionnaire items related to turnover by structure

equation model (SEM). Furthermore, we conducted a multiple groups analysis based on the number of employees and whether the company was listed on the stock exchange. Then, we discuss what specific efforts should be focused on to reduce turnover rates at a company-wide level.

In this study, which measures the effectiveness of promotions, we construct a machine learning model that predicts whether or not an e-mail magazine will be opened and interpret the prediction results using distribution data and purchase data from an electronics retail store. We then consider distribution methods that are likely to be opened and the characteristics of customers who are likely to open the newsletters. We also propose future delivery methods.

This paper analyzes factors influencing customer satisfaction in hair salons using review data. Specifically, customer reviews were vectorized and categorized through cluster analysis, and the proportion of reviews in each cluster for each salon was calculated. Using these proportions as explanatory variables and the average satisfaction rating for services as the objective variable, a prediction model was built, and the importance of each feature was examined. The results suggest that customer satisfaction may be influenced more by the overall experience at the salon than by the quality of individual services like cuts or coloring. Additionally, the study highlights the need to approach repeat customers more effectively.

Using data on words searched for in parenting websites, this paper analyses the relationship between initial registration behavior and continuity of website use. In particular, the clustering results are compared using three methods in the dimensionality reduction method of distributed representation of textual data, and continuity is discussed.

Labor shortages present a significant challenge for tourist destinations in Japan. One potential solution to address this issue is the implementation of tourism demand forecasting. However, numerous tourist destinations in Japan currently rely on intuition and experience to make such predictions. This study developed and quantitatively evaluated a hypothetical tourism information gathering and transmitting model to enhance the accuracy of daily tourism demand forecasting. These results indicate that the hypothetical model cannot be fully elucidated. Conversely, it cannot be conclusively stated that the hypothetical model is inapplicable. Future research will involve verifying the model by analyzing alternative websites and reconstructing them through structured interviews.

In this study, we examined how the interaction between university students in the early stages of their career development and workers who have already had working experience changes their narratives about their own careers and work. We conducted a structural topic model to examine how the frames that students and workers have about careers change as a result of interaction between them. The results of the analysis showed that the frames “objective corporate excellence", “corporate culture", and “work motivation" diverged from each other as the students' and the workers' perspectives on their careers changed over time, while the frames “connection in the workplace" and “difficulty in conveying the attractiveness of the company" tended to become closer to each other.

Generative AI is rapidly being adopted across various fields, including universities, where it is having a significant impact on education and research. In education, AI provides individualized instruction and automates the analysis of students' learning progress, creating optimal learning environments. Additionally, generative AI enhances efficiency in tasks like writing papers and preparing presentation materials, and it is expected to transform the future of university education and research. In this presentation, we created a virtual university, “Department of ChatGPT, Faculty of Management and Information Sciences, Tama University," on the Web, using generative AI to produce images and videos. This project offers a chance to rethink the future of university education, and we report on its development.

As digital technologies prevail, the companies offering internships and job shadowing expect universities to include these technologies into their courses. When it improves courses of digital technologies, the opinions of companies are just important as same as feedback from teachers and students. It examined 10 indicators to evaluate the syllabus of ICT courses by corporate perspective. The Principal Component Analysis (PCA); PC1 confirms the usefulness of these indicators, while PC2 emphasized the importance of career development. The regression analysis further suggests that both components influence the necessity of the indicators. By reflecting data into the prototype of syllabus evaluation system, there is potential to assist teachers.

While Transactive Memory Systems (TMS) have been examined within the context of group dynamics as a means of efficiently sharing specialized knowledge, the need to expand this focus to the entire organization has been acknowledged. This study investigates knowledge sharing among individual employees within a firm by exploring the antecedents of out-group TMS. The results indicate that while social media use is essential for identifying the location and information of knowledge, it is necessary that employees be in an environment where they can make decisions to access expertise outside their group.

This study conducted an original survey targeting companies listed on the Tokyo Stock Exchange (TSE) in response to the growing importance of generative AI as a key management issue for Japanese businesses. Responses were received from 273 firms, representing 7.1% of all TSE-listed firms, and the relationship between the utilization of generative AI and the development of digital culture within organizations was analyzed. The overall use of generative AI was found to be limited, with particular delays in areas such as customization and performance measurement. A correlation between the use of generative AI and organizational digital culture was observed, suggesting the potential for further analysis in future research.

The world-shaking COVID-19 triggered the rapid spread of telecommuting in Japan. The results of our survey on working hours have revealed that there are a variety of work styles, including cases in which workers come to work even on telecommuting days and cases in which they perform their work only from home. The purpose of this study is to conduct an online semi-structured interview survey of telecommuters and to use grounded theory techniques to extract the characteristics of the results.

In preparation for a society with a declining population, the government is beginning to move toward cloud sharing and standardization using DPI in IT systems.

However, even if efforts are made to promote sharing and standardization, the introduction and switchover times for each IT system are different. As a result, it cannot necessarily be said that a consensus has been reached among the parties concerned regarding the life cycle of IT systems and the timing of considering sharing and standardization.

Therefore, we will extract and analyze the systems and cost-effectiveness that government agencies have introduced, and discuss measures that will enable parties concerned to reach a consensus on the cost-effectiveness of digitalization that incorporates the life cycle.

This study analyzes the formation of trust in digital technology and government, with a particular focus on the My Number system. As the adoption of My Number cards increases, the study aims to explore how citizens' understanding and acceptance influence the formation of trust. By comparing data from 2016 to 2023, the research examines whether trust in the government and local authorities has increased alongside the rise in My Number card applications. The study reassesses the notion that, for the government and local authorities to further promote digitalization, it is essential to focus on building trust, as relying solely on incentive measures, like those that increased My Number card applications, has its limitations.

In this presentation, we discuss a possibility of enhancing current information system for disaster victim support to information system for not creating disaster victims. We also propose AISAS model for disaster prevention.

MNCs can build competitiveness through knowledge transfer among foreign subsidiaries. However, inter-organizational knowledge transfer can only be effective with the absorptive capacity of foreign subsidiaries as recipients of transferred knowledge. This study focuses on the influence of managers' transformational leadership in foreign subsidiaries as an organizational factor that enhances their absorptive capacity. Based on data from a questionnaire survey of 100 managers of foreign subsidiaries in China, this study uses regression analysis to analyze the relationship between transformational leadership, absorptive capacity, and knowledge transfer. The analysis shows that transformational leadership has a direct positive effect on absorptive capacity, and absorptive capacity has a direct positive effect on knowledge transfer.

This study aims to explore methods for preventing frailty by using a large-scale language model to focus on conversational continuity. It is well-established that older adults with frailty are approximately twice as likely to require assistance or care. Therefore, the goal of this study is to create prompts for frailty prevention that can enhance the quality of life for individuals. The analysis will utilize a large-scale language model (LLM) to assess the feasibility and challenges of implementing multiple prompts.

In this paper, we develop a new theory that combines matching theory and the graph model for conflict resolution (GMCR). Specifically, we construct GMCR based on a model that matches three players and three teams using the immediate acceptance method, and calculate the stability of each state regarding Nash, GMR, SMR, and SEQ by program. Of the total 216 states, Student 1 achieved equilibrium for all four stability concepts in 92 states, Student 2 achieved equilibrium in 128 states, and Student 3 achieved equilibrium in 128 states. There were 32 ways in which all students were Nash.

To respond to the rapidly changing global society, it is necessary for Japan to design a healthy venture ecosystem (VES) and create an environment where unicorn companies are continuously founded. A comparison of countries that have successfully created VES shows that there are five common stages in the process of VES ("transformation," "development," "progress," "expansion," and "sustainable growth"), and that important events in each stage impact VES. On the other hand, in Japan, the "progress" stage, where values change and innovation is created through cultural exchange, is weak, and the growth of VES is slowing down. This study clarifies the impact of cultural and value changes on the development of VES from the perspective of risk acceptance in entrepreneurship.

The research question of this study is how teachers with master's or doctoral degrees in compulsory education influence students and the community. The objective of the research is to demonstrate the impact of graduate teachers in compulsory education on student academic achievement, student well-being, and community well-being. The analysis uses data from sources such as the "National Academic Performance Survey Reports and Aggregated Results" provided by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) and employs multiple regression analysis. The novelty of this study lies in its empirical demonstration of how teachers' academic qualifications affect students academically and psychologically using national data. This research provides interdisciplinary insights for education and psychology.