According to a recent survey on labor shortages, many companies are facing labor shortages and the problem is especially severe in the hotel industry. The food and beverage industry is also struggling to find enough workers. To address this issue, many stores have started using self-checkout machines. This is expected to make employees’ jobs easier and more efficient. However, some studies suggest that the introduction of self-checkout systems has actually increased the workload for cashiers. This study examines how self-checkout machines are affecting employees’ jobs through text mining of online article comments and surveys of cashiers. The analysis revealed the following key findings: (1) at present, self-checkout counters need to be managed in a manner similar to manned checkouts, and (2) compared to self-checkout systems, semi-self-checkout systems, where customers only handle payment transactions, are likely more effective in reducing the stress of employees working at checkout counters.

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Artificial intelligence (AI)-driven decision support has expanded across various fields; however, its widespread adoption has revealed diverse technical challenges related to the explainability, fairness, and optimization of AI decision-making processes. This study proposes an integrated AI-based decision-making (IADM) framework to comprehensively address these challenges. It compares traditional decision support systems (DSSs) with data-driven decision-making (DDDM) to identify the major challenges faced by AI-based decision support. Based on this analysis, the IADM framework integrates explainable AI (XAI) technologies to enhance transparency, fairness technologies to mitigate bias, and optimization technologies to support efficient, accurate, and adaptive decisionmaking. The IADM framework comprises three key phases: data input, AI analysis, and final decisionmaking, each applying technologies to overcome the technical limitations in conventional approaches. Furthermore, this study examines the applicability of IADM in corporate management, public policy, healthcare, and sports, and categorizes the technical, ethical, and operational challenges associated with its practical implementation. The findings of this study are expected to provide a foundation for the advancement of AI technologies and their practical applications in decision support.

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Amid the serious issue of a shortage of caregiving professionals, workplace relationships remain a leading cause of staff turnover. This study focuses on building relationships and implementing a training program for mindset transformation based on the Appreciative Inquiry (AI) approach. The program was conducted with 25 leaders from special nursing homes and other caregiving facilities, and its effects were examined.A comparative survey using the Appreciative Inquiry Scale was conducted before and after the training, and after a two-year interval, 12 on-site leaders were interviewed to assess changes in their awareness.The results showed statistically significant differences in at least one question item across all expected subscales in the comparative survey, notably including all questions related to self-respect.Subsequently, relationships continued to develop over time, utilizing the positive core to foster sustained growth. The leaders recognized that their appreciative attitudes and behaviors, which emphasized respect for both themselves and others, had a positive influence on their surroundings. This led to more productive team dialogues and a proactive approach to problem-solving.Moving forward, it is essential to establish purpose-driven educational systems, such as specialized training on interpersonal relationships, to further enhance caregiving environments.

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As the size of Japanese companies decreases, their progress in digital transformation tends to lag. In this study, we sought to identify the success factors for non-IT small and medium-sized enterprises (SMEs), which are expected to face significant challenges in achieving organizational transformation through IT. We conducted case studies of eight companies and found the following: (1) transformation leaders in non-IT SMEs often possess “heterogeneous prior knowledge” gained from their previous careers; (2) these leaders, when in positions of influence, typically exhibit a strong sense of urgency regarding the company's future; (3) even if the leaders are not IT experts, they may start with a vague understanding of IT utilization but can gradually refine their vision through learning and experimentation; and (4) transformation leaders frequently achieve significant results by employing an iterative approach, leveraging small successes to build momentum. Additionally, we observed that “heterogeneous prior knowledge” plays a critical role in enabling these four processes.

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The question arose as to whether the valuation of the property of privately held stocks should be based on the Circular or on a professional valuation. In this paper, we conclude that the valuation based on the Circular should be used. Since non-public stocks are traded without going through the market, it is difficult to measure their fair market value. Therefore, in order to ensure fairness in taxation among taxpayers, valuation by the Circular is appropriate.

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