This study examines how office space and its utilisation policies influence knowledge sharing among employees in a Japanese company, focusing on the unique operational model of introducing mobile Group addresses, which involves forcibly changing the location of Group addresses in activity-based working (ABW). Group addressing is a unique approach commonly seen in Japanese companies that have adopted open-plan office layouts, allowing employees to freely select their seats within a broadly designated workspace assigned to their department (group). A new office policy was implemented involving the introduction of a mobile Group address for 587 employees at the headquarters of a large Japanese firm (Company A). Two questionnaire surveys were conducted before and after the introduction of the new policy, and responses were obtained at both time points from 224 employees. Statistical analyses using t-tests and paired t-tests were employed to test the hypotheses on knowledge sharing. The frequency of inter-group knowledge sharing during a Group address in the ABW system varied depending on the use of communication spaces, which are open areas that can be used without prior booking for meetings or discussions involving a few people. While the introduction of a mobile Group address did have a measurable effect on inter-group knowledge sharing, the effect was limited and dependent on prior communication space utilisation patterns.

The assessment and management of work-related psychosocial risks is mandated across many jurisdictions, yet a critical governance gap persists: there is little consensus on the point at which measured psychosocial hazard exposure constitutes an actionable risk requiring intervention. While physical and chemical risk assessment benefits from occupational exposure limits established through transparent, deliberative processes, psychosocial risk evaluation currently lacks equivalent mechanisms. The question of when exposure becomes an unacceptable risk is answered implicitly through methodological choices that are seldom articulated or justified. This position paper addresses this governance gap by distinguishing three logics of risk evaluation: predictive (risk as a statistical association with outcomes), normative (risk as a deviation from reference standards), and weighted (risk as frequency × severity prioritization). We analyze the conceptual foundations, governance implications, and practical trade-offs of each approach. Because each logic embeds different assumptions about when intervention is warranted, method choice is not a neutral technical decision but a governance choice that encodes assumptions about acceptable working conditions and employer obligations. We call for explicit governance processes that make methodological choices visible, debatable, and accountable, including documentation requirements, sector-level guidance on method selection, and inspector training on methodological adequacy. The challenge is not a lack of knowledge but the need for coordinated action to develop governance infrastructure for psychosocial risk evaluation comparable to that achieved in chemical and physical risk assessment.

In recent years, absenteeism and presenteeism caused by stress and mental illness have become serious issues for companies, resulting in reduced labor productivity and economic losses. This paper examines the potential of data-driven approaches to employee health management and preventive health activities and presents practical considerations for balancing data utilization with privacy protection. Shionogi & Co., Ltd. conducted a formal research project on employee stress prediction using wearable devices, questionnaires, and attendance information, as well as an internal employee-support initiative using daily pulse surveys. Rather than emphasizing technical performance alone, this paper focuses on practical governance issues involved in handling employee-related data, including purpose limitation, consent, pseudonymization, access control, and restrictions on secondary use. The 2 cases differed in legal and organizational character: the first was conducted as formal research subject to ethics review, whereas the second was implemented as an internal support activity. These cases suggest that employee-related data may help organizations understand employee conditions and provide timely support when used within an appropriate governance framework. At the same time, the use of such data requires a careful distinction among legal requirements, guidance-based good practices, and ethical considerations. Sustainable data-driven employee health management depends not only on technical feasibility but also on trust, transparency, and appropriate institutional design.

Artificial intelligence (AI) is increasingly being introduced into workplaces, raising expectations for improved efficiency and safety while also generating new challenges for occupational safety and health (OSH). However, empirical evidence on how AI is adopted, perceived, and governed in everyday workplace settings remains limited, particularly from the perspective of workers. This descriptive, cross-sectional, web-based survey aimed to describe the current state of AI adoption in Japanese workplaces and to examine participants’ perceptions of AI-related OSH risks, organizational responses, and accountability related to AI use. The survey was conducted in June 2025 among 1000 employed adults registered with an online survey panel in Japan. AI adoption was still limited in this sample: 5.8% of participants reported that AI had been fully implemented in their organizations, and 16.6% reported partial adoption. In addition, 78.9% reported no work experience related to AI. Across AI-related OSH risk items, approximately 39%–42% selected “Not applicable/AI not used,” and responses among those for whom at least 1 risk item was applicable were frequently concentrated in the “Neither” category. Organizational measures to address AI-related OSH risks appeared unevenly implemented, although human final review of AI outputs was among the more frequently reported measures. Regarding the perceived need for preventive measures, 36.5%–44.6% of participants considered measures such as transparency and explainability, workplace applicability verification, clear allocation of responsibilities, and collaboration with OSH experts to be necessary to some extent or very necessary. Responsibility for AI-related decisions was most frequently attributed to organizations and senior management that decided to adopt AI, and 59.5% of participants indicated that legal reform mandating accountability for AI decisions and actions was necessary or conditionally necessary. These findings suggest that AI use in Japanese workplaces remains at an early stage for many participants, with limited direct experience and uncertainty regarding AI-related OSH risks and organizational responses. Responsible AI use in OSH may require workplace-level guidance to identify AI-related risks, preserve appropriate human oversight, and clarify responsibility and accountability across multiple stakeholders.