Language：Japanese   Publisher：安全工学会  

In process industries as petrochemical industries, many researcher stated levels of safety culture are key factors to affect industrial accidents.<b> </b>However, definitions of safety culture are implicit, and there are many issues to measure safety culture.<b> </b>We developed 110 item questionnaire based on 8 axis model of safety culture. This questionnaire responded by 9,647 employees from 86 Japanese plants, which included thermal power plants, oil refineries, chemical plant and rubber plants.<b> </b>As a result, the first principal component score, which could be used as a composite indicator of safety（Safety Culture Index）, showed a negative correlation with the incidence of occupational and process accidents. Also, result of this study indicates this assessment has the possibility to streamline plants safety measure planning.

DOI： 10.18943/safety.55.1_49

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Language：Japanese   Publisher：安全工学会  

<p>海外における保安力評価開発の動向を示すため，主に欧州の事例について解説を行う．ノルウェーDet Norske Veritas 社のISRS，イギリスHealth & Safety Laboratory のSafety Climate Tool，の概要を説明すると共に，フランスInstitut pour une culture de la sécurité industrielle（産業安全文化研究所：ICSI） のHSE Culture survey の詳細について解説する．さらに，評価システムを支える人材育成の仕組みをどのように構築しているか並びに今後の保安力評価が目指すべき姿についての整理を行った．</p>

DOI： 10.18943/safety.51.6_395

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：AMER SOC MECHANICAL ENGINEERS  

Scenario Graph is a structured mind mapping methodology that aids design teams to generate potential scenarios for new products and services while visually organizing contextual information. Since its introduction in industry and academia, the Scenario Graph has helped design teams to capture new values and behaviors of potential customers during the problem formulation stage. At the same time, the Scenario Graph, sharing a common challenge with various design methods, has faced difficulty regarding validation of its effectiveness as a design method.
This paper describes a validation framework for a method in problem formulation stages and an experiment, which compare ideation results of 87 people-43 people with the Scenario Graph method (as a test group) and 44 people with the Brainstorming (as a control group) to solve an identical problem. While the results show no statistically significant difference in the number of ideas generated, the data reveals statistically significant differences in the quality of ideas. The test group, which used the Scenario Graph, yielded ideas that were more novel, feasible, and abstract than the control group, which used the Brainstorming, did. These metrics represent a way to measure the quality of ideas in the domain of engineering design.
Our experiment confirms the hypotheses that the Scenario Graph is effective in improving the performance of idea generation sessions, which is consistent with qualitative evaluations. The lessons, gained from this experiment, provide an insight on how this method can be effectively used during the early stages of concept generation of a company's process for product and/or service development.

DOI： 10.1115/DETC2011-48047

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Language：Japanese   Publisher：一般社団法人 日本人間工学会  

In this paper, in order to develop a group dynamics training that suits a specific occupational team, studies were carried out on how to set the training target and to measure training results. Teamwork Importance Factors were determined by implementing a questionnaire survey. A Teamwork Target Scale and a Teamwork Condition Scale were outlined by using a factor model of Teamwork Importance Factors. There was no significant difference between the average on the Teamwork Target Scale and the high score group's average on the Teamwork Condition Scale. There was a significant difference between the average on the Teamwork Target Scale and the low score group's average on the Teamwork Condition Scale. Further, there was a significant difference between the high score group and the low score group's average on the Teamwork Condition Scale. Additionally, the study shows that important factors differ with regard to the types of occupation. Finally, this study shows the possibility for target setting and measuring the training results by using these scales.

DOI： 10.5100/jje.47.171

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Authorship：Last author   Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Authorship：Last author  

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Authorship：Last author   Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Language：Japanese   Publisher：The Japan Society of Mechanical Engineers  

DOI： 10.1299/jsmetsd.2019.g190416

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Language：Japanese   Publisher：日本芳香族工業会  

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Language：Japanese   Publisher：化学工業日報社  

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Event date： 2023.6

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

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Event date： 2022.12

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

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Event date： 2022.12

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

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Event date： 2022.12

Language：Japanese   Presentation type：Oral presentation (general)  

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Event date： 2019

Language：Japanese  

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Event date： 2013.4

Language：Japanese  

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Event date： 2012

Language：English  

Scenario Graph is a structured mind mapping methodology that aids design teams to generate potential scenarios for new products and services while visually organizing contextual information. Since its introduction in industry and academia, the Scenario Graph has helped design teams to capture new values and behaviors of potential customers during the problem formulation stage. At the same time, the Scenario Graph, sharing a common challenge with various design methods, has faced difficulty regarding validation of its effectiveness as a design method.
This paper describes a validation framework for a method in problem formulation stages and an experiment, which compare ideation results of 87 people-43 people with the Scenario Graph method (as a test group) and 44 people with the Brainstorming (as a control group) to solve an identical problem. While the results show no statistically significant difference in the number of ideas generated, the data reveals statistically significant differences in the quality of ideas. The test group, which used the Scenario Graph, yielded ideas that were more novel, feasible, and abstract than the control group, which used the Brainstorming, did. These metrics represent a way to measure the quality of ideas in the domain of engineering design.
Our experiment confirms the hypotheses that the Scenario Graph is effective in improving the performance of idea generation sessions, which is consistent with qualitative evaluations. The lessons, gained from this experiment, provide an insight on how this method can be effectively used during the early stages of concept generation of a company's process for product and/or service development.

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