Scholarlink

In the current big data era, excessively rich data may cause information overload in employees, resulting in reduced productivity. Therefore, this study seeks to address the question: “Is it more effective to generate ideas by showing factors at the factor level, as with structural equation modeling (SEM), rather than showing the effects of a large number of individual variables, as with multiple regression analysis (MRA)?” We randomly assigned 131 students to use the brand image factors of 10 sports in Japan as reference data for ideas. We provided the control group with the MRA analysis results and the treatment group with the SEM analysis results, and asked them to plan a new gym concept. As a result of a randomized controlled trial conducted on 2,521 people in their 20s to 60s, 29.1% (control group: MRA analysis) and 36.2% (treatment group: SEM analysis) found the concept attractive (quality of the concept); a significant difference was detected. However, the amount of concepts was fewer in the treatment group. Presumably, the motivation to generate more ideas decreases once a good idea is generated through SEM. Therefore, SEM should be actively adopted for data analysis within a company to reveal employees’ idea-creating abilities. Additionally, the motivation to produce more ideas decreases once a good idea is produced; hence, managers should not easily demand large amounts of output from their subordinates. This study is novel since it demonstrates the impact of the comprehensive analytical results of aggregated information from SEM on human creativity.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

SEM Generates Good Ideas! Differences in the Quality of Concept Planning Depending on the Method of Visualizing Consumer Attitude Factors

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Takumi Kato,
  • Ryosuke Ikeda,
  • Emiko Yamada,
  • Kenta Kasahara

摘要

In the current big data era, excessively rich data may cause information overload in employees, resulting in reduced productivity. Therefore, this study seeks to address the question: “Is it more effective to generate ideas by showing factors at the factor level, as with structural equation modeling (SEM), rather than showing the effects of a large number of individual variables, as with multiple regression analysis (MRA)?” We randomly assigned 131 students to use the brand image factors of 10 sports in Japan as reference data for ideas. We provided the control group with the MRA analysis results and the treatment group with the SEM analysis results, and asked them to plan a new gym concept. As a result of a randomized controlled trial conducted on 2,521 people in their 20s to 60s, 29.1% (control group: MRA analysis) and 36.2% (treatment group: SEM analysis) found the concept attractive (quality of the concept); a significant difference was detected. However, the amount of concepts was fewer in the treatment group. Presumably, the motivation to generate more ideas decreases once a good idea is generated through SEM. Therefore, SEM should be actively adopted for data analysis within a company to reveal employees’ idea-creating abilities. Additionally, the motivation to produce more ideas decreases once a good idea is produced; hence, managers should not easily demand large amounts of output from their subordinates. This study is novel since it demonstrates the impact of the comprehensive analytical results of aggregated information from SEM on human creativity.