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The authors contribute to the literature on leadership by investigating how characteristics of principal investigators (PIs) affect innovation performance, and how collaborative and non-collaborative projects moderate this relationship within the context of inter-organisational research projects.

The authors analysed panel data from the National Science and Technology Information Service on 171 research projects within a biomedical and regenerative medicines programme overseen by the Korea Health Industry Development Institute. The authors used a hierarchical regression model, based on the ordinary least squares method, to examine the relationship between PI characteristics and performance, considering both quantity and quality.

The results show that the characteristics of PIs have diverse effects on the quantity and quality of innovation performance. Gender diversity within PIs negatively affects the quality of innovation performance, while the capacity of PIs positively influences it. Moreover, the degree of PI’s engagement is positively associated with the quantity of innovation performance but does not have a significant relationship with the quality of performance. In terms of moderating effects, collaborative projects with multiple leaders seem less reliant on PI capacity than non-collaborative projects led by a single leader, in terms of innovation performance.

The results contribute significantly to the literature on innovation management by examining the role of leadership in collaborative environments to enhance innovation performance, addressing the need for empirical evidence in this area. Analyses of PI characteristics in government R&D management can lead to improved team performance, more efficient processes and effective resource allocation, ultimately fostering innovation.

We aimed to establish criteria for determining the deformed lateral torso type to identify individuals requiring measurement methods different from standard methods before extracting dimensions from three-dimensional (3D) scan data.

We collected the 3D body scan data of 119 women aged 70–85 years collected in the 6th Size Korea. Three axes were defined to determine the deformation of the lateral shape, and the angle of each reference axis was used for the analysis. Additionally, to classify the lateral torso shape, 14 experts made visual judgments on the side-view images of the participants.

To identify the axis that best represented the lateral torso shape, we used each angle value of the three reference axes as an independent variable and the expert’s visual classification as a dependent variable. Each discriminant function was obtained and accuracy calculated. The whole torso axis exhibited the highest accuracy. Next, an assessment scale was developed to determine the shape of the lateral torso using the angular value of the whole torso axis.

The scale developed in this study has the potential to reduce measurement errors arising from elderly deformed torso shapes, thereby enhancing data reliability.