Researchers have found that approximately 70% of information systems development projects in Japan have failed, thus increasing the demand for solutions that will raise expected project success rates. It is said that to improve success rates, it is essential that risk management should be conducted at an early stage. Although risk management is an important process that a project focuses on, it is difficult to mitigate the influence of all the risks through only the project. Therefore, it is noted that support should be provided by the organization to which the projects belong. The study aims to identify projects that an organization should support preferentially by predicting a project outcome that utilizes the assessment results of the project risks at an early stage. The risks of 88 projects of a Japanese IT vendor were assessed using a risk assessment sheet, and a project outcome prediction model was developed by utilizing the results of the assessment. As a consequence, a model with 73.9% predictive accuracy was obtained. It is thought that the system, including the risk assessment sheet and the project outcome prediction model, will contribute to identify projects that an organization should support preferentially.

Researchers have found that approximately 70% of information systems (IS) development projects in Japan have failed, thus increasing the demand for solutions that will increase expected project success rates. One study that explored the identification of factors that affect project performance found that six factors of organizational culture influence the project outcome for information technology (IT) vendors. This study uses those results to determine several unrevealed points of knowledge that contribute to reducing project failure rates by identifying differences in organizational culture based on an organization's size. Analysis of variance demonstrated that project performance and project planning show positive results in large organizations. Further, the three factors of "organizational policy toward project performance," "IS development process," and "project manager skills" show positive results in large organizations, and one factor, "information sharing within organization," shows a positive result in small organizations. The other factors, "senior manager involvement" and "trust between senior manager and project manager," do not show differences based on the size of the organization. Given these results, we proposed several organizational solutions for organizations of each size.

A method is proposed to estimate the friction coefficient between a planar surface and an elastic finger-shaped sensor by only pressing a sensor against the surface of an object. The contact condition between a planar surface and a half-cylindrical finger is considered, using finite-element analysis. The deformation of the elastic finger, contact forces, and strain distribution inside the elastic finger are calculated for various friction coefficients between the finger and the surface. Results show that the shear strain differs when the friction coefficient differs. In addition, in this paper, an elastic finger-shaped sensor made of silicone rubber is designed and constructed. In an experiment using this newly designed sensor, the friction coefficient between the finger and the planar surface is estimated using the strain inside the finger.

The authors have proposed a method for detecting a friction coefficient between a planar object and an elastic finger-shaped sensor by only pressing the sensor against the object in our previous study. In the present study, an elastic finger-shaped sensor made of silicone rubber is designed and produced. First, geometry of the finger-shaped sensor is designed using finite element analysis (FEA). Results of FEA revealed that strain detected using strain gage incorporated near the edge of the contact surface varies when the friction coefficient between the finger-based sensor and a planar object varies. Then, in a experiment using the newly designed sensor, it is confirmed that the friction coefficient between the finger and the object is detected using the strain inside the finger when the sensor is pressed against the object.