Investment in start-ups is highly uncertain. Therefore, a rational basis can be found for collective formation through syndicated investment, and the members of such syndication formations can be regarded as a ‘mutually supportive group’. However, a possible side effect is that it might create groups with similar traits, which may lead to a loss of diversity and the homogenisation of information within the group. In short, although group formation may positively impact the performance of start-ups investors, information homogenisation may have a negative impact. Therefore, this study sought to elucidate the actual situation of mutually supportive groups through syndicated investment in Japan and examine differences in the performance of investors depending on their position in a group. Using data on 4287 investors, we prepared a graphical representation of the investor network based on an adjacency matrix and identified cut-points and bi-components in the network. The relationship between cut-points and investment performance was then examined, and the investment performance across investor position types was compared using Kruskal–Wallis and Dunn–Bonferroni tests. The analysis confirmed the existence of one large bi-component in the network, with numerous smaller bi-components and isolated points around it. This suggests that the network through syndicated investment in Japan is structured such that similar information is easily propagated and the network is prone to information homogenisation. Moreover, it is suggested that to improve investment performance it is not sufficient to simply form a group, but that it is important to belong to multiple groups and play a role in the inter-group exchange.

View full abstract

Companies’ intangible asset ratios have been increasing year by year, and their importance is growing. Although valuation methods for intangible assets have been extensively studied in accounting, it is difficult to accurately evaluate intangible asset values, because no clear rules for doing so exist. Consequently, balance sheets do not record latent intangible fixed assets, meaning that companies’ balance sheets do not accurately represent the value of their assets. In this study, I developed a method that uses machine learning to calculate the predicted value of intangible fixed assets, then proposed and verified a method to extract industries with high potential for unrecorded intangible fixed assets based on the deviation rate between the predicted value of intangible fixed assets and the value of intangible fixed assets recorded on the balance sheet. First, I constructed a machine learning model that provides a highly accurate prediction of intangible fixed asset value for a sample of Nikkei 225 stocks by comparing several algorithms using automated machine learning. Next, I used the coefficients of determination, prediction error plots, and learning curves to evaluate the constructed model and confirm that it met an acceptable performance level. The deviation rate between the constructed model’s predictions and the value of intangible fixed assets recorded on the balance sheet was used to identify companies with a high probability of having intangible fixed assets that were not recorded on the balance sheet. Finally, by calculating the mean deviation rate for each industry type, I identified industries with a relatively high probability of latent unrecorded intangible assets.

View full abstract

The Balanced Scorecard (BSC) is a strategic management system introduced in 1992 by R.S. Kaplan and D. P. Norton. Over the past 3 decades, the BSC has a significant impact on both practice and academia. Due to its practical application, the BSC has undergone the evolution. Recently, there has been a growing interest among researchers and practitioners in managing the organization assets, which are considered key components of intangible assets. This is because the organization assets could contribute to sustainable corporate value creation. However, intangible assets are more difficult to manage than tangible assets due to limitations in their identification and measurement. In this context, there is an idea that the BSC supports to manage the organization assets. This article provides insight into how researchers and practitioners should approach managing the organization assets with the BSC, based on the evolutionary history of the BSC. It also offers modest opinions about the future potential of the BSC.

View full abstract

Bass (1985) developed the social psychological framework of transformational-transactional leadership based on Burns’ (1978) discourse on leadership and concluded that both types of leadership are found within an individual. When the discussions later sprang around which type of leadership is more effective, Bass (1985) presented his assumption that when a leader exercises the transformational type of leadership that has bases on the transactional type, his/her leadership is augmented to be most effective. The present study empirically examined the augmentation hypothesis of transformational-transactional leadership, which has been frequently discussed but least put the discussions to the test. In this study, 105 participants were sampled from a Tokyo Stock Exchange-listed (parent) company and its three subsidiaries. As a result, the following three things became clear: (1) no augmentation effect was found when transactional leadership joined to transformation leadership, (2) an augmentation effect was found when transformational leadership joined to transactional leadership, and (3) the augmentation hypothesis was verified by this study. the present study.

View full abstract