The purpose of this paper is to fill two critical research gaps in the study of cross-border lending determinants by financial institutions. The first research gap is that a few previous studies have analyzed normative factors such as institutional and cultural distance in financial institutions' lending decisions. The second research gap is that previous studies have not analyzed financial institutions' decision-making separately into space (distance) and place (context) effects. The major scope of this research is the second research gap. This study conducts an empirical analysis applying a four-step hierarchical multiple logistic regression model based on 4,928 lending judgment observations of 176 financial institutions that made lending decisions on 28 liquefied natural gas (LNG) projects. What this study empirically found is that financial institutions make lending decisions influenced not only by economic factors, but also by normative factors. In addition, we found that the influence of the normative factor was confirmed not only in the space (distance) effect but also in the place (context) effect. This study focuses on making theoretical contributions and ensuring research originality through the identification of critical research gaps by a thorough review of previous studies. The limitation is that the research applies a logistic regression model with dependent variable being a binary value, 1 for lending and 0 otherwise. If the loan amount, which is a continuous variable, can be used as the dependent variable, it will be possible to estimate the magnitude of the impact of other variables.

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Intercompany alliances formed through investment syndicates are an effective risk aversion strategy. In Japan, where the direct financing market is still in its infancy, a high percentage of syndicates are formed. However, some aspects of these alliances are unclear, and questions regarding the relationships that are created when syndicates are formed, the commonalities in their bonding patterns, and alliance trends remain. Identifying the essential structural characteristics of Japanese investor networks triggered by syndicated investments would provide important clues for gaining insight into startup ecosystems. Therefore, in this study, we attempt to construct an image matrix and a reduced investor network structure graph using the block model. This study is novel in this respect, as no previous study has examined the visualisation of investor networks in Japan, focusing on social positions. If the essential structure of the chaotic investor network can be succinctly expressed, it will help us understand the formation patterns of the startup ecosystem. The period from 1 April 2014 to 31 March 2022 was selected for analysis, and a dataset of 7,599 cases related to 4,508 investors was prepared from investment statement data by investment round. An adjacency matrix was constructed and divided into nine blocks using CONCOR software. We depict a reduced graph from the image matrix and interpret the divided blocks. The results suggest that as more investors participate in syndicated investments, the structural characteristics of their networks can be seen as center-periphery.

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Despite the increase in the economic value of intangible assets in recent years, a problem remains regarding the inadequate reflection of these assets in corporate balance sheets. Moreover, although machine-learning models to evaluate intangible fixed assets exist, their versatility remains inadequate for dealing with complex models. This study aims to develop a new methodology for identifying companies and industries likely to have unrecorded intangible fixed assets on their balance sheets. Synthetic data were generated using the financial statement data of companies listed on the Prime Market of the Tokyo Stock Exchange, employing generative artificial intelligence (AI) techniques, following which a regression model was constructed based on machine learning. This study uses synthetic data to imitate the latent features and patterns of the real data, thereby providing new insights into the valuation of intangible assets. Generative AI techniques were employed to generate a large synthetic dataset that was applied to train a machine-learning model, and enhance its predictive accuracy and generalization ability beyond the limitations of the real data. Explainable AI techniques were also applied to increase model transparency and interpretability, allowing experts and general stakeholders to easily understand the predictive results of the model. The results suggest that intangible fixed assets are more likely to have higher values than their recorded values in certain industries. This study showcases the applicability of AI technology to financial analysis, specifically to the accurate pinpointing of the presence of unrecorded intangible fixed assets. Such an application can potentially provide additional information for investors and creditors when making investment decisions, and may contribute to decision-making effectiveness and a true understanding of corporate value. Future researchers are suggested to improve the model’s intangible asset valuation accuracy through dataset expansion and model refinement.

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If a Global Niche Top (GNT) Company can continue to grow with the growing niche market, it can become a Global Mega (GM) Company. However, the growing market is also attractive to other companies, which may increase the number of competitors and reduce the GNT company’s market share. This study compares and analyzes SMC Corporation from Japan, which has maintained high growth rates in the pneumatic equipment market and is on the verge of becoming a GM, with two competitors, Festo (Germany) and CKD (Japan), to identify what a company needs to grow faster than the growing market. Regarding hypothesis, we add “market expansion”, which Namba et al. (2016) state that the conditions driving the growth from being GNT companies, to concepts of hidden champion companies by Simon (2009, 2012). This study consists of comparison analyses and hypothesis verification by conducting case studies of; disclosures from SMC, Festo, and CKD; semi-structured interviews with these companies’ key persons, their competitors, customers, and securities analysts. Namba et al. (2016) states that conditions of GNT companies becoming GM companies is necessary for additional research. This study shows factors to the requirement from GNT companies to GM companies in the pneumatic equipment market. Concepts for hidden champion companies and previous research on GNT companies observe technological concentration and progress as “requirements” for growth but this study shows it is not absolutely necessary requirement. This study suggests to add the following factors to the requirement list for a leap forward to become a GM company: 1) acquiring “organizational capabilities” to respond to customer’s true-cause needs (customization), 2) standardizing the customized solution (standardization), and 3) having a “strategic cycle in practice”, standard products to customized upon customers’ request, back to standardized for anticipated larger demands, greatly contributes to sales growth with cost reduction, according to their business environmental change.

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