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This study aims to analyze the emergence of a new structural configuration of globalization, with the 2008 global financial crisis serving as the first symptom of this change. By introducing the “Evolutionary Structural Triptych” (EST), this research seeks to understand the basic components of the new evolutionary trajectory of global capitalism post-2008. The study places emphasis on its interdependent and coevolving economic, political and technological dynamic facets.

This research introduces the EST framework, critically contrasting it with conventional understandings in international political economy (IPE) to provide a comprehensive and structured analysis of global developments after 2008. It traces the phases of global capitalism since Second World War, examines the central dynamic dimensions during each evolutionary phase, identifies the basic patterns and delves into the foundational elements of the emerging era of globalization.

The analysis reveals three key findings. First, the emerging restructured globalization indicates a need for a new balance in the contemporary world system; however, this balance cannot be achieved within the architecture of the old system. Second, the new era of globalization necessitates a re-equilibrated approach across different dimensions of geopolitical stability, economic development and innovation. This approach should emphasize sustainability, adaptability, resilience and inclusivity and lean toward responsible, open and organic innovation models for a revamped global structure. Third, while many current IPE theories tend to compartmentalize aspects of the new globalization, the EST advocates for a holistic perspective that integrates politics, economics and technology within the framework of global trends. This perspective bridges existing gaps and offers actionable insights for a dynamic and inclusive global future.

The paper presents the EST as a novel analytical instrument in the realm of the modern IPE. This tool uniquely places technology and innovation at the forefront, parallel to economic and political spheres, to comprehend the progression of globalization. In doing so, it highlights the intertwined relationship of these structural dimensions in shaping the future of the subject of the IPE.

This study aims to investigate the impact of facial-based perceived trustworthiness on stock valuation, particularly, in the initial public offering (IPO). IPO settings provide the opportunity to investigate whether information asymmetry resulting from company newness in the market would influence the incorporation of soft information in the form of executive facial trustworthiness in stock valuation.

We use a recent machine learning algorithm to detect facial landmarks and then calculate a composite facial trustworthiness measure using several facial features that have previously been observed in neuroscience and psychological studies to be the most determining factor of perceived trustworthiness. We then regress the facial trustworthiness of IPO firm executives to IPO underpricing.

Utilizing machine learning algorithms, we find that the facial trustworthiness of the company executive negatively impacts the extent of IPO underpricing. This result implies that investors incorporate the facial trustworthiness of company executives into stock valuation. The IPO underpricing also shows that the cost of equity is higher when perceived trustworthiness is low. With regard to the higher information asymmetry in IPO transactions, such a negative impact implies the role of facial trustworthiness in alleviating information asymmetry.

This study provides evidence of the impact of top management personal characteristics on firms’ financial transactions in the Indonesian context. From the perspective of investors and other fund providers, this study shows evidence that heuristics still play an important role in financial decision-making. This is also an indication of investor reliance on soft information. Our research method also provides a new opportunity for the use of machine-learning algorithms in processing non-conventional types of data in finance research, which is still relatively rare in emerging markets like Indonesia. To the best of our knowledge, our study is the first to use personalized measures of trust generated through machine-learning algorithms in IPO settings in Indonesia.