Research Article
A Bibliometric Analysis of Market Sector and Cross-Country Impact on IPO Underpricing: Current Status, Development, and Future Research Directions
Abhrajit Sarkar*
Issue:
Volume 1, Issue 3, September 2026
Pages:
125-143
Received:
23 February 2026
Accepted:
9 March 2026
Published:
28 July 2026
DOI:
10.11648/j.ib.20260103.11
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Abstract: This study explores current trends in IPO (Initial Public Offering) underpricing by combining macroeconomic and microeconomic viewpoints while also tracking the development of scholarly research in the field. To achieve this goal, the research uses a three-step method that includes citation analysis, keyword analysis, and qualitative content analysis. First, citation analysis is used to identify the most influential publications, authors, and journals that have shaped the understanding of IPO underpricing. This step helps trace the roots of the literature and highlights key contributions that have significantly influenced theoretical and empirical discussions. Second, keyword analysis is performed to identify emerging research themes, main ideas, and changes in scholarly focus over time. By analyzing keyword co-occurrence patterns, the study uncovers the evolving research areas and thematic groups related to IPO underpricing. Lastly, content analysis interprets the insights gained from citation and keyword analyses, providing a deeper understanding of how historical views connect with current empirical findings. The results show that the factors influencing IPO underpricing vary widely across countries and industries due to differences in institutional settings, market maturity, and investor behavior. In developed markets, investors tend to focus more on corporate governance features when evaluating IPO firms. Aspects such as gender diversity on boards, director qualifications, CEO duality, and board independence are seen as signals of transparency, accountability, and strong oversight, which in turn influence investor confidence and pricing decisions. Conversely, investors in developing markets rely more on firm-specific traits, including firm age, size, and the reputation of the underwriter, which act as signs of credibility and reliable information in environments where institutional frameworks and disclosure standards may be weaker. Sector dynamics further demonstrate the diversity of IPO results. In developed economies, sectors like technology, healthcare, finance, and service industries lead IPO activities, driven by rapid digital advances, innovation-focused growth, and investor demand for high returns. On the other hand, in developing economies, sectors such as agriculture, real estate, and fast-moving consumer goods (FMCG) show stronger IPO performance because of their key role in economic growth and the increasing consumer demand linked to expanding middle-class populations. Overall, these findings emphasize the importance of contextual and sectoral factors in shaping IPO underpricing patterns across global markets.
Abstract: This study explores current trends in IPO (Initial Public Offering) underpricing by combining macroeconomic and microeconomic viewpoints while also tracking the development of scholarly research in the field. To achieve this goal, the research uses a three-step method that includes citation analysis, keyword analysis, and qualitative content analys...
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Research Article
Unlocking Value in Agricultural Value Chains: How
AI-Driven Decision Intelligence Improves Value Realisation Across Stakeholders, with a Tur Dal Pilot in Kalaburagi
Tarak Dhurjati*
Issue:
Volume 1, Issue 3, September 2026
Pages:
144-151
Received:
12 January 2026
Accepted:
30 January 2026
Published:
28 July 2026
DOI:
10.11648/j.ib.20260103.12
Downloads:
Views:
Abstract: India’s agricultural value chains remain fragmented and inefficient, characterised by information asymmetry, weak price discovery, excessive post-harvest losses, and poor coordination among farmers, traders, processors, and retailers. These structural inefficiencies result in a large price spread between farm gate and consumer, with farmers capturing a disproportionately small share of final value while consumers face high and volatile prices. This paper hypothesizes on how artificial intelligence (AI), deployed as a value-chain-wide decision intelligence platform, can significantly improve value realisation for all stakeholders. By integrating soil, weather, crop, price, demand, quality, and logistics data, AI can enable demand-aligned production, transparent quality-based price discovery, reduced processing losses, and optimised distribution. The paper elaborates four dimensions of AI intervention—production, procurement and price discovery, processing and value addition, and distribution and consumption—and illustrates the hypothesis through a tur dal (pigeon pea) pilot in Kalaburagi district, Karnataka. The analysis demonstrates that AI can compress the price spread by 20–25%, reduce processing losses from 10–15% to 9%-11%, increase farmer price realisation, and improve consumer affordability. The paper concludes with policy and institutional implications for scaling AI-enabled value chains in India.
Abstract: India’s agricultural value chains remain fragmented and inefficient, characterised by information asymmetry, weak price discovery, excessive post-harvest losses, and poor coordination among farmers, traders, processors, and retailers. These structural inefficiencies result in a large price spread between farm gate and consumer, with farmers capturi...
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