International Business & Economics Studies
International Business & Economics Studies (IBES) is an international, Refereed, double-blind peer-reviewed, open-access journal published by Scholink. The journal aims to provide a high-level platform for scholars and researchers all over the world to share latest findings and views in the field of Business & Economics Studies. We would welcome scholars and researchers engaging in the related field to submit your manuscripts which are complete unpublished and original works and not under review in any other journals to IBES. Both of online submission and E-mail submission (ibes@scholink.org) are acceptable. ------------------------------------------------------------------------------ The journal includes, but is not limited to the following fields: Business Entrepreneurship Marketing Commerce Economics Financial Economics Regional Economics Economic Growth Economic Policies Economic Indicators Industrial Economics International Economics Managerial Economics Regional Development Agricultural Economics Management Finance Banking Accounting Buyer Behavior Corporate Governance Organizational Behavior Quantitative Methods and Their Applications Financial Management Decision and Risk Analysis Human Resource Management ----------------------------------------- Index/List/Archive CNKI-Scholar CrossRef WANFANGDATA LinkedIn ResearchGate ResearchBib Yahoo Google Scholar Canadiana ResearchBib Base Search CrossRef Worldcat PKP Open Archives Harvester Lockss Publons ISSN PORTAL Refereed Journal: International Business & Economics Studies is a Refereed Journal. Open access: International Business & Economics Studies is available online to the reader "without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself." Peer review: International Business & Economics Studies takes peer review policy. Peer review is the evaluation of work by one or more people of similar competence to the producers of the work (peers).
Authors who publish with this journal must agree to the following terms: Authors should promise that their works are entirely original, and all the references in their works should be appropriately cited or quoted. The works have not been published previously. The behavior of submitting the same work to other journals is unacceptable. The journal editor has the right to edit the format of works.
Social Insurance Burden and Firms’ Outward FDI: Evidence From Chinese Enterprise Data
This study examines how the social insurance burden affects firms’ decisions to invest abroad. We construct a structural investment model to show that higher domestic labor costs induced by social insurance contributions diminish the relative profitability of local operations and consequently enhance the incentive for firms’ outward foreign direct investment (OFDI). Using panel data on Chinese listed firms from 2007 to 2022, we employ a two-way fixed effects regression to test the causal relationship between firms’ social insurance burden and their decision and scale of OFDI. The results indicate that heavier social insurance burden significantly increase firms’ OFDI activities. Furthermore, this relationship is moderated by firms’ productivity and risk preference. In addition, we find that the positive effects are more pronounced among non-state-owned firms, those operating in the manufacturing sector, and firms located in the eastern regions of the country. The results demonstrate that social insurance policies play an important role in firms’ globalization strategies. Policymakers should balance social welfare financing with measures to maintain firms’ competitiveness in international markets.
Copyright (c) 2026 Gan Lin, Ruyan Tang, Zhen Wu, Yisheng Ning http://creativecommons.org/licenses/by/4.0
Board Decision-making Logic, Equity Nature, and Bank Environmental Lending
This article explores the impact of bank governance on bank environmental lending, using a manually collected sample of China’s city commercial banks from 2008 to 2018. The empirical results show that government ownership can promote bank environmental lending and a board of directors with a preference for social logic can positively moderate the relationship between them. In addition, the positive moderating effect is more pronounced in banks with state-owned shareholders as the largest shareholder and banks located in provincial capital cities. This paper is beneficial for expanding the understanding of the interaction between equity nature and board decision-making process in bank governance.
Copyright (c) 2026 Yana Zhou http://creativecommons.org/licenses/by/4.0
AI and MIST in Circular Economy and Resource Recovery: A Comparative Analysis of Ecommerce Industries
The rapid expansion of e-commerce has altered retail, but it has also exacerbated environmental issues such as packaging waste, product returns and product recovery, and resource depletion. This research evaluates the implementation of Artificial Intelligence (AI) and Modern Information Systems and Technologies (MIST) to achieve Circular Economy (CE) development and resource recovery in the context of e-commerce above the major e-commerce platforms of the United States, United Kingdom and Indian markets—Amazon, ASOS, IKEA Online, Big Basket, and Nykaa. This study used a quantitative research design and Power BI analysis to compare the extent to which these firms utilize AI and MIST to retrieve circularity, reduce carbon emissions, and improve operational efficiency. The findings revealed a clear relationship between high levels of AI and MIST relative to operations—such as predictive analytics, IoT-enabled tracking, blockchain transparency, and cloud-based logistics—and significant improvements in resource efficiency, waste reduction, and customer satisfaction. The e-commerce platforms with the greatest levels of CE performance were Amazon and IKEA Online platform, although emerging firms like Big Basket and Nykaa were beginning to adopt these models. In conclusion, both AI and MIST drive sustainable development, create customer trust, and increase competitive advantages, suggesting that their role in e-commerce is a key focus for digital transformation for sustainable e-commerce.
Copyright (c) 2026 Dr. Surjadeep Dutta, Pritam Chowdhury, Anindita Sinha, Tisha Biswas, Dr. Uma Padmini Ema http://creativecommons.org/licenses/by/4.0
Bridging Information Asymmetry through AI-driven FinTech: The Role of Digital Footprint Analytics in Financial Inclusion
Financial inclusion—broadly defined as the availability and equality of opportunities to access financial services—is widely recognized as critical for fostering economic growth, reducing poverty, and promoting equitable development (Berg, T., Burg, V., Gombović, A., & Puri, M., 2020). Nevertheless, despite global initiatives aimed at expanding financial access, a substantial number of individuals and small businesses, particularly in developing countries, remain excluded from traditional financial systems due to insufficient credit histories and inadequate financial documentation (Demirgüç-Kunt et al., 2022). Central to this issue is information asymmetry, a longstanding theoretical challenge articulated by foundational economic theories, including those of Akerlof (1970) and Stiglitz & Weiss (1981). These indicate how asymmetrical information between borrowers and lenders generates adverse selection and moral hazard, ultimately resulting in credit rationing and the systematic exclusion of otherwise creditworthy but information-poor segments of society.In recent years, the rapid development of financial technology (FinTech) powered by artificial intelligence (AI) has fundamentally reshaped the possibilities for overcoming informational barriers. Unlike traditional credit assessment methodologies that depend heavily on structured financial data such as credit bureau reports, income verification, and collateral evaluations, emerging AI-driven credit scoring systems incorporate large-scale behavioral data—often termed “digital footprints”—derived from non-traditional sources including smartphone metadata, social media interactions, e-commerce behaviors, and even geolocation patterns (Berg, T., Burg, V., Gombović, A., & Puri, M., 2020). Recent empirical studies have demonstrated that these novel data sources can outperform traditional financial data in predicting loan repayment behavior, thus substantially reducing information asymmetry and enabling lenders to extend financial services to previously underserved groups (Berg et al., 2020). Leading fintech companies such as Tala in the United States (which primarily serves Southern Africa and Southeast Asia) and Sesame Credit in China’s Ant Financial Services Group exemplify the transformative potential of AI-driven financial innovation. Tala, for instance, utilizes machine learning algorithms that analyze smartphone usage patterns to reliably estimate creditworthiness, enabling real-time unsecured loan approvals for individuals with no formal credit histories (Björkegren, D., & Grissen, D., 2019). Similarly, Zhima Credit has leveraged diverse behavioral indicators—ranging from online transaction consistency to social interaction networks—to deliver precise risk assessments, thereby broadening access not only to credit but also to various consumer services (Zhang, Q., & Li, X. 2023). These case studies highlight how digital footprint analytics can be broadly applied to help mitigate adverse selection and significantly reduce financial exclusion.Despite the transformative benefits, the integration of AI into credit assessment systems raises profound ethical and regulatory concerns. Critical issues include the opacity of algorithmic decision-making processes (“black box” models), the potential perpetuation of existing biases and inequalities embedded in historical datasets, and the privacy implications of intensive personal data use (Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. 2020). For instance, recent research has highlighted the unintended amplification of gender bias in AI-driven financial services, wherein ostensibly neutral algorithms disproportionately disadvantage women due to embedded socio-economic inequalities within training data (Arora & Gupta, 2025). Addressing these challenges requires robust governance frameworks, algorithmic transparency standards, and informed regulatory oversight, such as those advocated by recent developments in the European Union’s General Data Protection Regulation (GDPR) and emerging algorithmic fairness guidelines (Binns, 2024). Building upon these insights, this paper critically examines the role of AI in bridging information asymmetry within FinTech, with an emphasis on how digital footprints and behavioral analytics are reshaping credit access and financial inclusion. By synthesizing theoretical perspectives on asymmetric information with cutting-edge empirical evidence from recent studies and practical case analyses, this research aims to elucidate both the opportunities and limitations inherent in the AI-enabled transformation of financial decision-making. Furthermore, the paper offers actionable policy recommendations designed to balance technological innovation with ethical responsibility, alongside clearly defined directions for future interdisciplinary research in economics, data science, and regulatory policy.Building on existing theories, this study further incorporates the “Rice Theory” (Talhelm et al., 2014; Dong et al., 2024), which argues that cultural orientations influenced by agricultural practices (particularly rice farming) affect individuals’ social behaviors and cooperative tendencies. Applying this theory to financial technology (FinTech) adoption, we propose that users from collectivist cultural backgrounds—commonly associated with regions historically reliant on rice farming—may exhibit distinct patterns of interaction and acceptance toward digital financial services, thereby influencing the degree of information asymmetry and financial inclusion outcomes within FinTech ecosystems.
Copyright (c) 2026 Mingxuan Cao http://creativecommons.org/licenses/by/4.0