This paper empirically investigates the determinants and performance implications of capital structure for two dominant Indian conglomerates, Reliance Industries Limited (RIL) and the diversified Tata Group, utilizing annual data spanning the critical 2011â2021 period. The study addresses the ambiguity regarding optimal financing choices in large emerging market firms, focusing on the contrasting centralized, capital-intensive structure of RIL versus the industry-aligned, decentralized financing strategies of major Tata subsidiaries (TCS, Tata Steel, Tata Motors). A dynamic panel data approach, utilizing the System Generalized Method of Moments (Sys-GMM), is employed across the 11-year period to address issues of endogeneity, unobserved firm heterogeneity, and, critically, to accurately estimate the speed of leverage adjustment, given the observed persistence of financing decisions. The results confirm a dual-theory application dictated by corporate strategy and industry alignment. RILâs financing choices, particularly its aggressive leveraging followed by deleveraging toward zero net debt by 2021, are predominantly explained by the Pecking Order Theory (POT), where high profitability negatively predicts reliance on external debt. Conversely, the Tata Groupâs sub-entities strongly align with the Trade-Off Theory (TOT), with asset tangibility significantly dictating debt capacity (e.g., high debt for Tata Steel vs. minimal debt for TCS). Crucially, the analysis confirms that leverage generally showed a significant negative impact on RILâs operational performance [Return on Assets (ROA) and Return on Equity (ROE)], validating its strategic shift towards an equity-heavy model. The findings underscore the critical role of strategic corporate philosophy (centralized flexibility versus decentralized industry alignment) in shaping capital structure efficiency and shareholder value creation within complex conglomerates.
Open access
Working Capital and Financial Performance
Innovations and Analysis in Business and Education
Purpose Cryptocurrencyâs novelty and volatilityâcombined with the absence of standardized reporting prior to 2023âcreated an opaque information environment. This study explores whether such conditions enabled assertive impression management in corporate reporting. We examine how firms not only varied the volume of cryptocurrency disclosures over time, but also strategically manipulated their readability . Additionally, we use this context to demonstrate the utility of machine learning and natural language processing tools for consistent analysis of complex financial narratives. Study design We analyze full-text annual reports, MD&A sections, and proxy statements from five publicly traded U.S. firms with diverse cryptocurrency involvements. Our methodology includes machine learning-based topic modeling, readability assessment using standardized indices, and visualization tools. Findings (i) Information Demand: Google search trends for target firms are strongly associated with Bitcoin price movements, reflecting external attention cycles. (ii) Impression Management: Firms increase both the frequency and readability of crypto disclosures in favorable markets and reduce or obscure them in downturns, consistent with strategic impression management. (iii) Readability: Crypto-related disclosures are significantly more readable than non-crypto sections from the same reports suggesting deliberate simplification. Contributions This study advances the limited literature on cryptocurrency disclosure by offering a textual and behavioral lens on corporate impression management. A key contribution is the integration of readability metrics, public attention signals, and NLP tools into disclosure analysis. We highlight how firms use both narrative framing and readability engineering as tools to influence perceptionâespecially in periods of regulatory uncertainty. Implications Our findings have direct implications for policy and practice: (i) Policymakers should consider not only disclosure quantity but also its linguistic clarity and comparability, especially for volatile assets. (ii) Investors and analysts can use automated text analysis to detect subtle impression management tactics and to interpret the strategic use of clarity in disclosure narratives.
The emergence of Decentralized Autonomous Organizations (DAOs) presents a fundamental challenge to the traditional corporate form, which has dominated economic organization for over a century. Built on blockchain technology, DAOs propose a new model for coordinating economic activity. This study addressed the critical question of institutional efficiency by applying the lens of Transaction Cost Economics (TCE) to compare DAOs and traditional corporations. A comparative institutional analysis was conducted using a mixed-methods approach. We employed a multiple case study design, analyzing two representative DAOs and two analogous traditional corporations from Q1 2023 to Q4 2024. Data collection involved the systematic analysis of archival records, including 215 DAO governance proposals and corporate filings, and 32 semi-structured interviews with key participants. A novel analytical framework was developed to categorize transaction costs into ex ante (search, bargaining) and ex post (monitoring, enforcement), further distinguishing between 'on-chain' and 'off-chain' costs. The study revealed significant trade-offs between the two organizational forms. Traditional corporations exhibited high ex ante bargaining costs (legal, negotiation) and ex post monitoring costs (managerial overhead), but benefited from established legal frameworks that reduced enforcement uncertainty. Conversely, DAOs significantly lowered specific transaction costs through automation via smart contracts, particularly in on-chain bargaining and enforcement for codified tasks. However, DAOs incurred substantial, often hidden, new transaction costs related to off-chain social coordination, governance participation, and navigating legal ambiguity. This was termed the 'Governance Overhead Paradox'. In conclusion, DAOs do not represent a universally superior organizational form but rather a new point on an institutional possibility frontier. They are highly efficient for tasks that are global, permissionless, and computationally verifiable. Traditional firms retain advantages in contexts requiring complex, subjective decision-making and legal certainty. The future of the firm is likely not a replacement of one form by the other, but a pluralistic ecosystem where hybrid models emerge.
Hong Qu, Krzysztof Gogol, Florian GrĂśtschla, Claudio J. Tessone
Decentralized Finance (DeFi) lending enables permissionless borrowing via smart contracts. However, it faces challenges in optimizing interest rates, mitigating bad debt, and improving capital efficiency. Rule-based interest-rate models struggle to adapt to dynamic market conditions, leading to inefficiencies. This work applies Offline Reinforcement Learning (RL) to optimize interest rate adjustments in DeFi lending protocols. Using historical data from Aave protocol, we evaluate three RL approaches: Conservative Q-Learning (CQL), Behavior Cloning (BC), and TD3 with Behavior Cloning (TD3-BC). TD3-BC demonstrates superior performance in balancing utilization, capital stability, and risk, outperforming existing models. It adapts effectively to historical stress events like the May 2021 crash and the March 2023 USDC depeg, showcasing potential for automated, real-time governance.
This paper investigates the dynamic relationship between geopolitical uncertainty and decentralized finance (DeFi) token prices using a nonlinear, time-series-based framework. Leveraging the GPRD index as a proxy for global risk sentiment, the study examines seven prominent DeFi tokens representing diverse functional roles within the ecosystem. Through a layered empirical strategy - including Transfer Entropy, Mutual Information, Kernel-based Granger Causality, and Structural Time Series. Modeling - the analysis identifies both predictive and structural dependencies between GPRD and token valuations. The results reveal that tokens associated with financial-layer functions such as lending, collateralization, and liquidity rebalancing (e.g., Maker, Aave, BAL) exhibit stronger and more persistent exposure to geopolitical shocks than exchange-layer tokens like Uniswap or PancakeSwap. Kernel Granger causality confirms significant nonlinear predictive power of GPRD across all tokens, while structural decomposition shows that GPRD systematically depresses the long-term trend component of financial DeFi tokens. These findings indicate that global uncertainty operates not only through shortterm volatility, but also as a sustained driver of DeFi asset repricing. By combining information-theoretic and structural techniques, the study provides a comprehensive empirical lens through which to evaluate systemic risk transmission into DeFi markets. The results underscore the heterogeneous macro-financial sensitivity of decentralized protocols and suggest the need for differentiated risk assessment frameworks in crypto-asset research and governance.
We construct an empirically founded model of a repo trade intermediated by two broker-dealers and prove multiple equilibrium and the existence of equilibrium at the joint profit maximizing volume of trade. We then present a smart contract that resolves multiple equilibrium by requiring each broker-dealer to report its client schedule and its minimum hurdle spread, and implementing a selection rule that filters out hurdle-infeasible outcomes. Whenever there exists an equilibrium that exceeds both hurdle spreads, the protocol selects the joint profit maximizing feasible trade and thereby avoids a collapse to no trade. The smart contract is a machine executed algorithm which eliminates the need for trust. Hardware and cryptography are used to prevent leakage of broker-dealer client trade schedules, and to enable privacy-protected auditing with zero-knowledge proofs of the integrity of computations. The outcome can be implemented by a myopic strategy where a broker-dealer truthfully reports its own variables without anticipating its counterparty's reports. This minimizes cognitive and computational complexity, thereby making our smart contract suitable for real-world deployment.
Leonardo Henrique Lima de Pilla, Alketa Peci, Rodrigo de Oliveira Leite
ABSTRACT Corporatization in the public sector entails decentralizing the provision of public goods and services to more autonomous entities, including state-owned enterprises (SOEs). Research indicates that the decision to corporatize is driven, among other factors, by the pursuit of financial sustainability in public organizations. A continuing debate revolves around whether the political ideology of incumbents is linked to the creation of SOEs. However, limited attention has been given to understanding if incumbentsâ ideology shapes SOEsâ financial performance and, hence, financial sustainability. This is concerning because SOEs operate beyond political cycles, facing pressures from ideologically different governments over time. Herein, we investigate whether the incumbentsâ ideologies shape SOEsâ financial performance. We hypothesize that the more right leaning the incumbent, the greater the SOEsâ financial performance. However, given that incumbentsâ decisions are influenced by their political partiesâ behaviors, the effects of ideology may be contingent on these factors. Thus, we investigate whether the association of incumbentsâ ideology with SOEsâ financial performance is weaker when incumbentsâ political parties display non-policy behaviors (e.g., by prioritizing electoral outcomes or office occupation). We analyze a 2019â2022 panel of 317 SOEs controlled by 27 subnational governments in Brazil with both FGLS and instrumental variable regression approaches. The data comprising 1,116 SOE-year observations confirm our hypotheses. Our research contributes to scholarship on the drivers of public organizationsâ financial performance and sheds light on the role of political contingencies, such as incumbentsâ ideology and party predominant behaviors regarding SOEsâ financial performanceâa commonly overlooked gap in current research.
ABSTRACT In the burgeoning marketplaces of digital assets, nonâfungible tokens (NFTs) revolutionize digital asset ownership and intellectual property (IP) protection, but high minting costs create barriers to marketplace entry and growth. This study examines the impact of âlazy minting,â a new NFT production method introduced by major NFT marketplaces to lower minting costs by deferring blockchain certification until the first sale. In response to the call for further research on emerging technologies in operations management, we explore how this policy affects the net sales performance of existing sellers in the NFT marketplaces. Based on transaction cost economics (TCE) and the literature about different IP protection methods, we distinguish between lazyâ and regularâminted NFTs by their differential transaction costs and utilize the staggered differenceâinâdifferences (DID) method to conduct our analysis. We find that lazy minting adoption significantly boosts the net sales performance of existing sellers. This is attributed to their costâadaptive IP protection behavior. Specifically, they achieve this by minting more NFTs with a larger proportion of styleâconsistent NFTs through lazy minting, while strategically employing regular minting for styleâbreaking NFTs, which is contingent upon their reputation. Our study has important theoretical and practical implications for operations management under the emerging technological revolution.
Abstract This study investigates whether corporate social responsibility (CSR) serves as a financial tool to mask corporate financialization. Using data from publicly listed nonâfinancial firms from 2008 to 2020 in China, we analyze the effects and mechanisms of CSR on corporate financialization. The results show that CSR, particularly those targeting stakeholders such as investors, customers, and the community, is positively associated with corporate financialization, suggesting that CSR acts as a financial tool that supports, rather than curtails, financialization. Employing the BâZ threeâstep method, we find that financing constraints partially mediate the effect of CSR on financialization, implying that CSR activities can ease financing constraints, thus providing funds for financial investments. This supports the view that enterprises' allocation of financial assets may be driven more by profitâseeking motives than precautionary liquidity management. Further analysis reveals that the financial tool hypothesis is primarily reflected in the stage where financing constraints impact financing and is particularly pronounced in nonâstateâowned enterprises, firms with decentralized ownership structures, and those led by management teams prioritizing shortâterm returns. This research offers a reference for studying the dualâedged implications of CSR in global corporate practices.
Decentralized finance (DeFi) lending platforms often require over-collateralization, excluding users without substantial crypto holdings. This paper introduces LFG, a novel DeFi protocol that leverages on-chain social profiles and tokenized reputation to assess creditworthiness. By integrating Ethereum smart contracts with Layer-2 solutions (Ethereum, Polygon), decentralized storage (IPFS) and zero-knowledge proofs, LFG enables undercollateralized loans while preserving privacy. We present a technical architecture, analyze security risks, and compare LFGs with traditional models using quantitative metrics. The results show a 40% reduction in collateral requirements for users with high reputation scores on the chain.
Ilan Alon, Haakon Stole Klemetsen, AuĹĄrinÄ Ĺ ilenskytÄ, Ilan Gildin
Purpose This study aims to explore the innovative governance structures enabled by blockchain-based decentralized autonomous organizations (DAOs) in the context of international business (IB). As nonhierarchical entities managed through collective voting and peer-to-peer interactions, DAOs challenge traditional assumptions of multinational corporation (MNC) governance. Design/methodology/approach The authors perform a literature review, which combines qualitative content analysis of both academic papers on DAO and whitepapers with quantitative analysis of bibliometrics using VosViewer. Findings The findings highlight DAOsâ ability to generate new research questions and offer a foundation for future studies on decentralized governance in IB. Through a bibliometric analysis of 73 papers from Web of Science and qualitative content analysis, the authors identify four primary research streams in the DAO literature. A deep dive into financial DAOs, or DeFi DAOs, reveals insights into their governance mechanisms and implications for the studies in the field of IB. Research limitations/implications Research on DAOs is in its early stages, and the practice in IB is nascent. As the regulatory, economic and technological landscape for DAOs changes, more research will be needed on the governance mechanisms and their sustainability. Originality/value This paper advances the field by providing a multilevel analysis of DAOsâ potential impact on IB, considering macro (country), meso (MNC/DPE) and micro (multicultural team and individual) levels.
Enterprise innovation investment is influenced by the actions of innovation subjects, whereas regulating shareholders' equity pledge behavior facilitates innovation investment and finance but also carries dangers and affects enterprise innovation investment. Methods:This paper builds an unbalanced panel model to empirically analyze the impact of controlling shareholders' equity pledges on corporate innovation and its heterogeneous characteristics. It also looks at the moderating role of corporate financing constraints and the mediating role of equity incentives, using data from A-share listed companies in China's pharmaceutical manufacturing industry from 2015 to 2022. Innovation investment is substantially inversely correlated with controlling shareholders' equity pledge; that is, firms' creative behavior and intensity are inhibited by equity pledge. Results and conclusions:The results also show that controlling shareholders' equity commitments have a more pronounced negative impact on enterprises' ability to innovate than non-state-owned and decentralized equity firms. The relationship between company innovation and the equity pledge of controlling shareholders is somewhat mediated by equity incentives. The relationship between controlling shareholders' equity promises and enterprises' innovation is negatively moderated by financing limitations, which also reduces R&D expenditure and stifles innovation.
Amidst the frenzy surrounding Non-Fungible Tokens (NFTs) in 2021, the concept of digital assets and trading was redefined. Although the initial hype may have subsided, NFTs continue to drive innovation in ownership, with substantial revenue streams flowing through the market. This transformative shift underscores the importance of discerning the factors that shape this ecosystem. This paper delves into the intricate dynamics of the NFT market, particularly focusing on the impact of creation methodsâwhether hand-drawn or artificial intelligence (AI)-generatedâon market behavior. In a comprehensive analysis of the NFT market, we have analyzed a vast dataset comprising 1,478,556 transactions of NFT art from the OpenSea marketplace in 2023 to explore correlations and patterns between key transactional features. Furthermore, we employed regression models to predict the sales of an NFT and classification models to distinguish between hand-drawn and AI-generated NFTs. Finally, by comparing different machine learning models, we identified the most appropriate model for analyzing the market, considering the non-linear relationships and complex nature of the NFT market. Overall, the results provided in this research can lead to making more informed decisions regarding investment, creation, and trading.
Unyime Ufok Ibekwe, Uche M. Mbanaso, Nwojo Agwu Nnanna, Umar Adam Ibrahim
Smart contracts have emerged as a transformative technology within the blockchain ecosystem, facilitating the automated and trustless execution of agreements. Their adoption spans diverse sectors such as education, agriculture, healthcare, government, real estate, transportation, supply chain, and global initiatives like Central Bank Digital Currencies (CBDCs). However, the security of smart contracts has become a significant concern, as vulnerabilities in their design and implementation can lead to severe consequences such as financial losses and system failures. This systematic review consolidates findings from 78 selected research articles, identifying key vulnerabilities affecting smart contracts and categorizing them into a taxonomy encompassing code-level, environment-dependent, and user-related vulnerabilities. It also examines the threats that exploit these vulnerabilities and the most effective detection techniques. The domain-based classification presented in this review aims to assist researchers, software engineers, and developers in identifying and mitigating significant security flaws related to the design, implementation, and deployment of smart contracts. A comprehensive understanding of these issues is essential for enhancing the security and reliability of the blockchain ecosystem, ultimately fostering the development of more secure and robust decentralized applications for end users.