11 Chapter II Literature Review II.1 Literature Review II.1.1 Financial Performance Financial performance refers to an organization’s ability to generate profit and sustain growth over a specific period, and it serves as a key indicator of the company’s operational efficiency and financial health, and this concept encompasses aspects such as revenue, profitability, cash flow, and return on investments, according to Oleiwi et al., (2019), financial performance assessment involves evaluating whether a company is achieving its objectives and effectively utilizing resources, also this evaluation aids stakeholders in making informed decisions regarding investment, creditworthiness, and strategic planning. Financial ratio analysis is a method used to assess a company’s financial health by examining the relationships between different financial statement items, ratios are grouped into categories such as profitability, liquidity, solvency, and efficiency, each providing distinct insights (Sharifi et al., 2019), liquidity ratios measure a company’s capacity to meet short-term obligations, while profitability ratios evaluate its ability to generate earnings relative to revenue or assets, and this analysis simplifies complex financial data, enabling comparisons across time periods and industries, as stated by Ross et al. (2021), ratio analysis plays a crucial role in identifying financial trends and potential risks, guiding managerial and investment decisions. The combination of financial performance evaluation and ratio analysis offers a comprehensive view of a company’s financial standing, this dual approach allows businesses to identify strengths, weaknesses, and areas requiring improvement, insights gained from these analyses help organizations adapt strategies to enhance operational efficiency and financial stability, stakeholders rely on these tools to assess value creation and predict future performance, as highlighted by Yadav et al., (2022), robust financial analysis contributes to the long-term sustainability of organizations in dynamic economic environments. 12 II.1.2 Altman Z-Score The Altman Z-Score is a financial model designed to predict the likelihood of corporate bankruptcy within a specific timeframe, Introduced by Edward Altman in 1968, the model combines multiple financial ratios into a single score to evaluate a company’s financial health, it is widely recognized for its accuracy in assessing the risk of insolvency, especially for manufacturing firms (Awwad & Razia, 2021), The Altman Z-Score integrates key financial metrics, providing a reliable measure of distress, and the model has become an essential tool in financial analysis, particularly for stakeholders aiming to assess company stability (Ebaid, 2024). The primary purpose of the Altman Z-Score is to estimate the financial risk associated with a company, It helps investors, creditors, and management identify early warning signs of financial distress, allowing proactive decision-making, By measuring the risk of bankruptcy, this model supports efforts to safeguard investments and maintain financial stability (Mehmood & Luca, 2023), Its predictive accuracy has made it a cornerstone in risk management and credit evaluation, and the approach ensures that stakeholders are equipped to mitigate potential financial losses. Numerous Altman Z-Score models have been developed to assess financial health, predict bankruptcy, and evaluate the relative performance of firms across industries and economic conditions, rach model caters to specific contexts, addressing unique challenges in financial analysis, such as non-normal data distributions, outlier sensitivity, and varying industry characteristics, the foundational Altman Z-Score is widely recognized for its predictive power, while other models, such as the Standardized Altman Z-Score and Modified Altman Z-Score, refine the approach for broader applicability and robustness, Revised versions, including the Revised Altman Z-Score and Zeta Model, enhance the framework by incorporating additional variables and adapting to non-manufacturing sectors, and all of these models, along with specialized versions like the Log-Z Score, provide analysts and 13 researchers with versatile tools for comprehensive financial evaluation (Karim et al., 2021). Altman Z-Score is a widely applied financial tool designed to predict corporate bankruptcy and assess financial stability, The formula combines five key financial ratios: working capital to total assets, retained earnings to total assets, earnings before interest and taxes to total assets, market value of equity to total liabilities, and sales to total assets, and the model is especially effective for evaluating manufacturing firms, offering clear indications of financial health, a Altman Z- Score above 2,99 suggests financial stability, while scores below 1,81 indicate high bankruptcy risk, Its simplicity and predictive accuracy make it a preferred choice for financial analysts, and the formula is as follows Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 1.0X5 The formula represents the original Altman Z-Score model, which is used to predict the likelihood of a company’s bankruptcy, each variable X1 through X5 represents a financial ratio that measures different aspects of a firm’s financial health, Specifically, X1 is the working capital to total assets ratio, X2 is the retained earnings to total assets ratio, X3 is the earnings before interest and taxes (EBIT) to total assets ratio, X4 is the market value of equity to total liabilities ratio, and X5 is the sales to total assets ratio, the coefficients assigned to each ratio reflect their relative importance in predicting bankruptcy, with higher values for X3 and X5 indicating stronger financial stability, and the formula generates a Altman Z-Score that helps assess a company’s financial distress risk, with lower Altman Z-Scores indicating higher risk of bankruptcy. Standardized Altman Z-Score is a statistical metric used to determine the position of a specific data point relative to the dataset’s mean, expressed in terms of standard deviations, and the measure is essential for standardizing data, enabling comparisons across different datasets or variables, it is particularly useful in detecting outliers and assessing relative performance, Its broad applicability 14 extends to finance, social sciences, and research, ensuring normalized values for easier interpretation, analysts benefit from its simplicity in comparing values across diverse contexts, the formula is as follows OANRA@ 8=HQAF/A=J 5P=J@=N@ &ARE=PEKJ Modified Altman Z-Score offers an alternative method for identifying outliers by replacing the mean and standard deviation with the median and median absolute deviation (MAD), and the modification enhances robustness against non-normal distributions and datasets with extreme values, The approach is especially valuable in fields where traditional metrics are skewed by outliers, By focusing on medians, it provides more reliable insights, maintaining stability in the presence of data anomalies, Its adoption improves accuracy in financial and scientific analyses, The formula is as follows OANRA@ 8=HQAF/A@E=J; /A@E=J #>OKQHPA &ARE=PEKJ Revised Altman Z-Score modifies the original Altman model to accommodate non- manufacturing firms by excluding the sales-to-total-assets ratio, and the adjustment allows for a more accurate assessment of financial stability in industries such as services, technology, and other non-manufacturing sectors, It retains the effectiveness of the original model while broadening its applicability, The simplified formula remains user-friendly and highly relevant for financial evaluations in diverse industries, The revised version ensures meaningful insights without unnecessary complexity, The formula is as follows: HAO OQ?D =O %=OD (HKSO =J@ %D=JCAO EJ 9KNGEJC %=LEP=H; Log-Z Score applies a logarithmic transformation to variables before standardizing, effectively addressing datasets with exponential or skewed distributions, and the approach is particularly useful in fields like finance where growth rates or returns often follow a log-normal pattern, By stabilizing variance and reducing skewness, it enhances the accuracy and interpretability of financial metrics, Its application ensures better handling of non-linear data relationships, providing meaningful insights, the transformation makes it an indispensable tool in quantitative analyses, The formula is as follows: < ���L log:1>OANRA@ 8=HQA;F:.KC=NEPIE. /A=J; .KC=NEPIE. 5P=J@=N &ARE=PEKJ In this study, the Altman Z-Score is employed to analyze the financial ratios of Unilever, making it an appropriate and relevant choice for assessing the company’s financial health, The Altman Z-Score is widely recognized for its ability to predict the likelihood of bankruptcy by evaluating key financial indicators, such as liquidity, profitability, and leverage, which are crucial for any large corporation like Unilever, Given Unilever’s diverse operations and international presence, the Altman Z-Score provides a comprehensive overview of its financial stability, making it an ideal tool for this analysis, The model’s emphasis on multiple financial ratios ensures a balanced and thorough assessment of the company’s financial performance, Furthermore, the Altman Z-Score’s proven success across different 16 industries and its simplicity in application make it particularly suitable for analyzing a company of Unilever’s scale and complexity. II.1.3 Beneish M-Score The Beneish M-Score, developed by Beneish (1999), is a statistical model designed to detect potential earnings manipulation in financial statements, It evaluates whether a company’s accounting practices deviate significantly from expected norms, indicating the likelihood of fraudulent reporting, and the metric incorporates various financial ratios, each serving as a red flag for manipulation, a higher Beneish M-Score suggests increased probability of earnings manipulation, while a lower score indicates compliance with accounting standards, and the model has become a valuable tool for auditors, investors, and regulators in identifying financial misrepresentation Khatun et al., 2022), The purpose of the Beneish M-Score is to provide a quantifiable framework for detecting earnings manipulation in corporate financial reports, It assists stakeholders in making informed decisions by identifying companies that may engage in aggressive accounting practices, The model enhances transparency by flagging firms that inflate revenues, defer expenses, or misstate assets and liabilities (Marzuki et al., 2024), Its predictive power is particularly valuable in environments where financial irregularities undermine trust, The Beneish M-Score fosters accountability by promoting adherence to ethical financial reporting (Toit, 2024), The Beneish M-Score calculation involves eight financial ratios, each representing distinct indicators of potential manipulation, and all of these ratios are Days Sales in Receivables Index (DSRI), Gross Margin Index (GMI), Asset Quality Index (AQI), Sales Growth Index (SGI), Depreciation Index (DEPI), Sales General and Administrative Expenses Index (SGAI), Leverage Index (LVGI), and Total Accruals to Total Assets (TATA), and all of these metrics collectively measure anomalies in revenue recognition, expense deferral, asset valuation, and capital structure, The formula integrates these ratios into a logistic regression model to 17 yield the Beneish M-Score (Khatun et al., 2024), and the comprehensive approach ensures a robust analysis of financial data, The DSRI measures changes in receivables relative to sales, indicating whether receivables growth is outpacing revenue, a higher ratio suggests potential revenue inflation through credit sales (Khatun et al., 2024), The GMI evaluates gross margin trends, where a decline may indicate pressure to manipulate profits, The AQI captures changes in non-current assets other than property, plant, and equipment, signaling improper asset capitalization, Each of these metrics reflects specific financial manipulations that collectively inform the Beneish M-Score, The SGI identifies rapid sales growth, which may pressure management to manipulate earnings to meet expectations, The DEPI examines changes in depreciation expense, where a decline suggests adjustments to prolong asset lifespans artificially, The SGAI measures changes in administrative expenses, which can indicate efforts to mask operational inefficiencies, The LVGI assesses changes in leverage, highlighting the potential for debt-related financial strain (Khatun et al., 2024), The TATA, calculated as the difference between operating cash flow and net income divided by total assets, assesses the extent of accrual-based earnings management, a higher TATA reflects reliance on accruals, increasing the likelihood of earnings manipulation (Khatun et al., 2024), The combined weights and coefficients for all eight ratios produce the Beneish M-Score, Companies with Beneish M-Scores greater than -2,22 are considered likely manipulators, and the threshold has been validated through empirical research as a reliable predictor of earnings manipulation, The Beneish M-Score provides a systematic approach to identifying companies at risk of financial misreporting, Its application extends beyond forensic accounting, supporting investment analysis and regulatory oversight, and the model has proven effective in detecting manipulation, fostering greater confidence in financial 18 markets (Khatun et al., 2024), Its adoption underscores the importance of rigorous tools for financial analysis, the Beneish M-Score remains a cornerstone in the fight against corporate financial misconduct. Table II.1 Beneish M-Score y
�g��� L F�
���E�
���p~uE�
����syuE�
����m}uE�
����su 4�
����pq|u F�
����smuE�
�����m�mF�
����x�su Variables Formula Days Sales in Receivables Index &54+L :0AP 4A?AER=>HAO �5=HAO �; :0AP 4A?AER=>HAO �. 55=HAO �. 5; Gross Margi Index )/+L )NKOO /=NCEJ �. 5 )NKOO /=NCEJ � Asset Quality Index #3+L : sF:%# �E22' �;6# �; : sF:%# �. 5E22' �. 5;6# �. 5; Sales Growth Index 5)+L 5=HAO � 5=HAO �. 5 Depriciation Index &'2+L :&ALN �. 5:&ALN �. 5E22' �. 5;; :&ALN �:&ALN �E22' �;; Sales General and Administrative Expenses Index 5)#+L :5)# �5=HAO �; :5)# �. 55=HAO �. 5; Leverage Index .8)+L :.KJC 6ANI &A>P �E%QNNAJP .E=>EHEPEAO �;6KP=H #OOAPO �; :.KJC 6ANI &A>P �. 5E%QNNAJP .E=>EHEPEAO �. 5;6KP=H #OOAPO �. 5; Total Accruals to Total Assets 6#6#L 6KP=H #??NQ=HO � 6KP=H #OOAPO � Figure II.1 Beneish M-Score Interpretation 19 The Beneish M-Score calculations give a number of outputs which can be interpreted as below M-score > -1.78 = RED FLAG; likely earnings manipulation -2.22 < M-score < -1.78 = YELLOW FLAG; possible earnings manipulation M-score < -2.22 = GREEN FLAG; unlikely profit manipulation II.1.4 Piotroski F-Score The Piotroski F-Score, introduced by Piotroski (2000), is a fundamental analysis tool designed to evaluate a company’s financial health and predict its future performance, It uses nine financial metrics categorized into three main areas: profitability, leverage/liquidity, and operating efficiency, and the scoring system assigns a binary value to each metric, with a score of 1 indicating positive performance and 0 for negative performance, The total Piotroski F-Score ranges from 0 to 9, where higher scores suggest stronger financial health, and the model is particularly useful for identifying undervalued firms with potential for growth (Park et al., 2021), The purpose of the Piotroski F-Score is to assess the financial strength of companies and guide investment decisions, It enables stakeholders to differentiate between firms with improving fundamentals and those with deteriorating financial conditions, and the metric is valuable for value investors seeking to identify stocks with strong financial foundations, The Piotroski F-Score emphasizes historical performance to ensure objective and data-driven evaluations, Its adoption reduces reliance on subjective judgment and enhances the reliability of financial assessments (Park et al., 2021), The measurement of the Piotroski F-Score begins with the profitability dimension, which includes three metrics, Return on Assets (ROA) indicates a company’s efficiency in generating profits relative to its total assets, Positive ROA reflects strong profitability and earns a score of 1, Operating Cash Flow (OCF) measures the cash generated by core operations, with positive cash flow indicating financial 20 stability, The leverage and liquidity dimension assesses a firm’s financial stability through three additional metrics, Change in Leverage evaluates whether a firm has reduced its debt levels over the past year, with reductions earning a score of 1, Current Ratio measures short-term liquidity by comparing current assets to current liabilities, rewarding improvements in this ratio, Issuance of Equity detects whether the firm has avoided diluting shareholder value by issuing additional equity, assigning a score of 1 when no new shares are issued (Festa et al., 2021), The operating efficiency dimension focuses on changes in asset utilization and cost management, Change in Gross Margin examines whether a firm’s gross profit as a percentage of sales has increased, with improvements earning a score of 1, Change in Asset Turnover measures how effectively a company utilizes its assets to generate revenue, rewarding positive changes with a score of 1, and all of these metrics collectively highlight improvements in operational performance and resource allocation (Festa et al., 2021), The calculation of the Piotroski F-Score aggregates binary scores for all nine metrics to derive a final value, Firms with an Piotroski F-Score of 8 or 9 are considered financially strong and likely to outperform in the future, Scores of 0 to 3 indicate financial weakness and higher investment risk, Piotroski (2000) demonstrated that high Piotroski F-Score firms generate superior returns compared to low Piotroski F-Score firms (Festa et al., 2021), Table II.2 Piotroski F-Score Variables Score Return on Assets (ROA) Score 1 if ROA > 0 (profitable company) Change in ROA (ROA) Score 1 if ROA increases compared to the previous year. Cash Flow from Operations (CFO) Score 1 if CFO > 0 (positive operating cash flow). 21 Accruals Score 1 if CFO > ROA (indicating low accrual manipulation. Change in Leverage (Leverage) Score 1 if Leverage (ratio of long-term liabilities to total assets) decreases compared to the previous year. Change in Liquidity (Liquidity) Score 1 if the current ratio (current assets to short-term liabilities) increases. Equity Issuance Score 1 if the company does not issue new shares (no equity dilution). Change in Gross Margin (Gross Margin) Score 1 if gross margin increases compared to the previous year. Change in Asset Turnover (Asset Turnover) Score 1 if the asset turnover ratio (sales to total assets) increases. Figure II.2 Piotroski F-Score Interpretation The Piotroski F-Score calculations give a number of outputs which can be interpreted as below F-score 7-9 = Good, strong, best score; Indicates very healthy situation of the company F-score 4-6 = Average score; Financial situation is typical for a stable company F-score 0-3 = Worst, bad, or low score; Indicates financially weak company II.1.5 Financial Disclosure Analysis Financial disclosure information analysis involves the systematic evaluation of the information provided by companies in their financial statements and supplementary disclosures, The primary aim of this analysis is to assess the transparency, reliability, and completeness of the financial information disclosed by companies, 22 The analysis also seeks to understand how well the disclosed financial data reflects the actual financial position, performance, and cash flows of the company, According to Goldstein and Yang (2017), financial disclosure provides stakeholders with critical insights into a company’s financial health and performance, allowing them to make informed decisions, Transparent financial disclosures enable investors and analysts to accurately evaluate a company’s risk, growth potential, and sustainability, The foundation of financial disclosure information analysis lies in the recognition that financial statements alone do not fully capture a company’s financial condition, a thorough analysis requires examining the accompanying notes, management discussions, and other supplementary reports that provide context to the raw financial data (Neto et al., 2023), and all of these disclosures help to clarify the assumptions and estimates used in preparing financial statements, such as valuation methods for assets and liabilities, or accounting policies that impact the reported figures, According to Nor et al. (2019), this supplementary information is crucial for understanding the underlying factors that may influence a company’s financial performance, The ability to interpret this information effectively enhances the quality of the analysis, The role of financial disclosure information analysis extends beyond simply reviewing compliance with accounting standards, It requires critical scrutiny of how accurately a company’s financial performance is reflected through its disclosures, Inaccurate or misleading disclosures can result in investors being misinformed, leading to flawed investment decisions and market distortions (Vitale et al., 2022), and the underscores the importance of assessing the quality of the information presented, including the level of detail, clarity, and consistency in the disclosures, a well-structured and transparent financial disclosure system ensures that all stakeholders have access to the necessary data to evaluate the true financial position of a company, 23 Another critical aspect of financial disclosure analysis is the evaluation of the consistency and comparability of disclosed financial information, According to Neto et al. (2023), comparability allows users to analyze financial data across different time periods and between companies within the same industry, Consistent disclosure practices enable analysts to detect trends, assess financial performance over time, and benchmark companies against industry standards, Lack of comparability, due to inconsistent application of accounting standards or poor disclosure practices, limits the usefulness of financial statements for decision- making, Thus, ensuring consistency in disclosures across reporting periods is essential for maintaining the integrity of financial reporting, The significance of financial disclosure analysis is also highlighted in its role in enhancing investor confidence and supporting efficient capital markets, When companies provide clear, accurate, and timely disclosures, they foster trust among investors, which can positively impact their stock prices and overall market reputation (Vitale et al., 2022), Companies that maintain high standards of financial transparency are likely to attract more investment, as stakeholders feel more confident in the reliability of the disclosed information, Additionally, transparent financial disclosures can help to minimize the cost of capital for companies, as investors demand lower risk premiums when they trust the financial information being presented, Therefore, effective financial disclosure is a key factor in ensuring the stability and efficiency of capital markets. a growing area of concern in financial disclosure analysis is the increasing complexity of financial reporting in the context of globalization and technological advancements, According to Goldstein and Yang (2017), globalization has led to more diverse and complex financial structures, requiring enhanced disclosure practices to address new risks and accounting challenges, Additionally, advancements in technology have introduced new tools for financial analysis, allowing analysts to assess larger volumes of data more efficiently, However, these developments have also raised concerns about the potential for companies to engage in aggressive or misleading disclosure practices, Therefore keeping up with 24 evolving financial reporting standards and technological tools is essential for maintaining the relevance and accuracy of financial disclosure analysis. Finally, financial disclosure information analysis requires an ongoing commitment to improving disclosure practices, Regulatory bodies and standard-setting organizations continually refine accounting standards to improve transparency and enhance comparability in financial reporting (Goldstein & Yang, 2017), Analysts must stay updated with these changes to provide accurate assessments of financial statements, Continuous improvements in disclosure practices ensure that financial reporting remains relevant and that companies meet the growing expectations of investors, regulators, and other stakeholders, Effective financial disclosure analysis is not only about assessing the past performance of a company but also about anticipating future risks and opportunities based on the information provided. II.1.6 External Factors The financial performance of a company is not only influenced by internal factors such as management decisions and operational efficiency but also by external factors that are often beyond the company’s control, all of these external factors include macroeconomic conditions, industry trends, regulatory changes, geopolitical events, and social movements, also understanding these factors is crucial for a comprehensive analysis of UNVR’s financial health, as they provide context for the challenges and opportunities the company faces in the broader economic and social environment. Macroeconomic conditions, such as inflation, exchange rates, and economic growth, play a significant role in shaping the financial performance of companies in the FMCG sector, and for multinational companies like UNVR, fluctuations in exchange rates can directly impact the cost of imported raw materials, while inflationary pressures can reduce consumer purchasing power and demand for non- essential goods (Bank Indonesia, 2023; World Bank, 2022) all of these factors create a challenging environment for maintaining profitability and financial stability, with an example a weakening Indonesian Rupiah (IDR) against the US 25 Dollar (USD) increases the cost of imported raw materials, thereby squeezing profit margins (Bank Indonesia, 2023) inflationary pressures can lead to higher operational costs, reducing consumer purchasing power and demand for non- essential goods (World Bank, 2022). The FMCG industry is characterized by rapid changes in consumer preferences and technological advancements, which further complicate the financial landscape for companies like UNVR, trends such as the shift towards healthier and sustainable products, as well as the rise of e-commerce, require significant investment in research and development (R&D) and digital transformation (McKinsey & Company, 2021; PwC, 2021), while these trends offer opportunities for growth, they also pose financial challenges, particularly for companies that must balance innovation with cost efficiency, for instance the rise of e-commerce and digital marketing has altered traditional distribution channels, requiring companies to invest in new technologies and platforms to remain competitive (PwC, 2021). Regulatory changes in Indonesia, particularly in areas such as environmental policies and taxation, can also have a direct impact on a company’s financial performance, for an example the Indonesian government has introduced stricter regulations on plastic packaging to reduce environmental waste, which may increase production costs for FMCG companies (Ministry of Environment and Forestry, 2022) changes in corporate tax rates or import tariffs can affect profitability and cash flow, requiring companies to adjust their financial strategies accordingly (Deloitte, 2023) with these regulatory challenges add another layer of complexity to UNVR’s financial management, as the company must navigate compliance while maintaining profitability. Geopolitical events, such as trade wars, pandemics, and regional conflicts, can disrupt global supply chains and affect market demand, also for multinational companies like UNVR, these events can lead to increased costs, delays in production, and reduced sales, the COVID-19 pandemic for instance, caused significant disruptions in global supply chains, leading to shortages of raw materials 26 and increased logistics costs (World Health Organization, 2021) geopolitical tensions between major trading partners, such as the US and China, can lead to trade restrictions or tariffs, further complicating the business environment for multinational companies (IMF, 2022) all of these disruptions highlight the importance of risk management and supply chain resilience in maintaining financial stability. Finally, political and social movements, such as boycotts against multinational companies, can significantly impact a company’s sales and brand reputation also in recent years, there has been a rise in nationalist sentiments and calls to support local brands, which has led to boycotts of foreign-owned companies like UNVR (Jakarta Post, 2023) all of these movements highlight the importance of understanding and responding to local consumer sentiments in maintaining market share and financial stability, with an example boycotts driven by nationalist sentiments can lead to a decline in sales and damage brand reputation, particularly for companies that rely heavily on consumer trust and loyalty (Jakarta Post, 2023). II.1.7 Study of Previous Relevant Studies This study aims to analyze the risk of bankruptcy, potential financial statement manipulations, and the overall financial health quality of Unilever during the 2020– 2024 period, It is grounded in prior research that has developed methods for evaluating corporate financial risks and detecting manipulations in financial statements, By leveraging insights from these studies, this research seeks to provide a comprehensive overview of UNVR’s financial condition, Table II.3 Study of Previous Relevant Studies No, Author Title Method Findings 1 Mehmood and Luca (2025) Financial distress prediction in private firms: Developing a model for troubled debt restructuring This study developed a model for financial distress prediction using a sample of 312 distressed and 312 non-distressed firms from France, Spain, and Italy, with data The findings show that the modified Z”- Score model offers improved prediction accuracy for financial distress, and the TDR probability index effectively reflects the 27 extracted from the ORBIS database, The model is built by modifying the Z”- Score, estimating coefficients using linear discriminant analysis (LDA), and developing a TDR probability index through logistic regression probability trends for both distressed and non-distressed firms 2 Festa et al. (2021) The contribution of intellectual capital to financial stability in Indian pharmaceutical companies This study examines the financial structures of the top five pharmaceutical companies in India, assessing their soundness and risk of bankruptcy using operating and profitability ratios, Altman Z-Scores for bankruptcy prediction, and Piotroski F-Scores for financial structure attractiveness, with a specific focus on the role of intellectual capital (IC) The findings indicate that the financial structures of these companies are stable, but evolving patent regulations in India necessitate a greater emphasis on IC to enhance innovation, ensure sustainability, and remain competitive internationally 3 Paletta dan Alimehmeti (2022) The efficiency of the Italian preventive agreement: a legal, economic and organizational perspective This study examines the ex ante and ex post efficiency of the preventive agreement process under Italian Bankruptcy Law, analyzing data from 728 Italian companies that filed for preventive agreement in 2016 using nine logit regressions The findings reveal that debt structure, ownership structure, corporate governance, management systems, and effective court control significantly influence the outcomes of the preventive agreement 4 Useche et al. (2024) Taking ESG strategies for achieving profits: a This study analyzes the relationship between financial performance and The findings reveal that stronger ESG practices and disclosures are 28 dynamic panel data analysis ESG (environmental, social, and governance) practices using dynamic panel data regressions on a sample of 114 companies listed on the Latin American Integrated Market (MILA) across Chile, Colombia, Mexico, and Peru during the 2011–2020 period, with financial indicators including Altman Z-Scores, Piotroski F-Scores, EVA, and Jensen’s alpha associated with lower bankruptcy risk, greater financial strength, improved economic value added (EVA), and superior risk-adjusted returns 5 Liu et al.