37 Chapter III Research Methodology III.1 Research Design This research employs a mixed-method research approach, combining quantitative and qualitative methodologies to thoroughly evaluate PT Unilever Indonesia Tbk’s financial condition. the quantitative methods employed are the Altman Z-Score, Beneish M-Score, and Piotroski F-Score, while a qualitative Financial Disclosure Analysis complements these methods to obtain comprehensive insights into internal financial conditions influencing the company's performance, the rationale for employing a mixed-method approach is to leverage the strengths of quantitative rigor combined with qualitative contextual understanding, enhancing the robustness and relevance of research outcomes. This research follows a structured approach, as illustrated in Figure III.1, to systematically address the defined research objectives, the process begins by clearly outlining research objectives, which guide the collection of secondary data from PT Unilever Indonesia Tbk’s financial reports for the period 2020–2024, also three distinct quantitative methods the Altman Z-Score, Beneish M-Score, and Piotroski F-Score are applied to quantitatively evaluate bankruptcy risk, earnings manipulation potential, and overall financial health quality, respectively also each quantitative model is calculated and analyzed carefully using Microsoft Excel software to ensure accuracy and transparency, also after interpreting results from these quantitative analyses based on their respective predefined criteria, a qualitative Financial Disclosure Information Analysis is conducted, with this qualitative analysis serves to enrich the quantitative findings by examining detailed disclosures provided by UNVR, uncovering deeper insights into internal factors that might be causing the company's decline, and also based on the combined insights gained from the quantitative assessments and the qualitative financial disclosure analysis, strategic business solutions and practical recommendations are proposed, aiming to address identified internal issues and support the company's financial recovery. 38 Figure III.1 Research Design 39 III.2 Data Collection Method Data for this study was collected from publicly available secondary sources, primarily consisting of audited financial statements and official disclosures published by PT Unilever Indonesia Tbk for the period 2020-2024 all of these include comprehensive annual reports containing balance sheets, income statements, cash flow statements, and the notes accompanying the financial statements, all of these documents were sourced from the company's official website, the Indonesia Stock Exchange (IDX) website, and reliable financial databases also additional financial market data such as market value and stock price history, was retrieved from trusted financial information platforms such as IDX website, Yahoo Finance, and other reputable financial databases. III.3 Data Analysis Technique All quantitative analyses and calculations performed in this research utilized Microsoft Excel to ensure precision, transparency, and reproducibility with this research employed established financial models, also the potential bankruptcy risk was assessed through the Altman Z-Score, also for this purpose data from Unilever’s balance sheets and income statements were collected to calculate the five financial ratios required in the Altman Z-Score model, including Working Capital to Total Assets (X1), Retained Earnings to Total Assets (X2), Earnings Before Interest and Taxes (EBIT) to Total Assets (X3), Market Value of Equity to Total Liabilities (X4), and Sales to Total Assets (X5) all of these ratios were integrated using Altman’s original Z-Score formula: Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 1.0X5. The resulting Z-Score was interpreted against standard benchmarks, where scores above 2.99 indicate financial stability, scores between 1.81 and 2.99 suggest moderate financial distress, and scores below 1.81 indicate high bankruptcy risk. To detect potential manipulation in financial statements, the Beneish M-Score model was employed. This analysis began by calculating eight distinct financial ratios—Days Sales in Receivables Index (DSRI), Gross Margin Index (GMI), Asset 40 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) also all of these ratios were integrated into the Beneish logistic regression model, represented by the following formula: M- Score=−4.84+0.92(DSRI)+0.528(GMI)+0.404(AQI)+0.892(SGI)−0.115(DEPI)−0 .172(SGAI)+4.679(TATA)−0.327(LVGI) nterpretation of the resulting M-Score was guided by the established thresholds, classifying results into likely earnings manipulation (M-score > -1.78), potential manipulation (-2.22 < M-score < -1.78), and unlikely manipulation (M-score < -2.22) The overall financial health quality was assessed using the Piotroski F-Score, a robust scoring system based on historical financial performance metrics, with this method evaluated nine fundamental indicators grouped into three categories profitability, leverage/liquidity, and operational efficiency also each indicator was assigned a binary score of either 0 or 1, with the total Piotroski F-Score ranging from 0 (weakest) to 9 (strongest), the specific indicators included Return on Assets (ROA), changes in ROA (ΔROA), positive Operating Cash Flow (CFO), CFO greater than ROA (indicating low accrual manipulation), decrease in leverage, improved current ratio, absence of new equity issuance, increases in gross margin, and improvement in asset turnover ratio, and the scores ranging between 7–9 indicated strong financial health, 4–6 represented average financial stability, while scores of 0–3 implied weak financial health. Finally, a qualitative Financial Disclosure Analysis was conducted to provide deeper insights into internal conditions influencing Unilever’s financial results with this approach involved critically reviewing management discussions, detailed notes to the financial statements, and supplementary financial disclosures provided in the annual reports from 2020–2024 with specific attention was given to identifying internal challenges, such as negative working capital trends, rising liabilities, cost management issues, and other strategic or operational decisions affecting profitability with this qualitative analysis served to triangulate and provide context 41 to the quantitative findings, uncovering internal factors and pressures that contributed to the company’s declining financial and stock performance..