Evaluation of Financial Risk Management of Digital Services Companies Using Integrated Entropy-Weight TOPSIS Model

Abstract

Digital services companies help in the digitalization and transformation of the industry in driving Malaysia by advancing the economy of the country. However, digital services companies often face financial risks in terms of liquidity, solvency, efficiency, profitability, and operational risks. These risks increase the chances of failure and financial volatility, which put the companies at a serious disadvantage. This paper proposes an integrated Entropy-Weight TOPSIS model to analyze the financial risks of the listed digital services companies within Malaysia. The entropy method helps to prevent subjective weights by reflecting on information obtained from the financial reports of the companies. This study also provides an analysis to show possible improvements for the companies. The interest coverage ratio (ICR), which measures the capability to settle interest on debt, shows the highest weight followed by the basic indicator approach (BIA) and return on asset (ROA) based on the entropy weighting method. In addition, CLOUDPT, ITMAX, and MSNIAGA are ranked as the top three digital services companies that give the highest relative closeness to the ideal solution. The results help the risk managers to identify the criteria that caused the greatest financial risk in digital services companies to formulate targeted strategies to improve the companies’ financial health.

1. Introduction

According to Deloitte’s survey (Deloitte, 2025), millennials and Generation Z are expected to comprise more than 70% of the total manpower worldwide by 2030. These generations are digital adopters and integrators whose personal and professional lives revolve around digital services for task settlement. According to Bursa Malaysia (2024), digital services refer to internet-related services such as web design, hosting, emails, e-commerce, or e-payments. Digital services help in task simplification and increase the rate of business-operation accomplishments. The application of digital services in the current world is multifold. Web3, a blockchain-based web, aims to enhance the privacy and ownership of consumers (Guan et al., 2023). SDG 11 highlights sustainable cities wherein digital solutions such as artificial intelligence (AI), cloud computing, and data analytics help in creating smart cities with sustainable and safer transport, energy, and risk management (Sengupta & Sengupta, 2022). Even though digital services companies play huge roles in innovation and societal transformation, they often face valuation and systematic risks which are driven by return on equity (ROE) and intangible assets (Perilla, n.d.). A slight disruption of an important digital services company could lead to billions of losses, as seen in the Cloudflare outage in November 2025 (Times of India, 2025). Digital services companies are customer-centric and encounter multifaceted customer expectations which are constantly evolving (Javaid et al., 2024).

This study aims to assess the performance of digital services companies in terms of risks associated with liquidity, default, solvency, cash flow, profitability, and operations. Digital services companies offering services such as Software as a Service (SaaS) and cloud services often require enormous initial investments on product development while clients tend to subscribe to the services on a monthly or annual basis, making revenues less predictable and leading to liquidity risks (C. H. Wu & Pambudi, 2025). Moreover, the churn rate of digital services companies such as providers of SaaS, could reach more than 15%, which is alarming for their liquidity position (Saias et al., 2022). The high capital expenditure on data centers and other intangible assets also add on to the liquidity risk of the companies (Greenberg et al., 2009). Liquidity risk is well-measured by the current ratio (CR) to assess a company’s ease of meeting short-term liabilities (Tarawallie et al., 2025). Solvency issues in digital services companies are critical problems which have rarely been discussed in the literature, even with real cases of bankruptcy (Parry, 2021). Solvency leads to outage in services, which are catastrophic to clients in terms of data losses and tedious retrieval (Parry & Bisson, 2020). The United States Federal Reserve (McCoy et al., 2020) noted that the interest coverage ratio (ICR) could potentially indicate vulnerabilities with a low ICR, hinting at greater chances of default and bankruptcy. The ICR serves as a criterion to analyze insolvencies with low error, with a high potential to spot a risk of financial distress (Ji, 2019). The debt-to-equity ratio (DER) is also a primary ratio in financial risk management. The DER is applied to determine the financial structure of a company (Lee, 2023). A high DER indicates instability and solvency risk (Tunçay et al., 2025).

Operational capability can be represented by the receivable turnover ratio (RTR) to evaluate the efficiency of a company when collecting payments (Lee, 2023). A high RTR signifies better cash flows with a lower risk of bad debt (Yin & Guo, 2026). When clients pay promptly, there is a stable cash flow to manage operational expenses such as cloud hosting and utilities to power data centers (T. Huang et al., 2025). Late client payment poses a high risk in a company’s short-term commitments, which could cause the company to resort to hefty third-party financing and adding to the existing financial burden (Lee, 2023). Profitability is also an important financial aspect for a company. Larger profit values portray a positive image of the company while showcasing good management behavior. Return on equity (ROE) explains a company’s equity deployment which investors look at when studying a company’s profile (Ichsani & Suhardi, 2015). Return on asset (ROA) discusses the efficiency of digital services companies in utilizing their intangible assets. The combination of ROA and ROE could well-measure a company’s financial risk and debt management (Tutcu et al., 2024). Technical glitches and vendor issues could increase the operational risk of a digital services company. As introduced by the Basel Committee (BCBS, 2017), the basic indicator approach (BIA) could measure the risks from internal and external process failures. The exposure to operational risk is highlighted by the capital charge calculated using BIA (Mitra, 2013).

In this study, CR, ICR, DER, RTR, ROA, ROE, and BIA are applied to the financial risk management of the digital services companies. The criteria are chosen based on their significance in assessing the financial health of a company. For a comprehensive review, liquidity, solvency, profitability, and cash flow ratios are adopted to evaluate a company to aid in management decision-making (Santos et al., 2021). CR and DER are important ratios which are widely applied to examine liquidity and solvency in financial risk assessments (M. R. Karim et al., 2021). ROA and ROE serve as industry benchmarks for company performance measurement and financial distress (Marozva et al., 2026; Metwally et al., 2025). The ICR assesses whether digital companies show vulnerabilities while meeting interest-expenses obligations, as a strong ICR has been reported to reflect low credit and solvency risks (Lee, 2023; McCoy et al., 2020). RTR serves as a critical ratio in measuring cash flow, especially in digital companies as they often receive payments on later dates (Yin & Guo, 2026). Meanwhile, BIA, an internationally accepted indicator, measures operational risk in digital services companies to assess the risk of potential day-to-day disruption (Mitra, 2013). These seven criteria are carefully considered for independence and significance in financial risk management. This complicated financial risk management process is commonly viewed as multi-criteria decision-making (MCDM) problems since different risks are to be considered for optimal decision-making. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a MCDM model which aims to select the leading alternative by comparing the separation of decision alternative towards the positive (PIS) and negative ideal solution (NIS) with vast applications in various fields such as information technology risk (Alshahrani et al., 2022), supply chain risk (Abdel-Basset & Mohamed, 2020; Mukherjee et al., 2024), and safety management (Tian et al., 2020). PIS maximizes the benefit criterion by presenting the largest value of the criterion while NIS displays the lowest value of the criterion for the minimization of non-benefit criterion (Dehdasht et al., 2020). In this study, the Shannon entropy weighting method is applied to determine the weight of the criteria by reflecting on the amount of valuable information that each criterion carries. The main assumption in entropy weighting is the measurement of dispersion in criteria (Y. Zhu et al., 2020). The greater the variability or dispersion, the smaller the entropy value, the more valuable information carried, and the greater the entropy weight of the criterion (Srivastava & Singh, 2025). The entropy weighting method is used in different fields to obtain the objective weights (Libório et al., 2024). Entropy is found to have high accuracy and sensitivity to assess criteria weights (R. M. X. Wu et al., 2022) The entropy weighting method is then incorporated into the TOPSIS model to analyze the financial risk management of decision alternatives (Le Roux et al., 2023).

Financial risk management is important for the development of companies (Slassi-Sennou et al., 2025). Based on past research, there is very limited comprehensive analysis performed in financial risk management of digital services companies for benchmarking purposes based on the financial ratios using the MCDM model. Zainuddin et al. (2018) and Eng et al. (2023) conducted prediction bankruptcy analyses for technology companies in Malaysia with financial data without a comprehensive focus on the listed digital services companies or the application of MCDM models. Using financial ratios, Abdullah (2020) performed a financial distress analysis of listed companies in Malaysia without the inclusion of digital services companies. Financial risk management has also been studied in the oil and gas industry (Y. F. A. Karim et al., 2017) and Shariah-compliant companies (Ghazali et al., 2018) but it is largely neglected in the digital services companies in Malaysia. Therefore, this study aims to fill the research gap by proposing a MCDM model to evaluate the financial risk of Malaysian listed digital services companies with the Entropy-Weight TOPSIS model. The idea of entropy is proposed to avoid subjective weights and to utilize the data obtained from the financial reports of the digital services companies (Chen, 2021). In addition, the TOPSIS model is proposed in this study because it is a well-known MCDM model which selects the optimal alternative based on the distance measurement (Y. Huang, 2025). Entropy reduces the bias and subjectivity of the decision-maker to improve the robustness of the TOPSIS model (Chen, 2021). Entropy, when combined with TOPSIS, provides high discriminatory power to evaluate the alternatives (Abughazalah & Khan, 2025; Jiang, 2025). The entropy-TOPSIS model has been widely applauded in construction and civil engineering (Dehdasht et al., 2020; Zhao et al., 2022), materials science (Dasgupta et al., 2025), and manufacturing (Tiwari et al., 2019). This section is followed by Section 2 which introduces the methods of this analysis that includes the development of the proposed Entropy-Weight TOPSIS model. Section 3 presents the empirical results with the proposed model. Section 4 concludes the paper.

2. Materials and Methods

2.1. Research Development

In this research, a MCDM model is proposed based on the hybrid of the entropy weighting method and TOPSIS model for the evaluation of financial risks among digital services companies (Y. Huang, 2025; Wang & Mao, 2023; H. Zhu & Mao, 2024). The advantage of the entropy weighting method is to determine the objective weight based on the measurement of indicators’ information (Chen, 2021). In addition, the optimal decision can be determined based on the PIS as well as a NIS with the TOPSIS model (Dehdasht et al., 2020). Figure 1 presents the three stages of analysis.

As shown in Figure 1, the analysis of this paper consists of three stages as follows.

Stage 1: Identify the decision criteria and alternatives for the analysis of financial risk management.

Stage 2: Determine the weights of decision criteria with the Entropy Weighting method.

Stage 3: Rank the decision alternatives with the TOPSIS model. The alternatives include all the listed digital services companies in Bursa Malaysia. The financial risk of digital services companies is evaluated and compared based on the PIS and NIS.

Table 1. Hierarchy Structure.

Levels	Description
Main Objective (First Level)	Evaluate financial risk of listed digital services companies in Malaysia using Entropy-Weight TOPSIS model.
Decision Criteria (Second Level)	Current ratio (CR)
	Interest coverage ratio (ICR)
	Receivables turnover ratio (RTR)
	Debt-to-equity ratio (DER)
	Return on asset (ROA)
	Return on equity (ROE)
	Basic indicator approach (BIA)
Decision Alternatives (Third Level)	CTOS
	HTPADU
	ZETRIX
	DNEX
	CLOUDPT
	THETA
	MSNIAGA
	ITMAX
	REVENUE
	DIGISTAR
	AWANTEC
	MICROLN
	OMESTI
	NEXG

presents the hierarchy structure for the evaluation of financial risk among listed digital services companies in the Malaysian stock market with the proposed Entropy-Weight TOPSIS model. All the companies under the “Digital Services” subsector in Bursa Malaysia are the decision alternatives of this study.

This study investigates listed digital services companies in Malaysia through data analysis in the companies’ financial reports from 2020 to 2024 with the proposed Entropy-Weight TOPSIS model. All the listed digital services companies, with complete financial statements from 2020 to 2024 on Bursa Malaysia, are included in this study. Based on past studies, the decision criteria such as CR, ICR, RTR, ROA, and ROE are needed to be maximized whereas DER and BIA are needed to be minimized (Lin et al., 2013; Osemudiamwen et al., 2025).

2.2. Entropy Weighting Method

Entropy weighting method is proposed to determine the objective weights based on the measurement on indicators’ information. Entropy is a concept that determines the importance of criterion based on the differences between the criterion’s values across given alternatives (Y. Zhu et al., 2020). A higher difference implies a larger weight for the criterion. Weights obtained from the entropy suggest more accuracy and higher reliability as the subjective weighting method may cause biasness (Qu et al., 2022). The steps for the entropy weighting method are shown as follows:

Step 1. Formulate a decision matrix $\left({{\displaystyle \left({{\displaystyle x}}_{ij}\right)}}_{n\times m}\right)$ with m decision criteria and n decision alternatives.

$${{\displaystyle \left({{\displaystyle x}}_{ij}\right)}}_{n\times m}=\left[\begin{array}{cccc}{{\displaystyle x}}_{11}& \dots & \dots & {{\displaystyle x}}_{1m}\\ {{\displaystyle x}}_{21}& \ddots & & {{\displaystyle x}}_{2m}\\ ⋮& & \ddots & ⋮\\ {{\displaystyle x}}_{n1}& \dots & \dots & {{\displaystyle x}}_{nm}\end{array}\right],$$

(1)

Step 2. Construct a normalized decision matrix using normalization as follows:

$${{\displaystyle p}}_{ij}={\displaystyle \frac{{{\displaystyle x}}_{ij}}{{\displaystyle \sum _{i=1}^n{{\displaystyle x}}_{ij}}}},i=1,2,\dots ,n,j=1,2,\dots ,m,$$

(2)

Step 3. Compute entropy value ${{\displaystyle e}}_j$ that measures the utility value of the indicator’s information.

$${{\displaystyle e}}_j=-k{\displaystyle \sum _{i=1}^n{{\displaystyle p}}_{ij}}\cdot \ln \left({{\displaystyle p}}_{ij}\right),j=1,2,\dots ,m,$$

(3)

with

$$k={\displaystyle \frac{1}{\ln \left(n\right)}},$$

(4)

Step 4. Calculate objective weight of each criterion.

$${{\displaystyle w}}_j={\displaystyle \frac{1-{{\displaystyle e}}_j}{{\displaystyle \sum _{j=1}^m\left(1-{{\displaystyle e}}_j\right)}}},j=1,2,\dots ,m,$$

(5)

2.3. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)

In this study, the entropy weight is incorporated into the TOPSIS model. The steps of TOPSIS model are as follows (An & Wang, 2025):

Step 1. Normalized the decision matrix ${{\displaystyle \left({{\displaystyle x}}_{ij}\right)}}_{n\times m}$ into $R={{\displaystyle \left({{\displaystyle r}}_{ij}\right)}}_{n\times m}$ using the normalization method below:

$${{\displaystyle r}}_{ij}={\displaystyle \frac{{{\displaystyle x}}_{ij}}{\sqrt{{\displaystyle \sum _{i=1}^n{{\displaystyle x}}_{ij}^2}}}},i=1,2,\dots ,n,j=1,2,\dots ,m,$$

(6)

Step 2. Form a weighted normalized decision matrix using the entropy weight obtained from Equation (5).

$$Y={{\displaystyle \left({{\displaystyle y}}_{ij}\right)}}_{n\times m}={{\displaystyle \left({{\displaystyle w}}_j{{\displaystyle r}}_{ij}\right)}}_{n\times m},i=1,2,\dots ,n,j=1,2,\dots ,m,$$

(7)

Step 3. Determine the positive ideal solution and negative ideal solution.

$$\begin{array}{cc}\mathrm{P}\mathrm{o}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{I}\mathrm{d}\mathrm{e}\mathrm{a}\mathrm{l}& =\left\{\left(\max y_{ij}|j\in J\right),\left(\min y_{ij}|j\in J^{\prime }\right),i=1,2,\dots ,n\right\}\hfill \\ & =\left\{y_1^{+},y_2^{+},\dots ,y_m^{+}\right\}\hfill \end{array}$$

(8)

$$\begin{array}{cc}\mathrm{N}\mathrm{e}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{I}\mathrm{d}\mathrm{e}\mathrm{a}\mathrm{l}& =\left\{\left(\min y_{ij}|j\in J\right),\left(\max y_{ij}|j\in J^{\prime }\right),i=1,2,\dots ,n\right\}\hfill \\ & =\left\{y_1^{-},y_2^{-},\dots ,y_m^{-}\right\}\hfill \end{array}$$

(9)

where

$$\begin{gathered} J=\left\{j=1,2,\dots ,m\mathrm{a}\mathrm{n}\mathrm{d}j\mathrm{i}\mathrm{s}\mathrm{a}\mathrm{s}\mathrm{s}\mathrm{o}\mathrm{c}\mathrm{i}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{b}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{f}\mathrm{i}\mathrm{t}\mathrm{c}\mathrm{r}\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{a}\right\} \\ {J}^{\prime }=\left\{j=1,2,\dots ,m\mathrm{a}\mathrm{n}\mathrm{d}j\mathrm{i}\mathrm{s}\mathrm{a}\mathrm{s}\mathrm{s}\mathrm{o}\mathrm{c}\mathrm{i}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{l}\mathrm{o}\mathrm{s}\mathrm{s}\mathrm{c}\mathrm{r}\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{a}\right\} \end{gathered}$$

Step 4. Calculate separation measures using Euclidean distance. Separation measure is the separation distance of each alternative from the PIS and NIS.

$${{\displaystyle s}}_{{{\displaystyle i}}^{+}}=\sqrt{{\displaystyle \sum _{j=1}^m{({{\displaystyle y}}_{ij}-{{\displaystyle y}}_{{{\displaystyle j}}^{+}})}^2}},i=1,2,\dots ,n,$$

(10)

$${{\displaystyle s}}_{{{\displaystyle i}}^{-}}=\sqrt{{\displaystyle \sum _{j=1}^m{({{\displaystyle y}}_{ij}-{{\displaystyle y}}_{{{\displaystyle j}}^{-}})}^2}},i=1,2,\dots ,n,$$

(11)

Step 5. Measure the relative closeness $\left({{\displaystyle C}}_i^{*}\right)$ of each decision alternative towards the ideal solution as follows.

$$C_i^{*}={\displaystyle \frac{s_i^{-}}{s_i^{+}+s_i^{-}}}\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e}0\le C_i^{*}\le 1\mathrm{a}\mathrm{n}\mathrm{d}i=1,2,\dots ,n$$

(12)

The higher value of $c_i^{*}$ denotes that the alternative has longer distance from the NIS and shorter distance to the PIS. Therefore, the alternative that has a higher value of c_i gives better performance.

3. Empirical Results and Discussion

This section presents the results and discussion of the financial risk of the listed digital services companies with the Entropy-TOPSIS model (Duong & Thao, 2021; Öztürk & Gökçen, 2023). Based on the entropy weighting method, Figure 2 presents the weight of the decision criteria for the evaluation of financial risk of the listed digital services companies. From Figure 2, the weights for the decision criteria are ICR (0.5297), BIA (0.2412), ROA (0.0662), ROE (0.0608), DER (0.0394), CR (0.0343), and RTR (0.0283). ICR, which indicates a digital services company’s capability to settle interest payments of its liabilities, is weighted greatly in this study.

The weight generated from the entropy method is incorporated into the TOPSIS model to form a weighted normalized decision matrix. Based on the matrix, the PIS and NIS are identified for each criterion using Equations (8) and (9). Figure 3 presents the value for the PIS and NIS of all criteria. A shorter distance from the PIS and longer distance from the NIS indicate the highest significant criterion (Barbat et al., 2022).

The separation measure for all decision alternatives from the PIS and NIS are calculated using Equations (10) and (11). Figure 4 and Figure 5 present the separation distance of each company from the PIS and NIS, respectively.

As shown in Figure 4, CLOUDPT is the closest to the PIS with a separation of only 0.0099, which is the best among all of the fourteen listed digital services companies. This is followed by ITMAX (0.3436), REVENUE (0.4978), NEXG (0.4981), MICROLN (0.5014), CTOS (0.5025), MSNIAGA (0.5058), AWANTEC (0.5063), HTPADU (0.5073), THETA (0.5079), DIGISTAR (0.5082), OMESTI (0.5095), DNEX (0.5169), and ZETRIX (0.5272).

Based on Figure 5, CLOUDPT (0.5411) has the furthest location to the NIS, indicating the best risk management among all companies. ITMAX (0.2418), MSNIAGA (0.1865), THETA (0.1859), and AWANTEC (0.1830) are the top two to five companies which are far away from the NIS. REVENUE, MICROLIN, OMESTI, HTPADU, NEXG, CTOS, DNEX, and ZETRIX are located 0.1773, 0.1763, 0.1612, 0.1574, 0.1507, 0.1438, 0.0823, and 0.0668 away from the NIS.

Table 2. Relative closeness and ranking of listed digital services companies.

Companies	Relative Closeness	Ranking
CLOUDPT	0.9820	1
ITMAX	0.4131	2
MSNIAGA	0.2694	3
THETA	0.2680	4
AWANTEC	0.2654	5
REVENUE	0.2626	6
MICROLN	0.2601	7
DIGISTAR	0.2600	8
OMESTI	0.2404	9
HTPADU	0.2369	10
NEXG	0.2323	11
CTOS	0.2225	12
DNEX	0.1374	13
ZETRIX	0.1125	14

presents the relative closeness and ranking of the financial risk of the listed digital services companies. The last step in Equation (12) identifies financial risk management as well as the relative closeness of decision alternatives towards the ideal solution.

The best alternative has the shortest distance to the PIS and yet the farthest distance from the NIS. As shown in Table 2, CLOUDPT, ITMAX, and MSNIAGA are ranked as the top three digital services companies with the best financial risk management based on the highest relative closeness of 0.9820, 0.4131, and 0.2694, respectively. CLOUDPT is the best digital services company in financial risk management because CLOUDPT has the highest value for ICR and ROA, which lies directly on the PIS of the ICR (0.5043) and ROA (0.0335). CLOUDPT has the second proximity to the PIS of ROE, immediately after ITMAX. CLOUDPT also has the lowest DER value directly on the PIS of DER (0.0001). This implies that CLOUDPT has the best management of solvency and profitability. The success of CLOUDPT, a key information technology (IT) company focusing on delivering IT infrastructure and software, can be observed as the company took only two years to ascend from the Access, Certainty, Efficiency (ACE) market to the Main Market of Bursa Malaysia in October 2025 (Fathina, 2025). CLOUDPT has also secured a large number of contracts to aid in long-term cashflows (Jalil, 2025). ITMAX has the best ROE value, which falls on the PIS of ROE (0.0320). ITMAX is the closest to the PIS of CR and ICR, and third closest to the PIS of ROA, hence, it is ranked second in financial risk management. MSNIAGA has the smallest BIA value falling on the PIS of BIA (0.0057) and the second closest to the PIS of DER, hence, it obtains the third place in financial risk management. These top three companies provide business-to-government (B2G) and business-to-business (B2B) services. THETA, AWANTEC, REVENUE, MICROLN, DIGISTAR, OMESTI, and HTPADU are placed fourth to tenth in the financial risk management based on the Entropy-Weight TOPSIS model. The four lowest-ranking companies are NEXG, CTOS, DNEX, and ZETRIX with the relative closeness of 0.2323, 0.2225, 0.1374, and 0.1125, respectively. ZETRIX lies directly at the NIS of BIA and is the third closest to the NIS of RTR, which causes ZETRIX to be ranked last in financial risk management. ZETRIX faced operational stability issues due to low corporate governance which led to a reprimand by Bursa Malaysia, and this may be one of the reasons for low financial risk management of the digital services company (Zainul, 2025). The ranking is crucial in financial risk management because the ranking of competitors in the same sector can be identified for benchmarking purposes (Abdel-Basset et al., 2020). The results of this study show that the proposed Entropy-Weight TOPSIS model is capable to solve MCDM problems in evaluating and ranking of financial risks. MCDM is an important process of analyzing decisions by considering multiple decision criteria in the selection of the best alternative.

Table 3. Comparison between Entropy-TOPSIS and Entropy-WSM.

Companies	Entropy-TOPSIS		Entropy-WSM
	Relative Closeness	Ranking	Score	Ranking
CLOUDPT	0.9820	1	0.8996	1
ITMAX	0.4131	2	0.4046	2
MSNIAGA	0.2694	3	0.2833	3
THETA	0.2680	4	0.2747	4
AWANTEC	0.2654	5	0.1709	5
REVENUE	0.2626	6	0.1639	7
MICROLN	0.2601	7	0.1612	8
DIGISTAR	0.2600	8	0.1444	11
OMESTI	0.2404	9	0.0230	14
HTPADU	0.2369	10	0.0722	13
NEXG	0.2323	11	0.1524	10
CTOS	0.2225	12	0.1540	9
DNEX	0.1374	13	0.0731	12
ZETRIX	0.1125	14	0.1643	6

displays the validation of ranking results based on the comparison of the financial risk management between the proposed Entropy-TOPSIS model and Entropy-Weighted Sum Model (WSM) (Al-Bayati & Al-Zubaidy, 2020; O’Shea et al., 2026).

Based on Table 3, the results of the Entropy-TOPSIS are consistent with the results of Entropy-WSM. The rankings of the top five companies, which are CLOUDPT, ITMAX, MSNIAGA, THETA, and AWANTEC, are identical in both models. The results are consistent with the findings by Mulliner et al. (2016) when comparing MCDM models. Spearman’s rho correlation between the proposed Entropy-TOPSIS and Entropy-WSM is 0.7363 (Kabassi, 2021b, 2021a).

Sensitivity analysis is carried out by creating a 10% increase and 10% decrease in the weight of the most influential criterion, which is the ICR (Roy & Shaw, 2021; Więckowski & Sałabun, 2023). The outcome of the sensitivity analysis is shown in

Table 4. Sensitivity analysis.

Companies	Entropy-TOPSIS		+10%		−10%
	Relative Closeness	Rank	Relative Closeness	Rank	Relative Closeness	Rank
CLOUDPT	0.9820	1	0.9851	1	0.9785	1
ITMAX	0.4131	2	0.3876	2	0.4442	2
MSNIAGA	0.2694	3	0.2295	3	0.3126	3
THETA	0.2680	4	0.2283	4	0.3109	4
AWANTEC	0.2654	5	0.2260	5	0.3082	5
REVENUE	0.2626	6	0.2235	6	0.3052	6
MICROLN	0.2601	7	0.2211	8	0.3027	7
DIGISTAR	0.2600	8	0.2213	7	0.3019	8
OMESTI	0.2404	9	0.2039	9	0.2802	9
HTPADU	0.2369	10	0.2006	10	0.2766	10
NEXG	0.2323	11	0.1967	11	0.2716	11
CTOS	0.2225	12	0.1880	12	0.2607	12
DNEX	0.1374	13	0.1149	13	0.1626	13
ZETRIX	0.1125	14	0.0951	14	0.1314	14

.

The sensitivity analysis highlights that the rankings of the companies remain highly consistent in Table 4. When there is a 10% increase in the ICR, Spearman’s rho correlation is 0.9956; a 10% increase in the ICR shows that all the rankings remain similar, hence Spearman’s rho correlation is 1.0000.

4. Conclusions

The entropy-Weight TOPSIS model is proposed in this study to evaluate the financial risk of listed digital services companies in Malaysia. The entropy weighting method is used to avoid biasness on subjective weighting to determine the objective weight for each criterion prior to the ranking of the digital services companies with the TOPSIS model. The TOPSIS model is proposed in this study because it can determine the ranking of the financial risk management of the companies based on the distance of alternatives from the PIS as well as NIS. The optimal alternative gives the shortest distance towards the PIS yet the farthest distance from the NIS. The results of this study show that the ICR is the most important criterion in financial risk management, followed by BIA, ROA, ROE, DER, CR, and RTR. In addition, CLOUDPT achieves the best financial risk management, followed by ITMAX, MSNIAGA, THETA, and AWANTEC. Apart from academic implications, this research also provides practical implications to listed digital services companies for benchmarking and future improvement. This research provides an analysis on the separation distance from the PIS as well as NIS of each digital services company. The company can identify their strengths and weaknesses based on the criteria examined. The results help the risk managers to identify the criteria that caused the greatest financial risk in digital services companies to formulate targeted strategies to improve the companies’ financial health. This study helps investors to distinguish low-risk and high-risk digital services companies beyond traditional financial ratio analysis. The risk profiles of the digital services companies are clearly presented by the results of this Entropy-TOPSIS model in this study to aid investors in adjusting their portfolios or investment decisions and is part of the risk-adjusted return. This study focuses on the financial risk management of the listed digital services companies only. In the future, non-financial criteria such as churn risk or cybersecurity risk can be analyzed to uncover the non-financial risk mitigation of the companies. The application of the proposed Entropy-Weight TOPSIS model can be extended to the MCDM problems in the other fields for future research.