1. Introduction
In the context of the deep integration of the digital economy and logistics industry, the value creation mechanism of data assets and its quantitative assessment have become core topics in both academia and industry. With the penetration of blockchain, the Internet of Things (IoT) and other technologies, the logistics industry is experiencing a paradigm shift from a traditional supply chain to a smart asset network, where data elements not only reconfigure the operation mode of logistics enterprises but also give rise to the emerging value dimension of excess returns [
1]. Meanwhile, the impact of supply chain risk management (SCRM) on the innovation performance of SMEs has also received much attention. Awain et al. (2025) not only showed that SCRM significantly enhanced the product innovation performance of Omani SMEs but also found that entrepreneurial networks and technological turbulence played a positive synergistic moderating role in this relationship [
2].
However, the existing research still faces three challenges in data asset valuation methodology: first, traditional financial indicators have difficulty capturing the network effect and zero marginal cost of data assets, resulting in systematic bias in valuation models [
3]; second, the complexity of the ownership definition and value transmission path of data assets in the logistics scenario requires the construction of multidimensional valuation frameworks [
4]; and third, the mechanism of excess return generation has not yet been quantitatively correlated with the inputs of data elements, which lacks theoretical support for investment decisions [
5]. To address these bottlenecks, scholars have begun to explore hybrid valuation models that integrate hierarchical analysis (AHP) and machine learning, which significantly improve valuation accuracy by deconstructing the value hierarchy of data assets (e.g., user behavioral data, logistic path optimization data, and equipment status data) and establishing a dynamic weight allocation mechanism [
6]. Notably, recent studies have shown that the value spillover effect of logistics data assets can generate excess returns of 12–18% per annum, mainly due to data-driven demand forecast optimization and inventory turnover efficiency improvement [
2,
5].
According to the International Data Corporation (IDC) Global Data Asset Valuation Report 2024, the average annual growth rate of data asset value in the logistics industry is 27%, which is significantly higher than that of traditional asset classes. This trend is particularly prominent in the Chinese market, where headline companies represented by “Shunfeng Holding Co., Ltd.” (hereinafter referred to as SFH) have accumulated a significant amount of data assets during their digital transformation process, including logistics information, customer data, transaction records, and real-time transportation data. These data assets not only provide critical support for the company’s operations but also help maintain its leading position in the highly competitive logistics market. However, the current research on the value of data assets has focused primarily on the internet industry, with limited and less systematic or practical studies targeting logistics enterprises. Therefore, this paper takes SFH as a case study to explore the value assessment of data assets in logistics enterprises, aiming to provide a scientific and actionable evaluation method for the industry. This paper first constructs a comprehensive model applicable to the data asset value assessment of logistics enterprises by integrating the multiperiod excess return method and hierarchical analysis method and then verifies the applicability and validity of the model by taking SFH as a case study. The theoretical value of this study lies in overcoming the limitations of the traditional DCF model and market approach, whereas the practical significance is reflected in providing pricing benchmarks for data asset securitization and cross-border data transactions of logistics enterprises.
The rest of the paper is arranged as follows: the second part presents the literature review, the third part describes the construction of the valuation models, the fourth part presents the case study, and finally, the conclusion is presented.
Author Contributions
Conceptualization, L.Y. and S.Q.; Methodology, S.Q.; Software, S.Q. and N.Z.; Validation, L.Y., S.Q. and Z.Y.; Formal analysis, L.Y. and S.Q.; Investigation, S.Q.; Resources, N.Z. and Z.Y.; Data curation, S.Q.; Writing—original draft preparation, L.Y. and S.Q.; Writing—review and editing, L.Y. and Z.Y.; Visualization, S.Q.; Supervision, Z.Y.; Project administration, N.Z.; Funding acquisition, L.Y. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the 2021 Key Projects for General of Universities of Guangdong Province, grant number 2021ZDZX3028.
Institutional Review Board Statement
Ethical review and approval were waived for this study in accordance with the local legislation and institutional requirements (Article 32 of Measures for Ethical Review of Life Sciences and Medical Research Involving Human Beings of China; detailed information can be found at (
https://www.gov.cn/zhengce/zhengceku/2023-02/28/content_5743658.htm, accessed on 20 September 2023), as it did not entail clinical trials or manipulations involving humans or animals.
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The data presented in this study are available on request from the corresponding author due to containing business sensitive information protected under non-disclosure agreements.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Rachana Harish, A.; Liu, X.L.; Zhong, R.Y.; Huang, G.Q. Log-flock: A blockchain-enabled platform for digital asset valuation and risk assessment in E-commerce logistics financing. Comput. Ind. Eng. 2021, 151, 107001. [Google Scholar] [CrossRef]
- Awain, A.M.S.B.; Asad, M.; Sulaiman, M.A.B.A.; Asif, M.U.; Shanfari, K.S. Impact of supply chain risk management on product innovation performance of Omani SMEs: Synergetic moderation of technological turbulence and entrepreneurial networking. Sustainability 2025, 17, 2903. [Google Scholar] [CrossRef]
- Elvina, P.S. The effect of company size, profitability, economic growth rate, market traction and competitive advantage on startup valuation in logistics aggregators. Am. J. Econ. Manag. Bus. 2023, 2, 164–174. [Google Scholar] [CrossRef]
- Lim, S.; Lee, C.-H.; Bae, J.-H.; Jeon, Y.-H. Identifying the optimal valuation model for maritime data assets with the Analytic Hierarchy Process (AHP). Sustainability 2024, 16, 3284. [Google Scholar] [CrossRef]
- Yuswadi, M.R.A.-N.; Soekarno, S. Digital transformation and corporate valuation: Unveiling the influence of digital maturity in stocks return in Indonesian FMCG industry. Int. J. Curr. Sci. Res. Rev. 2024, 7, 4553–4558. [Google Scholar] [CrossRef]
- Bang, S.-H.; Lee, K.-H.; Jang, J.-Y.; Seon, H.-N.; Shin, K.-S. Prioritization on information sharing in digital platform for linking online retail and logistics systems using AHP. Korean Logist. Res. Assoc. 2023, 33, 45–57. [Google Scholar] [CrossRef]
- Tang, Z. Research on the accounting recognition and measurement problems of enterprise data assets. Int. J. Glob. Econ. Manag. 2024, 3, 242–253. [Google Scholar] [CrossRef]
- Fu, J.; Xiao, B.; Wang, F. Evaluation of data asset value based on Hierarchical Analysis Method. Front. Bus. Econ. Manag. 2024, 12, 240–244. [Google Scholar] [CrossRef]
- Yin, C.; Jin, T.; Zhang, P.; Wang, J.; Chen, J. Assessment and pricing of data assets: Research review and prospect. Big Data Res. 2021, 7, 14–27. [Google Scholar]
- Simanjuntak, D.; Nurjanah, S.; Willy; Muda, I. Historical cost vs current cost accounting method. Braz. J. Dev. 2023, 9, 31828–31840. [Google Scholar] [CrossRef]
- Gad, I. Challenges in asset and liability valuation: Bridging fair value and historical cost accounting. South Asian J. Soc. Stud. Econ. 2024, 21, 10–17. [Google Scholar] [CrossRef]
- Nada, E.Q.; Novitasari, S.B.; Putri, Q.L.; Wahyuni, N. Market value analysis of Dalwa Syariah Hotel with cost approach and income approach methods. J. Akunt. Terap. Dan Bisnis 2022, 2, 1–11. [Google Scholar] [CrossRef]
- Martin, D.; Heinz, D.; Glauner, M.; Kuhl, N. Selecting data assets in data marketplaces: Leveraging machine learning and explainable AI for value quantification. Bus. Inf. Syst. Eng. 2025. [Google Scholar] [CrossRef]
- Zhang, J.; Dong, W.; Wei, Y. Business big data analysis: Research on valuation methods of transactional data assets. J. Intell. 2023, 7, 1–33. (In Chinese) [Google Scholar]
- Li, W.; Yang, W.; Zhou, Z.; Wu, S.; Mo, C. Analyzing the game pricing mechanism of data assets: Theoretical evidence considering market structure and competitive characteristics. Int. Rev. Econ. Financ. 2025, 99, 104043. [Google Scholar] [CrossRef]
- Lev, B.; Feng, G. The End of Accounting and the Path Forward for Investors and Managers; John Wiley & Sons: Hoboken, NJ, USA, 2016. [Google Scholar]
- Zhou, X.; Zhang, B. Valuation of enterprise data assets by using the improved multi-period excess-earnings method. J. Ind. Eng. Manag. 2023, 1, 24–31. [Google Scholar] [CrossRef]
- Feng, L.; Hu, X.; Zhao, X. Research on data asset valuation based on lifecycle theory: A case study of Bilibili. Friends Account. 2024, 13, 15–21. (In Chinese) [Google Scholar]
- Saaty, T.L. A scaling method for priorities in hierarchical structures. J. Math. Psychol. 1977, 15, 234–281. [Google Scholar] [CrossRef]
- Kaewfak, K.; Huynh, V.-N.; Ammarapala, V.; Charoensiriwath, C. A fuzzy AHP-TOPSIS approach for selecting the multimodal freight transportation routes. In Communications in Computer and Information Science; Springer: Singapore, 2019; pp. 28–46. [Google Scholar]
- Rashidi, K. AHP versus DEA: A comparative analysis for the gradual improvement of unsustainable suppliers. Benchmarking Int. J. 2020, 27, 2283–2321. [Google Scholar] [CrossRef]
- Cao, X. E-commerce platform risk identification using AHP hierarchical analysis and BPNN neural network. In Proceedings of the 2022 2nd International Signal Processing, Communications and Engineering Management Conference (ISPCEM), Montreal, ON, Canada, 25–27 November 2022; pp. 316–323. [Google Scholar]
- Yang, H.; Zhang, Y.; Ma, H. Evaluation model and promotion of logistics transportation D and A system based on AHP. In Proceedings of the 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, Tangshan, China, 24–26 March 2023; p. 175. [Google Scholar]
- Surucu-Balci, E.; Iris, Ç.; Balci, G. Digital information in maritime supply chains with blockchain and cloud platforms: Supply chain capabilities, barriers, and research opportunities. Technol. Forecast. Soc. Change 2024, 198, 122978. [Google Scholar] [CrossRef]
- Wang, J.; Li, H.; Guo, H. Coordinated development of logistics development and low-carbon environmental economy base on AHP-DEA model. Sci. Program. 2022, 1, 5891909. [Google Scholar] [CrossRef]
- Yang, Y.; Guo, Z.; Zhang, L.; Sun, L. Analysis of the implementation path of data asset valorization. Inf. Commun. Technol. Policy 2024, 50, 24–33. (In Chinese) [Google Scholar]
- Li, Z. Research on the valuation system of off-balance-sheet intangible assets. Commer. Account. 2019, 13, 90–92. (In Chinese) [Google Scholar]
Figure 1.
Fitted Model of SFH’s Operating Revenue.
Figure 2.
Hierarchy of the value of off-balance sheet intangible assets of SFH.
Figure 3.
Sensitivity test plot of the data asset valuation to discount rate vs. period of return.
Table 1.
Scale of Relative Importance.
Scale | Description of Importance |
---|
1 | Two factors are equally important. |
3 | The former factor is slightly more important than the latter factor. |
5 | The former factor is significantly more important than the latter factor. |
7 | The former factor is strongly more important than the latter factor. |
9 | The former factor is extremely more important than the latter factor. |
2, 4, 6, 8 | Indicates an intermediate level of importance between the two factors above. |
Reciprocal of 1–9 | Represents the importance when the order of the two factors is reversed. |
Table 2.
Standard Values of the Random Consistency Index (RI).
n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|
RI | 0.00 | 0.00 | 0.52 | 0.89 | 1.12 | 1.26 | 1.36 | 1.41 | 1.46 | 1.49 |
Table 3.
SFH’s Operating Revenue from 2019 to 2023.
Year | 2019 | 2020 | 2021 | 2022 | 2023 |
---|
Operating Revenue (RMB 100 million) | 1121.93 | 1539.09 | 2071.90 | 2674.90 | 2584.10 |
Table 4.
SFH’s Operating Revenue Forecast for 2024–2028.
Year | 2024 | 2025 | 2026 | 2027 | 2028 |
---|
Operating Revenue (RMB 100 million) | 3054.01 | 3334.03 | 3597.27 | 3846.66 | 4084.36 |
Table 5.
SFH’s Related Costs and Expenses from 2019 to 2023.
Year | 2019 | 2020 | 2021 | 2022 | 2023 |
---|
Operating Costs (RMB 100 million) | 926.50 | 1288.10 | 1815.50 | 2340.70 | 2252.70 |
Average Proportion of Operating Revenue | 85.71% |
Taxes and Surcharges (RMB 100 million) | 2.80 | 3.79 | 4.79 | 4.77 | 5.02 |
Average Proportion of Operating Revenue | 0.22% |
Selling Expenses (RMB 100 million) | 19.97 | 22.52 | 28.38 | 27.84 | 29.92 |
Average Proportion of Operating Revenue | 1.36% |
Administrative Expenses (RMB 100 million) | 96.99 | 116.00 | 150.30 | 175.74 | 176.33 |
Average Proportion of Operating Revenue | 7.37% |
Financial Expenses (RMB 100 million) | 6.83 | 8.53 | 15.63 | 17.12 | 18.66 |
Average Proportion of Operating Revenue | 0.66% |
Table 6.
SFH’s Related Costs and Expenses Forecast for 2024–2028.
Year | 2024 | 2025 | 2026 | 2027 | 2028 |
---|
Operating Costs (RMB 100 million) | 2617.50 | 2857.50 | 3083.11 | 3296.86 | 3500.59 |
Taxes and Surcharges (RMB 100 million) | 6.72 | 7.33 | 7.91 | 8.46 | 8.98 |
Selling Expenses (RMB 100 million) | 41.60 | 45.41 | 49.00 | 52.40 | 55.64 |
Administrative Expenses (RMB 100 million) | 224.93 | 245.56 | 264.94 | 283.31 | 300.82 |
Financial Expenses (RMB 100 million) | 20.03 | 21.87 | 23.59 | 25.23 | 26.79 |
Table 7.
SFH’s Capital Expenditures and Working Capital from 2019 to 2023.
Year | 2019 | 2020 | 2021 | 2022 | 2023 |
---|
Cash Paid for the Acquisition of Fixed Assets, Intangible Assets, and Other Long-term Assets (RMB 100 million) | 64.21 | 122.67 | 191.96 | 141.84 | 124.72 |
Net Cash Received from the Disposal of Fixed Assets, Intangible Assets, and Other Long-term Assets (RMB 100 million) | 0.49 | 0.65 | 1.47 | 1.76 | 3.36 |
Capital Expenditures (RMB 100 million) | 63.72 | 122.02 | 190.49 | 140.08 | 121.36 |
Average Proportion of Operating Revenue | 4.97% |
Current Assets (RMB 100 million) | 428.97 | 516.77 | 941.12 | 906.73 | 909.91 |
Current Liabilities (RMB 100 million) | 309.82 | 418.09 | 760.22 | 776.77 | 739.90 |
Working Capital (RMB 100 million) | 119.15 | 98.68 | 180.90 | 129.96 | 170.01 |
Increase in Working Capital (RMB 100 million) | 63.45 | −20.47 | 82.22 | −50.94 | 40.05 |
Average Proportion of Operating Revenue | −1.62% |
Table 8.
SFH’s Capital Expenditures and Increase in Working Capital Forecast for 2024–2028.
Year | 2024 | 2025 | 2026 | 2027 | 2028 |
---|
Capital Expenditures (RMB 100 million) | 151.68 | 165.59 | 178.66 | 191.05 | 202.85 |
Increase in Working Capital (RMB 100 million) | −49.38 | −53.91 | −58.16 | −62.19 | −66.04 |
Table 9.
SFH’s Depreciation and Amortization Amounts from 2019 to 2023.
Year | 2019 | 2020 | 2021 | 2022 | 2023 |
---|
Depreciation and Amortization (RMB 100 million) | 45.03 | 52.93 | 125.72 | 163.48 | 160.62 |
Average Proportion of Operating Revenue | 6.13% |
Table 10.
SFH’s Depreciation and Amortization Forecast for 2024–2028.
Year | 2024 | 2025 | 2026 | 2027 | 2028 |
---|
Depreciation and Amortization (RMB 100 million) | 187.26 | 204.43 | 220.57 | 235.87 | 250.44 |
Table 11.
Forecast of Free Cash Flow for SFH (2024–2028).
Year | 2024 | 2025 | 2026 | 2027 | 2028 |
---|
Operating Revenue (RMB 100 million) | 3054.01 | 3334.03 | 3597.27 | 3846.66 | 4084.36 |
Operating Costs (RMB 100 million) | 2617.53 | 2857.53 | 3083.14 | 3296.89 | 3500.62 |
Taxes and Surcharges (RMB 100 million) | 6.71 | 7.33 | 7.91 | 8.45 | 8.98 |
Selling Expenses (RMB 100 million) | 41.60 | 45.41 | 49.00 | 52.40 | 55.64 |
Administrative Expenses (RMB 100 million) | 224.93 | 245.56 | 264.94 | 283.31 | 300.82 |
Financial Expenses (RMB 100 million) | 20.03 | 21.87 | 23.59 | 25.23 | 26.79 |
EBIT (RMB 100 million) | 143.23 | 156.36 | 168.71 | 180.40 | 191.55 |
Income Tax Rate | 25% |
Net Operating Profit After Tax (RMB 100 million) | 107.42 | 117.27 | 126.53 | 135.30 | 143.66 |
Capital Expenditures (RMB 100 million) | 151.68 | 165.59 | 178.66 | 191.05 | 202.85 |
Depreciation and Amortization (RMB 100 million) | 187.26 | 204.43 | 220.57 | 235.87 | 250.44 |
Increase in Working Capital (RMB 100 million) | −49.38 | −53.91 | −58.16 | −62.19 | −66.04 |
Free Cash Flow (RMB 100 million) | 192.38 | 210.02 | 226.60 | 242.31 | 257.29 |
Table 12.
Forecast of Contribution Value of Current Assets for SFH (2024–2028).
Year | 2024 | 2025 | 2026 | 2027 | 2028 |
---|
Operating Revenue (RMB 100 million) | 3054.01 | 3334.03 | 3597.27 | 3846.66 | 4084.36 |
Proportion of Current Assets | 37.27% |
Beginning Balance of Current Assets (RMB 100 million) | 909.90 | 1138.09 | 1242.44 | 1340.54 | 1433.48 |
Increase in Current Assets (RMB 100 million) | 228.19 | 104.35 | 98.10 | 92.94 | 88.58 |
Ending Balance of Current Assets (RMB 100 million) | 1138.09 | 1242.44 | 1340.54 | 1433.48 | 1522.06 |
Average Balance (RMB 100 million) | 1024.00 | 1190.27 | 1291.49 | 1387.01 | 1477.77 |
Return on Investment | 4.35% |
Contribution Value (RMB 100 million) | 44.54 | 51.78 | 56.18 | 60.33 | 64.28 |
Table 13.
Forecast of the Contribution Value of Fixed Assets for SFH (2024–2028).
Year | 2024 | 2025 | 2026 | 2027 | 2028 |
---|
Operating Revenue (RMB 100 million) | 3054.01 | 3334.03 | 3597.27 | 3846.66 | 4084.36 |
Proportion of Depreciation of Fixed Assets | 2.39% |
Depreciation of Fixed Assets (RMB 100 million) | 30.05 | 35.81 | 45.89 | 58.60 | 66.12 |
Proportion of Fixed Assets | 17.28% |
Beginning Balance of Fixed Assets (RMB 100 million) | 539.30 | 527.63 | 576.00 | 621.48 | 664.57 |
Ending Balance of Fixed Assets (RMB 100 million) | 527.63 | 576.00 | 621.48 | 664.57 | 705.64 |
Average Balance of Fixed Assets (RMB 100 million) | 533.46 | 551.82 | 598.74 | 643.03 | 685.10 |
Return on Investment | 4.65% |
Contribution Value (RMB 100 million) | 97.91 | 105.46 | 113.95 | 121.98 | 129.62 |
Table 14.
Forecast of Contribution Value of On-Balance-Sheet Intangible Assets for SFH. (2024–2028).
Year | 2024 | 2025 | 2026 | 2027 | 2028 |
---|
Operating Revenue (RMB 100 million) | 3054.01 | 3334.03 | 3597.27 | 3846.66 | 4084.36 |
Proportion of Amortization of On-Balance-Sheet Intangible Assets | 0.75% |
Amortization of On-Balance-Sheet Intangible Assets (RMB 100 million) | 20.79 | 24.88 | 26.85 | 28.71 | 30.48 |
Proportion of On-Balance-Sheet Intangible Assets | 7.77% |
Beginning Balance of On-Balance-Sheet Intangible Assets (RMB 100 million) | 181.47 | 237.37 | 259.13 | 279.59 | 298.97 |
Ending Balance of On-Balance-Sheet Intangible Assets (RMB 100 million) | 237.37 | 259.13 | 279.59 | 298.97 | 317.45 |
Average Balance of On-Balance-Sheet Intangible Assets (RMB 100 million) | 209.42 | 248.25 | 269.36 | 289.28 | 308.21 |
Return on Investment | 4.65% |
Contribution Value (RMB 100 million) | 32.53 | 36.43 | 39.37 | 42.16 | 44.82 |
Table 15.
Weighted Average Cost of Capital for Peer Companies.
Company | D/(D + E) | E/(D + E) | Rd | Rf | Rm | Re | T | WACC |
---|
SFH | 52.88% | 47.12% | 4.17% | 3.12% | 10.7% | 13.15% | 25% | 7.95% |
YTO Express | 33.95% | 66.05% | 4.17% | 3.12% | 10.7% | 8.5% | 25% | 6.68% |
Yunda Express | 50.23% | 49.77% | 4.17% | 3.12% | 10.7% | 9.18% | 25% | 6.14% |
Table 16.
Combined Returns on Intangible Assets for SFH.
Company | | | | | | | |
---|
SFH | 7.95% | 59.73% | 4.35% | 27.79% | 4.90% | 12.48% | 31.94% |
YTO Express | 6.68% | 41.85% | 4.35% | 43.27% | 4.90% | 14.88% | 18.39% |
Yunda Express | 6.14% | 47.24% | 4.35% | 40.75% | 4.90% | 12.02% | 17.39% |
Average | 22.58% |
Table 17.
Assignment Results of the Criterion Layer Judgment Matrix.
Indicator | Price Advantage | Sales Growth | Competitiveness Improvement | Cost Savings |
---|
Price Advantage | 1 | 1.4579 | 1.8734 | 0.6410 |
Sales Growth | 0.6851 | 1 | 0.9891 | 0.3183 |
Competitiveness Improvement | 0.5338 | 1.0111 | 1 | 0.2739 |
Cost Savings | 1.5602 | 3.1421 | 3.6506 | 1 |
Table 18.
Weights of Criterion Layer Indicators.
Indicator | Price Advantage | Sales Growth | Competitiveness Improvement | Cost Savings | Eigenvector | Weight |
---|
Price Advantage | 0.2646 | 0.2205 | 0.2494 | 0.2870 | 1.0215 | 0.2554 |
Sales Growth | 0.1815 | 0.1513 | 0.1316 | 0.1425 | 0.6069 | 0.1517 |
Competitiveness Improvement | 0.1412 | 0.1529 | 0.1331 | 0.1227 | 0.5499 | 0.1375 |
Cost Savings | 0.4128 | 0.4753 | 0.4859 | 0.4478 | 0.8217 | 0.4554 |
Table 19.
Consistency Check Results of the Criterion Layer Judgment Matrix.
| | CI | RI | CR |
---|
16.0622 | 4.0156 | 0.0052 | 0.90 | 0.0058 |
Table 20.
Weights of the indicators in the solution layer.
Goal Layer | Criterion Layer | Solution Layer |
---|
Off-Balance-Sheet Intangible Asset Earnings | Price Advantage | 0.2554 | Goodwill or Organizational Image | 0.2171 |
Customer Relationships | 0.1374 |
Information Technology Systems | 0.2463 |
Corporate Culture | 0.2407 |
Corporate Strategic Planning and Policies | 0.2172 |
Human Resources | 0.3829 |
Data Assets | 0.3084 |
Sales Growth | 0.1517 | Goodwill or Organizational Image | 0.1623 |
Customer Relationships | 0.0791 |
Information Technology Systems | 0.2849 |
Corporate Culture | 0.2528 |
Corporate Strategic Planning and Policies | 0.1738 |
Human Resources | 0.4426 |
Data Assets | 0.3545 |
Competitiveness Improvement | 0.1375 | Goodwill or Organizational Image | 0.1120 |
Customer Relationships | 0.1749 |
Information Technology Systems | 0.2290 |
Corporate Culture | 0.3350 |
Corporate Strategic Planning and Policies | 0.2252 |
Human Resources | 0.4039 |
Data Assets | 0.2702 |
Cost Savings | 0.4554 | Goodwill or Organizational Image | 0.3235 |
Customer Relationships | 0.1708 |
Information Technology Systems | 0.1502 |
Corporate Culture | 0.3740 |
Corporate Strategic Planning and Policies | 0.2008 |
Human Resources | 0.2751 |
Data Assets | 0.2555 |
Table 21.
Valuation Results of SFH’s Data Assets (Unit: RMB 100 million).
Year | 2024 | 2025 | 2026 | 2027 | 2028 |
---|
Free Cash Flow | 192.38 | 210.02 | 226.60 | 242.31 | 257.29 |
Contribution Value of Current Assets | 44.54 | 51.78 | 56.18 | 60.33 | 64.28 |
Contribution Value of Fixed Assets | 97.91 | 105.46 | 113.95 | 121.98 | 129.62 |
Contribution Value of On-Balance-Sheet Intangible Assets | 32.53 | 36.43 | 39.37 | 42.16 | 44.82 |
Contribution Value of Off-Balance-Sheet Intangible Assets | 17.40 | 16.35 | 17.10 | 17.84 | 18.57 |
K | 0.2861 |
Contribution Value of Data Assets | 4.9778 | 4.6789 | 4.8934 | 5.1047 | 5.3122 |
Discount Rate | 22.58% |
Present Value of the Contribution Value of Data Assets | 4.0610 | 3.8172 | 3.9921 | 4.1645 | 4.3338 |
Total | 20.3686 |
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