Hyongmook Cheong

Hyongmook Cheong


Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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Verified
Journal Article
Open access
PDF file
HCHyongmook Cheong
Hyongmook Cheong
보영(보영) 김 (김)보영(보영) 김 (김)
보영(보영) 김 (김)
IUIvan Ureta
Ivan Ureta
As both investment attraction and mergers and acquisitions targeting information technology and platform companies are becoming more important in the digital-centric economic environment, interest in valuing corporate data assets is increasing. Accordingly, among the income approaches used in business valuation, this study presents a data valuation model based on discounted cash flow. This model is expected to be useful for corporate investment decision-making. The assumptions used in this study for the estimation of data income include intangible asset value, exclude net asset value, and data attribution is centered on technology, human resources, and market factors. In particular, data attribution accounts comprise ordinary data research and development, data labor costs, and data advertising expenses. Data costs were divided into those incurred during collection, storage, curation, analysis, and utilization. Financial statements and related data from a real estate information platform operator over three years were collected and used to simulate the data valuation model. The simulation reveals that the operator possesses KRW 472.6 billion in data assets. Ultimately, the data valuation model developed in this study can contribute to strengthening platform operators’ investment attraction, guaranteeing financial sustainability, and transparency and data assetization.

Verified
Journal Article
Open access
PDF file
HCHyongmook Cheong
Hyongmook Cheong
보영(보영) 김 (김)보영(보영) 김 (김)
보영(보영) 김 (김)
IUIvan Ureta
Ivan Ureta
The importance of data assets as intangible corporate assets is being emphasized as more business activities based on digital technology are being carried out. This study proposes the development of a data valuation model that can enable companies to use data assets as collateral for loans in financial transactions. To this end, a model was designed with a focus on the cost approach, which is less likely to involve arbitrariness and error among other valuation model approaches. Furthermore, a model simulation was conducted after securing transaction data of a Korean secondhand marketplace provider. Among the total costs of this marketplace provider, the cost of using data reflecting the ratio of data activities was derived, focusing on financial statements and tangible and intangible assets for the last five years. The data asset acquisition costs were derived, and the data replacement costs were calculated by reflecting the past price and wage growth rates. The results revealed that simulation companies could use a total of KRW 26.8 billion worth of data as collateral for a loan. Accordingly, the data valuation model developed in this study will contribute to reinforcing the value of corporate data assets and proposing a new means of corporate financing.