Standalone Valuation Method for Software-as-a-Service Operational Knowledge Derived from Human Intellectual Capital Qualitative Changes

Suguru Sakuma () and Tomoyuki Furutani
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Suguru Sakuma: Faculty of Policy Management, Graduate School of Media and Governance, Keio University, Fujisawa-shi 252-0882, Kanagawa, Japan
Tomoyuki Furutani: Faculty of Policy Management, Graduate School of Media and Governance, Keio University, Fujisawa-shi 252-0882, Kanagawa, Japan

Administrative Sciences, 2024, vol. 14, issue 4, 1-12

Abstract: This study focuses on digital operational knowledge belonging to natural persons and proposes a greenfield approach to differentiate the value of intangibles from that of human intellectual capital. Our research approach involves two assessments. Assessment 1 evaluates intangible assets using the internally generated goodwill (IGG) measure. We analyze time-series IGG data for six digital sectors, using the top 90 software-as-a-service (SaaS) companies as a benchmark. The results indicate that the IGG of the SaaS benchmark is higher than the total IGG of the six sectors. Assessment 2 focuses on the correlation between digital labor investment and digital investment returns before and after 2013 for the six sectors to identify positive and negative correlations from 2013 onward. The results indicate that, since 2013, a qualitative change has occurred in digital labor capital that has not been reflected in financial statements because of accounting distortions and that the returns on investment for digital labor have been underestimated. The standalone valuation of digital know-how that belongs to natural persons, previously based on operating expense, will be based on capital expenditure. In addition, amortization will have the same contribution as depreciation of tangible assets to value creation.

Keywords: SaaS; digital intangible; value transfer; human intellectual capital; accounting distortion; digital labor productivity (search for similar items in EconPapers)
JEL-codes: L M M0 M1 M10 M11 M12 M14 M15 M16 (search for similar items in EconPapers)
Date: 2024
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