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

Author

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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)

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.

Suggested Citation

  • Suguru Sakuma & Tomoyuki Furutani, 2024. "Standalone Valuation Method for Software-as-a-Service Operational Knowledge Derived from Human Intellectual Capital Qualitative Changes," Administrative Sciences, MDPI, vol. 14(4), pages 1-12, April.
    • Handle: RePEc:gam:jadmsc:v:14:y:2024:i:4:p:71-:d:1370665
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    References listed on IDEAS

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    1. Valerie Walker & Gail Bowkett & Isabelle Duchaine, 2018. "All Companies Are Technology Companies: Preparing Canadians with the Skills for a Digital Future," Canadian Public Policy, University of Toronto Press, vol. 44(S1), pages 153-158, November.
    2. Grigorios Emvalomatis, 2017. "Is productivity diverging in the EU? Evidence from 11 Member States," Empirical Economics, Springer, vol. 53(3), pages 1171-1192, November.
    3. Nadim Ahmad & Paul Schreyer, 2016. "Are GDP and Productivity Up to the Challenges of the Digital Economy?," International Productivity Monitor, Centre for the Study of Living Standards, vol. 30, pages 4-27, Spring.
    4. Kourentzes, Nikolaos & Petropoulos, Fotios, 2016. "Forecasting with multivariate temporal aggregation: The case of promotional modelling," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 145-153.
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