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Microfoundations of collaborative networks: The impact of social capital formation and learning on investment risk assessment

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  • Rodgers, Waymond
  • Degbey, William Y.
  • Housel, Thomas J.
  • Arslan, Ahmad

Abstract

Both traditional financial and intangible asset (IA) performance measures aid in the design of micromanagement organizational systems. We shed light on the microfoundational processes of collaborative networks and their impact on investment risk assessment by exploring IA performance measures in response to decomposing macro-level constructs. The IA measures focus on the exploration of individual human capital and their actions and interactions that influence investment risk assessments, which is critical for long-term prosperity. Additionally, human capital herein includes social factors such as social capital, which research has demonstrated can be developed from intellectual capital, and vice versa. Findings from an experiment with 40 professional investors (resulting in 160 independent observations) suggested that belonging to a company's collaborative networks—where they would gain access to IA performance information—led them to adjust their investment risk assessments downward or upward in response to material weakness or strength disclosures pertaining to IA performance. Additionally, a laboratory experiment revealed that 121 novice investors who learned how to interpret and use their social networks to gain access to IA performance information also led them to adjust their investment risk assessments in response to material IA information deficiencies in target companies. The results showed IA knowledge can be learned and transferred to impact social change.

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  • Rodgers, Waymond & Degbey, William Y. & Housel, Thomas J. & Arslan, Ahmad, 2020. "Microfoundations of collaborative networks: The impact of social capital formation and learning on investment risk assessment," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
  • Handle: RePEc:eee:tefoso:v:161:y:2020:i:c:s0040162520311215
    DOI: 10.1016/j.techfore.2020.120295
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