Waymond Rodgers, William Y. Degbey, Thomas J. Housel, Ahmad Arslan, Microfoundations of collaborative networks: The impact of social capital formation and learning on investment risk assessment, Technological Forecasting and Social Change, Volume 161, 2020, 120295, ISSN 0040-1625, https://doi.org/10.1016/j.techfore.2020.120295
Microfoundations of collaborative networks : the impact of social capital formation and learning on investment risk assessment
|Author:||Rodgers, Waymond1,2; Degbey, William Y.3; Housel, Thomas J.4;|
1University of Texas, El Paso, El Paso, Texas 79968, U.S.A.
2University of Hull, Cottingham Rd, Hull, North Humberside, HU6 7RX, U.K.
3Department of Marketing & International Business, Turku School of Economics, University of Turku, FI-20014, Turku, Finland
4Naval Postgraduate School, Department of Information Sciences, Root Hall 239, Monterey, CA 93943, California, USA
5Department of Marketing, Management & International Business, Oulu Business School, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020092170651
|Publish Date:|| 2022-09-12
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.
Technological forecasting and social change
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
512 Business and management
William Degbey acknowledges the Foundation for Economic Education (Liikesivistysrahasto) and Marcus Wallenberg Foundation in Finland for supporting this research.
© 2020 Elsevier Inc. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.