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A policy-making model for evolutionary SME behavior during a pandemic recession supported on game theory approach.
Hafezalkotob, Ashkan; Nersesian, Lia; Fardi, Keyvan.
  • Hafezalkotob A; La Trobe Business School, La Trobe University, Melbourne, Australia.
  • Nersesian L; College of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran.
  • Fardi K; Faculty of Industrial Engineering, Urmia University of Technology (UUT), Urmia, Iran.
Comput Ind Eng ; 177: 108975, 2023 Mar.
Article in English | MEDLINE | ID: covidwho-2165161
ABSTRACT
The global economy has experienced a tremendous shock caused by the Covid-19 pandemic and its effects on the normal activities of SMEs, which provide essential driving economic force. Considering that there is currently no precise prediction about the end of this pandemic, many SMEs must make critical decisions about whether to remain in the market during the pandemic or to leave it, investing their assets in a more secure sector of the economy. However, in order to convince SMEs to remain in the market, thus maintaining the damaged economy, governments may variously apply punitive or supportive measures. In this regard, the interaction between SMEs strategies and government measures can be considered as an evolutionary game, in which the governments impose various policies after observing the evolutionary behaviors of SMEs. An evolutionary stable strategy (ESS) is derived through a replicator dynamic system, and the available payoff of each player is calculated by Nash equilibrium (NA). Finally, a numerical example is presented, and related managerial insights are proposed at the end of the current study. For instance, contrary to general belief, it can be inferred from investigating possible scenarios that punitive policies are more effective than supportive measures in convincing SMEs to remain in the market.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Comput Ind Eng Year: 2023 Document Type: Article Affiliation country: J.cie.2022.108975

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Comput Ind Eng Year: 2023 Document Type: Article Affiliation country: J.cie.2022.108975