Abstract
A “virtual enterprise” is an effective organization formed by enterprises and partners under market opportunity which can flexibly adapt to the dynamic market demand and improve the competitiveness of enterprises. To select virtual enterprise partners objectively and scientifically, this study proposes the evaluation model of the innovation resource capability of the alternative enterprises under the unknown weight. In the multigranularity hesitation fuzzy language environment, the unknown weight is solved by using fuzzy entropy theory. The risk attitude of decision-making enterprises is introduced by using the improved prospect theory and the selection of partners is comprehensively considered. Finally, a case study is presented to demonstrate the effectiveness of the proposed approach. The research intends to enable the virtual enterprise to choose the partners swiftly such that they can compensate for the shortcomings and optimize the allocation of innovation resources.
Highlights
Most of the research on VE partner selection is based on the assumption of rationality
Based on the utility function proposed by Bernoulli, we introduce a new parameter based on the utility curve to render the relative loss of income more sensitive [53]. e expression of the value function of the improved prospect theory is shown in the following equation: φΔxα, Δx ≥ 0, v(Δx) −θ(−Δx)β, Δx < 0, (17)
To avoid the influence of experts’ subjective tendency weight, this study uses information entry and fuzzy theory to calculate the weight of the evaluation criteria when the weight of VE partner selection is unknown
Summary
Partner selection has received considerable attention for its significant effect on successful organization management. In 2010, Ye [27] proposed an extended TOPSIS for group decision-making with interval-valued intuitionistic fuzzy numbers, which is used to solve the problem of partner selection in VEs under the condition of information asymmetry. Such solutions do not address the flexibility of enterprise collaboration and rarely consider collaboration risk, unsettling the rationality of the selection results. Erefore, this study proposes to solve the unknown weight in the multigranularity hesitation fuzzy language environment, and on this basis, combined with the risk attitude of decision-making members, to screen the VE partners. A case study was used to evaluate the impact of different types of partners on the collaborative innovation efficiency of VEs to optimize resource allocation and give full play to the collaborative ability of VE
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