Research on negotiation-based partner selection approach

2002 ◽  
Vol 15 (01) ◽  
pp. 15
Author(s):  
Li Li
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Na Zhang ◽  
Xiaopeng Deng ◽  
Bon-Gang Hwang ◽  
Muchao Bi ◽  
Amin Mahmoudi

PurposeThis paper aims to develop a partner selection approach for the high-speed rail (HSR) firms from the perspective of achieving competitive advantage in the international competitive bidding sphere.Design/methodology/approachThe current study developed a partner selection approach based on the evolutionary game theory. Firstly, the current study identified the influencing variables and logical formation path of competitive advantage on the international HSR project by literature review and case analysis. After that, a pay-off model was developed based on the theoretical foundation. Meanwhile, the evolutionary stable strategy was analyzed for different combinations of initial pay-offs.FindingsA real-world case was simulated to verify the effectiveness of the developed approach. The simulation results secured support from three industry professionals, indicating the developed approach is valid.Originality/valueThe current study can help HSR firms to select their partners and develop a cooperation strategy from the perspective of winning awards. Also, the proposed approach is based on the advantage driving variables and formation path, which can contribute to HSR firms' understanding of the sources of competitive advantage.


2020 ◽  
Vol 17 (5) ◽  
pp. 243-265 ◽  
Author(s):  
Jiandong Xie ◽  
Sa Xiao ◽  
Ying-Chang Liang ◽  
Li Wang ◽  
Jun Fang

2019 ◽  
Author(s):  
Sierra Bainter ◽  
Thomas Granville McCauley ◽  
Tor D Wager ◽  
Elizabeth Reynolds Losin

In this paper we address the problem of selecting important predictors from some larger set of candidate predictors. Standard techniques are limited by lack of power and high false positive rates. A Bayesian variable selection approach used widely in biostatistics, stochastic search variable selection, can be used instead to combat these issues by accounting for uncertainty in the other predictors of the model. In this paper we present Bayesian variable selection to aid researchers facing this common scenario, along with an online application (https://ssvsforpsych.shinyapps.io/ssvsforpsych/) to perform the analysis and visualize the results. Using an application to predict pain ratings, we demonstrate how this approach quickly identifies reliable predictors, even when the set of possible predictors is larger than the sample size. This technique is widely applicable to research questions that may be relatively data-rich, but with limited information or theory to guide variable selection.


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