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Extreme support vector regression (ESVR) has been widely used in the design, analysis and optimization of engineering systems of its fast training speed and good computational ability. However, the ESVR model is only able to utilize one-fidelity information of engineering system. To solve this issue, this paper extends extreme support vector regression (ESVR) to a multi-fidelity surrogate (MFS) model which can make use of a few expensive but higher-fidelity (HF) samples and a lot of inaccurate but cheap low-fidelity (LF) samples, named ESVR-MFS.

In the ESVR-MFS model, a kernel matrix is designed to evaluate the relationship between the HF and LF samples. The root mean square error of HF samples is used as the training error metric, and the optimal hyper-parameters of the kernel matrix are obtained through a heuristic algorithm.

A number of numerical problems and three engineering problems are used to compare the ESVR-MFS model with the single-fidelity ESVR model and two benchmark MFS models. The results show that the ESVR-MFS model exhibits competitive performance in both numerical cases and practical cases tested in this work.

The proposed approach exhibits great capability for practical multi-fidelity engineering design problems.

A MFS model is proposed based on ESVR, which can make full use of the advantages of both HF data and LF data to achieve optimal results at same or lower cost.

Therefore, this study aims to explore the relationships among OC, KS, OCB and OI “Organization” is often seen as a company and few studies pay much attention to tribes and other related organizations and communities of Indigenous peoples. However, Indigenous peoples production organizations (IPPOs) would be certainly influenced by factors from the internal/external, including organizational culture (OC), organizational citizenship behavior (OCB), knowledge sharing (KS) and organizational innovation (OI). Therefore, this study aims to explore the relationships among OC, KS, OCB and OI.

Based on valid 139 Indigenous workers in IPPOs, this study used structural equation modeling to validate the relationships among OC, OCB, KS and OI.

The empirical findings indicate that OC would significantly influence OCB and OI, whereas KS would not have significant impact on OI. In addition, OC would not influence KS as usual, whereas OCB would do. Finally, OCB would impact KS.

As OCB acts as a complete mediator in OC–KS relationship, it means that these IPPOs already have OCB to motivate their staffs to do KS, but not enough to achieve more excellent performance on innovation.

Compared to past studies, this study aims to investigate the theory of organizational behavior and whether it is suitable between general businesses and IPPOs.