Purpose -The purpose of this paper is to explore the mediating effect of business strategy (BS) on intellectual capital (IC) and financial performance (FP). The impact of financial crisis is also a major topic of this research. Design/methodology/approach -This study applies BS as mediator to explore the relationships between IC, BS, and FP. Partial least squares is employed to test the reliability and validity of measurements and the significance of path coefficients, and therefore to examine the hypotheses. Findings -IC has significant impacts on BS and FP in all samples, as well as in those years before and after the financial crisis. BS has a partial significant mediating effect between IC and FP. BS has significant effects on FP in all samples and pre-financial crisis, but has not in post-financial crisis.Research limitations/implications -IC has significant impacts on BS and FP. Moreover, the relationships of IC, BS, and FP are different during pre-and post-financial crisis. The direct effect of IC on FP is confirmed and consistent, and the indirect effect of IC on FP by BS is dependent upon the environment status. Practical implications -Enterprises should pay attention to IC, BS, and the related changes in environment status. These help enterprises develop appropriate strategies, maintain competitive advantage, and upgrade FP. Originality/value -This study applies BS as mediator, and explores the relationships between IC, BS, and FP. The impact of financial crisis is also discussed. The results may serve as the criteria for strategic performance management.

Many nonlinear programs can be piecewisely linearized by adding extra binary variables. For the last four decades, several techniques of formulating a piecewise linear function have been developed. By expressing a piecewise linear function with m + 1 break points, the current method requires us to use m additional binary variables and 4m constraints, which causes heavy computation when m is large. This study proposes a superior way of expressing the same piecewise linear function, where only ⌈ log 2 m ⌉ binary variables and 8 + 8 ⌈ log 2 m ⌉ additive constraints are used. Various numerical experiments demonstrate that the proposed method is more computationally efficient than current methods.

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Many optimization problems are formulated as generalized geometric programming (GGP) containing signomial terms f(X)·g(Y), where X and Y are continuous and discrete free-sign vectors, respectively. By effectively convexifying f(X) and linearizing g(Y), this study globally solves a GGP with a lower number of binary variables than are used in current GGP methods. Numerical experiments demonstrate the computational efficiency of the proposed method.

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