PurposeThe purpose of this paper is to examine supply chain (SC) innovation for improving organisational performance in the healthcare industry.Design/methodology/approachA research model is proposed which describes the impact of SC innovation, supplier cooperation, SC efficiency, and quality management (QM) practices on organisational performance. The proposed research model and hypotheses were tested using structural equation modeling based on data collected from 243 hospitals.FindingsThe results of the study support that organisational performance is positively associated with constructs of each SC innovation factor. Innovative design of SC has a significant impact on selection of and cooperation with excellent suppliers, improved SC efficiency, and encouragement of QM practices.Research limitations/implicationsThe data used in this study were collected from relatively large hospitals with more than 100 beds in South Korea. The generalization of the study results may be limited by the size of sample hospitals.Originality/valueThis study provides useful planning information in the healthcare industry. The results suggest successful implementation of SC management is attained through continuous SC innovation with supplier cooperation, which in turn improves organisational performance.

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In this paper, we present a comparative analysis of the forecasting accuracy of univariate and multivariate linear models that incorporate fundamental accounting variables (i.e., inventory, accounts receivable, and so on) with the forecast accuracy of neural network models. Unique to this study is the focus of our comparison on the multivariate models to examine whether the neural network models incorporating the fundamental accounting variables can generate more accurate forecasts of future earnings than the models assuming a linear combination of these same variables. We investigate four types of models: univariate-linear, multivariate-linear, univariate-neural network, and multivariate-neural network using a sample of 283 firms spanning 41 industries. This study shows that the application of the neural network approach incorporating fundamental accounting variables results in forecasts that are more accurate than linear forecasting models. The results also reveal limitations of the forecasting capacity of investors in the security market when compared to neural network models.

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