Progressive performance modeling for the strategic determinants of market value in the high-tech oriented SMEs
Jooh Lee and
International Journal of Production Economics, 2017, vol. 183, issue PA, 91-102
The purpose of this paper is to present an adaptive performance model using neural networks in scrutinizing the impact of strategic factors on firm performance, especially within high-tech oriented small and medium-sized enterprises (SMEs) in the United States. This paper explores generalized learning of backpropagation neural network (BPNN) in conducting an explanatory and predictive analysis of the strategic determinants of the market value of SMEs. The progressive performance model through BPNN is designed to capture the different and unique significance of strategic determinants for better firm performance by dividing high-tech segments into two performance groups: high performers and low performers. In doing so, this paper introduces a salient BPNN approach for performance modeling and extends the applications of BPNN. Furthermore, efficiency measurement and performance prediction using BPNN adds meaningful value to the literature and highlights the potential advantages of using BPNN. The empirical results demonstrate the successful implementation of the model and clearly distinguish varying patterns at different performance levels, High and Low, which is a significant finding of this study. Overall, sales growth, R&D intensity, and current ratio can be used as major strategic determinants of market value performance of the technology-oriented SMEs.
Keywords: Progressive performance modeling; Neural networks; R&D intensity; Inventory turnover; Market value (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:183:y:2017:i:pa:p:91-102
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