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Forecasting the Innovation Potential Under Uncertainty

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Industrial Engineering and Operations Management I (IJCIEOM 2018)

Abstract

The nations are looking for ways to increase the capacity and potential of innovation at national and international level. In order for the nations to use the competitive advantages resulting from innovation practices, a country should predict its innovation potential and hence prepare its strategic plans accordingly. The traditional forecasting methods are usually insufficient where sudden and unexpected changes happen nationwide and/or worldwide and limited information is available. The aim of this study is to provide a forecasting approach to predict the future innovation potential. To forecast the innovation potential, the percentage of enterprises with innovative activities is used as the main indicator. In the case of predicting the Turkey’s innovation potential, there exist a few bi-yearly historical data where traditional forecasting methods are insufficient. Therefore, grey forecasting approach that can handle uncertain environments is used in this study. The results indicate that the grey forecasting approach achieved satisfactory results while constructing the grey model with a small sample. In the innovation potential of Turkey, the predicted percentage for organization and/or marketing innovator is found to be highest with 60% where the actual is approximately 51%, and the predicted percentage of enterprises with abandoned/suspended innovation is found to be lowest with 6.5% where the actual is 8%. These predictions of innovation potential can be used to evaluate the effects of national and international policies within the country. Moreover, according to these predictions, the national policies should be improved to enhance the country’s competitive advantage in terms of innovativeness.

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Correspondence to Ozmehmet Tasan Seren .

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Seren, O.T., Burcu, F. (2019). Forecasting the Innovation Potential Under Uncertainty. In: Reis, J., Pinelas, S., Melão, N. (eds) Industrial Engineering and Operations Management I. IJCIEOM 2018. Springer Proceedings in Mathematics & Statistics, vol 280. Springer, Cham. https://doi.org/10.1007/978-3-030-14969-7_10

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