• The Journal of Small Business Innovation
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• v.19 no.4
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• pp.1-17
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• 2016

The purpose of this study is to suggest an efficient way for ventures to achieve innovation performance through R&D cooperative arrangements. Achieving innovation is one of the critical factors for the survival of ventures. Unlike established firms, ventures often do not have the specialized assets necessary to take technological developments to the product and market stages. Young and resource-constrained firms can achieve innovation by finding and accessing to the complementary resources from R&D cooperation. In the current business environment, many firms are likely to engage in multiple simultaneous R&D cooperations with different partners. Recent research stream addresses the importance of efficient cooperation management from the holistic portfolio perspective. Since maintaining the multiple cooperative relations require substantial amount of time and effort, managing cooperative relationships play a more important role to resource-constrained firms. In order to find an efficient composition of R&D cooperative partners, we mainly focus on the diversity of partner type and dependence level in partnership. We analyze the data on Korean manufacturing ventures collected in the Korean Innovation Survey (KIS) which was conducted by the Science and Technology Policy Institute (STEPI). The KIS questionnaire assesses the existence of cooperative relationships with different types of partners respectively. The types of cooperating partners are affiliated companies, suppliers, clients & customers, competitors or other firms in the same industry, consulting firms, universities, and research institutes. We confirm that ventures obtain relatively higher benefits from R&D cooperation compared with established firms in terms of innovation performance. The results show that a moderate level of diversity in cooperative partner type composition increases innovation. Moreover, diversity of cooperation dependency among the partners enhances innovation performance. Likewise, concentrating on the quality aspects of cooperative composition, such as diversity of partners and degree of dependencies, this study offers some implications for ventures in managing partners from an integrative perspective.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.