Sustainable economic growth is closely linked to a national system of innovation’s (NSI) adaptability. The NSI of a country in catch-up mode is different than one at the technology frontier. In this exploratory paper we use a socio-cognitive approach to demonstrate that shared mental models (SMMs) need to change with the evolution of a NSI to sustain growth. For South Korea in particular, this insight offers a way for it realize better technology transfer and commercialization (TTC) performance and a new cognitive model for its TTC teams to transition to and operate at the technology frontier. We use cognitive mapping techniques to interpret the interviews of teams in South Korea’s public research institutes active in TTC. Their SMMs reveal that a top-down policy for catching-up NSIs reinforces SMMs around a linear commercialization process. Alternatively, the participatory policy approach of frontier innovation systems supports interaction and the active learning of their actors’ SMMs. This affords a wider variety of innovation and commercialization processes. Consequently, a policy of transitioning NSIs that remains top-down freezes TTC teams in their existing SMMs fettering growth. By extension, as a transitioning NSI, South Korea should adopt policy that reconfigures its existing SMMs to encourage a more open approach to TTC.

SolBridge, a young business school in South Korea, acquired an open-source learning management system (LMS) in early years of its operation. It switched to an on-premises proprietary LMS a few years later. However, before renewal of its annual license SolBridge was caught in a typical dilemma of information systems acquisition (IS acquisition), i.e., keeping the costs lowest while maximizing user and organizational benefits. The decision was made more complex by newly available cloud-hosting options. SolBridge CIO analyzed the total cost of ownership (TCO) and benefits of several options before recommending one of them to school management. This case provides senior undergraduate or graduate students with an opportunity to assume the role of SolBridge CIO and analyze TCO for its next LMS. Not only will they realize the managerial challenges involved in selection and acquisition of enterprise information systems but they will also learn about the economic feasibility of on-premises and cloud-hosting options. It also becomes visible to them that organizations grapple with unceasing technological and organizational changes. Therefore, flexibility and scalability emerge as key elements of modern IT infrastructure.

Introduction: Diverticulitis is the inflammation and/or infection of small pouches known as diverticula that develop along the walls of the intestines. Patients with diverticulitis are at risk of mortality as high as 17% with abscess formation and 45% with secondary perforation, especially patients that get admitted to the inpatient services are at risk of complications including mortality. We developed a deep neural networks (DNN) based machine learning framework that could predict premature death in patients that are admitted with diverticulitis using electronic health records (EHR) to calculate the statistically significant risk factors first and then to apply deep neural network. Methods: Our proposed framework (Deep FLAIM) is a two-phase hybrid works framework. In the first phase, we used National In-patient Sample 2014 dataset to extract patients with diverticulitis patients with and without hemorrhage with the ICD-9 codes 562.11 and 562.13 respectively and analyzed these patients for different risk factors for statistical significance with univariate and multivariate analyses to generate hazard ratios, to rank the diverticulitis associated risk factors. In the second phase, we applied deep neural network model to predict death. Additionally, we have compared the performance of our proposed system by using the popular machine learning models such as DNN and Logistic Regression (LR). Results: A total of 128,258 patients were used, we tested 64 different variables for using univariate and multivariate (age, gender and ethnicity) cox-regression for significance only 16 factors were statistically significant for both univariate and multivariate analysis. The mortality prediction for our DNN out-performed the conventional machine learning (logistic regression) in terms of AUC (0.977 vs 0.904), training accuracy (0.931 vs 0.900), testing accuracy (0.930 vs 0.910), sensitivity (90% vs 88%) and specificity (95% vs 93%). Conclusion: Our Deep FLAIM Framework can predict mortality in patients admitted to the hospital with diverticulitis with high accuracy. The proposed framework can be expanded to predict premature death for other disease.