Stevens Institute of Technology

About: Stevens Institute of Technology is a education organization based out in Hoboken, New Jersey, United States. It is known for research contribution in the topics: Computer science & Cognitive radio. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.

Knowledge spillover and innovation in technological clusters

Abstract: Knowledge spillovers have been used to explain the increased rate of innovation that is found in technological clusters. The last two decades have seen an increasing interest by researchers trying to capture and measure the effects of these spillovers. However, very little is known about the mechanisms of knowledge exchange that take place in clusters. In this paper we draw on the current body of knowledge and use the concepts of tacit and explicit knowledge to understand how knowledge spillovers actually take place. We present a conceptual model of knowledge accessibility as a mean for knowledge transfer, and we distinguish between knowledge transfer and knowledge spillover based on the knowledge holder's intention or lack of it to exchange such knowledge. We then review how tacit knowledge is being accessed in technological clusters and how it affects knowledge creation.

The Role of Corporate Culture in Bad Times: Evidence from the COVID-19 Pandemic

Abstract: After fitting a topic model to 40,927 COVID-19-related paragraphs in 3,581 earnings calls over the period January 22 to April 30, 2020, we obtain firm-level measures of exposure and response related to COVID-19 for 2,894 U.S. firms. We show that despite the large negative impact of COVID-19 on their operations, firms with a strong corporate culture outperform their peers without a strong culture. Moreover, these firms are more likely to support their community, embrace digital transformation, and develop new products than those peers. We conclude that corporate culture is an intangible asset designed to meet unforeseen contingencies as they arise.

Learning Dynamic Context Graphs for Predicting Social Events

Abstract: Event forecasting with an aim at modeling contextual information is an important task for applications such as automated analysis generation and resource allocation. Captured contextual information for an event of interest can aid human analysts in understanding the factors associated with that event. However, capturing contextual information within event forecasting is challenging due to several factors: (i) uncertainty of context structure and formulation, (ii) high dimensional features, and (iii) adaptation of features over time. Recently, graph representations have demonstrated success in applications such as traffic forecasting, social influence prediction, and visual question answering systems. In this paper, we study graph representations in modeling social events to identify dynamic properties of event contexts as social indicators. Inspired by graph neural networks, we propose a novel graph convolutional network for predicting future events (e.g., civil unrest movements). We extract and learn graph representations from historical/prior event documents. By employing the hidden word graph features, our proposed model predicts the occurrence of future events and identifies sequences of dynamic graphs as event context. Experimental results on multiple real-world data sets show that the proposed method is competitive against various state-of-the-art methods for social event prediction.