This book introduces a powerful new concept to the business world — Collaborative Innovation Networks (COINs). COINs have been around for hundreds of years, and many of us have already been a part of a COIN without knowing it. What makes COINs so relevant today is that the concept has reached its tipping point, thanks to the communication capabilities of the Internet and the World Wide Web. A COIN is a cyberteam of self-motivated people with a collective vision, enabled by technology to collaborate in achieving a common goal — an innovation — by sharing ideas, information, and work. Working this way is key to successful innovation, and it is no exaggeration to state that COINs are the most productive engines of innovation ever. This book explores why COINs are so important to business success in the new century. It explains the traits that characterize COIN members and COIN behavior. It makes the case for why businesses ought to be rushing to uncover their COINs and nurture them, and provides tools for building organizations that are more creative, productive, and efficient by applying principles of creative collaboration, knowledge sharing, and social networking. Through real-life examples of COINs in several business sectors, the book shows how to leverage COINs to develop successful products in R&D, grow better customer relationships, establish better project management processes, and build higher-performing teams. There is even a method offered for locating, analyzing, and measuring the impact of COINs on an organization.

Purpose – Social media provide an impressive amount of data about users and their interactions, thereby offering computer and social scientists, economists, and statisticians – among others – new opportunities for research. Arguably, one of the most interesting lines of work is that of predicting future events and developments from social media data. However, current work is fragmented and lacks of widely accepted evaluation approaches. Moreover, since the first techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains. Therefore, better understanding the predictive power and limitations of social media is of utmost importance. Design/methodology/approach – Different types of forecasting models and their adaptation to the special circumstances of social media are analyzed and the most representative research conducted up to date is surveyed. Presentations of current research on techniques, methods, and empirical studies ai...

This paper contributes to the ongoing stream of research correlating social network structure with individual and organizational performance. While teaching a course on optimizing online communication behaviour and social network analysis, we collected preliminary data on the relationship between dynamic social network structures and individual and team performance. Students from Helsinki University of Technology and University of Cologne, who had never met face to face, formed virtual interdisciplinary teams collaborating on a common task, the communication analysis of online communities. As part of their task, students correlated performance of the community they were analysing with social network structure. In this research, we compare social network structure and individual and team performance of participants in a multi-user online computer game with social network structure and performance among the student teams. While among computer gamers the number of communication links predicts performance, a balanced contribution index predicts performance of the student knowledge worker teams. We also give general recommendations for efficient virtual communication behaviour.

This paper presents new evidence regarding a firm’s probability for survival, based on the network structure of the firm’s managers. We found that start-ups that have larger informal communication networks increased their chance to survive external shock. Original data have been collected from Israeli software start-ups during the dot-com economic growth. About eight years later, we added information about their ability to survive the burst of the dot-com bubble. From a theoretical point of view, this paper highlights the power of the classic social networks approach in explaining organizational performance. From a practical point of view, these findings offer some guidelines for managers of start-ups. Our results show that the size of informal interfirm networks really matters.

This paper describes an emotion recognition system for dogs automatically identifying the emotions anger, fear, happiness, and relaxation. It is based on a previously trained machine learning model, which uses automatic pose estimation to differentiate emotional states of canines. Towards that goal, we have compiled a picture library with full body dog pictures featuring 400 images with 100 samples each for the states “Anger”, “Fear”, “Happiness” and “Relaxation”. A new dog keypoint detection model was built using the framework DeepLabCut for animal keypoint detector training. The newly trained detector learned from a total of 13,809 annotated dog images and possesses the capability to estimate the coordinates of 24 different dog body part keypoints. Our application is able to determine a dog’s emotional state visually with an accuracy between 60% and 70%, exceeding human capability to recognize dog emotions.