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Titlebook: Collaborative Innovation Networks; Latest Insights from Yang Song,Francesca Grippa,João Leitão Book 2019 Springer Nature Switzerland AG 201

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楼主: papyrus
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Mirror, Mirror on the Wall, Who Is Leaving of Them All: Predictions for Employee Turnover with Gatedcontaining 12 months of time series of e-mail communication from 3952 managers, our model reached an accuracy of 80.0%, a precision of 74.5%, a recall of 84.4%, and a Matthews correlation coefficient value of 61.5%. This paper contributes to turnover literature by providing a novel analytical perspective on key elements of turnover models.
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Show Me Your Moves: Analyzing Body Signals to Predict Creativity of Knowledge Workerscreativity reported by the individuals. Comparing those correlations with known predictors of creativity such as mood states and personality traits, we found that movement-related body signals predict creativity on the same accuracy level as mood states and personality traits do.
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Measuring Human-Animal Interaction with Smartwatches: An Initial Experimente way of attaching the Pebble influenced the measurement quality. In summary, the experiment indicates that there might be a connection between the heart rate (BPM), the average movement (VMC), and the mood data (pleasance and activation) of an animal and its owner during training sessions.
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The King’s Ring: A Matter of Trustthe “open strategy” at the same speed. Besides, we also suggest that increasing knowledge spillover will contribute to innovation efficiency and stabilization. Theoretically, our study explains the stabilization strategy of the game and provides reasonable recommendations for policy makers.
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Book 2019g of individual workers. The book also discusses how such networks can benefit startups, offering new self-organizing forms of leadership in which all stakeholders are encouraged to collaborate in the development of new products..
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Mirror, Mirror on the Wall, Who Is Leaving of Them All: Predictions for Employee Turnover with Gatedight help organizations to react to these mostly negative events with, e.g., improved employee retention strategies. Current studies use a “standard analysis approach” (Steel, Academy of Management Review 27:346–360, 2002) to predict employee turnover; accuracy in predicting turnover by this approac
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