Collaborative Innovation Networks

The purpose of this paper is to demonstrate how to build a shared future through Collaborative Innovation Networks (COINs) using a historical case study of the timely and unique Co-Wuity partnership between Henry Ford, the automotive industrialist, and Albert Kahn, the Detroit architect who designed and built over 1000 buildings for Ford over 34 years from 1908 to 1942.

Wuity is a higher cognition with the flexible and imaginative ability to gain intuitive understanding of underlying phenomena and connect deliberative thinking for insightful problem-solving through a process of mindful observation and visual analogy.

Ford was building an automotive manufacturing company, which was designing and manufacturing a vehicle in standardized high volumes and that needed a new building to meet that growing demand. Kahn was building an architectural firm, which was designing custom building plans on a project-by-project basis to meet the needs of his clients, also on a high-volume scale.

Both developed Wuity behaviors of mindful observation and visual thinking to gain insights for innovations, experimentation, and implementation to see and understand what worked in their respective ventures, influencing each other’s businesses in the process.

The focus of the case is on the emerging and future technologies at that time, on how COINs strengthened adaptability and transformability to leverage novel ideas as a competitive advantage using the two early nineteenth-century start-ups: the Ford Motor Company and Albert Kahn Architects and Engineers as our historical case study.

Our goal is to inspire today’s leaders by demonstrating the lessons these past leaders taught us about how to build a shared future through a Collaborative Innovation Network. In addition, this case extends the analysis of Wuity to a Co-Wuity Collaborative Innovation Network.

Employee turnover is a serious issue for organizations and disrupts the organizational behavior in several ways. Hence, predicting employee turnover might 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 approach is only low to moderate. To address this shortcoming, we conduct a deep learning experiment to predict employee turnover. Based on a unique dataset containing 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.

In the last decade, researchers have increasingly analyzed the interaction between business and environment under new perspectives. We join this effort focusing on the positive change that can arise from new collaborative opportunities between business and the environment, in order to embrace environmental challenges and pursue reciprocal benefits. We evaluate this synergetic activity using system thinking which identifies pivotal center, has the power to originate connections, and regulates a system’s behavior. In this regard, we believe education and technology are among the most important elements in the system. They play a major role in determining and weighing the interactions between environment and business. The two scenarios presented here are (1) the status quo system and (2) a system in which technology and education are empowered and serve as leverage points to develop a more efficient use of the world resources, thus using a smaller portion of the world’s physical capacity. This last scenario focuses on one environmental sphere (water), but the same tool could be applied to other environmental aspects. We build our analysis balancing the contributions coming from both professional and academic spheres. Progress has been made to integrate sustainability into strategy, such as the adoption of science-based targets by organizations aiming to reduce the effect of climate change. The authors believe that this process explains new ways in which business and society can thrive for generations to come.

After Jeff Bezos, the founder and CEO of Amazon, acquired The Washington Post in late 2013, the newspaper’s neutrality and independence from Bezos’ agenda has been in question. This paper takes a first step at exploring whether the neutrality of The Washington Post has changed after the acquisition in 2013. By analyzing the content of newspaper articles based on the sentiment and the emotional tone and by comparing results to a baseline consisting of The New York Times and The Guardian, this paper tries to establish if and how the Post’s reporting on Amazon has changed. Albeit results of this study are limited to the scope and the distortion of the analyzed data, we find an increasing number of articles about Amazon in all newspapers after the acquisition. Furthermore, it can be shown that the overall positive sentiment of The Washington Post decreases, while the emotional tone intensifies.

This paper introduces Tribefinder, a novel system able to reveal Twitter users’ tribal affiliations. Tribefinder establishes to which tribes individuals belong through the analysis of their tweets and the comparison of their vocabulary. These tribal vocabularies are previously generated based on the vocabulary of tribal influencers and leaders selected using Tribecreator. To demonstrate its functionality, in the case presented in this paper, the system was calibrated in three specific tribal macro-categories: alternative reality, lifestyle, and recreation. Apart from describing the methodology we used to create this system, we also provide some practical examples of its use, thus giving a first indication of its potential. Finally, we present the results of the adoption of a t-SNE visualization approach, useful to verify whether tribe members cluster closely together.

In this paper, we elaborate relevant theoretical and empirical foundations from the literature and prepare a conceptual framework on how social media teams of hospitals can act as mediators of various parties in their digital health ecosystem. Furthermore, we present our analytical approach as well as preliminary findings from our ongoing research. The paper is a research in progress from the second phase of a project on strategic use of social media in hospitals, which started in April 2018. Its regional focus is on Austria, Germany, and Switzerland, where we conduct empirical studies on the research questions.

China is a multicultural society made up of 56 ethnic groups. In addition to the complex cultural healthcare beliefs and traditions, China’s 1.38 billion people live under diverse social and economic conditions. Providing holistic care in such a multifaceted context is challenging for nurses and nursing educators. If nursing students are to provide holistic patient care, then nurse educators must design courses and pedagogic strategies that enhance student capacity to assess and address unique needs including biological, psychological, social, and spiritual problems and concerns. In particular, beginning with spiritual dimension of health is promising because spiritual beliefs are interwoven within the cultural fabric of Chinese people. However, Chinese people do not readily share private thoughts and feelings, and thus even introducing the topic of the provision of spiritual care as part of nursing curricula is challenging. In this article we share the educational content and pedagogical approach that was implemented in an undergraduate multicultural nursing care course that was designed to overcome these challenges. The cultural competency model developed by Camphina-Bacote (Journal of Transcultural Nursing 13:181–184, 2002) was instrumental in structuring learning around spirituality as part of cultural competency training.

This case illustrates the use of mixed methods for analyzing structure, content, and sentiment of a city council task force appointed to guide the city (note, “the city’s” name remains anonymous as per research agreement) in updating their oil and gas drilling ordinance. The novel method approach integrates linguistic conversation analysis and social network influence to analyze the interactions among stakeholders over time as they occur both inside and outside formal meetings. The case study also represents a practical example of conducting e-Research where a small team of four researchers shared a cloud-based PC as a collaborative space for cost efficiency and data sharing and as an educational tool for team members working across mixed PC and Mac operating systems.

Although this research is a single case study of one community, the data sources and methods can be scaled to include multiple communities or a larger geographic area that could include a county, state, region, or an entire country. The authors identify traditional methods that can take advantage of data analytics enabled by new technologies.

This paper studies the “evolutionarily stable strategy” (ESS) between industry and university during collaborative innovation processes based on evolutionary games. By designing knowledge sharing models, we analyze the impact factors of knowledge input, knowledge transfer, and innovation cost on collaborative innovation. Furthermore, we use simulation to verify the knowledge sharing model. Our results suggest that the “open innovation strategy” is actually the fact that players choose “evolutionarily stable strategy” in the long-term collaborative innovation process. When the number of game players is different, the small group takes the lead in achieving stabilization strategy. When the number of game players is similar, both groups adopt the “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.

The paper describes and evaluates an explorative approach to quantify the relationship between trainable animals and their owners. Data on human-animal interaction has been collected by using Pebble smartwatches and by observing different kinds of animal training sessions. Tracking movement of horses and dogs with the Pebble Watch was successful with horses but not with dogs. Besides the breed and behavior of the animal, weather conditions and the 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.

We propose a novel approach to measuring the collaboration of knowledge workers, using body sensing smartwatches to capture psychometric data about individuals in a team. In a proof of concept study, we collected 2653 samples of body signals by equipping 15 people with our body sensing smartwatch over the course of 3 days during a design workshop. Additionally, we polled the users about their self-perceived team creativity at the end of each day. By employing multiple linear regression models, we found that body signals tracked by the smartwatch correlate significantly with the perceived team creativity 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.

In this paper, we present a pattern language comprising 23 patterns for collaborative activities for biodiversity conservation. These patterns have been created based on interviews with people working in Kitahiroshima which is known in Japan for its successful model in biodiversity conservation. We extracted the knowledge from practice: how they have been constructing Collaborative Innovation Networks. We expect this outcome to be applied to various areas and support the next generations of the communities for biodiversity conservation.

This paper illustrates the core principle of COINs (collaborative innovation network) of rotating leadership by the example of Jazz musicians, who take turns grooving together. These musicians are exemplars of team members seamlessly transferring the leadership role from one to the other, leading to a “flow” experience of superb quality for their audience. As we show, so-called honest signals from Jazz can play a key role for organizational development to create an “organizational groove.”