Sascha Friesike

33 Erfolgsprinzipien der Innovation

Alle Unternehmen wollen innovativ sein, nur wenigen gelingt es. Warum schaffen einzelne Unternehmen echte Durchbruche, wahrend die meisten nicht uber lustlose Me-too-Produkte hinauskommen? Innovation lasst sich nicht einfach verordnen - aber es gibt Erfolgsprinzipien, denen die besten Unternehmen folgen und die ihnen immer wieder einen Innovationsvorsprung gegenuber ihren lahmen Wettbewerbern verschaffen. nnDieses Werk zeigt Ihnen ebenso lehrreich wie unterhaltsam die 33 besten dieser Erfolgsprinzipen. Zum Beispiel das Ford-Prinzip, das darstellt, wie wichtig Losungen sind, die nachhaltig begeistern, das Kleine-Schwarze-Prinzip, das auf Produkte verweist, die genau das einhalten, was der Kunde braucht, oder das Aikido-Prinzip, das zeigt, dass es sich lohnt, anders zu denken.

Towards Another Scientific Revolution

In this introductory chapter we establish a common understanding of what are and what drives current changes in research and science. The concepts of Science 2.0 and Open Science will be introduced. As such we provide a short introduction to the history of science and knowledge dissemination. We explain the origins of our scientific culture which evolved around publication methods. Interdependencies of current concepts will be elucidated and it will be stated that the transition towards Open Science is a complex cultural change. Reasons as to why the change is slow are discussed and the main obstacles are identified. Next, we explain the recent changes in scientific workflows and how these cause changes in the system as a whole. Furthermore, we provide an overview on the entire book and explain what can be found in each chapter.

Profiting from innovation in China

- Gives advice how Western firms can benefit from innovation in China n- Provides real-life cases n- Offers useful checklists for managers ? nnChina is dramatically catching up and is rapidly becoming a leading technological innovator on the global scale. The number of Chinese firms with global ambitions is growing fast, more and more technological innovation is coming from China, and the number of patents in China is also growing steadily.The negative side of this development is the still insufficient protection of intellectual property in China. The phenomenon of counterfeits originating from China has increased constantly over the past two decades. Moreover, within the past ten years the scale of intellectual property theft has risen exponentially in terms of its sophistication, volume, the range of goods, and the countries affected.This book addresses managers dealing with innovation in China, and offers concrete advice on how Western firms can benefit from these innovations. Among others, it provides examples and checklists to help decision-makers active in China.?

Copy, transform, combine: exploring the remix as a form of innovation

AbstractnThe reuse of existing knowledge is an indispensable part of the creation of novel ideas. In the creative domain knowledge reuse is a common practice known as “remixing”. With the emergence of open internet-based platforms in recent years, remixing has found its way from the world of music and art to the design of arbitrary physical goods. However, despite its obvious relevance for the number and quality of innovations on such platforms, little is known about the process of remixing and its contextual factors. This paper considers the example of Thingiverse, a platform for the 3D printing community that allows its users to create, share, and access a broad range of printable digital models. We present an explorative study of remixing activities that took place on the platform over the course of six years by using an extensive set of data on models and users. On the foundation of these empirically observed phenomena, we formulate a set of theoretical propositions and managerial implications regarding (1) the role of remixes in design communities, (2) the different patterns of remixing processes, (3) the platform features that facilitate remixes, and (4) the profile of the remixing platform’s users.

Open Science: Many Good Resolutions, Very Few Incentives, Yet

In recent years, a movement has emerged, which assembles itself under the umbrella term “Open Science”. Its intent is to make academic research more transparent, collaborative, accessible, and efficient. In the present article, we examine the origins, various forms, and understandings of this movement. Furthermore, we put the aims of individual groups associated with Open Science and the academic realities of their concepts into context. We discuss that much of what is known as Open Science can be viewed through the prism of a social dilemma. From this perspective, we explain why the concept of Open Science finds a lot of support in theory, yet struggles in practice. We conclude the article with suggestions on how to foster more Open Science in practice and how to overcome the obstacles it is currently facing.

Crowdsourcing as an Innovation Tool

Crowdsourcing – a neologism created from the words ‘crowd’ and ‘outsourcing’ – describes giving tasks to the crowd for completion. The use cases for crowdsourcing are diverse, and new ideas are constantly emerging on the internet, making it all the more important for companies to engage with the topic and to learn how to use it effectively. Various formats of crowdsourcing projects are used depending on the purpose and the initiator of the project, such as intermediate platforms, free solutions, corporate platforms, and marketplaces. The crowdsourcing process is divided into five stages, which consist of preparation, initiation and implementation of the project, and the evaluation and utilization of the ideas collected. Each step is associated with particular challenges and needs to be managed so that crowdsourcing can be used successfully as a problem-solving tool. Aside from the process-related factors, a good understanding of the web 2.0 culture, professional preparation, and a clear understanding of the goals and responsibilities are the fundamental cornerstones of successful crowdsourcing projects.

Setting up crowd science projects

Crowd science is scientific research that is conducted with the participation of volunteers who are not professional scientists. Thanks to the Internet and online platforms, project initiators can draw on a potentially large number of volunteers. This crowd can be involved to support data-rich or labour-intensive projects that would otherwise be unfeasible. So far, research on crowd science has mainly focused on analysing individual crowd science projects. In our research, we focus on the perspective of project initiators and explore how crowd science projects are set up. Based on multiple case study research, we discuss the objectives of crowd science projects and the strategies of their initiators for accessing volunteers. We also categorise the tasks allocated to volunteers and reflect on the issue of quality assurance as well as feedback mechanisms. With this article, we contribute to a better understanding of how crowd science projects are set up and how volunteers can contribute to science. We suggest that our findings are of practical relevance for initiators of crowd science projects, for science communication as well as for informed science policy making.

Data sharing as social dilemma: Influence of the researcher’s personality

It is widely acknowledged that data sharing has great potential for scientific progress. However, so far making data available has little impact on a researcher’s reputation. Thus, data sharing can be conceptualized as a social dilemma. In the presented study we investigated the influence of the researchers personality within the social dilemma of data sharing. The theoretical background was the appropriateness framework. We conducted a survey among 1564 researchers about data sharing, which also included standardized questions on selected personality factors, namely the so-called Big Five, Machiavellianism and social desirability. Using regression analysis, we investigated how these personality domains relate to four groups of dependent variables: attitudes towards data sharing, the importance of factors that might foster or hinder data sharing, the willingness to share data, and actual data sharing. Our analyses showed the predictive value of personality for all four groups of dependent variables. However, there was not a global consistent pattern of influence, but rather different compositions of effects. Our results indicate that the implications of data sharing are dependent on age, gender, and personality. In order to foster data sharing, it seems advantageous to provide more personal incentives and to address the researchers’ individual responsibility.

Opening Up Science: Towards an Agenda of Open Science in Industry and Academia

The shift towards open innovation has substantially changed the scientific and practical perception of corporate innovation. While scientific studies on open innovation are burgeoning, present research underlies a business-centric view that has focused on the back-end of the innovation process. The impact and implications of open innovation on academic and industrial science at the very front-end of the innovation process have so far been neglected. Our paper presents a conceptualization of open science and research as a peculiarity under the roof of open innovation. We propose four perspectives, outline current trends, and present directions for future developments.

A nod to public open access infrastructures

In March, the European Commission discussed the creation of an open access publishing platform where the scientists it funds could publish their papers (“Science funders plunge into publishing,” M. Enserink, News In Depth, 31 March, p. [1357][1]). Modeled after the platforms set up by the