Enterprise crowdsourcing transforms the way in which traditional business tasks can be processed by harnessing the collective intelligence and workforce of a large and often diver-sified group of people. At the present time, data and information residing within enterprise crowdsourcing systems and other business applications are insufficiently interlinked and are rarely made publicly available in an open and semantically structured manner – neither to the corporate intranet nor to the World Wide Web (WWW). However, the semantic annotation of enterprise crowdsourcing activities is a promising research and application domain. The Semantic Web and its related technologies, methods and principles for publishing structured data offer an extension of the traditional layout-oriented Web to provide more intelligent and complex services. This technical report describes the efforts toward a universal and lightweight yet powerful Semantic Web vocabulary for the domain of enterprise crowdsourcing. As a methodology for developing the vocabulary, the approach of ontology engineering is applied. To illustrate the purpose and to limit the scope of the ontology, several informal competency questions as well as functional and non-functional requirements are presented. The subsequent con-ceptualization of the ontology applies different sources of knowledge and considers various perspectives. A set of semantic entities is derived from a review of existing crowdsourcing applications and a review of recent crowdsourcing literature. During the domain capture, all partial results of the review are integrated into a consistent data dictionary and structured as a UML data schema. The designed ontology includes 24 classes, 22 object properties and 30 datatype properties to describe the key aspects of a crowdsourcing model (CSM). To demonstrate the technical feasibility, the ontology is implemented using the Web Ontology Language (OWL). Finally, the ontology is evaluated by means of transforming informal to formal competency questions, comparing it to existing semantic vocabularies, and calculat-ing ontology metrics. Evidence is shown that the CSM ontology covers the key representa-tional needs of the enterprise crowdsourcing domain. At the end of the technical report, cur-rent limitations are illustrated and directions for future research are proposed.

Die stetig wachsenden Herausforderungen für Unternehmen machen es erforderlich, dass die Ressourcen eines Unternehmens möglichst effizient eingesetzt werden. Einen wesentlichen Beitrag zur Erfüllung dieser Aufgabenstellung kann das Controlling leisten. Aufgrund ihrer Besonderheiten wird das Controlling bei der Hervorbringung von Innovationen häufig noch stark vernachlässigt, obwohl auch in diesem Bereich eine Unterstützung durch das Controlling möglich ist. So kann das Controlling bei der Auswahl der Projektmitarbeiter, der Ideenfindung und –bewertung, durch eine Unterstützung der Planung, der Sicherstellung eines adäquaten Berichtswesens, durch eine Unterstützung der Kontrollfunktion, der Sicherstellung eines Risikomanagements und der zielgerichteten Nachbereitung abgeschlossener Projekte zu einer Steigerung der Effizienz im Innovationsbereich beitragen. Diese Unterstützung sollte allerdings nicht auf Sachgutinnovationen beschränkt bleiben. Auch bei der Hervorbringung von Dienstleistungsinnovationen kann das Controlling zu einer besseren Abwicklung der Innovationsprozesse beitragen.

The last couple of years have seen a fascinating evolution. While the early Web predominantly focused on human consumption of Web content, the widespread dissemination of social software and Web 2.0 technologies enabled new forms of collaborative content creation and problem solving. These new forms often utilize the principles of collective intelligence, a phenomenon that emerges from a group of people who either cooperate or compete with each other to create a result that is better or more intelligent than any individual result (Leimeister, 2010; Malone, Laubacher, & Dellarocas, 2010). Crowdsourcing has recently gained attention as one of the mechanisms that taps into the power of web-enabled collective intelligence (Howe, 2008). Brabham (2013) defines it as “an online, distributed problem-solving and production model that leverages the collective intelligence of online communities to serve specific organizational goals” (p. xix). Well-known examples of crowdsourcing platforms are Wikipedia, Amazon Mechanical Turk, or InnoCentive. Since the emergence of the term crowdsourcing in 2006, one popular misconception is that crowdsourcing relies largely on an amateur crowd rather than a pool of professional skilled workers (Brabham, 2013). As this might be true for low cognitive tasks, such as tagging a picture or rating a product, it is often not true for complex problem-solving and creative tasks, such as developing a new computer algorithm or creating an impressive product design. This raises the question of how to efficiently allocate an enterprise crowdsourcing task to appropriate members of the crowd. The sheer number of crowdsourcing tasks available at crowdsourcing intermediaries makes it especially challenging for workers to identify a task that matches their skills, experiences, and knowledge (Schall, 2012, p. 2). An explanation why the identification of appropriate expert knowledge plays a major role in crowdsourcing is partly given in Condorcet’s jury theorem (Sunstein, 2008, p. 25). The theorem states that if the average participant in a binary decision process is more likely to be correct than incorrect, then as the number of participants increases, the higher the probability is that the aggregate arrives at the right answer. When assuming that a suitable participant for a task is more likely to give a correct answer or solution than an improper one, efficient task recommendation becomes crucial to improve the aggregated results in crowdsourcing processes. Although some assumptions of the theorem, such as independent votes, binary decisions, and homogenous groups, are often unrealistic in practice, it illustrates the importance of an optimized task allocation and group formation that consider the task requirements and workers’ characteristics. Ontologies are widely applied to support semantic search and recommendation mechanisms (Middleton, De Roure, & Shadbolt, 2009). However, little research has investigated the potentials and the design of an ontology for the domain of enterprise crowdsourcing. The author of this thesis argues in favor of enhancing the automation and interoperability of an enterprise crowdsourcing environment with the introduction of a semantic vocabulary in form of an expressive but easy-to-use ontology. The deployment of a semantic vocabulary for enterprise crowdsourcing is likely to provide several technical and economic benefits for an enterprise. These benefits were the main drivers in efforts made during the research project of this thesis: 1. Task allocation: With the utilization of the semantics, requesters are able to form smaller task-specific crowds that perform tasks at lower costs and in less time than larger crowds. A standardized and controlled vocabulary allows requesters to communicate specific details about a crowdsourcing activity within a web page along with other existing displayed information. This has advantages for both contributors and requesters. On the one hand, contributors can easily and precisely search for tasks that correspond to their interests, experiences, skills, knowledge, and availability. On the other hand, crowdsourcing systems and intermediaries can proactively recommend crowdsourcing tasks to potential contributors (e.g., based on their social network profiles). 2. Quality control: Capturing and storing crowdsourcing data increases the overall transparency of the entire crowdsourcing activity and thus allows for a more sophisticated quality control. Requesters are able to check the consistency and receive appropriate support to verify and validate crowdsourcing data according to defined data types and value ranges. Before involving potential workers in a crowdsourcing task, requesters can also judge their trustworthiness based on previous accomplished tasks and hence improve the recruitment process. 3. Task definition: A standardized set of semantic entities supports the configuration of a crowdsourcing task. Requesters can evaluate historical crowdsourcing data to get suggestions for equal or similar crowdsourcing tasks, for example, which incentive or evaluation mechanism to use. They may also decrease their time to configure a crowdsourcing task by reusing well-established task specifications of a particular type. 4. Data integration and exchange: Applying a semantic vocabulary as a standard format for describing enterprise crowdsourcing activities allows not only crowdsourcing systems inside but also crowdsourcing intermediaries outside the company to extract crowdsourcing data from other business applications, such as project management, enterprise resource planning, or social software, and use it for further processing without retyping and copying the data. Additionally, enterprise or web search engines may exploit the structured data and provide enhanced search, browsing, and navigation capabilities, for example, clustering similar crowdsourcing tasks according to the required qualifications or the offered incentives.

Ein entscheidender Erfolgsgarant für die Abwicklung von FuE-Projekten durch ein internationales Projektteam liegt in der Kombination des Wissens aller Mitarbeiter. Manche Unternehmen können das spezielle Know how ihrer Mitarbeiter noch stärker nutzen und Entscheidungsbefugnisse für bestimmte Projektaufgaben delegieren. Zu empfehlen ist dies z. B. für die Innovationsaufgaben: Erstellung des Gesamtentwicklungskonzeptes und Ideenfindung. Unterscheiden sich die Unternehmenskulturen der beteiligten Projektmitarbeiter stark voneinander, ist die Erstellung des Gesamtentwicklungskonzeptes durch das Team nicht mehr sinnvoll. Die Aufgaben: Suche nach Projektmitarbeitern und Auswahl der Zulieferer sollte der Geschäftsführer aufgrund seiner besseren Zugangsmöglichkeiten zu anderen Unternehmensbereichen und externen Einrichtungen überwiegend selbst ausführen. Generell ist es ratsam, dass international agierende Konzerne individuelle Partizipationsmodelle gestalten und Probleme, die durch unterschiedliche Kulturausprägungen entstehen, verhindern bzw. beseitigen.

This paper describes the background, objectives and methods of my doctoral research concerned with the support of the initiation of research collaboration with the help of information and communication technologies. The research is based on the assumption that providing more information about potential partners would lead to better-grounded decisions about collaboration. The purpose of this research is also to support and utilize Open Research. To address these aims, I suggest the design and creation of an ePortfolio that will aggregate information from the Internet, particularly Web 2.0 applications.

Immer kürzer werdende Innovationszyklen lassen vor allem das strategische Zeitfenster für Innovationen schmaler werden. Ein ganzheitliches Time-to-Market Management (TtMM) gewinnt daher zunehmend an unternehmerischer Bedeutung. Das TtMM hat die zeitliche Optimierung des Innovationsprozesses zum Ziel. Sämtliche marktorientierten Innovationen eines Unternehmens sollen von der Idee bis hin zu ihrer Markteinführung zeitlich so geplant werden, dass sie innerhalb ihres strategischen Zeitfensters bzw. zu einem ökonomisch lohnenswerten Zeitpunkt am Markt eingeführt werden. Das Ziel der vorliegenden Dissertation ist die Darlegung einer ganzheitlichen, mit den Erfordernissen der Praxis korrespondierenden TtMM-Konzeption. Zur Erreichung dieses Ziels wird ein zweigeteilter Ansatz verfolgt. In einem ersten Schritt werden die in der Praxis auftretenden Problembereiche bzw. Anforderungen an ein ganzheitliches TtMM analysiert. In einem zweiten Schritt werden Methoden identifiziert, die den in der Praxis aufgedeckten Anforderungen gerecht werden.

Die Arbeit untersucht die Rolle des Wissens- und Technologietransfers in nationalen Innovationssystemen und insbesondere im Innovationsprozess. Bisher vorliegende Arbeiten zu diesem Thema behandeln entweder den Wissens- und Technologietransfer als einen eigenständigen Prozeß oder den Aufbau nationaler Innovationssysteme. Die Verbindung beider Konzepte ist Gegenstand der Arbeit.