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Artificial intelligence is an emerging topic and will soon be able to perform decisions better than humans. In more complex and creative contexts such as innovation, however, the question remains whether machines are superior to humans. Machines fail in two kinds of situations: processing and interpreting “soft” information (information that cannot be quantified) and making predictions in “unknowable risk” situations of extreme uncertainty. In such situations, the machine does not have representative information for a certain outcome. Thereby, humans are still the “gold standard” for assessing “soft” signals and make use intuition. To predict the success of startups, we, thus, combine the complementary capabilities of humans and machines in a Hybrid Intelligence method. To reach our aim, we follow a design science research approach to develop a Hybrid Intelligence method that combines the strength of both machine and collective intelligence to demonstrate its utility for predictions under extreme uncertainty.

In this conceptual paper, we propose crowdsourcing for opportunity creation as a new field of further research in both in the information systems and entrepreneurship domain. Building on previous re-search on entrepreneurial opportunity creation, we elaborate on the benefits of employing a crowdsourcing approach to reduce uncertainty and iteratively develop an opportunity into a new ven-ture. Based on this assessment we develop a research agenda that highlights the need to adapt previous crowdsourcing mechanisms for the special context of entrepreneurial opportunity creation. In doings so, we expand research of crowdsourcing to the field of entrepreneurship by extending the principles of crowdsourcing for innovation for entrepreneurial opportunity creation. Further, by highlighting the requirements of crowdsourcing for opportunity creation, we point towards potential future research issues. Such research should examine novel participation architectures that enable the iterative co-creation of an opportunity through different maturity stages, thereby overcoming the limitations of previous crowdsourcing efforts that rather focus on the generation of novel ideas than its evolution. Finally, we propose crowdsourcing as a practical way for entrepreneurs to validate their assumptions about their opportunity, thereby achieving fast and early product-market fit.

Complementors are the cornerstone of innovation and a vital part for every platform ecosystem. Nevertheless, their perspective is commonly neglected in academic research. By drawing on external resources, they develop complementary products, use distribution channels and benefit from the development of the platform and the surrounding ecosystem. Their accelerating dependence on platform owners, however, has not only created new business opportunities but also introduced essential new risks. These co-innovation risks are especially accelerated by the dominant position of the platform owner and the related uncertainty of the transaction environment. Hence, we apply a quantitative survey among 42 complementors of five leading cloud platforms to explore the drivers of such risk and the role of platform specificity, governance as well as app architecture in such settings. We thus provide valuable insights for both practice as well as theory on platform ecosystems.

The creative potential from innovative contributions of the crowd constitutes some critical challenges. The quantity of contributions and the resource demands to identify valuable ideas is high and remains challenging for firms that apply open innovation initiatives. To solve these problems, research on algorithmic approaches proved to be a valuable way by identifying metrics to distinguish between high and low-quality ideas. However, such filtering approaches always risk missing promising ideas by classifying good ideas as bad ones. In response, organizations have turned to the crowd to not just for generating ideas but also to evaluate them to filter high quality contributions. However, such crowd-based filtering approaches tend to perform poorly in practice as they make unrealistic demands on the crowd. We, therefore, conduct a design science research project to provide prescriptive knowledge on how to combine machine learning techniques with crowd evaluation to adaptively assign humans to ideas.

This demonstration paper proposes a hybrid intelligence system that combines the complementary strengths of human and machines for complex decision-making problems that require human “gut feeling” (i.e. success prediction for startups). The architecture extends principles of previous interactive machine learning systems by using continuous input from an expert crowd and explicitly leveraging the advantages of collective intelligence. This approach allows to augment machine learning techniques for generating features, intuitive and analytic labeling as well as troubleshooting.

Business incubators are an important mechanism to accelerate the success of new ventures. The emergence of ubiquitous IT allows to provide several support services for start-ups via online platforms. One particularly promising approach is the concept of crowd-based incubation. To shed light on this novel topic we conducted a single case study at the crowd-based incubator JumpStartFund. The results provide several interesting insights and a preliminary conceptual model of crowd-based incubation that contributes on research of business incubators in general and provides valuable hints for practical applications that might extend the service offering of existing business incubators.

Startups are the fresh blood of our economy and, besides uni- versities, are thriving innovation. One of the rst steps in this enriching journey to success, where they create jobs and new products, is getting funded. Decision makers, such as investors, incubators, and accelerators, receive a large amount of funding proposals. Currently, new ventures are evaluated "manually", often without any machine learning or any decision support at all. The implicit rules that every experienced decision maker has developed through years of failures and successes can not be codied, let alone be explained. It is intuition. We envision a learning system that is able to learn the intuition from multiple decision makers and guide them in their judgment. Our envisioned learning system relies on hybrid intelligence, which we dene as the ability to accomplish complex goals by combining human and articial intelligence to collectively achieve superior results and continuously improve by learning from each other.

The emergence of platforms is significantly changing the organizing logic of software development. Platform owners are increasingly engaging vibrant ecosystems around their platform to foster third-party innovation. Despite all the potential benefits for complementors, however, innovation in platform ecosystems also introduced essential new risks that remain under investigated. To discover the complementors ́ risk in software ecosystems we utilize 21 interviews and 42 survey respondents to examine risk and governance mechanisms such as control structures and architectural choices to handle such.

Digital technology is rapidly changing the organisation of work. On form of digital work is crowd work that is gaining increasing importance in the field of software development. However, only few platforms are able to sustainably create and capture value. We therefore provide an examination of how to design crowd work platforms by conducting a multiple case study in the software development industry.

The focus of the submission is to discuss concepts of early customer integration for startups. Recent literature suggests various concepts that aim to integrate customers in the development as well as in the testing of a startups product- and service ideas. Two popular concepts include the Lean Startup (Ries, 2011) and the Customer Development (Blank, 2013). While both concepts refer to certain activities undertaken by startups during the product development process these have not been empirically investigated and tested so far. Against this background, one aim of this study is to analyze both concepts in more detail so as to derive a detailed process model for developing and testing startup ideas. The thereby derived process model, referred to as customer feedback process, provides a first integrated model that researchers as well as practitioners can use to further investigate the phenomenon of customer integration for startups. Moreover, this research examines the extent to which individual process steps of the customer feedback process are supported by current IT solutions as IT bears great potential to support as well as enable entrepreneurship (Del Giudice & Straub, 2011). Based on the findings of this study, the paper further demonstrates important areas for future research concerning "IT support for customer feedback".

Platform-as-a-service (PaaS) providers are increasingly engaged in nurturing vibrant ecosystems of independent software vendors (ISVs) by offering standardized services. However, cloud ecosystems have also been known for its fluctuation and high rates of desertion. A currently under-researched explanation for this low traction and high rates of fluctuation may lie in the fact that ISVs face considerable costs when joining and acting on a specific platform. If these costs are too high, they can rapidly outweigh the additional value generated by the ecosystem. This study therefore explains the role of different configurations of cost-inducing factors and resource benefits in influencing an ISV´s platform loyalty. By using a configurational approach based on fuzzy-set qualitative comparative analysis (FsQCA), we display complex interactional effects of cost and benefits as causal conditions on ISVs’ intention to stay in the ecosystem and thus provide valuable insights for both practice as well as theory on platform ecosystems.

Setting a new venture is a challenging tasks which leads to dramatic numbers of failures. To support early stage ventures and accelerate their growth support service providers such as business incubators and accelerators gain increasing popularity. Yet, in particular the latter one is still on its rise and current practices of supporting startups have several limitations such as limited capabilities, networks or are faced with the bound rationality of individual mentors. To overcome these deficiencies we propose the Hybrid Intelligence Accelerator as a novel service system to support early stage startups through connecting multiple actors via an online platform and combining the idiosyncratic benefits of collective and artificial intelligence. In the context of a design science research approach we developed an initial prototype version of the Hybrid Intelligence Accelerator and provide insights in the procedures as well as design principles. We thus provide prscriptive knowledge abou novel forms of startup support service systems that might constitute a blueprint for future progress in the fields of entrepreneurship and information systems.

Platform-as-a-Service (PaaS) Anbieter haben großes Interesse daran, durch das Angebot standardisierter Services das Entstehen dynamischer Ökosysteme unabhängiger Softwareanbieter (Independent Software Vendor; ISV) zu fördern. Allerdings neigen Plattform-Ökosysteme oft zu hoher Fluktuation und niedriger Loyalität von ISVs. Eine bislang kaum erforschte Erklärung für dieses Verhalten ist, dass aus Sicht der ISV Kosten entstehen, wenn sie einem Plattform-Ökosystem beitreten und dort Apps entwickeln. Diese Kosten können hierbei schnell die durch das Ökosystem generierten Mehrwerte überwiegen. Dieser Beitrag untersucht, wie verschiedene Konfigurationen kostenverursachender Faktoren und ressourcenbasierter Nutzungspotentiale von Plattform-Ökosystemen die Loyalität eines ISV beeinflussen. Im Rahmen einer konfigurationstheoretischen Herangehensweise werden komplexe Interaktionseffekte von Kosten und Nutzen als kausale Bedingungen identifiziert. Basierend auf den Ergebnissen einer fuzzy-set Qualitative Comparative Analysis (fsQCA) extrahiert dieser Beitrag Muster, die die Loyalität von ISV erklären und die als Blueprint für die Gestaltung von Plattform-Ökosystemen dienen können.

The high uncertainty of creating business models demands entrepreneurs to re-evaluate and continuously adapt them. Therefore, incubators offer validation services. However, systematic, and scalable information systems to enable interaction with a crowd of potential customers, investors, or other stakeholders and entrepreneurs do not exist. Our aim is thus to develop tentative design principles for crowd-based business model validation (CBMV) systems. Such systems should support entrepreneurs to reduce the uncertainty about the validity of their business model. Thus, we apply a theory-driven design approach based on knowledge drawn from literature and complemented by empirical insights. For developing such information systems, we combine the concept of crowdsourcing with findings from research on decision support systems to propose theory-grounded design principles for a CBMV system. The identified design principles describe a potential solution to a problem that previous research proved as viable.

For entrepreneurs, one of the most pivotal tasks is to develop their business model. Therefore, entrepreneurs try to collect information that might support them in their decision making. Such information includes feedback from other actors to assess the validity of their assumptions and make decisions. However, entrepreneurs are constraint by bounded rationality, which prevents them from making optimal decisions. To solve this problem, the aim of this research is to develop a decision support system (DSS) for supporting entrepreneurs’ decisions regarding their business model to support accessing, processing, and the interpretation of relevant information. To achieve this, we follow a design science approach to develop a Hybrid Intelligence DSS that combines the strength of both machine and collective intelligence. Our contributions will consist of preliminary prescriptive knowledge, extending the scope of DSS to business model innovation, and a novel approach to support decision making by combining machine and collective intelligence.

The paper at hand examines if the crowd can offer valuable support in evaluating start-ups. In doing so, we plan to conduct an experiment 1.) to test if the crowd is capable to support experts in evaluating start-ups 2.) to examine how differences in task-representation (i.e. rating scales vs. a crowdfunding mechanism) influences cognitive processing of the crowd and 3.) to examine how types of cognitive processing (i.e. system 1 thinking vs. system 2 thinking) relate to predic-tion accuracy of the crowd. To this end, we plan to introduce crowdfunding as a new evaluation mechanism to support the crowd in coming up with more accurate predictions of start-up value. Our theoretical contribution is twofold. First, we aim to show if the crowd can be used to sup-port Venture capitalists in evaluating start-ups, in the sense that their evaluations agree with expert evaluations. Second, we plan to contribute to a better understanding about how the de-sign of evaluation mechanisms influences peoples cognitive processing and the crowds ability to predict start-up value.

Setting a new venture is a challenging tasks which leads to dramatic numbers of failures. For entrepreneurs, one of the most pivotal tasks is to develop their business model. Therefore, entrepreneurs try to collect information that might support them in their decision making. Such information includes feedback from other actors to assess the validity of their assumptions and make decisions. However, entrepreneurs are constraint by bounded rationality, which prevents them from making optimal decisions. To solve this problem, the aim of this research is to develop a decision support system (DSS) for supporting entrepreneurs’ decisions regarding their business model to support accessing, processing, and the interpretation of relevant information. To achieve this, we follow a design science approach to develop a Hybrid Intelligence DSS that combines the strength of both machine and collective intelligence. Our contributions will consist of preliminary prescriptive knowledge, extending the scope of DSS to business model innovation, and a novel approach to support decision making by combining machine and collective intelligence.

Complementors are the cornerstone of innovation and a vital part for every platform ecosystem. Nevertheless, their perspective is commonly neglected in academic research. By drawing on external resources, they develop complementary products, use distribution channels and benefit from the development of the platform and the surrounding ecosystem. Their accelerating dependence on platform owners, however, has not only created new business opportunities but also introduced essential new risks. These co-innovation risks are especially accelerated by the dominant position of the platform owner and the related uncertainty of the transaction environment. Hence, we apply a quantitative survey among 42 complementors of five leading cloud platforms to explore the drivers of such risk and the role of platform specificity, governance as well as app architecture in such settings. We thus provide valuable insights for both practice as well as theory on platform ecosystems.

Platforms enable third-parties to develop new software artefacts like applications and become the locus of digital innovation. Prior studies have mainly focused on the benefits that initially motivate external complementors to join an ecosystem. However, little is known about the costs that are associated with this choice and how such costs actually influence the decision to interrelate with a platform owner. We develop the overarching idea that the complementor´s costs are mainly influenced by the interplay of the micro-architecture of single extensions and control modes applied to govern the ecosystem. The purpose of our research is therefore to shed light on the nature of complementors’ costs on the micro-level of transactions between the platform owner and the complementor and their effects on the intention to join an ecosystem. Using data from a quantitative survey among complementors of five leading platforms in the Enterprise Application Software industry hypothesized relationships are tested.