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Advances in AI technology affect knowledge work in diverse fields, including healthcare, engineering, and management. Although automation and machine support can increase efficiency and lower costs, it can also, as an unintended consequence, deskill workers, who lose valuable skills that would otherwise be maintained as part of their daily work. Such deskilling has a wide range of negative effects on multiple stakeholders –– employees, organizations, and society at large. This essay discusses deskilling in the age of AI on three levels - individual, organizational and societal. Deskilling is furthermore analyzed through the lens of four different levels of human-AI configurations and we argue that one of them, Hybrid Intelligence, could be particularly suitable to help manage the risk of deskilling human experts. Hybrid Intelligence system design and implementation can explicitly take such risks into account and instead foster upskilling of workers. Hybrid Intelligence may thus, in the long run, lower costs and improve performance and job satisfaction, as well as prevent management from creating unintended organization-wide deskilling.

In recent years, business model design has received increased attention as a perspective to associ-ate business model innovation with firm performance. In this view, fitness between a business model’s elements leads to high performance and conversely, high performance requires fitness between the design elements. However, so far there have been few insights into the concrete con-tent and structure of design element configurations that enable high performance. Based on an investigation of 188 entrepreneurial ventures operating in the context of the Internet of Things (IoT), this study develops a taxonomy of IoT business models comprising a total of 108 design elements. By applying a k-Modes clustering approach, we further identify such high performing business model configurations. Finally, based on a random forest analysis, our study finds that the design of the business model components customer relations, revenue model, market ap-proach, and solution are the most important in determining firm performance.

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.

Organizations must be increasingly able to adapt to ever-changing environmental conditions. For the resulting transformations, companies need a solid knowledge of their current business models to transform them towards prospective target models. However, established business model creation frameworks are manual, error-prone, time- consuming and highly subjective. Following a design science research approach, this work proposes the development of an artefact for business model mining to overcome the weaknesses of manual approaches. In a set of meta-requirements, design principles and a prototype, it will be shown how qualitative value propositions can be extracted from companies’ web pages. Embedded in a comprehensive data-driven artefact, it should be possible to extract the whole business model of a company through organizational information and its systems. This provides a broader view on a company’s value creation next to existing top-down approaches and enlarges existing business model research.

Setting up Internet of Things (IoT) business models is a challenging tasks which leads to dramatic numbers of failures. To support both corporates and startups, business accelerators gain increasing popularity. However, most of such efforts have several restrictions such as limited capabilities, networks or expertise. To overcome these deficiencies we propose the Hybrid Intelligence Accelerator as a novel service system to support the design arocess of IoT business models 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 tentative design principles.

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.