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Facilitated by AI technology, the phenomenon of cognitive automation extends the scope of deterministic business process automation (BPA) through the probabilistic automation of knowledge and service work. By transforming work systems through cognitive automation, organizations are provided with vast strategic opportunities to gain business value. However, research lacks a unified conceptual lens on cognitive automation, which hinders scientific progress. Thus, based on a Systematic Literature Review, we describe the fundamentals of cognitive automation and provide an integrated conceptualization. We provide an overview of the major BPA approaches such as workflow management, robotic process automation, and Machine Learning-facilitated BPA while emphasizing their complementary relationships. Furthermore, we show how the phenomenon of cognitive automation can be instantiated by Machine Learning-facilitated BPA systems that operate along the spectrum of lightweight and heavyweight IT implementations in larger IS ecosystems. Based on this, we describe the relevance and opportunities of cognitive automation in Information Systems research.

Crowdfunding is now established as a valid alternative to conventional methods of financing for startups. Unfortunately, to date, research has not investigated how backers can be encouraged to support entrepreneurs beyond funding. The aim of this study is to design and evaluate certain design elements for reward-based crowdfunding platforms that can engage backers in co-creational activities for product development. The study uses a design science research (DSR) approach and the theoretical concept of psychological ownership to inform a new design and then experimentally test that design. The results suggest that the derived artifacts positively influence co-creational activities in crowdfunding and that feelings of psychological ownership play an important mediating role. The contribution of this research is threefold. First, this paper extends crowdfunding’s application potential from merely a method of financing to a method of value creation with customers for product development. Second, the study advances DSR by applying a new DSR approach that shows whether a design performs as hypothesized by theory. Third, this research allows the exploration of backers’ individual behavior as opposed to their collective behavior.

One of the most critical tasks for startups is to validate their business model. Therefore, entrepreneurs try to collect information such as feedback from other actors to assess the validity of their assumptions and make decisions. However, previous work on decisional guidance for business model validation provides no solution for the highly uncertain and complex context of early-stage startups. The purpose of this paper is, thus, to develop design principles for a Hybrid Intelligence decision support system (HI-DSS) that combines the complementary capabilities of human and machine intelligence. We follow a design science research approach to design a prototype artifact and a set of design principles. Our study provides prescriptive knowledge for HI-DSS and contributes to previous work on decision support for business models, the applications of complementary strengths of humans and machines for making decisions, and support systems for extremely uncertain decision-making problems.

Ceramides are mediators of inflammatory processes. In experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis (MS), we observed that CerS6 mRNA expression was upregulated 15‐fold in peripheral blood leukocytes before the onset of EAE symptoms. In peripheral blood leukocytes from MS patients, a 3.9‐fold upregulation was found. Total genetic deletion of CerS6 and the selective deletion of CerS6 in peripheral blood leucocytes exacerbated the progression of clinical symptoms in EAE mice. This was associated with enhanced leukocyte, predominantly neutrophil infiltration and enhanced demyelination in the lumbar spinal cord of EAE mice. Interferon‐gamma/tumor necrosis factor alpha (IFN‐γ/TNF‐α) and granulocyte colony‐stimulating factor (G‐CSF) both drive EAE development and induce expression of the integrin CD11b and the chemokine receptor C‐X‐C motif chemokine receptor 2 (CXCR2), and we found they also induce CerS6 expression. In vivo, the genetic deletion of CerS6 enhanced the activation/migration of neutrophils, as reflected by an enhanced upregulation of CD11b and CXCR2. In vitro, the genetic deletion of CerS6 enhanced the activation status of IFN‐γ/TNF‐α‐stimulated neutrophils, as shown by increased expression of nitric oxide and CD11b and an increased adhesion capacity. In G‐CSF‐stimulated neutrophils, the migration status was enhanced, as reflected by an elevated level of CXCR2 and an increased migration capacity. These data suggest that CerS6/C16‐Cer mediates feedback regulation by inhibiting the formation of CD11b and CXCR2, which are induced either by IFN‐γ/TNF‐α or by G‐CSF, respectively. We conclude that CerS6/C16‐Cer mediates anti‐inflammatory effects during the development of EAE and MS possibly by suppressing the migration and deactivation of neutrophils.

In an atmosphere of rapidly changing business environments and intense competition, adequate and timely business models are crucial for companies. Current research mainly focuses on business model development that often neglects the legacy of established companies. The paper at hand addresses this research gap by a process design which allows established companies to rethink, improve, and continually innovate their business models. Following a design science research approach, requirements for improving business models are identified by the analysis of existing literature and by expert interviews. Collaboration Engineering and a multilevel evaluation are applied to create a continuous and implementable process design for business model improvement – including specific activities, instructions, and tools. The process design represents a nascent design theory in form of an “invention” type of knowledge contribution. Moreover, going beyond existing literature, the importance of collaboration between participants in a business model improvement project is highlighted. From a practical perspective, the developed process design enables companies for continuous and recurring business model improvement without the ongoing support of professional moderators or consultants.

One of the most critical tasks for startups is to validate their business model. Therefore, entrepreneurs try to collect information such as feedback from other actors to assess the validity of their assumptions and make decisions. However, previous work on decisional guidance for business model validation provides no solution for the highly uncertain and complex context of earlystage startups. The purpose of this paper is, thus, to develop design principles for a Hybrid Intelligence decision support system (HI-DSS) that combines the complementary capabilities of human and machine intelligence. We follow a design science research approach to design a prototype artifact and a set of design principles. Our study provides prescriptive knowledge for HI-DSS and contributes to previous work on decision support for business models, the applications of complementary strengths of humans and machines for making decisions, and support systems for extremely uncertain decision-making problems.

Research has a long history of discussing what is superior in predicting certain outcomes: statistical methods or the human brain. This debate has repeatedly been sparked off by the remarkable technological advances in the field of artificial intelligence (AI), such as solving tasks like object and speech recognition, achieving significant improvements in accuracy through deep-learning algorithms (Goodfellow et al. 2016), or combining various methods of computational intelligence, such as fuzzy logic, genetic algorithms, and case-based reasoning (Medsker 2012). One of the implicit promises that underlie these advancements is that machines will 1 day be capable of performing complex tasks or may even supersede humans in performing these tasks. This triggers new heated debates of when machines will ultimately replace humans (McAfee and Brynjolfsson 2017). While previous research has proved that AI performs well in some clearly defined tasks such as playing chess, playing Go or identifying objects on images, it is doubted that the development of an artificial general intelligence (AGI) which is able to solve multiple tasks at the same time can be achieved in the near future (e.g., Russell and Norvig 2016). Moreover, the use of AI to solve complex business problems in organizational contexts occurs scarcely, and applications for AI that solve complex problems remain mainly in laboratory settings instead of being implemented in practice. Since the road to AGI is still a long one, we argue that the most likely paradigm for the division of labor between humans and machines in the next decades is Hybrid Intelligence. This concept aims at using the complementary strengths of human intelligence and AI, so that they can perform better than each of the two could separately (e.g., Kamar 2016).

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.