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Recent technological advances, especially in the field of machine learning, provide astonishing progress on the road towards artificial general intelligence. However, tasks in current real-world business applications cannot yet be solved by machines alone. We, therefore, identify the need for developing socio-technological ensembles of humans and machines. Such systems possess the ability to accomplish complex goals by combining human and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other. Thus, the need for structured design knowledge for those systems arises. Following a taxonomy development method, this article provides three main contributions: First, we present a structured overview of interdisciplinary research on the role of humans in the machine learning pipeline. Second, we envision hybrid intelligence systems and conceptualize the relevant dimensions for system design for the first time. Finally, we offer useful guidance for system developers during the implementation of such applications.

This paper presents a framework for developing tool support for the design and management of new business models. Existing IT tools supporting the process of designing, innovating, and evaluating a company's business model are currently not leveraging the full potential of tool support, because they do not make use of theoretical and empirical knowledge around business model development. Against this backdrop, we analyze existing knowledge on business model design and management, resulting in a first systematization of the activities that are necessary for developing and managing new business models. In order to complement this knowledge and to identify the requirements for supporting these activities, a series of expert interviews is conducted. Based on the results of the interview series, a new business model development tool is created and evaluated. The learnings of this development process are then consolidated in a unified framework. This framework constitutes a new solution for systematically designing tool support for business model development and extends existing literature by highlighting the importance of collaboration between participants in a business model development project. It also provides designers of new business model development tool with an empirically based conceptualization to guide their efforts.

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.

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.

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.

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4. Зміст роботи (перелік питань, які потрібно розробити) Вступ. 1 Аналіз предметної області інноваційного концепту Індустрія 4.0. 1.1 Інформаційнотехнологічний та цифровий контекст виникнення Індустрії 4.0. 1.2 Означення терміну Індустрія 4.0. 1.3 Технологія, гнучкість та продуктивність Індустрії 4.0. 1.4 Стійкість та людиноцентрованість Індустрії 4.0. 2 Аналіз застосування інформаційних технологій для організації виробництва за принципами Індустрії 4.0. 2.1 Бізнес-процеси виробництва за принцмпами Індустрії 4.0. 2.2 Ключові цінності Індустрії 4.0. 2.3 Інформаційно-технологічне забезпечення Індустрії 4.0. 2.4 Допоміжні технології що інтегруються в Індустрію 4.0. 2.5 Розробка нового продукту з врахуванням концепту Індустрія 4.0. 2.6 Перспективи подальших досліджень в галузі Індустрії 4.0. 3. Безпека життєдіяльності, основи охорони праці. Висновки. Перелік джерел. 5. Перелік графічного матеріалу (з точним зазначенням обов'язкових креслень, слайдів) 1 Титульна сторінка. 2 Тема та мета роботи. 3 Завдання роботи. 4 Актуальність роботи. 5 Прогресивна глобалізація. 6 Для Індустрії 4.0 базовим… 7 Коли CPS взаємодіють. 8 Хмарні інформаційні технології. 9. Інноваційні інформаційні технології. 10 Поява нових технологій. 11 Сфера найбільшого впливу Індустрії 4.0. 12 Індустрія 4.0 є темою. 13 Модель еталонної архітектури. 14 RAMI 4.0. 15 Ключові цінності Індустрії 4.0. 16 Інформаційнотехнологічне забезпечення. 17 Допоміжні технології. 18. Технологічні чинники. 19. Висновки. 20 Доповідь завершено.