Christian Engel

Intelligent agents (IAs) are permeating both business and society. However, interacting with IAs poses challenges moving beyond technological limitations towards the human-computer interface. Thus, the knowledgebase related to interaction with IAs has grown exponentially but remains segregated and impedes the advancement of the field. Therefore, we conduct a systematic literature review to integrate empirical knowledge on user interaction with IAs. This is the first paper to examine 107 Information Systems and Human-Computer Interaction papers and identified 389 relationships between design elements and user acceptance of IAs. Along the independent and dependent variables of these relationships, we span a research space model encompassing empirical research on designing for IA user acceptance. Further we contribute to theory, by presenting a research agenda along the dimensions of the research space, which shall be useful to both researchers and practitioners. This complements the past and present knowledge on designing for IA user acceptance with potential pathways into the future of IAs.

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.

Big data analytics (BDA) gained importance in scholarly and practitioner literature alike. There is some disagreement, however, whether BDA is merely an evolution of established phenomena, most particularly business intelligence, or whether BDA represents a novel technology-driven innovation with potentially disruptive market impacts. Using the technology-organization-environment theory as our lens of analysis, we take a critical stance and conduct a systematic literature review to offer guidance for future research by pinpointing pivotal concepts and providing conceptually and empirically validated propositions as well as research avenues for IS research on BDA adoption. While the research avenues are intended to trigger future research, the developed propositions shall provide guidance to research endeavors that empirically analyze the adoption of BDA in organizational settings. By discussing open research issues and providing potentially fruitful theoretical perspectives for enriching our knowledge in this domain, this shall ultimately contribute to advancing BDA research.

The deployment of Artificial Intelligence (AI) in businesses is said to provide significant benefits to organizations. However, many businesses struggle to align single AI use cases with the overall strategic business value contribution. Thus, we investigate the strategic characteristics that determine the business value contribution of AI use cases at an organizational level. We draw on academic literature and 106 AI use cases to develop a conceptually sound and empirically grounded taxonomy of the organizational business value of AI use cases. With the developed taxonomy, decision-makers are presented with a tool to systematically align AI use cases with strategic objectives. Moreover, our findings reveal how an AI use case can generate different business value contributions in different contexts, which provides researchers with a conceptual frame for informing their empirical research endeavors at the organizational level.

Many organizations struggle to measure, control, and manage agility in a manner of continuous improvement. Therefore, we draw on Design Science Research to develop and test a tool for Continuously Assessing and Improving Agile Practices (CAIAP). CAIAP helps agile practitioners to monitor the alignment of “as is” agile practices on individual, team levels with the overall agile strategy of the organization. To develop CAIAP, we first empirically gather requirements, draw on the ICAP framework to base the tool development on a solid conceptual and theoretical basis. CAIAP helps agile practitioners to constantly monitor their agile practices on individual and team levels and to identify areas for improvement to gain greater organizational agility. To researchers, CAIAP helps to make the unit of analysis of agile work explainable, predictable and helps researchers to guide their own empirical research as well as serve as a basis for designing further tool support.

The ever-increasing complexity of the music industry and the intensified resentment of artists towards collecting societies call for a transformation and a change of behavior within the music ecosystem. This article introduces a hybrid intelligence system, that ameliorates the current situation by combining the intelligence of humans and machines. This study proposes design requirements for hybrid intelligence systems in the music industry. Using a design science research approach, we identify design requirements both inductively from expert interviews and deductively from theory and present a first prototypical instantiation of a respective hybrid intelligence system. Overall, this shall enrich the body of knowledge of hybrid intelligence research by transferring its concepts into a new context. Furthermore, the identified design requirements shall serve as a foundation for researchers and practitioners to further explore and design hybrid intelligence in the music industry and beyond.