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Christian Engel

Wissenschaftlicher Mitarbeiter
Müller-Friedbergstrasse 8
9000 St Gallen
+41 71 224 3363


  • Cognitive Automation
  • Management Künstlicher Intelligenz
  • Weitere Forschungsgebiete

  • Datengetriebene Geschäftsmodelle
  • Datengetriebene Dienstleistungen
  • Publikationen

    get_appChristian Engel, Philipp Ebel, Jan Marco Leimeister
    Wissenschaftlicher Artikel
    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.

    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.

    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.

    The role of the Chief Information Officer (CIO) in the organization has received a lot of attention in recent years. While traditionally most CIOs had faced the difficulty of stepping out of the shadow of being coined as a "Utility & Infrastructure Director", they have been found to establish themselves as a driving force in defining and shaping the digital agenda and strategic direction of their organization (Peppard et al. 2011). This crisis-driven development is, however, now paving the way for a new era of the CIO role. As research shows, crises usually do not lead to a trend reversal, but to a trend acceleration (Gassmann and Ferrandina 2021). Therefore, this opportunity should be seized by CIOs in order to leverage the digitalization momentum gained through the COVID-19 crisis, and to build lean digital organizational structures and use strategic sourcing of services for cost efficiency. Thus, the focus here should not be on rebuilding old barriers, but to use the crisis induced dynamic to empower the CIO to successfully master the future challenges of efficiency, flexibility, resilience, scalability and innovation in the organization.

    Facilitated by Artificial Intelligence technology, cognitive automation means to front and back offices what the pervasive automation through physical machinery and robots meant to production plants. Thus, we can automate tasks and processes that were unimaginable to be automated one decade ago. However, organizational adoption of cognitive automation is way below its possibilities, as this novel class of automation technology is perceived to be risky by organizations. This demands structured approaches for assessing the suitability of use cases for cognitive automation. Following the Design Science Research paradigm, we develop a method for assessing cognitive automation use cases. This enables practitioners to make more informed decisions on selecting, specifying, and embedding cognitive automation use cases in their organizations. For researchers, the method serves as a conceptual frame, which they can adapt to guide their empirical research or to use it for developing future decision support to shape the future of work.

    Artificial Intelligence (AI) provides organizations with vast opportunities of deploying AI for competitive advantage such as improving processes, and creating new or enriched products and services. However, the failure rate of projects on implementing AI in organizations is still high, and prevents organizations from fully seizing the potential that AI exhibits. To contribute to closing this gap, we seize the unique opportunity to gain insights from five organizational cases. In particular, we empirically investigate how the unique characteristics of AI – i.e. experimental character, context sensitivity, black box character, and learning requirements – induce challenges into project management, and how these challenges are addressed in organizational (socio-technical) contexts. This shall provide researchers with an empirical and conceptual foundation for investigating the cause-effect relationships between the characteristics of AI, project management, and organizational change. Practitioners can benchmark their own practices against the insights to increase the success rates of future AI implementations.

    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.

    The journey to become an ecosystem orchestrator for an Internet of Things (IoT) platform poses considerable strategic challenges for industry incumbents, which arise along three dimensions: platform, ecosystem and value co-creation. We describe how “TelcoCorp,” a large European telecoms operator, addressed these challenges as it established its enterprise IoT platform ecosystem. Based on the TelcoCorp case study, we provide recommendations that IT and business executives can use to become orchestrators in the IoT instead of fearing platform competition.

    Natural Language Processing (NLP)-based machine learning receives continuous attention in Information System (IS) research and practice. Despite the success of deep learning models, NLP feature engineering still plays a vital role in contexts where only little annotated data is available, and in which explainability is a precondition for productive deployment. However, NLP feature engineering is a labor-intensive and time-consuming endeavor, and there is still limited shared knowledge about the distinctive characteristics of NLP features from an interdisciplinary perspective. To address this gap, we draw on a systematic literature review and develop a five-dimensional NLP feature taxonomy based on 133 unique features from 211 scientific studies. This helps IS researchers and practitioners to classify, compare, and evaluate their NLP studies. Moreover, we used cluster heat mapping analysis to derive three clusters and several white spots to provide further assistance for designing new NLP solutions in IS.

    Cognitive automation moves beyond rule-based automation and thus imposes novel challenges on organizations when assessing the automation potential of use cases. Thus, we present an empirically grounded and conceptually operationalized model for assessing cognitive automation use cases, which consists of four assessment dimensions: data, cognition, relationship, and transparency requirements. We apply the model in a real-world organizational context in the course of an action research project at the customer service department of ManuFact AG, and present unique empirical insights as well as the impact the application of the model had on the organization. The model shall help practitioners to make more informed decisions on selecting use cases for cognitive automation and to plan respective endeavors. For research, the identified factors affecting the suitability of a use case for cognitive automation shall deepen our understanding of cognitive automation in particular, and AI as the driving force behind cognitive automation in general.



    • 2018 - M.Sc., Wirtschaftsingenieurwesen @ Karlsruher Institut für Technologie (KIT)
    • 2016 - Auslandssemester @ University of Connecticut (UCONN), USA
    • 2015/16 - Stanford ME310 Design Thinking Class als Teil des Sugar-Netzwerks
    • 2015 - B.Sc., Wirtschaftsingenieurwesen @ Karlsruher Institut für Technologie (KIT) 


    • 2021 Bestes Minitrack-Paper und nominiert für Best Paper Award bei Hawaii International Conference on System Sciences (HICSS 2021) 


    • Association for Information Systems (AIS)
    • Swiss Chapter of the Association for Information Systems (CHAIS)