Generic filters
Exact matches only
Filter by content type

Prof. Dr. Ivo Blohm

Assoziierter Professor für Wirtschaftsinformatik, insb. Business Analytics
Büro 52-6014
Müller-Friedberg-Strasse 6/8
9000 St. Gallen
+41 71 224 3321


  • Business Analytics
  • Data Science
  • Crowdsourcing
  • Forschungsgebiete

  • Business Analytics
  • Data Science
  • Crowdsourcing
  • Publikationen

    Agile work organization has experienced a significant increase in acceptance in recent years. However, literature falls short in explaining the transformation process that results from the implementation of agile work organization as a means of achieving increased adaptability, rate of speed and flexibility. We apply a process ontology to agile work organization by following three multinational firms that apply agile work organization, utilizing a case study approach over three years. At the macro level, our theory describes the transformation process set in motion by agile work organization as a three-phase process. At the micro level, we show that this transformation process is driven by specific design decisions on individual elements. Thus, our process theory contributes to a better understanding of agile work organization as a means to achieve organizational agility and to STS theory by showing that the emergence and constitution of STS are mainly driven by micro-level processes.

    Algorithmic forecasts outperform human forecasts in many tasks. State-of-the-art machine learning (ML) algorithms have even widened that gap. Since sales forecasting plays a key role in business profitability, ML based sales forecasting can have significant advantages. However, individuals are resistant to use algorithmic forecasts. To overcome this algorithm aversion, explainable AI (XAI), where an explanation interface (XI) provides model predictions and explanations to the user, can help. However, current XAI techniques are incomprehensible for laymen. Despite the economic relevance of sales forecasting, there is no significant research effort towards aiding non-expert users make better decisions using ML forecasting systems by designing appropriate XI. We contribute to this research gap by designing a model-agnostic XI for laymen. We propose a design theory for XIs, instantiate our theory and report initial formative evaluation results. A real-world evaluation context is used: A medium-sized Swiss bakery chain provides past sales data and human forecasts.

    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.

    With looming uncertainties and disruptions in today’s global supply chains, such as lockdown measures to contain COVID-19, supply chain resilience has gained considerable attention recently. While decision-makers in procurement have emphasized the importance of traditional risk assessment, its shortcomings can be complemented by resilience. However, while most resilience studies are too qualitative in nature and abstract to inform supplier decisions, many quantitative resilience studies frequently rely on complex and impractical operations research models fed with simulated supplier data. Thus there is the need for an integrative, intermediate way for the practical and automated prediction of resilience with real-world data. We therefore propose a random forest-based supervised learning method to predict supplier resilience, outperforming the current human benchmark evaluation by 139 percent. The model is trained on both internal ERP data and publicly available secondary data to help assess suppliers in a pre-screening step, before deciding which supplier to select for a specific product. The results of this study are to be integrated into a software tool developed for measuring and tracking the total cost of supply chain resilience from the perspective of purchasing decisions.

    The Swiss Journal of Business Research and Practice invited submissions related to the question of how the digitalization may shape the future of work. The rapid development of innovative digital technologies and the associated digital transformation have changed the way in which we live, communicate, and work. Digital platforms and the increasing pursuit of becoming more effective and flexible have affected many traditional work structures within and across organizations. Work is potentially becoming more distributed, flexible, and autonomous. At the same time, many approaches of digital work are associated with inferior working conditions, low payment, or even increasing surveillance (Durward et al. 2020, Aloisi/Gramano 2019). Phenomena such as artificial intelligence, smart devices, or robotics might further accelerate these developments and could lead to an augmentation and automation of knowledge work - work that requires extensive education and training and that is today performed by humans. Similarly, organizations and management practices may become more digital such that new jobs, roles, and skill profiles as well as innovative modes of management and leadership could emerge. These developments will not only impact individuals and organizations, but also our society in its entirety.

    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.

    The increasing availability of data and advances in data processing and analysis methods have led to a flourishing of data science and business analytics. This not only constitutes new research efforts in information systems research (e.g. artificial intelligence (AI), processing of unstructured data, decision support systems, or visualization), but also has a significant impact on established topics in information systems research such as business intelligence and decision support systems. In this track, we welcomed the entire diversity of information systems research efforts in the fields of data science and business analytics and were open to all methodological approaches.

    Investors increasingly use machine learning (ML) algorithms to support their early stage investment decisions. However, it remains unclear if algorithms can make better investment decisions and if so, why. Building on behavioral decision theory, our study compares the investment returns of an algorithm with those of 255 business angels (BAs) investing via an angel investment platform. We explore the influence of human biases and experience on BAs’ returns and find that investors only outperformed the algorithm when they had extensive investment experience and managed to suppress their cognitive biases. These results offer novel insights into the role of cognitive limitations, experience, and the use of algorithms in early stage investing.

    Internal crowdsourcing showed a substantial increase of use in recent years, since it describes a promising alternative to traditional orchestration of employees in today’s digital era. However, literature falls short in explaining the transformation process that is enacted by such approaches of platform-based work organization. By using a work organizational perspective with the existing body of knowledge in combination with a revelatory case study, this paper develops a process theory explaining the transformation process of internal crowdsourcing over time and how the organization of work transform during this process. Moreover, we discovered four different forms of organizing work with a completely new form of work organization: the “Hybrid Flash Organization”. Scholars can identify critical incidents and process phases, while practitioners use our findings as a transformation guideline of internal crowdsourcing to detect potential threads, opportunities and constraints along the way of a successful implementation."



    • 2021 Habilitation "Leveraging Business Analytics in Organisations with Crowdsourced Data" an der Universität St.Gallen 
    • 2017 CAS Teaching and Learning in Higher Education an der Universität St.Gallen
    • 2009-2012 Promotion am Lehrstuhl für Wirtschaftsinformatik (Prof. Krcmar) der Technischen Universität München (Deutschland)
    • 2003-2009 Studium Technologie- und Managementorientierte Betriebswirtschaftslehre an der Technischen Universität München (Deutschland)
    • 2005-2006 Erasmus-Austausch an der Università degli Studi di Verona (Italien)


    Der Kern meiner Lehre ist im Bereich Business Analytics:

    • Business Analytics und Data Science Applications (Bachelor, Data Science Fundamentals Program) 
    • Big Data und Data Science (Master)
    • Data Science und Artificial Intelligence für das Management (Master) 
    • FPV Big Data - Developing Smart Business Models (Master)
    • CAS "Big Data and Artfificial Intelligence for Managers" und weitere Executive Education Formate (Executive)

    Darüber hinaus, unterrichte und unterrichtete ich zahlreiche Kernfächer der Wirtschaftsinformatik:

    • Theories in Organization and Information Systems (Ph.D.)
    • Business & IT Strategy Alignment (Master)
    • Case Study Seminar Ineternetökonomie (Bachelor)
    • Einführung in die BWL aus informationswirtschaftlicher Perspektiver (Bachelor) 


    • Seit 2022 Associate Professor für Wirtschaftsinformatik insb. Business Analytics an der Universität St.Gallen
    • Seit 2019 Akademischer Direktor und Programm Manager "CAS Big Data & Artificial Intelligence for Managers" 
    • 2016-2022 Assistenzprofessor für Data Science und Management an der Universität St. Gallen
    • 2009-2012 Wissenschaftlicher Mitarbeiter an der Technischen Universität München im Bereich Wirtschaftsinformatik (Prof. Dr. Krcmar)
    • 2013 Invited Visiting Researcher an University of Queensland (Australien)
    • 2012 Invited Visiting Researcher an Harvard University (USA)


    Die Wirtschaftswoche sieht Ivo Blohm auf Platz 28 (von 3346) der forchungsstärksten BWL-Forscher im deutschprachigen Raum auf Basis der Publikationsleistung der letzten 5 Jahre.

    • 2021 Best Associate Editor, European Conference on Information Systems 
    • 2021 Best Associate Editor, Internationale Tagung Wirtschaftsinformatik
    • 2020 Latsis-Preis der Universität St. Gallen (Bester Nachwuchsforscher)
    • 2019 Runner Up Best Associate Editor, International Conference on Information Systems
    • 2019 Nominee HICCS Best Paper Award
    • 2019 Platz 40 (von 2854) BWL-Forschern im Wirtschaftswoche-Ranking
    • 2018 Nominee VHB Best Paper Award (Best IS Paper in 2016/2017)
    • 2018 Runner Up, Best Associate Editor, International Conference on Information Systems
    • 2018 Finalist TUM Research Excellence Award 
    • 2017 Nominee Most Innovative Short Paper, International Conference on Information Systems
    • 2017 Nominee European Research Paper of the Year, CIONET
    • 2016 2nd Runner Up Most Innovative Research-In-Progress Paper, International Conference on Information Systems
    • 2016 Best Paper in Track "Digital Collaboration and Social Media", International Conference on Information Systems 
    • 2016 Best Reviewer Award Academy of Management Annual Meeting, OCIS Division
    • 2016 International Conference on Information Systems, Junior Faculty Consortium
    • 2015 Nominee Best Paper Award Internationale Tagung Wirtschaftsinformatik               
    • 2011 Academy of Management Annual Meeting, Doctoral Consortium OCIS Division
    • 2010 Auszeichnung der Technischen Universität München für herausragende Leistungen
    • 2007-2008 Leonardo-Stipendium
    • 2006-2012
    • 2005-2006 Erasmus-Stipendium  



    • AIS
    • AOM
    • VHB

    Editorial Board

    Editorial Board “Journal of Information Technology”


    • BeeUp GmbH
    • Perspective Food AG


    Speaker, Keynote-Speaker und Panellist für Business Analytics, Aufbau daten-getriebener Organisationen und Innovationen, künstliche und kollektive Intelligenz sowie Crowdsourcing und andere Formen der digitalen Zusammenarbeit. Eine Auswahl aktueller Talks:
    • Weg von der Krise zur Exzellenz. Wie Tech "Business Resilienz" schafft und "Organisationelle Agilität" ermöglicht, Swisscom CAP 2021 Virtual Kickoff-Event, 15.01.2021
    • Building Data-driven Organizations, CIE Virtual Roundtable, 12.11.2020
    • Analytics für Manager, Handelsblatt Workshop, Berlin, 16.04.2020
    • What must Managers know about Analytics? Digital Week, Executive School University of St.Gallen, 03.03.2020
    • How to Build a Data-Driven Organization, Webinar Executive School University of St.Gallen, 30.06.2019
    • How to Build Data-Driven Innovations and Organizations, AppliedAI KI für den Mittelstand, München, 09.05.2019
    • How to apply AI for small data problems? Simulating survival and profitability of startups, AI Suisse Meetup Zürich, 20.02.2019
    • The Nature of Crowd Work and its Effects on Individuals’ Work Perception, IDHEAP University of Lausanne, 26.06.2019
    • Verbindung künstlicher und kollektiver Intelligenz zur Entwicklung skalierbarer Software Testing Lösung, BDLI TecDays, Airbus, Friedrichshafen, 18.03.2019
    • Re-Imagining Education: The Solution to embrace Artificial Intelligence into Society, Swiss Cognitive, Zurich (CH), 05.11.2018