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Prof. Dr. Ivo Blohm

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

Schwerpunkte


  • Business Analytics
  • Data Science
  • Crowdsourcing
  • Forschungsgebiete


  • Business Analytics
  • Data Science
  • Crowdsourcing
  • Publikationen


    The centrality of information systems (IS) customization to match companies' needs with software systems available in the market has been researched extensively. The distinctive characteristics of Artificial Intelligence (AI) systems compared to other types of IS suggest that customization needs a new conceptualization in this context. We draw on evidence from expert interviews to conceptualize customization of AI systems as composed of four layers: data, models, algorithms, infrastructures. We identify a continuum of levels of customization, from no to complete customization. Since companies customize AI systems in response to business needs, we develop a theoretical model with six antecedents of AI systems' customization choices. In so doing, we contribute to both AI management research, by introducing the IS customization perspective in the field, and IS customization literature, by introducing AI systems as a novel class of systems and enlarging the understanding of customization for a specific class of software systems.

    Mehr
    Algorithmic forecasts outperform human forecasts by 10% on average. State-of-the-art machine learning (ML) algorithms have further expanded this discrepancy. Because a variety of other activities rely on them, sales forecasting is critical to a company's profitability. However, individuals are hesitant to use ML forecasts. To overcome this algorithm aversion, explainable artificial intelligence (XAI) can be a solution by making ML systems more comprehensible by providing explanations. However, current XAI techniques are incomprehensible for laymen, as they impose too much cognitive load. We contribute to this research gap by investigating the effectiveness in terms of forecast accuracy of two example-based explanation approaches. We conduct an online experiment based on a two-by-two between-subjects design with factual and counterfactual examples as experimental factors. A control group has access to ML predictions, but not to explanations. We report results of this study: While factual explanations significantly improved participants' decision quality, counterfactual explanations did not.

    Mehr
    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.

    Mehr
    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.

    Mehr
    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.

    Mehr
    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.

    Mehr
    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.

    Mehr
    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.

    Mehr
    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.

    Mehr
    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.

    Mehr

    Ausbildung


    • 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)
       

    Lehraktivitäten


    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) 

    Berufserfahrung


    • 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)

    Awards


    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 e-fellows.net-Stipendium
    • 2005-2006 Erasmus-Stipendium  

     

    Mitgliedschaften


    • AIS
    • AOM
    • VHB

    Editorial Board


    Editorial Board “Journal of Information Technology”

    Spin-Offs


    • BeeUp GmbH
    • Perspective Food AG

    Vorträge


    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