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Prof. Dr. Ivo Blohm

Assistenzprofessor
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 ongoing discussion of the Agile Work Organization (AO) in research and practice permeates a multitude of research areas. However, no clear conceptualization of the AO has been provided. In this paper, we conduct a Systematic Literature Review to investigate what constitutes and defines the AO. The SLR reveals three dimensions in the research field of the AO: Strategic, Functional and Operative Agility. These dimensions define the AO through different unique capabilities by influencing and enhancing the overall goal of the AO in adaptation and flexibility. Building up on the insights from the review, we develop proposition which describe the interrelationship between the dimensions and towards the AO. Furthermore, implications for academia and practice as well as a research agenda are provided in order to trigger and guide further discussions and research surrounding the AO.

    Mehr
    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.

    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
    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."

    Mehr
    Crowd work reflects a new form of gainful employment on the Internet. We study how the nature of the tasks being performed and financial compensation jointly shape work perceptions of crowdworkers in order to better understand the changing modes and patterns of digital work. Surveying individuals on 23 German crowd working platforms, this work is the first to add a multi-platform perspective on perceived working conditions in crowd work. We show that crowd workers need rather high levels of financial compensation before task characteristics become relevant for shaping favorable perceptions of working conditions. We explain these results by considering financial compensation as an informational cue indicating the appreciation of working effort that is internalized by well-paid crowd workers. Resulting boundary conditions for task design are discussed. These results help us understand when and under what conditions crowd work can be regarded as a fulfilling type of employment in highly developed countries.

    Mehr
    get_appIvo Blohm, Shkodran Zogaj, Ulrich Bretschneider, Jan Marco Leimeister
    Wissenschaftlicher Artikel
    Crowdsourced tasks are very diverse – and so are platform types. They fall into four categories, each demanding different governance mechanisms. The main goal of microtasking crowdsourcing platforms is the scalable and time-efficient batch processing of highly repetitive tasks. Crowdsourcing platforms for information pooling aggregate contributions such as votes, opinions, assessments and forecasts through approaches such as averaging, summation, or visualization. Broadcast search platforms collect contributions to solve tasks in order to gain alternative insights and solutions from people outside the organization, and are particularly suited for solving challenging technical, analytical, scientific, or creative problems. Open collaboration platforms invite contributors to team up to jointly solve complex problems in cases where solutions require the integration of distributed knowledge and the skills of many contributors. Companies establishing crowdsourcing platforms of any type should continuously monitor and adjust their governance mechanisms. Quality and quantity of contributions, project runtime, or the effort for conducting the crowdsourcing project may be good starting points.

    Mehr
    Crowd work reflects a new form of gainful employment on the Internet. We study how the nature of the tasks being performed and financial compensation jointly shape work perceptions of crowd workers in order to better understand the changing modes and patterns of digital work. Surveying individuals on 23 German crowd working platforms, this work is the first to add a multi-platform perspective on perceived working conditions in crowd work. We show that crowd workers need rather high levels of financial compensation before task characteristics become relevant for shaping favorable perceptions of working conditions. We explain these results by considering financial compensation as an informational cue indicating the appreciation of working effort that is internalized by well-paid crowd workers. Resulting boundary conditions for task design are discussed. These results help us understand when and under what conditions crowd work can be regarded as a fulfilling type of employment in highly developed countries.

    Mehr
    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. We apply a process ontology on internal crowdsourcing as platform-based mode of work organization, following two organizations employing internal crowdsourcing in a case study approach for over four years. On a macro level, our theory describes the transformation process enacted by internal crowdsourcing as three-phased process. On the micro-level, we illustrate that this transformation process is driven by specific design choices on single elements. In so doing, our process theory contributes to a better understanding of internal crowdsourcing as means for transformation work organization and to STS theory by showing that the emergence and constitution of STS is mainly driven by processes on a micro-level.

    Mehr

    Ausbildung


    • 2021 (erwartet): 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 2019 Akademischer Direktor und Programm Manager "CAS Big Data & Artificial Intelligence for Managers" 
    • Seit 2016 Assistenzprofessor für Data Science und Management an der Universität St. Gallen
    • Seit 2013 Leiter der Competence Center Crowdsourcing 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.

    • 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

    Spin-Offs


    • BeeUp GmbH
    • Perspective Food GmbH

    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