Research

  1. Home
  2. Research

At the IBT, we study behavior and technology from a human-centered perspective. We use latest methods to conduct fundamental research and to study technological applications and their implications for individuals, organizations, and society at large.

  • Affective Computing

    Our studies are part of a burgeoning field of research, seeking to develop systems and devices that can recognize, interpret, process, forecast, and simulate human emotion. Specific examples include the development of a hybrid machine learning approach to improve emotion recognition, based on facial or vocal characteristics. Further, we investigate how affect-related consumption outcomes such as satisfaction can be predicted from written user input. In another research project, we investigate how to engineer empathetic and vulnerable conversational agents able to transmit emotions via simulating human vocal features. Such agents could potentially adapt to the emotional state of the user and possibly motivate prosocial behaviors, for instance in the context of donation advertisements. All the aforementioned projects can be leveraged in both research and industry contexts.

    Exemplary publications
    • Busquet, F., & Hildebrand, C. (2020). Black-Box Emotion Detection: On the Variability and Predictive Accuracy of Automated Emotion Detection Algorithms. ACR North American Advances. [Link]
  • Autonomous Products & Robots

    Technologies are becoming increasingly autonomous, from smart kitchen devices and robotic vacuum cleaners to self-driving cars and service robots. In fact, some voices argue that we are about to move from the age of automation to the age of autonomy. Autonomous technologies can make decisions and complete tasks on behalf of humans, promising unprecedented levels of convenience and efficiency. At the same time, this novel class of technology endangers some fundamental human motives. At the IBT, we examine how these changes affect the relationship between humans and technology, which barriers to consumer adoption exist, and what the societal consequences may be in the long run.

    Exemplary publications
    • Whillans, A., de Bellis, E., Nindl, F., & Schlager, T. (2020). Robots Save Us Time—But Do They Make Us Happier?. Harvard Business Review. [Link]
    • De Bellis, E., & Johar, G. V. (2020). Autonomous shopping systems: Identifying and overcoming barriers to consumer adoption. Journal of Retailing, 96(1), 74-87. [Link]
    Image description
  • Conversational AI

    The use of conversational AI ranges from text-based chatbots that automate service operations to voice-based interfaces such as Amazon Alexa or Google Home that take over every-day tasks in consumers’ homes. Building on prior work in human-to-human communication and interpersonal psychology, we examine the impact of conversational AI on consumer decision making, consumer trust, and how to design competent while empathic conversational AI. We further examine how the proliferation of AI-enabled technologies that appear increasingly more human-like impact mind perception, entire markets, and consumer self-expression.

    Exemplary publications
    • Hildebrand, C., & Bergner, A. (2021). Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making. Journal of the Academy of Marketing Science, 49(4), 659-676. [Link]
    • Hildebrand, C. A., & Bergner, A. (2021). Wie Chatbots die Bankenwelt verändern (How Chatbots Change the Future of Banking). Die Volkswirtschaft, (4), 52-53. [Link]
  • Customization & Personalization

    The combination of modern information technology and digital behavior offers new possibilities for tailor-made solutions in domains such as food, insurance, and ad targeting. On the one hand, an increasing number of firms allows individuals to self-customize their own products according to their specific preferences. On the other hand, websites are personalized to customers’ implicit wishes and needs by leveraging large amounts of customer profile data. At the IBT, we explore these two central one-to-one marketing concepts—customization and personalization—and examine both their benefits and risks for individuals and companies.

    Exemplary publications
    • de Bellis, E., Hildebrand, C., Ito, K., Herrmann, A., & Schmitt, B. (2019). Personalizing the customization experience: a matching theory of mass customization interfaces and cultural information processing. Journal of Marketing Research, 56(6), 1050-1065. [Link]

    Image description
  • Digital Ethics & Fairness

    Digital technologies that can make decisions autonomously and are used pervasively in daily life, can have unintended effects on individuals and society at large. Unfair algorithmic decisions, biases in AI applications, and a lack of privacy and transparency are only a few examples. Through an interdisciplinary lens, researchers at the IBT investigate how digital technologies affect our everyday lives, how technology can change people's behavior, and how unintended technological consequences can be prevented and counteracted.

    Exemplary publications
    • Hildebrand, C., & Bergner, A. (2021). Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making. Journal of the Academy of Marketing Science, 49(4), 659-676. [Link]
    • Hampton, W. H. (2020). Covid-19 tracking: Knowing where you are without knowing who you are. [Link]



    Image description
  • Interface Haptics

    The modern technology landscape increasingly relies on haptic technologies to control a device. Mobile technologies such as smartphones, tablets, or even smart watches rely on the human sense through touch, emitting vibrations to the user, and increasingly allow gestural input to control a device. We study how the haptic sensation (from touch, gestures, and vibrations) impact consumer shopping decisions and examine the effective gestural design of haptic interfaces.

    Exemplary publications
    • Melumad, S., Hadi, R., Hildebrand, C., Ward, A. (2020): Technology-Augmented Choice: How Digital Innovations are Transforming Consumer Decision Processes, Customer Needs and Solutions, pp. 1–12. [Link]
    • Hampton, W. H., & Hildebrand, C. A. (2021). Pavlov’s Buzz? Mobile Vibrations as Conditioned Rewards and Modifiers of Consumer Decision-Making. [Link]






  • Mobile Sensing & Digital Behavior

    Mobile phones are the most personal device in many people's lives. While phones once were only used for communication, the technical sophistication of modern smartphones provides users with a wide range of functionalities. Many of these functionalities allow users to do things on their phone anytime and anywhere. These functionalities rely on an array of sensors and logging routines that can also be used to measure when and where people do certain things. Sensor-based behavioral metrics are increasingly being used to identify, describe, and characterize individuals and their activities. At the IBT, we investigate how mobile sensing can be used to study human behavior, decisions, as well as the environments and situations people spend time in.

    Exemplary publications
    • Stachl, C., Au, Q., Schoedel, R., Gosling, S. D., Harari, G. M., Buschek, D., ... & Bühner, M. (2020). Predicting personality from patterns of behavior collected with smartphones. Proceedings of the National Academy of Sciences, 117(30), 17680-17687. [Link]


    Image description
  • Multi-Modal Behavioral Analytics

    We all leave a broad range of physical and digital footprints that are increasingly used to study human behavior. From analyzing people’s movements through environmental sensors and GPS trackers to analyzing features in the human voice. We employ and further develop feature-extraction tools from sound data and other forms of behavioral data sources (such as physiological measurements). We further conduct research on the ethical implications of building multi-modal databases for business and society.

    Exemplary publications
    • Hildebrand, C., Efthymiou, F., Busquet, F., Hampton, W. H., Hoffman, D. L., & Novak, T. P. (2020). Voice analytics in business research: Conceptual foundations, acoustic feature extraction, and applications. Journal of Business Research, 121, 364-374. [Link]






  • Personality Computing & Assessment

    In addition to situational aspects, the personality of a person is one of the most important characteristics to understand and anticipate behavior. Personality also plays a key role in peoples' everyday decisions, preferences, and experiences. At the IBT, we study how personality is expressed in everyday behavior and how machine learning can be used to understand, assess, and conceptualize personality and individual differences.

    Exemplary publications
    • Harari, G. M., Vaid, S. S., Müller, S. R., Stachl, C., Marrero, Z., Schoedel, R., ... & Gosling, S. D. (2020). Personality sensing for theory development and assessment in the digital age. European Journal of Personality, 34(5), 649-669. [Link]
    • Stachl, C., Pargent, F., Hilbert, S., Harari, G. M., Schoedel, R., Vaid, S., ... & Bühner, M. (2020). Personality research and assessment in the era of machine learning. European Journal of Personality, 34(5), 613-631. [Link]









    Image description

Our studies are part of a burgeoning field of research, seeking to develop systems and devices that can recognize, interpret, process, forecast, and simulate human emotion. Specific examples include the development of a hybrid machine learning approach to improve emotion recognition, based on facial or vocal characteristics. Further, we investigate how affect-related consumption outcomes such as satisfaction can be predicted from written user input. In another research project, we investigate how to engineer empathetic and vulnerable conversational agents able to transmit emotions via simulating human vocal features. Such agents could potentially adapt to the emotional state of the user and possibly motivate prosocial behaviors, for instance in the context of donation advertisements. All the aforementioned projects can be leveraged in both research and industry contexts.

Exemplary Publications:

  • Busquet, F., & Hildebrand, C. (2020). Black-Box Emotion Detection: On the Variability and Predictive Accuracy of Automated Emotion Detection Algorithms. ACR North American Advances. [Link]

OPEN POSITIONS

Menu