The proliferation of Internet of Things (IoT) devices has embedded major technology brands like Amazon, Google or Xiaomi more deeply into customers daily lives and home environments. These brands offer not just single products, but interconnected ecosystems of smart speakers, cameras, doorbells, and media devices. The success of these ecosystems depends on user trust, a belief in the brand's reliability, competence, and ethical conduct.

However, recent research reveals a troubling trend: the use of "dark patterns", which are user interface designs used to manipulate users into actions they did not intend by themselves, such as unintentionally sharing data or making unwanted purchases. Authors like Kowalczyk et al. (2023) suggest that dark patterns are particularly pervasive in the Smart Home domain, with devices from major manufacturers like Amazon containing a high number of these manipulative designs. For example, a smart speaker might use a pre-selected checkbox to gain consent for data sharing during setup, or a streaming device might make it very easy to sign up for a free trial to a subscription service but difficult to cancel.

While the immediate harm of a single dark pattern might seem small, the cumulative effect on user cognition (i.e. mental models, trust, continuance intention) and associated behavior is largely unknown. For instance, when a user encounters a dark pattern in one product, does the damage to trust remain confined to that specific product, or does it erode trust in the overall brand and/or ecosystem as well? 

Potential research questions: 

• How does the discovery of dark patterns in a specific smart home product (e.g., a smart speaker) affect a user's cognition (in particular trust) and behavior? 
• Up to which extent does the discovery of dark patterns impact the user's overall trust in and behavior toward the brand?
• What factors moderate this relationship? For instance, does the perceived severity of the dark pattern or the user's pre-existing brand loyalty influence the degree of trust erosion?

The following research methods, for example, can be used for the thesis:

• Quantitative experimental design (e.g. laboratory experiment, scenario-based online experiment)

Requirements:

This thesis proposal is suitable for students in business and economics, business informatics, industrial engineering or related fields, who meet the following requirements:

• Strong interest in current research on user trust and behavior, Smart Home and the Internet of Things as well as the impact of dark patterns in digital ecosystems.
• High degree of independence and personal responsibility.
• Experience with scientific research methods and strong analytical skills.

Literature: 

• Kowalczyk, M., Gunawan, J. T., Choffnes, D., Dubois, D. J., Hartzog, W., & Wilson, C. (2023, April). Understanding dark patterns in home IoT devices. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-27).
• Mathur, A., Kshirsagar, M., & Mayer, J. (2021, May). What makes a dark pattern... dark? Design attributes, normative considerations, and measurement methods. In Proceedings of the 2021 CHI conference on human factors in computing systems (pp. 1-18).
• Hoff, K. A., & Bashir, M. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human factors, 57(3), 407-434.
• Chou, E. Y., Hsu, D. Y., & Myung, N. (2022). Once bitten, twice shy: The negative spillover effect of seeing betrayal of trust. Journal of Experimental Psychology: Applied, 28(2), 360.