Relevance & Research Question: The emerging field of sensor-based journalism relies on data beyond human reach collected by sensors (Diakopoulos, 2019; Loebbecke & Boboschko, 2020). Research on sensor-based journalism (Boboschko & Loebbecke, 2025) studies the impact of identity cues and outlet reputation on content credibility (Sundar, 1999). Communication and media studies (Boller et al., 1990; Wathen & Burkell, 2002) analyze how testimonial-based 'argument strength' drives journalistic content credibility. Aiming to complement both research streams, we ask how argument strength influences content credibility in the context of sensor-based journalism.

Methods & Data: This study deploys a between-subjects online experiment (N= 853) followed by multi-group covariance-based structural equation modeling. Two treatment groups read an article on traffic affecting air pollution in London, one drawing evidence from sensor data, the other from testimonials. As endogenous latent variables, we measure argument strength with four items and content credibility with five items. Measurement items, wording of all items, descriptive statistics, standardized factor loadings, squared multiple correlations for each indicator, construct-level reliability, and convergent validity are available upon request.

Results: Sensor-based journalism fosters argument strength and credibility formation; statistical details and interpretations are available upon request. Controlled online experiments allow for realistically simulating (journalistic) media consumption.

Added Value: Promoting research in sensor-based journalism in times of AI-based hallucinations – an increasingly relevant phenomena in today's democracies.

References:

Boboschko, I. & Loebbecke, C. (2025). Identity cues influencing article credibility in sensor-based journalism, European Conference on Information Systems (ECIS), Amman, Jordan.

Boller, G., Swasy, J., & Munch, J. (1990). Conceptualizing argument quality via argument structure, Advances in Consumer Research, 17(1), 321-328.

Diakopoulos, N. (2019). Automating the news: how algorithms are rewriting the media, Harvard University Press, Cambridge, MA, US.

Loebbecke, C., & Boboschko, I. (2020). Reflecting upon sensor-based data collection to improve decision making, Journal of Decision Systems, 29(Sup1), 18-31.

Sundar, S. (1999). Exploring receivers' criteria for perception of print and online news, Journalism & Mass Communication Quarterly, 76(2), 373-386.

Wathen, C., & Burkell, J. (2002). Believe it or not: factors influencing credibility on the web, Journal of the American Society for Information Science and Technology, 53(2), 133-144.

Relevance & Research Question
Armed conflicts fundamentally disrupt daily life, yet their impact on digital media behavior remains understudied, particularly across different population groups. This study examines how the October 2023 Israel-Hamas war affected media consumption and social media activity among Jewish and Arab citizens of Israel, investigating the relationship between anxiety and online behavior changes. The research addresses: How did the war affect social media activity across populations? Did anxiety levels differ between groups? What is the relationship between anxiety and social media behavior during conflict?
Methods & Data
We conducted representative online panel surveys with 505 Jewish and 146 Arab respondents through Ipanel during October-November 2023. The survey measured retrospective media consumption (before vs. during war), social media activity changes, and generalized anxiety using the GAD-7 scale. Media consumption was categorized into four daily time brackets. Social media activity changes were measured on a five-point scale. We employed correlation analyses examining relationships between anxiety and social media behavior across population groups.
Results
Jewish respondents showed dramatic media consumption increases, with heavy users (4+ hours/day) rising from 19.2% to 38.6%, while Arab respondents showed minimal change. Approximately 31% of Jewish and 6% of Arab respondents reported increased social media activity. No significant anxiety differences emerged between populations (Jewish M=9.38, Arab M=8.67). Critically, opposing anxiety-behavior patterns emerged: among Arabs, higher anxiety correlated with decreased social media activity (r=-.237, p<.01), while Jewish respondents showed non-significant positive trends (r=.059), suggesting anxiety drives increased engagement in majority populations but withdrawal in minorities during intergroup conflict.
Added Value
This research challenges assumptions that populations respond uniformly to crisis events, revealing that majority and minority groups exhibit fundamentally different digital coping mechanisms during intergroup conflicts. The opposing anxiety-behavior relationships suggest social media serves different psychological functions across populations—as an information-seeking and community tool for majorities versus a stress source for minorities. These findings have critical implications for targeted crisis communication, mental health interventions in conflict zones, and understanding how digital platforms may amplify psychological disparities during emergencies. Results indicate researchers and practitioners must account for population-specific responses when designing crisis communication strategies and evaluating social media's role in conflicts.

Relevance & Research Question

Generative AI has emerged in recent years as a key technology shaping both consumers’ everyday lives and the media and advertising industry. It competes with major search engines such as Google in searching information and opens up new efficient and profitable opportunities for advertisers to engage consumers – for example, through AI-generated advertising, synthetic brand influencers or AI shopping agents.
This study addresses three core questions: (1) How do consumers in Germany use generative AI in daily life and how have their attitudes evolved over time? (2) What opportunities and risks are perceived regarding GenAI? (3) To what extent do usage and attitudes toward GenAI influence the acceptance of AI-generated advertising?

Methods & Data

The research applies a mixed-method design comprising three modules:

• Module 1: In-depth interviews (n=18) with non-users, occasional users, and heavy users to explore attitudes and everyday usage of GenAI as well as perceptions of AI-generated advertising by blind-testing AI-generated TV commercials (A/B test).
• Module 2: Online survey (n≈4,000; ages 16–69; online-representative) to quantify acceptance and perception of AI-generated advertising by blind-testing AI-generated TV commercials (A/B test).
• Module 3: Tracking study (three annual waves since 2023; n≈1,000 per wave; ages 16–69; online-representative) to analyze temporal changes in usage, attitudes and acceptance of GenAI.

Results

Preliminary findings indicate a steady increase in familiarity and regular usage. GenAI is perceived as indispensable for information gathering. While efficiency is highly valued, concerns about data privacy, misinformation and job displacement persist but do not significantly affect the rate of adoption and usage. AI-generated advertising is considered forward-looking but evokes mixed reactions: younger, tech-savvy users show higher acceptance, whereas older cohorts remain skeptical. Synthetic influencers face the strongest resistance, while AI-generated TV commercials and shopping assistants receive comparatively higher acceptance.

Final results will be available in January 2026.

Added Value

The study provides empirically grounded insights for advertisers to strategically leverage the potential of generative AI. It identifies target groups open to AI-based advertising formats and highlights acceptance barriers. These findings support the development of effective communication strategies in an increasingly AI-driven media landscape.