Relevance & Research Question

Research on AI has a long history, spanning seven decades (Jiang et al., 2022), but only recently have scholars began exploring AI's impact on everyday activities (Ertal, 2018). Over the last two years, one could witness a surge in the use of large language models like ChatGPT, Bard, and Dall-e2. This study investigates people's concerns about AI replacing their roles and their willingness to embrace these technologies, focusing on traditional predictors of fear and adoption: income, education, and age.

Methods & Data

A representative survey of the adult (18+) Jewish population in Israel was conducted (n=502) via an internet panel (iPanel) in the beginning of 2023. It was comprised of demographic and perspectives on AI technologies questions.

Results

Results indicate a significant negative correlation between income, education, and age with fears of AI replacing jobs (rs = -.179, p < .001; rs = -.108, p < .01; rs = -.096, p < .05). Additionally, a borderline significant positive correlation between willingness to adopt AI models and education (rs = .071, p = .055) and a significant negative correlation with age (rs = -.088, p < .05) were found. No correlation was observed between income and the willingness to adopt these technologies (rs = .019, p > .05).

Added Value

Notably, this research reveals a unique finding; Contrary to previous studies showing negative correlations between fear of technology and income or education, the fear of adopting new technologies is inversely related to age. As people grow older, their fear of adopting technology diminishes, likely because these tools offer a user-friendly interface resembling existing chat bots, requiring no new technological literacy. Another possible explanation is that the respondents feel secure in their workplace positions regardless to the new technologies.

Moreover, the lack of a correlation between income and willingness to adopt may stem from the low (sometimes free) cost associated with these technologies.

In an era of rapid AI development and integration into daily life, studies like this one hold significance in understanding public sentiments surrounding these tools and their implications for personal and professional life.

Relevance & Research Question: The research addresses a significant question in the era of advanced digital technology: "Are humans ready to detect AI-generated content?" This question is pivotal as it explores human perception and understanding in the face of rapidly evolving AI capabilities in times of deep fakes on all media platforms.

Methods & Data:

The empirical approach is using a digital interview with n>1000 Germans exposed to a variety of AI-generated and human-created content. In three categories (pictures, audio, videos) the participants were asked to identify the source of each piece of content, whether it was produced by AI or by a human..The content itself was created using AI Tools and stock content sources. The questionnaire is using implicit measurement and pairwise comparisons using the Analytic Hierarchy Process (AHP) methodology.

Results: The findings reveal intriguing insights into the human ability to discern AI-generated content. A significant proportion of participants are heavily challenged in correctly identifying the nature of the content, with varying degrees of accuracy across different types of media. These results highlight the sophistication of current AI technology in mimicking human creativity and the challenges faced by individuals in distinguishing between the two.

Added Value: This study adds substantial value to the discourse on AI and human interaction. It provides empirical evidence on the current state of human perception regarding AI-generated content in Germany, offering a foundation for further research in this area. The findings have implications for fields ranging from digital media and communication to AI ethics and policy-making, emphasizing the need for increased awareness and understanding of AI capabilities among the general public.

Relevance & Research Question
The use of various AI technologies in market research has increased significantly in recent years, and 2023 was a special year: industry publications clearly show that since the beginning of the year, large language models have also been used and new application areas have been tested. These new models are often described as game changers, particularly in qualitative research and analysis. However, there has been little cross-industry sharing of lessons learned. There is a limited understanding of how qualitative researchers use and experience these technologies in their day-to-day work, and how their work may change as a result. Our study aims to fill these gaps by collecting experiences and identifying concerns and challenges. We want to find out what qualitative researchers are actually doing after this year and how the sector has evolved. The aim of our study is to learn what experiences have been gathered so far and what methodological implications, expectations, challenges and opportunities exist.
Methods & Data
German-speaking qualitative researchers in the fields of market, social and UX research are invited to take part in the study. The survey consists of open and closed questions to capture different perspectives on the topic and takes approximately 7 minutes to complete. Questions cover experiences, methods, workflows and the real benefits of AI. Participants will answer completely anonymously so that experiences can be shared openly. Invitations will be sent out via newsletters, social media and industry media to reach as wide an audience as possible.
Results
The results are not yet available, but we will be able to present them at the conference. The data will be collected in December and January.
Added Value
The use of AI poses several challenges for our industry. Sharing experiences is essential to properly assess the potential and develop common standards. We will make the results available to interested parties and communicate them through a variety of channels to encourage a dialogue within the industry.