The market research industry has reacted to the massive uncertainties regarding future consumer behaviour emerging from the corona pandemic. It is providing the various stakeholders from industry and society with numerous studies, which are intended to guide them through the thicket of the New Normal: What (changed) attitudes do consumers show because of Corona? How do priorities in purchasing behaviour change, as do our needs? Most studies published follow the classical “explicit” attitude measurement paradigm using scaled answers. As a consequence, most findings, pretending to predict future or describe current consumer behaviour in the pandemia, suffer from the well-researched “say-do gap” and the general weakness of explicit attitude measure to predict real behaviour. In an international study we applied an implicit, reaction-time based methodology to assess Covid-related attitudes (towards politics, nutrition, vaccination, health-related behaviours) to highlight differences between countries in coping with Corona and showing a methodological approach to separate pure lip service from real behaviour intentions.

Led by our Polish research partner NEUROHM a large-scale global comparative study “COVID-19 Fever” was conducted between the late April and early May 2020, followed by a national wave in Germany in January 2021 to assess attitudes towards vaccination in more detail. The international study was conducted in ten countries with 1000 respondents each as a syndicated project involving universities and commercial research agencies specializing in behavioural economics. The theoretical basis of the applied measurement model of NEUROHM (iCode, see also Ohme, Matukin & Wicher 2020) is the “Attitude Accessibility” model of Fazio (1989). iCode is an algorithm that allows the calculation of a confidence index (CI), which integrates the explicit and implicit measures of attitudes in one score showing the tension between rationalizing opinions and the underlying security and trustworthiness in the form of implicit confidence.

Results clearly showed the need to distinguish between superficial, socially desirable answers and implicit, well-internalised beliefs when it comes to coping with Covid-19. If politicians or companies want to develop sound strategies based on highly predictable behaviours of consumers or citizens, they should add research paradigms from behavioural economics in their studies.

Relevance & Research Question:

Technical progress through digitalisation is constantly increasing. Currently, the most relevant and technically sophisticated technology is artificial intelligence (AI). Due to the strong influence of AI, it is necessary that it meets with broad social acceptance. However, it is apparent that the prerequisites for this are distributed differently according to gender. Women are less frequently involved in research and development on AI. What are the differences between men and women in their perception, evaluation, development, and use of AI in the workplace?

Methods & Data:

A quantitative online survey consisting of 45 items was conducted among company representatives and students from July to September 2020 [N = 382 (age; M = 35.9, SD = 13.5, 69.6% female, 61.4 % university degree)]. To determine differences in the variables of interest, a t-test or ANOVA was calculated, if the prerequisites were fulfilled.

Results:

The results show that men, in contrast to women, see more opportunities in AI (t(317) = -2.88, N = 319, p = .004), rate their own AI-competence higher (t(317) = -6.65, N = 319, p < .001), and trust more in AI (U = 8401.00, Z = -3.604, p < .001). One reason for the significant results could be the fact that men are more involved and have more experience with AI than women (χ² (2, N = 319) = 7.902, p = .019). Men and women agree in their desire for better traceability in AI-decision-making processes (t(317) = .375, N = 319, p = .708), and both show a high motivation for further training (t(317) = -.522, N = 319, p = .602).

Added Value:

Developing one's own AI-competence takes away fears and promotes trust and acceptance towards AI – an important prerequisite for openness towards AI. Promoting interest in and the willingness to deal with AI can at the same time sensitize people to the possible risks of AI applications in terms of prejudice and discrimination and mobilize more women to engage in AI development.