2.4 AI—The Good, the Bad, and the Scary

Divisive products and services can create disagreement or division among consumers due to cultural, ethical, environmental, or personal beliefs. Divisiveness has been associated with many goods and services, such as some health care services, which can be very difficult to market. The case study is the mRNA COVID-19 vaccine. Data science and machine learning tools, a subset of artificial intelligence, are used with the intent to visually monitor information flows (data visualizations), understand the sentiment of the divisive audience (sentiment analysis), and align with the key underlying topics that are attributable to the divisiveness (topic modelling). These tools can influence patterns and anomalies for humans, and possibly AI, to study, which leads to early detection of barriers to acceptance of the vaccine.

This paper proposes Artificial Intelligence (AI) as a solution to cope with the researcher's subjectivity when interpreting qualitative material. Applied to the Album On Line (AOL) method, we combine the use of generative AI (8 different chatbots) and semantic AI using Natural Language Processing (NLP) to improve the validity of the outcoming mapping. Ultimately, the outcomes generated by artificial intelligence (AI) are compared with those derived from researchers' efforts. This study aims to provide a broader vision of the world of AI and its possible contribution to research. In this regard, AI enhances efficiency, saves time, and improves result quality. This study contributes to the enrichment of the researcher's toolbox by presenting how to exploit AI in favor of research.

We apply the theory of reasoned action to hypothesize that personal outcome expectations influence attitudes toward reckless driving controls. Our unique theoretical contribution is to add not normative influence but norms as a complementary psychological antecedent to attitude. Three experiments are undertaken to test the integrity of our measures and our hypotheses. Three of the four dimensions of the "libertarianism" norm that we empirically identified (and confirmed in replication) explained 20-25% of overall attitudes to forty reckless driving control (RDC) concepts. This was a far greater influence than the rationally based antecedent, self-reported reckless driving behavior. Future research that differentiates between the vehicle taking control and authorities taking control through "big brother" tracking of reckless behavior is discussed.

This research empirically explores the effects of epistemic curiosity on consumers' stickiness and attitudes toward an audiobook mobile app influence consumers' behavioral responses. Drawing on evolutionary psychology and decision-making research, the authors find that the performance expectancy, social influence, and facilitating condition of the audiobook mobile app positively influence behavioral outcomes. Social influence and effort expectancy have a positive impact on attitudes toward audiobook mobile apps. Consumers' stickiness to audiobook mobile apps mediates the relationship between attitude and purchase intention. Consumers' attitude positively influences their use intention, thereby influencing their use behavior. Moreover, as one of the important personal traits, the effects of epistemic curiosity on consumers' behavioral outcomes are also discussed. High D-type epistemic curiosity inspires users to adopt audiobook mobile apps and also positively influences mobile app behaviors. We find that I-type epistemic curiosity has an inverse relationship with consumers' use behavior.