In a working environment facing increasing digitalization and the Fourth Industrial Revolution, the acceptance of new technologies remains an important factor. Based on the Technology Acceptance Model (TAM) and the Artificial Intelligence Acceptance Model (AIAM), this study examines if individual adaptability, trust in technology, as well as openness and willingness to experiment influence technology acceptance in the working environment. Using an online questionnaire approach, a sample of N = 769 participants ( M = 51.25, SD = 13.88 years; 390 female) was recruited to test the theoretically derived model with structural equation modeling. Results suggest including trust in technologies in future technology acceptance models in the working environment. Furthermore, the TAM-variable perceived ease of use was not a significant predictor, which is discussed in detail. Regarding the antecedents derived from AIAM, individual adaptability proves to be relevant, while first insights on the non-significant influence of openness to experiment are gained. The final model explains more than two-thirds of the intention to use new technology and thus provides an important contribution to understanding the acceptance of new technologies in a working environment.

Explainable AI (XAI) is discussed as an important feature of AI systems that is required for professional, ethical and commercial reasons. XAI is particularly needed in AI systems that remain relatively closed, i.e. black box, on how to make decisions whether for complexity or commercial sensitivity. Still, many factors play an important role on whether people perceive a need for XAI. The personal relevance of the AI system might play a role, but also individual differences in personality traits or cognitive functions. In order to test these assumptions, 215 participants (129 female) underwent an online experiment testing their objective working memory capacity and completed the Intellect facet of the Big Five Personality traits (as a proxy for self-assessed cognitive capacity). Further, all participants were presented three AI systems scenarios of relatively different degrees of (personal) relevance and, hence, personal investment: skin cancer detection (i), the personalization of social media feeds (ii), and the amount to pay for a car insurance (iii). We observed that people reported a higher need for XAI in scenarios where they were more personally invested (health, payment for insurance > social media); for signficant differences between these scenarios see the result section. Interestingly, working memory capacity was not associated with XAI needs across the three domains, but self-reported Intellect showed a mild positive association with need for an AI explanation in the context of the insurance story. The present study represents a first attempt to investigate objective working memory capacity in the context of need for XAI. Beyond that, the study indicates that personal investment is crucial for determining the need for XAI.

This paper explores the potential for connected public-transport (PT) mobility as an alternative to motorized private transport (MPT) in medium-sized cities. Despite the high demand for MPT, it occupies a lot of space and contributes to conflicts and reduced livability. The more sustainable mobility solution of PT, however, is often considered slow, unreliable, and uncomfortable. To overcome these issues, the authors investigate the state-of-the-art research of connected PT mobility, including ways to quantify mobility behavior, micro- and macro-simulations of traffic flow, and the potential of not-yet-established modes of transport such as Mobility on Demand (MoD) for last-mile transportation. MoD could reduce the drawbacks of PT and provide sufficient and sustainable mobility to all citizens, including those in rural areas. To achieve this, precise information on individual traffic flows is needed, including origin–destination (OD) relations of all trips per day. The paper outlines a two-step approach involving the expansion of OD relations to include all modes of transport and diurnal variation, followed by microscopic traffic simulations and macroscopic optimization to determine potentials for on-demand offers within inner-city traffic. The paper concludes by calling for critical questioning of the approach to validate and verify its effectiveness.

Individual mobility behavior continues to pose a challenge to achieving climate goals, as motorized individual transportation is still favored over public transportation. The present study examines five possible drivers of more sustainable transportation mode choices: two infrastructural factors, specifically city center accessibility and railway accessibility, and three psychological variables: adaptability, climate change perception, and car orientation. A sample of N = 187 participants was collected in a German city in the Lower Rhine region. Our findings, based on ordinal logistic regression models, indicate that railway accessibility and car orientation are associated with both the use of motorized and public transportation. While center accessibility and adaptability predicted the use of motorized individual transportation, these variables did not significantly relate to the use of public transportation. Also, our results indicate that climate change perception does not relate to transportation use. This surprising finding is discussed in detail. On a more general level, the study’s insights reinforce previous findings and stress the importance of considering not only infrastructural factors in urban spaces but also the characteristics and attitudes of their inhabitants.