Since the formation of Extinction Rebellion in 2018 protest groups applying similar forms of civil disobedience (CD) were founded across 12 western countries forming the A-22 network. We define CD as any non-violent act breaching the law who´s intentionality originates from maximizing reach within society while harm to particular individuals or groups is instrumental within certain constraints. Thus far, the Netherlands are the only government responding to protesters demands and committed to phase out inefficient fossil subsidies by 2025. But even when considering softer determinants of success as government response such as citizens decreasing reactance against climate policies (see Kountouri et al. 2023 & Ozden et al.,2022). The current impact of present climate crisis directed CD appears rather minor considering that 92% of authoritarian states (n=50) in which CD played a salient role between 1971 and 2004 underwent a at least partially successful democratic transition as illustrated by Karatnycky (2004). This lab-in-the-field experiment examines behavioural impacts of CD on recipients from civil society in the context of climate crisis mitigation. It thereby contributes to an economic assessment of changes in individual decision making moderated by CD. Participants play in groups of 6 in a sequential public good game in which they can contribute to a real public good CO2-emission reduction by either spending it on purchasing CO2 emission rights or donating to the german protest group Letzte Generation. The central treatment condition of the study is the participation of a protester of the Letzte Generation. Group members are being informed if they are paired with an activist. We hypothesize that contribution decisions change in the presence of a protester whereas the direction of the effect may be determined by the degree to which participants belief in counteracting narratives such as a lack of performative legitimacy, technological optimism and cynicism.

Mit der Verabschiedung des European Green Deal 2020 durch die Europäische Union wird der Nachhaltigkeit im europäischen Wirtschaftraum ein gesetzlicher Rahmen gegeben. Zielstellung ist die Umgestaltung der EU-Wirtschaft für eine nachhaltige Zukunft. Dies betrifft nicht nur Großunternehmen. Auch kleine und mittlere Unternehmen (KMU) müssen sich ihrer Verantwortung bewusst werden. Sie haben einen erheblichen Anteil an der Bruttowertschöpfung (OECD, 2021). KMU bieten rund 70 Prozent der Beschäftigungsverhältnisse (OECD, 2021). Schätzungsweise 60 Prozent der CO2 Emissionen werden durch KMU verursacht (Hillary, 2004). Im Gegensatz zu Großunternehmen gibt es in der Literatur für KMU bis dato nur wenig Ansätze, für eine nachhaltige Unternehmensgestaltung. Ansätze für Großunternehmen sind nicht auf KMU übertragbar. Um hier einen Beitrag leisten zu können, muss verstanden werden, wie KMU mit dem Themenfeld der Nachhaltigkeit umgehen. Gründe und Motive, sich nachhaltig auszurichten, sind für KMU hinlänglich bekannt. In dieser Arbeit wird die Frage gestellt, wie sich die Art der Motivation (intrinsisch vs. extrinsisch) in KMU auf die Umsetzung der Unternehmensnachhaltigkeit auswirkt. Untersucht wird die Frage anhand einer Fallstudie in KMU entlang der lebensmittelproduzierenden Wertschöpfungskette.

ChatGPT disrupted the application of machine-learning methods and drastically reduced the usage barrier. Chatbots are now widely used in a lot of different situations. They provide advice, assist in writing source code, or assess and summarize information from various sources. However, their scope is not only limited to aiding humans; they can also be used to take on tasks like negotiating or bargaining. To understand the implications of Chatbot usage on bargaining situations, we conduct a laboratory experiment with the ultimatum game. In the ultimatum game, two human players interact: The receiver decides on accepting or rejecting a monetary offer from the proposer. To shed light on the new bargaining situation, we let ChatGPT provide an offer to a human player. In the novel design, we vary the wealth of the receivers. Our results indicate that humans have the same beliefs about other humans and chatbots. However, our results contradict these beliefs in an important point: Humans favor poor receivers as correctly anticipated by the humans, but ChatGPT favors rich receivers which the humans did not expect to happen. These results imply that ChatGPT’s answers are not aligned with those of humans and that humans do not anticipate this difference.