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Social networking technologies such as social media, crowd concepts, or gamification represent key resources for the integration of customers, value network partners, and the community into sustainable business models. However, there is a lack of understanding of how sustainable enterprises apply such technologies. To close this gap, we propose a taxonomy of design options for social networking technologies in sustainable business models. Our taxonomy comprises eight dimensions that deal with relevant questions of the design of social networking technologies. When creating our taxonomy, we built on existing literature and use cases and involved experienced practitioners in the field of sustainable business models for the validation of our taxonomy. In this way, our study contributes to knowledge on the use of social networking technologies in sustainable business models and how such technologies influence the boundaries of sustainable business models. Likewise, we provide practical insights into the use of social networking technologies in sustainable business models.
In recent years, various studies have highlighted the opportunities of artificial intelligence (AI) for our society. For example, AI solutions can help reduce pollution, waste, or carbon footprints. On the other hand, there are also risks associated with the use of AI, such as increasing inequality in society or high resource consumption for computing power. This paper explores the question how corporate culture influences the use of artificial intelligence in terms of sustainable development. This type of use includes a normative element and is referred to in the paper as sustainable artificial intelligence (SAI). Based on a bibliometric literature analysis, we identify features of a sustainability-oriented corporate culture. We offer six propositions examining the influence of specific manifestations on the handling of AI in the sense of SAI. Thus, if companies want to ensure that SAI is realized, corporate culture appears as an important indicator and influencing factor at the same time.