Privacy policies are essential for understanding how platforms collect, use, and manage user data, reflecting compliance with legal standards such as the General Data Protection Regulation (GDPR) and providing transparency in handling personally identifiable information. In the context of the rapidly growing gig economy, examining these policies is crucial to ensure that user data is protected in line with current regulations and best practices. Enhanced digital surveillance, especially through remote work, necessitates identifying gaps and inconsistencies in data protection practices, thereby highlighting areas for improvement and fostering greater transparency and accountability. This study systematically analyzes the privacy policies of platform companies based in Germany, with a focus on gig workers. By evaluating these documents, the research aims to uncover risks and challenges in data protection and to assess the compliance of these platforms with GDPR principles, particularly regarding the rights of data subjects. Utilizing a sample of 52 German gig-working companies, privacy policies were collected and analyzed between August and October 2023. New data processing laws are addressing the new challenges, with the main objective being to improve the transparency of data processing practices. However, the study highlights the need for standardization of data processing, as communication about data processing is handled so differently, which can make workers feel insecure about how their data is handled, especially with the focus on data portability.

<p>The globalization and digitalization of the workplace, accelerated by the COVID-19 pandemic and the ongoing political crisis, are leading to higher rates of virtual collaboration and, therefore, more places and communities to meet virtually. In addition, a shortage of skilled workers is causing companies to prioritize internal communities to retain employees, share knowledge, build trust, and develop employees. These internal communities must be managed, whether communities of practice or enterprise social networks. However, managing internal communities requires more full-time resources, HR or executive support, and financial backing. It is economically viable to support the most time-consuming tasks to unlock the untapped potential of managing internal communities: creating and planning content, moderating, and campaigning. Artificial intelligence (AI) could be used to increase the efficiency and productivity of internal community managers. AI is a machine's ability to imitate human abilities, such as reasoning, learning, planning, and creativity. Thus, the role of AI in internal community management will be explored in three research questions, including (1) the state of research, (2) potential, and (3) challenges.</p>

<p>A preliminary search of academic databases revealed few results related to AI and internal community management, prompting an exploratory qualitative approach to the topic. First, a systematic literature review (SLR) was conducted in four academic databases to synthesize the current research state, revealing a knowledge and empirical gap. Second, to gather insights and data on the topic, four expert interviews were conducted to inductively discover the potential and challenges of AI from German internal community managers with and without knowledge of AI.</p>

<p>The interviews are expected to reveal the potential of AI for internal community managers, such as automating manual tasks, text suggestions, and using chatbots for time-consuming tasks to increase productivity and innovation. In addition, expected challenges include data ethics, AI literacy, transparency, trust, and community engagement.</p>

<p>Limitations to the significance of the findings are acknowledged due to the limited number of interviews. Thus, more internal community managers should be interviewed in focus groups or Delphi surveys to reduce this limitation. In addition, relevant articles may have been excluded during the SLR due to the search string or databases used. To justify the validity of the SLR, the process was transparently documented and described.</p>

<p>This article is the first to qualitatively explore the potential and challenges of AI in internal community management. It provides valuable insights into the use of disruptive technologies in corporate communities and is, therefore, highly relevant for academics and practitioners.</p>

<p>The sharing of private charging infrastructure for electric cars through an IT-supported sharing platform represents an innovative concept that could contribute to closing the gap in public charging infrastructure. As factors influencing the participation behavior of providers and users in such concepts of the sharing economy have not been sufficiently researched, this paper investigates the acceptance and diffusion factors of a peer-to-peer charging infrastructure. A theory-driven qualitative research approach involving expert interviews was applied for this purpose. The results show that the diffusion of the concept is particularly influenced by the charging point provider. Monetary incentives, convenience requirements, perceived uncertainties, technical requirements, and compatibility with living conditions are relevant aspects in the consideration of participation. This paper contributes to acceptance research in the field of the sharing economy by providing differentiated explanations for acceptance and diffusion.</p>