AI, Organizing, and Management
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Item Does Workforce’s Reliance on Conversational AI Depend on Their Cultural Background?(2025-01-07) Elippatta, Jijin; Intezari, AliConversational Artificial Intelligence (CAI) is revolutionizing various aspects of business and can have a direct impact on individual’s lives and on social discourse in the workplace. This has raised concerns about the level of reliance on CAI among the workforce. This study examines the impact of workforce’s country of origin and age on their reliance on CAI. Informed by the Social Impact theory, and the Computers as Social Actors (CASA) theory, this research seeks to understand whether individuals who come from countries with distinctively different cultural characteristics (Australia vs India) and in different ages vary in their reliance on CAI. The study was motivated by the substantial influx of highly educated young Indian professionals into Australia, particularly in technology-intensive sectors, and the possibility of the impact of such a demographic shift on Australia’s overall CAI-reliance profile. We conducted an experimental study using ChatGPT-generated vignettes to compare the two workforce groups. Our findings reveal that Indians tend to rely less on CAI and more on human judgment compared to Australians, suggesting the need for greater cultural awareness and diversity in the design and use of CAI. We did not find a significant relationship between age and reliance on AI. Our findings offer theoretical and practical contributions to understanding the dynamics of CAI reliance, with implications for the integration of CAI in diverse work environments. Directions for future studies are offered.Item The Systems Engineering Approach in Times of Large Language Models(2025-01-07) Cabrera, Christian; Bastidas, Viviana; Schooling, Jennifer; Lawrence, Neil D.Using Large Language Models (LLMs) to address critical societal problems requires adopting this novel technology into socio-technical systems. However, the complexity of such systems and the nature of LLMs challenge such a vision. It is unlikely that the solution to such challenges will come from the Artificial Intelligence (AI) community itself. Instead, the Systems Engineering approach is better equipped to facilitate the adoption of LLMs by prioritising the problems and their context before any other aspects. This paper introduces the challenges LLMs generate and surveys systems research efforts for engineering AI-based systems. We reveal how the systems engineering principles have supported addressing similar issues to the ones LLMs pose and discuss our findings to provide future directions for adopting LLMs.Item Between Enablement and Control – Generative Artificial Intelligence-based Systems Development(2025-01-07) Wu-Gehbauer, Mei; Rosenkranz, ChristophThe proliferation of generative artificial intelligence (GenAI)-based systems with embedded large language models (LLM) has raised the interest of organizations in leveraging their potential to enhance work performance. However, for GenAI systems, the focus of systems development should extend beyond the artifact to managing the human-AI interaction. Through an exploratory case study of three GenAI systems development projects in a large enterprise we find that GenAI systems are shaped through an interplay of enablement and control of the user-AI interaction. We explain how these mechanisms practically unfold for systems supporting broad versus narrow user intents. Our findings demonstrate that through enablement the divergent nature of GenAI systems can be strengthened, while control ensures their convergence with requirements in the organizational context. With this we contribute to the understanding of how to develop GenAI systems that support the work in organizations.Item Responsible Management for AI-augmented Knowledge: A Knowledge Ecosystem Approach(2025-01-07) Wang, Belinda; Tan, Barney; Boell, Sebastian; Yu, JieThis study adopts a knowledge ecosystem approach to explore how organizations manage AI-augmented knowledge responsibly. Drawing from a case study on a large AI solution provider for manufacturing sector, our study reveals a dynamic knowledge management process that shapes and was shaped by the interdependent interactions among ecosystem actors. The preliminary findings in this study contribute to responsible AI and knowledge management literature, enhancing the understanding on actors’ role to responsible knowledge management.Item Human-Centered AI Design Principles for the Public Service Domain: An Action Design Research Study(2025-01-07) Schmager, Stefan; Pappas, Ilias; Vassilakopoulou, PolyxeniMost existing design and development guidelines for Human-Centered AI primarily cater to a commercial context, they are not tailored to the specific needs of public services. This paper presents an Action Design Research study proposing public service-specific design principles for Human-Centered AI. The design principles are informed by multiple iterations of empirical research with citizens and public service employees acknowledging the multi-stakeholder nature of Human-Centered AI. The study recognizes an evolving understanding towards prioritizing human values and well-being in AI technologies. Furthermore, it considers the relationship between technology features in the public sector and citizens' needs to ensure that AI systems are developed with a commitment to fostering public trust and welfare. The study contributes to theory and practice by advancing the scholarly discourse on Human-Centered AI and informing AI strategies and implementations within the public sector.Item Ready for Managing AI Projects? An Analysis of AI Project Management Frameworks(2025-01-07) Wrobel, Lasse; Dietzmann, Christian; Alt, RainerRecent advances in AI models and their integration in cloud service platforms like Microsoft Azure accelerate the options to develop individual AI solutions. Despite expanding technical possibilities, organizations still fail to successfully execute AI projects and sustainably integrate the solutions. Hence, it remains questionable whether existing project management frameworks cover the holistic complexity of individual AI projects and enable decision-makers to navigate the manifold challenges associated with AI projects. The present study aims to fill this research gap through a qualitative research approach. A structured literature review and 12 expert interviews examined by thematic analysis provide 54 AI project management challenges along 16 requirement clusters. These requirements are applied to evaluate twelve established AI-related project management frameworks. Seven areas for improvement were identified, relating to AI ethics, regulation, culture, evaluation, sourcing, impact, and modularity guidelines. Thus, this work serves as a fundament for designing novel AI project management artifacts.Item The Impact of AI Usage in HRM on Interpersonal Justice and Perceived Innovativeness: A Cross-Cultural Investigation(2025-01-07) Moritz, Josephine; Zahs, Dominik; Schmodde, Lynn; Wehner, Marius; Kulkarni , SumedhArtificial intelligence (AI) is increasingly used in personnel selection. Despite its associations with increased efficiency and objectivity, the adoption of AI often encounters resistance due to algorithm aversion. Previous theoretical frameworks suggest that AI perceptions may depend, among other aspects, on cultural factors. We examined cross-cultural differences between India and Germany regarding perceptions of AI usage, specifically interpersonal justice and innovativeness within the context of personnel selection. We hypothesize that AI evaluation is negatively associated with perceptions of interpersonal justice and positively associated with perceptions of innovativeness, with these relationships moderated by country. To test our hypotheses, we conducted a vignette experiment. Our results indicate a positive relationship between AI usage and perceived innovativeness, which is more pronounced in Germany. With our study, we provide insights into how AI usage is perceived across different cultural contexts and underscore the need for organizations to consider cultural factors when implementing AI.Item AI Use in Auditing - A Technology Dominance Perspective(2025-01-07) Seethamraju, Ravi; Hecimovic, AngelaAI technologies’ use in audit work is expected to deliver cost efficiencies and improve decision-making. However, some deleterious effects of reliance on technology are discussed in the literature. Using the effects of technology dominance on audit work, enunciated in the theory of technology dominance (TTD) as theoretical anchor and qualitative methodology, our study investigates the consequential effects such as deskilling, automation bias, complacency, and illusion of competence on audit work and how audit firms deal with those effects. Our study observed that audit firms are prepared to deal with these risks through controlled deployment and use of AI tools, visibility of the tool, upskilling of early career auditors, and matching the tool with the auditor’s skills. Balancing the automation of task with the retention of expertise; sharing of data, models and economic benefits with the audit clients; and loss of interest and therefore the expertise to do mundane tasks are challenges observed.Item Leveraging Generative AI and ChatGPT in SMEs: A Grounded Model(2025-01-07) Walke, Fabian; Klopfers, Lee; Winkler, Till J.ChatGPT and other generative artificial intelligence have garnered substantial media attention for their ability to generate text and code, presenting great potential for both personal and professional use. Less research has been conducted in the field of generative AI usage in small and medium enterprises, especially across different enterprise departments. This study examines with a grounded theory approach the characteristics of generative AI usage within management, marketing, and development departments of a small and medium enterprise. Ten employees from this enterprise were interviewed. Three causal and intervening conditions, as well as strategies were identified, to leverage the use of generative AI. The results indicate a productivity increase in all departments, especially in marketing. Improvements in learning processes were noted in development, while management saw shorter communication pathways. Across all departments, significant cost and time savings were observed, along with risks, limitations, and future potential.Item AI Paradoxes in Organizations: Collection, Typology, and Clarification(2025-01-07) Uebach, Carolin; Stein, VolkerThis paper addresses the increasing number of AI paradoxes that appear in the academic literature. It appears that research in this area is still in its infancy, with a great deal of conceptual confusion still prevailing. The objective of this paper is to collect the AI paradoxes that appear in the literature, systematically assign them to the phases of the AI life cycle, and finally assess whether they are actually paradoxes or rather other forms of contradictory decision situations. The findings of this study provide a foundation for the precise regulation of terminology, which will facilitate further research in this field.