圣加仑大学

Artificial intelligence increasingly contributes to strategic work, yet research has not conceptualised an AI role that contributes to this work at the top of the firm. Drawing on role theory and research on managerial work, we conceptualise this role as the AI CEO: a role in which an AI system performs the chief executive's strategic work. We develop two configurations of the role and propose how augmenting or automating the human CEO affects firm performance, relative to a human CEO acting alone. Cases from practice show that existing systems already perform this work but are rarely integrated across the role's activities or deeply institutionalised. We therefore derive the technical and organisational infrastructure the role requires and propose how its integration, institutionalisation and configurability shape the AI CEO's effect on firm performance. We discuss implications for future research about how the chief executive's work can be integrated into a role that an AI system can hold and that firms can design. This has implications for multidisciplinary research in strategy, technology management and information science.

Co-founder selection is a pivotal decision in new venture formation, yet how lead founders evaluate potential partners remains underexplored. Drawing on insights from marriage market theory, we conceptualize co-founder selection as an act of individuated judgment guided by cooperative logic and associated joint utility projection, wherein lead founders assess co-founder candidates based on social-emotional, instrumental, and distributive considerations reflected in interpersonal attraction, resource contributions, and equity demand. We further propose that lead founders' dark triad shifts this judgmental process from joint utility logic to personal utility maximization. Using a conjoint experiment with 206 entrepreneurs making 3296 co-founder selection decisions, we find that interpersonal attraction and resource contributions positively influence willingness to select a co-founder candidate, whereas higher equity demand reduces candidate attractiveness. Critically, lead founders higher in the dark triad display distinct patterns: they discount interpersonal attraction and display heightened sensitivity to equity dilution—assessments that indicate their prioritization of personal over joint value creation. Taken together, these findings reveal how dispositional tendencies influence the judgments underlying co-founder selection, indicating a shift away from the cooperative logic assumed in marriage market theory and co-founder selection toward greater emphasis on personal utility considerations. We discuss theoretical implications of these findings and offer practical guidance for navigating the complexities of co-founder selection.

Early regional adoption is crucial for platform-enabled innovations because their viability depends on enough people within a region adopting them to generate network effects. Existing research on regional innovation diffusion explains early adoption largely through demographic, economic, infrastructural, or institutional conditions, paying limited attention to regional population psychology. We conceptualize regional psychological openness as an invisible social infrastructure that is relevant for understanding early platform adoption. To test this idea, we introduce a newly curated dataset that allows us to trace the initial development of three major platform innovations (i.e., accommodation sharing platforms, ride-sharing platforms, social media platforms) across hundreds of U.S. metropolitan regions. Across all three platform types, regions differ markedly in early adoption. We link these adoption data to large-scale geo-referenced personality information from millions of U.S. residents (N = 3,234,899). Results show that regional psychological openness consistently predicts earlier uptake even after accounting for demographic, socio-economic, social, infrastructural, geographical, and other psychological characteristics. In sum, the study advances a behavioral perspective on regional innovation adoption by integrating population psychology into explanations of platform diffusion.

As it has developed into a defining paradigm of the 21st century, the sharing economy is widely regarded as providing opportunities for environmental and social benefits in addition to economic advantages. Yet, it remains unclear whether sharing business models that incorporate environmental ambitions and public alignment require supplementary changes to other business model elements to scale successfully. Drawing on configurational theory and the business model literature, we model market scaling as a function of five business model design choices: environmental ambitions, alignment with public stakeholders, value-proposition portfolio size, digital intensity, and revenue-stream diversification. Fuzzy-set qualitative comparative analysis (fsQCA) of twenty-one sharing-mobility providers identifies six configurations (four associated with successful scaling, two with scaling failure) and yields three implications. First, business models with strong environmental ambitions and close public alignment can scale as successfully as their counterparts that lack these characteristics. Second, irrespective of a firm’s level of environmental ambitions and public alignment, value-proposition portfolio size and revenue-stream diversification consistently emerge as the keys to successful scaling. Third, successfully scaled business models exhibit congruence between environmental ambitions and public alignment. Overall, business models with strong environmental ambitions and close public alignment and business models without these characteristics share similar paths to scale: multiple value propositions or diversified revenue streams. These findings advance configurational business model theory, resolve ambiguities in the sharing economy literature, and offer a cautiously optimistic message for management practice: Designing a business model with genuine environmental ambitions neither requires sacrificing scalability nor defying the common commercial logic.

Research increasingly uses voice and audio features to understand human-technology relationships. But findings regarding AI and spoken communication often appear somewhat contradictory. People can be blunt or even aggressive toward chatbots, speak more politely to voice assistants, and self-censor themselves when AI listens in the background during a call. Why? To address this heterogeneity, we propose a Conversational Role Framework that distinguishes whether the AI is (1) a conversation Partner that is directly addressed (e.g., a chatbot), (2) a Filter that transforms speech signals before another person receives it (e.g., a real-time translator that re-voices a speaker), or (3) an Ambient Listener that silently records, transcribes, and summarizes a human exchange (e.g., an AI note-taker summarizing a doctor-patient conversation). This framework helps explain seemingly conflicting findings and sheds light on why the same vocal cue can capture different meanings depending on AI's role in a given interaction.