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The integration of legitimacy in entrepreneurial ecosystem theory is under-researched, resulting in scholarly vagueness about how entrepreneurs acquire resources. Our qualitative study with 31 (co-)founders of startups following the triple bottom line investigates entrepreneurs' daily practices for building legitimacy in entrepreneurial ecosystems. We identify that entrepreneurs follow a sequential process to build legitimacy: 1) engaging and assimilating with culture, 2) establishing and utilizing networks, 3) enhancing visibility, and 4) leveraging the sustainable mission. Following this sequential process builds different levels of legitimacy. Each level grants access to resources from the entrepreneurial ecosystem. We contribute to the scholarly conversation on legitimacy in entrepreneurial ecosystems and provide practical implications for entrepreneurs.
Adopting AI-based solutions is now widely regarded as an essential consideration in organisations’ innovation strategies. For healthcare institutions, such solutions are an especially promising means to address societal and organisational challenges, including rising demand combined with shortages of qualified staff. The technology may enhance the efficiency of, for example, detecting diseases and planning treatments, which are time-consuming when executed manually. However, empirical research related to how AI can be effectively adopted in healthcare to harness these opportunities remains scarce. To address this gap, we conduct an exploratory multiple case study comprising 13 cases in the radiotherapy domain. Taking over an adoption theory perspective, we uncover that organisational, environmental, technological and individual factors are decisive for effective adoption of AI and contribute to the emergence of efficiency gains and standardisation. Our analysis reveals that organisational factors such as pursuing a dedicated innovation strategy within the radiotherapy department as well as a holistic AI implementation strategy are most crucial. In determining and relating the identified relevant factors, we contribute to adoption theory and AI-enabled value creation in healthcare. Further, we advise managers of healthcare institutions on how to effectively adopt AI to overcome challenges at organisational and societal levels.
Unternehmen stehen zunehmend vor der Herausforderung, ihre Steuerungssysteme an neue Marktanforderungen anzupassen. Mit dem vorliegenden Beitrag soll die Entwicklung der Steuerungssysteme der DAX-Unternehmen zwischen 2020 und 2023 untersucht werden. Die Analyse zeigt, dass finanzielle Kennzahlen weiterhin eine zentrale Rolle spielen. Gleichzeitig hat die Bedeutung nichtfinanzieller Kennzahlen zugenommen. Diese Verschiebung ist Ausdruck eines stärkeren regulatorischen und gesellschaftlichen Drucks hin zu nachhaltigen Geschäftsmodellen. Häufige Anpassungen der Steuerungssysteme bergen indes das Risiko von Inkonsistenzen, weshalb eine transparente Kommunikation dieser Änderungen essenziell ist. Die Ergebnisse der Analyse unterstreichen die Relevanz eines ganzheitlichen Steuerungsansatzes, bei dem finanzielle und nichtfinanzielle Kennzahlen integriert, regelmäßig evaluiert und an strategische Ziele angepasst werden.
More than 15 years after the introduction of 'familiness' into the literature, the term has evolved into one of the most popular concepts in family firm research. Despite the steadily growing body of studies that build on familiness, recent calls suggest a need to revisit its conceptualisation due to a lack of a common understanding that could affect future research endeavours. In our systematic literature review of 25 studies, we find support for this notion and show that the discussion on the concept has reached a dead end. We present a systematisation of familiness research that highlights an inconsistent conceptualisation, a lack of validation and even a partial hijacking of the term into contexts different from those originally proposed by Habbershon and Williams (1999). Based on these findings, we present a research agenda aimed at overcoming the current limitations and rejuvenating familiness as a suitable approach to understanding family firm heterogeneity.
Nichtfinanzielle Leistungsindikatoren im Steuerungssystem der DAX-, MDAX- und SDAX-Unternehmen
(2025)
Für Aktionäre gewinnen Nachhaltigkeitsthemen zunehmend an Bedeutung. Als Reaktion darauf haben zahlreiche Unternehmen ihre Nachhaltigkeitsberichtserstattung in den letzten Jahren kontinuierlich erweitert. Rund ein Drittel der Unternehmen integriert nichtfinanzielle Leistungsindikatoren bereits in das unternehmensweite Steuerungssystem, um Nachhaltigkeit fest in der Unternehmensstrategie zu verankern. Für das Geschäftsjahr 2023 liegt der Prüfungsschwerpunkt der BaFin unter anderem auf der Darstellung des Steuerungssystems in den Konzernabschlüssen. Vor diesem Hintergrund gibt der vorliegende Beitrag einen aktuellen Einblick über die nichtfinanziellen Leistungsindikatoren, die in den Steuerungssystemen der Unternehmen des DAX, MDAX und SDAX integriert sind. Die Analyse zeigt, dass dort vor allem die Themen Klimawandel und eigene Belegschaft bereits berücksichtigt werden.
C-Level-Kommunikation
(2025)
Sehr geehrte Frau Stoetzel, in den vergangenen Jahren nimmt die Bedeutung der sog. C-Level-Kommunikation und damit auch die Bedeutung des C-Level Personal Branding immer mehr zu. Die Plattform LinkedIn spielt dabei eine Hauptrolle. Was steckt dahinter und was bewirkt diese Entwicklung?
JS: Die Digitalisierung hat die Erwartungen an Führungskräfte grundlegend verändert. Wo früher quartalsweise Geschäftsberichte und jährliche Hauptversammlungen ausreichten, erwarten Stakeholder heute einen kontinuierlichen Dialog auf Augenhöhe. Eine neue Generation von Investoren recherchiert digital, bevor sie in persönlichen Kontakt tritt. Diese Entwicklung wurde durch die ESG-Thematik noch verstärkt – Nachhaltigkeit und Governance erfordern transparente, regelmäßige Kommunikation.
The concept of coopetition - simultaneous collaboration and competition between organizations to achieve mutually beneficial outcomes - plays a pivotal role in shaping business performance, particularly during periods of rapid technological advancements. This is especially evident the manufacturing sector, where innovation and competitive dynamics intersect with economic and social forces. The current academic discourse predominantly focuses on the qualitative identification and analysis of coopetition attributes, leaving a significant gap for large-scale quantitative studies to enable empirical assessment. This study aims to examine the significance of three groups of coopetition attributes for coopetition performance classified into two strategic (dynamics, paradoxicality), six relational (asymmetry, complexity, coopetition intensity, mutual dependence, strength, tensions), and five behavioral attributes (competition intensity, conflict, formality, investments, trust). Using data from 1216 manufacturing firms in Poland and employing a generalized Covariance based Structural Equation Model (CB-SEM), this study offers nuanced insights to the global discourse at the intersection of technological change and social dynamics. The results indicate that the strategic attribute paradoxicality, the relational attribute strength, and most of the behavioral attributes (trust, competition intensity, investments, formality) positively impact coopetition performance. Additionally, a significant negative impact of the strategic attribute dynamics was demonstrated, while no significant influence was identified for the remaining relational attributes (asymmetry, tensions) as well as the behavioral attribute conflict. Diverging from prior qualitative approaches, this study offers data-driven insights for decision-makers navigating societal and technological change, highlighting which attributes should be stimulated to enhance coopetition performance while minimizing the level of dynamics within coopetition strategies.
The Future Combat Air System (FCAS), a sixth-generation advanced combat aircraft, is assumed to be the largest European defence development and production programme of the 21st century and a core pillar of European cooperation in the defence sector. The 2017-initiated collaboration programme is led by Germany and France, with Spain and Belgium joining as partners, and is a who-is-who of the largest European defence companies, including Airbus Defence and Space, Thales, Safran, MBDA, and MTU. While such defence programmes have significant military, political, industrial, and economic impacts, the economic dimension in terms of economic costs and benefits is often under-represented in academic discussions. As the FCAS is still a nascent programme, only rough estimates of the total programme cost are available, and assumptions range from €100 bn to more than a trillion. With our research, we aim to contribute to the academic discussion by estimating the true economic costs and benefits of the FCAS. We use a mixed approach of data triangulation by cross-checking open source intelligence (OSINT) with expert interviews to estimate the full programme life-cycle costs. Furthermore, we estimate its economic impact on the European gross value added, employment, and tax by utilising an input – output (IO) model.
Artificial intelligence (AI) emerges as a promising technology to address burgeoning challenges resulting from shifting demographics, coupled with a shortage of qualified personnel. Thus, the adoption of AI creates especially interest within the talent acquisition (TA) domain to realize anticipated efficiency gains. However, evidence suggests that AI adoption may foster the emergence of harmful forms of practices (HFP) within TA practices. Despite the importance, respective empirical studies collecting data to generate insights remain sparse. Thus, the aim of this study is to investigate HFP and underlying drivers through a mixed-method approach. At the first stage, we conducted in-depth interviews with 42 TA experts. The resulting insights informed the development of the 'Adoption of AI in TA: Framework on Negative Consequences.' This model suggests that a confluence of technological, individual, and organizational factors can result in the emergence of HFP post-AI adoption. Such potential HFP include biased decision-making, data privacy violations, and efficiency reduction. Then, we validated our qualitative findings and confirmed our hypotheses by employing a quantitative, survey-based approach with 303 valid study participants. By shedding light on potential HFP through AI adoption in TA and respective catalysts, our research empowers both information technology and TA professionals to proactively engage in mitigation strategies. In this vein, they may successfully navigate the complex landscape of AI adoption. Hence, this study adds to research on effective AI adoption in TA.
Artificial intelligence (AI) is increasingly being recognized as a critical tool when it comes to addressing the most pressing challenges facing modern industries, including the pursuit of sustainability. The use of AI is aiding businesses in navigating corporate sustainability challenges, but existing research lacks a comprehensive exploration of how corporations leverage AI to boost their sustainability. By exploiting an inductive concept-development approach and incorporating data from 24 companies, this study provides valuable insights into the role that AI plays in shaping organizational sustainability strategies, identifying operational enablement and technical capacity as key drivers of AI adoption for corporate sustainability. These drivers are incorporated into the technology, organization, and environment (TOE) framework alongside the strategic steps and capabilities necessary for organizations to effectively adopt and implement AI in the development of their sustainability strategies. Ultimately, this study proposes an integrative model for sustainability-oriented AI adoption that emphasizes the importance of aligning AI initiatives with organizations’ sustainability objectives in order to maintain a competitive advantage and drive progress. Correspondingly, it underscores the need for robust data management, system integration, and continual performance monitoring to reduce resistance to AI adoption allowing for the potential of AI to be fully harnessed in pursuit of sustainability. Furthermore, this study offers practical guidance by exploring the direct and indirect use cases of AI in corporate sustainability. The study concludes by highlighting potential avenues for future research in this evolving field.