Rapid scaling amidst resource constraints is crucial for established firms and startups in today’s business landscape. Despite its increasing popularity and research interest, little is understood about dynamic capabilities in the context of growth hacking. This study explores how growth hacking activities contribute to developing dynamic capabilities by employing a flexible pattern matching approach based on 29 qualitative interviews with growth hacking experts. By filling the gap in understanding dynamic capabilities in the context of growth hacking, the research enriches growth hacking’s theoretical foundations and provides practical insights. The results emphasize effectively managing capability dimensions dualities, distinguishing between startup and corporate challenges, and prioritizing rapid experimentation with big data. The study also examines artificial intelligence’s (AI) potential for automating growth hacking processes, while highlighting data privacy risks. This paper suggests future research avenues for further exploring the interplay of dynamic capabilities and growth hacking.

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.

Organizational agility (OA), the ability of an organization to adapt to rapidly changing environments and reconfigure its structure and operations, has become a critical capability for achieving and sustaining competitive advantage. However, existing frameworks for OA often adopt narrow, industry-specific perspectives, neglecting the broader interplay of contextual factors, internal and external antecedents, and multidimensional capabilities. This study addresses this gap by conducting a systematic literature review (SLR) of 110 peer-reviewed articles from three leading academic databases. Guided by an inductive approach and the grounded theory methodology, this research identifies and categorizes the key antecedents of OA into four interconnected dimensions: contextual factors, fundamental attributes, vital enablers, and dynamic capabilities. The findings reconceptualize OA as a bundle of interrelated dynamic capabilities—encompassing macro-agility (e.g., strategic and partnering agility) and micro-agility (e.g., operational, innovation, and workforce agility)—rather than a singular construct. This multidimensional concept bridges theoretical gaps, integrating insights from contingency, agency and resource-based theories to explain the dynamic relationship between internal and external antecedents. The study offers a foundation for future research and provides actionable guidance for practitioners seeking to design agile organizations capable of thriving in dynamic capabilities.

Purpose Small businesses are facing evolving environments, with a resulting need to shift their traditional approaches toward new business models (BMs). Many face difficulties within this transition process due to their specific resource constraints. Based on this, incremental changes to the BM – business model transition (BMT) – are proposed as comprising a suitable framework for entrepreneurial small businesses. Design/methodology/approach This study conducts a systematic literature review (SLR) to cover a broad range of relevant literature within a final sample of 89 articles. The SLR method was chosen to integrate research in a systematic, transparent and reproducible way. For qualitative analysis and framework derivation, the study draws on a thematic ontological analysis. Findings The broad search criteria, focusing on BM, incremental BM changes and small businesses, pave the way for a comprehensive overview of multiple research streams of BM concepts (e.g. digital and sustainable BM). The main contribution of this work is the resulting holistic BMT framework, comprising the main parts BM innovation, external antecedents (transition of environment, entrepreneurial ecosystem), internal antecedents (dynamic capabilities, entrepreneurial orientation, resilience, strategy) and output (firm performance). Practical implications The framework provides guidance for entrepreneurs and entrepreneurial managers to implement and complete BMT in small businesses. Furthermore, the presented paper sets a future research agenda focusing on small businesses structured according to the derived framework. Originality/value This study provides the first SLR of existing BM concepts with a small-business specific perspective on BMI and a focus on various incremental BM changes.

Recent years have seen a surge in research on artificial intelligence (AI)-driven business model innovation (BMI), reflecting its profound impact across industries. However, the field’s current state remains fragmented due to varied conceptual lenses and units of analysis. Existing literature predominantly emphasizes the technological aspects of AI implementation in business models (BMs), treating BMI as a byproduct. Additionally, there is a lack of coherent understanding regarding the scope of BMI propelled by AI. To address these gaps, our study systematically reviews 180 articles, offering two key contributions: (1) a structured analysis of evolving research dimensions in AI-driven BMI, differentiating between static and dynamic views of BMI, and (2) a framework presenting distinct research perspectives on AI-driven BMI, each addressing specific managerial focuses. This synthesis facilitates a comprehensive understanding of the field, enabling the identification of research gaps and proposing future avenues for advancing knowledge on the management of AI-driven BMI.