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.

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.

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.

This paper investigates the emerging potential of metaverse technology and the diverse opportunities it presents for companies across industries. Although the metaverse remains in its nascent stages, its swift evolution has introduced a broad spectrum of use cases that hold significant promise for businesses. However, despite the evident potential, there remains a limited understanding of how metaverse technology can be effectively applied to benefit business operations and strategy. To address this gap, this study employs a scoping review methodology, systematically collecting and analyzing data from academic literature, publicly available sources, and company websites. The comprehensive review identified 101 distinct use cases of metaverse technology, which were subsequently categorized into three primary application fields: developing new product and service offerings, enhancing customer experience, and optimizing internal business processes. These findings not only provide a compelling rationale for companies contemplating the adoption of metaverse technology but also represent the first extensive exploration of its applications across diverse fields and industries. The study offers valuable insights that are crucial for both academic researchers and business practitioners who are keen to understand and leverage the transformative potential of the metaverse. By mapping out the current landscape of metaverse applications, this paper contributes to a deeper understanding of how companies can harness this technology to drive innovation, improve operational efficiency, and create new value propositions in an increasingly immersive and interconnected world.

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.