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.
In today's data-driven era, ubiquitous concern about environmental issues pushes more startups to engage in business model innovation that promotes environmentally friendly technologies. The goal of these startups is to create technology-based products and services that enhance environmental sustainability. In this context, artificial intelligence promises to be a key instrument to create, capture, and deliver value. However, the existing literature lacks a deep understanding of how startups using AI innovate their business models to achieve a positive environmental impact. Therefore, this paper investigates how green technology startups utilize AI from a business model innovation perspective for environmental sustainability. We conduct a qualitative, exploratory multiple-case study using the Eisenhardt methodology, based on interview data analyzed using qualitative content analysis. We derive five predominant manifestations for AI-driven business model innovation and identify archetypical connections between business model dimensions. Further, we establish three overarching archetypical associations among the cases. In doing so, we contribute to theory and practice by providing a deeper account of how green technology startups attempt to maximize their positive environmental impact through AI. The results of this study also highlight how business model innovation driven by AI can support society in securing a more environmentally sustainable future.
Enacting disruption
(2023)
Purpose
Entrepreneurial ventures aspiring to disrupt existing market incumbents often use business-model innovation to increase the attractiveness of their offerings. A value proposition is the central element of a business model, and is critical for this purpose. However, how entrepreneurial ventures modify their value propositions to increase the attractiveness of their comparatively inferior offerings is not well understood. The purpose of this paper is to analyze the value proposition innovation (VPI) of aspiring disruptors.
Design/methodology/approach
The authors used a flexible pattern matching approach to ground the inductive findings in extant theory. The authors conducted 21 semi-structured interviews with managers from startups in the global electric vehicle industry.
Findings
The authors developed a framework, showing two factors, determinants and tactics, that play a key role in VPI connected by a continuous feedback loop. Directed by the determinants of cognitive antecedents, development drivers and realization capabilities, aspiring disruptors determine the scope, focus and priorities of various configuration and support tactics to enable and secure the success of their value proposition.
Originality/value
The authors contribute to theory by showing how cognitive antecedents, development drivers and capabilities determine VPI tactics to disrupt existing market incumbents, furthering the understanding of configuration tactics. The results have important implications for disruptive innovation theory, and entrepreneurship research and practice, as they offer an explanatory framework to analyze strategies of aspiring disruptors who increase the attractiveness of sustainable technologies, thereby accelerating their diffusion.
Since its inception over two decades ago, the theory of disruptive innovation has sparked heated discussions. Especially because of the increasing importance of societal influences and novel forms of competition and technology, questions about its theoretical value and practical relevance remain. Researchers have focused on firm-internal factors of disruptive innovations to resolve discussions about the validity of the theory. However, the literature lacks an integrated understanding of contextual factors, such as demand, market structure, culture, and regulation, that influence disruptive innovation because of its dispersed, fragmented character across disciplines. Our study addresses this fragmentation and lack of integrated understanding by systematically reviewing 62 articles. The study makes three main contributions. First, we integrate and synthesize the literature on contextual factors of disruptive innovations. Second, we derive a three-phase framework of contextual factors: (1) disruptive susceptibility, (2) emergence and diffusion, and (3) endgame and outcome. Third, we contribute to resolving discussions about the theory's core elements and its predictive value by showing how, depending on the societal, cultural, or market context, the implications of the theory can change. Overall, this article shows how disruptive innovation can start, and be started, by social change. We conclude by suggesting areas for future research.