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Browsing by Author "Rezazadeh, Arash"

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  • A Delphi study of business models for cycling urban mobility platforms
    Publication . Sá, Elisabete; Carvalho, Ana; Silva, Joaquim; Rezazadeh, Arash
    The movement towards sustainable and liveable cities is gaining momentum and is projected to continue to shape the future of cities. Bicycles are one of the fastest-growing transportation modes that can contribute to more sustainable and smart urban mobility. New digital service platforms will likely arise to support an enhanced cycling mobility experience while also offering value to other stakeholders of a connected urban mobility ecosystem. Exploring suitable business models is critical to sustaining digital urban mobility platforms, but approaches that consider multiple stakeholders are scarce in previous research. Aiming to reduce this gap, this Delphi research with experienced professionals and academics adopts an ecosystem approach and explores two important components of business models for future cycling urban mobility platform services and the data they would generate: value propositions and value capture models. Results show that experts participating in the study generally agree on the potential attractiveness of the services of such a platform and mobility data for the studied stakeholders. However, lower and diverging estimates regarding the expected willingness to pay suggest that a business model that combines revenues from platform services and data services may be needed and that cross-subsidisation of some stakeholders could be necessary.
    2022-12Journal article Open access Show more
  • Generative AI for growth hacking: how startups use generative AI in their growth strategies
    Publication . Rezazadeh, Arash; Kohns, Marco; Bohnsack, René; António, Nuno; Rita, Paulo
    This study explores how startups and scaleups in Europe and the US use generative AI in their go-to-market strategies across product-led, sales-led, and operational efficiency-driven growth. Through interviews with 20 cases spanning pre-seed to Series E funding stages, we 1) analyze generative AI's role in growth strategies, 2) identify large language model use cases for tackling growth challenges such as customer churn, and 3) develop a framework for AI capabilities that guides managers in building, refining, and reflecting on their knowledge of using generative AI for growth hacking. Key findings include the implications of generative AI for technical and non-technical content creation in product-led growth, promotional content creation and repurposing, and customer experience personalization in sales-led growth, and market research, market entry strategies, and customer engagement in operational efficiency-driven growth. Findings empower managers to develop effective generative AI-driven growth hacking strategies while proactively managing unintended organizational, competitive, and societal consequences.
    2025-04Research article Open access Show more