Architecting value propositions for driving data-driven innovations: unveiling the role of data-driven innovation capability of marketing through a configurational approach

Ludivine Ravat (), Olivier Furrer and Aurélie Hemonnet-Goujot ()
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Ludivine Ravat: CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon
Olivier Furrer: UNIFR - Université de Fribourg = University of Fribourg
Aurélie Hemonnet-Goujot: CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon, AMU IAE - Institut d'Administration des Entreprises (IAE) - Aix-en-Provence - AMU - Aix Marseille Université

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Abstract: Data-driven innovation is increasingly vital for gaining a competitive advantage in business markets, underscoring the strategic role of firm competencies and data-driven innovation capability (DDIC) of marketing in crafting effective value propositions. However, the contribution of DDIC of marketing to value proposition design for data-driven innovation has been minimally explored. Based on an empirical complementary study, we intend to bridge this gap. First, we conducted a preliminary inductive analysis of data from 15 B2B international manufacturing companies engaged in data-driven innovation, leading us to determine three main value practices: (1) value capture, (2) value articulation, and (3) value leverage, each divided into three sets of inherent sub-practices for structuring value propositions for identified DDI types. Second, supported by a sample of 103 professionals from marketing, digital, IT, R&D, finance, and sales departments, we used Fuzzy set Qualitative Comparative Analysis (fsQCA) to depict the interactions of DDIC of marketing and firm resources within value proposition practices leading to specific DDI types. Our results highlight five configurations for three DDI types: (1) digitalized service innovation, (2) data monetization, and (3) global integrated solution, showing similarities and differences, and stressing the key role of DDIC of marketing resources in each solution. We conclude by discussing the theoretical and managerial implications of our findings and offering future research avenues.

Keywords: Value proposition; Data-driven innovation; Data-driven innovation capability of marketing; Marketing strategy; Business-to-business firms; fsQCA (search for similar items in EconPapers)
Date: 2024-04-12
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Published in The 2024 Haring Symposium, Kelley School of Business, Indiana University, Bloomington, USA, Apr 2024, Bloomington (Indiana), United States

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