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Although a growing number of companies are using growth hacking (GH) to grow their businesses, scholars know little about its operationalization, namely through growth hacking capability (GHC), its antecedents and its effectiveness in improving their performance. Indeed, there are no studies that have examined the role of intellectual capital (IC) in this sense. The aim of this study is to fill these gaps and explore the effects of IC (composed of human, relational and structural capital) in influencing GHC and – in turn – whether GHC influences companies’ financial and market performance.

Empirical research was conducted using partial least squares structural equation modelling (PLS-SEM) to examine the validity of the proposed hypotheses and research model. Quantitative data were collected from 38 SMEs in the Italian context through a specifically designed questionnaire.

The results of the analysis show that IC has a positive and significant impact on SMEs’ GHC, thus confirming its role as a relevant antecedent; at the same time, the empirical results underscore the positive effect GHC has on SMEs’ financial and market performance.

First, the present body of work operationalizes GH, thereby, following previous work on lean startup and explores for the first time in literature the effect of IC on it. Second, from a contextual standpoint, the article deepens scholars' understanding of GHC by focusing on SMEs. Lastly, the adopted method represents a novel approach to investigating GHC, as scholarly literature has primarily focused on qualitative and theoretical dimensions.

The advantages of applying big data analytics for organizations to boost innovation performance are enormous. By collecting and analysing substantial amounts of data, firms can discern what works for their customer needs and update existing products while innovating new ones. Notwithstanding the evidence about the effects of big data analytics, the link between big data analytics and innovation performance is still underestimated. Especially in today's fast-changing and complicated environments, companies cannot simply take big data analytics as one innovative technical tool without fully understanding how to deploy it effectively.

This study tries to investigate this relationship by building on the knowledge absorptive capacity perspective. The authors conceptualized effective use of big data analytics tools as one general absorptive capacity rather than a simple technical element or skill. Specifically, effectively utilize big data analytics tools can provide values and insights for new product innovation performance in a turbulent environment. Using online survey data from 108 managers, the authors assessed their hypotheses by applying the structural equation modelling method.

The authors found that big data analytics capacity, which can be conceptualized as one absorptive capacity, can positively influence product innovation performance. The authors also found that environmental turbulence has strong moderation effects on these two main relationships.

These results establish big data analytics can be regarded as one absorptive capacity, which can positively boost an organization's innovation performance.