Business Model Innovation: the role of internal and external drivers

Many contributions examine the concept of Business Model Innovation (BMI), underlying that BMI enhances a firm's competitive advantage (Mitchell and Coles, 2003) exploiting both newly and already existing internal/external resources without investing significantly in R&D (Amit and Zott, 2010), and rethinking the firm's purpose, value creation and value perception (Bocken et al., 2014). Moreover, when dealing with BM and its innovation, the literature suggests a linkage with new technologies or existing technologies applied to other purposes (Casprini et al., 2014), and the appearance of new business models (Baden-Fuller and Haefliger, 2013; Zott et al., 2011). From the internal perspective, many scholars described the key role of internal drivers (Achtenhagen et al., 2013; Zhang et al., 2016), highlighting that new and distinctive resources can not only reconfigure the BM (Morris et al., 2005), but also enhance efficiency and efficacy of value creation, provision, and capture (Pucci et al., 2013; Pucci et al., 2017). However, the ability to choose, integrate and adapt new resources determines the level of efficacy and efficiency in terms of value creation (George and Bock, 2011). Since Process Innovation (PI) acts as a crucial driver to obtain the sought efficiency and productivity improvement and effective competitive advantage (Terjesen and Patel, 2017; Trantopoulos et al., 2017), we aim to test the following hypothesis: (Hp.1) PI increases the probability to have BMI. From the external perspective, the literature showed the ability of external drivers to change BMs (De Reuver et al., 2009). Indeed, cocreation and co-innovation networks (Romero and Molina, 2011), with higher Supply Chain (SC) integration (Atti, 2018), and social innovation (Alegre and Berbegal-Mirabent, 2016), may offer new opportunities and BMs re-configuration. Focusing on SC, since collaborations within SC are fruitful for any player involved (Chen and Paulraj, 2004), we identified and investigated three set of external stimuli, namely: upstream (e.g. suppliers), downstream (e.g. customers) and horizontal (e.g. universities). Thus, we tested the following hypothesis: (Hp.2) stimuli coming from SC increase the probability to have BMI. Furthermore, since proximity can be viewed as a pre-condition for innovation especially because of its role in fostering knowledge and technology transfer among actors (Knoben and Oerlemans, 2006), we investigated the impact of the following proximity dimensions on BMI: geographical (GP), technological (TP), organisational (OP), and social proximity (SP) (Geldes et al. 2017; Marrocu et al., 2013). Our analysis does not investigate: institutional proximity, as differences in formal institutions are not relevant on this research contest and informal institutions are likely to overlap the notion of OP (Knobben and Oerlemans, 2006); cognitive proximity, since we are investigating specific technologies and their related knowledge. Thus, our last hypothesis is: (Hp.3) GP, TP, OP, and SP increase the probability to have BMI. Data collection has been performed through a structured survey, submitted to 107 Italian manufacturing firms between July and October 2018. In particular, since BMI (Foss and Saebi, 2017) has been defined and measured in several ways (Casprini, 2015), we rely upon a definition considering the firms' own perception about their innovations and BMI. For what concerns Hp. 1, PI shows a positive and significant effect on the probability of having BMI, thus Hp. 1 is supported. This shades the light on the possibility of a double positive effect of PI for firms. Not only PI is directly beneficial for firms, it also has additional positive effects by increasing the probability of having BMI. On the other hand, Hp. 2 is partially supported since only Downstream Stimuli show a positive and significant effect on the likelihood of having BMI. Conversely, for Hp. 3, while the effect is positive and significant for TP, it is negative and significant for SP and not significant for OP. A peculiar result comes from GP which shows a significant curvilinear effect, therefore both a too high or a too low GP is detrimental to the probability of having BMI. Hence, only an adequate distance fosters BMI. Therefore, Hp. 3 is partially supported. This research contributes both to academical and practical understanding of factors fostering, or hindering, BMI. It underlines that firms may innovate their business model not only relying on internal resources, but also taking advantage of stimuli coming from different SC actors at the right geographical distance and with whom they share technological affinity.