Companies in asset heavy industries face a shift from selling hardware to launching own digital platforms. These platforms differ from the more widely analyzed consumer platforms due to their layered architecture. Therefore, we explore the role of network effects and platform strategies to gain competitive advantage in the agricultural industry.
We build on two distinct literature strands: network economics and its implications on platform strategy (e.g., Rochet and Tirole 2003), and on the resource-based view in ecosystems (e.g., Alexy et al. 2018). Platform literature has studied strategies to leverage existing and induce new direct and indirect network effects. Strategic decisions include which side to discount (Parker and van Alstyne 2005), entry timing and quality (Zhu and Iansiti 2012), and design choices such as openness (Boudreau 2010; Eisenmann et al. 2009). One essential difference of asset heavy digital platforms with a layered architecture is that competitive advantage needs to be assessed on an ecosystem level since the critical resources do not solely rest within one firm (Dattée et al. 2018; Jacobides et al. 2018). Alexy et al. (2018) showed how strategic openness can lead to competitive advantage. These insights can be adopted for industrial platforms to extend traditional platform theory.
While extant knowledge on digital platforms is largely based on analyses of platforms in asset light industries, empirical evidence from industrial platforms is scant. This lack is surprising as the economics of layered platforms to achieve competitive advantage differ substantially. Given the rise of these platforms, more research is warranted.
We aim to integrate platform strategies with RBV in ecosystems to assess: Under which conditions do platform businesses in asset heavy industries lead to competitive advantage? Subsequently, we explore the role of network effects, propensity of Winner-take-all outcomes, and platform strategies with special focus on strategic openness.
This study follows a two-phased approach: First, a qualitative multiple-case design is conducted in accordance with the exploratory nature of the topic. Second, the insights from the case study inform a simulation study. The aim of the in-depth inductive research approach (Gioia et al. 2013) is to disentangle relevant decision factors and competitive dynamics. Primary data will be conducted through semi-structured interviews whereas secondary data will be based on media documentation, websites and documents provided by the interviewees. Based on the insights an optimization model will simulate the decisions under uncertainty (e.g., Gorissen et al. 2015).
The agricultural industry serves as an example for three reasons: First, while deeply rooted in the production of physical products farming is increasingly digitized (Bovensiepen et al. 2016), representing typical asset heavy digital platforms with a layered architecture. Second, digitized agriculture will create new value pools requiring a shift in business model and completely redefining the competition (Dressler 2015). Third, food and agribusiness does not only have a massive economic footprint but also a significant social and environmental relevance and digital offerings could be part of an answer for securing sufficient, safe and healthy food (Kritikos 2017). We follow an embedded multiple-case design based on a literal replication logic (Yin 2018). We study two cases: one machinery company and one producer of seeds and crop protection. Both represent key players within the agricultural value chain, are key partners to farmers, at the same time positioned upstream, hence not directly consumer-facing, and their focal product is of physical nature. Our study is based on interviews with executives of the focal companies, customers, suppliers, investors, and complementors. To corroborate our findings, we include extensive secondary data, such as customer usage data, market analyses, internal reports, etc.
This study sheds light on the role of network effects in asset heavy industries and the subsequent implications on platform strategies to gain competitive advantage. Our research will identify the relevant parameters and strategic decisions (e.g., openness, pricing, entry timing) to maximize market share for certain scenarios. Special focus is put on the question of strategic openness. Our hypothesis is that a value migration strategy (i.e. commoditizing the physical layer through openness and shifting competition towards a digital platform) will create network effects, which in turn increases market share. Thus, it adapts existing knowledge on platform strategies to a new type of platforms and empirical context. Thereby, unsolved questions are explored, such as the tension of adoption and appropriability (West 2003) and the relative importance of platform quality and network effects (Zhu and Iansiti 2012). In addition, the simulation study allows to model decisions under uncertainty based on the identified parameters. It will also help to identify the appropriate levers to reach the tipping point of network effects required to create a sustaining competitive advantage (clarity cut-off point) (Dattée et al. 2018).
Contribution to Scholarship
With our paper, we are expanding the network economics and platform strategy literature as well as the resource-based view in ecosystems. By focusing on asset heavy industries, our study extends existing research to asset heavy industries with a layered architecture in which the ecosystem approach and network effects are untested. The integration of platform strategies with the RBV-approach allows us to enrich the knowledge on network economics and market dynamics with a special focus on openness questions. Thereby, we follow an integrated approach as proposed by Gawer (2014). Our findings thus not only help to better depict the economic mechanics of digital platforms in industrial settings but also sheds light on unsolved strategy issues for digital platforms in general.
Contribution to Practice
Companies in asset heavy industries increasingly embrace the platform play. Yet, shifting from a product focus to a business model based on network effects involves a significant leap for incumbent players. Specifically, the question of openness triggers resistance. In addition, they often cannot afford the cash burn of consumer platforms to achieve critical mass. It is therefore crucial to assist the decision making under uncertainty for managers in industrial contexts. Our results on market dynamics, such as WTA propensities, will also inform policymakers on regulatory measures which are especially important in the agricultural industry.
Digital platforms in asset heavy industries fundamentally change the business model towards openness in ecosystems. Moreover, the agricultural industry studied here is highly relevant for our society. Hence, this research lies at the core of the innovation challenge and enlarges the perspective on digital platforms.
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