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Network Externalities, Incumbent’s Competitive Advantage and the Degree of Openness of Software Start-Ups

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Abstract

This paper proposes a formal model that analyzes the degree of openness chosen by start-ups when entering the software industry. In line with the literature, we label as degree of openness the extent to which software start-ups mix open source (OS) and proprietary solutions in the portfolio of software products they offer. We relate the choice of the degree of openness to two key characteristics of the market segments in which software start-ups operate: the strength of the network externalities and the competitive advantage of the incumbent. Specifically, by modelling (price) competition between an incumbent and an entrant in two ways, i.e., the entrant is price-setter or price-taker, we derive the necessary condition(s) in terms of the strength of network externalities for observing the adoption of a business model that comprises the offering of both proprietary and OS solutions by the entrant (i.e., hybrid business model). Then, we highlight that, if a hybrid business model is the choice, the degree of openness chosen in equilibrium increases along with both the strength of the network externalities and the competitive advantage of the incumbent. This result holds indifferently whether the software start-up is modelled as a price-setter or a price-taker. An empirical test run on a sample of European start-ups in the software industry supports these theoretical predictions.

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Notes

  1. This multi-dimensional construct refers to the proportion of products and services based on OS software, as measured by the share of items on offer, the license schemes adopted, and the total turnover. These objective elements (products and services, licenses, turnover) are integrated with a subjective judgment about the strategic importance of OS for the firm (see Bonaccorsi et al. 2006, p. 1090).

  2. For sake of completeness, it is worth mentioning that formal models have been used also to study other aspects on the OS phenomenon. For instance, Lee and Kim (2013) have resorted to a formal model to study open source project participation by software programmers, who differ in terms of ability and intrinsic motivation.

  3. In particular, Casadesus-Masanell and Llanes (2011) have recently developed a model that studies firms’ choice of mixing the offering of proprietary and OS software in the framework of the competition between a for-profit firm and an OS community. Their analysis points to the intrinsic quality of the software modules developed by the incumbent as an important driver of its business model. Among other things, the authors find that, when OS and proprietary solutions are compatible, a business model that mixes the offering of the two types of software (i.e. hybrid business model) is optimal “when some (but not all) of the firm’s modules are of substantially higher quality than those of the opensource competitor” (p. 2).

  4. Incumbent’s competitive advantage is here defined as the premium that an incumbent may obtain in terms of higher demand with respect to a newcomer apart from price and intrinsic product quality considerations.

  5. It is worth noting that in most cases it is not a matter of “real” incompatibility as OS developers make efforts to render OS solutions compatible with proprietary ones to promote their diffusion. However, users may still ‘perceive’ OS solutions as highly incompatible with proprietary ones. Perceived incompatibility may hamper network externalities to unfold if users restrain to adopt OS solutions thinking that they cannot work with proprietary ones or, more generally, that OS software is more difficult to use.

  6. The ELISS II (European Libre Software Survey) directory has been developed by a consortium of universities and public research institutions (ETLA The Research Institute of Finish Economy, Finland; Fraunhofer Institute for Systems and Innovation Research, Germany; Instituto Superior de Ciências do Trabalho e da Empresa de Lisboa, Portugal; Universitad Carlos III de Madrid, Spain; Sant’Anna School of Advanced Studies, Italy) within the PRIME Network of Excellence (http://www.prime-noe.org/), founded by the European Community within the Sixth Framework Program. See Sect. 4 for further details.

  7. It is worth noting that even some large incumbents, like IBM, Oracle and Sun Microsystem, have embraced open source and promoted it (Pisano 2006).

  8. The fact that the demand of the incumbent decreases with the degree of openness of the entrant is not, of course, a general assumption, but fits well the recent evolution of the software market (see the Introduction and Sect. 2). The case where a higher degree of openness of the entrant increases the demand of the incumbent could be handled within the same framework developed here: clearly, in this case, the results would be reverted.

  9. It can be easily checked that the second-order condition is satisfied. The same holds when firm E is considered (see later).

  10. The second term in (3) vanishes in the case of linear demand functions.

  11. Note that the threshold value of the network externalities in case of variations of e and o may be different from \(e^{*}\). Details are available from the authors.

  12. The analysis developed up to this point may be easily extended to a T-period framework. Suppose that there are T periods, indexed with \(t=1,\ldots ,T\). The incumbent has entered the market at period \(t=1\), while the entrant enters at time \(\tau \in [2,\ldots ,T-1]\) (note that when \(\tau =T-1\) we are in the two-period model described in the text). When \(\tau \) is low, the direct effect tends to be low while the indirect effect tends to be high, because there is a scarce installed base and a great incentive to exploit future demand. Therefore, we expect that the critical value \(e^{*}\) depends positively on \(\tau \), or: \({\partial e^{*}}/{\partial \tau }>0\).

  13. Both maximal and minimal openness are invariant by definition with the variables of the model, and therefore they are of less interest for our analysis.

  14. When the necessary condition for non maximal openness holds, two situations are possible: partial openness, originating a hybrid business model for the entrant, and zero openness, meaning that the entrant sells no good in OS format.

  15. Note that, for any variable \(\xi _j \in {\bar{\xi }}\), Proposition 1 applies directly if \({\partial \alpha }/{\partial \xi _j}\ge 0\), while it applies inversely if \({\partial \alpha }/{\partial \xi _j}\le 0\).

  16. In Fig. 2 (and in Fig. 3, later), the case of minimal openness is not represented. As indicated in Lemma 4, a necessary condition for its occurrence in equilibrium is that \(e\ge e^{*}\).

  17. As suggested by a referee, openness may be divided between “compatible” openness and “incompatible” openness, as discussed in the Introduction. A simple way to consider this dichotomy within the model is the following. The positive impact of o on the competitive advantage of the entrant is likely to be stronger the more compatible is the openness strategy adopted by the entrant, as consumers are more prompt to choose an open source format when they anticipate that they will not be locked-in by software incompatibility. In terms of our model, this amounts to say that \({\partial \alpha }/{\partial o}\) is larger, in absolute value, the more the open format is compatible. Therefore, one may expect that the incentive to choose a larger degree of openness is stronger the more compatible is the openness strategy under consideration.

  18. It is immediate to note that \({\partial ^{2}\tilde{\Pi }^{E}}/{\partial o^{2}}<0\). Therefore, if an interior solution of \({\partial \tilde{\Pi }^{E}}/{\partial o}=0\) exists, it must be a maximum.

  19. Alternatively, it would have been possible to proxy the degree of openness by counting the OS solutions offered by a start-up over the total software solutions offered. However, the ELISS II dataset does not provide this information. Moreover, note that the use of this measure would have exposed our analysis to the criticism that some OS products might be irrelevant in terms of sales.

  20. All subjective data refer to 2004, while data on OS turnover shares are based on the level of sales in 2003, except for 3 firms that were born in the early 2004, for which respondents were asked to take the date of the survey as reference. Note that excluding these latter observations from the analysis does not alter in any sensible way the findings of the empirical test (results are reported in Table 5).

  21. The resulting variable is strongly correlated with both the direct (r = 0.79) and indirect (r = 0.63) components. Results (available upon request from the authors) are similar to those obtained substituting the variable EXTERNALITY with either components. Finally note that in the case of a missing value in one of the two types of externality, we attributed to EXTERNALITY the value assumed by the other externality component. The original Likert scale included 5 points, ranging from 1 (not important) to 5 (very important). As most of the start-ups choose medium (3) or high values (4 and 5) for network externality variables, we decide to recode EXTERNALITY, assigning to it value 1 if the firm chose an average score ranging from 1 to 3, value 2 if the average score was greater than 3 but less or equal to 4, and value 3 if the average score was greater than 4.

  22. An empirical testing of the determinants of OS firms’ degree of openness was also included in Bonaccorsi et al. (2006). However, in that case, the authors did not ground their econometric analysis in a formal model. Moreover, they focused mainly on the impact on the degree of openness of firms’ structural characteristics and relationships with OS communities. The role of the competitive environment in which firms are embedded was set aside in their investigation.

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Authors and Affiliations

  1. Istituto di Economia e Finanza, Università Cattolica del Sacro Cuore di Milano, via Necchi 5, 20123 , Milano, Italy

    Stefano Colombo

  2. Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini 4/b, 20156 , Milano, Italy

    Luca Grilli & Cristina Rossi-Lamastra

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  1. Stefano Colombo
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  2. Luca Grilli
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  3. Cristina Rossi-Lamastra
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Correspondence to Cristina Rossi-Lamastra.

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Colombo, S., Grilli, L. & Rossi-Lamastra, C. Network Externalities, Incumbent’s Competitive Advantage and the Degree of Openness of Software Start-Ups. Comput Econ 44, 175–200 (2014). https://doi.org/10.1007/s10614-013-9385-8

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