Attempting to enter a mature and multisided business ecosystem with a potentially disruptive technology may reveal a thorny path. Mature business ecosystems revolving around multisided platforms appear to possess solid means of preventing any new player posing threat to existing players from entering the sphere; so much that a potential disruptor’s success vastly depends on the support of the to-be-disrupted incumbents, a conundrum recently referred to as the disruptor’s dilemma (Ansari et al., 2016). Although it is not difficult to understand why the potential disruptor would want to seek support from incumbents, it is much less clear why incumbents would be willing to engage in any sort of collaboration. Such an observation suggests that disruption in such an ecosystem counts as a triumph. Yet, disruptions not only happen, but it is the fate of all businesses.
We present a game theoretic model of incomplete information towards analysing the dynamics of decision-making by incumbents based on their beliefs formed (at least partly) via information divulged, either privately or publicly, by the potential disruptor. More precisely, we use a global game model (Carlsson & van Damme, 1993; Morris & Shin, 2001; Frankel et al., 2003), and even more precisely a global game of regime change (Angeletos et al., 2007). Following a similar development as in the work of Angeletos et al. (“Dynamic Global Games of Regime Change: Learning, Multiplicity, and the Timing of Attacks,” 2007), we start by a static game which differs from the one discussed by Angeletos et al. (“Dynamic Global Games of Regime Change: Learning, Multiplicity, and the Timing of Attacks,” 2007) only in the payoff structure. We then examine a more realistic scenario in which incumbents can make their decision to support the potential disruptor in a second round, where further information is gained from observing the noisy outcome of the first one. We succinctly discuss the generalization to an arbitrary number of rounds.
In games of imperfect information (also known as Bayesian games), the assumed rationality implies that each incumbent acts in a way to maximize its payoff. Each incumbent’s payoff depends on its decision, on the decision of other incumbents in the ecosystem and on some unknown economic fundamentals (Morris & Shin, 2001); therefore, the incumbent’s decision must repose on its beliefs regarding economic fundamentals, and on its appreciation of the other players’ beliefs as well, which gives raise “to an infinite regress in reciprocal expectation on the part of the players” (Harsanyi, 1967, p. 5) due to endless higher order beliefs—although, in practice, there seem to be a limit (Strzalecki, 2014). Considering the entire set of higher order beliefs together with a high-dimensional representation of economic fundamentals may become rapidly overwhelming, to the point of not being practicable. Global games offer a simplified representation of such a situation where economic fundamentals are captured by a single parameter θ, and each player receives a different signal of θ—the difference is due to noise (Morris & Shin, 2001).
Although a global game model may lack sophistication, or may even appear overly stylized, to fully capture the intricate dynamics of the disruptor’s dilemma, “[r]emember that all models are wrong; the practical question is how wrong do they have to be to not be useful” (Box & Draper, 1987, p. 74). The work introduced throughout this paper should be regarded as a preliminary step in constructing tools to help both the potential disruptor and the incumbents in making strategic decisions when confronting the dilemma. Those tools should belong to a broader portfolio of tools and other existing frameworks (as a complement), and are not intended to provide definite answers.
A brief look at the literature in global games reveals an obvious frequently recurring pattern consisting in, first, presenting a set of assumptions, on top of which a model is developed; then, subsequently, in giving formal proofs of the existence of equilibria and other properties together with conditions that guarantee a unique equilibrium (Carlsson & van Damme, 1993; Chamley, 1999; Angeletos et al., 2007; Frankel et al., 2003; Edmond, 2013). We do not follow this pattern. Instead, we rely on extensive simulations to discover equilibria. One can argue that simulations’ results should be loosely considered and are unsuitable for making conclusive claims about a larger context than the one in which simulations are conducted (i.e., generalizations are at best insubstantial). On the other hand, such results are meaningful for developing insights on the matter. Such an insight can be valuable in practice; besides, it can be a first exploratory step towards devising formal proofs of the suggested/observed results.
The rest of this paper is structured as follows: In Section 1 we extensively delve into the disruptor’s dilemma. Our proposed model and conducted simulations are presented in Section 2. Finally, in Section 3, we discuss the limitations of our model and suggest some future directions of investigation.
The Disruptor’s Dilemma
The simplest description of the dilemma can trivially be grasped by the observation that “[f]irms introducing disruptive innovations into multisided ecosystems confront the disruptor’s dilemma: gaining the support of the very incumbents they disrupt” (Ansari et al., 2016, p. 1). Central to this description of the dilemma are the terms disruptive innovations and multisided ecosystems, which calls for further elaboration.
Ansari et al. (“The Disruptor’s Dilemma: TiVo and the U.S. Television Ecosystem,” 2016, n. 1) define disruptive innovations as:
[N]ew technologies, products or business models that are financially unattractive to incumbents … . They can be (1) “low-end” innovations that target customers “overserved” by the functionality of their current provider, such as discount department stores (e.g., Walmart), (2) “new-market” innovations that target “non-served” customers, i.e., those unable to access, use or even afford the product, such as online auctions (e.g., eBay), or (3) hybrids, that combine both overserved and non-consumers, such as low-cost airlines (e.g., Southwest Airlines).
Technology-wise, disruption is not necessarily achieved via performance superiority, as common sense may suggest—as well as suggested in some preliminary works (Utterback & Abernathy, 1975)—but through a complex evolutionary mechanism where the trajectory of offered performance grows much faster than the trajectory of needed performance. Consequently, the performance of the quasi-unnoticed and seemingly inferior technology eventually becomes what users need (Christensen, 1997). Based on Christensen (The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail, 1997)’s work, Adner (Adner, 2002, p. 2) contends that “[d]isruptive technologies are technologies that introduce a different performance package from mainstream technologies and are inferior to mainstream technologies along the dimensions of performance that are most important to mainstream customers.”
By contrast, Kavadias et al. (“The Transformative Business Model,” 2016) argue that disruptions happen via a combination of both new technologies and new business models, for a new technology alone might not suffice. They identified six traits that seem to be recurrently recognized in a number of recent successful business models (and absent from the ones that proved unsuccessful). They also observe that the odds of success for a potential disruptor remain meager: “Most attempts to introduce a new model fail—but occasionally one succeeds in overturning the dominant model, usually by leveraging a new technology” (Kavadias et al., 2016, p. 3).
It is worth noting that, although disruptions appear to be exceedingly rare, this is, paradoxically, the fate of all successful business organizations (Christensen, 1997); we further elaborate on that point in the next section (Section 1.2).
In spite of having received much attention by academics and industrials, given the absence of precise consensus, remarking the fact that many works, such as the ones cited above, justify their claim by finding a few examples of situations that somehow corroborate the claim—a context obviously prone to the confirmation bias, for the cases that disconfirm the theory are conveniently ignored (Nickerson, 1998)—and noting the undeniable alarmingly high number of failed attempts (Marmer et al., 2011), the mechanism leading to successful innovations (and therefore disruptions) is still to be identified. Further, the possibility that such a mechanism does not exist cannot be ruled out, those radical changes might happen by sheer luck or serendipity—a phenomenon falling under the umbrella of the so-called black swans (Taleb, 2008). Whether such a mechanism exists seems to be still an open question that some scholars have been attempting to elucidate; Christensen (The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail, 1997), one of them, expresses strong presumptions in favour of its existence.
The disruptive consequences of some innovations are not always intentional. There are cases where both the instigator of a new technology and the incumbents had not foreseen the full disruptive power of the new technology. As a case in point, consider the introduction of the iPhone in 2007, “[Apple] aims to sell 10 million iPhones through the end of 2008, which would account for about 1% of the annual global handset market” (Yuan & Bryan-Low, 2007, para. 5); whereas some analysts from prominent financial institutions reported that 2 million units could be sold at best (Yuan & Bryan-Low, 2007). A modest ambition from Apple and an amusing prediction from analysts when we now know the actual figures: Apple exceeded its target for 2008 (Cheng, 2008), and went on to reach a staggering cumulative sales of 1 billion units during 2016 (Apple Inc., 2016), making this product “the most successful product of all time” (Reisinger, 2017, para. 3). Not only has the iPhone been generating tremendous wealth for the shareholders, but it has also disrupted the entire personal computer paradigm and industry. From targeting a niche market to the decade-longed so-called Post-PC era (Press, 1999; Weiser, 1991), the iPhone and its ecosystem offers the quintessence of an unexpected vastly disruptive and far-reaching innovation.
Disruption: An Inevitable Destiny
It appears that being disrupted is inherent/intrinsic to any business endeavour, and thus unavoidable. That is to say, present successful disruptors will eventually and quasi-inexorably be, in turn, disrupted. The stratagem consists then in postponing the occurrence as much as value creation can be sustained; and, in due course, gracefully exiting (e.g., by being part of the superseding business).
High barriers to entry might delay the downfall, but not prevent it. Agarwal and Gort (“The Evolution of Markets and Entry, Exit and Survival of Firms,” 1996) substantiate that, in spite of strong barriers to entry, increased innovation and increase in market entries are correlated.
In his book, Christensen (The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail, 1997) provides numerous examples of once renown firms praised and acclaimed for their seemingly flawless management and subsequent well-deserved success (e.g., Sear, IBM, DEC and Xerox to which we can add the like of Kodac, RIM, Nokia, Sun Microsystems); yet that have since then either vanished altogether or are now hostilely criticized for the disappointment they have been causing to the stockholders. In many of those examples, the recurrent pattern is that those firms, while enjoying and resting on their seemingly eternal laurels, failed to recognize the turning wind (or at least failed to act on it), and in retrospect, many of those firms appeared to have been adequately positioned to benefit from the new wind direction. Those observations should justify and fuel the fear of certain healthy companies of missing “the next big thing”—in the words of the late former Apple CEO Steve Jobs, who is often regarded as a visionary and a serial disruptor (Isaacson, 2011) —making them more inclined to collaborate1 with potential disruptors; e.g., Google was acquiring over one company per week on average a few years ago (Rusli, 2011). Apple, Facebook, IBM or Microsoft, among other titans of the Internet—a sector driven by innovations and thus prone to disruption—are exhibiting a similar behaviour (Wikipedia, 2017; Wikipedia, 2016; Wikipedia, 2017; Wikipedia, 2017).
Multisided ecosystems revolve around platforms that synergistically link the different sides together (Hagiu, 2014). Throughout this paper, we use interchangeably the terms multisided ecosystems and multisided platforms (although this is a slight abuse of language, as the former concept is larger than the latter).
Multisided platforms (MSPs) systematically exhibit, or even are characterized by, a strong network effect—also referred to as network externalities in the literature (Rochet & Tirole, 2003). That is to say, for one side, the perceived value of the MSP increases with the sizes of the other sides. The terms cross-side network effects or indirect network effects are used to describe this setting—by contrast, direct network effect of goods refers to a situation where the perceived value to a user increases with the total number of users (Hagiu, 2014).
Once well-established, an MSP enjoys a comfortable position and is likely to last—due to the stability offered by the network effect and switching costs mechanisms2 (Farrell & Klemperer, 2006; Zhu & Mitzenmacher, 2008). “[S]uccessful MSPs occupy privileged and often hard-to-assail positions in their respective industries” (Hagiu, 2014, p. 3). However, establishing a successful MPS is not an easy task. Failure at building such a platform remains the norm (Hagiu, 2014). The so-called chicken-and-egg problem lies at the heart of the challenge. Hagiu (“Strategic Decisions for Multisided Platforms,” 2014) identifies four critical decisions to be made: number of sides, platform design, pricing structure and governance rules. Rochet and Tirole (“Platform Competition in Two-Sided Markets.,” 2003) thoroughly address the pricing structure decision in a two-sided market. It has been observed and theorized that, often, the MSP must subsidize at least one side (loss leader) to reach and sustain a critical mass of participants. Rochet and Tirole (“Platform Competition in Two-Sided Markets.,” 2003) provide a number of such occurrences, including, e.g., platforms as diverse as video games, credit cards, operating systems, newspapers, TV networks and a lot more.
The concept of MSP is related to the notion of complementor coined by Brandenburger and Nalebuff (Co-Opetition, 2011). “A complementor is the opposite of a competitor. It’s someone who makes your products and services more, rather than less, valuable” (Brandenburger & Nalebuff, 2011, chap. Forword). At least one side of the platform appears as a complementor to the MSP; besides, the sides of the platform other than the consumers are complementors to one another as well (Boudreau & Hagiu, 2008). For instance, a video game developer and a joystick manufacturer for a given video game console platform are complementors. Bear in mind, though, that complementors are not necessarily linked via a MSP (Gawer & Henderson, 2007).
Disruption in Multisided Ecosystems
Let’s examine why and how disruptions happen in multisided ecosystems. Attacking a new market is hard: “Newcomers to a business face many disadvantages. They lack proven products, brands, loyal customers, manufacturing experience, and relationships with suppliers. As a challenger, if you go head-to-head with an incumbent, you’re likely to lose”3 (Brandenburger & Nalebuff, 2011, p. 236). Yet, the difficulty is exacerbated many folds in a multisided environment, due to the network effects, possible switching costs and other barriers to entry, as discussed above.
The distinction between a new entrant as yet-another-player in the ecosystem and a new entrant as a potential disruptor is essential. MSPs surely benefit from the new comers docilely filling their role on one side of the platform, e.g., Markman and Waldron (“Small Entrants and Large Incumbents: A Framework of Micro Entry,” 2014, p. 6) note that “the legions of micro entrants, including startups, that collectively offer more than 700,000 iPhone and Android applications … bolster Apple’s and Google’s positions in the smart phone ecosystem.” Encouraging a multitude of such new entrants can even constitute a divide-and-conquer strategy in order to keep the full control of the platform, as implemented by Nintendo, which limited the number of games a firm could develop per year or short-supplied retail chains (Brandenburger & Nalebuff, 2011). By sharp contrast, the emergence of an innovation with a potential for disruption is most assuredly not welcomed.
Considering the tremendous difficulties to develop a mature MSP (Hagiu, 2014), it is without surprise that ecosystem’s participants adopt an overly conservative stance when facing a potential disruptor. Even if the innovative technology appears appealing to the platform itself or to one side, the MSP may demonstrate a high level of scepticism and be extra-cautious, for it might have the potential to wipe out the benefit of the platform to at least one other critical side4, which would then jeopardize the critical balance/link between sides, to finally endanger the survival of the whole ecosystem. “[T]he need to please many different and heterogeneous platform constituents greatly constrains an MSP’s ability to innovate by introducing truly ground-breaking features” (Hagiu, 2014, p. 5).
In spite of that, disruptions in MSP ecosystems happen—coherently with our discussion in Section 1.2—as illustrated by the TiVo case (Ansari et al., 2016) or by the upheaval in the music industry caused by the advent of digitalization in the recent decades (Easley et al., 2003; Dolata, 2011; Moreau, 2013).
This massive, to the point of being seemingly insurmountable, hurdle for a potential disruptor to enter a multisided ecosystem leads to the disruptor’s dilemma (Ansari et al., 2016). The aspirant disruptor is doomed to failure unless it gains the support of the ecosystem’s incumbents.
From the Incumbents’ Perspective
Why mighty incumbents would deliberately collaborate with a frail threatening challenger in lieu of proactively crushing it straightaway? Although we do not pretend to fully elucidate this question, various elements of response can be advanced.
Drawing on the literature, Markman and Waldron (“Small Entrants and Large Incumbents: A Framework of Micro Entry,” 2014) studied the entrant-incumbent relations, and observed that although incumbents typically retaliate when a new player of comparable size attempt an entry, they might tolerate the entry of much smaller entrants. However, the argumentation is based on the assumption that the “micro entrant” has no disruptive capacity (i.e., poses no threat); an assumption that does not hold in our context.
Intuitively, several factors can be hypothesized. Healthy firms are constantly in the look up for growth opportunities (ailing firms might just be focusing on staying afloat—but reposing on similar tactics). For a MSP, one way consists in growing each side, while keeping an adequate balance, which is not trivial, Hagiu (“Strategic Decisions for Multisided Platforms,” 2014, p. 6) indicates that:
The most difficult MSP design decisions are those that involve features putting the interests of different sides of the MSP at odds with each other or with those of the MSP. Such features create strategic trade-offs for the MSP because they generate positive value for some participant groups or for the MSP itself, but negative value for other participant groups.
The TiVo case provides us with a perfect illustration. TiVo generates positive value to TV viewers—advertising is quantified in terms of hindrance for the audience (Ambrus et al., 2016; Kind et al., 2007; Reisinger, 2012)—and negative value for advertisers. Another example is given by Hagiu (“Strategic Decisions for Multisided Platforms,” 2014) with Microsoft and the do-not-track feature in Internet Explorer 9.
When making those decisions, MSPs might underestimate the disruptive power of the new feature (let a seemingly harmful player in). A conjecture in line with the principles of disruptive innovation elaborated by Christensen (Christensen, 1997). Conversely, without underestimation, an MSP might be deliberately applying this principle5 (and not being a victim of it) and see an opportunity (especially in a context where there are competing platforms). In other words, MSPs might anticipate their disruption, and as a remedy, thrive to be part of the superseding platform. The discussion in Section 1.2 about the titans of the Internet acquiring startup companies on an industrial scale perfectly embodies this view. In that same vein, an ailing MSP—e.g., which might have been struggling to meet its objectives, unable to thwart a declining position, attempting in vain to fight emerging alternative platforms—might have become more keen on taking risks and thus on collaborating with its possible successor. The implantation of the iTunes platform in the music industry corroborates this hypothesis (Dolata, 2011).
Speculations from incumbents about the possible success of the innovative technology might engender keen interest in the technology. In parricular, the fear of becoming obsolete in the ecosystem should other participants adopt the technology—a situation somewhat akin to a Keynesian beauty contest (Wikipedia, 2016) and related to beliefs of higher order (Harsanyi, 1967)—might lead to a self-fulfilling prophecy, where a number of incumbents start to cooperate, paving the way to success for the disruptor. For instance, in the TiVo case, a number of incumbents (including “AOL Time Warner, DirecTV, CBS, NBC, and Disney”) started to cooperate soon after its inception: “Some incumbents were prompted to engage due to a desire to “keep tabs” on TiVo’s new technology as well as the threat it posed to them” (Ansari et al., 2016, p. 10).
From the Potential Disruptor’s Perspective
In the context of a multisided ecosystem consisting of various sides (themselves formed by a multiplicity of competitors), it appears that one strategy consists for the potential disruptor in gaining a critical mass of supporters as soon as possible; as quoted by Ansari et al. (“The Disruptor’s Dilemma: TiVo and the U.S. Television Ecosystem,” 2016, p. 11) in the TiVo case:
The long-term success of TiVo depends on its ability to quickly build a large subscriber base, integrate its functionality into a broad range of consumer electronics products, and develop new services and programming to enhance the TiVo service. In order to achieve these goals, the company has aggressively pursued strategic partnerships with cable and satellite network operators, television programmers, consumer electronics manufacturers, marketing support partners and suppliers of key components of the TiVo technology (Miller, 2000, p. 12).
The disruptor’s dilemma is, in essence, ingrained in the concept of coopetition (Brandenburger & Nalebuff, 2011). As maintained by Ansari et al. (Ansari et al., 2016), the potential disruptor must shrewdly promulgate information in order (i) to convince incumbents that they will find long-term benefits—“intertemporal coopetition”—(ii) to lessen the perceived threat—“dyadic coopetition”—and (iii) to smooth out friction between incumbents—“multilateral coopetition”—caused by one side’s favourable attitude towards the disruptor upsetting other sides (Hagiu, 2014). ■
The full paper is available on Apollo—The University of Cambridge’s Repository—and can be accessed at doi.org/10.17863/CAM.9207.
(Cover image: Lightspring / Shutterstock.com)
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The term collaborate might spur some controversies, for the term is arguably not adequate for, e.g., hostile takeovers or acquisitions to kill, a phenomenon often triggering heated polemics in the specialized press—e.g., Yahoo is notorious for this practice (Lapowsky, 2014). ↩
Hagiu (“Strategic Decisions for Multisided Platforms,” 2014) notes that the network effect alone does not necessarily constitute a strong barrier to entry, as illustrated by the Groupon example which saw its valuation plummet when investors realized the absence of switching costs. ↩
McCann and Vroom (“Pricing Response to Entry and Agglomeration Effects,” 2010) shows that the impact of new entrants on incumbents are not always negative, and can even be positive. However, their argumentation, largely based on the agglomeration effect (Potter & Watts, 2010), may certainly not hold when the new entrant is perceived as a potential disruptor. ↩
For example, for an MSP consisting of “one side as a profit center and the other as a loss leader” (Rochet & Tirole, 2003, p. 2), if the technology appeals to the former, but repel the latter. The TiVo case offers a concrete example (Ansari et al., 2016); while TV viewers may appreciate the option to skip advertisings, advertising companies would desert TV network platforms, which would then, in all likelihood, collapse. ↩
It is quasi-undeniable that this work had an impact not only in academia—according to Google Scholar the book has been cited in a ridiculously large number of publications—but also in the industry as well, e.g., Isaacson (Steve Jobs, 2011) reveals that Steve Jobs—who has taken over the helm of the soon-to-go-bankrupt Apple and made it the most valuable company in the world in terms of market cap—was influenced by it. ↩