Platform Ecosystems: Aligning Architecture, Governance, and Strategy (2014)
Part V. The Road Ahead
Chapter 12. Every Product Is a Platform Waiting to Happen
This chapter describes how managers in non-technology industries can actively apply this book’s core ideas to their business. We first summarize the broader implications of this book’s three key ideas for nurturing business ecosystems, emphasizing how they offer a biologically-inspired way of thinking of the design of economic entities. The emphasis in this chapter is on how managers in traditional, non-technology industries can use ideas in this book to spot opportunities to reconceive their bread-and-butter businesses as platform businesses.
business ecosystems; platform models; transformation; biologically inspired design
You can’t connect the dots looking forward; you can only connect them looking backwards.
In This Chapter
• Translating software ecosystem ideas to business ecosystems in nontechnology industries
• Summary of the three key ideas developed in this book:
• Migration from product and service business models to business ecosystems
• How ecosystem evolution drives survival
• How interlocking of ecosystem architecture and governance helps orchestrate ecosystem evolution
Platform thinking predates by almost a century the likes of Amazon, Google, and eBay, which were built natively around the idea. It was instrumental in the rise of General Motors over Ford in the 1920s, Black and Decker’s dominance in powered hand tools beginning in the 1930s, and the rise of Komatsu over the market leader Caterpillar in the heavy machinery industry in the 1960s. General Motors, for example, leveraged a common set of base components across multiple models of cars, creating a product platform that could be used as the foundation for cost-effectively creating different cars targeted at different customer segments. This was unlike Ford, whose founder famously said that a customer could have any Ford she wanted as long as it was black. While platform thinking is not new, what is new is that platform-centric business models are becoming the engines of innovation across a variety of unexpected industries such as autos, healthcare, publishing, services, manufacturing, and consumer goods. This chapter describes how managers in just about any industry can actively apply this book’s core ideas to their business instead of remaining helpless bystanders in a coming metamorphosis.
In this chapter, we summarize the broader implications of this book’s key ideas for firms in industries outside the software industry. Platform-based business models are less an organizing logic and more a biologically inspired way of thinking of the design of economic entities. The emphasis in this chapter is on how managers in traditional, nontechnology industries can use ideas in this book to spot opportunities to reconceive their bread-and-butter businesses as platform businesses. Given the broader focus of this chapter, we therefore refer to business ecosystems rather than just platform ecosystems, complementors rather than app developers, customers rather than end-users, business process architecture, product, and service architecture rather than software architecture, and the ecosystem orchestrator rather than platform owner. Table 12.1 summarizes the broadened application of this book’s core themes.
Translating Software Platform Concepts to Broader Business Ecosystems
Focus of This Book
Software platform ecosystem
Business process and product/service architecture
Figure 12.1 summarizes the book’s three key ideas extrapolated to business ecosystems. This book’s introduction previewed the three ideas on an index card. These are the crux of this book’s message and are recapped next in their broader context.
FIGURE 12.1 A recap of the three key ideas developed in this book.
12.1 Idea 1: migration to ecosystem competition
The emergence of business ecosystems is changing the first rule of competition that firms compete with rival firms. Competition, in a growing variety of industries, is increasingly migrating to being among rival ecosystems rather than among rival products or services. After this transition, a good product or service without a compelling ecosystem has no shot in the market. The industry-agnostic migration toward business ecosystems is enabled and fueled by the confluence of (1) digitization as well as the growing software intensity of products, services, and business processes across industries and (2) the ubiquity of cheap and fast Internet-based networks. Although these drivers appear to be mere curiosities in isolation, their confluence is creating a perfect storm. Digitization allows products and services to be converted into bits of digital information and ubiquitous connectivity wipes out the costs of acquiring and delivering them from just about any location. This increasingly allows a market offering—product or service—to be pieced together using globally dispersed components and then delivered digitally just about anywhere.
The drivers that are hastening the emergence of business ecosystems are identical to those driving software platforms: the growing costs of innovation coupled with shorter lead times to capitalize on them (a new mass-market prescription drug, for example, costs $900 million in research and development before it hits the shelves), uncertainty in markets composed of demanding customers with diverse needs, pressures to focus on a few core activities that a firm can be really good at, and the rising knowledge content of industries.
12.1.1 Business ecosystems in nontechnology industries
Although the shift toward business ecosystems is most visible in the technology industries, it is a sign across a plethora of nontechnology industries of a looming new blueprint for industrial organization that blends the disciplining power of markets with the partitioning of innovation work among thousands of outside complementors. The key property of business ecosystems is the presence of a central firm—the orchestrator—and a vast network of independent complementors collaboratively creating valuable complements driven by pure self-interest. The pursuit of self-interest by business ecosystem participants is the source of the competitive advantage of business ecosystems. A software system, independent of industry, is often the vehicle that enables large-scale coordination among these ecosystem participants. Even if you are not in the information technology business, it is dangerous to overlook platform thinking; even shoe makers (Nike), retailers (Sears), and sports networks (ESPN) now offer their own APIs.
While this book does not imply that every industry will or should become ecosystem-based, the shift is broader than one might initially realize from casual observation. Industries that have ecosystem potential include telecommunications, publishing, manufacturing, electrical equipment, consumer goods, chemicals, education, healthcare (e.g., health management organizations), textile, credit card businesses, banking, job search services, real estate brokerage, travel, gaming, pharmaceuticals and genetics research, food and beverage, and automotives (including electric vehicles). Consider a few examples of fledgling business ecosystems emerging—but yet to develop into true multisided ecosystems—outside the technology industries. Many of these are examples of crowdsourcing mechanisms and distributed innovation systems that are not yet truly platforms but have the potential to become platforms. (A true platform must have at least two sides (i.e., two distinct groups), such as complementors and consumers, who interact with each other through the platform.)
• Product and service innovation. Starbucks uses its MyStarbucksIdea crowdsourcing site to have its customers generate ideas for new products for the company. Several hundred ideas—all generated by Starbucks’ customers—have become highly successful products. For example, one customer wanted Starbucks to make ice cubes out of coffee so frozen drinks don’t get diluted as they melt. About 10,000 customers agreed. Another customer wanted something to plug the hole on the top of disposable coffee cups to minimize spilling while driving (it is common in the United States to drink coffee while driving). Starbucks introduced a simple “splash stick” suggested by a customer to fix precisely that problem, in turn boosting its drive-through sales of larger-sized drinks that were more prone to spilling in the car. In the Starbucks business ecosystem, customers are the primary source of ideas for new business process innovations, ideas to enhance the in-store experience, and new products. This approach also allows Starbucks to foster a strong sense of ownership of the ideas among its customers, almost guaranteeing their market success. Similar ecosystems have emerged for new product development (e.g., Quirky) and social innovation projects (OpenIDEO).
• The food and drinks industry. Atlanta-based Coca Cola Company introduced an innovative 125-flavor Coke FreeStyle soda dispenser in 2009 that centrally collects data on sales of over a hundred individual brands of Coke drinks. The system allows Coke to alter its mix of products multiple times a day at individual stores, by centrally analyzing real-time sales data from thousands of deployments around the United States. A computer embedded in the machine calculates with “surgical precision” the ingredients of over a hundred brands of Coke products, allowing real-time market experiments with dynamic pricing and promotions in even the most staid of industries (Levin, 2013). Coke also uses technology to cultivate direct relationships with its customers and to create a demand-driven supply chain. For example, its MyRewards app has 18 million users in over a hundred countries; it is tweaked and customized to the local markets within each of those countries. This allows Coke to tap into its markets’ long-tails.
• Matching human cognitive and physical labor in markets. ODesk matches its 3 million workers with expertise in a variety of fields with small employers, who contract by the job. Without ODesk in the middle the buyers and providers of professional services would not have found each other nor would they be able to cost-effectively transact. ODesk therefore lowers search and transaction costs on either side of its business ecosystem. Amazon Mechanical Turk similarly serves as a platform that provides a simpler way to efficiently trade human brain power for white-collar tasks. Similarly, other platforms such as Task Rabbit and Moving Help match buyers and providers of physical labor, contracted by the task. Similar platforms have also emerged specializing in cartographic mapping (Open Street Map), advertising design (Prova), navigation (Waze), and encyclopedia maintenance (Wikipedia and Quora).
• Production networks. Hong Kong-based Li & Fung orchestrates a network of over 8000 independent manufacturers in about 40 countries. It aggregates diverse capabilities of an ecosystem of loosely organized partners to produce garments and home goods sold under a variety of major independent and store brands in North America, South America, Europe, and Asia.
• Innovation and R&D networks. Proctor and Gamble uses a platform called Connect + Develop to co-create with its customers new product ideas in a variety of personal care and household supplies market segments. Similarly, Eli Lilly uses an open innovation platform called Innocentive to tap into a network of about 300,000 scientists in nearly 200 countries to solve difficult R&D challenges in the biosciences. Similar innovation platforms also have emerged for systems biology (SBV Improver) and genetics research (Merck Gene Index).
• Finance and venture capital. Crowd funding platforms such as Kickstarter increasingly provide capital for startups in industries as diverse as music, publishing, game development, and entertainment.
For all enablers in Figure 12.1, it is the injection of software into the value chain of diverse product and service businesses that most opens them up to ecosystem business models that share strong commonalities such as network effects, noncoercive lock-in, increasing returns to scale, and envelopment potential historically found only in the technology industries. More and more businesses and industries are either run on software or deliver some part of their end product as online services. Even the mature auto industry insreasingly relies on software to run engines, control safety features, and navigate cars. The growing digitization of products, services, and business processes coupled with ubiquity pushes the constraint of geography to the backstage. The emerging Internet of Things is only likely to accelerate this trend. Although estimates vary widely, about 20 billion mobile “things”—give or take a few billion—are expected to be connected to the Internet by 2020 (Jacobson et al., 2012, p. 2). So, it is safe to assume that more everyday objects in your own industry will be connected to the Internet. What ones does with them is limited by imagination, not technology. New technology often starts out as a solution in search of a problem. It remains a curiosity until someone figures out a killer application for it. This matching of technology with a problem usually comes from deep immersion in a problem domain. This is precisely why managers in the trenches of an industry—not IT professionals—are better positioned to spot ecosystem opportunities. But one must use a platform lens to see them: our lenses determine not only opportunities that we see but also those that we do not see.
But what about physical objects themselves? The doom-and-gloom predictions of the coming demise of manufacturing has been old news for about 200 years. Yet, no amount of digital bytes is going to replace my need for lunch, or a physical automobile, or a brick house. What is going to change eventually is how those physical objects are produced and delivered. Even the constrain of physical form is weakening with the advent of 3D printing (additive manufacturing), which is preparing ground for new models for digitized manufacturing and plausibly a substitute for shipping physical objects just like the MITS Altair and the ZX 81 computers created ground for the subsequent personal computing era. We are in the infancy of 3D printing, perhaps where the Internet was in 1974, but the advances in materials and objects possible to construct (chips, tools, guns, even human organs) appear to be reported with surprising frequency in The Economist, Business Week, New York Times, and in various scientific journals. It is important to recognize adoption dynamics for new innovations can vary markedly by industry; telephones took 50 years, cell phones 20 years, and the Internet about 40 years to enter the mainstream. This will make it increasingly unclear where one industry ends and another begins, further blur the boundary between manufacturing and services, and wipe the distinction between patents (which last 20 years) and copyrights (which last 70 years after the death of the creator).
Attempting to predict how fast new innovations such as 3D printing and Internet of Things will mature is likely as reliable as the weatherman’s prediction of rain in August in Georgia. It is better to assume that it will rain and accept that we can’t be sure just when. Carrying an umbrella just in case is akin to the idea of investing in real options in ecosystems (discussed in Chapter 8). It is also important to assume that digitization-driven convergence will ensure that truly disruptive substitutes in your own industry are more likely to come from nontraditional competitors and colliding ecosystems.
Companies in every industry must assume that a business ecosystem revolution is coming. You don’t want to be stuck on the wrong side of this disruption. Watch out for one-sided businesses moving to two sides, for potential “sides” with high search and transaction costs, and for segments of your prospective customer base whose needs are oversupplied or undersupplied by your existing industry business models. Companies that fail to recognize this ongoing shift from products to platforms in their own industries are on an irrecoverable death march. Managers—independent of their industry—should therefore grasp the ideas described in this book to recognize emergent opportunities to introduce business ecosystems in their industries. The Red Queen dynamic inherent in business ecosystems means that firms that get platform thinking will see outsized returns when ecosystems become viable in their industry or in adjacent industries.
12.1.2 Properties unique to business ecosystems
Common business sense and fundamental economics still hold true in business ecosystems, but three less visible properties become more pronounced. First, evolutionary dynamics that historically took 30–40 years can unfold in 5–7 years in platform markets. This requires unprecedented attention to designing ecosystems for evolvability and then orchestrating their evolution. Second, over the past 500 years of modern commerce, major disruptive innovations across every industry have almost always come from industry outsiders, typically new entrants who do not share the assumptions and legacies of industry incumbents. They trigger value erosion by introducing new, more effective, and legacy-free business designs. Every time you open your refrigerator now, I wish you’ll remember to curb the reaction of Minnesota’s thriving ice harvesters when they first saw the refrigerator—dismissal—when you see such a crude design first emerge in your own industry. Business ecosystems described in this book simply allow such disruption to be harnessed more productively and less destructively, offering opportunities to industry incumbents to earn their keep in the new order in their industries. They also allow incumbent firms to enter adjacent markets through strategies such as envelopment that are more abundant in platform markets. But such silver linings also have a dark cloud: Your competitors can come from unexpected places, and overfocusing on your existing rivals and existing customers can startle you when you face an envelopment attack. Ecosystems’ unique properties also expand managers’ competitive repertoire. Their multisidedness offers fertile opportunities to create hard-to-break network effects; value-driven, sustainable lock-ins; and the prospects of swallowing adjacent platforms as well as the dangers of being swallowed by one. They also offer a capacity to penetrate long-tail market microsegments typically inaccessible to product and service businesses. Third, the architecture of a business ecosystem is inseparable from how it ought to be governed. Their diversity can be their biggest liability but can become their biggest strength if the two interlock. A misfit between them imposes eventually fatal evolutionary penalties. This requires not just codesigning but also coevolving them as an ecosystem progresses through different stages in its lifecycle.
12.1.3 The three stooges of a business ecosystem
Building an ecosystem is not always an optimal strategy. They offer an advantage only when the effectiveness of the loosely coupled ecosystem exceeds that of a traditional organization. Three distinctive groups—the ecosystem orchestrator, complementors, and consumers—must be better off with a business ecosystem for it to be more attractive than a traditional business model. For ecosystem orchestrators, the value proposition is to innovate faster and cheaper around their core business than rivals, to reach potential untapped market segments, and to focus more deeply on their core competencies. For complementors, the value proposition is a scalable foundation for their own work that allows them to better focus on their own expertise and to reach a vastly expanded market. For consumers, the value proposition is benefiting from faster innovation, increased customization, and lower search and transaction costs.
12.2 Idea 2: ecosystem orchestration drives evolutionary survival
The second key idea developed in this book is that the evolution of a business ecosystem determines its prosperity and survival.
12.2.1 The Red Queen race to survive
Business ecosystems that survive evolutionary competition are not the strongest, largest, or the cleverest but the ones that are most adaptive to their environment. Four evolutionary principles must remain center stage: (1) The Red Queen effect (the pressure to keep accelerating ecosystem evolution just to keep up with rival ecosystems, or pay the penalty of irrecoverably falling behind), (2) emergence, (3) the Goldilocks rule of having a just-right degree of modularization in an ecosystem, and (4) the need to deliberately coevolve architecture and governance to maintain their interlocking as an ecosystem ages. Red Queen dynamics require recognizing that adapting to new rivals might require adaptations different from those that worked well against earlier rivals (Barnett and Hansen, 1996).
We used the analogy of a bathtub to describe ecosystem evolution, where the potential basis of competitive advantage is the difference between the inflow of innovations into the ecosystem and those that are copied by rivals (the outflow). This difference—a business ecosystem’s stock of innovations—can be increased by either speeding up the inflows or slowing down the outflows. Speed with which new resources are added is therefore the only reliable—but always temporary—source of competitive advantage. But only some resources in a business ecosystem—recognized using a four-part resource litmus test in Chapter 10—foster a competitive advantage. A resource must be valuable and rare to create a competitive advantage and be inimitable and nonsubstitutable to sustain it. Few resources will simultaneously meet all these conditions.
12.2.2 Business ecosystems thrive on orchestration
Business ecosystems can appear to be messy and disorderly. They differ markedly from their market potential, structure, and management approaches from product and service businesses. The constant challenge in business ecosystems is about how to attract and sustain the contributions of outsider complementors, while allowing the orchestrator firm to capture some portion of the new value created. An ecosystem orchestrator’s success depends heavily on mobilizing outside resources that are not owned by the firm, organically coordinating the work of complementors with which it does not have authority-based relationships, and learning to compete with rivals for not just customers but also for complementors. Delivering a compelling value proposition to both complementors and end-customers demands orchestration of a business ecosystem, not management, ownership, or control. This paradigm shift is a hard pill for most managers to swallow because it flies in the face of the tenets of ownership and control that have historically been instrumental to the success of traditional business. However, emergent innovation in business ecosystems cannot be planned—only facilitated—by an ecosystem orchestrator. This requires making it cheap and easy for complementors to contribute (the realm of architecture) and creating incentives to motivate them (the realm of governance). Unfortunately, thinking at the architecture–governance nexus does not come naturally to either IT professionals or to managers.
12.2.3 You cannot win the Grand Prix by watching the fuel gauge
Ecosystem evolution is a journey navigated with evolutionary metrics. Such metrics can be operational or strategic, and span the short, medium, and long term. They encompass resilience, scalability, and composability in the short term; stickiness, orchestrator synergy, and plasticity in the medium term; and envelopment, durability, and mutation in the long term. Among the nine evolutionary metrics described in this book, you’ll find curiously absent those familiar to managers (e.g., return on investment, efficiency, and effectiveness) and to software professionals (e.g., the schedule–budget–quality triad, code quality, and code complexity). We assume those to be necessary for short-term operational performance but insufficient to guide evolutionary survival. Metrics of evolution help steer evolution, separate signals from noise, and help both ecosystem orchestrators and complementors better manage tradeoffs. Three principles guide metrics choices: an outside-in vantage point, focus on the short term without obscuring the long term, and an acceptable cost–benefit balance.
12.2.4 Challenges in business ecosystems evolve over their lifecycle
Strategies that are appropriate for orchestrating ecosystems vary with their stage in their lifecycle. Fledgling business ecosystems must overcome the challenge of getting two sides onboard, each of which will find it attractive to join the ecosystem only when a critical mass already exists on the other side (the chicken-or-egg problem). It must also break the paralyzing dynamic where adopters on one side are waiting for others to join before they commit as well (the penguin problem). Governance fine-tuned to different stages of an ecosystem’s lifecycle can help overcome these startup problems. However, a business ecosystem rarely starts out as one; the preceding chapters emphasized that the most ecosystems started out as a product or service that was valuable to one side in and of itself. The silver lining in this observation is that existing products and services that are successful in the market will increasingly encounter opportunities to become a robust foundation for business ecosystems.
Once an ecosystem successfully takes off by overcoming these problems, it enters an evolutionary Red Queen race with rival ecosystems. A different set of challenges then replace the chicken-or-egg and the penguin problem. These include the need to balance complementor autonomy with ecosystem-wide integration (the seesaw problem), need for the work of complementors to be separable from the orchestrator but also to be easy to subsequently reintegrate it with the outputs of other ecosystem participants (the Humpty Dumpty problem), and the need to interlock ecosystem governance with ecosystem architecture (the mirroring principle). Architectures—of business processes and product and service designs—built around modularized, self-organizing principles are key.
12.3 Idea 3: Orchestration Requires Interlocking of Ecosystem Architecture and Governance
The third idea developed in this book is that interlocking an ecosystem’s architecture with ecosystem governance is key to orchestrating its evolution.
12.3.1 Architecture is an ecosystem’s DNA
Conventional coordination and control mechanisms that underpinned the success of many traditional businesses and their value chains can become the albatross of business ecosystems. They fall flat because business ecosystems are rarely conducive to conventional notions of authority-based control, because they have widely fragmented ownership, and because of their sheer scale. Architecture—of business processes, products, and services—is a business ecosystem’s DNA that imprints its evolvability, irreversibly preordaining the evolutionary trajectories open and closed to an ecosystem. Architecture instead must provide the blueprint for both partitioning innovation work across the many participants in a business ecosystem and integrating it. For this, architecture must accomplish two things: (1) Partition an ecosystem into an ecosystem core and autonomous complements that use it as a foundation for their own work and (2) allow these moving pieces to be rapidly and inexpensively pieced back together into a coherent product or service offering. Perfect ecosystem architectures—which exist only in theory—are simple, resilient, maintainable, and evolvable.
Tempered modularization endows such properties to architectures. Modularization is accomplished by decoupling the orchestrator’s work from its complementors’ work and then using stable and explicitly documented interfaces among them. The simple heuristic for partitioning an ecosystem is to keep low-variety, high-reusability functions and business processes with the ecosystem’s orchestrator and high-variety, low-reusability ones with the complementors. This distribution must evolve as an ecosystem ages.
Architectural modularization across a business ecosystem also creates the flexibility to cope with an unforeseeable future. It does this by potentially embedding six real options—the flexibility without the obligation to do something in the future—in ways that limit risks while preserving yet-unknown future opportunities. Such flexibility is of value only in the presence of technology and market uncertainty. This flexibility is exercised through five types of discrete evolutionary actions, which we call modular operators. A linear representation using modular operators compactly conveys its evolutionary trajectory.
12.3.2 Ecosystem governance is the catalyst for evolution
The old saying that you can drag a horse to the water but you can’t make him drink applies astutely well to ecosystems. Architecture, however, only decreases the structural complexity of business ecosystems; decreasing behavioral complexity requires thoughtful governance. Architectures conducive to running an ecosystem provide the means but not the motivation for outside complementors. Even the most thoughtful ecosystem architecture cannot nurture a vibrant ecosystem unless it is governed effectively. A business ecosystem’s architecture is useful as a coordination device only when everyone follows the same rules. Motivating complementors through incentives to co-opt them and enforcing compliance with architectural rules are the two key roles of ecosystem governance.
Governance is how the ecosystem orchestrator influences its ecosystem. The ideal governance structure is one that is simple, transparent, realistic, and fair. Governance has three dimensions: (1) who decides what across the ecosystem (decision rights), (2) how an ecosystem orchestrator controls ecosystem complementors (control mechanisms), and (3) pricing policies. Decision rights encompass strategic and implementation decisions about the ecosystem’s core and its complements. Control uses a mix of formal (gatekeeping, metrics, and process) control and informal (relational) control mechanisms. Pricing policies involve decisions about whether complementors or end-users are subsidized, for how long, whether the ecosystem orchestrator charges fees, and the pie-splitting structure. Such policies must match an ecosystem’s business model, its stage in its lifecycle, and its architecture.
12.3.3 Architecture and governance as the interlocking gears of an ecosystems’ evolutionary motor
The interlocking of architecture and governance shapes ecosystem-wide evolution in the short, medium, and long term. These evolutionary effects are manifested in their resilience, scalability, and composability in the short term; stickiness, synergy, and plasticity in the medium term; envelopment, competitive durability, and mutation in the long term. The motor of ecosystem evolution stalls if its two gears—architecture and governance—fall out of alignment. The reward for maintaining alignment is the potential for a Cambrian explosion of innovation across a business ecosystem. Fostering emergent innovations across the business ecosystem requires attention to a different set of evolutionary metrics spanning multiple time horizons and correcting emergent misalignments between ecosystem governance and architecture. Most innovations in business ecosystems emerge from their complementors’ selfish pursuit of self-interest and rarely from some grandiose vision of selfless collectivism. The tripartite design of ecosystem governance spanning decision rights allocation, control, and pricing by the orchestrator ensures that such pursuit of self-interest also furthers the interests of its ecosystem.
The fundamental premise of biologically inspired business ecosystems is as old as our species: Encouraging everyone to do what they can do best makes everyone better off.
1. Barnett W, Hansen M. The Red Queen in organizational evolution. Strateg Manag J. 1996;17(1):139–157.
2. Jacobson D, Brail G, Woods D. APIs: A Strategy Guide. Sebastopol, CA: O'Reilly; 2012.
3. Levin R. Driving the top line with technology: an interview with the CIO of Coca-Cola. McKinsey Q. 2013;(March):1–4.
*“To view the full reference list for the book, click here or see page 283.”
About the Author
Amrit Tiwana is a professor in the Terry College of Business at the University of Georgia. He has also held joint appointments in computer science and management departments, giving him a unique vantage point to author Platform Ecosystems. Professor Tiwana regularly advises in the United States, Europe, and Japan industry consortia, government agencies, and major technology companies such as IBM, UPS, NTT Japan, Fujitsu, Hitachi, Toshiba, Mitsui, Mitsubishi Electric, Sumitomo Steel, Kansai Electric, Sony, Eli Lilly & Company, Japan Electronics and IT Industry Association, and Finland’s INFORTE. His ongoing research involves Mozilla and Blackberry developer communities.
Platform Ecosystems builds on recent research developments in information systems, software engineering, and business strategy. Professor Tiwana has been a direct contributor to research in peer-reviewed journals in all three fields. He serves on the editorial boards of leading information systems journals (such as Information Systems Research, Journal of Management Information Systems, and IEEE Transactions on Engineering Management) and business strategy journals (Strategic Management Journal). Dr. Tiwana is the best-selling author of The Knowledge Management Toolkit (Prentice Hall), which is translated into several foreign languages, widely used in business schools, and has continuously been in print since it first appeared 15 years ago. He received his doctorate from Georgia State University. Home Page: PragmaticTheory.com
1. Adner R. The Wide Lens. New York: Portfolio; 2012.
2. Adner R, Kapoor R. Value creation in innovation ecosystems: how the structure of technological interdependence affects firm performance in new technology generations. Strateg Manag J. 2010;31(3):306–333.
3. Adner R, Levinthal D. Demand heterogeneity and technology evolution: implications for product and process innovation. Manag Sci. 2001;47(5):611–628.
4. Adner R, Snow D. Bold retreat. Harv Bus Rev. 2010;88(March):1–7.
5. Agarwal R, Bayus B. The market evolution and sales takeoff of product innovations. Manag Sci. 2002;48(8):1024–1041.
6. Anderson S, Dekker H. Management control for market transactions. Manag Sci. 2005;51(12):1734–1752.
7. Anderson P, Tushman M. Technological discontinuities and dominant designs: a cyclical model of technological change. Adm Sci Q. 1990;35:604–633.
8. Andreessen M. Why software is eating the world. Wall Street J. 2011; August 20th. http://online.wsj.com/article/SB10001424053111903480904576512250915629460.html.
9. Ang S, Straub D. Production and transaction economies and IS outsourcing: a study of the U.S banking industry. MIS Q. 1998;22(4):535–552.
10. Argyres N, Bigelow L. Innovation, modularity, and vertical deintegration: evidence from the early US auto industry. Organ Sci. 2010;21(4):842–853.
11. Armstrong M. Competition in two-sided markets. RAND J Econ. 2006;37(3):668–691.
12. Armstrong M, Wright J. Two-sided markets, competitive bottlenecks and exclusive contracts. Econ Theory. 2007;32(2):353–380.
13. Arthur B. The Nature of Technology. New York: Free Press; 2009.
14. Arthur B. The second economy. McKinsey Q. 2011;(October):1–9.
15. Athey S, Roberts J. Organizational design: decision rights and incentive contracts. Am Econ Rev. 2001;91(2):200–205.
16. Baldwin C. Where do transactions come from? Modularity, transactions, and the boundaries of firms. Ind Corp Change. 2008;17(1):155–195.
17. Baldwin C, Clark K. Design Rules: The Power of Modularity. Cambridge, MA: MIT Press; 2000.
18. Baldwin C, Clark K. The architecture of participation: does code architecture mitigate free riding in the open source development model? Manag Sci. 2006;52(7):1116–1127.
19. Baldwin C, von Hippel E. Modeling a paradigm shift: from producer innovation to user and open collaborative innovation. Organ Sci. 2011;22(6):1399–1417.
20. Baldwin C, Woodard J. The architecture of platforms: a unified view. In: Gawer A, ed. Platforms, Markets and Innovation. Cheltenham, UK: Edward Elgar; 2009:19–44.
21. Banker RD, Kauffman RJ, Kumar R. An empirical test of object-based output measurement metrics in a computer aided software engineering (CASE) environment. J Manag Inf Syst. 1992;8(3):127–150.
22. Banker RD, Kauffman RJ, Wright C, Zweig D. Automating output size and reuse metrics in a repository-based computer-aided software engineering (CASE) environment. IEEE Trans Softw Eng. 1994;20(3):169–187.
23. Barnett W, Hansen M. The Red Queen in organizational evolution. Strateg Manag J. 1996;17(1):139–157.
24. Bernheim B, Whinston M. Incomplete contracts and strategic ambiguity. Am Econ Rev. 1998;88(4):902–932.
25. Bester H, Krähmer D. Delegation and incentives. RAND J Econ. 2008;39(3):664–682.
26. Bollen N. Real options and product life cycles. Manage Sci. 1999;45(5):670–684.
27. Boudreau K. Open platform strategies and innovation: granting access vs devolving control. Manag Sci. 2010;56(10):1849–1872.
28. Bradach J, Eccles R. Price, authority, and trust: from ideal types to plural forms. Annu Rev Sociol. 1989;15(1):97–118.
29. Brusoni S, Prencipe A. Making design rules: a multidomain perspective. Organ Sci. 2006;17(2):179–189.
30. Burrows P. How apple feeds its army of app makers. BusinessWeek 2011;(June 13–19):39–40.
31. Caillaud B, Jullien B. Chicken and egg: competition among intermediation service providers. RAND J Econ. 2003;34(2):309–328.
32. Cardinal LB. Technological innovation in the pharmaceutical industry: the use of organizational control in managing research and development. Organ Sci. 2001;12(1):19–36.
33. Chui M, Loffler M, Roberts R. The Internet of things. McKinsey Q. 2010;2:1–9.
34. Coff RW. When competitive advantage doesn't lead to performance: the resource-based view and stakeholder bargaining power. Organ Sci. 1999;10(2):119–133.
35. Courtney H. 20/20 Foresight. Boston: Harvard; 2001.
36. de Weck O, Roos D, Magee C. Engineering Systems. Cambridge, MA: MIT Press; 2011.
37. Dekleva S, Drehmer D. Measuring software engineering evolution: a Rasch calibration. Inf Syst Res. 1997;8(1):95–104.
38. Dhanaraj C, Parkhe A. Orchestrating innovation networks. Acad Manag Rev. 2006;31(3):659–669.
39. Dougherty D. A practice-centered model of organizational renewal through product innovation. Strateg Manag J. 1992;13(1):77–92.
40. Dougherty D, Dunne D. Organizing ecologies of complex innovation. Organ Sci. 2011;22(5):1214–1223.
41. Economist. Bigger and better than Wi-Fi. Economist 2010a; www.economist.com/node/17647517; (accessed 9.08.2012).
42. Economist. Power from thin air. Economist 2010b; www.economist.com/node/16295708; (accessed 25.10.2012).
43. Economist. Make your own angry birds. Economist 2012a;(July 21):55.
44. Economist. Outsourcing is so last year. Economist 2012b; www.economist.com/blogs/babbage/2012/05/future-customer-support; (accessed 12/6/2012).
45. Eick S, Graves T, Karr A, Marron J, Mockus A. Does code decay? Assessing evidence from change management data. IEEE Trans Softw Eng. 2001;27(1):1–12.
46. Eisenhardt K. Agency theory: an assessment and review. Acad Manag Rev. 1989;14(1):57–74.
47. Eisenmann T, Parker G, van Alstyne M. Strategies for two-sided markets. Harv Bus Rev. 2006;84(10):1–10.
48. Eisenmann T, Parker G, Van Alstyne M. Platform envelopment. Strateg Manag J. 2011;32(12):1270–1285.
49. Ethiraj S, Levinthal D. Bounded rationality and the search for organizational architecture: an evolutionary perspective on the design of organizations and their evolvability. Adm Sci Q. 2004a;49(3):404–437.
50. Ethiraj S, Levinthal D. Modularity and innovation in complex systems. Manag Sci. 2004b;50(2):159–173.
51. Ethiraj S, Levinthal D, Roy R. The dual role of modularity: innovation and imitation. Manag Sci. 2008;54(5):939–955.
52. Evans D, Schmalensee R. Catalyst Code. Boston, MA: Harvard Press; 2007.
53. Evans D, Hagiu A, Schmalensee R. Invisible Engines: How Software Platforms Drive Innovation and Transform Industries. Cambridge, MA: MIT Press; 2006.
54. Farrell J, Saloner G. Standardization, compatibility, and innovation. RAND J Econ. 1985;16(1):70–83.
55. Farrell J, Saloner G. Installed base and compatibility: innovation, product preannouncement, and predation. Am Econ Rev. 1986;76:940–955.
56. Farrell J, Saloner G. Converters, compatibility, and the control of interfaces. J Ind Econ. 1992;XL(March):9–35.
57. Fenton N, Neil M. Software metrics: successes, failures and new directions. J Syst Softw. 1999;47(3):149–157.
58. Fichman R. Real options and IT platform adoption: implications for theory and practice. Inf Syst Res. 2004;15(2):132–154.
59. Fichman R, Keil M, Tiwana A. Beyond valuation: real options thinking in IT project management. Calif Manage Rev. 2005;47(2):74–96.
60. Fichman R, Moses S. An incremental process for software implementation. Sloan Manage Rev (Winter). 1999;39–52.
61. Gamba A, Fusari N. Valuing modularity as a real option. Manag Sci. 2009;55(11):1877–1896.
62. Garud R, Kumaraswamy A. Changing competitive dynamics in network industries: an exploration of sun microsystems' open systems strategy. Strateg Manag J. 1993;14:351–369.
63. Gawer A, Cusumano M. How companies become platform leaders. Sloan Manag Rev. 2008;49(2):28–35.
64. Gulati R, Singh H. The architecture of cooperation: managing coordination costs and appropriation concerns in strategic alliances. Adm Sci Q. 1998;43:781–814.
65. Hagui A. Pricing and commitment by two-sided platforms. Rand J Econ. 2006;37(3):720–737.
66. Heller M. The Gridlock Economy: How Too Much Ownership Wrecks Markets, Stops Innovation, and Costs Lives. New York: Basic Books; 2008.
67. Henderson R, Clark K. Architectural innovation: the reconfiguration of existing product technologies and the failure of established firms. Adm Sci Q. 1990;35:9–30.
68. Hibbs C, Jewett S, Sullivan M. The Art of Lean Software Development. Sebastopol, CA: O'Reilly; 2009.
69. Hilhorst C, Ribbers P, van Heck E, Smits M. Using Dempster-Shafer theory and real options theory to assess competing strategies for implementing IT infrastructures: a case study. Decis Support Syst. 2008;46(1):344–355.
70. Hoetker G. How much you know versus how well I know you: selecting a supplier for a technically innovative component. Strateg Manag J. 2005;26:75–96.
71. Hoetker G. Do modular products lead to modular organizations? Strateg Manag J. 2006;27(6):501–518.
72. Huchzermeier A, Loch CH. Project management under risk: using the real options approach to evaluate flexibility in R&D. Manage Sci. 2001;47(1):85–101.
73. Iansiti M, Levien R. Keystone Advantage: What the New Dynamics of Business Ecosystems Mean for Strategy, Innovation, and Sustainability. Boston, MA: Harvard Business Press; 2004.
74. Jackson N. Infographic: the Internet of things. Atlantic 2011; www.theatlantic.com/technology/archive/2011/07/infographic-the-internet-of-things/242073/; (accessed 21.9.2012).
75. Jacobson D, Brail G, Woods D. APIs: A Strategy Guide. Sebastopol, CA: O'Reilly; 2012.
76. Jensen M, Meckling W. Specific and general knowledge and organizational structure. In: Werin L, Wijkander H, eds. Contract Economics. Oxford, UK: Blackwell; 1992:251–274.
77. Kamel R. Effect of modularity on system evolution. IEEE Softw. 1987;(January):48–54.
78. Kapoor R, Adner R. What firms make vs what they know: how firms’ production and knowledge boundaries affect competitive advantage in the face of technological change. Organ Sci. 2012;23(5):1227–1248. doi 10.1287/orsc.1110.0686.
79. Katz M, Shapiro C. Systems competition and network effects. J Econ Perspect. 1994;8(2):93–115.
80. Kim S, Mcfarland R, Kwon S, Son S, Griffith D. Understanding governance decisions in a partially integrated channel: a contingent alignment framework. J Market Res. 2011;48(3):603–616.
81. Kirsch LJ. Portfolios of control modes and IS project management. Inf Syst Res. 1997;8(3):215–239.
82. Langlois R. Modularity in technology and organization. J Econ Behav Organ. 2002;49:19–37.
83. Langlois R, Garzarelli G. Of hackers and hairdressers: modularity and the organizational economics of open-source collaboration. Ind Innov. 2008;15(2):125–143.
84. Levin R. Driving the top line with technology: an interview with the CIO of Coca-Cola. McKinsey Q. 2013;(March):1–4.
85. MacCormack A, Rusnak J, Baldwin C. Exploring the structure of complex software designs: an empirical study of open source and proprietary code. Manag Sci. 2006;52(7):1015–1030.
86. Macher J, Boerner C. Technological development at the boundaries of the firm: a knowledge-based examination in drug development. Strateg Manag J. 2012;33(9):1016–1036.
87. McGrath R. A real options logic for initiating technology positioning investments. Acad Manage Rev. 1997;22(4):974–996.
88. Meadows D. Thinking in Systems. White River Junction, VT: Chelsea Green Publishing; 2008.
89. Messerschmitt D, Szyperski C. Software Ecosystem. Cambridge, MA: MIT Press; 2003.
90. Meyer M, Selinger R. Product platforms in software development. Sloan Manag Rev. 1998;40(1):61–74.
91. Mezias JM, Mezias SJ. Resource partitioning, the founding of specialist firms, and innovation: the American feature film industry, 1912–1929. Organ Sci. 2000;11(3):306–322.
92. Moneverde K. Technical dialog as an incentive for vertical integration in the semiconductor industry. Manag Sci. 1995;41(10):1624–1638.
93. Monteverde K, Teece D. Supplier switching costs and vertical integration in the automobile industry. Bell J Econ. 1982;13(1):206–213.
94. Nault B. Information technology and organization design: locating decisions and information. Manag Sci. 1998;44(10):1321–1335.
95. Ostrovsky M, Schwarz M. Adoption of standards under uncertainty. RAND J Econ. 2005;36(4):816–832.
96. Ouchi W. A conceptual framework for the design of organizational control mechanisms. Manag Sci. 1979;25(9):833–848.
97. Ouchi W. Markets bureaucracies and clans. Adm Sci Q. 1980;25(1):129–141.
98. Parker G, Van Alstyne M. Two-sided network effects: a theory of information product design. Manag Sci. 2005;51(10):1494–1504.
99. Parnas D. On the criteria to be used in decomposing systems into modules. Commun ACM. 1972;15(9):1053–1058.
100. Parnas D. Designing software for ease of extension and contraction. IEEE Trans Softw Eng. 1979;5(2):128–137.
101. Parnas D, Clements P, Weiss D. The modular structure of complex systems. IEEE Trans Softw Eng. 1985;11(3):259–266.
102. Penrose E. The Theory of the Growth of the Firm. Oxford: Basil Blackwell; 1959.
103. Pil F, Cohen C. Modularity: implications for imitation, innovation, and sustained advantage. Acad Manag Rev. 2006;31(4):995–1011.
104. Prentice C. The washers and dryers that talk back. BusinessWeek 2010;(August 9):23–24.
105. Rochet J, Tirole J. Platform competition in two-sided markets. J Eur Econ Assoc. 2003;1(4):990–1029.
106. Rochet J, Tirole J. Two-sided markets: a progress report. RAND J Econ. 2006;37(3):645–667.
107. Rogers E. Diffusion of Innovations. fourth ed. New York: Free Press; 1995.
108. Rohfls J. Bandwagon Effects in High-Technology Industries. Cambridge, MA: MIT Press; 2003.
109. Rysman M. The economics of two-sided markets. J Econ Perspect. 2009;23(3):125–143.
110. Saloner G, Shepard A. Adoption of technologies with network effects: an empirical examination of the adoption of automated teller machines. RAND J Econ. 1995;26(3):479–501.
111. Sanchez R. Strategic flexibility in product competition. Strateg Manag J. 1995;16:135–159.
112. Sanchez R, Mahoney J. Modularity, flexibility, and knowledge management in product organization and design. Strateg Manag J. 1996;17(1):63–76.
113. Schilling M. Toward a general modular systems theory and its application to interfirm product modularity. Acad Manag Rev. 2000;25(2):312–334.
114. Schilling M. Strategic Management of Technological Innovation. Boston, MA: McGraw Hill; 2005.
115. Schrage M. Serious Play. Boston, MA: Harvard Business School Press; 2000.
116. Shy O. Economics of Network Industries. Cambridge, UK: Cambridge University Press; 2001.
117. Simon H. The architecture of complexity. Proc Am Philos Soc. 1962;106(6):467–482.
118. Simon H. Rationality as process and as product of thought. Am Econ Rev. 1978;68(2):1–16.
119. Simon H. Near decomposability and the speed of evolution. Ind Corp Change. 2002;11(3):587–599.
120. Sosa M, Eppinger S, Rowles C. The misalignment of product architecture and organizational structure in complex product development. Manag Sci. 2004;50(12):1674–1689.
121. Strietfeld D. As boom lures app creators, tough part is making a living. New York Times 2012; In: www.nytimes.com/2012/11/18/business/as-boom-lures-app-creators-tough-part-is-making-a-living.html.
122. Tadelis S. Complexity, flexibility, and the make-or-buy decision. Am Econ Rev. 2002;92(1):433–437.
123. Thompson A. Good morning, this is your coffeemaker calling. BusinessWeek 2010;(September 20):41–42.
124. Tiwana A. Does interfirm modularity complement ignorance? A field study of software outsourcing alliances. Strateg Manag J. 2008a;29(11):1241–1252.
125. Tiwana A. Does technological modularity substitute for control? A study of alliance performance in software outsourcing. Strateg Manag J. 2008b;29(7):769–780.
126. Tiwana A. Governance-knowledge fit in systems development projects. Inf Syst Res. 2009;20(2):180–197.
127. Tiwana A, Bush A. A comparison of transaction cost, agency, and knowledge-based predictors of IT outsourcing decisions. J Manag Inf Syst. 2007;24(1):263–305.
128. Tiwana A, Keil M. Does peripheral knowledge complement control? An empirical test in technology outsourcing alliances. Strateg Manag J. 2007;28(6):623–634.
129. Tiwana A, Konsynski B. Complementarities between organizational IT architecture and governance structure. Inf Syst Res. 2010;21(2):288–304.
130. Tiwana A, Konsynski B, Bush A. Platform evolution: coevolution of architecture, governance, and environmental dynamics. Inf Syst Res. 2010;21(4):675–687.
131. Trigeorgis L. The nature of option interactions and the valuation of investments with multiple real options. J Financ Quant Anal. 1993;28(1):1–20.
132. Utterback J. Mastering the Dynamics of Innovation. Boston, MA: Harvard Press; 1996.
133. van Schewick B. Internet Architecture and Innovation. Cambridge, MA: MIT Press; 2012.
134. Vazquez X. Allocating decision rights on the shop floor: a perspective from transaction cost economics and organization theory. Organ Sci. 2004;15(4):463–480.
135. von Hippel E. Lead users: a source of novel product concepts. Manag Sci. 1986;32(7):791–805.
136. von Hippel E. Sources of Innovation. Cambridge, MA: MIT Press; 1988.
137. Wernerfelt B. A resource-based view of the firm. Strateg Manag J. 1984;5:171–180.
138. Williamson O. The Economic Institutions of Capitalism. New York, NY: Free Press; 1987.
139. Williamson OE. Comparative economic organization: the analysis of discrete structural alternatives. Adm Sci Q. 1991;36(2):269–296.
140. Williamson O. Strategy research: governance and competence perspectives. Strateg Manag J. 1999;20:1087–1108.
141. Williamson O. Transaction cost economics: the natural progression. Am Econ Rev. 2010;100(3):673–690.
142. Williamson P, De Meyer A. Ecosystem advantage: how to successfully harness the power of partners. Calif Manag Rev. 2012;55(1):24–46.
143. Young-Ybarra C, Wiersema M. Strategic flexibility in information technology alliances: the influence of transaction cost economics and social exchange theory. Organ Sci. 1999;10(4):439–459.
144. Zook C, Allen J. Growth outside the core. Harv Bus Rev. 2003;81(December):2–9.
145. Zweben SH, Edwards SH, Weide BW, Hollingsworth JE. The effects of layering and encapsulation on software development cost and quality. IEEE Trans Softw Eng. 1995;21(3):200–208.