This paper examines the relative importance of platform quality, indirect network effects, and consumer expectations on the success of entrants in platform‐based markets. We develop a theoretical model and find that an entrant's success depends on the strength of indirect network effects and on the consumers' discount factor for future applications. We then illustrate the model's applicability by examining Xbox's entry into the video game industry. We find that Xbox had a small quality advantage over the incumbent, PlayStation 2, and the strength of indirect network effects and the consumers' discount factor, while statistically significant, fall in the region where PlayStation 2's position is unsustainable. Copyright © 2011 John Wiley & Sons, Ltd.
U ncertain and dynamic environments present fundamental challenges to managers of the new product development process. Between successive product generations, significant evolutions can occur in both the customer needs a product must address and the technologies it employs to satisfy these needs. Even within a single development project, firms must respond to new information, or risk developing a product that is obsolete the day it is launched. This paper examines the characteristics of an effective development process in one such environment-the Internet software industry. Using data on 29 completed development projects we show that in this industry, constructs that support a more flexible development process are associated with better-performing projects. This flexible process is characterized by the ability to generate and respond to new information for a longer proportion of a development cycle. The constructs that support such a process are greater investments in architectural design, earlier feedback on product performance from the market, and the use of a development team with greater amounts of "generational" experience. Our results suggest that investments in architectural design play a dual role in a flexible process: First, through the need to select an architecture that maximizes product performance and, second, through the need to select an architecture that facilitates development process flexibility. We provide examples from our fieldwork to support this view.
At the heart of any innovation process lies a fundamental practice: the way people create ideas and solve problems. This "decision making" side of innovation is what scholars and practitioners refer to as "design." Decisions in innovation processes have so far been taken by humans. What happens when they can be substituted by machines? Artificial Intelligence (AI) brings data and algorithms to the core of the innovation processes. What are the implications of this diffusion of AI for our understanding of design and innovation? Is AI just another digital technology that, akin to many others, will not significantly question what we know about design? Or will it create transformations in design that current theoretical frameworks cannot capture?This paper proposes a framework for understanding the design and innovation in the age of AI. We discuss the implications for design and innovation theory. Specifically, we observe that, as creative problem-solving is significantly conducted by algorithms, human design increasingly becomes an activity of sensemaking, that is, understanding which problems should or could be addressed. This shift in focus calls for the new theories and brings design closer to leadership, which is, inherently, an activity of sensemaking.Our insights are derived from and illustrated with two cases at the frontier of AI-Netflix and Airbnb (complemented with analyses of Microsoft and Tesla)-which point to two directions for the evolution of design and innovation in firms. First, AI enables an organization to overcome many past limitations of human-intensive design processes, by improving the scalability of the process, broadening its scope across traditional boundaries, and enhancing its ability to learn and adapt on the fly. Second, and maybe more surprising, while removing these limitations, AI also appears to deeply enact several popular design principles. AI thus reinforces the principles of Design Thinking, namely: being people-centered, abductive, and iterative. In fact, AI enables the creation of solutions that are more highly user centered than human-based approaches (i.e., to an extreme level of granularity, designed for every single person); that are potentially more creative; and that are continuously updated through learning iterations across the entire product life cycle.In sum, while AI does not undermine the basic principles of design, it profoundly changes the practice of design. Problem-solving tasks, traditionally carried out by designers, are now automated into learning loops that operate without limitations of volume and speed. The algorithms embedded in these loops think in a radically different way than a designer who handles the complex problems holistically with a systemic perspective. Algorithms instead handle complexity through very simple tasks, which are iterated continuously. This paper discusses the implications of these insights for design and innovation management scholars and practitioners.
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