Recent studies on design management have helped us to better comprehend how companies can apply design to get closer to users and to better understand their needs; this is an approach usually referred to as user-centered design. Yet analysis of design-intensive manufacturers such as Alessi, Artemide, and other leading Italian firms shows that their innovation process hardly starts from a close observation of user needs and requirements. Rather, they follow a different strategy called design-driven innovation in this paper. This strategy aims at radically change the emotional and symbolic content of products (i.e., their meanings and languages) through a deep understanding of broader changes in society, culture, and technology. Rather than being pulled by user requirements, design-driven innovation is pushed by a firm's vision about possible new product meanings and languages that could diffuse in society. Design-driven innovation, which plays such a crucial role in the innovation strategy of design intensive firms, has still remained largely unexplored. This paper aims at providing a possible direction to fill this empty spot in innovation management literature. In particular, first it proposes a metamodel for investigating design-driven innovation in which a manufacturer's ability to understand, anticipate, and influence emergence of new product meanings is built by relying on external interpreters (e.g., designers, firms in other industries, suppliers, schools, artists, the media) that share its same problem: to understand the evolution of sociocultural models and to propose new visions and meanings. Managing designdriven innovation therefore implies managing the interaction with these interpreters to access, share, and internalize knowledge on product languages and to influence shifts in sociocultural models. Second, the paper proposes a possible direction to scientifically investigate the management of this networked and collective research process. In particular, it shows that the process of creating breakthrough innovations of meanings partially mirrors the process of creating breakthrough technological innovations. Studies of design-driven innovation may therefore benefit significantly from the existing body of theories in the field of technology management. The analysis of the analogies between these two types of radical innovations (i.e., meanings and technologies) allows a research agenda to be set for exploration of design-driven innovation, a relevant as well as underinvestigated phenomenon.Ã This paper is the result of a decade of research on design-driven innovation, which benefited from collaborations and interactions with several scholars. For their insightful inspirations and comments, I gratefully thank Tommaso Buganza, Claudio Dell'Era, and Alessio Marchesi (at the School of Management of Politecnico di Milano);

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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.

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The growing social and regulatory concern for the environment is leading an increasing number of companies to considering ‘green’ issues as a major source of strategic change. In particular, this trend has major and complex implications on the technological strategy of a company and on its product innovations. Indeed, most authors acknowledge that eco‐efficiency will be one of the major challenges for R&D practice and theory in the next decade. Unfortunately, studies usually focus on large corporations. There is a debate as to whether this factor will affect R&D practices and product innovation in small and medium enterprises (SMEs). A superficial glimpse at the problem could lead one to think that SMEs will not be major green innovators, especially as far as product technologies are concerned, and that they will simply try to comply with environmental regulations (mainly on production processes). This paper shows that ‘green’ product innovation may occur and may also have strategic implications in SMEs. Starting from the analysis of four selected case studies and using a Precursors Events methodology, this paper illustrates why ‘green’ product innovation cannot be considered a marginal issue for most SMEs, even for those that are not directly affected by environmental regulations. Hence, the paper suggests a contingent framework to support SMEs in the analysis of the drivers of ‘green’ product innovation and in the choice of a proper R&D strategy that explicitly accounts for the eco‐efficiency of product technologies.

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.

Nowadays, design is recognized as a strategic resource. Customers are increasingly paying attention to the aesthetic, symbolic, and emotional value of products, a value that is conveyed by the design language-that is, the combination of signs (e.g., form, colors, materials) that gives meaning to a product. As a consequence firms are devoting increasing efforts to define a proper strategy for the design language of their products. An empirical analysis was conducted on the product language strategies in the Italian furniture industry; in particular, the present article explores the relationship between innovation and variety of product languages. Companies are usually faced by two major strategic decisions. The first one concerns the innovation of product languages: To what extent should a firm proactively propose new design languages or, rather, should adopt a reactive strategy by rapidly adopting new languages as they emerge in the market? The second decision concerns the variety and heterogeneity of languages in their product range. Should a firm propose a single product language to communicate a precise identity, or should it explore different product languages? Of course, the two strategic decisions-innovativeness and variety of product languages-are closed connected. Analyzing more than 2.000 products launched by 210 firms, the present article explores how the variety of product languages is approached in the strategy of innovators and imitators. The empirical results illustrate an inverse relationship between innovativeness and heterogeneity of product signs and languages. Contrary to what is expected, innovators have lower heterogeneity of product languages. They tend to be strongly proactive and limit experimentations of new languages in the market. Imitators, instead-which would be expected to have low variety since they can invest only in languages that have been proven successful in the market-tend on the contrary to have higher product variety. Eventually, by having lower investments in research on trends of sociocultural models, they miss the capability to interpret the complex evolution of products signs and languages in the market. Strategic decisions on innovativeness and variety of product languages are therefore interrelated; counterintuitively companies should carefully analyze these decisions jointly.

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