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Kaplan and Vakili-Emergence of Nanotech-April 7 2011

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Kaplan and Vakili-Emergence of Nanotech-April 7 2011 Patterns in the Emergence of Nanotechnology: The Case of Fullerenes Sarah Kaplan* University of Toronto, Rotman School 105 St. George Street Toronto, ON, M5S 3E6, Canada 416-978-7403 [email protected] Keyvan Vakili University of Toronto, Rotman School 105 St. George Street Toronto, ON, M5S 3E6, Canada 607-708-4960 [email protected] This draft: April 11, 2011 Preliminary. Grace Gui, Sara Sojung Lee, and Neal Parikh provided much-appreciated research assistance. We are grateful to Michael Lounsbury for the suggestion to study fullerenes and nanotubes within the broad domain of nanotechnology, Juan Alcácer for help and advice in creating the initial data set, to Hanna Wallach for her early test-driving of topic modeling on these data. This work is partially supported by the Mack Center for Technological Innovation at the Wharton School, University of Pennsylvania (where Kaplan is a Senior Fellow) and the Canadian Social Sciences and Humanities Research Council under grant # 410-2010-0219. The usual disclaimers apply. *Corresponding author. Patterns in the Emergence of Nanotechnology: The Case of Fullerenes Abstract: We propose a new methodology – topic modeling – to evaluate the emergence and interpretation of new technologies and use this approach to examine trends in the field of nanotechnology. We treat patents not as measures of innovation but rather as historical documents written by particular human beings, in particular places, at particular times. We argue that studying the language in patents provides a closer reading of interpretations of an emerging technology than can be obtained through indicators such as USPTO patent classifications. By analyzing the text of the abstracts of 2,826 fullerene patents, we find that interpretations of what this “general purpose technology” was and how it might be used changed over time. Our descriptive results contrast with extant literature on sources and value of inventions. 1. Introduction There is increasing interest in understanding technologies and markets in their emergent or nascent phases (Aldrich & Fiol, 1994; Santos & Eisenhardt, 2009). While we know a great deal about the development of technological trajectories once competing new technologies emerge (Christensen & Rosenbloom, 1995; Dosi, 1982; Sahal, 1981; Utterback, 1994; Nelson & Winter 1977), we know much less about what goes on in the era of ferment where variation has been assumed to be due to stochastic forces (Tushman & Rosenkopf, 1992). Traditional research in science and technology studies (Bijker, Hughes & Pinch, 1987) and in the management of technology (Henderson, 1995; Tushman & Rosenkopf, 1992; Utterback, 1994) has shown that social processes are at work in the construction of technologies over the course of the technology life cycle but offered few insights into how the technologies emerge initially. More recently, scholars have begun to examine the potential for social factors in shaping the quantity and quality of technological variations in the era of ferment. Some attribute variation to the backgrounds of individual inventors or organizations (Fleming, 2002; Fleming & Singh, 2010). Others have suggested that institutional actors such as social movements, the Emergence of Nanotechnology - 1 - media, communities or firms affect which technologies are funded and explored (Kaplan & Radin, 2011; Kennedy, 2008; Weber, Heinze & DeSoucey, 2008; Wry, Greenwood, Jennings & Lounsbury, 2010). Implicit in these models is an assumption that such social factors as career backgrounds or external forces shape inventors’ and entrepreneurs’ interpretations and choices about which technologies to pursue. In these highly uncertain settings, actors’ cognitive frames become the basis for action. Because new technologies are inherently “equivocal” (Weick, 1990: 2), they are subject to sensemaking by actors (scientists, managers, regulators, patent examiners, etc.) who need to make choices about how to act. These interpretations and actions can, in a co- evolutionary manner, shape the direction of the technical change itself (Kaplan & Tripsas, 2008). While cognition is receiving increasing attention in management research in general (Elsbach, Barr & Hargadon, 2005; Walsh, 1995), research on the emergence of new technologies has been largely silent about cognition’s role (Kaplan & Tripsas, 2008; Nightingale 1998). Filling this gap requires addressing important methodological challenges. Tracking the emergence of a new technological field risks falling into a “Whiggish” progressivist history in which the past is retrospectively understood based on the outcomes that we observe (Lamoureaux, Raff & Temin, 2004). We may miss the paths not taken, the “flotsam and jetsam” of history (Schneiberg 2007), that disappear once a particular path is followed. Or, we may impose categories developed later in time on interpretations occurring at the period of emergence. These challenges dog large-scale studies using data such as patents or scientific publications to track knowledge flows systematically over time. These studies have drawn conclusions about technology evolution and the emergence of technological novelty based on Emergence of Nanotechnology - 2 - proxies such as US Patent and Trademark Office (USPTO)-designated technology classifications (e.g., Fleming & Sorenson, 2004) or key words (Azoulay, Ding & Stuart, 2007) that are potentially problematic. In the case of USPTO patent classes, these are pre-established categories used to facilitate patent examiners’ searches for prior art (Benner & Waldfogel, 2008) and therefore may lag substantially the emergence of new technological arenas. The USPTO does, from time to time, update its classifications and then reclassifies existing patents according to the new system, but these classifications risk ex post interpretation of the prior emergence of a technological arena. They may also miss truncated paths in which a new idea emerges but dies off quickly. So far, the field has not had a means for identifying new technological ideas as they emerge nor for tracking their evolution over time. In this paper, we introduce a new method – topic modeling of text – for addressing these challenges and show how its application in the analysis of patent texts provides new avenues into avoiding retrospective bias while analyzing the emergence of new technologies. The idea behind the use of textual (or content) analysis is that language is tightly connected with human cognition (Duriau et al 2007). Topic modeling uses Bayesian statistical techniques to determine the meanings of words by looking at co-presence with other words in the same document or block of text. This method is becoming established in computer science research, but is only beginning to emerge as a technique for the social sciences (Ramage et al 2009, McCallum et al 2006, McCallum et al 2007). It generates categories (topics) from the texts themselves rather than imposing pre-determined categories on the information. This approach allows an examination of interpretations as they develop contemporaneously and provides a systematic way of tracking the evolution of interpretations over time, even over very large bodies of texts. We illustrate this method’s potential through a descriptive analysis of the emergence and Emergence of Nanotechnology - 3 - evolution of a new nanotechnology: fullerenes. Specifically, we use the texts from the abstracts of all fullerene-related patents to examine how this technology has been understood over time. We treat patents not as measures of innovation (Jaffe and Trajtenberg 2002, Griliches 1998) but as historical documents written by scientists at particular periods of time (Alcacer & Gittelman 2006). Topic modeling provides us with a direct method for tracking the interpretations embodied in the texts of patents. Using this method, we develop a portrayal of the evolution of fullerenes and nanotubes that contributes to our knowledge about the nature of technological emergence and how to measure it. We show that the understanding of fullerenes and nanotubes evolved over time, from an early focus on methods to a later focus on different types of applications. The sources of these inventions, both in terms of the types of inventors and their geographic location, tended to be from outside the core of technology development. That is, the inventors of the important breakthroughs tended not to have much targeted experience in the domain and tended not to be located in the standard technology clusters (Silicon Valley and Route 128). Only later ideas, particularly those about specific applications, tended to be generated within those clusters. Using topic modeling allows us to disaggregate measures of the creativity of the invention and measures of its value (where in the past, the calculation of the breakthrough nature of an invention and of value were based in a count of subsequent citations to the patent). In doing so, we find that topic-generating patents (those that initiate a new idea or theme) were more likely to have higher forward citations than follow on patents, yet not all highly cited patents were those that created breakthroughs. Controlling for a patent’s degree of cognitive breakthrough, we find that social factors such as the number of inventors listed on the patent contributed to its subsequent number of citations, factors that might be less associated with a Emergence of Nanotechnology - 4 - patent’s “value” than with its position in a social network. These analyses
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