What Drives Demand-Pull Innovation? An Empirical Investigation
in the Computer Hardware Industry*
Sali Li
The University of Wisconsin–Milwaukee
Lubar School of Business
P. O. Box 742
Milwaukee, WI 53201
Phone: 414-229-6257
Fax: 414-229-5999
Email: li9@uwm.edu
Richard L. Priem
Texas Christian University
Neeley School of Business
TCU Box 298530
Fort Worth, Texas 76129
Phone: 817-257-7550
Fax: 817-257-7227
Email: r.priem@tcu.edu
and
LUISS Guido Carli University
Department of Business and Management
Viale Pola, 12 - 00198 Rome, Italy
Email: rpriem@luiss.it
Gianmario Verona
Università Luigi Bocconi
Institute of Technology and Innovation
Department of Management and Technology
Via Roentgen 1, 20136, Milan, Italy
Phone: ++39‐02‐58366522
Fax: ++39‐02‐58366888
Email: gianmario.verona@unibocconi.it
*Authors contributed equally.
Please do not quote or cite without authors’ permission
January 10, 2012
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What Drives Demand-Pull Innovations? An Empirical Investigation
in the Computer Hardware Industry
ABSTRACT
Despite extensive research in the fields of economics and the management of innovation over the
past thirty years, the sources of demand-pull innovations have yet to be empirically validated. In
this paper we pursue that empirical validation. We first review the literature on demand-pull
innovation. Then, we argue that the likelihood of demand-pull innovations occurring depends
upon: a firm’s relative focus on marketing versus R&D and a technology’s modularity, and
further that these two factors interact as complements. We test these hypotheses using a sample
of innovations patented in the computer hardware industry from 2001-2004. Based on our
results, we discuss the importance of demand-pull as a contributor to innovation, and we link our
findings to the broader debate comparing resource-focused versus demand-focused approaches to
strategic management.
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INTRODUCTION
The seminal contributions of Joseph Schumpeter (1934; 1942) initiated research in the
fields of economics and the management of technology aimed at increasing our understanding of
innovation. This work is important because innovation is a critical issue for both scholars and
practitioners. For scholars, a better understanding of innovation can help to explain competitive
advantage in the increasingly dynamic markets that characterize modern competitive landscapes
(Teece, Pisano & Shuen, 1997). For managers and entrepreneurs, an understanding of innovation
can provide important guideposts for exploring new markets (March, 1991).
A rich debate over “technology push” versus “demand pull” as mechanisms driving
innovation has taken place in the literatures on the economics of technological change (e.g.,
Freeman, 1974; Mowery & Rosenberg, 1979; Dosi, 1982) and on technology-innovation
management (e.g., Abernathy & Utterback, 1978; Tushman & Anderson, 1986; Christensen &
Bower, 1996). Di Stefano, Gambardella and Verona’s (2011) review of empirical work in these
fields confirms the persistent presence of the technology-push and demand-pull views in the
literature. They also note, however, that compared to technology-push innovations relatively
little attention has been paid to either the situations that increase the likelihood of demand-pull
innovations or to the organizational factors that may facilitate them.
Yet understanding the drivers of demand-pull innovation is increasingly important, in
part because its role appears to be growing with the accelerating pace of technological change.
For instance, advances in information and communication technologies have allowed firms to
expand their knowledge bases more quickly by engaging users in their innovation processes
(Chesbrough, 2003; Prahalad & Ramaswamy, 2004; von Hippel, 2005). This in turn has required
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new ways of organizing for innovation in which the contribution of “pull” from the demand side
has become pivotal (Arora, Fosfuri & Gambardella, 2001; Verona, Prandelli & Sawhney, 2006).
This increasing importance of demand-pull innovation is reinforced by research highlighting the
extreme challenges firms sometimes face when trying to convert technology-push innovations
into successful products (Danneels, 2007). Concurrently, more attention now is being paid to
users by the nascent demand-side approach to strategy (Adner & Snow, 2010; Priem, 2007; Ye,
Priem & Alshwer 2012), which aims at better understanding the sources of value creation and
competitive advantage sustainability from customers’ perspectives. Zander and Zander (2005),
for example, argued that close customer relationships can help uncover information that is useful
in guiding innovation. Faulkner and Runde (2009) provided a specific example when they
explained how user innovations by hip-hop “DJs” reinvigorated the turntable industry. In sum, a
deeper understanding of demand-pull innovation is fundamental to a broader and more accurate
comprehension of value creation by firms in a dynamic marketplace.
We contribute to developing this deeper understanding, first, by providing a method for
empirically distinguishing between technology-push and demand-pull innovations and, second,
by identifying key technology- and firm-level factors that contribute to demand-pull innovation.
We explore two central research questions: Does technology modularity affect the likelihood of
demand-pull innovation? And does the attention paid by a firm’s managers’ to technology
relative to customers: (1) affect its production of technology-push versus demand-pull
innovations, and (2) moderate the effects of technology modularity on the likelihood of demandpull innovation? Answers to these questions are likely to be helpful to scholars interested in
factors contributing to innovation success, and to practitioners who wish to pursue opportunities
available from customer-focused innovations.
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THEORY DEVELOPMENT
Studies in the economics of technological change have emphasized the fundamental roles
of science and technology in the development of innovations across many economic sectors (e,g,
Freeman, 1974; Rosenberg, 1982; Pavitt, 1984). Within industries, technology evolves along
trajectories that often are punctuated by paradigm changes due to scientific advances (Dosi,
1982). This view of science and technology as drivers of innovation has come to be recognized
as the “technology-push” approach to innovation, wherein entrepreneurs and firms develop
marketable products primarily thanks to scientific discoveries. Although demand is seen as
useful in this view for understanding how innovations could be marketed successfully, it
historically has been viewed as more of a selection force rather than a generating force that spurs
innovation (Di Stefano et al., 2011). In their classic review of demand pull and technology push,
for example, Mowery and Rosenberg (1979) argued that, given the interrelated nature of the
curves of demand and supply, it is impossible to distinguish any pull of demand from the
dominant push of technology which typically generates innovation in most industries.
Despite this dominant interpretation, pioneering work by scholars in the late Sixties
highlighted a more generative role for demand. Schmookler (1966), for example, showed that
fluctuations in investments in multiple U.S. industries were influenced more by external events
such as upswings in demand than by inventive activity itself. Meyers and Marquis evaluated 567
successful technological innovations and concluded that “recognition of demand is a more
frequent factor in innovation than recognition of technical potential” (1969: 60). In another study
of successful innovations, Langrish et al. (1972) distinguished innovations triggered by customer
needs from those originating in the firm itself. And von Hippel’s (1976) study of innovations in
scientific instruments determined that innovative labor could be classified as either that of firms
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providing engineering and manufacturing functions, or that of motivated users who are capable
of innovating. Even scientists such as the famous robotics innovator Joseph Engelberger (1982),
have noted that a recognized need, relevant technology, and financial support are critical
elements for innovation.
Overall, these studies confirm the importance of user demand – not simply as a selective
force, but also as a generative force that spurs innovations. Following the lead of these early
contributions, we distinguish between technology-push and demand-pull innovations as follows:
Technology-push innovations are mainly the product of building upon science and technology,
independent of specific customer or market needs. Demand-pull innovations are mainly the
product of direct attempts to satisfy specific market needs. That is, if an innovation is primarily
the result of scientific development, then it can be considered a technology-push innovation. If
an innovation instead results from explicit or latent demand by a market segment, then it can be
considered a demand-pull innovation. These distinctions between technology-push and demandpull innovations are matters of degree, however. Our use of the term “demand-pull” applies to
innovations that integrate consumer needs and that are more focused on the commercialization
aspects of technology advancement; any given innovation may be more (or less) technology
push, or more (or less) demand pull, along a continuum. We do not mean to suggest that all
innovations can be divided neatly into these two categories.
Strategic Attention Focus and Demand-Pull Innovation
Organizational context can influence innovators’ behaviors and, thereby, the types of
innovation achieved. For instance, Tellis, Prabhu and Chandy (2009) showed that corporate
culture can play a preeminent role in producing radical innovations, and similarly Ernst and Vitt
(2002) found that key inventors change their invention patterns if an acquisition results in a
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change in corporate culture. Wu, Levitas, and Priem (2005) determined that the job tenures of
U.S. biotechnology companies’ CEOs influence their firms’ innovative outputs. These studies
among others suggest that the “direction” of strategic attention paid by a firm’s managers can
affect the overall orientation of the firm’s innovations. Following Ocasio, we define strategic
attention as the “noticing, interpreting, and focusing of time and effort” (1997: 188). We next
examine the focus of a firm’s strategic attention, as managers’ allocate scarce attention resources
to marketing versus R&D, and the effects of this strategic attention focus on the likelihood of
demand-pull innovations.
The resource allocation of a firm reflects where its managers’ attention is focused and
affects the firm’s culture and activities (Ocasio, 1997). As a firm focuses employee attention
within or across different functions (Gifford 1998), “a discipline-dominant culture (such as a
marketing or engineering culture) can impact the final outcome of the [innovation] process by
attracting, motivating, and holding talented people from specific disciplinary fields (LeonardBarton, 1992)” (Verona, 1999: 173). Thus, from an attention-based view, firm strategic action is
an outcome of how firms channel and distribute limited attention resources. As a firm increases
attention to marketing activities relative to R&D activities, for example, a greater emphasis will
be given to how to satisfy customer demands. Accordingly, a strategic attention focus on
marketing will direct employees’ (including inventors and engineers) attention toward types of
innovation that can better satisfy heterogeneous customer demands (Verona, 1999). On the other
hand, as a firm decreases attention to marketing activities relative to R&D activities, a greater
emphasis will be given to refining details of the firm’s existing technology. Accordingly, this
strategic attention focus will direct employees’ attention toward innovations based on
technological interdependence, and as inventors increase the depth of technology-based search
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and exploration the breadth of external search for new knowledge and for market-based solutions
will narrow. Thus:
Hypothesis 1: The likelihood of demand-pull innovation increases as the strategic
attention focus of a firm’s managers places a great emphasis on marketing
activities relative to R&D activities.
Technology Modularity and Demand-Pull Innovation
Technology evolution is characterized by discontinuities that initiate periods of
experimentation followed by convergence upon a dominant design. Anderson and Tushman
(1990) argued that after each technology discontinuity there occurs an era of ferment followed by
an era of incremental change. The delimiting event between these two eras is the emergence of a
dominant design. Henderson and Clark (1990), for example, have demonstrated that firms tend to
focus on fundamental architectural innovations before a dominant design emerges, but tend to
focus on component innovations after the field converges on a dominant design.
During an era of ferment, firms face substantial pressures to develop the next dominant
design (Tushman & Anderson, 1986; Abernathy & Utterback, 1978), and the associated
uncertainty makes it especially difficult to anticipate market needs. Managers and inventors
therefore tend to focus more attention their own technology and on developing technological
design superiority, so their design can become dominant. Once a dominant design is established,
however, the era of incremental change begins and firms’ inventors and managers focus on
incremental improvements to the dominant design that will make it more useful for customers.
The critical innovation task becomes effectively commercializing the dominant design through
incremental innovations incorporating customers’ increasingly heterogeneous demands.
Modular product architecture standardizes the means through which product components
interface. This in turn makes incremental innovations, via component replacements and
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reconfigurations, easier for technology companies. Technology modularity thereby “facilitates
‘modular innovation’ (Henderson & Clark 1990): innovation in product components that does
not significantly affect the design of other components. This kind of innovation allows firms to
produce families of products based on the same overall product architecture, thereby helping
them to address heterogeneity in customer demand (e.g., Langlois & Robertson 1992)” (Argyres
& Bigelow, 2010: 842). In eras of incremental yet rapid innovation (Garud & Kumaraswamy,
1995; Galunic & Eisenhardt 2001), modularity allows more configuration alternatives from a
given set of inputs, which in turn allows technology companies to better address the changing
needs of increasingly heterogeneous segments of market demand following a dominant design
(Schilling, 2000). Because modular innovation helps firms to more directly target customer
needs, increasing heterogeneity of demand in a technological field spurs companies toward
innovations that enhance inter- and intra-firm product modularity (Argyres & Bigelow, 2010;
Schilling, 2000). Surprisingly, modular innovation may be especially likely among competitors
following a much-demanded, “breakthrough” dominant design such as the Model T in
automobiles (Argyris, Bigelow & Nickerson, 2011). The fundamental advantage of more
modular systems over more technologically interdependent ones in fast-changing industries
facing heterogeneous demand segments is exemplified by the mini-computer industry during the
1980s. Those companies with more modular product architectures, such as Digital Equipment
Corporation, Data General and Hewlett Packard, responded to consumers’ needs for acrossbrand mini-computer and mainframe communications networks and distributed data-processing
relatively quickly through flexible, modular innovations. Other mini-computer companies such
as Wang Laboratories and Prime Computers, however, focused innovation instead on improving
efficiencies in their proprietary, integrated systems. Due in part to the resulting inflexibility and
9
therefore inability to respond to customers’ needs, these firms eventually faded as competitors in
mini-computer hardware (Schilling, 2000).
Thus, modularity – i.e., increasing the possible configurations achievable from a set of
inputs – makes it possible to better exploit customer preference heterogeneities. In order to
respond to increasing market pressures from heterogeneous segments of demand, firms tend to
further uncouple product components through finer specialization and more modular innovations
(Schilling, 2000). This in turn fosters loose coupling of a firm's learning processes at the levels
of product components and architectures (Sanchez & Mahoney, 1996), and enables firms to
coordinate loosely coupled networks of suppliers and customers by acting as system integrators,
thereby benefiting from both integration and specialization (Brusoni, Prencipe & Pavitt, 2001).
Particularly important is the insight that the potential for division of innovative labor extends to
technology users; i.e., in loosely coupled modular networks the locus of innovation may be
spread across producers and users (Arora & Gambardella, 1994).
On the other hand, during periods of ferment the benefits of adopting modular innovation
are scant, in part because the market has yet to provide the clear signal of consumer preferences
represented by a dominant design (Argyres et al., 2011). In such contexts innovations with high
interdependency are more likely, but such innovations tend to occur through different processes
(Baldwin & Clark, 2000; Fleming & Sorenson, 2001). Innovations that are high in technological
interdependence are more difficult because they require that innovators delve more deeply into a
technology’s details to understand of how components interact (Fleming & Sorenson, 2001). The
process of building upon interdependencies redirects innovators’ cognitive and social resources
toward technological priors and nuances (i.e., technology push), rather than toward exploring
new ways of recombination to address customer needs (i.e., demand pull). Thus, although
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interdependent innovations may be more difficult for competitors to imitate, they also are more
difficult to achieve and, later, to build upon or modify. In sum, when technology firms face
incremental eras with changing and heterogeneous segments of demand they seek flexibility and
move toward modular innovations that allow component recombination in ways that better match
the demand heterogeneity in the marketplace. These modular innovations are likely to be driven
by consumers and, therefore, will tend to be demand pull. When technology firms face eras of
ferment without strong demand signals from a dominant design, on the other hand, innovations
built upon interdependencies with their own prior, proprietary technologies are likely. Such
innovations are likely to be technology driven and, therefore, will be technology push. Thus:
Hypothesis 2: The likelihood of demand-pull innovation increases with the degree of
modularity in a technological field.
Complementary Effects of Strategic Attention Focus and Technology Modularity
The previous arguments leading to H1 explained why the strategic attention focus of a
firm’s managers may influence the likelihood of demand-pull innovations by the firm, but they
do not address the potential complementary effects that strategic decision focus might have on
the relationship of technology modularity to the likelihood of demand-pull innovation that was
proposed in H2. We discuss this issue next, building theory supporting complementary,
interactive effects of managers’ strategic attention focus and technology modularity on the
likelihood of demand pull.
As Siggelkow has noted, “Two activities interact as complements if the marginal benefit
of each activity increases in the level of the other activity” (2002: 901). Ocasio’s principle of
situated attention “provides a link between how individuals think and decide in any particular
organizational situation, and how the organization and its environment shape the situations that
individuals find themselves in” (1997: 191). This principle suggests that a firm’s strategic
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attention focus may complement the firm’s technology modularity, as follows. A marketingbased strategic attention focus is most pertinent and useful during eras of incremental yet rapid
change when segments of demand with heterogeneous needs are becoming clearer in the
marketplace. Because demand-pull innovations are most appropriate in such contexts, the
efficacy of technology modularity will be increased by a firm-wide strategic attention focus on
marketing that emphasizes the consumer needs. Moreover, the marketing-based strategic
attention focus in turn will be legitimized and reinforced for employees by the need for modular
innovation. In sum, the efficacy of technology modularity will be increased by firm-wide
attention focus on customers, and the efficacy of a firm-wide attention focus on customers will
be increased by technology modularity. Thus, there is a high likelihood that these factors will
interact as complements, with increases in each together contributing to the likelihood of
demand-pull innovations beyond their individual, main-effect contributions.
The dual effects – main and interactive – of a firm’s strategic attention focus indicate that
strategic attention focus can be classified as a “quasi-moderator” of the relationship between
technology modularity and likelihood of demand-pull innovation. A quasi-moderator is a
specification variable that is related to the predictor or criterion variables and that also affects the
relationship between the predictor and criterion (Sharma, Durand, & Gur-Arie, 1981; Prescott
1986). In our case strategic attention focus is related to the demand-pull innovation criterion, and
strategic attention focus also moderates the relationship between technology modularity and the
criterion. In sum, we have argued that a firm’s strategic attention focus interacts with the
previously described effect of technology modularity on demand-pull innovation, such that the
effect of technology modularity on the likelihood of demand-pull innovation will be greater with
increasing strategic attention focus on marketing relative to R&D. Thus,
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Hypothesis 3: As a firm’s strategic attention focus on marketing versus R&D increases,
a unit increase in technology modularity will have an increasing effect on
the likelihood of demand-pull innovation.
METHODS
We test our hypotheses using sample firms from the U.S. computer hardware industry
between 2000 and 2004. We chose this high-technology industry because computer firms need to
bridge technology and customer demand in the innovation process (Randall et al., 2007), and
innovations in this industry are well documented through firms’ patenting activities. Our
computer hardware industry sample included SICs 3571, 3572, 3575, 3577 and 3578. We
utilized the Compustat dataset and the USPTO patent dataset to construct our sample. We first
selected all US computer hardware firms with R&D and advertising expenditure variables
available in Compustat for the 5 year period of 2000-2004. During this process we eliminated
foreign firms (i.e., ADRs) because our focus is on patents filed in the USPTO system. We then
traced the remaining computer firms’ patenting activities using the NBER dataset and USPTO.
In particular, we used the match indexes (PDPCOHDR and DYNASS) developed by the NBER
led by Bronwyn Hall to merge the patent data with Compustat data. The PDPCOHDR index
provides an accurate match between patent assignee name and Compustat’s GOVKEY, and
DYNASS helps track the change of patent ownership in a given period of time (Bessen, 2009).
This approach led to a total of 8828 patents from 74 U.S. computer hardware firms.
During the 2000-2004 period 19 firms in our sample changed assignee numbers due to
ownership structure changes. Nine of those firms terminated their business, but 10 changed to a
new assignee number. We consistently tracked the patent activities of these 10 firms by matching
their new and old assignee numbers.
Dependent variable
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Demand-pull innovation. We used the description found in each patent’s abstract to code
the degree to which that patent reflects market pull versus technology push. A patent abstract
provides important information on the essence of a patented technology. Patent abstracts have
been used previously in technology research to identify the nature of a patented technology.
Brusoni, Prencipe and Pavitt (2002), for example, used patent abstracts to code whether a patent
represents an architecture or a component innovation. We took a number of steps when coding to
ensure the validity of this measure. First, we consulted two industry experts, each of whom has
multiple patents in the computer hardware industry and extensive experience with patenting. We
explained our definitions of technology-push and demand-pull innovation in detail to each
expert. We also used Randall et al.’s (2007) specific examples as a “roadmap” for ensuring
agreement on the technological dimensions and user needs in the computer industry. After the
experts agreed on the definitions of technology-push and demand-pull innovation, we selected 10
computer hardware patents, each of which was developed by one of these experts, and asked
them to code the degree of demand pull for each patent on a Likert scale ranging from 1 (“purely
technology push”) to 7 (“purely demand pull”) based on an initial coding scheme we developed.
After discussion with the inventors, we refined the coding scheme for patents. The refined scale
included: 1. Pure theory or method oriented; 2. Pure technology (such as chemical materials); 3.
Technology that can be used in product, indirectly related to customer product, or technology
breakthrough for certain specialty; 4. Technology that can be used in product that is directly
related to customer product; 5. Technology that can be directly used by customer through a
product or service provided by the company; 6. Technology that can be directly used by
customer; 7. Innovation or technique created by customers or created purely based on customers’
14
interests. In the appendix, we show examples of patent abstracts in each category to demonstrate
coding the different levels of demand pull.
Second, we hired two independent coders, each of whom have electronic engineering
degrees and who had 18 and 7 years of professional experience at the time of our study. We
explained the constructs of demand-pull and technology-push innovation to these coders, and
then asked them to independently score the 10 patent abstracts that had been coded by the patent
inventors along the 1-7 Likert scale of demand pull. If they arrived at the same score as did the
patent inventors, we discussed their reasoning further to confirm their rationale. If they gave a
different score than did the patent inventors, we discussed the differences and explained why the
patent inventor coded that patent differently. Third, we then asked each coder to independently
code another set of 100 patent abstracts, and compared the two coder’s results to ensure
consistency. Thereafter, the coders independently coded all of the remaining patents. Overall,
there was an initial 68 percent agreement between our independent coders, and Cohen’s kappa
was .77, which is above the satisfactory level of .70 that was determined based on first 100
patents coded. When there were disagreements in the two coders’ ratings, they later discussed
and resolved the disagreements, and their consensus was used in the analyses.
Independent Variables
Strategic attention focus. This construct was measured as the ratio of marketing expenses
over R&D expenses. Prior research (Durand, 2003) has used the firm’s relative expenditures
related to marketing and R&D to reflect a firm’s strategic attention focus. As noted by Ocasio
(1997), a firm’s strategic attention focus reflects how the organization distributes and allocates
critical resources.
Technology modularity. Following Fleming and Sorenson (2001) and Sorenson, Rivkin,
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and Fleming (2006), we measured technology modularity by calculating the “readiness” with
which a patent’s technology subclasses can be recombined with other technology subclasses:
n
Technology modularity of patent j= ∑ E i/Count of subclasses on patent j
where Ei denotes the easiness of recombination of subclass i, measured as the ratio of the
count of subclasses previously combined with subclass i over the count of previous patents in
subclass, and n is the count of subclasses on patent j.
In this measure, technology subclasses serve as proxies for the technology elements that
constitute the patented innovation. Although a patent’s subclasses may not always perfectly
correspond to physical components of a patent, they are the defining, building blocks of the
knowledge that comprises the patented innovation. The face validity of this measure has been
validated via a survey of inventors (Fleming & Sorenson, 2004; Sorenson, Rivkin & Fleming,
2006).
Patent class and subclass data were drawn from the Patent Network Dataverse by Lai et
al. (2011). To derive the measure of easiness of recombination for a given subclass, we first
counted all the prior patents containing the particular subclass from 1990 to 1999 as the
denominator. We then counted of the number of different subclasses that have been combined
with the particular subclass on previous patents as the numerator. Lastly, we calculated the
average of the ease of recombination scores for a given patent to derive the measure of
technology modularity. The basic intuition behind this measure is that if a patent’s subclasses
can be easily “mixed and matched” with a variety of other subclasses, then the patent receives a
high value of technology modularity. If, on the other hand, a patent’s subclasses have only been
combined with a small set of other subclasses, which implies that the patent’s subclasses are
interdependent upon each other and therefore its modularity is low.
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Control Variables
We controlled for the potential effects of knowledge searching by including a patent’s
self-citations and basic science linkages in our analyses. Patent citation information provides
valuable data concerning the flow of information to a firm from external sources (Griliches,
1990). Self-citation ratio is measured by the number of self-citations relative to the total number
of prior citations in a four-year period. A higher number of self-citation indicates a higher level
of embeddedness of the focal patent in the prior patents in the company (Hoetker & Agarwal,
2007). An important source of knowledge that patent inventors rely on is scientific publications.
The more inventions incorporate recent scientific discoveries that are linked to basic science, the
more citations a firm will list on its patents (Narin, Noma, & Perry, 1987; Bierly & Chakrabarti,
1996). Therefore, we followed prior research (e.g., Meyer, 2000; Harhoff, et al., 2003) by using
the number of non-patent references in a patent as a proxy for the patent’s science linkage1. We
also controlled for those firm characteristics that may affect innovation. Firm age was used as a
proxy for the length of the firm’s patenting experience, because firms with longer patenting
histories are more likely to build inertia and become less responsive to market needs. We
calculated firm age as the difference between year t and the first year a firm appeared in the
Compustat database. Also, firms with a rich stock of patents are likely to have a broader
knowledge base and better capabilities from which inventors can draw. We therefore measured a
firm’s patent size as the number of patents filed by the inventor’s firm from year t-3 to t. We also
controlled for firm ROS in year t.
1 Not all non-patent references are from scientific journals – some are from industry journals and firm publications.
This issue has been studied in detail by Schmoch (1993). As noted by Harhoff, et al (2003), however, most nonpatent references are from scientific journals, and it is very difficult to directly identify the number of explicit links
to the scientific literature. We therefore used non-patent literature as a proxy for a patent’s science linkage and
expect, despite the measurement issues, because non-patent literature references likely have explanatory power in
science-based industries such as the computer industry.
17
We also controlled for the potential effect of alliance formation on innovation. Prior
research has noted that alliances can affect the pattern of a firm’s innovation (Mowery, Oxley &
Silverman, 1996; 1998). We followed Lavie and Rosenkopf (2006) by using alliance types to
code the extent to which an alliance is functionally explorative. Using SDC data, we coded a
categorical indicator of whether each alliance involved a knowledge-generating R&D agreement
(coded 1); an agreement based on existing knowledge, involving joint marketing and service,
OEM/VAR, licensing, production, or supply (0); or a combination of R&D and other agreements
(0.5). The alliances’ functional exploration was assessed as the average value of the alliance type
indicator across all alliances formed by firm i in year t-1. Values ranged from 0 to 1, where a
high value indicates the exploration nature of a firm’s alliance portfolio, while a low value
indicated the exploitation nature of a firm’s alliance portfolio. More emphasis on exploration
alliances may be associated with technology-push innovation, while more emphasis on
exploitation alliances may be associated with demand-pull innovation.
Finally, we controlled for industry segment, technology category, and year effects. There
are five major industry segments in the computer industry. We therefore included industry
dummies for the following four industry segments: electronic computer (SIC 3571), computer
storage devices (SIC 3572), computer communications equipment (SIC 3576), computer
peripheral equipment (SIC 3577), with calculating and accounting machines (SIC 3578) as the
base. Since there may be a systematic pattern of demand pull across patent technology classes,
we included a control variable of patent technology category. But given the fact that our sample
represents 188 unique technology classes, which is too large to serve as controls, we used the
NBER-classification developed by Hall, Jaffe, and Trajtenberg (2001) to control the different
effects of technological categories. Given our sample period ranging from 2000-2004, we
18
included dummy variables for year (with 2000 as base) to control for any systematic, yearspecific effects that may influence patents applied for in a particular year.
Statistical Approach
Because our measure of demand pull innovation ranges from 1 to 7, we employed the
double-censored Tobit procedure with a lower limit of 1 and an upper limit of 7. Tobit regression
is preferred to ordinary least squares (OLS) regression because OLS can lead to inconsistent
parameter estimates when the dependent variable is left and/or right censored (Greene, 1993).
Moreover, given that the 8,828 patents in our sample belong to 74 firms, we controlled for firmlevel effects by clustering firms in the regression analysis. This approach is similar in its
assumptions and interpretation to a random-effects estimation in a linear model, but allowed us
to account for interdependent errors within firms and heterogeneous errors across firms (Glomb
& Liao, 2003; Barkema & Shvyrkov, 2007). Because patents can be considered as nonindependent observations with in a firm, the ‘cluster’ function in STATA helped us account for
this within-firm dependence and offered greater efficiency for test the heterogeneous errors
across firms than would the traditional fixed-effects specification.
RESULTS
Table 1 reports descriptive statistics and a correlation matrix for the variables used in
testing our hypotheses. Stata linear regression collinearity diagnostics produced VIF values that
all were less than 4.58 – well below the rule-of-thumb cutoff of 10 (Neter, Wasserman & Kutner,
1985) – indicating that multicollinearity is not a concern in our data. For ease of interpretation,
the variables reported in Table 1 are not mean-centered. All the independent variables were
centered in all equations in Table 2, however, regardless of whether they were used for
interaction terms (Aiken & West, 1991).
19
[Insert Table 1 about here]
[Insert Table 2 about here]
Table 2 presents the results estimating the likelihood of demand pull innovation. Model I
is the baseline model incorporating all control variables. Model II augments Model I by adding
the main effects for Technology modularity and Strategic attention focus. Model III adds the
Strategic attention focus x Technology modularity interaction term. Chi-square tests show that
Models II and III provide significant improvements compared to Model 1, indicating that the
independent variables add significant predictive power beyond the control variables.
Hypotheses 1 and 2 proposed that strategic attention focus and technology modularity
each would be positively associated with demand pull innovation. The results in Model III
support both H1 and H2. For the interactive effect, Hypotheses 3 proposed that states that the
positive relationship of technology modularity on demand-pull innovation would be enhanced
with increases in strategic attention focus on marketing. In Model III, the coefficient of the
Strategic attention focus x Technology modularity interaction term supports H3.
Among the control variables, Firm patent size has a negative and significant coefficient
(p<0.05) and Explorative alliances has a positive and significant coefficient (p<0.05, except for
Model 1). These results suggest that firms with a large patent portfolio tend to create technology
pushed innovation, while allying with explorative partners will enhance the likelihood to create
demand pull innovation. Finally, the year fixed effects and technology class fixed effects are
jointly significant, indicating that macroeconomic conditions and technology heterogeneity have
an impact on the tendency to create demand pull innovation.
We investigated the robustness of our findings in several ways. First, we aggregated the
patent-level data to the firm level and calculated an overall demand-pull innovation score for all
20
patents filed by each of our sample firms in each year. This produced a panel-data sample that
allowed us to examine the firm-level degree of demand-pull innovation each year for the 74
computer hardware companies, totaling 269 firm-year observations. For this unbalanced paneldata sample, we performed the Hausman specification test to determine the choice of a fixed- or
random-effects model (Hausman, 1978). Results suggested that a random effects specification
was appropriate, and we therefore used random-effects regression for all our models.
To test the overall degree of demand-pull innovation at the firm level, we retained the
firm-level predictors used in the patent-level analyses while we aggregated the patent-level
explanatory variables to the firm level. We calculated the average self-citation ratio for each
firm’s total patent applications in a given year to measure Self-citation ratio (firm). Similarly, we
used the ratio of total science linkages over total number of patents to calculate Science linkage
(firm). We again applied the method of Fleming and Sorenson (2001) and Sorenson, et al (2006),
but at the firm level, to measure Technology modularity (firm). That is, this measure was based
on the ease of recombination for all subclasses encompassed in the total patent applications of a
firm in a given year. The other explanatory variables, with the exception of Technology class
dummies, were already at the firm level, so we kept them in the panel-data analysis without
modification. Because the panel data were at the firm level, we were no longer able to control for
technology-class effects in the regression. Hence these were the only controls dropped from the
panel-data regression.
The descriptive statistics and a correlation matrix for the panel sample are reported in
Table 3. The means of the variables are slightly different from the numbers in Table 1 due to the
unbalanced panel structure. We used Stata linear regression collinearity diagnostics to check the
variance inflation factors (VIFs) for all independent variables and interaction terms. All VIFs
21
were less than 5.27, suggesting that multicollinearity is not a concern (Neter, Wasserman, and
Kutner, 1985).
[Insert Table 3 about here]
[Insert Table 4 about here]
Table 4 presents the estimation results for the factors affecting a firm’s overall degree of
demand-pull innovations. Wald Chi-square ratio tests show that Models 2 and 3 provide
significant improvements compared to Model 1, indicating that our independent variables add
predictive power to the control variables. The variable Technology modularity (firm) is positively
related to a firm’s overall market-pull innovations in Model 3, consistent with our findings at the
patent level. Strategic attention focus on Marketing versus R&D is positively related to a firm’s
overall market-pull innovations in Model 3, again consistent with our patent-level findings. We
also found a positive interaction effect between a firm’s Strategic attention focus on Marketing
versus R&D and Technology modularity (firm) on a firm’s overall market-pull innovations in
Model 3. The control variables Firm patent size and Explorative alliances are no longer
significant at the firm level in Table 4, while the coefficient of Self citation ratio (firm) is
marginally negative. For our hypothesis tests, however, the overall pattern of firm-level results
closely matches the results pattern we found at the patent level in Table 2.
We next performed two additional robustness tests (details of these tests are available
from the authors). First, we replicated the patent-level analyses by adopting a dichotomous
dependent variable, Demand pull dummy, to measure demand-pull innovation. This variable took
on the value 1 when the score of demand pull was greater than 4.0, 0 otherwise. Using logistic
regression, we observed a similar pattern of results as that reported in Table 2. In addition, we
followed Sorenson, et al, (2006) by using a longer window to calculate the Technology
22
modularity variable, in order to test the stability of this variable over differing time windows.
Using a window of 25 years (i.e., 1975-1999), the new measure was highly correlated with the
one based on a 10-year window and led to qualitatively similar results.
DISCUSSION
Technological advances often have been viewed as the most important source for firms’
innovations, which in turn can create and sustain competitive advantage. Yet comparatively little
is known about demand-pull side of innovation. Our study contributes to the technology
innovation and strategy literatures by investigating factors affecting the degree to which a firm’s
innovations are likely to be technology push or demand pull. Results indicate that increases in
technology modularity and strategic attention focus on marketing each increase the likelihood
that a firm’s innovations will be demand pull. Moreover, the effect of technology modularity on
the likelihood of demand-pull innovation is further enhanced as a firm’s strategic attention
emphasis on marketing increases. We discuss the implications of these findings next.
Implications for innovation
Although prior research has discussed the increasingly important role of demand pull
innovation in the current dynamic market place (e.g., Di Stefano, et al., 2011; von Hippel, 2005),
little is known about which technological conditions or organizational factors can best foster
demand-pull innovations. Our study is one of the first investigations this topic. Results indicate
that technology modularity facilitates the development of demand-pull innovations. While
scholars have noted the role of modularity in addressing heterogeneous market needs (Argyres et
al., 2011; Schilling, 2000), much empirical research on technology modularity has focused on
the imitablity or transferability of modular innovation (Sorenson, et al., 2006; Fleming &
Sorenson, 2001). Our study is one of the first to utilize patent data to investigate the effects of
23
modularity in affecting innovators’ ability to search and integrate market demands into
innovations. We provided additional empirical support for the prior theoretical premises
regarding modularity, but we also have opened new doors for further exploring the effects of
modularity in a broader research context associated with firm strategy.
This broader research context adds strategic attention focus as an important construct for
investigating firms’ innovation activities. According to Ocasio’s (1997) attention-based view,
innovation activities are a type of firm behavior that will be affected by allocation of the limited
attention resources in the firm. We found support for Ocasio’s (1997) arguments in the
innovation context, because firm’s strategic attention focus on marketing relative to R&D
positively affected the likelihood of demand pull innovation in our sample. This finding, together
with prior research showing that innovation activities can be influenced by organizational culture
(Tellis, et al., 2009), CEO characteristics (Wu, et al., 2005), and CEO attention (Yadav, et al.,
2007), highlights influence of context in determining the nature of firms’ innovative activities.
Furthermore, we have added an important boundary condition concerning the influence
of technology modularity on innovation. Our results show that when strategic attention focus on
marketing relative to R&D increases, the positive effect of technology modularity on demandpull innovation is enhanced. This finding confirms that managerial activities that align strategic
attention focus with technology characteristics have important effects on innovation outcomes.
Implications for the demand-side perspective in strategy
Our study also provides support for the nascent demand-side perspective on strategic
management, which examines how the heterogeneity of market demand is related to firms’
heterogeneity and opportunities for competitive advantage (see Priem et al., 2012, for a review).
One way to gauge demand-side effects is, as we have done, to determine the extent to which
24
important strategic activities such as innovation are spurred by internal technological resources
or market segment demands. We did this by responding to calls in the innovation literature for
empirical examination of demand-pull and technology-push innovations.
Our emphasis on the demand side in this study in no way denies or diminishes the
usefulness of technology-push innovation, however. Instead, we demonstrated that innovation
can result technology push, demand pull, or a combination of the two; that is, from the joint
contribution of both technological competences and market-related competences (Verona, 1999).
The computer hardware industry has evolved from dominance by mainframe computers
maintaining centralized data, to mini-computers with more distributed data processing, to
personal computers giving consumers local data control and manipulation solutions, and now
toward “cloud” computing service providers. This evolutionary process has been guided by
technology breakthroughs that push the development of innovative products and by market
demand that pulls industry competitors toward novel solutions. Neglecting either factor will
prevent researchers from reaching a comprehensive understanding of technology innovation.
Potential managerial implications
Beyond the direct managerial implications that can be drawn from our results, that
strategic attention focus and technology modularity affect the likelihood of demand-pull versus
technology-push innovations, our results also have managerial implications that can be drawn
from our classification of innovation drivers. Classification is the foundation of science
(McKelvey, 1982) and is a necessity for effective managerial prescription. Prior research and
business textbooks have classified innovations as either: product or process (Abernathy &
Utterback, 1978), incremental or radical (Abernathy & Utterback, 1978; Abernathy & Clark,
1985), competence-destroying or competence-enhancing (Tushman & Anderson, 1986;
25
Anderson & Tushman, 1990), modular or architectural (Henderson & Clark, 1990; Langlois &
Robertson, 1992), sustaining or disruptive (Christensen & Bower, 1996) and, more recently,
radical or generational (Gatignon et al. 2002; Turner, Mitchell, & Bettis, 2010). These
classifications have improved our understanding of connections between innovation types, firm
competences and innovation performance. Yet, they have tended to emphasize the technological
attributes of an innovation (e.g., how radical it is compared to existing technology) rather than
the drivers of innovations as either fundamental technological change or the emergence of new
consumer needs in a heterogeneous market.
Our study contributes to technology classification by highlighting the different sources of
innovations labeled demand pull and technology push. These distinctions are important because
they give us insight into why innovative firms often have challenges in commercializing their
innovations (Danneels, 2003). For example, we can extend Abernathy and Clark’s (1985) notion
of “transilience” by examining how firms may integrate demand-side factors and technological
details in innovation. They noted that: “Novelty and connection with scientific advance may
have little to do with an innovation's competitive significance…reinforces the notion that the
competitive significance of an innovation depends on what it does to the value and applicability
of established competence – that is, on its transilience (between market/customer linkage and
technology impact)” (Abernathy & Clark, 1985: 7). By jointly considering the marketing and
technology impacts of innovations, they depicted radical innovation as the product of a firm’s
joint marketing and technological competences, and niche innovation as producing new products
made from the same technology but that used different market-related competences. Similarly,
Danneels (2003) proposed a classification of innovation according to the ability of the firm to
use its market-related competences (which he labeled customer competences) and its
26
technological competences. Extending the ideas above, we can derive a two-by-two matrix of
technology innovations by jointly considering the two dimensions of: technology push versus
demand pull, and incremental versus radical innovation (see Figure 1). This matrix shows the
interactions between the direction of knowledge sourcing and the design attributes of an
innovation, and thereby provides a richer context and guidance through which managers can
evaluate the potential success or failure of an innovation based on their specific context, firm
competencies and strategic attention focus. Surprisingly, for example, dominant incumbents
often are able to respond effectively to disruptive technologies by serving segments of
heterogeneous demand while eschewing imitation of the new technology (Adner & Snow, 2010).
This is shown by the Swiss watchmakers that were previously dominant in mechanical
movement systems. They were not replaced by the emergence of quartz movement systems, but
instead focused on a new market niche comprised of consumers who have high regard for the
beauty of mechanical timepieces. This phenomenon is hard to explain through the traditional
framework focusing on radical vs. incremental innovation. Adner and Snow (2010), however,
attribute this “anomaly” to the existence of heterogeneous market demand. On the other hand,
GM’s electric vehicle, EV1, and Apple’s Newton in the 90s should be classified as architectural,
radical or even disruptive technologies. Yet these breakthroughs didn’t lead to financial success
as promised by received theory. Instead, the failures of GM’s EV1 and Apple’s Newton were
largely due to their state-of-the-art attributes that diverged from realistic consumer needs. By
expanding the traditional classifications of innovation to include technology-push and demandpull sources, managers can better understand their firms’ positioning for innovation, as shown in
Figure 1.
[Insert Figure 1 here]
27
Limitations and directions for future studies
Our study has several limitations that also provide opportunities for future research. First,
our study was conducted in the context of the computer hardware industry. Although our sample
was large and representative of computer firms, these firms are more likely to be highly
dependent on technology innovation to maintain their competitive advantages than are firms in
some other industries. This raises potential questions regarding the generalizability of our results.
Arguably, our conceptualization can be applied to explain innovation activities in other
innovation-intensive industries. We hope that our work may trigger future studies of demandpull innovation in different industry settings.
Second, we focused on technology modularity and strategic attention focus as sources of
demand-pull innovations. Demand pull is a complicated phenomenon, however, and there likely
are other explanatory variables that could facilitate demand-pull innovations. Future research
might integrate other theoretical perspectives and variables to further advance our understanding
of this complex topic.
Finally, some possible concerns about using patent data to measure demand-pull
innovation are warranted. One such concern is related to the comprehensiveness of patents as
measures of innovations. Because the decision to patent is itself a strategic choice, not all
technological innovations are patented. This concern is minimized in part because the nature of
competition in the computer hardware industry encourages active innovation patenting (Stuart &
Podolny, 1996). Another concern has to do with “noise” in the patent abstract due to the unique
format of patent writing, which can make it difficult to pull data from patent abstracts. This
concern is minimized in part because the validity of patent abstracts as a valuable source of
information for examining innovation has been established in prior studies (Brusoni et al, 2002),
28
and also because we had active patenting inventors in the computer hardware industry validate
our abstract-based demand-pull scale. We hope our approach may help future researchers to
capture, or to develop even better methods for capturing, the extent to which an innovation is
driven by demand pull.
29
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34
APPENDIX A
An illustration of dimensions of product design and user needs in computers (adopted from
Randall, et al., 2007).
Product design (parameter-based systems)
Processor
Display
Memory
Package
XGA/SXGA/UXGA
Video card
Hard drive
User needs (needs-based systems)
Display density
Viewing distance
MS-office performance
Affordability
Gaming
Data storage
Integrated devices
35
APPENDIX B
Examples of patents along the following 7-point Likert scale of demand-pull: 1. pure theory
or method oriented; 2. pure technology (such as chemical materials); 3. technology that can be
used in product, indirectly related to customer product, or technology breakthrough for certain
specialty; 4. technology that can be used in product that is directly related to customer product;
5. technology that can be directly used by customer through a product or service provided by the
company; 6. technology that can be directly used by customer; 7. innovation or technique created
by customers or created purely based on customers’ interests.
1. Patent 7130278 by Cisco applied in 2001.
Multi-layered cross-sectional diagram of a network
A multi-layered cross-sectional diagram and a method of diagramming a network by using
the multi-layered cross-sectional diagram are described. The multi-layered cross-sectional
diagram exhibits sufficient modularity, scalability, and size flexibility to handle a wide range of
networks and to deal with a wide variety of network sizes, including small networks, growing
networks, and large networks. In addition, the multi-layered cross-sectional diagram facilitates
localized modifications to reflect changes in the network.
3. Patent 6809572 by Cirrus Logic applied in 2002
Integrated circuit with automatic polarity detection and configuration
An integrated circuit on a system board is used, for example, in a digital audio device (such
as a DVD or A/V receiver). The integrated circuit includes a digital-to-analog converter and the
system board may include circuitry to mute the analog output of the device under certain
predefined conditions. Because it may not be known in advance by the designer of the integrated
circuit whether the circuit is activated by a signal in a high state (polarity) or a low state, the
integrated circuit includes a detector which detects and stores the required polarity. When it is
necessary for the circuit to be activated, the detector provides a signal of the correct polarity.
5. Patent 7152168 by Cisco applied in 2003
Recharging power storage devices with power over a network
A network device is disclosed. The network device includes an incoming power port, an
outgoing power port, an internal circuit, and a power storage system connected to the incoming
power port, the outgoing power port and the internal circuit. In alternative embodiments, the
device may include a power regenerator, a power detector and divider, or a power splitter.
7. Patent 6510048 by Apple applied in 2001.
Keyboard arrangement
The invention generally pertains to a computing device. More particularly, the invention
pertains to an improved keyboard arrangement for use in the computing device. One aspect of
the invention pertains to a movable keyboard that can be opened or removed to gain easy access
to internal components of the computing device. Another aspect of the invention pertains to a
magnetic keyboard securing system suitable for holding the movable keyboard relative to the
computing device. The invention is particularly useful in computing devices such as portable
computers.
36
TABLE 1. Descriptive statistics and correlation matrix
1. Demand-pull innovation
2. Strategic attention focus on
Marketing v.s. R&D focus
3. Technology modularity
4. Self citation ratio
5. Science linkage
6. Firm patent size
7. Firm age
8. Return on sales (ROS)
9. Explorative alliances
Mean
3.344
S.D.
0.012
0.182
1.658
0.093
4.761
857.998
14.026
0.08
7.196
0.003
0.008
0.002
0.149
7.871
0.076
0.006
0.056
1
2
0.19
0.04
0.00
-0.04
-0.14
0.02
0.00
0.00
0.06
0.05
-0.08
-0.26
0.00
0.00
-0.10
3
0.00
-0.04
-0.10
0.06
0.00
0.02
4
0.01
-0.01
0.11
0.06
0.14
5
0.00
-0.11
-0.10
-0.06
6
7
8
0.19
0.21
0.33
0.18
0.29
0.16
6
7
8
0.19
0.13
0.50
0.20
0.39
0.18
Notes: N=8828. Significant at the 0.05 level (two-tailed test) when Pearson correlations ≥0.02 or ≤-0.02.
TABLE 3. Descriptive statistics and correlation matrix (firm level)
1. Demand-pull innovation (firm)
2. Strategic attention focus on
Marketing versus R&D
3. Technology modularity (firm)
4. Self citation ratio (firm)
5. Science linkage (firm)
6. Firm patent size
7. Firm age
8. Return on sales (ROS)
9. Explorative alliances
Mean
3.520
S.D.
0.055
0.172 0.018
1.763 0.034
0.070 0.006
6.503 0.949
131.677 19.084
10.751 0.560
-0.251 0.069
3.844 0.273
1
2
0.31
-0.06
-0.21
0.11
-0.07
0.03
-0.01
0.00
0.19
0.06
-0.10
0.03
0.06
0.10
0.02
3
0.17
-0.12
-0.02
0.07
0.08
0.05
Notes: N=269. Significant at the 0.05 level (two-tailed test) when Pearson correlations ≥0.12 or ≤-0.12.
37
4
-0.12
0.13
0.22
0.18
0.16
5
-0.06
-0.12
-0.07
-0.10
TABLE 2. Tobit regression results for the determinants of the likelihood of demand-pull
innovationa
Variable
Constant
Self-citation ratio
Science linkage b
Firm patent size b
Firm age
Return on sales (ROS)
Explorative alliances
Model
(I)
3.33***
(0.30)
-0.16
(0.17)
-0.33
(0.24)
-0.03**
(0.01)
0.01
(0.01)
0.01
(0.04)
0.02†
(0.01)
Yes
Yes
Yes
Yes
Yes
Yes
Model
(IV)
3.15***
(0.24)
-0.23†
(0.13)
-0.24
(0.25)
-0.02*
(0.01)
0.01
(0.01)
0.02
(0.05)
0.02*
(0.01)
0.65**
(0.21)
0.08**
(0.03)
0.16**
(0.06)
Yes
Yes
Yes
14.77***
13.14***
12.67***
-13604.33
-13468.72
-13463.53
271.22***
281.60***
Strategic attention focus on Marketing
versus R&D
Technology modularity
Model
(II)
3.17***
(0.25)
-0.23†
(0.13)
-0.24
(0.25)
-0.02*
(0.01)
0.01
(0.01)
0.02
(0.05)
0.02*
(0.01)
0.65**
(0.21)
0.08**
(0.03)
Strategic attention focus x Technology
modularity
Fixed industry segment effect
Fixed technology class effect
Fixed year effect
F
Log Pseudo likelihood
-2[L(Reduced)-L(Full)]~ χ2
a
N=8828. Standard errors adjusted for 74 firm clusters.
Coefficients are multiplied by 100 for reporting convenience.
†
p<0.10, * p<0.05, ** p<0.01, *** p<0.001.
b
38
TABLE 4. Regression results for the determinants of firm-level likelihood of demand-pull
innovationa
Variable
Constant
Self citation ratio_firm
Science linkage_firm b
Firm patent size b
Firm age
Return on sales (ROS)
Explorative alliances
Model
(I)
3.45***
(0.48)
-1.24†
(0.66)
0.34
(0.33)
-0.01
(0.03)
0.00
(0.01)
0.02
(0.05)
0.01
(0.02)
Yes
Yes
Yes
Yes
Model
(III)
3.40***
(0.45)
-1.09†
(0.66)
0.51
(0.33)
-0.01
(0.03)
0.01
(0.01)
0.01
(0.05)
0.01
(0.02)
1.35**
(0.36)
0.20†
(0.10)
0.84†
(0.46)
Yes
Yes
0.63
17.59
0.59
34.25**
0.58
38.33**
Strategic attention focus on Marketing
versus R&D
Technology modularity (firm)
Model
(II)
3.24***
(0.45)
-1.01
(0.66)
0.51
(0.33)
-0.01
(0.03)
0.01
(0.01)
0.01
(0.05)
0.01
(0.02)
1.17**
(0.35)
0.20*
(0.10)
Strategic attention focus x Technology
modularity (firm)
Fixed industry segment effect
Fixed year effect
Rho
Wald χ2
a
N=269. b Coefficients are multiplied by 100 for reporting convenience.
p<0.10, * p<0.05, ** p<0.01, *** p<0.001.
†
39
Figure 1. Illustration of a transilience view of innovation
Demand pull
Risk of inventor dilemma
(i.e., if short-term focused,
then it can lead to inventor
dilemma in Christensen’s
sense)
Challenging but potential for
revolutionary technology (e.g.,
iPhone, Wii)
Incremental innovation
Risk of sandy foundation (i.e.,
technology improvement
potentially to be trumped by
disruptive technologies)
Radical innovation
Risk of brilliant failure (e.g.,
Apple’s Newton, EV1)
Technology push
40