Why do Category Labels Stick? Unpacking the Innovation
Paradox
Stine Grodal
Fernando Suarez
Diego Zunino
Boston University Questrom School of Business
ABSTRACT
Adding to the traditional studies of technology adoption by scholars in economics and
management, scholars with socio-cognitive and institutional perspectives have recently proposed
an apparent paradox in the adoption of new technologies: The need to balance the perceived
familiarity and novelty of a new technology. This idea has become well accepted, but scant
empirical research has been done to confirm or deepen our theorizing. By examining an
important element of how innovations are understood, their related category labels, we show how
adoption is affected by the degree to which a new product label is perceived as familiar or
creative. Drawing on a large data set from the smartphone industry from 1998 to 2011, we track
firms’ adoption of category labels over time. We find support for an inverted U-shape
relationship between the firms’ decision of which label to adopt and the degree to which labels
are perceived as familiar or creative. We confirm these results with an online experiment
designed to address potential endogeneity concerns. Our paper expands our understanding of the
innovation paradox and further builds theory around why some category labels stick while others
are left in the dustbin of history.
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INTRODUCTION
The question of how new technologies get adopted has been a key concern of scholars in
technology management and economics for decades (Hannan and McDowell, 1984; Rogers,
2010; Majumdar and Venkataraman, 1998). A more recent literature has begun to explore the
role that socio-cognitive and institutional factors play in the stakeholders’ framing and
understanding of new technologies, and their consequent influence on how each technology fares
in the market (Kennedy and Fiss, 2013). This literature has highlighted an apparent paradox that
every new technology needs to overcome. On the one hand, new technologies have to
differentiate themselves from existing technological offerings; in other words, they must convey
ideas of novelty, creativity, and originality. However, at the same time, new technologies have to
appear familiar enough to stakeholders and invoke existing understandings, so as to minimize the
natural reluctance to something new and unproven (Hargadon and Douglas, 2001; Bingham and
Kahl, 2013).
A growing body of research has examined the socio-cognitive aspects surrounding new
technologies and markets (Rosa et al., 1999; Pontikes, 2012; Suarez, Grodal and Gotsopoulos,
2014). This research has stressed the role of categories and their associated labels in shaping
stakeholders’ understanding and perception of new technologies and products (e.g. Navis and
Glynn, 2010). Categories are defined as socially constructed partitions or taxonomies that divide
the social space into groupings of objects perceived to be similar (Bowker and Star, 2000).
Category labels --words or phonemes that are used to reference objects that are perceived to be
distinct or belong together-- are the first instantiation of categories. As such, they are one of the
earliest factors that shape stakeholders’ understandings about a new product.
When introducing an innovation to the market, producers often lack terms for how to
reference their new product (Kaplan and Tripsas, 2008). In order to communicate the meaning of
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their product to stakeholders they often invent category labels, like such as “pocket PC”, “camera
phone” or “smartphone”. When confronted with a newly created category label, stakeholders
have no prior experience with the kind of products that such label may refer to; consequently, at
the beginning, the meaning of most new category labels tends to be shallow. Extant research has
suggested that stakeholders begin to make sense of new labels by linking the label’s constituent
parts via a relation to existing objects, or by projecting properties of a constituent (Wisniewski,
1996; Smith, Osherson, Rips and Keane, 1988). Through an iterative and negotiated process by
which labels are adopted and used by different stakeholders, labels become infused with value
and began to be associated with pre-existing categories, and thus gradually become meaningful
categories themselves (Peirce, 1931; Grodal et al., 2014). As the meaning of the new categories
develop, they begin to form “rules” or “boundaries” that dictate which objects can claim
membership to a given category (Hannan, Polos, and Carroll, 2007; Navis and Glynn, 2010).
Category labels that do not get traction fail to get infused with meaning and are ultimately
abandoned. As this process continues, emerging industries often coalesce in around a dominant
category, or “the conceptual schema that most stakeholders adhere to when referring to products
that address similar needs and compete for the same market space” (Suarez et al., 2013).
Despite the importance that category labels have in shaping stakeholders’ understanding
about new technologies, we know relatively little about the process by which some of these labels
are selected over others and how they “emerge and fall out of use” (Kennedy and Fiss, 2013, p. 1).
This is an important gap in our theory, since the performance of entrants into a new industry can
depend on the categorical labels they choose when positioning their products (Rosa et al., 1999;
Pontikes, 2012). In this paper, we tackle this gap by presenting an empirical study of the creation
and selection of categorical labels in what became to be known as the “smartphone” industry. By
collecting data from the official product launches of each smartphone in the period 1998 to 2011
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in the U.S., we trace the categorical labels used by smartphone producers over time. Borrowing
from linguistics and extant research on categories, we test and expand existing theory about why
some labels tend to stick while others are abandoned.
In particular, we revisit and test the idea that, to secure adoption, producers have to find
ways to convey both notions of change and stability when introducing new products (Hargadon
and Douglas, 2001). We posit that one of the key ways in which producers convey these notions
to consumers and other stakeholders is through their choice of category labels. The literature,
however, has not been consistent or specific enough as to what kind of change and stability is
needed to maximize adoption. Some authors seem to imply some type of trade-off between these
dimensions (e.g. Bingham and Kahl, 2013), while other authors seem to suggest that both can be
achieved at once (Hargadon and Douglas, 2001). Studies of adoption in other contexts that also
use this change-stability distinction are equally unspecific. For instance, Uzzi et al. (2012), in one
of the few large scale empirical studies besides ours, use the opposite sides of a single measure to
operationalize both constructs (in their terms, novelty and conventionality) in their study of how
atypical combination of knowledge affect the adoption (cites) of scientific papers, which would
suggest some kind of trade-off. However, then they argue that most-cited papers score high in
both novelty and conventionality. Mckinley et al. (1999), studying adoption of schools of thought
in organizational theory, recognize the existence of a tension between continuity and novelty, but
then they go on to argue that adoption requires an “adequate” level of each (p.637).
In this paper, we provide a fresh empirical analysis from which our existing theory and
understanding can be improved. By focusing on why some category labels are adopted more than
others, we provide both granularity and specificity to the discussion of these important issues. In
particular, we hypothesize a non-linear relationship between label adoption and a construct
denoting stability and conventionality, which hereon we refer to as label familiarity; we also
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hypothesize a non-linear relationship between label adoption and a construct denoting change and
novelty, which hereon we will refer to as label creativity. Our results largely support our
hypotheses, thus representing not only one of the first large empirical studies of category label
adoption, but arguably the most detailed data-based work to date on the change-stability paradox.
Moreover, we complement our econometric analysis by carrying out and reporting the result of
an online experiment specially designed to provide further confirmation of our findings, and to
address possible concerns about endogeneity in our regressions. The results of the experiment
strongly confirm our empirical findings, making our paper also, to our knowledge, the first
multiple-methods work on this topic.
THEORY AND HYPOTHESES
Categories are arguably the most widely-researched construct among several that have
been proposed to capture the socio-cognitive dimension of technology emergence and adoption,
such as “field frames” (Lounsbury 2001; Lounsbury, Ventresca and Hirsch, 2003); “schemas”
(Bingham and Kahl, 2013), and “technological frames” (Kaplan and Tripsas, 2008; Orlikowski
and Gash, 1994). Categories help group similar objects and can indeed determine the set of
characteristics that the objects belonging to a given category are expected to posses, and the
elements that differentiates them (or not) from members of other categories (Vygotsky, 1986).
As the first instantiation of categories, category labels help make sense of new products
by allowing stakeholders to develop semantic links to other categories and their associated labels,
relating a label to other objects or concepts and borrowing from the inherited properties of the
label components (Wisniewski, 1996). It is through this process that the “deepening” of meaning
takes place (Grodal et al, 2014) through which some of these labels eventually become
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established categories (Peirce, 1931; Bingham and Kahl, 2013). When stakeholders observe a
category label, they construct the group of objects that they perceive as being associated with it
(Yamauchi and Markman, 1998). While producers are the main creators of labels in the process
of introducing their products to an emergent industry, but labels are socially constructed and thus
other stakeholders such as users, industry analysts or observers, bloggers, etc, can also create
them. For instance, the “mountain bike” label was created by users who tweaked their standard
bikes for racing down hills, the “robot” label was created by a writer of a science fiction book,
and the label “impressionism” was created by art critics (originally with a negative connotation)
to refer to the unconventional work of Monet and other painters of the time.
Extant research has sought to understand the extent to which firms spanning categorical
boundaries face performance penalties (Zuckerman, 1999; Hsu et al. 2009), and the conditions
under which spanning categorical boundaries might be permissible (Ruef and Patterson, 2009;
Fleischer, 2009; Pontikes, 2012; Granqvist et al. 2013). Despite notable progress in our
understanding of categories, scant research, theoretical or empirical, has been conducted to unveil
the dynamics of competing categories and their associated labels; i.e. the contestation process that
leads to the adoption of some labels and the abandonment of others. In order to tackle this
challenge, in this study we test and extend the theoretical argument that, in order to drive higher
adoption, category labels have to resolve the paradox of being simultaneously (1) creative enough
to convey notions of novelty and change so as to attract the attention of stakeholders, and (2)
familiar enough that they convey notions of continuity and stability, so as to be readily
comprehensible. Similar versions of this broad argument have been proposed by a handful of
authors. For instance, Hargadon and Douglas (2001) suggest that, “Without invoking existing
understandings, innovations may never be understood and adopted in the first place. Yet, by
hewing closely to existing institutions, innovators risk losing the valued details, representing the
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innovation’s true novelty, that ultimately change those institutions. Success, then, requires
entrepreneurs to locate their ideas within the set of understandings and patterns of action …in
order to gain initial acceptance, yet somehow retain the inherent differences” (p. 478).
While the general argument of a paradox is appealing, it is by no means widely accepted,
particularly when it comes to the form and limits of the tension between the two constructs.
Moreover, the argument has, to our knowledge, never been empirically tested. The existing
literature has been rather unspecific and even in apparent disagreement with respect to the
existence of a familiarity-creativity tension, and the role that it may take during technology
adoption. The quote from Hargadon and Douglas above would suggest that the authors believe
that familiarity and creativity can be achieved at the same time, and that having more of each is
always beneficial for adoption. However, no conclusion can be drawn from their theorizing as to
how much of each dimension maximizes adoption; for instance, should a technology sponsor
always try to convey more familiarity and more creativity in their label to secure stronger
adoption? Bingham and Kahl (2013), in contrast, perceive a trade-off. They claim that their study
resolves, “a conundrum related to the process of emergence – how to manage the simultaneous
existence of two inconsistent states, familiarity and novelty” (p. 15). They propose that these
“inconsistent states” can be overcome by focusing on one first (familiarity) and then the other
(novelty). Studies of adoption that have used the familiarity-creativity dilemma in contexts other
than technology are equally unspecific. McKinley et al. (1999) state that, “Our central thesis
concerning novelty and continuity is that although there exist tension between them, adequate
level of each are necessary [for adoption].” Not surprisingly, they give no clues to determine
what an “adequate” level is.
We contribute to existing literature by providing a fresh perspective to study these open
issues in the literature, based on a careful empirical study complemented by an online experiment
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that allows us to add precision to our theorizing. We focus on the structure and content of
category labels in order to develop a more nuanced account of the familiarity and creativity
constructs. In our view, these constructs are best understood through the prism of recombination.
An established literature has shown that new creations are formed through the recombination of
existing elements (Schumpeter, 1939; Wisniewski, 1996), and that the level of creativity in these
recombinations can influence both the success of the innovation (Fleming, 2001; Fleming, Mingo,
and Chen, 2007) and how widely they diffuse (Grodal and Thoma, 2014).
A category label can be creative while still using very familiar words of phonemes. For
example, when John Burton Carpenter in the 1970s created the compound label “snowboard” it
was creative, because it combined two words “snow” and “board” that had seldom been used
together, but at the same time it was familiar, because even stakeholders who were exposed to the
compound for the first time were able to associate it with elements they knew and that helped
them make sense of the new product. Indeed, compounds, defined as “the simple concatenation
of any two or more nouns [or other words] functioning as a third nominal” (Downing, 1977: 810),
are an important way for stakeholders to create category labels that: (a) build links to existing
categories and meanings, thus invoking continuity and familiarity but, at the same time, (b) allow
for novelty and change by supporting creative recombinations (Lieber, 1983; Wry, Lounsbury
and Jennings, forthcoming).
A central tenet of our theorizing in this paper is that, when it comes to adoption, there are
first increasing and later decreasing returns to both creativity and familiarity. In other words, the
more familiar a category level, the higher the adoption, but only up to a point. The more creative
a category label is, the higher the adoption, but also up to a point. Both of econometric analysis
and online experiment below are geared to test these hypotheses.
Labels become familiar by establishing links to existing categories and their associated
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labels. Category labels that fail to make these connections will most likely be abandoned because
stakeholders will be confused about the labels’ meaning. As we noted before, the label
“snowboard” became dominant in that industry and was widely adopted, as it represented a
creative recombination of words that conveyed both change and familiarity.
However,
snowboard was not the label used by the first such product introduced to the market. The first
product was introduced using a different label, the “snurfer,” which failed to make enough
connections to existing labels and categories and therefore was later was abandoned in favor of
snowboard. While the snurfer label drew from the words “snow” and “surfer,” these associations
were disguised, making it hard for stakeholders encountering the label for the first time to make
sense of it. While being unfamiliar might be problematic, being too familiar has its disadvantages
as well, because when category labels are perceived to be too familiar they become taken-forgranted and thereby fail to elicit interest and scrutiny by stakeholders (Hsu and Grodal, 2014).
Too much familiarity may render the label obvious or uninteresting and thus fail to capture the
attention of stakeholders. For instance, one of the early labels in the smartphone industry was
“camera phone,” directly derived from the fact that one of the first technological features that the
new type of phones added was a digital camera. The label was an obvious combination of two
very well known labels that had existed for a long time, and as such did not capture major
attention from stakeholders.
We therefore hypothesize,
H1: There is an inverted U-shape relationship between the familiarity of a
category label and its degree of adoption in an emerging industry.
A similar dynamic occurs with creativity in labels. While label creators should strive to
have their labels be as novel and creative as possible so as to make them attractive to other
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stakeholders, the recombination of words in the creation of category label (like the recombination
of novel technologies) can be taken to an extreme: “The set of potential combinations and, a
fortiori, the possible ways that each set of potential combinations can be combined has become
essentially infinite” (Fleming, 2001). In other words, label sponsors have limitless possibilities to
come up with creative labels by recombining words and phonemes that have never been
recombined before. However, if a label’s creativity is too high, it lead to inconsistent semantic
connections to existing labels or categories, thus bringing confusion to stakeholders that
encounter the label. Inconsistent semantic connections mean that stakeholders cannot easily find
informational value in the creative combination of words, thus hampering their understanding.
Labels that are too creative risk being difficult to comprehend by stakeholders and thus are likely
to be abandoned. Consistent with our thinking here, Fleming (2001) finds that recombinations
based in local search tend to be more successful than those based on more distant search. It
follows that,
H2: There is an inverted U-shape relationship between the creativity of a category
label and its degree of adoption in an emerging industry.
METHODS
Setting: The “Smartphone” Industry
We chose to study the smartphone industry, a suitable context in which to test our hypotheses,
due to several characteristics. First, this industry represents a market space that emerged recently
and therefore data can be retrieved on the entire set of category labels created by the key
stakeholders. Second, many categorical labels were introduced during a relatively short period of
time, which highlights the label contestation dynamics we are interested in. Third, due to the
extensive and far-reaching technological possibilities offered by smartphones and the rapid pace
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of technological change, there was much categorical uncertainty, particularly in the early years:
that is, the meaning of the many categorical labels introduced was often ambiguous and
overlapping. It was not easy for customers and other stakeholders to make sense of the different
categorical labels they were presented with.
Regression Analysis
In our regressions, the unit of analysis is label-year. Because we deal with count data, a
Poisson model is appropriate. The Poisson model assumes expected value and variance equal to 𝜆,
known as the dispersion index. This assumption can be too strict, so we relax it by using a
Negative Binomial regression, a generalization of the Poisson model that does not rely on the
identity of variance and expected value of the dispersion index. The Binomial distribution,
however, assumes overdispersion --that is, the variance is larger than the expected value. We can
see from the summary statistics table (Table 3) the face validity of assuming overdispersion, but
we also provide the estimate of ln(alpha) for each regression. In a Poisson model, alpha is
constrained to zero; thus, whenever ln(alpha) is not different from zero, alpha is equal to 1.
Overdispersion parameter equal to 1 implies that the negative binomial is of little use compared
to a Poisson regression.
The distribution of Negative Binomial is given by:
The negative binomial regression equation is then:
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Where X is the vector of time-invariant characteristics of the category label, and 𝜏 is a set of
dummy for each year in the sample. We include both a linear and a quadratic term for the
interaction between familiarity and originality.
DATA AND VARIABLES
To test our hypotheses, we constructed a unique dataset of category labels and their
adoption in what we know today as the smartphone industry, which emerged in the late 1990s.
We identified category labels by examining the press releases that smartphone manufacturers
used when introducing their new products to the market. Press releases are a reliable record of the
category labels chosen and used by different companies over time, since they represent the firms’
best efforts to communicate the position of their products vis a vis pre-existing market categories.
Indeed, press releases have already been used in extant research on categories; Pontikes (2012),
for instance, reports that each producer issues more than 1 unique category label every year
(mean 1.5, median 1.3).
We coded 390 category labels from 382 press releases between 2000 and 2010, for a total
of 1,924 label-year observations. Our dataset tracks category labels from 31 companies used to
position 308 devices identified as belonging to what would later be commonly referred as to the
“smartphone” category. The reference markets are the United Kingdom and the United States,
and the time covered goes from from 2000 to 2010.
Figure 1 shows the average number of labels per press release by company. The chart gives
an idea of the different strategies followed by the firms entering the industry in the use of
category labels. For instance, while Apple uses very few categorical labels (1.7 per press release),
LG and Motorola do the opposite, using almost six category labels per press release. The figure
also suggests that the number of category labels used by a producer is not necessarily correlated
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with the producer’s performance in the market. For example, Motorola and Nokia, among the
companies using the largest number of category labels, failed to remain in the industry as
independent companies and were later acquired by Google and Microsoft, respectively.
Figure 2 shows that, by 2010, the last year in our data, the total number of labels used by
producers is still increasing, suggesting that a dominant category (Suarez et al, 2013) has not yet
emerged.
We collected press releases from different sources. Table 1 shows the relative importance
of each of our sources: three quarters of the press releases come from smartphone manufacturers
themselves, and remaining quarter come predominantly from operators (23.7%). The residual is
divided in OS developers (0.8%) and retailers (0.5%).
*** INSERT FIGURE 1 HERE ***
*** INSERT TABLE 1 HERE ***
*** INSERT FIGURE 2 HERE ***
To construct indexes for the familiarity and originality of each label, we broke down the
label into words. For the case of labels with two or more words, we also deconstructed the
compounds, even when the words were written together as one word. For instance, the label
“smartphone” was deconstructed into the words “smart” and “phone”. In total, there were 206
words used to construct 390 category labels.
We constructed a measure of a category label’s familiarity by counting the number of times that
each word in the label appeared in the Factiva database of major US newspapers.
For originality, given the computational requirements, we drew on data coming only from three
major U.S. newspapers: The New York Times, The Wall Street Journal, and USA Today. We
randomly selected one day per month for each year in our data collection period, excluding
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weekends because of possible differences in weekend editions. We then collected all the articles
that were published by the three newspapers on that particular day: on average, each random day
provided 400 to 450 articles, with exceptions for holidays when the article count dropped to 190
to 320. Table 2 shows that we collected more than 4,500 articles per year over 11 years, with a
total of 54,161 articles. Finally, we used MemeStat, a software for content analysis, to obtain
both counts of words and particularly counts of words co-occurrence at the paragraph level.
*** INSERT TABLE 2 HERE ***
Dependent Variables
Count of press releases citing the label. The dependent variable in our analysis is a
measure of the level of adoption of a given category label. This measure provides greater
granularity over alternative measures of category label success, such as survival (i.e. label still in
use). We compute a label’s count of press releases as the number of press releases by any
smartphone producer that use a focal label, within the first year from the label’s first use in any
manufacturer’s press release. The label showing the highest adoption under this measure was
used in 82 press releases (in 2010), while many category labels were mentioned in just one press
release.
Count of labels citations. Counting press releases makes no difference between a press
release that contains just one mention of a given category label, and another that uses the label
several times. To take this difference into account, we use an alternative dependent variable that
counts the total number of times a label is used in all the press releases by any smartphone
producer, within the first year from the label’s first use by any manufacturer. This dependent
variable can be thought of as measuring the intensity of use of a given category label.
Explanatory Variables
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Two-word compound is a dummy variable that assumes the value of one if the category
label is a recombination of two words (as for example in “pocket computer”), and zero otherwise.
We created another dummy variable to control for the rather rare occurrence of compounds with
three or more words (as for example in “electronic messaging device”).
Familiarityt-1. We constructed a familiarity index by searching our corpus of newspaper articles
for the frequency of each word used in the labels within any given year. We then used this metric
to estimate how common each word was relative to other words used in the category labels for
that year. For labels with more than one word, we averaged the frequency of each word in the
label. To obtain a scale from 0 to 1, we divided the average frequency by the highest frequency
in that year. Because of the shape of the distribution, we use the logarithm of the familiarity index
in the regressions. Given our hypotheses, familiarity enters the regressions both in linear and
quadratic terms.
Originalityt-1. This variable represents, for any given category label, the level of originality in the
recombination of words. We measured this by identifying how common it was for two or more
words to appear together in the same press release or story for any given year. For example,
consider this excerpt from The Wall Street Journal, July 10, 2007 part of our sample:
“The colorful computers and the ads are part of an effort by Dell to redefine its brand,
which had lost its focus in recent years as the PC business changed. Dell became famous
with the direct-distribution sales model pioneered by founder Michael Dell, in which it
sold computers over the phone and on the Internet. Instead of aiming to create an image
for the brand, Dell's ads used to focus on the technical specifications of its computers,
such as the speed of its processors.”
In the excerpt above we would code the words “PC” and ”phone” as co-occurrences in a given
paragraph. A high number of co-occurrences like this would signal that the combination of those
two words is not that original. We measure originality as the difference between one and the ratio
of the number of co-occurrences of two words over the number of times the two words are
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mentioned individually. We subtracted the intersection from the sum of the two words in order to
avoid counting the intersection twice; the originality index is logically illustrated in Figure 3. For
multiple-word labels, we computed all the possible two words combinations within the label and
then averaged them to calculate the originality ratio.
*** INSERT FIGURE 3 HERE ***
Category labels with more than two words are only 7% of the category label-year
observations and just 17 of them receive between one and three mentions. For simplicity, given
the minimal loss, we limit the analysis of originality to two-word category labels. Theoretically, a
value of originality equal to zero means that there words A and B are always mentioned together
(always co-occur), while a value of originality equal to one means that the two words have not
co-occurred together in that year. Because of the shape of the distribution, we used the logarithm
of originality in the regression. The originality index enters the regression both in linear and
quadratic terms. As we did with familiarity, we performed the analysis of originality at the article
and paragraph level using the 3-newspaper sample, and also at the article level using the larger
set of 69 newspapers.
Control Variables
We controlled for several other characteristics of category labels. We include a set of
dummy variables to capture the number of words used in a label. We use dummy variables to
control for the use of trademarks (e.g. Galaxy), the reference to a particular technology (e.g.
LCD), to an operating system (e.g. Android), or to a technology generation (e.g. 3G). We control
also for the use of suffixes such as “enabled”, “powered”, or “enhanced”. We take into account
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the age of the category label, defining it as the difference between the focal year t and the label’s
year of introduction. Finally, in some robustness checks, we use a full set of dummies to capture
the “head” of the category label, meaning “the word that is obligatory and controls the other
words, its dependents. […] heads convey central pieces of information and their dependents
contribute extra information” (Brown & Miller, 2013). For example, in the category label “smart
device”, the words that controls the other word is “device”; while in pocket PC the head is “PC”.
Some of these heads are repeated several times in our data, such as in “smart device,” “portable
device,” “mobile device,” etc. When we control for the head we look at the familiarity and the
originality of the dependents, given a particular head. This is why this control enters as a
robustness check.
RESULTS
Table 3 reports the descriptive statistics of the variables, while table 4 reports the
correlation matrix. The table shows that category labels use on average a little more than three
words. On average, the age of a category label is 3 years and a quarter, while the oldest category
label is fourteen years old in 2010. Almost half of the labels reference technological features,
13% reference an operating system, 9% reference a technology generation, 7% of the category
labels contained a trademark term, and about 13% of labels use the suffixes “enabled”,
“powered”, or “enhanced”. From the correlation matrix we observe that familiarity and
originality are negatively correlated; this correlation gets attenuated when we take the logarithms
in the regressions.
Table 5 shows the results for the test of Hypotheses 1 and 2. Models 1 and 2 include the
control variables as described in the section above. The two models differ in the dependent
variable used: in model 1 the dependent variable is the count of press releases citing the label,
while in model 2 is the count of label citations. In the appendix (Table A-1) we report the
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hierarchical models with progressive inclusion of controls. Model 3 and 4 add to the model a set
of dummies for the “head” nouns. We first analyze the control variables and then the explanatory
variables. Category labels with more words are associated with less adoption as measure by either
of the two dependent variables. Comparatively, two-words labels perform better than labels with
more than two words: in all the four models the negative coefficient associated with two words
labels is smaller than that associated with more-than-two-word labels. The dummy for labels with
trademark is not significant in models (1) and (2) but becomes significant in models (3) and (4)
when we control for the “head” nouns, suggesting an association with a lower level of adoption.
This same pattern holds with age: older labels tend to be adopted less, and this negative
association is stronger after we control for the “head” nouns. There is no significant difference
between category labels that refer to a technological generation with respect to those that do not.
Nevertheless, we observed negative association between adoption and category labels that refer
to a particular technology (e.g., Bluetooth, MMS, or megapixel camera). In contrast, category
labels that refer to an Operating System, and those that have “enabled”, “powered”, or “enhanced”
as suffix, show a positive association.
As for the explanatory variables, the first two rows of Table 5 show the results for
familiarity. The linear term is positively related to the dependent variables, while the quadratic
term is negative. The linear and quadratic terms are significant at 1% and 5% level respectively
for the count of press releases citing the label, and even more significant (0.1% and 1%
respectively) for the count of label citations. Once we controlled for head nouns in Models 3 and
4, the quadratic term becomes larger and more significant, while the linear term coefficient is
smaller in case of the first dependent variable and larger for the second one. Overall, these results
suggest substantial evidence that familiarity and degree of adoption of a category label follow an
inverted U-shape relationship, which lends support to Hypothesis 1.
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The third and fourth rows show the coefficients for originality. At first glance, one notices
that the coefficient for the originality score is very high – even when using a logarithm
transformation. This is because of the skewedness in the originality distribution: in fact, most of
the scores lie between 71 and 100, with a large number of them around decimals of 99. The
coefficients for the linear term of originality are positive and significant when each of the two
dependent variables is used, at 1% and 5% significant levels, respectively. The coefficient for the
quadratic term is also negative and significant in both Models 1 and 2, same significance levels
similar to those of the linear term. When controlling for the head nouns in Models 3 and 4 the
coefficients become larger and more significant in both cases. Our results therefore provide
evidence that originality and degree of adoption of a category label have an inverted U-shape
relationship. This evidence supports Hypothesis 2.
In Table 6 we analyzed how familiarity and originality interact. In the appendix (Table A2) we report the hierarchical models with progressive inclusion of controls. The four models in
Table 6 have the same specification as those in Table 5, and in Table 6 we have added the
interaction terms, both linear and quadratic, between originality and familiarity (first two rows).
We first describe the main effects, and then we describe the interaction.
The main effects for familiarity and originality show no change in the sign of the
coefficients with respect to those of Table 5. Possibly due to the effect of multicollinearity, the
significance of the linear coefficient for familiarity drops from 1% to 10% in Model 1, and from
0.1% to 1% in Model 2. With the addition of head dummies, the significance of familiarity drops
both in linear and quadratic terms when compared to those from Table 5, for both of the
dependent variables. The significance for originality does not seem to drop when compared to
Table 5 and even increases for the case of Model 2.
The interaction between familiarity and originality enters the regression both in linear and
19
quadratic terms. The sign of the linear coefficient for the interaction is negative, for all the model
specifications in Table 6, and significant. The coefficient for the squared interaction term is
positive and significant, again across all model specifications. Thus, overall we show evidence of
a U-shaped relationship between the familiarity-originality interaction and our two measures of
category label’s adoption. One way to interpret this result is to think of familiarity mitigating the
inverted U-shape relationship between label originality and label traction.
These results hold overall to two robustness specifications, which we report in table 7 and
8. In table 7, we only select one category label for each press release – the one most used as
measured by the frequency of use within the press release. With this specification, we omit other
labels that are used but that can be considered secondary to a firms’s attempts to associate its
product with a category. The number of observations drops from 1900 to 338. The specification
of models 1 and 2 is the same than in models 1 and 2 of Table 5, while the specification of
models 3 and 4 is the same than in models 1 and 2 in Table 6. When compared to the larger
sample, the signs of the coefficients do not change. The coefficients for familiarity and originality
still suggest an inverted U-shape relationship with the level of adoption of a label, while the
interaction terms still shows a U-shape relationship. The significance of the coefficient is still
robust for familiarity, ranging from 5% to 0.1%. Originality suffers from lack of power because
of its skewedness: when no interactions are added, it is only significant at the 10% level.
In table 8, in a further robustness test, we dropped from the analysis a category label
containing the words “phone” or “device” unless they were the only labels used to describe the
product in a given press release. These two words are among the most used in the press releases,
but they differ from other label words insofar as “phone” refers to the previous (and broader)
category, while “device” is a very generic term for a technology object.
The specifications of Table 8 do not differ from those in Table 7. Moreover, the signs in
20
Table 8 are consistent with the signs of Tables 5 and 6, and with our hypotheses. It should be
noted that the significance is lower for familiarity when there are no interactions; indeed, the
quadratic term is not significant in models 1 and 2. When interactions are added, however, the
significance of coefficients for familiarity become significant for both the linear and quadratic
terms.
Figures 2 and 3 provide a graphical interpretation of the interaction between originality
and familiarity. We first segmented familiarity into three terciles, and labeled each of them as
“low”, “medium”, and “high” familiarity, respectively. In Figure 2 we show the quadratic fit of
the count of press releases citing the label over the values originality for each of the three
familiarity terciles. The figure shows that the highest predicted number of count of press releases
for a category label occurs when originality and familiarity are both “medium”. Moreover, the
shape of the curve gets more skewed for the case of medium familiarity, and almost flat for the
case of high familiarity. This suggests that, when the familiarity of the label words is high,
increasing originality produces a small but monotonic positive effect on the traction of the label.
This result does not change when we use the alternative dependent variable. Therefore,
consistently with our theory, we find evidence that the highest number of counts appears when
both originality and familiarity are high but not too high.
DISCUSSION AND FINAL REMARKS
Our research augments our understanding of the socio-cognitive dimension of industry
emergence. Drawing from the literatures on categories and industry evolution we present an indepth look and empirical test of the socio-cognitive dynamics that take place as an industry
develops. Our study shows that, as theorized by recent research (see for instance, Suarez et al.,
21
2014), an initial period of categorical divergence where the number of categories in use increases
over time, is followed by a period of convergence where the number of categories in use is
reduced -- a pattern illustrated in Figure 4. In doing so our study begins to answer the call by
Kennedy and Fiss (2013) to study the process through which categories are created, adopted and
fall out of use.
*** INSERT FIGURE 4 HERE ***
We focus on category labels, because they represent the first instantiation of categories.
During the early period of industry evolution producers introduce new category labels as they
struggle to find the right term to describe their innovative products. As the industry evolves
producers gradually cease to introduce new category labels and instead converge on using the
same category label(s). In this study we set out to understand why some category labels are
adopted while others are abandoned. We argue that, in order to be successful, category labels
have to overcome an inherent tension between familiarity and originality. Drawing from recent
literature (Kennedy and Fiss, 2013; Grodal et al., 2014), we hypothesized a non-linear, inverse Ushape relationship between adoption and both familiarity and originality. In other words,
successful category labels are familiar, but not too familiar to be uninteresting, and original, but
not too original that stakeholders cannot relate to them. Our results, using data we collected on
what is today called the smartphone industry, largely support our hypotheses. Indeed, smartphone,
the “dominant category” (Suarez et al., 2013) that emerged in this industry, out competed more
than 200 different labels that firms used to refer to their products. Moreover, as Table 4 shows,
the smartphone label falls in that sweet spot of familiarity and originality that we propose.
The arguments and results of this article have direct and important implications for firms’
22
strategies. It is likely that firms, when introducing their products, may not be fully aware of the
socio-cognitive dynamics that take place in their emergent industry, let alone its consequences.
As existing research has shown (Zuckerman, 1999; Pontikes, 2012), choosing a categorical
positioning that is not consistent with what customers and other stakeholders begin to accept as
the major categories in an industry, can have important consequences for the success of the firm’s
products and the firm’s overall performance. While it is always possible for a firm to reposition
its products using a different category label than that used when introducing the product, making
such changes are costly. At the end of the day, even the most powerful firms have to conform to
the dominant category once it has emerged. In the smartphone industry, for instance, Apple
initially resisted for years the use of the “smartphone” label in their communications and
advertising, emphasizing the power and customer awareness of their “iPhone,” first launched in
2007. However, by 2012 Apple had begun using the “smartphone” category label to refer to the
iPhone. Firms have to conform to the dominant category label because if they don’t, they run the
risks of not being in the preference consideration set of customers when they look for a product in
that category. When meaningful categories form from category labels, they create “rules of
membership” or “boundaries” (Navis and Glynn, 2010)—that is, rules by which stakeholders
determine which products belong to the category and which don’t—that can be delineated and
unforgiving.
The implication of our study is, therefore, that firms should not only pay more attention to
the labels they create and use when introducing their products, but also simultaneously follow
closely the evolution of other categorical labels in the industry. Balancing familiarity and
originality is not trivial, and there may be different strategies open to firms, such as hedging by
trying to position an early product in more than one category while learning and collecting
information about which labels seem to works best. It is also clear from our results that firms are
23
better off using two-word compounds when creating category labels than longer compounds or
single words. Compounds make it easier for firms to overcome the tension between originality
and familiarity than single words, as one word in the compound can provide the familiar link
while the other an original twist. Moreover, two familiar words that are not often used together
often may provide the originality needed for a label to succeed.
Despite our contributions, there are still several limitations to our study. First, we have
only studied what drives the adoption of category labels, but have not explored in our regressions
the link to product or firm performance. Given that many of the firms competing in the
smartphone industry are large firms that produce many different products for different industries,
it has not yet been possible yet to obtain a reliable measure of smartphone performance for all
firms in the sample.
However, it would be beneficial for future research to address this
relationship. The increased understanding of the dynamics of category creation and adoption that
our study provides is an important issue in its own right.
Second, one may question the external validity of our study, because we have only
studied one industry. However, the dynamics of categorical evolution have been investigated in
other industries (e.g. Rosa et al., 1999; Pontikes, 2012) and, while those studies do not contain
the kind of empirical analysis that we present here, the basic pattern of category creation and
adoption seem to be similar to what we observed and document for the smartphone industry.
24
LIST OF TABLES AND FIGURES
Figure 1. Average number of category labels used by the different companies in the study, 20002010
Kyocera
LG
Motorola
Huawei
Siemens
HTC
Nokia
ZTE
Sendo
AlphaCell
Samsung
HP
Pantech & Curitel
Hitachi
GarminAsus
Ericsson
Dell Inc
ALVA
RIM
Palm
Sony Ericsson
ASUS
Sagem
Google
Danger
Audiovox
Apple
Novatel Wireless
0
1
2
3
4
5
6
7
8
Figure 2. Number of labels used by all smartphone producers each year, 2000-2010
120
100
80
60
40
20
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
25
Figure 3. Label Adoption over time – Ten Labels (percent of total adoption per year)
Table 1. Sources of Smartphone Press Releases in US and UK, 2000-2010
26
Source
Count
Manufacturer
68.4%
Operator
23.4%
OS developer
0.8%
Manufacturer and Operator
5.0%
Manufacturer and OS developer
0.8%
Operator and OS developer
0.3%
Manufacturer, Operator, and OS developer
0.8%
Retailer
0.5%
Total
100%
Table 2. Number of random articles considered each year
Year
Count of Articles
Share
1999
4709
9%
2000
4279
8%
2001
4861
9%
2002
4555
8%
2003
4866
9%
2004
4319
8%
2005
4492
8%
2006
4275
8%
2007
4285
8%
2008
4975
9%
2009
4376
8%
2010
4382
8%
Total
54,374
100%
Notes: Random draws from Factiva.
27
Table 3. Summary Statistics
Variable
N
Mean
S.D
Min
Max
Number of Articles
1924
0.81
3.34
0
84
Cites per label
1924
2.14
13.22
0
345
Familiarityt-1
1924
12.40
15.4
0.012
100
Originality t-1
1900
98.93
1.62
77.22
100
Age
1924
3.25
2.96
0
14
Number of words
1924
3.19
1.32
1
9
Trademark
1924
0.07
0.24
0
1
Generation
1924
0.09
0.29
0
1
Technology
1924
0.51
0.50
0
1
Operative System
1924
0.13
0.34
0
1
“enabled” suffix
1924
0.13
0.34
0
1
Notes: Summary Statistics of Category Labels between 2000 and 2010, which appeared at least in
a given year.
28
Table 4. Correlation Matrix
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11) (12)
Number of Articles (1)
1
(2) 0.873
Cites per label
1
(3) -0.01 -0.026
Familiarityt-1
1
(4) -0.03 -0.004 -0.583
Originality t-1
1
(5) -0.163 -0.164 0.148 -0.111
Age
1
(6) -0.03 -0.024 0.106 -0.018 -0.125
Number of words
1
(7) 0.147 0.151 0.085 -0.042 -0.072 0.087
Trademark
1
(8) -0.033 -0.036 -0.057 0.055 0.104 0.024 -0.086
Generation
1
(9) -0.121 -0.117 -0.128 0.109 0.415 -0.085 -0.117 0.308
Technology
1
Operative System (10) -0.019 -0.03 0.006 -0.05 0.285 -0.096 -0.095 0.047 0.381 1
(11) -0.04 -0.045 -0.101 0.061 0.313 -0.095 -0.092 -0.025 0.284 0.332 1
“Enabled” suffix
(12) 0.015 0.015 -0.054 0.101 0.018 -0.037 0.253 0.076 0.084 0.082 0.078 1
Year
Notes: Summary Statistics of Category Labels between 2000 and 2010, which appeared at least in a given year.
29
Table 5. Test of Familiarity and Originality on Count
(1)
(2)
Dependent
Count of press
Count of
Variable
releases citing the
label
label
citations
Log(Familiarityt-1)
Log(Familiarity2t-1)
Log(Originalityt-1)
Log(Originality2t-1)
(3)
Count of press
releases citing the
label
Head dummies
(4)
Count of label
citations
Head dummies
0.748**
(0.28)
-0.146*
(0.07)
1305.357**
(487.76)
-144.983**
(54.14)
0.700***
(0.16)
-0.119**
(0.04)
1183.899*
(548.45)
-131.576*
(60.96)
0.587**
(0.18)
-0.148***
(0.04)
1495.177***
(282.65)
-165.827***
(31.28)
0.851**
(0.27)
-0.207***
(0.06)
1562.395***
(352.03)
-173.405***
(38.94)
-2.168**
(0.71)
-3.225***
(0.67)
-3.787***
(0.66)
-4.362***
(0.67)
-4.273***
(0.71)
-3.522**
(1.25)
-4.865***
(0.70)
-4.470***
(0.81)
-2.845***
(0.81)
-4.512***
(0.71)
-5.177***
(0.72)
-5.954***
(0.73)
-5.886***
(0.78)
-5.085***
(1.34)
-6.549***
(0.83)
-6.159***
(0.95)
-2.430***
(0.59)
-3.520***
(0.43)
-4.135***
(0.56)
-4.639***
(0.56)
-4.721***
(0.54)
-3.846***
(0.93)
-5.277***
(0.33)
-4.841***
(0.76)
-3.239***
(0.68)
-4.894***
(0.45)
-5.661***
(0.57)
-6.222***
(0.59)
-6.550***
(0.54)
-5.405***
(1.24)
-7.072***
(0.39)
-6.781***
(0.82)
-0.044
(0.26)
-0.067
(0.04)
0.218
(0.19)
-0.529**
(0.18)
0.613**
(0.21)
0.528*
(0.22)
-0.020
(0.30)
-0.088*
(0.04)
0.239
(0.24)
-0.706**
(0.25)
0.914***
(0.24)
0.584*
(0.25)
-0.337**
(0.10)
-0.075***
(0.01)
0.120
(0.23)
-0.707**
(0.22)
0.698***
(0.17)
0.556**
(0.18)
-0.350*
(0.15)
-0.111***
(0.02)
0.078
(0.19)
-0.914***
(0.24)
0.855***
(0.16)
0.741**
(0.23)
# of Words
2 words
3 words
4 words
5 words
6 words
7 words
8 words
9 words
Trademark(=1)
Age
Generation(=1)
Technology(=1)
Operating System(=1)
Enabled suffix
30
Year
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Head Dummies
0.596
(0.43)
1.075*
(0.43)
1.049**
(0.40)
1.028**
(0.39)
1.147**
(0.38)
0.670+
(0.40)
0.745+
(0.39)
0.570
(0.40)
0.592
(0.41)
1.237**
(0.43)
0.813+
(0.45)
1.194*
(0.48)
1.028*
(0.44)
0.992*
(0.43)
1.304**
(0.45)
0.816+
(0.45)
0.622
(0.44)
0.423
(0.44)
0.536
(0.45)
1.276*
(0.51)
0.574
(0.84)
0.856
(0.91)
0.759
(0.85)
0.700
(0.86)
0.673
(0.96)
0.150
(0.85)
0.272
(0.88)
0.070
(0.88)
0.015
(1.07)
0.675
(1.04)
0.619
(1.06)
0.605
(1.09)
0.404
(0.96)
0.398
(1.00)
0.399
(1.22)
-0.185
(1.07)
-0.213
(1.09)
-0.415
(1.12)
-0.424
(1.33)
0.313
(1.31)
No
No
Yes
Yes
Ln(alpha)
0.897***
1.440***
0.628***
1.210***
(0.10)
(0.09)
(0.11)
(0.09)
N
1900
1900
1900
1900
Notes: Negative Binomial Regression with label-clustered standard errors for models 1 and 2, and head
clustered standard errors for models 3 and 4 in parentheses.
Significance levels: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001
31
Table 6. Test of Interaction between Familiarity and Originality on Count
(1)
(2)
(3)
Dependent
Count of
Count of label
Count of press
Variable
press
citations
releases citing the
releases
label
citing the
label
Head dummies
(4)
Count of label
citations
Head dummies
Log(Familiarityt-1)*
Log(Originalityt-1)
-36.482+
(19.68)
-61.038**
(22.25)
-16.469*
(8.03)
-24.091*
(10.80)
Log(Familiarityt-1) 2*
Log(Originalityt-1) 2
0.824*
(0.37)
1.280**
(0.42)
0.405**
(0.14)
0.502*
(0.21)
168.524+
(90.62)
-17.557*
(7.78)
1567.068**
(577.63)
-169.991**
(62.13)
281.712**
(102.44)
-27.226**
(8.80)
1774.230**
(682.83)
-189.311**
(73.44)
76.194*
(37.03)
-8.671**
(3.02)
1473.929***
(374.85)
-161.983***
(40.26)
111.620*
(49.75)
-10.800*
(4.37)
1700.132***
(446.82)
-185.571***
(47.94)
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Log(Familiarityt-1)
Log(Familiarityt-1) 2
Log(Originalityt-1)
Log(Originalityt-1) 2
Control Variables
Year Dummies
Head Dummies
Ln(alpha)
0.987***
1.535***
0.715***
1.294***
(0.12)
(0.11)
(0.11)
(0.13)
N
1900
1900
1900
1900
Notes: Negative Binomial Regression with label-clustered standard errors in parentheses for models 1
and 2, head-clustered standard errors in parentheses for models 3 and 4. Control Variables are
Derivation, Trademark, Generation, “Enabled” suffix, and Operating System dummy variables, Age,
and a set of dummy variables for the number of words.
Significance levels: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001
32
Table 7. Robustness Check – Only principal labels per press release
(1)
(2)
(3)
Dependent
Count of press
Count of label Count of press
Variable
releases citing the
citations
releases citing
label
the label
Log(Familiarityt-1)*
Log(Originalityt-1)
Log(Familiarityt-1) 2*
Log(Originalityt-1) 2
Log(Familiarityt-1)
Log(Familiarityt-1) 2
Log(Originalityt-1)
Log(Originalityt-1) 2
Control Variables
Year Dummies
Ln(alpha)
(4)
Count of label
citations
-48.373*
(21.18)
1.246*
(0.55)
-87.770**
(29.69)
2.314**
(0.79)
2.344**
(0.90)
-0.381*
(0.18)
587.981+
(312.53)
-65.962+
(35.24)
3.417***
(1.02)
-0.559**
(0.19)
828.391+
(428.80)
-92.625+
(48.33)
223.989*
(97.13)
-26.521*
(11.68)
941.923*
(378.18)
-100.096*
(40.68)
405.975**
(136.28)
-49.198**
(16.69)
1464.964**
(532.49)
-154.093**
(57.10)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.743*
1.583***
0.737*
1.561***
(0.32)
(0.20)
(0.33)
(0.20)
N
338
338
338
338
Notes: Negative Binomial Regression with label-clustered standard errors in parentheses. Control
Variables are Derivation, Trademark, Generation, “Enabled” suffix, and Operating System dummy
variables, Age, and a set of dummy variables for the number of words.
Significance levels: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001
33
TABLE 8: Robustness Check – Labels excluding “phone” and “device” if not used as only label
(1)
(2)
(3)
(4)
Dependent
Count of press
Count of label Count of press
Count of label
Variable
releases citing the
citations
releases citing
citations
label
the label
Log(Familiarityt-1)*
Log(Originalityt-1)
Log(Familiarityt-1) 2*
Log(Originalityt-1) 2
Log(Familiarityt-1)
Log(Familiarityt-1) 2
Log(Originalityt-1)
Log(Originalityt-1) 2
Control Variables
Year Dummies
Ln(alpha)
-55.383*
(25.18)
1.195**
(0.46)
-80.453**
(26.36)
1.648***
(0.49)
0.541+
(0.30)
-0.098
(0.08)
1502.156*
(613.03)
-166.564*
(68.02)
0.493**
(0.16)
-0.068
(0.05)
1301.807*
(659.39)
-144.480*
(73.31)
255.283*
(115.94)
-25.351**
(9.72)
2001.157**
(768.51)
-215.210**
(82.29)
370.889**
(121.37)
-34.970***
(10.30)
2217.440**
(812.65)
-235.331**
(87.14)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.978***
1.578***
0.949***
1.541***
(0.14)
(0.12)
(0.15)
(0.12)
N
1875
1875
1875
1875
Notes: Negative Binomial Regression with label-clustered standard errors in parentheses. Control
Variables are Derivation, Trademark, Generation, “Enabled” suffix, and Operating System dummy
variables, Age, and a set of dummy variables for the number of words.
Significance levels: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001
34
Figure 4: Mediation of familiarity on originality (Count of press releases citing the label as DV)
Figure 5: Mediation of familiarity on originality (Count of labels citations as dependent variable)
35
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38
APPENDIX
TABLE A1: Nested Regression Table for Test of H1 and H2.
Dependent
Variable
Log(Familiarityt-1)
Log(Familiarity2t-1)
Log(Originalityt-1)
Log(Originality2t-1)
(1)
Number
of Citing
Press
releases
Baseline
(2)
Number of
Citing Press
releases
(3)
Number of
Citing Press
releases
(4)
Number of
Citing Press
releases
(5)
Cites of Label
(6)
Cites of Label
(7)
Cites of Label
(8)
Cites of Label
Controls
Year FE
Head FE
Baseline
Controls
Year FE
Head FE
0.290
(0.23)
-0.072
(0.07)
403.693
(719.74)
-45.058
(79.87)
0.763**
(0.28)
-0.150*
(0.07)
1566.355***
(417.42)
-173.927***
(46.31)
0.747**
(0.28)
-0.145*
(0.07)
1311.585**
(485.80)
-145.624**
(53.92)
0.583***
(0.18)
-0.147***
(0.04)
1518.810***
(308.38)
-168.385***
(34.04)
0.170
(0.16)
-0.066
(0.06)
-393.402
(940.83)
43.043
(104.25)
0.688***
(0.16)
-0.117**
(0.04)
1534.633***
(451.14)
-170.465***
(50.10)
0.700***
(0.16)
-0.119**
(0.04)
1208.141*
(540.23)
-134.213*
(60.05)
0.848**
(0.26)
-0.207***
(0.06)
1611.497***
(356.78)
-178.782***
(39.41)
-2.265**
(0.70)
-3.331***
(0.66)
-3.888***
(0.65)
-4.513***
(0.66)
-4.451***
(0.69)
-3.430**
(1.31)
-4.957***
(0.70)
-4.533***
(0.82)
-0.004
(0.25)
-0.068+
(0.04)
0.286
-2.164**
(0.71)
-3.217***
(0.67)
-3.775***
(0.66)
-4.347***
(0.66)
-4.275***
(0.70)
-3.511**
(1.25)
-4.849***
(0.70)
-4.458***
(0.81)
-0.045
(0.26)
-0.067
(0.04)
0.219
-2.431***
(0.59)
-3.514***
(0.42)
-4.128***
(0.55)
-4.630***
(0.56)
-4.730***
(0.53)
-3.839***
(0.93)
-5.270***
(0.32)
-4.834***
(0.76)
-0.338**
(0.10)
-0.075***
(0.01)
0.123
-3.004***
(0.80)
-4.661***
(0.69)
-5.354***
(0.71)
-6.140***
(0.72)
-6.181***
(0.74)
-5.006***
(1.38)
-6.737***
(0.81)
-6.198***
(0.95)
0.012
(0.28)
-0.092*
(0.04)
0.325
-2.845***
(0.81)
-4.510***
(0.71)
-5.172***
(0.72)
-5.946***
(0.72)
-5.904***
(0.77)
-5.079***
(1.34)
-6.538***
(0.82)
-6.153***
(0.95)
-0.020
(0.30)
-0.089*
(0.04)
0.243
-3.247***
(0.67)
-4.896***
(0.45)
-5.664***
(0.56)
-6.222***
(0.59)
-6.578***
(0.51)
-5.405***
(1.23)
-7.071***
(0.38)
-6.784***
(0.82)
-0.351*
(0.15)
-0.111***
(0.02)
0.083
# of Words:
2 words
3 words
4 words
5 words
6 words
7 words
8 words
9 words
Trademark(=1)
Age
Generation(=1)
39
Technology(=1)
Operating System(=1)
Enabled suffix
(0.20)
-0.492**
(0.18)
0.621**
(0.22)
0.534*
(0.23)
(0.19)
-0.538**
(0.18)
0.618**
(0.21)
0.526*
(0.22)
(0.22)
-0.718**
(0.22)
0.703***
(0.17)
0.553**
(0.18)
No
0.532
(0.43)
1.017*
(0.43)
0.982*
(0.40)
0.985*
(0.38)
1.090**
(0.37)
0.612
(0.40)
0.687+
(0.39)
0.513
(0.39)
0.535
(0.40)
1.183**
(0.43)
No
0.452
(0.80)
0.746
(0.85)
0.639
(0.80)
0.606
(0.81)
0.564
(0.90)
0.037
(0.80)
0.160
(0.84)
-0.041
(0.83)
-0.097
(1.01)
0.564
(0.98)
Yes
(0.26)
-0.651**
(0.25)
0.919***
(0.26)
0.538*
(0.25)
(0.24)
-0.716**
(0.25)
0.918***
(0.24)
0.583*
(0.25)
(0.19)
-0.925***
(0.25)
0.860***
(0.16)
0.741**
(0.24)
No
0.768+
(0.45)
1.150*
(0.47)
0.980*
(0.43)
0.961*
(0.42)
1.263**
(0.45)
0.772+
(0.45)
0.579
(0.44)
0.381
(0.44)
0.494
(0.44)
1.234*
(0.51)
No
0.505
(1.01)
0.515
(1.01)
0.305
(0.90)
0.320
(0.93)
0.308
(1.13)
-0.278
(0.99)
-0.306
(1.02)
-0.507
(1.04)
-0.516
(1.24)
0.218
(1.22)
Yes
Year
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Head Dummies
Ln(alpha)
No
No
1.564***
1.051***
1.006***
0.716***
2.194***
1.603***
1.562***
(0.16)
(0.12)
(0.12)
(0.11)
(0.12)
(0.11)
(0.11)
N
1900
1900
1900
1900
1900
1900
1900
Notes: Negative Binomial Regression with label-clustered standard errors in parentheses. Head clustered standard errors for models 4 and 8.
Significance levels: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001
40
1.290***
(0.13)
1900
TABLE A2: Nested Regression Table for Test of H3
(1)
Number of
Citing
Press
releases
Baseline
(2)
Number of
Citing Press
releases
(3)
Number of
Citing Press
releases
(4)
Number of
Citing Press
releases
(5)
Cites of
Label
(6)
Cites of Label
(7)
Cites of Label
(8)
Cites of Label
Controls
Year FE
Head FE
Baseline
Controls
Year FE
Head FE
188.041
(139.03)
-18.531+
(11.10)
650.873
(925.35)
-67.345
(98.95)
208.869*
(86.35)
-20.769**
(7.49)
1983.664***
(476.40)
-214.580***
(51.15)
176.303*
(89.38)
-17.838*
(7.76)
1658.206**
(561.76)
-179.493**
(60.49)
79.927**
(29.73)
-8.595***
(2.38)
1600.921***
(354.20)
-175.616***
(38.21)
389.182+
(200.18)
-31.975*
(15.08)
503.037
(1110.13)
-42.571
(121.66)
344.477***
(93.50)
-32.338***
(8.16)
2351.324***
(520.53)
-250.926***
(55.84)
299.248**
(98.57)
-28.232**
(8.61)
1931.374**
(641.07)
-205.738**
(69.04)
127.112**
(43.32)
-11.612**
(3.86)
1905.966***
(406.92)
-207.508***
(43.77)
Log(Familiarityt-1)*
Log(Originalityt-1)
-40.807
(30.19)
-45.245*
(18.75)
-38.169*
(19.41)
-17.274**
(6.45)
-84.454+
(43.43)
-74.673***
(20.30)
-64.845**
(21.41)
-27.449**
(9.40)
Log(Familiarity2t-1)*
Log(Originality2t-1)
0.873+
(0.52)
0.976**
(0.35)
0.838*
(0.37)
0.401***
(0.11)
1.502*
(0.71)
1.521***
(0.38)
1.327**
(0.41)
0.540**
(0.18)
-2.376***
(0.72)
-3.394***
(0.67)
-4.019***
(0.67)
-4.520***
(0.69)
-4.636***
(0.71)
-3.639**
(1.27)
-5.212***
(0.73)
-4.761***
-2.281**
(0.73)
-3.293***
(0.68)
-3.903***
(0.68)
-4.375***
(0.69)
-4.458***
(0.72)
-3.688**
(1.22)
-5.066***
(0.73)
-4.674***
-2.473***
(0.61)
-3.537***
(0.44)
-4.171***
(0.58)
-4.624***
(0.60)
-4.769***
(0.55)
-3.908***
(0.86)
-5.362***
(0.34)
-4.895***
-3.198***
(0.82)
-4.776***
(0.71)
-5.602***
(0.72)
-6.219***
(0.74)
-6.539***
(0.77)
-5.445***
(1.33)
-7.201***
(0.83)
-6.611***
-3.040***
(0.82)
-4.649***
(0.72)
-5.414***
(0.73)
-6.051***
(0.75)
-6.258***
(0.79)
-5.458***
(1.29)
-6.930***
(0.86)
-6.541***
-3.291***
(0.69)
-4.920***
(0.46)
-5.724***
(0.57)
-6.249***
(0.61)
-6.635***
(0.53)
-5.536***
(1.16)
-7.204***
(0.37)
-6.874***
Dependent
Variable
Log(Familiarityt-1)
Log(Familiarity2t-1)
Log(Originalityt-1)
Log(Originality2t-1)
# of Words:
2 words
3 words
4 words
5 words
6 words
7 words
8 words
9 words
41
Trademark(=1)
Age
Generation(=1)
Technology(=1)
Operating System(=1)
Enabled suffix
Year Dummies
Head Dummies
Ln(alpha)
No
No
(0.85)
-0.009
(0.24)
-0.076*
(0.04)
0.283
(0.20)
-0.487**
(0.18)
0.612**
(0.22)
0.560*
(0.24)
No
No
(0.85)
-0.043
(0.25)
-0.075+
(0.04)
0.214
(0.19)
-0.527**
(0.18)
0.603**
(0.21)
0.546*
(0.23)
Yes
No
(0.79)
-0.319**
(0.10)
-0.079***
(0.01)
0.139
(0.22)
-0.703**
(0.22)
0.705***
(0.16)
0.546**
(0.18)
Yes
Yes
No
No
(0.98)
-0.014
(0.27)
-0.109**
(0.04)
0.305
(0.25)
-0.646**
(0.24)
0.860***
(0.25)
0.648*
(0.26)
No
No
(1.00)
-0.032
(0.29)
-0.106*
(0.04)
0.221
(0.23)
-0.696**
(0.24)
0.861***
(0.24)
0.671**
(0.26)
Yes
No
(0.85)
-0.336*
(0.15)
-0.116***
(0.02)
0.090
(0.19)
-0.907***
(0.25)
0.863***
(0.15)
0.750**
(0.23)
Yes
Yes
1.544***
1.035***
0.993***
0.718***
1.572***
1.537***
1.291***
(0.16)
(0.12)
(0.12)
(0.11)
(0.11)
(0.11)
(0.13)
N
1900
1900
1900
1900
1900
1900
1900
1900
Notes: Negative Binomial Regression with label-clustered standard errors in parentheses. Poisson Regression for Model 5 because of non-convergence of the
Negative Binomial model (problem of mulicollinearity). Head clustered standard errors for models 4 and 8.
Significance levels: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001
42