for peer review · for peer review 3 this work shows that because the “true” underlying value...

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The social origins of valuation: Entrepreneurial arguments

and stock market reactions in new markets

Journal: Administrative Science Quarterly

Manuscript ID Draft

Manuscript Type: Original Articles

Keywords: valuation, emergence, ..........Institutional entrepreneurship, IPOs, ..........Capital markets, arguments

Methodology Keywords: Regression analysis

Abstract:

This paper investigates how a new way of valuing organizations emerges in financial markets. We theorize that organizations in the early stages of a new market can actively shape how investors over time come to value firms in that space. Using internet initial public offerings from 1997 to

2012, we show that organizations argued for and eventually altered the way the stock market now values internet firms. This emergence process unfolds over two stages. First, the earliest organizations in the market introduce and legitimate a new understanding of value, thus establishing a shared basis of intelligibility with investors. Second, once this new understanding is established, organizations that subsequently enter the market are able to use new valuation metrics that were previously deemed nonsensical. Moreover, supplementary analyses suggest that high levels of excitement surrounding the new market may help new ways of valuing get off the ground more quickly. This study offers insight into how new understandings of value and their associated valuation metrics can emerge over time and lays the groundwork for future research on the social origins

of valuation in financial markets.

http://mc.manuscriptcentral.com/asq

Administrative Science Quarterly

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THE SOCIAL ORIGINS OF VALUATION:

ENTREPRENEURIAL ARGUMENTS AND STOCK MARKET REACTIONS

IN NEW MARKETS

ABSTRACT

This paper investigates how a new way of valuing organizations emerges in financial markets. We theorize that organizations in the early stages of a new market can actively shape how investors over time come to value firms in that space. Using internet initial public offerings from 1997 to 2012, we show that organizations argued for and eventually altered the way the stock market now values internet firms. This emergence process unfolds over two stages. First, the earliest organizations in the market introduce and legitimate a new understanding of value, thus establishing a shared basis of intelligibility with investors. Second, once this new understanding is established, organizations that subsequently enter the market are able to use new valuation metrics that were previously deemed nonsensical. Moreover, supplementary analyses suggest that high levels of excitement surrounding the new market may help new ways of valuing get off the ground more quickly. This study offers insight into how new understandings of value and their associated valuation metrics can emerge over time and lays the groundwork for future research on the social origins of valuation in financial markets. Words: 183 Keywords: valuation, arguments, emergence, entrepreneurship, IPOs, financial markets

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INTRODUCTION

“The formation of criteria used to assess value can emerge spontaneously in the market, developing gradually into taken-for-granted conventions. Often, however, established criteria have their origin in organizational action.” (Beckert and Aspers, 2011: 23)

Why do we value organizations the way we do? Our complex financial system is built on

a set of valuation methods that are often taken for granted and embedded in our everyday

decision-making (Abolafia, 2001). Yet how market players come to agree upon and begin using

these valuation methods is far less clear (Beunza and Garud, 2007). This paper examines the idea

that the origin of new ways of valuing might derive from the actions taken by organizations

during the earliest stages of a market. In fact, it is in this early period that meanings are most

uncertain (Tushman and Anderson, 1986; Santos and Eisenhardt, 2009) and the “rules of the

game” are still in question (Hargadon and Douglas, 2001; Kaplan and Murray, 2010), providing

organizations with the opportunity to influence how audiences perceive and value them (Aldrich

and Fiol, 1994).

Research on cultural entrepreneurship and categorization has explored this new market

emergence process, showing how organizations can use symbolic strategies to reduce uncertainty

surrounding themselves and the new market they are entering. Lounsbury and Glynn (2001), for

example, point to the benefits of entrepreneurial storytelling, which can help new organizations

craft a recognizable identity (Wry, Lounsbury, and Glynn, 2011; Jha and Beckman, 2017) and

gain legitimacy (Navis and Glynn, 2011), leading to higher market valuations (Martens,

Jennings, and Jennings, 2007; Zott and Huy, 2007; Sørensen and Feng, 2017). Being seen as a

member of an already legitimate category can also be beneficial (Porac, Wade, and Pollock,

1999; Zuckerman, 2000), and new organizations that successfully signal this category affiliation

tend to be viewed more favorably (Rosa et al., 1999; Kennedy, 2008; Navis and Glynn, 2010).

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This work shows that because the “true” underlying value of organizations entering a new

market is often unclear, at least with respect to their core business operations, such symbolic

efforts are one way organizations can reduce investor uncertainty and be seen as more valuable.

Yet there is reason to believe that these organizations might also try to construct a new

way of being valued that reflects what value means in their business and this new market. In fact,

because the market is new, existing valuation methods may not reflect what organizations in this

space believe they have to offer investors (Aldrich and Fiol, 1994). As a result, organizations

may be motivated to look for other ways to convey their value. In practice, we actually see that

many industries eventually develop alternative valuation metrics (e.g., “available seat mile” in

the airline industry, or “reserve life index” in the oil industry) that complement existing financial

metrics but are uniquely tailored to how value is derived and measured in that space (Demirakos,

Strong, and Walker, 2004; Orlikowski and Scott, 2013). However, little work has been done to

understand how these new ways of valuing are created, adopted by market players, and become

taken-for-granted (Khaire and Wadhwani, 2010; Durand and Khaire, 2017). And while scholars

have pointed to the possibility that organizations themselves might construct the very method

they want investors to use when valuing them (Beckert and Aspers, 2011), it is unclear how this

process would unfold.

This study explores the process by which entrepreneurial organizations in a new market

work to introduce and legitimate a new way for investors to value them. To do so, we integrate

research on the structure of linguistic arguments (Harmon, 2018) and theories of value

(Zuckerman, 1999) to theorize a two-stage process of how new ways of valuing emerge. We

theorize that organizations must first legitimate the assumptions underlying a new understanding

of value, which creates a shared understanding with investors about what value means and can

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serve as a mutual basis for intelligibility (Suchman, 1995) and comparison processes (Espeland

and Stevens, 1998). Once this shared understanding has emerged, organizations will then be able

to use new valuation metrics, alongside existing metrics, which investors can use to further

differentiate these organizations from their peers. The theory developed in this study thus builds

on and extends existing research that emphasizes how new identities are legitimated in the early

stages of a new market (e.g., Lounsbury and Glynn, 2001; Zott and Huy, 2007; Navis and Glynn,

2010) by showing how new ways of valuing organizations can originate and emerge out of these

same new market conditions.

We examine these ideas in the context of internet initial public offerings (IPOs) from

1997 to 2012 by looking at how entrepreneurial arguments evolved over time and changed the

way the stock market valued firms entering this space. This context is useful for two reasons.

First, the category identity of firms, which has been the focus of prior work (Rosa et al., 1999;

Kennedy, 2008; Navis and Glynn, 2010), was not in question. Most parties knew what an

internet company was, and “entrepreneurs [were] not expending energy on creating stories to

legitimate the Internet since financial gatekeepers had already bought in” (Lounsbury and Glynn,

2001: 558). What was in question, however, was the metrics one should use to value internet

firms and, more generally, what value actually meant in this space (Cassidy, 2003). Second, this

setting is a case of a broader phenomenon that we increasingly see in emerging technology

markets—such as the Internet of Things, artificial intelligence, and blockchain—where

technological advancements create enormous yet undefined opportunities and the way to value

these organizations and broader markets is still up for grabs (Manyika, 2015; Manolache and

Rusu, 2016; Lannquist, 2018).

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THE SOCIAL ORIGINS OF VALUATION

Theories of Value

Stable classification systems help reduce uncertainty in exchange relationships by

establishing a basis for shared meanings that people can reliably use to compare and evaluate

aspects of everyday life (Espeland and Stevens, 1998; Zuckerman, 2000; Durand and Khaire,

2017). A theory of value, as we define it here, is a socially shared framework between relevant

market players for how to value organizations in the market.1 As depicted in figure 1, a theory of

value is grounded in a shared understanding of what value means in a specific market context.

This understanding of value, in turn, validates the types of metrics producers can offer when

trying to convince an audience of their value and what audiences can accept when assessing that

value.2 For example, consider the discounted cash flow (DCF) method, a common theory of

value used to evaluate new investment opportunities. To value an investment using this

framework, market participants look for and perform a simple calculation using agreed-upon

metrics (i.e., cash flow and discount rate). By using these metrics, however, these actors

implicitly acknowledge a shared understanding of what value means (i.e., a company’s earning

power), an assumption that has not always been true (e.g., Graham and Dodd, 1934).

[Insert figure 1 here]

1 Our conceptualization of a theory of value is broadly consistent with the definition offered by Paolella and Durand (2016: 330–331), which is an “audiences’ identification of issues and solutions, and their ascription to solution providers of a value order, which, in competitive markets, translates into a willingness to pay and higher prices” (see also Paolella and Sharkey, 2017). However, their definition prioritizes the audiences’ perspective and its effects on market value, whereas our interest centers on the construction of a shared understanding between organizations and investors. As a result, our definition prioritizes this shared component (cf. Zuckerman, 2017). 2 We acknowledge that other market players, like intermediaries (Zuckerman, 2000; Beunza and Garud, 2007) and the media (Pollock, Rindova, and Maggitti, 2008; Harmon, 2018), play a role in financial market meaning-making processes. However, we focus on the interface between organizations and investors because the construction of value, while it may be influenced by a multitude of parties, must fundamentally originate with the valuation claims by sellers and the acceptance of those claims by buyers (Espeland and Stevens, 1998; Beckert and Aspers, 2011). However, we do examine the role of the media in our supplemental analyses.

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Two important observations are worth noting. First, theories of value are socially

contingent. “There is nothing inherent or timeless” about the use of a particular valuation

approach, Zuckerman (1999: 1431–32) notes, since the meaning of value and its associated

metrics in a given market context are always open to revision (Dalpiaz, Rindova, and Ravasi,

2016). Second, theories of value are often context-dependent. While the DCF approach is used

across most industries, many other theories of value (e.g., about how to value firms in the

pharmaceutical, electronic, or journalism sectors) are industry-specific (Demirakos, Strong, and

Walker, 2004; Orlikowski and Scott, 2013; Christin, 2018). Indeed, the taken-for-granted

valuation methods in a given market context tend to reflect the theories of value that have

“gained currency among prevalent market players” (Zuckerman, 1999: 1431; see also Durand,

Granqvist, and Tyllström, 2017).

Yet how do these theories of value actually emerge or “gain currency” in financial

markets? Building on research that suggests that the uncertainty of new markets can provide

organizations the opportunity to actively shape investors’ perceptions about their identities

(Santos and Eisenhardt, 2009; Navis and Glynn, 2010; Wry, Lounsbury, and Glynn, 2011), we

suggest that these same conditions might also provide organizations the space to begin

constructing an entirely new way of being valued. To understand how organizations might do

this, we suggest looking at the underlying structure of their linguistic arguments.

Entrepreneurial Arguments

Arguments are a way to reason with others. The Toulmin Model (1958), the most

authoritative framework for understanding how arguments are structured, suggests that

arguments contain at least three major components: claim, data, and backing (see figure 2).3

3 Toulmin (1958) also discussed a fourth argument component, called the warrant, which is the “if-then” reasoning that links data to a claim (see also Harmon, Green, and Goodnight, 2015). However, recent empirical research on

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Organizations, for example, can reason with stakeholders by asserting a claim and then justifying

this claim with data. Underlying this data and claim is a shared understanding, or backing, which

reflects the assumptions for how actors in this type of interaction should proceed. Note the

striking parallel between the structure of an argument and that of a theory of value (in figure 1).

This is not coincidental, and scholars have theorized that the structure of arguments uniquely

maps onto the underlying structure of social classification systems (Harmon, Green, and

Goodnight, 2015; Harmon, 2018). We argue here that we can leverage this structural similarity

to understand how entrepreneurial organizations might construct a new way of being valued.


To understand how, consider the most basic argument any entrepreneurial organization

makes to investors when going public and trying to acquire resources. Its primary claim is that

“you should invest in us,” and much of its IPO documentation is geared toward convincing

prospective investors of this assertion. In an ideal scenario, these claims might be supported by

data related to well-established financial metrics (e.g., return on assets) that typically signal a

firm’s ability to generate a return for investors. Once these data are provided, often nothing more

needs to be said. The reason is that the backing underlying these particular data—that value is an

organization’s earning power—is an assumption most of us already take for granted. As a result,

this backing simply goes without saying (Green, Li, and Nohria, 2009; Harmon, 2018).

Yet in reality, this is not the situation many entrepreneurial organizations face. It was

certainly not the case for organizations going public in the internet sector in the late 1990s. Most

of these organizations, since they often did not have sufficiently long operating histories, lacked

strong financial metrics. Despite lacking these traditional signals of value, many of these

linguistic arguments in financial market contexts (Harmon, 2018) finds that warrants are rarely observable in discourse, because people tend to assert their data and claims separately instead of making full “if-then” statements. For this reason, we exclude the warrant from our theorizing and empirical investigation.

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organizations nevertheless believed that they had something unique to offer investors and that

this had something to do with the potential opportunities of conducting their business operations

online (Cassidy, 2003). A handful of organizations in this early period even experimented with

quantifying this value into data in the form of new valuation metrics. For instance, when NK2, a

music entertainment company, went public on October 17, 1997, it tried in its prospectus to use

page impressions in the same way other organizations might use return on assets to justify a

higher market valuation:

The Company estimates that the number of page impressions per month for its music retail website has grown from approximately 767,000 in July 1996 to approximately 9.4 million in July 1997. Page impressions for the Company’s music retail website are defined as the number of full computer screens of content served to users of the website.

Yet these early attempts to provide new forms of data were unsuccessful, revealing a

more fundamental problem that we suggest organizations in the early stages of new markets

collectively face—that the backing upon which to ground new forms of data does not yet exist.

That is, new data cannot successfully justify a claim if the parties involved have not yet

developed a shared understanding or backing that enables the cause-and-effect relationship

between data and claim to make sense (Toulmin, 1958; Toulmin, Rieke, and Janik, 1984). For

example, because most investors already understand that value is an organization’s earning

power, it makes sense that providing data or metrics related to earning power (e.g., return on

assets) will positively affect market valuations. But what this means is that if internet firms want

to use new metrics related to connectivity and interaction (e.g., impressions, click-through rate,

unique visitors) to justify higher market valuations, then they must somehow convince investors

that value in this new space has something to do with an organization’s ability to enhance

connectivity and interaction with and between consumers online. To do this, organizations would

need to articulate this new backing to get investors to alter their understanding of how value

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works in the online space. For example, Digitas, which went public on March 14, 2000,

attempted to do just that by writing the following in its prospectus:

The Internet is fundamentally changing the way consumers and businesses interact. To fully capture the potential of the Internet and maximize value, companies must transform their businesses to a “bricks and clicks” business model, in which their existing assets are integrated with a digital strategy.

What we are proposing is that because the structure of arguments can be mapped onto the

underlying structure of a theory of value, we can trace organizations’ usage of backing- and data-

related arguments and their corresponding impact on market valuations over time as one way to

observe organizations’ attempts and success of constructing a new way of being valued.

The Social Origins of Valuation

We conceptualize the emergence of new ways of valuing as a two-stage process that can

unfold as a new market develops. In the first stage, the earliest organizations in the market will

offer backing-related arguments as a way to introduce and legitimate, in the eyes of investors, a

new understanding of what value means in this space. If this new understanding of value

becomes legitimate, then, in the second stage, organizations that subsequently enter the market

will be able to successfully offer data-related arguments, since these new metrics will now be

based on a shared understanding that grounds their intelligibility.4

During the earliest stages of a new market, organizations trying to convince investors of

their value may use backing-related arguments as a way to introduce a new understanding of

value that they believe better reflects their novel business operations in that space. Scholars have

theorized that articulating a new backing might be one way to introduce a new way of thinking

4 This two-stage process echoes the density-dependence hypothesis of organizational ecologists, which explains how an increasing number of organizations entering a new industry can first help to define the new space, but then at some point, more organizational entries unleashes competition (Hannan and Freeman, 1977; Carroll, 1985). Yet as Rao (2000: 306) notes, the ecologist’s story emphasizes “the intermediate outcomes such as foundings and failures rather than direct outcomes such as acceptance by financiers.” Put this way, our story is not about foundings and failures but rather about the process by which investors come to value organizations in a specific way.

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(Harmon, Green, and Goodnight, 2015; Harmon, 2018) because it sketches out the background

assumptions that may ultimately make new valuation metrics comprehensible (Suchman, 1995;

Suddaby and Greenwood, 2005). And because “market coordination presupposes a shared

understanding” of what value means (Beckert and Aspers, 2011: 14), or else the processes of

valuation will grind to a halt (Espeland and Stevens, 1998), these organizations may be

motivated to explain what value means in their business and this new space so that they might

acquire the resources they need to survive.

Yet because these backing-related arguments are brand-new, investors are unlikely to

immediately understand or respond positively to them (Khaire and Wadhwani, 2010). However,

as this new market develops and more organizations offering similar arguments go public,

investors will start to understand and adopt this new understanding of value. This should occur

for two reasons. First, organization theorists have suggested that audiences may need repeated

exposure to similar arguments over time before buying into and acting upon them (Green, 2004;

Bonardi and Keim, 2005; Suddaby and Greenwood, 2005). Moreover, an audience is more likely

to accept a new argument when it is articulated by multiple parties (Hoefer and Green, 2016).

What this means is that as more organizations go public, backing-related arguments will become

more widely available and investors should be more likely to recall and act upon these arguments

when making their investment decisions (Kuran and Sunstein, 1998; Pollock, Rindova, and

Maggitti, 2008). Second, since investors actively seek ways to reduce their uncertainty when

valuing startups in new markets (Sanders and Boivie, 2004), they will be more open to seeking

and accepting new ways of understanding value if it helps them make sense of new entrants.

Thus as the market develops and organizations going public continue offering backing-

related arguments, this new backing will become legitimate in the eyes of investors. With this

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new understanding of what value means in this space now intact, entrepreneurial organizations

going public that use these backing-related arguments will pose less uncertainty to investors and,

in turn, garner higher market valuations.

Hypothesis 1 (H1): As a market develops, an organization’s use of backing-related arguments that define a new understanding of value will emerge as having a positive effect on its market valuation.

Once this new understanding of value has successfully emerged, we expect three related

dynamics to ensue as this new market develops.

First, the use of these once novel backing-related arguments will become boilerplate and

no longer positively influence market valuations. Scholars have suggested that once an argument

is understood and accepted, it becomes implied in the overall line of reasoning and will no longer

be persuasive (Green, 2004). As with metaphors (Rosa et al., 1999; Navis and Glynn, 2010),

once the novelty is gone, it becomes “dead” and is “no longer surprising or unfamiliar, but

routine and taken-for-granted” (Powell and Colyvas, 2008: 294). In fact, continuing to use an

argument that has become taken-for-granted can even have a negative effect. Harmon (2018) has

shown that the Chair of the Federal Reserve explicitly reaffirming the taken-for-granted backing

underlying the Fed’s monetary policy framework creates uncertainty in the broader financial

market. We thus expect that once a new understanding of value has emerged as legitimate,

organizations that subsequently enter this market but still use this boilerplate backing-related

argument will find that doing so no longer positively affects their market valuation.

Second, organizations that subsequently enter this market will shift from using backing-

related arguments to using data-related arguments. Prior research shows that organizations tend

to shift their language usage as a new market develops (Rosa et al., 1999; Kennedy, 2008). Navis

and Glynn (2010), for example, described how satellite radio providers shifted from using

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collective identity statements to using individual identity statements as they worked first to

define a new market category and then differentiate themselves within it. In our case,

organizations work first to define a new understanding of value so that this understanding can

ground the intelligibility and usage of new valuation metrics, which organizations can use to

justify their own valuation claims. In fact, were they to introduce these metrics prior to the

emergence of this shared understanding, these data would be nonsensical (Suchman, 1995) and

they would likely have had either no effect or even a negative effect on market valuation (e.g.,

Zuckerman, 1999). However, once this shared understanding emerges, organizations no longer

need to state the backing and they can move on to providing new data (Green, Li, and Nohria,

2009). Thus we expect that once a new understanding of value has emerged, organizations will

begin to drop backing-related boilerplate arguments in favor of data-related arguments.

Third, as the market continues to develop, these data-related arguments will eventually

become more persuasive and start to positively influence market valuations. Now that a shared

understanding of what value means has emerged (Khaire and Wadhwani, 2010), there is a valid

basis upon which investors can start to make sense of these new data (Suchman, 1995). Yet

having a basis for making sense of new metrics does not necessarily mean that investors will

instantly use them in their valuation decisions. Similar to our reasoning for H1, as the market

continues to develop and organizations entering this space continue offering similar arguments,

these new data will become more widely available and increasingly persuasive (Green, 2004;

Pollock, Rindova, and Maggitti, 2008; Hoefer and Green Jr, 2016). And as investors continue to

seek ways to reduce uncertainty (Sanders and Boivie, 2004), they will be increasingly attracted

to these new metrics as a way to differentiate among startups. Thus as the market continues to

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develop, we expect that organizations entering the market that use these data-related arguments

will eventually pose less uncertainty to investors and garner higher market valuations.

Hypothesis 2 (H2): Once a new understanding of value has emerged, the positive effect of an organization’s use of backing-related arguments on its market valuation will diminish.

Hypothesis 3 (H3): Once a new understanding of value has emerged, organizations will shift from using backing-related arguments that define this new understanding of value to using data-related arguments that contain new valuation metrics.

Hypothesis 4 (H4): Once a new understanding of value has emerged, an organization’s use of data-related arguments that contain new valuation metrics will emerge as having a positive effect on its market valuation.

RESEARCH DESIGN

The internet sector emerged in the late 1990s out of a more general excitement for the

“New Economy,” which reflected the hope that new information technologies like the internet

would fundamentally change the rules of the game for how organizations would operate in

society (Webber, 1993; Blinder, 2001). Yet no one really knew how, or even if, the New

Economy would actually change anything (Pohjola, 2002). Even still, this excitement prompted a

flurry of internet-related organizations to go public between 1997 and 2000, which is where they

initiated the first attempts to construct a new way of being valued. By drawing on the themes in

the “New Economy” discourse, and cobbling together information from analysts, underwriters,

and research firms like International Data Corporation (IDC) and Forrester (Cassidy, 2003),

firms going public during this early period tried to make sense of and articulate for investors

what value uniquely meant in this new market. Although the bursting of the dot-com bubble in

March 2000 chilled this early excitement and slowed the pace at which internet firms went

public, the foundation for this new understanding of value had already been firmly established,

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and the steady stream of internet firms going public between 2001 and 2012 continued to

construct this new way of being valued.

Sample

Our sample consists of all organizations that went public in the internet sector of the

NASDAQ Stock Market from 1997 to 2012. We first consulted Loughran and Ritter’s (2004) list

of internet firms, identifying 493 U.S.-based organizations with IPOs during this period. We then

augmented the list with 48 additional organizations that used internet-related keywords (i.e.,

internet, online, web, electronic commerce, e-commerce, and e-business) to describe their

businesses, since self-descriptions reflect the type of business that organizations intend to be

(Pontikes, 2012). After excluding firms with missing data, our final sample was 523 firms.

Analyses

Consistent with existing research on emergence and legitimation processes (Kennedy,

2008; Green, Li, and Nohria, 2009; Navis and Glynn, 2010), our analyses emphasize the

longitudinal nature of this story. Since internet IPOs were unevenly distributed across time, with

two-thirds of IPOs occurring in 1999 or 2000, our primary analyses control for time and bubble-

period effects and treat the order in which organizations went public as our main longitudinal

variable. However, we also run and report our analyses with calendar time as a longitudinal

variable to show when the patterns in our data emerged and to rule out alternative explanations.

To examine H1, H2, and H4, we use three empirical tests. First, we analyze the full

sample using the Interflex5 command in Stata. Since existing interaction models “assume a linear

interaction effect that changes at a constant rate with the moderator” (Hainmueller, Mummolo,

and Xu, 2018: 1), we are unable to explore whether an independent variable might influence a

5 We specified the variance-covariance estimator as “robust” and used Gaussian kernel reweighting to allow the marginal effects to be fully flexible. The Stata guide is here: http://yiqingxu.org/software/interaction/StataGuide.pdf.

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dependent variable nonlinearly over time. The Interflex command, therefore, enables us to model

how the effects of backing- and data-related arguments on valuation vary as the market develops.

Second, to provide a finer-grained examination of this first analysis, we used a rolling regression

technique (Smith and Taylor, 2001), which is an “important tool in the econometric analysis of

time series” (Banerjee, Lumsdaine, and Stock, 1992: 272) and is commonly used in economics

and finance (Harris, 1989; Braun, Nelson, and Sunier, 1995; Fama and French, 1997) to

demonstrate how the relationship between variables can vary nonlinearly over time. Consistent

with this work, we conduct rolling regressions using windows of 100 IPOs each (e.g., IPO 1

through 100, IPO 2 through 101, etc.) across the full sample, and then graph all regression

coefficients for our two predictor variables. Third, we report the full regression tables for six key

windows of time, specifically when the backing- and data-related arguments have the strongest

effect on valuation, in order to determine effect sizes and show the underlying statistics.

To examine H3, we regressed the backing and data ratio on both the order in which firms

went public as well as calendar time (for a similar approach, see Green, Li, and Nohria, 2009),

and then graphed the rolling averages of these measures over the development of the market to

visually demonstrate the shift in organizational usage from backing- to data-related arguments.

Finally, to triangulate the interpretations of our findings based on these analyses, we also

conducted 12 interviews with people who were involved in the IPO process in the internet sector

during our sample period and could speak directly to the process by which IPO prospectuses

were prepared. Interviews were conducted with lawyers, underwriters, venture capitalists,

executives of issuer firms, and valuation experts, with each interview lasting 30 to 90 minutes.

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Dependent Variable

Market valuation is measured as the difference between the offer price and the closing

price on the first day of trading, expressed as a percentage of the offer price: ([(priceclosing –

priceoffer)/priceoffer]). The offer price was obtained from the final prospectus, and the first-day

closing price was collected from the CRSP Daily Stock File. Market valuation, by definition, is

therefore affected by both the firm’s offer price and the market’s reaction, leading to two

different interpretations of this outcome. Studies examining IPOs unrelated to the internet

typically refer to this outcome as underpricing (Certo, 2003; Dalton et al., 2003), because the

assumption in such contexts is that the firm made a mistake and “underpriced” their offering,

thereby leaving money on the table. These studies thus interpret high market valuations (or

underpricing) as a negative performance indicator. In contrast, studies examining IPOs in the

internet space argue that firms going public during this time were typically seeking high market

valuations (DuCharme, Rajgopal, and Sefcik, 2001). In fact, if an IPO did not experience a

considerable “pop,” or a high market valuation, the IPO was assumed to be unsuccessful for not

generating enough interest or demand. Based on this, studies examining internet- and

technology-related IPOs have interpreted high market valuations as indicators of investor interest

and demand (Ritter and Welch, 2002; Ljungqvist and Wilhelm, 2003; Ljungqvist, Nanda, and

Singh, 2006), which is consistent with our interpretation here.

Independent Variables

Backing ratio (BR). The backing ratio measures the relative amount of backing discussed

in the Company Background section of each IPO prospectus.6 Consistent with the methodology

6 For any firm undertaking an IPO, the Securities and Exchange Commission (SEC) requires a registration statement, which includes the prospectus. The prospectus provides information on the offering, such as company background, potential risk factors, and management and ownership information. Since issuing firms are held legally accountable for the accuracy of information disclosed in the prospectus (Drake and Vetsuypens, 1993; Hanley and

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used by Harmon (2018), the first author and three business school undergraduate students

familiar with IPOs and financial markets coded each sentence within the Company Background

section of each prospectus as one that either does or does not discuss the backing. The average

number of sentences in a Company Background section was 38, which resulted in the manual

coding of roughly 18,000 sentences. We calculated interrater reliability at the sentence level

(Neuendorf, 2001; Krippendorff, 2003) for the first 80 IPOs (Krippendorff’s alpha = 0.84) as

well as for the last 20 IPOs (Krippendorff’s alpha = 0.80) to demonstrate consistency. We then

used this sentence-level coding scheme to calculate the backing ratio for each IPO prospectus:

BR = (number of sentences that discuss the backing / total number of sentences)

To be coded as pertaining to the backing, a sentence had to discuss the new

understanding of value in this new market. This commonly took the form of organizations

defining or elaborating on the role of the internet as a revolutionary new global medium and

specifically included observations about how the success of firms in this space would likely be

derived from the enhanced possibilities for connectivity and interaction with and between

consumers. For example, Broadbase Software, which went public on September 22, 1999, wrote

in the Company Background section of its prospectus:

The Internet is emerging as an important channel for businesses to interact with their customers [backing]. The rise of the Internet as a new business channel has led to a dramatic increase in the number of ways that businesses interact with their customers [backing]. Today, these include not only traditional storefronts, catalogs and call centers, but also websites, e-mail marketing campaigns and online customer service [backing].

Similarly, Calico Commerce, which went public on October 7, 1999, wrote:

Hoberg, 2010), and since the investment community heavily relies on the quality of prospectus information (Bhabra and Pettway, 2003; Dalton et al., 2003; Loughran and McDonald, 2013), many studies have examined the impact of prospectus information on IPO-related outcomes such as market valuations (Dalton et al., 2003).

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The Internet has created a new means for businesses to interact directly with new and existing customers worldwide, thereby transforming the ways companies market, sell and support their product and service offerings [backing]. The Internet allows for enhanced interactivity, greater personalization and the ability to offer a broad array of complex, configurable goods and services, all at the time of purchase [backing].

In contrast, sentences that do not contain this new backing either contain data (see below)

or just provide descriptive information about the organization’s products, relationships, or

business. For example, descriptive sentences in the Company Background sections of the

prospectuses for Broadbase Software and Calico Commerce, respectively, include:

Broadbase develops and markets software that integrates and analyzes customer information from Internet and traditional business channels, enabling businesses to improve their customer acquisition, retention and profitability. Broadbase EPM integrates information from numerous points of customer interaction, or touch points, by pulling information from multiple data sources and transforming it into a standard format that can be analyzed.

We provide software and services that enable our customers to engage in electronic commerce by selling complex products and services over the Internet. Based on the number of our customers, we believe that we are a leading provider of software and services that enable our customers to implement greater electronic business strategies.

Data ratio (DR). The data ratio measures the relative amount of new data (i.e., use of new

valuation metrics) in the Company Background section of each IPO prospectus. Consistent with

our approach to measuring the backing ratio, the first author and three business school

undergraduate students coded every sentence as one that either does or does not discuss new

data. We again calculated interrater reliability at the sentence level for the first 80 IPOs

(Krippendorff’s alpha = 0.96) as well as for the last 20 IPOs (Krippendorff’s alpha = 0.94), and

used this sentence-level coding scheme to calculate the data ratio for each IPO prospectus:

DR = (number of sentences that discuss data / total number of sentences)

For a sentence to be coded as containing data, it had to explicitly define or use internet-

specific metrics, sometimes in conjunction with traditional financial metrics, to justify or

demonstrate to investors the company’s value. This was typically done by providing specific

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metrics—such as online traffic, unique visitors, page impressions, views per day, monthly active

users (MAUs), or daily active users (DAUs)—all of which were associated with trying to

quantify the enhanced possibilities for connectivity and interaction with and between consumers,

which was the new backing or understanding of value that emerged in this context. For example,

Buy.com, which went public on February 8, 2000, wrote:

We are a leading multi-category Internet superstore based on our net revenues and the amount of traffic to our Web site [data]. “Unique visitor” is an industry term used to describe an individual who has visited a particular Internet site once or more during a specific period of time [data].

Similarly, Zillow and Zynga, which went public on July 20, 2011, and December 16, 2011,

respectively, wrote:

Measuring unique users is important to us because our marketplace revenues depend in part on our ability to enable our users to connect with real estate and mortgage professionals and our display revenues depend in part on the number of impressions delivered [data]. We count a unique user the first time a computer or mobile device with a unique IP address accesses our website [data].

We measure our business by using several key financial metrics, which include bookings and adjusted EBITDA, and operating metrics, which include DAUs, MAUs, MUUs and ABPU [data]. Our operating metrics help us to understand and measure the engagement levels of our players, the size of our audience, our reach and overall monetization of our players [data].

As with the backing ratio, sentences coded as not including new data either contain a

discussion of the new backing (see above) or descriptive information about the organization.7

Market development. To account for the longitudinal aspect of this story, our primary

analyses used a continuous count variable that we call IPO order, which sequentially assigned a

number to each IPO in our sample in temporal order of when they went public. We also

replicated our analyses with calendar time as a secondary proxy for market development.

7 The denominator in the calculation of the backing ratio includes sentences coded as “data,” and the denominator in the data ratio calculation includes sentences coded as “backing.” Theoretically, this is reasonable because prior work shows that each component of an argument has separate persuasive force on an audience (Toulmin, 1958; Harmon, Green, and Goodnight, 2015; Harmon, 2018). Nevertheless, our results remain consistent if we use only the sentences not coded as backing or data as the denominator in the calculation of our variables.

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Control Variables

Firm-related factors. We controlled for metrics that have been shown to influence

investor uncertainty and market valuations. We controlled for firm performance by using logged

revenue for the year before the offer date. We controlled for logged total assets prior to the IPO

as a proxy for firm size, which has been shown to affect IPO outcomes (Ibbotson, Sindelar, and

Ritter, 1994; Dalton et al., 2003). We controlled for firm age by taking the log value of the

difference between the founding date and the filing date, because mature firms offer less

“liability of newness” (Ritter, 1991; Arthurs et al., 2008). Founding date was identified from the

prospectus and cross-checked with Ritter’s list of founding dates.

We also accounted for characteristics of IPO endorsers that are known to signal quality.

We controlled for whether the firm was venture backed and accounted for the venture capital

(VC) reputation (Fitza and Dean, 2016). Firms were coded 1 if they were backed by VC funding,

and 0 otherwise, and we used the Lee-Pollock-Jin Venture Capital Reputation Index of Lee et al.

(2011) to calculate the average reputation for venture capital firms involved in an IPO. We also

controlled for the number of underwriters and the lead underwriter reputation, since prior work

has shown that IPOs led by a greater number of underwriters and by underwriters with high

reputations are associated with reduced uncertainty (Higgins and Gulati, 2003; Pollock, 2004;

Brau and Fawcett, 2006; Chemmanur and Krishnan, 2012). Data on the number of underwriters

were collected from the SDC New Issues Database, and data on underwriter reputation were

obtained from Loughran and Ritter’s (2004) IPO underwriter reputation ranking.

We controlled for ownership and management characteristics that can influence

perceptions of value, including the size of the board of directors (BOD), percent of outside

directors, and CEO age. We also controlled for founder presence by coding 1 if a founder was

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an executive or a board member, and 0 otherwise, since individual investors tend to react more

favorably when a founder is present (Certo et al., 2001; Nelson, 2003). Finally, to control for

different types of IPO firms in the internet sector, we created two dummy variables for business-

to-consumer (B2C) and business-to-business (B2B) that were coded 1 if a firm engaged in

business transactions with end consumers or with other businesses, respectively, and 0 otherwise.

Prospectus-related factors. Following prior studies that examine the effect of prospectus

information on IPO outcomes (Hanley and Hoberg, 2010; Loughran and McDonald, 2013), we

controlled for offer price, which can influence the market’s baseline interpretation of the

offering. We controlled for the word count in the Company Background section of each IPO

prospectus, because longer descriptions provide more information and can reduce uncertainty

(Van Buskirk, 2012). Since prior work has demonstrated that simply saying that a company is

associated with the internet can affect IPO outcomes (Lee, 2001), we controlled for the

proportion of internet words (i.e., internet, online, web, World Wide Web) used in the Company

Background section of the IPO prospectus. Since stories about potential growth can increase

legitimacy and value (Wry, Lounsbury, and Glynn, 2011), we controlled for whether the IPO

prospectus included information on the growth of the market by citing the prominent research

companies offering this information (i.e., IDC or Forrester). If the prospectus cited IDC or

Forrester, we coded 1, and 0 otherwise.

Media- and market-related factors. Consistent with work that shows that media attention

shapes investors’ valuations during the IPO process (Pollock and Rindova, 2003; Pollock,

Rindova, and Maggitti, 2008; Guldiken et al., 2017), we control for media attention by looking at

the 30 IPOs prior to the IPO of the focal firm and taking the average number of media articles

found in the LexisNexis Major Newspapers database in the five days after those firms went

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public. Consistent with work showing that the broader media climate can create an interpretative

context that shapes investors’ evaluations (Pfarrer, Pollock, and Rindova, 2010; Harmon, 2018),

we also control for the average amount of media buzz in the seven days prior to the focal firm’s

IPO date. We calculated media buzz by using the Thomson Reuters Market Psych Indices

proprietary database (see Harmon, 2018), which provides a daily measure of the relative volume

of media (or buzz) expressed in United States business media (e.g., Reuters, The Wall Street

Journal, the Financial Times) related to all firms in the technology sector. Finally, since

sentiment can also influence IPO outcomes (Bhattacharya et al., 2009; Bajo and Raimondo,

2017), we controlled for media sentiment by again using the Thomson Reuters Market Psych

Indices database as a measure of average sentiment related to all firms in the technology sector

during the seven days prior to the focal firm’s IPO date.

Finally, we controlled for broader market factors that can influence market valuations.

Consistent with prior IPO research in the internet context (Martens, Jennings, and Jennings,

2007), we controlled for IPO hotness by measuring the number of IPOs issued in the seven days

prior to the focal firm’s IPO date (Jenkinson, Ljungqvist, and Ljungqvist, 2001). We also

controlled for the bubble period by coding the IPO as 1 if it occurred on or before March 31,

2000, and 0 otherwise (Ritter and Welch, 2002). For the analyses that control for time, we

included dummy variables for year fixed-effects.

RESULTS

Table 1 shows descriptive statistics and correlations for all variables for the full sample.8

[Insert table 1 here]

8 While our theory and analyses seek to examine how the effect of backing- and data-related arguments on market valuation changes nonlinearly over time, for informational purposes, we also provide the OLS regression tables for the full sample of 523 IPOs in table A1 of the Appendix. These models include all controls and year fixed-effects.

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Hypotheses 1 and 2, when considered together, predict that the effect of backing-related

arguments on market valuations will change as a new market develops—from not influencing

market valuations in the earliest stages, to positively influencing market valuations as investors

come to accept this new understanding of value, to no longer influencing market valuations as

these arguments become boilerplate. Using the Interflex command in Stata, figure 3 graphs the

marginal effect and confidence intervals of the backing ratio on market valuation, with IPO order

along the x-axis. This analysis includes all control variables listed above, including year fixed-

effects. Consistent with Hypotheses 1 and 2, we see that the effect of using backing-related

arguments on market valuations emerged as positive (H1) and then diminished thereafter (H2).


To further examine this pattern, we conducted rolling regressions in windows of 100

IPOs, controlling for all variables except year fixed-effects, given the small number of IPOs in

each regression. Figure 4a graphs these rolling regressions using IPO order as the longitudinal

variable, while figure 4b uses calendar time. Both graphs plot the coefficients for the backing

ratio predicting market valuations in each regression, with the colors of the dots representing the

coefficient’s level of significance in that regression. Also plotted is the rolling average of the

backing ratio used by organizations to provide the usage of such arguments alongside their

effectiveness. Note that the organizations’ usage of backing-related arguments remained

reasonably consistent (i.e., averaging between 10 and 12 percent) through the first 400 IPOs.

Importantly, this shows that, during this period, what changes is not the usage of these

arguments, but investors’ reaction to these arguments.

[Insert figures 4a and 4b here]

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Based on these figures, we see that backing-related arguments emerged as a significant

driver of market valuations with the 180th IPO in July 1999, peaking in late October 1999, and

then falling below significance after the 297th IPO in December 1999. The fall in significance of

backing-related arguments occurred three months before the dot-com bubble burst, which

happened on March 10, 2000 (at IPO 349), suggesting that this change in investors’

interpretations of value did not result from the bursting of the bubble. While our theory cannot

speak to whether this rise and fall in the significance of backing-related arguments over a six-

month period is fast or slow, one might speculate that the excitement associated with the

concurrence of the dot-com bubble might have contributed to the pace at which this new

understanding of value was accepted. We explore such considerations in the supplementary

analyses. Nevertheless, figures 4a and 4b depict the same story shown in figure 3, again

supporting Hypotheses 1 and 2.

To show the underlying statistics for these analyses, table 2 reports the regression tables

for three windows of time during which the backing ratio significantly influenced market

valuation (from July 1999 to December 1999, as denoted by the blue, green, and red dots). The

three windows were: 1) the 100-IPO regression window that occurs exactly one-third of the way

into this period (Models 1 and 2), 2) the 100-IPO regression window that occurs two-thirds of

the way into this period (Models 3 and 4), and 3) a regression window for the entire period

(Models 5–7). During this period in which the backing ratio significantly influenced market

valuations, a one standard deviation increase in the backing ratio—which is equivalent to adding

three backing-related sentences to the Company Background section in a prospectus—increased

market valuations by 16 percent. To put this in perspective, during this same period, a one

standard deviation increase in VC reputation, underwriter’s reputation, and media attention

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increased market valuations by 16 percent, 29 percent, and 15 percent, respectively.9 Thus,

although a 16 percent increase is large, market valuations during this period were higher than

normal on average and, furthermore, this effect size is similar in size to other drivers of valuation

identified in prior research.


Hypothesis 3 predicts that after this new understanding of value emerges, as evidenced by

the backing ratio positively driving market valuations, we should see organizations shift from

using backing-related to data-related arguments. To test this prediction, we first regressed the

backing ratio and data ratio on both IPO order and calendar time. Tables 3a and 3b show that as

the market developed, the use of backing-related arguments decreased (Models 8 and 9), whereas

the use of data-related arguments increased (Models 10 and 11).

[Insert tables 3a and 3b here]

To examine the timing of this shift in the usage of arguments, we plotted the rolling

averages of the backing and data ratio over IPO order (figure 5a) and calendar time (figure 5b).

We used 25 IPOs instead of 100 to show the finer-grained detail of our data, although the

inferences are the same. While backing- and data-related arguments were used at some level over

most of our sample period, the major drop in the use of backing-related arguments coincided

with a major rise in the use of data-related arguments. The crossover point, where data-argument

usage started to exceed backing-argument usage, was after the 427th IPO in September 2000,

which was 130 IPOs, or nine months, after the effect of backing-related arguments on market

valuations fell below significance. Again, our theory cannot predict the precise timing of this

crossover point, but our supplementary analysis suggests how our context might shed light on

9 Effect sizes were calculated after removing 13 outliers with market valuations over two standard deviations above the mean. Our results are robust to the inclusion or exclusion of these outliers. However, we removed these outliers only when evaluating effect sizes so as not to inflate the economic significance of our findings.

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why this happened when it did. Nevertheless, these results support Hypothesis 3, confirming that

the crossover occurred after the new understanding of value had already emerged.


Hypothesis 4, which predicts an eventual positive effect on market valuations of using

data-related arguments, was tested in the same manner as H1 and H2 were. Using the Interflex

command in Stata, figure 6 graphs the marginal effect and confidence intervals of the data ratio

on market valuation, with IPO order along the x-axis. As before, this analysis includes all control

variables, including year fixed-effects. Consistent with Hypothesis 4, we can see that the effect

on market valuations of using data-related arguments emerged as positive after the period in

which the backing ratio had emerged as positively affecting valuations.


To examine this pattern further, we used the same rolling regressions as above but instead

plotted the coefficients for the data ratio. Figure 7a graphs these rolling regressions using IPO

order as the longitudinal variable, while figure 7b uses calendar time. As before, we also plotted

the rolling average of the data ratio used by organizations. Both figures depict the same story

shown in figure 6, whereby the use of data-related arguments had either no effect or, in some

cases, a negative effect on market valuations until the new understanding of value had fully

emerged, after which the effect of using data-related arguments started to positively drive market

valuations. The use of data-related arguments emerged as a significant driver of market

valuations with the 455th IPO in November 2004 and remained a significant driver throughout

the rest of our sample period, thus supporting Hypothesis 4.

A comparison of figures 4a and 4b with figures 7a and 7b shows that 158 organizations

went public over five years between the time when backing-related arguments stopped driving

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market valuations (December 1999) and when data-related arguments started driving market

valuations (November 2004). To put this lag into perspective, backing-related arguments started

driving market valuations after 180 organizations went public over 2.5 years (January 1997 to

July 1999). Again, although our theory cannot predict the precise moment during the

development of a market when backing- or data-related arguments would emerge as significant

drivers of valuation, our supplementary analyses explore the possibility that the excitement

during the dot-com bubble might have accelerated the pace at which this new understanding of

value took off.


Finally, to show the statistics underlying these analyses, table 4 reports the regression

tables for three additional windows of time. Consistent with table 2, we report three windows

during which the data ratio significantly influenced market valuation (from November 2004

through December 2012, as denoted by the blue, green, and red dots): 1) a window that occurs

one-third of the way into this period (Models 12 and 13), 2) a window that occurs two-thirds of

the way into this period (Models 14 and 15), and 3) a window for the entire period (Models 16–

18). During the period in which the data ratio significantly influenced market valuations, a one

standard deviation increase in the data ratio—which is equivalent to including adding one data-

related sentence to a company background section—increased market valuations by 7 percent.

To put this in perspective, a one standard deviation increase in VC reputation during this period

increased market valuations by 8 percent.


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Excitement of New Technology Markets: The Potential Role of the Dot-com Bubble

This section explores additional factors that were present in our context that might have

contributed to the emergence of a new way of valuing. Recall that the mechanisms for why

backing-related arguments took off and were adopted by investors, as predicted by H1, were

based on two theoretical arguments. First, repeated arguments made by multiple organizations

made them more widely available and persuasive as the market develops. Second, these

arguments are more likely to be persuasive when investors have a strong motivation to reduce

their uncertainty about startup valuation issues. However, in our context, investors’ acceptance

of this new understanding of value also coincided with the dot-com bubble period. Far from

ignoring the role of the bubble, we suggest that it produced three factors that commonly arise in

new technology markets, and that these three factors might have created a context in which

investors adopted these backing-related arguments more quickly.

First, the intensity with which other firms went public during this period might have

provided investors with social proof (Rao, Greve, and Davis, 2001; King, 2005) that there really

was something credible about backing-related arguments, thus prompting investors to buy into

these arguments more quickly (cf. Pollock, Rindova, and Maggitti, 2008). As Cialdini (1993:

131–132) explains, social proof presumes that “if a lot of people are doing the same thing, they

must know something we don’t. Especially when we are uncertain, we are willing to place an

enormous amount of trust in the collective knowledge of the crowd.” To explore this possibility,

we took all IPOs up until the backing ratio’s effectiveness in driving valuations peaked in

October 1999, since we wanted to isolate and explore the period during which the backing ratio

took off. Using the same controls as before, we conducted a three-way interaction between

backing ratio, IPO hotness, and time. IPO hotness, which measures how many firms went public

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in the seven days before the IPO date of a focal firm, captured the relative intensity of social

proof. Time, measured using a month count variable, captured when an organization went public.

This revealed a significant three-way interaction effect (β = .094, σ2 = .046, p = .041,

adjusted R-squared = .281). Figure 8 plots this interaction by graphing a line for the predicted

values of backing ratio on market valuation at two levels of IPO hotness (plus or minus one

standard deviation) and two levels of time (August 1998 and August 1999). As we show,

backing-related arguments did not significantly drive market valuations in August 1998,

regardless of IPO hotness. In fact, even in August 1999, when IPO hotness was low, backing-

related arguments still did not significantly affect market valuations. But when the market had

developed more by August 1999 and IPO hotness was high, we can see that the persuasiveness

of using backing-related arguments took off and started to positively drive market valuations (see

the yellow line). This suggests that the intensity of firms going public during this period appears

to help explain the conditions under which the backing-related arguments became persuasive for

investors. Regression tables for these analyses are shown in table A2 of the Appendix.


Second, the excitement of this period manifested not only in the number of firms going

public but also in the media buzz in the broader technology sector (Cassidy, 2003). To explore

the possibility that media buzz played a role in helping this new understanding of value take off,

we conducted the same three-way interaction as above, but used our media buzz variable instead

of IPO hotness. This revealed a marginally significant three-way interaction effect among the

backing ratio, media buzz, and time (β = 4.742, σ2 = 2.824, p = .095, adjusted R-squared = .264).

Figure 9 plots this interaction by graphing a line for the predicted values of backing ratio on

market valuation at two levels of media buzz and time. Similar to the effect of IPO hotness, we

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see in figure 9 that the persuasiveness of using backing-related arguments took off when the

market was more developed and media buzz was high (see the yellow line). This again suggests

that the excitement of this period, this time as evidenced through media buzz, appears to help

explain the conditions under which backing-related arguments became persuasive for investors.

Regression tables for these analyses are shown in table A3 of the Appendix.


Third, the excitement of the internet also attracted a diverse set of organizations. For

example, roughly one-third of our sample were business-to-consumer firms (B2C), like eBay,

Zillow, and Facebook, while the rest were business-to-business firms (B2B), like Concur

Technologies, Earthlink Network, and Broadbase Software. Although all these firms took an

interest in the internet, the differences between the two types of firms suggests that they might

have played different roles in how this new way of valuing emerged. To explore this possibility,

we replicated figures 5a and 5b from above, which plotted the rolling averages of the backing

ratio and data ratio usage over the development of the market, but did so for B2C and B2B firms

separately. Figure 10a shows them over IPO order; figure 10b shows them over calendar time.

What we see is that both types of firms contributed to the construction of the new

understanding of value, evidenced by a high backing ratio across most of the sample. However,

unlike B2C firms, B2B firms never actually started using data-related arguments. One possible

interpretation of these patterns is that in the earliest stages of this new market, different types of

organizations could contribute to constructing the new understanding of value, which at that

point was just an abstract possibility. Yet with the emergence of a clearer interpretation of what

value actually meant to internet-related companies, B2B firms realized that the new

understanding of value was less applicable to their businesses. In contrast, B2C firms realized

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that this new understanding of value, which was built on the potential for connectivity and

interaction with and between consumers, applied more readily to their own business operations.

This is perhaps why we see increases in B2C firms’ use and investors’ acceptance of data-related

arguments later on.

These observations point to the possibility that the early flurry of IPOs from 1997 to

2000, which encouraged B2B firms to jump on the bandwagon, may have been an important

factor that contributed to the pace at which this new understanding of value took off. Indeed, by

inviting more firms into the fray than would normally have been there, this flurry fanned the

broader excitement (the number of firms going public, the media buzz) that contributed to the

conditions under which investors were more likely to accept backing-related arguments. While

we do not believe that the excitement associated with these technology markets is required for a

new understanding of value to emerge, we suggest that such factors contribute to the conditions

that might accelerate the emergence process.


Robustness

We also conducted an empirical test to assess the robustness of the effects the backing

ratio and data ratio have on market valuation. To do so, we reran Models 11 and 18, which were

the two periods during which the backing ratio and data ratio significantly influenced valuations,

respectively. Using the method developed by Frank (2000) and recently used by organizational

theorists (Hubbard, Christensen, and Graffin, 2017; Harmon, 2018), we calculated how strong a

correlated omitted variable would have to be to overturn the significance of these two main

effects.10 To invalidate the inference related to the backing ratio, the impact of an omitted

variable must be 0.11. This is greater than the impact of the three largest covariates during this

10 For the Stata code to conduct this analysis, see https://msu.edu/~kenfrank/research.htm.

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period: B2C (0.05), VC reputation (0.03), and underwriter reputation (0.03). To invalidate the

inference related to the data ratio, the impact of an omitted variable must be 0.08. This too is

greater than the impact of the three largest covariates during this period: offer price (0.07), VC

reputation (0.06), and venture backed (0.02). What this means is that the size of the omitted

variable needed to invalidate our primary results is larger than every other control variable used

in the existing literature. Assuming that we have included a reasonable set of control variables,

this suggests that our results are unlikely to be driven by an omitted variable.

DISCUSSION

This paper set out to investigate how new ways of valuing organizations emerge in

financial markets. Using the internet sector as our context, we show how organizations argued

for and eventually altered the way the stock market now values internet firms. This new way of

valuing emerged over two stages, with the earliest organizations in the market establishing a new

understanding of value with investors, thereby enabling subsequent organizations to use new

valuation metrics that had become intelligible. The practical implications of these efforts are

visible today as well, with investors using these new valuation metrics (e.g., click-through rate,

unique visitors, daily active users), along with other financial metrics, to value internet

companies when they go public (Amigobulls, 2014). By examining entrepreneurial arguments as

one possible avenue through which new ways of valuing can emerge, this study highlights a need

to better understand why we value organizations the way we do in financial markets.

Surprisingly, this topic has received little scholarly attention. Strategy and

entrepreneurship scholars, for example, have long been interested in the factors that drive market

valuations (Sanders and Boivie, 2004; Martens, Jennings, and Jennings, 2007; Zott and Huy,

2007) as well as how valuations change over time (Zuckerman, 2000; Zajac and Westphal,

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2004). But this work largely takes for granted the origin of such factors and has not explored

how or why new ways of valuing might emerge. And while the growing body of research on new

market emergence sheds some preliminary light on this matter (Kennedy, 2008; Navis and

Glynn, 2010; Wry, Lounsbury, and Glynn, 2011; Granqvist, Grodal, and Woolley, 2013), the

focus in this space has centered around how organizations in new markets construct their

identities to be viewed as more legitimate. This line of work tends to view valuation as a

byproduct of the identity construction and categorization process, where an organization garners

a higher valuation to the extent it is intelligible and fits into a recognizable symbolic or cultural

category (Zuckerman, 1999; Lounsbury and Glynn, 2001).

Our paper seeks to place the study of valuation at center stage. By focusing on what value

means to organizations in a new market, this study highlights how entirely new understandings

of value and their associated valuation metrics can be constructed over time. Taken together, this

study opens up new research opportunities in an area of study that we refer to as valuation

entrepreneurship and, more broadly, lays the groundwork for future research on the social origins

of valuation in financial markets.

Valuation Entrepreneurship

We define valuation entrepreneurship as the process of constructing the meaning of value

and its associated valuation metrics in a market. Valuation entrepreneurship, therefore, places the

attempts to influence what value means or which valuation metrics to use in a given context as

the explicit object of study. Given the importance of valuations in a variety of financial market

contexts (e.g., IPOs, mergers or acquisitions, investment decisions, etc.) and by different market

players (e.g., firms, retail and institutional investors, analysts, auditors, etc.), a substantial

opportunity exists for the exploration of how new ways of valuing emerge or change over time.

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This study proposes just one way to begin exploring these issues. By mapping the

structure of linguistic arguments onto the structure of theories of value, we are able to separate

arguments about what value means (i.e., the backing) from arguments about the metrics used to

justify a valuation (i.e., the data). This distinction enables scholars to disambiguate between

whether actors are trying to influence or change the basis upon which valuation judgments are

made or whether they are merely offering a justification for their valuation given an already

accepted set of assumptions. The potential insights garnered from mapping argument structure

onto our cultural-cognitive frameworks has been demonstrated in other market contexts as well.

Harmon (2018) showed that once the backing underlying the monetary policy framework had

become taken-for-granted, the Fed Chairperson discussing these assumptions creates financial

market uncertainty because it reopens the very considerations that people thought were taken-

for-granted. Our study reverses this story by examining how a new understanding of value is

introduced and becomes taken-for-granted. We show how organizations introduced a new

understanding of value to investors through backing-related arguments, which eventually became

taken-for-granted and enabled the use of new data or valuation metrics.

This separation in the underlying structural components of arguments and theories of

value also lends insight into why valuation entrepreneurship sometimes fails. Consider WeWork,

a middleman real-estate company that rents buildings from others and then rents them back to

individuals at a markup, and its attempt to introduce a new valuation metric in its 2018 bond

offering (Levine, 2018). The new metric, called “community adjusted EBITDA,” was a non-

GAAP income measure that excluded not only interest, taxes, depreciation, and amortization, but

also marketing, general and administrative, and development and design costs. Adam Neumann,

WeWork’s CEO, defended this new metric by saying, “to assess WeWork by conventional

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metrics is to miss the point. WeWork isn’t really a real estate company. It’s a state of

consciousness.” The markets immediately became squeamish. “I’ve never seen the phrase

‘community adjusted Ebitda’ in my life,” said Adam Cohen, founder of Covenant Review, a

bond research company. The market’s reaction might be summed up by analyst Jesse

Rosenthal’s report in response to WeWork’s bond offering, titled “WePass” (Smith, 2018).

Why did the market pass? Our findings suggest that WeWork mistakenly believed that an

entirely new understanding of value, beyond what was commonly accepted in the real estate

industry, had already emerged and was shared in this space. But the market’s reaction suggests

this was a miscalculation. Without a new and shared understanding of value, novel metrics such

as “community adjusted EBITDA” appear nonsensical to the market. Interestingly, we also

found some evidence of this same reaction in the earliest stages of the internet market, when

organizations tried to offer new valuation metrics before a new understanding of value had fully

emerged. As figures 7a and 7b show, the data ratio coefficients on market valuations were, on

average, negative from the beginning of our sample through December 1999, with several

periods where this negative effect reached significance. These sorts of premature attempts to use

nonstandard metrics appear to be quite common too (Merced, 2011; Wolcott, 2016; Lugo, 2106)

as organizations face increasing pressures to differentiate themselves from their peers. The

theory developed in this paper sheds light on the potential implications of such efforts.

Our study also suggests the possibility of the complete opposite problem from what

WeWork faced. That is, a new market may struggle in its development and maturation because

market participants are stuck debating what value means and never get around to defining clear

valuation metrics for organizations, analysts, and investors to use to establish reliably consistent

valuations. In some respects, the biotech industry has faced this challenge over the years. The

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biotech space is a highly fragmented and complex industry with an excess of interpretations of

what value could mean. As a result, much of the valuation entrepreneurship effort over the years

have gone towards trying to define what value means in this space (Kaplan and Murray, 2010),

potentially limiting the industry’s ability to develop consistent valuation metrics that investors

might use to value biotech startups (Pukthuanthong, 2006). Our study thus highlights the

importance of paying attention not only to the valuation metrics organizations use in financial

markets but also to the shared understanding of value (or lack thereof) that grounds the

legitimate use of such metrics.

Finally, while our approach leverages an argumentation lens and focuses mainly on

organizations as the valuation entrepreneurs, there are other avenues for promising research. In

particular, existing research on the linguistic microfoundations of institutions (Cornelissen et al.,

2015) discusses how rhetoric (Green, 2004), narratives (Vaara, Sonenshein, and Boje, 2016), and

framing (Cornelissen and Werner, 2014) can shape our understanding of collective market

phenomena. Researchers might explore how different entrepreneurial strategies might be more

successful in affecting the understanding of value as opposed to more successful in offering

metrics to justify one’s value. Moreover, exploring the entrepreneurial activities of market

players other than organizations presents enormous opportunities for research. For instance,

given the important role analysts play in determining how organizations are valued, and given

the central role auditors play in validating the use of valuations that organizations present in their

financial statements, scholars might begin to explore the assumptions and metrics these actors

use to justify their valuation assessments over time.

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The Social Origins of Valuation

This study also highlights the broader need to better understand the social origins of

valuation in financial markets. Scholars have long been interested in how value is socially

constructed (Zelizer, 1978; Espeland and Stevens, 1998; Beckert and Aspers, 2011; Stark, 2011).

Most of the work in this space, however, has focused on the valuation of intangible social objects

(Fourcade, 2011) or products in creative industries, such as art (Khaire and Wadhwani, 2010) or

high fashion (Khaire, 2014). This literature, for example, asks questions like, how do we put a

price on a human life, or a Picasso painting? While these questions seem to call for a social

answer, since the worth of such things are more obviously socially constructed, financial market

valuations are often treated differently. When we look at our financial and capital markets, we

tend to see valuation as a more rational process that is grounded by seemingly objective numbers

in a prospectus or on a financial statement. The theory developed here suggests that these

valuation frameworks underlying our complex financial system may be more socially derived

than we often assume.

Perhaps one reason valuation in financial markets has tended to overlook its social

origins is due to the overwhelming emphasis on valuation metrics. Business school students are

taught that valuation can be justified using different frameworks, such as the DCF, industry

multiples, and so forth, focusing on the metrics that one needs to apply in order to draw their

conclusions. Based on our interviews with analysts and valuation experts in the industry, this

metric-centric mentality only gets more pronounced over time. Yet what this metric-centered

focus omits is the recognition that these metrics are legitimate only because they are grounded in

a shared set of assumptions underlying what value means in that context (see figure 1), which

could, of course, be otherwise. Even our seemingly objective financial metrics rely on

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assumptions that are embedded in generally accepted accounting principles, which itself is an

evolving social system contingent upon a shared understanding within the financial community

(cf. Carruthers and Espeland, 1991). By distinguishing between these two structurally distinct

components of valuation, this study shines a spotlight on the often-overlooked shared

understandings that underlie the valuation practices commonly used in financial markets.

This focus on the social origins of valuation is perhaps more important today than ever

before. We are amidst the emergence of a number of new markets—the Internet of Things,

artificial intelligence, and blockchain among them—where, as with the internet in the late 1990s,

technological advances are quickly creating enormous yet undefined opportunities that

promise—or threaten—to ripple across countless industries. As entrepreneurial organizations

flood into these spaces and overall excitement in these markets grows, we can see that the way to

value these organizations and the broader markets is still up for grabs. In the years to come, these

valuation practices will likely get sorted out. Just as the market today takes for granted that

“unique visitors” or “monthly active users” are reasonable metrics to justify the value of internet

companies, so too will new metrics emerge in these other markets as equally commonplace. This

study seeks to lay the groundwork for future research in this space and begin the conversation

about developing a broader theory on the social origins of valuation in financial markets.

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TABLES

Table 1. Descriptive Statistics and Pearson Correlation Statistics*

Variable Mean S.D. 1 2 3 4 5 6 7 8 9 10 11 12

1. Market valuation 0.76 0.97

2. Backing ratio 0.08 0.10 0.15

3. Data ratio 0.01 0.03 -0.05 -0.10

4. IPO order 262.00 151.12 -0.18 -0.29 0.21

5. Time 1425.52 1165.29 -0.23 -0.34 0.21 0.75

6. Revenuea 6.83 1.09 0.00 -0.04 0.05 0.27 0.35

7. Total assetsa 7.46 0.54 -0.05 -0.18 0.09 0.39 0.40 0.46

8. Firm agea 3.14 0.42 -0.11 0.00 0.02 0.24 0.30 0.25 0.11

9. Venture backed 0.79 0.41 0.07 0.07 0.11 0.15 0.12 0.08 0.15 0.18

10. VC reputation 17.67 19.42 0.13 0.01 0.11 0.07 0.14 0.08 0.16 0.11 0.46

11. Number of underwriters 3.72 2.34 -0.04 -0.09 0.20 0.20 0.31 0.18 0.39 0.07 0.04 0.11

12. Underwriter reputation 8.34 3.05 0.04 -0.04 0.01 0.13 0.16 0.18 0.33 0.07 0.02 0.07 0.04

13. Size of BOD 6.72 1.77 0.01 -0.11 0.08 0.10 0.10 0.04 0.27 0.06 0.21 0.14 0.07 0.12

14. Outside directors 0.69 0.20 -0.05 0.01 0.08 0.17 0.21 0.07 0.18 0.27 0.44 0.30 0.08 0.10

15. CEO age 43.11 8.03 -0.07 -0.07 -0.07 0.12 0.17 0.09 0.13 0.13 0.05 0.04 -0.03 0.03

16. Founder presence 0.68 0.47 0.11 0.08 -0.06 -0.05 -0.06 -0.12 -0.06 -0.01 0.10 0.08 0.03 -0.05

17. B2C 0.30 0.46 -0.10 -0.15 0.30 -0.04 0.07 -0.08 -0.01 -0.13 -0.08 0.00 0.04 0.06

18. B2B 0.52 0.50 0.04 0.14 -0.19 0.08 0.01 0.07 -0.12 0.07 0.00 -0.06 -0.03 -0.04

19. Offer price 14.94 7.56 0.31 0.00 0.07 0.00 -0.02 0.09 0.20 -0.07 0.04 0.11 0.32 0.06

20. Word count 1173.52 658.49 -0.26 -0.16 0.18 0.17 0.49 0.20 0.29 0.06 -0.05 -0.02 0.18 0.04

21. Internet reference 0.79 0.81 0.04 0.30 -0.14 -0.26 -0.29 -0.17 -0.19 -0.12 -0.04 -0.03 -0.15 -0.07

22. Growth of market 0.28 0.45 -0.02 0.21 -0.05 0.10 0.06 0.05 0.01 -0.04 0.01 -0.06 0.01 -0.03

23. Media attention 1.92 0.64 0.02 -0.13 0.02 -0.14 0.12 0.09 -0.07 0.02 -0.06 -0.01 -0.03 0.00

24. Media buzz 0.13 0.03 0.12 0.12 -0.05 0.20 -0.28 -0.15 -0.08 -0.12 0.01 -0.13 -0.13 -0.04

25. Media sentiment -0.08 0.03 0.21 0.19 -0.14 -0.50 -0.47 -0.16 -0.20 -0.10 -0.07 0.00 -0.08 -0.04

26. IPO hotness 4.23 3.89 0.10 0.19 -0.14 -0.20 -0.39 -0.21 -0.18 -0.11 0.03 -0.04 -0.09 -0.05

27. Bubble period 0.69 0.46 0.28 0.36 -0.20 -0.80 -0.70 -0.24 -0.36 -0.22 -0.09 -0.10 -0.17 -0.12

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Variable 13 14 15 16 17 18 19 20 21 22 23 24 25 26

14. Outside directors 0.37

15. CEO age 0.14 0.09

16. Founder presence 0.06 -0.04 -0.08

17. B2C 0.06 -0.02 -0.12 -0.02

18. B2B -0.11 -0.06 0.05 -0.05 -0.61

19. Offer price 0.01 -0.06 -0.01 0.08 -0.01 -0.04

20. Word count 0.12 0.11 0.12 -0.13 0.18 -0.09 -0.06

21. Internet reference -0.14 -0.06 -0.02 0.04 -0.18 0.07 0.07 -0.22

22. Growth of market 0.00 0.03 -0.02 0.04 -0.11 0.05 0.01 -0.06 0.12

23. Media attention 0.00 0.00 0.08 -0.07 0.08 -0.02 0.03 0.13 -0.05 -0.09

24. Media buzz -0.06 -0.12 -0.13 0.10 -0.13 0.09 0.06 -0.39 0.13 0.04 -0.23

25. Media sentiment 0.02 -0.04 -0.03 0.06 -0.04 0.00 0.03 -0.13 0.12 0.03 -0.21 -0.28

26. IPO hotness -0.07 -0.03 -0.10 0.07 0.01 0.00 0.04 -0.35 0.21 0.11 -0.20 0.25 0.24

27. Bubble period dummy -0.11 -0.14 -0.14 0.09 0.03 -0.02 0.07 -0.26 0.23 0.04 -0.01 -0.08 0.58 0.47

* 523 IPOs from 1/1/1997 to 12/31/2012. a Variable is logged.

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Table 2. Regression Models Predicting Market Valuation, Stage 1.

Window 1b Window 2

b Window 3

b

IPOs 118 to 219 N=100

IPOs 158 to 259 N=100

IPOs 79 to 297 N=217

Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Backing ratio 4.207*** 4.573*** 2.609*** 2.710*** (0.962) (1.116) (0.762) (0.786) Data ratio -4.810 -5.994* -3.520 -1.489 0.475 0.219 -1.303 (4.343) (3.261) (5.536) (5.552) (4.027) (4.222) (4.292) Revenuea -0.096 -0.102* 0.068 -0.050 0.030 0.003 -0.009 (0.087) (0.061) (0.119) (0.096) (0.050) (0.045) (0.047) Total assetsa 0.308 0.254 0.278 0.416** 0.066 0.084 0.108 (0.185) (0.176) (0.226) (0.205) (0.151) (0.149) (0.152) Firm agea 0.417* 0.088 0.167 -0.146 -0.151 -0.246 -0.222 (0.243) (0.208) (0.219) (0.195) (0.153) (0.159) (0.154) Venture backed -0.018 -0.104 0.039 0.106 -0.077 -0.113 -0.179 (0.216) (0.172) (0.252) (0.203) (0.157) (0.151) (0.158) VC reputation 0.006 0.005 0.012* 0.008 0.010*** 0.009** 0.009** (0.004) (0.003) (0.007) (0.005) (0.004) (0.004) (0.004) Number of underwriters 0.090** -0.035 0.037 -0.011 0.008 -0.014 -0.029 (0.042) (0.045) (0.037) (0.031) (0.033) (0.025) (0.029) Underwriter reputation -0.025 0.022 0.170** 0.092 0.125** 0.123** 0.118** (0.052) (0.050) (0.079) (0.072) (0.051) (0.050) (0.053) Size of BOD 0.052 0.043 -0.015 -0.015 -0.016 -0.012 -0.011 (0.044) (0.041) (0.062) (0.060) (0.040) (0.039) (0.041) Outside directors -0.258 -0.421 -0.458 -0.738* -0.236 -0.270 -0.221 (0.352) (0.319) (0.499) (0.426) (0.298) (0.307) (0.313) CEO age -0.004 0.004 0.016 0.017* 0.001 0.003 0.002 (0.009) (0.011) (0.010) (0.010) (0.008) (0.008) (0.009) Founder presence 0.321** 0.348** 0.109 0.085 0.206 0.163 0.211 (0.159) (0.149) (0.229) (0.208) (0.130) (0.127) (0.133) B2C -0.037 0.067 -0.605* -0.419 -0.406* -0.359* -0.342* (0.217) (0.164) (0.353) (0.296) (0.209) (0.198) (0.199) B2B 0.113 0.118 -0.251 -0.342 -0.202 -0.271 -0.268 (0.191) (0.164) (0.423) (0.375) (0.199) (0.197) (0.201) Offer price 0.062*** 0.063*** 0.004 0.006 0.013 0.016 0.016 (0.019) (0.015) (0.016) (0.013) (0.023) (0.022) (0.023) Word count -0.000 -0.000 -0.000* -0.001** -0.000** -0.000** -0.000** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Internet reference -0.054 -0.163** -0.062 -0.193 -0.016 -0.081 -0.081 (0.080) (0.073) (0.124) (0.122) (0.077) (0.083) (0.093) Growth of market 0.167 -0.059 -0.076 -0.398* -0.211 -0.369** -0.389** (0.162) (0.150) (0.194) (0.207) (0.149) (0.157) (0.159) Media attention -0.297 -0.207 -1.460** -1.181* -0.040 0.001 0.107 (0.449) (0.378) (0.655) (0.601) (0.140) (0.145) (0.156) Media buzz 25.423 35.120** 1.284 18.388 14.531* 16.118* 5.456 (17.966) (14.337) (33.062) (28.607) (8.157) (8.202) (15.073) Media sentiment -1.333 -0.341 22.570 16.484 11.601 12.075* 4.792 (18.318) (13.705) (27.641) (23.826) (7.176) (6.904) (9.593)

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IPO hotness -0.064** -0.049** -0.034 -0.009 -0.026 -0.016 -0.018 (0.027) (0.022) (0.040) (0.037) (0.019) (0.019) (0.019) Constant -5.788* -6.254** 0.830 -1.055 -0.885 -0.932 -0.450 (3.149) (2.499) (6.037) (5.309) (1.789) (1.787) (2.253) Month fixed-effects N N N N N N Y R-squared 0.368 0.587 0.369 0.513 0.243 0.300 0.318 Adjusted R-squared 0.187 0.462 0.189 0.365 0.157 0.216 0.212 D.f. 22 23 22 23 22 23 29

*** p<0.01, ** p<0.05, * p<0.10 Note: Results show robust regressions with robust standard errors in parentheses. Significance tests are two-tailed. a Variable is logged. b These regression tables examine the windows during which the backing ratio significantly influenced market valuation. Window 1 examines the 100 IPOs that occur exactly one-third of the way into this period; Window 2 examines the 100 IPOs that occur two-thirds of the way into this period; and Window 3 examines the entire period during which the backing ratio significantly influenced market valuation.

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Table 3a. Regression Models Predicting Backing Ratio, N = 523.

Variable Model 8 Model 9

IPO order -0.020**** (0.003) Time -1.110****

(0.136)

Constant 13.565**** 14.157****

(0.858) (0.825)

Adjusted R-squared 0.0845 0.112 F 49.17 67.06

**** p<0.0001 (standard errors in parentheses) Note: Backing ratio rescaled by multiplying by 100.

Table 3b. Regression Models Predicting Data Ratio, N = 523.

Variable Model 10 Model 11

IPO order 0.004**** (0.001) Time 0.193****

(0.038)

Constant -0.039 -0.068

(0.235) (0.229)

Adjusted R-squared 0.041 0.046 F 23.07 26.22

**** p<0.0001 (standard errors in parentheses) Note: Data ratio rescaled by multiplying by 100.

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Table 4. Regression Models Predicting Market Valuation, Stage 2.

Window 4b Window 5

b Window 6

b

IPOs 376 to 477 N=100

IPOs 399 to 500 N=100

IPOs 354 to 523 N=168

Variable Model 12 Model 13 Model 14 Model 15 Model 16 Model 17 Model 18

Backing ratio -1.100 -1.041 -1.269 -1.189 -0.977 -0.900 -0.903 (0.849) (0.874) (1.158) (1.041) (0.632) (0.634) (0.649) Data ratio 2.848** 3.215*** 2.420*** 2.587*** (1.257) (0.794) (0.791) (0.913) Revenuea 0.017 0.023 -0.027 -0.024 0.009 0.002 0.006 (0.041) (0.037) (0.043) (0.041) (0.034) (0.034) (0.035) Total assetsa 0.034 0.025 -0.086 -0.087 -0.049 -0.028 -0.021 (0.125) (0.122) (0.105) (0.099) (0.081) (0.080) (0.086) Firm agea -0.070 -0.084 -0.064 -0.080 0.006 0.021 0.020 (0.133) (0.135) (0.104) (0.109) (0.106) (0.113) (0.122) Venture backed 0.186 0.135 0.190 0.137 0.122 0.081 0.078 (0.217) (0.207) (0.125) (0.116) (0.131) (0.128) (0.137) VC reputation 0.005* 0.004* 0.003 0.002 0.005* 0.004* 0.004* (0.005) (0.005) (0.003) (0.002) (0.003) (0.003) (0.003) Number of underwriters -0.118*** -0.133*** -0.087** -0.104*** -0.064*** -0.068*** -0.069*** (0.042) (0.043) (0.034) (0.033) (0.019) (0.018) (0.021) Underwriter reputation 0.011 0.012 0.008 0.009* 0.004 0.004 0.005 (0.008) (0.007) (0.006) (0.005) (0.007) (0.006) (0.007) Size of BOD -0.028 -0.040 -0.016 -0.026 -0.034 -0.037 -0.038 (0.037) (0.038) (0.034) (0.033) (0.026) (0.025) (0.027) Outside directors 0.063 0.063 -0.278 -0.335 0.262 0.284 0.298 (0.315) (0.331) (0.320) (0.327) (0.237) (0.238) (0.272) CEO age -0.002 0.000 -0.002 0.000 -0.002 -0.001 -0.002 (0.005) (0.005) (0.005) (0.005) (0.004) (0.004) (0.004) Founder presence 0.032 0.072 -0.018 0.022 -0.050 -0.033 -0.024 (0.125) (0.126) (0.089) (0.085) (0.077) (0.076) (0.090) B2C -0.315 -0.462** -0.143 -0.307** -0.316** -0.422*** -0.410*** (0.209) (0.214) (0.140) (0.132) (0.124) (0.126) (0.138) B2B -0.005 -0.020 0.109 0.063 -0.113 -0.132 -0.122 (0.199) (0.190) (0.124) (0.115) (0.120) (0.118) (0.127) Offer price 0.045*** 0.049*** 0.036*** 0.040*** 0.037*** 0.036*** 0.035*** (0.015) (0.014) (0.012) (0.011) (0.009) (0.009) (0.009) Word count 0.000 0.000 0.000* 0.000* 0.000 0.000 0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Internet reference -0.111 -0.108 -0.031 -0.026 -0.100 -0.098 -0.101 (0.082) (0.081) (0.085) (0.081) (0.063) (0.064) (0.069) Growth of market 0.163 0.164 0.078 0.087 0.126 0.130* 0.142* (0.118) (0.116) (0.106) (0.100) (0.077) (0.077) (0.084) Media attention -0.019 -0.041 -0.061 -0.093 -0.186** -0.194** -0.210 (0.146) (0.148) (0.101) (0.098) (0.086) (0.084) (0.148) Media buzz 4.973** 3.950 1.698 0.777 3.732** 3.395** 3.734 (2.453) (2.495) (1.398) (1.254) (1.481) (1.397) (4.375) Media sentiment 3.589 3.523 2.282 2.122 0.139 -0.034 0.585 (3.756) (3.764) (1.975) (1.832) (1.882) (1.821) (2.693)

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IPO hotness -0.025 -0.026 -0.006 -0.008 0.005 0.005 0.004 (0.029) (0.028) (0.030) (0.026) (0.023) (0.023) (0.025) Constant -0.406 -0.065 1.313 1.672 0.290 0.203 0.155 (0.931) (0.968) (1.126) (1.110) (0.798) (0.793) (1.017) Year fixed-effects N N N N N N Y R-squared 0.459 0.483 0.390 0.463 0.436 0.459 0.469 Adjusted R-squared 0.295 0.318 0.216 0.301 0.346 0.368 0.323 D.f. 22 23 22 23 23 24 35

*** p<0.01, ** p<0.05, * p<0.10 Note: Results show robust regressions with robust standard errors in parentheses. Significance tests are two-tailed. a Variable is logged. b These regression tables examine the windows during which the data ratio significantly influenced market valuation. Window 1 examines the 100 IPOs that occur exactly one-third of the way into this period; Window 2 examines the 100 IPOs that occur two-thirds of the way into this period; and Window 3 examines the entire period during which the data ratio significantly influenced market valuation.

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FIGURES

Figure 1. Theory of value.

Figure 2. The Toulmin Model of argument.

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Figure 3. Backing ratio on market valuation.

-10

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Figure 4a. Backing ratio on market valuation, over IPO order.

(100 IPO rolling regressions)

Figure 4b. Backing ratio on market valuation, over time.


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Figure 5a. Shift in backing ratio to data ratio, over IPO order.

(25 IPO rolling averages)

Figure 5b. Shift in backing ratio to data ratio, over time.


0.00

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Aver

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Average backing ratio Average data ratio

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Figure 6. Data ratio on market valuation.

-15

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Marg

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ratio o

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IPO order

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Figure 7a. Data ratio on market valuation, over IPO order.


Figure 7b. Data ratio on market valuation, over time.


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<.01 <.05 <.10 Not significant Average data ratio

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Figure 8. Interaction between backing ratio, IPO hotness, and time, N = 231.

Figure 9. Interaction between backing ratio, media buzz, and time, N = 231.

02

46

Mark

et valu

ation (pre

dic

ted v

alu

es)

.1 .2 .30 .4Backing ratio

Aug 1998, low IPO hotness Aug 1998, high IPO hotness

Aug 1999, low IPO hotness Aug 1999, high IPO hotness

02

46

Mark

et valu

ation (pre

dic

ted v

alu

es)

.1 .2 .30 .4Backing ratio

Aug 1998, low media buzz Aug 1998, high media buzz

Aug 1999, low media buzz Aug 1999, high media buzz

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Figure 10a. Shift in backing ratio to data ratio, by firm type, over IPO order.


Figure 10b. Shift in backing ratio to data ratio, by firm type, over time.


0.00

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0.121

13

25

37

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61

73

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145

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B2C Internet Company

Average backing ratio

Average data ratio

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30

59

88

117

146

175

204

233

262

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349

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B2B Internet Company


Average data ratio

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0.12

Aver

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B2C Internet Company


Average data ratio

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Aver

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B2B Internet Company


Average data ratio

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APPENDIX

Table A1. Regression Models Predicting Market Valuation, N = 523.

Variable Model 1 Model 2 Model 3 Model 4

Backing ratio 0.686 0.675

(0.522) (0.523)

Data ratio 1.314 1.230

(1.384) (1.394)

Revenuea 0.061** 0.054* 0.061** 0.054*

(0.028) (0.028) (0.028) (0.028)

Total assetsa -0.042 -0.031 -0.036 -0.026

(0.097) (0.099) (0.097) (0.099)

Firm agea -0.185* -0.200* -0.184* -0.199*

(0.102) (0.103) (0.102) (0.103)

Venture backed 0.073 0.060 0.062 0.050

(0.115) (0.113) (0.115) (0.113)

VC reputation 0.007*** 0.006*** 0.007*** 0.006***

(0.002) (0.002) (0.002) (0.002)

Number of underwriters -0.025 -0.026 -0.027 -0.028

(0.031) (0.031) (0.031) (0.031)

Underwriter reputation 0.014 0.012 0.014 0.013

(0.010) (0.010) (0.010) (0.010)

Size of BOD 0.016 0.019 0.015 0.018

(0.026) (0.025) (0.026) (0.026)

Outside directors -0.152 -0.172 -0.147 -0.166

(0.230) (0.232) (0.231) (0.233)

CEO age -0.005 -0.005 -0.005 -0.005

(0.005) (0.005) (0.005) (0.005)

Founder presence 0.051 0.044 0.056 0.048

(0.078) (0.078) (0.079) (0.079)

B2C -0.223* -0.222* -0.241** -0.240**

(0.118) (0.118) (0.122) (0.122)

B2B -0.055 -0.069 -0.055 -0.068

(0.106) (0.108) (0.106) (0.108)

Offer price 0.032 0.033 0.032 0.033

(0.021) (0.021) (0.021) (0.021)

Word count -0.000** -0.000*** -0.000*** -0.000***

(0.000) (0.000) (0.000) (0.000)

Internet reference -0.082* -0.097** -0.081* -0.097**

(0.047) (0.047) (0.047) (0.047)

Growth of market -0.077 -0.107 -0.077 -0.106

(0.085) (0.090) (0.085) (0.091)

Media attention 0.101 0.112 0.106 0.116

(0.074) (0.075) (0.075) (0.075)

Media buzz -4.149 -4.280 -4.355 -4.471

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(3.544) (3.573) (3.550) (3.575)

Media sentiment 9.304*** 9.543*** 9.098*** 9.346***

(3.441) (3.413) (3.438) (3.410)

IPO hotness -0.037** -0.035** -0.036** -0.035**

(0.015) (0.015) (0.015) (0.015)

Bubble dummy 0.340** 0.303* 0.344** 0.307*

(0.166) (0.168) (0.167) (0.169)

Constant 2.317** 2.435** 2.258** 2.378**

(0.973) (0.963) (0.983) (0.973)

Year fixed-effects Y Y Y Y

Observations 523 523 523 523

R-squared 0.294 0.297 0.295 0.298

Adjusted R-squared 0.240 0.242 0.239 0.242

D.f. 36 37 37 38

*** p<0.01, ** p<0.05, * p<0.10

Note: Results show robust regressions with robust standard errors in parentheses. Significance

tests are two-tailed. a variable is logged.

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Table A2. Regression Models Predicting Market Valuation, IPO Hotness Analysis, N = 258.

Variable Model 5 Model 6 Model 7

Backing ratio (BR) 2.139*** -0.140

(0.642) (2.096)

IPO hotness -0.034* -0.027 0.155

(0.019) (0.019) (0.223)

Time 0.029 0.036 0.017

(0.031) (0.032) (0.037)

BR*IPO hotness*time 0.094**

(0.046)

Data ratio -0.477 -1.380 -1.179

(3.300) (3.079) (2.909)

Revenuea 0.032 0.011 0.031

(0.038) (0.037) (0.038)

Total assetsa 0.086 0.115 0.107

(0.154) (0.150) (0.148)

Firm agea -0.060 -0.139 -0.243

(0.148) (0.145) (0.157)

Venture backed -0.154 -0.193 -0.134

(0.171) (0.164) (0.164)

VC reputation 0.009** 0.008** 0.008**

(0.003) (0.003) (0.003)

Number of underwriters 0.013 0.004 0.009

(0.020) (0.017) (0.017)

Underwriter reputation 0.092** 0.093** 0.072*

(0.043) (0.041) (0.040)

Size of BOD 0.043 0.053 0.052

(0.039) (0.039) (0.036)

Outside directors -0.616* -0.711** -0.597*

(0.337) (0.341) (0.340)

CEO age 0.001 0.001 0.001

(0.008) (0.008) (0.008)

Founder presence 0.229* 0.199* 0.155

(0.123) (0.120) (0.119)

B2C -0.137 -0.136 -0.121

(0.169) (0.164) (0.162)

B2B -0.213 -0.284* -0.255*

(0.163) (0.162) (0.152)

Offer price 0.009 0.010 0.009

(0.019) (0.017) (0.016)

Word count -0.000*** -0.000*** -0.000***

(0.000) (0.000) (0.000)

Internet reference 0.015 -0.033 -0.022

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(0.053) (0.053) (0.055)

Growth of market -0.404*** -0.532*** -0.582***

(0.143) (0.149) (0.146)

Media attention 0.181** 0.226*** 0.201**

(0.083) (0.087) (0.084)

Media buzz -4.224 -3.068 -1.462

(6.366) (6.503) (6.330)

Media sentiment 7.093 9.132 7.731

(6.151) (6.095) (6.060)

Constant -0.152 -0.319 0.385

(2.017) (2.054) (2.200)

Year fixed-effects Y Y Y

Observations 258 258 258

R-squared 0.265 0.306 0.364

Adjusted R-squared 0.186 0.227 0.281

D.f. 25 26 30

*** p<0.01, ** p<0.05, * p<0.10

Note: Results show robust regressions with robust standard errors in parentheses.

Significance tests are two-tailed. a variable is logged.

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Table A3. Regression Models Predicting Market Valuation, Media Buzz Analysis, N = 258.

Variable Model 8 Model 9 Model 10

Backing ratio (BR) 2.139*** 2.473

(0.642) (3.449)

Media buzz -4.224 -3.068 11.608

(6.366) (6.503) (10.138)

Time 0.029 0.036 0.063

(0.031) (0.032) (0.053)

BR*media buzz*time 4.742*

(2.824)

Data ratio -0.477 -1.380 -1.026

(3.300) (3.079) (2.793)

Revenuea 0.032 0.011 0.032

(0.038) (0.037) (0.039)

Total assetsa 0.086 0.115 0.107

(0.154) (0.150) (0.149)

Firm agea -0.060 -0.139 -0.223

(0.148) (0.145) (0.150)

Venture backed -0.154 -0.193 -0.144

(0.171) (0.164) (0.161)

VC reputation 0.009** 0.008** 0.008**

(0.003) (0.003) (0.003)

Number of underwriters 0.013 0.004 -0.001

(0.020) (0.017) (0.018)

Underwriter reputation 0.092** 0.093** 0.088**

(0.043) (0.041) (0.041)

Size of BOD 0.043 0.053 0.049

(0.039) (0.039) (0.039)

Outside directors -0.616* -0.711** -0.563

(0.337) (0.341) (0.344)

CEO age 0.001 0.001 0.003

(0.008) (0.008) (0.008)

Founder presence 0.229* 0.199* 0.191

(0.123) (0.120) (0.118)

B2C -0.137 -0.136 -0.101

(0.169) (0.164) (0.163)

B2B -0.213 -0.284* -0.240

(0.163) (0.162) (0.153)

Offer price 0.009 0.010 0.012

(0.019) (0.017) (0.016)

Word count -0.000*** -0.000*** -0.000***

(0.000) (0.000) (0.000)

Internet reference 0.015 -0.033 -0.019

(0.053) (0.053) (0.053)

Growth of market -0.404*** -0.532*** -0.543***

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(0.143) (0.149) (0.144)

Media attention 0.181** 0.226*** 0.215**

(0.083) (0.087) (0.096)

Media sentiment 7.093 9.132 5.753

(6.151) (6.095) (5.941)

IPO hotness -0.034* -0.027 -0.023

(0.019) (0.019) (0.019)

Constant -0.152 -0.319 -1.037

(2.017) (2.054) (2.198)

Year fixed-effects Y Y Y

Observations 258 258 258

R-squared 0.265 0.306 0.350

Adjusted R-squared 0.186 0.227 0.264

D.f. 25 26 30

*** p<0.01, ** p<0.05, * p<0.10

Note: Results show robust regressions with robust standard errors in parentheses.

Significance tests are two-tailed. a variable is logged.

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