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Entrepreneurship, Legitimacy and Online Social Communities:

An Empirical Analysis

Sofia Bapna and Mary Benner

Carlson School of Management, University of MN

We seek to address the question of how entrepreneurial firms can leverage online

social networking communities to establish legitimacy. We propose that one

measure of a firm’s legitimacy is the size of its online community, and we

hypothesize about the specific ways that the use of online symbolic actions can

facilitate growth in a firm’s online social community, and therefore, its legitimacy.

We test the impact of four categories of online symbolic actions on online social

community growth. We find that online symbolic actions that i) show product and

industry knowledge, a component of actions reflecting credibility, are positively

associated with online social community growth, ii) show firm accomplishments, a

component of actions reflecting organizational achievements, are also positively

associated with online social community growth, and iii) seek opinions have a

negative impact on online social community growth.

The failure rate for new businesses is staggering. A full quarter of new ventures don’t make it

past the first year, 44% of ventures fail by the end of the third year, and 55% of ventures fail by

the end of the fifth year from founding (Shane 2008). Institutional theorists associate legitimacy,

that is, the acceptance of an organization’s actions as proper or appropriate (Suchman 1995),

with new venture success and survival (Meyer and Rowan, 1977, DiMaggio and Powell 1983).

Stinchcombe (1965) suggests that legitimacy helps new ventures, particularly new forms of


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organization, overcome the liability of newness. Aldrich and Fiol (1990) refer to new venture

legitimacy as “especially critical”. Legitimacy is viewed as a resource that can be used to acquire

other resources, to gain unrestricted access to markets (Brown, 1994), to thwart questions about

an organization’s competence, and to raise the status of an organization in its community (Oliver

1991). Meyer and Rowan (1977) note that organizations that lack legitimacy “are more

vulnerable to claims that they are negligent, irrational or unnecessary."

Managers can purposely seek legitimacy through specific actions (Suchman 1995) and previous

literature has provided insights into some of these actions. Suchman (1995) suggests the use of

symbolic actions and public discussion as approaches to enhance legitimacy. Zimmerman and

Zietz (2002) theorize that new ventures can gain legitimacy by adherence to socio-political

regulations, norms and widely held beliefs within a venture’s domain. Lounsbury and Glynn

(2001) propose that new ventures can increase their legitimacy through the use of stories that

reflect the firm’s stock of entrepreneurial capital, which includes patents, credentials,

endorsements, prior performance, and social connections.

Apart from legitimatization in the context of new ventures, past literature has also examined

legitimacy creation in various unique settings including legitimacy in the context of

multinational enterprises (Kostova and Zaheer 1999), legitimacy of networks of firms (Human

and Provan 2000), legitimacy in contexts such as hospitals where technical and managerial

legitimacy are created in ways distinct from each other (Reuf and Scott 1998), and legitimacy in

the context of deep institutional change within a firm (Suddaby and Greenwood 2005).


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However, none of these studies have examined the use of online social communities to establish

legitimacy. Several unique features of social networking sites make it conducive to the

legitimation process. Before we delve into these reasons it is important to define social

networking sites and their relevance to businesses. Ellison and Boyd (2013) define a social

networking site as:

“A networked communication platform in which participants i) have uniquely

identifiable profiles that consist of user-supplied content, content provided by

other users, and/or system-provided data; ii) can publicly articulate

connections that can be viewed and traversed by others; and iii) can consume,

produce, and/or interact with streams of user generated content provided by

their connections on the site.”

Henceforth, we refer to a firm’s connections on a social networking site as the firm’s online

social community, community, fans, or fan base. The widespread use of such sites by businesses

is evident in the fact that 11 million businesses used the social networking site, Facebook, in

2012. A survey of business professionals indicates that 55% of them devote employee resources

to manage their social media initiatives (Mzinga Inc. and Babson Executive Education 2009).

Further, the features of social networking sites lead us to four arguments about why such sites

are potentially effective channels for establishing new venture legitimacy.

Firstly, social networking sites allow a firm to communicate with its community members

several times a day via unique content that can be spontaneous, that is, the communications don’t


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necessarily require pre-planning as is the case with traditional advertising and marketing. These

characteristics allow for a large variety of content, and give the firm the flexibility to tailor

content to current issues. This makes social networking sites an optimal channel for the use of

symbolic actions (Suchman 1995), which make people draw interpretations about the character

of the organization (Brown 1994), and for story telling (Lounsbury and Glynn 2001). For

example, a firm sharing a photo on a social networking site of products being quality checked is

a symbolic action that can cause viewers to perceive the firm as having professional processes.

Secondly, community members are able to respond to content published by a firm on the social

networking site, and firms are able to respond back either with a generic response that is geared

to all community members, or with a response that is geared to a specific member. The cycle of

communication is quick with the possibility of an exchange of several messages over a single

day. Further, social networking sites allows for communication between community members

who don’t necessarily know each other, and the communication between community members is

visible to the firm. This three-way communication, between firm and community member,

between community member and firm, and between community members, is akin to a

conversation. This is consonant with the idea of participating in public discussion to create

legitimacy (Suchman, 1995).

Thirdly, social networking sites allow for social diffusion via peer influence (Aral and Walker

2010). People who are connected to members of the firm’s community, but not necessarily to the

firm can see interactions between the firm’s community member and the firm and are thus

exposed to new information and stories about the firm. A community member can choose to


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indicate her interest in a story shared by the firm on the social networking site. The community

member can also comment on the story or share it. Everyone that the community member is

connected to can see such actions. Thus social networking sites allow firms to leverage their

community members to diffuse information and stories. This is aligned with the idea of

broadcasting stories about the firm’s entrepreneurial capital (Lounsbury and Glynn 2001) and

also about the firm’s adherence to commonly held beliefs and norms in the domain that the firm

operates (Zimmerman and Zietz 2002).

Lastly, social networking sites publicly articulate information about how many people a firm is

connected to on the site. A firm’s connections on the site, which is also referred to as fan count,

is the most popular metric for measuring social media success (Chief Marketer’s 2011 Social

Marketing Survey, via Comscore 2012). These connections or fans are people who have

affirmatively stated their affinity for a particular organization, for reasons that may include “self-

expression, communicating positive associations of that brand to others, staying in

communication with that brand, or receiving deals and promotions” (Lipsman, Mudd, Rich and

Bruich 2011). Facebook fans as well as their friends on Facebook who have been exposed to

content by a retailer on the social networking site are found to be significantly more likely to

make a purchase from that retailer than an average Internet user (Lipsman, Mudd, Aquino and

Kemp 2012). With the intent of leveraging their fans, some iconic brands have amassed more

than twenty million fans. Based on arguments of mimetic isomorphism which suggest that under

conditions of uncertainty, firms seek to gain legitimacy by following the practices of other firms

that they perceive to be successful (DiMaggio and Powell 1983), we expect that new firms will

attempt to establish a presence on social networking sites and grow their fan base if this is a


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popular practice in their industry. This is because new firms, by virtue of their nascency are

faced with uncertainty.

These arguments point to an important gap in research in our understanding of legitimacy. Thus,

in this empirical study, we seek to address the question of how entrepreneurial firms can leverage

online social networking communities to establish legitimacy. We propose that one measure of a

firm’s legitimacy is the size of its online community, and we hypothesize about the specific ways

that the use of online symbolic actions can facilitate growth in a firm’s online social community,

and therefore, its legitimacy.

Online symbolic actions are actions that, like traditionally defined symbolic actions, carry

subjective meaning that is interpreted by the observer (Brown, 1994). However, online symbolic

actions are limited to those that can be carried out on a social networking site via posts. A post is

publicly visible content that is published by the firm on a social networking site. Posts can

include text, photos and icons. Posts can also include links to articles, blog posts and videos. The

following examples differentiate a symbolic action from an online symbolic action. An

entrepreneur can take a venture capitalist to a supplier meeting to display the venture capitalist’s

endorsement, or indicate reliability through a fashionably decorated office (Zott and Huy, 2007).

These are symbolic actions. However, on a social networking site the entrepreneur can share a

publicly available story about being funded by a venture capitalist or share photos of their office.

Because online symbolic actions are limited to posts on a social networking site, such actions

often take the form of representation of actions through media such as text and photos. However,

social networking sites also permit the firm to perform some unique actions like conducting a


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public discussion in which the firm seeks an opinion. Fans can view each other’s opinions,

interact with one and other, and the firm can participate in the discussion. All posts have the

potential of being online symbolic actions whether they are intended as such or not. A manager

may publish some content on a social networking site with the intention of it being viewed as an

online symbolic action, or just go about sharing something without intending it as an online

symbolic action. Either of these actions is an online symbolic action if the audience attaches a

symbolic meaning to the action.

To compile a comprehensive list of possible online symbolic actions and measure their impact on

fan growth, we started with symbolic actions suggested in past literature that are believed to be

associated with legitimacy, such as those noted by Zott and Huy (2006). From this list we

selected those symbolic actions that could potentially translate to the social media domain. We

augmented the list with additional symbolic actions that can be carried out over a social

networking site. Based on past literature and our theory we identified thirteen online symbolic

actions that can potentially lead to legitimacy. We grouped each of these online symbolic actions

into one of four broad categories of online symbolic actions. They are those that: reflect

credibility, reflect professional organizing, reflect organizational achievements, and seek

opinions. We propose that the frequent use of these four categories online symbolic actions by a

new venture leads to the growth of its online social community. We empirically test our

hypotheses by manually analyzing the content of close to 10,000 posts by new ventures.

Our empirical analysis points to three main findings. The first finding is that online symbolic

actions that show product and industry knowledge, which is a component of online symbolic


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actions reflecting credibility, are associated with online social community growth. Examples of

posts that fall under this category are posts that show how to use a product, posts that contain

broad educational content or posts that relay industry related news. The second finding is that

online symbolic actions that show firm accomplishments, which is a component of online

symbolic actions reflecting organizational achievements, are also associated with online social

community growth. Examples of posts that fall under this category are posts that show awards

won by the firm, as well as milestones achieved by the firm. These two findings indicate a form

of preferential attachment at work in the dynamics of social networking sites. People want to

connect and interact online with firms that they perceive to be knowledgeable and noteworthy in

terms of achievements. The final empirical finding is that contrary to our prediction online

symbolic actions that seek opinions have a negative impact on online social community growth.

We believe this may be due to an over use of questions posed by some firms to their community

members.

While previous studies have examined the impact of symbolic actions on legitimacy, this study

extends our understanding of legitimacy and symbolic actions to the domain of social media and

has three distinguishing factors. First, the study defines online symbolic actions and describes

how they are performed. The study also identifies which symbolic actions from previous

literature translate effectively to the social domain and also suggests some additional online

symbolic actions that are relevant to social media sites. Next, the study empirically examines the

impact of various types of online symbolic actions on online social community growth. Last, the

study suggests a theoretical model whereby legitimacy is created via social networking sites.


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This study has implications for managers of new ventures because it suggests a path to

legitimacy and growing online social communities. While managers have access to up to the

minute data analytics information such as engagement level and fan count, there are few large-

scale empirical studies that suggest a mechanism and path for growing online social

communities.

The remainder of this paper is organized into five sections. The first section describes the

boundaries of the study and the choice of social networking site for this study along with a

description of the site’s features and functionality. In the second section we review past literature

on symbolic actions in the context of legitimacy, develop four hypotheses that relate online

symbolic action to online social community growth and describe our theoretical model for

legitimacy creation. In the third section, we elaborate on our research context, methodology

employed, and the process we used for data collection and validation. In the fourth section this,

we present our empirical findings based on the analysis of about 10,000 Facebook posts for 15

entrepreneurial firms from a single industry, with a median of 67 weeks of data per firm. In the

final section, we discuss our contribution to research, practice and to the social networking sites

phenomenon in the context of e-commerce businesses.

Boundaries of the Study and Institutional Context

In the following two paragraphs we describe the boundaries of this study, and the choice of

social networking site for this study along with a description of the site’s features and

functionality.


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We limit our attention to entrepreneurial firms that have a significant online component, and are

business-to consumer (B2C) firms that sell physical products. An entrepreneurial context is

important for this study because as a firm becomes well established it is difficult to determine

whether the firm’s online social community is growing because of brand effects, existing status,

existing legitimacy or due to other factors. This is addressed, at least in part, by the fact that our

sample consists of new ventures, that is, those within the first three years of founding, that don’t

have the history and the legitimacy that is associated with well-established firms. Further, online

businesses typically engage in online social networking activities since such business target

customers who are also likely to be users of social networking sites. Thus, online businesses are

a good context for this study. Additionally, this study is limited to B2C and product firms

because the dynamics of usage of social networking sites by business-to-business and service

firms are considerably different from that of B2C product focused firms. This study focuses on

firms that sell physical products rather than digital products (such as a digital newspaper or

software) because physical and digital products lend themselves to different dynamics on social

networking sites in terms of community member contribution to discussions and content. Finally,

this study analyzes posts by the firm rather than user generated content, because we seek to

understand what online symbolic actions by the firm lead to legitimacy.

We focus our study on the social networking site, Facebook, because of the site’s extensive

adoption. This is illustrated by the fact that in the United States Facebook accounts for 90

percent of the time spent on social networking sites (comScore 2011). Furthermore, 11 million

businesses used the site in 2012. Firms have their own uniquely identifiable profile page on


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Facebook, where they periodically share content via posts. Visible to each Facebook user is a

live updated feed of the activity on the site by users she is connected to. Users can also see which

of their connections are fans of a particular firm. To become a fan or a community member of a

firm, a user needs to ‘like’ the firm’s Facebook page by clicking the ‘like’ button after visiting

the firm’s Facebook page. When a user becomes a fan of a page, posts by the firm are displayed

on that user’s live feed. The number of Facebook fans that a firm has at any given point in time

is publicly visible. Site users can interact with a firm by commenting on a post by a firm, sharing

it, or by indicating their interest in a post by ‘liking’ it. A user can like a post by clicking the

‘like’ button associated with the post. When a user likes, shares or comments on a post, the

action is publicly visible on the firm’s profile page and is also displayed on the feed page of the

user’s connections on the social networking site. If the firm permits them to do so, users of the

social networking site can add content on the firm’s profile page. This is called user generated

content. Firms can consume and interact with user-generated content on the social networking

site.

Symbolic Actions in the Context of Social networking sites

All objects and actions can potentially convey both an instrumental dimension and a symbolic

dimension. The instrumental dimension is based on objective criteria and relates to the attributes

of the products or service (Lievens and Highhouse 2003). It is measured by economic or

performance criteria such as speed and profitability (Rafaeli and Vilnai-Yavetz 2004). In

contrast, the symbolic dimension is subjective (Lievens and Highhouse 2003) and evaluated

according to criteria like values, feelings, and predilections of the observer (Rafaeli and Vilnai-


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Yavetz 2004). Symbolic actions are those actions to which subjective meaning is associated

beyond the substantive impact of the action (Brown 1994), that is, beyond the instrumental

dimension.

Several researchers have suggested specific symbolic actions as a means to establish legitimacy,

which in turn allows entrepreneurs to gain resources. Aldrich and Fiol (1994) propose that

symbolic communication can enable the creation of cognitive legitimacy. Santos and Eisenhardt

(2009) propose that firms that use identity claiming mechanisms such as templates, stories and

leadership signals become cognitive referents, that is the organization is accepted as

characterizing the new market. Lounsbury and Glynn (2001) posit that entrepreneurial stories

help legitimate a new venture by crafting its identity. Higgins and Gulati (2003) theorize that in

the biotechnology industry, upper echelon experience serves as symbols of a new venture’s

legitimacy to critical outsiders such as prominent underwriters.

Drawing a parallel form these studies, we suggest that a firm can use online symbolic actions to

establish legitimacy. We propose that there are four categories of online symbolic actions that

can lead to legitimacy. These are ctions that reflect credibility, reflect professional organizing,

reflect organizational achievements, and seek opinions. We explain each of these actions in the

following sub-sections.

Symbolic Actions Conveying Firm Credibility

Credibility refers to “the quality of being believable or worthy of trust” (Collins English

Dictionary, 2013). Past research has shown that entrepreneurs have depicted themselves as being


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credible by displaying personal capability and commitment to their ventures (Zott and Huy

2007), using association with prominent others (Higgins and Gulati 2003) and via obtaining

endorsements (Starr and MacMillan 1990).

We define online symbolic actions that convey firm credibility as those that fall into one of three

categories

i) Actions conveying the capability of the founder or key employees: Specifically these are

posts that portray the entrepreneur(s) or key team members as capable company builders.

Such capabilities can be displayed through posts that talk about the founder(s) or key

employees winning awards, being speakers at an event or conference, being interviewed

by the media, or being mentioned in the media. From an instrumental perspective, such

actions show past accomplishments, while from a symbolic perspective such actions are

an indicator of future competence and capability of team members.

i) Actions conveying association with industry experts: These are posts showing content

such as an interview with an external expert; product picks, tips or suggestions by an

external expert; or a live Q&A session with an external expert. The instrumental

dimension of such actions is that they communicate the expert’s opinions. Symbolically

these actions convey that established people recognize and want to associate with the

firm.

ii) Actions conveying product or industry knowledge: These are posts that provide

educational or value added informative content related to the firm’s products or the

industry to which the firm belongs. Instrumentally, educational or informative content

disseminates knowledge and views while symbolically such posts portray the firm as

being knowledge and having expertise (or having access to knowledge and expertise)


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about the products the firm sells and the industry in which it operates.

These ideas suggest the following hypothesis:

Hypothesis 1: The more frequently that an entrepreneurial venture performs online symbolic

actions that reflect credibility the more its online social community grows.

Symbolic Actions Conveying Professional Organizing

Entrepreneurs convey the quality of their venture’s organizing efforts by showing that the firm

has adopted professional structures and processes, for instance firms us fashionable front offices,

dress codes and professional looking websites in order to convey professional organizing (Zott

and Huy 2007). We define online symbolic actions that convey professional organizing as those

that fall into one of the following two categories. The first category is posts that communicate

the use of professional processes or procedures. For example, a post that includes a photograph

of products being quality checked before they are shipped to a customer indicates that the firm

employs professional processes. The second category constitutes posts that convey the existence

of professional structures. An example of a post that conveys professional structures is an

interview with an employee in which the employee describes her role in the firm. Instrumentally,

both these types of posts provide behind the scenes information about the firm. Symbolically,

such posts depict the professional nature of the firm’s structures and procedures, thus portraying

the firm as being both professionally run and experienced. Thus we hypothesize:



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actions that reflect professional organizing the more its online social community grows.

Symbolic Actions Conveying Organizational Achievement

In previous studies, entrepreneurs have been shown to demonstrate organizational achievement

such as prototypes, awards and demonstrations, venture age and number of employees to

establish legitimacy (Zott and Huy 2007). Santos and Eisenhardt (2009) propose that firms that

use stories and leadership signals such as setting standards become cognitive referents in a new

market. Rao (1994) in his study of the auto industry discusses how winning certification contests

legitimate organizations and create favorable reputations.

We define online symbolic actions that convey organizational achievement as posts that either i)

convey milestones, partnerships and awards won by the firm ii) convey that the firm sells award

winning products or those that are featured in the media. An example of a post that describes a

milestone is a photograph of the firm’s employees celebrating the firm’s first anniversary.

Instrumentally, such posts document past accomplishments or past certification of the products

sold by the firm. Symbolically, posts that convey information about milestones indicate that the

firm has persisted over time or grown. Posts that convey information about partnerships or the

fact that the firm sells award-winning products symbolically indicate that the firm is recognized

by established entities its environment. We therefore hypothesize:


actions that reflect organizational achievements the more its online social community grows.


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Symbolic Actions Seeking Thoughts or Opinions

We define online symbolic actions that seek others’ thoughts or opinions as posts that show that

the firm wants to engage in a conversation with their audience by asking questions. An example

of such a post is sharing pictures of two different prints for a product and asking which one the

reader prefers. On a social networking site, such questions are not rhetorical because readers are

able to respond to the questions and are also able to view and react to the responses by others.

The instrumental dimension of such posts is that the firm is seeking an opinion or input, while

the symbolic dimension is that the firm wants to connect with their audience and that the firm

cares about what its audience thinks. Thus we hypothesize:


actions that seek opinions the more its online social community grows.

We summarize these four hypotheses in Figure 2.

Theoretical model

Our proposed theoretical model (see figure 1) that describes how new ventures use online

symbolic actions to create legitimacy is partly based on arguments of mimetic isomorphism,

which suggest that under conditions of uncertainty firms mimic the actions of other firms that

they perceive to be successful, in order to be seen as legitimate (DiMaggio and Powell 1983).

New ventures by virtue of their nascency are faced with uncertainty. Therefore, we expect that in

industries where established firms have large online social communities, new ventures will seek

to establish a presence on social networking sites and grow their fan base. When a new venture


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uses an online symbolic action, the post acts as a stimulus around which existing community

members engage because they perceive the firm as being knowledgeable, and noteworthy in

terms of achievements. Community members engage by posting comments to, sharing, or

registering an interest in the post (Ellison and Boyd 2013). People who are connected to

members of the firm’s community, but not necessarily to the firm can see interactions between

the firm’s community member and the firm, and are thus exposed to new information and stories

about the firm. This serves as a trigger for activities on the site (Ellison and Boyd 2013). Thus,

the process of social diffusion of information, that is, getting information about the firm through

their connections may cause users of the site to join the focal firm’s online social community.

The firm continues to infuse the online social community with online symbolic actions to keep

its existing community engaged and simultaneously grow the firm’s online social community. In

this model, a high frequency of online symbolic actions is important in order to remain visible

through the torrent of information that is received by typical users of social networking sites.

Legitimacy that is established through a large online social community can be either

consequential or exchange legitimacy depending on the lens of the stakeholder. Exchange

legitimacy is awarded based on “expected value” or “practical consequences” to a particular set

of constituents (Suchman 1995). Thus, it is conceivable that when a prospective financial backer

is evaluating the legitimacy of a venture, she considers the potential economic benefits of a large

online social community such as leveraging the community for future revenues and for diffusing

information about the firm. Consequential legitimacy, in contrast, is based on what an

organization has accomplished (Meyer and Rowan 1991). Scott and Meyer (1991) note that

consequential legitimacy may be awarded based on criteria such as hospital mortality rates and


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automobile emission standards. In a similar vein, it is likely that a stakeholder such as a potential

customer may make inferences about the quality or value of a firm’s products based on the size

of the online social community that has voluntarily associated itself with the firm.

Alternate explanations

An alternate explanation to using symbolic action for online social community growth is the use

of more traditional marketing related posts. Such posts are characterized by a focus on firm or

product related content as well as product promotions or discounts. These are posts that do not

simultaneously convey any of the four online symbolic actions hypothesized in this study.

Additionally, it is conceivable that inducements such as sweepstakes, contests, giveaways, and

rewards may motivate people to join the online social community of a firm. To understand the

impact of these alternate explanations on online social community growth, our study measures

the impact of product and promotion related posts as well as posts that involve inducements.

Furthermore, previous literature indicates that user generated content (UGC) which refers to

posts by community members, is positively correlated to economic outcomes (Duan, Gu, and

Whicston 2008). In a similar vein it can be argued that UGC creates greater engagement and

hence larger online social communities. Our sample has only two firms that do not allow user

generated content that is allow their community members to create new posts on the firm’s

Facebook page. Since our sample does not have adequate variability with respect to firms that do

not allow UGS vs. firms that allow UGC, we do not control for UGC.


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Empirical Setting, Data, and Model

Empirical Setting and Sample

We test our hypothesis with a sample of firms in the online flash sales segment of the retail

industry. Such firms meet our criteria for analysis because they sell physical products and their

business model includes a significant online component. Further, the online flash sales segment

is one of the fastest growing e-commerce segments. The segment’s growth has accelerated since

2007 when the declining economy led to an unprecedented supply of excess inventory. Flash

sales were seen as a solution to move excess merchandise off the market quickly while

maintaining an aura of exclusivity (Cocotas 2012). These firms operate by purchasing excess or

out-of-season inventory at steep discounts from various brands. The products are then sold online

at a deep discount for a limited time (typically between a day and a week) to customers who

usually have to be members; members are invited by either the firm or by other members. The

need to market a new set of products on a weekly, or even daily, basis has led these firms to

depend heavily on email and marketing via social networking sites. In addition, the growth of the

segment since 2007 has given us a sample of several start-up firms founded around the same

time and operating under the same business model.

Our sample is based on listings from two directories of flash sales firms, Lokango.com and

FashionInvites.com as well as the business press. The latter source was included to compensate

for the time lag for some newly created firms to appear in the directories. The selected firms

covered all the major categories of flash sales businesses, namely apparel, household goods,


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travel and life style items. The criteria for inclusion in our sample are that the firm was in

business as of the time of data collection, has operations or an office location in the US, sells

products in the US, was founded in 20101, and has a Facebook fan page

2. We identified 23 firms

that met our criteria, and our goal was to obtain all Facebook content that is post text and weekly

fan count from the date each firm joined Facebook until July 1, 2012.

Dependent Variable

Our dependent variable is online social community size. We obtained the total Facebook fan

count per week for each firm in our sample from monitor.wildfireapp.com, This weekly data

spans the period of our study.

Independent Variables

Based on a literature review and our theory we identified thirteen dimensions associated with our

four online symbolic action constructs that reflect credibility, reflect professional organizing,

reflect organizational achievements, and seek opinions. Following Zaheer and Soda (2009), we

invited a panel of three experts to validate our constructs using the Q-Sorting technique (Segars

and Grover 1998). The list of 13 dimensions was randomly ordered and provided to a panel of

experts, who were asked to sort the dimensions into the four constructs. The instructions

included options for panel members to say that a dimension did not belong to any of the

constructs, or to say that a dimension belonged to multiple constructs. Based on the experts’

comments we reworded the dimensions related to two constructs. We then conducted another

round of surveys to validate our constructs; the percentage of correct classification and

1 In the case of firms with many subsidiaries, we include in the sample firms whose flash sales subsidiary was founded between 2010. 2 For firms that operate both a regular retail business and flash sales business we included only those firms that had a separate Facebook fan page for the flash sales part of their business.


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agreement was 100% for all three experts. Our independent variables were constructed by

categorizing each Facebook post in our sample along these dimensions.

The Facebook application programming interface (API) was used to collect the text of all past

posts for the firms in our sample, up to July 1, 2012. For some firms, we were not able to gather

posts all the way back to the date the firm joined Facebook, possibly because of restricted access

setting by some firms, or limits set by Facebook on historic data. This limitation combined with

the fact that different firms joined Facebook on different dates resulted in an unbalanced panel of

posts by each firm, that is each firm in the sample does not have posts for the same number of

weeks. A total of 12689 posts were collected.

Raters were asked to categorize each post in our sample of 12689 posts. This involved

determining whether the post belonged to any of the 13 dimensions associated with the

constructs. A web based questionnaire was given to the raters to perform the categorization

process. Measurement error due to context effects like grouping of items (Kline, Sulsky, and

Rever-Moriyama 2000) and item priming effects (Salancik 1984) was avoided by randomizing

the order of items that form the various constructs (Tourangeau and Rasinski 1988). The

questionnaire was peer reviewed to ensure principles of good item writing such as avoiding

double barreled questions, jargon, leading items, and negatively worded items. Further, the

questionnaire was pre-tested via a “think aloud” (Sudman, Bradburn, and Schwarz 1996) and via

a pilot test for 100 posts. We modified the questionnaire based on feedback from the think aloud

and the pilot tests. While we use a questionnaire for our independent variables, our dependent

variable is obtained from a different source, thus avoiding common source bias (King, Liu,


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Haney and He 2007).

Studies that analyze the content of textual data from social media typically use text mining tools

because the volume of data involved makes manual analysis difficult. For instance, Goh, Heng

and Lin 2013 use commercial text mining tools to capture the informative and persuasive nature

of content shared on Facebook. Text mining however, has many limitations. For example, it does

not analyze text embedded in images, or consider what the image itself conveys, nor does it

allow the analysis of the content of videos linked to posts. Also, text mining does not analyze

icons and symbol incorporated in posts. Depending on the way the text mining data is collected

and configured, the analysis may not include content linked to posts such as blogs and articles, as

wells as captions associated with photos. Further, fine-grained analysis of text can be challenging

when using text-mining tools. For example, in our study, we needed to differentiate between

three types of awards that may be mentioned in post content, that is awards to founders or key

employees, awards to the firm, and whether the firm sells award winning products. To allow for

a granular and nuanced analysis of posts we opted for manual rating of posts and used Amazon’s

Mechanical Turk (AMT) to find our raters. These raters not only analyzed the text of the post but

went to Facebook to find the actual post (based on identifying information) and rated the post

after reading articles linked to the post, watching videos embedded in the post, and scrolling

through photos associated with the post.

Since workers on AMT are significantly more diverse than workers from typical American

college samples (Buhrmester, Kwang and Gosling 2011) the probability of measurement error

due to response biases such as social desirability, and acquiescence (Bagozzi and Yi 1991) is


23

reduced by using AMT workers. In addition, a study by Buhrmester et all (2011) indicates that

the data obtained from workers on Mechanical Turk are “at least as reliable as those obtained via

traditional methods” and that AMT can be used to obtain, “high-quality data inexpensively and

rapidly.” This is supported by the fact that workers AMT receive negative ratings that are

publicly displayed as part of the workers profile if their work does not meet the expectations or

quality standards or of the job requestor.

Further, to ensure consistency of understanding of the questions, terminology and task, raters

were trained via videos that we posted online. Additionally, to filter out raters who had a poor

understanding of the questions or the task we “pre-qualified” each rater by asking them to rate a

set of 15 test posts. The degree of agreement between raters was measured using Cohen’s kappa,

where the K value is interpreted as the degree of agreement between raters after taking into

account probability (Cohen 1960). Literature on using kappa, suggests that a coefficient of .61

indicates reasonably good overall agreement (Kvalseth 1989), so a rater was qualified if his or

her categorization of the test posts resulted in an overall kappa coefficient of > .61.

A total of 15 raters were qualified using this metric. Qualified raters were given online access to

categorize posts for this study and we were available by email to answer any questions. To

ensure that inter rater reliability was maintained, we periodically selected random posts for each

firm and checked the reliability of the rating by asking another rater to rate the randomly selected

posts. This scalable, manual process applicable to high volume content analysis that we

developed for qualifying and managing workers, and for evaluating their output, is illustrated in

Figure 3.


24

For reliability testing, 370 randomly selected posts, approximately 3% of our original sample,

were examined by a second rater. The overall kappa coefficient for the 740 posts was 0.79. We

also examined the kappa coefficient for inter rater reliability for each firm. Three firms had a

kappa coefficient of <0.6 indicating that the raters did not have reasonably good overall

agreement. These three firms were dropped from our sample. Thus, this study develops and

employs a scalable and high quality manual content analysis process using the online work force

available through Amazon’s Mechanical Turk. This process enabled us to overcome the

previously stated limitations of text mining tools and resulted in nuanced content analysis.

An additional five firms were also dropped from our sample. Of these, two firms were dropped

because post content was not available possibly due to restricted access by the firms, and two

others were dropped because significant post content was unavailable (because the firm deleted

the content) after the firm had initially posted something. One firm was dropped because its

product line was focused on Indian fashion and home décor, and thus the post content targeted a

very narrow audience.

The three online symbolic actions that convey credibility, professional organizing, and

organizational achievements are multi-dimensional aggregate meta constructs (Law, Wong, and

Mobley 1998) that are formed as a composite of two or three sub-constructs (as indicated Figure

2). The sub-constructs under a particular construct may or may not co-vary. Further, each of

these sub constructs is composed of one or more dimensions. The construct corresponding to

seeking opinions has no sub-constructs and has only one dimension. Each dimension


25

corresponds to a question that the rater rates about the post content. Figure 2 depicts the

relationship between the constructs and the sub-constructs. Table 1 lists the constructs, sub-

constructs, and dimensions underlying the sub-constructs along with their corresponding survey

question(s).

Control Variables

We include two control variables that relate to the alternate explanations discussed earlier. The

first is a control for posts that invite people to participate in contests, giveaways and

sweepstakes. The second control variable is for posts that focus on firm or product related

content as well as product promotions or discounts. These are posts that do not simultaneously

convey any of the four online symbolic actions hypothesized in this study.

Model Specification

The final sample included 15 firms and a total of 9,826 posts. The median number of posts

analyzed per firm is 674 (mean 655). The median number of weeks of data per firm that was

analyzed is 67 (mean 57). Only two firms that were analyzed had less than 50 weeks of data.

We constructed a weekly panel data set that included our independent and dependent variables

and the controls. The independent variables and controls were computed by summing weekly

scores corresponding to sub-constructs and constructs.

We estimate the following model:


26

Fan Countit = β0ij + β1Fan Counti(t-1) + β2Credibilityit + β3Professional Organizingit +

β4Organizational Achievementit + β5Opinionit + β5Contestit + β7 ProductOrSaleit + uit (1)

In the equation above, Fan Countit is the size of the Facebook community for firm i during week

t and Fan Counti(t-1) is its lagged value by one week. The equation also includes the four

constructs and the two control variables discussed earlier.

There are a few potential concerns with a simplistic regression of the model presented in

equation 1. First, it is conceivable that an unmeasured variable like existing status or legitimacy

of a firm is driving growth in the fan base. This is addressed, at least in part, by the fact that our

sample consists of new ventures that don’t have the history and the legitimacy that is associated

with well-established firms. Second, omitted variables such as the sub-segment that the firm

belongs to for example apparel or household goods, may impact one or more of the explanatory

variables and also the fan count. This form of endogeneity causes biased estimates (Wooldridge

2002). Third, the presence of the lagged dependent variable Fan Counti(t-1) gives rise to

autocorrelation. To address the latter two issues, we use the to use the Arellano-Bond (1991)

difference GMM estimator. The second concern, omitted variables, is addressed so long as the

omitted variables are time invariant, that is, they are fixed effects (Mileva 2007). All of the firm

fixed effects are contained in the error term, uit, in equation 1, which is composed of both the

unobserved firm specific effects, vit,, and the observation-specific errors, eit,. The difference

GMM uses first-differences to transform equation (1) into:

ΔFan Countit = β0ij + β1ΔFan Counti(t-1) + β2ΔCredibilityit


27

+ β3ΔProfessional Organizingit + β4ΔOrganizational Achievementit

+ β5ΔOpinionit + β5ΔContestit + β7 ΔProductOrSaleit + uit (2)

Since the unobserved firm specific effects, vit, are time invariant, the fixed firm-specific effect is

removed, and from equation 2 we get:

uit-ui,t-1= (vi− vi)+( eit - ei,t-1) = eit - ei,t-1 (3)

The third concern, autocorrelation, is addressed because the dynamic panel model instruments

the first-differenced lagged dependent with its lagged levels (Mileva 2007).

For robustness we also estimate our equation using Negative Binomial, and OLS models. For

these two models our dependent variable was the growth in fan count each week, which is

computed as the fan count for the current week minus the fan count for the previous week. While

estimating these models we included the previous week’s fan count value as a control variable

and dummy variables for firm and week. For the Negative Binomial model we had to shift he

dependent variable because this model accepts only positive values, and in our study there are

instances where the fan count drops across weeks, resulting in a negative value. Further, this

count model may not be the most appropriate for very large count values (as is the case for the

fan count in our study) that are heavily skewed.


28

Results

Table 1 provides descriptive statistics for our variables, and Tables 2 and 3 provide correlations

for the variables at the construct and sub-construct levels respectively. We do not find

collinearity among the independent variables to be an issue.

Table 4 presents the results of the estimation from equation (1) that examines the impact of

online symbolic actions on fan count. The results reveal a positive and statistically significant

impact of symbolic actions reflecting credibility, and thus support H1. The coefficients for

symbolic actions reflecting professional organizing and organizational achievements are not

statistically significant. Hence we do not find initial support for H2 and H3. Further, contrary to

our expectations the results reveal a negative and statistically significant impact of symbolic

actions seeking opinions. Thus for H4 we find that the effect is in the opposite direction from

what we had predicted. We explain this result later in this section. With respect to our controls,

we find that contests are not associated with online social community growth, while posts with

content about the firm’s products or about promotions (that do not simultaneously exhibit any of

the for online symbolic actions hypothesized in this study), are associated with online social

community growth.

For comparative purposes, results from the dynamic panel model are show along with the results

from the OLS and the Negative Binomial models in Table 4. The R–square value from the OLS

model is 38%. The results from the OLS the Negative Binomial models for the four main

variables from our hypotheses are consistent with those of the dynamic panel model, except in


29

the case of the Negative Binomial model for symbolic actions seeking opinions, which is found

to be not statistically significant.

To understand what is driving our results we examine the impact of each of the sub-constructs

associated with the four symbolic actions on fan count. The equation we estimate at the sub-

construct level is:

Fan Countit = β0ij + β1Fan Counti(t-1) + β2Capabilityit + β3Association With Expertsit + β4 Product

or Industry Knowledgeit + β5 Professional Processit + β5 Professional Structureit + β7 Firm

Accomplishmentit + β8 ProductAffirmationit + β9 Opinionit + β10Contestit + β11ProductOrSaleit

+uit (4)

Results from this estimation are shown in Table 5. Of the three sub-constructs that constitute

credibility, namely, personal capability, access to experts and product and industry knowledge

we find that product and industry knowledge (for example content that shows how to use a

product, broad educational information, and industry related news) is positively associated with

online social community growth. In addition, we find that of the two sub-constructs that

represent organizational achievement that is product affirmation and firm level accomplishments

such as awards and milestones, firm accomplishments are positively associated with online

social community growth. This finding provides partial support for H3. These two findings

indicate a form of preferential attachment wherein people want to associate online with firms that

they perceive to be knowledgeable and noteworthy in terms of achievements. None of the


30

coefficients of the sub-constructs that constitute professional organizing were found to be

statistically significant. Thus analysis at the sub-construct level analysis lends no support for H2.

As with the previous model (at the construct level) we find that seeking opinions is negatively

associated with online social community growth. The volume of information that users on social

networking sites are exposed to exceeds their capacity to consume it (Weng Flammini,

Vespignani and Menczer 2012). Although questions posed on social media don’t necessarily

require a response, asking a question places additional demands on users’ limited time, and a

firm that asks too many questions may seem onerous to its community members. We attribute

the negative association between asking for opinions and online social community growth to the

fact that some firms are posing too many questions to their community members. We find that

some firms in our sample ask on average between 6 and 12 questions a week.

Once again, for comparative purposes, results from the dynamic panel model are show along

with the results from the OLS model in Table 5. The R–square value from the OLS model is

39%. The results from the OLS model for the sub-constructs associated with our hypotheses and

for the two control variables are consistent with those of the dynamic panel model.

To be able to better interpret our coefficients we estimate our model using the natural log of the

dependent variable. Given the skewed nature of our dependent variable this improves the fit for

the OLS model. A normally distributed dependent variable, however is not an assumption for the

dynamic panel model. The equations we estimate at the construct and sub-construct levels are


31

Ln(Fan Count) it = β0ij + β1Fan Counti(t-1) + β2Credibilityit + β3Professional Organizingit +

β4Organizational Achievementit + β5Opinionit + β5Contestit + β7 ProductOrSaleit + uit (5)

Ln(Fan Count) it = β0ij + β1Fan Counti(t-1) + β2Capabilityit + β3Association With Expertsit + β4

Product or Industry Knowledgeit + β5 Professional Processit + β5 Professional Structureit + β7

Firm Accomplishmentit + β8 ProductAffirmationit + β9 Opinionit + β10Contestit +

β11ProductOrSaleit +uit (6)

Estimates for the models at the construct level, that is, equation 5, are shown in Tables 6.

Estimates for the models at the sub-construct level, that is equation 6, are shown in Table 7. The

significance and the direction of the coefficients are the same as the estimates for the model

represented by equation 1. We find that a unit increase in online symbolic actions reflecting

product and industry knowledge is associated with an average increase in fan count by 0.2%3,

Similarly a unit increase in online symbolic actions showing firm achievements is associated

with an average increase in fan count by 1.3%. However, a unit increase in online symbolic

actions seeking opinions is associated with an average decrease of fans by 0.2%. Mangers we

talked to feel that if using online symbolic actions contributes to even a small (5%) increment in

their online population growth, it is worth pursuing. In one entrepreneur’s words, “Every little

increase matters.” This information gives us an organizational benchmark to evaluate magnitude

of effect for symbolic action on online social community size.

3 computed as 100(exp(b)-1)


32

Contributions

Our study makes three contributions to research. To begin with, the study defines online

symbolic actions and describes how they are performed. The study identifies which symbolic

actions from previous literature translate effectively to the social domain and also suggests some

additional online symbolic actions that are relevant to social media sites. In addition, the study

empirically examines the impact of various types of online symbolic actions on online social

community growth. Furthermore, the study suggests a theoretical model whereby legitimacy is

created via social networking sites.

Our empirical analysis points to three important findings. First, online symbolic actions that

show product and industry knowledge, which is a component of online symbolic actions

reflecting credibility, are associated with online social community growth. Next, online symbolic

actions that show firm accomplishments, which is a component of online symbolic actions

reflecting organizational achievements, are also associated with online social community growth.

Last, contrary to our predictions we find that online symbolic actions that seek opinions have a

negative impact on online social community growth. The first two findings indicate a form of

preferential attachment at work in the dynamics of social networking sites. The third finding can

be attributed to an over use of questions on social networking sites.

This study is relevant to managers and timely because both new ventures and incumbents are

rapidly adopting the use of social networking sites. While several social networking analytics

sites provide entrepreneurs with up to the minute social networking sites statistics such as:

number of fans and engagement level, the analytics sites leave it to the firm to determine what


33

leads to better or worse performance on these metrics. By associating online symbolic actions

with online social community growth, the study provides one possible path to growing online

social communities and to legitimacy. The latter facilitates resource acquisition, which is critical

to the survival of new ventures. Conversely, the study also provides some understanding about

what kinds of online actions will lead to a decline in online social community size. Our study has

wide applicability as it encompasses online B2C new-ventures from various industries that are

product focused.

Limitations and Directions for Future Research

One limitation of this study is that it does not consider the quality of the posts by the firm. Apart

from the fact that quality of posts is a subjective measure that is hard to define and measure, the

volume of data in this study would make it difficult to systematically hand code the quality of

each post.

Another limitation is that while it is conceivable that some firms purchase ‘likes’ or fans on

Facebook and thus increase the size of their online social community. Our study is unable to

control for this directly. However, to the extent that this may be an ongoing practice by a

particular firm, the practice can be thought of as a time invariant fixed effect that gets washed

out. The reason that this may be an ongoing practice rather than a one-time activity is that a

sudden surge in fans would seem suspect. Further, Facebook has never permitted the purchase or

sale of Facebook Likes, and their website indicates that detection of such activity could result in

action against the firm by Facebook. This may be a deterrent, since the firms in our study belong

to the Flash sales industry that relies heavily on social networking sites marketing due to their


34

constantly changing inventory

In terms of future research, there are several interesting research questions related to the creation

of legitimacy through the use of social networking sites. For instance, what impact does user

generated content have on legitimacy, what types of user generated content leads to legitimacy

and what actions can the firm take to stimulate such user generated content, are promising

questions for future research.


35

Figure 1: Symbolic action and social diffusion work together to grow the online social

community


36

Figure 2: Online symbolic actions (construct and sub-construct level) that lead to online social

community growth


37

Figure 3: Process for qualifying workers and rating posts

Working paper. Please do no not redistribute or cite without author’s permission. 38


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