Contributing to Public Document Repositories: A Critical Mass Theory Perspective
Naren B. Peddibhotla
npeddibhotla@csom.umn.edu
Mani R. Subramani
msubramani@csom.umn.edu
3-365, Department of Information and Decision Sciences
Carlson School of Management
University of Minnesota
321, 19th Ave South
Minneapolis, MN 55455
USA
March 30, 2006
Under second-round review at Organization Studies for the Special Issue on Online
Communities
Comments are welcome.
Public document repositories
Contributing to Public Document Repositories: A Critical Mass Theory Perspective
Abstract:
Public document repositories (PDRs) are valuable resources available on the Internet and are a
component of the broader information commons freely accessible to the public. Instances of
PDRs include the repository of reviews at Amazon.com and bn.com and Wikipedia, the online
encyclopedia. These repositories are created and sustained by the voluntary contributions of
individuals who are not compensated for their inputs. While the potential value of these
repositories is recognized, there has been little prior examination of the fundamental mechanisms
underlying the willingness of individuals to take the time and effort to make contributions.
This paper draws on Critical Mass theory to examine the benefits, motivations and dynamics of
contributions by the critical mass of contributors and is based on profiles of contributors and data
on their contributions of reviews to Amazon.com. It identifies a small critical mass of
contributors that makes a significant contribution to the maintenance of the PDR. The paper
contributes to the development of a theory of collective action related to public repositories of
information goods.
Keywords: Public document repository; critical mass theory, collective action, dynamics,
information technology.
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Public document repositories
Critical Mass – “a small segment of the population that chooses to make a big contribution to the
collective action while the majority do little or nothing. These few individuals are precisely those who
diverge most from the average. …the number of such deviants and the extremity of their deviance - is one
key to predicting the probability, extent and effectiveness of collective action”. (Oliver et al., 1985: 524)
Introduction
A number of websites that are freely accessible over the Internet provide users with useful
content. Instances of such sites, which we term public document repositories (PDR) include the
repository of book reviews, movies and music at amazon.com, repositories of travel and tourism
information at travelpost.com and lonelyplanet.com and the large body of reviews of consumer
products at epinions.com. Such repositories are created by the largely uncompensated efforts of
individuals contributing content e.g. book reviews, comments on hotels and tourist destinations
for the benefit of others who may be considering reading the books, choosing hotels or visiting
these destinations. The scale of many of these repositories is truly non-trivial. For instance,
Dooyoo.co.uk had over 200,000 reviews available on its site contributed by over 20,000
individuals and Amazon.com had over 3.5 million reviews available on its site in 2004
contributed by over a million individuals.
Such repositories are termed discretionary databases by Thorn and Connolly (1987) since they
comprise private information that is shared by individuals, at their discretion, with others.
Advances in information technologies facilitating large scale storage and retrieval make it
increasingly feasible to consolidate the collective knowledge and resources of even widely
geographically dispersed individuals into shared repositories that can be extremely useful to the
general public. However, motivating individuals to contribute to collective repositories is a
daunting challenge and initiatives to establish such repositories, even when they are seen as
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Public document repositories
serving the common good overwhelmingly fail (Fulk et. al 2004). Viewed against this backdrop,
evidence that popular public repositories of reviews such as Amazon.com and bn.com are
thriving suggests the need for a closer examination of the factors linked to their success. While
the value of such publicly accessible online repositories is generally recognized, there has been
little prior research examining contribution and participation in PDRs. Thorn and Connolly
(1987) observe that “the technology of storing and distributing information is advancing rapidly;
but we see relatively little evidence of parallel growth in the understanding of how this
technology can best be harnessed” (page 527). Their observation, made nearly two decades ago
still continues to be valid today. In this paper, we attempt to move the field forward towards a
greater understanding of the dynamics of collective action in Public Document Repositories. We
apply critical mass theory to the context of PDRs and suggest propositions related to the
dynamics of repository contributions. Using data on contributors to the large PDR at
Amazon.com, we identify a critical mass of reviewers and the factors linked to their repository
contributions.
Public Document Repositories
The act of making a repository contribution has several unique characteristics that set it apart
from instances of helping behavior in physical contexts (Clary et al. 1998) as well as
contributions in the context of technology mediated forums such as email and listservs (Constant
et al. 1996; Butler 2001). First, a repository contribution such as the posting of a book review is
independently initiated by an individual with the expectation that this might be useful to others.
Such contributions are made not only without a request for help but also without specific
information on the individuals being helped by the action. Second, while helpful actions
generally occur in a dyadic context of individuals or groups linked by social ties, PDR
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Public document repositories
contributions represent attempts by individuals to help unknown others with whom they typically
have no discernable ties other than those arising from participation in the PDR. Third, repository
contributions are accomplished through impersonal interactions with a database. Users typically
log into a website, fill out a form describing the contribution and either attach a document or
copy and paste their contribution into a text box. Contributions thus occur in a context devoid of
social cues – a rather peculiar feature since helping is a fundamentally social act. Fourth, tangible
incentives for contribution are mostly nonexistent and are at best, minimal. Though
organizations maintaining PDRs encourage contributions, they usually provide no direct
incentives for contributions. Finally, contributors usually get no feedback when (or if) their
contributions are viewed by others. Repositories such as Amazon.com provide mechanisms for
users viewing reviews to provide feedback on its quality but leaving feedback is optional and is
generally meager. Thus, the individuals spending their time and taking the effort to make
repository contributions appear to be doing so in spite of impediments that inhibit contributions.
Further, the technologies of PDRs create a unique environment with two important
characteristics: a) PDRs are not excludable since they cannot be withheld from any individual
once they become available, regardless of whether or not he or she contributed to its creation and
b) Use of PDRs is non-rivalrous since one person’s use of the PDR does not affect its
availability or its utility to other individuals. PDR are therefore collective goods or public goods
(Hardin 1982). This view that PDRs are public goods is consistent with the arguments of Thorn
and Connolly (1987) and Fulk et. al (2004) regarding repositories of discretionary information.
PDRs exhibit another characteristic of collective goods termed the jointness of supply, the costs
associated with creating the public good are fixed, regardless of the number of individuals that
take advantage of it. The costs of writing and submitting content remain the same whether the
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Public document repositories
content is used by one individual or a very large number. As a result, free riding is not a burden
and PDRs can potentially be created and sustained for the collective through the efforts of a
relatively small minority. These features of PDRs create conditions that Olson (1965) described
as the exploitation of the great by the small. The theory of critical mass (Oliver et al. 1985) that
proposes a framework to explain collective action with respect to public goods therefore
provides a useful lens to study contributions to PDRs.
Critical Mass Theory and Collective Action
Critical mass theory (Oliver et al. 1985; Marwell and Oliver 1993) presents a framework to
explain collective action. The central insight suggested by the theory is that the presence of a
critical mass, a sub-group of the population that shoulders most of the initial cost, can trigger
broader participation and the creation of the public good. Another important insight is the
interdependence of contribution by individuals in the population. An illustration of collective
action where the initial efforts of a critical mass are important is political lobbying by a
neighborhood to fight school closure where an affluent minority can jump-start the movement by
hiring a lawyer with their own funds before others join (Oliver et al. 1985). This theory has been
applied to study phenomena involving collective action by researchers in a variety of domains
(Oliver and Marwell 2001). It has been used by Markus (1987) in the literature on computer
mediated communications (CMC) to explain the diffusion of interactive media, by Thorn and
Connolly (1987) and Fulk et. al (2004) to explain contributions to discretionary organizational
databases and by Monge et al. (1998) to study pooled information in inter-organizational
alliances. However, to the best of our knowledge, there has been no prior application of this
theory to study collective action related to PDRs. In applying the theory to the context of PDRs,
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Public document repositories
we advance propositions extending the theory in the light of empirical observations of PDR
contribution and use.
Critical Mass Theory and Public Document Repositories
Role of the critical mass of contributors: Public goods present a social dilemma since any
individual can derive benefits from their use irrespective of his or her participation in creating
them, leading to the temptation for individuals to free ride on the contributions of others. Free
riding leads to sub-optimal provisioning of public goods. In the context of PDRs, widespread
free riding can lead to PDRs having very little useful content. When most users withhold their
own contribution to the PDR in the hope of expecting to benefit from the contributions of others,
there are few contributions ever made to take advantage of. The central premise of critical mass
theory applied to PDRs is that a small minority in the population – the critical mass – that is
interested in the PDR can make most of the contributions and lead to the creation of a useful
PDR that the majority of users exploit. In the absence of prior work on the validity of this
perspective to PDRs, our first research question is:
Are public document repositories created and sustained by a critical mass of
contributors?
Thorn and Connolly (1987) in their examination of discretionary databases in organizations
conclude that discretionary information will be chronically undersupplied. However, anecdotal
evidence of successful PDRs on the Internet such as those at Amazon.com and Wikipedia.com
appear to be inconsistent with these predictions. For instance, the online encyclopedia at
Wikipedia.com that was launched in 2001 had over 3.7 million articles (over 1million articles in
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Public document repositories
English) contributed by users as of March 2006. Wikipedia, one of the few PDRs for which
detailed contribution statistics are available reports receiving over 46 million content updates by
users on 3.7 million pages since July 2002. Wikipedia reported having over 17,000 active
contributors (those submitting 5 or more times in a month) in December 2005 and the number of
active contributors has been growing consistently every month since January 2001. While such
detailed statistics are unavailable from other PDRs operated by commercial entities such as
Amazon.com and epinions.com, a steady growth of contributors is reported for these sites as
well.
This evidence suggests the need to revisit some of the assumptions underlying models of
contributor behavior employed in prior research on discretionary databases. Consistent with the
uses and gratification paradigm in which outcomes observed are linked to diverse sources of
benefits that can influence the nature of technology use by participants (Katz et al. 1974), we
focus on the nature of the uses and gratifications from repository contributions.
Self oriented usage of technology: One central assumption in the models of Thorn and Connolly
1987) and Fulk et al. (2004) is that contributions by individuals can only benefit other people but
not the contributor and benefits to contributors accrue only from access to the contribution of
others. This assumption is unlikely to be valid in the context of PDRs such as Amazon.com
since the process of making repository contributions can be expected to be useful for individuals
in various ways. For instance, in contributing a review of a book to Amazon.com, the process of
reflecting on the book and providing a critique helps contributors develop skills related to critical
analysis and composition. Posting a review can also serve as a means of self-expression.
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Public document repositories
While private benefits have been recognized in the general case of public goods (Kim and
Bearman 1997), they have been assumed away as non-existent or ignored in prior research on
discretionary databases (e.g. Thorn and Connolly 1987; Fulk et. al 2004). The presence of such
private benefits can lead to greater repository contributions than predicted by prior theory since
the overall value to individuals from the PDR is enhanced. This leads to the following research
question:
Do contributors to PDRs derive direct benefits from making their contributions?
Motivations for PDR contributions: In view of the unique context of PDRs contributions
highlighted earlier, an understanding of the set of salient motivations for contributions is an
important issue. The work of Thorn and Connolly (1987) suggests reciprocity as the sole motive
for repository contributions.
Since PDRs are contexts with fluid memberships that individuals can join or drop-out at any time
and where individuals can participate anonymously, it is hard to imagine that reciprocity, a
feature that is prevalent in stable groups of identified individuals, can provide a dominant
motive. In the same vein, motivations for action such as altruism and social affiliation
highlighted in instances of pro-social behavior (such as caring for a stranger who collapses on
the street) are unlikely to be as salient in potentially de-individuating contexts at PDRs where
relatively anonymous individuals contribute and retrieve documents. Prior research indicates
that individuals sharing common interests can develop social bonds in virtual communities
(Butler et al. 2002). However, while it is also likely that the motives for PDR contribution may
be social and other-oriented, there is little guidance from prior theory regarding the motives
operative in the case of repository contributions. This leads to the following research question:
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Public document repositories
What are the key motivations for contributors to PDRs?
Interdependence of individual contributions: An important focus of prior work on collective
action (Marwell and Oliver 1993) is the nature of interdependence among individual
contributors. Thorn and Connolly (1987) viewed individual contributions to discretionary
databases as being independent since all players makes simultaneous decisions about
contributing in each period of a multi-period game. This clearly is unlikely to be the case in
PDRs since the technology can provide considerable transparency regarding prior contributions
by others and this can influence the willingness of individuals to make their own contributions.
Critical mass theory suggests two patterns in the interdependence of incremental contributions.
Incremental contributions of individuals can be accelerating (Oliver et al. 1985) with
contributions being more valuable in the presence of prior contributions. Alternatively,
incremental contributions can be decelerating, with contributions being less valuable in the
presence of prior contributions.
The key issue determining the nature of interdependence is the interpretation by potential PDR
contributors of information on the contributions by others (Kim and Bearman 1997). Does the
availability of prior reviews inhibit subsequent PDR contribution (the marginal value of an
additional review is considered to be small)? Or does the availability of prior reviews encourage
contribution (an additional review is considered an important contribution to the ongoing
articulation of the value of a book or movie)? There is little guidance in the literature to
determine the nature of interdependence of PDR contributions.
We therefore examine the following research question:
What is the nature of interdependence of incremental contributions in a PDR?
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Public document repositories
Methods
We examined these questions using self-disclosed reviewer profiles and data available on their
contributions to the review repository at Amazon.com, a site visited by about 40 million users
every month (Nielsen NetRatings 2006). The PDR of reviews at Amazon.com has over 3.5
million reviews contributed by over a million reviewers. While the repository is owned and
operated by a commercial firm– it is freely accessible without exclusions to the public over the
web and it is searchable in a variety ways - using keywords, book title, author or topic. While
users can choose to purchase items they see listed on the site, no purchase is necessary to use the
content or the facilities provided by the site. Participation as a contributor has minimal prerequisites. Any individual with an email address, irrespective of his or her location in the world
can sign up for an Amazon.com account and begin to contribute content - reviews of books,
music, videos and other products sold on the site or provide comments on content contributed by
other users. Amazon.com provides basic guidelines for reviews and all submissions are
moderated. A small group of Amazon.com editors using automated text search programs deletes
or replaces inappropriate or offending content from contributions before posting them online. To
eliminate confounds from the characteristics of the user interface and features provided by
different public repositories, we focused our data collection on this large PDR.
Contributing reviewing to the Amazon repository is not compensated – it is entirely voluntary.
The only reward, if any, is intangible – in the form of a higher rank among Amazon reviewers.
Amazon.com ranks reviewers using a composite measure based on the number of reviews
submitted and the average number of helpful votes received by reviews from users. A reviewer’s
categorization as a #1 Reviewer, Top 10, Top 50, Top 500 or Top 1000 reviewer is displayed
along with the text of his or her reviews. The possibility of joining the ranks of reviewers in any
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Public document repositories
of these five tiers represents the only formal incentive offered to contributors. Amazon.com also
provides all reviewers the option to disclose personal information (up to 4000 words) and upload
a photograph. This profile information is made available on a personal page that is linked to
each reviewer’s name when it appears alongside the review. Users are free to provide as much
or as little information that they see fit in these profiles. Another feature of the Amazon.com site
is the facility for users to select one or more reviewers as a ‘favorite person”. Individuals receive
email notifications with a URL to the contribution whenever one of their favorite persons posts a
review.
Reviewers at Amazon.com come from a wide variety of backgrounds and include teachers,
librarians, a former Speaker of the US House of Representatives, journalists, lawyers, consultants
and college students. While the total number of reviewers is large, those contributing a total of
10 or more reviews number only about 47,000. The critical mass of reviewers, the focus of this
study, is likely to be a subset of this population of active reviewers and by definition, comprises
the highest ranked members of this active group. Reviewers in the Top 1000 are clearly likely to
be part of the critical mass. We therefore collected detailed data only on this select group of
prolific contributors.
In the first phase of data collection, we conducted hour-long semi-structured interviews with two
of the Top 50 reviewers at Amazon.com. In the second phase, we collected contribution data on
all reviewers at Amazon.com. For each contributor to the PDR, we gathered data on the number
of reviews and helpful votes each review had received.
In the third phase, we collected detailed profile information provided by reviewers in the Top
1000 list. This final sample has 1009 individuals since multiple individuals shared ranks in the
Top 1000 list. These self disclosed profiles (collected only for reviewers in the top 1000 list)
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Public document repositories
often contained a variety of personal details such as their email address, URL of their personal
webpage, their location, their professional career, their hobbies and interests, details of their
families and pets, factors motivating them to write reviews, their favorite books and the music
that they liked. The disclosure of profile information is voluntary and some contributors provided
only their name (or pen-name) and little else. Three illustrative profiles with different amounts
of profile information are shown in Appendix 1. We also collected data on the number of
reviews already available at the time that each reviewer on the Top 1000 list contributed each of
his or her reviews.
Data Analysis
Quantitative Analyses: We used SPSS to fit a curve to data on repository contributions by all
reviewers. For the top 1000 reviewers, we examined correlations among variables using the data
on review contributions and variables coded from their profiles.
Qualitative Analysis: We read each profile and coded them into categories that reflected the
various motivations that reviewers had revealed. In analyzing the data, we followed the
techniques of open coding and axial coding advocated by Strauss and Corbin (1998). We used
open coding to categorize the text in the reviewer profiles into categories suggested by prior
theory. We identified keywords suggesting different categories of motives such as reciprocity
and enriched this set with keywords we encountered in profiles. We also used explanations
suggested by the data in the profiles. In creating new categories derived from the data, we often
backtracked to earlier profiles if any of them could be recoded into the new category created.
After coding the data, we grouped the categories that reflected similar concepts and themes,
consistent with the notion of axial coding. These steps highlighted the core phenomenon of the
motivations underlying contribution of reviews and we used the linkage between the categories
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Public document repositories
to infer the theoretical explanation. An individual’s profile was coded into multiple categories
when the profile indicated multiple motives for contribution.
Findings
Critical Mass: Is there a critical mass of reviewers at the Amazon.com review repository? To
examine the evidence for this, we examined the distribution of contributions among the
reviewers. The earliest in the set of reviews submitted by reviewers in the Top 1000 was in
March 1997. 55% of the reviewers wrote their first review in 1999 or earlier. 97% wrote their
first review in or before 2000. Clearly, this group comprises a set of longstanding, prolific
contributors to the PDR.
We grouped reviewers in order of their Amazon.com reviewer ranks (100 ranks in each group).
For each of the groups (e.g. ranks from 1-100, 101-200, and so on), we calculated the total
number of reviews contributed. The number of contributions by each of the groups and the
curve fitting this distribution is in Figure 1.
---------Figure 1 about here
----------
The Top 100 reviewers contributed 95,995 reviews while those ranked between 900-1000
contributed 14730 reviews, those ranked between 5,000 and 5,100 contributed 4923 reviews, and
those ranked between 9900 and10000 contributed 2533 reviews. This pattern indicates review
contributions being significantly lower for reviewers of lower-rank. The curve that best fits this
distribution (R2 = 0.96) is a power-law function:
Y = 82756.1 * X (-0.7217)
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Public document repositories
Such a distribution indicates that small contributions are extremely common whereas large
contributions are uncommon (Adamic 2002). This pattern is described by Juran (1992) as
reflecting the vital few and useful many and indicates a small group exerting a disproportionately
large influence compared to the rest of the population. Further, this suggests the possibility that
reviewers in the Top 1000 represent a key component of the critical mass of contributors to the
repository.
The data also indicate that the PDR has the characteristics of a large-group solution where no
single individual makes a perceptible difference to the collective (Olson 1965). We find that the
contribution of the critical mass is small compared to the overall volume of contributions of the
larger group (see table below). On average, members of the critical mass in our case each
contribute 148 reviews but their contributions amount to just over seven percent of the reviews in
the repository; the rest comes from a very large number of individuals, each contributing, on
average, just one review. It is interesting to note that members of the critical mass contributing
the greatest number of reviews per person are also the most helpful– members of the critical mass
on average receive 1177 helpful votes while the rest on average receive just three. Each of the
reviews of the critical mass of contributors, on average, received 8.03 helpful votes while those
of the others on average receive 2.12. This indicates the empirical validity of the feature of
critical mass theory - the exploitation of the great by the small (Oliver et al. 1985) - in the case of
PDRs.
---------Table 1 about here
----------
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Public document repositories
One of the central roles of the critical mass highlighted by the theory is the early contribution of
resources to collective action. The logic suggested by critical mass theory is that a minority of
the population - the critical mass, - through their early contribution to the collective good
enhances the probability of success of collective action. This in turn creates conditions for the
majority to join in and the collective goal is achieved by the participation of the majority.
(Marwell and Oliver 1993).
Applying this logic to the case of PDRs, we suggest that the critical mass of contributors- by
their early contributions - makes early content available in the repository. This supply of content
is important in enabling the repository to be a useful resource for the general public, a key
feature that helps generates participation and use of the repository by the public at large. This
participation by the larger group subsequently creates the potential for discretionary
contributions to the PDR by the others. In the case of a review repository like Amazon.com
where the set of books, movies and other products needing reviews is constantly expanding, we
argue that the critical mass plays an ongoing role in providing the early set of reviews of
products so that reviews are available when the average user comes to the site to look up the
newly added products. In contrast to the role of critical mass in other contexts where the group
makes an early and important but one-time effort to get collective action started, the critical mass
in the case of PDRs performs the ongoing role of providing early content in the different
categories created as the repository expands.
To assess this role of this set of reviewers in the ongoing maintenance of the PDR, we examined
the extent to which they provided the early reviews of products on the site. For each of the
98799 reviews contributed by the 466 reviewers in the Top 1000 list for whom we had profile
information, we calculated the frequency with which the review was among the set of early
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Public document repositories
reviews available on the site. The number of reviews available prior to the reviews contributed
by the 466 reviewers is in Figure 2. The data indicates that 16 percent of the reviews submitted
were the first ones available on Amazon.com for the book or movie. 32 percent of the reviews
were among the first three reviews available and 42 percent of the reviews were among the first
five available. Overall, 55% of the reviews posted by the critical mass were among the first ten
reviews posted on the site. This statistic provides compelling evidence regarding the central role
of the critical mass of reviewers as early contributors of content to the PDR. Even though the
overall number of reviews contributed by the critical mass is small compared to the total number
of reviews available on the site (about 7 percent of the total), these reviews are among the
earliest that are available to the users of the site.
---------Figure 2 about here
---------The critical mass thus comprises resourceful individuals who step up to make contributions to
the public good in instances where there are few alternatives available. This clearly indicates
that the critical mass plays an extremely valuable role in not only setting collective action in
motion, but also in maintaining the quality of the public goods available in the PDR on an
ongoing basis. Drawing from critical mass theory, we therefore suggest the following
proposition:
Critical mass proposition: Public document repositories rely heavily on a small number
of active contributors both for their establishment and their sustenance.
We now turn to an analysis of the personal benefits and motivations of this critical mass using
data from the profiles of reviewers in the Top 1000 list.
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Public document repositories
Among the critical mass of reviewers, profile information was available for 900; 109 reviewers
had no profile text at all. 258 of the 900 profiles were brief and had less than 50 words. 466 of
the profiles disclosed at least one motivation. Our analysis is based on the information on
motivations provided by this subset of the Top 1000 reviewers.
To preclude biases on account of the focus on this subset of 466 reviewers, we compared
attributes of this subset with those of the set of 543 reviewers who either did not provide profiles
or provided no information on motivations or benefits in their profiles. The median reviewer
ranks for these two sets were 482.5 and 533 respectively. In addition, those who had disclosed
motivations had contributed a median of 162 reviews, had received 1223 helpful votes for their
reviews, and had on average 7.91 helpful votes per review. Those who had not disclosed
motivations had written a median of135 reviews, had received 1134 helpful votes and had on
average 8.10 helpful votes per review. The similarity of the two groups suggests that the set of
reviewers disclosing profile details is representative of the critical mass of reviewers in the top
1000 list.
Personal benefits to critical mass: Information on the private benefits to contributors from
making contributions indicated were coded into five categories: self-expression, development of
writing skills, enhanced understanding of the topic, utilitarian benefits and personal enjoyment.
The frequency of occurrence and examples of these private benefits in the profiles is indicated in
Table 2 (in decreasing order of frequency).
---------Table 2 about here
----------
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Public document repositories
The data thus provides evidence that contributors to PDRs do obtain personal benefits from
making their own contributions. The existence of direct, private benefits suggests a broader view
of benefits to contributors than recognized in prior literature. We therefore present the following
proposition highlighting influence of direct benefits to contributors on contributions:
Direct benefits proposition: Contributions to PDRs by the critical mass are linked to
direct benefits obtained by contributors from their own contributions.
Motivations of critical mass: Mentions of motivations for contribution in the profiles were coded
into three categories of motivation: social affiliation, altruism and reciprocity. The frequency of
occurrence of these motivations in the profiles is indicated in Table 3 (in decreasing order of
frequency).
---------Table 3 about here
---------In a context that appears devoid of social cues, the presence of social motives is interesting.
Nearly half the reviewers indicated social affiliation as a motivation for contributing. This
suggests a social view of contributions to PDRs, quite different from that of interactions with a
database. Rather than being created by individuals seeing their actions as being one-on-one
interactions with a repository, PDRs are created by contributors who are aware of the presence of
a wider audience for their inputs. The profile data reveal altruism and reciprocity as the other
motives for contribution. It is interesting to note that reciprocity, the sole motivation for
contribution considered by Thorn and Connolly (1987), is the least frequently mentioned among
motives.
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Public document repositories
We therefore propose that:
Social motives proposition: Contributions to PDRs by the critical mass are linked to
social motives of contributors.
Interdependence among contributors: The data in the profiles indicated that reviewers’ choices
regarding the topic and areas to contribute in were influenced by the contributions of others. 64
profiles (13.73% of the set of profiles) contained details indicating that reviewers tried to pick
topics where there were few prior contributions or where they believed prior reviews were
inadequate. This clearly suggests that contributions to PDRs are influenced by the contributing
behavior of other reviewers. The pattern of interdependence suggested by the profile data fits
that described by (Oliver et al. 1985) as decelerating, a situation where early contributions are
most valuable and incremental contributions are less valuable:
Do I read more conventional books? Yes, but if there are already reviews that approximate what
I think needs to be known, I don't bother to review them.”
“For new music CDs, I review everything. But for catalog material (stuff that is on my shelves at
home), I do not write reviews if others have written reviews.”
We therefore propose that
Interdependence proposition: PDR contributions by the critical mass are more likely
where there are fewer prior contributions and the likelihood of contribution decreases
with increases in prior contributions by others.
Private benefits, social motives and contribution behavior
In addition to analyses guided by our research questions, we examined the data to gain insights
into the nature of contributions by the critical mass of reviewers.
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Public document repositories
The PDR at Amazon.com provides two measures of reviewer contribution – quantity (number of
reviews) and quality (helpful votes received). Prior theory suggest that incentives to contribute
are likely to lead to increased volume of contributions but decreased quality of contributions
(Thorn and Connolly 1987), indicating a trade-off between contribution quantity and
contribution quality. Our data supports this view - the number of reviews submitted by critical
mass of reviewers was negatively correlated with the quality of reviews (correlation = -0.15,
p<0.01, N=466).
The correlations among contribution quantity and quality of contribution, benefits and motives
are indicated in Table 4.
---------Table 4 about here
---------Factors linked to contribution quantity: Table 4 indicates that private benefits (utilitarian
benefits, self-expression) are positively correlated with quantity of contributions (Table 4, rows 5
and 6). This parallels the finding of Thorn and Connolly (1987) that raising the benefits from
contributions increases the quantity of contributions. It is surprising that mention of the social
affiliation motive is negatively correlated with the quantity of reviews (Table 4, row 3). It is
likely that individuals for whom the social affiliation motive is important seem to be less inclined
to contribute reviews on an ongoing basis. Perhaps, they restrict their contributions to specific
areas where they are aware of other reviewers, thus adversely impacting the overall quantity of
contributions.
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Public document repositories
Quantity of contribution proposition: The quantity of contributions by the critical mass
will tend to be
a) Positively related to personal benefits to contributors from contributions.
b) Negatively related to social motives of contributors.
Factors linked to contribution quality: The data suggests that reciprocity has a significant
positive correlation with quality (Table 4, row 2), while altruism has a weak positive correlation
(Table 4, row 1). This suggests that individuals contributing to reciprocate provide higher quality
content, indicating the useful role of the social context. It is likely that higher attention-to-task
observed in contexts of greater self-presentation (Sproull et. al 1996) is applicable to PDRs as
well. It may also arise from reviewers’ attempts to be equitable in reciprocating help (Adams,
1965). The association of altruism and quality of reviews suggests that people who are out to
help ensure that their reviews are useful.
Table 4 also indicates that private benefits (development of writing skills) and quality of reviews
are negatively correlated. This suggests that contributors with a focus on private benefits tend to
focus less on the usefulness of content to potential users.
Quality of contribution proposition:
Contributions of higher quality content by the critical mass will tend to be
a) Negatively related to personal benefits to contributors from making contributions;
b) Positively related to social motives of contributors.
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Discussion
Publicly accessible repositories that rely on voluntary contributions of content are increasingly
emerging as important sources of information for the public but have received little attention by
researchers. To understand the dynamics of contribution and use of such systems we need
theoretically grounded models of collective action in these contexts. To this end, we drew on
Critical Mass theory and applied it to examine behavior at one large PDR – the repository of
reviews at Amazon.com. Our work suggests the applicability of the theory to the context of
PDRs and also suggests several aspects along which received theory needs to be modified.
In this study, we make several contributions. First, we confirm the presence and identify the
important role of a relatively small group of individuals, the critical mass, whose ongoing
contributions are central to the sustenance of the PDR as a public good. In the Amazon PDR, the
critical mass comprises the core set of 1009 individuals (of the population of 1.3 million
reviewers) who contributes the most number of reviews per person and the most useful reviews.
Over half of the reviews they contribute are also among the first ten reviews available to users on
the site. As the coverage of the repository continually expands, the critical mass plays an
important ongoing role in creating content in areas where few prior reviews exist. This is
remarkable, considering that there is no coordination between the addition of new products to the
site and the contributions of reviews by the critical mass. Second, we highlight that individuals
obtain private benefits from their own PDR contributions. This runs counter to views in prior
research on discretionary databases (e.g., Thorn and Connolly 1987; Fulk et al. 2004) that
contributors derive benefits only from the information contributed by others and do not directly
benefit from making contributions. Our study suggests that the process of making a contribution
– a process that involves framing one’s thoughts, composing opinions and presenting them– is an
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exercise with personal benefits of various kinds. These private benefits may be important
motivations to contribute to PDRs, a feature that has been overlooked in prior research. Third,
we find social motives to be salient for contribution. This is a surprising finding given that
repository contribution and use is a context characterized by minimal social interaction. These
motives are another set of potential motivations overlooked in prior research and that are
highlighted in our work as important in understanding repository contributions. Fourth, we find
that individuals in the critical mass are less likely to contribute on topics that already have
contributions from others. This provides a new perspective on the congestion / information
overload phenomenon recognized in prior research on online forums (Butler 2001; Jones et al.
2004). Prior work suggests that congestion leads users to completely avoid participation in
online forums. In our study, we find individuals recognizing differences in the levels of
congestion within individual topics and rather than avoiding the PDR as a whole, contribute to
less congested topics with few prior contributions.
Finally, our study suggests that the determinants of quantity and quality of content are distinct.
This contrasts with the results of Wasko and Faraj (2005) in the context of an online bulletinboard that found several common antecedents of contribution quality and quantity.
Limitations
Our study has several limitations. First, our results are based on data collected at one PDR, the
repository of reviews at Amazon.com. While this choice minimized confounds due to contextual
differences between multiple sites, it is likely that the specific features implemented at
Amazon.com may have influenced our findings. Second, the work is based on the sample of the
critical mass of contributors at Amazon. The critical mass is recognized as being distinct in
attitudes, motives and behaviors from larger public and the results are likely to not be
24
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generalizable to the average repository contributor. Third, the qualitative data on reviewer
profiles is in form of statements that were not solicited by the authors of this paper. They were
willingly provided by many reviewers using the facilities provided by Amazon.com. Since we
used self-disclosed profile data, it is likely that our results are biased by the self-presentation of
contributors. Fourth, the cross-sectional nature of our study limits inferences of causality among
the variables. Longitudinal examinations of repository contributions and the role of motivational
and contextual factors can provide a deeper understanding of cause and effect relationships
explaining repository contributions.
Despite these limitations, our study makes a number of contributions to research and practice.
This represents one of the first field studies identifying the critical mass of contributors and
examining the benefits and motives of this group. The study highlights the importance of social
motives even in a context where the act of contribution does not involve social interaction. It
provides evidence of private benefits to contributors from their own contributions. This study
also provides empirical evidence regarding the relationship between key outcome variables –
quantity and quality –and contributor motives and private benefits. Our exploratory study
suggests an initial theoretical framework that explains contribution behavior in online PDRs. In
the figure below, we depict a summary of our findings in line with our earlier propositions:
---------Figure 3 about here
---------Based on our findings we suggest two sets of factors influencing quality and quantity of
contribution among individuals in the critical mass of contributors. For quality of contribution,
social motives are proposed to be antecedents. On the other hand, we propose that private
25
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benefits influence quantity of contribution. Also, social motives and private benefits are
negatively related with quantity and quality of contributions respectively.
Implications for practice:
Our findings also have implications for practice. The current study was based on a specific but
important type of public document repository. An increasing number of e-commerce sites are
providing facilities that allow people to submit reviews on products they have bought
(Kawakami 2005). According to a recent Forrester Research study, nearly 26% of online retailers
provide product review forums on their websites (Mendelsohn and McNabb 2005). The
procedure used by us to identify the critical mass and study its characteristics can be usefully
applied to direct incentives to the appropriate set of key participants. The findings can similarly
be applied within organizations seeking to identify the critical mass of contributors in knowledge
management initiatives that seek to develop repositories based on discretionary contributions of
content by employees (Fulk et. al 2004).
Our results suggest that attending to the private benefits and tapping social motives are important
levers to encourage the critical mass to contribute to PDRs. Our results also suggest that the
factors linked to quality and quantity of contribution are different and can guide the development
of incentive mechanisms for the key group of prolific contributors.
Conclusion
This paper draws on Critical Mass theory to develop a theory to explain collective action in the
development of public document repositories. Our results, based on data from the large PDR of
reviews at Amazon.com highlight the complex role of the critical mass of contributors in
establishing and sustaining collective action. The results also suggests the utility of a broader
view of benefits from contribution and the recognition of social motives linked to contributions
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by the critical mass. Further, our results contribute to a more nuanced view of determinants and
outcomes of contribution since the factors linked to the quality of contribution are distinct from
those linked to the quantity of contribution. Our approach also opens up several areas for further
theoretical and empirical work to understand the complexity of the establishment of collective
action to create publicly accessible information goods.
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References
Adamic, Lada
2002 ‘Zipf, power-laws, and Pareto – a ranking tutorial’ Accessed online at
http://www.hpl.hp.com/research/idl/papers/ranking/ranking.html on March 13, 2006
Adams, John S.
1965 ‘Inequity in social exchange’ In L. Berkowitz (Ed.) Advances in experimental social
psychology Volume 1: 267-299, New York: Academic Press.
Butler, Brian S.
2001 ‘Membership size, communication activity, and sustainability: A resource-based model of
online social structures’. Information Systems Research 12 / 4: 346-362.
Butler, Brian, Lee Sproull, Sara Kiesler, and Robert Kraut
2002 ‘Community effort in online groups: Who does the work and why?’ In Leadership at a
distance. Suzanne P. Weisband and Leanne Atwater (Eds.) Mahwah, NJ: Lawrence Erlbaum.
Clary, Gil, Mark Snyder, Robert Ridge, John Copeland, Arthur Stukas, Julie Haugen, and Peter
Miene
1998 ‘Understanding and assessing the motivations of volunteers: A functional approach’.
Journal of Personality and Social Psychology, 74: 1516-1530.
Constant, David, Sara Kiesler, and Lee Sproull
1994 ‘What’s mine is ours, or is it? A study of attitudes about information sharing’. Information
Systems Research, 5: 400-421.
Constant, David, Lee Sproull, and Sara Kiesler
1996 ‘The kindness of strangers: The usefulness of electronic weak ties for technical advice’.
Organization Science 7: 119-135.
Fulk, Janet, Rebecca Heino, Andrew J. Flanagin, Peter R. Monge, and Francois Bar
2004 ‘A test of the individual action model for organizational information commons’
Organization Science 15: 569-585.
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Hardin, Russell
1982 Collective Action. Baltimore: Johns Hopkins University Press.
Jones, Quentin, Gilad Ravid, and Sheizaf Rafaeli
2004 ‘Information overload and the message dynamics of online interaction spaces: A theoretical
model and empirical investigation’. Information Systems Research 15: 194-210.
Juran, Joseph
1992 Juran on Quality by Design. New York: Free Press
Katz, Elihu, Jay Blumler, and Michael Gurevitch
1974 ‘Uses and gratifications research’ Public Opinion Quarterly 37: 509-523.
Kawakami, Laurie
2005 ‘Giving reviews the thumbs down’ Wall Street Journal August 4, 2005. Accessed at
http://online.wsj.com/public/article/SB112311985732004654x3qD8ODU_jwTvTplbvV6uhXfyBc_20060803.html on March 13, 2006
Kim, Hyojoung, and Peter Bearman
1997 ‘The structure and dynamics of movement participation’. American Sociological Review,
62: 70-93.
Markus, Lynne
1987 ‘Toward a critical mass theory of interactive media: Universal access, interdependence and
diffusion’ Communication Research, 14: 491-511
Marwell, Gerald, and Pamela Oliver
1993 The critical mass in collective action: A micro-social theory. New York, Cambridge
University Press.
Mendelsohn, Tamara, and Kyle McNabb
2005 ‘Using web content management to drive e-commerce’ Forrester Research, October 20,
2005 teleconference. Accessed at
http://www.forrester.com/Events/Overview/0,5158,1211,00.html on March 13, 2006
29
Public document repositories
Monge, Peter, Janet Fulk, Michael Kalman, Andrew Flanagin, Claire Parnasa, and Suzanne
Rumsey
1998 ‘Production of collective action in alliance-based interorganizational communication and
information systems’ Organization Science, 9: 411-433.
Nielsen NetRatings
2006 ‘Two-Thirds of Active U.S. Web Population Using Broadband, Up 28 Percent Year-OverYear to an All-Time High’ Accessed at http://www.netratings.com/pr/pr_060314.pdf on March
13, 2006
Oliver, Pamela, and Gerald Marwell
2001 ‘Whatever happened to critical mass theory? A retrospective and assessment’. Sociological
Theory, 19: 292-311
Oliver, Pamela, Gerald Marwell, and Ruy Teixeira
1985 ‘A theory of critical mass I: Interdependence, group heterogeneity, and the production of
collective action’ American Journal of Sociology 91: 522-556.
Olson, Mancur
1965 The Logic of Collective Action: Public Goods and the Theory of Groups. Cambridge, MA:
Harvard University Press.
Rafaeli, Sheizaf
1986 ‘The electronic bulletin board: A computer-driven mass medium’. In Computers and the
Social Sciences 2. Paradigm Press: Osprey, FL.
Sproull, Lee, Mani Subramani, Sara Kiesler, Janet Walker, and Keith Waters
1996 ‘When the interface is a face’. Human-Computer Interaction 11: 97-124.
Strauss, Anselm L and Juliet M. Corbin
1998 Basics of qualitative research: Techniques and procedures for developing grounded theory,
2nd ed., Thousand Oaks, CA: Sage.
Thorn, Brian K and Terry Connolly
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1987 ‘Discretionary data bases: A theory and some experimental findings’. Communications
Research, 14 / 5: 512-528.
Wasko, Molly M. and Samer Faraj
2005 ‘Why should I share? Examining social capital and knowledge contribution in electronic
networks of practice’. Management Information Systems Quarterly 29 / 1: 35-57.
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Figures
Distribution of reviews
100000
80000
60000
40000
20000
0
0
20
40
60
80
100
120
Reviewer rank (in 100s)
Figure 1: Distribution of contributions by rank
Note: Graph plotted for top 10,000 reviewers only due to scaling considerations
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Cumulative distribution of prior reviews
70.00
60.00
50.00
40.00
30.00
20.00
10.00
30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0.00
0
Cumulative percentage of reviews
80.00
Number of prior reviews
Figure 2: Reviews available prior to contribution by critical mass (N=98799)
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+
Quality of contribution
Social motives
_
_
+
Private benefits
Quantity of
contribution
Figure 3: Model of critical mass contribution at PDRs
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Tables
Critical mass
(N=1009)
257,773
148
All other reviewers
(N=1,321,493)
3,428,054
1
Total number of reviews
Median number of reviews per
reviewer
Median number of helpful
1177
3
votes per reviewer
Median number of helpful
8.03
2.12
votes per review per reviewer
Table 1: Contribution volume, helpfulness of critical mass
Personal benefit
Self-expression
Frequency
of mentions
139
(29.8 %)
Illustrative comments
“(Writing reviews on Amazon) gives me the opportunity to
express my opinion on the items that I have purchased.”
82
(17.6 %)
“I think what people listen to or watch (or don’t) says a little
bit about who they are… I try to compare and contrast
within a genre. I also try to compare an artist’s work with
his / her past accomplishments rather than with someone
else’s work…”
“Writing reviews has enabled me to use some of the writing
skills that I learned in law school.”
Enhancing
understanding of
topic
39
(8.4 %)
“I am a technical writer by profession; reviews allow me to
take out my adjectives and brush the dust off them.”
“I write reviews on Amazon.com's website … to clarify and
organize my own thoughts.”
Utilitarian
benefit
29
(6.22 %)
Enjoyment
23
(4.9 %)
Developing
writing skills
“I review largely to fix the book for myself in my head”.
“I get promo copies of CDs from record companies … I have
realized that putting reviews on Amazon impresses record
companies as much as writing reviews for print weeklies. I
often send links of my reviews to record companies”.”
“I enjoy free gift certificates and would appreciate any!”
“Reviewing is fun. I do it for my own enjoyment.”
“I am doing this for fun and imagine that, besides myself and
I, no one else will ever read this.”
Table 2: Personal benefits to critical mass (N=466)
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Motivation
Frequency
of mentions
Illustrative comments
Social affiliation
208
(44.6 %)
“This is so cool that Amazon permits us book lovers the
space to share our thoughts about what we're reading. I
love to peruse other peoples' thoughts on the books I'm
about to buy and enjoy exchanging comments and ideas
with other readers.”
Altruism
136
(29.2 %)
Reciprocity
49
(10.6 %)
“Most pals, buddies know about my writing reviews. They
do not look at my reviews. Feedback from readers of my
magazine reviews is usually from people whom I know.
What is noteworthy is the feedback from customers at
Amazon: people who do not know you. I get mail from
people all over the world.”
“Wanting to help is the primary reason I write book
reviews on Amazon.com
“I am trying to help others in a purchase decision.”
“I know I read these reviews prior to buying any book and
they have been excellent help, so if I can steer someone to
one they will enjoy, well, then I've paid my dues.”
“I have consulted Amazon's public reviews for years before
making a purchase and I decided to start giving back to the
Amazon community”
Table 3: Motives of Critical Mass (N=466)
S. No.
Mentions of
Quantity of
Quality of
benefits and
contributions
contributions
motives
(# reviews)
(Votes per review)
1
Altruism
0.08†
2
Reciprocity
0.15**
3
Social affiliation
-0.12*
4
Development of
-0.13*
writing skills
5
Utilitarian benefits 0.10*
6
Self-expression
0.14**
Table 4: Correlation of Motives, Benefits and Attributes of Contribution (N=466)
**: p < 0.01, *: p < 0.05, †: p < 0.1.
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Appendix 1
Illustrative profiles provided by Amazon.com reviewers
(Identifying details removed)
Name:
Nickname:
Reviewer Rank: 20
At a glance
Reviews written: 514
(1319 helpful votes)
Name:
Nickname:
E-mail:
Reviewer Rank: 153
About me: I hope you find my reviews helpful. They were not helpful, please let me know. I
want to give you the best information to make your choice. Evaluating cookbooks is something I
love to do, and I hope you can benefit from some of my experience.
Reviews written: 158
(1084 helpful votes)
Name:
Nickname:
E-mail:
Reviewer Rank: 339
About me: I am a senior engineer for network security operations at a Fortune 500 firm. From
1998 through 2001 I defended global American information assets as a captain in the Air Force
Computer Emergency Response Team (AFCERT). Now I provide network security monitoring
to protect my employer and our clients. My professional interests include intrusion detection,
incident response, digital forensics, system administration, and multibooting of operating
systems on the Intel architecture. I read and review to learn, assist, and contribute. My reviews
are not always popular, since telling the truth is more important to me than inflating sales. If I
decide a book offers nothing new or useful, I will skim it. If I believe a book is lacking technical
accuracy, I may critique it. My goal is to give you straight advice on books. I will earn your
trust!
Reviews written: 66
(722 helpful votes)
37