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DOI: 10.1177/0170840607076002

2007 28: 327Organization StudiesNaren B. Peddibhotla and Mani R. Subramani

ontributing to Public Document Repositories: A Critical Mass Theory Perspective

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Contributing to Public Document Repositories:A Critical Mass Theory Perspective

Naren B. Peddibhotla and Mani R. Subramani

Abstract

Public document repositories (PDRs) are valuable resources available on the Internet andare a component of the broader information commons freely accessible to the public.

Instances of PDRs include the repository of reviews at Amazon.com and the online ency-clopedia at Wikipedia. These repositories are created and sustained by the voluntary con-tributions of individuals who are not compensated for their inputs. This paper draws onand extends critical mass theory in the context of PDRs. Using data on the reviews writ-ten by prolific reviewers at Amazon.com and the text of their personal profiles, we findthe critical mass of contributors at the PDR not only to be prolific and contributing high-quality reviews, but also to be among the earliest contributors of reviews on products.Reviewer profiles revealed the presence of multiple self-oriented motives (self expression,personal development, utilitarian motives, and enjoyment) and other-oriented motives(social affiliation, altruism, and reciprocity) for contribution. We find that the quality andquantity of contributions are inversely related and the motives for quantity of contributionare different from those related to the quality of contribution. The study highlights that

PDRs are viewed by contributors as social contexts even though making contributions isan individual act that does not involve social interaction.

Keywords: public document repository, critical mass theory, collective action, dynam-ics, information technology

Critical Mass a small segment of the population that chooses to make a big contri-bution to the collective action while the majority do little or nothing. These few individ-uals are precisely those who diverge most from the average. the number of suchdeviants and the extremity of their deviance is one key to predicting the probability,extent and effectiveness of collective action. (Oliver, Marwell and Teixeira 1985: 524)

Introduction

A number of websites that are freely accessible over the Internet provide users

with useful content submitted by other individuals. Instances of such sites,

which we termpublic document repositories (PDRs) 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

article title

OrganizationStudies28(03): 327346ISSN 01708406Copyright 2007SAGE Publications

(Los Angeles,London, New Delhiand Singapore)

Naren B. PeddibhotlaUniversity of

Minnesota, USA

Mani R. SubramaniUniversity ofMinnesota, USA

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others who may be considering reading the books, choosing hotels or visiting

these destinations. Many of these repositories hold a massive amount of content

submitted by vast legions of contributors. 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 con-

tributed by over a million individuals.

Such repositories are termed discretionary databases by Thorn and Connolly

(1987) since they house content that is shared by individuals, at their discretion,

with others. However, motivating individuals to contribute to collective reposito-

ries is a daunting challenge and initiatives to establish such repositories, even

when they are seen as serving the common good, overwhelmingly fail (Fulk et. al

2004). Thorn and Connolly (1987) observed 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 har-

nessed (page 527). These observations made nearly two decades ago continue tobe valid today. Though studies in the past two decades have examined factors

linked to participation in online spaces, such as discussion groups and virtual

communities, the nature of these interactive environments are markedly different

from those at PDRs where individuals interact with a database of content through

a web interface and make individual choices of what to contribute and when.

PDRs use technologies that aggregate the voluntary contributions of content

by widely dispersed individual contributors to create a publicly accessible

resource.1 The repository of reviews at Amazon.com, a typical PDR, provides

facilities for users to examine the content on the site and if they choose, to con-

tribute a review of a book or movie listed on the site. PDRs on the Internet suchas Wikipedia and the repository of reviews at Amazon.com that are populated

by voluntary contributions of content by individuals, are increasingly emerging

as important resources serving the needs of the public at large. Theoretically

grounded models of contribution behavior are therefore critical to understand

the factors influencing the establishment and sustenance of such repositories.

We drew on critical mass theory and applied it to the context of PDRs to derive

insights on the nature of user contributions. We used empirical data gathered

from one large PDR the repository of reviews at Amazon.com to assess

the level of support for the predictions based on critical mass theory.

Public Document Repositories and Critical Mass Theory

PDRs incorporate facilities for individuals to search and retrieve contentas well

as facilities for individuals to contribute contentto the repository. In that sense,

PDRs serve both constituencies those demanding contentand those supply-

ing content. While the processes of supplying content and of demanding con-

tent are related, probably recursively, and users of content are often contributors

as well, our focus in this paper is exclusively on the supply side the contribu-

tion of content to PDRs by individuals.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.

328 Organization Studies 28(03)



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1998) as well as participation in the context of technology-mediated interactive

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 ini-

tiated 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 and interchanges on bulletin boards generally occur in the con-

text of individuals or groups linked by social or electronic ties, PDR contribu-

tions represent attempts by individuals to help unknown others with whom they

typically have no direct ties, other than a shared interest in the use of the PDR.

Third, repository contributions are accomplished through impersonal interac-

tions with a website. Users typically log into the PDR website and either attach

a document or copy and paste their contribution into a text box. Even the mini-

mal level of social context cues such as the login IDs of participants or reference

to prior postings, etc. that are available during the posting of messages to anemail list are absent while making repository contributions. Contributions thus

occur in a context devoid of social cues a rather peculiar feature since help-

ing 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 spend-

ing their time and taking the effort to make repository contributions appear to bedoing so in spite of impediments that inhibit contributions.

The technologies underlying PDRs create a unique environment with three

characteristics. First, PDRs are open to the public; content in the repository is

open to any individual that desires to access it, regardless of whether or not he or

she contributed to its creation. Individuals can thus use the content of PDRs with-

out making contributions and free-ride on the contributions of others. Second, use

of PDRs is non-rivalrous, i.e. one persons use of the PDR does not affect its

availability or its utility to other individuals. Third, the costs of writing and sub-

mitting content are constant and invariant, whether they are used by one individ-

ual or a very large number. These properties create the context where the presenceof a small number of individuals contributing on an ongoing basis can lead to the

creation of a repository with useful content available for use by a much larger

number.2 PDRs can thus potentially be created and sustained for the collective

through the efforts of a relatively small minority. The small, highly motivated

minority of contributors is termed the critical mass (Marwell and Oliver 1993).

Prior work on repositories of voluntarily contributed content by Thorn and

Connolly (1987) concludes that discretionary information will be chronically

undersupplied, implying there would be less contributed content than what the

repository designers or participants would want (Thorn and Connolly 1987:

520). However, anecdotal evidence of successful PDRs on the Internet such asthose at Amazon.com and Wikipedia.com appear to be inconsistent with this

prediction. For instance, the online encyclopedia at Wikipedia.com that was

Peddibhotla and Subramani: Contributing to Public Document Repositories 329



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launched in 2001 had over 3.7 million articles (over 1 million articles in English)

contributed by users as of March 2006. Wikipedia reports receiving over 46

million content updates by users on 3.7 million pages since July 2002. The site

had over 17,000 active contributors (defined as those submitting five or more

times in a month) in December 2005 and the number of active contributors has

been growing consistently every month since January 2001. This evidence that

is in variance with theoretical predictions suggests the need for a more refined

model of contributor behavior in content repositories.

To this end, we draw on critical mass theory as a source of insight on reposi-

tory contributions to advance our understanding of phenomena in PDRs. The

central insight of this theory is the explication of the significant role of a small

subset of contributors that makes a significant difference to outcomes. From the

perspective of critical mass theory (Marwell and Oliver 1993), the defining char-

acteristic of the critical mass is not just that this group of active contributors is

a minority of the population, but that this minority makes the most useful contri-

butions to the creation of the public good. While the theory of critical mass

explains the emergence of outcomes in instances of collective action by individ-

uals, we suggest that it serves as a useful lens in the context of PDRs where the

content is created by the aggregation of individual actions by contributors.

Disproportionate participation by a small minority is a characteristic of many

communication systems such as online discussion groups. Therefore, the applic-

ability of critical mass theory to explain phenomena in PDRs hinges on the small

proportion of heavy contributors also being those who make useful contributions

to PDRs. This leads to the following research questions:

Are disproportionate contributions to public document repositories made by a minorityof contributors? Does this minority make useful contributions?

Motivations of the Critical Mass to Contribute

While critical mass theory suggests that the critical mass comprises individu-

als with greater interest in the collective good and with superior resources,

there is little attention paid to differences among motivations of members of

the critical mass.

Prior research on individual contributions suggests multiple perspectives on the

motivations to contribute. For instance, Thorn and Connolly (1987) and Fulk et.al(2004) view contributions as driven by the individual-level calculus of costs and

benefits. Individuals are seen as contributing when benefits from participation

exceed the cost of contribution. Further, contributions are viewed as benefiting

other participants but not the contributor. Individual benefits to contributors are

viewed as accruing solely from access to the contribution of others. Consequently,

the expectation of reciprocity is assumed to be the key driver of contribution

behavior. The work of Goodman and Darr (1998) on repository contributions

views social motives such as altruism and a sense of community as having a lim-

ited role in PDRs since potential contributors are viewed as individuals with no

personal or direct work ties with each other. On the other hand, the perspective inWasko and Faraj (2005) incorporates a more central role for social factors in influ-

encing contributions. In their examination of participation in discussion groups,




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the authors suggest that issues such as prior ties between individuals and their

social identities play a role in determining the motivations to contribute.

In the context of PDRs, the motivations for contribution by members of the

critical mass are likely to span both these perspectives. PDR contributions are

essentially individual actions that are aggregated by the technology underlying

PDRs. In the context of PDR, there are inherent costs borne by individuals in

making contributions. Writing and formulating reviews takes time and effort.

However, there are benefits to the activity as well, as contributors are likely to

develop and refine their conceptual thinking and composition skills. Potentially,

helping unknown others is likely to provide intrinsic rewards and reinforce ones

competencies and feelings of self-esteem. Further, the awareness of the aggre-

gation of their reviews by the PDR and the creation of a unified online reposi-

tory where individual contributions are housed can approximate the context of

virtual communities where individuals sharing common interests develop social

bonds (Butler et al. 2002). It is likely that active contributors develop a sharedsense of cooperation and citizenship that in turn can motivate contribution.

The nature of motivations is also recognized as an important determinant of

how individuals contribute. For instance, Thorn and Connolly (1987) found that

incentives for participation enhanced contribution quantity but it reduced con-

tribution quality. A more detailed view of the motivations of the critical mass in

making contributions and how these motivations influence contribution behav-

ior can be very useful in explaining a key factor determining the success of

PDRs. This leads to the following research questions:

What are the motives of the critical mass for contributing to PDRs?

How are the motives of the critical mass linked to their contribution behavior?

Methods

We examined these questions using data on contributions to the review reposi-

tory at Amazon.com, a site visited by about 40 million users every month

(Nielsen NetRatings 2006).

Details of PDR at Amazon.com

The PDR of reviews at Amazon.com has over 3.5 million reviews contributed

by over 1.3 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 of 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 vote on content contributed by oth-

ers. 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 ofbooks, music, videos, and other products sold on the site. Amazon.com provides

basic guidelines for reviews and all submissions are moderated. A small group




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of Amazon.com editors using automated text search programs delete or replace

inappropriate or offending content from contributions before posting them

online. To eliminate confounds due to the characteristics of the user interface

and features provided by different public repositories, we focused our data col-

lection on this large PDR.

Contributing reviews 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 cumulative number of reviews submitted and

the average number of helpful votes received by reviews from users. A

reviewers 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 possi-

bility of joining the ranks of reviewers in these five tiers represents the only

formal incentive offered to contributors. Amazon.com also provides to all review-

ers the option to disclose personal information (up to 4000 words) and upload aphotograph. This profile information is made available on a personal page that

is linked to each reviewers name when it appears alongside the review. Users

are free to provide as much or as little information that they see fit in these pro-

files. 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 notifica-

tions with a URL to the contribution whenever one of their favorite persons

posts a review. All reviews on a book or movie contributed to the site are pre-

sented to users sequentially, in reverse chronological order of their contribution,

so that the most current reviews received are presented first.

Reviewers at Amazon.com come from a wide variety of backgrounds andinclude 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.

We carried out the study in two steps. In the first step, we focused on the

quantitative data: the volume of reviewer contribution and the timing of their

contributions. In the second step, we examined the profile information made

available by reviewers, to assess the nature of motivations.

Step 1: Examination of Reviewer Contributions for Evidence of aCritical Mass

Given the large number of reviewers on Amazon.com (1.3 million), we catego-

rized the set of reviewers into groups of 100, based on the number of reviews

they had contributed. For each of the groups (e.g. the top-100 reviewers, the

next 100, and so on), we calculated the total number of reviews contributed by

reviewers in the group. The total number of contributions by each of the groups

and the curve fitting the distribution of contributions is in Figure 1.

The top-100 reviewers contributed 95,995 reviews while those ranked3

between 900 and 1000 contributed 14,730 reviews, those ranked between 5000and 5100 contributed 4923 reviews, and those ranked between 9900 and 10,000

contributed 2533 reviews. This pattern indicates review contributions being




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of top-1000 reviewers, on average, received 8.03 helpful votes while the reviews

of other contributors, on average, received 2.12 votes. Overall, each member of

the set of top-1000 reviewers received an average of 1177 helpful votes for their

reviews while the rest, on average, received just three. Clearly, this suggests that

the set of 1000 prolific viewers also contribute content considered very useful.

Another role of the critical mass of contributors highlighted by critical mass

theory is their early contribution of resources to collective action. The logic is

that a minority of the population the critical mass through their early con-

tributions, 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).

In the context of PDRs this logic implies that the critical mass of contribu-

tors, by their early contributions, play a part in a PDRs success by ensuring that

users can turn to the repository with the expectation that content is likely to be

available. For instance, when a contributor writes the first review of a productat a product review site like epinions.com, she provides a review when there are

no other product reviews available on the site. The availability of content is

important in enabling a repository to be viewed as a useful resource, a key fea-

ture that encourages the use of the repository by the public at large. Thus early

contributions are important for the continued sustenance of a PDR.

To assess this role for the set of the 1000 prolific reviewers, we examined the

extent to which they provided early reviews of products on the site. For each of

the 98,799 reviews contributed by the 466 reviewers in the top-1000 list for

whom we had profile information, we determined the chronological ordering of

each submission among the total set of reviews available for the product. Thisallowed us to assess the frequency with which submissions by reviewers in the

critical mass were among the earliest reviews available on the Amazon.com site

for products being reviewed. The number of reviews on the site prior to the

reviews contributed by the 466 reviewers is plotted in Figure 2. The data indi-

cate that 18 percent of the reviews submitted by the critical mass of contribu-

tors were the first ones available on Amazon.com for the book or movie being

reviewed. Thirty-two percent of the reviews were among the first three reviews

available and 42 percent of the reviews were among the first five available. Over

half (54 percent) of the reviews posted by the critical mass were among the first

10 reviews posted on the site.In the case of a review repository like Amazon.com where the set of books,

movies, and other products needing reviews is constantly expanding, the critical


Top-1000 reviewers All other reviewers(N = 1000) (N = 1,321,493)

Total number of reviews 257,773 3,428,054Median number of reviews 148 1

per reviewerMedian number of helpful votes 1177 3

per reviewerMedian number of helpful votes 8.03 2.12

per review per reviewer

Table 1.Contribution Volume,Helpfulness of CriticalMass



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Details of Profile Data: Profiles were available for 900 of the top 1000 review-

ers; the others had provided no profile information. Of the 900 profiles, 258 were

brief and had fewer than 50 words. Of the profiles 466 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 the attributes of this subset with

those of the rest of the critical mass who either did not provide profiles or pro-

vided no information on motivations in their profiles. The median reviewer ranks

for these two sets were 482.5 and 533 respectively. In addition, those who had

disclosed motivations contributed a median of 162 reviews, received 1223 help-

ful votes for their reviews, and had on average 7.91 helpful votes per review.

Those who had not disclosed motivations had written a median of 135 reviews,

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 pro-

file details is representative of the set of critical mass of reviewers.We coded the top-1000 reviewers into categories based on the motivations that

they mentioned in the text of the profile. The content analysis was guided by the

framework of motives suggested by Snyder and Omoto (2000) that views motiva-

tions as comprising two distinct orientations: self-orientation and other-orienta-

tion. Drawing from this framework, we coded the profiles into five types of

self-oriented motivations: self-expression, development of writing skills, enhanced

understanding of the topic, utilitarian motives and personal enjoyment) and three

types ofother-oriented motivations: social affiliation, altruism and reciprocity).

Details of the motives and coding scheme are provided in the Appendix 1.

In analyzing the data, we followed the techniques of open coding and axialcoding advocated by Strauss and Corbin (1998). We used open coding to cate-

gorize the text in the reviewer profiles into categories based on the motivations

mentioned in the profiles. We identified keywords suggesting different cate-

gories of motives (e.g., reciprocity) and enriched this set with keywords we

encountered in profiles (see the Appendix for the definitions and sample phrases

under each motive). We also used explanations suggested by the data in the pro-

files to create new categories reflecting motivations. In creating new categories

from the data, we 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 axialcoding. An individuals profile was coded into multiple categories when the

profile indicated multiple motives for contribution.

Reliability of coding: To assess the reliability of the coding, we compared the

coding of a random sample of 100 profiles by one of the authors to that per-

formed independently by a coder not connected with the current study. Since

each profile could be coded under more than category, we calculated Cohens

Kappa for each category. The values of Cohens Kappa for each of the motives

were 0.74 or higher, suggesting adequate reliability.

Motivation for contribution: The frequency of mentions of motives and illustrative

examples of motives in reviewer profiles are in Table 2 and Table 3. The evidence




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from the profile data in Table 2 with illustrative comments from reviewer pro-

files indicates the prevalence of self-oriented motives underlying contribution

behavior. This is a particularly interesting since prior work in PDRs has recog-

nized only other-oriented motives for contribution. For instance, in the modeling

of contributions to discretionary databases, Thorn and Connolly (1987) viewed

individual contributors as benefiting only from the contribution of others and

thus viewed contributions as being driven largely by motivations of reciprocity

arising from the value gained from the contributions of others. Our data provide

strong evidence of the presence of a broader range of motivations. The motiva-tions also include self-expression, personal development through the sharpening

of skills and understanding of topics, utilitarian benefits and personal enjoyment.


Self-oriented Frequencymotive of mentions Illustrative comments

Self-expression 139 (Writing reviews on Amazon) gives me(29.8%) the opportunity to express my opinion

on the items that I have purchased.

I think what people listen to or watch

(or dont) says a little bit about who

they are I try to compare and

contrast within a genre. I also try tocompare an artists work with his/her

past accomplishments rather than

with someone elses work

Developing 82 Writing reviews has enabled me to usewriting skills (17.6%) some of the writing skills that

I learned in law school.

I am a technical writer by profession;

reviews allow me to take out myadjectives and brush the dust off them.

Enhancing understanding 39 I write reviews on Amazon.comsof topic (8.4%) website to clarify and organize

my own thoughts.

I review largely to fix the book for

myself in my head.

Utilitarian motive 29 I get promo copies of CDs from record(6.22%) companies I have realized that

putting reviews on Amazon impresses

record companies as much as writing

reviews for print weeklies. I often sendlinks of my reviews torecord companies.

I enjoy free gift certificates andwould appreciate any!

Enjoyment 23 Reviewing is fun. I do it for my own(4.9%) enjoyment.

I am doing this for fun and imaginethat, besides myself and I, no

one else will ever read this.

Table 2.Self-oriented Motivesof Critical Mass(N = 466)



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The results in Table 3 provide a very fascinating perspective on the social

context of repository contributions. In a context that appears to be devoid of

social cues, the significant presence of social motives is interesting. Over a third

of the reviewers indicated social affiliation as a motivation for contributing. The

view of contributions to PDRs in prior literature, as exemplified in the work of

Goodman and Darr (1998), is that they are instrumental interactions of individ-

uals with a database. The extracts from contributor profiles provided in Table 3

(row 1) provide a rather startling contrast. Rather than seeing their actions as

being one-on-one interactions with a repository, contributors in the critical massare aware of the presence of a wider audience for their inputs and appear to be

motivated by bonds developed with a larger community of book lovers through

the PDR. The profile data reveal altruism and reciprocity as the other motives

for contribution. It is interesting to note that reciprocity, usually considered the

sole motivation for contribution in prior literature (e.g. Thorn and Connolly

1987), is the least frequently mentioned motive in Table 3.

Motivations and Contribution Behavior

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


Other-oriented Frequencymotive of mentions Illustrative comments

Social affiliation 176 This is so cool that Amazon permits(37.8%) us book lovers the space to share our

thoughts about what were reading.

I love to peruse other peoples thoughts

on the books Im about to buy...

Most pals, buddies know about my writingreviews. 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 feedbackfrom customers at Amazon: people who do

not know you. I get mail from people

all over the world.

Altruism 136 Wanting to help is the primary reason(29.2%) I write book reviews on Amazon.com.

I am trying to help others in a

purchase decision.

Reciprocity 49 I know I read these reviews prior to buying(10.6%) any book and they have been excellent

help, so if I can steer someone to one

they will enjoy, well, then Ive paid my dues.

I have consulted Amazons public reviewsfor years before making a purchase and I

decided to start giving back to the

Amazon community.

Table 3.Other-oriented Motivesof Critical Mass(N = 466)



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contributions but decreased quality of contributions (Thorn and Connolly 1987),

indicating a trade-off between contribution quantity and contribution quality.

The two metrics of contribution are therefore expected to be negatively corre-

lated. This is supported in our data the number of reviews submitted by criti-

cal mass of reviewers was negatively correlated with the quality of reviews

(correlation = -0.15, p < 0.01, N = 466).

The correlations between the mentions of the motives and the measures of

quantity and quality of contribution are in Table 4.

Factors linked to contribution quantity: The results in Table 4 indicate that for the

top-1000 reviewers, two self-oriented motives (utilitarian benefits, self-expres-

sion) are positively correlated with quantity of contributions (rows 5 and 6). This

parallels the finding of Thorn and Connolly (1987) that raising the level of bene-

fits linked to contributions increases the quantity of contributions. It is surprising

that mention of the social affiliation motive is negatively correlated with the quan-tity of reviews (row 3). It is likely that individuals for whom the social affilia-

tion motive is important experience evaluation anxiety (Sproull et. al 1996) that

inhibits contribution. Perhaps they focus their contributions on topics where they

are familiar with other contributors, thus adversely impacting the overall quantity

of contributions. This is an interesting issue for future research.

Factors linked to contribution quality: The results in Table 4 suggest that reci-

procity has a significant positive correlation with quality (row 2), while altruism

has a weak positive correlation (row 1). Individuals motivated to contribute by

feelings of reciprocity provide higher-quality content, indicating the useful roleof the social context. It is likely that higher attention-to-task observed in contexts

of greater self-presentation (Sproull et al. 1996) is operative during PDR contri-

bution. It may also arise from reviewers need to be equitable in reciprocating

help, thus raising the resources committed to contributions (Adams 1965). The

association of altruism and quality of reviews suggests that contributors moti-

vated to help others ensure that their reviews are useful. The correlation between

the motivation to develop writing skills and quality of reviews contributed (row

4 of Table 4) is negative and significant and is consistent with the reasoning that

reviewers attempting to develop writing skills through contributions, on average,


Quantity of Quality ofMentions of benefits contribution contribution

S. No. and motives (# reviews) (Votes/review)

1 Altruism 0.05 0.082 Reciprocity 0.04 0.13**3 Social affiliation 0.12* 0.024 Development of writing skills 0.08 0.12*5 Utilitarian motives 0.10* 0.036 Self-expression 0.14** 0.01

7 Enjoyment

0.02

0.018 Enhanced understanding 0.02 0.05

** p < 0.01; * p < 0.05; p < 0.1.

Table 4.Correlations of Motivesand Attributes ofContribution (N = 466)



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do not write as well as other contributors and are thus likely to contribute lower-

quality documents.

Overall, our results provide an interesting view of the link between motiva-

tions and contribution behavior: self-oriented motives are positively related to

the quantity of contribution while other-oriented motives are positively related

to the quality of contribution.

Summary of Findings and Propositions

We analyzed data on contributions by a very large population of reviewers (1.3

million) at Amazon.com. Based on an examination of the distribution of contri-

butions, we focused on the set of 1000 most prolific contributors to examine the

validity of the predictions of critical mass theory that the critical mass com-

prises the most useful set of contributors. In our examination of the 1000 most

prolific reviewers, we find that members of this set are extraordinarily activeand the volume of contributions by each of them, on average, is over two orders

of magnitude larger (148 times) than the average for other contributors to the

PDR. In addition to being extremely prolific contributors, our data suggests that

their contributions, on average, receive about four times the number of helpful

votes from users than the average contribution of the rest of the population of

contributors. Moreover, they make their contributions in the early periods after

books or movies become available, when there are few other reviews available

to users. The data thus suggest strong support for our first research question and

leads to our first proposition that we term the critical mass proposition high-

lighting the role of the critical mass in PDRs:Critical mass proposition: Among contributors to a PDR, a small minority of active con-tributors makes a disproportionately high volume of contributions; these contributions aremore helpful than those made by the majority. This minority is among the earliest con-tributors of content on various topics to the PDR. This group the critical mass thusplays a central role in populating and sustaining the PDR.

We used the text of personal profiles disclosed by the critical mass to infer the

motives of these individuals to contribute to the PDR. Our results confirm the

role ofreciprocity, the central other-oriented motive recognized in prior research.

However, the evidence suggests the existence of two more other-oriented moti-

vations: social affiliation and altruism. Our evidence of the role of social motivesis an important contribution as it highlights that PDRs are social contexts. Our

results also suggest the presence of self-oriented motivations for contribution, a

category less recognized in the literature. These include self-expression,personal

development (e.g. development of writing skills and understanding), utilitarian

motives, and personal enjoyment. Drawing on these results, we suggest the fol-

lowing propositions regarding the motives for contribution:

Other-oriented motives proposition: Contributions to PDRs by the critical mass are linkedto other-oriented motives of contributors such as social affiliation, altruism, and reciprocity.

Self-oriented motives proposition: Contributions to PDRs by the critical mass are linkedto self-oriented motives of contributors such as self-expression, personal development,utilitarian motives, and enjoyment.




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Our examination of two different measures of contribution quantity and

quality reveal the inherent tradeoffs between these two dimensions: the quan-

tity of contributions by an individual contributor is negatively related to the qual-

ity of their contributions. This reflects a central dilemma in the establishment and

maintenance of PDRs. We therefore suggest the following proposition:

The quantity of contributions to a PDR by individuals in the critical mass is negativelyrelated to the quality of their contributions.

Our examination of the association of the motivations for contribution by

individuals with the quantity and quality of their contribution suggests that self-

oriented motivations are positively related to the quantity of contribution while

other-oriented motives are positively related to the quality of contribution.

Further, other-oriented motivations (such as social affiliation) were negatively

related to the quantity of contributions. Moreover, self-oriented motivations

(such as development of personal skills) were negatively related to quality of

contributions. Both of these associations suggest again the inherent tradeoffsbetween an emphasis on quality and an emphasis on quantity by contributors.

This leads to the following propositions:

Contributor motivation propositions:

(a) Self-oriented motivations of individuals in the critical mass are:

Positively associated with the quantity of contributions Negatively associated with the quality of contributions

(b) Other-oriented motivations of individuals in the critical mass are:

Negatively associated with the quantity of contributions Positively associated with the quality of contributions

Discussion

The combination of developments in networking, storage, and search technolo-

gies have made it possible to establish public document repositories on the

Internet that aggregate the voluntary contributions of content by widely dis-

persed individual contributors to create a valuable publicly accessible resource.

PDRs on the Internet such as Wikipedia and the repository of reviews atAmazon.com that are populated by voluntary contributions of content by indi-

viduals are increasingly emerging as important resources serving the needs of

the public at large. We provide a theoretically grounded model of contribution

behavior drawing on critical mass theory to understand the factors influencing

the establishment and sustenance of such repositories. We used empirical data

gathered from one large PDR the repository of reviews at Amazon.com to

assess the level of support for theoretical predictions.

Consistent with theoretical predictions, we found evidence of the presence of a

critical mass of contributors, each of whom makes a very significant volume of

contributions that are also viewed as being considerably more helpful than the con-tributions of the average reviewer. These contributors also make a large proportion

of the early contribution of reviews on books or movies, populating the repository




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with content when there are few reviews available for a product in the repository.

Our results also highlight the variety of motives underlying contribution behavior

and their links to the quality and quantity of reviews. Self-oriented motives are pos-

itively associated with the quantity of contribution while they are negatively related

to quality. Other-oriented motives, in contrast, are positively associated with the

quality of reviews while they are negatively related to the quantity of reviews.

Our study has several limitations. First, our results are based on data col-

lected at one PDR, the repository of reviews at Amazon.com. While this choice

minimized problems due to contextual differences between multiple sites, it is

likely that the specific features implemented at Amazon.com may have influ-

enced our findings. Second, the work is based on the sample of the critical mass

of contributors at Amazon.com. This is currently the largest repository of prod-

uct reviews accessible to the public and it is likely that its high-profile role in

establishing electronic commerce may have attracted a unique set of contribu-

tors that are distinct in attitudes, motives, and behaviors from those of otherrepositories. Third, the qualitative data used in our analyses were based on per-

sonal profiles voluntarily provided by individuals for public viewing. It is likely

that our results are biased by the self-presentation of contributors. Finally, the

cross-sectional nature of our study limits inferences of causality among the vari-

ables. 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. To the best of our knowledge, this is one of the earliest field studies

identifying characteristics of a critical mass of PDR contributors and examiningthe motives of this group. The study highlights the importance of social motives

even in a context where user actions do not involve social interaction. It provides

evidence of benefits to contributors from their own contributions. This study also

provides empirical evidence regarding the relationship between key outcome vari-

ables quantity and quality and contributors other-oriented motives and self-

oriented motives. Our study highlights critical mass theory as a useful theoretical

lens to provide insights on phenomena in PDRs. Further, the focus on our study

has exclusively been on contribution behavior, the supply side of PDRs. The exam-

ination of user behavior, the demand side of PDRs, and the interaction of supply

side and demand side factors are important directions for future research wherecritical mass theory and the methodology used by us can provide useful guidance.

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 percent 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 identify and direct incentives to the appropriate set of participants. The findings

can similarly be applied within organizations seeking to identify the critical massof contributors in knowledge management initiatives that seek to develop reposi-

tories based on discretionary contributions of content by employees (Fulk et al.




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2004). Our results suggest that contributor motives are important levers to encour-

age the critical mass to contribute to PDRs. Our results also suggest that the fac-

tors linked to quality and quantity of contribution are different and can guide the

development of incentive mechanisms for prolific contributors.

Conclusion

This paper draws on critical mass 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 role of the critical mass

of contributors in establishing and sustaining collective action. The results also

suggest the need for a broader view of contributor motives incorporating

both self-oriented and other-oriented motives since these are differentially

linked to different aspects of contribution behavior. Our results contribute to amore nuanced view of the determinants of contribution and highlight that the

motivations linked to the quality of contribution are distinct from those linked

to the quantity of contribution. Our approach also opens up several avenues for

further theoretical and empirical work to understand the complex factors

involved in the establishment and sustenance of publicly accessible document

repositories

Acknowledgements

This article is based on part of the first authors doctoral dissertation. We would like to thank hiscommittee: Shawn Curley, Gordon Davis, and Mark Snyder. We are also grateful to the guest edi-tor of the special issue and reviewers ofOrganization Studies for their insightful comments and sug-gestions that have helped us improve the quality of the article significantly. Earlier versions of thisarticle were presented at the Academy of Management conference in Atlanta (2006), the DoctoralConsortium at the International Conference on Information Systems in Washington DC (2004), andat seminars at the University of Minnesota. We thank the participants there who provided us withvaluable feedback.

Appendix 1. Definition of Motives and Sample Phrases from

Reviewer Profiles

Self-oriented Motives

Utilitarian motive

Definition: Contributing reviews because it provides me tangible rewards from

others.

Personal recognition (e.g. as a reviewer on the blurb of a book, as top

reviewer, etc.)

Benefiting ones profession, career, or business

For monetary gain (e.g. Amazon gift certificate) Obtain free copy of a book, CD, etc.

See ones writing in print




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Adams, John S.1965 Inequity in social exchange in

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motivations of volunteers: A functionalapproach.Journal of Personality andSocial Psychology 74: 15161530.

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usefulness of electronic weak ties fortechnical advice. OrganizationScience 7/2: 119135.

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for organizational informationcommons. Organization Science 15:569585.

Goodman, Paul S., and Eric D. Darr1998 Computer-aided systems and

communities: Mechanisms fororganizational learning in distributedenvironments. MIS Quarterly 22/4:417440.

Juran, Joseph1992 Juran on Quality by Design.

New York: Free Press

Kawakami, Laurie2005 Giving reviews the thumbs down.

Wall Street Journal August 4. http://online.wsj.com/public/article/SB112311985732004654-


Benefited from reviews by others

Helping others in repayment for help received in past

Write reviews in the expectation that others will help me

Social affiliation

Definition: Contributing reviews in order to associate with other reviewers,

readers of reviews and authors/creators of the products that are reviewed.

Bond with others with similar interests in books/music/movies, etc.

Bond with others with similar experiences NOT connected with books/music/

movies

Reviewing to receive feedback

Influence the lives/thoughts of others

Contribute to literary cycle

Encourage authors with praise and feedback

1 The distinction between individual actions and collective actions is important. We are gratefulto the editors for highlighting the nuanced view of PDR contributions as individual actions thatthrough the technology of PDRs are converted into public goods.

2 We are grateful to an anonymous reviewer for suggesting this parsimonious framing of the keyproperties of PDRs.

3 All references to rank are to the rank of reviewers in our dataset, calculated based on thenumber of reviews contributed.

4 Readers are referred to Amazon.com for samples of reviewer profiles. We do not reproducesamples here owing to privacy concerns.

Notes

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Naren Peddibhotla is a Lecturer in the Information and Decision Sciences Department atthe Carlson School of Management, University of Minnesota. His research interests arein the areas of organizational use of information technologies for competitive advantage,knowledge management, and the role of information technologies in individual work.His work has been presented at the Academy of Management annual meeting and theBehavioral Decision Research in Management conference.

Address: 3365, Department of Information and Decision Sciences Carlson School ofManagement, University of Minnesota, 321, 19th Ave South, Minneapolis, MN 55455, USAEmail: [email protected]

Mani Subramani is an Associate Professor in the Information and Decision SciencesDepartment at the Carlson School of Management, University of Minnesota. His researchfocuses on the strategic role of information technology within the organization and ininter-organizational relationships. His current areas of research are knowledge manage-ment and the leveraging of organizational capabilities using information technologies. Hiswork has been published in theAcademy of Management Journal, Communications of theACM,Information Systems Research,Journal of Management Information Systems,MISQuarterly, and Sloan Management Review.

Address: 3365, Department of Information and Decision Sciences, Carlson School ofManagement, University of Minnesota, 321, 19th Ave South, Minneapolis, MN 55455, USAEmail: [email protected]

Naren

Peddibhotla

Mani Subramani

organization studies 2007 peddibhotla 327 46

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