A Contingency View of Transferring and Adapting Best Practices within Online Communities

Online communities, much like companies in the business world, often need to transfer 'best practices' internally from one unit to another to improve their performance. Organizational scholars disagree about how much a recipient unit should modify a best practice when incorporating it. Some evidence indicates that modifying a practice that has been successful in one environment will introduce problems, undercut its effectiveness and harm the performance of the recipient unit. Other evidence, though, suggests that recipients need to adapt the practice to fit their local environment. The current research introduces a contingency perspective on practice transfer, holding that the value of modifications depends on when they are introduced and who introduces them. Empirical research on the transfer of a quality-improvement practice between projects within Wikipedia shows that modifications are more helpful if they are introduced after the receiving project has had experience with the imported practice. Furthermore, modifications are more effective if they are introduced by members who have experience in a variety of other projects.


INTRODUCTION
Online communities, like companies in the business world, often need to transfer best practices internally from one unit to another to improve their performance. For example, communities in the Stack Exchange network of question and answer websites use a common reputation system modeled on Stack Overflow's original one. Similarly, many non-English language Wikipedia versions have borrowed policies and procedures originally developed in the English Wikipedia. Barnstars, the badges Wikipedia editors give to each other to reward meritorious work and motivate each other, originated in the MeatballWiki and were imported into Wikipedia in 2003 [47]. Since then Wikipedia has developed over 100 distinct Barnstars, and thousands of Wikiprojects have created their own specialized Barnstars. Similar tales could be told of Wikipedia's various quality improvement programs, such as Collaborations of the Week (CotW), a practice designed to increase the quality of under-developed content areas, that has diffused across hundreds of Wikiprojects [46,51].
While the effectiveness of particular practices has been studied in isolation [10,27,33,46,51], we are aware of no research that examines how the process of acquiring and changing these practices influences their effectiveness. Understanding the factors that determine how practices are internally transferred and effectively adapted could provide insights into community success that go beyond individual practices. This is also one of the central topics in the field of organization research in the last two decades [1,30,41]. As organization scholar Szulanski noted, "Identification and transfer of best practices is emerging as one of the most important and widespread management issues" [43].
One important question regarding best practice transfer within organizations is the extent to which recipients need to modify an original practice to make it effective in a local context [49]. Organization scholars have a long-standing debate on this topic.
According to the re-creation perspective, strict replication leads to incompatibility between the new practice and the recipient's environment, rendering the imported practice less effective [1,11,36,37,25]. The recipient units need to continuously modify the original practice to create their own practice that better fits with their culture, structure and approach. For example, according to this approach, McDonalds, which sells billions of beef-based burgers in the US, needed to change its menu by introducing localized products like McVeggie™ for India, where half of the population is vegetarian [24].
In contrast, the replication perspective argues that modifying a successful practice for a new environment increases the risk that the modifications will harm performance (e.g., [1,16,34,40,48,49]). Some empirical evidence shows that in a large franchise organization changing a successful practice (by selling non-standard products) harms franchisees' survival. A one-standarddeviation increase in revenue derived from nonstandard products more than doubles a franchise unit's hazard of failure [49, p. 678].
In this paper, we propose that in online communities neither replicating an original practice without modification nor freely implementing modifications is a successful approach to transfer best practices. Instead, we propose a contingency perspective and hypothesize that modifications are most successful if they are introduced after the receiving unit has had experience with the imported practice. This allows for a form of iterative organizational design, in which a receiving site can tweak an imported practice based on experience. We also hypothesize that modifications will be more effective if they are introduced by people who are core members of the receiving unit and who participate in a variety of other communities. These are the people who likely to be knowledgeable about what their unit needs and about alternative practice tweaks used by others.
To test these hypotheses, we analyzed historical data about Collaborations of the Week (CotW) in Wikipedia. A Collaboration of the Week is quality-improvement practice in Wikiprojects that organizes editors collaboratively to improve a designated article in a limited time period. Collaborations of the Week spread from project to project and are often modified both before they are adopted and as they are used. We collected the history of CotW in 146 Wikiprojects and measured how different types of modifications influenced their success, in terms of the length of time the CotW continued to be used in a project, the amount of work they elicited from project members and the number of unique editors who contributed to them. The results generally supported the hypotheses.

THEORY AND HYPOTHESES The Best Practice Adaptation Dilemma
Practice refers to an organization's routine use of knowledge for conducting a particular function [43]. According to organization scholars, the ability to transfer best practices internally within a firm provides a competitive advantage [2] and is one reason they can be more effective than other institutional arrangements such as markets [5,28]. The benefits of transferring good practices between parts of a single organization have been documented in many different organization settings (see [2] for a review). For example, Darr et al. showed how pizza franchises benefited from learning from other franchise stores how to place pepperoni [12]. Similarly, Baum and Ingram [7] found that hotels within a single chain benefited from the experience of other hotels in their chain in the same environment.
An important question is the extent to which units within a larger organization benefit by modifying practices received from another parts of the organization to fit their local environments. On one hand, modifying a successful practice increases the risk that the modifications will harm performance. On the other hand, strict replication might lead to incompatibility between the imported practice and the recipient's environment, reducing the benefit derived from the imported practice. In this section we review existing evidence relevant to this debate. Based on this review, we suggest a contingency perspective to understand when and how to modify best practices. We develop and test hypotheses about the conditions under which modifications to source practices lead to more successful organizational performance.

Not to Modify: The Replication Approach
Winter and Szulanski [48] claimed that knowledge transfer is maximally effective when only value-creating facets of the knowledge are replicated, and no time or effort is devoted to the creation of addition features, which could harm performance. There is evidence showing that attempting to modify a successful working practice could be harmful, even when these modifications initially seemed sensible, promising, or desirable. Work in population ecology has found negative survival effects of modifying core features of organizations in a variety of contexts, including voluntary social service organizations [40]; Finnish newspapers [1]; U.S. medical diagnostic imaging firms [34]; U.S. bicycle manufacturers [16]; and French, German, and British auto manufacturers [15]. Recent work on franchises provides empirical evidence supporting the replication perspective: deviations from a franchisor's template (i.e., a source practice) have negative consequences for the survival of franchise units within a larger franchise organization [49]. According to the replication perspective, modification of a working practice introduces risks, and the risk increases when the practice is complex. Modification of complex practice can lead to unanticipated deleterious interaction effects that are causally ambiguous and difficult to interpret [49,31].

Modify: The Re-creation Approach
However, the problem with replication is that a practice might encounter incompatibility problems when moving from a source environment to the recipient one. According to Argote and Ingram [2], practice is often embedded in structural elements of an organization, such as its people and their skills, technical tools, or other routines and systems used by the organization, as well as in the networks formed between and among these elements. Failure of transferring practice often results from incompatibility with the new context. And the risk of failure caused by incompatibility increases when the practice is more complex [2,18].
The re-creation approach advocates modifying and adapting the source practice in the recipient site to reduce incompatibility. The re-creation perspective is influenced by literature in organization innovation, technological adaptation and organization routine [11,17,25,43]. Kim and Nelson examined learning and innovation in newly industrializing economies and proposed that knowledge transfer is a dynamic learning process where organizations continually interact with customers and suppliers to innovate or creatively imitate. Orlikowski [36] explored the introduction of groupware into an organization to understand the changes in work practices and social interaction it facilitated. She found that people's mental models and an organization's structure and culture significantly influenced how the technology was actually used. She further proposed that change is endemic to the practice of organizing and is enacted through the situated practices of organizational actors as they improvise, innovate, and adjust their work routines over time [37]. Feldman and Pentland [17] challenged the traditional understanding of organization routines as creating inertia in organizations. They argued that organization routines are a source of change that create on-going opportunities for variation, selection and retention of new practices. Synthesizing these perspectives, practice is seen as being continuously modified in the transfer process. Practice transfer is a dynamic learning process, involving the continuous modification, re-configuration and re-creation.

Contingency view of best practice modification
Prior research suggests that modifying practices when adopting them can ameliorate the incompatibility between a source practice and the local environment, but can also increase the risk of introducing deleterious features to a successful working practice. The risks of both incompatibility and deleterious modifications increase when the practice is more complex.
Neither strictly replicating an original practice without modification nor freely modifying it will optimize the effectiveness of the imported practice. Instead, we need to understand the conditions under which modifications are more or less effective. In the following sections, we develop testable hypotheses about when and who should make modifications to improve the performance of the imported practice. Specifically, we propose hypotheses about the effectiveness of modifications at an early stage (i.e., pre-implementation) versus later (i.e., postimplementation), and the influence of characteristics of the people involved in the modification on their success.

When to modify: Effectiveness of Pre-versus Postimplementation Modification
Tyre and Orlikowski [45] examined the temporal pattern of modifications to a new technology in organizations. They found modifications disproportionately occurred when the technology was first introduced (and even before its official use). Thus, they suggested that managers and engineers have only a relatively brief window of opportunity to explore and modify new technology. However, the authors only examined when modifications occurred, not their effectiveness at different stages.
We propose that modifications early in the adoption process are often based on presumptions (i.e., predictions about which components of the new practice might go wrong). These presumptions may be wrong because they are not based on evidence. In contrast, when people modify a practice after implementing it, they are basing their changes on experience. Their changes are more likely to be responses to actual compatibility problems between the imported practice and the receiving site. Making changes to a practice after implementing it is a form of iterative organizational design, in which a receiving site can tweak an imported practice based on experience. Therefore, we hypothesize that post-implementation modifications are less likely to introduce deleterious changes compared to preimplementation modifications and, thus, will be more effective than pre-implementation modifications.
The idea that experience-based, post-implementation modifications will be effective is consistent with the organization learning and knowledge creation literature (see [4] for a recent review). According to organization learning theories, new knowledge is iteratively created as experience interacts with context. We propose to use an iterative organization design model to depict the postimplementation modification of source practice as an ongoing use-mismatch-create cycle. In this cycle, the recipient site adopts and implements the new practice, uses it, detects mismatch, fixes the mismatch, and creates a new iteration. Each iteration results in more effective utilization of the practice. The re-creation process does not end when the new practice achieves satisfactory results at the recipient site. Even after successfully incorporating the new practice for a period of time, changes in the local context at the recipient site (e.g., environmental change, member turnover, introduction of new tools or policies) might result in a new mismatch and thus prompt a new iteration.
The process of post-implementation, iterative organizational design is analogous to the iterative userinterface design [35,39]. Nielson proposed that software improves more rapidly when users use the interface and developers learn from their feedback, rather than designing and iterating without evidence [35]. He provided data to show that redesigning user interfaces on the basis of user testing substantially improved usability [35].
This hypothesis might reconcile differences between the replication and re-creation perspective discussed above. Szulanski and Jensen [42] and Winter et al. [49] provided empirical evidence showing that deviation from corporate templates negatively affected the survival chances of franchise units within a large organization. However, those studies only focused on presumptive modifications (i.e., modification based on managers' assumptions, without evidence, about what should work) [42] or conflated presumptive modifications and post-implementation modifications [49]. We suggest that modifications made before implementation (i.e., presumptive modifications) will generally not lead to successful use of the practice, while the post-implementation modifications should significantly improve its successful utilization.

H1.
Modifications made after implementing the practice are more effective than modifications made before implementation.

Who should modify: Effectiveness of Modifications Created by People in Different Network Positions
We next consider how the network structures of the individuals who propose and implement modifications to a practice in a recipient site influence the success of the practice.
We hypothesize that members who are central in the local site can create better modifications because they know more about the local environment. Central people should be better able to identify a mismatch between the new practice and local needs and to craft a good solution to fix the mismatch.
In addition, members' network ties to other sites that have already adopted the practice can also affect whether they will create successful post-implementation modifications. Prior research on the transfer of knowledge in organizations has shown that individuals with ties outside their organization are often successful at searching for and importing new ideas into their organization [20,21]. Therefore, we propose that people's external ties will also help them create successful post-implementation modifications at the recipient site.
This proposal about the usefulness of external ties is based on the concept of "learning in a world of learners" from Levitt and March [32] and an ecological approach to understanding the role of external ties in successful knowledge transfer. The key element of creating an effective modification is to resolve a mismatch between a new practice and the local environment. Since many recipient sites may have modified the source practice to fit it into their local environments, some of their solutions might be relevant to another site that is trying to incorporate the practice. Members in a local site with ties to other sites that have already adopted the practice are in a good position to search for solutions from those other sites. Furthermore, according to research on analogical reasoning [44], even though a local site is not experiencing problems that are identical to other in other recipient sites, exposure to the mismatch-fixing cycle in the other sites might inspire good solutions at the local site.
Although people who have external ties with other recipient sites are more likely to generate good solutions for mismatches at the local site, acceptance of their solutions cannot be taken for granted. Gruenfeld et al. [19] investigated the consequences of temporary membership changes for itinerant members (i.e., those who leave their group of origin temporarily to visit a foreign work group) and indigenous members of those origin and foreign groups. They found that, although itinerant members produced more unique ideas than indigenous members, their ideas were significantly less likely to be utilized by the group. Kane et al. [23] later found that groups were more likely to adopt the ideas from itinerant members when they shared a superordinate social identity with them than when they did not. Therefore, our final hypothesis is that people with external ties who are also central in the local units can generate good solutions that result in a higher acceptance rate. Those persons, therefore, are most likely to create more effective modifications.

STUDY PLATFORM
We conducted our research in the context of Wikiprojects in Wikipedia. Wikiprojects are subgroups of Wikipedians who organize to curate articles around different topics. Our research examines how Wikiprojects adopt and modify a project-based practice called Collaboration of the Week (CotW).

Collaborations of the Week (CotW)
CotW is a practice whereby members of a project designate one or two articles to improve within a week or other defined time period. Collaborations of the Week started as a Wikipedia-wide activity, not tailored to any specific project. However, hundreds of Wikiprojects have adopted and modified this practice since 2004 and created their own project-specific variants, which often have dedicated project pages. Figure 1 shows the CotW project page in Wikiproject Video Games (WVG).
CotWs have two phases: selection and collaboration. In the selection phase, project members nominated candidate articles for improvement and then voted on which they would work on. During the collaboration phase, the project tagged the chosen article(s) with a special template in its talk page. In addition, the project typically announced on its project pages which articles were targets of the collaboration for that period.
CotW is an important practice to direct volunteer editors' attention to articles that are important to the group but which may not attract individual members' interests. As Zhu et al. [51] have shown, without coordination techniques like CotWs, editors often gravitate to work on popular articles and neglect less popular ones. CotWs can motivate project members to contribute to these less popular, but important, articles. Their research also showed that, in addition to motivating contributions on important but less popular articles, CotWs have other benefits. For example, during periods when CotWs were active, project members also increased their work on non-CotW-target articles. Furthermore, editors who worked on CotWs seemed to learn from central members in the project, who served as role model; after participating in a CotW, editors became more likely to perform similarly to the role models, and they increased their work on assessment and anti-vandalism activities.
Despite the benefits of CotWs, their use by Wikiprojects varies widely. Among 146 Wikiprojects that had ever hosted a CotW, 72 hosted only a single one, 74 hosted more than one and 55 successfully hosted more than five collaborations.

CASE STUDY: COTW IN WVG
To better understand how Wikiprojects used and modified the Collaboration of the Week practice, we conducted an in-depth case study on the Wikiproject Video Games (WVG)'s Collaboration of the Week, named "Gaming Collaboration of the Week" (GCOTW). The case study can help us better understand the hypotheses in the context of Wikipedia and CotW.

Method
We analyzed the complete revision history of GCOTW project page (3431 revisions) and discussions on WVG's talk page that mentioned GCOTW. We also cross-linked key participants' activities in GCOTW and other parts of Wikipedia during the period the CotW were active. Wikipedia records almost all actions by editors and provides an API for researchers to conveniently retrieve and analyze the activities. We rely on these complete records to reconstruct WVG's experience using CotW.  1. Illustrate the goal of CotW. For instance, this page says: "Each week a Gaming Collaboration of the week will be picked using this page"…"The aim of this project is to improve the quality of Wikipedia's computer and video game articles through widespread cooperative editing." "The project is also used to fill gaps in Wikipedia, to give users a focus, and to give us all something to be proud of. " 2. Template designed to announce targets of the collaboration each week. The template shows "the current focus of collaboration of the week is XX. The last article was XX -see how it improved." 3. Policies and guidelines about running the collaborations. The policy on this iteration includes five parts: how to vote, how to deal with vote ties, how to nominate a candidate, what to consider before nominations, and how to prune nominations that do not receive enough votes. For instance, the policy for voting says "Please vote for as many of the following candidates as you like. Please add only support votes. Opposing votes will not affect the result, as the winner is simply the one with the most support votes (see Approval voting). Remember: Any registered user is encouraged to vote." 4. This is the area for editors to participate in the nomination and voting. They post the title (with a link) of the article they nominate and reasons why they want to nominate this article. Other users will support the nominations or leave comments about the nominations. Table 1 shows five iterations of the GCOTW as examples to illustrate what we mean by "modifications" in the context of CotW. The first example discusses guidelines for nomination. The original guideline inherited from the source CotW simply reminded people to justify why they nominated particular articles. Editor pie4all88e had a concern that members of WVG might be enthusiastic about a particular niche topic yet not consider its importance for the whole gaming community. Therefore, in the new iteration, pie4all88 added a new guideline to remind nominators to consider the impact to the wider gaming community of the articles they nominated.

Modifications of GCOTW
The second example considers the pruning policy, which defines the threshold to prune unsuccessful nominations (i.e., those that fail to receive adequate support). After implementing the original pruning policy that would drop articles that failed to receive five votes within a week, users stated that this threshold was too high. In the talk page, people proposed lowering the number of needed votes per week because "this CotW does not get as much traffic as the original CotW." That change is reflected in the new iteration.
The third example relates to the voting policy. The original policy encouraged members to "vote for as many of the following candidates as you can." That policy, however, encouraged people to vote but not contribute. Because of this, articles selected as GCOTW targets received little contribution during the collaboration period. One member expressed this problem in the discussion and suggested that the weekly improvement drive (itself a variant of the source CotW) create a template to remind voters to contribute. As a result, two changes were made in the new iteration. First, the description was changed to "A vote … shows your commitment to support and aid in collaborating on that specific article if it is chosen." This change highlighted votes as a commitment to contribute rather than a simple social gesture. Second, an editor created a new template to remind voters when the articles they voted for were chosen.
The fourth example also concerns voting policy. The original policy stated that any registered user was encouraged to vote. To increase the likelihood that their preferences would be selected, some members created "sockpuppets" (i.e., fake accounts) to cast false votes. In the new iteration, sockpuppets were forbidden from voting.
The final example relates to the selection mechanisms in GCOTW. After hosting GCOTWs for over four years, member enthusiasm eroded. Low participation frustrated members who were still actively organizing the nomination and voting. To address this problem, the nominate-voteselect schema was changed to a bot-selecting schema. Each week, a bot would randomly select an article from the lowquality-high-importance category and post it as the GCOTW. In the discussion, people believed that this change could remove the stress caused by nomination and voting and focus on the contribution. Also, the random nature of the selection was more enjoyable. After implementing the new bot-selecting schema, GCOTW ran successfully for another 2.5 years.

Pre-and Post-implementation Modifications
The first example modification was made before the WVG officially implemented the GCOTW (i.e., the date of announcing the first GCOTW). The remaining four example modifications were made after the GCOTW was officially implemented. Prior to the official implementation, the modifications were created based on people's predictions about which component might go wrong. For instance, in the first example, editor pie4all88e predicted that members of WVG might be enthusiastic about a niche topic without considering its importance for the whole gaming community. No discussion was found related to the problem of proposing a niche topic. In other words, it was uncertain whether nominating niche topic articles would be problematic. In contrast, the remaining four examples were all based on lessons learned from previous iterations, such as the high pruning threshold, the lack of contributions despite the number of votes, false votes, and decreased enthusiasm. We found discussion histories related to each of these four examples. The post-implementation modifications are more targeted to actual problems compared to pre-implementation modifications.

People in the modification process
The third example, about voters not contributing, shows how people with external ties can generate good solutions to resolve problems of using new practice at the local site by borrowing solutions. The editor (Jacoplane) mentioned that another project created a template that "gets put on every user's talk page that vote". The editor suggested borrowing this solution: "I think we should do something similar to remind people that they voted." We checked Jacoplane's editing history and found that this editor participated in nine other Wikiprojects that hosted CotWs that year. Despite the multiple project participation, the editor was based in WVG (87.7% of his/her project page contributions are devoted to WVG at that year). In WVG, the editor was a top 3 contributor among the group's 347 members. The central role of this editor in WVG might make it easier for him/her to identify the problem. Second, the external relationship with other projects was an advantage for him/her to find a solution. Finally, the central role of this editor made it easier for his/her suggestions to be accepted.
The case study provides real examples to help better understand the hypotheses about modification of best practice in the context of CotW. In the following section, we conduct quantitative analysis to test the hypotheses.

Discussion New Iteration
Guidelines for nominations -Giving reasons as to why an article should become the COTW may assist others in casting their vote.
No discussion found specifically related to this change.
Guidelines for nominations -Giving reasons why an article should become the GCOTW may convince others to support your nomination.
-Can the wider gaming community easily contribute to the article? Or is it something only a small number of people will know about?
Pruning policy:

Reactivating Collaboration of the Week -with ROBOTS!!!
(Propose the plan of having robots randomly select one article from the category of low quality but high importance as collaboration) "Removing the stress of nomination and voting will reduce frustration, and make participation the focus, not bureaucracy (this isn't an RfA). The random nature will make it more fun, as part of it is wondering which article will be chosen. "

Introduction:
The WikiProject Video games collaboration is a collective effort to improve related articles covered by the project's scope. An article is chosen every Monday, by a bot that randomly selects one video gamerelated article that is rated Stub or Start or C class, and Top or High priority for WP:VG. The bot then updates Template:Collab-gaming with the pick, and the collaboration begins. If there is consensus that a selected article is not felt to be suitable for collaboration, then the bot will be requested to "re-roll" and select a different article. Articles that have previously been chosen for collaboration will not be chosen again. Previous collaborations can be found at /History.

Method
We ran a quantitative analysis on 146 Wikiprojects that adopted CotW. The first step is to identify the modifications of CotW in these projects.

Automatically identify modifications in CotW
We wanted to automatically identify modifications from the CotW pages' historical revisions. We defined modifications as the changes to the practice, which modify the way of organizing and operating CotW. Not all the historical revisions of CotW pages were "modifications". The goal of this section is to automatically identify the modifications.
We found that a large proportion of the historical revisions on the CotW pages are actually candidate nominations or votes to select collaboration articles, rather than modifications to the CotW rules. To rule out these types of nomination and voting activities, we excluded the revisions that only modified the sections of nomination and voting. Results show that 88.6% of the revisions on the CotW pages are the nomination or voting activities.
To further detect the modifications in the remaining 11.4% revisions we used a machine-learning approach in which we hand-coded 335 non-nomination-voting revisions from two Wikiprojects' CotWs as a training set. We then created a feature set containing nine different features (see Table 2 for details). We trained statistical models (rule-based model generated based on our domain knowledge, decision-tree, and SVM) on the training set and evaluated them using a separate set of hand-coded data (113 non-nominationvoting revisions from another two Wikiprojects). Details of the feature set and model shown in Table 2.
We compared the performance of rule-based model, decision-tree, and SVM. Results are shown in Table 3. The rule-based model and decision tree outperformed SVM on both the training set and test set. On the training set, the decision-tree performed slightly better than the rule-based model. However, in the test set, the rule-based model performed slightly better than the decision-tree model. Because the rule-based model performed the best in the test set and is easy to interpret we used it in the following analysis.

Analysis overview
This analysis seeks to identify the effects of different types of modification on the successful utilization of CotWs. We measured the success of CotW according to three criteria: (1) the survival of CotW (i.e., the likelihood that projects continuously use CotW), (2) the number of contributions on CotW target articles during the collaboration period, and (3) the unique contributors to CotW target articles during the collaboration period. The analysis was conducted on the project-collaboration-period level. We predicted outcomes (i.e., survival, contribution, and participants) in the current collaboration period according to whether the project made a new iteration in the last collaboration period.
Since we used observational data to run the analysis, the creation of a new iteration is not a true experimental treatment. New iteration creation (i.e., modification on CotW), as with most events in the real world, is endogenous in the sense that it is caused by other factors inside the system. In our data, Wikiproject activity correlates to project members' participation in CotW, as well as their tendency to modify its procedures of CotW. Not controlling for confounding factors that influence both the treatment (CotW modifications) and the outcome (CotW utilization) can lead to biased estimates of the treatment effects. To ameliorate the endogeneity problem, we used propensity score matching (PSM). We will discuss the details of PSM method later.

Data preparation
The data were longitudinal, following the same project across multiple collaboration periods. The data comprised 1588 project-collaboration-period observations.

Dependent variables
• Practice Death. We defined a CotW as near abandonment (i.e., dying) if the project did not have at least two collaborations after a focal collaboration period (a sensitivity analysis with differing threshold values showed no difference in the pattern of results). This variable was assigned to 1 if the project's CotW was dying (i.e., had no more than two collaborations in the future); it was assigned 0 if the project's CotW was still active (had more than two collaborations in the future).
• Contributions. We measured the number of revisions to the target articles during the collaboration period, controlling for the number of revisions on these articles during the non-collaboration period. Particularly, we divided the number of revisions on the target articles during the collaboration period by the number of revisions on the target articles during the pre- collaboration period. The pre-collaboration and collaboration periods lasted the same length (e.g., normally between a week and a month long). This variable is log transformed in the analysis.
• Participants. We measured the number of unique contributors who edited the target articles during the collaboration period, controlling for the number of unique contributors during the non-collaboration period. Specifically, we divide the number of contributors during the collaboration period by the number of contributors during the pre-collaboration period. Both periods lasted the same length. This variable is also log transformed in the analysis.

Independent variables
• Post-implementation modification. We measured the number of modifications the project's CotW had in postimplementation periods.
• Pre-implementation modification. We measured the number of the modifications the project's CotW had in the pre-implementation period (i.e., the preparation period).
We further divided the modifications according to which editors would implement the modifications.
• "Modification made by core members in the recipient project" versus "modification made by non-core members in the recipient project". We defined core members as those whose overall contributions to the project are among the top 10%. We then divided the modifications into two groups: those made by core members versus those made by non-core members.
• "Modification made by members with more external ties" versus "modification made by members with fewer external ties". We measured external ties as multiple memberships in other projects that also adopt CotWs. If a member participated in three projects in addition to the focal project, he/she had three external ties. We defined members with more external ties as those with external ties greater than or equal to the median (external ties 3). Similarly, we define member with fewer external ties as those with ties lower than the median (external ties 3).
In addition to the above variables designed to measure the main effects of core-ness in the focal project and external ties, we also measured the interaction effects. We defined four more interaction measurements: (1) modifications made by core members in the recipient project and that have more external ties, (2) modifications made by core members in the recipient project but do not have many external ties, (3) modifications made by non-core members in the recipient project but have more external ties, and (4) modifications made non-core members in the recipient project and do not have many external ties.
In addition, we measured the popularity of the source.
• Popularity of the source. In our data, we observed that Wikiprojects have different sources. Many of the earliest projects learned and copied rules and policies from the Wikipedia-level CotW (which has since been terminated). Some projects started by copying other Wikiprojects' CotW. The very first revision of the CotW page is likely to be the source CotW. We calculated the popularity of the source by comparing the structural similarity of the given project's first CotW page revision with all the other CotWs in other projects at that time period. Higher similarity indicated that more projects were using the same structure, and that focal project was starting with a more popular template.

Propensity score matching
The basic idea of PSM is to pair the treated project and the control project. For a given project that had modifications, we selected a comparison project that was most similar on confounding variables but did not have modifications. We used Propensity Score Matching (PSM) to pair the projects (more precisely, project-collaboration-periods).
Using PSM involved three steps. In the first step, we estimated the propensity score (i.e., the tendency of having modifications) from a set of conditioning variables. We chose four variables indicating the activity level of the project listed below as conditioning variables. In the second step, we matched each project that had modifications in a particular week with another project that did not have modifications, but which had the most similar propensity score based on our four activity indicators. Propensity scores allow researchers to control for many variables simultaneously by matching on a single scalar variable. To conclude the second step, we tested whether the treatment group and control group were well matched in terms of the conditioning variables. In the third step, we ran fixed effects regression analyses to estimate the effect of modifications on the treated groups and matched controls.
Step 1: Estimate propensity score We first used logistic regression to estimate the probability of having modifications based on the project activity level. The estimated probability is the propensity score. The four predictors are listed below.
• Active members. We measured the number of active members during the period of time.
• Number of CotW hosted before. We measured how many CotW were hosted.
• Project page activities. Project pages are places where Wikiproject organize activities. CotW is one of activities organized through project pages. We measured the amount of contributions on the project pages during the given period, indicating whole project activity during the given period of time.
• Number of project pages. We measured the number of pages the project had during the given period, which indicates the size of the project.
Step 2: Matching based on propensity score.
In this step, we matched projects that modified their CotWs with projects that did not, based on the estimated propensity score. To do this, we ordered the treated and control projects according to their propensity scores. For each treated project, we then selected a control project with the closest propensity score within a maximum distance (we defined maximum distance as 0.1 in the analysis). Figure 2 reports the histogram of the propensity score (i.e., the tendency of making modifications) for treated groups and control groups before and after matching. Here the treated group contains projects that indeed made modifications at the given time period and the control group contains projects that did not make modifications at the given time period. Figure 2 shows that the treated group and control group are balanced on the tendency of making modifications after matching. Table 4 reports the details of the matching process. Note that variables that correlate highly with the treatment (also having higher risk to introduce bias) will be balanced better than variables with lower correlation with the treatment. This explains why PSM tends to favor page activities, active members and previous CotWs over the number of project pages during balancing.
There is an interesting observation that the bias (i.e., unbalance) between the treatment group and control group is not that serious even before matching. In Zhu et al's [52] study where they used PSM to match an editor who received messages with editors who did not receive messages, the bias was 79%-110% before matching. In this analysis, the bias is only 5%-8% before matching.
The statistical results are consistent with our observations. We observe that project activity and project size do not correlate with the number of modifications made on the CotWs (and the success of CotWs although the latter project generally much more active than the former project.
Step 3: Run the analysis on the match sample Using the matched sample, we then examined the effects of modifications on the outcomes (survival, contributions and participants). We used fixed effects linear regression to predict outcomes, with each treated control pair as a group.

Results
The temporal patterns of the modifications are shown in Figure 3. The results are consistent with Tyre and Orlikowski's (1994) findings ( Figure 4) that a substantial proportion (about 30%) of modifications happened in the pre-implementation stage. Far fewer modifications happened in each post-implementation CotW period. A hazard ratio is the ratio of the risk of a CotW being abandoned in a given time period associated with a one-unit change in the explanatory variables. A hazard ratio smaller than 1 indicates decreased rate of abandonedness (i.e., 1 Note that here we do not use the traditional interaction model (e.g., with modification, modification X pre-post (interaction), and modification X pre-post X the types of people (interaction) as explanatory variables in the regression) but divide the number of modifications into different groups. Our analysis is essentially the same as the traditional interaction method but is easier to interpret.  Table 4. Comparison between treatment projects that made modifications (Treat) and control projects that did not make modifications (Control) before and after propensity score matching (Full vs. Match).

Bias is calculated as follows: ,
where and are the sample means in the treated and control groups, and and are the corresponding sample variance. increased survival rate), while a hazard ratio larger than 1 indicates increased rate of abandonedness (i.e., decreased survival rate).
Models 5-8 show the results of the random effects linear regression models (with each project as a group) predicting how modifications affect the amount of contributions received by CotW target articles. Models 9-12 show the results of the random effects linear regression models (also with each project as a group) predicting how modifications affect the number of unique contributors in CotW. Models 5-12 report the regular coefficients. Coefficients smaller than 0 indicate decreased contributions/participants, while coefficients larger than 0 indicate increased contributions/participants. Model 1 shows that a one-unit increase in preimplementation modification decreases the hazard ratio by 3%, while a one-unit increase in post-implementation modification decreases the hazard ratio by 62%. The difference between the pre-and post-implementation modification is significant (χ2=14, P < .01). The results confirm Hypotheses 1, showing that post-implementation modifications have a much stronger positive effect on the practice survival. Models 2-4 show that modification effectiveness is influenced by editor type (e.g., core vs. non-core member and strong external ties versus weak external ties). Model 2 shows that the modification created by core members were more effective in decreasing hazard rate (68%) than non-core members (24%) and the difference is marginally significant (χ2=3.0, P = .09). Model 2 confirmed Hypothesis 2a partially. Model 3 shows that the modifications introduced by contributors with more external ties were more effective (decreasing the hazard rate by 83%) than modifications introduced by people with fewer external ties (decreasing the hazard rate by 13%). This difference is also statistically significant (χ2=14, P <.01). The results of Model 3 confirmed Hypothesis 2b. Regarding the interaction effects of being a core member with external ties, Model 4 provides mixed results. The modifications introduced by core members with more external ties (V7) significantly decrease the hazard rate by 82%. The modifications introduced by the other three types of contributors (core members with fewer external ties-V8, non-core members with more external ties-V9 and noncore members with fewer external ties-V10) did not significantly decrease the hazard rate. Also, core members with more external ties tend to create more effective modifications than those with fewer external ties (χ2=8.5, P <.01), which indicates that external relationships help core members create effective modifications. However, among the people with external ties, the difference between being core members and non-core members is not significant (χ2=.62, P =.43). The results support Hypothesis 2c partially.
Models 5-12 present similar patterns as Models 1-4. For example, similar to Model 1, Model 5 shows that a one-unit increase in pre-implementation modification increases the contributions received by CotW target articles by 0.7% 2 (P > .05), while a one-unit increase in post-implementation modification increases the contributions by 19% 3 (P < .01).
The results collectively support Hypotheses 1, and 2b, and provide partial support for Hypothesis 2a and 2c.

Modification timing of imported practice
Research by Tyre and Orlikowski [45] as well as our own, although conducted in different organization settings, reveal similar patterns of new practice modifications (see Figure 9, top and bottom). Specifically, we find that a substantial proportion of modifications were made relatively soon after receiving the new practice and far fewer modifications were made afterwards. The underlying psychological process might be as follows: when the recipient site receives a new practice, people are excited to adopt it yet believe that they can improve its potential value by modifying it. However, after implementing the practice for a while, people tend to become reluctant to make changes. When the imported practice does not achieve expected performance, they might simply abandon rather attempt to further modify the practice.
However, empirical analysis reveals that modifications introduced before implementation are less effective than those introduced after implementation. Results show that the benefits of pre-implementation modifications are one order of magnitude lower than post-implementation modifications. A one-unit increase in pre-implementation modification decreased the hazard of failure by only 3%, while a one-unit increase in post-implementation modification decreased the hazard of failure by 62%. Similarly, a one-unit increase in pre-implementation modifications increased member contributions on targeted articles by only 0.7%, while a one-unit increase in postimplementation modifications increased the contributions by 19%.
The results suggest an alternative way to treat an imported practice. It might be better for a recipient unit to change the imported practice only slightly-if at all-before trying it because pre-implementation modifications (although initially deemed sensible and promising) minimally improve practice utilization. In contrast, more resources 2 0.7% = e^0.007-1 3 19% = e^0.17-1 should be devoted to modifying the practice after the receiving units have experienced it.

Effects of modifications introduced by core members
Hypotheses related to core members (2a and 2c) are weakly supported by the data. For instance, modifications created by core members decreased hazard rate of CotW by 68% and those modifications created by non-core members decreased hazard rate by 24%, but the difference is only marginally significant (p=.09).
One possible reason why the effects are not as strong as anticipated is that the operationalization of core memberstop 10% contributors-might be arbitrary. According to this operationalization, some peripheral members might be labeled as core members or vice versa, which might explain the relatively low significance.
Second, the current core-ness measurement, which essentially measures people's contribution levels, might not be a good proxy. There are two possible underlying  mechanisms of the effects of modifications introduced by core members. The "expertise-based" mechanism suggests that core members are more experienced and better understand the local project. Thus they can better identify or proactively search for effective modifications. The "influence-based" mechanism suggests that core members are more influential in the project and thus their modification suggestions are more likely to be accepted by other project members. Contribution levels might be a first order of approximation of the expertise or influence people have in the projects. However, this study will benefit from a closer examination on the roles of core members play in the practice adaptation process and more nuanced and precise measurements of member core-ness. Future work should attempt to address these aspects.

Generalization to offline organizations
This chapter proposes a contingency theory aimed at answering one management question that applies to any online community or offline organization that attempts to transfer best practices from one unit to another. The empirical study presented in this chapter provides evidence that the theory holds in the context of online communities. However, it remains unknown to what extent the findings may be generalized to an offline context.
One conjecture is that the findings might be easier to translate to offline organizations that share the some of the same features as online communities, especially those "organic organizations". Roughly fifty years ago, Burns and Stalker [9] proposed the concept of "organic management system" as an alternative to bureaucratic management systems (what they called a "mechanistic system"). They suggested that organic systems and mechanistic systems represent two poles of organizing forms: a mechanistic system is highly formal, rigid and centralized, while the organic system is informal, dynamic and flat. Organic management systems feature "the contributive nature of special knowledge and experience to the common task" and "lateral rather than a vertical direction of communication through the organization" ( [9], Page 121). Organizations fall on different positions on the organic-mechanistic spectrum. For example, universities, offline volunteer organizations, design studios and research labs are more organic and thus more similar to online communities in terms of organization structures than, for example, the military and government, which are more mechanistic.
Given the similarity between organic organizations and online communities, we conjecture that the findings of this work might be easier to transfer to organic offline organizations as compared to mechanistic organizations. However, this conjecture must be regarded with caution until it is confirmed by empirical work. Our intent of connecting online communities and organic organizations is to stimulate readers to bridge the CSCW and organization science areas, and consider new perspectives in studying important organizational phenomenon in both new and traditional organization forms.

CONCLUSION
In this paper, we propose a contingency perspective to understand the process of incorporating and adapting best practice within online communities. We conducted quantitative analysis on the transfer of a qualityimprovement practice between 146 Wikiprojects within Wikipedia. The results show that modifications were more helpful if they were introduced after the receiving project already had experience with the imported practice.
Modifications were more effective if they were introduced by people who had experience in a variety of other projects.