Capturing & Visualizing Knowledge Work: Results & Implications of a Pilot Study of Proposal Writing Activity William Hart-Davidson
Clay Spinuzzi
Mark Zachry
Michigan State University Suite 7 Olds Hall East Lansing, MI 48824 517-432-2560
University of Texas at Austin 1 University Station B5500 Austin, TX 78712-1122 512-471-8707
University of Washington 14 Loew Hall, Box 352195 Seattle, WA 98195-2195 206-616-7936
[email protected]
[email protected]
[email protected]
The aims of our research project were
ABSTRACT
•to visualize the contributions technical communicators make to the knowledge economy
This paper reports on a pilot study of three proposal writers conducted by the authors during late Fall 2005 and Spring 2006. In this report we discuss how well the data collection, data analysis, and data visualization methods served the interests of our project and of the participants, along with implications for future research. Among the methodological issues we address: how to capture rich accounts of fragmented work without taxing participants too much, how to filter rich datasets that result from automated recording of work sessions to focus on specific issues, and how to visualize data to elicit follow-up information from participants.
•to develop a software tool that allows technical communicators to visualize and modify their work themselves To make the value a technical communicator contributes to a project easier to assess, our project concentrated on a genre that has a relatively unambiguous success condition: technical proposals. The value of a technical communicator to a project team preparing a technical proposal is, at present, difficult to assess. This difficulty is largely attributable to the fact that much of the work technical communicators do is invisible or, when it succeeds, becomes seamlessly integrated into the successful outcomes that other team members own. We sought to develop the ability to construct systematic representations of the nature and success of technical communication work so that technical communicators and their employers might have a greater capacity for determining return on investment for technical communication work.
Categories and Subject Descriptors K.4.3 [Organizational collaborative work
Impacts]
--
Computer-supported
General Terms Management, Documentation, Design, Human Factors
Keywords
In keeping with this focus, our project addressed the problem of how to adequately study writing processes in fragmented work— work distributed in time, space, and among multiple participants— while also generating ways for workers to better regulate their own contributions to such work [2]. In particular, the research involved a study of how interdisciplinary teams write grant proposals, focusing on the sequences of actions, tools, and material conditions workers use to regulate their work.
Knowledge work, visualization, proposal writing
1. BACKGROUND How can we assess the value of a technical communicator’s contribution to a project team? This paper reports on a pilot study that was meant to test methods for answering this question. In August of 2006, we learned that our research team was a finalist for a Society of Technical Communication major research grant award. We proposed to pilot methods for a multi-site participatory study of proposal writing [1]. This paper reports findings from that pilot study, showing our data gathering and analysis methods in some detail for the purpose of aiding other researchers who may be interested in work visualization of this type.
2. METHODS Our methodological approach for this study combined processtracing methods (preliminary interviews & training sessions, participant diaries, computer event logs) with stimulated recall interviews to produce rich accounts of proposing and grant-seeking activity.
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Our multimodal data collection process was designed to examine rich, contextual data while supporting model building, allowing us to connect visualizations with workers’ activities. Our data collection involved several methods for aggregating, visualizing, and exploring the communicative patterns and resources that teams use to sustain proposal writing activity:
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professional preparation for and engagement in the work of proposing due in large part to the nature of their position and the type of organization in which they worked.
•Diary studies: Workers kept diaries of their communicative events and the artifacts they used during these events. Diary entries included information about the nature of events, when and where they occurred, and also about the substance of the events and users perceptions of their purpose.
Our first participant, “Colette,” is Communications Manager and Proposal Manager at a research corporation that for over 45 years has focused on applying basic research to technological challenges in space, including the development of satellites, sensors, and reconnaissance data visualization hardware. As the lead Proposal Manager at this nonprofit research corporation that is funded by private, national, and international contracts, Colette plays a central role in the financial stability of her organization. Supervising a team of communication specialists as well as serving as the coordinator with the organization’s research scientists and engineers, Colette’s professional life is devoted to proposal development work.
•Recorded work sessions: Workers recorded episodes of proposing activity completed on their workplace computers using a commercial software application called Morae. The resulting logs offer a comprehensive recording of all system events that took place in their computing environment during a work session. •Post-observational interviewing: We conducted semistructured interviews with workers to triangulate analysis of the diaries and recorded work sessions. The interviews added to the data in the diaries and recorded activity logs information about users affect, decision-making, and level of satisfaction— dimensions that are impossible to capture completely with the two other recording methods.
Our second participant, “Emily”, is a special projects director at a nonprofit organization dedicated to improving web accessibility for people with disabilities. Although she prospects for grant opportunities and writes grants, she also arranges training sessions, competitions, high school and college outreach, and connections with university research projects. All of these other activities are opportunities for building relationships with other nonprofits and related organizations, and she draws on this informal network extensively as she finds local partners for the grant proposals she sends to foundations.
•Site visits & artifact documentation: Researchers visited the work sites to document and collect artifacts such as drafts, email, and memory aids. We did this for several reasons: 1) to determine how complete the diary records were, making sure that informal genres and brief oral and written exchanges that may be crucial are were not being overlooked, 2) to make sure that the self-initiated recording of work practices did not leave out important, but tacit, work habits, and 3) to discover how the work records and interview responses gathered from participants meshed with broader organizational strategies and issues related to workplace culture.
Our third participant, Dave, is an academic at a large public research institution and assistant director of a research center in the humanities and social sciences. Grant seeking and proposing are very important components of his work, but he has many other demands on his time including teaching responsibilities, project work, and other management related duties related to his position as an assistant director.
The data collection methods described above created a rich eventbased log of project activity for each participating team. We then constructed visualizations of these logs to facilitate analysis of the teams’ work efforts [3]. Communication Event Models [4] (CEM) allow us to examine whether writing processes are proceeding efficiently, that is, with the fewest number and most effective types of communication needed to complete a task or achieve a certain goal. Genre Ecology Models [5] (GEM) allow us to examine and track the many texts that are simultaneously deployed to mediate and create conditions for successful work.
3. TESTING AND REFINING DATA COLLECTION METHODS An important goal for the pilot study was to determine whether our proposed data collection protocol was effective at producing comparable data across sites, reasonable for participants to complete given their busy schedules, and reasonable for us as researchers in terms of handling the potentially huge amounts of data that result from computer event logging. These were questions raised by reviewers in our initial LOI round of reviews as well. Our answers follow below.
In this project, we sought to relate these two models by modifying and extending sociotechnical graphs [6]; these allow for the conventions of an activity to be systematically inferred, tested, and compared across narrative accounts such as diaries, interviews, and our own field observations. CEMs describe sequences (“What communicative events constitute a given episode?”), while GEMs describe associations (“What genres are brought to bear during the episode but are not highlighted because they are not being used transactionally?”) and substitutions (“How did alternate genres get used to perform the same activity? Given x conditions, what genres are people likely to use to perform y type of activity?”). In the pilot study, we hoped to discover how the two formats, brought together, might prove useful for creating accounts of heretofore “unaccountable” work.
3.1 Does the mix of process tracing & stimulated recall methods ask too much of participants?
2.1 Site and Participant Profiles
Our answer to this question is yes, potentially, though we believe we have refined the diary and logging methods sufficiently to make them no more obtrusive than keeping a project log for billable hours or other similar work-tracking process. In one case, our participant reported that our protocol helped her to keep a more thorough and accurate version of the log required by her employer.
We conducted a full round of data collection at each of three different sites, and a second round of data collection using a refined participant diary format and an enhanced training session at two of the three sites. Our sites and participants reflected a diverse set of
We realized very quickly that making diary entries quick to complete was very important. Otherwise, this important bit of record keeping could easily get lost in the very busy work routines of our participants. We have revised our diary entry form and
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Purpose: Meeting follow up and web editing,
format to decrease the time it takes to make an entry. We have also explored and mocked up a web-based form for making entries that would allow participants to store common entry types to avoid repetition and to encourage more frequent entries. For one of our participants, making entries took about as much time as checking email, something he does often throughout the day. This seems to be a reasonable target for future participants as well.
Goals: Get Workshop dates set and place and get web site moving, Time: 2/28/06, 8:00 AM - 9:15 AM Location: MATRIX, Dave's office Genres used: Email, vitae, web, notes, discussion, laptop, Applications used: email, joomla, ms word, ms onenote
Logging work sessions proved a bit less obtrusive for our participants. For the pilot we used Morae, a computer program that makes comprehensive recordings of all system events, producing both video and text-based logs. A small recording application was installed on the participants’ computer and they were asked to record work sessions when they were doing work that corresponded to items in their diary. Participants had few problems with this, as the recording goes on in the background and doesn’t cause interruptions. This caused a problem for one participant, though, who left the recorder running for nearly 24 hours because he had forgotten it was on. We’ve altered our training protocols a bit to help users manage both the diary and logging software. But we expect that until users can begin to actively see benefits from monitoring their own work – for instance, producing useful visuals on the fly for their own purposes – logging will continue to be an operational challenge for our already busy participants.
Technologies used: telphone, l-soft listserv, listserv logs, im, Participants: Dave R., Stephon Cole, Matthew McCain. Description: Talk to Cole about meeting. Follow up email to list about possible dates and place for workshop. Follow up to follow up when one member did not like place. Worked on vita for IMLS grant. Began to add materials to web site. The automated log gives us touchstones as to when certain parts of such a complex event begin – here we see the participant launching a web browser, for example, via a desktop shortcut. This type of “high threshold” (that is, operating system level) event occurs reliably at the beginning of sequences that involve looking for resources on the web, using a web-based application or editing content on a web-site. Morae event log data
3.2 Are the large data sets from the Morae logs reducible in systematic ways?
Time Stamp: 0:00:00.40
Thankfully, yes. Morae not only makes a screen-capture video of logged work sessions, it also produces an event log that can be exported as a .CSV spreadsheet file. By applying filters to the log before exporting the spreadsheet, we found that we could easily focus on events that corresponded with diary entries (e.g. application launches or application window focus changes), with process steps (e.g. invoking the printer), or more fine-grained actions like editing a document (keystrokes). Morae also allowed us to save and share the filters applied to one data set so that they can be applied across multiple data sets for consistent analysis.
User Action: Shortcut to
Event Type: Screen Text System Threshold: high Applications: Program Manager, Windows Explorer Time:10:39:17 Date: Nov 16_ 2005 A minute later, we see the user accessing their web-based e-mail application:
Working with the Morae logs and the participant diaries in tandem proved to be a very powerful way to extract patterns for stimulated recall interviews as well. Morae allows us to put markers in the video playback stream that correspond to our analysis points. In this way, we can go back and watch events that stood out in the spreadsheet as they unfold in real time and play these back for participants in recall interview sessions.
Morae event log, as CSV exported data 0:01:01.20,Screen Text,,,,,,,,Internet,medium,,,,,,,MSU Webmail Microsoft Internet Explorer,Internet Explorer,10:40:17 / Nov 16_ 2005 In between these two events, Morae recorded about three hundred other system events. But we can filter out most of these rather easily if we want to isolate a specific sequence of actions. In this case, we can ignore other high-threshold events and watch for the triggering “medium threshold” (or application-level event) that signals the sequence we are interested in. Invoking a particular web-based email client shows up as such a medium-threshold event. We can also drill down to low-threshold events (keystrokes, mouse-clicks, etc.), filter by specific types of high, medium, or low threshold events, etc. For example, if we know that a user might leave their browser running rather than launch it to start a given sequence, we can watch for the high-threshold event category of window focus change, singling out changes that put the user’s browsing application window into focus.
Diary entries give us high-level information about user activities and goals, while the Morae logs give us operational details and point to actions that may be tacit and/or too low-level to attend to. Here is a sample participant diary entry (taken from pilot data) followed by an event from the Morae log that might represent the beginning of an operational sequence for this type of event (not from the same user, in this case). In this entry, the participant logs a work session that involves multiple genres, multiple applications, and multiple sub-tasks: Participant Diary Data Project: SLC,
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Worker Emily 4/25/2006 11:00am Emily 4/27/2006 6:00am Emily 4/28/2006 5:45am Karen 4/26/2006 11:00am Karen 4/27/2006 7:30am
Project General
Event Genres Organization-K WeekStatus meeting Grant Meeting
Writing "the sell" XYZ Foundation Proposal
Table of grant opportunities
Proposals
Proposal
XYZ Foundation Proposal Fine tune "the issue"Proposal sell"
ABC Center of Excellence Powerpoint charts Copies of ABC proposal copies of slides notes on recommend Site visit by improvements summarizing propos selection/review mem Q&A ABC Center of Excellence Mod. Charts (2) for fPowerpoint charts Proposal (harveste the review leader's notes taken at site v submittal to review t graphics) softcopysuggested chart meeting content/requirement s/etc. Draft response, propoDEF Organization + Karen DEF Organization Meet with PI/Co-PI Meeting solicitation docs Organization website 4/28/2006 10:30am Kim email im chat SPG (Strategic PartnWorked on writing 3 Proposal 2/21/2006 Grant) preposal 12:20pm Kim SPG redrafted proposal Proposal Discussion 2/22/2006 8:00am Kim SPG Revised Proposal Proposal email discussion 2/22/2006 11:48am Kim JKL Meeting preparation agenda proposal workshop proposalnotes press release email 2/24/2006 8:00am SLC partner meeting Notes JKL Workshop proposallists calendar Kim 2/24/2006 1:30pm
web site
minutes
Table 1: Genres used in proposing activity across three participants at three research sites resources created and/or accessed during each event. This kind of view is useful for monitoring ongoing work on a project, particularly in collaborative settings, as it answers the question “what’s been going on?” The view in fig. 1 shows a sequence of events that corresponds with work on a particular proposal project for one of our participants.
3.3 Can we get comparable data across sites with this mix of methods? Yes. The data collected highlights similarities and differences in proposal writing practices in several important areas. The aggregated data in Table 1, for example, show genres used across three proposal writers at three different research sites. Proposal genres are highlighted, but just as interesting in this picture is the range of other genres that accompany proposal writing. This sort of view, based on Latour’s socio-technical graphs [6], can help writers to identify useful assemblages of genres in their own work as well as in the work of others.
Figure 2 shows another view that combines event sequences on different projects during the same workweek, answering the question “what projects have I worked on this week?”
Fig. 2 Combined CEM: Weekly Overview
3.3.2 Showing Genres as They Relate to Specific Projects Using the Genre Ecology Model format, we can show how specific genres came into play during a particular project over a specified period of time. The diagram below combines work completed on three specific projects as well as on general grant-seeking work for a single week:
Fig. 1 CEM for one proposing project
3.3.1 Showing Event Sequences Diary events can be visualized as time-ordered sequences, allowing for a range of possible views of proposing work that include the
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Fig. 3 GEM showing three proposing projects
Fig. 4 CEM/GEM hybrid mockup
This diagram emphasizes the relationships that exist among genres as they function to mediate the work of grant seeking and proposing. In a view like this, it is possible to trace where a particular worker’s value-added contributions to a product are likely to show up in specific artifacts.
In this mock-up, upon mousing over an icon representing a proposal-drafting event, the user is presented with the project name (SPG) and a genre-ecology diagram showing the resources linked to that event.
4.2 Can we aggregate data across projects (within participants) in such a way that the visualizations are “organizational accounting devices?”
4. TESTING AND REFINING DATA ANALYSIS METHODS Because we expected the data we collected to be especially rich, we had several explicit data analysis questions that we sought to answer during the pilot study. These were questions that dealt with how well the information we were able to collect could be transformed into visual formats like those above, as well as how the overall data set could be reduced in systematic ways to produce useful displays for workers. We address these questions in this section.
We borrow the concept of organizational accounting device (OAD) from Dourish [7], who understands OADs as providing a framework for those charged with accounting the activity of an organization to make such an account understandable. In this capacity, an organizational accounting device has two key features: 1. It “organizes a view of the activity itself” – the view becomes the activity, so that interacting with the view becomes doing work. “That is, at its most fundamental level, it does not simply describe the activity, but renders it observable-and-reportable as being the activity.”
4.1 Can we produce Communication Event Models & Genre Ecology Models from the same data set? Yes. This is probably the most exciting finding from the pilot study, as it represents something of a theoretical breakthrough as well as a methodological advance for the project. Exploring the value of viewing work practice data from these two complementary perspectives is a significant aim of the full study we are planning. But we can already begin to see how shifting back and forth between event sequences and genre/artifact relationships, or even viewing both at the same time, can yield rich accounts of work. When these visualization options are presented in the context of an interactive system, users can browse representations of work for a range of specific purposes, from quickly accessing the latest version of a document to reasoning in more robust ways about the best course of action to take.
2. It establishes, discursively, who can make and understand an account. This necessarily involves limiting the types of accounts that are possible by focusing on the interests (questions, concerns) of those for whom the account is constructed. Do our visualizations, taken together, act as OADs? We believe they can. The “weekly view” Communication Event Model shown above in Figure 3 is an example. Figure 5 offers another version showing a different participant’s data for a single workweek.
Figure 4 shows one way that a user of such an interactive system might access both CEM and GEM views in an effort to answer a question like: “what documents did I access while I was composing the initial draft of the proposal?”
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2. Measures of output created are inadequate because they cannot account for the value of “products” that are not client deliverables Our system of data gathering, aggregation, and visualization addresses these two problems by conceiving of and capturing knowledge work as a series of communicative events and textual resources. As a result, return on investment (ROI) calculations might be performed that give a more accurate account of time spent and work produced. “Work” is visible as both product and process. And we believe that both the immediate and ongoing value of work as measured by the continued use or reuse of particular processes or artifacts can be calculated over time. We can imagine, for example, asking questions such as “how valuable has creating a proposal template for our team been?” and answering that question with a combination of data about how often the template was used, with how many changes each time. Or perhaps the question is slightly different: “does having a template allow us to get proposal done faster? With fewer face-to-face meetings? With less churn in the drafting process?”
Fig. 5 CEM: Project View
These are ROI questions that do not focus on the value of a single individual, but rather focus on the value of specific practices and strategies that teams may adopt in systematic or highly ad-hoc ways.
For this participant, grant seeking and proposal writing are only two aspect of his work, though they are quite important to the overall success of the research center. In the context of a busy and fragmented work schedule, it can be difficult to track what kinds of work one is doing. The view above shows what kinds of grant writing work was done during a particular week and on which projects the work focused. Much of this work might otherwise be invisible to many people within the organization, even if they are making explicit attempts to recognize and value it. When we showed this view to the participant, he suggested that an interesting consequence of this problem is that work days can get scheduled full with meetings leaving individuals with no time to write or do the research needed to complete a proposal. Having access to a shared view of a teams’ grant-writing work over time might allow the team to schedule meetings and individual work time more effectively.
5. FUTURE DIRECTIONS As these questions make clear, we will need to continuously work to develop data analysis, aggregation, and visualization methods to support the kinds of in-depth reasoning about technical communication practices for which this pilot project only begins to suggest directions. This is why we have structured the next phase of our work as a participatory research project that will continuously place our theoretical concepts and research methods into a practical framework: an interactive knowledge work visualization system that supports users as they create accounts of their work. Many design challenges lie ahead for this larger project. Below, we discuss three significant ones that will drive our future research.
4.3 Can we compare data across sites in ways that synch with common return on investment metrics?
5.1 Challenge: Capture Ambient Work Practice Data Unobtrusively
Or, to put this questions another way: can the data be used to show “costs” and “benefits?” Can we expand the range of stakeholders’ interests possible for such calculations by allowing for multiple accounts?
Doing knowledge work tasks such as those that make up the work of grant seeking requires the coordination of multiple tools and genres, and, of course, dedication of significant cognitive resources on the part of workers. It stands to reason, therefore, that keeping detailed records of this work while doing it is very difficult and not really practical for extended periods of time. The participants in our study were gracious enough to monitor their work for us, but they let us know that it was a struggle to do that and to keep on task at the same time.
We believe that our system of data collection and analysis capture more detailed and more accurate accounts of grant seeking and proposal writing work than were previously possible to produce. Further, the ability to construct “accounts” of work in variety of visual formats allows for a variety of Return-On-Investment calculations. Traditional ROI calculations for technical communication have focused on people only - salary costs measured against the value of work products. These methods have proven difficult to implement consistently in real work situations for several reasons:
Part of the difficulty our participants encountered was due to our diary study data collection method for the pilot study. With that technique, we were asking users to record data about their work in ways that also filtered that data into categories that we thought would be useful. In contrast to the automated work-session recording method using Morae, the diary studies required much more effort on the part of users to complete. To record a work session, all users had to do was to launch the recording application, click on a record button to begin logging, and click stop when the work session the wanted to record was complete. The data that
1. Accounts of work done are incomplete because they are associated with major product milestones rather than with routine knowledge work
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resulted were, of course, much harder to deal with for us as researchers precisely because it did not come pre-sorted into categories that corresponded readily to those we had associated with proposing.
5.3 Challenge: Visualize Data in Ways that Help Users Account for Their Work Perhaps the most exciting challenge we see ahead lies in the development of visualization techniques that are accessible to users for the purpose of representing their own work. One of the most positive outcomes of our pilot study was the favorable reaction we received when we showed participants what their work looked like. They reacted with surprise, often, and delight. Generally, they were pleased to see someone (finally) recognizing the complexity of grant-seeking activity. Many thought that they could use visualizations of routine work for purposes internal to their organizations such as training as well.
The fact that work completed online is so readily traceable is something that, increasingly, software developers and workers themselves are taking more advantage of. As we discuss elsewhere (Spinuzzi, Zachry, & Hart-Davidson 2007), the “lifestreaming” trend is beginning to extend to the workplace. This may mean that, in the future, some combination of automatic logging and lightweight status monitoring by users could produce a stream of data from key proposing applications suitable for producing the sorts of visuals we have shown above. If these workstreams are combined with logged events that are part of work already (e.g. shared to-do lists, Gantt charts, or billable hour logs) they might become routine aspects for certain types of knowledge workers. Proposal writing lends itself well to this sort of “ambient” data collection because the work tends to be bounded by firm deadlines and further traceable by the status of the document artifacts associated with proposals: drafts of project descriptions, budgets, etc.
But we have really just begun to determine whether and how the visualization formats we show above are useful for proposal writers and proposal writing teams. There are exciting possibilities, for example, to explore how a successful team or individual might develop as a result of monitoring their work and the work of others. Might the use of workstreaming visualizations be a counter-measure against the fragmentation of work associated with information-age technologies? We look forward to exploring this question further.
5.2 Challenge: Aggregate and Filter Data from Multiple Sources
6. WORKS CITED [1] Zachry, M., Spinuzzi, C., & Hart-Davidson, W. In SIGDOC ’06: Proceedings of the 24th annual international conference on design of communication (New York, NY, USA, 2006), ACM Press, pp. 70–77.
As more data are available that make knowledge work traceable, the need to aggregate and filter data from multiple sources becomes more critical. With proprietary logging software like Morae, gathering work session data from multiple participants to create displays of collaborative team processes requires that all participants log using the same software. Even in our small study, this did not prove feasible due to the system requirements for the Morae recorder application. During our study, Morae recorder was not available on the Macintosh platform, for example, and it required a fast processor and a lot of memory to run unobtrusively on the participant’s computer.
[2] Spinuzzi, C., Hart-Davidson, W., and Zachry, M. Chains and ecologies: Methodological notes toward a communicativemediational model of technologically mediated writing. In SIGDOC ’06: Proceedings of the 24th annual international conference on design of communication (New York, NY, USA, 2006), ACM Press, pp. 43–50. [3] Hart-Davidson, W., Spinuzzi, C., and Zachry, M. Visualizing writing activity as knowledge work: Challenges & opportunities. In SIGDOC ’06: Proceedings of the 24th annual international conference on design of communication (New York, NY, USA, 2006), ACM Press, pp. 70–77.
To be clear, we were using Morae for a purpose other than its stated purpose, and so complications of the type we mention were to be expected. Morae is marketed primarily to users who need to conduct usability tests and need the full-video playback that Morae’s recording technology provides. We were interested in the text-based event logs that Morae created in our project, and moreover, in a small fraction of the total number of events that showed up in these logs. We see an opportunity to create a different kind of aggregation application that “listens” a bit more selectively for significant events from specific applications associated with proposing activity. And while we haven’t yet found one specific application that does this sort of aggregation, we do see some movement in this direction. Wakoopa is one example of an application/web service that permits users to build and share with others a profile of the applications they use on their desktop machines. Even more exciting, perhaps, is a trend toward making commonly used applications such as office software a bit more “aggregation-ready.” Key events such as a new document version posted or a milestones reached, for example, can be streamed to users via syndication formats such as RSS. These trends suggest to us that collecting and aggregating information about work is not simply a matter of concern for researchers, but increasingly a matter for end users seeking to better understand and represent their work to others.
[4] Hart-Davidson, W. Seeing the project: Mapping patterns of intrateam communication events. In SIGDOC ’03: Proceedings of the 21st annual international conference on computer documentation (New York, NY, USA, 2006), ACM Press, pp. 28-34. [5] Spinuzzi, C. and Zachry, M. (2000). Genre ecologies: An opensystem approach to understanding and constructing documentation. ACM J. Comput. Doc., 24(3):169–181. [6] Latour, B., Mauguin, P., and Teil, G. (1992). A note on sociotechnical graphs. Social Studies of Science, 22:33–57. [7] Dourish, P. Process descriptions as organizational accounting devices: The dual use of workflow technologies. In SIGGROUP ’01: Proceedings of the 2001 international conference on supporting group work (New York, NY, USA, 2001), ACM Press, pp. 52-60. [8] Wakoopa.http://www.wakoopa.com. Accessed 29 May, 2007.
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