Designing for Meaningful Social Interactions

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The age of cheap “like”-hunting needs to come to an end. It all started innocently enough with likes and tweets. Then in a few years, we suddenly ended up with governments scoring people and masses manipulated into meaningless activities to generate more ad revenue.

But it doesn’t have to be that way. Now the time has come for us—designers, working on digital products—to step up our game and act like real gatekeepers.

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Second-hand UX

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Something that I feel is overlooked by a lot of product designers is the second-hand experience of their product. That is to say, above and beyond the target user, who is affected by the product—and most importantly—what is their experience?

If the UX team has satisfied all the needs and desires of the target user, minimized their pain-points, and maximized their ability to enjoy the most common process flows, that is truly awesome—but how does the experience they design affect that person’s social circle? Do product designers currently see that as a question worth spending additional time and resources to answer?

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Researching User Experience: A Knowledge Ecology Model

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When we think of learning environments, we think of books, lectures, databases perhaps. But in my recent research, I discovered that the interactions we have with people in our networks play an even more important role in what we learn and how we turn information into actionable knowledge.

All of the people in my study were learning how to be lecturers and how to progress their careers after spending considerable amounts of time as practitioners in a variety of industries such as business and marketing, health, psychology, education, environmental sciences and entertainment. I focused on exploring what informed their learning and professional development and how it informed their learning.

After a series of interviews and qualitative data analysis, I found that what is primarily informing their learning activities is knowledge–knowledge of oneself and knowledge from a range of people in their professional and personal networks such as informal and formal mentors, industry and academic colleagues, family, friends, and even inspirational figures they have never met. Some of the key learning experiences include:

  • hearing from experienced leaders as ‘role models’ at professional development programs,
  • seeking and attracting developers (informal mentors or peers) while taking formal courses,
  • presenting papers at events such as conferences, thus gaining peer feedback and making friends,
  • getting known through volunteering within professional communities and internal committees,
  • maintaining personal foundations around the home, family, and social life, and
  • seeking or attracting new opportunities for expansion using a range of social media.

Five types of knowledge emerged from the data:

Knowledge Types Examples
Experiential lessons from past experience, tacit knowledge, know-how
Personal social savvy, common sense, trust, empathy
Technical how-to guides, user reviews
Disciplinary conversations or reviews within similar discipline or field
Interdisciplinary conversations or reviews between different disciplines

Each knowledge type refers to knowledge co-created within relationships: knowledge from the new lecturer (knowledge of self) and knowledge from their developers (knowledge of others).

I also found that, for these new university lecturers, what they gleaned from informal interactions is key to meaningful learning experiences. All of the above forms of knowledge are created and used during the key learning experiences within the informal sphere of learning. The informal sphere is where trust is built and where people can ‘be themselves’ and choose to learn what matters most to them.

Contrastingly, information is discussed as useful for learning but is experienced as secondary to knowledge. My participants view the knowledge types as listed above as more important to their learning than information types listed below. Although they are both useful for learning, the lecturers first ‘relate’ to information types–they select information that they can relate to or they have something in common with–that becomes knowledge stored in the mind which strongly informs their learning.

From the data, I have identified the following categories of information resources used for learning experiences.

Information Types Examples
Texts articles, books, websites, multimedia, emails
Tools software, hardware, mobile devices, equipment
Humans elevator speeches, business cards, online profiles
Culture organizational or community
Environments work/home space design, geographical location or political climate

Once a person interacts with these forms of information by relating to them personally, the selected information turns into knowledge inside a person’s head, to be used and re-used for learning experiences.

Relationships between people (in particular, reciprocal relationships based on trust and empathy) can be viewed as complex knowledge contexts, where knowledge is created from relating to information. By asking how particular forms of knowledge from people inform learning and development, we begin to see processes associated with the experiences of knowing oneself, knowing other people, and recognizing multiple layers of relationships. Processes involved in knowledge user experience include:

  • Knowing self by identifying, testing, feeling, discovering, reflecting on, and offering knowledge of oneself;
  • Knowing others by accessing, monitoring, aligning, seeking, applying and sharing knowledge of other people; and
  • Recognizing multiple layers of relationships by selecting communication modes, exploring personal dimensions, navigating across boundaries, balancing roles, and changing over time.

My findings reflect the experiences of a group of people who are moving between different contexts, such as industry to academia or research. The conceptual model described above is a ‘knowledge ecosystem’ which could also have implications not only for UX practice in designing, learning, and professional development experiences (both online and offline) for user groups who transition between different worlds, but also possibly for building bridges between them. Some general implications for UX practice are below.

At first glance, it seems that the current generation of UX practice is geared towards users’ experiences of information (texts, humans and tools) and also context (culture and environment), as in the case of service design, for example.

If information is only secondary to knowledge in terms of usefulness to achieve a particular goal or purpose, this finding suggests that the UX field could advance by looking beyond interacting with information and towards a more holistic, ecological view that encompasses both information and knowledge user experiences.

A key question here could be: How do we create a user experience that facilitates tapping into the different forms of knowledge found within people’s heads?

Thinking about people as users of knowledge rather than just users of information opens up a whole new terrain of potential design, thus moving from information user experience to knowledge user experience.

At the heart of people’s user experience is the concept of the human relationship, the processes of informing our relationships through knowledge, and strengthening our social networks to achieve one’s life purpose. Relationships are not just between the interface of human-to-computer/website but also, more importantly for knowledge user experience, human-to-human interaction, whether that interaction occurs online or offline.

Designing for Social Interaction

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It took both the telephone and the mobile phone 15 years to amass 100 million users, but Facebook did it in 9 months. We see more and more people becoming connected on online social networks, and it seems our networks are growing exponentially. But the reality is, social networks rarely add to our number of connections. We’ve already met almost all the people we’re connected to on social networks. We’re already connected to these people offline. Social networks simply make the connections visible. For example, we often connect with old school friends, and catch up over a couple of wall posts. But rarely do we continue the conversation once we’ve connected, and over time we forget that the connections exist. In fact, Facebook users often have no interactions with up to 50% of their connections.1 When we study how people are interacting on social networks, we see that most interactions are with a very small subset of the people we’re connected to.

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5 Steps to Building Social Experiences

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Nowadays everyone wants social in their sites and applications. It’s become a basic requirement in consumer web software and is slowly infiltrating the enterprise as well. So what’s a designer to do when confronted with the requirements to “add social”? Designing social interfaces is more than just slapping on Twitter-like or Facebook-like features onto your site. Not all features are created equal and sometimes a little bit can go a long way. It’s important to consider your audience, your product—what your users will be rallying around and why they would want to become engaged with it and each other, and that you can approach this in a systematic way, a little bit at a time.

These concepts derive from a book I wrote recently with Christian Crumlish, “Designing Social Interfaces“. They are quick and easy things to remember when infusing social into your site. Each points offers some simple suggestions and points to consider when designing. Potential design patterns are recommended (and linked to) as examples for what could be done in your interface as you design and grow your service. Keep in mind that your context will dictate different specific solutions but the questions and concepts to think about will still be applicable.

Step 1 – What’s your social object? Make sure there is a “there” there. Give users a reason to rally. Why would someone come to your site?

Most people are drawn to a site based on their particular interests, in hopes of learning more or meeting others like themselves. They may be looking for information or they may have information to share. They have a passion—such as making handcrafted jewelry or taking landscape photographs—and at some point, they will want to share that with other people. That passion, that thing that people rally around is often referred to as the social object. It’s the object around which conversations emerge and thrive.

Remember that sometimes, the social object is a person – or the conversations between people. But don’t forget history (remember Friendster? or SixDegrees?), if the only thing to do is build a profile, people will eventually go somewhere else to have conversations or to do things around objects of interest.

Step 2 – Give people a way to identify themselves and to be identified.

This can be as simple as an “attribution” line when contributing and signing content.

Attribution of a comment on flickr
Attribution of a comment on flickr

It could be an “identity card” that shows a little bit about the person and is attached to every thing they do or can be as robust and complex as a “full profile” that is linked from all their contributions. The method can start out simple and grow over time.

Identity or Contact card as seen on FriendFeed
Identity or Contact card as seen on FriendFeed

It’s important to give people credit for their words and contributions. It helps others recognize their friends and disambiguate them from other people with the same name and builds a “reputation of quality” or lack thereof for their participation on your service.

Public display of relationships allows viewers to find others they might know by allowing them to browse contacts for the person whose info they are viewing.

Public display of relationships allows viewers to find others they might know by allowing them to browse contacts for the person whose info they are viewing. Module shown from MyBlogLog
Relationship module shown from MyBlogLog

Once you have given people the ability to identify themselves, allow them to “find each other” and claim their tribe. “Relationships” make the world go round and online it’s no different.

Step 3 – Give people something to do.

Provide a path for participation so lurkers as well as early adopters can be engaged at the level of effort that is appropriate for them. Things like ratings (“1-5” or “thumbs up“) are easy ways to get low participation people involved by letting them quickly register their opinion with little effort.

Thumbs up and down ratings for restaurants on GoodRec let people quickly register their opinion with little effort
Thumbs up and down ratings for restaurants on GoodRec

Allow them to “share items” they find interesting with their friends or family and “curate and collect their favorites“. The latter requires a little bit more effort, but lets your users have ownership over what they find meaningful.

Flickr allows users to “favorite” images they like and collect them for display to others.
Flickr allows users to “favorite” images they like and collect them for display to others.

At the other end of the spectrum is full authorship of content with “reviews“, “comments“, “blog postings“, and “wiki entries” all the way through to participation as a moderator or guide in your service.

Wikimedia allows collaborative editing of content on sites built with the software.
Wikimedia allows collaborative editing of content on sites built with the software.

Start simple, with light features, and gradually add more complexity if it is really needed. Keep the structure flexible enough for your users to mold and adapt to their needs. In the book, we discuss several principles related to this including “Deliberately Leave Things Incomplete“, which reminds designers to allow features to emerge from the community behavior rather than forcing behavior to fit the UI and “Strict vs. Fluid Taxonomies” which merges a strict taxonomy like your site navigation with user generated groupings and organization with features like Groups, Message Boards, Tagging, etc.

Allowing behavior to guide your features and giving your users ownership of the structure make the site much more personal for them which in turn encourages repeat and longer term usage.

Step 4 – Enable a bridge to real life (groups, mobile, meetings, face-to-face).

Don’t be afraid to build in tools that allow your users to bring their community into the real world. In many online groups, a majority of people know each other personally.

Upcoming shows local events and allows people to add events to their calendar and view events their friends are interested in.
Upcoming shows local events and allows people to add events to their calendar and view events their friends are interested in.

Providing tools to help plan face-to-face meetings and then archive those happenings will strengthen your site and the community. Consider incorporating “geo” features like “GeoMapping“, and “GeoMashups“.

Additional features might entail creating “subspaces (groups)” and coordinating real time “face-to-face meetings” and gatherings among users of your service.
Meetup lets people affiliate with groups of interest and the site helps coordinate real life - in person meetings and gatherings between members.
Meetup lets people affiliate with groups of interest and the site helps coordinate real life – in person meetings and gatherings between members.

Step 5 – Gently Moderate. Let the community elevate people and content they value.

This can be through simple things like ratings or “reputation labels“.

Reputation labels on the intranet at Yahoo!.
Reputation labels on the intranet at Yahoo!

The community can help you surface contributions of quality which in turn should help attract future participants and will help keep the interactions lively. This process also helps push bad quality content down and out of sight.

Keep an eye on the community, participate yourself, welcome people as they join, set yourself up as a role model.

Hunch founder, Catarina Fake, acts as a role model for the community being built on the site.
Hunch founder, Catarina Fake, acts as a role model for the community being built on the site.

Notice who is passionate and who is potentially causing trouble. Conversations should run their course. Let the “community moderate itself” and provide tools to allow them to do that, like allowing them to mark content as spam or block trolls or “report abuse“. Step in only when necessary.

Report Abuse is available on every comment in Yahoo! Answers and allows users to moderate the content quality.
Report Abuse is available on every comment in Yahoo! Answers and allows users to moderate the content quality.

Make sure people are aware of the “terms of service” and “license” implications of content they create – both as it relates to your site as well as what they can permit others to do with their content.

Go out and get started

These are a few of the things to consider when building a social application or when adding social features to an existing site. There are a lot more features and concepts available within the social ecosystem but these should get you started and will build a good foundation from which more robust and complex features can be added to.

It is important to remember that you don’t have to do it all at once. You can apply features sparingly and let the community tell you when you need to expand. Consider the bare minimum while fleshing out your infrastructure. Add complexity as your community grows and scales. Remember that you are building a container for activity and conversation and that you don’t have to have everything figured out. The people will create their own paths of interaction making their own meaning and experience.

Wanted/Needed: UX Design for Collaboration 2.0

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No current software supports the full process of collaboration.

That’s a bold claim, and I hope that someone can prove me wrong.

This article is more of a “Working Towards …” position paper than the final word; written in the hope that the ensuing discussion will either bring to light some software of which I’m not aware, or motivate the right people to develop what’s needed.

There is plenty of hype about “Collaboration 2.0” at the moment, but the bugle is being blown too loudly, too soon. Take, for instance, the Enterprise Collaboration Panel at last year’s Office 2.0 Conference. Most of the discussion was really about communication rather than collaboration, with only a hint that beyond forming a social network (“putting the water cooler inside the computer”) there was still a lack of software that actually helped groups of people get the work done. What’s missing from the discussion is any formulation of what the process of collaboration entails; there’s no model from which collaborative applications could arise. If we can figure out a model then we in the UX community should be able to make a significant contribution to it.

I want to start this discussion by proposing a model for collaboration1 that links the various elements of collaboration, comment on the so-called “collaboration software” currently available, and make some tentative suggestions about IA and UX requirements for a real collaboration platform.

A proposed model


Collaboration is a co-ordinated sequence of actions performed by members of a team in order to achieve a shared goal.

The main concepts in this definition are:

  1. Collaboration is action-oriented. People must do something to collaborate. They may exchange ideas, arrange an event, write a report, lay bricks, or design some software. To collaborate is to act together and it is the combined set of actions that constitutes collaboration.
  2. Collaboration is goal-oriented. The reason for working together is to achieve something. There is some purpose behind the actions: to create a web site, to build an office block, to support each other through grief, or some other human goal. The collaborators may have varying motivations, but the collaboration per se focuses on a goal that is shared.
  3. Collaboration involves a team. No-one can collaborate alone. Collaboration requires a group of people working together. The team may be any size, may be geographically co-located or dispersed, membership may be voluntary or imposed, but there is at least some essence of being part of the team.
  4. Collaboration is co-ordinated. That is, the team is working together in some sense. The co-ordination may follow some formal methodology, but can equally well be implicit and informal. There needs to be some sense at least that there are a number of things to be done, some sequences of actions, some allocation of tasks within the group, and some way to combine the contributions of different team members.

Components of collaboration

Any collaboration process involves interactions between six elements, as shown in the following diagram:

The basic components of collaboration.

Figure 1. The basic components of collaboration


The Artifacts are the tangible objects relating to the collaboration. They include the outcomes of the process – the office block that progressively gets built, the web site that finally gets commissioned – as well as a variety of objects that were used along the way to promote, direct and record collaboration – such as design documents, project schedules, and meeting agendas.


The Team element includes the collaborators and the interactions between them: Team membership and authorization, inter-personal dynamics, personal identity, decision making processes, and communication.


The Tasks element includes the list of things to be done in order to reach the goal, along with all the processes necessary to manage that list. How do tasks get formulated? How is their status recorded and tracked over time? How is the list prioritized and scheduled? How are tasks assigned to team members and how are personal ‘To Do’ lists presented?


Most collaboration is extended across time, and consequently requires some degree of time-management: setting deadlines, milestones and task completion dates; scheduling team meetings; and keeping an historical record of events.


Team members perform Actions based on the Tasks assigned to them. The Actions might just involve searching or viewing the Artifacts, but more typically mean modifying the Artifacts in some way. There might also be some meta-Actions such as maintaining the Artifact repository, keeping a log of Actions and commenting on the Artifacts.


Resources enable the Team members to perform the Actions. They include physical equipment, money, external advice, and all manner of software (project management, Wiki, spreadsheet, and content management systems, among others).

The current state of collaborative software

There are three primary ways in which humans interact: conversations, transactions, and collaborations. There is plenty of software that enables conversation–email, VOIP, chat, IM, forums–and plenty of software for transactions–eBay, PayPal, internet banking, shopping carts. But what is available for collaboration?

There are many software applications that seek to enable collaboration2. But let’s see what happens when they are evaluated under these three categories:

  • The extent to which the software provides the required functional components (i.e. the boxes in Figure 1)
  • The extent to which the software supports the interaction between those components (i.e. the lines in Figure 1)
  • The usual criteria that apply to all software , such as ease of interaction, security, integration with other applications, and so on.

It is true that there are software packages for most of the individual components of collaboration:

  1. Artifacts: we have software for maintaining and accessing a repository of digital Artifacts (e.g. any number of CMS applications–well-established ones like Documentum or Stellent, more recent one’s like Joomla! or any of the 925 others listed at The CMS Matrix), and we can easily construct databases for tracking the status of non-digital Artifacts.
  2. Team: software for maintaining team membership, facilitating group-based decision support, and managing remote meetings (e.g. WebEx) and video conferencing. There is even some possibility that virtual worlds like Second Life may provide an effective environment for team interaction. In Growing Pains: Can Web 2.0 Evolve Into An Enterprise Technology?, Andy Dornan quotes a business manager as saying that “Second Life allows more user engagement than traditional video or phone conferencing.” I know of one company whose preliminary experiments with Second Life found that there was a more relaxed and open interaction via avatars than when a team interacted face-to-face.
  3. Tasks: software for maintaining task lists (e.g. Jira, ScrumWorks); task dependencies and scheduling, Gantt Charts (Microsoft Project, @task); brainstorming; workflow and process modeling; and others.
  4. Calendar: Microsoft Outlook (along with Microsoft Exchange Server so that the calendar is shared), Google Calendar, among other similar software.
  5. Resources and Actions: Many software applications act as Resources for implementing diverse Actions. For instance, Wikis enable editing of shared documents, and there are any number of calculators, electronic dictionaries, encyclopedias, search engines, web design tools – software that team members might use as they do their work.

These ‘point’ solutions may address their targeted functions effectively and even showcase the core ideals of Web 2.0 – user-generated content and taxonomies, broad-based participation, software-as-a-service (SaaS), and rich user-interfaces within a web browser. But they can’t just be lumped together and called “Collaboration” (with or without the 2.0 suffix). If you buy into the definition and model described above, it should be clear that true collaboration software must go beyond a set of disconnected point solutions and reach for the broader goal of enabling the whole collaboration process.

A key shortcoming of current so-called “collaborative software” is that there is no compelling metaphor or unifying vocabulary. We have many of the necessary pieces, but they do not interact at either the backend or user interface levels.

Some major contenders

Computer-Supported Co-operative Work (CSCW) and Computer-Supported Collaboration (CSC)

CSCW and CSC both promised such systems, but where are the practical results? While these research areas from previous decades generated many novel and hopeful ideas, there seems to have been an overly academic orientation rather than much focus on software design. Although the theory made useful distinctions, such as the categorization of collaboration by time and space, the software that resulted from these efforts dealt more with communication and co-ordination than with real collaboration.


Google offers an assortment of products that promote collaboration: Google Calendar, Google Apps, and more are promised. I was hoping that their acquisition of JotSpot in 2006 might result in a broader Wiki-based collaboration platform that unified those other offerings. But to date JotSpot has been silent. At this stage, Google’s offering is still an “assortment” rather than a clearly-conceived package.


The Zoho suite encapsulates virtually all the point-solutions mentioned above. It includes the standard office tools (word processing, spreadsheet, presentations, email), remote conferencing, chat, meeting organizer, calendar, project management and a Wiki. All of that and more is delivered via a SaaS model through your web browser. Zoho is way ahead of any competition because of its unified user interface. However, there are still important aspects lacking in Zoho: not primarily additional modules but some key IA and UX characteristics that I outline below.


Perhaps the closest we have today is from Microsoft. Combine SharePoint, Outlook and the Office suite and this provides remarkably effective functionality for team management, scheduling meetings, communication and shared workspaces. Our organization makes heavy use of this combination, and it pushes teamwork and information sharing a long way ahead of where we once were. On the down-side, the Task management in that environment is quite simplistic, with little support for maintaining a complex task list, or prioritization, or comprehensive status reports. The Wiki facility shipped with SharePoint is very primitive3. Microsoft has implemented a “Collaboration 1.0” approach rather than “Collaboration 2.0”, by which I mean it requires a large degree of centralized control rather than drawing on the power of social networking. Of course, the content of email, announcements, uploaded documents, and so on is completely open to freedom of expression, but the constrained environment and heavy IT infrastructure make the system as a whole feels complex and unwieldy.

Multi-user editing

Perhaps something specific needs to be said about one type of so-called collaborative software – the type that enables multi-user editing of electronic documents. Most of these applications are primarily interested in version control: they maintain a repository of documents and control access to that repository. Authorized people can view documents and a subset of those can edit the documents. The software provides some process for giving each editor a copy of the document and when the changes have been made, the software merges the changes back into the master copy, while keeping some form of historical change log. Examples are clearspace and the various text-based code-management tools such as Subversion.

While revision control has an important role, it is a meager offering in terms of the extent of collaboration that it enables. In most cases, such applications assume that individuals work independently of each other. One user edits this part of the document and, as a quite separate task, another user may edit another part of the same document. Two people editing the same part of the document is treated as a problem, and typically the last person to submit changes trumps any previous changes.

A more significant level of collaboration requires the assumption that multiple people will be working together to edit the document simultaneously. That requires a single shared document rather than separate copies of a master document for each editor. See Wikipedia article for a list of such real-time collaborative editors.

XMPP (the Extensible Messaging and Presence Protocol) has extensions for both multi-user text editing and multi-user whiteboarding, so there is at least discussions about how such interaction can be standardized. But tools that use that protocol are few and far between.

The Challenge for IA and UX

There are many human and business activities mediated by computer systems where IA and UX practitioners have provided design guidance to make the interaction more effective. Given that collaboration is fundamentally about interacting effective to jointly achieve some goal, IA and UX can play an even more substantial role than usual.

So, what principles would you apply to collaboration software? Here are my suggestions:

1.      Build the user interface around a consistent, unifying metaphor.

  • The metaphor should be goal-oriented. That is, a stated goal should take center-stage, with the Team, Tasks, Calendar, Resources, and Artifacts being other players in the drama.
  • The user interface needs to enable and encourage interactions between collaborators. Perhaps the metaphor of a sport team would be effective.
  •  A “portal”/dashboard pattern allows simple movement between team management, task list, calendar, documentation management and the like. That approach can collate the answers to core concerns like: What collaboration projects am I part of? What’s the current status of each? What’s on my To Do list?

2.      Build an open, extensible, modular framework: a collaboration platform rather than a single application.

  • The scope of collaboration is too extensive to expect that a single vendor will be able to provide all the pieces. It is important to allow modules to be gathered from multiple sources and plugged into a shared framework.
  • For instance, Jira might be the first choice for the maintaining the Task list, but the framework should allow that to be substituted with alternatives. Similarly, in a basic system there may be a limited reporting feature (e.g. to view the change history for the Artifact), but it should be possible to plug in a more substantial reporting application later on.
  • Most importantly, it will be important to provide a standard API to the Artifact repository, so that any number of applications can view, add and modify Artifacts.

3.      Include at least the following functions “out of the box:”

  • Team management: functions to define and authorize team members, and for individuals to update their personal profiles
  • Task management: functions to add and prioritize tasks, allocate responsibilities to team members, and maintain current status
  • Calendar management: all team members can add events to a single shared calendar
  • Communication: integration with email, IM, and other technologies
  • Meetings: ability to schedule a meeting and invite specific team members, publish an agenda, record notes and decisions from the meeting.

4.      The platform itself should maintain a collaboration history rather than leave that function to the plug-in components. All meetings, decisions, changes to Artifacts, Task status changes and other events are recorded in that history. The history should be displayed as a journal along a time-line as well as being exposed as a life-stream via RSS/Atom.

5.      Connect to other enterprise applications and data stores. A collaboration application will gain significant value if it can interact with existing databases, content management systems, security mechanisms, and if it can exchange data with other applications via some standard like Web Services.

6.      Implement all this as a Rich Internet Application. The complexity of interactions between team members who are potentially geographically scattered indicates the platform needs to be web-based. The complexity of interactions between users and the system indicates that the user interface needs to be very dynamic, with near-real-time synchronization between all concurrent users and a shared Artifact repository.


Maybe all I’ve done here is scratch an itch. But I hope that the itch is contagious.

Collaboration is an essential part of human endeavor and information technology is at a stage where it should be able to add value to collaboration in more ways that just connecting people in a social network. We have many web-based applications that address parts of the process, but who’s going to create the framework to bring it all together?


1 This model was first presented at BarCamp Sydney in August 2007.

2 Capterra’s Web Collaboration Software Directory lists “174 Solutions”. See also the Wikipedia article on collaborative software.

3 Lawrence Liu comments that the SharePoint Wiki is not intended to be best-of-breed, just something that “is sufficient for a very large percentage of our customer base”. Even that is wishful thinking, but fortunately, the guys at Atlassian have made a SharePoint Connector for Confluence that can easily replace the default SharePoint Wiki.

On A Scale of 1 to 5

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Where would we be without rating and reputation systems these days? Take them away, and we wouldn’t know who to trust on eBay, what movies to pick on Netflix, or what books to buy on Amazon. Reputation systems (essentially a rating system for people) also help guide us through the labyrinth of individuals who make up our social web. Is he or she worthwhile to spend my time on? For pity’s sake, please don’t check out our reputation points before deciding whether to read this article.

Rating and reputation systems have become standard tools in our design toolbox. But sometimes they are not well-understood. A recent post at the IxDA forum showed confusion about how and when to use rating systems. Much of the conversation was about whether to use stars or some other iconography. These can be important questions, but they miss the central point of ratings systems: to manage risk.

So, when we think about rating and reputation systems, the first question to ask is not, “Am I using stars, bananas, or chili peppers?” but, “what risk is being managed?”


What Is Risk?

We desire certainty in our transactions. It’s just our nature. We want to know that the person we’re dealing with on eBay won’t cheat us. Or that Blues Brothers 2000 is a bad movie (1 star on Netflix). So risk, most simply (and broadly), arises when a transaction has a number of possible outcomes, some of which are undesirable, but the precise outcome cannot be determined in advance.


Where Does Risk Come From?

There are two main sources of risk that are important for rating and reputation systems: asymmetric information and uncertainty.

Asymmetric information arises when one party to a transaction can not completely determine in advance the characteristics of the other party, and this information cannot credibly be communicated. The main question here is: can I, the buyer, trust you, the seller, to honestly complete the transaction we’re going to engage in? That means: will you take my money and run? Did you describe what you’re selling accurately? And so on.

This unequal distribution of information between buyers and sellers is a characteristic of most transactions, even in transactions where fraud is not a concern. Online transactions make asymmetric information problems worse. No longer can we look the seller in the eye and make a judgment about their honesty. Nor can we physically inspect what we’re buying and get a feel of its quality. We need other ways to manage our risk generated by asymmetric information.

The other source of risk is not knowing beforehand whether we’ll like the thing we’re buying. Here honesty and quality are not the issue, but rather our own personal tastes and the nature of the thing we’re buying. Movies, books, and wine are examples of experience goods, which we need to experience before we know their true value. For example, we’re partial to red wine from Italy, but that doesn’t mean we’ll like every bottle of Italian red wine we buy.


Managing Risk with Design

Among the ways to manage risk, two methods will be of interest to user experience designers:

  1. Signaling is where participants in a transaction communicate something meaningful about themselves.
  2. Reducing information costs involves reducing the time and effort it takes participants in a transaction to get meaningful information (such as: is this a good price? is this a quality good?).

Reputation systems tend to enable signaling and are best utilized in evaluating people’s historical actions. In contrast, rating systems are a way of leveraging user feedback to reduce information costs and are best utilized in evaluating standard products or services.

 It is important to note that reputation systems are not the only way to signal (branding and media coverage are other means, among others), and rating systems are not the only means of reducing information costs (better search engines and product reviews also help, for example). But these two tools are becoming increasingly important, as they provide quick reference points that capture useful data.

As we review various aspects of rating and reputation systems, the key questions to keep in mind are:

  1. Who is doing the rating?
  2. What, exactly, is being rated?
  3. If people are being rated, what behaviors are we trying to encourage or discourage?


Who is doing the Rating?

A random poll of several friends shows about half use the Amazon rating system when buying books and the other half ignore it. Why do they ignore it? Because they don’t know whether the people doing the rating are crackpots or if they have similar tastes to them.

Amazon has tried to counteract some of these issues by using features such as “Real Name” and “helpfulness” ratings of the ratings themselves (see Figure 1).

Figure 1

Figure 1: Amazon uses real names and helpfulness to communicate honesty of the review.

This is good, but requires time to read and evaluate the ratings and reviews. It also doesn’t answer the question, how much is this person like me?

Better is Netflix’s system (Figure 2), which is explicit about finding people like you, be they acknowledged friends or matched by algorithm.

Figure 2

Figure 2: Netflix lets you know what people like you thought of a movie.

Both these systems implicitly recognize that validation of the rating system itself is important. Ideally users should understand three things about the other people who are doing the rating:

  1. Are they honest and authentic?
  2. Are they like you in a way that is meaningful?
  3. Are they qualified to adequately rate the good or service in question?

The last point is important. While less meaningful for rating systems of some experience goods (we’re all movie experts, after all), it is more important for things we understand less well. For example, while we might be able to say whether a doctor is friendly or not, we may be less able to fairly evaluate a doctor’s medical skills.


What is being rated?

Many rating systems are binary (thumbs up, thumbs down), or scaled (5 stars, 5 chili peppers, etc.), but this uni-dimensionality is inappropriate for complicated services or products which may have many characteristics.

For example, Figure 3 depicts a rating system from the HP Activity Center and shows how not to do a rating system. Users select a project that interests them (e.g., how to make an Ireland Forever poster) and then complete it using materials they can purchase from HP (e.g., paper). A rating system is included, presumably to help you decide which project you should undertake in your valuable time.

Figure 3

Figure 3: The rating system on the HP Activity Center site: what not to do.

A moment’s reflection raises the following question: what is being rated? The final outcome of the project? The clarity of the instructions? How fun this project is? We honestly don’t know. Someone thoughtlessly included this rating system.

Good rating systems also don’t inappropriately “flatten” the information that they collect into a single number. Products and services can have many characteristics, and not being clear on what characteristics are being rated, or inappropriately lumping all aspects into a single rating, is misleading and makes the rating meaningless.

RateMDs, a physician rating site, uses a smiley face to tell us about how good the doctor is (Figure 4).

Figure 4

Figure 4: rating system for doctors.

Simple? Yes. Appropriate? Perhaps not.

Better is Vitals, a physician rating site that includes information about doctors’ years of experience, any disciplinary actions they might have, their education, and a patient rating system (Figure 5).

Figure 5

Figure 5: The multi-dimensional rating system on

While Vitals has an overall rating, more important are the components of the system. Each variable – ease of appointment, promptness, etc. – reflects a point of concern that helps to evaluate physicians.

When rating experiences, what is being rated is relatively clear. Did you enjoy the experience of consuming this good or not? Rating physical goods and products can be more complicated. An ad hoc analysis of Amazon’s rating system (Figure 6) should help explain.

Amazon's rating system

Figure 6: Amazon’s rating system is not always consistent.

In this example the most helpful favorable and unfavorable reviews are highlighted. However, each review is addressing different variables. The favorable review talks about how easy it is to set up this router, while the unfavorable review talks about the lack of new features. These reviews are presented as comparable, but they are not. These raters were thinking about different characteristics of the router.

The point here is that rating systems need to be appropriate for the goods or services that are being rated. A rating system for books cannot easily be applied to a rating system for routers, since the products are so entirely different in how we experience them. What aspects we rate need to be carefully selected, and based on the characteristics of the product or service being rated.


What behaviors are we trying to encourage?

Any rating of people is essentially a reputation system. Despite some people’s sensitivity to being rated, reputation systems are extremely valuable. Buyers need to know whom they can trust. Sellers need to be able to communicate – or signal based on their past actions – that they are trustworthy. This is particularly true online, where it’s common to do business with someone you don’t know.

But designing a good reputation system is hard. eBay’s reputation system has had some problems, such as the practice of “defensive rating” (rate me well and I’ll rate you well; rate me bad and I’ll rate you worse). This defeats the purpose of a rating system, since it undermines the honesty of the people doing the rating, and eBay has had to address this flaw in their system. What started out as an open system now needs to permit anonymous ratings in order to save the reputation (as it were) of the reputation system.

While designing a good reputation system is hard, it’s not impossible. There are five key things to keep in mind when designing a reputation system:


1. List the behaviors you want to encourage and those that you want to discourage

It’s obvious what eBay wants to encourage (see Figure 7). A look at a detailed ratings page shows they want sellers to describe products accurately, communicate well (and often), ship in a reasonable time, and not charge unreasonably for shipping. (Not incidentally, you could also view these dimensions as source of risk in a transaction.)

Figure 7

Figure 7: eBay encourages good behavior.



2. Be transparent

Once you know the behaviors you want to encourage, you then need to be transparent about it. Your users need to know how they are being rated and on what basis. Often a reputation is distilled into a single number — say, reputation points — but it is impossible to look at a number and derive the formula that produced it. While Wikinvest (Figure 8) doesn’t show a formula (which would be ideal), they do indicate what actions you took to receive your point total.

Figure 8

Figure 8: Wikinvest’s reputation system

Any clarity that is added to a reputation system will make your users happy, and it will make them more likely to behave in the manner you desire.


3. Keep your reputation system flexible

Any scoring system is open to abuse, and chances are that any reputation system you design will be abused in imaginative ways that you can’t predict. Therefore it’s important to keep your system flexible. If people begin behaving in ways that enhance their reputation but don’t enhance the community, the reputation system needs to be adjusted.

Changing the weighting of certain behaviors is one way to adjust your system. Adding ratings (or points) for new behaviors is another. The difficulty here will be in keeping everything fair. People don’t like a shifting playing field, so tweaks are better than wholesale changes. And changes should also be communicated clearly.


4. Avoid negative reputations

When possible, reputation systems should also be non-negative towards the individual. While negative reputations are important to protect people, negative reputations should not always be emphasized. This is specifically true in community sites where users generate much of the content, and not much is at stake (except perhaps your prestige).

Looking at our example above (Figure 8), Wikinvest uses the term “Analyst” (a nice, non-offensive term … if you’re not in investment banking), to mean, “this person isn’t really contributing much.”


5. Reflect reality

Systems sometimes fail on community sites when people belong to multiple communities and their complete reputations are not contained within any one of them. While there are exceptions, allowing reputations earned elsewhere to be imported can be a smart way to bring your system in line with reality and increase the value of information that it provides.



Our discussion of rating systems and reputation systems is certainly incomplete. We do hope that we’ve given a good description of risk in online transactions, and how understanding this can help user experience designers better manage risk via the design of more robust rating and reputation systems.

In addition, we’d like to begin a repository of rating and reputation systems. If you find any that you’d like to share, feel free to submit them at

Social Networks And Group Formation

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This is the first in a three-part series on academic research that illuminates social networks, one of the most important trends in design today.

Humans suffer from information overload; there’s much more information on any given subject than a person is able to access. As a result, people are forced to depend upon each other for knowledge. Know-who information rather than know-what, know-how or know-why information has become most crucial. It involves knowing who has the needed information and being able to reach that person (Johnson et al. 2000).

In this context, understanding the formation, evolution and utilization of online social networks becomes important. A social network is “a set of people (or organizations or other social entities) connected by a set of social relationships, such as friendship, co-working or information exchange.” (Garton et al., 1997) While the Internet contributes to the information overload, it also provides useful tools to effectively manage one’s social networks and through them gain access to the right pieces of information.

This field is of particular interest to researchers working at the intersection of information systems, sociology and mathematics. These researchers study the uses of social networks and the ways in which they are mediated in society and in the workplace through information communication technologies (ICTs) such as (but not limited to) the Internet. This literature review explores how social networks that take advantage of information communication technologies—specifically, web based technologies—begin, evolve and are utilized.

The online social network field is broad, and any literature review can only focus on a selection of articles. The present article highlights recent research in the field and focuses on centrality, linkage strength, identity, trust, activity and benefits. By no means is this review comprehensive, but it should give practitioners some useful concepts to consider as they design social network based web applications.

The Strength of Weak Ties

Social networks were first researched in the late 1940s. With the advent of the Internet, online communities and social networking websites, their significance has only increased. Any review hoping to be meaningful must begin with the normative contributions of the sociologist Mark Granovetter and the mathematician Linton C. Freeman who both wrote influential articles well before the Internet was popularized.

Granovetter (1973) argued that within a social network, weak ties are more powerful than strong ties. He explained that this was because information was far more likely to be “diffused” through weaker ties. He concluded that weak ties are “indispensable to individuals’ opportunities and to their incorporation into communities while strong ties breed local cohesion.”

Granovetter’s doctoral thesis demonstrated that most people landed jobs thanks to their weak ties and not their strong ones. It was the people that they did not know well, the ones with whom they did not have shared histories and did not see on a regular basis who were of most help. This is because people with strong ties generally share the same pieces of information and resources. Therefore they are of less help to one another.

Similarly, Granovetter identified absent ties (also called nodding ties) – those ties that lack the emotional intensity, time, intimacy and reciprocity to even qualify as weak ties. Someone living on the same street that you nod to everyday is an absent tie. An absent tie is someone that exists in your life but with whom you have no connection whatsoever. That person is not helpful in the way that a weak tie can be.

Depending upon the type of application you are building, you may want to design it so that people are encouraged to form weak ties with people that they do not know very well. They are more likely to benefit from those weak ties than from strong ones. But it is important to recognize the difference between a weak tie and an absent one. On social network sites like MySpace and Facebook, where self worth is garnered through the number of ties, the difference becomes important. Yet, the fact that you can search and connect to all kinds of ties on these networks has influenced their growth.

According to Granovetter’s theory, there would be value in the visual depiction of weak ties. LinkedIn tells you how many ties you have at each degree of separation, but other than that you are not given much information about those ties. Are they strong, weak, or absent ties? LinkedIn has another problem too: It makes it difficult for you to connect with your weak ties. You often have to ask a common friend for permission to establish that connection. No wonder LinkedIn is being eclipsed by other social network services!

Centralization in a Network

An understanding of social networks needs also to include accounts of centrality and of one node’s relationship to other nodes in a network. This is why Linton C. Freeman’s article on centrality in social networks is important (Freeman, 1979). Freeman explored how “graph centralization” was based on differences in point centralities. He also outlined three competing theories regarding the definition of centrality based on degree of a point, control and independence.

Degree of a point refers to the number of nodes connected to a given node. In simple terms, this means counting the number of friends you have in a social network. The more friends, you have, the more important you are.

Control refers to the extent to which nodes depend on one specific node to communicate with other nodes. For example, if hundreds of friends are connected to each other only when you serve as the bridge connecting them, then your centrality is high. You are the node that controls the communication flows.

And finally independence means that a node is closely related to all the nodes considered – so that it is minimally dependent on any single node and is not subject to control. This means you can reach the maximum number of people through the shortest number of links, without being dependent on a particular few nodes.

Figure 1: A depiction of centrality.
* Degree point: C and K have the most nodes connected to them.
* Control: D serves as the bridge between the most nodes and controls the flow of information.
* Independence: K is most closely connected to the other nodes by multiple nodes (I and Q).

Because social networks are fundamentally social tools in which people are constantly monitoring and growing their social network, most social network media depict growth using the degree of point definition. However, control and independence can be more useful definitions. For example, a person who controls information flows is more important than one who may have more friends in the network. Centrality can also indicate which members are the most useful or well connected and therefore the best information resources.

Learning from Flickr & Yahoo

The principles of node structures, tie strength and centrality have been applied to understand nodes in modern day online social networks. A good example of this is in the explanatory research conducted by Kumar, Novak and Tomkins (2006). They compared two online social networks, Flickr and Yahoo 360, which together had more than five million users at the time. These researchers noticed that the social networks follow a standard pattern of growth, namely, rapid early growth followed a period of decline and then slow but steady growth.

Kumar, Novak and Tomkins also saw that network activity is of three types:
* “Singletons,” who have no connections and are least central
* The “giant component,” which is the largest group of nodes tightly connected to the central nodes and to each other
* The “middle region,” which represents isolated groups which interact amongst themselves but not with the rest of the network, forming isolated stars. These groups grow one user at a time. Over time they merge with the giant component.


Figure 2a: The red section represents the giant component. The blue is the middle region comprising of isolated networks while the gray are singletons.

The node analysis of these networks showed that more than half of a social network is outside the giant component where the greatest centrality lies. They used the “control” definition of centrality to determine this. The research also highlighted a prevalence of “stars” in the middle region which are mini social networks, typically driven by one dynamic member who serves as the point of centrality with others serving as satellite nodes – connected to the dynamic member but not to each other. In Kumar, Novak and Tomkins’ analysis the middle region represented one-third of users on Flickr and about ten percent of users on Yahoo! 360.

Also keep in mind that the most growth happens in the middle region where dynamic members influence others to join their network. These sub-networks can gradually join the giant component over time. Once they do, the importance of the dynamic member diminishes. Even if that dynamic member were to leave the network, the others would stay in the network.


Figure 2b: A connection is made between one of the isolated networks from the middle region connects to the giant component.


Figure 2c: The formerly isolated network becomes part of the giant component.

What are the implications of this? When designing your social network, be aware that most of the network will be outside the giant component. In a sense, social networks themselves are thousands of sub-networks. The more mechanisms that you provide for those sub-networks to flourish, greater the overall network growth. Social networks are fundamentally virtual ghettos. Networks like MySpace and Facebook that encourage ghettos grow the most. Ning, which lets you create your own network and join others too, cleverly understands this concept and leverages it.

Live Journal, DBLP & Adoption Behavior

Most online social networks grow based on the initiative of early adopters who transfer their offline networks online and serve as “stars.” But it is also important to look at the evolution of social networks based on intentional activity within a network. Backstrom, Huttenlocher and Kleinberg (2006) analyze group formation in large social networks. They used LiveJournal data from its ten million users and DBLP, a database of co-authorship in conference publications to study how the communities grew based on the underlying social networks. They showed that a person was more likely to join a social network if friends of the person were already closely linked together on it. Having several friends closely connected in an online social network builds trust. For those of us who are active members of social networks, this makes obvious sense.

The article conclusively showed that the most growth happened in the giant component (without using the term explicitly) where the nodes were most central. In highlighting the importance of the giant component, Backstrom, Huttenlocher and Kleinberg validated the Kumar et al. (2006) theory. Their article raises a critical question: Once a node becomes aware of its neighbors’ behavior, under what conditions and based on what network relationships will the node adopt that behavior itself?

Another group of researchers who studied the DBLP database were Cai et al. (2006). They pointed out that each node belongs to several different social networks, with the other networks affecting the group formation patterns, evolution and information sharing on the social network. As a result, they felt that a network can’t be analyzed independently but needs to be studied in the context of other networks. It may also influence whether a node leaves a network based on the activity of nodes on its other networks. This raises an important question for practitioners: Do you know how much of the activity on your social network is influenced by activity on other social networks?

This is of particular interest when examined in the context of the new Google lab efforts around Social Stream, which hopes to be a meta-social-network aggregating different networks together. Developed in partnership with Carnegie Mellon University, Social Stream s currently in private beta. The question that social network designers worry about is, once you can understand network activity on different networks via a single, consolidated interface, how will that affect your own network preferences?

It is clear that online social networks are always evolving because of both outside influences and activity within them. Butler (2001) emphasized this when they showed that network size has a complex influence on the network such that more member gains results in more member losses too. They argued that it is necessary to balance the positives and negatives of size and communication activity. A final question to consider is which type of membership activity and where (giant component, middle layer or among singletons) most affects an online network?


Researchers studying group formation have incorporated the normative social network theories discussed by Granovetter and Freeman. They recognize that these are socio-technical systems that must account for human agency, meaning that the ability of human beings to make unique choices heavily influences a network’s evolution. As a result, one can apply social networking theory to a web product, but one must remember that because these are human systems it is difficult gauge the potential success of a given network.

The next part of this series will explore information-sharing patterns on social networks. The third part will cover some workplace scenarios.

Authors Note: By no means is this review comprehensive, moreover it should serve as just a starting point for gaining familiarity with some of the academic contributions.


Backstrom, L., Huttenlocher, D., Kleinberg, J., and Lan, X. (2006.) Group formation in large social networks: membership, growth, and evolution. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press: Philadelphia, PA, USA.

Butler, B. (2001.) Membership size, communication activity, and sustainability: a resource-based model of online social structures. Information Systems Research, 12 (4), p. 26.

Cai, D., Shao, Z., He, X., Yan, X., and Han, J. (2005) Mining hidden community in heterogeneous social networks. In Proceedings of the 3rd International Workshop on Link Discovery. ACM Press: Chicago, Illinois.
Freeman, L. C. (1979.) Centrality in social networks conceptual clarification. Social Networks, 1 pp. 215-239.
Garton, L., C. Haythornthwaite and B. Wellman. (1997.) Studying online social networks. Journal of Computer Mediated Communication, 3 (1).
Granovetter, M. S. (1973) The strength of weak ties. American Journal of Psychology, 78 (6), pp. 1360-1380.
Johnson, B., Lorenz, E. and Lundvall, B. (2002.) Why all this fuss about codified and tacit knowledge? Industrial and Corporate Change, 11 (2), pp. 245-262.
Kumar, R., J. Novak and A. Tomkins. (2006.) Structure and evolution of online social networks. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 611-617. ACM Press: Philadelphia, PA, USA.

A Web 2.0 Tour for the Enterprise

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“The architecture of participation is baked into the architecture of the software.”

Thanks to the hype generated by Business Week, The New York Times, Fortune, and Newsweek (among others), Web 2.0 has captured the imagination of consumers and businesses alike. But knowing how to leverage Web 2.0 concepts to fuel collaboration and innovation among employees, partners, and customers is another story. Web 2.0 can change an enterprise but recognizing how, and determining whether you should, do so is confusing. This article aims to dispel some of the myths surrounding Web 2.0 while discussing its practical applications within organizations. Then the enterprise—businesses and their practices—can embrace and extend Web 2.0 to Enterprise 2.0.

What is Web 2.0?
To paraphrase a definition by Tim O’Reilly, who was one of the first to use the term, “Web 2.0” is web-based software which is continually collaboratively updated. This means that the software gets more useful the more people who consume and remix it. Remixing is a key concept of Web 2.0. In music, remixing means taking established songs and editing them together, potentially adding your own elements as well. With Web 2.0, individual users add their own data and services to collaborative web software, remixing the Web 2.0 sites into increasingly useful tools and creating an exponential growth effect.

For example, Digg publishes news stories from around the web. Users contribute their own news stories as well as noting other publications’ stories, and all users “digg” or rate them. The Diggers also add comments to the stories and rate the comments of others, too, determining the stories’ prominence on the site. The more users who contribute and rate stories and comments, the more effective the service gets.

The unique properties of the web—rolling releases and nearly universal accessibility—gave birth to the Web 2.0 architecture of participation. The web has always been a fertile medium for collaboration but new technologies have increased the possibilities—as well as the complexities. AJAX in particular, is being used to make the web experience faster and richer. This combination of XML and JavaScript frees interaction from slow page refreshes, creating a desktop-like responsiveness, but it also breaks down the simple click-read-click model. Ajax can create a more satisfying user experience by offering drag-and-drop, resizing, and partial-page refreshes, or a significantly worse one as designers struggle to communicate this new behavior to visitors who are used to clicking, not dragging.

Ruby on Rails is another groundbreaking technology. A combination of an elegant programming language and a framework for speeding development of web applications, Ruby on Rails is allowing dozens of tiny start-ups to create potential businesses overnight. Those web applications, from blogging tools to photo galleries to wikis, are turning site visitors into site participants. Ruby is an remarkably clear language, readable by designers as well as programmers, and Rails has many best practices built into the framework, so that these new applications are more accessible and usable than ever. Meanwhile RSS and APIs are freeing data from presentation on sites all over the web, making it easier than ever to get information the way you want it, or to remix it with other websites’ data into something new and exciting.

It’s a mistake to think Web 2.0 is all about the technology, but it’s also a mistake to dismiss the technology. The architecture of participation is baked into the architecture of the software. Web 2.0 lets you share and incorporate multiple voices— your customers, your service reps, your employees—who quickly take the product, service, or idea in a direction that you could not alone. Often the technology will let you behave no other way.

It seems most important aspect of Web 2.0 is the values it espouses. Web 2.0 purports to be collaborative, participatory, simple, accessible, efficient, lightweight, approachable, action-oriented, and user-driven. These values are found in companies like Google, Yahoo!, Netflix, Flickr, Technorati, Skype, and eBay. When you think about Web 2.0, first think about the values before you think about potential applications of the technology. The technology is nifty; the values are competitive.

What does it mean for the enterprise?
So far, other than the new technologies associated with Web 2.0, very little of the Web 2.0 advances have been brought to the internal workings of business. At first blush, it appears that the concepts don’t apply to the enterprise. The open, freewheeling discussion of a Digg seems inapproapriate for a corporation. But closer examination reveals some key opportunities.

First, Web 2.0 can change the way you reach your customers, build relationships with them, and further your brand objectives. Successful companies are using Web 2.0 concepts to encourage their customers to build communities around their products, provide feedback on products, and, in some cases, even inform strategy. But Web 2.0 concepts are not effective unless you examine how you are connecting with your customers and relinquish the idea you can dictate to them. It takes courage to let go of control, through collaborative design with the customer, or through communication within the enterprise. Rather than “aligning supply chains, communications, marketing initiatives” what if you co-create new supply chain approaches with your suppliers, or what if marketing initiatives come from the customers? While pronouncements and offerings feel safer and more familiar than participation and collaboration, the rewards are higher when you open your processes up to more input.

Take General Motors. They have been running promotions inviting customers to create advertisements for their Chevy Tahoe brand. The customers visit a website where they can choose a video clip, add sound, text, create sequences, and publish the result as a complete advertisement. Recently a number of anti-SUV customers used this platform to create ads about global warming, to protest the war on Iraq, and to demean the product. This has resulted in numerous new articles and remarkable traffic. While this violates most brand managers’ rules-of-thumb, General Motors is leaving all but the profane up on the site. This repositions them as unafraid and honest, and allows the traffic to continue unabated. PT Barnum said any publicity is good publicity; we’ll see if General Motors agrees.

Companies are also using Web 2.0 approaches to communicate more effectively with customers. The Sun Microsystems CEO, Jonathan Schwartz, publishes a popular blog in which he discusses his company’s strategies, products, opportunities, and challenges. Customers can then follow the company’s progress via a more intimate and digestible form than a press release which then becomes a format for open dialog between the company and the consumer. Normally corporations shy from allowing customers to express their opinions publicly, much less let the CEO engage in published discussion with them. Sun promotes loyalty and gains invaluable knowledge with this simple tool.

The collaborative value of Web 2.0
Consider how your enterprise works with its network of partners. Whether it is with suppliers, distribution partners, or service providers, there are opportunities for collaboration. Ask yourself how you develop your go-to-market strategy for new products. How are you involving your business partners? And more specifically, how are you involving the foot soldiers in your partner companies?

Luxury brands like Chanel and Estee Lauder work very closely with their retailer partners to make sure that their brands are accurately represented in the department stores. These luxury brands share their marketing strategies with the senior executives from their retail partners. Maybe it is time for them to share those plans with the employees at the retailer who will actually be tasked with selling the product? Most of this communication happens over the phone, through email, and with on-site visits. Web 2.0 technologies increase the reach and improve the richness of the interaction.

Imagine if the next time Estee Lauder was determining the look of its retail presence at a Macy’s or a Bloomingdales it used Web 2.0 technologies to ask saleswomen to evaluate or even remix counter display concepts. By asking the saleswomen to vote on counter display concepts via a dynamic Web 2.0 website, Estee Lauder would learn vital information. If it allowed saleswomen to rearrange, add to, and combine those display concepts, Estee Lauder might discover new ways to reach the consumer. In fact, if the communication on the Web 2.0 sites were allowed to live on post launch, Estee Lauder salespeople could continually refine the concepts based on store usage patterns, and could share the knowledge of what works and what doesn’t across stores almost instantly.

The audience within
What Web 2.0 values should be corporate values? The more collaborative the employees of a company are, the more successful the company becomes over time. Employees that collaborate efficiently by leveraging each other’s intellect and resources create stronger and more successful products. Unfortunately, it is also recognized that current communication and collaboration “solutions” are woefully inadequate. Most software touted to enable collaoration is difficult to use, cumbersome, limiting, and does not empower employees to share their content. Rather than fueling collaboration, they hinder it.

Why do these existing approaches fail? They fail because they’re driven by technology requirements rather than by human needs. Because the current crop of tools are built on values of control, containment, and secrecy in environments where employees are encouraged to compete more than collaborate with one another, installing another knowledge managment tool does little to remedy the problem. Until the enterprise is willing to examine its values and its behavior, poor choices in policy and in technology are inevitable.

Web 2.0-driven solutions for collaboration are different because the values are baked into the functionality. RSS feeds do not force employees to visit an intranet or website but can bring the information to them in the employee’s choice of format. By allowing anyone in a company to publish RSS feeds, and by letting employees choose which ones to subscribe to with the tools they want, the best feeds rise to the top, employees are better informed, and the employee authors get the recognition they deserve. In this manner, the company itself also learns what’s valuable, instead of telling employees its abstract ideas of what employees should value. Courageous companies could even learn what direction to take the corporate strategy by tapping into the “wisdom of crowds.”

Similarly, a company that uses a wiki-based solution for collaboration will have more success than a traditional, highly permission-driven intranet tool. Wikis allow anyone to edit anything, and require no special privileges or knowledge to contribute. They work the way a smart team does, permitting people to riff on each others ideas and expand on each other’s knowledge. Moreover, if wiki authors have a comprehensive profile describing their professional interests, listing their previous posts and their contact information, an atmosphere of trust and familiarity arises, and employees will be more likely to collaborate and share their personal knowledge.

In a nutshell, Web 2.0 concepts like wikis and integrated chat can make a big difference in acheiving Web 2.0 values. Companies that are more collaborative, participatory, efficient, user-driven, and action-oriented are recognized as the most successful. IBM, for example, has just launched “Innovation Jams” where thousands of IBM employees are encouraged to participate in virtual chatrooms simultaneously on a given day. IBM hopes to uncover transformative business ideas through these virtual discussions. As discussed in a recent Businesssweek article, IBM CEO J. Palmisano believes that the opinions of 100,000 IBM employees will result in “catalytic innovations” that can lead to new business for IBM.

But what can you do today?
It’s all well and good to discuss major shifts in corporate culture, but we all know those take time. What specifically can you do today to understand Web 2.0 better and to learn how to use it in your company to support employees, customers, and partners? Don’t task your information technology department to make every web-based application Web 2.0 ready, or push your product managers to start blogging 25 times a day. Instead, step back and learn more about this space, then think how the underlying concepts can help you improve in small ways. And the easiest way to do that is to look at a few examples currently on the web.

Revolutionizing the phone book
The first place to start is with a networking site like LinkedIn. Sign up and invite your peers to join as well. Create a profile of yourself. Play around with some of the linking features. Try searching for someone. And then ask yourself whether your company would benefit from an application like this for all employees and partners. Is it easier to use than your current intranet employee directory? Does it have some nifty features you wish you had on your intranet? By making the internal social networking more explicit, employees will discover connections with each other and further knowledge management between departments and offices.

Simplifying the spreadsheet
The second place to visit is the Google Spreadsheet application. At first glance, Google’s spreadsheet application may seem lacking in many respects. From a functionality perspective, it does pale in comparison to Microsoft Excel. However, after playing around with it for a while, you’ll discover that it includes the most-used functionality of Excel and offers something a bit different. Click on the “share this spreadsheet” link, and suddenly you’re collaborating in real time. This can be invaluable now that companies’ business units are seperated not just by buildings but by countries.

Web 2.0 applications are optimized for multi-users, easy to use, and mostly free. Now ask yourself, are there any applications in your work environment you wish were web-based with more collaborative features and better usability? Are there applications that have unnecessary gating features, locking out great minds? Are your most important documents living in people’s inboxes and on their hard drive? You may discover that you don’t need to upgrade to the next version of Microsoft Office again. A Web 2.0 application similar to Google’s spreadsheet application may meet your online collaboration needs.

A bottom-up knowledge management system
Once you’ve finished playing around with the Google spreadsheet application, make your way over to Wikipedia. Wikipedia is the largest living encyclopedia on the web. From the home page of Wikipedia, search for the word “collaboration.” You’ll be taken to a page filled with definitions, explanations, and references. You may even notice that the collaboration article has been written in collaboration with another wiki called Now try it yourself: click on one of the edit links on the right-hand side. You can edit the page yourself in real time. Click on the History tab at the top of the page, and you can see who else has edited the page. Now leave your mark and see how fast it’s corrected.

Imagine if you had a wiki to share information and brainstorm with parts suppliers. Wouldn’t they feel far more invested in the success of programs that they had co-created? Imagine if your whole intranet was a wiki where anyone could create, edit, or even remove a page. You’d probably get a lot more content publishers and a lot more tacit knowledge online.

Afraid of vandalism? Because wikis have revision histories that contain who has done what to each page, responsibility for good and bad acts is transparent. An employee who might vandalize an intranet is one who might be doing worse privately. An employee who tirelessly caretakes the accuracy of the wiki is someone to appreciate, and work to retain.

Getting news that you want when you want it
Next, visit Technorati. You will discover that Technorati is the largest directory of blogs. Search for a word—maybe something like innovation—and in a matter of seconds, you’ll see which of 46 million blogs contains it. Go to one of those blogs and bookmark the RSS (really simple syndication) feed. If you use a browser such as Firefox, you’ll be able to view live news feeds from that website right in your bookmarks panel.

Alternatively, you could use a reader like Bloglines, which allows you to not only watch but share news feeds. You could also start publishing in RSS—sites like Feedburner make it easy. Think about how you could communicate with your employees or your partners using news feeds. All they would have to do is subscribe to a newsfeed, and they’d get news from you or your department as soon as you published it.

Training tapes in the car
Talking about staying on top of news, jump to Business Week, and scroll down to the bottom of the page. Choose a podcast—maybe the CEO’s Guide to Technology—and download it to your machine. Plug in your iPod and listen to the podcast on what Web 2.0 is. If you can learn about Web 2.0 via a podcast, there’s no reason why you can’t publish podcasts about products, communication strategies, training, and industry statistics for your employees. National Semiconductor recently issued iPods to all 8,000 of its employees for this purpose.

Group decision-making on steroids
The last website to visit on your Web 2.0 tour is Yahoo! Tech Buzz. Tech Buzz is a prediction market, taking advantage of the wisdom of the crowds concept. This means users can buy and sell a concept’s contract which is similar to buying a stock for a company. The value of the contract can be interpreted as a prediction for a future event. The people who think the idea is a good one invest in it driving the price up and vice versa. So go invest your fantasy dollars in trends you think are going to flourish. Use the prices of other trends to understand what the rest of the world thinks of those trends. It is like the stock market, but instead of trading shares of a company, you’re trading concepts.

Next time you need to make a decision between two advertising concepts, consider publishing them on an internal predication market on your intranet. Let your employees buy shares in each concept based on which one they think is strong. Before you know it, you will have learned which one is better. Hewlett-Packard pioneered applications in sales forecasting and now uses prediction markets in several business units.

Making sense of it all
There is no doubt Web 2.0 concepts are starting to have an impact in the workplace with blogs, wikis, and prediction markets cropping up everywhere. Just as the original spreadsheet changed business, Web 2.0 will find its place in the corridors of some of America’s largest companies. Already companies like Ernst & Young, Nokia, Kodak, Lucent Technologies, and IBM are testing different Web 2.0 concepts for their enterprises.

The technology is relatively simple to adopt, especially thanks to open source, service-oriented architectures, and advancements in XML and presentation layer technologies. Out of the box Web 2.0 wiki and blogging solutions geared for the enterprise, such as Social Text and Traction Software, also make the move easier. You can dabble with the tools first before committing to complete adoption of a Web 2.0 approach.

Web 2.0 (its technology and values) is here to stay. The web is not about publishing content and making it available to employees, partners, and customers. That was Web 1.0. This time it’s about letting those customers, partners, and employees take control of the online experience.

Allow it to happen. It may change your business forever.

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Communicating Complex Ideas

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“Prediction markets are appealing and useful because they can make predictions about future events, but getting people to use them effectively requires a bit of work.”For those who don’t follow the latest popular economics books, the relatively recent publication of The Wisdom of the Crowds by James Surowiecki has generated a burst of activity and interest in the prediction market arena. Prediction markets–markets which explicitly take advantage of the “wisdom of the crowds,” in that the price of a contract can be interpreted as a prediction for a future event–seem very simple and straightforward. People can buy and sell a contract based on a event, much like they would buy and sell a stock, and the resulting price says something about the probability for that event occurring.

Take, for example, a popular prediction market contract: Hillary Clinton will be the next U.S. president. If I think Hillary will be President, I can buy the contract. If I don’t think Hillary will succeed in her bid, I can sell the contract. Straightforward enough, but things can quickly get complicated.

What if you believe that Hillary has a pretty good shot at winning, say a 70% chance, but the contract is priced as if there is a 90% chance that Hillary will be the next president. Would you buy it then, or sell it? Suddenly you need to think about the strength of your belief. And how would you sell it if you don’t own the contract already?

Now imagine the contract is conditional, so that it is priced based on the outcome of whom the Republicans nominate, say John McCain. Would that increase her chances, or decrease them? And what if there is a market, say the Democratic Party Nominee Market, where Hillary is just one of several contracts trading? And furthermore, what if you were only one of 10 traders in the market, so that every time you bought or sold a contract you moved the market by over 10%?

Now add to this the fact that most people don’t know how to trade. For most people investing involves a 401K and maybe an IRA. Shorting stock is an alien concept to most. While day traders rabidly tapping out buy and sell orders into brokerage sites gripped the imagination for a while in 2000, these types of traders are a very small percentage of the U.S. population. Most people, it turns out, aren’t very good at buying and selling stocks at all.

And finally, why would one even care about buying contracts on Hillary’s presidential aspirations?

“These challenges become even harder when one thinks about implementing a prediction market within the corporate environment. Here, the cost of playing can be high (trading instead of working), and the contracts can be relatively arcane–predictions of future sales, for example. Selecting a large enough group of the right traders appears to be a difficult task.”Making prediction markets work
Prediction markets are appealing and useful because they can make predictions about future events, but getting people to use them effectively requires a bit of work. They need to make themselves relevant, attract the right types of traders, and teach people who are new to trading how to trade.

Several websites have taken on this daunting task with mixed success. These public prediction markets offer markets and contracts that cover a range of issues and events. They each have different functionality, and have designed their markets in different ways, but all are trying to solve the same problem: how to engage users to participate in these markets, and produce meaningful predictions.

What follows are some basic and general rules, gleaned from audits of these sites (with selected examples to help explain), about how to improve the communication and simplification of complex ideas about trading, and how to engage users, particularly when developing a prediction market. Some of these rules will be familiar and appropriate to all websites (but with a different emphasis for prediction markets), and some will be unique to prediction markets.

The following is a brief summary of these rules:

  • Liquidity is important to a market, so make people want to play.
  • Brief examples on the homepage will sometimes be the only learning users will have the patience for. Use these examples widely.
  • The stock market is a good metaphor for prediction markets, but don’t take it too far.
  • Simplify. Allow users to focus on the things that really matter.
  • Use tools that indicate and reveal what is happening. Trading is a nerve racking experience and users want to know what is happening.
  • Use contextual help. More so than elsewhere, contextual help guides users through complicated processes.

Make people want to play
Call it the usefulness of a website, or the hook which gets users to stay, the most essential part for any prediction markets website is to have markets and contracts that people are interested in participating in. As one recent book (1) put it, “As the wonkishness of the contract rises, however, volume and liquidity fall rapidly.” John Delaney, the founder/CEO of, puts it more directly: “there is very little point in listing a contract that no one is interested in.”

The Hollywood Stock Exchange (HSX) is a master at getting people to play. With well over 50,000 users using play money to buy and sell contracts and derivatives based on the revenue of movies and actors, the site is clearly popular. It is also predictive. As academics seek to understand how well these markets perform they have measured the predictive power of HSX against Hollywood experts in how well they can predict who will win Oscars (2). HSX, it turns out, is superior.

As an indication of how passionate some users are about HSX, top portfolios on HSX can sometimes be found on eBay where people are willing to pay real money for fake money accounts.

Protrade is another public prediction market and has extended the concept from movies to baseball, football and basketball. On this site users can “invest” in players who they think are under-valued, and “sell” players who they think are over-valued.

Sports, movies, and actors make good subjects for prediction markets, not only because they are popular, but because accessing relevant information and forming an informed opinion is easy. Sports statistics are readily found on any number of sites, and talking about movies and actors is virtually a global past-time. Who doesn’t know about Tom Cruise? Who doesn’t have an opinion on him? Who doesn’t think they know better?

Making prediction markets work in the corporate world partly depends on overcoming this hurdle. Unless you work for the Boston Red Sox or at Universal Studios there probably aren’t that many compelling issues that workers can get passionate about. Motivating people in the workplace to take part in prediction markets takes imagination.

Use your examples wisely
As we all know users don’t tend to read much online unless they have to, or they have found the specific content they’re interested in. First impressions are also very important, and communicating the content of the prediction market, and how it works at a high level, are usually accomplished on the home page. Therefore most prediction markets have some sort of example or explanatory text on the home page. Unless the users are diligent enough to read the help text, this is often the only chance a site will get in explaining itself.

Different sites choose different tactics, but perhaps the best and simplest approach is that chosen by the Yahoo Tech Buzz Game.

Yahoo’s Tech Buzz Game homepage example

In four simple bullets it explains how the game works, what you will be trading, and importantly, how prices are moved.

On you buy and sell words on the expectation of their frequency of appearance in English speaking newspapers. For example, buy the word Google just before a big announcement by the company and the contract in that word rises once the announcement hits the newsstand. Trendio tries to drive their point home graphically. homepage example

But they end up only confusing matters more. First, the example refers to ideas and not words. Second, without labeling and a consistent y axis it’s difficult to know what you’re comparing and how to even understand the charts.

When people are learning, examples are their guideposts. Use them wisely.

The stock market is a good metaphor, but don’t get stuck in it
Prediction markets are almost always compared to stock markets, and it makes sense to do so. Despite the fact that most people aren’t traders, most everyone has a passing familiarity with it. And almost everyone knows the most basic trading strategy of all time: Buy low, sell high!

But this model can also work against you. In the stock market there are many ways to enter an order. You can enter a market order, a limit order, an all or none order, and so on. While heavy traders might be happy to see these features imbedded into prediction markets, most people will be confused by them. Despite this some sites make the attempt.

Smarkets, though, a prediction market game that allows you to buy or sell contracts based on Amazon products and what their sales rank is, has simplified the trading interface to a point where it’s almost ridiculously simple.

Smarkets Trading Widget, Step 1 (contract information is to the left on the site)

Smarkets Trading Widget, Step 2 (contract information is to the left on the site)

The Smarkets trading widget dispenses with many of the complicated aspects of trading stock. Of course it can afford to as a play money game, but why make users deal with bid ask spreads and limit orders when they don’t need to. The majority of users will be just happy with a trading widget like the one above. If there are users for your site who want such functionality, then an advanced trading screen can be created for them, such as on Hollywood Stock Exhange (HSX).

Experts may quibble that a bid ask spread is important to price discovery, and so it is, but if you can make do without exposing it to the user, why not?

HSX Trading Widget

The HSX trading widget is even simpler and allows for even greater flexibility. Mouse over the movie you want to buy and the widget pre-populates. Max quantity allows you to purchase the maximum allowed amount. If you already own shares in this movie, to sell all of them just sell max, and you never have to worry about remembering how many shares you already own.

Both sites also take another idea from stock trading that people may be familiar with and twist it in a way that’s easy to understand and use. Shorting stock is a difficult concept to understand, and in the stock market it requires that you borrow shares from someone else (your broker figures out whom), sell them on the market, and then repurchase them later, hopefully at a cheaper amount. You pocket (or pay) the difference.

There is some complicated accounting involved and the trader who does this must have a margin account. In other words, you must always have a certain amount of money available in case the market turns against you.

Prediction markets handle the desire for traders to bet against an event or movie by allowing traders to buy shares in the “opposite outcome.” But exposing two contracts for the same event (or movie) to neophyte traders can, perhaps, be a bit confusing (both could potentially be bought and sold), so Smarkets and HSX disguise buying an opposite outcome contract as “selling short.”

In this way they’ve jettisoned the complicated accounting and they’ve removed the “opposite outcome” and, as a result, simplified the user experience. Of course, traders aren’t actually “selling short” but it makes no difference to them. They’ve accomplished what they wanted to accomplished – bet against the movie, or event.

Simplify, simplify, simplify
Yahoo’s Tech Buzz Game does something very interesting and quite logical. They’ve dispensed with allowing the user to enter the amount of shares they want to purchase. Why make the user do the calculation at all? The user only cares about how much money they’re going to invest in a certain contract not in how many shares they’re going to buy.

Yahoo Tech Buzz Game Contract page

By only allowing users to enter an amount of money they’ve actually saved the user time and an extra step. However, they have not carried this innovation over to the selling side of things. When selling, you begin with how many shares you’d like to sell, rather than how much money you’d like to cash out (which would be nice, wouldn’t it?). This is entirely appropriate, since a user may only want to sell half their shares instead of all of them.

Use tools that support the trading process and reveal what’s happening
For those unaccustomed to it, trading can be a nerve racking experience, even when trading with play money. While you bank account may be safe in these games, your ego is not. Nothing is quite so dispiriting as seeing your “assets” dwindle because you were wrong. Therefore, tools that help a user feel “safe” when trading, by explaining what is happening, and exactly what everything means, have value.

Inkling, for example, draws a direct connection between the expected probability of the event and the price. It then sets up three possible trades for you based on how different your expectations for the event happening are from the probability implied in the price. It also allows user to enter their own trades, if they so wish, but neophyte traders can allow themselves to be guided through the process of aligning their expectations for the event occurring and the price and number of contracts they purchase.

Inkling Trade Assistant, Step 1

Inkling Trade Assistant, Step 2

This guidance through the trading process is akin to training wheels on a bicycle. Experienced traders will have no need for this level of hand holding but beginning traders will appreciate it.

HedgeStreet, a real money peer to peer trading site includes a neat tool that helps the trader foresee any potential gains and losses.

HedgeStreet Trading Tool

In this case (Euro-US$ futures contracts) the user can enter in how many shares they would like to purchase. The price is automatically entered for them and their potential gain (upside) and loss (downside) is automatically calculated. It immediately tells you the amount of risk and potential reward you are accepting before making the purchase.

Use contextual help … a lot
And finally, as my father used to say, when all else fails read the instructions. Most prediction market websites have relatively robust Help sections, since there is a lot to learn and get help on. Better, though, are the sites that present their help contextually, and in this way support the user while they are in the middle of a task rather than forcing them to leave the page and go to the Help section.

NewsFutures has the humorously (but appropriately) named “I Need Help!” link which opens up a pop-up window with detailed instructions on how to read what is probably (certainly to most new traders), a difficult to understand bid and ask price list.

NewsFutures “I Need Help” link

NewsFutures “I Need Help” pop-up

While the help diagram is busy, the idea is right.

Smarkets makes it even simpler. With the link placed right under the order type drop down, the user can quickly access an explanation of what they should be doing.

Smarkets contextual help

Contextual help, while not the most sexy recommendation, is just one of the little things that public prediction markets should get right, more so than most sites, in order to create a useable and enjoyable experience for users.

As prediction markets push beyond being just games and interesting experiments, particularly as they expand into the corporate world, simplifying and communicating complex ideas about trading, contracts, and markets will become crucial. These examples show just some of the ways that these complex tasks and ideas can be communicated.

The most successful sites are those that understand the experience range of their users. Some are veteran traders who know what to do, while others can’t tell a bid from an ask price. Accommodating the novice traders is crucial to the success of these markets, as well as moving them along as they gain experience.

Prediction markets hold great promise. Research has shown a well designed prediction market can predict the future better than experts most (if not all) of the time. This promise encourages us to apply these markets to new areas of knowledge and in new environments. As more people use prediction markets, though, the more usable prediction markets must be.