Partially as a result of the different settings in which these activities take place, the VCR has remained an underused piece of electronics, while the automobile continues to play a central role in our culture. This example is just one of many that the authors cite in weaving a cautionary tale about relying exclusively on technology — especially information technology — to drive the future of our organizations, institutions, and societies. Instead, we must recognize how social needs — especially around learning — influence our acceptance and successful application of new technologies. If we fail to do so, we’ll continue to build products that people can’t use, design strategies that people won’t implement, and recommend changes that people fail to embrace — regardless of how elegant or sophisticated those solutions may be.

Broken Promises of the Information Age

To bolster their argument, Seely Brown, director of the famed Xerox Palo Alto Research Center, and Duguid, research specialist in social and cultural studies in education at the University of California at Berkeley, explore some of the broken promises of the Information Age. What ever happened to visions of the “paperless office”? Or predictions that the organizations of the 21st century would be flatter and less centralized than their 20th-century counterparts? Or the idea that most of us will soon be working for “virtual corporations,” dialing into the office every day from our homes? Despite now having the technical means to make such divinations realities, we have yet to do so. Are we merely creatures of habit, stubbornly standing in the way of progress? Or are there deeper reasons why the digital revolution hasn’t changed our world as quickly and as completely as some soothsayers had prophesized?

Seely Brown and Duguid believe that many of the predictions about the transforming impact of bits and bytes fail to take human needs and desires into account. They state, “The tight focus on information, with the implicit assumption that if we look after information everything else will fall into place, is ultimately a sort of social and moral blindness.” The authors argue that “rather than condemning humanity as foolish, primitive, or stubborn for sticking with the old and rejecting the new, it seems better to stop and ask why.”

Their probing questions produce interesting — and sometimes counterintuitive — results. For instance, why has the rise of digital communication corresponded with an unfortunate jump in paper consumption, when many predicted that computers would replace the need for printed documents? In exploring this query, Seely Brown and Duguid found that paper is more than just a carrier of information; it offers certain qualities that are challenging to duplicate in electronic form. Documents bear smells, textures, and smudges that convey meaning. For instance, think of the reactions that a letter on high-quality bond, a perfumed notecard, or a tearstained letter can provoke in the recipient — characteristics that are difficult to emulate by computer.

The authors sense that we have found cutting-edge technologies and old-fashioned pen and paper to be complementary rather than competitive. They cite the case of the fax machine, which has grown in popularity even as seemingly more efficient modes of communication have evolved. People still find it useful to be able to scrawl comments on a document and drop it in the fax for instant — and accurate — transmission.

Likewise, for years, pundits have predicted that the rise of e-mail, the Internet, and the World Wide Web would lead to flatter organizations, with information systems replacing middle managers. What these futurists failed to recognize is that managers add value to the flow of information; they aren’t simply conduits that can easily be replaced by machines. And technology can actually lead to greater centralization. With the compression of space and time made possible by digital communication, the main office can now maintain tighter control over branch offices than it could when information flowed more slowly. Thus, technology won’t automatically cause more egalitarian organizational structures; managers still must choose to share power and authority with others.

Knowledge and the Knower

Seely Brown and Duguid also address the topic of knowledge management. In an effort to leverage employees’ learnings and insights, numerous companies have invested fistfuls of money in knowledge databases. But many have found that, despite their best intentions, they have created only static repositories of information. True knowledge is notoriously difficult to “detach” from the knower. As a case in point, the authors cite several companies that have successfully identified best practices in one plant but have been unable to implement those practices in another factory just across town.

Why is transferring knowledge from one plant to another, or from one person to another, so difficult? This question brings us back to the example of the video-cassette recorder — and the social nature of learning. Seely Brown and Duguid refer to anthropologist Julian Orr’s study of the spread of knowledge among Xerox technical representatives — which occurred in spite of the company’s information systems. Orr found that the company-supplied documentation was inadequate for all but the most routine tasks that the reps faced. So the reps found ways to engage in collaborative problem-solving, knowledge sharing, and knowledge creation outside the organization’s formal processes — through telling stories over breakfast or while troubleshooting breakdowns together.

“Become a member of a community, engage in its practices, and you can acquire and make use of its knowledge and information. Remain an outsider, and these will remain indigestible.”

The reps formed a community that was linked by their common practice of servicing copiers. “The members of this community spent a lot of time both working and talking over work together. . . .The talk made the work intelligible, and the work made the talk intelligible. . . . Become a member of a community, engage in its practices, and you can acquire and make use of its knowledge and information. Remain an outsider, and these will remain indigestible.” The reps ultimately adopted a knowledge database that succeeded in becoming a valuable resource because they themselves determined what tips and insights to include. In this case, the technology supported — rather than sought to replace — the workers’ social network and processes.

Learning as a Social Process

Based on their findings, the authors have several recommendations for moving from an information-based to a knowledge-based model of learning. They highlight the power of collaboration, storytelling, and improvisation. They cite the example of a problem-solving session at Xerox that resembled “a series of alternating, improvisational jazz solos, as each [rep] took over the lead, ran with it for a little while, then handed it off to his partner, all against the bass-line continuo of the rumbling machine until finally all came together.” This kind of learning would be difficult to glean from a user’s manual or information database.

Seely Brown and Duguid also advocate balancing formal and informal processes, as well as structure and spontaneity. Too many constraints can limit creativity; too few can hinder productivity. They comment that “The use of deliberate structure to preserve the spontaneity of self-organization may be one of humanity’s most productive assets.”

The authors are careful to point out that knowledge creation and sharing mustn’t remain the purview of the folks in product development. “Businesses have to create new business models, new financial strategies, new organizational structures, and even new institutional frameworks to deal in these new markets.” Companies must look beyond their own walls to view their formal and informal connections with other businesses — especially those located close by. Seely Brown and Duguid point out the synergies present in “clusters” of companies in similar industries, such as the high-tech cluster in Silicon Valley, the Formula 1 cluster of race-car designers outside of London, and the golf-club cluster outside of Los Angeles. Such hotbeds of knowledge on a particular subject can offer economies of scale and broad-reaching networks of practice for all players.

Far from being a pessimistic diatribe about the limits of technology, The Social Life of Information highlights the potential that exists in the human mind and spirit. Time and again, though, the authors remind us that machines, software, and datalines must serve human needs — and that humans don’t exist merely to fulfill a destiny predetermined by our tools. In order to make the most of the incredible technical resources that we’ve created, we need to tailor them to help bring us together rather than allow them to push us farther apart. By remembering that learning and knowledge creation are social processes, we can ultimately leverage the promise of technology to build a better future for all.

Janice Molloy is content director at Pegasus Communications and serves as managing editor of THE SYSTEMS THINKER.

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A Starting Point for Theory-Building

Quality pioneer Dr. Edwards Deming once said, “No theory, no learning.” In order to make sense of our experience of the world, we must be able to relate that experience to some coherent explanatory story. Without a working theory, we have no means to integrate our differing experiences into a common picture. In the absence of full knowledge about a system, we must create a theory about what we don’t know, based on what we currently do know.

Each systems archetype embodies a particular theory about dynamic behavior that can serve as a starting point for selecting and formulating raw data into a coherent set of interrelationships. Once those relationships are made explicit and precise, the “theory” of the archetype can then further guide us in our data-gathering process to test the causal relationships through direct observation, data analysis, or group deliberation.

Each systems archetype also offers prescriptions for effective action. When we recognize a specific archetype at work, we can use the theory of that archetype to begin exploring that particular system or problem and work toward an intervention.

For example, if we are looking at a potential “Limits to Success” situation, the theory of that archetype suggests eliminating the potential balancing processes that are constraining growth, rather than pushing harder on the growth processes. Similarly, the “Shifting the Burden” theory warns against the possibility of a short-term fix becoming entrenched as an addictive pattern (see “Archetypes as Dynamic Theories” on pp. 9–10 for a list of each archetype and its corresponding theory).

Systems archetypes thus provide a good starting theory from which we can develop further insights into the nature of a particular system. The diagram that results from working with an archetype should not be viewed as the “truth,” however, but rather a good working model of what we know at any point in time. As an illustration, let’s look at how the “Success to the Successful” archetype can be used to create a working theory of an issue of technology transfer.

, “Success to the Successful” Example

An information systems (IS) group inside a large organization was having problems introducing a new email system to enhance company communications. Although the new system was much more efficient and reliable, very few people in the company were willing to switch from their existing email systems. The situation sounded like a “Success to the Successful” structure, so the group chose that archetype as its starting point.

'SUCCESS TO SUCCESSFUL' EMAIL

Starting with the “Success to the Successful” storyline (top), the IS team created a core dynamic theory linking the success of the old email systems with the success of the new system (middle). They then identified structural interventions they could make to use the success of the old systems to fuel the acceptance of the new one (loops B5 and B6, bottom).

The theory of this archetype (see “‘Success to the Successful’ Email” on p. 8) is that if one person, group, or idea (, “A”) is given more attention, resources, time, or practice than an alternative (, “B”), A will have a higher likelihood of succeeding than B (assuming that the two are more or less equal). The reason is that the initial success of A justifies devoting more of whatever is needed to keep A successful, usually at the expense of B (loop R1). As B gets fewer resources, B’s success continues to diminish, which further justifies allocating more resources to A (loop R2). The predicted outcome of this structure is that A will succeed and B will most likely fail.

When the IS team members mapped out their issue into this archetype, their experience corroborated the relationships identified in the loops (see “Core Dynamic Theory”). The archetype helped paint a common picture of the larger “elephant” that the group was dealing with, and clearly stated the problem: given that the existing email systems had such a head start in this structure, the attempts to convince people to use the new system were likely to fail.

Furthermore, the more time that passed, the harder it would be to ever shift from the existing systems to the new one.

Using the “Core Dynamic Theory” diagram as a common starting point, group members then explored how to use the success of the existing system to somehow drive the success of the new one (see “Extended Dynamic Theory”). They hypothesized that creating a link between “Usefulness of Existing Email” and “Usefulness of New Email” (loop B5) and/or a link between “Use of Existing Email” and “Usefulness of New Email” (loop B6) could create counterbalancing forces that would fuel the success loop of the new system. Their challenge thus became to find ways in which the current system could be used to help people appreciate the utility of the new system, rather than just trying to change their perceptions by pointing out the limitations of the existing system.

Managers As Researchers and Theory Builders

Total Quality tools such as statistical process control, Pareto charts, and check sheets enable frontline workers to become much more systematic in their problem solving and learning. With these tools, they become researchers and theory builders of their own production process, gaining insight into how the current systems work.

Similarly, systems archetypes can enable managers to become theory builders of the policy- and decision-making processes in their organizations, exploring why the systems behave the way they do. As the IS story illustrates, these archetypes can be used to create rich frameworks for continually testing strategies, policies, and decisions that then inform managers of improvements in the organization. Rather than simply applying generic theories and frameworks like Band-Aids on a company’s own specific issues, managers must take the best of the new ideas available and then build a workable theory for their own organization. Through an ongoing process of theory building, managers can develop an intuitive knowledge of why their organizations work the way they do, leading to more effective, coordinated action.

ARCHETYPES AS DYNAMIC THEORIES

Limits to success dynamic theory

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