Design

We are surrounded by things that have been designed—from the utensils we eat with, to the vehicles that transport us, to the machines we interact with. We use and experience designed artifacts everyday. Yet most people think of designers as only having applied the surface treatment to a thing conceived by someone else. Eli Blevis created an illustration to emphasize the gulf between the general public’s notion of design and designer’s views of design (Blevis et al., 2006) (see Figure 19.1).

Figure 19.1 – A caricature of the popular conception of design vs. all other concepts.

Ultimately, everything that has not come from nature has been designed—it just may not have been consciously designed. As early as 1938, Moholy-Nagy described design as more than just facade making. He suggested that design was “a complex and intricate task … and the integration of technological, social and economic requirements, biological necessities, and the psychophysical effects of materials, shape, color, volume, and space’’ (Moholy-Nagy, 1938). Most design definitions also include planning as a critical element. Janet Murray, author of Hamlet on the Holodeck, describes the designer’s role as making ‘‘something new that fits in with what already exists or changes it in a positive way.’’ This description of design is consistent with Herbert Simon’s seminal work in which he says, ‘‘Everyone designs who devises courses of action aimed at changing existing situations into preferred ones’’ (Simon, 1996). Marty Neumeier simplifies further by suggesting that ‘‘design is change’’ (Neumeier, 2009). Of course, change (or the process of change) can be changed. That is, change can be designed; thus, design can be designed.

Service

There are many definitions of service in the literature. On one hand, services are viewed as performances: choreographed interactions manufactured at the point of delivery that form a process and coproduce value, utility, satisfaction, and delight in response to human needs (Zeithaml and Bitner, 1996; Evenson, 2005; Engine, 2006). On the other hand, activities or events in a service process are described as forming a perceivable set or ‘‘product’’ through interaction with designed elements or resources from representatives of the service organization, the customer, and any mediating technology.

For purposes of this discussion, we put forth the definition described by Jean Gadrey and based on Peter Hill’s 1977 work (Gadrey, 2002): ‘‘a service may be defined as a change in the conditions of a person or a good belonging to some economic unit, which is brought about as the result of the activity of some other economic unit with the prior agreement of the former person or economic unit.’’ Gadrey goes on to explain that a service should first be considered a process, and illustrates service as a triangle that includes three primary elements: service provider, customer/client/user, and transformation of a reality (Figure 19.2).

Figure 19.2 – The service triangle as illustrated and defined by Jean Gadrey. (2002)

Are services in support of ‘‘changes in the conditions of a person’’ similar to changing existing situations into preferred ones? Are services change? Are people participating in the service designing as they cocreate the service? The concepts Gadrey presents with respect to service relations, interactions, operation, and activity are well suited for defining service as design.

We view designing for service as a meta activity: conceiving and iteratively planning and constructing a service system or architecture to deliver resources that choreograph an experience that others design. When a company provides the optimal mix it will have produced a resonating service system and delivers an experience advantage (Evenson, 2005).

Designing for service is a process that brings together skills, methods, and tools for intentionally creating and integrating (not accidentally discovering and falling into) systems for interaction with customers to create value for the customer, and, by differentiating providers, to create long-term relationships between providers and customers.

Figure 19.3 – A service as design triangle. After Gadrey. (1996A)

Experiences Matter

Our lives are shaped by—and emerge from—the experiences we have. How we are greeted when we enter a store shapes the experience that we will have while in the store. When Apple introduced the iPhone, they consciously designed the journey that their new phone customers would have—from learning about the features they would use on the phone in advance of sale of the phone, to making the activation (once a torturous event with most cell providers) a self- service affair that could be done at home with ease. Smart companies work hard to provide the appropriate resources for customers to have experiences that they value.

Pine and Gilmore (1999) suggest that we seek out experiences that fulfill our needs and satisfy our wants. Today (having satisfied many basic needs), people are looking for more (and more meaningful) experiences. Many people are willing to pay more for their coffee or their hotel stays if the brand reinforces their image of themselves. Consider the shift in just one generation’s experience. Many baby boomers grew up in small town America, purchasing through the Sears, Roebuck catalog. In that shopping experience, the catalog arrived and the customer poured over the pages to select just the right thing. The customer either called or mailed an order form back to Sears. Weeks later the purchase arrived and the customer was either pleased or not. If the customer was not pleased, there was a lot of work to be done to return the item and receive credit. Fast-forward to today: Nike offers customers the opportunity to design their own shoes (items that are notoriously hard to fit) online. Zappos also sells shoes online. From the get-go they understood the need for an experience that would exceed customer expectations (Taylor, 2008). They began by offering overnight delivery, which in part was made possible by the technical infrastructure they have in place. Customers report ordering shoes at 8 p.m. and having them arrive at 8 a.m. the following morning. Both examples contrast with the customer experience with Sears decades earlier. Customer expectations have changed dramatically, and if they want to be successful, organizations need to provide the resources for exceptional customer experience. Zappos and Nike are raising the standards for their competitors and for all online retailers.

But not only have expectations changed for online retail, expectations are changing in health care. In a recent McKinsey survey of more than 2000 patients with commercial insurance, ‘‘75% would consider switching hospitals to become better informed about treatments or if appointments were kept on time. If forced to choose between information and timeliness, 3 times as many patients said they valued information more’’ (Grote et al., 2007). Because there is so much more information available generally, people’s expectations have been raised to want better information, tailored for them personally.

People today also want experiences that support their values, whether it is their concern for the environment or their belief in natural foods. Perhaps this fulfillment behavior has gone too far (or at least lacks substance) when people with means can purchase ‘‘carbon offsets’’ to ease their guilt over behaviors that conflict with their personal value of not contributing to pollution. People are seeking meaningful experiences as part of a community as evidenced by the doubling in recent years of people who planned to volunteer on their vacations (Dalton, 2008).

Great experiences are leading to a demand for even better experiences. As expectations for service experiences rise—are the people participating or cocreating those experiences becoming more skilled at leveraging the resources for their experience and designing their service? If so, then what are the implications for designing-for-service experiences?

In designing-for-service experiences we must provide the opportunity for customers to have meaningful, compelling, and fulfilling experiences that address their needs and satisfy wants. We need to provide the resources for people to design, so that they can create their own experiences (Tempkin, 2008).

Given the current cultural, social, and economic contexts, the resources need to meet or exceed people’s expectations, and encourage participation so that customers become advocates for the brand. (In a sense, they invest in the brand, taking ownership and cocreating the brand itself.) The technology is now in place as a key differentiator in service delivery. What happens at Zappos today simply was not possible just a few years ago. They have raised the table stakes for all other companies.

Creating an Experience Advantage by Providing the Resources for Cocreation

Ganz and Meiren (2002) suggest a need for knowledge about social interaction activities. This is due to an intense awareness that service work is ‘‘people work,’’ and too little is known about the human aspect of both the provider and the client in service definition. The consideration of this human aspect is a key differentiator in the design of a service system. People-centered research can drive innovation.

Designing for service, from our perspective, assumes the participants are the starting point or lens for this exploration. This is essential because the service designer is providing the ‘‘clay’’ (or perhaps the potter’s wheel and kiln) for participants to design for themselves. Through the use of creative, human-centered and participatory methods, we model how the service could be performed or provided.

At the same time, service design identifies and integrates the means to provide a service with the desired qualities within the economic and strategic intent of an organization. Collaborators ‘‘visualize, express and choreograph what other people can’t see, envisage solutions that do not yet exist, observe and interpret needs and behaviors and transform them into possible service futures, and express and evaluate, in the language of experiences, the quality of design’’ (Service Design Network, 2005). As a discipline, service design should not be viewed in isolation, but as complement to service development, management, operations, and marketing (Service Design Network, 2005; Mager, 2002; Edvardsson et al., 2000).

In our approach to designing for service innovation, we integrate these activities across a service development process that includes exploratory, generative, and evaluative research that spans the entire development process—from discovery to release The process differs from conventional approaches, such as those defined by Booz and Hamilton (1982), Bowers (1985), Khurana and Rosenthal (1997), and Zeithaml et al. (2006), where strategy is defined prior to investigation, creating an outline of the service that has to then be filled in. We argue that the right strategy cannot be known a priori. Instead of trying to define a service from the top down, we start with exploratory or immersive research to lead to opportunities for innovation in strategy. This, in turn, provides context (or the fill) from which the service can be created.

People-centered Research Drives Innovation

The approach we have taken to service design is based on our experience in interaction design and approaches developed and published primarily in Europe (Erlhoff et al., 1997). At Carnegie Mellon University we have organized our approach within a conventional design process framework, leveraging exploratory, generative, and evaluative research methods along the way.

Exploratory Research—Uncovering and Understanding Latent and Masked Needs. In exploratory research, techniques are used to define ‘‘what is’’ in the current situation or context. Methods used in exploratory research are typically drawn from ethnography and include shadowing, participant observation, and contextual inquiry. The goal of this type of research is to immerse the researcher–designer in the context of the inquiry and to provide a deep under- standing of not only the category of people under observation, but also their goals and needs.

In a recent project at Carnegie Mellon, students were asked to improve service flow at the Transportation Security checkpoint at the local airport. Students first documented stories of their experiences at the Pittsburgh airport and other airport checkpoints. This directed storytelling exercise immersed them in the context of the experience even before going onsite. After just a few hours of observation, the students uncovered a latent need and documented it. They found that passengers and their friends and loved ones had no place to say goodbye. The service as designed for the critical security-checking goal provided resources for security officials and a few for passengers to participate in the process, but the physical space, in particular the area leading up to the security checkpoint, the communication products such as the signs and cue markers, and the service providers offered little support for another fundamental activity in the traveling process—people simply saying goodbye.

Generative Research—Determining What Is Meaningful. In generative research, the goal is to verify the framing of the ‘‘what is’’ and assumptions about how to respond to the needs identified with representatives of the service participants. Early on in generative research the activities are more projective and include exercises that help people express ideas, emotions, and desires around the service experience, The exercises are designed to help people express or explore what is usually hard for them to communicate—how they feel about the given service experience on an emotional level. Later activities are more constructive and are designed to validate specific reactions to service concepts, flows, and evidence. Figure 19.4 illustrates the projective and constructive faces of generative research (Hanington, 2007).

Figure 19.4 – Model of generative reserach (Hanington, 2007)

The later activities are usually design collaborations between designers and participants in sessions that may include people, process, and artifacts that encourage creativity and conversations (Sanders, 2000). In these sessions designers and participants engage in the meta-design of the experience resources when they coproduce prototypes and enactments of the service experience. In a recent project with UPMC (the University of Pittsburgh Medical Center) students teams engaged in two very different activities to elicit patients’ emotional needs with regard to their health-care experiences. In the first case, students provided patients with a set of stimulus cards that had images of different environments in which the ambiance ranged from a baby sleeping in a room to a pianist playing in a concert hall. The participants were asked to select images that best represented the experience they would like and to explain why. Another team took a slightly different approach. They provided respondents with sets of four images of the same thing, such as four orange juicers or four magazine covers, and asked respondents to compare the images to what they wanted from the service setting and explain why one of the images was most appropriate and another was least.

The resulting conversations from both of these participatory exercises helped the design team suggest appropriate resources (places, products, and people’s behavior) for the ultimate service users to design a health-care experience that would be right for them.

Evaluative—From Concepts to Recommendations. Evaluative research helps validate whether the needs and expectations people bring to the service experience are actually met by the resources as designed. Ultimately, the goal is to determine if the resources provided for the experience are useful, usable, and desirable for the intended service users and providers (Sanders, 1992). Methods may be tightly controlled as in a lab experiment or loosely defined as an extension of generative activities (Hannington, 2007). The purpose is to evaluate the resources while they are still easy to change and before major investment is made in producing the service process, service products or evidence, or the setting for service delivery.

An Integrated Service Design Process

An integrated service design and implementation process is key to the success of any service experience. We have found a multidisciplinary effort with a modeling-centric approach to be most effective for service design. The process is illustrated in Figure 19.5 in the context of the previously described people-centered research model. Though the process as shown is illustrated in a linear fashion in practice, it is fluid and iterative.

Figure 19.5 – Integrated design process and people-centered research.

The Five Major Stages in Designing for Service. There are many models of the design process, and many service design organizations opt for their own variations, while others prefer not to be confined to a single process. We have refined our process through practice, but admit that it is fluid and should change according to the design challenge (Evenson, 2005). The activities in the stages of our current process are described briefly in Table 19.1.

Table 19.1 – Process Overview

Service designers must account for the complexity of service resources that must be accessible to the appropriate participants to design the service experience for themselves. Methods that service designers use to address this complexity in particular are service ecologies, experience prototyping, and service blueprinting. Service ecologies are maps of the participants and entities affected by a service and the relationships between them. Ecologies or mappings of the research findings reveal new opportunities and inspire ideas, and they help to establish the overall service concept (livejwork, 2004). Experience prototyping brings the service experience to life. First designers, and then stakeholders in the experience, act out the service experience with specific roles and rough props. This is similar to Brenda Laurel’s design improvisation (Laurel, 2003). The goal is theater that enables the designers to better understand the contextual level of the design experience. This understanding is crucial because experience emerges from the activity of persons acting in a setting and is embedded in context and ongoing social practices.

G. Lynn Shostack developed service blueprinting. She states, ‘‘a service blueprint allows a company to explore all the issues inherent in creating or managing a service.’’ She goes on to explain that there are four aspects to the blueprint. They are process identification, isolation of fail points, establishing the time frame, and analyzing profitability (Shostack, 1984). We have extended this approach to include opportunities for service innovations that are derived from immersive research.

Service Design Languages

Just as spoken languages are the basis for our conversations with people, so design languages are the basis for conversations with services—they are building blocks of the service experience. People use spoken language to express themselves; services designers use service design languages to express the service, what it does, how it is to be used, and what experiences or journeys are made possible through it. Service design languages are used to visualize, express, and choreograph the resources that mediate the service experience. A design language consists of a system of elements (with associated meanings) through which designers signal purpose and users ‘‘read’’ intent (interacting with expectations), for example, ‘‘grip here’’ or ‘‘this is a button that can be pressed.’’ A design language also includes a set of organizing principles (the rules and conventions) for combining elements.

Spoken languages consist of words and rules of grammar. Design languages consist of design elements that are combined into constructs, such as a touch point, and the principles for their combination. Spoken language supports the production of meaningful expressions by allowing people to combine well-known sets of words and rules of grammar to create previously unknown but usually comprehensible expressions. In addition, spoken language is generative and inherently open. Research into creating a service language, so it is similarly open, will be invaluable.

With a service design language it is possible to visualize, express, and choreograph the resources for interaction. Design languages are general to a servicescape, such as a coffee shop with a condiment station for tailoring the coffee that has a flat place large enough to hold several drinks, trash receptacles, sugars, creamers, and so on, and specific to a particular brand (e.g., in the way Starbucks expresses a condiment station) (Bitner, 1992). Essentially, design languages are the means by which

• Designers build meaning and create coherence in the service interface
• Service interfaces express themselves and their meanings to people
• People learn to understand and use the service and engage in experiences associated with the service journey
• Companies establish new industry standards for quality, market presence, and customer satisfaction

When an effective service design language is deployed consistently, people who use or access services become fluent in their interactions with the service. Designers and developers are also articulate and skilled at the production of the resources for service delivery. Research into design languages is likely to influence service design in multiple ways. An exploration of service design languages will augment or change existing business process description or blueprinting methods that are used for describing the current state of service experiences. This work is a natural compliment to research into specification, choreography, improvisation, and, most importantly, implementation.

Cocreating and Experience Advantage – Designing Design

Approaching service as designing will lead to new ways of thinking about service innovation. Service as designing means service itself is fundamentally a creative process. As service designers we are engaged in meta-design—designing design—and are producing resources for people to creatively engage with a service. The position explains why the metaphor of choreography that is so often used with service experience may not be a metaphor at all. The choreographer creates a plan for the dance, but the dancer also creates the dance as he brings his own point of view to performing it.

What will the impact of a ‘‘service as designing’’ mindset be on the design of services such as a healthcare experience? In recent projects with the University of Pittsburgh Medical Center and the Mayo Clinic, Carnegie Mellon students have shown that a design approach and design mindset can lead to innovative solutions to serious service challenges. At a small scale it can mean simply better understanding the relationships that are created through interaction around the service. This is illustrated through the suggestion that catheterization lab team members wear ‘‘gear’’ that unifies them as a group and allows the patient and family to see them as their team. On a broader scale, the service as design mindset leads to service innovation concepts that put the patient more in control of their experience—both in proactive and in chronic primary care situations. In this case, the patients would then be provided with the resources to change their existing situations into preferred ones. We hope that more efforts to frame service as design can lead to even more innovative solutions for these and other important challenges.

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Editor’s Note:
In last year’s January + February issue Usman Haque, Paul Pangaro, and I described several types of interaction—reacting, regulating, learning, balancing, managing, and conversing. In the July + August 2009 issue, Paul Pangaro and I described several types of conversing—agreeing, learning, coordinating, and collaborating—and we proposed using models based on Gordon Pask’s Conversation Theory as a guide for improving human-computer interaction. Peter Jones responded, noting that there are other models of conversation and prior work in bringing conversation to human-computer interaction in particular Winograd and Flores 1986 work with The Coordinator. We agree on the importance of The Coordinator and invited Peter to outline the history of models of conversation and their relationship to HCI. His response follows.

—Hugh Dubberly

This article will step back in time to retrieve alternative, influential views of conversation for design, and then bring the discussion forward to current situations where we might learn from this history.

Three historically parallel pathways can be shown as influenced by a common circle of systems theorists: the well-known language/action perspective (LAP) [1], Rittel’s argumentation perspective [2], and the dialogic design school, emerging from Christakis’s structured dialogue [3] and Warfield’s science of generic design [4].

Distinctions between these three perspectives are readily apparent in the embodiments of their design languages in software, with very different routines for conversation modeling. They also share a central concern with the role of generative conversation for design outcomes. The current article series attempts to coordinate common elements and concerns among perspectives in the attempt to establish a workable common ground.

This article focuses on the theory of conversation embodied in LAP—an influential framework of phenomenology, pragmatics, and speech act theory. While LAP has received significant attention in prior ACM publications, the framework deserves further consideration in light of renewed interest in the systemic view of conversation in design. The emergence of massive social media networks has inspired interest in social design and social systems, particularly in applications to network systems, including business models, online social activism, and organizational systems.

A Conversation about Conversation

What are the contexts for conversation? Most theories of communication assume a dyad model of information exchange: two individuals talking with each other. Cherry defined “communication” as the exchange of normatively defined meanings and creating understanding between purposeful social participants [5]. Conversation is seen as a form of communication in which a particular exchange takes place between at least two people at a time, representing individual interests or intentions, or collective interests represented by individuals.

In everyday parlance, we subscribe to a more inclusive view. In fact, many and perhaps most conversations occur as or start with small talk. Known as phatic communication, it is present to some extent in most real conversations, and is identified as orientation” in the LAP model. While its power to reinforce relationships should not be minimized, here we focus on purposeful conversations that enable the coordination of multiple perspectives in the activity of designing.

Any design activity is guided by the intention to change a situation in accordance with a communicated desire or intention. Conversations for design must reflect and preserve the positions and contributions of multiple participants included (and excluded) in the model of change. By “merely” speaking, the designer creates a context for the relative inclusion of stakeholders or users, an ethic explicitly revealed by his or her conversational model. By extension of this assumption, the way we converse may also be seen as, perhaps unwittingly, reflecting our working philosophy of designing.

Several implicit models of conversation can be identified that guide participation in very different ways. Three epistemological orientations include the rational, pragmatic, and phenomenological.

The rational perspective may be viewed as an instrumental and purposive individual communications system used by designers to achieve sophisticated design outcomes. Conversation can be understood as a set of patterns employed as skillful means in facilitating the relationship between designers, stakeholders, and product or materials. This is the mainstream perspective in our technological culture, and perhaps the way most readers view conversation in design. This perspective is observable in practices that employ a well-defined set of methods and communications with every problem situation.

A pragmatic perspective considers design an inherently communicative practice, where design activities enact the creation of a linguistic system of meanings applicable to a problem in context. In practice, we create a unique coupling of appropriate language to the design situation, following stakeholders and their lifeworlds rather than promoting our own language of design. When we customize design methods to suit a particular purpose, rather than pull methods “off the shelf,” we reveal a pragmatic philosophy.

A phenomenological perspective acknowledges that all meaning arises in language, that human activity is not separate from language. This view suggests that design itself is a conversation, products and services are networks of other conversations, and designing acts are performed and recognized by language. Conversation is not a tool for outcomes; rather, language uses us, shaping and constraining our work and experience.

These are not mutually exclusive perspectives; designers may adopt different perspectives to calibrate responses to a situation, while scholars may be adherents of one school of thought. And while not an inclusive list, perspectives from sense making and constructivism, for example, range beyond this current focus of conversation for design. Elements of all three perspectives, and more, could inform responses to a single problem. The language/action artifacts appear to embrace elements from all three schools, even though the foundation text presents a phenomenological perspective.

Conversation as Designable Action

Readers of interactions and Communications of the ACM may be familiar with Winograd and Flores’s (1986) LAP work [6]. Flores demonstrated successes in software (The Coordinator and Action Workflow), education (Logonet and Landmark), and management (Business Design) based on an integral philosophical system. While LAP’s critique of the artificial intelligence field had an enormous humanist impact, its longevity was disrupted by critiques of the embedded conversational model in The Coordinator. Today we may consider the irony of how the LAP, a critique of the micro-cognitive and rationalist view of AI, was itself critiqued as socially deterministic (macro-cognitive) and insensitive to natural human interaction. However, LAP reenvisioned cognition and agency as responsive to action in the world, a humanistic concern. Winograd and Flores’s unit of analysis for

Table 1: Three Perspectives

embodied cognition was conversation, expressed in an explicit phenomenological approach known as ontological design. Ontological design was construed as a practice of formulating conversations to invent new modes of being and co-create action. Conversation was deemed the appropriate way not only to explore the possibilities invented in design activity, but also to generate those possibilities in reality by intentional speech acts.

Types of Speech Acts

LAP adopted Searle’s speech act theory, wherein language performs an action represented by the content and intent of the utterance. Performative speech acts instantiate the action referred to in speech itself. Five basic speech acts, called illocutionary points, are specified as:

• Assertives commit a speaker to the truth of an expression.
  
• Directives (such as requests, commands, and advice) cause the listener to follow a requested action.
  
• Commissives (such as promises and oaths) commit the speaker to future actions.
  
• Declarations change the circumstances of reality to accord with a proposition (e.g., pronounce a couple as married).
  
• Expressives convey a speaker’s attitudes or emotions about a proposition (e.g., praise, gratitude).

The applicability of performative speech acts in design was pointedly critiqued, essentially based on the hermeneutic problem that a listener might interpret an illocutionary point different from the speaker’s intention [7]. However, Searle’s model provides a descriptive power of language as action helpful in understanding and even guiding the messy dynamics of design practices. And since conversation (and hermeneutics) is recursive, continuous, and correctable, the interpretive critique seems overwrought.

Speech Acts in Conversation

While a conversation must be “about something,” conversations often have no purpose other than social mediation and acknowledgement of phatic communication. Conversations that lead to action exhibit intentionality, and differences in conversational structure are apparent.

Winograd describes three types of purposeful conversations based on the LAP model. His nomenclature reveals intention by the preposition for,” as conversations for”:

• Orientation
• Possibility
• Action

Orientation is maintained by conversation that mutually regards a shared referent object (e.g., the weather), “creating a shared background as a basis for future interpretation of conversations.” The intent of this so-called phatic communication is merely acknowledgement.

Conversations for possibility include interpersonal queries, inquiries, and propositions that “open a context.” Winograd notes the importance of common ground(background), including prior intent, upon which speakers can instantiate new contexts for conversation. There are no “goals” in conversations for possibility, but rather the co-construction of understanding and novelty.

A conversation can be observed as moving through progression of stages, where an opening affords the potential for action. The coordination of action requires meeting what Searle calls conditions of satisfaction [8]. Conditions may include some agreed outcome, and agreements about necessary quality and future dates. While some may consider these conditions goals, LAP does not refer to goals in the objective sense . This difference is crucial, as LAP suggests that we honor the commitment, as if spoken between persons, not the objectives.

This model has much in common with the discovery orientation in design practice. Designers are taught to “challenge the brief” and to help clients reformulate a problem as given so that the right framing of a problem is adopted in a design project. The skills for mediating conversations for possibility are learned through the experience of navigating different frames of possible visions or outcomes in conversation. Other distinct “conversations for” that were not proposed in LAP show in a designing context, as they occur as patterns of sense-making between committed participants. Conversations for understanding (or dialogue) and for clarification (convergence) are two that might be further distinguished.

Figure 1: Conversation for Possibility

Figure 2: Conversation for Action

Moving the “right possibility” toward a conversation for action is another embodied skill. The ability to move stakeholders in social design situations is not seen as a rhetorical, persuasive skill, but one that turns on what Searle identifies as illocutionary force. This is the extent to which action is performed by words, not by the semantic content, but by the speaker’s intent. The variable capacity to move together toward action is embodied by the speaker at the time of utterance. This distinction is inherent in LAP’s formulation of ontological design—design actions are co-created by speaker and listener at the time of conversation in a mutual grounding of understanding and agreement.

Learning from The Coordinator

A 2006 issue of Communications recapped the language/action perspective, but it included no mention of the early email activity management system released by Flores in 1986 The Coordinator was (primarily) designed for ultimately managing conversations for action, by instantiating requests, offers, counter-offers, promises, and other commitments as mediated transactions. These illocutionary points were identified in Flores’s earlier research on effective business conversations in the workplace, and were formulated in his notion that “organizations exist as networks of directives and commissives.”

Early email systems followed a slow adoption curve, given the limitations of computing and networks. Free-form email was initially perceived to be unnecessarily constrictive, a “cold” medium that was not at all conversational. During the years The Coordinator was available, early conventional email systems were used for sporadic and discretionary communications. The ubiquitous acceptance of email required a span of five years to alter communicative practices, even in dedicated organizations. While The Coordinator did not fit the cognitive style or tasks of existing organizations, even unstructured electronic communications were fraught with resistance and halting advances. Since The Coordinator also required a commitment to managing accountable communications, its use was limited to fairly small and dedicated networks.

The design and flaws of The Coordinator might still teach us about structuring conversations and accountable communicative actions. Perhaps the system’s intent was, as Lucy Suchman said, “to remedy the carelessness of organization members regarding their commitments to each other through a technologically based system of intention-accounting” [9]. Yet this critique focuses on the functions of The Coordinator, as originally designed. Speech act theory was certainly not perfectly matched to the intended domains of conversation. Searle’s explicitly-described theory does not preordain a “rationalist” implementation. As a conversation theory, it retains constructive power for formulating social (and therefore design) commitments as acts by their very communication.

One can agree with the underpinning concern of Suchman’s critique while identifying significant exceptions. For one, regulated organizations could benefit from Searlean communication by filtering today’s overwhelming volumes of data by displaying information by action: requests, commitments, dates, and implicit promises to network participants.

LAP-structured conversations might enhance communications in complex, high-reliability organizations. Winograd’s 1987 case study of hospital conversation flow foresaw the usability nightmare of electronic medical records systems. In regulated environments the coordination of commitments is as important as data quality. In operations such as healthcare, transportation, and the military, the ability to manage and respond to commitments fosters operational resiliency by managing actions that occur “as speech,” such as orders, responses, announcements, and outcomes. The entire chain of commitments following a medication order would be tracked as a directive conversation, rather than as “workflow.” It instantiates a process based on verbs, action, rather than nouns and objects. While Google’s adoption of the “conversation” as unit of communication appears to build on this perspective, in practice, few email threads are true conversations. The meaningful verbs that prompt action are hidden in today’s electronic communications.

While The Coordinator software passed into collective memory without further enhancement, Winograd and Flores’s bold experiment in organizing communication should be evaluated from an innovation perspective. Consider the audacity of introducing a dedicated, tightly structured email system in the late 1980s. As an early adopter, I found its most significant difficulty was the macrocognitive problem of its lack of organizational fit (as suggested by Suchman’s critique) and the necessity of changing communicative practices. For it (or any email system) to be of value, all participants in an action network had to agree to use it consistently.

Figure 3: The Basic Conversation for Action
This diagram translates Winograd and Flores’ original state diagram into a flow diagram, in the hope of making it more accessible.]

Conclusion

A major contribution of LAP was creating a design language for the construction and monitoring of commitment. As Web-based systems have greatly enabled the ability to collaborate, people are easily overloaded by multiple communication channels. Managing commitment and attention remains the weak link in our technology panorama. A conversation design perspective can enhance our coordination of attention as well as action.

With respect to The Coordinator, I would make the personal observation that successful software systems are rarely treated as newsworthy in scholarly publications, and failures are typically ignored. Successful software products are discussed only peripherally. With no venue for cooperative constructive critique of social and interactive artifacts, we collectively risk losing the value of learning from the wisdom embodied in such artifacts and their adoption by real users. We also suffer the loss of shared meaning from collective memory by not sustaining an academic tradition of a balanced interpretive review and critique of artifacts we design and endorse. Perhaps interactions might host such a critique as a shared conversation toward creating a critical discourse, in support of creating a constructive shared memory.

Finally, the emerging perspective of purposive design, of “designing for” (e.g. sustainability, thrivability, transformation, care) shares an ontological basis with “conversation for” in terms of intentionality and social teleology. When designing for a purpose, our “conversation for” that purpose brings it forth, a distinctly different view from a design method perspective. These and other proposals ought to be considered in the emerging reconfigurations of design thinking and practice.

About the Author
Peter Jones, Ph.D. founded the Redesign innovation research firm in 2001, and conducts independent and client-based research. Redesign, specializes in information and process strategies for scientific, organizational and healthcare practices. Jones is writing Design for Care (Rosenfeld Media. 2010), exploring how new design thinking is transforming healthcare. He resides in Toronto, where he is on faculty at Ontario College of Art and Design. Find Peter at Design Dialogues.

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Much of human behavior is directed toward goals: finding food, selling services, curing cancer, making meaning.

Achieving goals requires action. Action requires effort. Effort requires energy and attention applied over time. Effort overcomes obstacles. Obstacles tax our patience, sap our resolve, and cause us stress.

English (as well as many other languages) includes many metaphors that frame effort as a cost:

• I enjoy spending time with you.
• You’re wasting your energy.
• You’re not paying attention.
• This job is not worth the stress.
• It all takes its toll.

These metaphors suggest an economics of human behavior—a framework for understanding the human cost of living and the trade-offs we make momentby- moment as we choose one course of action over another. This paper begins the development of such a framework for everyday living and suggests how it might be applied to business and design. The authors hope to provide a means for us all to learn to act in better accord with our interests and thereby improve productivity and satisfaction, both individually and in concert with others.

Bio-cost measures human effort

Bio-cost is the energy, attention, and stress that people expend over time to achieve their goals—to get what they want” in Ashby’s sense. [Ashby 1956]

All of life’s activities carry some bio-cost. Most often, we “feel” bio-cost when we meet resistance—when we can’t enter a flow and act simply to get what we want. We experience the drain of bio-cost every day— when we find a stone in our shoe; when traffic slows us; when we struggle to change a channel with a remote control; when the bureaucracy requires we submit another form; when the boss makes contradictory requests; when the stock market sends mixed signals. Bio-cost limits what we can achieve because we may not have the resources to get what we want, or we might spend too much for what we get in return. This is true for individuals, groups, organizations, and species. While we may not be able to quantify bio-cost with precise measures— whether in anticipation of expending it or after the fact—the authors have found considerable utility in construing bio-cost as comprising distinct quantitative components.

Bio-cost is a function of time

All tasks take time to accomplish. The effort required to complete a task can be mapped against time (in basic cases, at least). Graphing against time we see an ebb and flow of effort—e.g., walking to a destination requires relatively constant bio-cost expenditure over time, while flagging down a cab and getting in requires an initial burst of effort followed by a period of relative rest during the ride, as in Figure 1.

Figure 1: Bio-cost of physical effort to travel by taxi (cyan) versus walking (black)

Bio-cost has physical, mental, and emotional components

In the case above, the physical effort can be measured as calories—the greater the effort, the more calories required. There are limits to our physical efforts; when taken to an extreme, we can experience muscle fatigue or exhaustion.

Bio-cost also has a mental component. Mental effort means attention paid to perform a task or even to think about how to perform it. As with physical effort, this use of our brains and all the components of our nervous system that coordinate our thinking and acting also requires effort and also has limits. Some tasks require more concentration than others, so the attention we pay will vary.

Similarly, we reach emotional limits as palpable as physical and mental ones when we get “stressed out” due to factors such as uncertainty and fear.

Bio-cost reveals trade-offs

Because the chemical and hormonal pathways overlay the nervous system, feeling has impact on thinking and vice versa. [von Forester 1973] A second-order awareness of the toll that a task is taking—whether in physical, mental, or emotional terms—may add further stress or alleviate it. This becomes part of a feedback loop that helps us to estimate the bio-cost expenditure required to be successful. When the task is to “save our life”— for example, to undergo invasive surgery to remove a tumor—our stress is increased because the stakes and uncertainties are high. When there are negative consequences for not completing a task by some deadline, such as getting to the airport in time to board a flight, perceived limitations of time can contribute to stress. Even non-time threatened situations raise our stress levels: Will I get fired for that mistake? Will I pass the test? Will she like me?

By reflecting on the bio-cost of specific activities in our daily lives, we can usually make trade-offs among the components—time, energy, attention, and stress as shown in Table I—to minimize the overall cost of getting what we want or need. At any point we may also decide to spend money to lower one or more dimensions of bio-cost. (Here we note without further exploration that this has the side benefit of allowing us to calculate a monetary equivalence for bio-cost, at least in a specific context. For example, avoiding the additional time and physical effort of walking is often worth the $10 monetary cost of a taxi—plus the stress of not knowing whether we can find one in time and whether traffic will cooperate.)

Table 1: Bio-cost components.

Can we replenish our “reserves”?

Clearly, we cannot recover time once spent, but given more time, we may be able to replenish our energy, our ability to concentrate, and our capacity to absorb stress.

After periods of intense activity, we often seek a better “life balance,” that is, we seek to counter-act activities that carry significant bio-cost with those that allow us to restore our physical, mental, and emotional systems. For example, we often say that we “make time” for family and friends, so that we can “recharge our batteries.”

Sleep appears to restore our energy, refresh our brains, and reduce our stress such that we can use our time more efficiently and make better choices. Many other activities also fit this category, such as meditation, the pursuit of sports, crafts, and the arts, or even mastery of a skill.

How do we assess bio-cost trade-offs?

In monetary transactions we commonly consider cost versus gain. This paper argues that the same is true for actions that involve the expenditure of physical, mental, and emotional effort, and that explicit awareness of this affords us the opportunity to reflect on trade-offs and improve the choices we make.

It is important to keep in mind, however, that we can’t always easily calculate the value of reducing bio-cost in monetary terms nor can we translate or commute a given valuation to other circumstances or individuals. Still, we maintain a belief in the gain and a sense of the cost, and we remain capable of generating an opinion as to what we will base our actions on right now. Put another way, we think the view is worth the climb.

In order to characterize a progression of variations of goal setting, taking action, and reaping rewards, the next set of figures start from a single participant and proceed to cover cases of cooperation and collaboration with others.

1 Bio-cost for single participant

Figure 2 draws from Pask’s model of goal/action systems [Pask 1975; Pangaro 2003], reinterpreted such that the “goal” level (L1 in Pask’s original) becomes the gain, while the “means” level (L0 in the original) becomes the cost. Per Pask, the goallevel controls the execution of procedures at the means-level, as indicated by the vertical arrow on right side. Results from execution are returned and compared to the original goal, as indicated by the line on the left with comparator sign.

Figure 2: First canonical form shows goals are achieved via separate means, where the means has a cost and achieving the goal creates a gain.

2 Bio-cost for cooperative participants

The next case involves a distinction between Participant A, who sets the goal, and Participant B, who agrees to perform the actions required to achieve that goal. The components are the same as in Figure 2. However, in Figure 3, there is a clear division (the vertical line) as goal-setting and action-taking are executed by different participants.

Participant B expends the bio-cost to achieve the goal on behalf of Participant A, who compares the result of B’s actions with the goal. We call the interaction cooperation” because there are clear roles and actions for A and for B—they co-operate, that is, they operate together but within agreed boundaries.

Figure 3: Second canonical form shows the allocation of goal-setting to Participant A, and action-taking to Participant B.

3 Bio-cost for collaborative participants

The third case also involves two participants but is more open-ended in that the distribution of roles and actions between participants is not predetermined. Rather, participants A and B collaborate— they “co-labor” or work together—to create and agree on the goals themselves, as well as to agree on who does what to achieve them.

In Figure 4, participants A and B converse at two levels: about goals (upper horizontal loop) and about the means to achieve them (lower horizontal loop). They likely also cooperate about means, and use feedback to check whether goals have been achieved (loops that cross from upper to lower level). In an ongoing collaboration, participants may maintain some sense of the trade-offs across time and situations, and they may seek a balance over time.

Figure 4: Second canonical form shows the allocation of goal-setting to Participant A, and action-taking to Participant B. Third canonical form shows that A and B “co-labor” to create goals and share bio-cost to achieve them.

Bio-cost in business and design

Society has benefitted greatly from—one could say society can arise because of—the sharing of bio-cost. As early as the Stone Age, social groups learned how coordinated action could achieve goals that would otherwise have been impossible. A group could successfully hunt a swift and powerful animal for food, whereas a single hunter might have only a slim chance of success and a high risk of injury or death. By sharing such responsibilities, groups could achieve net bio-cost reduction thereby freeing up resources to explore new lands, create new arts and cultures, and develop new means of associating and collaborating.

Since the Renaissance, the corporation has provided one such structure for collaboration. The success of modern corporations is a measure of the huge scale on which they reduce collective bio-cost expenditures. Yet, modern corporations also exact a huge toll in frustration and stress from their employees. In other words, working in a corporation often comes with a high bio-cost. For example, on a mundane level the noise and interruptions of cubicle life” can make focused attention difficult. On a more critical level, uncertainty about goals and criteria can lead to rework; uncertainty about roles and responsibilities can lead to unproductive conflict; and uncertainty about continued employment can lead to fear. Such bio-costs are an extraordinary and persistent waste of “human resources.”

Transforming a corporation from a current state of high bio-cost to a more efficient state requires a complex system that learns as it goes—and the bio-cost of learning, even for those who thrive on it, is very high. [Geoghegan and Pangaro 2003] This appears to be one reason why corporations often fail to find new paths to success when markets change. [Dubberly, Esmonde, Geoghegan & Pangaro 2002]

On the other hand, “strong teamwork” means that there is mutual trust (itself a huge bio-cost reducer) as well as clarity of direction, role and proper action (all proxies for low uncertainty and hence low bio-cost situations). At best, the beliefs and goals of the individuals in a corporation are highly aligned.

In addition to applying the framework of bio-cost to organizational design, we can also apply it to product and service design. Minimizing or at least reducing a user’s bio-cost can be an important design goal. Even though the precision of bio-cost measures is limited, a focus on bio-cost permits a deep conversation during the design process. Instead of seeking to make products “simple” or intuitive”—laudable goals but not very specific— designers can use the dimensions of bio-cost to participate in a more directed design process where trade-offs are made explicit and clear.

Why bio-cost is important

We see an opportunity for organizations to create value by focusing on bio-cost. First, bio-cost provides a framework for improving productivity; by getting better at understanding bio-cost, we can get better at reducing it. In addition, bio-cost provides a framework for innovation; identifying bio-cost is identifying inefficiency, identifying an unmet user need, identifying an opportunity for new products and services.

In summary, it is our conviction that reducing biocost leads to:

• greater efficiency in achieving goals, which leads to…
• greater capacity or resources in the system, which allows the cultivation of…
• greater variety, which means…
• greater ability to generate higher-level plans for reducing bio-cost even further, resulting in…
• even lower bio-cost—a positive feedback loop and a virtuous cycle.

Reducing bio-cost creates value. It expands the space in which additional choices may be generated and evaluated. It can be an ethical motivation in the design process and lead to a more humane world. We believe that a bio-cost economy underlies all exchanges of value, and it always will, because it involves the management of the least fungible and most valuable aspect of life: how we spend our time.

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Editor’s Note:
This article proposes several models of community, including a model of “mobile community”—an extension of physical community merged with online community. The authors also provide examples of how these models have contributed to the development of community applications in their work at Samsung.

—Hugh Dubberly

For the past decade, mobile phones have been used primarily to make phone calls. However, with an increase in the number of mobile phone users and improvements to mobile phone technology, new forms of interaction and new kinds of applications become possible. Now the role of mobile phones is expanding to support forming and maintaining “community”—both geographic based communities and communities based on diverse cultural interests—creating new ways for people to connect and communicate.

The rise of online communities is one of the most exciting commercial and social opportunities of this decade. Today anyone working in the converging worlds of communications, media, and technology knows that communities are perhaps the most influential factor and value-added service in the emerging market, potentially exceeding games, voting or polling applications, or music and video downloads because of their long-term sustainability. In fact, a public report estimates that the market value of community will be around €673 billion by 2010 [1].

Traditionally, the term “community” defined a group of people living in a common location [2]. But as the Internet reduced the limitations of distance, “community” has expanded to include groups organized around common values and common interests. Early Internet community applications limited online interaction for members—the community was active only when members were in front of a personal computer.

In contrast, mobile phones support interaction virtually anywhere, but until recently mobile phones did a poor job of supporting community. A new generation of mobile phone applications is beginning to support not just one-to-one communications, but also one-to-many, many-to-one, and many-to-many communications—an essential part of creating, reinforcing, and managing a community (see Figure 1).

Expansion of Mobile Communication
Figure 1. Mobile communication has focused on one-to-one connections for many years; however, the role will expand to group communication, which is needed for efficient and effective community management.

Consumers expect mobile devices to support rich forms of media: audio, text, photo, and video. And they expect to use rich media to communicate. Consumers will also expect mobile community applications to support rich media. And they will expect applications to be aware of users’ context—both their physical environment as well as their virtual environment:

• their location,
• the tasks in which they are engaged,
• the information they are browsing,
• the people with whom they are interacting,
• and the history of each.

These contextual elements (location, task, domain, contacts, and history) may combine to “trigger” realization of both individual and group goals. Some goals and activities will already be “in play”, while others will emerge from interaction. Browsing a library shelf may lead to the discovery of a new book. Stepping into a cafe may involve running into a friend. Mobile community applications become especially valuable when they support serendipity—spontaneous or unplanned events—and aid the formation of ad hoc communities or “flash groups” (which may dissolve after the event). This last feature is a main difference between mobile communities and communities in the online and physical worlds.

A mobile community, therefore, can be defined as a group of people with shared interests (i.e., health, safety, entertainment, and so on) getting together first online and then in person to define common goals, agree on actions to achieve them, and then carry out their plans. A mobile community can be built up in private (consisting of friends, family, or colleagues—people who are well known to each other) or created in public (a flash group assembled because of shared interests and coincidences of space and time).

Broadly, the mobile community model encompasses two varieties: those centered on relationships and those centered on tasks. The former are typically informal, grassroots-oriented communities that revolve around shared interests, ideas, topics, and goals. In these communities, the development of relationships is the primary goal. In contrast, task-centered communities tend to be more structured and impersonal. The relationships established or augmented online are a means to a mutual end, such as efficiently making a satisfying purchase.

More specifically, the communities are established

• between business partners,
• between businesses and their customers,
• between different groups of customers within companies,
• and between individuals and groups devoted to particular topics.

Based on two continuums—for profit versus nonprofit (or financial capital versus social capital) and strong personal ties versus looser social connections—the model articulates four types of communities (see Figure 2). Any individual might be a member of all four types of communities; this case is visualized by the face in the center of Figure 2.

Mobile Community
Figure 2. Mobile communities lie within a space defined by two dimensions: focus of community goals and community longevity or stability. Mobile communities in the first column (1 and 3) focus on financial goals (more explicit transactions), while those in the second column (2 and 4) focus on social goals (softer, less tangible exchanges). Mobile communities in the first row (1 and 2) are longer-lived and change slowly, while those in the second row (3 and 4) are shorter-lived and more ad hoc. Most people are members of all four types of mobile communities.

Groups of people in the left-hand column (the first and third quadrants) are likely to value “efficiency” more than groups in the right-hand column (the second and fourth quadrants). On the other hand, groups in the right-hand column are organized around social goals, not profits or business interests. For example, moral obligations or personal connections can motivate and sustain community. Almost by definition, communities require high levels of interaction between members to remain viable. Members of a family interact with each other according to defined social roles. We expect that groups based on common interests will also develop shared social norms for interaction.

Groups in the top row (the first and second quadrant) are more stable or fixed by nature than groups in the bottom row (the third and fourth quadrants). The goal of friend and family groups existing in the top row, for instance, is to maintain relationships and reinforce the tribe through active participation. Those goals often lead members to share memorable events. Likewise, members of a work group make efforts to reinforce team spirit and build relationships in support of shared goals, such as project milestones, market share, or net income. On the other hand, the group-formation process in the bottom row is relatively dynamic and temporary. An auto service (in the third quadrant) can improve its service by adding individual personalization. One who has an accident, for example, requires a speedy and systematic interactive service of a community, composed of hospital, police and emergency services, and insurance company. The systematic service and interactivity supported by these parties forms a temporary community around the “event” (the accident) and the specific time and place where it happens.

The need for rich and affordable communication increases as a community grows and matures. This circumstance suggests we may be able to develop rules or heuristics regarding communication within community services. Of course, flexible and easy-to-use user interfaces for sharing media and collaborating on projects are prerequisites for creating successful new mobile experiences. New opportunities for mobile community require rich, affordable, and effortless digital interaction for sharing, contacting, collaborating and being entertained.

Communication within a community is not limited to the explicit dialogue between members; rather it must also expand to include delivery of tacit knowledge in a broad sense, including sharing events, emotions, and experiences across time and place, which bring closer relationships and increased trust. We call this range of exchanges rich social communication. For example, sharing views on a wide range of issues with some or all members of the group may be more important to building and maintaining community than optimizing direct communication, such as SMS or calling. That may be because exchanging members’ intentions or views encourages creating tacit knowledge that leads to more and deeper interactions among community members. Likewise, a single video file of combined clips created by siblings becomes another form of tacit knowledge, standing for family love and encouraging interaction between family members. Sharing one’s status or schedule with other community members implies that one wants to meet or keep in touch. Broadcasting personal music or video (whether to friends or to people unknown) presents a virtual identity and may lead to forming a flash tribe around a favorite song, band, or genre. All these functions can be summarized by three key features—sharing, contacting, and collaborating (see Figure 3).

A Taxonomy of Mobile Community Activities
Figure 3. Key features of mobile community applications include sharing, contacting, and collaborating—all of which support socializing. In addition, other functions (for example, personal information management and voting as personalization) are added to deal with information generated in a community.

When designing a UI to support rich social communication, there can be a deliberate process for fashioning features, such as sharing, contacting, and collaborating [3]. This process is started by writing a sentence that describes a social behavior pattern. Sharing, for example, is characterized as a subject-verb-object construct. (Families share photos, or fans share music.) Similarly, constructs are made for other relevant social behavior patterns. (Friends exchange information about their whereabouts, or members create new videos.) To conclude, three core components (people, goal, and content—or actor, action, and objective) are wedded coherently together in order to visualize three key features of socializing (see Figure 4).

A Conceptual UI for Mobile Community
Figure 4. UI components are created by composing sentences that suggest ways to organize community features.

With these three constructs, UI for mobile community is conceptualized (see Figure 5). The top row sets up a community’s common goal. Sharing, contacting, and collaborating with people in the group who have something in common is automatically located in a second row, along with user actions dealing with the goal (features).

Figure 5. Collective (e.g., friends or family) decisions are visually presented and delivered to the members within a voting or polling application.

Finally, content such as various multimedia objects and text are attached at the bottom.

Additional features necessary for community activity, such as schedule sharing and personal broadcasting, can be customized for various mobile devices. For example, an SMS thread in Figure 6 entitled “rolling paper” expresses tacit intention through messages collected from the participants. A group schedule-sharing application shows members’ schedule status from the community server, which encourages participation.

Figure 6. Actual screen shots of the implementation of the UI on a mobile device, which required us to modify the original “wireframe” due to specific requirements of the device; however, the key features are consistent with those described earlier.

The infrastructure of a mobile community consists of hardware, software, an interface, and services that knit together everything. People working in the mobile device industry already understand the importance of user interface and interaction for complicated mobile services. We hope our approach contributes to this understanding and suggests ways of adding new and exciting features that encourage end-user adoption, without sacrificing ease-of-use.

Ultimately, all characteristics, including environment, people, objects, and processes, should be considered when tailoring a UI to the specific needs of a community. While the communication tools available for communities are often highly attractive, we must keep in mind that the tools should fit the community, not the other way around.

As we previously pointed out, mobile devices have been designed primarily for private communication for the past decade. Thus most mobile phone UIs are optimized for private (one-to-one) use. The role of a mobile device as a personal multimedia manager is now expanding into social media and connecting groups of people. These emerging applications ask us to develop features that support rich social communication, both within and across applications. We believe that our novel UI approach is capable of enabling rich interaction for groups of people who are forming and maintaining community.

About the Authors
Dr. Youngho Rhee is a senior UX designer at SAIT (Samsung Advanced Institute of Technology). His main research interests are in the areas of context awareness, agents, and social networking. He has been actively engaged in numerous projects dealing with mobile Internet service design and methodologies for subjective evaluation of mobile applications. He holds a Ph.D. in industrial system engineering from the University of Wisconsin-Madison. He is on the program board for Human Interface and the Management of Information at HCII (Human Computer Interaction Institute).

Juyoun Lee is a UX designer at SAIT. Her main research themes are pervasive computing, context awareness, social networking, augmented reality, and interactive media. Recently, she has been working on service design and user behavior studies of mobile Internet services. She has a degree in industrial design and computer science from Korea Advanced Institute of Science and Technology (KAIST).

Mobile Devices Should Be About Neither Mobility Nor Devices. Discuss.

Written by Paul Pangaro.

In the beginning every human-to-human connection was unmediated and local. We lived each day in communities where contact and conversation helped us to share goals and coordinate actions. With today’s complexities, it ain’t that simple. Today technology mediates, enables, and spreads our conversations across divergences of time, space, and experience. Despite touch-based UX and because of cloud-based connectivity, human networks have intricate fractal structures, making our interactions fragmented and fl awed. The complexities of distributed communication mean that we’re as confused as we are elated when we add tweets to SMS or GPS to GSM.

Is there any way ahead here?

I’ve found that by returning to universals it is possible to see beyond the latest add-on app and to situate collections of features in the unifying context of human need. This is what Youngho Rhee and Juyoun Lee have done by using “sharing, contacting, and collaborating” as the basis for designing wireframes and developing UI features. The result is a clear hypothesis of benefits, and a clear relationship between intent and design. Which raises the question, how far can we go with universals? For example, can universals say more about mobility and community? I believe so.

One universal we may forget is that our bodies are naturally untethered—that is, wireless is our natural state. Being tied to a desktop computer and then to a wired connection was a temporary, historical anomaly. Having our devices always with us—as if part of our bodies—and seamlessly connected to the human network is much more “biological.” Put another way, to be mobile is to be human. Let’s get beyond the thrill of mobility; we’re only getting closer to what it should have been all along. So I suggest we say, “Noted. Thank you. Can we move on?”

Here’s another universal: Human beings live in a social world, which they co-create in conversation. Enriching our conversations with shared experiences brings us closer together. We naturally want to share our photos and videos and ideas and to meet together. It is in our nature. And when we share experiences, we increase trust, which lowers anxiety and frees up mental and emotional bandwidth to live freer and potentially better lives.

So just as “mobility” is a natural state and hence a distinction we can lose, “social networking” is a natural state, to which 50 years of computing is just now catching up. Since all media is social media, I hope we can move beyond the vague and redundant “social” tag and focus on better ways of living together, through shared experience, through better conversations—even those mediated by technology. For example, how can we make these fabulous digital channels carry more than 140 characters of “great burger at shake shack just now”? Where do tweets fit with everything else we have? And what’s missing? In the universals, answers may be found.

Mobile devices, check. Social media, check. Next up, shall we have a go at expanding the number of cool apps, or perhaps design for being human? Think about this and then ask what it would mean to carry a thousand friends in your pocket?

About the Author
Paul Pangaro is the CTO at Cybernetic Lifestyles in New York City, where he consults at the intersection of product strategy, marketing, and organizational dynamics. He is recognized as an authority on search and related conversational impedances in human-machine interaction, and on entailment meshes, a highly rigorous framework for representing knowledge. He was CTO of several startups, including Idealab’s Snap.com, and was senior director and distinguished market strategist at Sun Microsystems. Paul has taught at Stanford University.

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Editor’s Note:
Use-based design is a new model of product development. It is a process of measuring user behavior and applying the resulting data to improve the next version of a product—creating a feedback loop between user and designer. (We might refer to the process as data-driven design, but that term has another meaning among software developers.)

Basing design decisions on customer behavior has roots in mail-order catalogs of the late 19th century, such as Montgomery Ward and Sears, Roebuck. Use-based design grew along with direct mail in the mid-20th century. More recently, the Web created opportunities for collecting data on user behavior. Google is famous for driving decisions with use data. But few designers have experience in basing decisions on data, and many are still uncomfortable with the practice.

Now the use-based design model is being applied to hardware. For consumer electronics and office products especially, connecting to networks has become almost standard, creating new opportunities to collect information about user behavior (and about device behavior). In a sense, networked hardware products are very much like Web services—and in many cases hardware products are being integrated with Web services. For a growing class of “monitoring” hardware and services (e.g., network management, security, sports, and health products), collecting user information may even be the primary mission.

Taking advantage of these capabilities requires a new model of design as well as a plan to integrate them into the first version of a product.

—Hugh Dubberly

All Products want to be Websites

Increasingly, hardware products (especially consumer electronics) include computers, sensors, and connections to the Internet. These capabilities enable changes in what products “know,” how they are used, and how we develop them. They are becoming more like websites. This simple fact became apparent to me during my work at Dash Navigation as their design director.

There, I worked on the Dash Express, a personal navigational device (PND) that included GSM networking and exploited the following networked-services principles.

Networked services differ from traditional hardware products in at least four important ways:

1. Networked services can recognize their users and respond uniquely.

2. Networked services collect information as a natural part of operation.

3. Networked services change continuously, largely based on user actions that feed product improvements.

4. Networked services may also enable field upgrades of software, continually improving the product.

At first, however, I didn’t understand how designing an integrated system of hardware, software, and network applications requires a new way of thinking about a product and its development. On one hand, we design end-user applications. On the other, we design platforms for both services and user feedback on these services.

This allows us to adopt the essayist Clay Shirky’s theme of building “systems where having good participants produces better results than having good planners.” [1] Internal discussions change from “what feature or quality status do we think our products need?” to “what data can we collect about our features and quality?”

The Product Development Experience

Getting from the standard 18-month hardware development cycle to a rapid cycle of data-driven verification requires that the product team commit itself to building the infrastructure to:

• send updated software to all, or just segments, of its user base

• obtain and analyze data from the users themselves

• provide explicit, periodic methods for the users to submit their opinions from the device.

Thematic features of this magnitude require strong executive and product management sponsorship to succeed. This evolution cannot happen without explicitly specifying these needs as a core product feature. Back-haul data, in particular, benefits from discussions between many departments (operations, support, QA, and development), as well as the user experience (UE) team. In fact, it’s quite likely that the other departments will be the initial drivers of the feature. However, UE participation is required to create the kind of infrastructure that allows design iteration based on behavioral data.

Obtaining Customer Feedback for Traditional Hardware Products
With traditional hardware products, designers have limited knowledge of customer use patterns. Support calls can provide important data on trouble areas (if properly cataloged), but other information is only available from small samples and observers may bias results. Feedback is incomplete and lags actual use considerably.

Obtaining Customer Feedback for Websites
With web sites, designers can have almost complete knowledge of how customers use a service: Which links receive the most traffic? Where in a process do customers drop out? Web sites also allow A/B testing: Which of several examples “performs” better? With realtime feedback, use-based design becomes possible. In this regard, web site management is more like direct marketing than traditional hardware or even software development.

Obtaining Customer Feedback for Networked Hardware Products
Consumer electronics (and office products) are increasingly connected to the Internet. That means use patterns can be recorded and sent to an online database, giving designers almost complete knowledge of how customers use a device: Which features are used most often? What’s not used at all? With this knowledge, designers can refine software and content— and they can send updated versions to the device. In this way, networked hardware design becomes like web site design.

Uses of Back-haul Data

In contrast to data submitted by users through emails, phone calls, forum posts, and surveys, back-haul data enables direct observation of user and system behavior. (“Back-haul” refers to moving data from a remote site, e.g., a networked device, to a central site, e.g., an application server.)

One great place to start prioritizing your needs is by brainstorming with a broad range of stakeholders about what questions they’d like to answer. Some of the most obvious will be questions that you can’t reasonably or reliably ask users to answer themselves. With behavioral data, we can collect answers from the total user population, not just those who respond to survey requests. Another good place to start is with the statistics that measure, from a product management perspective, whether the feature was successful against business goals.

Secondarily, questions asked in user surveys can be crosschecked against behavioral data to assess the degree to which users accurately answer survey questions. For example, when asked how many times a month they use a device, the estimate from survey respondents is often larger than the observed degree of device usage. While this is a well-known bias of surveys, unsophisticated product teams often believe that what a user says is the unvarnished truth. They have either forgotten or not learned the principle that says, “Don’t trust users; observe them.” [9]

Common Networked Functions in Connected Devices

Many connected devices will use the following networking functions:

• Transmission of sensing data from the device back to a server. For Dash, the sensing data is the driver’s current road and speed; for a medical device, the data might be the monitored patient’s heart rate, blood oxygen level, or glucose level.

• Automated distribution of software updates to the device. Dash advertises its auto-update feature as an important advantage over competitors.

• Ability for the manufacturer to configure and diagnose the device. At Dash, the networking-derived functions of the device (real-time traffic, Internet search) are disabled if users let their service plan lapse [10].

• Ability for the user to send data from the Web to the device, thus eliminating more tedious device side entry. For the Dash Express, a well-loved function is entering an address by sending it from the web to the car [11].

All of these connected functions can potentially be exploited by UE for research needs. For example, variants of the software can be sent to target user populations for either their subjective feedback or to generate “A/B” test cases. A population’s configuration settings can be analyzed to determine the most and least popular setting changes.

Most important, the software development organization can rapidly update the product during usability testing and then observe users for their reactions to the device and its new software.

Many Groups Use Back-haul Data

User experience is hardly the only group motivated to scoop up back-haul data. Back-haul data collection is essential for other functions to fulfill their missions.

The ability for UE to understand, leverage, and champion these other needs will maximize its influence over the form that back-haul infrastructure takes. In particular, UE needs to realize its stake in the design of these infrastructure components when initial implementations are sketched. A common mistake for typical small UE teams is not seeing the potential at early stages.

For example, the operations group will require usage data for authentication and billing. IT needs aggregate usage metrics that trigger alarms when usage levels drop, whether due to instability in its own infrastructure or to service-provider outages. Support needs some form of back-haul data to understand customer issues more effi ciently, such as versioning, configuration settings, stack traces [12], and prior usage. QA values back-haul data on common performance metrics, such as “time until first GPS fix” and “time to first network connection” so that it can assess release readiness. QA also values back-haul data to help track metrics of system performance and stability. With this data, the QA team can better evaluate release readiness. Finally, everyone wants some method to “file a problem report” from the device to reduce the time to file, reproduce, and fix bugs.

Issues Unique to UE Needs

The UE group has needs distinct from the other functional groups. Both QA and operations, by and large, are more concerned with aggregate numbers than an individual’s experience. Their numbers may say the product is performing as specified, but they don’t indicate if the users are actually happy with that level of performance.

For UE design, it is beneficial to triage use cases into buckets such as “frequent for all,” “frequent for some,” or “infrequent for all.” In order to do this, the data source must maintain a marker of individuality along with the data, so the data analyst can slice and dice the data to discover such relationships.

In addition, the UE group desires the ability to run longitudinal queries, particularly the ability to see that new user features lead to perceptible user benefits. This requires the UE group itself to create and maintain over time a database of high-level user events in a normalized format.

Infrastructure Needs

In order to exploit the back-haul data, central sampling issues such as who, what, and when (how frequently) need addressing.

Behavioral logging benefits from the ability to understand and filter the results through the lens of various user-grouping mechanisms. For example, stakeholders will question unwelcome results because various outlier groups may have skewed the results.

Invariably, both business and operational groups have an interest in creating user segmentation; this functionality is thus almost certainly available to the UE group. However, the grouping mechanisms will be driven by customer purchasing segments, and this in turn can constrain an experiment’s design.

Problem Reports
Since users have problems, they need a mechanism to explain what those problems are: the problem report. There are two different design approaches:

• Mostly passive, where the problem report is created on behalf of the users. They just need to submit the report.

• Mostly active, where the user initiates the problem report.

Both are, of course, useful. But for devices, the active report is particularly useful, as it is often very difficult to create bug reports independent of a complex environment that only the user fully understands.

The work required is not just the data transmission and recording. In implementing the problem report feature, I recommend budgeting a fair amount of time for implementing tools that facilitate analysis of the reports. Tools that facilitate categorization and visualization of data pay for themselves in short order. Otherwise, time will be spent telling the bug reporter, “I can’t reproduce your problem.”

Server-side Logging
The simplest and most cost-effective technique is to execute server-side system logging. Then scripts can be run to collect and analyze the logged data.

The main advantage of this approach is that little planning is required to generate data because there are no changes to the device code. In the simplest case, an IT operator or engineer adds some logging code (using the existing logging framework) to a server-side component. These changes are usually low risk to any release. This modification gets deployed to the main user population at the next server-side software release.

However, back-end logging alone doesn’t provide a rich picture of the user experience because many behavioral issues can be resolved only via data from the device side.

In addition, tools construction is required to “extract knowledge from data.” Mike Kuniavasky gives an overview of both the benefits and issues with server-side logging [13].

Device Logging
Logging from the client side is more complex than logging from the server side, because device logging has to handle the transmission of state back to the mother ship. In addition, the organization has to be willing to pay the bandwidth cost required to send back the data.

The simplest code approach is to just add a logging function to each “user event” (such as a key-press). This is analogous to a website “click-stream.” The UI framework may have a very small number of places where this logging code could occur. So the implementation can be both simple and broad. The downside, of course, is that an enormous amount of data is generated and transmitted and most of the data is not germane to the user experience questions at hand.

Fundamentally, this functionality is only useful for debugging purposes when focused on a small number of users.

A more complex approach is to log, for the purposes of a targeted experiment, only the data germane to the researcher’s question. This requires custom code to map between various system states and the user state. This code can be more complex because it is integrated into the application logic as opposed to targeted at a base-system level. In addition, you need to remember to turn it off after the experiment is complete.

My experience is that adding this instrumentation is unnecessarily costly when done after the design and initial implementation phase of a project. Don’t shoehorn it in during the beta cycle.

Conclusion

From my inefficient, but valuable, initial experience, a central theme emerges for UE professionals:

Think through the data you want to collect as a fundamental part of sketching and specifying your design. This facilitates a cheaper engineering implementation, but it also allows you to drive design directions from an initial, limited implementation published to a specific user population.

In addition, all functional groups can feel enriched by the quality of user data derived from an Internet-connected device. Customarily, these user research and debugging techniques were available only to Web services. But they are now possible with today’s connected, physical devices, provided your product team invests in the key infrastructure items such as:

• User segmentation
• Over-the-air software updates
• User and system state logging
• Data analysis tools for the user and system logs
• User research resources devoted to designing, maintaining, and analyzing back-haul data

With such infrastructure, you can implement a truly iterative, use-based (or data-driven) design approach for your device, just as website designers do today. But to get this infrastructure built in the first place and to exploit it fully, you’ll also need to convince key stakeholders that your complex, sophisticated, stand-alone product can in fact be conceived by your product managers, your designers, and your users, as just another very profitable website.

About the Author
Tim Misner is senior director of software engineering at Oracle. Before joining Oracle, he was director of software engineering at Dash Navigation, makers of Dash Express, a two-way, Internet-connected GPS-based navigation system. Misner also served a stint as director of user experience engineering at Sun. He has a background in product management, engineering, and mathematics.

Customer-Data-Driven Business: 10 Enabling Trends

by Hugh Dubberly

Google and Amazon have built big businesses by collecting and analyzing previously unheard of amounts of data. Their businesses are not accidents. They are not corner cases. They are signals of an emerging future.

Several trends point to the same thing: the value of large amounts of data and the ability of that data to support tailoring, learning, and decision making—to enable new categories of business, or perhaps a new model for all businesses.

Today, these trends may still appear separate:

1. Big Data
Big data refers to the assembly of very large databases, perhaps first by research in the physical sciences and intelligence gathering by the NSA and the military; followed closely by telephone companies, securities exchanges, credit card companies, and consumer list consolidators (e.g., Acxiom, ChoicePoint, Equifax, Experian, and TransUnion); more recently, Web-based services have begun to generated huge amounts of data.

2. Conversation-based Marketing
Conversation-based marketing refers to a broad shift from one-size-fits-all broadcast advertising to real one-on-one conversations with customers. The shift began with direct mail that broadened into direct response and direct marketing. Direct mail was one of the first areas to apply big data to drive design decisions and improve customer service.

3. CRM Systems
Customer relationship management systems collect and store information on a business’s customers (and potentially other constituents in service delivery). CRM systems are one part of the complex community of systems that sophisticated businesses need to manage conversations with constituents. CRM systems and practices are related to, but typically separate from, DM/DR activities. A business will install a CRM system internally, but serious players in DM/DR outsource customer database management, because it’s a specialized skill not available from in-house IT groups. (For example, your IT folks are not likely to know where to begin to “de-dupe” your customer list or perform other forms of regular “data cleansing.”)

4. Crowd Sourcing
Crowd sourcing is a system (often electronic) that uses large groups of people to collect and organize data. We might propose crowd sourcing as an umbrella term for a range of activities involving large numbers of people: Open source projects that rely on volunteers (e.g., the OED, Wikipedia, Linux); collaborative filtering, the process of making recommendations based on the actions of people with shared traits; Google-style search, which uses links as one of the predictors of relevance; and flash mobs, which quickly form, take collective action, and disperse.

5. Data Visualization
Data visualization refers to the application of graphic design principles to making large volumes of data easier to understand—that is, making pictures out of lots of numbers. Today data visualization is a subspecialty at the intersection of the hard sciences, computing, and information design, drawing on the disciplines of statistics, animation, and filmmaking. It will become increasingly important to communications design, interaction design, and service design. Closely related are design of service dashboards and augmented realities (virtual overlays).

6. Use-based Design
Use-based design refers to a process by which the actions of users are recorded, analyzed, and used to make decisions on changes in the next version of a product or service. Use-based design has roots in the world of direct marketing. Websites created a new level of opportunity. Sites can log every view and click, and enable the testing of alternatives. Google is famous for its data-driven decision making. And now, as more products are connected to the Internet, consumer electronics (and office products) can also log every action users take, providing data to drive decisions for the next version of a product or for interim software upgrades.

7. Massive Cloud Computing
Cloud computing involves Internet-based services that provide large amounts of computing power on an as needed or lease basis, much as utilities provide electric power. Massive cloud computing refers to recent initiatives by IBM+Google and HP+Yahoo supporting research and development efforts to increase the power and speed of cloud computing systems.

8. Sensors
London is under surveillance by 400,000 closed-circuit video cameras. Wal-Mart has mandated that all its suppliers build RFID chips into packages that go through its distribution system. Every iPhone includes at least six types of sensors. We are on the cusp of a huge wave of sensor technology. Sensors will be embedded in everything, and they will pour out a continuous flood of data.

9. Service Design / Service Science
Service design and service science refer to the process of developing and managing services. As hardware becomes increasingly commoditized, services offer opportunity for differentiation. A customer’s experience with a brand may extend across a family of services, each with a collection of touch points. These touch points are increasingly networked, and thus customer behavior may be logged, analyzed, and used to drive improvements.

10. Social Media
Social media refers to media (communications channels, most often enabled by the Internet) in which users create most (or all) of the content. Users engage in conversations with each other—sometimes about and with businesses that serve them. Participating in social media and its attendant conversations is a growing part of managing relations with customers. Social media are forms of crowd sourcing—or crowd sourcing from another perspective.

The next generation of computing will assemble vast stores of data—from a growing array of physical and virtual sensors. These technical and social changes will create opportunities for a wide range of companies. Consumer electronics makers like Apple will monitor their hardware and supporting services. Network-tools makers like Cisco will instrument their customers’ networks. Google (and other Web-services providers) will continue to instrument search and everything you do with their tools. Health care device makers like Johnson & Johnson will increasingly offer services to complement products that continuously monitor your vital signs. Sports and apparel makers like Nike will also build biometric sensors into the soles of their shoes and the fabrics of their clothes, supporting another form of continuous monitoring.

Apple, Cisco, Google, Johnson & Johnson, Nike, and others like them will find themselves in essentially the same business, certainly dealing with the same customer management, design management, and information technology management questions. They will have entered the era of customer-data-driven business.

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Interaction describes a range of processes. A previous “On Modeling” article presented models of interaction based on the internal capacity of the systems doing the interacting [1]. At one extreme, there are simple reactive systems, such as a door that opens when you step on a mat or a search engine that returns results when you submit a query At the other extreme is conversation. Conversation is a progression of exchanges among participants. Each participant is a “learning system,” that is, a system that changes internally as a consequence of experience. This highly complex type of interaction is also quite powerful, for conversation is the means by which existing knowledge is conveyed and new knowledge is generated.

We talk all the time, but we’re usually not aware of when conversation works, when it doesn’t, and how to improve it. Few of us have robust models of conversation. This article addresses the questions: What is conversation? How can conversation be improved? And, if conversation is important, why don’t we consider conversation explicitly when we design for interaction? This article hopes to move practice in that direction. If, as this forum has often argued, models can improve design, we further ask, what models of conversation are useful for interaction design?

We begin by contrasting “conversation” with “communication” in a specific sense. We then offer a pragmatic but not exhaustive model of the process of conversing and explore how it is useful for design.

What Isn’t Conversation?

Claude Shannon developed a rigorous model of a transmission channel used to convey messages between an information source and a destination. While his context was analog telephones with wires highly susceptible to noise, Shannon produced a model that applies to a wide range of situations.

Shannon’s Model of Communication:
A message flows from an information source through a transmitter that encodes a signal. The communication channel, shown as the tiny square box subject to noise, conveys the signal to a receiver, which decodes the signal into a message that is delivered to a destination.

In Shannon’s model an information source selects a message from a known set of possible messages, for example, a dot or a dash, a letter of the alphabet, or a word or phrase from a list. Human communication often relies on context to limit the expected set of messages. If you receive a call from a friend (the source) arriving by train, you expect to hear “I’m getting on the train,” or “I’m on the train,” or “the train is late,” and so on—messages that are drawn from a set of possibilities known to both of you. The channel is effective if it enables you (the destination) to select which of the possible messages is currently being transmitted. (Voice communication is more than sufficient for this, and Shannon’s interest was highly encoded transmission. But this simplified example draws useful distinctions for the discussion that follows.)

Communication in the sense of distinguishing among possible messages known in advance is important for much of our daily life. It allows us to synchronize a wide range of actions with others. But it has limits. Shannon’s model captures a fundamental limit of nearly all human-to-computer interaction: Our input gestures can only activate an existing interface command (select a message) from the preprogrammed set. While we can automate sequences of existing commands, we can’t ask for something novel. If our software application does anything novel, we file a bug report!

In Shannon’s model, how can we say something novel to one another? The answer is, we can’t. It’s not designed for that. We need the capacity for new messages to be generated and the resultant understanding confirmed or denied. We call interaction with these capacities “conversation.” Only in conversation can we learn new concepts, share and evolve knowledge, and confirm agreement. To describe how this works, we draw on the cybernetic models of conversation theory and Gordon Pask, because they are based on a deep study of human-to-human and human-to-machine interaction and because of their prescriptive power [2].

What Is the Process of Conversation?

Conversation at its simplest takes place when participants perform these tasks:

1) Open a channel.
When participant A sends an initial message, the possibility for conversation opens. For conversation to follow, the message must establish common ground; it must be comprehensible to participant B.

Conversation for Agreement:
As a result of conversation, participants agree on their understanding of a concept in that they share a similar model, and they believe that they agree.

2) Commit to engage.
Participant B must pay attention to the message and then commit to engaging with A. Such a commitment may amount to nothing more than continuing to pay attention. For conversation to persist, the commitment must be symmetrical, and either side may break off for any reason, at any time. Put another way, each participant must see value in continuing the conversation, which offsets the personal cost of being engaged: what we call the “bio-cost,” or the energy, time, attention, and stress required [3].

3) Construct meaning.
Conversation enables us to construct (or reconstruct) meaning, including meaning that is new to the destination. Conversation theory has a highly detailed model that we must leave to other descriptions though it is useful even in this skeletal form [4].

Messages are composed with topics or distinctions that are already shared, on the basis of prior conversation or shared contexts, such as common language and social norms. Participant A uses the message channel to convey what these topics are and how they are distinct from one another (descriptive dynamics), along with a kind of “glue” that explains just how these topics interact to make up the new concept (prescriptive dynamics). Participant B “takes all this in” and “puts it all together” to reproduce A’s meaning (or something close enough).

This can occur because, first, the descriptive and prescriptive dynamics come together to express an inherent coherence for the concept—they fit together like gears in a watch and only in a limited way or ways. Second, the human nervous system has evolved especially to make sense of the messages that arrive [5]. This “meaning making” (the taking all this in and putting it all together) is a mini AHA moment, every time we “get” what someone is saying [6].

4) Evolve.
Participant A or B (or both) are different after the interaction. Either or both hold new beliefs, make decisions, or develop new relationships, with others, with circumstances or objects, or with themselves.

Here we define an “effective conversation” as an interaction in which the changes brought about by conversation have lasting value to the participants.

5) Converge on agreement.
Participant B may wish to confirm understanding of A’s concept. To do so, B must create and transmit a different formulation of the topic(s) under discussion, one that captures his model of the concept. On receipt, participant A attempts to make sense of B’s formulation and compares it with her original intention. This may lead to further exchanges. When both A and B judge that the concepts match sufficiently, they have reached “an agreement over an understanding.” Such agreement may involve a fact about the world or merely shared belief. Sometimes participants agree on the qualities of a song, or that they like each other enough to continue talking.

6) Act or Transact.
Sometimes one or more of the participants agrees to perform an action as a result of, and beyond, the conversation that has taken place. For example, they may agree to play a game together or enter into a relationship. Or they may agree to an exchange, as when money is traded for a product or service.

Thus we have a simplified description of conversation. All of us experience breakdowns in conversations; it is near miraculous that we understand each other at all. But if you comprehend this, the process of conversation is working right now.

What Does Conversation Offer?

Conversation enables participants to:

1) Learn.
We learn a great deal via conversation, including conversations with ourselves. We learn highly valuable life lessons, for example, ways to avoid being run over by a bus. At an opposite extreme, what we learn might seem simple: Our partner prefers drinking noncarbonated, room-temperature water; registering a credit card on a website saves time when buying airline tickets. Trivial as these examples may seem, learning basic things may save time later, freeing our future attention for other, less trivial, things. This is a valuable benefit of interactions that have memory and that evolve into relationships.

Conversation to Learn:
Conversation is a means to convey concepts and to confirm agreement. When a conversation changes one of the participants, we say the participant has “learned.”

2) Coordinate.
We spend a great deal of time with others not merely synchronizing (“You’ve arrived, so let’s start!”), but also coordinating our actions in ways that are mutually beneficial. Anytime we negotiate one favor for another, we use conversation to reach an agreement to transact:

“I’ll pick up the laundry if you stop for groceries, OK?”

“No, you have to take the car in for servicing.”

“I can do both, but you’ll have to cook if you want to eat on time.”

“That still works for me.”

“OK, good.”

In practice, society is a complex market of coordination based in conversation. Money is often used in the transaction, but not always. Subsets of the population agree to perform some actions (grow food, manufacture products, educate children, enforce the law) paid for by others who are free to do what they do, for (hopefully) mutual benefit.

Individuals and society become more efficient by coordinating work. This frees resources for other activities—including the design of more efficient products and services, in a recursive and generative process—which supported the Industrial Revolution. Conversation is the primary mechanism for complex human social coordination. It is a highly effective form of bio-cost reduction and therefore an engine of society.

3) Collaborate.
Coordination of action assumes relatively clear goals, but many times social interaction involves the negotiation of goals. (Horst Rittel believed this to be a fundamental challenge of design [6].) We may want to eat together, but one of us prefers Italian food, while the other doesn’t want to spend too much or listen to opera while eating. Or, we need to redesign our Web service but have conflicting demands for features, quality of experience, and development time. Or we would like to see a more equitable healthcare system. Conversation is a requisite for agreeing on goals, as well as for agreeing upon, and coordinating, our actions.

Conversation to Coordinate:
Participant B agrees to trade an action for payment from participant A. B performs the action and confirms that his action has created the correct result. A confirms her goal is achieved and compensates B as agreed. Compensation may be monetary, return of favor, barter, etc.

What Are the Limits To a Conversation?

When designing for conversation, it is critical to consider what cannot happen. What can’t be talked about can’t be learned, conveyed, agreed on, or transacted. Conversations may be limited in two fundamental ways:

1) Conversational infrastructure.
We are frustrated when we can’t open a channel for conversation or when the channel is full of noise (experienced by every U.S. mobile phone user). Or we’re frustrated when we can’t use the available interface functions to get what we want. So, when software is the connection between participants, we ought to ask, “How well does the infrastructure support the conversational connection?”

2) Conversational participants.
Inherent in the capacities for a given conversation are the individual limits of its participants. Individuals contribute both what they know in depth and breadth and their style of interaction. Given a specific group of participants, conversations may go nowhere—they have no value; they create no lasting change in the participants. Other conversations create their own energy and go places—they are generative, have momentum, and lead to new and unexpected knowledge. We prize the individuals with whom we achieve this. “When assembling a design team we ought to ask, What expertise and what collaborative style(s) do we need? What variety is required to succeed?”

Conversation to Collaborate:
Agreeing on goals and coordinating actions to achieve them

Types of Participants

A human participant in conversation is usually a single person, although Pask suggests additional possibilities [7].

Conversations may take place between groups. For example, different political parties, religious groups, or nations interact with each other—they send messages, commit to engage (or not), evolve each other’s beliefs, and sometimes lead to transactions such as trade or war.

Similarly, we often have internal conversations—conversations with ourselves. I explore alternative perspectives, exchange points of view, come to a stable viewpoint about a belief or action (or, when I can’t, remain conflicted)—all inside my own mind.

We generate new ideas by combining old topics in new ways. This is important to interaction design because we spend so much time in front of screens talking to ourselves. Interaction design is as much about connecting humans across the murky “Internet cloud” (fostering community and conversation) as connecting an individual with his or her own capacity to explore what is possible and generate new possibilities (supporting internal conversations).

Why Does Conversation Matter?

Conversation matters to any community of interest (including our community of a single mind), but nowhere is the value of conversation more clear than in commerce, because commerce cannot flourish, or even exist, without conversation.

Requirements for conversation

Establish environment and mindset—context
Use shared language
Engage in mutually beneficial, peer-to-peer exchange
Confirm shared mental models
Engage in a transaction—execute cooperative actions

Marketplace example from user perspective

What’s new in mobile phones?
How is this like a Blackberry?
Can I use this in Europe?
What will that cost?
Yes, this product suits me.
I accept your price and terms; here is my payment.

But many products and services, on the Web and off, connect individuals for broader reasons. Social networks such as Facebook and LinkedIn match two ends of a channel for mutual benefit, whether or not money changes hands. Sometimes what occurs is a sharing of interests, ideas, or even intimacy. But in all these cases, conversation is required.

Summarizing, conversation is infrastructure for commerce because:

• Long-term success means ongoing commerce.
• Ongoing commerce needs ongoing trust.
• Ongoing trust is built via ongoing relationships.
• Ongoing relationships are built via agreeing on goals and actions.
• Agreeing on goals and actions is possible only through effective conversation. So, effective conversation is essential to commerce.

What Can Designers Do?

If conversation is important to “users,” we should explicitly model conversation as we design. Here are four broad proposals:

View every user (persona) as a participant in a conversation, and every scenario as a conversation to define or achieve one or more goals. Use models of conversation to make design decisions, such as:

1. What channel is being opened to begin the conversation? Is the interruption reasonable in how and when it intrudes? What is the bio-cost of the intrusion relative to its benefit? Are there better ways to interrupt?

2. Is the first message clear? Does it offer something to the recipient?

3. Once accepted, does the ongoing exchange convey the potential benefits in continuing the engagement? Is there learning or delight? Is curiosity or interest stimulated? At what bio-cost? How can it be improved?

4. Is meaning easily understood; that is, do the messages speak to the participants’ context, needs, interests, values, and in their language? How difficult is it for users to “put together”? How can messages be made more efficient or clear or entertaining, as appropriate?

5. How can users convey intention and meaning to the software? Are those means sufficiently expressive or easy or delightful? Where do they fall short?

6. Do participants evolve during the interaction? Aside from entertainment or delight, do they acquire something useful, learn a new point of view, or gain new knowledge? (This applies to human participants as well as software, which may evolve a model of the user for the sake of having more effective or more efficient conversations in the future.)

7. Do both sides agree? Can the participants agree to disagree?

8. Can sharing or exchange or transaction continue beyond this conversation, whether in the form of commerce or barter or simply agreeing to continue the conversation at a later time? In other words, has the conversation begun or continued a relationship?

Invest in a better understanding of conversation:

1. Review past projects and recast them as conversations: How could design outcomes be improved?

2. Look at new technologies or techniques in terms of conversation: Do they help generate more effective conversations?

3. When developing new projects, do models of conversation help in choosing technologies or techniques?

4. Can we design for conversations that directly improve trust, and therefore create stronger communities or greater lifetime customer value?

Investigate trends, tools, and technologies that will change online conversations in the next five years:

1. Personal journeys: How do physical age and technology exposure change predilections for media, modes of collaboration, and personal values?

2. Social computing: How will conversational technology transform individuals and organizations?

3. Portable and secure identity tools: How do OpenID and equivalents create secure and controllable online identities? How do they build trust? What can’t they do?

4. Cloud computing: How can we deliver the same experience everywhere, at lower cost?

5. Sensors: How does a seamless “network of objects,” when capable of conversational interaction, better extend our capacity for learning, coordinating, and collaborating?

Invest in design of conversations via prototyping:

1. For stakeholders: Build trust and value for employees, shareholders, clients, partners, competitors, and communities of interest.

2. Inside the organization: Instill coevolution as the process for understanding the market, defining and delivering the offering, and increasing customer satisfaction and shareholder value.

3. Across organizational and cultural boundaries: Explore a “marketplace of ideas.”

The Impact

Imagine a design movement that takes conversation seriously. Could it create a revolution?

The Industrial Revolution harnessed physical machines to extend and enhance our muscles. The Information Revolution harnessed virtual machines to extend and enhance our nervous systems. A “Conversation Revolution” would harness the existing infrastructure of physical machines and virtual machines to create a mesh out of “networks of objects” and networks of individuals and organizations. Such a mesh would enhance coordination and collaboration and create wealth by introducing new efficiencies. It would also expand opportunities to generate new knowledge.

Imagine a search engine designed for effective conversation, with all the knowledge on the Web participating. We would no longer be focused on “search,” nor would we be using an “engine.” What should it be called? Who will build it first?

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Models are ideas about the world—how it might be organized and how it might work. Models describe relationships: parts that make up wholes; structures that bind them; and how parts behave in relation to one another.

Models are ideas about the world— how it might be organized and how it might work.

For example, the sun rises in the east, moves across the sky, and sets in the west. Or the earth orbits the sun. Models support communication and learning. Models help bridge the gap between observing and making, between research communities and design communities [1]. Models are especially important in interaction and service design.

A representation of the Ptolemaic model of the “world system” — a geo-centric view.

A representation of the Copernican model of the “solar system” — a helio-centric view.

Making Sense and Guiding Action

Models help us make sense of things. Stafford Beer wrote, “Now in trying to account for the behavior of a complicated system, the scientist has first to represent it in the formal terms he knows how to manipulate…. The formal representation of the system that he builds is called a model. This model is something different than the diagrams that are drawn.” [2] Alan Kay noted, “Models are our voodoo dolls. We do most of our thinking in models.” [3] Models begin with things or events that we observe. We want to describe or explain what we see. Pieces fit together; patterns emerge; we posit causes and effects. Under this frame, evidence leads to models.

Observations can be a source of new models.

Models are conjectures—hypotheses. They are not formed by deduction or induction but by abduction—inferring the most likely story to explain the evidence. Abduction is the creative heart of science, engineering, and design. Its mechanism remains unknown—though preparation and persistence may aid the process. Models are not the special province of science. We use them all the time. Models help us recognize new situations as similar to others we have encountered. Without a model, recognizing the similarities might be difficult. Models also help us predict likely futures: what actions other actors may take, consequences of those actions, and what actions best respond to threats or most efficiently help us pursue our goals. Armed with our models’ predictions, we act accordingly.

Chris Argyris wrote, “Although people do not [always] behave congruently with their espoused theories [what they say], they do behave congruently with their theories-in-use [their mental models].” [4] Under this frame, models lead to action.

Our models guide our actions.

Learning As Forming and Reforming Models

If we are “present and engaged” (that is, paying attention) and yet we have an accident or make a mistake, the cause may be some defect in our models. That is, our models suggested one outcome, but we have found another. The difference between expectation and outcome creates an opportunity for learning.

Learning involves forming models and reforming them based on feedback. We observe some behavior in our environment; it suggests models, which we use to predict future behavior and guide our actions. Additional observations provide feedback, which helps us revise and refine our models. We learn.

When outcomes do not match our predictions, we have two choices:

1) Reject the data

• Were measurements inaccurate?
• Was the test procedure flawed?
• Was the reporter biased?

2) Accept the data

• Is it relevant to our model?
• Is it a special case? Meaning our model is less useful at the extremes or our model needs refinement or extension,
• Was previous data inaccurate or insufficient? Meaning we need to revise our model.

Under this frame, we modify our models based on the results of our predictions—we subject them to feedback.

We evolve our models as we test their predictions.

Learning involves:

Creating new models

Revising existing models

• Extending a model so that it corresponds to more observations (broadening)
  

For example, Ptolemy introduced cycles within cycles to account for the retrograde motion of Mars.

Refining a model so that it more closely corresponds to observation (deepening)

For example, Kepler found that Brahe’s observations showed that the planets’ follow an elliptical (not circular) path around the sun.

Generalizing models—reframing a model of a specific event as a model of a more general set of phenomena

For example, the shift from the Ptolemaic to Copernican model is an example of a general case that recurs throughout the history of science as one important model gives way to another. Kuhn named this a “paradigm shift.”

Identifying model primitives—finding patterns which recur across many models, often based on fundamental rules of geometry or topology

For example, the earth orbiting the sun is a special case of a more general model of satellites orbiting primary bodies, which describes other cases such as the moon orbiting the earth or suns orbiting the center of a galaxy. A system in which one element revolves around another is a fundamental pattern—a “primitive” or building block of models.

We use models and learn through them, not only as individuals but also as groups. Learning takes place on at least four scales:

1. Individual
   
2. Work-group (or play team), which is composed of individuals
   
3. Organization, which is composed of work-groups
   
4. Culture, which is composed of organizations

Learning—forming and reforming models—begins with individuals. It can expand to work-groups, organizations, and even entire cultures. That is, a model may be highly idiosyncratic, rarely shared with others. Or it may be highly conventional, widely shared by others.

At each scale (individual through culture), three levels of process are at work:

1. Primary—the activity at hand
   understood through models
   

2. Second-order—direct learning (and designing)
   improving primary processes that is, refining models of primary processes
   

3. Third-order—meta-learning (learning about learning)
   improving second-order processes that is, improving models of learning and models of models
   

Passing models from one generation to the next is a responsibility of teachers and managers. Models are what students take away from school and what young people take away from early jobs. Models are what you remember after leaving.

Peter Senge noted that developing and sharing models is fundamental to “learning organizations.” He suggests that a leader’s role is to improve both his or her own mental models and those of the organization—to test and add to the mental models of others [5].

Design is a young profession; design practices that operate as learning organizations are rare. Typically, models remain implicit. Students learn by watching teachers, managers, and colleagues. Universities, professional organizations, and design practitioners have much opportunity to improve the way designers learn—to develop systems for forming and reforming models of design processes.

Limits and Costs

Earlier, I described observation shaping models; but models also shape what we see—what we let ourselves notice. Our models tell us what is important, what counts, what to look for. Peter Senge wrote, “Models [are] so powerful in affecting what we do…because they affect what we see. Two people with different mental models can observe the same event and describe it differently, because they’ve looked at different details.” [5] Under this frame, models also lead to evidence.

Our models affect what we see.

In a similar way, models already shared within an organization may limit its ability to see new evidence, understand changing situations, or act in its own interest. Old models often resist new ones and inhibit learning. That’s why organizations need to expose the fundamental models that guide them and periodically challenge those models.

Creating or revising a model is meta-activity, taking us outside the primary activity in which we were engaged. It requires attention, energy, and time.

But a new or improved model may pay dividends; it may reduce accidents or other unexpected outcomes, or it may make an individual or group more competitive. In this way, forming and reforming models may “pay for itself.”

Sharing models may reduce group costs and thus create value. But the cost of adopting new models can also inhibit their spread. Adoption requires value that clearly outweighs cost.

Agreement and Understanding

Models are closely tied to stories. We explain models by telling stories, and when we tell stories, listeners form models—mental pictures of the actors, how they are related, and how they behave.

Models are explained by stories; stories build models.

Shared models support discussions. They are examples of what Susan Star called “boundary objects,” artifacts that enable discourse at the boundaries between communities of practice [6]. By sharing our models, we may be able to confirm where we agree—and discover where we disagree.

Models provide a basis for shared understanding, agreement, and group action. They also build trust and enable collaboration.

Agreement begins with individual understanding—forming our own models. Through conversation, we begin to understand each other’s models—to form models of the other’s models. We compare our model with the other’s model. Are our models congruent? Do we agree? And then, do we agree that we agree? If so, we have reached “an agreement over and understanding.” We have a basis for trust, collaboration, and action [7].

Shared models are the basis for understanding, agreement, and action.

Models in Design

As designers increasingly focus on systems and communities of systems, we need to improve our modeling skills.

Without modeling, system design is not possible. Often service systems and computer-based applications are partly hidden or invisible, or they stretch across time and space and cannot be seen all at once or from a single vantage point. In such cases, models must stand in for systems during analysis, design, and even operation.

Using models, designers can unify otherwise separate artifacts and actions. Interaction models unify interface widgets. Service models unify customer touch points. Brand models unify messages. Platform models unify individual products.

Drawing has long been an essential skill for designers and the heart of design education. Bill Buxton, Dick Powell, and others assert that “drawing is the essence of design.” [8] Are they correct? Perhaps—if designers focus on objects. But when attention turns to systems, modeling becomes the essence of design. Design education and practice must adapt to this changing reality.

Von Bertalanffy wrote, “The advantages and dangers of models are well known. The advantage is in the fact that this is the way to create a theory—i.e., the model permits deductions from premises, explanation and prediction, with often unexpected results. The danger is oversimplification: to make it conceptually controllable, we have to reduce reality to a conceptual skeleton—the question remaining whether, in doing so, we have not cut out vital parts of the anatomy. The danger of oversimplification is greater, the more multifarious and complex the phenomenon is.” [9]

Keeping in mind the multifarious and complex nature of design—and the attendant dangers—we must bring more rigorous modeling to our work.

Observation may suggest models, but models also frame and filter observations.

Thinking about how to explain our observations may lead us to think of alternatives or related ideas.

The process of representing an idea may change the idea itself.

Sharing a model may also change it.

All these feedback loops, and more, act simultaneously-shaping and reshaping our models.

Questions to Ask When Making Models

For any set of observations (or system), we may imagine many models. And for any (mental) model, we may imagine many representations.

What processes lead to good models?
What processes lead to good representations?

How do we recognize a good model?
How do we recognize a good representation?

All models have a purpose and serve constituents. Models have a point of view; and they advocate it. Models are always political.

Acknowledge the subjectivity of modeling: Considering your constituents. Speak with them to learn their needs and their views of the system (situation).

Directly observe the system; record your observations. If you are modeling a system that does not exist, observe similar systems.

Constituents’ goals and system observations form the criteria against which we judge both model and representation.

Why are we making a model?
What decisions or actions will it support?
Who are the constituents for the model?
What are their goals?
How can the constituents be involved in the modeling process?
How will decisions about the model and representation be made?

Models are not objective. They leave things out. They draw boundaries between what is modeled and what is not; between the system and its environment; and between the elements of the system.

Framing a system—defining it—is editing. What we think of as natural boundaries, inside and outside, are somewhat arbitrary. The people making the model choose what boundaries to draw and where to draw them. That means, they have to agree on the choices.

What should the model attempt to predict?
What is in the system, and what is not?
Who or what are the actors?
What resources do they use?
How do they affect one another?
What level of abstraction or degree of granularity is appropriate?

Enlist others to work with you. Begin with discussion. Use a white-board to record comments. Record the white board in photographs.

Write a working title for the model.

Create quick, low-fidelity sketches. Identify the system’s elements and write the name of each on a Post-It note. At the beginning, don’t worry about having too many elements or the wrong elements. Editing comes later.

Arrange the Post-It notes to describe the system’s structure. Group similar elements. Place elements that often interact near each other. Avoid repeating elements. Label connections.

Review your proto-model to see which model primitives or patterns it includes. Are these appropriate or would others be better? Does the proto-model build on or suggest already established or generalized models?

Revise your proto-model.

Present the proto-model to your constituents; tell them the model’s story. Observe their reactions; ask for feedback; reflect on what was easy or difficult to explain. Record these results; create an “issues” list for debugging the model.

Revise. Increase fidelity and detail as appropriate. (Determining what’s appropriate becomes easier with practice—as your model of modeling grows.)

The quality of models and representations increases with iteration. So: Iterate.

When Judging (Mental) Models,
Consider 4 Primary Criteria:

1) Fit
How does the model fit the evidence?
Is our evidence relevant?
Is it reliable?
Is it sufficiently granular? (depth)
Do we have enough evidence to draw meaningful conclusions? (breadth)
Are the elements of the model necessary and sufficient?
Are the elements of the model “MECE”—mutually exclusive and collectively exhaustive?

2) Least Means
Is there a simpler way to explain the evidence?
Given two models explaining the same evidence, Ockham told us to prefer the simpler.

3) Consistency
Is the model internally consistent?
Is it free from contradiction?

4) Predictive Value
What predictions does the model make?
Are our model’s predictions consistent with later observations?
Do the model’s predictions help us make decisions that might have been more difficult without them?

When Judging Visual Representations,
Consider 5 Primary Criteria:

1) Fit
Is the representation congruent with the model?
Do representation and model have similar structures?
Are all the elements in the model explicit in the representation?

2) Least Means
Could the model be represented in a simpler way?
What can be removed without changing the meaning? (Remove decoration.)
Could conventional symbols or other standard patterns make reading easier?

3) Consistency
Are the means of representation consistent?
(Similar forms should represent similar functions or similar content.
Likewise, similar functions or similar content should be represented by similar forms.)
Are all elements and their connections labeled?

4) Contrast
What about the model should appear to be most important?
Does the most important thing appear most important?
(Not everything is equally important. Important elements of the model should stand out in the representation. One way to achieve contrast is through scale, making more important items larger than less important items.)

5) Hierarchy
How do the elements of the system appear to fit together?
Is the structure of the system clearly visible?
Do we know where to look first?
Can we find a clear path through the model?

The final test of the model (and representation) is with the audience.

Does the audience understand it?
Do they agree with it?
Do they agree that they agree?
Will they act on it?

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When we discuss computer-human interaction and design for interaction, do we agree on the meaning of the term “interaction”? Has the subject been fully explored? Is the definition settled?

A Design-Theory View

Meredith Davis has argued that interaction is not the special province of computers alone. She points out that printed books invite interaction and that designers consider how readers will interact with books. She cites Massimo Vignelli’s work on the National Audubon Society Field Guide to North American Birds as an example of particularly thoughtful design for interaction [1].

Richard Buchanan shares Davis’s broad view of interaction. Buchanan contrasts earlier design frames (a focus on form and, more recently, a focus on meaning and context) with a relatively new design frame (a focus on interaction) [2]. Interaction is a way of framing the relationship between people and objects designed for them—and thus a way of framing the activity of design. All man-made objects offer the possibility for interaction, and all design activities can be viewed as design for interaction. The same is true not only of objects but also of spaces, messages, and systems. Interaction is a key aspect of function, and function is a key aspect of design.

Davis and Buchanan expand the way we look at design and suggest that artifact-human interaction be a criterion for evaluating the results of all design work. Their point of view raises the question: Is interaction with a static object different from interaction with a dynamic system?

An HCI View

Canonical models of computer-human interaction are based on an archetypal structure—the feedback loop. Information flows from a system (perhaps a computer or a car) through a person and back through the system again. The person has a goal; she acts to achieve it in an environment (provides input to the system); she measures the effect of her action on the environment (interprets output from the system—feedback) and then compares result with goal. The comparison (yielding difference or congruence) directs her next action, beginning the cycle again. This is a simple self-correcting system—more technically, a first-order cybernetic system.

In 1964 the HfG Ulm published a model of interaction depicting an information loop running from system through human and back through the system [3].

Man-Machine System

Gulf of Execution and Evaluation

Don Norman has proposed a “gulf model” of interaction. A “gulf of execution” and a “gulf of evaluation” separate a user and a physical system. The user turns intention to action via an input device connected to the physical system. The physical system presents signals, which the user interprets and evaluates—presumably in relation to intention [4].

Norman has also proposed a “seven stages of action” model, a variation and elaboration on the gulf model [5]. Norman points out that “behavior can be bottom up, in which an event in the world triggers the cycle, or top-down, in which a thought establishes a goal and triggers the cycle. If you don’t say it, people tend to think all behavior starts with a goal. It doesn’t—it can be a response to the environment. (It is also recursive: goals and actions trigger subgoals and sub-actions) [6].”

Seven Stages of Action

Interaction

Like Norman’s models, Bill Verplank’s wonderful “How do you…feel-know-do?” model of interaction is also a classic feedback loop. Feeling and doing bridge the gap between user and system [7].

Representing interaction between a person and a dynamic system as a simple feedback loop is a good first approximation. It forefronts the role of information looping through both person and system [8]. Perhaps more important, it asks us to consider the user’s goal, placing the goal in the context of information theory—thus anchoring our intuition of the value of Alan Cooper’s persona-goal-scenario design method [9].

In the feedback-loop model of interaction, a person is closely coupled with a dynamic system. The nature of the system is unspecified. (The nature of the human is unspecified, too!) The feedback-loop model of interaction raises three questions: What is the nature of the dynamic system? What is the nature of the human? Do different types of dynamic systems enable different types of interaction?

A Systems-Theory View

The discussion that gave rise to this article began when Usman Haque observed that “designers often use the word ‘interactive’ to describe systems that simply react to input,” for example, describing a set of Web pages connected by hyperlinks as “interactive multimedia.” Haque argues that the process of clicking on a link to summon a new webpage is not “interaction”; it is “reaction.” The client-server system behind the link reacts automatically to input, just as a supermarket door opens automatically as you step on the mat in front of it.

Haque argued that “in ‘reaction’ the transfer function (which couples input to output) is fixed; in ‘interaction’ the transfer function is dynamic, i.e., in ‘interaction’ the precise way that ‘input affects output’ can itself change; moreover in some categories of ‘interaction’ that which is classed as ‘input’ or ‘output’ can also change, even for a continuous system [10].”

For example, James Watt’s fly-ball governor regulates the flow of steam to a piston turning a wheel. The wheel moves a pulley that drives the fly-ball governor. As the wheel turns faster, the governor uses a mechanical linkage to narrow the aperture of the steam-valve; with less steam the piston fills less quickly, turning the wheel less quickly. As the wheel slows, the governor expands the valve aperture, increasing steam and thus increasing the speed of the wheel. The piston provides input to the wheel, but the governor translates the output of the wheel into input for the piston. This is a self-regulating system, maintaining the speed of the wheel—a classic feedback loop.

Of course, the steam engine does not operate entirely on its own. It receives its “goal” from outside; a person sets the speed of the wheel by adjusting the length of the linkage connecting the fly-ball governor to the steam valve. In Haque’s terminology, the transfer function is changed.

Our model of the steam engine has the same underlying structure as the classic model of interaction described earlier! Both are closed information loops, self-regulating systems, first-order cybernetic systems. While the feedback loop is a useful first approximation of human computer interaction, its similarity to a steam engine may give us pause.

The computer-human interaction loop differs from the steam-engine-governor interaction loop in two major ways. First, the role of the person: The person is inside the computer-human interaction loop, while the person is outside the steam-engine-governor interaction loop. Second, the nature of the system: The computer is not characterized in our model of computer-human interaction. All we know is that the computer acts on input and provides output. But we have characterized the steam engine in some detail as a self-regulating system. Suppose we characterize the computer with the same level of detail as the steam engine? Suppose we also characterize the person?

Types of Systems

So far, we have distinguished between static and dynamic systems—those that cannot act and thus have little or no meaningful effect on their environment (a chair, for example) and those that can and do act, thus changing their relationship to the environment.

Within dynamic systems, we have distinguished between those that only react and those that interact—linear (open-loop) and closed-loop systems.

Some closed-loop systems have a novel property—they can be self-regulating. But not all closed-loop systems are self-regulating. The natural cycle of water is a loop. Rain falls from the atmosphere and is absorbed into the ground or runs into the sea. Water on the ground or in the sea evaporates into the atmosphere. But nowhere within the cycle is there a goal.

Types of Systems

A self-regulating system has a goal. The goal defines a relationship between the system and its environment, which the system seeks to attain and maintain. This relationship is what the system regulates, what it seeks to keep constant in the face of external forces. A simple self-regulating system (one with only a single loop) cannot adjust its own goal; its goal can be adjusted only by something outside the system. Such single-loop systems are called “first order.”

Learning systems nest a first self-regulating system inside a second self-regulating system. The second system measures the effect of the first system on the environment and adjusts the first system’s goal according to how well its own second-order goal is being met. The second system sets the goal of the first, based on external action. We may call this learning—modification of goals based on the effect of actions. Learning systems are also called second-order systems.

Some learning systems nest multiple self-regulating systems at the first level. In pursuing its own goal, the second-order system may choose which first-order systems to activate. As the second-order system pursues its goal and tests options, it learns how its actions affect the environment. “Learning” means knowing which first-order systems can counter which disturbances by remembering those that succeeded in the past.

Water Cycle

Linear system

Self-regulating system

Learning system

A second-order system may in turn be nested within another self-regulating system. This process may continue for additional levels. For convenience, the term “second-order system” sometimes refers to any higher-order system, regardless of the number of levels, because from the perspective of the higher system, the lower systems are treated as if they were simply first-order systems. However, Douglas Englebart and John Rheinfrank have suggested that learning, at least within organizations, may require three levels of feedback:

• basic processes, which are regulated by first-order loops
• processes for improving the regulation of basic processes
• processes for identifying and sharing processes for improving the regulation of basic processes

Of course, division of dynamic systems into three types is arbitrary. We might make finer distinctions. Artist-researcher Douglas Edric Stanley has referred to a “moral compass” or scale for interactivity “Reactive > Automatic > Interactive > Instrument > Platform” [11].

Cornock and Edmonds have proposed five distinctions:
(a) Static system
(b) Dynamic-passive system
(c) Dynamic-interactive system
(d) Dynamic-interactive system (varying)
(e) Matrix [12]

Kenneth Boulding distinguishes nine types of systems [13].

Levels of systems

System Combinations

One way to characterize types of interactions is by looking at ways in which systems can be coupled together to interact. For example, we might characterize interaction between a person and a steam engine as a learning system coupled to a self-regulating system. How should we characterize computer-human interaction? A person is certainly a learning system, but what is a computer? Is it a simple linear process? A self-regulating system? Or could it perhaps also be a learning system?

Working out all the interactions implied by combining the many types of systems in Boulding’s model is beyond the scope of this paper. But we might work out the combinations afforded by a more modest list of dynamic systems: linear systems (0 order), self-regulating systems (first order), and learning systems (second order). They can be combined in six pairs: 0-0, 0-1, 0-2, 1-1, 1-2, 2-2.

0-0 Reacting
The output of one linear system provides input for another, e.g., a sensor signals a motor, which opens a supermarket door. Action causes reaction. The first system pushes the second. The second system has no choice in its response. In a sense, the two linear systems function as one.

This type of interaction is limited. We might call it pushing, poking, signaling, transferring, or reacting. Gordon Pask called this “it-referenced” interaction, because the controlling system treats the other like an “it”—the system receiving the poke cannot prevent the poke in the first place [15].

A special case of 0-0 has the output of the second (or third or more) systems fed back as input to the first system. Such a loop might form a self-regulating system.

0-1 Regulating
The output of a linear system provides input for a self-regulating system. Input may be characterized as a disturbance, goal, or energy.

Input as “disturbance” is the main case. The linear system disturbs the relation the self-regulating system was set up to maintain with its environment. The self-regulating system acts to counter disturbances. In the case of the steam engine, a disturbance might be increased resistance to turning the wheel, as when a train goes up a hill.

Input as “goal” occurs less often. A linear system sets the goal of a self-regulating system. In this case, the linear system may be seen as part of the self-regulating system—a sort of dial. (Later we will discuss the system that turns the dial. See 1-2 below.)

Input as “energy” is another case, mentioned for completeness, though a different type than the previous two. A linear system fuels the processes at work in the self-regulating system; for example, electric current provides energy for a heater. Here, too, the linear system may be seen as part of the self-regulating system.

1-0 is the same as 0-1 or reduces to 0-0. Output from a self-regulating system may also be input to a linear system. If the output of the linear system is not sensed by the self-regulating system, then 1-0 is no different from 0-0. If the output of the simple process is measured by the self-regulating system, then the linear system maybe seen as part of the self-regulating system.

0-2 Learning
The output of a linear system provides input for a learning system. If the learning system also supplies input to the linear system, closing the loop, then the learning system may gauge the effect of its actions and “learn.”

On the other hand, if the loop is not closed, that is, if the learning system receives input from the linear system but cannot act on it, then 0-2 may be reduced to 0-0.

Today much of computer-human interaction is characterized by a learning system interacting with a simple linear process. You (the learning system) signal your computer (the simple linear process); it responds; you react. After signaling the computer enough times, you develop a model of how it works. You learn the system. But it does not learn you. We are likely to look back on this form of interaction as quite limited.

Search services work much the same way. Google retrieves the answer to a search query, but it treats your thousandth query just as it treated your first. It may record your actions, but it has not learned—it has no goals to modify. (This is true even with the addition of behavioral data to modify ranking of results, because there is only statistical inference and no direct feedback that asserts whether your goal has been achieved.)

1-1 Balancing
The output of one self-regulating system is input for another. If the output of the second system is measured by the first system (as the second measures the first), things are interesting. There are two cases, reinforcing systems and competing systems. Reinforcing systems share similar goals (with actuators that may or may not work similarly). An example might be two air conditioners in the same room. Redundancy is an important strategy in some cases. Competing systems have competing goals. Imagine an air conditioner and a heater in the same room. If the air conditioner is set to 75, and the heater is set to 65—no conflict. But if the air conditioner is set to 65 and the heater is set to 75, each will try to defeat the other. This type of interaction is balancing competing systems. While it may not be efficient, especially in an apartment, it’s quite important in maintaining the health of social systems, e.g., political systems or financial systems.

If 1-1 is open loop, that is, if the first system provides input to the second, but the second does not provide input to the first, then 1-1 may be reduced to 0-1.

1-2 Managing and Entertaining
The output of a self-regulating system becomes input for a learning system. If the output of the learning system also becomes input for the self-regulating system, two cases arise.

The first case is managing automatic systems, for example, a person setting the heading of an autopilot—or the speed of a steam engine.

The second variation is a computer running an application, which seeks to maintain a relationship with its user. Often the application’s goal is to keep users engaged, for example, increasing difficulty as player skill increases or introducing surprises as activity falls, provoking renewed activity. This type of interaction is entertaining—maintaining the engagement of a learning system.

If 1-2 or 2-1 is open loop, the interaction may be seen as essentially the same as the open-loop case of 0-2, which may be reduced to 0-0.

2-2 Conversing
The output of one learning system becomes input for another. While there are many possible cases, two stand out. The simple case is “it-referenced” interaction. The first system pokes or directs the second, while the second does not meaningfully affect the first.

More interesting is the case of what Pask calls “I/you-referenced” interaction: Not only does the second system take in the output of the first, but the first also takes in the output of the second. Each has the choice to respond to the other or not. Significantly, here the input relationships are not strict “controls.” This type of interaction is a like a peer-to-peer conversation in which each system signals the other, perhaps asking questions or making commands (in hope, but without certainty, of response), but there is room for choice on the respondent’s part. Furthermore, the systems learn from each other, not just by discovering which actions can maintain their goals under specific circumstances (as with a standalone second-order system) but by exchanging information of common interest. They may coordinate goals and actions. We might even say they are capable of design—of agreeing on goals and means of achieving them. This type of interaction is conversing (or conversation). It builds on understanding to reach agreement and take action [16].

There are still more cases. Two are especially interesting and perhaps not covered in the list above, though Boulding surely implies them:

• learning systems organized into teams
• networks of learning systems organized into communities or markets

The coordination of goals and actions across groups of people is politics. It may also have parallels in biological systems. As we learn more about both political and biological systems, we may be able to apply that knowledge to designing interaction with software and computers.

Having outlined the types of systems and the ways they may interact, we see how varied interaction can be:

• reacting to another system
• regulating a simple process
• learning how actions affect the environment
• balancing competing systems
• managing automatic systems
• entertaining (maintaining the engagement of a learning system)
• conversing

We may also see that common notions of interaction, those we use every day in describing user experience and design activities, may be inadequate. Pressing a button or turning a lever are often described as basic interactions. Yet reacting to input is not the same as learning, conversing, collaborating, or designing. Even feedback loops, the basis for most models of interaction, may result in rigid and limited forms of interaction.

By looking beyond common notions of interactions for a more rigorous definition, we increase the possibilities open to design. And by replacing simple feedback with conversation as our primary model of interaction, we may open the world to new, richer forms of computing.

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Design research is in a state of flux. The design research landscape has been the focus of a tremendous amount of exploration and growth over the past five to 10 years. It is currently a jumble of approaches that, while competing as well as complementary, nonetheless share a common goal: to drive, inspire, and inform the design development process.

Conflict and confusion within the design research space are evident in the turf battles between researchers and designers. Online communities reveal the philosophical differences between the applied psychologists and the applied anthropologists, as well as the general discontent at the borders between disciplines. At the same time, collaboration is evident in the sharing of ideas, tools, methods, and resources in online design research communities. We can also see an increase in the number and quality of global design research events and a growing emphasis on collaborative projects between industry and the universities, particularly in Europe.

Why Make a Map?

When asked to write a paper about the state of design research, I found that I had to make a map so that I could see what I was writing about [1]. People who know me are aware that orienting and finding my way around physical space is not one of my strengths. Making a map is a way to hold a domain still for long enough to be able to see the relationships between the various approaches, methods, and tools. Maps are good for visualizing relationships.

Maps can be useful for showing complexity and change. For example, the underlying landscape of the map may be relatively permanent, changing only as major forces affect it. But the tools and methods shift and change somewhat like trends. And the people who inhabit the landscape may come and go. As in the real world, some people like to stay put and others like to travel. So maps are good for layering complexity and for revealing change as it occurs.

In making the map, I found that I needed to name the dimensions of the design research space in a way that would help bring clarity and light to the landscape. Once this happened, everything else fell quickly into place.

How Is the Map Organized?

The design research map is defined and described by two intersecting dimensions: One is defined by approach and the other is defined by mind-set. Approaches to design research have come from a research-led perspective (shown at the bottom of the map) and from a design-led perspective (shown at the top of the map). The research-led perspective has the longest history and has been driven by applied psychologists, anthropologists, sociologists, and engineers. The design-led perspective, on the other hand, has come into view more recently.

Figure 1
Map of design research-underlying dimensions

There are two opposing mind-sets evident in the practice of design research today. The left side of the map describes a culture characterized by an expert mind-set. Design researchers here are involved with designing for people. These design researchers consider themselves to be the experts, and they see and refer to people as “subjects,” “users,” “consumers,” etc. The right side of the map describes a culture characterized by a participatory mind-set. Design researchers on this side design with people. They see the people as the true experts in domains of experience such as living, learning, working, etc. Design researchers who have a participatory mind-set value people as co-creators in the design process. It is difficult for many people to move from the left to the right side of the map (or vice versa), as this shift entails a significant cultural change.

The largest and most developed of the areas on the map is the user-centered design zone. Thousands of people in this zone work to help make new product and services better meet the needs of “users.” They use research-led approaches with an expert mind-set to collect, analyze, and interpret data in order to develop specifications or principles to guide or inform the design development of product and services. They also apply their tools and methods in the evaluation of concepts and prototypes. The three large areas of activity in the user-centered zone come from the applied social and behavioral sciences and/or from engineering: human factors/ergonomics, applied ethnography, and usability testing. There are also two smaller bubbles within the user-centered territory: contextual inquiry and lead-user innovation. (More information about the map can be found in my 2006 Design Research Quarterly article [1].)

The participatory design zone spreads across both the research-led and design-led approaches on the right side of the map. Participatory design is an approach to design that attempts to actively involve the people who are being served through design in the process to help ensure that the designed product/service meets their needs. Its origins are generally traced back to work done with trade unions in several Scandinavian countries in the 1960s and 1970s [2]. Participatory design attempts to involve those who will become the “users” throughout the design development process to the extent that this is possible. A key characteristic of the participatory design zone is the use of physical artifacts as thinking tools throughout the process, common among the methods emanating from the research-led Scandinavian tradition.

Figure 2
Map of design research-research types

The design and emotion bubble emerged in 1999 with the first Design and Emotion Conference in Delft, the Netherlands. It represents the coming together of research-led and design-led approaches to design research. Today it is a global phenomenon, with practitioners as well as academics from all over the world contributing to its development. Interested readers can learn more about it at the website of the Design and Emotion Society.

The critical design bubble (in the top left corner) is design-led, with the designer playing the role of the expert. The emergence of this bubble came about as a reaction against the large user-centered zone, with its overwhelming focus on usability and utility. Critical design evaluates the status quo and relies on design experts to make things that provoke our understanding of the current values people hold. Critical design “makes us think” [3]. Cultural probes is a methodology in the critical design bubble [4]. Probes are ambiguous stimuli that designers send to people who then respond to them, providing insights for the design process. Probes are intended to be a method for providing design inspiration rather than a tool to be used for understanding the experiences of others.

The generative design bubble (in the top right corner) is design-led and fueled by a participatory mind-set. Generative design empowers everyday people to generate and promote alternatives to the current situation. Generative tools is a methodology in the generative design research bubble. The name “generative tools” refers to the creation of a shared design language that designers/researchers and the stakeholders use to communicate visually and directly with each other. The design language is generative in the sense that with it, people can express an infinite number of ideas through a limited set of stimulus items. Thus, the generative tools approach is a way to fill the fuzzy front end with the ideas, dreams and insights of the people who will be served through design [5].

Both critical design and generative design aim to generate and promote alternatives to the current situation. But they operate from opposing mind-sets. Many of the new tools and methods that have emerged in the last five years are design-led and sit along the top of the map, spanning the range from the critical design bubble to the generative design research bubble.

How Have I Used the Map?

The map has already been useful in a number of different ways. In my academic role, the map has been very useful for teaching about the changing state of design practice and design research. At the graduate level in particular, I see a trend toward a broader mix of disciplines wanting to learn how to do design research. The map can help students from different disciplinary backgrounds to understand each others’ mind-sets, approaches, and tools for doing research. The map can help students recognize where their past training and/or experience positions them as researchers, and it can also show them new directions for exploration and learning. I have used the map to support and scaffold different modes of exploration and experimentation in the design research process.

For example, graduate students (from design and engineering at the Ohio State University) who took a class in design research were asked to show where they stood on the map as a result of their previous research experiences [6]. The students located themselves primarily on the expert-driven side of the map, spanning research-led (the engineers were here) and design-led (the designers were here) approaches. The students formed teams (made up of people from both disciplines), and each team selected a topic to explore through design research. They were then asked to decide where on the map they would like to explore. All of the teams decided to move away from the expert-driven side of the map in order to explore participatory, design-led approaches to design research. Each team made a successful learning journey on the map. The engineers were surprised to learn that research can be a creative process that can open up ideas and new opportunities. They had previously been more familiar with research for problem solving. The designers learned how to think and work with a participatory mind-set, inviting non-designers to become their partners in the creative process.

On a more strategic side, I am currently using the design research map as a framework for establishing new curricula to ensure the effectiveness of learning experiences for students from diverse disciplines. One question that arises is this: Should we make separate design research maps for the different design domains such as industrial design, interior space design, interaction design, architecture, etc.? That may be useful as an interim step, particularly in academia where the design disciplines have not yet been integrated for the most part. A more useful end goal is to begin to connect the separate maps to help show the relationships between research tools and methods across all the different design domains. After all, people are people, whether they are finding their way around a building, using a product, reading a package, or using a software application. With the increased interest in and application of participatory design thinking, we will see that the professionals who understand people (whether designers or not) will be the ones to lead design in the future.

In my role as a practitioner, I have used the map as a framework for writing proposals and workplans. It can also be used to explain to clients (as well as team members from other disciplines) why a variety of research approaches are needed to address different points along the design development process. On a more strategic side, I am currently using the design research map as a landscape in which to position changes taking place on the competitive front and as a framework for future scenario development.

For example, by looking at changes in activity on the map over time, you can see where design research is heading and how fast it is getting there. This long view can be very useful in making strategic business decisions.

Figure 3
Dialogic design overlaid on map of design research

How Have Others Used the Map?

The map was originally offered as a scaffold to support conversation and to spark future thinking and doing. It was presented as a collage that is still taking shape. I invited readers to contribute additional dimensions, layers, zones, clusters and bubbles [1].

A few people have taken me up on that offer. Peter Jones, managing principal at Redesign Research, Inc., used the map to position his primary area of expertise called Dialogic Design. This adds new content to the map and enriches it tremendously.

“Design Dialogues imagines the possibilities of design as a transformative revisioning of systems that matter. We require new tools of design thinking and social engagement to energize the wisdom of participants. Dialogue is between perspectives, around a multi-perspective design canvas of products, systems, organizations & societies. In a world of complex, wicked problems, design has many cultural instruments, of dialogue, arts, research, and action [7].” 


Jaime Barrett, a recent MAA in design graduate from Emily Carr Institute of Art and Design, found the map to be useful in helping her find her way on the thesis journey [8]. “When Liz presented her cognitive map at Emily Carr Institute, I became acutely aware of the spaces where designers and researchers could learn from one another. It was astounding to see the work Liz has done to show just how different disciplines overlap. Liz painted a larger picture for me that day: I had always wondered if many different disciplines and fields actually do the same thing, but we all just call it something different. This inspired me to actually see myself and my work as sitting in both fields of research and a design; and it has especially allowed me to feel as if I could contribute and make a difference. Even just knowing that there are others out there with similar interests has inspired me to continue looking for new and interesting ways to contribute from a design perspective. And all of this came from such a small little map [9].”

Figure 4
People-centered innovation overlayed on map of design research

Anne Kirah, a consultant in People Centered Concept Making, on the other hand, adds no new content to the map. She modifies it to serve her needs, i.e., to reflect her own perspective and perhaps that of a European audience. (From a presentation called: “Methods or Mind-set? Issues of concern in designing for a global world and with the goal to improve lives.”)

Anne has changed the map by relabeling some of the areas (e.g., participatory design becomes people centered innovation) and by changing the size and manipulating the areas of overlap between some of the bubbles. She also chooses to leave certain bubbles off the map (e.g., generative design research) [10].

How Is the Map Evolving?

The map of design research methods can be used as a framework for organizing design research tools and methods and also as a net for capturing and revealing ideas about possible futures. It is clear that the current growth in design research is on the design-led (versus the research-led) side of things. We can expect to see more definition on this side of the map in the near future as we look to the arts and design for inspiration. Some of the new tools and methods for design research are listed below. It is interesting to note that most of them are from the European design research community.

• design games [11]
• design probes [12]
• design documentaries [13]
• visualization and storytelling [14]
• playful triggers [15]
• designing with video [16]
• Mobile Diaries [17]
• Situated Make Tools [18]

Figure 5
Map of design research-new tools and methods

Loose Ends

An unresolved issue is what to do with the explosion of interest in co-creation from a marketing perspective. This view appears to be focused primarily on digital forms of co-creation that takes advantage of the social networks in harnessing enormous amounts of input at a low cost. Marketing-driven approaches to co-creation are generally not being practiced from a participatory mind-set as is evidenced by their (over) use of the phrase “customer co-creation.” If people were truly valued as co-creators, they would likely be seen and referred to as “partners” or “co-creators,” not “customers.” It is as though the co-creative marketers are not on the map, but are seeing/sensing the landscape and figuring out how to take advantage of the activity for their own gain. It is interesting to see how this will turn out.

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In the early twentieth century, our understanding of physics changed rapidly; now, our understanding of biology is undergoing a similar rapid change.

Freeman Dyson wrote, “It is likely that biotechnology will dominate our lives and our economic activities during the second half of the twenty-first century, just as computer technology dominated our lives and our economy during the second half of the twentieth [1].”

Recent breakthroughs in biology are largely about information—understanding how organisms encode it, store, reproduce, transmit, and express it—mapping genomes, editing DNA sequences, mapping cell-signaling pathways.

Changes in our understanding of physics, accompanied by rapid industrialization, led to profound cultural shifts—changes in our view of the world and our place in it. In this context, modernism arose. Similarly, recent changes in our understanding of biology are poised to create new industries and may bring profound cultural shifts—new changes in our view of the world and our place in it.

Already we can see the process beginning. Where once we described computers as mechanical minds, increasingly we describe computer networks with more biological terms—bugs, viruses, attacks, communities, social capital, trust, identity.

How is design changing?

Over the last 30 years, the growing presence of electronic information technology has changed the context and practice of design.

Changes in production tools designers use (software tools, computers, networks, digital displays and printers) have altered the pace of production and the nature of specifications. But production tools have not significantly changed the way designers think about practice. In a sense, graphic designer Paul Rand was correct when he said, “The computer is just another tool, like the pencil [2],” suggesting the computer would not change the fundamental nature of design.

But computer-as-production-tool is only half the story; the other half is computer-plus-network-as-media.

Changes in the media designers use (the internet and related services) have altered what designers make and how their work is distributed and consumed. New media are changing significantly the way designers think about practice. New types of jobs have emerged. For many of us, both what we design and how we design are substantially different than they were a generation ago.

What do electronic media and designing have to do with biology?

Emerging design practice is largely information based—awash in the technologies of information processing and networking. Increasingly design shares with biology a focus on information flow, on networks of actors operating at many levels and exchanging the information needed to balance communities of systems.

Modern design practice arose alongside the industrial revolution. Design has long been tied to manufacturing—to reproduction of objects in editions or “runs.” The cost of planning and preparation (the cost of design) was small compared to the cost of tooling, materials, manufacturing, and distribution. A mistake in design multiplied thousands of times in manufacturing is difficult and expensive to fix.

The realities of manufacturing led to certain practices and in turn to a mindset or even a way of thinking. In the “modern” era, design practice adopted something of the point-of-view or even the philosophy of manufacturing—a mechanical-object ethos.

Now as software and services have become a large part of the economy, manufacturing no longer dominates. The realities of producing software and services are very different than those of manufacturing products.

The cost of software (and “content”) is almost entirely in planning, preparation, and coding (the cost of design). The cost of tooling, materials, manufacturing, and distribution is small in comparison. Delaying a piece of software to “perfect” it invites disaster. Customers have come to expect updates and accept their role as an extension of developers’ QA teams, finding “bugs” that can be fixed in the next “patch.”

Services also have a different nature than hardware products. “Services are activities or events that create an experience through an interaction—a performance co-created at point-of-delivery [3].” Services are largely intangible, as much about process as final product. They are about a series of experiences across a range of related touch-points.

The realties of software and service development lead to certain practices and to a mindset or even a way of thinking. Emerging design practice is adopting something of the point-of-view or even the philosophy of software and service development—an organic-systems ethos.

Models of change

Several critics have commented on facets of the change from technical-object ethos to organic-systems ethos. This article brings together a series of models outlining the change and contrasting each ethos.

The models are presented in the form of an “era analysis.” Two or more eras (e.g., existing-emerging eras or specified time periods) are presented as columns in a matrix with rows representing qualities or dimensions, which may change across each era, characterizing aspects of the era.

The eras are framed as stark dichotomies to characterize the nature of changes. But experience is typically more fluid, lying along a continuum somewhere between extremes.

John Rheinfrank [4] provides a broad summary of the change, which may serve as an introduction and an overview. He begins by describing a change in world-view, similar to the change in ethos described above.

We may expand Rheinfrank’s model, to describe how things come to be and the role of designers and their clients in the process.

A Concern for Users

Austin Henderson and Jed Harris [6] have noted that many computer systems are constrained by a mechanistic world-view. They cite automation projects avoiding errors by drastically reducing options available to users (narrowing language or variety) but in the process crippling communication and organizational flexibility. Henderson and Harris contrast coherent systems to responsive systems. Coherent systems require consistency and predictability; responsive systems support messiness and improvisation. “In a given system, as responsiveness increases, coherence tends to decrease and vice versa—a classic tradeoff. Scaling makes this tradeoff sharper. As systems get larger, they have to work harder to maintain their coherence, and this increasingly makes them unresponsive. Conversely, large systems that allow great local responsiveness (such as the World Wide Web) have difficulty maintaining coherence.”

Henderson [7] pointed out that consistency is an ideology, that other choices are possible, “the core ideology of computer system design is totally permeated with the assumption that computers are rule-following machines, and more generally, that all human activities can and should be described in terms of a consistent set of rules.”

He argues that “feedback loops . . . actually make organizations work, and the constant negotiation that these loops entail . . . computing systems tend to break those loops . . . so people have to bear the brunt of patching them up, and usually have to fight the computer system to do it.” Henderson and Harris propose a new approach, which they describe as “Pliant Computing.”

At the heart of Henderson’s call for “Pliant Computing” is a deep concern for people who use computers. Henderson sees the relationship between designer and audience changing. As Rheinfrank pointed out, the designer is moving from detached expert to collaborator. And the relationship between designer and constituent is moving from expert-patient to what Horst Rittel called, “a symmetry of ignorance (or expertise) [8]” where the views of all constituents are equally valid in defining project goals.

Liz Sanders [9] presents a similar argument with slightly different eras, explicitly introducing the idea of moving beyond human-centered or user-centered design.

Co-development is also a fundamental tenet of open-source software. Eric Raymond [10] wrote, “Treating your users as co-developers is your least-hassle route to rapid code improvement and debugging.” He added, “Even at a higher level of design, it can be very valuable to have lots of co-developers random walking through the design space near your product.” Raymond famously contrasted “cathedrals carefully crafted by individual wizards or small bands of mages working in splendid isolation” to “a great babbling bazaar of differing agendas and approaches.” He suggested traditional “a priori” approaches will be bested by “self-correcting systems of selfish agents.”

The Rise of Service Design

The shift from industrial age to information age mirrors, in part, a shift from manufacturing economy to service economy. In the new economy, as former WiReD editor Kevin Kelley put it, “commercial products are best treated as though they were services. It’s not what you sell a customer, it’s what you do for them. It’s not what something is, it’s what it is connected to, what it does. Flows become more important than resources. Behavior counts [11].”

Early on, Shelley Evenson saw the importance of service design, and she has led U.S. designers in developing the field. She has provided a framework contrasting traditional business-planning methods with service-design methods. Her framework parallels the larger change in ethos we’ve been discussing.

Typically, responsibility for designing individual artifacts rests pretty much with one individual, but systems design almost by definition requires teams of people (often including many specialties of design). The need for teams of designers can be seen easily in the design of software systems and service systems, where many artifacts, touch-points, and sub-systems must be coordinated in a community of cooperating systems. For example, “web-based services” or “integrated systems of hardware, software, and networked applications” require development and management teams with many specialties.

The work of an individual designer on an individual artifact has often been characterized as “hand-craft.” In contrast, Paul Pangaro and I have proposed “service-craft” to describe “the design, management, and ongoing development of service systems.” Design practice in a hand-craft context differs markedly from design practice in a service-craft context. Having assembled a team, care must be taken to negotiate goals, set expectations, define processes, and communicate project status and changes in direction. Care must also be taken to create opportunities for new language to emerge and to create capacity for co-evolution between service and participants.

We also noted, “hand-craft has not gone away, nor is service-craft divorced from hand-craft. Hand-craft plays a role in service-craft (just as in developing software applications, coding remains a form of hand-craft). While service-craft focuses on behavior, it supports behavior with artifacts. While service-craft requires teams, teams rely on individuals. Service-craft does not replace hand-craft; rather service-craft extends or builds another layer upon hand-craft [13].”

Characterizing Services

Robert Lusch [14] wrote about changes in marketing, describing a service-dominant logic in which “value is defined by and co-created with the consumer rather than embedded in output.” The “make-and-sell” strategy of linear value chains gives way to the “sense-and-respond” strategy of self-reinforcing “value cycles.” Lusch described traditional goods-centered dominant logic as focused on “operand resources,” tangible assets with inherent value. He contrasted that logic with emerging service-centered dominant logic focused on “operant resources,” intangible assets, which create value in their use, such as skills, technologies, and knowledge. He also pointed out that service logic is not only compatible with the idea of a learning organization, but it may actually require one.

Nicholas Negroponte has famously contrasted “atoms and bits.” The physical, tangible, here-and-now aspect of products-as-objects makes them relatively easier to evaluate than services. This characteristic is one of the things that make products easier to manage than services. A CEO can pick up a product appearance model and immediately evaluate it, compare it to another, and decide how to proceed. Even a complex product like a car can be evaluated relatively quickly. But services are much harder to evaluate. Services cannot be apprehended all at once; they must be experienced over time. And sometimes service varies from one experience to the next.

Sustainable Design

The mechanical-object–organic-system dichotomy also appears vividly in discussions about ecology. Much of our economy still depends on “consumers” buying products, which we eventually throw “away.” William McDonough and Michael Braungart have pointed out that there is no “away,” that in nature, “waste is food,” They urged us to think in terms of “cradle-to-cradle” cycles of materials use, and they suggested manufacturers lease products and reclaim them for reuse [15]. Theirs is another important perspective on the idea of product-as-service.

Architects, too, have begun to design for disassembly and reconfiguration. Herman-Miller recently published a manifesto on programmable environments, talking about the need for “pliancy” in the built environment and echoing the language The Cathedral and the Bazaar while discussing building design [16].

Sustainable design is emerging as an issue of intense concern for designers, manufacturers, and the public. The same sort of systems thinking required for software and service design is also required for sustainable design. This provides further impetus for changing our approach to design education.

Stuart Walker, Professor of Environmental Design at University of Calgary, has written, “Only by fundamentally changing our approaches to deal with the new circumstances can we hope to develop new models for design and production that are more compatible with sustainable ways of living. Wrestling with existing models and trying to modify them is not an effective strategy.”

Early Parallels

The current shift from a mechanical-object ethos to an organic-systems ethos has been anticipated in earlier shifts.

In the mid-1960s, architects and designers began to focus on “rational” design methods, borrowing from the successes of large military-engineering projects during the war and the years following it. While these methods were effective for military projects with clear objectives, they often proved unsuccessful in the face of social problems with complex and competing objectives. For example, methods suited to building missiles were applied to large-scale construction in urban redevelopment projects, but those methods proved unsuited to addressing the underlying social problems that redevelopment projects sought to cure.

Horst Rittel [8] proposed a second-generation of design-methods, effectively reframing the movement, casting design as conversation about “wicked problems.” His proposal came too late or too early for the design world, which had already moved on to “post-modernism” but had not yet encountered the internet.

Rittel’s work did attract attention in computer science (he was a pioneer in using computers in design planning), where “design rationale” (the process of tracking issues and arguments related to a project) continues as a field of research. More recently, Rittel’s work has attracted attention in business school publications addressing innovation and design management [18][19].

Paul Pangaro and I have also noted that Rittel’s framing of first- and second-generation design methods parallels Heinz von Foerster’s framing of first- and second-order cybernetics. Von Foerster described a shift of focus in cybernetics from mechanism to language and from systems observed (from the outside) to systems-that-observe (observing-systems).

In 1958, von Foerster formed the Biological Computer Laboratory at the University of Illinois Urbana-Champaign. He brought in Ross Ashby as a professor and later Gordon Pask and Humberto Maturana as visiting research professors. The lab focused on problems of self-organizing systems and provided an alternative to the more mechanistic approach of AI followed at MIT by Marvin Minsky and others [22]. In a way, von Foerster anticipated the shift from mechanical-object ethos to organic-systems ethos in computing, design, and perhaps the larger culture.

What do these changes mean for design education?

As design moves into the Age of Biology and shifts from a mechanical-object ethos to an organic-systems ethos, we should reflect on how best to prepare for coming changes in practice. At a recent conference on design education, Meredith Davis described, “the distance between where we are going in the practice of graphic design and longstanding assumptions about design education [23].” (The full text of her talk is included on page 28 of the September issue of Interactions.)

Davis (building on Poggenpahl and Habermas) distinguished between two models of practice, “know how” and “know that,” “design as a craft and design as a discipline.” This distinction parallels the distinction between hand-craft and service-craft Pangaro and I proposed above. Davis asserted “college design curricula, and the pedagogies through which we deliver them, are based almost exclusively on the first model of practice, on know-how, and don’t acknowledge issues that drive emerging practices.”

Davis’ argument and framing are closely related to changes described in this article. Changes Davis advocates are consistent with the spirit of the new ethos and aimed at helping designers grasp the nature of organic-systems work and preparing them for practice in the Age of Biology.

Of course, not all designers welcome the coming change. Form-giving remains a large part of design practice and design education. Will some designers be able to continue to practice primarily as form-givers? That seems likely. But already a schism is developing both in design practice and design education, as individuals and institutions choose to focus on either form-giving or on planning. It remains to be seen if one person, one firm, or one school can bridge the divide and excel at both.

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