Normative Analysis and Preparing the Proposal

  1. Logic of Normative Analysis
  2. Analyzing requirements
  3. Creating the proposal
  4. Participative Methods

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Logic of Normative Analysis

Normative approach aims at finding out not only how things are, but above all how they should be, which means that it will be necessary to define the subjective point of view that shall be used, in other words to select the people who shall evaluate the proposals which aim at improving the object of study.

For the task of defining how the present state of things should be improved, there are several possible logical chains of reasoning that can be used. Three of them are delineated in the diagram on the right. Each of them uses a different starting point:

Any two, or all three approaches can also be used in parallel in order to give a more reliable basis for the proposal.

In any case, the final stage of the normative process usually consists of an alternation of preparing detailed tentative proposals (often by professional designers or researchers) and evaluating these proposals, preferably by people from the interest groups or at least by simulating their points of view.

For the entire process of normative analysis nobody has yet found a reliable and generally applicable model, but quite often one or more of the following logical procedures are used in the process:

Analyzing requirements

When considering the need of improving an object of study, the normal approach is to define those properties of the object which need improvement, and perhaps also to enumerate those other characteristics of it that should be kept as they are. In other words, you make a list or a specification that contains the requirements that the final proposal of the project shall meet.

The procedure for creating a specification depends on the information that is available as its starting point. Usual approaches for this include the following:

Adding a normative dimension to a descriptive analysis

One approach for managing the characteristics of the future proposal is to apply the methods of descriptive analysis (such as case study, comparison, classification etc.) to the object of study, and to amplify these methods by adding a normative dimension into the analysis. A few examples are given below.

Normative Case Study. Normative case study Case study is a usual approach in descriptive research projects, but it can easily be amplified with a normative aspect in order to give grounds for later improvements in the object of study, be it an existing circumstance, product, or work routine. For example, much descriptive research has been done just to find out why the celebrated tower in Pisa slants and which factors contribute to the problem. Only after these mysteries have been clarified can engineers successfully draft the plan for improvement.

Obvious candidates for normative case studies are large and expensive one-off products like houses, theatre or film productions and computer programs which sometimes need updating or betterment during their long life spans.

Probably the most common use for a normative case study is to give a starting point for developing a new version of an existing product or procedure. This starting point is normally defined - after necessary studies - as an exemplar, see below.

Normative Study of Development. Many traditional histories have had a hidden normative essence even when the author tried to present his work as "pure description" with the methods of Analyzing Development. Innumerable historians have been appointed by organizations like the Church, the government or the royal family, and this attachment has guided the selection of facts. Historians were expected to describe mostly those events that had some relation to the patron, emphasized its importance and preferably its admirable deeds and the wrongs of its opponents.

There is no absolute divergence between descriptive and normative study of history, because we have so immense amount of past incidents recorded that it would be impossible to write any history without first having an idea of which events and associations are important. Usually importance means an attitude to the event as well, in other words a normative view about which events in history were proper and useful and which not.

There is no hiding for the fact that historical studies can assist in making existing things better, and if you intend to make that kind of study it becomes easier if you acknowledge this goal consciously. In practice this means that you already at the outset should define what is the problem that you expect to alleviate with the help of your study, or which type of product is going to be improved. There is no point in speaking of 'improvement' without defining also whose point of view shall be used. All these things should be defined as early as possible during the study, to prevent gathering irrelevant material and doing unnecessary analyses.

Once you have gathered and analyzed your material and discovered the inherent structure or "dynamic invariance" of historical evolution, this invariance can then be used to predict the future development of the object of study. In the case that the predicted development seems to go in the wrong direction, the causalities found in past evolution can often be used to change the direction of future progress.

Normative Comparison. The difference between descriptive and normative styles of comparison is that in normative analysis one of the principal criteria is evaluative like "satisfaction", "usefulness" etc., and the aim of the study is to point out the best (in this respect) among the alternatives that are being studied. Sometimes the final aim is not only to find the best, but also to improve it or similar objects later on, and comparative analysis is expected to provide grounds for the planning of improvements to existing circumstances or products.

Sometimes you can make use of already existing sources of evaluations, like the Customer Feedback System, if the company has one, or the public critique and the tests of new products that some institutions, associations and journals generate and publish habitually.

Car model:AB
Number of seats58
Number of doors24
Air bags24
Fuel consumption5,87,5
Special merits(Descriptive text)
Price85009900
In these tests a number of competing products are judged according to a standard list of questions which includes only those aspects where the rivalling products differ.

The comparison table works well even in the case that some of the items are expressed as numerical variables, others as qualitative verbal descriptions, and nothing prevents including in the table even pictorial presentations of the objects. However, in the final phase you have to translate all these descriptions into evaluations so that you can sum them up and point out the best alternative. Note that all the items on your list are seldom equally crucial, therefore you will need to define the weight of each item when summing them up.

A normative classification can similarly be constructed from almost any descriptive cross tabulation by adding a dimension which expresses an evaluation.

Normative comparisons and classifications are effective in pointing out cases which agree with the criteria of the normative project - or at least with some of the criteria. These cases can then be used as exemplars, i.e. as starting points in developing the final normative proposal of the project. Methods for this are Combination of exemplars and the method of An exemplar and improvements.

Combination of exemplars

Exemplars are existing procedures, lines of action, artefacts or their details, in which at least some characteristics are regarded meritorious from the point of view of the present normative project. They may include also some features that are not quite acceptable.

The normative or educative purpose is clearly visible already in the first case studies that are known to us from antiquity. They explained the merits of respected statesmen, or of admired works of architecture, later also the lives of saints and esteemed artists. The purpose obviously was to present these to the younger generation as meritorious, or sometimes as avoidable, examples. Even today this approach is often used in the field of various arts where reputed critics select exemplars which are then published in exhibitions and professional journals and are hoped to guide the work in the field later on. They can substitute or complement theory in artistic design professions for topics for which it is difficult to develop more explicit doctrines and for topics where knowledge exists only in tacit form, which is often the case in questions of beauty and taste.

Exemplars can provide useful points of reference in a product design project, particularly in the early phase of preparing a detailed product concept, when it is difficult to find other patterns for describing the future product. A new product idea can be defined either by selecting an exemplar and enumerating the necessary improvements to it, or alternatively by giving a set of exemplars and pointing out their benefits which should be re-created in the new product. Both these methods are explained in Logical patterns for presenting a product concept.

Already one exemplar can be useful for normative purposes, but more common is that you have found two or more exemplars, of which no one is perfect in all respects, but nevertheless each of them entails something that is excellent. It is then these characteristics that the normative project shall try to combine in its proposal. If this can be achieved, the resulting product or activity will be more competitive than any single one of the exemplars, even in the case that none of its properties in itself are surpassing the exemplars.

When the selection of the exemplar depends on factual sales records, the method has the advantage of reflecting truthfully the customers' views. An alternative but much more arduous method for collecting the opinions of potential users of the product would be a survey.

The exemplars can be selected from your firm's own production or activity, but it is also possible to take one or more of them from your keenest competitors, that is, they are the products or activities that shall supersede the competition in the marketplace if all goes well. When the exemplar is selected among the most successful companies and products in the market, it can also be used for "benchmarking": it defines an optimal level of operation which is known and which can be taken as a target for less successful companies.

Working with a set of exemplars is easy and straightforward; there is no great risk of misunderstanding. The weakness of this method is that it does not encourage searching new alternatives faraway from the old and proven traditions, and it is difficult to generate anything really new which could revolutionize the market. The result will contain only such qualities that your competitors already offer, if you or your company cannot add something of its own invention.

Exemplars are also widely used in the education of business economics. Many universities possess large collections of case reports of companies that work in the vicinity. Typically an educational case describes the history, organization, environment, key products, system of management and perhaps a few typical managerial problems that the students then can try to solve.

Medicine is another field of education where exemplars are essential. Here they describe clinical operations that have successfully cured an illness.

An exemplar and improvements

Defining improvements by referring to existing products or activities is an ancient method. In its time, it was even possible to bypass designating the exemplar because the standard models for most products and work processes were defined by tradition. Today, the method is especially suitable if the activities or products of your firm are relatively constant and need only minor annual improvements. In such a case, you will normally want to select the existing state of the activity or product as the starting-point exemplar. Another possibility is to select a competing product or activity from a competing enterprise that you wish to surpass.

Which properties of the exemplar then require improvements, and in what degree, that is a question perhaps already answered in the files of the customer feedback system of your company. Otherwise, you will have to do market research with suitable survey methods among either potential customers, or among the salespersons of your company.

Advantages of the exemplar-and-improvements method are that it is simple to use, concrete and realistic. Misunderstandings are not likely. From the designer's viewpoint an exemplar is an expedient point of departure, and it is often relatively easy to continue the design with the method of iteration. An advantage is also that usually only small modifications to the production line will be necessary.

A disadvantage can be that it is all too easy to point out details that might need improvement, without realizing how the components of the finished proposal depend on each other (for example, costs often rise too much when quality is improved). A too optimistic use of this method in the product concept phase can result in high aspiring targets which cannot be met in practice. To prevent it, it is advisable to avoid giving too absolute targets but instead use scales of satisfaction (see below) when defining desirable improvements.

Specification on theoretical basis

In the case that no suitable exemplar can be found, the methods that refer to an exemplar are out of question. Nevertheless, it is also possible to specify the desired properties of the future state of things or of the new product directly, on the basis of earlier research and theory. This method has, besides, the advantage that it allows defining even radically new products and processes with no known precursor.

On the other hand, the method is demanding, because you have to work with abstract theoretical concepts. It can be difficult to define the attributes of an activity or product not yet existing. It is difficult to see the relations between various attributes of the object, and that is why the requirements concerning them often conflict. Moreover, it is too easy to forget essential properties and their side effects, especially if they affect mostly outsiders.

The scope of properties that are relevant when developing new products is very large. It includes topics like:

The list above contains mostly items that interest or benefit either the user or the manufacturer of the product. Besides, you should keep in mind that there can be side effects, perhaps detrimental ones, to outsiders. Such effects are, unfortunately, often quite difficult to enumerate and assess. See a general list of various "stakeholders" in a development project.

Every product possesses an infinite number of attributes, but not all of them are so important in regard to production, marketing or use that they need be included in the product concept. Moreover, we can disregard "self-evident" questions: those topics on which all the people in the design team agree and which thus probably will be regarded without any special mention in the product concept. Even then, the list of properties often grows difficult to handle. Some procedures which can help managing a lengthy list of properties and requirements are:

Obligatory requirements. These often concern the safety of use of the product, especially the mechanical, chemical or electrical risks of its use. Obligatory regulations are often given by the public authorities, or they are published as "voluntary" industry standard guidelines which are specified through separate research projects carried out by specialists like physicians, occupational health and safety engineers, etc. In a normative project it is usually advisable not to mix obligatory and voluntary requirements. Instead, make separate lists of them. In that way it will be easier, in the final inspection of the proposal, see if they are met.

Interdependent requirements. When planning changes to anything that concerns several people with different values and life styles, it is not unusual that preferences conflict in some degree. You should try to eliminate or arbitrate such conflicts as soon as possible, otherwise there is a risk of running into a dead end when making the final proposal of the project.

Optimizing Sometimes it is possible to arbitrate the goals that ostensibly conflict, by uncovering their mutual relationship. An example of this method is finding the optimal thermal insulation for a new building. When selecting the thickness of the insulating layer, the cost of building materials (B, in the figure on the right) and the future heating costs (A) seem to conflict. Nevertheless, the values of both of these expenditures can be translated into annual costs which then can be added up and the minimum of the sum A+B is easily found. This new variable (A+B) can then supplant the two original variables of building and heating costs.

The science of operations analysis includes other comparable analysis methods such as, for example, the algorithm of linear programming which can be used to find the common optimum of several quantifiable attributes of a product. Most of these methods accept only quantitative variables. Of course, it is possible to "operationalize" any qualitative attribute, by constructing an arbitrary scale for its various levels of intensity and thus transforming it into a quantitative variable; but the conversion often overlooks some subtler aspects of the attribute and the validity of the results will then suffer. Therefore, this technique should be used only with discretion.

Target Weight
Capacity is at least 55 units/hour 40
Design is striking and personal;
unlike all the other models in the market
10
All materials can be recycled 10
Production will cost not more than $100 40
Total weights 100
Defining the mutual weights of requirements. The longer you prepare the list of requirements, the lengthier it grows. Nevertheless, not all of the requirements are vitally important while most of them are more or less optional.

Of course, the goal is to fulfil all the requirements of pertinent people if possible, but in the final detailed design phase it often turns out that accomplishing completely one target will prevent achieving another one, particularly the target of moderate costs. This is why it is advisable to define the order in which the requirements can be relinquished if it turns out to be necessary, for example to make possible the fulfilment of more important goals. The order of importance can be given by assigning weights to the requirements. An example of a small table of weights is presented on the right.

A table of requirements helps handling a large number of questions, but only if its content is given in a logical order. Preferably it should be possible to comprehend all the principal contents in one complete panorama. To this end, it is advantageous to locate groups of associated properties and combine them into one category. Such families of attributes can be found by contemplating or discussing them in a team, or for quantifiable variables with the help of factor analysis.

Usability The resulting groups of properties can then be presented in the pattern of a logical tree, a Venn diagram or another topological model. On each level of the tree the sum of all the weights is constant, for example 100%, but of course the absolute weights on the more detailed lower levels will be smaller. An example of a logical tree is on the right, where Shackel has analyzed the concept of usability of products.

Another logical tree (in table form, below) depicts the goals of building (by Niukkanen, 1980, p.20).

SATISFACTION / FIT
Inputs Outputs
Costs / Resources Usefulness / Function Experience / Perception
-building costs
-costs of use
-decrease to output
Spaces:
-indoor climate
-equipment.
-durability
Environmental factors:
-exteriors
-interiors

One of the advantages of a logical tree is that in the evaluation stage of the development project you can easily transform it into a table for a cost benefit analysis.

Property: Ease of use Merit
Most operations are automatic. 5
Several operations are automatic.
The instruction booklet is detailed and explicit.
4
The operation and the instructions are mediocre. 3
The operation is sometimes clumsy or confusing. 2
The machine reacts not as described in the booklet. 1
Degrees of satisfaction. When developing a product, a service or a state of things, the limit of acceptance is vague: the more you get of a certain property, the better. On the other hand, producing superior quality, capacity etc. usually means extra costs. It can be advantageous to postpone the final dimensioning of such a property to a later moment in the development project, when we have more knowledge of the available alternatives and their costs. This means that even when it would be perfectly possible to define, in the analysis phase, an acceptance limit for this property, we define instead a series of degrees of satisfaction or "levels of merit". These are often defined numerically on an arbitrary scale, for example on a scale from 1 to 5, like in the table on the right. Numerical scale facilitates handling several properties simultaneously and finding their common optimum, though the disadvantage is that some minor aspects of the property may get forgotten.

There are also properties which you can easily measure numerically, but in spite of this the measurement does not directly correlate with satisfaction. Let us consider a bicycle: if it possesses two gears it is much better than a single-gear vehicle. Three gears is still a little better, but if there are over ten gears the addition of one or two more does not give much increase in usefulness or satisfaction. The relationship between the number of gears and satisfaction can suitably be expressed as a curve, like in the diagram on the left.

Another example of a scale of utility is on the right. According to it, a CD player is to be rated poor (utility value=1) if it only transmits voice until the 5 kHz limit. 10 kHz is a little better, while 20 kHz is excellent (utility value=5). Thereafter higher performance gives no additional merit, because anyway the human ear could never hear a voice over 20 kHz.

Aesthetic value It is possible to give a measurable value, beside materialistic and utilitarian benefits, also to humanistic pleasures like the experience of beauty. An example is seen in the diagram on the left. The graph purports to indicate that there is a measurable optimum of the visual complexity of a work of art. The philosophy behind this type of aesthetic measurement is discussed in Beauty of Products.

Creating the proposal

"Rational planning" techniques

Logical reasoning as a planning or design method aims at finding just one, final and optimal solution on the basis of given targets and factual circumstances. This technique is possible only if the planner knows exactly all the targets and restrictions as well as their mutual relations, and if these are accepted by all parties involved.

The process using logical deduction is sometimes called "rational planning process" and it consists ideally of the following operations:

  1. Description of the starting situation
  2. Description of the target situation
  3. The difference between 1 and 2 gives the objectives for the project
  4. Conceiving alternatives to reach the objective
  5. Predicting the consequences of each alternative
  6. Assessing the consequences
  7. Selecting the best alternative.

The initial three phases of the process are often readily feasible with the usual methods of descriptive research. The phases 4 and 5 are conventional routines for a proficient designer. In those fields of industrial production where this type of task is usual, it is even possible that researchers develop standard patterns of deduction or calculation which the designers can use in most tasks for finding the optimal (or at least adequate) solutions. Such is the case in many fields of engineering where the prevalent theory of design includes algorithms and formulas of deduction for the design of, for example, traffic network or bridge construction. They can often be carried out by a computer, which can accelerate the design a great deal, especially when computer aided design (CAD) is used.

The weakest point in the model are phases 6 and 7 where the designer has to consider simultaneously a multitude of requirements from different parties: the evolving needs of various groups of people, the environment, production technology and conjunctures. Their common evaluation is obviously possible only if the consequences of alternatives are exactly known and there are not too many personal differences in their evaluation. Such a lucky condition does not always exist in the design of products for personal use.

Many researchers of planning methods have proposed dealing with complicated problems by applying the method of Descartes, given as the rules #2 and #3 in the Discourse on the Method (1637): by dividing the problem into "manageable parts" and solving separately each of these, beginning with the simplest issues and ascending to the more complex. Thus, Christopher Alexander in the book Notes on the synthesis of form (1964, p.94) illustrates the method with the help of two logical trees (below). The one on the left presents the process of analysis: cleaving the requirements to the future product into their constituents. The second tree symbolizes synthesis where the solved problems, presented as diagrams, are added together. "At the apex is the last diagram, which captures the full implications of the whole problem, and is therefore the complete diagram for the form required" says Alexander. However, practicing architects and designers soon found that it is seldom possible to cleave design problems in so independent parts that these could be solved in isolation and again combined successfully. In other words, it seems that Alexander's method may work sometimes, but it is no Philosopher's stone for all the problems of planning.

Alexander's design method

 

Iteration

IterationIteration is a process often seen in normative analysis. It means that you make small "incremental" modifications to the proposal, compare the new proposal to the old one and continue with whichever of them is best. It is a powerful method, but when using it you should keep in mind two inherent weaknesses of it:

  1. The method of iteration cannot really well handle more than one feature of the object at a time. If the alternatives diverge from each other in more than one respect, it will be difficult to compare and rank order them.
  2. Another weakness of iteration is that while it usually leads to a better solution, it may nevertheless fail to find the best alternative of all. An example of this is illustrated in the figure on the left: if the iteration process is started at option A, it will eventually lead to alternative C. This is, however, only a partial optimum: while it certainly is better than the neighbours, it may be far from the radically different absolute optimum S, which thus never could have been found by iteration only. Because of this weakness it is usually advisable to complement the iterative approach with other, more innovative methods that are capable of finding alternatives that are more distant to the already known ones.

On the other hand, the iterative approach has a weighty advantage: it allows starting the work on a low level of precision and reliability. With less detailing and precision it is often easier to maintain innovation and create alternatives that are not too close to the ones known today. If you do not have enough facts to base your first proposals on, you can simply make guesses. Bad guesses will then be eliminated in the evaluation phase. In a development project there always is a decisive final assessment which detects effectively the errors made in the earlier stages of the project. This presupposes, of course, a final, careful evaluation of the proposals, if possible by appropriate random samples selected from the future users of the results. The final evaluation will have better validity already because the final proposals are more detailed and more realistic.

Trial and error

When you have the possibility of choosing among alternatives which have a large variation, and the number of the alternatives grows, the probability increases that there is an acceptable alternative among them. This holds true also when the average acceptability of the alternatives is quite low, and even when they have been generated with a random procedure which does not at all aim at the goals of the final selection. The last named logic is, in fact, the same that has governed the origin of species through natural selection, as has been explained by Charles Darwin.

An efficient use of the trial and error method requires a large number of alternatives, perhaps hundreds of them, and the range of their variation must be so wide that it includes at least one potentially fruitful alternative. Note that we need not yet find a perfect final proposal among the alternatives. Finding one or a few promising ones usually suffices, because a few imperfections in them can normally be corrected later quite easily with iteration, for example. You should thus evaluate the alternatives not as such, but as potential starting points for proposals. Great competency of the evaluator is thus essential.

For developing a large number of alternatives two methods are common:

For generating a large range of alternatives you might like to take an existing product as a starting point, but the difficulty in this method is that the proposals tend to remain too close to this origin and really new alternatives are never found. Nevertheless, the method is viable when combined with bold wilful variation, for example by modifying the existing product idea with transformations such as:

You should avoid criticizing the transformations or slowing down the process, because it might spoil the innovative spirit of the team. For this reason you should disregard all practical viewpoints and restrictions. Their turn comes later, when selecting the best candidates and improving them further.

Another method for encouraging variation when developing alternatives is to let random "distant ideas" merge into the creative process. These distant ideas could be introduced as items randomly picked from a prefabricated list which need not have any relevance with the problem in question. However, each distant idea when associated with the original product may help to produce new ideas by analogy.

Once a thinkable proposal - or a few of them - has been found with the "trial and error" method, it quite often turns out that there still is much to be improved in each proposal. For these final ameliorations, iteration is often a suitable method.

Subconscious maturing and innovation

The methods described above are meant to be used by the researcher or planner as planned and conscious procedures, but another possibility is to let his or her subconscious to take care of the work, perhaps by utilizing logical chains that we do not yet know.

The method of subconscious maturing and innovation is common in the artistic design of products. It is normal that the designer first lets the targets of the design mature in the subconscious for quite a time, and if all goes well, the solution eventually pops up. Such an event has been described by many professionals practising various arts, for example Mika Waltari (1980, p.398...400), writer of the best-seller "The Egyptian":

"This intensive experience is brief, sometimes a few seconds, sometimes minutes. ... In advance of it, I had already devised many outlines for the future book, but all of them had seemed pointless ... This veritable flash, the genuine innovation resembles a mystic occurrence and it does not last long. Afterwards you can consciously try to understand it and make it clear to yourself. Only thereafter you can start to collect new material from a novel point of view, and then follows the final concentration in writing which can take several years..."

Waltari emphasizes that the best arrangement comes from the subconscious, not by forthright planning on paper even in the case that the work will be based on a lot of collected written information:

"If I write down collected facts in a manner too definite, it becomes an impediment... It is better to let this collected intelligence submerge into the subconscious, and later when I really need these elements for my work, they will come back to conscious thinking as evident facts. If I should this way forget some details, I have concluded that those particulars were perhaps not really important after all. ... If I would try to write down longer passages [before their due time] their idea would die: the thought would stiffen prematurely so that I could no more exploit it." (ibid. p.406.)

Some creative artists believe that it would be detrimental to disturb or try to speed up the workings of the subconscious. The most ingenious ideas are the most elusive: they easily fade and disappear if the innovator too rashly formulates them on the paper:

"In hunting for ideas man's skill at staging represents the hunter's craft. Creativity is a question of staging a problem with such a setting, that something begins to happen, appear, and move within it. Now a being is "becoming" there, something becomes more visible, more credible. But it is only loosely trapped; it can escape if one approaches it too soon." ... "The seizing of an idea is a process which one doesn't seem to be able to influence consciously. Conscious cognition is too coarse an instrument." (Pietilä 1985 p. 26.)

As a matter of fact, we know very little of the working habits of our subconscious. It seems that to beget an invention the brain needs, beside the logical basis for the problem solution, also stimulation which the inmost layers of the brain normally produce all the time. This stimulation is in no way related to the conscious problem and (because we do not know its structure) it appears to be random. On the basis of these two stimuli (logical and random) the brain then produces tentative solutions for the problem, and cognition then starts to evaluate these in the same way than the natural selection screens out the unfit mutants in Darwin's theory. Eventually one of them becomes accepted in the conscious evaluation and the artist starts developing it further.

Innovative team work

Admitted that we do not know how innovations are born, it is nevertheless possible to stimulate innovation, at least in team work. Special techniques that have often been used for stimulating innovation, include:

All these methods share a few common principles:

The participants should try to forget attitudes which could inhibit innovation, such as (according to Johnsson and Varjoranta, 50):

Participative Methods

A normative project aims at developing a proposal which can be accepted by all the pertinent interest groups. One alternative for gathering the opinions of these people and prepare a proposal of action is to organize a contact with the interest groups so that a number of these people personally co-operate with the researchers or planners, not only when finally accepting or rejecting the proposal, but at several points of time during planning. This method of frequent consultation improves the chances of success for the project, because it helps to eliminate unacceptable proposals before too much work has been spent to them.

Frequent meetings and debates with sometimes a roomful of people make the working methods of the project quite different from the solitary work of an academic researcher. Typical of participating research methods are the following procedures and characteristics:

Work phases of the project merge together. The people who participate in a normative project are normally most interested in the final proposal. If they participate already in the earlier phases of the project, they will anyway want to discuss the final proposal "prematurely" as the researcher perhaps thinks.

Data collection gets a normative dimension. Simultaneously with descriptive facts which are gathered with objective measurements, normative opinions about the object of study can be registered. The participating laymen are often most interested in data and opinions which have relevance with the final proposal. Examples of the normative type of data gathering are given on the page Collecting Normative Data.

Different methods of data collection and analysis can be mixed. It is not necessary to avoid the disturbances that can ensue from using concurrently the methods of observation, interview and experiment. What is important in a normative project is not the reliability of objectively gathered data but the quality of the proposals of the project, and it will be evaluated in the final phase of the project.

It is not to be denied that evaluation and reaching unanimity can sometimes be arduous and slow. Often the normative project affects the lives of many interest groups, and when many people participate in the discussions the project can seldom proceed straight to the synthesis and proposal. Some usual reasons for complications are:

Methods that are often used for arbitrating opinions that are in contrast, are enumerated on the page Mediating Opinions in Contrast. It is normal that it takes time to reach general acceptance when opinions differ. On the contrary, it is typical of participatory analysis that sometimes part of the work has to be redone. If there are many such backward returns the process begins to resemble more a circle than a linear succession of decisions. Indeed, a spiral like the one on the right is sometimes seen as a typical model of a development project.
Normative spiralTypical phases in a "spiral of development" are as follows.

  1. evaluative description of the initial state and defining the need for improvements
  2. analysis of relationships and possibilities to change things
  3. synthesis: proposal for improvement
  4. evaluation of the proposal.

By repeating the sequence from 2 to 4, and by gradually improving the proposal, an acceptable result is usually found.

A few typical arrangements of normative participation are discussed below.

Tailor-made planning is the normal arrangement of co-operation for producing a unique product for one person or for a small group of people such as a family. When it is possible to gather all the designers and users of the product around a table, they can work as a team, deliberating together the problems of design. The solutions to the problems are often found and accepted collectively, but it is the task of the professional designer to explain the technical restrictions and to produce alternatives, perhaps with the methods of creating the proposal discussed earlier. The planner or designer is normally hired as a consult by the user.

When a large but unorganized cluster of people are willing to develop the product in co-operation, suitable methods can be found under the label of collective design, which method has sometimes been used when planning a neighborhood of houses, see Kukkonen (1984). It is not very common, but it can have some interest from the viewpoints of research and democracy.

Collective design is based on regular meetings of the designers and the future users, the number of which can amount to several hundreds. Because of the great number of users of the product, their opinions often differ, and a discussion to settle the conflict is called for. In order to speed up the discussion it can be advisable to organize the participants into temporary "interest groups". Beside these groups, there is always a "technical team" of professional designers (and possibly researchers) who continuously prepare alternative proposals to be discussed in weekly general meetings with the users.

Typical phases in the project are:

  1. Organizing the interest groups who then can decide to have private meetings if they feel it necessary.
  2. Analysis. The interest groups clarify their goals, first only to themselves.
  3. Design and negotiation between the interest groups. If a group seems to be suffering because of a proposal this group should perhaps receive a compensation for it.
  4. Ratification with so unanimous a decision as is possible. In more detail.

It is the task of the technical team, beside preparing the proposals, also to provide a "design language" so that all the participants can define their expectations and can understand the proposals of the technical team. For example, Kukkonen (1984) used a miniature model system in scale 1:15 for the design of the interior of the dwelling (see picture of it).

A pioneer work was the concise book Toward a Scientific Architecture by Friedman (1975). The writer states that to assist collective design, the designer must, in advance, prepare a repertoire that shows the user all the possible alternatives he has. Moreover, the repertory must contain warnings pertinent to each choice, e.g. its benefits, inconveniences and costs. But it is not up to the designer to criticize the choices of the user any more than the waiter of a restaurant criticizes the dishes his client chooses.

A well-known example of theoretical material, in advance prepared for collective design, is A Pattern Language (1977) developed by Christopher Alexander et al. It is based on research both with regard to practicality and to comfort. The "pattern language" consists of 253 design instructions or patterns. Each of these follows the same formula, described on page x of the book. The first picture is always an archetype-like example and there is also a short list of other forms that it is related to. The list is followed by a caption that clarifies what this pattern is all about. After this, an account is given of the empirical knowledge about the pattern and the variations of its application. An example of Alexander's patterns. The method of collective design with these patterns is explained in The Oregon Experiment, by Alexander et al. (1975).

Thanks to intensive research, some products possess now so reliable a theory that designing new products is quite easy. Some researchers, notably Yona Friedman (1975 B) and Nicholas Negroponte have proposed that on the basis of such a theory - the standards, algorithms, exemplars and rules of fine-tuning them - it would be possible to construct a design machine, a computer programme or an apparatus for producing designs for new products automatically. The benefit would be not only reducing the work of design, but to improve the service to the consumer who thus would have more options when buying a product.

There are few design machines in operation today, but they may become more frequent in future. The idea is theoretically interesting for researchers, because an explicit theory of design will be needed for defining the costs and other results that each alternative in the menu will produce when selected, and of course, for producing the design that the customer finally wants to order.

It is not difficult to construct a design machine for such products which consist of prefabricated parts and the customers need only choose the components they want to include in the product. In fact, this is what today already happens, not only in the traditional coffee maker, but also in the marketing systems of kitchen furniture and of computers, especially when bought in the internet.

The next step could perhaps be enlarging the customer's selection to include dimensions, colors and shapes of the product as well, and to transmit the finished design to the machines of manufacturing, thus connecting to the system of computer-assisted manufacture (CAM).

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Methods of evaluating normative proposals are explained on another page.

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August 3, 2007.
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Original location: http://www2.uiah.fi/projects/metodi