A gross-impact case study

394

A Cross-impact Case Study

Methodology A CROSS-IMPACT

Developments in the techniques employed in forecasting

CASE

The Dutch construction

STUDY

sector

Johan G. Wissema and Jan Benes The authors advocate the use of cross-impact models for scenario generation and describe a calibration technique which reduces problems of scaling. In a study of the Netherlands construction sector to Iggo, aggressive interviewing of experts produced quantified trends. After cross-impact analysis, an input-output table for 1990 was estimated from that for 1975. A number of scenarios were also developed, by adding events to the matrix. The authors review the strengths and weaknesses of the methods used and summarise the results of the study.

J. G. Wissema is a management consultant with Bakkenist Spits and Company, 5 Emmaplein, 1075 AW Amsterdam, Netherlands. J. Benes is an independent management consultant at 2 Beukenlaan, 5071 CJ Udenhout, Netherlands.

cross-impact method was selected for this purpose. We expected this method to be more exact than scenarios and more transparent than mathematical models, such as system dynamics. Another problem was how to visualise the ‘structure of the sector’. For this purpose it was decided to estimate the input-output table for the sector, an analysis that had not previously been done for the sector at a national level. Statistical material from various sources had to be supplemented by expert estimates in a few cases. For the cross-impact analysis, the KSIM version of the Institute for the Future was used. This program could handle some 40 variables, which meant that the number of trends in technology, society, and economy had to be reduced. It was chosen to do so by a procedure described below. An outline of the study procedure is shown in Figure 1. From interviews and literature search, a large number of single trends could be identified. These were aggregated and then quantified. The cross-impact method was

0016-3287/80/050394-11

Press

THE OBJECT of this study, commissioned by the Foundation for Construction Research SBR in Rotterdam, was to analyse the impact of developments in construction technology on the structure of the construction sect0r.l The latter includes the nature of the parties and companies and their relationships, the size of the companies and the size of such as extracompany production do-it-yourself ‘moonlighting’ and activities. Study procedure The above formulation not only fixes the starting point of the investigation (the technological trends) but also the finish (the structure of the sector). Between these points, heavy interaction with social, societal, and economical trends are likely. This calls for a multivariable method of analysis and the

$0!240 0 1980 IPC Business

FUTURES

October 1980

A Cross-impact Case Study

Ilniervws, IiierakL

395

Statistics, estimates -7

I

+ Single trends

I

Quantified trends

* Feted

trends

,,(,,

lDescrip%ianoi mechanisms/

Structure of sector In 1975

Y

pi?iizd~ Figure 1. Outline of the study procedure

used to balance the expectations about the quantified trends with the estimates of the impact coefficients to give a set of impacted trends. These trends, together with the input-output table 1975 and the description of certain mechanisms were used to formulate the input-output table 1990 to show changes that are likely to occur in the structure of the sector. A number of possible disaster events were also chosen and their impact on the quantified trends was estimated. The cross-impact program was arranged in such a way that it could compute the impacted trends for any combination of events occurring in any combination of years. These combinations were used as the basis of scenarios. Certain aspects of the procedure will

FUTURES

October 1980

be described in more detail, but only the most important results of the study will be noted.2 Single trends

and mechanisms

Single trends for technological developments, and other developments that interact strongly with them, were found for ten categories by interviewing US and European experts and by literature search (Table 1). A written report was made for each interview and summaries were prepared of relevant literature. The reports and summaries were then analysed for contradictions and common views. Many of these points of difference or agreement were used in the latter half of the interviews, in which a fairly

396

A Cross-impact Case Study

TABLE

1. CATEGORIES

OF SINGLE TRENDS IN TECHNOLOGICAL DEVELOPMENTS=

AND

OTHER Number of trends collected

Number of trends collected Category

Category Welfare and societal developments Technological change in the past Civil engineering construction Off-shore sector Foundations Note: a A full description TABLE

::

of the trends

2. TYPES

Structures Finishings Services (eg heating, utilities) Design and coordination Business or economic processes

48 88 1s

is given in reference

aggressive style was adopted-focusing on the bases of opinions and confronting the experts with what might happen. It was felt that this method of extracting information was more cost efficient than Delphi interrogation: at the same time the risk of interviewer subjectivity was believed to be very limited. In addition to the single trends, mechanisms governing behaviour in the were described construction sector (Table 2), the material stemming from the interviews and to a much smaller extent from the literature. An example of a mechanism is : “The innovation rate is higher when the costs of replacing faults are smaller”. This sounds obvious but explains why the bearing structure of present buildings is still similar to that used by the Romans, while the innovation rate in finishings is very rapid. We arrived at this mechanism because some interviewees maintained that the innovation rate in the construction sector is low while others had the opposite opinion. Aggregation

and quantification

To eliminate those trends unlikely to influence the structure of the sector and to aggregate those likely to have parallel effects, they were classified into five areas :

:: 31

2.

OF MECHANISM GOVERNING BEHAVIOUR CONSTRUCTION SECTOR

Differences between the USA and the Netherlands Kinds of innovation What fosters or impedes employment in building? What fosters or impedes innovation in building?

:;

IN THE

Rationalisation Moonlighting Trade and distribution Do-it-yourself activities

productivity, 0 quality of construction performance, 0 innovation potential, l organisational structure of the construction company, and o relation between construction parties. l

Next, each trend received a priority number, depending on its relevance to changes in the sector’s structure. Within each of the five areas, trends with a high priority were combined where possible. The trends which resulted were then quantified. A team of experts was asked to estimate the values for 1975 and 1990 and the likely range in which the values might fluctuate. To this end, the experts filled out forms at home and later discussed major differences in a meeting. The final estimates of quantified trends are shown in Table 3. Table 4 shows the events chosen by the same group of experts on the basis of relevance to the study. An event in this context is defined as an action or a situation that is likely to significantly influence the 1990 values of the trends. The trends however, have no influence on whether or not the events occur. In other words, the events influence the construction sector but they are not influenced by it. It is not necessary to have an opinion about

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October 1980

A Cross-impact Case Study

TABLE

3. QUANTIFIED

TRENDS

IN THE CONSTRUCTION

SECTOR TO 1990 Maximum,

Value (%) Trend Number of households actively seeking a home increases Internal reconstruction of private houses increases Application of separate bearer/built-in construction increases Energy consumption decreases Turbulence of society increases Number of small companies (including profit-responsible units of conglomerates) increases 7 Impact of information technology increases on society 8 Influence of nonconformists increases Number ot reaulations in buildina increases Volume of lo&s and mortgages iaken 1: by individuals to buy or improve housing decreases of land) 11 Density (ma of building/hectare increases 12 Added value realised outside the construction site (ie prefabrication) increases 13 Use of open systems of standardised elements increases 14 Percentage of new buildings in which modular coordination techniques are used increases 15 Number of independent or profitresponsible companies specialised in structures (especially finishing) increases 16 Sales volume of insulating materials increases 17 Sales percentage of finishing work carried out by specialist firms increases 18 Number of man/woman hours per year in do-it-yourself work increases 19 Turnover of Dutch sawmills for building timber decreases 20 Percentage of nonconventional heating systems increases 21 Share of hot-water systems using solar cells and/or heat pumps increases 22 Percentage of service work (eg gas, water, electricity, heating) carried out by do it yourself increases 23 Percentage of design man hours taken over by computers increases 24 Number of architects specialising in particular products or services increases 25 Turnover of building-trade companies increases Note: 5

1975 -

1990 -

-

1

Index, 19995 130 120

15

397

value (%) -

1500

1996 index 150 150

25

2500

-

-

2:: 125

-

120 500 150

-

-

200

-

1000

-

-

150

-

500

-

-

-

150 70

-

300 200

-

-

150

-

200

-

140

-

200

5

50

1000

80

8000

2.5

50

2000

75

3000

10

50

500

80

800

-

-

300

40

800

-

5 -

-

-

-

-

1000 75

400

-

50

-

1500 1000 150

5

60

1200

100

2000

1

50

5000

75

7500

5

30

600

50

1000

1

30

3000

50

5000

10

30

300

50

500

-

-

120

-

200

The index for 1975 is taken to be 100.

the likelihood of occurrence of an event. The effects of any scenariodefined here as a combination of events and the years in which they will occur-can be calculated by the crossimpact method, as will be demonstrated below.

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October 1980

The position

in 1975

The 1975 input-output table was constructed from various statistical sources, using some expert estimates where the exact figures were not documented. The resulting table can

398

A Gross-impact Case Study

TABLE El E2 ;: :: E7 E8

4. EVENTS

LIKELY TO SIGNIFICANTLY

INFLUENCE

1990 VALUES

OF TRENDS

There will be a law prescribing standardisat~oo of both sire and position (modular coordination} The government increases the share of privately owned houses to 306/, within 2 years The government makes overtime work free of taxes and social premiums The price of energy doubles in 2 years, in addition to a regular 5% price increase The price of land doubles in 2 years, in addition to a regular 10% price increase The bank interest rate doubles in 2 years due to capital shortages Labour cost in the construction sector decreases by 30% in 5 years, the national income staying on the same level (unempioyment~ The real net disposable income of people decreases by 3074 in 5 years, with cost of labour staying the same

be shown (see Table

to be at least 90% 5 for a summary).

correct

TABLE 5. BREAK-DOWN OF ADDED VALUE IN THE DUTCH CONSTRUCTION SECTOR, 1975 Area Contractors Prefabrication Subcontracting Unofficial circuit Design Distribution Do it yourself

Cross-impact

Share 46.8 26.5 Il.6

Integration

(2) can be written

as:

yiefds :

xi(t+

(%I

Ailt)=xt (t)p”(l),

(4)

jz (l~l--+j)xr w_..-.~...-

(5)

in which l+aAt p&) = ~

;:i 3.3 1-O

1f W

analysis

This technique has been described in other papers,8 and we assume that the reader is familiar with it, The method used in the Dutch study was designed by Kane, 4 and modified by Lipinski and Tydeman.5 The theory comes down to two basic assumptions. The first is that trends increase or decrease according to a logistic or S-shaped curve, described by: d In x =-ffxlnx, (1) dt in which x=x(t) is a function of time, t is time, OLis a constant (indicating the steepness of the S-curve), and 0
Equation

(2)

in which orlf are interaction coefficients, and Jv is the total number of trends. If fl= I, equation (2) reduces to equation ( 1) .

j:l

f 1% I + w) 3

This equation can be interpreted as follows. If clzj> 0, hence a positive impact by which the increase of trend j will stimulate the increase of trend i, there is no contribution in the numerator, whilst the contribution in the denominator is :

cigjx;r nt.

(6)

This corresponds to the magnitude of the impact as defined by Kane,Q ie impact is proportional to the magnitude of the impacting variable at moment t and with the interaction coefficients. In other words, one can write equation (5) as: ~~(~)=l+Atl~-veimpactson~~l l+At~Z;+veimpactsonx~~

_

(7)

If the negative impacts are stronger than the positive ones, then pi(t) > 1 and x6 will decrease. (Note: 0 -=z x < 1 as x is normalised by the upper range values.) If the positive impacts are larger, @a(t) < 1 and xf increases. A problem

of scaling

This outline shows an inherent weakness of cross-impact analysis, or indeed

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October 1980

399

A Cross-impact Case Stua’y

size of the impacting variable and thus find the coefficient. In practice, experts are unlikely to do all this, even if so instructed. In contrast, they will give their intuitive size and sign of the impact. If the ranges of the variables are of the same order of magnitude, the scaling problem does not play a serious role. However, in this study (see Table 3) the index in some cases (eg trends 21 and 23) has to be driven from 100 to 5000 and 3000, while others (eg trends 10 and 6) only move to 70 or 125. With the impact coefficients estimated by the experts (Table 6), one is not surprised to find the initial (unscaled) results shown in the third column of Table 7. In Table 7, the estimated values

of all forecasting techniques in which cross influencing is allowed, and that is the problem of scaling. In asking the experts to give their opinion about the the impact coefficients, we asked them, as is customary in these exercises, to give +5 for a very strong positive interaction, +4 for a less strong positive interaction, ranging through to -5 for a very strong negative interaction. The effect of the impact on the impacted variable, however, depends as much on the size of the impacting variable as it does on the coefficient, as equation (6) shows. Theoretically, the experts should estimate what, say, a 10% increase in the value of the impacting variable would do to the impacted variable; they would then have to divide this by the average TABLE6.lNTERACTlON

COEFFICIENTS

FOR PAIRS TRENDS"

OFTRENDS

Trend

Trend

z:ent

1

2

3 4

6 7 6

9

10

13

14 15 16

0

2 -

3(2)0 "; ;

002 ;lI;

1 2

2 21 (2)O 2

1 3

1011 3 3 1

0 30 001 11

000 323 o-o -00

2 1 2 0

0 (2)O2 2 0 (1)21 0 0 00 0

4 1 3 1051 4 1 2210230111 0 1 130 100

22 203 200 (I) 0 2 (I) 0 2 0 Y'(13) 2 221 0 (I) 010 1 0 0

0 2 1

0 1 0 -

1 2 2 1

:; 01 z:

:1 30 04 20

: 0 0

K 010 010 310

(l)lo- 4 : 0 4 431 430 (2)O 2 3 5 -2 0 1 0 00232-1410101 00 0 00-l

0 2 12 0 0

20 0 121 00 0

310 001 000

1 1 1

0 02 (I)1 1 0 02

:

:::

8::

:

:,1 8

8::

zl:,;: Xl 201 100110 13 211 (1)1 05 3 (3)4 3 1 1 (3)(3) 2 0 (2) (3)(2) 1 1 0 (4)(4) 0 2 0

21,: 2 4 ::,

:,

::

5

1 1

11 12

AND

FOR

EVENTS

AND

17

16

19

20

21

4

2 5

0 0

0 21

01

331213132 2 0

5

5

0 4

01 (2)O

0 1

0

0 0 3 0

3 01 4 1

1 1 0

12 000 2 (I)21 1 0 (2)2 0 0 11101

3 430111 2 2 2 2 3 2 0

01 0 2

0 21

(l)O (l)0 0 0 -

0 11 0

0

00000010103-1 (I)00 0 013

12

-

0 1

0

21 00

1211001 0

040321 0 010

x

:,

0

02

2112: 2 221

001 301 201 001 2 0 0 2 0 1 2 O(1)

3 1 0 3 2 1 0 3

(2)O03 0 (l)O 2 32 5 52 5 4 (2) 0 (5) 4

00 00 -1 0 0

0 1

3 1 0

010 324 3 10 2

0 (2)

341 103 210

4 0 0 0 520 0 0 12 0115 3 210 1 2 1 1 (I) (I) 0 0 0 0 1 0

0 1 0

2 1 0 0 1 0 0 0

0

0

3 0 1

0

0

3 0 3 (2)O 4 0 3 0 4 0 3 (1) 4 0

0

22

23 24 25

3

000 022

2

001 2 21 0 (I)01 300 2

21123 41 1 211 0 1 0 1 2 (1)

0

0 4 0

122 013 001

3

3

0 01 011

0

0

2 0

2-l Ol-

0

2 0

0 0 12 2 011 320 2 1 (2)O 0 5 5 4112 0 0 2 020 (2)(2)4 0 1 1 0 2 000 0 0 4 0 0

0 2

2 1 0

Nofe: a See Tables 3 and 4 for fulldescriptionof trends and events: negative values given in parentheses.

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October1980

400

A Cross-impact Case Study

TABLE7.RESULTSOFUNSCALEDANDSCALEDCROSS-IMPACTANALYSlS Estimated value Trend 1 : 4 zl 7 : 10 11

14 :: 17 18 19

Et 22 23 ;z

1975

1990

0.67 0.67 0.04 0.83 0.20 0.67

0.87 0.80 0.60 0.33 0.40 0.83 0.20 0.30 0.50 0.35 0.75 0.70 0.13 0.67 0.62 0.30 0.53 0.40 0.33 0.60 0.67 0.60 0.60 0.60 0.60

0.10

0.20 0.33 0.50 0.50 0.50 0.01 0.03 0.12 0.10 0.07 0.10 0.67 0.05 0.01 0.10 0.02 0.20 0.50

Computed value, 1990 0.68 0.78 0.13 0.71 0.24 0.75 0.23 0.25 0.45 0.55 0.59 0.67

0.11 0.16 0.32 0.24 0.22 0.29 0.61 0.17 0.05 0.25 0.06 0.39 0.65

Multiplier c2 f:; &3 1-4 3.2 1.2 1.1 3.55 2.5 1.35 2.6 1.45 3.4 7.2

;:; 2.15 0.65

Computed

value

in 1999 with event i in 1980a

12

3

4

5

6

7

8

0.70 0.85 0.75 0.20 0.27 0.89 0.24 0.28 0.52 0.58 0.76 0.73 0.15 0.71 0.71 0.40 0.58 0.37 0.61 0.76 0.83 0.54 0.67 0.63 0.64

0.67 0.84 0.68 0.04 0.33 0.88 0.26 0.29 0.63 0.52 0.88 0.76 0.17 0.79 0.72 O-54 0.62 0.60 0.61 0.89 0.96 0.83 0.76 0.68 0.65

0.64 0.87 0.82 0.18 0.28 0.86 0.26 0.29 0.59 0.46 0.40 0.77 0.23 0.83 0.74 0.36 0.67 0.56 0.60 0.70 0.79 0.76 0.71 0.73 0.62

0.63 0.78 0.76 0.19 0.21 0.88 0.24 0.26 0.56 0.46 0.88 0.74 0.20 0.78 0.72 0.36 0.59 0.57 0.61 0.62 0.60 0.81 0.67 0.68 0.63

0.63 0.78 0.68 0.21 0.25 0.87 0.23 0.27 0.51 0.60 0.75 0.68 0.12 0.68 0.64 0.36 0.54 0.57 0.63 0.71 0.75 0.72 0.60 0.60 0.61

0.62 0.76 0.62 0.15 0.25 0.87 0.23 0.23 0.60 0.65 0.87 0.71 0.14 0.75 0.65 0.35 0.56 0.54 0.61 0.68 0.78 0.79 0.64 0.61 0.61

0.69 0.84 0.75 0.23 0.25 0.87 0.29 0.26 0.61 0.57 0.79 0.79 0.26 0.89 0.77 0.36 0.71 0.55 0.54 0.69 0.77 0.75 0.86 0.74 0.62

0.72 0.85 0.76 0.26 0.27 0.85 0.24 0.28 0.55 0.60 0.76 0.71 0.15 0.72 0.67 0.37 0.62 0.53 0.61 0.67 0.82 0.74 0.65 0.67 0.63

aSeeTable4forfulldescription of events.

for 1975 and 1990 have been shown as a fraction of 1, ie the values of Table 3 have been divided by their maximum range values. Next the computed values are given. A comparison of the estimated 1990 values and the computed 1990 values shows that most computed values are much lower than the estimated values. This indicates that these trends are not driven powerfully enough (impact coefficients too low) or that the 1990 estimates were too high. The difference is especially high in trends 1, 3, 4, 5, 14, 15, 17, 19, 20, 21, 22, 23, and 24. The reverse is the case in trends 7 and 25. Only in one case, trend 10, is the direction of change of the computed value contrary to that of the estimated value. As its proponents claim, cross-impact analysis allows one to identify inconsistencies in one’s expectations about the future. An ideal, fully consistently thinking expert, would give the estimated values on the one hand, and the interaction coefficients on the other, in

such a way that the computed values would be identical to the estimated ones. In such a case, the estimated final values and the interaction coefficients are ideally balanced. When they are unbalanced, as shown by differences between computed and estimated final values, an analysis of the imbalance may result in a more penetrating vision about the cause-andeffect relationships that will shape the future. Tuned by multipliers In our case, we found a reasonable balance but differences remain due to the scaling problem mentioned earlier. This makes the interaction matrix unsuitable for analysis of the events. It was thus decided to activate a number of row multipliers by which the interaction of the impacts on any trendj could be amplified. A number of multipliers was manipulated by trial and error until the computed

FUTURES

October 1980

A Cross-impact Case Study

1990 values were equal to the estimated values. The effect is shown in Table 7. Unfortunately, the system does not allow the manipulation of all the multipliers, as all eigenvalues of the interaction matrix must be negative for the system to be bound. With the interaction tuned as described, the effects of the occurrence of events could also be computed. Table 7 shows the effects of each event taking place in 1980; computations of events taking place on a different date or with more events were not carried out. A powerful

technique

neglected in crossThis technique, impact analysis, was found to be very powerful. For example, a large increase in private over public housing (event 2) does not lead to a sharp increase of internal reconstruction of houses as one would expect (trend 1 grows from 100 to 128 instead of 120 in the base case). There is a sharp reduction of energy consumption (trend 4 from 100 to 31 instead of 40 in the base case). More private ownership tends to increase nonconformism as well as the volume of finance and the hours of do-ityourself activities. For the other scenarios, a similar debate can be extracted from Table 7. Strong points and limitations Our main conclusion about the method of cross-impact analysis is that its use as a quantitative generator of scenarios, after the interaction matrix and the expected values for the base case have been balanced, is at least as important as its use in generating insights from this balancing process itself. Both cross-impact and system dynamics are multivariable forecasting techniques with facilities for crossinfluencing of variables and allowances for quantitative scenarios, but the calibration problem is more easily and directly solved in system dynamics.

FUTURES

October 1980

401

Cross-impact, however, is more suitable when working with groups of experts or potential users of results. In such instances, the process can be an effective vehicle for sharpening the perceptions of those who participated in the identification of the estimated values and the impact matrix. In addition to the scaling problem, the cross-impact method still cannot deal with level impacts. Another severe limitation is the lack of reversibility. Harris has recently shown that Kane’s mathematical model is inconsistent with the behaviour of real systems since it is irreversible. 6 This implies that different conclusions would have been reached, had we formulated our trends negatively, ie in terms of decreasing rather than increasing parameters. The effects of irreversibility have not been studied due to lack of time and finance. Sensitivity tests have not been carried out for the same reason. The development of cross-impact techniques now seems to be focusing on stochastic models, as exemplified by two recent doctoral dissertations.697 The 1990 input-output

table

The construction of the 1990 inputoutput table from that for 1975 and the impacted trends was the most difficult part of the study. Since we carried out no market research, the estimated sales figure of the total construction sector in 1990 was borrowed from a parallel study. s The likelihood of the occurrence of the events was estimated and the effects of these events were checked for their impact on the input-output table. A similar analysis of the impacted trends resulted in a series of qualitative statements about the effects on the figures in the 1990 table. With these statements, the table was then constructed from the total sales figure and the 1975 table (see Figure 2 for summaries) .

402

A Cross-imbact

Case Study

Figure 2. Added

value shares in the Dutch

In evaluating the technique of inputoutput modelling, we felt that it represented the structure of the sector more than adequately, even though the necessary statistical material was not fully available. The construction

sector to 1990

A brief summary of the main conclusions reached in the study follows. Comparing the 1975-l 990 period with the 1960-1975 period, the accent in construction has shifted from solving a quantitative problem (shortage of houses and buildings) to solving a qualitative one. Cost control by scale increases in both technology and organisation has shifted to cost control by standardisation. This will result in greater accuracy on the construction sites, more prefabrication, shorter work periods on the site, increased accuracy in planning and coordination. In turn, this leads to an increase of scale in the prefabricating companies and specialisation, decentralisation, and increased subcontracting in the traditional construction companies. Exceptions to this are the off-shore and waterworks sectors where further integration can be expected, possibly leading to further

construction

sector, 1975 and 1990

mergers up to companies with annual sales in the order of $5 thousand million. Raw

materials

: limited

changes

Sand, gravel, cement, and clay will continue to be the major raw materials : the relative positions of concrete, bricks, mortars, and brick-blocks will hardly change. Even though metal-frame building and wooden-frame building will find more applications in specific areas, concrete-frame building is expected to remain by far the most important technique. New fillers and binding agents will be used in concrete. Wood will be used for decoration (nonstructural), at about 3 m3 per home. There is no fundamental danger of basic raw-material shortages. Inland production of sand, gravel, and cement could decrease drastically, but this can be compensated for by increased use of North Sea sand and gravel, by imports and by gravel reclamation of old concrete. Asbestos. lead, copper, and zinc will no longer be used in the construction sector. In new applications of plastics and resins, their specific properties will

FUTURES

October 1980

A Crass-impact Case Sk&

increasingly be exploited-especially by compounding with traditional materials. The use of relatively new materials (eg mineral wool, glass fibre) will continue to grow, both in combination with other materials or unmixed. However, no replacement of the traditional bulk materials is foreseen; the ‘houses of the future’, as shown in exhibitions, will never be built. Flexibility and energy In construction technology, the most fundamental change will be the use of separate bearing structures and finishings. This allows the adaptation of houses and buildings in the future. The installations and service sector will change drastically as new heating technologies emerge (eg total-energy systems, heat pumps, solar energy; and new kinds of area, block, or street heating). New insulation techniques and the continued simplification of the traditional instalIation techniques (through flexible ducting and connections and new jointing techniques) will add to this changing outlook of this subsector. More mobile equipment Changes in the foundation sector will include increased use of soil mechanics, increased accuracy in piling, many new piling techniques, and increased supervision of piling. The trend in equipment is towards smaller, transportabIe, multi-functional equipment with performance characteristics similar to that presently available. This greater mobility and the fact that equipment will increasingly be hired leads to a more flexible and cheaper use of equipment. Only in open-cast mining and off-shore operations is a further increase of scale expected. Perhaps the most significant trend in equipment is the continued development of cheap electrical or pneumatic

FUTURES 29

October lS80

403

hand tools, an area that experienced rapid growth during 1960-1975. As similar development continues over 1975-1990, workers will require ever less skill and experience. Do-it-yourself boom A drastic further increase in do-ityourself work is foreseen, both in renovation and new building activities. A second generation of do-it-yourself technology will arise, allowing lay people to carry out more sophisticated construction jobs. And the easier tasks (eg decorating, small timber work) will be tackled by an even greater part of the population. Design offices will maintain their position with a shift of their activities towards the parties involved in the prefabrication and the construction phases. Architects will tend to become industrial designers and specialists in regulations and planning. Acknowledgements

The authors are much indebted to the chairman of the study committee, Professor L. P, Sikkel, and the managing director of the Foundation for Construction Research, Dr W. J, Diepeveen, for their permission to publish this paper and for their constructive criticism. We thankfully acknowledge the efforts and comments of Ilubert Lipinski of the Institute of the Future, who modified the Institute’s K~IM program and who carried out the computations; Dr Roy Amara, president of the Institute; and P. Holroyd of the University of Bradford Management Centre. Notes and references 1. The two-year study was carried out by

a committee consisting of 13 specialists including the authors who were charged with the field work and reporting of the study. The Foundation for Construction Research SBR is an independent research centre and works largely on a contract basis. This study was financed by the Stichting Structuuronderaoek Bouwnijverheid.

404

A Cross-impact

Case Study

A more detailed description of results is given in J. G. Wissema and J. Benes, “The future of construction technology and sector structure in the Netherlands”, not yet published. T. J. Gordon and H. Hayward, “Initial experiments with the cross-impact matrix method of forecasting”, Futures, 2 (2)) December 1968, pages 100-116. E. Novaky and K. Lorant, “A method for the analysis of interrelationships between mutually connected events: a crossimpact method”, Technological Forecasting and Social Change, August 1978, 12(2/3), pages 201-212. J. Kane, “A primer for a new crossimpact language-xsn?‘, Technological

Forecasting and Social Change, 1972,4, pages 129-142. H. Lipinksi and J. Tydeman, “Crossimpact analysis : extended KSIM”, Futures, April 1979, 11(2), pages 151-154. D. J. Harris, “Analysis and simulation of qualitative forecasting models”, PhD thesis, University of Wales, Swansea, June 1979. S. Enzer, “An interactive cross-impact scenario generator for long range foredoctoral dissertation, Univercasting”, sity of Southern California, LA, 1980. “The development of the building market and employment in the construction sector: 1980-1990-2000”, (in Dutch), Central Bureau of Statistics, Amsterdam.

FUTURES

October 1880