Factors influencing innovation

Techovation,

IO:6 (1990)

379-387

Department of Marketing and Business Policy, Leicester Polytechnic, Leicester (U.K. I

Abstract

In u recent paper Pearson et al. (1989) examined factors influencing novation in a mature industry. The work reported here stems from

and supporting

in-

unease generated in the description of their measuring instrument. It suggests that alternative analytical tools drawn from the marketing sphere (Baron et al. 1971, Crimp 1981) give rise to firstly, a challenge of the accuracy of their instruments, secondly support of their major conclusion, and thirdly a revised interpretation of the nu.mber and relative importance of factors leading to innovative strength. *

The Pearson measure of anizational characteristics of firms I.

‘TABLE

Organizational

The authors attempt to measure characteristics of the surveyed firms by means of Likert scales in which employees rate their company on a total of 72 separate statements. These are made up of 12 blocks, each of six statements; half purporting to measure ‘Organizational Style’, half ‘Strategic Focus’. Innovative strength is then related to the mean score on each of these two factors. The structure of these factors is reproduced in Table 1, where each block is labelled with the letter and title accorded by Pearson (I 989). Considerations of this procedure immediately generate concern about such a simple instru-

‘Impe~t~~ for thi\ paper slcmmcd Pearson PI ol. akrthor\ for xcw

I.

A B C D E F

: : : : : :

Pearson

1989 organizational

style

Formality Communication Participation Management philosophyK Leadership style Motivational style

Strategic G H J

: : : : L : M :

factors fucus

Functional orientation Generic strategy Corporate integrity External communications Long-term vision Clarity of job result areas

ment. How can it be established a priori that cacll block may be accurately represented by the statements chosen? Why should each block and factor have exactly the same number of scales? Why should each scale contribute equally to the factor? Could some of the scales be measuring similar underlying perceptions

from early dl\cu\k)n\ wilh unc of the Pearhon (21ul. author\, Dr. Ball. Appreciation IO original data not available IO the editor and rcvicwcrs uf the original article.

novation, Volume 10 No 6

0166-4972/90/$03,50

cc:,1990 Elsevier Science

is expressed to the

Publishers

Ltd

379

t? J. Baron

and, therefore, be correlated? This correlation would imply an over-weighting of that component of perception. Indeed, how can it be certain that these scales represent just two faCtOrS? The thesis on which the paper was based (Pearson 1989) does consider one of these problems and identifies two blocks of scales (leadership and formality) as possibly incoherent on the basis of reliability coefficient, and other tests. This problem is, however, largely dismissed, and not referred to in the published article (Pearson et al. 1989) which persists with style and the two factors: organizational strategic focus. These problems are familiar to many disciplines and notably marketing, where perception studies assume a major interest. Product perception studies based on Likert and semantic difference scales have been used for this purpose by marketeers over many years, often under the name of attitude research. Exactly the same questions were raised of these studies, resulting in the adoption of the techniques originally developed in disciplines such as psychology. The most common approach to the interpretation of scaling type instruments is that of factor analysis. This technique explicitly addresses the question of what distinct characteristics (factors) are being recorded for each sample unit. It specifically avoids the danger of presuming the relationship between scales and factors, and even avoids presumption about the number of factors perceived by respondents. Simple descriptions of factor analysis typically explain the procedure as a statistical technique which identifies a relatively small number of factors or components that can be used to represent relationships among sets of many interrelated variables (SPSS 1988). Perceptions or concepts typically cannot be measured directly, but may be approached via a set of tests, scales, and other measures. This set of measures will be interrelated and contribute in varying proportions to the perception or concept itself. Factor analysis seeks sequentia linear combinations of the original variables

which explain as much variati-,n in the original data set as possible, and yet are constrained to be independent of each other factor. Mathematically there are as many factors as original variables. The selection procedure is, however, based on the criteria of maximizing variation explained. Thus, if there are only few real dimensions in the variable set, the first few factors will explain most of the variability. At the other extreme, if each original variable was independent, then all factors would be required to explain their variability. The resultant factors are each linear combinations of all variables. To assist in interpretation of the meaning of each factor a further mathematical procedure is usually added to the analysis. This is known as ‘rotation’ and involves a mathematical transformation so that each factor lies as close as possible to a distinct set of original variables. In the two-dimensional case this is equivalent to rotating the conventional vertical and horizontal axes about the origin; hence the name. It seems appropriate to re-examine the data used by Pearson et al. (1989) with this kind of technique to establish how many real dimensions of perception exist in interviewees’ minds, and select rationally the appropriate variables and their relative contribution to each revealed factor.

Data used in therefore, subject a re-analysis of strength. It must number of ob

the to a factor analysis, prior to their relation to innovative be admitted that the total

Factors influencing innovation

was a principal components extraction with orthogonal (Varimax) rotation.* The number of factors to be extracted is usually chosen on the basis of the Kaiser criterion or by means of the Scree test (SPSS 1988). The Kaiser criterion argues that any factor which explains more than 1% of total variation should be retained since it performs better than an original (standardized) variable. In cases such as this, where there are more than 50 variables, that criterion frequently fails: sug,gesting an excessive number of factors. Indeed for this data set the Kaiser criterion suggested 17 factors. The alternative Scree test is suggested as a more appropriate statistic in such

cases and was, therefore, used. In this test the number of factors is indicated by the “bottom of the cliff where the Scree begins”. As shown below, the plnt clearly suggests that the Scree begins at the fourth factor and four factors are consequently selected as the appropriate number. Already, therefore, there is a clear challenge to the claim of Pearson et al. (1990) that their instrument reflects but two factors. The next stage of the analysis is to rotate the original factor solution to a configuration in which each factor loads heavily on as few variables as possible. The Varimax rotation achieves this while preserving orthogonality of the factors and is reproduced in Table 2. In this

. VW

.

.

Fig.

1.

Scree plot of factors.

*Marketing research has for a variety of reasons had to contend with the problem of small data sets. Indeed the adequacy of this data set would be classified between poor (100 cases) and very poor (50 cases). 300 cases are required for a ciassification as good, and obviously more than that would be preferred. Monte Carlo exploration of poor data sets in marketing suggests that accurate results can still be obtained (Acito and Anderson 1980, 1986). In such cases accuracy is improved as the number of variables and communality is increased. This data set has a large number of variables and high estimated communalities. There is some argument that image factoring may be superior to principal components. Exploratory application of image factoring to this data set did not alter the conclusions to be derived SO the more easily understood principal components method has been presented.

Teohnovation,

Volume

10 No 6

381

d

F2 D5 D2 Cl D3 F4 D6 Bl B2 F3 6 C4

Ml M3 M4 M2 M5 L5 G2 K3 J5 32 L4 Mb 53 Jl HI K4 54 Ll 56 K6 K2 64 L3

TABLE 2.

-.OOOOl .?,I753 .I8713

.I9860

.30682 .28209

.16990

.35095

.I9207

-. 13024 -.00258 .02553

.68054 .67563 .64982 .64194 .64012

.70976

.74647 -73899 .72925 .7265 1 .71495

.46992 .27804 -.I8327 .20762 .26591 .39020 -.07509 .04050 .07142 .23408

.30780

.21928 .30216 -.02803 .30181

.I5087 .11460

-71941 ,69687 ,67294 866977 865343 65307 63655 61109 60391 60243 59242 58759 57476 56993 5625 1 51513 49057

.05726 .04308 .05832 .02369 .24703 .08499

.17344

.08716 .02812 .01513 .22307 .02890

.21698 44285 .11363

.19935

.01205 .34801 ,365 16 .0055 1 .38515 .324?6 .22238 .30459 .28616

.I7923

.25028 -.05362 .12531 .32716

.I9480

.12862 .09196

-76769

.00621 . .0792 1 .02088 -.01814

Factor 3

.14775 .09859 .21396 .22700

Factor 2

80291 .79587 .78454 .78174

.8377c

Factor 1

Varimax rotated factor matrix

.19678

-.04999 .11204 -.03338 .15725 .02990 .O4777

.I3764

.08314 .08953 -. 16302 -. 19608

.10687

-.20480 -.01617 .32557 -.22069 .34525 .08115 .26158 .01366

.30624

.10002 -. 10501 -.09530

.12123 .10665

.02384 .12007 .11593 .09013 -.00200 -. 15452 -.02139 .04070

Factor 4

scaie description

Employee achievements recognized Employees expected to enjoy work Management & workforce work together Encourage own initiative Equal opportunity to get on Can take responsibility Staff development is management res’bility Management communicate Hear from management first Work is satisfying People socialize outside work Employees consulted & influence decisions

Discuss contribution to Co. Costs versus quality Know Co. strategy & job contribution Helped to improve per iormance Know important part of job Technology improves, not threat Find customer wants & provide Takes advantage of technology Contributes to local community Innovator capable to cope with change Aware of next technology Working to same end Company’s products are best Co. honest to customers and suppliers Main strength is quality of products Employees know competitors Produce makes world a better place Management emphasis on long planning Good employer Contact with technological leaders Innovator able to cope with change Sales & marketing more import. prod’n. Invests in R&D, market research

Abbreviated

c3

6

3

&3)

g

s

CD

M3 Al A5 H2 L2 E3 E4 E6 E5 A3 El E2

Cl G5 G3 L6 K5 Kl H4 Fl H6 A2 G6 A6 H5

B5 B4 c2 B3

CS FS A4 C6 F6

Z d I. ; ?

t?

D4 Dl

Z? s-

-.02826 -.11700 .20919 .36875 -.24877 .26225 -.08227 -.I 1796 .34156 .08922 .I0919

.22839

.29431 .21162 -.08272 -.01365 .37525

.I4901

.06434 .13578 .30351 .41884 .31922 .23875 .46198

.32649 .16145 .11892 -.00131 .18291 .46223 .I9183 -.01836 .28890 .03203

.14625

.06753

.37237 .22489 .33 161 .02088 .21732 .05836

.19503

-.03225 .15015 .3 1248 - XI0547 .09022

.15958 -.04094 .15500 .02268 .05793 .15295 .23535 .49661 .29800 -.19144 .06174 .36246 .32808

.63856 .62897 .61950 .59974 I57207 .55268 ,47883 .47552 S45547 &I4204 b37196 ,32327

.07608 .06973 .02400 -.02739

.17463

.3 1474 -. 12719 .03675 .39458 .13799 .04237 -. 16074

.48040 .46809 .43086 .41845 40140

.54702

.78561 .72330 .71575 .67386 .65420 .58593 .55878

.28109

.16466

.23406 .I3747 .27948 .36940 -. 19836 .07043 -.11002 .15768 .00732 .01340

.57170 44584 44148 m.42758 s.41884 .40326 .40161 .39963 .37923

.57926

.60549

.69624

.I6566 .19717 .25960 -.07289 -.03827 -.09410 -. 14930 .07625 -.05684 .27205 -.30220 .21805 -09415

.15510 -.01952 .23643 -.03985 .I9895 .39998 .21442 -.09132 .08042 .37060 -.20426 .17119

Beat competition on price Seniority & privileges important at top .Encouraged to dress smartly Aim for lowest costs in industrv Know where market is going Litt!: authority to spend money Management delegates Management help & support Management instruct & use authority Written job descriptions Discipline key to success Chief exec. uses personal control

Costs down more import. market share Cost control most important Efficiency for profit not customer needs Development more important than profit Know why people buy from competition Keeps in contact with customers Leaders for quality Good pay Products are special Must show courtesy Management expert in technology Rules & reg’s. important Don’t comnete on low costs

Co. pays ftir training Employees seen as committed Clear roots for promotion Encouraged if make mistakes Seniors use employees’ first names Improvements suggesten to top management Working conditions good Management delegate People socialize outside work Management communicate when want something Other than chief exec. involved in decis’ns Departments communicate closely

P.J. Baron

table each factor is identified by enclosure of significant loadings in a box, terminated by a horizontal line which is drawn across the table to delimit the scales contributing to that factor. Each block of scales appears in a numerical sequence from Factor 1 through to Factor 4. aming or identification of the four factors remains subjective and is based on the scales contributing to the factor in question. In this case it is essentia! both to name the factor and to compare it with the two Pearson et al. factors. To assist in this exercise Table 3 is constructed showing the number of scales from each Pearson et al. block which contribute to each factor. While it is not possible to rely on this table in a mechanistic sense (because it is conceivable that where up to 23 scales contribute to a factor, one block of scales might fill the last 6 places), it gives an initial picture. TABLE 3. factor Block Factor Factor Factor Factor

Number uf scales from a block contributing to each

ABCDEFGHJKLM

I 2 I 3 2 4 3

216446 6

6

6

5

I 6

4

3 2

1

1 1

It is immediately apparent that the ‘strategic focus’ factor is substantially reproduced in Factor 1 and tends to be confirmed when an inspection is made of scale descriptions in the matrix of rotated factor scores shown in Table 2. Moreover, it makes the largest contribution (21%) to explaining the total variation in the data set. It is important to note that it does have a different makeup to the original strategic focus factor. For example, only two of the scales in Block 6 contribute to this new factor analysis measure of ‘strategic focus’. In total some 12 of the original scales new strategic focus measure. included do not contribute e

by the loadings of Table 2. Hence the factor score for each individual and for the mean across the sample on this component may well differ substantially from that based on a straight averaging of the original scales as used by Pearson et al. Similarly, it appears that ‘organizational style’ is substantially reproduced in Factor 2. It must be said, however, that on the basis of contrlbuting scales revealed in Table 2, ‘employee relations’ might be an equally appropriate title. In addition. however, there are revealed two other factors. By examining the contributory scales, Factor 3 might be labelled ‘productive efficiency and expertise’ since the scales are mainly about product knowledge and production control. Factor 4 contains all of the leadership scales and several others, which intuitively might be seen as related to leadership. Factor 4 might, therefore, be named ‘leadership’. Again it is worth emphasizing that in these three factors, component scales contribute in varying amounts as shown by the loadings of Table 2. It is worth noting in particular that scales E4 and E6 contribute negatively to ‘leadership’. This emphasizes the problem of using Pearson et al.‘s simple arithmetic means of the original scales where, of course, both of these scales would have counted positively. The effect of the changed makeup and weighting of contributory scales to ‘strategic focus’ and ‘organizational style’ is revealed in Table 4.

TABLE 4.

Comparison

of company mean scores

Stra. L,iC focus

Organizational

style

Baron

Pearson et al.

Baron

0.55 1.73 1.36

- 0.26 0.40 0.65

0.51 1.72 - 0.19

- 1.36

0.69 1.25 - 1.03 - 0.42

0.99 0.13 - 1.61 - 1.30

0.40 I.18 .- 0.60 0.13

- 0.59 0.08 - 1.36 - 0.12

Pearson et al. Company

A B C E” F G

0.44

1.06

Factors influencing innovation

3. characteristics

on innovation

It is next necessary to examine the contribution of each of these four factors to innovative success. Because there are now four explanatory factors, a simple graphical exposition is not possible. In the Pearson et al. (1989) study, the relationship is statistically explored using rank correlation between innovativeness ranking of firms and ranking of the mean score of each firm on each of their two characteristic scales. This choice is partly based on their doubts over the value of original rankings which placed each of the seven test firms within the total identified industry of 62 firms. In this re-analysis a similar procedure could be adopted and the Pearson rankings used as a dependent variable in a discriminate analysis. Examination of the Pearson et al. study shows, however, that such a procedure, implying equal distance between each firm in the ranking is not borne out by the data. Either the original ranking, or a comparison of total numbers of innovations, or of experts’ opinions suggest, for example, that B & C are much closer to each other in innovative TABLE

5.

strength, than say G & F. In the case of ranking the difference between B & C is only two places while the difference between G & E is 15 places. B has introduced no more innovations than C, while G has introduced two compared with zero for F. Accordingly, this re-analysis uses the original industry ranking and explores the relationship between it and the four identified factors USiilg multiple regression. * Table 5 presents the regression results with explanatory variables listed in order of entry to the regression. This estimate is a fairly severe test of the model since it is fitted to only seven observations; leaving only two degrees sf freedom for estimating the variance of residuals. The overall explanatory power of 92% is therefore highly satisfactory. Quite clearly the principal conclusion of Pearson et al. is confirmed since ‘strategic focus’ is by far the most important explanatory variable. ‘Organizational style’, however, is not significant and would contribute nothing to explanatory power. Interestingly, the next most important factor, adding a further 5% explanatory power, is ‘productive efficiency and expertise’. ‘n’his analysis suggests, therefore, that managers might support their companies’

Regression of innovative rank on firm characteristics Coefficient

t statistic

Contribution to explanatory power

Siguifican t variables Strategic focus Productive efficiency

- 17.584 - 5.763

- 7.301*** - 2.463*

0.83 0.05

Total variation explained: Overall significance :

F = 34.45**+

Explanatory variable

-

Insignificant variab!es Leadership Organizational style

R2 = 0.92

- 0 94J$N” - 0:419”5

-i firms ranked, and the robustness of regression, the concern over non-normality of ranked data can be dis*Given the large number J. counted. There remains, however, concern that this aepcndent variable is not measured independently for each of the respondents. For example, all 11respondents for firm A have the same dependent variable thus suggesting non-independence of the error terms. To avoid pI.iated for each firm and the regression fitted to the resulting seven observations. suc’l problems the mean factor scores are c~I_~

385

P. J. Baron

strategic focus with competencies making up the ‘productive efficiency’ factor rather than those of organizational style. onclusions Reexamination of the Pearson data raises serious questions as to the appropriateness of their measures of organizational characteristics. Further analysis suggests they are in fact recording four distinct perceptions of companies rather than the two postulated. Moreover, the values they obtain for the ‘strategic focus’ and ‘organizational style’ factors will have been inaccurate because of the inclusion of irrelevant variables with inappropriate weights. Not only must the measuring instrument be When related to innovative questioned. strength this re-analysis suggests that the redefined ‘strategic focus’ is indeed the crucial explanatory factor. It further suggests that ‘organizational style ’ is insignificant, and that the new characteristic of ‘productive efficiency and expertise’ should replace it as a distinct and important secondary characteristic contributing to innovative strength in an established industry. Clearly, the Pearson et al. study represents an extremely useful contribution to the study of innovation. The outcome of this paper is hopefully to suggest further attempts at refining the necessary measuring * instruments preparatory to their application to a more extensive range of innovation situations. eferences F. Acito and R.D. Anderson, A Monte Carlo comparison of factor analytic methods. Journal of Marketing Research, 17 (1980) 228-236. F. Acito and R.D. Anderson, A simulation study of factor score indeterminacy. Journal of arketing Research, 23 (1986) 111-118. P.J. Baron, W.J.A. Cowie, D.R. ughcs and D. Lesser, Mleasuring attitudes to iamb. ui-opean Journal of Marketing, 5 (1971) 36-

M. Crimp, The Marketing Research Process. Prentice Hall, Englewood Cliffs, NJ, 1981. G.J. Pearson, Factors which facilitate and inhibit innovation in a mature industry. Ph.D. Thesis, University of Manchester, 1989. G.J. Pearson, A.W. Pearson and D.F. Ball, Innovation in a mature industry: a case study of warp knitting in the U.K. Technovation, 9 (1989) 657-679. SPSS, Statistical Package for the Social Sciences: Advanced Statistics V3.0. SPSS, Chicago, IL, 1988.

RBuM~ Dans un article recent,

Pearson

et autres

Factors influencing innovation

ichti ABRISS RESUMEN

Li einem vor kurzem veroffentlichen Aufsatz untersuchen Pearson u.a. (1989) jene Factoren, die in einer vol entwickelten Industrie fur die Innovation marjgebend sind. Die erwahnte Arbeit ergab sich aus gewissen Zweifeln an den MaBstaben, die jetzt angelegt werden. Es wird empfohlen, Methoden ahnlich deren, die im marketing Bereich zu finden sind (Baron u.a. 1971,Crimp 1981) anzuwenden. Erstens wiirde dies die Exaktheit der jetzt angewandten Methoden in Frage stellen; zweitens wtirde es die SchluBfolgerung unterstreichen, und drittens wiirde es zu einer neuen Interpretation der Anzahl und der relativen Wichtigkeit der Faktoren fuhren, die fur effektive Innovation ausschlaggebend sind.

En una ponencia reciente de Pearson et al. (1989) se examinaron 10s factores que influyan en y apoyan a la innovation en una industra madura. Los trabajos que se exponen aqui surgieron a raiz de dudas relacionadas con la description de su instrument0 de medicion. Se afirma que 10s elementos de analisis alternativos tomados de1 campo de marketing (Baroil et al., 1971; Crimp, i981) ponen en duda, en primer lugar, la precision de sus instrumentos; en Segundo lugar apoya a su conclusion principal y en tercer lugar ofrece una interpretation nueva de cuantos factores puedan conducir a la fuerza en la innovation y de su relativa order de importancia.

387