Download - The psychological representation of corporate ‘personality’

Applied Cognitive Psychology, Appl. Cognit. Psychol. 25: 605–614 (2011)Published online 22 June 2010 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/acp.1729

The Psychological Representation of Cor
porate ‘Personality’
PHILIPP E. OTTO1*, NICK CHATER2 and HENRY STOTT3

1European University Viadrina, Frankfurt (Oder), Germany2University College London, London, UK3Decision Technology, London, UK

*CorrScharE-ma

Copy

Summary: As with any other object, people represent companies along a number of dimensions. But what are the key psychologicaldimensions that best describe companies, organizations, or brands? We apply research methods initially developed for studyingattitudes, including attitudes to other people, to look at how the public represents corporate ‘personality’. The major dimensionsthat psychologically differentiate companies resemble human factors of personality and can be labelled Honesty, Prestige,Innovation, and Power. These dimensions are confirmed after a time gap of 1 year, also capturing specific changes in the rating ofindividual companies. The proposed methodology not only has substantial commercial value in helping companies understand andtrack their public perception, but scales of this type can potentially guide and manage the decision-making of individuals or groupsinside and outside rated organizations, thus influencing their organizational culture. Copyright # 2010 John Wiley & Sons, Ltd.

In the continuous interaction with our environment, we need

not only to be able to quickly perceive new information but

also have to rely on existing information as a benchmark.

Behaviour always takes place under a specific frame or is

embedded in context, and evaluations are always in relation

to similar objects of the respective class of objects. But is

there a general mechanism which describes this formation

of differences between perceived objects? Do we apply

comparable processes in different domains? Kelly (1955)

proposed the theory of personal constructs for personality

evaluations, using methods that appear more generally

applicable. Osgood (1962) developed methods for finding

semantic ‘dimensions’ for objects of all kinds. More recently

computational corpus analysis has been applied to ‘dimen-

sionalizing’ semantic materials in a uniform way (Griffiths &

Steyvers, 2004; Landauer &Dumais, 1997). Moreover, in the

literature on categorization, it is typically assumed that

uniform principles guide the representation of diverse

categories, although certain fundamental distinctions (e.g.

the distinction between natural kind versus artefact concepts)

are sometimes viewed as representationally significant

(Gelman, 1988; Murphy, 2002; Sloman & Malt, 2003).

The focus in this work is how well existing research

methods developed for uncovering psychological dimen-

sions can be transferred to understanding how people

represent companies. Its public perception may be a

substantial factor within a company and for its outside

relations, guiding consumer purchasing decisions as well as

potentially influencing investors’ decision making. Thus, it

would be of considerable practical interest to have a

workable model of the dimensions of what we term corporate

‘personality’, which proposes a taxonomy of companies

based on their public perception. To tackle this question, we

derive an evaluation process for measuring generally

perceived company differences, which combines existing

methods to provide a new and unified approach.

espondence to: Philipp E. Otto, Europa-Universitat Viadrina, Grobernstrabe 59, D-15230 Frankfurt (Oder), Germany.il: [email protected]

right # 2010 John Wiley & Sons, Ltd.

PREVIOUS RESEARCH AND CONCEPTUAL

BACKGROUND

Research investigating how people represent complex objects

can be traced back to Osgood and colleagues, who postulated

the existence of general psychological dimensions for

evaluating objects (Osgood, 1962; Osgood, Tannenbaum, &

Suci, 1957). In their ‘semantic differential’ approach, lists of

adjectives were searched for which best capture meaningful

differences between items. The claim was made that a

restricted number of descriptive properties can be sufficient to

differentiate items within a wide range of categories of objects,

reaching from colours and shapes to stories and people. More

recent approaches have applied the semantic differential to a

range of specific domains, including product categories (Hsu,

Chuang, & Chuang, 2000; Katz, Aakhus, Kim, & Turner,

2002; Mondragon, Company, & Vergara, 2005), perceptual

categories (Ohnishi et al., 1996; Oyama, Yamada, & Iwasawa,

1998; Tessarolo, 1981) and names (Hartmann, 1985).

Another evaluation approach for complex objects is the

psychometric method. Here scales are developed for capturing

differences. Underlying factors are extracted which link

directly or indirectly to featural differences, but either way

explain variations in observed behaviour. This perspective is

common in personality research where different ‘personality

dimensions’ are used to explain individual differences

(compare Cattell, 1965; Eysenck & Eysenck, 1985; Goldberg,

1993). Prior research in brand perception has documented

interesting parallels between the perception of people and

the perception of brands (i.e. Epstein, 1977). Lievens and

Highhouse (2003) take a related approach in the evaluation of

organizational attractiveness. When describing an organiz-

ation, similar descriptors are used as when describing

categories like ‘friends’ or ‘strangers’. Thus, it appears that

people may represent organizations as having a corporate

‘personality,’ analogous to human personality. However, Aaker

(1997) who formalizes the specific dimensions for ‘brand

personality’ and Slaughter, Zicker, Highhouse, and Mohr

(2004) who did the same for ‘organization personality’ do

observe differences between human and company personality.

606 P. E. Otto et al.

So far these different studies stand next to each other

without providing integrated insights into human decision

making. To generate a more general taxonomy of perceived

company differences, these studies are integrated into a

single framework. To combine these different approaches

into one unifying concept of corporate personality, we begin

with an exploratory study, to establish some of the natural

dimensions along which people differentiate companies. In

the light of this pilot study, our first study allows us to

systematically evaluate these and other adjective dimensions

for companies, provided by the literature, to get an

understanding of relative importance of the company

descriptors. Our second study then uses a subset of

descriptors, which provided to be important for measuring

company differences, to position diverse companies on these

adjective dimensions and to generate the factors of corporate

personality. Here, relations to different economic charac-

teristics are highlighted. In the third study, the stability of the

derived corporate personality factors is tested for a time gap

of 1 year, also measuring changes in the perception of

individual companies over this time span.

PILOT STUDY: EXPLORING COMPANY

PERCEPTION

The main aim is to explore, in a relatively open-ended method,

the natural dimensions which best describe the concept

‘company’. Later, we want people to rate companies on these

different adjective dimensions. But first we need a method to

generate further candidates, which might usefully discriminate

between companies, to not miss out fundamental descriptors of

company differences. For this we use an experimental

technique called Repetory Grid (RepGrid) which was

introduced by Kelly (1955). RepGrid was first used by Kelly

for the evaluation of individual personality differences. Tomake

sure the concept is derived by the participant and not induced by

the procedure, he introduced an iterative method which is

content neutral. This keeps the guidance by the actual questions

asked at a minimum and only builds on formerly given answers,

without needing any specific material and only providing a

content free frame. This general method is especially suitable

for the generation of dimensions people naturally use to

evaluate objects and can directly be applied for the analysis of

any concept. The one difference in the case here is that

the objects of analysis are companies instead of people. The

selected companies and the derived adjective dimensions of the

individual concepts are to be included in the later analysis.

Method

Six postgraduate students or university staff (three male;

three female; average age 27) took part in the study and were

paid £6 ($11) each. The individual RepGrid session lasted

approximately 60minutes and took place as a one-to-one

interview. The material consisted of a card for each elicited

company (element) and an initially blank table intowhich the

adjectives would be written. The same table was later used

for the rating of the elicited companies on the different

derived adjective dimensions.

Copyright # 2010 John Wiley & Sons, Ltd.

First, each participant had to name nine different well-

known companies, which were written on cards. This was

followed by a procedure to derive the adjective dimensions

for the corresponding objects, which Kelly (1955) called

‘triadic elicitation’, in which randomly three cards as triples

of companies were selected by the experimenter. Two of the

company cards were contrasted here with a third one and the

participant’s task was to produce ‘bipolar’ pairs of adjectives

that differentiated the third company from the other two.

These descriptions were always depicted in bipolar dimen-

sions, where one adjective was describing the two companies

on the one side and another adjective was describing the

single company on the other side, presenting opposing

adjectives like ‘international’ versus ‘national’, ‘large’

versus ‘small’, ‘friendly’ versus ‘unfriendly’, etc. These

comparisons were done repeatedly, so that each company

was selected once as the ‘single’ company, resulting in nine

descriptions for every participant. In a last step all nine

companies were rated on a scale from 1 to 5 on these derived

bipolar adjectives, all in line with Kelly’s (1955) standard

RepGrid method. The results were analysed according to

concept homogeneity and inter-individual variability.

Results

All participants found it easy to generate bipolar adjectives

to separate their selected companies. Also the similarity

between the companies, based on the rating for their elicited

adjectives, were supported by the participants, as the

grouping of the clustering results was ad hoc confirmed

by the participants. An example of a derived solution is given

in Figure 1. The named companies, the generated dimen-

sions, and the ratings are shown for this participant. As in the

case of the other participants, the adjective dimensions group

into higher level dimensions, shown by the hierarchical

clustering result which is based on the individual ratings. For

all different RepGrid solutions see Appendix A.

Named companies mainly represent large retailers or

famous brands. They cover supermarkets and banks as well

as current or potential employers and favourite product

producers. Participants are similar in what companies they

select, with the same company picked once by four out of the

six participants. The frequency with which the selected

companies co-occur within the sample is ‘Tesco’ four times,

‘Sainsbury’ and ‘Costcutters’ three times, and ‘H&M’, ‘BT’

and ‘Barclays’ two times. For comparing companies people

use similar adjectives, as is apparent from inspection of

Table 1, where the same adjectives reoccur within a small

sample of six participants. These qualitative overlaps in

choice of adjectives used to distinguish companies, broadly

support the assumption of a naturally agreed concept of

company differences. Common themes appear to be quality,

price, general appearance and contact experiences.

Discussion

The elicited dimensions for company evaluations have

overlaps within the sample but also with company

descriptors developed in previous studies (Table 2). First

they show commonalities with the concept of brand

personality (Aaker, 1997) and organization personality

Appl. Cognit. Psychol. 25: 605–614 (2011)

Figure 1. RepGrid example solution

Table 1. Company differentiation dimensions elicited for the self-selected companies

Person 1 Person 2 Person 3 Person 4 Person 5 Person 6

Common Affordable Dominant Attractive Abstract AdversarialEnjoyable Close Freedom of action Cheapa Cheapa BigEssential Durable Identity Competitive Educated CompetentHidden importance Formal Internationala Distant Helpfula ConcernedNeeded Luxurious Powerful Feminine Influential ExploitativeNice Qualityb Qualityb Helpfula Physical Internationala

Prestigious Rare pos. experience Spacious Modern Professional Qualityb

Secondary Relaxeda High status Regular Trustworthy Socially responsibleSpecific Rigid Typical Relaxeda Useful Well priced

Here only the one side of the dimension is shown. The other pole is its opposite (i.e. big–small) or its negation (i.e. common–uncommon).aOccurs twice.bOccurs three times.

Corporate personality 607

(Slaughter et al., 2004), sharing one and two company

adjectives respectively; second with Osgood’s semantic

differentials (Heise, 1970; Osgood et al., 1957) sharing four

company adjectives. In the former a large spectrum is used to

find the most useful dimensions for companies. The latter

assumes more general dimensions which apply for different

Table 2. Co-occurrence of named adjectives from different sources

RepGrid Brand

Number in totala 48 42Brand 1 —Organization 2 9Semantic differential 4 1

aAll named 54 adjective dimensions resulting from the RepGrid technique were co1997, p. 354) and Organization (Slaughter et al., 2004, p. 89) personality adjectiveOsgood’s Semantic Differentials represent his three dimensions Evaluation, Potencyis 118 different company adjectives.


sorts of objects and which have been labelled Evaluation,

Potency, and Activity. By letting people derive the

dimensions for describing differences between companies

here, a more open-ended yet direct way is chosen to generate

the important dimensions for differentiating between

companies. The RepGrid method can be seen as a more

Organization Semantic differential

23 239 1— 11 —

nsidered of which six were redundant due to double naming. Brand (Aaker,s were taken from the respective studies and their derived descriptive factor., and Activity (Heise, 1970, p. 237). The total number for all sources together



direct approach for finding useful adjectives on which to

evaluate differences between companies, being positioned

somewhere in between these two separate approaches.

The elicited dimensions might prove useful for future

evaluations of companies. They are complementary to

adjectives generated in the existing literature, thus poten-

tially adding formerly neglected areas of systematic

company differences. But, of course, only a larger data set

will allow for reliable interpretation. Therefore, this pilot

study only prepares for the following studies. The results

derived here are used to produce a more systematic study. To

further evaluate the dimensions differentiating between

companies, in Study 1, we compare the newly derived

company concepts with the company descriptors used in

previous studies. Thereby, the number of potentially useful

adjectives is pruned to facilitate later evaluations.

Table 3. Most stable differentiating company adjectives

AdjectiveTotalSTD

STD means(over

companies)

STD meansdivided bytotal STD Source

Technical 1.18 0.92 0.78 BPLuxurious 1.22 0.90 0.74 RepGridInternational 1.38 0.98 0.71 RepGridUpper class 1.20 0.82 0.68 BPCool 1.20 0.78 0.65 BPQuiet 1.07 0.68 0.63 SDFormal 1.26 0.79 0.63 RepGridExploitative 0.97 0.59 0.61 RepGridLeader 1.19 0.72 0.60 BPOriginal 1.15 0.67 0.58 OP and BPPopular 1.07 0.62 0.58 OPNoisy 1.22 0.70 0.57 SDStatus 1.13 0.65 0.57 RepGridCheap 1.22 0.69 0.57 RepGridLow class 1.22 0.69 0.57 OPYoung 1.07 0.60 0.56 BP and SDQuality 1.20 0.67 0.56 RepGridOld 1.19 0.65 0.55 SDGood looking 1.21 0.66 0.54 BPWell priced 1.06 0.57 0.54 RepGridGlamorous 1.30 0.70 0.54 BPCreative 1.09 0.59 0.54 OPCompetitive 1.01 0.54 0.54 RepGridPowerful 1.20 0.64 0.54 RepGrid

and SDEducated 1.15 0.61 0.53 RepGridIntelligent 1.08 0.57 0.53 BPBusy 1.03 0.54 0.52 OPBig 1.06 0.55 0.52 RepGrid

and SDSecure 1.12 0.58 0.51 BPPrestigious 1.14 0.58 0.50 RepGridSecondary 1.10 0.55 0.50 RepGrid

Thirty-one adjectives where the proportion of variance explained by com-pany mean differences is above the cutoff of 50%. The sources are asfollows: RepGrid, the newly derived dimensions; SD, semantic differential;BP, brand personality; OP, organization personality.

STUDY 1: COMPANY EVALUATION DIMENSIONS

A simple rating method is used to evaluate the different

proposed descriptive adjectives. This is done to system-

atically compare the different sources of company descrip-

tors. Existing company personality adjectives, the semantic

differentials, and the RepGrid company adjectives generated

in our pilot study are used to measure a restricted number of

companies to figure out their descriptive values. Besides

finding the most useful dimensions, redundancies are

captured and the adjectives are put into relation. It also

enables us to determine a set of adjectives which best

describes company differences. This allows a reduction in

the number of adjectives required to then scale-up the

resulting methods in Studies 2 and 3.

Method

Participants rated a list of adjectives in relation to a set of

companies. As illustrated in the pilot study, the different

sources show overlaps in the adjectives used. Here we

included all proposed descriptors (Aaker, 1997; Slaughter

et al.; Heise, 1970) and our newly derived dimensions. Only

redundant adjectives, which described the same dimensions,

were left out.

Twenty students (10 male; 10 female; average age 26) took

part in the study who were paid £12 ($22) each. The computer

based rating lasted approximately 120minutes and took place

in separate rooms for each individual. Participants had to

rate 20 companies on all the 118 adjectives resulting from the

different studies in Table 2. A Likert Scale from 1 to 5 was

used for each adjective always taking both ends of the

dimension as a single evaluation, so that i.e. ‘good’ and ‘bad’

were rated separately with the complementary anchor of the

scale being its own negation (‘not good’ and ‘not bad’). The

companies were taken from the pilot study. The six companies

which were named more than once and in addition 14

representatives for different industries were used. The

companies were displayed together for each adjective to

keep the context in which the comparisons are made stable.

But the company order was randomized in each set and the

adjective order was randomized over participants.


Results

The company ratings were analysed according to the relation

of inter-company variability to inter-individual variability.

For this we introduce the measure of Company Differentia-

bility, which describes how strong a specific adjective

differentiates between companies, while yielding consistent

ratings for a single company across the different raters.

The Company Differentiability score equals the variance of

the company means divided by the total variance. This

measure of Company Differentiability relates the adjective’s

descriptive power of differentiating between companies to

their inter-individual variation. The more stable the adjective

is over participants and the better it distinguishes between

companies, the higher is its value. The highest adjectives in

Company Differentiability, correspondingly being the most

stable and strongest between-company differentiating adjec-

tives, are shown in Table 3. These adjectives are selected by a

criterion of stable Company Differentiability, meaning that

their Company Differentiability score is equal to or above .5.

Thus, for those 31 adjectives, at least half of the total

variance is explained by the differences between company

mean values. Adjectives below this cutoff of 50% were left



aside due to their relative high inter-individual variability.

With this criterion of stable Company Differentiability,

RepGrid adjectives were included the most with 14 out of 31

representing our newly elicited dimensions. For the other

sources there are ten, six, and five cases in the highly

differentiating group for the Brand Personality, Semantic

Differential, and Organization Personality adjectives

respectively. The number of 31 adjectives is in line with

the hierarchical clustering results, where a strong decrease in

the fit of the model, measured by R square (RSq), is observed

somewhere around this number (Figure 2).

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

110 100 90 80 70 60

RSq

Number of C

Figure 2. RSq for number of cluster

Figure 3. Hierarchical clustering tree for


The clustering results for the 31 highest scoring adjectives

in Company Differentiability are shown in Figure 3. These

clustering results are based on the average company values

over participants. The different clustering steps describe

different aggregation levels for company evaluation. The

adjectives group together into higher levels, forming

different aspects of company characteristics. They nicely

separate into four to six clusters which describe company

characteristics on a higher level. Representative classes as

higher order clusters are ‘quality/prestige’, ‘power’, and

‘price’, with the adjectives ‘technical’ and ‘quiet’ as outliers.

50 40 30 20 10 0lusters

s in the Pearson cluster history

the highly differentiating adjectives



These classes form different groups of adjectives for the

evaluation of companies.

Discussion

When evaluating a larger number of adjectives from different

sources, which all describe companies’ characteristics, the in

the pilot study newly elicited adjectives prove highly useful.

This illustrates the possible improvements we can achieve for

the evaluation of companies. By comparing the adjectives on

their Company Differentiability score, the more important

adjectives are isolated. These can be seen as the more general

descriptors for companies.

Interesting is not only how single adjectives describe

companies but also how these aggregate into factors. A first

step into this direction is done in this part by grouping the

adjectives into clusters. Note that these adjectives are broadly

in line with Osgood’s three general Semantic Differentials:

Evaluation (prestige/quality), Potency (power), and Activity

(price). However, a larger body of companies is needed for

generating the fundamental dimensions which guide

company evaluations. Thus, our next study applies a smaller

number of adjectives to a much wider range of companies.

STUDY 2: COMPANY POSITIONING

What are the fundamental factors for company perception?

To estimate the relative importance of the different company

dimensions and to learn more about their similarities, we ran

a further study to distill the most important factors for the

evaluation of companies. Study 2 uses the adjective

evaluation results of Study 1 and expands the analysis to

a large number of companies. This is done to derive a general

taxonomy of perceived company features which then can be

related to for example other company characteristics.

Method

Sixty-four large UK companies and globally operating

international companies (UK: 48 companies; international:

16 companies) were evaluated on 41 adjectives in an online

survey. These 41 adjectives represent the dimensions

Figure 4. Initial fa


captured with the 31 adjectives high on Company

Differentiability and in addition keeping social adjectives,

like friendly and helpful, and trust related adjectives, like

pleasant and personal, in the pool. The total number of

people participating in this study was 1282 (40% female,

38.5 average age). Participants were recruited and paid via

the Ipoints web-service, which is a UK nation-wide panel

system for online data collection, primarily used in

commercial market research. This study was run in

September 2005.

Each evaluation lasted approximately 10 minutes. Every

participant evaluated all 64 companies on four randomly

allocated adjectives. The companies were displayed together,

but were randomized for each adjective. Each adjective

was rated by at least 100 participants (average 125 ratings)

on a 5-point Likert Scale, as in Study 1. These ratings

were averaged for each company adjective. The resulting

company mean values on each adjective were then used in a

factor analysis to derive the underlying dimensions of

company evaluations.

Results

The usage of the adjectives on a broader range of companies

enables a grouping of the different adjectives. These groups

or factors illustrate the underlying categorical differences

and nicely link to economic measures for company

performance.

In the factor analysis of this data, the eigenvalues of the

principal factors flatten out after the fourth factor which is

illustrated in Figure 4. Although Factor 5 is, with an

eigenvalue of 1.08 slightly above one, due to the drop before

we only consider a 4-factor solution in the further analysis, in

line with Cattell (1966). The data gathered in the year 2006,

which also supports this selection, is the topic of the next

section. The complete Equamax rotated factor solution is

given in Appendix B. The rotated solution nicely separates

into four factors that can be labelled ‘Honesty’, ‘Prestige’,

‘Innovation’ and ‘Power’ (see top of Figure 5). The first

factor which we named ‘Honesty’ captures fairness and

trustworthiness of a company. ‘Prestige’ is a dimension of

how valued a company is. With ‘Innovation’ the vividness

ctor solution


Figure 5. Equamax rotated factor solution 2005 and 2006


and flexibility of a company is described. ‘Power’ as the

fourth factor captures the importance or dominance of a

company.

These factors are then put in relation to economic

company descriptors taken from Datastream, which is a data

provider for historical company information. Here the factor

values for the British companies where correlated with

economic measures of size, evaluation, growth, and profit.

Table 4 shows the Spearman correlation for the four factors

based on the 25 companies listed on the London Stock

Exchange. The derived factors show direct relations to

objective company characteristics. The Prestige factor

strongly correlates with the measure for company size and

company profit; the Innovation factor correlates with the

measure for company growth.

Discussion

These factors can be of potential use for the understanding

of company perception and the development of alternative

evaluation criteria for companies. The factor correlation also

indicates possible relations with economic measures, which

Table 4. Factor Spearman correlation for company performancemeasures

Factor 1 Factor 2 Factor 3 Factor 4Honesty Prestige Innovation Power

Size �0.02 0.56a �0.23 0.06Evaluation 0.13 0.24 �0.27 0.33Growth �0.14 �0.32 0.52a �0.14Profit �0.14 0.75a �0.31 0.27

Company measures are taken from Datastream, where Size stands for thetotal assets employed, Evaluation for the market to book value, Growth forthe 3-year growth in sales, Profit for the pre-tax profit.aSignificant on the p< 0.01 level.


might proof useful as performance predictors. Though,

evaluations over a longer time horizon and on a larger body

of companies are necessary to prove the strength and the

directionality of these relations.

Also interesting relations can be drawn from the derived

factors to Osgood’s concept of Semantic Differentials

(Osgood et al., 1957). The ‘Innovation’ factor fits with

their Activity dimension, ‘Prestige’ goes together with the

Evaluation dimension, and ‘Power’ with the Potency

dimension. Only ‘Honesty’ comes in as an additional factor

for the evaluation of companies, which seems not to be part

of Osgood’s original schema. The broader implications of

these results are discussed in more detail later. First, we test

the stability of the factor solution in a further study, to

evaluate how well these corporate personality factors capture

general company differences.

STUDY 3: COMPANY REEVALUATION

How stable are the corporate personality factors over time?

For estimating the stability of the factor solution, we ran

a re-evaluation of the companies on the same adjective

dimensions after a time gap of 1 year. This does not

only test the stability of the proposed measure but also

enables us to track changes in the perception of individual

companies on these factors. Study 3 reruns Study 2 after

exactly 1 year. The same method is used, but the number

of companies is further increased. Thereby, the stability

of the measure is tested in two ways. First, it is checked if

the measure generalizes to a larger set of companies.

Second, possible factor changes over time are evaluated.

For the subset of companies which are included in both

studies, also individual variations on this measure are

captured.



Method

Ninety-two large UK companies and globally operating

international companies (UK: 65 companies; international:

27 companies) were evaluated on 41 adjectives using the

same method as in Study 2. Fifty-nine companies from the

previous study were included and 32 new companies from

different sectors were added. Five from the 64 companies had

to be left out because they did not want to be included in a

further evaluation.

The study was run on 1374 people (44% female, 36.5

average age). These new participants were again recruited

and paid via the Ipoints web-service. The study was run in

September 2006. Each evaluation lasted approximately 10

minutes. Every participant evaluated all 92 companies on

three randomly allocated adjectives. The companies were

displayed together, but were randomized for each adjective.

Each adjective was rated by at least 80 participants (average

100 ratings). The resulting factor solution is compared to

the factor results of Study 2 and changes for companies,

which were included in both evaluations, are highlighted.

Results

The re-evaluation of the companies tests the developed

method of company differences. On the one hand, it is

interesting if the generated factors are similar to the ones in

the previous study. On the other hand, it is important to check

the stability of the factors.

The factor analysis of this in the year 2006 gathered data

nicely compares to the 2005 data. The eigenvalues of the

principal factors again flatten out after the fourth factor

(Figure 4). This supports our initial assumption of a 4-factor

solution for perceived company differences. The differences

of the Equamax rotated factor solution between the years

2005 (Study 2) and 2006 (Study 3) are shown in Figure 5.

Here the five highest positive and the two highest negative

loading adjectives are shown for each factor. The order of the

most important adjectives varies, but only one to two

important adjectives change in the factors. Also the replaced

adjectives appear to describe similar dimensions and the

rotated solution again nicely separates into the four factors

we labelled ‘Honesty’, ‘Prestige’, ‘Innovation’ and ‘Power’

Figure 6. Ten highest and lowest companies on ‘Innovation


(see Appendix C for the complete Equamax rotated factor

solution).When applying confirmatory factor analysis for the

2006 data based on the three highest loading adjectives of

each factor resulting from Study 2, the factor solution is

confirmed (see Appendix D), although independence of the

factors cannot be claimed (RMSEA¼ 0.17).

The stability of the factors is further supported by the high

correlation of the derived factor values for the companies

included in both evaluations. When based on the same factor

loadings, the Pearson correlation of the company factor scores

between 2005 and 2006 is .94 for ‘Honesty’, .87 for

‘Innovation’, .98 for ‘Prestige’ and .88 for ‘Power’. For

descriptive purposes the changes in the rank for the top and the

lowest 10 companies are shown for the least stable factor

‘Innovation’ in Figure 6. Between 2005 and 2006 mainly

German car producers gain in their perceived innovativeness.

The boost in innovativeness for the newspaper ‘The Sun’ could

possibly be attributed to its ongoing online marketing campaign.

Discussion

The re-evaluation with an extended number of companies

stresses the value of the corporate personality measure.

The factor solution holds after a time gap of 1 year and the

measure proofs to be robust. The high correlation between

the companies’ factor scores further supports the stability of

the factors. Besides, the re-evaluation tracks the overall

performance on these factors and captures changes of single

companies. Although isolated company activities can have

strong impacts on their public perception, long-term effects

for single actions close to the evaluation can be questioned and

corresponding influences are expected to be of short impact

for their corporate personality. These influences do not

undermine the stability of the derived factors, as the corporate

personality taxonomy captures generally perceived company

differences, which can vary over different time spans.

GENERAL DISCUSSION

The derived factors can potentially be applied in diverse

areas. This potential highly depends on the factors’

’ in 2006 with their changes in rank compared to 2005



universality and how stable they link to other company

characteristics of interest. These two questions guide the

general discussion.

How universal are the factors of corporate

personality?

A first, very positive, observation in relation to the likely

generalizability of factors of corporate personality is that

they connect well with Osgood et al.’s (1957) classic attempt

to find universal semantic differentials. This suggests that

there are general principles guiding the evaluations of objects

that are structuring how people judge companies. No doubt,

of course, the evaluation of companies has its idiosyncrasies

and possibly other objects might have their own peculiarities

too. Osgood et al.’s (1957) proposed general dimensions are

Activity, Evaluation and Potency. It is interesting to consider

how these original dimensions, which have a clear sense

when applied to, for example, living things, translate into

the related factors concerning companies. The translation of

the Activity dimensions, in the case of companies, into an

Innovation factor appears straight forward as innovation can

be seen as resulting from concerted activity within groups.

Activity is somehow equal to the rate of change at which an

entity updates itself. Our Prestige factor stands in close

relation to Osgood’s Evaluation dimension. The prestige of a

company is described by evaluative qualitative criteria.

Potency is a measure of strength and freedom of action, or

the ability to influence and create one’s environment.

The power of a company directly reflects this ability. The

additionally derived factor Honesty could somewhat be

understood as part of the Evaluation dimension, although

the results here show that a company’s honesty is somehow

distinct from the more general quality evaluations and should

be treated as a separate dimension. In passing, it is worth

noting that Honesty relates directly to questions of trust, the

public perception of which is presently a central concern in

many areas of commerce.

These conclusions are further supported by the study of

Slaughter et al. (2004) who also report an honesty dimension

for Organization Personality. Their first factor ‘Boy Scout’ is

described by adjectives like ‘friendly’, ‘family-oriented’,

‘pleasant’, ‘personal’, ‘attentive to people’, ‘helpful’,

‘honest’ and ‘cooperative’, which is quite similar to our

Honesty factor. Factor two is Innovativeness and factor three

is Dominance, which are directly in line with our results.

Their factors four (Thrift) and five (Style) are comparable to

our Prestige factor or Osgood’s general Evaluation dimen-

sion. Also Aaker’s (1997) first factor, named Sincerity,

describes an honesty dimension as the most important

dimension for their Brand Personality with the loading

adjectives ‘down-to earth’, ‘honest’, ‘wholesome’ and

‘cheerful’. Although receiving somehow differing labels,

also the Prestige factor (Competence and Sophistication) and

the Power factor (Ruggedness) find their representations. An

interesting question for future work is how far this viewpoint

holds for future evaluations of the company concept;

currently, it seems that some clear common themes arise

from different methods for evaluating how people perceive

companies. So far, research appears to be showing a


relatively consistent picture which represents the natural way

of evaluating companies.

The Semantic Differential has been used for attitudinal

research and the evaluation of diverse objects. Besides for

persons, it has not been applied for many living objects. But

when researching the affective characterization of cities,

Ward and Russell (1981) derive at similar results with

reporting as the main first two factors an evaluation

(‘angenehm’) and an activity (‘erregend’) dimension. In

the light of their work, and our own results on company

perception stressing similarities between individual and

organizational identities, it is natural to ask how far Osgood’s

dimensions apply to other types of objects, e.g. animals or

celebrities? Can we find similar regularities in these

domains? If so, there really might be cognitive simplification

processes involved which are similar across evaluations of

complex entities. If different classes of objects are mapped

according to similar dimensions, we might be able to derive

valuable information for the understanding of basic cognitive

processes. An important general question arises, such as

whether the underlying dimensions are separable or integral.

If these dimensions are separable (and hence can directly be

judged independently by participants); or if they are integral

(in which case, reconstruction of the dimensions will

necessarily be indirect, using methods such as that described

here).

Altogether, this study nicely combines previous research

and isolates the important adjectives for company percep-

tions. The corporate personality factors provide a general

measure of perceived company differences by integrating the

different approaches in this field into a universal taxonomy

for companies. An open question remains in which way

these adjective-based dimensions link to other models of

personality. For example the first four ‘Big Five’ personality

factors (Goldberg, 1993) could be represented on an

organizational level characterizing groups of people: with

Honesty describing ‘Agreeableness’, Prestige measuring

‘Extraversion’, Innovation capturing ‘Openness’ and Power

linking to ‘Consciousness’ (leaving ‘Neuroticism without an

organizational representation).

How stable and useful are dimensions of corporate

personality?

With the introduction of personality factors for companies, a

new way of describing companies is provided. This mainly

aims at an alternative description of companies, which

directly reflects the public understanding of companies. But

we have seen in Study 2 that the proposed corporate factors

also show links to economic variables and might in general

be useful as performance indicators. Thus, tracking measures

of corporate personality might add important dimensions to

economic measures of company performance and could be

used both in shaping marketing and brand strategy, and

potentially also in evaluating and predicting company

success. Here for example the first factor of corporate

personality, namely Honesty, might nicely link to marketing

concepts like trust building or corporate reputation. An

important restriction of the presented approach is its purely

outside perspective on organizations. These more generally



perceived differences from the customer perspective do not

necessarily represent the inside perspective, although over

the long run the outside perspective is assumed to represent

the internal perception or vice versa. Though, the linkage to

internal values or organizational practices, nicely described

as ‘Organizational Cultures’ by Hofstede et al. (1990),

remains to be established.

One important point for the application of the factors is

their variability. We assume that the values on the factors are

not stable over time and that changes can be expected over

longer time horizons. Study 3 provides some first examples

for this, which stresses the potential of the corporate factors

as indicators to track changes over time. The performance of

a company over time on these dimensions could be used, for

example, to evaluate the impact of a high-profile advertising

campaign. Also sources of variations, due to, for example,

regional differences, cannot be ruled out, but prior work on

the inter-cultural stability of factors supports the assumption

of stable dimensions for company evaluations. Geeroms,

Vermeir, Van Kenhove, and Hendrickx (2005) generated

preferred Brand Personality factors across 11 countries. The

four corporate factors find representations in their 8-factor

solution (i.e. ‘Belonging’, ‘Recognition’, ‘Vitality’, and

‘Power’). But perhaps more interesting is the link they built

between these factors and consumer motives, which stresses

the interactive component of the evaluation process and

illustrates that motives or goals can play a key role here.

Corporate Personality could have relationships to many

different areas of interest. It can not only represent company

structures in different regions but also influence the climate

within organizations (i.e. Argyris, 1958) and the behaviour of

a company in themarket (i.e. Podolny, 1993). If so, there could

be links to issues like consumer motives and demands,

attractiveness as an employer and employment confidentiality,

differences in short term and long-term performances, as well

as the general social acceptability of a company. Thus, this

improved measure of corporate personality provides the

cornerstone for a wide range of practical applications.

ACKNOWLEDGEMENTS

The authors thank the ESRC for a research grant to the first

author and Decision Technology for providing additional

resources.

REFERENCES

Aaker, J. L. (1997). Dimensions of brand personality. Journal of Marketing

Research, 34, 347–356.

Argyris, C. (1958). Some problems in conceptualizing organizational climate:

A case study of a bank. Administrative Studies Quarterly, 2, 501–520.

Cattell, R. B. (1965). The scientific study of personality. Baltimore: Penguin

Books.

Cattell, R. B. (1966). The Scree Test for the number of factors.Multivariate

Behavioral Research, 1, 245–276.

Epstein, S. (1977). Traits are alive and well. In D. Magnusson, & N. S. Elder

(Eds.), Personality at the crossroad (pp. 83–98). Hilsdale, NJ: Lawrence

Erlbaum Associates.

Eysenck, H. J., & Eysenck, M. W. (1985). Personality and individual

differences: A natural sciences approach. New York: London.


Geeroms, N., Vermeir, I., Kenhove, P. V., & Hendrickx, H. (2005). A

taxonomy of consumer motives through preferred brand personality:

Empirical findings for 11 countries. Ghent University Working Paper.

Gelman, S. A. (1988). The development of induction within natural kind

and artifact categories. Cognitive Psychology, 20, 65–95.

Goldberg, C. R. (1993). The structure of phenotypic personality traits.

American Psychologist, 48, 26–34.

Griffiths, T., & Steyvers, M. (2004). Finding scientific topics. Proceedings of

the National Academy of Sciences, 101, 5228–5235.

Hartmann, T. (1985). A concept-specific semantic differential for the rating

of German 1st and 2nd names. Journal of Social Psychology, 125, 787–

788.

Heise, D. R. (1970). The semantic differential and attitude research. In G. F.

Summers (Ed.), Attitude measurement (pp. 235–253). Chicago: Rand

McNally.

Hofstede, G., Neuijen, B., Ohayv, D. D., & Sanders, G. (1990). Measuring

organizational cultures: A qualitative and quantitative study across twenty

cases. Administrative Science Quarterly, 35, 286–316.

Hsu, S. H., Chuang, M. C., & Chuang, C. C. (2000). A semantic differential

study of designers’ users’ product form perception. International Journal

of Industrial Ergonomics, 25, 375–391.

Katz, J., Aakhus, M., Kim, H. D., & Turner, M. (2002). Young user’s

attitudes towards ICTs: A comparative semantic differential study of the

mobile telephone. Annals of Telecommunications, 57, 225–237.

Kelly, G. A. (1955). The psychology of personal constructs (Vol. 1). New

York: Norton.

Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem:

The Latent Semanctic Analysis theory of the acquisition, induction,

and representation of knowledge. Psychological Review, 104, 140–211.

Lievens, F., & Highhouse, S. (2003). The relation of instrumental

and symbolic attributes to a company’s attractiveness as an employer.

Personnel Psychology, 56, 75–102.

Mondragon, S., Company, P., & Vergara, M. (2005). Semantic differential

applied to the evaluation of machine tool design. International Journal of

Industrial Ergonomics, 35, 1021–1029.

Murphy, G. (2002). The big book of concepts. Cambridge, MA: MIT

Press.

Ohnishi, S., Shinkawa, A., Katto, M., Hamanaka, F., Kurioka, Y., Kawasaki,

M., et al. (1996). Evaluation of odor quality using a semantic differential

method for five odrants of a T&T olfactometer: The case for using

concrete adjectives. Chemical Senses, 21, 34–34.

Osgood, C. E. (1962). Studies of the generality of affectivemeaning systems.

American Psychologist, 17, 10–28.

Osgood, C. E., Tannenbaum, P. H., & Suci, G. J. (1957). The measurement of

meaning. Urbana: University of Illinois Press.

Oyama, T., Yamada, H., & Iwasawa, H. (1998). Synthetic tendencies as the

basis of sensory symbolism: A review of series of experiments by means

of semantic differential. Psychologia, 41, 203–215.

Podolny, J. M. (1993). A status-based model of market competition. Amer-

ican Journal of Sociology, 98, 829–872.

Slaughter, J. E., Zickar, M. J., Highhouse, S., & Mohr, D. C. (2004).

Personality trait inferences about organizations: Development of a

measure and assessment of construct validity. Journal of Applied Psy-

chology, 89, 85–103.

Sloman, S. A., & Malt, B. C. (2003). Artifacts are not ascribed essences, nor

are they treated as belonging to kinds. Language and Cognitive Processes,

18, 563–582.

Tessarolo, M. (1981). The musical experience: The semantic differential as a

research instrument. International Review of Aesthetics and Sociology of

Music, 12, 153–153.

Ward, L. M., & Russell, J. A. (1981). The psychological representation of

molar physical environments. Journal of Experimental Psychology, 110,

121–152.

APPENDICES

Due to the journal’s space limitations the additional material

can only be retrieved online at: http://econ.euv-ffo.de/�peo/

CM.html.


Download - The psychological representation of corporate ‘personality’

Top Related