3HenningTheron.qxd


Today, as organisations struggle to remain competitive in the

face of increasing foreign and domestic competition, interest

centres on the leader’s role of influencing the performance of

his/her subordinates in individual and work unit contexts. An

effective work unit leader is critical for successful unit

performance (Boss, 1978; Hirokawa & Keyton, 1995; Larson &

LaFasto, 1989). Teamwork, facilitated by effective leadership, is

one of the means used by organisations to increase productivity

(Barrett, 1987; Bettenhausen, 1991; Galagan, 1988; Hoerr, 1989).

Thus, a leader’s effectiveness is measured by the performance of

his or her work unit (Kolb, 2001). House (1988) reported that

changes in managerial effectiveness were directly related to

changes in organisational work unit effectiveness. Given this

focus on the leader’s role in influencing performance,

considerable practitioner interest and substantial research

efforts have focussed on the behaviours and competencies of

successful leaders (Alexander, Penley, & Jernigan, 1992; Luthans

& Lockwood, 1984; Trujillo, 1985; Wellmon, 1988; Yukl, 1987). In

the South African context, Spangenberg and Theron (2002a)

developed a comprehensive leadership behaviour index (LBI) to

identif y those latent leadership dimensions, on which a leader

performs relatively less well, in order to improve leader

effectiveness and ultimately unit performance.

Spangenberg and Theron (2002b) also developed a generic,

standardised unit performance measure (PI) that encompasses

the unit performance dimensions for which the unit leader could

be held responsible. Each of the eight unit performance

dimensions of the PI were item analysed and relatively high item

homogeneity was found for each dimension, as indicated by the

Cronbach alpha values (alpha values > 0,8310). Given the

intended use of the PI as a comprehensive criterion measure

against which to validate leadership and other competency

assessments, the relatively high item homogeneity found by

Spangenberg and Theron (2002b) for each dimension, as

indicated by the Cronbach alpha values are extremely gratif ying.

The intention of Spangenberg and Theron (2002b) to develop a

comprehensive structural model that would explain the manner

in which the various latent leadership dimensions affect the

endogenous unit performance latent variables, however, requires

an explanation of the manner in which the unit performance

dimensions affect one another. The development of this section

of the comprehensive structural model, furthermore, should

occur prior to trying to link the various dimensions of

leadership to unit performance. The objective of this paper thus

is to investigate the internal structure of the PI in order to

establish the interrelationships bet ween the eight unit

performance latent variables. Following a discussion of the

literature on organisational effectiveness, the development of

the PI by Spangenberg and Theron (2002b) will be described and

thereafter an argument will be presented as to how the eight unit

performance latent variables influence each other.

The literature describes two main approaches to organisational

performance and effectiveness, namely the goal approach and

the systems approach (Spangenberg and Theron, 2002b). The

goal model focuses on outcomes of the organisation - the more

closely an organisation’s outputs meet its goals, the more

effective it is considered to be. Financial measures of

performance, such as profitability, return on investment (ROI),

market share and return on assets (Etzioni, 1960; 1964) are used.

A discernable trend in performance measurement, however, has

been a move away from extensive and/or exclusive use of

financial measures, to the use of both financial and non-

financial measures. Furthermore, weaknesses of the goal model

ROLINE HENNING

CALLIE THERON
University of Stellenbosch

Department of Industrial Psychology

HERMANN SPANGENBERG
Centre for Leadership Studies, Graduate School of Business

University of Stellenbosch

ABSTRACT
The objective of this study was to investigate the internal structure of the Performance Index in order to

establish the interrelationships between the eight unit performance latent variables. The present study forms

part of a larger study aimed at validating the Leadership Behaviour Inventory (LBI) (Spangenberg & Theron,

2002b) against work unit performance. The validation sample, after imputation of missing values, consisted of

273 cases with observations on all 56 items. Item analysis and dimensionality analysis was performed on each

of the sub-scales using SPSS-windows. Thereafter, confirmatory factor analysis was performed on the reduced

data set using LISREL. The results indicated satisfactory factor loadings on the measurement model. Acceptable

model fit was achieved. Subsequently, the structural model was tested using LISREL. The results provided

statistics of good fit. Only four hypotheses failed to be corroborated in this study. The results are discussed and

suggestions for further research are made.

OPSOMMING
Die doel van hierdie studie was om die interne struktuur van die Performance Indexs (PI) (Spangenberg en Theron,

2002b) te ondersoek ten einde die interverwantskappe tussen die agt latente eeheidprestasiedimensies te bepaal.

Hierdie studie vorm deel van ’n meer omvattende studie wat daarop gemik is om die Leadership Behaviour Inventory

(LBI) teen werkeenheidprestasie te valideer. Die steekproef, na imputasie van ontbrekende waardes, het uit 273

gevalle bestaan met waarnemings ten opsigte van al 56 items. Item- en dimensionaliteitontledings is met behulp van

SPSS-windows op elke subskaal gedoen. Bevestigende faktorontleding is daarna met behulp van LISREL op die

verkleinde datastel uitgevoer. Die resultate het op bevredigende faktorbeladings vir die metingsmodel en ’n

bevredigende passing van die metingsmodel gedui. Daarna is die strukturele model met behulp van LISREL getoets.

Die resultate het bevredigende passing getoon, met slegs vier hipoteses wat nie deur die studie bevestig is nie. Die

resultate word bespreek en voorstelle vir verdere navorsing word gemaak.

THE INTERNAL STRUCTURE OF THE UNIT PERFORMANCE

CONSTRUCT AS MEASURED BY THE PERFORMANCE INDEX (PI)1

Requests for copies should be addressed to: CC Theron, Department of Industrial

Psychology, University of Stellenbosch, Private Bag X1, Matieland, 7602. E-mail:

ccth@sun.ac.za

26

SA Journal of Industrial Psychology, 2004, 30 (2), 26-36

SA Tydskrif vir Bedryfsielkunde, 2004, 30 (2), 26-36



led to the development of systems models of organisational

effectiveness, which focus on the means to achieve the

objectives of organisations, rather than on the ends themselves

(Miles, 1980). The main outcomes of the systems model are

survival, growth, and stability or decline (Denison, 1990). 

The systems approach led to the idea of measuring the

characteristics of major components of the systems model that

mediates in organisational survival and growth. Nicholson and

Brenner (1994) tested a four-element model of organisational

performance that comprised the elements of wealth, markets,

adaptability, and climate. The model described the management

process as a linkage between the elements, forming a cycle of

actions and outcomes. An additional measure, expected future

growth, served as an overall index of future expected

performance. 

An important factor that impacts on organisational effectiveness

is time. Considering that the organisation is part of a larger

system, namely the environment, over time the organisation

acquires, processes and returns resources to the environment.

The ultimate criterion of organisational effectiveness is

sustainable growth and performance. Survival of the

organisation is, therefore, the long-term criterion of

effectiveness. Gibson, Ivansevich and Donnelly (1991) described

a time-dimension model that defines organisational

effectiveness criteria over the short term, medium term and long

term. Short-term measures comprise three overall criteria of

effectiveness, namely production, efficiency and satisfaction. In

the medium term, effectiveness comprises adaptiveness and

development, while survival is the ultimate long-term criterion

of effectiveness. 

Spangenberg and Theron (2002b) extended the time-dimension

model by including an additional dimension, namely nature of

measurement, financial versus non-financial. This decision was

based on literature that emphasise the need for non-financial

measurements to facilitate the creation of value for the

organisation. Non-financial performance measures will only be

discussed briefly. 

Short-term non-financial performance measures include

outputs, efficiency and employee satisfaction. In their model,

medium range non-financial performance measures are

viewed to add considerable value to organisations. In order to

meet future environmental demands, organisations have to

invest resources for development carefully. This includes

continued investment both in production capacit y and

building out the capabilities of managerial and non-

managerial staff. Gibson et al. (1991) argue that future

oriented investment of resources may reduce production and

efficiency in the short term, but if properly managed,

development efforts often are the key to survival.

Systems theory stresses the importance for the organisation of

adapting to the external and internal environments and adapt

its visioning and strategising, management practices, and

policies in response to those changes (Denison, 1990). Using

Lisrel to test measures of interrelatedness amongst the four

elements of their systems model (Nicholson & Brenner, 1994),

some of the model’s predicted relationships were confirmed

and some light was shed on the possible significance of

relationships among the performance measures. Specifically,

they found that of the 18 possible directional paths of the

model, three emerged consistently across the three phases of

the project, namely wealth-climate, climate-adaptability, and

market- adaptability. In two cases, the directions of these

paths were reversed, and an additional proposed path between

adaptability and growth occurred irregularly. Irrespective of

the directions of these path coefficients, Nicholson and

Brenner (1994) drew two clear conclusions from the data.

Firstly, ‘adaptability emerges centre-stage as the lynch-pin of

effectiveness, either as directly associated with other

outcomes or when it mediated them.’ This finding is

consistent with their view, and supported by others, that

mastery of uncertainty is a survival and success requirement

in facing the demands of the modern corporation (Morgan,

1989; Peters, 1987). The second clear finding was the central

role played by global climate, both as an intervening variable

and as a predictor of perceived future success. Climate,

defined as the ambiance of an organisation as reflected in its

morale, conviviality, satisfaction, and shared commitment, is

essential for understanding organisational performance

(Nicholson and Brenner, 1994; Denison, 1990). Furthermore, a

favourable attit udinal climate is a precondition to the

continued effectiveness of the high performance, market-

client driven organisation. 

With regard to the long-term indicator of survival, Spangenberg

and Theron (2002b) replaced the concept of survival by survival

and future growth, with the emphasis on future growth.

Nicholson and Brenner (1994) include five variables in their

conceptualisation of future growth, namely market share,

profits, capital investments, staff levels and acquisitions.

THE PERFORMANCE INDEX (PI) OF 

SPANGENBERG AND THERON

Based on the literat ure, covering organisat ional 

effectiveness and financial and non-financial performance

measures, Spangenberg and Theron (2002b) compiled a base-

line struct ure for a model of work unit performance

effectiveness. The model reflects a synthesis of Nicholson

and Brenner’s (1994) systems approach, Conger and

Kanungo’s leadership outcomes (Conger & Kanungo, 1998)

and Gibson et al.’s (1991) t ime-dimension model of

organisational performance.

Dimensions from Nicholson and Brenner’s systems approach

(1994), namely wealth, markets, adaptabilit y and climate 

(as well as the parameter of future growth) form the core 

of the PI. The three dimensions of wealth, adaptabilit y, and

climate, and the parameter of f ut ure growth were 

retained, while the dimension of market share was expanded

to address the needs of non-profit organisations. Its name 

was changed to market standing. The dimension of climate

was split into work unit climate and individual climate

(satisfaction) because of a relatively large number of items

that pertain to individual employee sentiments, including

outcomes of leadership effectiveness (Conger and Kanungo,

1998). The short-term dimension of outputs/production-

efficiency (Cockerill, et al., 1993; Gibson, et al., 1991) was

added, with slightly changed items. The proposed model was

field-tested with a client-organisation of the Centre of

Leadership Studies.

The only major adaptation subsequently made to the proposed

model was the inclusion of the dimension of core people

processes. Core people processes represent Beckhard’s (1963) and

Beckhard and Harris’ (1989) criteria of organisational health and

effectiveness. It is believed that these people-related processes

and systems, e.g. communication, decision-making and

rewarding performance, would fulfil the need that arose from

field research. The critical role people-related dimensions such as

adaptabilit y and climate have been shown to play in

organisational effectiveness (Nicholson and Brenner, 1994),

supported this observation.

The final version of the Performance Index consists of 56

questions covering eight latent dimensions. Ratings are

obtained on a 5-point scale (well above standard, above

standard, satisfactory, below standard and well below

standard) with verbal anchors on scale points 5, 3 and 1. The

dimensions, with a brief description of each dimension, are

presented in Table 1. 

THE UNIT PERFORMANCE CONSTRUCT 27



TABLE 1

BRIEF SUMMARIES OF THE PI UNIT PERFORMANCE DIMENSIONS

1 Production and efficiency Refers to quantitative outputs such as 

meeting goals, quantity, quality and cost-

effectiveness, and task performance 

2 Core people processes Reflect organisational effectiveness criteria

such as goals and work plans, communication,

organisational interaction, conflict

management, productive clashing of ideas,

integrity and uniqueness of the individual or

group, learning through feedback and

rewarding performance. 

3 Work unit climate Refers to the psychological environment of the

unit, and gives an overall assessment of the

integration, commitment and cohesion of the

unit. It includes working atmosphere,

teamwork, work group cohesion, agreement

on core values and consensus regarding the

vision, achievement-related attitudes and

behaviours and commitment to the unit. 

4 Employee satisfaction Centres around satisfaction with the task and

work context, empowerment, and career

progress, as well as with outcomes of

leadership, e.g. trust in and respect for the

leader and acceptance of the leader’s influence. 

5 Adaptability Reflects the flexibility of the unit’s

management and administrative systems, core

processes and structures, capability to develop

new products/services and versatility of staff

and technology. Overall, it reflects the

capacity of the unit to appropriately and

expeditiously to change. 

6 Capacity (wealth of Reflects the internal strength of the unit, 

resources including financial resources, profits

and investment, physical assets and materials 

supply and quality and diversity of staff. 

7 Market share/scope/ Includes market share (if applicable), standing

competitiveness and market-directed diversity

of products or services, customer satisfaction

and reputation for adding value to the

organisation.

8 Future growth Serves as an overall index of projected future 

performance and includes profits and market 

share (if applicable), capital investment, staff 

levels and expansion of the unit. 

A PROPOSED UNIT PERFORMANCE 

STRUCTURAL MODEL

When evaluating the success of an organisational unit, all eight

aspects of the PI need to form part of the spectrum of unit

performance dimensions being assessed. What this study seeks

to establish is the nature of causal linkages amongst the eight

unit performance dimensions and, more specifically, the extent

to which these unit performance dimensions are directly and

indirectly dependent on one another. The proposed linkages

between the unit performance dimensions are based on the

following argument.

Organisational units exist for a definite reason and with a specific

purpose which is the provision of either a specific product or

service that satisfies the needs of society. In order to be

instrumental in the satisfaction of these needs, organisational

units have to combine and transform scarce production factors

into products and services with maximum economic utility.

Organisational units are then evaluated in terms of the efficiency

with which they produce these specific products or services. If an

organisational unit consistently succeeds in delivering a superior

output to its clients, over an extended period of time, it develops

an elevated market standing and a satisfied client base. An increase

in market standing enhances the overall reputation of the

organisational unit. The organisational unit tends to become

synonymous with the type of product/service in question. A

causal linkage is thus proposed between production and

efficiency (product) and market standing (market).

The environment in which organisational units operate is

characterised by instability and unpredictability; in other words

the environment is dynamic and complex. To ensure that current

high production will ensure future growth, it requires from the

organisational unit the ability to respond appropriately and

expeditiously to changes in the environment. However, in order

to respond in such a manner, it is essential that the unit be given

the appropriate direction in which change should occur. In

addition, the organisational unit should possess the structural

and procedural flexibility to timeously respond to such

directives. Only if the organisational unit has flexible

management and administrative systems, flexible core processes,

and flexible structures combined with versatile, multi-skilled

staff, can it respond appropriately and expeditiously to

environmental change so as to maintain its dominant market

position and achieve future growth. If an organisational unit

currently has a high market standing and the organisational unit

has the ability to adapt to internal and/or external environmental

changes, the unit will be characterised by high future growth

prospects. A causal linkage is thus hypothesised between market

standing (market) and future growth (growth), between capacity

(capacit) and future growth (growth), and between adaptability

(adapt) and market standing (market). Given the perceptual

nature of the PI, market standing is assumed to mediate the effect

of adaptability on future growth perceptions. Adaptability

(adapt) is also hypothesised to influence production and

efficiency (product) positively. Adaptability is thus assumed to

have both a mediated and an unmediated effect on market

standing. No direct causal linkage is proposed between

production and efficiency (product) and future growth (growth).

Current high market standing due to consistently efficient delivery

of a superior product or service cannot be achieved without at least

three additional broad prerequisites being met. Efficient core

people processes and structures represent a first, indispensable

requirement for high unit production and efficiency. Extensive

research evidence cited below supports the notion that human

recourses management practices (HRM) impact on productivity.

Cutcher-Gershenfeld (1991) found that organisations adopting

‘transformational’ labour relation practices – those emphasising

corporation and dispute resolution – had lower costs, higher

productivity and a greater return on direct labour hours than did

firms using ‘traditional’ adversarial labour relations practices. Katz,

Kochan and Weber (1985) demonstrated that highly effective

industrial relations systems, defined as those with fewer grievances

and disciplinary actions and lower absenteeism, increased product

quality and direct labour efficiency. Katz, Kochan and Keefe (1987)

further showed that a number of innovative work practices

improved productivity. Katz, Kochan and Gobeille (1983) and

Schuster (1983) found that quality of work life (QWL), quality

circles and labour-management teams increased productivity.

Bartel (1994) established a link between the adoption of training

programmes and productivity growth, while Holzer (1987) showed

that extensive recruiting efforts increased productivity. Guzzo,

Jette and Katzell’s (1995) meta-analysis demonstrated that training,

goal setting and socio-technical systems design had significant and

positive effects on productivity. Finally, links between incentives

and positive effects on productivity have consistently been found

(Gerhart & Milkovich, 1992; Weitzman & Kruse, 1990). It is,

therefore, with confidence that a direct positive linkage is

hypothesised between core people processes (core) and production

and efficiency (product).

Efficient core people processes, characterised by clear goals and

work plans, open communication, vibrant interaction and

productive clashing of ideas aimed at improving unit performance

in which contributions of individual unit members are valued and

rewarded, should result in high employee satisfaction. In as far as

clear purpose and fruitful, open, orderly interaction between unit

members constitute an expression of effective unit leadership,

efficient core people processes should also result in trust and

respect for the unit leader and acceptance of the leader’s influence.

Core people processes (core) is thus hypothesised to positively

HENNING, THERON, SPANGENBERG28



influence employee Satisfaction (satisf). A clear sense of purpose

combined with genuine unit member participation and

involvement should foster a highly cohesive, well-integrated work

unit with shared values, committed to a common vision. If unit

members have trust in the unit leader and they buy into what the

leader is trying to achieve and the way in which he or she is

approaching it (unit members being allowed the opportunity to

affect the operations of the unit) a positive work unit climate

should emerge. Core people processes (core) is thus hypothesised

to influence work unit Climate (climate) directly and indirectly

via employee satisfaction (satisf).

Continuous creative productive clashing of ideas, a willingness

to experiment with and learn from novel ideas and practices, in

addition, seems to represent an important prerequisite for the

unit to respond timeously and expeditiously to change in the

environment. A positive linkage is thus proposed between core

people processes (core) and adaptability (adapt), and between

core people processes (core) and future growth (growth).

Being a member of a unit with the capacit y to react

appropriately and expeditiously to environmental change

should foster a feeling of confidence, of being in control –

especially if such capacity, combined with efficient core people

processes – has resulted in sustained production and efficiency

over time. A positive causal linkage is thus hypothesised

between adaptability (adapt) and employee satisfaction (satisf). 

A second but equally indispensable requirement to achieve high

production efficiency is the continuous and sufficient access to

superior qualit y physical, financial, nat ural and human

resources. A causal linkage is thus hypothesised between

capacity (capacit) and production and efficiency (product). 

A third essential requirement to achieve high productivity

efficiency is a favourable global attitudinal work unit climate that

constitutes an expression of a set of shared core values and a

commitment to a shared unit vision and mission (Spangenberg &

Theron, 2002b). Nicholson en Brenner (1994) in their study

concluded and emphasised the central role of global climate as an

intervening variable between satisfaction and production, and

indirectly as a predictor of future growth. A favourable global

attitudinal climate is not just a desirable add-on to a profitable and

market-effective company, but a precondition for its continued

effectiveness. A linkage between work unit climate (climate) and

production and efficiency (product) is thus hypothesised.

Figure 1 provides a representation of the proposed unit

performance structural model. The structural model depicted in

Figure 1 differs from preliminary proposals in this regard by

Theron and Spangenberg (2002).

Figure 1: PI structural model

METHOD

Sample

For the purpose of this study, two sets of data were combined.

In both instances non-probability samples of organisational

units were selected. The objective initially was to obtain 360º

ratings from t wo subordinates, t wo peers and a single

superior. The need for as large as possible a sample size, in

conjunction with the difficulties encountered when trying to

apply a questionnaire of this length to respondents at this

high job level, however, necessitated a deviation from the

ideal in a number of cases. The first data set is that of

Spangenberg and Theron (2002b) and exists of a total of 257

completed questionnaires. The second data set is that of the

author and exists of 47 completed questionnaires obtained

from an initial sample of size 100. The latter sample was

drawn from three different functional departments in a large

FMCG company. No demographic information was obtained

from either sample.

Missing values

Missing values presented a problem that had to be addressed

before the data could be analysed. Various options to solve the

missing value problem were explored and it was subsequently

decided to use imputation as a method to solve the problem.

Imputation refers to a process of substituting real values for

missing values. The substitute values, which replace the missing

values of a case, are derived from one or more other cases that

have a similar response pattern over a set of matching variables

(Jöreskog & Sörbom, 1996b). 

The ideal is to use matching variables that will not be utilised in

the structural equation modelling. This was, however, not

possible in this case. The items least plagued by missing values

were firstly identified. A set of variables with three or less

missing values per variable was subsequently defined to serve as

matching variables. The PRELIS program (Jöreskog & Sörbom,

1996b) was used to impute missing values. The subsequent

PRELIS run on the reduced item set proved to be effective in

solving the missing value problem. By default, cases with

missing values after imputation are eliminated. After

imputation, 273 cases with observations on all 56 items

remained in the validation sample.

Statistical analysis

Item analysis

Item analysis was conducted on the validation sample before

and after imputation. Each of the eight PI sub-scales were item

analysed by means of the SPSS Reliability Procedure (SPSS,

1990) to identif y and eliminate possible items not contributing

to an internally consistent description of the sub-scale in

question. No items were rejected. The results of the item

analyses are shown in Table 2. Given the intended use of the PI

as a comprehensive criterion measure against which to validate

leadership and other competency assessments, the relatively

high internal consistency item homogeneity found for each

sub-scale in both cases (before and after imputation), as

indicated by the Cronbach alpha values in Table 2, are

extremely satisf ying. Table 2 clearly indicates that imputation

has a weak attenuating effect on the coefficient of internal

consistency calculated for each sub-scale.

Dimensionality Analysis

Unrestricted principal component analyses with Varimax

rotation were performed on each of the eight PI sub-scales,

each representing a facet of the multi-dimensional unit

performance construct. The objective of these analyses was to

confirm the uni-dimensionality of each sub-scale and to

remove items with inadequate factor loadings or to split

heterogeneous sub-scales into t wo or more homogenous

subsets of items (and make concomitant adjustments to the

underlying unit performance model). The eigenvalue greater

THE UNIT PERFORMANCE CONSTRUCT 29



than unity rule of thumb was used to determine the number of

factors to extract. SPSS (1990) was used for these analyses.

Hulin, Drasgrow and Parsons (1983), however, caution that

factor analysis as performed here on a matrix of product

moment correlations might not be the most appropriate

procedure for establishing the uni-dimensionality of a scale

due to the danger of extracting artefact factors reflecting

differences in item difficulty value or variance only. A series of

confirmatory factor analyses utilizing LISREL probably would

have provided more stringent tests of the dimensionality of

each sub-scale. 

Two of the eight sub-scales failed the uni-dimensionalit y

test. In these cases, moreover, the problem could not be

solved through the deletion of single wayward items. Both

sub-scales presented clear, relatively easily interpretable,

t wo-factor orthogonal struct ures. Each of the t wo sub-scales

was then subdivided into t wo orthogonal uni-dimensional

scales and defined, based on the common theme in the items

loading strongly on each factor. All items allocated to the

subdivided sub-scales loaded satisfactory (0.51< � < 0.893) on
a single factor. The Employee Satisfaction sub-scale could be

subdivided into t wo independent, uni-dimensional sub-

scales, namely (1) a Work Satisfaction sub-scale and (2) a

Leadership Satisfaction sub-scale. The first sub-scale refers to

the extent to which employees are satisfied with the task and

work context, salary and fringe benefits, career progression

and empowerment. The second sub-scale incor porates

outcomes of leadership e.g. trust in and respect for the

leader, acceptance of the leader’s inf luence and qualit y of

supervision. These results suggest that Spangenberg and

Theron’s (2002b) decision to delete t wo items from the

Satisfaction scale was probably not warranted. The Market

Standing sub-scale could also be subdivided into t wo

independent, uni-dimensional sub-scales, namely (1) a

Market dominance sub-scale and (2) a Reputation sub-

scale. The first dimension refers to market share,

competitiveness in markets and diversit y of markets. The

second dimension refers to the compet it iveness and

diversit y of products or services, customer satisfaction and

reputation for adding value.

Although in each case the factor fission was found to result in

a conceptually meaningful division of the original unit

performance dimension in question, and thus a theoretically

meaningful refinement of the unit performance model, the

original unit performance dimension will not be extended for

the purpose of this paper. To do so would further complicate

an already complex structural model. If the hypothesised

struct ural model satisfactorily fits the data, subsequent

analyses could investigate refinements suggested by the

foregoing results.

Structural Equation Modelling

Structural equation modelling (SEM) was used to perform a

confirmatory factor analysis on the sub-scales of the PI. The

eight latent variables could be divided into one exogenous

variable and seven endogenous variables in accordance with

the hypothesised structured model depicted in Figure 1 thus

resulting in two separate measurement models. Indicator

variables were obtained for each latent variable by calculating

the unweighted averages of the odd numbered items and the

even numbered items of each sub-scale. Two item parcels were

thus formed for each latent variable, thereby simplif ying the

event ual comprehensive Lisrel model by reducing the

manifest variables in the model from sixty-five to sixteen.

Apart from simplif ying the logistics of fitting the model the

creation of two indicator variables for each latent variable has

the added advantage of creating more reliable indicator

variables. However, rather than fitting the t wo separate

measurement models, a single confirmatory factor analysis

was performed on all eight dimensions. The exogenous

measurement model would have consisted of a single latent

variable (core) measured by two indicator variables. Despite

its simplicity the model would, however, not have been

identified (Diamantopoulos & Siguaw, 2000), thus preventing

the finding of a unique solution for the parameters to be

estimated (Kelloway, 1998). 

Evaluation of the measurement model

The measurement model underlying the PI is shown in matrix

format as equation 1.

X = �x� + � ————————————————————————————————-(1)

Where:

X is a 16x1 column vector of observable indicator variables;

�x is a 16x8 matrix of factor loadings;

� is a 8x1 column vector of latent exogenous variables; and

� is a 16x1 column vector of measurement errors in X. It
indicates systematic non-relevant, as well as random error

influences (Jöreskog & Sörbom, 1996).

LISREL 8.30 (Jöreskog, Sörbom, du Toit & du Toit, 2000) was

used to perform a confirmatory factor analysis on the PI to

HENNING, THERON, SPANGENBERG30

TABLE 2

RELIABILITY OF PI SUB-SCALE MEASURES

Sample after imputation (n=273) Sample before imputation

Scale Number items Alpha Mean Variance Sample size (n) Alpha Mean Variance 

Production & Efficiency 5 0,7446 18,7106 8,8240 276 0,7636 18,7391 9,0735

Core People Processes 9 0,8480 31,2381 34,4762 263 0,8661 31,1977 37,1058 

Work Unit Climate 7 0,8756 25,1465 25,7064 292 0,8908 25,3493 26,3449 

Employee Satisfaction 9 0,8870 30,9341 38,1133 279 0,8882 31,0143 3 7,9854 

Adaptability 7 0,8208 24,1575 21,0597 268 0,8233 24,4179 20,1393 

Capacity 7 0,8183 22,6593 23,9166 182 0,8248 22,6593 23,9496 

Market Share 7 0,7978 24,4908 21,2435 173 0,8367 24,5607 25,6315 

Future Growth 5 0,7290 16,1685 13,3318 126 0,8168 16,5079 16,1239 



determine the fit of the model shown as equation 1. For the

purposes of confirmatory factor analysis the measurement

model was treated as an exogenous model simply due to

programming advantages. The imputed data was first read

into PRELIS (Jöreskog & Sörbom, 1996b) to compute a

covariance matrix to serve as input for the LISREL analysis.

Maximum likelihood estimation was used to estimate the

parameters set free in the model. Instead of defining the

origin and unit of the latent variable scales in terms of

observable reference variables, the latent variables were rather

standardised (Jöreskog & Sörbom, 1993). All factor loadings

of each latent unit performance variable were set free to be

estimated, but only with regards to its designated observed

variables. All remaining elements of LX were fixed at zero

loadings to reflect the assumed factorial simplicit y of the

indicator variables (Tabachnick & Fidell, 1989). The elements

of the covariance/correlation matrix (�) and the diagonal
elements of the variance/covariance matrix (��) were treated
by default as free.

An admissible final solution of parameter estimates for the PI

measurement model was obtained after 10 iterations. Results

replicated the finding by Spangenberg and Theron (2002b) of

good or acceptable model fit. This, however, is not altogether

surprising since the data of the Spangenberg and Theron

(2002b) study formed part of the initial data set analysed in

this study. All indicator variables load significantly (p < 0,05)

on the latent variables they were designed to reflect. But for

product_2, a satisfactory proportion of the variance in each

indicator variable is explained by its underlying latent variable.

With regards to the production dimension, the second item

parcel’s (product_2) ability to reflect �1 seems to be somewhat
questionable. The operationalisation of the latent unit

performance dimensions in terms of the majority of the item

parcels formed on the PI sub-scales thus seems to have been

successful. The absence of crucial deficiencies in the

measurement part of the model justifies the subsequent

evaluation of the structural part of the model. ‘Unless we can

trust the quality of our measures, any assessment of the

substantive relations will be problematic’ (Diamantopoulos &

Siguaw, 2000, p. 89).

Evaluation of the full LISREL model

The proposed structural model that serves as the basis for this

study is portrayed in Figure 1. The specific paths depicted in the

structural model represent hypothesised causal linkages

between specific unit performance dimensions derived through

systematic theorising presented earlier. The design and structure

of the structured model implies a specific structural equation.

The structural model relevant to this study is shown in matrix

form as equation 2. 

� = �� + 	� + 
 —————————————————————————————-(2)

� is a 7x1 column vector of endogenous latent variables;

B is a 7x7 symmetrical matrix of path/regression coefficients (�)

describing the regression of �i on �j in the structural model;

	 is a 7x1 matrix of path/regression coefficients (�) describing

the regression of �i on �j in the structural model;

� is a 1x1 column vector of exogenous latent variables; and


 is a 7x1 vector of residual error terms or equation errors in the

structural relationship between � and � (Jöreskog & Sörbom,
1996; 1996a).

More specifically the causal relationships hypothesised earlier

and depicted in Figure 1 can be expressed as matrix equation 3.

Equation 3 implies the statistical hypotheses presented in Table

3 on the B and 	 population matrices.

TABLE 3

STATISTICAL HYPOTHESES ON THE � AND 	 POPULATION MATRICES

Hypothesis 1: Hypothesis 4: Hypothesis 7: Hypothesis 10: Hypothesis 13:

Ho: �31 = 0 Ho: �71 = 0 Ho: �12 = 0 Ho: �14 = 0 Ho: �76 = 0

Ha: �31 > 0 Ha: �71 > 0 Ha: �12 > 0 Ha: �14 > 0 Ha: �76 > 0

Hypothesis 2: Hypothesis 5: Hypothesis 8: Hypothesis 11: Hypothesis 14:

Ho: �21 = 0 Ho: �41 = 0 Ho: �34 = 0 Ho: �64 = 0 Ho: �61 = 0

Ha: �21 > 0 Ha: g41 > 0 Ha: �34 > 0 Ha: �64 > 0 Ha: �61 > 0

Hypothesis 3: Hypothesis 6: Hypothesis 9: Hypothesis 12: Hypothesis 15:

Ho: �11 = 0 Ho: �23 = 0 Ho: �15 = 0 Ho: �75 = 0 Ho: �54 = 0

Ha: �11 > 0 Ha: �23 > 0 Ha: �15 > 0 Ha: �75 > 0 Ha: �54 > 0

The manner in which the results of the evaluations of the structural

model fit are reported, is based on the guidelines of Raykov, Tomer

and Nesselroade (1991). LISREL 8.30 (Jöreskog et al., 2000) was

used to perform the structural equation modelling on the PI to

determine the fit of the model expressed as equation 2. The data

was read into PRELIS to compute a covariance matrix to serve as

input to the LISREL analysis. The method of parameter estimation

that was used in this study was Maximum Likelihood (ML). 

Assessing overall goodness-of-fit of the structural model

The full spectrum of indices provided by LISREL to assess the

absolute and comparative fit of the model is presented in Table 4. 

TABLE 4

GOODNESS-OF-FIT OF THE STRUCTURAL MODEL

Degrees of Freedom = 89 

Minimum Fit Function Chi-Square = 199,13 (P < 0,01) 

Normal Theory Weighted Least Squares Chi-Square = 195,81 (P < 0,01)

Estimated Non-centrality Parameter (NCP) = 106,81 

90 Percent Confidence Interval for NCP = (70,16 ; 151,21)  

Minimum Fit Function Value = 0,73 

Population Discrepancy Function Value (F0) = 0,39 

90 Percent Confidence Interval for F0 = (0,26 ; 0,56) 

Root Mean Square Error of Approximation (RMSEA) = 0,066 

90 Percent Confidence Interval for RMSEA = (0,054 ; 0,079)

P-Value for Test of Close Fit (RMSEA < 0,05) = 0,017  

Expected Cross-Validation Index (ECVI) = 1,07 

90 Percent Confidence Interval for ECVI = (0,93 ; 1,23) 

ECVI for Saturated Model = 1,00 

ECVI for Independence Model = 12,03 

Chi-Square for Independence Model with 120 Degrees of Freedom = 3240,62

Independence AIC = 3272,62 

Model AIC = 289,81 Saturated AIC = 272,00

Independence CAIC = 3346,37 

Model CAIC = 506,46

Saturated CAIC = 898,89  

Normed Fit Index (NFI) = 0,94 

Non-Normed Fit Index (NNFI) = 0,95 

Parsimony Normed Fit Index (PNFI) = 0,70 

Comparative Fit Index (CFI) = 0,96 

Incremental Fit Index (IFI) = 0,97 

Relative Fit Index (RFI) = 0,92  

Critical N (CN) = 168,93  

Root Mean Square Residual (RMR) = 0,023 

Standardised RMR = 0,043 

Goodness of Fit Index (GFI) = 0,92 

Adjusted Goodness of Fit Index (AGFI) = 0,87 

Parsimony Goodness of Fit Index (PGFI) = 0,60 

THE UNIT PERFORMANCE CONSTRUCT 31



The p-value associated with the Normal Theory Weighted Least

Squares Chi-Square value in Table 4 clearly indicates a highly

significant test statistic. A non-significant 
² indicates model fit
in that the model can reproduce the observed covariance matrix

(Bollen & Long, 1993; Kelloway, 1998). In this case the model is

not able to reproduce the observed covariance matrix to a degree

of accuracy that could be explained in terms of sampling error

only. However the 
² measure is distributed asymptotically as a

² distribution. This causes the frustrating dilemma that just at
the point where the distributional assumptions of the test

statistic become tenable the statistical power of the test also

becomes extremely high. It thus becomes extremely unlikely to

obtain the desired insignificant 
² statistic in a large sample even
when the model fits the empirical data quite well. Given the

sample size involved in this study it therefore seems somewhat

premature to conclude poor model fit based on the large and

significant 
² alone.

Expressing the 
² value in terms of its degrees of freedom has
been suggested as a way of getting round the aforementioned

problems associated with this measure. This is not routinely

provided by LISREL as part of its repertoire of fit measures and

thus not shown in Table 4. The evaluation of fit on the basis of

the ratio 
² /df ( 
²/df = 2,2001) for the structural model suggest
that the model fits the data well. Kelloway (1998), however,

comments that the guidelines indicative of good fit (ratios

between 2 and 5) have very little justification other than

researcher’s personal modelling experience and advises against a

strong reliance on its use.

The RMSEA value of 0,066 supports the notion of a good fit,

where a very good fit is indicated by a value of less than 0,05.

The RMR (0,023) and standardised RMR (0,043) also indicates

good fit. Values of less than 0,05 on the latter index are regarded

as indicative of a model that fits the data well (Kelloway, 1998).

The 90% confidence interval for RMSEA shown in Table 4 (0,054

– 0,079) indicates that the fit of the structural model could be

regarded as reasonable to good. A test of the significance of the

obtained value is performed by LISREL by testing Ho: RMSEA �
0,05 against Ha: RMSEA > 0,05. Table 4 indicates that the

obtained RMSEA value of 0,066 is significantly greater than the

target value of 0,05 (i.e. H0 is rejected; p < 0,05), and since the

confidence interval does not include the target value of 0,05, a

very good fit seems not to have been achieved. In terms of the

Browne and Cudeck (1993) guideline, however, the upper bound

of the confidence interval still suggests acceptable fit. This

conclusion is supported by the aforementioned Standardised

RMR value of 0,035. 

The goodness-of-fit index (GFI) measures are ‘based on a ratio of

the sum of the squared discrepancies to the observed variances

(for generalised least squares, the maximum likelihood version

is somewhat more complicated)’ (Kelloway, 1998, p. 27). The

adjusted GFI (AGFI) adjusts the GFI for degrees of freedom in the

model (Kelloway, 1998). Both these two measures should be

between zero and unity with values exceeding 0,9 indicating

good fit to the data (Jöreskog & Sörbom, 1993; Kelloway, 1998).

Evaluating the fit of the model in terms of these two indices

(0,92 & 0,87) a relatively favourable conclusion on model fit

emerges. Kelloway (1998), however, warns that these guidelines

for the interpretation of GFI and AGFI are grounded in

experience, are somewhat arbitrary and should therefore be used

with some circumspection.

Indices of comparative fit that use as a baseline an independence

model, contrast the ability of the model to reproduce the

observed covariance matrix with that of a model known a priori

to fit the data poorly, namely one that postulates no paths

between the variables in the model. The indices of comparative

fit reported by LISREL and shown in Table 4 seem to indicate

good model fit relative to that of the independence model. The

normed fit index (NFI = 0,94), the non-normed fit index (NNFI =

0,95), the incremental fit index (IFI = 0,97), the comparative fit

index (CFI = 0,96) and the relative fit index (RFI = 0,92) all can

assume values between 0 and 1 with 0,90 generally considered

indicative of a well fitting model (Bentler, 1990; Bentler &

Bonett, 1980; Hair et al., 1995; Kelloway, 1998). The values of all

of the aforementioned indices exceed the critical value of 0,90

thus indicating good comparative fit relative to the

independence model.

The assessment of parsimonious fit acknowledges that model fit

can always be improved by adding more paths to the model and

estimating more parameters until perfect fit is achieved in the

form of a saturated or just-identified model with no degrees of

freedom (Kelloway, 1998). The objective in model building is,

however, to achieve satisfactory fit with as few model parameters

as possible (Jöreskog & Sörbom, 1993). The objective is therefore

to find, in this sense, the most parsimonious model. Indices of

parsimonious fit relate the benefit that accrues in terms of

improved fit to the cost incurred (in terms of degrees of freedom

lost) to affect the improvement in fit (Hair et al., 1995; Jöreskog

& Sörbom, 1993). The parsimonious normed fit index (PNFI =

0,70) and the parsimonious goodness-of-fit index (PGFI = 0,60)

shown in Table 4 approaches model fit from this perspective. Its

meaningful use, however, necessitates a second, explicitly

formulated and fitted model that contains a number of

additional paths that can be theoretically justified so that the

initial model is nested within the more elaborate model. In this

case no such alternative model exists. The values of the expected

cross-validation index (ECVI = 1,07), the Aiken information

criterion (AIC = 289,81) and the consistent Aiken information

criterion (CAIC = 506,46) shown in Table 4 all suggest that the

fitted structural model provides a more parsimonious fit than

the independent/null model since smaller values on these

indices indicate a more parimonious model (Kelloway, 1998).

Examination of residuals

Residuals refer to the differences between corresponding cells in

the observed and fitted covariance/correlation matrices

(Jöreskog & Sörbom, 1993). Residuals, and especially

standardised residuals, provide diagnostic information on

sources of lack of fit in models (Jöreskog & Sörbom, 1993;

Kelloway, 1998). A stem-and-leaf plot of the standardised

residuals is provided in Figure 2.

Figure 2: Stem-and-leaf plot of standardised residuals ��2

Standardised residuals can be interpreted as standard normal

deviates (i.e. z-scores). Standardised residuals with absolute

values greater than 2,58 could thus be considered large

(Diamantopoulos & Siguaw, 2000). Large positive and negative

standardised residuals would be indicative of relationships (or

the lack thereof) between indicator variables that the model fails

to explain. Large positive residuals would indicate that the

model underestimates the covariance between two observed

variables. The problem could, therefore, be rectified by adding

paths to the model that could account for the covariance.

Conversely, large negative residuals would indicate that the

model overestimates the covariance between specific observed

variables. The remedy, in turn, would thus lie in the pruning

away of paths that are associated with the indicator variables in

HENNING, THERON, SPANGENBERG32



question. From the stem-and-leaf plot depicted in Figure 2, the

distribution of standardised residuals appears to be distributed

slightly positively skewed. This would suggest that the model

fails to account for one or more influential paths. The

leptokurtic nature of the distribution would suggest that

relatively few covariance terms in the observed covariance

matrix were inadequately accounted for by the fitted model.

However, although the negative standardised residuals seem to

be mostly of only modest magnitude (smallest, -3,51), the

presence of a number of large positive residuals do cause some

concern (largest 6,98). Twelve large positive residuals and four

large negative residuals thus indicate sixteen observed

covariance terms (out of 120) in the observed sample covariance

matrix (S) being poorly estimated by the derived model

parameter estimates. Inspection of the variables associated with

these standardised residuals reveal no clear specific suggestions

for possible model modification. The predominance of indicator

variables associated with capacity, markets and growth do,

however, suggest that these latent variables should be the focus

of future efforts to improve the model. A somewhat problematic

model fit is further indicated by the fact that the standardised

residuals for all pairs of observed variables tend to deviate from

the 45° reference line in the Q-plot in the upper and lower

regions of the x-axis. 

Model modification indices 

The proposed model depicted in Figure 1 seems to fit the data

reasonably well. The foregoing analysis of the standardised

residuals does, however, suggest that the addition of one or

more paths would probably improve the fit of the model. The

question subsequently arises which paths, when added to the

model, would significantly improve the parsimonious fit of the

model. The modification indices calculated by LISREL show the

decrease in the 
2 statistic if currently fixed parameters are set
free and the model re-estimated. Large modification index

values ( > 6,6349) thus indicate parameters that, if set free,

would improve the fit of the model significantly (p < 0,01)

(Diamantopoulos & Siguaw, 2000). Kelloway (1998), however,

cautions that model modifications suggested by modification

indices should be resisted unless such alterations to the model

can be supported by clear and convincing theoretical

justification. Examination of the modification indices

calculated for the B matrix indicates four additional paths that

would significantly improve the fit of the model. Results

suggest that markets influence capacity (37,47), and conversely

that capacity influence markets (37,03). A reciprocal causal

linkage bet ween market standing and capacit y is thus

suggested. Such a linkage does seem to make substantive

theoretical sense. Future growth is also indicated to influence

capacity (26,8) and markets (21,52). These linkages also do not

appear to be unreasonable. The standardised expected change

associated with the aforementioned paths is all of sufficient

magnitude to consider freeing them. Examination of the

modification indices and the completely standardised expected

parameter change associated with the fixed parameters in 	,
indicate that no paths originating from the single exogenous

latent variable, if added to the model, should result in a

significant decrease in the 
² measure at the 1% significance
level. If the parameter with the largest modification index (�56)
is relaxed and the model is re-estimated (Jöreskog & Sörbom,

1998), the fit of the model improves. Although the 
² statistic
remains significant (p<0,05), the RMSEA improves to 0,054. The

90% confidence interval for RMSEA (0,040 – 0,067) indicates

that the fit of the modified structural model could be regarded

as good to very good.

The obtained RMSEA value of 0,054 is not significantly greater

than the target value of 0,05 (i.e. H0: RMSEA�0,05 is not rejected;
p > 0,05), and since the confidence interval does include the

target value of 0,05, a good fit seems to have been achieved. The

standardised RMR of the modified model is a satisfactory 0,033.

The distribution of standardised residuals also improved in

terms of symmetry and dispersion with the addition of a

directional linkage between market standing and capacity.

Examination of the modification indices calculated for the

expanded B matrix indicates no additional paths that would

significantly improve the fit of the modified model.

Examination of the modification indices and the completely

standardised expected parameter change associated with the

fixed parameters in the �� matrix reveal nine covariance terms
that, if set free, would result in significant (p < 0,01) decreases in

the 
² measure. The expected magnitude of the completely
standardised covariate estimates, however, hardly warrants

seriously considering setting these parameters free. The expected

completely standardised covariance between the measurement

error terms associated with satisf_1 and satisf_2 (0,48) is the only

exception. The remaining completely standardised expected

change estimates are all sufficiently small. This in turn would

suggest that the assumption of uncorrelated error terms remains

largely tenable.

Examination of the modification indices calculated for the

variance-covariance matrix � reveal that allowing for
correlations amongst the residual error terms 
 would result in
a significant (p < 0,01) improvement in model fit in the case of

only one covariance term. The modification index value

associated with 
� (capacity) – 
� (markets) covariance (37,03)
seems to suggest that the pair of latent variables is both

influenced by at least one common latent variable not

recognised by the model. The magnitude of the standardised

expected change associated with these two correlation terms,

however, is not really substantial (< 0,24). Although not

necessarily the case, this result could possible be due to the

model’s inability to make provision for a reciprocal relationship

between these two latent variables. 

Assessment of the structural model

The analysis of the structural relationships should reveal

whether the theoretical structural model, and thus the research

hypotheses, could be confirmed. The relevant matrices for the

direct effects between the constructs are the beta (�) and gamma
(	) matrices reflecting the regression of �i on �j and the
regression of �i on �j respectively. The matrices are depicted in
Tables 5 and 6 respectively.

TABLE 5

COMPLETELY STANDARDISED BETA (�) MATRIX

PRODUCT CLIMATE SATISF ADAPT CAPACIT MARKET GROWTH 

0,24# -,02 0,04

PRODUCT - (2,15) (0,16) (0,21) - -

2,09* -,10 0,28 

0,37

CLIMATE - - (0,10) - - - -

3,70* 

0,32

SATISF - - - (0,06) - - -

3,67* 

ADAPT - - - - - - - 

0,84

CAPACIT - - - (0,07) - - -

7,49* 

0,10 0,67

MARKET (0,08) - - (0,09)

1,51 7,23* 

- - - - 0,25 0,52

GROWTH (0,11) (0,07)

2,56* 5,15* 

# Values represent the completely standardised � path coefficient, standard error and t-test
statistic respectively

* t-values >��1,96� indicate significant path coefficients

THE UNIT PERFORMANCE CONSTRUCT 33



Four issues are of relevance when evaluating the structural

model:

a) The significance of the parameter estimates (�i and �i)
representing the paths hypothesised between the latent unit

performance dimensions;
b) The consistency of the signs of the parameter estimates and

the hypothesised nature of the relationships between the

latent unit performance dimensions;

c) The magnitude of the parameter estimates indicating the

strength of the hypothesised relationships; and

d) The proportion of variance in each endogenous latent

variable that is explained by the latent variables linked to it in

terms of the hypothesised structural model.

From the t-values in the beta (B) matrix (Table 5), it can be seen

that for statistical Hypotheses 6, 7, 8, 11, 12, 13 and 15 (see

Table 3), Hoi: � = 0 can in each case be rejected in favour of Hai
(p < 0,05). Thus, the relationships that are postulated between

these respective endogenous latent variables in the structural

model (see Figure 1), are thereby corroborated. In addition the

signs associated with all the significant � parameter estimates
are consistent with the nat ure of the relationships

hypothesised to exist between these endogenous latent unit

performance dimensions. An insignificant (p > 0,05)

relationship is, however, evident bet ween capacit y and

production. Consequently, research hy pothesis 9 is not

corroborated (Ho9 can thus not be rejected in favour of Ha9).

The path coefficients associated with the hypothesised linkages

between adaptability and production and between production

and market standing also failed to reach significance (p>0,05).

H010 and H014 thus also are not rejected.

In the modified model the estimated standardised parameter

(0,50) associated with the influence of markets on capacity is

significant (p<0,05). The influence of capacity and adaptability

on production remains insignificant (p>0,05). The previously

insignificant path from production to markets, however,

becomes significant (p<0,05) in the modified model. The

influence of capacity on growth, although significant in the

original model, is insignificant in the modified model

(p>0,05).

TABLE 6

COMPLETELY STANDARDISED GAMMA (	) MATRIX

CORE

0,44

PRODUCT (0,22)

2,75* 

0,50

CLIMATE (0,12)

4,67* 

0,58

SATISF (0,11)

5,80* 

0,78

ADAPT (0,14)

9,02* 

CAPACIT -

MARKET - 

0,08

GROWTH (0,09)

0,93 

# Values represent the completely standardised � path coefficient, standard error and t-test
statistic respectively.

* t-values >� 1,96� indicate significant path coefficients

From the t-values in the gamma (	) (see Table 6) matrix it
can be inferred that the relationship hy pothesised bet ween

core processes and employee sat isfact ion, climate,

production and adaptabilit y respectively are all significant

(p < 0,05). H0i for statistical Hy potheses 1, 2, 3 and 5 are

therefore rejected. The signs associated with all the

significant � parameter estimates are consistent with the
nat ure of the relat ionships hy pothesised bet ween the

exogenous latent variable Core and the aforementioned

endogenous latent variables. The path coefficient associated

with the path hy pothesised bet ween core processes and

fut ure growth, however, is not significant (p>0,05). H04 is

therefore not rejected. The part icular path coefficient

remains insignificant (p>0,05) in the modified model.

The completely standardised � and � parameter estimates
ref lect the average change in standard deviation units in an

endogenous latent variable directly resulting from a one

standard deviation change in an endogenous or exogenous

latent variable to which it has been linked, holding the effect

of all other variables constant. Table 6 would thus suggest

that core people processes has a relatively strong impact on

four of the five endogenous unit performance dimensions it

has been linked to in the struct ural model, especially

adaptabilit y. Table 5 would, however, suggest that the direct

effect of capacit y on fut ure growth and the direct effect of

climate on production, although significant, are somewhat

less pronounced. The magnit ude of the remaining 

significant � parameter est imates in Table 5 indicate
moderate to relatively strong relationships. The direct 

effect of adaptabilit y on capacit y (0,84) shows up as the

most inf luential.

The squared multiple correlations for the endogenous latent

variables in the model are shown in Table 7.

TABLE 7

SQUARED MULTIPLE CORRELATIONS FOR STRUCTURAL EQUATIONS

SATISF CLIMATE PRODUCT CAPACIT ADAPT MARKET GROWTH 

0,73 0,70 0,43 0,71 0,61 0,53 0,58 

The proposed structural model satisfactorily succeeds in

explaining variance in four of the seven endogenous latent

variables (satisf, climate, capacit, and adapt). The model’s ability

to account for the variance in product, markets and growth,

although not all together problematic, nonetheless creates some

reason for concern.

The completely standardised �-matrix depicting the variance in
the residual error terms 
 is presented in Table 8.

TABLE 8

COMPLETELY STANDARDISED PSI (�) MATRIX

SATISF CLIMATE PRODUCT CAPACIT ADAPT MARKET GROWTH 

0,27 0,30 0,57 0,29 0,39 0,47 0,42 

The residual error terms 
 acknowledge the fact that all the
variance in the endogenous latent variables most probably will

not be explained by the model – some of the variance most

probably will be due to effects not included in the model. Large

residual error variance terms in Table 8 for product and, to a

lesser extent, markets and growth thus reiterate the conclusion

derived from Table 7 that the model achieves relatively less

success in accounting for the variance in these three unit

performance dimensions. Taken in conjunction with the finding

reported earlier on the nature of the possible path additions to

the structural model that would improve the fit of the model,

thus seems to suggest that the problem could be rectified by

HENNING, THERON, SPANGENBERG34



expanding the model with additional linkages between the

latent variables concerned. This inference seems to agree with

the findings derived from the modification indices calculated for

the �-matrix. With regards to the production dimension the
problem could possibly be explained in terms of the second item

parcel’s (product_2) failure to reflect �1.

DISCUSSION

The objective of this study was to establish the nature of causal

linkages between the eight unit performance dimensions and

more specifically the extent to which these unit performance

dimensions are directly and indirectly dependent on one

another. The ex post facto nature of the research design,

however, precludes the drawing of causal inferences from

significant path coefficients.

This study failed to find support for the hypothesis that there is a

directional linkage between production and efficiency (product)

and market standing/scope/share (market). Thus, although it

seems reasonable to propose that if an organisational unit

consistently succeeds in delivering a superior output to its clients

over an extended period of time, it thereby should develop an

elevated market standing and a satisfied client base, the available

empirical evidence does not corroborate this. The failure of the

second production item parcel to provide an uncontaminated

measure of the production latent variable, however, suggests that

it might be prudent to be a little cautious before abandoning this

hypothesis.  In the modified/expanded model the influence of

production on market standing is significant.

This study, however, does provide support for the hypotheses

that directional linkages exist bet ween market

standing/scope/share (market) and future growth (growth),

between capacity (capacit) and future growth (growth), and

between adaptability (adapt) and market standing/scope/share

(market). Market standing/scope/share is thus shown to mediate

the effect of adaptability on future growth perceptions. The

results moreover fail to show a positive directional linkage

between adaptability (adapt) and production and efficiency

(product). Adaptabilit y is thus shown to have only an

unmediated effect on market standing/scope/share. If an

organisational unit thus has a high market standing, and the

organisational unit has the ability to adapt to internal and/or

external environmental changes, should they occur, the unit will

currently be characterised by high future growth prospects. 

The results of the study confirm a direct positive linkage

between core people processes (core) and production and

efficiency (product) thus supporting the indispensable

requirement for a smooth running, quick response, low

friction, high-energy human system in order to pursue the

production objectives.

The results furthermore support the notion that there is a

positive directional linkage between core people processes

(core) and employee satisfaction (satisf). Core people processes

(core) influences work unit climate (climate) directly and

indirectly via employee satisfaction (satisf). The findings of the

study thus provides support for the positions held by Beckhard

(1969) and Beckard and Harris (1987) that vibrant, purposeful,

orderly interaction between unit members, characterised by

open communication, respect for the individual and his

contributions and a productive interchange of ideas focused on

the goals and work plans of the unit, constitute an important

prerequisite for a healthy (in terms of climate and satisfaction as

defined in Table 1) organisational work unit.

The study supports the notion of a positive linkage between core

people processes (core) and adaptability (adapt) but not between

core people processes (core) and future growth (growth).

Continuous creative productive clashing of ideas and a

willingness to experiment with and learn from novel ideas and

practices thus seem to be important prerequisites for the unit to

respond timeously and expeditiously to change in the

environment. A positive causal linkage is also supported

between adaptability (adapt) and employee satisfaction (satisf).

The study, moreover, does not confirm the hypothesis that

proposes a directional linkage between capacity (capacit) and

production and efficiency (product). This rather unexpected

finding could most likely be explained in terms of the failure of

the second production item parcel to comprehensively reflect

variance in the production and efficiency latent variable.

The results support the postulated linkage between work unit

climate (climate) and production and efficiency (product) thus

emphasizing the indispensable requirement of a favourable

global attitudinal work unit climate that constitutes an

expression of a set of shared core values and a commitment to a

shared unit vision and mission in order to achieve high

productivity efficiency.

The study somewhat tentatively suggests that as an

organizational work unit develops a strong market standing, a

satisfied client base and an enhanced overall reputation in which

the organisational unit becomes well-known for the product or

service they deliver, the unit tends to increase it wealth of

resources. Both in terms of financial investments and in terms of

the desirability of securing a position in a high flying unit, the

proposed modification to the model seems reasonable.

A complex, intricate interplay between the various facets of unit

performance is revealed. To fully capture this rich interplay in

words in such a way that it conveys the full flavour of the

complexity is, however, rather difficult to achieve. 

Suggestion for future research

Given the perceived pivotal role of leadership in organisational

unit performance, the nature of the presumed relationship should

be captured in a comprehensive leadership-unit performance

structural model that would explain the manner in which the

various latent leadership dimensions affect the endogenous unit

performance latent variables. The evidence on the validity of the

measurement and structural model underlying the PI reported in

this study, in conjunction with the results on the LBI reported in

Spangenberg and Theron (2002a), now paves the way for

proceeding with the extremely challenging task of explicating and

evaluating such a comprehensive leadership-unit performance

structural model. Core people processes, adaptability and capacity

seem to be possible vital portals through which unit leadership

could affect organisational work unit performance. The

explication of the second-order factor structure of the LBI,

however, seems to be a unavoidable hurdle that first would have

to be cleared before attempting to unfold an integrated

leadership-unit performance structural model.

REFERENCES

Alexander. E.R.III, Penley, L.E. & Jernigan, I.E. (1992). The

relationship of basic decoding skills to managerial

effectiveness. Management Communication Quarterly, 6, 

58-73.

Barrett, F. D. (1987). Teamwork: How to expand its power and

punch. Business Quarterly, 52 (3), 24-31.

Bartel, A. P. (1994). Productivity gains from the implementation of

employee training programs. Industrial relations, 33, 41-425.

Bettenhausen, K. L. (1991). Five years of groups research: What

we have learned and what needs to be addressed. Journal of

Management, 17, 345-381.

Beckhard, R. (1969). Organisation development: strategies and

models. Reading, Mass: Addison Wesley.

Beckhard, R. & Harris, R.T. (1987). Organisational transitions:

Managing complex change. Reading, Mass: Addison Wesley.

THE UNIT PERFORMANCE CONSTRUCT 35



Bentler, P.M. (1990). Comparative fit indexes in structural

models. Psychological Bulletin, 107 (2), 238-246.

Bentler, P.M. & Bonett, D.G. (1980). Significance tests and

goodness of fit in the analysis of covariance structures.

Psychological Bulletin, 88 (3), 588- 606.

Bollen, K.A. & Long, J.S. (1993). Testing Structural Equation

Models. Newbury Park: Sage Publications.

Boss, R.W. (1978). The effects of leader absence on a

confrontation team-building design. Journal of Applied

Behavioral Science, 14, 469-478.

Browne, M.W. & Cudeck, R. (1993). Alternative ways of 

assessing model fit. In K.A. Bollen & J.S. Long (Eds.): Testing

Structural Equation Models. Newbury Park: Sage Publications.

Cockerill, A.P., Schroder, H. M. & Hunt, J. W. (1993). 

Validation study into the High Performance Managerial

Competencies. Unpublished report, London Business School,

London.

Conger, J. A. & Kanungo, R. N. (1998). Charismatic leadership in

organisations. London: Sage Publications.

Cutcher-Gershenfeld, J. (1991). The impact on economic

performance of a transformation in industrial relations.

Industrial and Labor Relations Review, 44, 241-260.

Denison, D.R. (1990). Corporate culture and organisational

effectiveness. New York: Wiley.

Diamantopoulos, A. & Siguaw, J.A. (2000). Introducing LISREL.

London, New Delhi: Sage Publications.

Etzioni, A. (1960). Two approaches to organisational analysis: A

critique and a suggestion. Administrative Science Quarterly, 5,

257-258.

Etzioni, A. (1964). Modern Organisations. Englewood Cliffs. NJ:

Prentice-Hall.

Galagan, P. (1988). Donald E. Petersen: Chairman of Ford and

champion of its people. Training and Development Journal,

42, 20-24. 

Gerhart, B. & Milkovich, G.T. (1992). Employee compensation:

Research and practice. In M.D. Dunnette & L.M. Hough

(Eds.). Handbook of industrial and organisational psychology,

3, 481-569. Palo Alto. CA: Consulting Psychologists Press.

Gibson, J.L., Ivancevich, J.M. & Donnelly, J.H. (1991).

Organisations. Irwin: Boston.

Guzzo, R.A., Jette, R.D. & Katzell, R.A. (1985). The effect of

psychologically based intervention programs in worker

productivity: A meta analysis. Personnel Psychology, 38, 275-291.

Hair, J.F., Anderson, R.E., Tatham, R.L. & Black, W.C. (1995).

Multivariate data analysis with readings. New Jersey: Prentice-

Hall.

Hirokawa, R.Y. & Keyton, J. (1995). Perceived facilitators and

inhibitors of effectiveness in organizational work teams.

Management Communication Quarterly, 8, 424-446.

Hoerr, J. (1989). The payoff from teamwork. Business Week, 10

July, 56-62. 

Holzer, H.J. (1987). Hiring procedures in the firm: Their 

economic determinants and outcomes. In M.M. Kleiner, R.N.

Block, M. Roomkin & S.W. Salsburg (Eds.). Human resources

and the performance of the firm. Washington, DC: BNA Press.

House, R.J. (1988). Leadership research: Some forgotten, ignored, or

overlooked findings. In J.G Hunt, B.R. Boliga, H.P. Dachler &

C.A. Schriesheim (Eds.). Emerging Leadership vistas, 245 –

260. Lexington, MA: Lexington Books.

Hulin, Drasgrow and Parsons (1983). Item response theory,

application to psychological measurement. Homewood,

Illinois: Dow Jones-Irwin.

Jöreskog, K.G. & Sörbom, D. (1993). LISREL 8: Structural equation

modeling with SIMPLIS command language. Chicago:

Scientific Software International.

Jöreskog, K.G. & Sörbom, D. (1996a). LISREL 8: User’s reference

guide. Chicago: Scientific Software International.

Jöreskog, K.G. & Sörbom, D. (1996b). PRELIS 2: User’s reference

guide. Chicago: Scientific Software International.

Jöreskog, K.G. & Sörbom, D. (1998). Structural equation

modelling with the SIMPLIS command language. Chicago:

Scientific Software International.

Jöreskog, K.G., Sörbom, D., du Toit, S. & du Toit, M. (2000).

LISREL 8: New statistical features. Chicago: Scientific

Software International.

Kaplan, R.S. & Norton, D.P. (2001). Transforming the Balanced

Scorecard from performance measurement to strategic

management: Part 1. Accounting Horizons, 15 (1), 87-102.

Katz, H.C., Kochan T.A. & Gobeille, K.R. (1983). Industrial

relations performance, economic performance, and QWL

programs: An interplant analysis. Industrial and Labor

relations Review, 37, 3 – 17.

Katz, H.C., Kochan T.A. & Keefe, J.H. (1987). Industrial relations

and productivity in the U.S. automobile industry.

Washington, DC: Brookings Institution.

Katz, H.C., Kochan T.A. & Weber, M.R. (1985). Assessing the

effects of industrial relations systems and efforts to improve

the quality of working life on organisational effectiveness.

Academy of Management Journal, 28, 526.

Kelloway, E.K. (1998). Using LISREL for structural equation modelling;

a researcher’s guide. Thousand Oaks: Sage  Publications.

Kolb, J.A. (1996). A comparison of leadership behaviors and

competencies in high- and average-performance teams.

Communication Reports, 9 (2), 173-185.

Larson, C.E. & LaFasto, F.M. (1989). Teamwork: What must go

right, what can go wrong. Newbury Park, CA: Sage.

Luthans, F. & Lockwood, D.L. (1984). Toward an obser vation

system for measuring leader behavior in natural settings. In J.G.

Hunt, D. Hosking, C.A. Schriesheim & R. Steward (Eds.).

Leaders and managers: International perspectives on

managerial behavior and leadership, 117-141. New York:

Pergamon Press.

Miles, R.H. (1980). Macro-Organisational behaviour. Glenview, IL:

Scott, Foresman. 

Nicholson, N. & Brenner, S.O. (1994). Dimensions of perceived

organisational performance: tests of a model. Applied

Psychology: an International Review, 43 (1), 69-108.

Raykov, T., Tomer, A., & Nesselroade, J.R. (1991). Reporting

structural equation modelling results in psychology and

aging: Some proposed guidelines. Psychology and Aging, 6 (4),

499-503.

Schuster, M. (1983). The impact of union-management

cooperation on productivity and employment. Industrial and

Labor Relations Review, 36, 415-430.

Spangenberg, H.H. & Theron, C.C. (2002a). Development of a

uniquely South African Leadership questionnaire. South

African Journal of Psychology, 32 (2), 9-25.

Spangenberg, H.H. & Theron, C.C. (2002b). Development of a

performance measurement questionnaire for assessing

organisational work unit effectiveness. Manuscript presented

for publication to the Journal of Industrial Psychology.

Tabachnick, B.G. & Fidell, L.S. (1989). Using multivariate statistics

(Second edition). New York: Harper Collins Publishers.

Theron, C.C. & Spangenberg, H.H. (2002). Development of a

performance measurement questionnaire for assessing

organisational work unit effectiveness. Paper presented at the

Global Conference on Business and Economics. Paris.

Trujillo, N. (1985). Organisational communication as cultural

performance: Some managerial considerations. Southern

Speech Communication Journal, 50, 201 – 224.

Weitzman, M.L. & Kruse, D.L. (1990). Profit sharing and

productivity. In A.S. Blinder (Ed.). Paying for productivity, 95-

141. Washington, DC: Brookings Institution.

Wellmon, T.A. (1988). Concept ualizing organizational

communication competence: A rules-based perspective.

Management Communication Quarterly, 1, 515-534.

Yukl, G. (1987). A new taxonomy in integrating diverse perspectives

on managerial behavior. Paper presented at the American

Psychological Association Meeting, New York.

1 The valuable suggestions for improvement to this manuscript made by the

two anonymous referees are gratefully acknowledged.

2 Note. Stem values to the left of the vertical line represent integers. Each

leaf represents the first decimal value of each standardized residual.

HENNING, THERON, SPANGENBERG36