**Issues Facing Soft Systems Modelling:
Structural Modelling in**

** Relation to System Dynamics **

**Santanu**** Roy**

**P. S. Nagpaul**

**National
Institute of Science, Technology and Development Studies**

**Dr. K. S.
Krishnan Marg, New Delhi - 110 012, India**

**Pratap**** K. J. Mohapatra**

**Indian
Institute of Technology, Kharagpur - 721 302, India**

Interfacing system dynamics with various soft system
methodologies is currently engaging the attention of leading practitioners of
system dynamics. There is a great deal
of concern because of the isolation of system dynamics from other techniques
and because of methodological issues in system dynamics that the field of soft
OR has already begun to address. There is much benefit to be derived from a
dialogue between the practitioners of system dynamics and those of soft OR
(Lane, 1994). It is important to note that soft system management emerged from the failure of system
engineered concepts to be applied to the resolution of messy people based organisational problems (Bolton and Gold, 1994). Soft OR
involves an array of tools for coping with complexity, uncertainty, and
conflict. Checkland’s Soft Systems Methodology was
developed in response to the failure of hard systems methods (i.e. those based
on a means-end, functionalist approach) in analysing
complex organisational problem situations. A set of
activities, linked together so that the set was purposeful, was treated as a
new kind of system concept (a ‘human activity system’) (Tsouvalis
and Checkland 1996). A system is first of all a way of
looking at the world. In this sence, defining a system is viewpoint dependent (Espago, 1994). There is, therefore, a need for establishing a dialogue or
an interface between soft OR and related methodologies and the methodology and
paradigm of system dynamics as both are being used to try to implement the idea
of learning processes. Not all problems
can be addressed using system dynamics, and soft OR lacks a tool for examining
the time-evolutionary behaviour of systems. Knowledge
of soft OR would render more vigorous the methodological frame work of system
dynamics. Awareness of the strengths and weaknesses of the different systems
methodologies, and of the social consequences of using each type, leads to the
possibility of employing them in a pluralist or complementarist
manner - each used when and where it is most appropriate. Complementarism
at the level of methodology requires a meta-methodology that respects all the
other features of critical systems thinking and employs these, together with a full
understanding of each individual systems approach, to describe procedures for
operationalizing a pluralistic employment of methodologies in practice
(Jackson, 1995). Richardson (1996) while
commenting upon the problems for the future of system dynamics, states that the
field is experiencing the increasing use of qualitative tools - systems
archetypes, word-and-arrow diagrams under various labels (casual-loop diagrams,
influence diagrams, cognitive maps), and other approaches and techniques that
fall under the general rubric of qualitative systems thinking. The question of such an interface, therefore,
assumes criticality while modelling soft systems
using system dynamics.

There are inherent problems in modelling
soft systems. Conventional methods and models are based on hard (quantitative,
cardinally-measured)
information. The problems
are different in the analysis of soft, qualitative or categorically measured
data. Soft modelling
methodologies aim at taking into account the limitations caused by measuring
variables on a non-metric scale, and try to avoid the use of non-permissible
numerical operations on qualitative variables.
There is an increasing recognition that a qualitative approach need not
eschew measurement. Social scientists
have been more and more concerned with measuring qualities in order to grapple
with complex configurations and the ambiguities inherent in human perceptions
and behaviour. The problems occur at two stages in
such a modelling approach. Roy and Mohapatra
(1994) have earlier attempted to model the work climate of a research and
development (R&D) laboratory using the system dynamics framework. First, most of the variables encountered in
soft systems are measured using a quasi-quantitative framework. The problems of reliability and validity of
such measurement have to be addressed. Second, the relationships among the
indices have to be ascertained in a way that takes into account this
quasi-quantitative measurement approach.
Only thereafter could a system dynamics model of such a soft system be
developed. This would also help minimize
judgmental scaling error often encountered in such modelling
endeavours.
Structural equation modelling using LISREL
7.16 program is an approach to tackle these issues (Joreskog
and Sorbom, 1989).
Structural modelling can be viewed as a wholistic process in that the user aspires to gain an
overall appreciation of the system as a whole by studying a structural model of
the elements which comprise the system. The model incorporates unobserved
(latent variables), the relation between these and observed variables and an
allowance for errors of measurement in the independent and dependent latent varibles, and a causal model linking the latent variables
together. Such an exercise in modelling organizational climate in relation to the
performance or effectiveness of Research Units (RUs) were carried out .

The data was collected from a stratified random
sample of 236 RUs out
of a total population of 602 RUs from different laboratories of the Council of
Scientific and Industrial Research (CSIR), India. The effectiveness of RUs and other latent
variables conceptualizing various dimensions of organizational climate were
operationalized by observed variables or indicators measured on 5-point semantic
differential scales. This data was used
to develop two structural models involving these latent variables. The
respondent strata
consisted of RU head and the core scientists of the units (the
external evaluators - both scientific as well as administrative were there only
for the RU effectiveness measures) . The latent variables for RU effectiveness are R&D
effectiveness (REF), user-oriented effectiveness (USE), administrative
effectiveness(AEF) and recognition (REC). The rest of the ten latent variables
are applied research thrust (ART), technical services (TEC), leadership quality
(LSQ), supervisor contact effectiveness (SCE), innovative ethos (ETH),
administrative constraints (ADC), communication (COM), research orientation
(RES), conflict (CON) and research planning quality (RPQ). Figures 1 and 2 show
the two structural models developed after the hypothesized models among the
latent variables were run on LISREL 7.16 program. The exogenous concepts are indicated by xi ( x ) and the endogenous concepts are indicated
by eta ( h ). .Figure 4 shows the first structural model
involving the following exogenous variables - ART and TEC and the following
endogenous variables - ETH, ADC, COM, RES, USE, and AEF. Figure 5 shows the second structural model
involving the following exogenous variables - LSQ and SCE and the following
endogenous variables - ETH, COM, CON, RPQ, REF and REC. Only the significant causal linkages are
shown in the models (the t-values are ct 5 per cent
significant level). The values of the
structural coefficients gamma ( g ) between the exogenous and
the endogenous concepts and those of beta ( b ) among the endogenous
concepts are shown with each causal link along with their respective t-values
shown within brackets. An analysis of
the results indicate that in the first model (figure 1), the total Coefficient
of Determination for Structural Equations was found to be 0.597 indicating that
about 60 per cent of variance is captured by the model. Both the Root Mean Square Residual (RMSR) of
0.059 as compared to the average size of the S matrix (the actual observed covariances among the indicators) and the Goodness-of-Fit
index (GFI) of 0.961 are within acceptable limits. The t-values of the measurement errors of all
the endogenous concepts are found to be significant. In the second model (figure 2), the total
Coefficient of Determination for structural equations was found to be 0.431
indicating that about 43 per cent of variance is captured by the model. Both the RMSR of 0.069 as compared to the
average size of the S matrix and the GFI of 0.965 are within acceptable
limits. The t-values of the measurement
errors of all the endogenous concepts are found to be significant.

In conclusion, it is emphasized that the subjective
measures of soft variables are influenced by systematic and random measurement
errors. Hence it is essential that their
construct validity and reliability are assessed before these are used in
empirical studies. Further, the relationships
among the latent variables developed from the observed variables have to be
ascertained in a way that takes into account this quasi-quantitative
measurement approach. Structural Modelling using LISREL 7.16 programme
is an approach to tackle these issues and problems, and it also serves as a
pre-validation exercise for the System Dynamics model.

**References**

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Espago, Raul. 1994. What is Systemic
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Jackson, M.C. 1995. Beyond the Fads: Systems Thinking for Managers. *Systems Research.12(*1): 25-42.

Joreskog, K. and D.Sorbom. 1989. *LISREL
7.16: Analysis of Linear Structured Relationship by Maximum Likelihood and
Least Squares Method.* International Educational Services.
Chicago.

Lane, David, C. 1994. With a Little Help from our Friends:
How System Dynamics and Soft OR can Learn from Each Other. *System Dynamics Review.*
10(2-3): 101-134.

Richardson, George P. 1996. Problems
for the Future of System Dynamics. *System Dynamics Review.* 12(2):
141-157.

Roy, Santanu and Mohapatra, P.K.J. 1994. Study of Work Climate in R&D
Organizations: A System Dynamics Approach. *Proceedings
of the 1994 International System Dynamics Conference: Production and Operations
Management.* University of Stirling.
Scotland. 61-70.

Tsouvalis, Costas
and P.Checkland. 1996.
Reflecting on SSM: The Dividing
Line Between Real World and Systems Thinking World. *Systems Research.** *13(1): 35-45.

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