Issues Facing Soft Systems Modelling: Structural Modelling in
Relation to System Dynamics
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.
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