One of the premises of system dynamics is that the modeler would make assumptions about variable relationships with enough precision to make the model useful. A common validation method is to consult with field experts, but with the advent of the internet, and automated data collection methods, knowledge is diluted as companies store abundant information without time to process it. High turnover rates at companies paired with large amounts of data have reduced the number of "experts." Without experts, companies are data rich but not necessarily knowledge rich. Customers’ dislikes, perceptions, intentions, opinions, and service characteristics reside in data warehouses (e.g. survey data is stored as categorical, nominal, ordinal or qualitative without further analysis). We present an application of Classification and Regression Trees (CART), Chi-Square Automatic Interaction Detection (CHAID) which are known nonparametric predictive methodologies to uncover/confirm significant variable relationships and build the equations to feed the model. An illustrative example of CHAID/SEM to explore restructuring decisions in a large service organization will be briefly discussed.

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Date created

• 2007 July 29-2007 August 2

Type

• Document

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• Processing documentation available at: https://wiki.albany.edu/display/SCA/Processing+Ingested+Digital+Files

License

• Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International

  
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ITEM CONTEXT

Part of

298eed5a7fe199ac661be72f3a39f134

Scope and Contents

Part of

0619e689ce89476bfd3b88323c5a9410

Scope and Contents

Part of

23d738ba88f8333bc39725f9cb5bd0b8

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Collection

System Dynamic Society Records

Scope and Contents

Collecting area

• This collection is part of the University Archives