Operationalizing the Theory

Operationalizing the Theory

Lars Taxén (Linköping University, Sweden)
Copyright: © 2010 |Pages: 10
DOI: 10.4018/978-1-60566-192-6.ch007
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By operationalization, I refer to the transformation of the theoretical elements in ADT into mediational means that can be manipulated, measured, or observed in order to construct an activity domain. In line with the correspondence between mind and the socio-cultural environment, such means should be aligned with the activity modalities as much as possible to alleviate the construction of communal meaning. Moreover, the operationalized elements must be treated as dialectically related to each other; a change in one will impact all the others.
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The Information Model

Spatialization is operationalized by an information model, which signifies what information entities are subject to coordination and how these entities are related to each other1. The information model frames the spatial context of the domain; thus, it is also manifests contextualization. Moreover, since the information model captures the structure of informative things, the model expresses the information architecture of the domain.

Information models are usually conceptualized as some variant of entity-relationship models (Chen, 1976), such as the Object Modeling Technique (OMT: Rumbaugh, Blaha, Premerlani, Eddy, & Lorensen, 1991) and the Universal Modeling Language (UML: Jacobson, Christerson, Jonsson, & Övergaard, 1992). In order to alleviate the construction of communal meaning, the nomenclature should be easy to comprehend. UML might be advocated since it is a de facto standard for modeling software systems. However, the formalism of UML is hard to grasp for non-experts, which is a strong argument for using a simpler nomenclature such as OMT.

The information model needs to be defined at various levels of granularity depending on the purpose of the model. In Table 1, an overview of these levels is shown, derived from practical experience at Ericsson. The term “items” refers to anything needed to define the model, such as type definitions, relations, attributes, state sets, cardinalities, rule for revising elements, etc.

Table 1.
Various levels of granularity in an information model
LevelPurposeCharacteristicsNumber of items
I. Cross-activity domainCommon understandingMandatory items~20
II. Activity domainCommon understandingDomain specific~50 / activity domain
III. Activity domainIS specificationDetailed rules~100 / activity domain
IV. Activity domainIS implementationIS specific items> 1000 / activity domain

The first level focuses on achieving common understanding about the model across cooperating activity domains. A fairly small set of items is used, around 20. The next level, which comprises about 50 items, concerns common understanding about what should be subject to coordination within a specific domain. In Figure 1 an example of a level II information model is given.

Figure 1.

A level II information model for the A-domain at Ericsson (2001) (Taxén, 2005. ©Wiley Inter Science. Used with permission)

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