Developing a Dynamic View of Broadband Adoption

Developing a Dynamic View of Broadband Adoption

Herbert Daly (Brunel University, UK), Adriana Ortiz (TECNUN University of Navarra, Spain), Yogesh K. Dwivedi (Swansea University, UK), Ray J. Paul (Brunel University, UK), Javier Santos (TECNUN University of Navarra, Spain) and Jose M. Sarriegi (TECNUN University of Navarra, Spain)
DOI: 10.4018/978-1-59904-851-2.ch020
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Abstract

The widespread domestic use of broadband Internet technology has been recognized to have a positive influence on national economies and improve the life of citizens. Despite substantial investment to develop the infrastructure, many countries have experienced slow adoption rates for broadband. This chapter develops a view of UK broadband adoption using dynamic modeling techniques based on an existing statistical study. The contrasting approaches to modeling are compared. Principles of a dynamic modeling system are introduced and an appropriate form for broadband adoption chosen. The process of building a dynamic model based on an existing static model of broadband adoption is presented. Finally, the new perspective of the dynamic model is explored using the causal loop analysis technique.

Key Terms in this Chapter

Causal Loop Analysis: A qualitative method for evaluating influence diagrams, which are also know as causal loop diagrams. Re-enforcing and balancing loops are identified in the diagram, and causal loop analysis is the process of analyzing their interactions and the possible effects of loops and delays on the dynamic behavior of the problem. Roberts (1994) provides extensive coverage on this subject.

System Dynamics: A systems-oriented dynamic modeling approach first proposed in Forrester (1961). Models are based on the causal structure of the problem including the perceptions of the actors. Two levels of modeling are possible: qualitative modeling using influence diagrams, and causal loop analysis or quantitative modeling using Stock-Flow diagrams and computer simulation.

Static Modeling: An approach to modeling of a problem based on the state at a fixed point in time. A variety of mathematical approaches exist with different properties. For example, empirical or statistical models use collected data to create a view of the problem, whereas stochastic models reason about uncertainty in the problem.

Re-Enforcing Loop: A feature of a problem that may be observed using influence diagrams. Loops exist where a chain of cause and effect leads back to the same variable. In a re-enforcing loop, a positive relationship between each of the variables in the chain, or an even number of negative relationships, cause quantities to increase throughout the loop. In a model these relationships may represent potential exponential growth, or possibly decline, in the problems behavior.

Balancing Loop: A feature of a problem that may be observed using influence diagrams. Loops exist where a chain of cause and effect leads back to the same variable. In a balancing loop an even number of negative relationships between each of the variables in the chain causes quantities to decrease throughout the loop. In a model these relationships may represent potential goal-seeking behavior in the problem.

Dynamic Modeling: An approach to modeling of a problem that includes concept of change in the problem over time. There are many different methods available and the principles they based on varies. Their suitability to model a particular problem may be determined by the match between the features they support and the features of the problem.

Influence Diagrams: A method for conceptualizing a problem by representing the main variables that cause, or influence, them to increase or decrease. Relationships between variables are marked with either positive or negative polarity. Delays are also included, showing time lags in the effect of change on other variables. Coyle (1977) provides extensive coverage. Also known as causal loop diagrams.

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