A Generic Analytical Evaluation Framework

Given the analytical evaluation methods outlined and reviewed in Chapter 2, this section presents a generic high-level analytical evaluation framework for Inclusive Design. The aim is to provide the foundation for a systematic, quantitative and predictive framework that would allow designers and decision makers to ask and answer questions about the inclusivity of their product designs in the design process. Based on an examination of the advantages and limitations of current methods, and studies into the needs of designers, a list of six

requirements with corresponding research activities was generated as shown in Table 4-1.

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Table 4-1 Requirements for an analytical evaluation framework

Requirements Research Activities

1. General application to consumer products including products ranging from household electronic appliances to communication and information devices.

Classification of consumer product interfaces.

2. Comprehensive model of user capabilities encompassing the most common tasks that users perform with products.

Classification of human capabilities required for interaction and models of their interaction.

3. Facilitates valid estimation of exclusion and the numbers of people who are likely to experience various levels of difficulty under explicit and justified assumptions.

Establishment of an integrated user capability database with an accurate model to

calculate/predict task outcomes.

4. Sensitivity to variations in product attributes that can map to changes in excluded population.

Establishment of the required precision for sensitivity analysis.

5. Usable by designers and other

stakeholders, allowing ease of calculation of exclusion, visualisation to support decision making, and flexibility in using the

framework at various points in the design process.

Implementation of tools incorporating calculations and different forms of visual output for different stakeholders.

6. Accommodates revisions, upgrades, extensions and new data based on usage and feedback.

Refinement and expansion based on validation studies and new research.

The requirements deal with setting out the scope, underlying model, predictive ability, sensitivity, usability and adaptability of a generic framework for Inclusive Design evaluation.

These general parameters gave rise to a structured iterative evaluation process with three stages, as shown in Figure 4-9.

Figure 4-9 A Generic Analytical Evaluation Framework

The framework shows three stages of (1) establishing product demands on user capabilities, (2) comparing these demands to the distribution of capabilities in a population of interest and

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(3) using decision making techniques to improve the design of the product. Once changes are made to the design of the product, the profile of product demands changes, and thus the process is repeated. The first stage would involve the description and representation of three components: (1) the product interface features and attributes, (2) user goals, tasks, and sequences of actions and (3) a representation of the mental models required for using the product. These three components comprise the functional demands that the product places on the user’s sensory, cognitive and motor capabilities in a given use environment.

The second stage would involve the estimation of the proportions of people in a target population that may be excluded or have difficulty with the product design. This is achieved by comparing the demands to capability measures stored in a comprehensive capability database. By comparing these demands to distributions of capability levels in the wider population, an estimate of design exclusion can be obtained. Importantly, these comparisons should be multivariate and simultaneous across user capability domains. Considering the previous discussion on models of capability combination, the important factor in generating valid predictions would be the selection of a suitable model (linear, resource economic etc.) upon which to base these calculations of exclusion.

The third stage involves decision making and analysing user exclusion estimates via

sensitivity and trade-off analysis. Sensitivity analysis in this case will comprise asking ‘what- if?’ questions about design attributes and looking at the effects of making changes to these attributes on excluded population estimates. For example, a designer might find that a significant proportion of a population of interest is being excluded by the 8 point text size on a product interface (at a given viewing distance). By increasing the text size to 10 point, the change in excluded population could be recalculated given adequate data. Thus the designer can see not only what must be done to include more people in terms of increasing text size, but also by how much it should be increased to achieve a given reduction in exclusion.

Trade-off considerations are also important, such as the impact of increasing text size on the aesthetics of the product or other constraints such as button size that limit the size of the text.

In essence, the framework emphasises the making of informed decisions about design features and prioritises design problems based on objective user capability data. Due to the reliance on a capability database and the quantitative nature of the method, computational support will be required for a full implementation of such a framework. In addition, methods of visual decision making could be incorporated to make the framework usable. As Meister points out, the value of analytical methods could be found in the process itself, despite the final results (Meister, 1995). Re-envisioning an analytical evaluation framework for Inclusive Design as a

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non-prescriptive decision making tool may have advantages in terms of its acceptance and employment in industrial practice.

The critical precursor to the implementation and testing of such an evaluation framework, however, is the exploration and selection of an accurate model of capability combination which can predict task outcomes to an acceptable degree of accuracy. The studies presented in the following chapters of this dissertation aim to explore the relationships between capability measures and task outcomes in the context of requirements 1 and 2 in Table 4-1.