What-If Analysis on the Evaluation of User Interface Usability

What-If Analysis on the Evaluation of User Interface Usability

Saulo Silva, Mariana Carvalho, Orlando Belo
Copyright: © 2020 |Pages: 22
DOI: 10.4018/978-1-7998-2637-8.ch003
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Abstract

While interactive systems have the potential to increase human work performance, those systems are predisposed to usability problems. Different factors might contribute to these problems during the interaction process and as result, the decision-making process might be compromised. This work uses decision support system methods and tools to assist in the analysis of the usability of a university library website, measuring the constructs of effectiveness, efficiency, and learnability. The pilot study involved thirty-five subjects, and after collecting data, a multidimensional view of the data is created and discussed. Later, a What-if analysis is used to investigate the impact of different scenarios on system-use. The work has the potential to assist designers and system administrators at improving their systems.
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Introduction

Currently, industrial society is experiencing transformations without precedents. Technological improvements shape how people work and live. For the working sector, transformations that might improve work processes are always on demand. As work is an inseparable part of the human life, transformations in work processes have the potential to impact how humans live. While part of this impact is positive, challenges always exist and must be addressed. For instance, the use of steam power to mechanise production was introduced in First Industrial Revolution, dated around 1760s, creating plenty of opportunities, such as improving production processes and creating new factories and cities (Xu et al., 2018). General challenges to be addressed were to create good work and living conditions in the newly created factories and cities, respectively. Second Industrial Revolution (dated around 1870s) intended to improve production by employing electric power generated from combustion engines, inaugurating the mass production era (Xu et al., 2018). General challenges during that event were related to continue improving professional and social life in dimensions ranging from economy and politics, to urbanisation and transportation. Automated production era is introduced during the Third Industrial Revolution (dated around 1960s), based on electronics and information technology (Xu et al., 2018). Examples of general challenges were related to improvement of several dimensions, such as social aspects of work, diversification in energy sources used in production, development of production, management and governance systems, amid others.

Fourth Industrial Revolution programmes, represented by initiatives such as Industrie 4.0 (also referred to as Industry 4.0) in Germany or Smart Factoring in USA, implies the evolution of industrial workforce, i.e., the use of new types of interactions between human operators and machines (Lorenz et al., 2015). Thanks to technologies such as Big Data Analytics (Russom, 2011), Information Systems (Stair & Reynolds, 2013) and Industrial Internet of Things (Rawat et al. 2017), a new set of applications are possible, bringing improvements in industrial areas such as maintenance, coordination among jobs, decision-making, among others. To achieve production growth, industries escalate the use of technology for employees. Even though, this phenomenon is not restricted to industrial sector, this introduction provides a “thermometer” of how technology use is escalating. Therefore, an increasing number of systems are becoming part of the modern person’s routine, regardless of work position or salary.

Key Terms in this Chapter

What-If Analysis: Data simulation in which the goal is to explore and analyse the behaviour of a specific complex system, to test some given hypotheses.

Data Warehouse: Data repository that aggregates data from multiple sources.

Learnability: The quality of allowing users to easily become familiar with something.

Effectiveness: The degree to which some action is successful in producing a desired result and achieving success.

Satisfaction: the measurement of discomfort while making use of the product.

Decision Support System: Set of techniques, tools and data to assist with analysis and decision-making.

Efficiency: The state of achieving maximum productivity with minimum wasted effort or wasted expense.

Usability: the degree to which a specific piece of software can be used by specified users to achieve quantified objectives with effectiveness, efficiency and satisfaction in a use context.

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