| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 461433 | Journal of Systems and Software | 2014 | 18 Pages |
•Concept analysis of performance measures beyond ISO norms.•Full-fledged models of effectiveness, efficiency, effort, usability, satisfaction.•Empirical testing with structured questionnaires and mediation analysis.•Reduction of error is most important to explain stakeholder satisfaction.•Requirements and goals of opposite polarity prompt change requests.
Although it seems that software metrics have moved beyond mere performance measurement, it is not too clear how machine effectiveness, efficiency, and effort pertain to human requirements on such matters. In industry as well as academia, the ISO 9241-11 norm provides the dominant view on usability, stating that usability is a function of effectiveness, efficiency, and satisfaction. Although intuitively, usability requirements should be part of a software's design in an early stage, conceptually and empirically, it seems more likely that performance requirements (i.e., the absence of errors) should be the center of concern. This paper offers an elaborated view on usability, satisfaction, and performance. Certain theoretical conceptions are tested with data gathered from professional users of banking and hospital systems by means of a 4-year single-item survey and a structured questionnaire, respectively. Results suggested that performance factors (i.e., efficiency) are more important than usability in understanding why stakeholders are satisfied with a system or not. Moreover, it neither is dissatisfaction with a system nor that a system is less usable that predicate requirements change. Instead, avoiding machine inaccuracy best predicted the variability in agreement to “must have” requirements, while achieving human accuracy predicted the variability in agreement to the “won’t have” requirements. The present contribution provides a consistent research framework that can bring more focus to design (i.e., prioritization), clarify discussions about design trade-offs, makes concepts measurable, and eventually may lead to better-informed designs.
