A recent trend in technological innovation is towards the development of increasingly multifunctional and complex products to be used within rich socio-cultural contexts such as the high-end office, the digital home, and professional or personal healthcare. One important consequence of the development of strongly innovative products is a growing market uncertainty regarding 'if', 'how', and 'when' users can and will adopt such products. Often, it is not even clear to what extent these products are understood and interacted with in the intended manner. The mentioned problems have already become an evident concern in the field, where there is a significant rise in the numbers of seemingly sound products being complained about, signaling a lack of soft reliability. In this paper, we position soft reliability as a growing and critical industrial problem, whose solution requires new academic expertise from various disciplines. We illustrate potential root causes for soft reliability problems, such as discrepancy between the perceptions of users and designers. We discuss the necessary approach to effectively capture subjective feedback data from actual users, e.g. when they contact call centers. Furthermore, we present a novel observation and analysis approach that enables insight into actual product usage, and outline opportunities for combining such objective data with the subjective feedback provided by users.

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Currently, despite the explicit industrial consideration to improve the appeal and usability of technically sound electronics products, users increasingly seem to have dissatisfactory experiences in interacting with them. These unforeseen experiences (attributable to specifications omissions, usability/learnability problems, or specific usage context) lead to a large and increasing share of unknown field complaints. To correct and prevent such complaints or user reports, we promote effective exploitation of call centers: Valuable usage data is retrievable from the field by adopting a usercentered failure classification model, which we developed. We also report on the supporting results of a test from applying our model to a set of call center data.

This paper proposes a conceptual framework to distinguish between different classes of reliability problems encountered in strongly innovative products. Next to the conventional (hardware and software) problems, new classes of failures have emerged with a wide range of often strongly related definitions, such as: soft failures, "No Fault Found" failures, "Fault Not Found" failures, "Cause Not Found" failures, nuisance failures. The fact that these new classes of failures do not have precise and orthogonal definitions, leads to difficulties in failure identification and classification. A list of dimensions is proposed to identify and classify failures in an unambiguous manner. Contribution of this research is two-fold: From the academic point of view, it encourages precise reasoning about the emerging failure classes in the general context of reliability problems, as well as forming grounds for consistent use of terminology within the community. From the industrial point of view, it potentially provides more accurate and easier detection of failures, hence facilitating more effective and efficient ways to handle them.

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