Three studies were conducted with the goal to articulate and test models for integrating the concept of motivation to reduce uncertainty into the axiomatic structure of uncertainty reduction theory. Multiple models were considered, each model defining motivation to reduce uncertainty in a different way. Motivation to reduce uncertainty was dtfined as a scope condition (Model 2), as tolerance fo...

In industries like health care, consumer goods and agriculture, shortages are widely observed and the consequences can be costly. One of the main drivers of such shortages is the uncertain nature of supply and demand. To reduce uncertainties, sufficient information about supply and demand can be obtained by gathering relevant data (e.g., auditing suppliers and conducting market research). In th...

In this paper, a new method for modelling and estimation of reliability parameters of power transformer components in distribution and transmission voltage levels for preventive-corrective maintenance schedule of transformers is proposed. In this method, with optimal estimation of Weibull distribution parameters using least squares method and input data uncertainty reduction, failure rate and p...

Uncertainty reduction has a major role in communication theory of relational development. Two competing theories, Berger and Calabrese’s (1975) uncertainty reduction theory (URT) and Sunnafrank’s (1986) predicted outcome value (POV), provide different perspectives on the uncertainty reduction process during initial interactions. Research suggests that when forming a first impression, negative i...

This paper describes how the fusion of two different prognostic approaches produces a result that is more accurate and has more narrow uncertainty bounds than either approach alone. The fused prognostic estimate can be calculated by using both a physics-based as well as a datadriven approach. The individual approaches can have a plurality of input sources such as component properties (e.g., mat...