No abstract

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 for uncertainty (Model 3), as a weighted function of uncertainty by its importance (Model 4), and as the diflerence between one's uncertainty level and one's tolerance for uncertainty (Models 5a and 5b). Each of these models was compared to the baseline model (Model I ) derived from the original presentation of the theory where leuel of uncertainty, by itself, serues as a determinant of various communication behaviors. Tests of these models in terms of their ability to predict information seeking and attraction reveal that none of the models provides a consistent integration of motivation to reduce uncertainty into uncertainty reduction theory. Rather, folerance for uncertainty (Model 3) isone of threedeterminants of information seeking, while level of uncertainty (Model I ) is one of three determinants of attraction. This inability to integrate motivation to reduce uncertainty into uncertainty reduction theory can be attributed to the consistent failure to find support for deviance and incentive value as determinants of tolerance for uncertainty, the rejection of Axiom 3 in uncertainty reduction theory (which specifies a positive relationship between uncertainty and information seeking), and the rejection of Theorem 17 (which specifies a negative relationship between information seeking and liking). ncertainty reduction theory (Berger & Calabrese, 1975) was put forth over a decade ago as an explanation for certain U interpersonal communication behaviors displayed during initial interactions. The inability to predict and explain others' actions was offered as the central motivating force guiding behavior in first

No abstract

This research examines how problems with classification systems limit knowledge claims that can be made about compliance gaining message behavior. Strategies and their operationalizations from 74 compliance gaining message classifcation systems were previously integrated into a 64-category scheme that lists each strategy along with all definitions and examples located in the literature representing that strategy (Kellermann & Cole, 1991). Difficulties encountered during this cross-taxonomy strategy integration motivated an assessment, reported here, of the nature and structure of these different classification systems. The findings of this assessment of compliance gaining strategy taxonomies are threefold. First, current classification systems for compliance gaining messages include strategies that vary in myriad atheoretical ways, producing lists of techniques that are neither exhaustive nor mutually exclusive. Research findings reflect the structure of chaotic classifcation systems rather than the essence of compliance gaining message behavior. Second, conceptual definitions of the strategies are unclear, nonspecific, nonexclusive, and nondelimiting; these definitions make understanding the nature of the strategies difficult and class$cation unreliable. Third, approximately two-thirds of the operationalizations used in the research literature are not valid representatives of the strategies for which they were initially generated. As a result, many "accepted" knowledge claims about compliance gaining message behavior are suspect, misstated, and/or of unknown validity.

No abstract