Given that the experience of uncertainty, in some form, is a part of nearly every interaction, it should come as no surprise that new theoretical efforts to understand it are foundational to the study of interpersonal communication and continue unabated today. Uncertainty reduction theory (URT; Berger & Calabrese 1975) served as the field’s initial, and most influential, examination of uncertainty in interpersonal contexts, and the theory of motivated information management (TMIM; Afifi & Weiner 2004) represents the most recent effort. Interestingly, the range of theories also reflects an over-twenty-year struggle over the basic assumptions and perspectives that guide our understanding of people’s experience of, and response to, uncertainty.
A broad approach to uncertainty defines it as a lack of confidence in the predictive utility of information that one has about a topic (Brashers 2001). In other words, high uncertainty translates to a perceived inability to predict or explain a person, interaction outcome, or issue with confidence. The theoretical debates about uncertainty have mostly revolved around two issues. First, how do people experience uncertainty? Second, how do people respond to that experience?
The Experience Of Uncertainty
URT argued that individuals have an innate motivation to predict and explain people and events (at least up to a certain point). As such, uncertainty is almost inevitably experienced as a negative physiological and psychological state. In fact, the theory’s primary author has argued that the motivation to reduce uncertainty can be traced back to evolutionary needs (Berger 1987). Specifically, those who do not make efforts to reduce uncertainty about others are unprepared to defend themselves from the threats they may pose. Others have disagreed. Two communication scholars, in particular, have offered alternative viewpoints. Babrow (1992), in articulating problematic integration theory, and Brashers (2001), in advancing uncertainty management theory, have argued that people’s experience of uncertainty differs dramatically according to the context and culture in question. In some cases, uncertainty is experienced as a positive state and, in others, as a negative one.
In support of this position, they have focused on people’s experience of illness. For example, Brashers et al. (2000) have shown that people living with HIV often see uncertainty as hope – the otherwise certain outcome of death is less assured. Babrow has found similar positive experiences of uncertainty in patients with cancer, among others (Babrow and Kline 2000). Finally, some studies have suggested that uncertainty is more commonplace in certain cultures than in others, making inhabitants of those cultures less susceptible to aversive reactions to uncertainty (Goldsmith 2001). In an effort to account for this controversy, Afifi and Weiner (2004) recently advanced the notion of uncertainty discrepancy. They claimed that uncertainty is an anxiety-producing state only when the actual and desired levels of uncertainty in a specific circumstance do not match. Such a discrepancy could reflect cases in which individuals want less uncertainty than they have, or those in which they want more. While so doing, they also acknowledged that the more common situation is the former. Indeed, their studies have shown just that – most people want less uncertainty, but there is a subset of individuals who report wanting more uncertainty than they have on an issue (Afifi & Weiner 2006).
Unfortunately, this uncertainty paradigm has offered very little in the way of predictive specificity about individuals’ experience of uncertainty – only that uncertainty will be psychologically distressing for some, but not for others. One as yet untested possibility that marries the two theoretical camps is that uncertainty is experienced as positive only when individuals do not believe that its reduction would perform the preparedness/ protective function that the evolutionary explanation otherwise assumes it does. In other words, if people believe that the HIV diagnosis equals inevitable death (something that they cannot prevent or protect against), then the evolutionary utility of uncertainty reduction disappears and another evolutionary tool – maintaining emotional (vs physical) well-being – takes over. Such an explanation would account for most of the empirical discrepancies in this research area and deserves to be tested.
Responses To Uncertainty
Closely tied to the debate about the experience of uncertainty is the one that challenges the way people respond to uncertainty. Not surprisingly, given the supposed evolutionary disadvantage of uncertainty, Berger and Kellermann (1983) argue that individuals respond to uncertainty with immediate efforts at uncertainty reduction – attempts that wane once uncertainty is adequately reduced. They also offered three general ways in which people seek information to reduce uncertainty: (1) passive (i.e., through observation), (2) active (i.e., through third parties or by altering the environment and watching the target’s reaction), or (3) interactive (i.e., direct communication with the target) (for review, see Berger & Kellermann 1994). Several studies have confirmed that people prefer to start their uncertainty reduction efforts with passive strategies (a less efficient but more socially appropriate tactic), but inundate their targets with questions once in interactions. Investigations into uncertainty reduction in close relationships also show a preference for more indirect, socially appropriate strategies in these contexts.
Those scholars who see uncertainty as sometimes beneficial do not predict consistent efforts at its reduction. Individuals may make efforts to reduce it (when uncertainty is experienced as negative), but might instead choose to avoid information altogether (to prevent the reduction of uncertainty), cognitively re-assess the state of uncertainty, or even bask in its presence. Moreover, low levels of uncertainty do not always translate to a reduction in information-seeking efforts. In some cases they may lead to more vigorous efforts at information seeking – when uncertainty reduction means a loss of hope. Proponents of this model have produced considerable evidence supporting these possibilities. Examples include the avoidance of information when the expected outcome is negative or the search for third or fourth opinions in response to undesirable medical diagnoses.
Personality theorists have also argued that individuals differ systematically in their response to uncertainty. Some (e.g., blunters) avoid potentially threatening information at all costs while others (e.g., monitors) are voracious information seekers (Miller 1987).
Theory Of Motivated Information Management
Afifi and Weiner (2004) recently advanced TMIM as a way to address some of the inconsistencies in the literature and more fully capture the complexity of uncertainty management decisions within interpersonal encounters. The process starts with awareness of an uncertainty discrepancy about an important issue. That discrepancy then produces anxiety. In response, people ask themselves two general questions: “What are the costs and benefits of information seeking?” (labeled outcome expectancy), and “Am I able to seek and cope with the information?” (labeled efficacy). Three specific efficacy assessments are made: communication efficacy (i.e., can they skillfully seek the information from this person?), coping efficacy (i.e., can they cope with the outcome they expect?), and target efficacy (i.e., does the target have the information being sought and are they likely to provide it?).
Based on their perception of outcome expectancies and efficacy, individuals take one of three information-management routes: seek information (varying from direct to indirect methods), avoid information (varying from active avoidance to passive avoidance), or cognitively re-assess the level of actual or desired uncertainty. The theory proposes that individuals are increasingly likely to seek information directly to the extent that outcome expectancies are positive and the efficacy assessments all high. Afifi and Weiner (2004) argue that information providers go through similar assessments in determining what information to give and how to do it. They assess the costs and benefits of information provision and whether they are able to provide it. The end result of this process depends on the seeker’s strategy and the provider’s response. Studies of the theory have shown promise, although to date only the information seeker has been examined.
Methodological Problems And Future Directions
The literature on uncertainty in interpersonal encounters has been mostly dominated by quasi-experimental or experimental designs. The typical paradigm in the first few studies was to have two strangers interact in a lab (sometimes with varying instructions, sometimes not), then code the interactions for evidence of information-seeking efforts (e.g., questions asked).
Participants were also typically asked to rate their level of uncertainty about the target other. One of the most commonly used measures of self-reported uncertainty is the short version of Clatterbuck’s (1979) attributional confidence scale (CLUES). The measure is intended to capture general uncertainty about a target person and has consistently shown strong psychometric properties. Other measures of uncertainty have emerged since then, some with alternative foci such as relational uncertainty (Knobloch & Solomon 1999), but the question kernels used in these measures are generally adopted from the original
CLUES scale. Given the individualized, contextualized, and shifting nature of uncertainty’s meaning in Babrow’s and Brashers’ uncertainty management frameworks, it is not surprising that qualitative methods (e.g., interviews) rose to prominence in the mid- to late-1990s as a means of capturing uncertainty and related processes. Still, the dominant methodological paradigm in the area remains quantitative.
Much has changed in the uncertainty landscape since Berger and Calabrese’s uncertainty reduction theory was first introduced. Yet there remain many unanswered questions. Perhaps foremost among these is the explanatory mechanism that might lead to greater predictive precision regarding individuals’ experience of uncertainty. In addition, despite a large corpus of knowledge about behavior during initial interactions, there is still relatively little known about the fluidity of uncertainty states within and across interactions, or interactants’ shifting roles as provider and seeker. These limitations are in part due to an increasing reliance on one-shot survey designs and a noted absence of micro-coding of interactants in conversation and of longitudinal data. Finally, work by Babrow and Brashers has highlighted the need to attend more closely to the influence of emotion on the uncertainty management process, while research on TMIM has revealed the need to study more closely the role played by efficacy perceptions in the management of uncertainty.
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