2.1 Uncertainty reduction theory

Uncertainty reduction theory is the pioneering conceptual framework on uncertainty within interpersonal communication (Berger and Bradac 1982; Berger and Calabrese 1975; Berger and Gudykunst 1991). The theory tackles the issue of how individuals communicate when meeting for the first time. Uncertainty reduction theory proposes that initial interactions are structured toward reducing uncertainty about people’s own behavior and their partner’s behavior (Berger and Calabrese 1975). Following Shannon and Weaver’s (1949) information theory, it conceptualizes uncertainty in terms of the number of possible outcomes and the probability that each outcome may occur (Berger and Bradac 1982). Following Heider’s (1958) attribution theory, it assumes that individuals are motivated both to predict behavior proactively and to explain behavior retroactively (Berger and Calabrese 1975). Uncertainty reduction theory casts communication in dual roles: Communication is a vehicle for uncertainty reduction and is affected by uncertainty reduction (Berger and Calabrese 1975; Berger and Gudykunst 1991).

Uncertainty reduction theory defines two types of uncertainty within interpersonal episodes (Berger and Bradac 1982). Cognitive uncertainty denotes the questions people encounter about their own beliefs and their partner’s beliefs. Behavioral uncertainty encompasses the questions individuals face about their own actions and their partner’s actions. The theory identifies three contextual factors that especially motivate people to engage in uncertainty reduction (Berger 1979). Individuals are particularly driven to seek information when (a) a partner deviates from expectations, (b) they expect to communicate with the partner again in the future, and/or (c) their rewards and costs are controlled by the partner.

The theory proposes seven axioms, or self-evident premises about the connections among uncertainty, social cognition, and communication (Berger and Calabrese 1975):

Uncertainty reduction theory couples each of the seven axioms with every other axiom to deduce 21 theorems, or hypotheses about covariation between variables (Berger and Calabrese 1975; Berger and Gudykunst 1991). Subsequent work expanded on the original statement of the theory by adding to these axioms and theorems. For example, Parks and Adelman (1983) moved the theory into the context of established relationships and proposed Axiom 8 (shared social networks between partners lead to decreases in uncertainty). Gudykunst, Yang, and Nishida (1985) also nominated cultural similarity/dissimilarity as an important factor in uncertainty reduction.

The theory delineates three information-seeking strategies that people employ in the service of uncertainty reduction (Berger and Bradac 1982; Berger and Kellermann 1994). Passive information-seeking strategies involve observing the target of one’s uncertainty to obtain insight about him or her. Examples include reactivity searches (watching how the target responds to people in interpersonal interaction) and disinhibition searches (watching how the target behaves in casual situations). Active information-seeking strategies entail taking action without actually interacting with the target. Examples include asking third parties about the target and structuring the environment to see how the target behaves in a contrived situation. Finally, interactive information-seeking strategies involve communicating directly with the target. Examples include asking questions, soliciting reciprocated disclosures, and relaxing the target. All three approaches present risks and rewards: (a) passive strategies require minimal effort and incur minimal face threat but are not very efficient, (b) active strategies necessitate more investment but provide more control over the kinds of information acquired, and (c) interactive strategies endanger people’s face but offer an efficient means of soliciting specific information.

Subsequent theorizing has considered how individuals produce messages when they are grappling with uncertainty. Planning is one way people cope with the uncertainty that is inherent in crafting messages within interpersonal situations (Berger 1997). A plan is a hierarchical knowledge structure that contains goal-directed sequences of action; planning is a process that involves taking stock of the situation, setting goals, and creating plans (Berger 2008). Planning allows individuals to anticipate and manage the contingencies that arise when interacting under conditions of uncertainty, but according to the hierarchy hypothesis, when plans fail, people tend to alter their approach in ways that demand the least cognitive effort (Berger 1997). Hedging is another strategy that individuals use to aid message production when they are experiencing uncertainty (Berger 1997). Individuals may hedge against embarrassment in uncertain situations by framing messages using humor prospectively or backtracking retrospectively, employing ambiguity to hide the central thrust of the message, utilizing disclaimers to circumvent negative reactions, using retroactive discounting to soften a declaration, and controlling the floor by requiring the other person to do the talking. In sum, planning and hedging help people produce messages in the midst of uncertainty.

Uncertainty reduction theory has informed empirical investigations of initial encounters with both able-bodied (Douglas 1994; Kellermann and Reynolds 1990) and physically disabled (Grove and Werkman 1991) strangers. Not all of the evidence is consistent with the theory’s original axioms (e.g., Gudykunst et al. 1985; Kellermann and Reynolds 1990), but the theory has demonstrated substantial heuristic value in guiding research on how people communicate within romantic relationships (Parks and Adelman 1983; Knobloch 2008c), organizational situations (Kramer 2009), health contexts (Albrecht and Adelman 1984), and intercultural episodes (Gudykunst 1995). Recent work also has examined uncertainty reduction in online contexts (Antheunis, Valkenburg, and Peter 2010; May and Tenzek 2011). For example, Tidwell and Walther (2002) observed that individuals interacting with strangers were more likely to use direct uncertainty reduction strategies in computer-mediated conversations than in face-to-face conversations. Gibbs, Ellison, and Lai (2011) found that members of online dating sites used uncertainty reduction strategies to confirm the identity claims of potential partners before feeling confident to proceed with relationship development. Work in all of these contexts underscores the applicability of the theory to both initial interactions and established partnerships.

Principles from uncertainty reduction theory have been exported outside the field of communication as well. For example, scholars of marketing have drawn on the theory’s tenets to explain how retailers secure the commitment of powerful suppliers (Jap and Ganesan 2000). Psychologists have harvested ideas from uncertainty reduction theory to illuminate empathy (Ickes and Simpson 1997), the reduction of prejudice (Pettigrew and Tropp 2006), the socialization of newcomers into organizations (Bauer et al. 2007), and best practices for supervising clinicians (Holloway 1995). This scholarship highlights the reach of uncertainty reduction theory to research on human interaction conducted outside the communication discipline.

2.2 Predicted outcome value theory

Whereas uncertainty reduction theory (Berger and Calabrese 1975) follows Festinger’s (1954) social comparison theory in identifying uncertainty as a fundamental motivation driving people’s communication behavior, predicted outcome value theory characterizes reducing uncertainty as subordinate to people’s goal of maximizing their rewards and minimizing their costs (Sunnafrank 1986a, 1986b, 1990). Sunnafrank (1986a) offered predicted outcome value theory as an “expansion and reformulation” (p. 3) of uncertainty reduction theory. The crux of the difference between the two theories lies in the explanatory mechanism for behavior: Predicted outcome value theory relies on social exchange principles (e.g., Altman and Taylor 1973), rather than the attribution principles (e.g., Heider 1958) favored by uncertainty reduction theory, to explicate how people communicate during initial interaction.

Predicted outcome value theory contends that individuals seek information because they are motivated to forecast whether communicating with an acquaintance is likely to result in positive or negative outcomes (Sunnafrank 1986a, 1990). The theory proposes that people’s behavior hinges on the relational projections they make: They are likely to pursue involvement if they anticipate favorable outcomes, but they are likely to restrict contact if they forecast unfavorable outcomes. The theory advances three propositions about relationship initiation. First, individuals should be attracted to a partner when they expect the relationship will produce rewards. Second, as predicted outcome values increase, people should foster relationship development, but as predicted outcome values decrease, they should curb relationship development. Third, individuals should be eager to discuss rewarding topics but disinclined to discuss costly topics. The theory capitalizes on this reasoning to posit that people’s judgments about initial interaction should shape how relationships unfold into the future (Sunnafrank 1986a, 1990; Sunna-frank and Ramirez 2004).

The theory has received support in several empirical tests. For example, Sunnafrank (1990) pitted hypotheses drawn from uncertainty reduction theory against hypotheses drawn from predicted outcome value theory in a study of undergraduate students who interacted with each other during the first day of class. Among individuals who anticipated favorable reward values, uncertainty was negatively associated with ratings of the amount of verbal communication, nonverbal affiliative expressiveness, and liking. Conversely, among people who forecasted negative outcomes, uncertainty was not associated with ratings of the amount of verbal communication, nonverbal affiliative expressiveness, or liking. Sunnafrank (1990) interpreted these findings to suggest that people’s orientation toward acquaintance is motivated by a desire to maximize rewards and minimize costs.

Two more recent investigations have employed longitudinal data to evaluate the theory as relationships develop over time. In a first study, Sunnafrank and Ramirez (2004) asked undergraduate students to (a) have a brief conversation on the first day of class, (b) report their assessment of the conversation, and (c) assess the status of their relationship nine weeks later. When individuals estimated positive predicted outcome values after the initial conversation, they reported more communication with their partner and more attraction to their partner nine weeks later. Uncertainty did not correspond with the amount of communication or attraction after predicted outcome values were controlled. In a second study, Ramirez, Sunnafrank, and Goei (2010) invited undergraduate students to (a) report on a current relationship, and eight weeks later (b) describe a recent unexpected event, and (c) report on characteristics of their relationship since the event occurred. Findings indicated that changes in people’s predicted outcome values before and after the event predicted changes in attraction to a partner, perceived similarity to a partner, and the amount of communication with a partner. Moreover, the judgments individuals made about predicted outcome values before the event continued to predict their reports of attraction to a partner and amount of communication with a partner after their post-event judgments of predicted outcome values were covaried. Both studies imply that people’s appraisals of rewards and costs may play a key role in the subsequent trajectory of relationship development.

Other work has documented evidence in favor of the theory for explaining people’s behavior during conversations with visibly disabled strangers versus able-bodied strangers (Grove and Werkman 1991), the organizational commitment of new employees (Madlock and Horan 2009), the use of email within teacher-student relationships (Young, Kelsey, and Lancaster 2011), and students’ initial impressions of a teacher and their subsequent prosocial behavior in the classroom (Horan and Houser 2012). All of these studies illustrate the theory’s worth for explaining people’s motivations to pursue interpersonal interaction.

2.3 Problematic integration theory

Problematic integration theory takes up questions related to communication, uncertainty, and sense-making (Babrow 1992, 1995, 2001; Babrow and Matthias 2009; Russell and Babrow 2011). The theory distinguishes between two meanings of uncertainty (Babrow 2001). Whereas ontological uncertainty involves indeterminacy about the nature of the world (e.g., the causes and consequences of events), epistemological uncertainty entails questions about the nature of information (e.g., the sufficiency, reliability, credibility, and structure of knowledge). The theory conceptualizes the two meanings to be interrelated and interconnected in people’s experiences.

Problematic integration theory claims that human nature is fundamentally equipped with a desire to understand social circumstances (Babrow and Matthias 2009). It posits that individuals derive meaning from their experiences in terms of both probabilistic and evaluative orientations (Babrow 2001, 2007; Russell and Babrow 2011). Probabilistic orientations include the beliefs and expectations that guide how people understand their environment. Probabilistic orientations represent the knowledge judgments that provide individuals with a systematic understanding of their environment. The theory uses the label probabilistic because people hold these beliefs and expectations with varying degrees of confidence, assurance, and certainty. Evaluative orientations refer to the value-laden assessments individuals make about how their circumstances will affect their well-being. Evaluative orientations are valence judgments that span positive, negative, and neutral appraisals. At its core, problematic integration theory argues that people make sense of their surroundings via both knowledge judgments and value judgments.

A key premise of the theory is that people’s probabilistic and evaluative orientations are integrated in their everyday experiences (Babrow 1992, 2001; Babrow and Matthias 2009). The theory posits that the process of sense-making involves the interplay between knowledge judgments and value judgments; hence, people’s probabilistic and evaluative orientations are not isolated but are strongly intertwined. Although individuals often synthesize their beliefs and values without much conscious attention, problematic integration theory focuses on circumstances where the meshing of probabilistic and evaluative orientations is challenging in some way. Integration between the two orientations can take several forms, including (a) divergence between probabilistic and evaluative orientations (e.g., situations of regret, disappointment, guilt, and/or fear), (b) uncertainty (e.g., situations with an ambiguous outlook), (c) ambivalence (e.g., situations with equally attractive but mutually exclusive outcomes), and (d) impossibility (e.g., situations with unpleasant certainty about unfavorable outcomes).

The theory contends that problematic integration processes can alter meaning in at least two ways (Babrow 2001; Babrow and Matthias 2009). For example, the various forms of problematic integration can mutate as people seek to make sense of their circumstances over time (e.g., transforming impossibility into hope, transforming uncertainty into ambivalence). In addition, problematic integration can reverberate to related topics (e.g., shifting the focus from the original circumstance to closely allied issues). The theory, in sum, characterizes problematic integration as a dynamic process that individuals embark on during their ongoing quest for understanding.

Communication plays a central role in people’s problematic integration experiences (Babrow 1995, 2001). The theory casts communication as a fundamental medium, source, and resource in the sense-making process. Of course, communication can be a foundation of both probabilistic and evaluative orientations, but it also can provide a coping resource whereby individuals reappraise or reframe their circumstances. For these reasons, problematic integration encompasses both psychological and communicative processes.

Problematic integration theory has shown utility for understanding people’s communication behavior in a variety of domains. For example, McPhee and Zaug (2001) explicated the implications of the theory for organizational communication processes. Shi and Babrow (2007) drew on the theory to shed light on the dynamics of how adolescent and young adult Chinese Americans construct multicultural identities. Several projects have applied the theory to the context of cancer: Ford, Babrow, and Stohl (1996) analyzed how breast cancer patients evaluate social support messages, Gill and Babrow (2007) considered discourses surrounding breast cancer in women’s magazines, Dennis et al. (2008) examined coping within a breast cancer support group, and Cohen (2009) investigated how African American women understand cancer risk. Other scholars have relied on problematic integration theory to guide studies of Alzheimer’s disease (Polk 2005), hospice care (Planalp and Trost 2008), pregnancy and childbirth (Matthias 2009), and perceptions of the role of genetics in illness (Parrott et al. 2004). These investigations, taken together, underscore the value of the theory for illuminating how individuals make sense of challenging social circumstances.

2.4 Uncertainty management theory

Uncertainty management theory characterizes communication in the midst of ambiguity to be a complex, multifaceted, and ongoing process (Brashers 2001, 2007). The theory was constructed to illuminate how people negotiate uncertainty about health, wellness, and illness (Hogan and Brashers 2009). Uncertainty management theory draws on Mishel’s (1990) uncertainty in illness theory to argue that uncertainty (a) poses both opportunities and obstacles, (b) ignites both positive and negative emotions, and (c) can motivate people to reduce, maintain, increase, or acclimate to ambiguity depending on their goals (Brashers 2001, 2007).

The theory proposes that uncertainty (a) arises when people perceive they have insufficient knowledge about an issue, (b) contains both cognitive and emotional components, (c) stems from myriad sources and forms, (d) and prompts appraisals of meaning (Brashers 2001; Hogan and Brashers 2009). According to the theory, when individuals appraise uncertainty as detrimental, they will experience negative emotional responses such as anxiety, panic, and fear. Conversely, when people conceptualize uncertainty as beneficial, they will experience positive emotional responses such as hope, excitement, and optimism. When they frame uncertainty as inconsequential, they will experience neutral emotional responses such as indifference, disinterest, and apathy. Finally, when individuals see uncertainty as encompassing aspects of both threat and opportunity, they will experience combined emotional responses such as excitement paired with fear or happiness paired with anxiety.

The theory assigns an essential role to information in the uncertainty management process. It explicates information as “stimuli from a person’s environment that contribute to his or her knowledge or beliefs” (Brashers, Goldsmith, and Hsieh 2002, p. 259). According to the theory, information can function to diminish, preserve, or amplify people’s uncertainty, and acquiring new information can prompt individuals to reappraise their uncertainty. The theory views information management as a communal process that entails coordination and/or conflict among social network members (Brashers 2001; Hogan and Brashers 2009).

The theory conceptualizes uncertainty management as a three-pronged process of information acquisition, information handling, and information use (Hogan and Brashers 2009). To date, the theory has devoted the bulk of its attention to three purposive information acquisition strategies (Brashers 2007). Information seeking, for example, can both escalate and attenuate uncertainty (Brashers 2007; Brashers et al. 2000). Uncertainty management theory echoes uncertainty reduction theory in proposing that information seeking can help individuals predict and explain their environment, but it deviates from uncertainty reduction theory by contending that information seeking can heighten people’s uncertainty by uncovering alternatives or contradicting previously-held attitudes (Brashers 2007). Information seeking is particularly advantageous if knowledge about alternatives can help people stay optimistic in the face of a more probable negative outcome (Brashers et al. 2000). A second strategy is information avoidance. As Hogan and Brashers (2009) emphasize, “Just as individuals make conscious choices to seek information in response to uncertainty, they also may decide to make an intentional effort to thwart the entry of particular information into their lives” (p. 51). Dodging information that is threatening, intimidating, or overwhelming can help people ignore undesirable alternatives (Barbour et al. 2012; Brashers et al. 2000). A third strategy is reappraisal/adaptation. Individuals can adjust their perspective to value or tolerate uncertainty, change the way they plan for the future (e.g., by focusing on short-term goals instead of long-term goals), and/or embrace highly structured routines to cope with chronic uncertainty (Brashers 2007). Indeed, uncertainty may be so pervasive in some situations that people are compelled to adjust their thinking or accept prolonged states of ambiguity (Brashers et al. 2000).

Brashers and his colleagues originally developed uncertainty management theory in the context of people living with HIV/AIDS (Brashers et al. 1999; Brashers et al. 2000; Brashers et al. 1998). HIV/AIDS is an unpredictable disease marked by cycles of wellness and illness, and individuals with HIV/AIDS typically manage their uncertainty through a delicate balance of hope for a cure, optimism about periods of revival, fear of recurrence, and anxiety about rejection from others (Brashers et al. 1999; Brashers et al. 2000). People with HIV/AIDS experience multiple sources of uncertainty, including questions about the disease, outcomes of treatment, implications for finances, consequences for relationships, and the potential for stigma (Brashers et al. 2003). More recently, scholars have broadened the theory to identify the sources of uncertainty encountered by individuals dealing with organ transplantation (Martin et al. 2010), people living with diabetes (Vevea and Miller 2010), families coping with Alzheimer’s disease (Stone and Jones 2009), and survivors of cancer (Miller 2012). All of this research bolsters the theory’s multifaceted conceptualization of uncertainty in illness.

Other work on uncertainty management theory has considered how people employ media to manage questions about health and illness. Both online sources and direct-to-consumer pharmaceutical advertisements are popular (but not necessarily effective) channels for learning about medical issues (DeLorme and Huh 2009; Hurley, Kosenko, and Brashers 2011; Karras and Rintamaki 2012). On one hand, such channels can help individuals reduce their uncertainty by providing information they would not have access to otherwise (DeLorme and Huh 2009). On the other hand, such channels can be rife with ambiguous content that hampers uncertainty management (Hurley et al. 2011; Karras and Rintamaki 2012). Thus, using media for uncertainty management may present unique challenges.

Although the bulk of scholarship on uncertainty management theory has considered uncertainty in illness, scholars also have applied the theory to relationship contexts. For example, spouses may prefer to maintain uncertainty about each other’s past dating experiences to avoid discovering sensitive information that may harm their marriage (Wilder 2012). Similarly, adopted children may be reluctant to reduce uncertainty for fear of possible rejection by their birth parents or the revelation of negative information about their biological heritage (Colaner and Kranstuber 2010; Powell and Afifi 2005). These findings support the theory’s overarching premise that uncertainty management is more complex than merely uncertainty reduction.

2.5 Relational turbulence model

The relational turbulence model explicates how people negotiate times of transition as relationships develop (Solomon and Knobloch 2001, 2004; Solomon, Weber, and Steuber 2010). The model draws on theorizing about the processes of managing uncertainty (Berger and Bradac 1982) and establishing interdependence (Berscheid 1991) within intimate associations. Recent work has demonstrated the model’s utility for elucidating transitions as diverse as shifting from casual dating to serious involvement (Knobloch and Theiss 2010; Solomon and Theiss 2008), grappling with breast cancer (Weber and Solomon 2008), dealing with infertility (Steuber and Solomon 2008, 2012), entering parenthood (Theiss, Estlein, and Weber 2013), and reuniting following military deployment (Knobloch and Theiss 2011a, 2012).

The model characterizes transitions as periods of discontinuity punctuating the trajectory of relationship development that correspond with changes in how individuals define their partnership and act toward one another (Knobloch 2007). The model argues that people in the throes of a transition are vulnerable to overanalyzing and overemphasizing events that would be relatively mundane under more ordinary circumstances (Solomon and Theiss 2011). It defines relational turbulence as people’s propensity to be cognitively, emotionally, and behaviorally reactive when relationships are in flux (Solomon et al. 2010).

The relational turbulence model identifies relational uncertainty and interference from partners as two mechanisms that make individuals prone to reactivity during times of transition. Relational uncertainty is the degree of confidence (or lack of confidence) people have in their perceptions of involvement within close relationships (Knobloch 2010; Knobloch and Solomon 1999). It stems from three sources (Berger and Bradac 1982; Knobloch and Solomon 2002a): self-focused questions (“How certain are you about your view of this relationship?”), partner-focused questions (“How certain are you about your partner’s view of this relationship?”), and relationship-focused questions (“How certain are you about the future of your relationship?”). Interference from partners occurs when an individual’s goals are interrupted by a partner (Knobloch 2008b). According to the relational turbulence model, transitions spark upheaval because people are confronted with questions about the nature of their relationship and are susceptible to disrupting each other’s daily routines.

Although research suggests that both relational uncertainty and interference from partners may underlie people’s experiences of turmoil during times of transition (Solomon and Theiss 2011), we focus our attention on relational uncertainty given the goals of this chapter. One strand of work explicates the issues salient to individuals in diverse contexts. For example, dating couples encounter questions about each person’s desire, evaluation, and goals for the relationship, along with ambiguity about expectations for behavior, mutuality of investment, and the definition and future of the partnership (Knobloch and Solomon 1999). Married couples report being unsure about children, communication, career issues, finances, health, commitment, extended family, sexual intimacy, retirement, religious beliefs, leisure time, and household chores (Knobloch 2008a). When couples are confronting breast cancer, they experience relational uncertainty about identity changes, information regulation, and social support (Weber and Solomon 2008). When couples are coping with infertility, they grapple with questions about how much each person should invest in conception efforts, how to avoid violating assumptions, and where fault lies for the inability to achieve pregnancy (Steuber and Solomon 2008). When couples are negotiating depressive symptoms, they encounter questions about the cause of dysphoria, how to achieve understanding, how to deal with feelings of helplessness, and ways to maintain the viability of the relationship in the present and into the future (Knobloch and Delaney 2012). When military couples are reunited following deployment, they report questions about commitment, reintegration processes, household roles, personality shifts, and sexual intimacy (Knobloch and Theiss 2012). These examples illustrate the diverse themes of relational uncertainty that are salient to individuals at various junctures in relationships.

Other work links relational uncertainty with cognitive and emotional markers of turmoil. Under conditions of relational uncertainty, dating partners appraise unexpected events to be more negatively valenced (Knobloch and Solomon 2002b), hurtful incidents to be more intense (Theiss et al. 2009), their partner’s behavior to be more irritating (Solomon and Knobloch 2004; Theiss and Knobloch 2009; Theiss and Solomon 2006b), and their courtship to be more tumultuous (Knobloch and Theiss 2010; McLaren, Solomon, and Priem 2011). Similarly, spouses experiencing relational uncertainty react more negatively to sexual episodes (Theiss and Nagy 2010) and are less satisfied with their marriage (Knobloch 2008a; Knobloch and Knobloch-Fedders 2010). Relational uncertainty also shares close ties with people’s experience of emotion. Dating partners grappling with relational uncertainty report more anger, sadness, and fear (Knobloch, Miller, and Carpenter 2007; Knobloch and Theiss 2010); they also feel more jealousy (Knobloch, Solomon, and Cruz 2001; Theiss and Solomon 2006a). These findings are consistent with the model’s logic that relational uncertainty corresponds with upheaval in both cognitive and emotional forms.

Relational uncertainty also complicates communication (see Knobloch 2010, for a review). Whereas the process of reducing relational uncertainty may help partners build intimacy (Theiss and Solomon 2008), relational uncertainty itself may escalate the face threats of conversation, inhibit people’s willingness to communicate openly about sensitive issues, and hamper their ability to interpret messages accurately (Knobloch and Satterlee 2009; Knobloch and Solomon 2005). Indeed, individuals grappling with questions about involvement enact fewer relationship maintenance behaviors (Guerrero and Chavez 2005; Malachowski and Dillow 2011; Theiss and Knobloch in press), avoid more topics (T. D. Afifi and Schrodt 2003; Bevan et al. 2006; Guerrero and Chavez 2005; Malachowski and Dillow 2011), craft less fluent messages (Knobloch 2006), and are less willing to talk about the status of their relationship (Knobloch and Theiss 2011b). Dating partners experiencing relational uncertainty are less inclined to confront their partner about irritations (Theiss and Solomon 2006b) and to express jealous feelings (Theiss and Solomon 2006a). Similarly, spouses who are unsure rate conversations with their mate to be more threatening to themselves and to their marriage (Knobloch et al. 2007). All of these results support the premise of the relational turbulence model that relational uncertainty may heighten the difficulty of communication.

2.6 Theory of motivated information management

The theory of motivated information management details the process by which individuals strategically seek or avoid information about issues they deem important (W. A. Afifi 2010; W. A. Afifi and Morse 2009; W. A. Afifi and Weiner 2004). At its core, the theory proposes that people become emotionally activated when they experience more uncertainty or less uncertainty than they desire, and their feelings motivate them to action (W. A. Afifi 2010). The theory traces the interpersonal interaction between an information seeker and an information provider as they move through a series of phases (W. A. Afifi 2010; W. A. Afifi and Weiner 2004).

The interpretation phase begins when people perceive a gap between the amount of uncertainty they have and the amount of uncertainty they desire. The theory labels this gap an uncertainty discrepancy, which was originally theorized to activate feelings of anxiety in the information seeker (W. A. Afifi and Weiner 2004). A more recent expansion of the theory hypothesizes that an uncertainty discrepancy could produce a wide range of emotions (e.g., anger, sadness, fear, pride, jealousy, hope; W. A. Afifi and Morse 2009). For example, in the context of caregiving discussions between adult children and their aging parents, happiness about an uncertainty discrepancy may be a better predictor of an information seeker’s behavior than anxiety (Fowler and Afifi 2011).

In the evaluation phase, individuals gauge the advantages and disadvantages of various information management strategies. People use this cost-benefit analysis to construct outcome assessments that range from very beneficial to very detrimental (W. A. Afifi and Afifi 2009; W. A. Afifi and Weiner 2006). Outcome assessments stem from estimates of both process-based expectancies and results-based expectancies (W. A. Afifi 2010). Process-based expectancies are the outcomes individuals anticipate from enacting a strategy (e.g., the act of information seeking may present rewards or costs regardless of what information is gained), whereas results-based expectancies are the consequences individuals forecast from receiving the information (e.g., the information gained may itself be rewarding or costly).

Individuals also engage in efficacy assessments during the evaluation phase. As part of these efficacy assessments, information seekers judge (a) their skills to successfully enact the communication strategy under consideration (communication efficacy), (b) their ability to handle the possible outcomes (coping efficacy), and (c) their estimation of the information provider’s orientation (target efficacy), which includes judgments of the information provider’s ability to access the desired information (target ability) and willingness to provide truthful information (target honesty; W. A. Afifi and Weiner 2004). People’s efficacy judgments are an important factor in how they choose to manage an uncertainty discrepancy (W. A. Afifi 2010; W. A. Afifi and Afifi 2009; W. A. Afifi et al. 2006).

During the decision phase, an information seeker adopts an information management strategy (W. A. Afifi and Weiner 2004). The theory identifies three broad categories of information management strategies: (a) information seeking, (b) information avoidance, and (c) cognitive reappraisal of the situation. People tend to enact information-seeking strategies when they feel low anxiety, hold positive expectations about the discussion, and possess a strong sense of efficacy (W. A. Afifi, Dillow, and Morse 2004). Conversely, individuals tend to engage in avoidance strategies when they have high anxiety, pessimistic expectations, and low judgments of efficacy; avoidance strategies help people guard against encountering information that might make their situation worse (W. A. Afifi and Afifi 2009). A third information management strategy, cognitive reappraisal, occurs when individuals shift their mindset to reduce the size of the uncertainty discrepancy or diminish the importance of the issue. Cognitive reappraisal may weaken people’s drive for information management because the uncertainty discrepancy no longer warrants resolution, the meaning of the uncertainty changes, or they no longer see the issue as important (W. A. Afifi and Weiner 2004).

After the decision phase occurs, the theory shifts its attention from the information seeker to the information provider. The theory conceptualizes information management as a dyadic process by delineating an evaluation phase and a decision phase for the information provider as well (W. A. Afifi 2010). During the evaluation phase, information providers make their own outcome assessments and judge their own communication efficacy and coping efficacy. During the decision phase, information providers draw on their outcome and efficacy forecasts to choose an information provision strategy. The theory has not evaluated information providers in empirical tests of the model to date, but longitudinal evidence implies that individuals who come away from an initial acquisition attempt feeling efficacious are likely to continue seeking information in the future (Jang and Tian 2012). Hence, information providers may play an important role in the information management process by encouraging or discouraging future conversations.

Several studies have documented the three-phase process experienced by information seekers. For example, W. A. Afifi et al. (2004) showed that when individuals considered the issue to be important, their uncertainty discrepancy was positively associated with their feelings of anxiety (i.e., the interpretation phase). People’s anxiety, in turn, predicted less positive perceptions of potential outcomes and diminished feelings of efficacy (i.e., the evaluation phase). Moreover, people’s negative outcome expectancies predicted avoidance strategies, but high levels of efficacy predicted information seeking (i.e., the decision phase). Subsequent studies have produced similar results while also evaluating efficacy as a mediator of people’s outcome expectancies (W. A. Afifi and Afifi 2009; W. A. Afifi et al. 2006; W. A. Afifi and Weiner 2006). For instance, in family discussions of organ donation, communication efficacy linked people’s outcome assessments and their directness of talk (W. A. Afifi et al. 2006). Efficacy also predicts information management regarding relational uncertainty (Jang and Tian 2012), a romantic partner’s sexual health (W. A. Afifi and Weiner 2006), parent-child discussions of elder-care (Fowler and Afifi 2011), the comfort and frequency of religious conversations among dating partners (McCurry, Schrodt, and Ledbetter 2012), illicit stimulant use among young adults (Morse et al. 2013), avoidance following a dating partner’s deception (Jang 2008), and adolescents’ avoidance of conversations about their parents’ relationship (W. A. Afifi and Afifi 2009). In sum, initial work suggests that the theory shows substantial promise for explaining how people manage information when they encounter a mismatch between their current and desired level of uncertainty.