Show Summary Details

Page of

Printed from Oxford Research Encyclopedias, Communication. Under the terms of the licence agreement, an individual user may print out a single article for personal use (for details see Privacy Policy and Legal Notice).

date: 23 February 2024

Motivated Information Management and Other Approaches to Information Seekingfree

Motivated Information Management and Other Approaches to Information Seekingfree

  • Walid A. AfifiWalid A. AfifiDepartment of Communication, University of California, Santa Barbara


The turn of the 21st century has seen an explosion of frameworks that account for individuals’ decisions to seek or avoid information related to health risks. The four dominant frameworks are Risk Perception Attitude Framework, the Risk Information Seeking and Process model, the Planned Risk Information Seeking Model, and the Theory of Motivated Information Management. A comparison of the constructs within each and an examination of the related empirical tests reveal important insights into (a) factors that have consistently been shown to shape these decisions across these approaches and (b) constructs in need of additional theorizing and empirical testing. Specifically, the analysis suggests that uncertainty, efficacy, affect, risk perceptions, and subjective norms all play crucial roles in accounting for decisions to seek or avoid risk-related information. However, inconsistencies in the direction of influence for uncertainty or information discrepancy, risk perceptions, and negative affect argue for the need for considerably more theoretical clarity and empirical rigor in investigations of the ways in which these experiences shape decision making in these contexts.


  • Communication Theory
  • Health and Risk Communication
  • Interpersonal Communication

Information Seeking about Risk: A Comparison of Theories

The turn of the 21st century has seen a dramatic growth in theoretical attention to factors that influence individuals’ decisions to seek or avoid risk-related information. Four such frameworks are particularly well suited to do so: the Risk Perception Attitude Framework (RPA) (Rimal & Real, 2003), the Risk Information Seeking and Process model (RISP) (Griffin, Dunwoody, & Neuwirth, 1999), the Planned Risk Information Seeking Model (PRISM) (Kahlor, 2010), and the Theory of Motivated Information Management (TMIM) (Afifi & Morse, 2009; Afifi & Weiner, 2004). Each framework will be briefly reviewed and contrasted. A summary of empirical findings produces recommendations for a scholarly agenda moving forward.

The Risk Perception Attitude Framework

The Risk Perception Attitude Framework (RPA) takes as its foundation the notion that health behaviors are motivated by a combined impact of risk perceptions (“individuals’ beliefs about their vulnerabilities to various disease and risk factors,” Rimal & Turner, 2009, p. 148) and self-efficacy assessments (“people’s confidence in their ability to exert personal control over specific behaviors” (Rimal & Turner, 2009, p. 150). Consistent with Witte’s (1992) Extended Parallel Process Model, the framework argues that individuals take action to minimize threat if they believe that a high risk exists and simultaneously feel able to take the necessary steps to avert it. The combination of risk perception levels and strength of efficacy beliefs leads to four attitudinal groups: (1) responsive: individuals with high risk perceptions and strong efficacy beliefs and, as such, are both motivated to act and able; (2) avoidant, which represents those with elevated risk perceptions but weak efficacy beliefs, thus individuals who often avoid by turning away from any information that increases their sense of a threat that they feel unable to manage; (3) indifferent: people who have low risk perceptions and weak efficacy beliefs, thereby unmotivated to act (due to low risk perception) and unconvinced of the impact of action (due to low efficacy); and (4) proactive: those with low risk perceptions but high efficacy beliefs—people who feel confident in their ability to act but do not see a reason to do so. The RPA’s expectation is that these four groups’ relative positions on risk perceptions and efficacy leads to differences in their likelihood of health-related information seeking: responsives generally seek information, avoidants mostly avoid, indifferents are acutely unmotivated to seek (and, as such, least likely to seek), and proactives are often lulled into inaction. The empirical evidence in support of RPA’s predictions is strong (for review, see Grasso & Bell, 2015). Several studies confirm the four groupings and show them to differ on information-seeking and protective action in the ways predicted. Responsive attitudes seek information the most, indifference attitudes the least, and avoidant and proactive attitudes lie somewhere in the middle. The applied take-away is that interventions intended to elicit health-protective behaviors or information seeking should focus attention on simultaneously increasing the priority audience’s sense of risk and their perceptions of efficacy. Addressing one without the other is a recipe for failure. If they are both already high in that audience, interventions must be developed to maintain them through reinforcement.

A critical analysis of the framework, however, has also shown spaces in which additional inquiry is warranted. First, Turner, Rimal, Morrison, and Kim (2006) have shown that anxiety from perceived risk sometimes motivates avoidants to seek information, despite low efficacy beliefs. Importantly, though, Rimal and Turner (2009) note that the type of information sought is an essential, yet under-studied, aspect of understanding individuals’ decision-making. Specifically, individuals may take steps to avoid information that suggests an elevated risk for a negative outcome, while simultaneously pursuing information that minimizes the threat or their susceptibility therein. In other words, anxiety seems to play an important role in understanding information-seeking, but its impact may differ according to the expected valence of the information being sought. Second, the framework suggests that uncertainty about a health outcome for which individuals perceive elevated risk is likely to promote information-seeking intended to reduce that knowledge or confidence gap. Yet, little empirical research testing the RPA has closely examined that predicted moderating role of uncertainty. Third, some evidence (e.g., Turner, 2007) shows that extending the RPA’s reach to emotions other than anxiety may improve predictive success. Specifically, Rimal and Turner (2009) argue that emotions like anger may be sufficiently activity-motivating to overcome lowered efficacy levels among those with avoidant and indifferent attitudes.

Risk Information Seeking and Process Model

The Risk Information Seeking and Process Model (RISP) (Griffin, Dunwoody, & Neuwirth, 1999; Griffin, Dunwoody, & Yang, 2013) was one of the first comprehensive frameworks to focus on risk-related information-seeking and has been widely tested, empirically across multiple disciplines. It combines theoretical ideas introduced by the Heuristic-Systematic Model (HSM) of information process (Eagly & Chaiken, 1993), the Theory of Planned Behavior (TPB) (Ajzen, 1991), and other approaches. It places information insufficiency—the judgement that one’s current level of information is not where the person wants it to be in regard to a particular risk-related event (adopted from HSM’s sufficiency principle)—at the center of the information-seeking process. More broadly, though, the framework examined the impact of six factors on individuals’ information-seeking and processing behavior: (a) individual characteristics, (b) perceived hazard characteristics, (c) affective responses, (d) motivation (where information is sufficiently conceptually located), (e) relevant channel beliefs, and (f) perceived information gathering capacity. Individual characteristics include demographic factors, past experience with the issue in question, and other individual difference and sociocultural variables. The model argues that these factors shape the information-seeking process by influencing the level of perceived hazard characteristics (i.e., perceived risk), which, in turn, impacts both motivation and affective responses. Specifically, elevated perceptions of risk are predicted to produce negative affect (e.g., fear, worry) and to elevate motivation to seek information by creating a heightened belief in the need for more information (i.e., through increasing information insufficiency). More generally, negative affect such as worry is proposed to have a strong impact on information-seeking, both directly and indirectly (by affecting information insufficiency; Griffin, Neuwirth, Dunwoody, & Giese, 2004).

The heart of the RISP model is the motivation factors, which includes information insufficiency and informational subjective norms. The concept of informational subjective norms is adapted from TPB’s notion of subjective norms to address perceived pressures to seek information to manage risk. Specifically, RISP proposes that individuals who perceive that important others expect them to seek information about a negative outcome for which they are at risk will be more likely to do so. Moreover, those informational subjective norms will impact information-seeking directly and indirectly, by increasing information insufficiency. The two final impacts on information seeking are relevant channel beliefs (whether individuals believe that the information channel in question provides useful and credible information) and perceived information gathering capacity (also referenced as perceived behavioral control), which speaks to the question of whether individuals believe that they have the skills, the ability, the access, and the time and energy to seek the necessary information from the appropriate sources. Together, these factors shape information-seeking and systematic processing outcomes in the following ways: high perceived hazard characteristics, combined with elevated motivation through large information insufficiency and elevated pressure from subjective norms, strong negative affective responses, positive channel beliefs, and perceived information-gathering capacity work in concert to produce information-seeking and systematic processing.

Results to date have generally supported the predictive success of the RISP predictions. In fact, a recent meta-analysis of 13 RISP studies concluded that the “RISP model exemplified excellent explanatory power” (Yang, Aloe, & Feeley, 2014, p. 34), with the largest variance on information-seeking outcomes coming from current knowledge (a critical aspect ties to perceived information insufficiency) and informational subjective norms, the two primary components that make the central driving engine of the RISP model (motivation). A critical analysis of the framework, however, reveals some avenues for additional inquiry. First, Yang et al.’s (2014) meta-analysis revealed that current knowledge was a stronger predictor than information insufficiency, noting that the latter depends somewhat on familiarity with how much information one should have (e.g., how much the person doesn’t know), an awareness that is often missing on many health-related issues for which health professionals hope to encourage information-seeking. Second, negative affective responses, especially worry, have been consistently shown to have a strong impact, both directly and indirectly, on information-seeking (e.g., Noh, Lee, & Choi, 2016). Finally, as in the Risk Perception Attitude Framework (RPA), tests of RISP generally show perceived risk to play an important role in individuals’ decisions to seek information.

Planned Risk Information Seeking Model

The Planned Risk Information Seeking Model (PRISM) (Kahlor, 2010), developed by a scholar who also worked with the development of Risk Information Seeking and Process Model (RISP), integrates work from a wide range of information-seeking and heuristic-systematic processing models, and, like the other three frameworks described, has been mostly applied to health-related contexts. It is primarily an expansion of RISP, done by incorporating theoretical models that extended existing approaches to the study of risk information-seeking. According to the framework, the seven primary factors shaping information-seeking intent (the primary outcome for PRISM) are (a) seeking-related subjective norms, (b) attitude toward seeking, (c) perceived current knowledge, (d) perceived seeking control, (e) perceived knowledge insufficiency, (f) risk perception, and (g) affective response to risk. Seeking-related subjective norms, like RISPs informational subjective norms, reflect the perceived pressures from important others to seek information. Those perceptions are proposed to impact information-seeking intent both directly and indirectly and, in many ways, drive the engine that is captured in the PRISM framework. Those subjective norms are argued to have a bi-directional relationship with attitudes toward seeking and perceived seeking control (i.e., a perceived ability to seek the sought-after information), and uni-directional impacts on both perceived current knowledge and perceived knowledge insufficiency (akin to RISP’s information insufficiency, but importantly focused on “knowledge,” as opposed to information discrepancies). PRISM proposes risk perception as an independent factor that impacts information-seeking intent through its influence on affective responses to risk (i.e., the emotional responses to perceived risk, similar to RISP’s attention to affective responses), which in turns shapes the size of the perceived knowledge insufficiency, the most proximal impact on seeking intent.

The framework predicts that elevated pressure from perceived norms, more positive attitudes toward seeking, high levels of perceived seeking control, heightened risk perceptions, strong negative affect, and an elevated knowledge insufficiency together contribute to information-seeking intent. Although PRISM’s relatively young age has limited the number of empirical tests, those that have been completed generally support its predictive success. For example, Hovick, Kahlor, and Liang (2014) tested the model’s fit in the context of cancer risk, with an online sample of adults in the United States. They found good fit for the model in predicting information-seeking intention about cancer risk, with especially strong impact of seeking-related subjective norms, leading the authors to conclude that health interventions should take seriously the potential impact of pressure from important others for information-seeking. They also noted an important role for affective responses to risk, both as a direct and indirect effect. However, they tested an expanded model that included outcome expectancies (the expected costs and rewards of information-seeking on the issue of cancer risk), among other variables, and found the expanded elements to not add significantly to model fit. More recently, Willoughby and Myrick (2016) tested the model’s fit for two health contexts (cancer risk and sexual health) with a population of college students. Like Hovick et al. (2014), they found a particularly strong impact of seeking-related subjective norms and of affective responses to risk, across both contexts. Importantly, though, perceived knowledge insufficiency, a key component in PRISM, and an assumed proxy for information insufficiency in RISP, was not a significant predictor of information seeking intent across either context.

Theory of Motivated Information Management

The Theory of Motivated Information Management (TMIM) (Afifi & Weiner, 2004; Afifi & Morse, 2009) is similar to the other three frameworks in some ways and different in others. It is unique in that it is a framework that (a) was developed to explain information-seeking decisions exclusively within interpersonal contexts (for exceptions, see Tokunaga & Gustafson, 2014), (b) includes both the information seeker and provider, and (c) is not exclusively focused on risk-related information, although typically applied in related contexts. Relying heavily on the contributions of the Heuristic-Systematic Processing model (for review, see Chen & Chaiken, 1999) and Social Cognitive Theory (for review, see Bandura, 1997). The framework starts with scope conditions that limit its applicability to issues that individuals consider personally important and, as noted, to interpersonal contexts. It proposes four primary factors that predict information-seeking decisions (for the information seeker/avoider), articulated across three phases of the theory. The first phase—interpretation phase–begins with an awareness that a discrepancy exists between the amount of uncertainty one has and her or his preferred amount of uncertainty on an important issue. That notion, labeled uncertainty discrepancy, and, like information insufficiency (RISP) and knowledge insufficiency (PRISM), was adapted from the notion of a sufficiency principle. The theory argues that uncertainty discrepancy is then emotionally appraised (Afifi & Morse, 2009), with the most common appraisal being anxiety. That appraisal then has both direct and indirect impacts on the information-seeking process. The emotion that results from the uncertainty discrepancy initiates the second phase of the model by influencing two beliefs made in the evaluation phase: outcome expectancies and efficacy. Specifically, a negative appraisal of the uncertainty discrepancy is argued to negatively impact both assessments. Outcome expectancies, defined as the expected reward and costs of information-seeking about the particular issue from a particular source (Afifi & Weiner, 2004), are predicted to shape efficacy beliefs related to information seeking about the issue. TMIM distinguishes between three types of efficacy: (a) communication efficacy, the perceived ability to competently seek information from the target other; (b) coping efficacy, the perceived ability to manage the range of possible discoveries from the information search; and (c) target efficacy, which combines target ability (the assessment of whether the target other has relevant information) and target honesty (the assessment of whether the target other will be truthful). Efficacy is then argued to have the strongest impact on the decision to seek information, avoid information, or cognitively re-appraise the situation (e.g., decide that the issue is not as important as once believed or that the uncertainty discrepancy no longer exists), reflected in the decision phase. The theory predicts that more positive outcome expectancies and higher efficacy assessments predict a decision to seek information, but, counter to the other models, proposes a set of paths that make it such that higher uncertainty discrepancy ultimately leads to less information-seeking, by typically producing negative emotion appraisals, which in turn is proposed to negatively impact outcome expectancies and dampen efficacy assessments.

Several empirical tests have shown the framework to offer a good fit for efforts to explain information-seeking decisions across a wide range of contexts, most with health implications. Among the specific risk-related contexts in which the theory has been tested are sexual health, sexual assault, health care, and financial health (for review, see Afifi & Robbins, 2014). Most striking is the framework’s accuracy in proposing that increases in uncertainty discrepancy are associated with a reduced motivation to seek information. In other words, the greater the difference between individuals’ current levels of uncertainty and their desired levels of uncertainty about an important issue, the more they avoid additional information-seeking that may reduce that discrepancy (see Afifi, 2016). There are obviously exceptions to this trend, and a critical analysis of TMIM’s empirical tests reveals areas that could help elucidate these conditions. First, Afifi and colleagues (for review, see Afifi & Robbins, 2014) have called for greater attention to the role of emotions outside of the appraisal of uncertainty discrepancy. Affective states that accompany proximal appraisals of uncertainty discrepancy may override the impact of those appraisals on information-seeking decisions. Moreover, additional investigations needs to examine whether the direct influence of uncertainty-discrepancy appraisals differs systematically from their indirect effects through outcome expectancies and efficacy. Preliminary investigations suggest that may be the case. Second, outcome expectancies have played an inconsistent role in predicting information-seeking decisions. Afifi and Weiner (2004), in their original articulation of the framework, argued that positive expectancies may not have the same effect on decision-making as more negative expectancies. Several studies to date support that intuition and call for more attention to the specific conditions under which outcome expectancies shape decision-making about whether to seek or avoid information.

Comparison of Frameworks

By providing the first analysis of these distinct frameworks side-by-side, we are able to identify similarities and differences across four of the most commonly applied frameworks for understanding information-seeking about risk-related information. Such an exercise necessarily oversimplifies these approaches by examining the role of single factors within their complex theoretical infrastructures. As such, some may consider it an unfair, or at least incomplete, treatment of certain variables within each framework. Still, it brings attention to the ways in which four highly respected explanations of risk-related information-seeking offer sometimes contradictory predictions about the impact of the same (or related) construct. As such, it shines a spotlight on knowledge gaps that need filling.

First, it is noteworthy that all four frameworks examine uncertainty in some form (i.e., uncertainty, information, knowledge) as an important element of the information-seeking process. Moreover, three of the four rely on a version of the Heuristic-Systematic Model’s notion of information sufficiency in accounting for its impact. Those similarities are also where distinctions lie: the Risk Information Seeking and Process model (RISP) relies on information sufficiency, the Planned Risk Information Seeking Model (PRISM) identifies knowledge sufficiency, and the Theory of Motivated Information Management (TMIM) utilizes uncertainty discrepancy. The differences among the constructs of information, knowledge, and uncertainty (e.g., confidence in the state of knowledge) deserve additional empirical scrutiny in the context of risk because each suggests unique paths to interventions. For example, the solution to information insufficiency (i.e., the perceived lack of information) is more information. However, information often increases uncertainty, so if uncertainty discrepancy is the driving force behind information-seeking (as TMIM suggests), then increasing information may be counter-productive. In a similar vein, if an important goal is to increase knowledge (as PRISM suggests), then the goal may be to focus on informational campaigns that are known to reduce the knowledge gap. However, the goal to reduce the knowledge gap may sometimes be incompatible with the goals of reducing uncertainty or increasing information.

Second, all four approaches include some form of efficacy (broadly defined) as a central predictor of information-seeking. However, they differ in the type of efficacy on which they focus. PRISM conceptualizes efficacy through the discussion of individuals’ “perceived information gathering capacity” labeled in more recent articulations of the theory as “perceived behavioral control” (Kahlor, 2010), both of which focus on individuals’ perceived ability to acquire risk information and act on that information in appropriate ways. PRISM and the Risk Perception Attitude Framework (RPA) share that conceptualization. TMIM, in part because of its focus on interpersonal interaction, broadens the conceptualization of efficacy to include communication efficacy (i.e., the ability to seek the information), coping efficacy, and target efficacy (i.e., the target’s capacity to provide the sought-after information). TMIM’s inclusion of target efficacy has many similarities with RISP’s relevant channel beliefs (e.g., the trustworthiness, accessibility, and credibility of the information source), a construct dropped in PRISM in favor of an attitudinal assessment toward information-seeking (see Yang & Kahlor, 2013 for discussion). Tests of all four models consistently support the utility of efficacy constructs in predicting information-seeking decisions, but additional inquiry may help determine whether the decision to seek or avoid risk information is impacted most strongly by a more general assessment of perceived ability to act or, as TMIM and RISP propose, by more specific considerations of efficacy.

Third, all four approaches address the role of affect and argue for greater attention to its important role for understanding risk-related information-seeking, including the need to more thoroughly understand the relative impacts of both positive and negative affect. Specifically, RPA discusses the role of anxiety that emerges from risk perceptions, RISP and PRISM predict that negative affect increases information-seeking, and TMIM includes appraisal about the uncertainty discrepancy, specifically, and broader impacts of affect, more generally (Afifi & Robbins, 2014). Both theorizing and empirical data show that affect plays a critical role in individuals’ decisions to seek or avoid risk-related information. Yet, there remains insufficient understanding of the specific ways in which that occurs.

Fourth, three of the four frameworks are exclusively developed to understand response to risk and, as such, sensibly include risk perceptions as a critical element of their explanatory calculus. TMIM, because of its more general nature, does not do so, but captures some of the impact of that construct through a combination of restricting its scope to important issues, measuring outcome expectancies (which include the potential costs of information-seeking—learning about risk), and examining coping efficacy (which would likely be negatively impacted by high risk perception). Still, the strong effects of risk perception suggested by tests across a wide range of risk contexts suggests that its explicit inclusion within the TMIM framework would improve model fit when tested therein. Indeed, Wong (2014) showed that risk perception about HPV added significantly to TMIM’s model fit within that health context.

Finally, tests of RISP and PRISM suggest that the variable of subjective norms tied to risk information-seeking may play the most powerful role of all predictors in their framework, yet the construct is absent from RPA and TMIM considerations. The closest to its inclusion in TMIM is through its assessment of outcome expectancies, again, which includes benefits that would come from information seeking or costs associated from not doing so. Future investigations should more closely examine whether the separation of subjective norms as a specific and independent construct within TMIM would increase predictive utility to the extent that its value exceeds the drop in parsimony.

Remaining Questions

The comparison of these models also reveals four key questions that need to be addressed for our understanding of risk-related information-seeking to advance: (1) When does insufficiency motivate information-seeking about risk and when does it demotivate? (2) When is heightening perceptions of risk beneficial and when is it harmful? (3) When do negative emotions motivate information-seeking and when then do they demotivate? and (4) Are there conditions under which pressure from important others to seek information backfires?

The Role of Insufficiency

Perhaps the most glaring of inconsistent findings across the four approaches is whether uncertainty/uncertainty discrepancy/information insufficiency motivates information seeking (as the Risk Perception Attitude Framework [RPA], the Risk Information Seeking and Process model [RISP], and the Planned Risk Information Seeking Model [PRISM]) suggest or whether it demotivates that action, as the Theory of Motivated Information Management (TMIM) predicts and related empirical tests have shown. The logic for the assumption that uncertainty/information insufficiency encourages information seeking is strong (for review, see Afifi, 2009): individuals experience such insufficiencies as anxiety-producing, which leads them to the seek information in pursuit of the reduction in that state. That approach is consistent with the notion that individuals are knowledge and information seekers. In contrast, TMIM advances a logic by which that insufficiency typically create negative affect which, in turn, ultimately discourages information-seeking by biasing perceptions toward potential negative outcomes that individuals choose to avoid. That logic—that individuals makes assessments related to behavioral costs and avoid those feared outcomes—also has a rich history across disciplines. And importantly, the TMIM model has shown good fit across a wide range of information-seeking contexts, thereby making the case more strongly for the applicability of that logic. Of course, the likely answer to this question regarding the direction of impact is that it depends on moderating factors, but it is clear that the current state of empirical evidence is far from a consensus on this critical question. Future investigations should use experimental designs to test the relative success of the two contrasting explanations and explore moderators that might further advance our understanding of the conditions under which insufficiency about risk motivates action and those under which it motivates inaction. RPA and TMIM would suggest efficacy as a moderator, TMIM would add outcome expectancies, and both associated authors have argued that affect (e.g., mood) may play a critical—and understudied—moderating role on the impact of insufficiency. RISP and PRISM explicitly include affect as a factor in the decision-making process but not one that moderates the impact of insufficiency. Yet the evidence is beginning to mount that it may play precisely that role. More examination of that potential function for affect seems essential to advancing our knowledge in this area.

The Role of Perceived Risk

Like the expected influence of insufficiency, the models, and tests therein, also differ dramatically in the expected and proposed role of perceived risk on information-seeking. This inconsistency is also mirrored in reviews of literature regarding the impact of risk perception on health action (see Rimal & Turner, 2009). The RISP and PRISM argue strongly for a motivating effect of perceived risk on the decision to seek information, with increases in perceived risk likely leading to a heightened desire to seek information related to that risk, albeit mediated by other factors. In contract, RPA takes a position that the influence of heightened risk perception is moderated by self-efficacy, that its effect on action depends on one’s perceived ability to act. Finally, TMIM, while not explicitly including risk perception as a predictor, proposes that negative outcome expectancies (of which heightened risk perceptions would be an example) lead to a weakened likelihood of information seeking, both directly and, like RPA, through the moderating role of efficacy. Moreover, empirical data seem to support each of these accounts. What gives? Somewhat akin to the Extended Parallel Process Model’s (Witte, 1992) approach, the answer may be that the influence of perceived risk on information-seeking depends on the severity of that perceived risk, with moderate levels of that assessment motivating action and high levels doing the opposite. Moreover, under high levels, the impact is likely still moderated by perceptions of efficacy to act. Future investigations should explore that possibility more systematically within the context of risk-related information-seeking.

The Role of Negative Emotions

Here again, with results tied to the impact of negative emotions, we see a fault line in both the theoretical expectations and empirical results: Are negative emotions motivating toward action (as RISP and PRISM propose), or are they demotivating (as TMIM argues and RPA suggests)? The former argument rests on the notion that individuals are motivated to alleviate negative affect through action, while the latter is based on the belief that individuals are motivated to alleviate negative affect through avoidance. Indeed, this review makes clear that the basic and long-studied question of approach-avoidance motivations (for review, see Elliot, Eder, & Harmon-Jones, 2013) is at the heart of inconsistencies in what we know about risk-related information-seeking. An examination of the literature on the intersection of emotion and motivation suggests that the preponderance of the evidence links negative affect with avoidance and the cessation of goal pursuits. In fact, Aarts, Custers, and Holland (2007), in their review of a diverse set of theoretical approaches to the motivation force of affect, concluded that “there is common ground [across a wide range of approaches to emotion] to suggest that positive affect is associated with the preparation and motivation of action, whereas negative affect attached to goal pursuits reduces the motivation of the goal and puts it on hold” (p .167). These authors further advanced our understanding of the link between negative affect and goal pursuits by showing across five studies that the activation of negative affect in concert with the priming of a desired goal led to a nonconscious reduction in goal pursuit efforts. But not only were participants more likely to stop pursuit of a desired goal when an unrelated negative affect was induced, the goal also lost some of its desirability for subsequent pursuit efforts. Hart and Gable (2013) expanded this analysis by varying the intensity of the affective state and assessing whether differential levels of affective intensity impacted goal pursuit. Although their interest was restricted to positive affect, they supported their prediction that the simultaneous activation of high-intensity positive affect during the priming of a goal led to stronger goal pursuits than the activation of moderate-intensity positive affect.

There is strong evidence, then, that affect impacts goal pursuits in significant ways, with negative affect discouraging action and positive affect promoting it. Moreover, these effects occur regardless of whether the affect is consciously tied to a particular goal or simply co-occurs with the primed goal. As such, efforts to increase information-seeking about health risk should be especially careful that their interventions do not unintentionally elicit negative affect, and theoretical efforts to understand risk-related information-seeking should more carefully consider the proposed role of affect therein. In the same vein, scholars should devote significant effort to understanding variables that moderate the link between affect and action.

Next Steps

A review of four frameworks at the forefront of efforts to understand information-seeking about risk has revealed that uncertainty, efficacy, risk perception, affect, and subjective norms all play important roles in individuals’ decisions in these contexts. However, it also revealed critical inconsistencies that call for additional scrutiny, with the primary fault line across framework reflecting a difference in assumptions about the conditions that motivates action. These inconsistencies dramatically impact the types of recommendations given to health professionals working to improve population responses to health-related risk information, and, as such, require immediate attention and careful scrutiny.

Further Reading

  • Afifi, T. D., & Afifi, W. A. (Eds.). (2009). Uncertainty, information management, and disclosure decisions: Theories and applications. New York: Routledge.
  • Eastin, M. S., Kahlor, L. A., Lang, M. C., & Abi Ghannam, N. (2015). Information-seeking as a precaution behavior: Exploring the role of decision-making stages. Human Communication Research, 41, 603–621.


  • Aarts, H., Custers, R., & Holland, R. W. (2007). The nonconscious cessation of goal pursuit: When goals and negative affect are coactivated. Journal of Personality and Social Psychology, 92(2), 165–178.
  • Afifi, W. A. (2009). Uncertainty and information management in interpersonal contexts. In S. Smith & S. Wilson (Eds.), New directions in interpersonal communication research (pp. 94–114). Thousand Oaks, CA: SAGE.
  • Afifi, W. A. (2016). Theory of Motivated Information Management. In C. R. Berger & M. Roloff (Eds.), International encyclopedia of interpersonal communication (Vol. 3, pp. 1755–1765). Chichester, U.K.: Wiley Blackwell.
  • Afifi, W. A., & Morse, C. R. (2009). Expanding the role of emotion in the Theory of Motivated Information Management. In T. D. Afifi & W. A. Afifi (Eds.), Uncertainty, information management, and disclosure decisions: Theories and applications (pp. 87–105). New York: Routledge.
  • Afifi, W. A., & Robbins, S. (2014). Theory of Motivated Information Management: Struggles with uncertainty and its outcomes. In D. O. Braithwaite & P. Schrodt (Eds.), Engaging theories in interpersonal communication (2d ed., pp. 143–156). Thousand Oaks, CA: SAGE.
  • Afifi, W. A., & Weiner, J. L. (2004). Toward a Theory of Motivated Information Management. Communication Theory, 14, 167–190.
  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
  • Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.
  • Chen, S., & Chaiken, S. (1999). The heuristic-systematic model in its broader context. In S. Chaiken & Y. Trope (Eds.), Dual-process theories in social psychology (pp. 73–96). New York: Guilford Press.
  • Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth, TX: Harcourt Brace Jovanovich College Publishers.
  • Elliot, A. J., Eder, A. B., & Harmon-Jones, E. (2013). Approach–avoidance motivation and emotion: Convergence and divergence. Emotion Review, 5(3), 308–311.
  • Grasso, K. L., & Bell, R. A. (2015). Understanding health information seeking: A test of the risk perception attitude framework. Journal of Health Communication, 20(12), 1406–1414.
  • Griffin, R. J., Dunwoody, S., & Neuwirth, K. (1999). Proposed model of the relationship of risk information seeking and processing to the development of preventive behaviors. Environmental Research, 80, S230–S245.
  • Griffin, R. J., Dunwoody, S., & Yang, Z. J. (2013). Linking risk messages to information seeking and processing. Annals of the International Communication Association, 36(1), 323–362.
  • Griffin, R. J., Neuwirth, K., Dunwoody, S., & Giese, J. (2004). Information sufficiency and risk communication. Media Psychology, 6(1), 23–61.
  • Hart, W., & Gable, P. A. (2013). Motivating goal pursuit: The role of affect motivational intensity and activated goals. Journal of Experimental Social Psychology, 49(5), 922–926.
  • Hovick, S. R., Kahlor, L., & Liang, M. C. (2014). Personal cancer knowledge and information seeking through PRISM: The planned risk information seeking model. Journal of Health Communication, 19(4), 511–527.
  • Kahlor, L. (2010). PRISM: A planned risk information seeking model. Health Communication, 25(4), 345–356.
  • Noh, G. Y., Lee, S. Y., & Choi, J. (2016). Exploring factors influencing smokers’ information seeking for smoking cessation. Journal of Health Communication, 21(8), 845–854.
  • Potocki, B. L. (2012). Preventing sexual assault: Applying the theory of Motivated Information Management (PhD diss.). Ohio State University.
  • Rimal, R. N., & Real, K. (2003). Perceived Risk and Efficacy Beliefs as Motivators of Change. Human Communication Research, 29, 370–399.
  • Rimal, R. N., & Turner, M. M. (2009). Use of the risk perception attitude (RPA) framework for understanding health information seeking: The role of anxiety, risk perception, and efficacy beliefs. In T. D. Afifi & W. A. Afifi (Eds.), Uncertainty, information management, and disclosure decisions: Theories and applications (pp. 140–163). New York: Routledge.
  • Tokunaga, R. S., & Gustafson, A. (2014). Seeking interpersonal information over the Internet: An application of the theory of motivated information management to Internet use. Journal of Social and Personal Relationships, 31(8), 1019–1039.
  • Turner, M. M. (2007). Using emotion to prevent risky behavior: The anger activism model. Public Relations Review, 33, 114–119.
  • Turner, M. M., Rimal, R. N., Morrison, D., & Kim, H. (2006). The role of anxiety in seeking and retaining risk information: Testing the risk perception attitude framework in two studies. Human Communication Research, 32(2), 130–156.
  • Willoughby, J. F., & Myrick, J. G. (2016). Does context matter? Examining PRISM as a guiding framework for context-specific health risk information seeking among young adults. Journal of Health Communication, 21(6), 696–704.
  • Witte, K. (1992). Putting the fear back into fear appeals: The extended parallel process model. Communication Monographs, 59, 329–349.
  • Wong, N. C. (2014). Predictors of information seeking about the HPV vaccine from parents and doctors among young college women. Communication Quarterly, 62(1), 75–96.
  • Yang, Z. J., Aloe, A. M., & Feeley, T. H. (2014). Risk information seeking and processing model: A meta‐analysis. Journal of Communication, 64(1), 20–41.
  • Yang, Z. J., & Kahlor, L. (2013). What, me worry? The role of affect in information seeking and avoidance. Science Communication, 35(2), 189–212.