Uncertainty reduction theory

From Wikipedia, the free encyclopedia
Jump to: navigation, search

The uncertainty reduction theory, developed in 1975 by Charles Berger and Richard Calabrese, is a communication theory from the post-positivist tradition. The theory asserts the notion that, when interacting, people need information about the other party in order to reduce their uncertainty. In gaining this information people are able to predict the others behavior and resulting actions, all of which according to the theory is crucial in the development of any relationship.

Charles Berger and Richard Calabrese explain the connection between their central concept of uncertainty and seven key variables of relationship development with a series of axioms. Within the theory two types of uncertainty are identified; cognitive uncertainty or behavioral uncertainty. There are three interactive strategies which people may use to seek information about someone, these are passive, active, or interactive. Furthermore the initial interaction of strangers can be broken down into individual stages, these interactional behaviors can be used as indicators of liking and disliking, the entry stage, the personal stage, and the exit stage. According to the theory, people find uncertainty in interpersonal relationships unpleasant and are motivated to reduce it through interpersonal communication.


In 1975, Charles Berger and Richard Calabrese created Uncertainty Reduction Theory "to explain how communication is used to reduce uncertainties between strangers engaging in their first conversation together.) [1] In an effort to comprehend initial interactions, Berger and Calabrese believe people attempt to increase the predictability in communication.

There are two primary sub-processes of uncertainty reduction, prediction and explanation.[1] Prediction refers to the ability to forecast one's and other's behavioral choices. Explanation refers to the ability to interpret the meaning of behavioral choices.[1] In initial meetings, individuals attempt to predict what the other may want to hear based off the meaning they acquired from previous statements, observations, or information ascertained.

The foundation of the uncertainty reduction theory stems from the information theory, originated by Claude E. Shannon and Warren Weaver.[1] Shannon and Weaver suggests, when people interact initially, uncertainties exist especially when the probability for alternatives in a situation is high and the probability of them occurring is equally high.[2] They assume uncertainty is reduced when the amount of alternatives is limited and/or the alternatives chosen tend to be repetitive.


There are seven assumptions associated with the uncertainty reduction theory:[1]

  • People experience uncertainty in interpersonal settings.
  • Uncertainty is an aversive state, generating cognitive stress.
  • When strangers meet, their primary concern is to reduce their uncertainty or to increase predictability.
  • Interpersonal communication is a developmental process that occurs through stages.
  • Interpersonal communication is the primary means of uncertainty reduction.
  • The quantity and nature of information that people share change through time.
  • It is possible to predict people's behavior in a lawlike fashion.

Axioms and theorems[edit]

Berger and Calabrese propose a series of axioms to explain the connection between their central concept of uncertainty and seven key variables of relationship development: verbal communication, nonverbal warmth, information seeking, self-disclosure, reciprocity, similarity, and liking.[3] The uncertainty reduction theory uses scientific methodology and deductive reasoning to reach conclusions.[4]


  • Axiom 1 - Verbal communication: Given the high level of uncertainty present at the onset of the entry phase, as the amount of verbal communication between strangers increases, the level of uncertainty for each interactant in the relationship will decrease. As uncertainty is further reduced, the amount of verbal communication will increase. It is also important to consider recently published work by Berger, in which, he states the importance of appropriate levels of verbal communication, where too much verbal communication may lead to information seeking by the other party.
  • Axiom 2 - Non-verbal warmth: As non-verbal affiliate expressiveness increases, uncertainty levels will decrease in an initial interaction situation. In addition, decreases in uncertainty level will cause increases in non-verbal affiliative expressiveness
  • Axiom 3 - Information seeking: High levels of uncertainty cause increases in information-seeking behavior. As uncertainty levels decline, information-seeking behavior declines
  • Axiom 4 - Self-disclosure: High levels of uncertainty in a relationship cause decreases in the intimacy level of communication content. Low levels of uncertainty produce high levels of intimacy
  • Axiom 5 - Reciprocity : High levels of uncertainty produce high rates of reciprocity. Low levels of uncertainty produce low rates of reciprocity.
  • Axiom 6 - Similarity : Similarities between persons reduce uncertainty, while dissimilarities produce increases in uncertainty
  • Axiom 7 - Liking : Increases in uncertainty level produce decreases in liking; decreases in uncertainty produce increases in liking.

Two additional axioms have since been added to the theory:

  • Axiom 8 - Shared Networks : Shared communication networks reduce uncertainty, while lack of shared networks increases uncertainty.
  • Axiom 9 - Communication satisfaction: There is an inverse relationship between uncertainty and communication satisfaction.[5]


Berger and Calabrese formulated the following theorems deductively from their original seven axioms:

  • Amount of verbal communication and nonverbal affiliative expressiveness are positively related.
  • Amount of communication and intimacy level of communication are positively related.
  • Amount of communication and information seeking behavior are inversely related.
  • Amount of communication and reciprocity rate are inversely related
  • Amount of communication and liking are positively related.
  • Amount of communication and similarity are positively related.
  • Nonverbal affiliative expressiveness and intimacy level of communication content are positively related.
  • Nonverbal affiliative expressiveness and information seeking and information seeking are inversely related.
  • Nonverbal affiliative expressiveness and reciprocity rate are inversely related.
  • Nonverbal affiliative expressiveness and liking are positively related.
  • Intimacy level of communication content and intimacy are positively related.
  • Information seeking and liking are negatively related.
  • Information seeking and similarity are negatively related.
  • Reciprocity rate and liking are negatively related.
  • Reciprocity rate and similarity are negatively related.
  • Similarity and liking are positively related

Viewed collectively, the theorems provide a framework for examining and predicting the process of getting to know someone.[6]

Verbal Nonverbal Info seeking Disclosure Reciprocity Similarity Liking
Verbal + - + - + +
Nonverbal + - + - + +
Info seeking - - - + - -
Disclosure + + - - + +
Reciprocity - - + - - -
Similarity + + - + - +
Liking + + - + - +

Types of uncertainty[edit]

According to the uncertainty reduction theory, in initial interactions there are two types of uncertainty. You will either have cognitive uncertainty or behavioral uncertainty. Cognitive uncertainty pertains to the level of uncertainty associated with the cognition (beliefs and attitudes) of each other in the situation.[7] Uncertainty is high in initial interactions because individuals are not aware of the beliefs and attitude of the other party.[7] Behavioral uncertainty pertains to "the extent to which behavior is predictable in a given situation."[7] In most societies there are behavior norms, that we all tend to abide by, and if in initial conversations one chooses to ignore those norms there are risks of increasing behavioral uncertainty and reducing the likelihood of having future interactions. A great example of ignoring societal norms is engaging in inappropriate self-disclosure.

In addressing these uncertainties there are two processes for reduction, proactive uncertainty reduction and retroactive uncertainty reduction, proposed by Berger and Calabrese. Proactive uncertainty reduction is strategic communication planning prior to interaction.[8] Retroactive uncertainty reduction is the process of analyzing the situation post interaction.[8]

Interactive strategies[edit]

People engage in passive, active, or interactive strategies to reduce uncertainty with others.

According to Berger, If a person were to observe another in their natural environment, intentionally unnoticeable, to gain information on another, would be categorized as using a passive tactic for reducing uncertainties.[9] For example, watching someone in class, cafeteria, or any common area without attracting attention.

An active strategist would result to means of reducing uncertainties without any personal direct contact.[9] For example, if one were to ask a friend about a particular person, or ask the particular person's friend for some information without actually confronting the person directly.

An interactive strategist would directly confront the individual and engage in some form of dialog to reduce the uncertainties between the two.[9]

Stages of relational development[edit]

Berger and Calabrese separate the initial interaction of strangers into three stages: the entry stage, the personal stage, and the exit stage. Each stage includes interactional behaviors that serve as indicators of liking and disliking.[10]

The entry stage of relational development is characterized by the use of behavioral norms. Meaning individuals begin interactions under the guidance of implicit and explicit rules and norms, such as pleasantly greeting someone or laughing at ones innocent jokes. The contents of the exchanges are often demographic and transactional. The level of involvement will increase as the strangers move into the second stage.[11]

The second stage, or personal phase, occurs when strangers begin to explore one another's attitudes and beliefs. Individuals typically enter this stage after they have had several entry stage interactions with a stranger. One will probe the other for indications of their values, morals and personal issues. Emotional involvement tends to increase as disclosure increases.[6]

The final stage of interactional development is the exit phase. Here, the former strangers decide whether they want to continue to develop a relationship. If there is not mutual liking, either can choose not to pursue a relationship.[6]

Understanding the cycle of relational development is key to studying how people seek to reduce uncertainty about others.

Incentives to reduce uncertainty[edit]

Berger suggests that an individual will tend to actively pursue the reduction of uncertainty in an interaction if any of the three conditions are verified:

  • Anticipation of future interaction: A future meeting is a certainty.
  • Incentive value: They have or control something we want.
  • Deviance: They act in a manner that is departing from accepted standards

Example: There is a new manager in your place of work for a couple of weeks, therefore future interaction with the person is a certainty. The manager is assigning projects to the people in your department. Every project returns a different commission and this will directly influence your income. Arguably, being assigned a higher paying project has a greater incentive value for anyone in the department. The manager has a sibling in your department. This could influence the manager's decision on project assignment.

According to the theory, any single afford mentioned factor or all three of them combined can result in an increase in our desire to reduce the uncertainty in interpersonal interactions.[6]

Contemporary use[edit]

The uncertainty reduction theory has been applied to new relationships in recent years. Although it continues to be widely respected as a tool to explain and predict initial interaction events, it is now also employed to study intercultural interaction (Gudykunst et al., 1985), organizational socialization (Lester, 1986), and as a function of media (Katz & Blumer, 1974). Gudykunst argues it is important to test the theory in new paradigms, thus adding to its heuristic value (Gudykunst, 2004).

A study was conducted on 704 members of a social networking site to see what reduction theory strategies they used while gaining information on people they had recently met in person. All respondents used passive, active and interactive strategies, but the most common and beneficial strategy was the interactive strategy. This strategy reduced the most uncertainty of the target person by showing a perceived similarity and increasing social attraction.[12]

The uncertainty reduction theory also lead to the formation of a model originated by Michael W. Kramer. Kramer presents some major tenets and criticisms of the uncertainty reduction theory and then propose a Motivation to Reduce Uncertainty (MRU) model.[13]

MRU suggests that different levels of motivation to reduce uncertainty can lead to certain communication behaviors depending on competing goals.[13]

MRU suggests at least four different reasons for low motivation to seek information:[13]

  • People do not experience uncertainty in every event or encounter. Predictable or easily understood situations will not result in significant levels of uncertainty.[13]
  • Individuals have different levels of tolerance for uncertainty. The more one tolerates uncertainty the less information one seeks.[13]
  • Because communication always has social or effort costs,[14] minimizing those costs with limited effort may be preferable to information seeking.[13]
  • Individuals may also create certainty with minimal information seeking and without overt communication. For example, classification systems, such as stereotyping, create certainty out of uncertain situations.[13]

Research demonstrates that MRU could be used to examine how employees manage uncertainty during adjustment processes. MRU uses theoretical explanations for examining the approaches to understanding group decision making. “When groups are highly motivated to reduce the uncertainty surrounding a decision and there are no competing motives such as time or cost limitations, highly rational behaviors lead to information seeking to reduce uncertainty to optimize decisions.”[13] MRU could be used at the organizational level to examine communication related to organizational strategy.[13]

Computer-mediated communication examples[edit]

Given that uncertainty reduction theory was primarily developed for face-to-face interactions, critics have questioned the theory's applicability to computer-mediated communications. Pratt, Wiseman, Cody and Wendt argue that the theory is only partially effective in asynchronous, computer-mediated environments.[15] Although many computer mediated communications limit the possibility of utilizing many traditional social cues theories such as, Social Information Processing and Hyperpersonal Model, suggest individuals are quite capable of reducing uncertainties and developing intimate relationships.[16]

Antheunis, Marjolijn L., et al. investigated whether language-based strategies, employed by computer-mediated communication (CMC) users, would aid in reducing uncertainties despite the absence of nonverbal cues.[17] This study examined three interactive uncertainty reduction strategies (i.e., self-disclosure, question asking, and question/disclosure intimacy) in computer mediated communications.[17] Also, this study probed whether these uncertainty reduction strategies enhanced the verbal statements of affection in CMC.[17] This study questioned the use of language-based strategies to three communication options: face-to-face, visual CMC supported by a webcam, or text-only CMC.[17] “Content analysis of the verbal communication revealed that text-only CMC interactants made a greater proportion of affection statements than face-to-face interactants. Proportions of question asking and question/disclosure intimacy were higher in both CMC conditions than in the face-to-face condition, but only question asking mediated the relationship between CMC and verbal statements of affection.”[17]

Online dating[edit]

Online dating sites typically bring together individuals who have no prior contact with one another and no shared physical space where nonverbal cues can be communicated. Online dating sites produce a different set of concerns for individuals, as well as a different set of tools for reducing uncertainty. Gibbs, Ellison and Lai report that individuals on online dating websites attempt to reduce uncertainty at three levels: personal security, misrepresentation, and recognition. The asynchronous nature of the communications and the added privacy concerns may alter the Uncertainty Reduction model. Individuals who participate in online dating sites may engage in interactive behaviors and seek confirmatory information sooner than those who engage in offline dating.[16]

Online dating mainly supports passive strategies for reducing uncertainties. The option to view profiles online without needing to directly contact an individual is the main premise of passively reducing uncertainties. As one reviews another's profile they become equipped with enough knowledge to effectively predict and explain particular behaviors in initial interactions.

When one encounters initial interactions they’re commonly exposed to many risks. In online dating, many participants consider risks resulting from self-disclosure.[16] For example, a prospective suitor may disclose information on a profile that is dishonest or omits important details. Because reciprocity norms persuade individuals to reveal personal info in response to others’ self-disclosures, opportunities for misleading another is increased.[16] If one offers details in response to deceptive communication from others, with expectations of initiating face-to-face meetings and/or romantic relationships, the probability of succumbing to an act of violence is heightened.[16]

Gibbs, et al. found that “participants who used uncertainty reduction strategies tended to disclose more personal information in terms of revealing private thoughts and feelings, suggesting a process whereby online dating participants proactively engage in uncertainty reduction activities to confirm the private information of others, which then prompts their own disclosure.”[16]

Online surrogacy ads[edit]

Parents and surrogate mothers have great incentive for reducing uncertainty, taking optimal control, and finding a suitable third party for their pregnancy process. May and Tenzek assert that three themes emerged from their study of online ads from surrogate mothers: idealism, logistics, and personal information. Idealism refers to surrogates' decision to share details regarding their lifestyle and health. Logistics refers to the surrogates' requested financial needs and services. Personal information refers to the disclosure of details that would typically take several interactions before occurring, but has the benefit of adding a degree of tangible humanness to the surrogate (e.g. the disclosure of family photos). Idealism, logistics and personal information all function to reduce potential parents' uncertainty about a surrogate mother.[18]

Ethnicity and cultural differences[edit]

Studies have been conducted to determine the differences in the uses of uncertainty reduction strategies among various ethnicities. A study, conducted in the United States, suggests that significant differences are apparent. Self-disclosure has a pan-cultural effect on attributional confidence but other types of uncertainty reduction strategies appeared to be more culture-specific.[19]

“A multiple comparisons analysis using a least significance difference criterion indicated that for both self- and other-disclosure, African-Americans used greater self-disclosure than Euro-Americans, Hispanic-Americans, and Asian-Americans and perceived greater other intraethnic disclosure. The only other significant differences found in the multiple comparisons test were between self- and other-disclosure levels for Hispanic-Americans and Asian-Americans, namely, the former perceived greater self- and other-disclosure levels than Asian-Americans.”[19]

Another study suggests that cultural similarities between strangers influence the selection of uncertainty reduction strategies by increasing the intent to interrogate, intent to self-disclose, and nonverbal affiliative expressiveness.[20] The study also expressed an individual’s culture influences their selection of uncertainty reduction strategies.[20] For example US students exhibit higher levels of interrogation and self-disclosure than in Japanese students.[20]

Anxiety/uncertainty management theory[edit]

Inspired by Berger's Theory, the late California State, Fullerton, communication professor William Gudykunst began to apply some of the axioms and theorems of uncertainty reduction theory to intercultural settings. Despite their common axiomatic format and parallel focus on the meeting of strangers, Gudykunst's anxiety/uncertainty management theory (AUM) differs from Berger's uncertainty reduction theory in several significant ways. First, AUM asserts that people do not always try to reduce uncertainty. When uncertainty allows people to maintain positive predicted outcome values, they may choose to manage their information intake such that they balance their level of uncertainty. Second, AUM claims that people experience uncertainty differently in different situations. People must evaluate whether a particular instance of uncertainty is stressful, and if so, what resources are available.[21]

Example: online cancer research[edit]

Hurley, Kosenko and Brashers argue that 65% of internet-based cancer news is associated with the increase of uncertainty. In order of their degree of magnitude, information regarding treatment, prevention, detection, survivorship, and end-of-life issues yielded the most uncertainty. Given the inverse relationship between information-seeking behavior and uncertainty reduction, Hurley, Kosenko and Brashers assert that Uncertainty Management Theory may be more accurate and effective than uncertainty reduction theory. More research is needed to determine what computer-mediated communications exacerbate and help individuals manage their uncertainty regarding their health.[22]


Uncertainty reduction theory has sparked much discussion in the discipline of communication. Critics have argued that reducing uncertainty is not the driving force of interaction. Michael Sunnafrank's predicted outcome value theory (1986) indicated that the actual motivation for interaction is a desire for positive relational experiences. In other words, individuals engaging in initial interactions are motivated by rewards opposed to reducing uncertainties. According to Sunnafrank, when we communicate we are attempting to predict certain outcome to maximize the relational outcomes. Kellerman and Reynolds (1990) pointed out that sometimes there are high level of uncertainty in interaction that no one wants to reduce.[23] As a result of the critique, researchers formed the Uncertainty Management theory. This theory contrasts uncertainty reduction theory by identifying reduction as only one of the many actions that people take when uncertainty arises.[24] Gudykunst points out that uncertainty reduction theory was formulated to describe the actions and behaviors of middle-class, white strangers in the United States. This is the demographic in the studies Berger and Calabrese used to develop the theory.[25] Another issue is the scope of the axioms and theorems. If a particular theorem is disproved, it destroys the axiological base upon which it rests.


Eleven years after uncertainty reduction theory was introduced, Berger published Uncertain Outcome Values in Predicted Relationships: Uncertainty Reduction Theory Then and Now. His aim was to defend his theory in new contexts and modify it, as necessary. Berger later proposed three types of information seeking behavior: passive (watching the interactant for clues in reactions to stimuli), active (posing questions to other individuals about the interactant), and interactive ( posing direct questions to the interactant).[26] Later research by Berger and Bradac (1982) indicated that disclosures by interactants may lead them to be judged as more or less attractive.[7] The judgment will determine whether the judge will continue to reduce their uncertainties or end the relationship. Berger also acknowledges the works of Gudykunst, et al. (1985) and Parks & Adelman (1983) to extend uncertainty reduction theory to the realm of more established relationships.[27]

Planalp & Honeycutt (1985)[28] studies the introduction of new uncertainty to existing relationships. Their findings indicate that uncertainty in long-term relationships usually impacts negatively on the relationship.

See also[edit]


  1. ^ a b c d e West, Turner, and L. Turner. "Introducing communication theory: analysis and application with powerweb." (2003).
  2. ^ Shannon, Claude E., and Warren Weaver. "The mathematical theory of communication (Urbana, IL." University of Illinois Press 19.7 (1949): 1.
  3. ^ Griffin, Em. (2012) A First Look At Communication Theory. New York: McGraw-Hill.
  4. ^ Miller, K. (2005). Communication theories: Perspective, processes and contexts (2nd ed). NY: McGraw Hill, 176-183.
  5. ^ Turner, L.H. & West, R. (2010). "Introducing Communication Theory" (4th ed). NY: McGraw Hill. p.147-165
  6. ^ a b c d Berger, C. R., Calabrese, R. J. (1975). "Some Exploration in Initial Interaction and Beyond: Toward a Developmental Theory of Communication". Human Communication Research, 1, 99–112.
  7. ^ a b c d Berger, Charles R., and James J. Bradac. Language and social knowledge: Uncertainty in interpersonal relations. E. Arnold, 1982., pg. 7
  8. ^ a b Berger, Charles R., and Richard J. Calabrese. "Some explorations in initial interaction and beyond: Toward a developmental theory of interpersonal communication." Human communication research 1.2 (1975): 99-112.
  9. ^ a b c Berger, Charles R. "Inscrutable goals, uncertain plans, and the production of communicative action." Communication and social influence processes (1995): 1-28.
  10. ^ Berger, C. R., Calabrese, R. J. (1975). "Some Exploration in Initial Interaction and Beyond: Toward a Developmental Theory of Communication".Human Communication Research, 1, 99–112.
  11. ^ Berger, C. R., Calabrese, R. J. (1975). "Some Exploration in Initial Interaction and Beyond: Toward a Developmental Theory of Communication". Human Communication Research, 1, 99–112
  12. ^ Antheunis, M. L.; Valkenburg, P. M.; Peter, J. (2010). "Getting acquainted through social network sites: Testing a model of online uncertainty reduction and social attraction". Computers in Human Behavior 26: 100. doi:10.1016/j.chb.2009.07.005.  edit
  13. ^ a b c d e f g h i Kramer, MW. 1999. Motivation To Reduce Uncertainty: A Reconceptualization of Uncertainty Reduction Theory., Management communication quarterly, 13(2), 305. (ISSN: 0893-3189).
  14. ^ Miller, V. D., & Jablin, F. M. (1991). Information seeking during organization entry: Influences, tactics, and a model of the process. Academy of Management Review, 16, 92-120.
  15. ^ Pratt,L., Wiseman, R.L., Cody, M.J. & Wendt, P.M. (1999). Interrogative Strategies and Information Exchange in Computer-Mediated Communication. Communication Quarterly, 47 (1), 44-66
  16. ^ a b c d e f Gibbs, J.L. , Ellison, N.B. & Lai, C. (2011). First Comes Love, Then Comes Google: An Investigation of Uncertainty Reduction Strategies and Self-Disclosure in Online Dating. Communication Research, 38 (1), 70-100
  17. ^ a b c d e Antheunis, Marjolijn L., et al. "Interactive uncertainty reduction strategies and verbal affection in computer-mediated communication." Communication Research 39.6 (2012): 757-780.
  18. ^ May, A. & Tenzek, K.E. (2011). Seeking Mrs. Right: Uncertainty Reduction in Online Surrogacy Ads. Qualitative Research Reports in Communication, 12 (1), 27-33
  19. ^ a b Sanders, Judith A. & Wiseman, Richard L., (1993) Uncertainty Reduction Among Ethnicities in the United States Intercultural Communication Studies III:1
  20. ^ a b c Gudykunst, William B., and Tsukasa Nishida. "Individual and cultural influences on uncertainty reduction." Communications Monographs 51.1 (1984): 23-36.
  21. ^ Maguire, K.C. (2007). ‘‘Will It Ever End?’’: A (Re)examination of Uncertainty in College Student Long-Distance Dating Relationships.Communication Quarterly, 55 (4), 415-432
  22. ^ Hurley, R.J., Kosenko, K.A. & Brashers, D. (2011). Uncertain Terms: Message Features of Online Cancer News. Communication Monographs, 78 (3), 370-390
  23. ^ Miller, K. (2005). Communication theories: Perspective, processes and contexts (2nd ed). NY: McGraw Hill.
  24. ^ Knoblock, Leanne (2010). "New Directions in Interpersonal Communication Research." New York: Sage.
  25. ^ Gudykunst, W. B. (1985). "The Influence of Cultural Similarity, Type of Relationship, and Self-Monitoring on Uncertainty Reduction Processes". Communication Monographs, 52, 203–217.
  26. ^ Miller, K. (2005). Communication theories: Perspective, processes and contexts (2nd ed). NY: McGraw Hill, 176-183
  27. ^ Berger, C. R. (1986). Uncertain Outcome Values in Predicted Relationships: Uncertainty Reduction Theory Then and Now Human Communication Research, 13, 34-38
  28. ^ Planalp, S., & Honeycutt, J. (1985). "Events that increase uncertainty in personal relationships." Human Communication Research, 11, 593-604.

Further reading[edit]

  • Griffin, Em. (2012) A First Look At Communication Theory. New York: McGraw-Hill.
  • Deyo, J., Price, W. & Davis, L. (2011). Rapidly Recognizing Relationships: Observing Speed Dating in the South. Qualitative Research Reports in Communication, 12 (1), 71-78
  • Flanagin, A.J. (2007). Commercial markets as communication markets: uncertainty reduction through mediated information exchange in online auctions. New Media & Society, 9 (3), 401-423
  • Koester, J., Booth-Butterfield, M. & Booth-Butterfield, S. (1988). The Function of Uncertainty Reduction in Alleviating Primary Tension in Small Groups. Communication Research Reports, 5(2), 146-153
  • Ramirez, A. (2009). The Effect of Interactivity on Initial Interactions: The Influence of Information Seeking Role on Computer-Mediated Interaction. Western Journal of Communication, 73 (3), 300-325
  • Witt, P.L. & Behnke, R.R. (2006). Anticipatory Speech Anxiety as a Function of Public Speaking Assignment Type. Communication Education, 55(2), 167-177
  • Gudykunst, W. B., Shapiro, R., "Communication in Everyday Interpersonal and Intergroup Encounters," International Journal of Intercultural Relations, Vol. 20, 1996, pp. 19–45.
  • Goldsmith, D, J. (2001). A Normative Approach to the Study of Uncertainty and Communication. Journal of Communication, 514- 533
  • Sunnafrank, M. (1986), Predicted Outcome Value During Initial Interactions A Reformulation of Uncertainty Reduction Theory. Human Communication Research, 13: 3–33
  • Gudykunst, W. B., Yang, S.-M. and Nishida, T. (1985), A Cross-Cultural Test of Uncertainty Reduction Theory. Human Communication Research, 11: 407–454
  • Bradac, J. J. (2001). Theory Comparison: Uncertainty Reduction, ProblematicIntegration, Uncertainty Management, and Other Curious Constructs. Journal Of Communication,51(3), 456