uncertainty reduction theory case study

Communication Studies

The #1 resource for the communication field, uncertainty reduction theory.

An employer tells two unacquainted employees that they will be working together on a big project for the next six months. The startled individuals stare at each other awkwardly for a few seconds. Each one is thinking the same thing: “Does my new working partner have any strange quirks? Will we get along? Will my partner take the project seriously–or get us yelled at by the project committee?”

Finally, the two begin to chat casually. The first employee, Sarah, finds out her new co-worker, Rob, is from the corporate office in New York and has a big incentive to make sure their project succeeds. Rob discovers that Sarah is obsessed with keeping everything in her office in its place, but the aggravation pays off in always knowing where the project files are.

This example illustrates the concepts of Uncertainty Reduction Theory. The theory states that people often feel uncertainty about others they don’t know and are motivated to communicate in order to reduce that uncertainty. Professors Charles Berger and Richard Calabrese invented the theory in 1975 after noting that initial interactions between individuals followed predictable patterns of information-gathering. Uncertainty reduction is particularly important in relationship development, as the information gathered through observation and interaction can be used to predict a person’s behavior.

Core Concepts and Assumptions

Uncertainty Reduction Theory rests on several basic assumptions. The main assumption is that uncertainty creates cognitive discomfort, which people will try to reduce. Uncertainty reduction occurs primarily by questioning new acquaintances in an attempt to gather information about them. This information can then be used to predict people’s behavior, or the outcome of starting a relationship with them. The process of information seeking goes through predictable developmental stages, indicating changes in the quantity and type of information shared between individuals. Berger and Calabrese outlined seven concepts related to these assumptions:

1. Verbal Output — High levels of verbal output correlate positively with a greater reduction in uncertainty, higher levels of communication intimacy, similarity between individuals and liking.

2. Nonverbal Warmth — Refers to positive signs in a person’s gestures and body language that indicate a willingness to communicate or form a relationship.

3. Information Seeking — Occurs when individuals wish to know more about each other. Information can be obtained passively through observation or interactively through conversation.

4. Self-Disclosure — Individuals willingly divulge information about themselves to reduce uncertainty in the other person, thus encouraging them to communicate openly.

5. Reciprocity — Individuals interested in reducing uncertainty or starting a relationship will reciprocate uncertainty-reducing behavior, such as asking questions. The higher the uncertainty between individuals, the more reciprocity a person can expect.

6. Similarity — Individuals who are alike or share interests will feel less uncertain about each other and achieve communication intimacy more quickly. Dissimilar individuals experience higher levels of uncertainty.

7. Liking — Feelings of approval and preference between individuals likewise speed up the uncertainty-reduction process. Feelings of dislike discourage relationship formation.

However, only in certain circumstances do individuals feel the need to reduce uncertainty. After all, people rarely strike up conversations with others while riding an elevator or the subway. Theorists have identified three situations in which people will seek to reduce uncertainty:

1. Anticipation of Future Interaction — People will seek information about others they expect to see again, such as co-workers and neighbors.

2. Incentive Value — People desire information about individuals who have the power to influence their lives either positively or negatively, such as employers, teachers and politicians.

3. Deviance — People want to reduce their uncertainty about odd, eccentric individuals who behave contrary to one’s expectations or social norms.

Information-Seeking Strategies

People use three basic strategies to obtain information about others: passive, active and interactive. With the passive strategy, the individual of interest is observed in various situations, including those in which the person may be presenting himself to others in a strategic way (i.e., self-monitoring), such as in a classroom or at a party. The active strategy involves setting up a situation where the person of interest can be observed or approached for interaction. With the interactive strategy, people simply communicate with the person they wish to reduce uncertainty about.

Stages of Communication

Finally, Berger and Calabrese described three stages of communication through which uncertainty reduction advances:

1. Entry — Individuals exchange demographic information such as their age, gender, occupation and place of origin. Communication generally follows social rules and norms.

2. Personal — Communicators begin to share more personal data, including attitudes, beliefs and values. Communication is less constrained by social norms.

3. Exit — Communicators decide whether they will interact in the future or continue a relationship. In some cases, interaction will end at this point.

Although Uncertainty Reduction Theory has greatly influenced communication studies, it’s not without its critics. Some scholars say that uncertainty reduction is not always the factor motivating communication; some people interact out of a genuine desire to connect positively with others. Others note that Berger and Calabrese’s studies included only one U.S. demographic: middle-class white people. Still others have pointed out that the scope of the theory’s assumptions may be too large and, therefore, easily disproved–which ultimately weakens the theory.

Applications

Uncertainty Reduction Theory has been used in recent years to study intercultural interaction, organizational socialization and interactions on social media.

I am not a communication student, but the article has switched on some insights within. I can use the info to kick off engaging convos

Leave a Reply Cancel reply

Your email address will not be published.

Save my name, email, and website in this browser for the next time I comment.

Copyright Communicationstudies.com 2022

Home > ETDS > Dissertations and Theses > 3040

Dissertations and Theses

International students' experiences in higher education: a case study examining uncertainty reduction theory in communication classrooms.

Susan Kuhn , Portland State University

Portland State University Department of Speech Communication

First Advisor

Susan B. Poulsen

Date of Publication

Document type, degree name.

Master of Science (M.S.) in Speech Communication

Speech Communication

Foreign students -- Attitudes, Teacher-student relationships, Intercultural communication

10.15760/etd.3035

Physical Description

1 online resource (vi, 176 pages)

This was an exploratory case study which focused on international students' experiences in higher education. In particular, this study investigated the efficacy of uncertainty reduction theory in communication classrooms. The research asked four exploratory questions: (a) What are the students’ perceptions of the teacher/student relationship? (b) Do international students experience uncertainty in communication classrooms? (c) If uncertainty is experienced, what is its source(s)? (d) If uncertainty is experienced, do students seek to reduce it, and if so, how?

A phenomenological perspective was utilized in this study as the organizing, theoretical framework. Relevant literature on uncertainty reduction theory was reviewed as well as literature specific to international education, the communication classroom, the role of the teacher, and teacher self-disclosure. Focus group interviews, individual interviews, and member checks were conducted with international students who had taken communication classes at Portland State University in the 1998-1999 academic year. Using a set of analytic measures, 21 initial categories were identified and subsequently collapsed into 4 key categories: international education, teacher/student relationship, uncertainty in the communication classroom, and approaches to managing uncertainty.

Based on analyses of the data, this study revealed findings significant to understandings of both international education and uncertainty reduction theory. First, a model of classes within international education was derived from the data and served to deepen understandings of international education, in particular the international students’ perceptions of classes across countries.

Second, this research tested the extant claims of uncertainty reduction theory and raised questions regarding its conceptualization. The data revealed that the students' definitions of uncertainty and uncertainty reduction differed from those previously postulated, resulting in the formulation of new definitions. Also, context was found to strongly influence students' experiences of uncertainty; the context of the classroom not only determined the sources of uncertainty, but also influenced how uncertainties were coped with when they were not reduced. These alternative understandings of uncertainty reduction theory are significant as they could aid in further research that explores the theory’s extant claims.

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

If you are the rightful copyright holder of this dissertation or thesis and wish to have it removed from the Open Access Collection, please submit a request to [email protected] and include clear identification of the work, preferably with URL

Persistent Identifier

http://archives.pdx.edu/ds/psu/17734

Recommended Citation

Kuhn, Susan, "International Students' Experiences in Higher Education: A Case Study Examining Uncertainty Reduction Theory in Communication Classrooms" (2000). Dissertations and Theses. Paper 3040. https://doi.org/10.15760/etd.3035

Since July 28, 2016

Included in

International and Intercultural Communication Commons , Interpersonal and Small Group Communication Commons

Advanced Search

• Notify me via email or RSS
• Featured Collections
• All Authors
• Schools & Colleges
• Dissertations & Theses
• PDXOpen Textbooks
• Conferences
• Collections
• Disciplines
• Faculty Expert Gallery
• Submit Research
• Faculty Profiles
• Terms of Use
• Feedback Form

Home | About | My Account | Accessibility Statement | Portland State University

Privacy Copyright

Amanote Research

International students' experiences in higher education: a case study examining uncertainty reduction theory in communication classrooms, doi 10.15760/etd.3035.

Available in full text

January 1, 2000

Portland State University Library

• Search Menu

Sign in through your institution

• Advance Articles
• Author Guidelines
• Submission Site
• Open Access
• Why Submit?
• About Journal of Communication
• About International Communication Association
• Editorial Board
• Advertising and Corporate Services
• Journals Career Network
• Self-Archiving Policy
• Journals on Oxford Academic
• Books on Oxford Academic
• < Previous

Theory Comparison: Uncertainty Reduction, Problematic Integration, Uncertainty Management, and Other Curious Constructs

• Article contents
• Figures & tables
• Supplementary Data

James J. Bradac, Theory Comparison: Uncertainty Reduction, Problematic Integration, Uncertainty Management, and Other Curious Constructs, Journal of Communication , Volume 51, Issue 3, September 2001, Pages 456–476, https://doi.org/10.1111/j.1460-2466.2001.tb02891.x

• Permissions Icon Permissions

This paper compares 3 theories examining the role of communication in producing and coping with subjective uncertainty. Uncertainty reduction theory offers axioms and derived theorems that describe communicative and noncommunicative causes and consequences of uncertainty. The narrow scope of the theory and its axiomatic form are both advantageous and disadvantageous. Problematic integration and uncertainty management theories are comparatively broad, and they exhibit an open, web-like structure. The former theory scrutinizes the complex intersection of probability assessments and evaluations of the objects of these assessments, whereas the latter examines the various ways in which people cope with uncertainty, including sometimes attempting to increase it. The paper also compares meanings of “uncertainty” in the 3 theories as well as the roles played by natural language in the communication-uncertainty interface.

International Communication Association members

Personal account.

• Sign in with email/username & password
• Get email alerts
• Save searches
• Purchase content
• Activate your purchase/trial code
• Add your ORCID iD

Institutional access

Sign in with a library card.

• Sign in with username/password
• Recommend to your librarian
• Institutional account management
• Get help with access

Access to content on Oxford Academic is often provided through institutional subscriptions and purchases. If you are a member of an institution with an active account, you may be able to access content in one of the following ways:

IP based access

Typically, access is provided across an institutional network to a range of IP addresses. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account.

Choose this option to get remote access when outside your institution. Shibboleth/Open Athens technology is used to provide single sign-on between your institution’s website and Oxford Academic.

• Click Sign in through your institution.
• Select your institution from the list provided, which will take you to your institution's website to sign in.
• When on the institution site, please use the credentials provided by your institution. Do not use an Oxford Academic personal account.
• Following successful sign in, you will be returned to Oxford Academic.

If your institution is not listed or you cannot sign in to your institution’s website, please contact your librarian or administrator.

Enter your library card number to sign in. If you cannot sign in, please contact your librarian.

Society Members

Society member access to a journal is achieved in one of the following ways:

Sign in through society site

Many societies offer single sign-on between the society website and Oxford Academic. If you see ‘Sign in through society site’ in the sign in pane within a journal:

• Click Sign in through society site.
• When on the society site, please use the credentials provided by that society. Do not use an Oxford Academic personal account.

If you do not have a society account or have forgotten your username or password, please contact your society.

Sign in using a personal account

Some societies use Oxford Academic personal accounts to provide access to their members. See below.

A personal account can be used to get email alerts, save searches, purchase content, and activate subscriptions.

Some societies use Oxford Academic personal accounts to provide access to their members.

Viewing your signed in accounts

Click the account icon in the top right to:

• View your signed in personal account and access account management features.
• View the institutional accounts that are providing access.

Signed in but can't access content

Oxford Academic is home to a wide variety of products. The institutional subscription may not cover the content that you are trying to access. If you believe you should have access to that content, please contact your librarian.

For librarians and administrators, your personal account also provides access to institutional account management. Here you will find options to view and activate subscriptions, manage institutional settings and access options, access usage statistics, and more.

Short-term Access

To purchase short-term access, please sign in to your personal account above.

Don't already have a personal account? Register

Email alerts

Citing articles via.

• Recommend to your Library

Affiliations

• Online ISSN 1460-2466
• Print ISSN 0021-9916
• Copyright © 2024 International Communication Association
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Rights and permissions
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

ScienceBeta

Uncertainty Reduction Theory in Interpersonal Communication

Uncertainty Reduction Theory (URT), developed by social psychologists Charles R. Berger and Richard J. Calabrese in 1975, is a pivotal communication theory focused on how human beings understand and reduce uncertainty during initial interactions. This theory posits that when people meet for the first time, they face a high level of uncertainty, and their primary goal is to decrease this uncertainty by gaining more information about each other.

According to uncertainty reduction theory, everyone engages in two procedures to minimize uncertainty. The first is a proactive approach that anticipates what someone might do. The second is a retroactive procedure that examines how people interpret what others do or say .

URT is segmented into two types of uncertainty:

• Cognitive Uncertainty: Pertains to the beliefs and attitudes of the person.
• Behavioral Uncertainty: Relates to the predictability of the person’s behavior.

In the quest to reduce these uncertainties, individuals engage in various strategies of information collection. Berger and Calabrese lay out a framework suggesting that as interaction progresses, the levels of uncertainty decrease, conversely increasing predictability and relational development.

Principal Axioms of Uncertainty Reduction Theory

A set of axioms derived from prior research and common sense is put forth by Berger and Calabrese to explain the relationship between their central concept of uncertainty and the following seven important relationship development variables: verbal and nonverbal communication, information seeking, intimacy level, reciprocity, likeability, and similarity.

Axiom 1: Verbal Communication

Given the high level of uncertainty at the start of the entry phase, as strangers engage in more verbal conversation, the level of uncertainty for each interactant in the connection decreases.

As uncertainty decreases, verbal communication will grow. With the ambiguity reduced, communication between the two people will not only grow, but also become much more engaging as the individuals get to know each other better. Berger’s more recent work emphasizes the need of proper amounts of verbal communication, stating that excessive verbal communication may lead to the other person seeking information.

While verbal communication has a negative relationship with reciprocity and information seeking, it has a positive relationship with nonverbal affiliative expressiveness, intimacy level, likeability, and similarity.

Axiom 2: Nonverbal Warmth

Nonverbal affiliative expressiveness includes eye contact , head nods, arm motions, and physical distance between interactants (closeness). As nonverbal affiliate expressiveness increases, uncertainty levels in an initial interaction setting drop. Furthermore, decreases in uncertainty lead to increases in nonverbal affiliative expressiveness.

In contrast to its negative correlation with reciprocity and information seeking, nonverbal affiliative expressiveness has a positive correlation with verbal communication, intimacy level, likeability, and similarity.

Axiom 3: Information Seeking

Interactants are expected to ask questions during initial contacts, and the queries may merely require brief responses, such as requesting information about one’s occupation, hometown, previous residences, and so on. If one of the interactants remains doubtful, the other interactants are more likely to give relatively little sensitive or personal information.

High degrees of uncertainty lead to an increase in information seeking activity. As uncertainty levels diminish, information-seeking behavior decreases.

Axiom 4: Intimacy Level

High degrees of uncertainty in a relationship reduce the closeness level of communication content. Low degrees of uncertainty result in a high degree of intimacy. For example, during the initial engagement, communication content with a low intimacy level, such as demographic information, is expected rather than high intimacy level content, such as attitudes and opinions.

Similarity, liking, nonverbal affiliative expressiveness, and verbal communication are all positively correlated with intimacy level; conversely, reciprocity and information seeking are negatively correlated with intimacy level.

Axiom 5: Reciprocity

Reciprocity refers to the degree to which interactants expect another person to give similar knowledge after one has shared something. High degrees of uncertainty lead to high rates of reciprocity. Low levels of uncertainty lead to low rates of reciprocity.

Berger and Calabrese think that the simplest method to reduce mutual uncertainty is to request and provide the same types of information at the same rate of exchange, and that as uncertainty decreases, there is less need for symmetric exchanges of information at a quick rate.

Axiom 6: Similarity

Uncertainty reduction is facilitated by similarity. When individuals find shared attributes or attitudes, it acts as a confirmation of predictability, which, in turn, lessens uncertainty.

Similarity is inversely correlated with reciprocity and information seeking, but positively correlated with verbal communication, nonverbal affiliative expressiveness, intimacy level, and approval.

Axiom 7: Liking

Increased uncertainty leads to decreased liking, while less uncertainty leads to increased liking. A number of thinkers have offered evidence that suggests a positive association between similarity and like.

It is considered that if conversation participants have pleasant feelings for each other, the total level of uncertainty will be much reduced, and the frequency of conversations between persons will increase dramatically. According to Axiom 6, people’s desire to seek out similar persons in order to lessen uncertainty should lead to liking.

Axiom 8: Shared Networks

Uncertainty is decreased by shared communication networks and increased by their absence. This axiom is based on additional study on relationships beyond the entry stage conducted by Berger and William B. Gudykunst in 1991.

Axiom 9: Communication Satisfaction

Uncertainty and communication satisfaction have an adverse relationship. “An affective response to the accomplishment of communication goals and expectations” is the definition of communication satisfaction. This additional axiom was proposed by James Neuliep and Erica Grohskopf in 2000, and links uncertainty to a particular communication outcome characteristic.

Strategies and Information-Seeking Behavior

To lessen uncertainty with others, people use interactive, passive, or active methods. According to the general principle of uncertainty reduction, in order to lessen their degree of ambiguity regarding other people’s conduct, people should obtain broad demographic data about them.

A passive strategy involves observing others in social contexts without directly interacting with them. Individuals may learn about others’ behaviors and preferences, allowing them to anticipate future interactions and reduce uncertainty. For example, scrutinizing someone’s social media activity falls under this unobtrusive form of information collection.

In contrast, an active strategy encompasses efforts to gather information about others indirectly. Individuals using this method might ask third parties about the person of interest or manipulate social situations to elicit information. Research indicates that active strategies can successfully reduce uncertainty, especially during the initial stages of relationship development.

Finally, the interactive strategy involves direct communication with the other party to alleviate uncertainty. This approach can include asking questions, self-disclosure, and shared activities. It provides the most immediate and accurate form of information-seeking behavior, fostering connectedness and predictability in the relationship.

In 2002, Ramirez, Walther, Burgoon, and Sunnafrank proposed a novel approach to reduce uncertainty that goes along with the advances in technology and computer-mediated communication. The fourth uncertainty reduction method, known as extractive information seeking, refers to using online resources to collect knowledge about an individual given the abundance of information available about them. For instance, using a social media site like Facebook or Instagram as a tool to look for personal details about that individual.

Stages of Uncertainty Reduction

The theory outlines three definitive stages: the entry stage, the personal stage, and the exit stage. Each of these stages represents a distinct phase in the process of individuals increasing familiarity and reducing uncertainty about each other.

The Entry Stage is marked by rule-guided interaction. It is the initial phase where individuals adhere to social norms and scripts to make a good first impression and to avoid faux pas.

In this stage, information exchange is superficial and typically revolves around general topics that are not controversial or deeply personal. The focus is on nonverbal cues and behaviors rather than intimate disclosure.

As individuals progress to the Personal Stage, interaction becomes more relaxed and communication shifts to a more personal level. Individuals begin to share attitudes, beliefs, and values, discovering commonalities and differences. This stage fosters a deeper sense of connection as self-disclosure increases and both parties begin to dismantle their facades, revealing their true selves.

The Exit Stage is approached when one or both individuals decide whether to continue deepening the relationship or to part ways. It is a conclusive phase where the future of the interactions is considered based on the information gathered in the previous stages.

Decisions made in the exit stage are influenced by the perceived rewards or drawbacks experienced during the relational development and the degree to which original uncertainties have been resolved.

Extensions and Applications

In intercultural communication, URT informs the strategies individuals use to reduce uncertainty when interacting with those from different cultural backgrounds. For example, in initial intercultural interactions, individuals may apply uncertainty reducing strategies to enhance understanding and minimize potential misunderstandings that can arise from cultural differences.

Research has been done to find out how different ethnic groups apply tactics for reducing uncertainty differently. According to a study done in the US by Judith Sanders and Richard Wiseman, there are clear and noticeable variances. On attributional confidence, self-disclosure had a cross-cultural impact, while other approaches to reducing uncertainty seemed to be more culturally unique.

According to a study on intercultural communication between Americans and Korean-Americans, verbal communication between the two groups did not reduce the degree of uncertainty that the Korean-Americans had about the Americans. Nonetheless, Korean-Americans’ degree of uncertainty about Americans declined as their closeness level of communication content rose. However, this articulation of these two proven axioms is only partially helpful in understanding such cross-cultural communication.

Organizational Socialization

Within organizational socialization, URT has been used to understand how new employees adapt to their workplace. It helps clarify the processes through which new members seek information to reduce uncertainty about their roles and organizational norms. Effective reduction of uncertainty during organizational socialization can lead to better adjustment and job satisfaction.

Group identification, such as nationality, religion, gender, ethnicity, and many more related groupings, influences how an individual categorizes themselves. People therefore keep trying to fit in with even more specialized groups in an effort to lessen the uncertainty they have about who they are. Additionally, research shows that individuals with high levels of self-doubt are more prone to identify with more homogeneous groups in an effort to settle their doubts and come to a more settled condition.

Organizations can harness URT to enhance dyadic communication. For instance, understanding individual differences in uncertainty can help tailor communication behaviors to improve collaboration and productivity. Training programs focused on URT can equip employees with active and interactive strategies for information-gathering, ensuring efficient communication.

Long-distance Relationships

For a variety of reasons, long-distance love relationships can be difficult for both parties. It makes sense that in long-distance relationships, doubts could arise when there is a lack of regular in-person interactions. Certainty can also be troublesome in long-distance romantic relationships if it is certain that a circumstance will result in an unsatisfactory outcome.

Uncertainty can lead to bad relational results. Katherine C. Maguire states that a relationship will terminate if reducing ambiguity results in an anticipated negative consequence. Research indicates that using techniques to reduce ambiguity in long-distance romantic relationships is good for the relationship overall, even while it is true that certainty may result in an unfavorable relational outcome.

Critics claim that minimizing uncertainty is not the motivating motivation behind interaction. According to Michael Sunnafrank’s predicted value theory (1986), the underlying motive for interaction is a desire for positive relational experiences.

In other words, those engaging in first contacts are motivated by incentives rather than eliminating uncertainty. According to Sunnafrank, when we communicate, we are aiming to forecast specific outcomes in order to maximize relational results.

Furthermore, the subjectivity of people’s self-assessment makes the assumption of uncertainty reduction difficult. Uncertainty is caused by people’s lack of knowledge about themselves, information, and the environment.

However, doubt is mostly caused by people’s perceptions of their own cognitions and abilities, which are difficult to measure. In Brashers’ study on the application of uncertainty management to health communication, he describes the uncertainty of self-perception, which states that people’s feelings of uncertainty do not always correlate to their own assessments of available knowledge.

Other critics have questioned the theory’s narrowed focus on initial interactions, potentially oversimplifying lengthy or more intimate associations where different rules and behaviors emerge.

Sally Planalp and James Honeycutt argue that people’s potential changes, lack of understanding, or impetuous behavior will increase uncertainty in communication beyond the initial interaction. Their research calls into question the assumption that increased knowledge of other people and relationships will help social actors function more effectively in the social world.

References:

• Berger, Charles R. (1995). Inscrutable goals, uncertain plans, and the production of communicative action. In Berger, Charles R.; Burgoon, Michael (eds.). Communication and Social Influence Processes. Michigan State University Press ISBN 978-0-87013-380-0
• Brashers, Dale E. (2001). Communication and Uncertainty Management. Journal of Communication. 51 (3): 477–497. doi: 10.1111/j.1460-2466.2001.tb02892.x
• Carr, Caleb T.; Walther, Joseph B. (July 2014) Increasing Attributional Certainty via Social Media: Learning About Others One Bit at a Time . Journal of Computer-Mediated Communication. 19 (4): 922–937. doi: 10.1111/jcc4.12072
• Kim, Byung-Kil (1990). Reexamination of uncertainty reduction theory in intercultural communication: a case study of Korea- Americans (Thesis). OCLC 26954252
• Maguire, Katheryn C. (2007) Will It Ever End?’: A (Re)examination of Uncertainty in College Student Long-Distance Dating Relationships. Communication Quarterly. 55 (4): 415–432. doi: 10.1080/01463370701658002
• Miller, Katherine (2005). Communication Theories: Perspectives, Processes, and Contexts. McGraw-Hill Companies,Incorporated ISBN 978-0-07-293794-7
• Neuliep, James. (2012). The Relationship among Intercultural Communication Apprehension, Ethnocentrism, Uncertainty Reduction, and Communication Satisfaction during Initial Intercultural Interaction: An Extension of Anxiety and Uncertainty Management (AUM) Theory. Journal of Intercultural Communication Research. 41. 1-16. 10.1080/17475759.2011.623239
• Planalp, S., & Honeycutt, J. M. (1985). Events that increase uncertainty in personal relationships . Human Communication Research, 11(4), 593–604
• Sanders, Judith A. & Wiseman, Richard L., (1993) Uncertainty Reduction Among Ethnicities in the United States Intercultural Communication Studies III
• Sunnafrank, M (1986). Predicted outcome value during initial interactions: A reformulation of uncertainty reduction theory. Human Communication Research. 13 (1): 3–33
• West, Richard L; Turner, Lynn H (2010). Introducing communication theory: analysis and application. McGraw-Hill ISBN 978-1-283-38719-4

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Med Internet Res
• v.23(2); 2021 Feb

The Role of Transparency, Trust, and Social Influence on Uncertainty Reduction in Times of Pandemics: Empirical Study on the Adoption of COVID-19 Tracing Apps

Andreas oldeweme.

1 University of Muenster, Muenster, Germany

Julian Märtins

Daniel westmattelmann, gerhard schewe, associated data.

Background information for participants.

Items of the survey and their sources.

Contact tracing apps are an essential component of an effective COVID-19 testing strategy to counteract the spread of the pandemic and thereby avoid overburdening the health care system. As the adoption rates in several regions are undesirable, governments must increase the acceptance of COVID-19 tracing apps in these times of uncertainty.

Building on the Uncertainty Reduction Theory (URT), this study aims to investigate how uncertainty reduction measures foster the adoption of COVID-19 tracing apps and how their use affects the perception of different risks.

Representative survey data were gathered at two measurement points (before and after the app’s release) and analyzed by performing covariance-based structural equation modeling (n=1003).

We found that uncertainty reduction measures in the form of the transparency dimensions disclosure and accuracy, as well as social influence and trust in government, foster the adoption process. The use of the COVID-19 tracing app in turn reduced the perceived privacy and performance risks but did not reduce social risks and health-related COVID-19 concerns.

Conclusions

This study contributes to the mass adoption of health care technology and URT research by integrating interactive communication measures and transparency as a multidimensional concept to reduce different types of uncertainty over time. Furthermore, our results help to derive communication strategies to promote the mass adoption of COVID-19 tracing apps, thus detecting infection chains and allowing intelligent COVID-19 testing.

Introduction

At the onset of the COVID-19 pandemic, people, organizations, and governments worldwide were plunged into uncertainty [ 1 ], leading to changes in everyday behavior [ 2 , 3 ]. To control the pandemic and protect lives, the authorities have implemented different policies, which range from recommendations (eg, enhanced hand and respiratory hygiene or ventilating rooms) over relatively mild measures (eg, maintaining social distance or mandatory face masks) to far-reaching interventions in civil rights (eg, restrictions in human mobility or lockdowns) [ 4 - 6 ].

Fighting the pandemic effectively is a complex challenge, since limited resources in the health care system and restrictions in everyday life need to be considered simultaneously [ 7 , 8 ]. Consequently, measures to control the pandemic have to be coordinated [ 9 ]. Especially when there is no widely available appropriate vaccine, testing, contact tracing, and isolation are considered to be the most essential measures against COVID-19 [ 9 , 10 ]. From a health care management perspective, testing is a key element and provides valuable information regarding the spread of the virus over time [ 9 ]. However, limited testing budgets and test resources such as trained personnel, indicator reagents, polymerase chain reaction devices, or laboratories are the bottlenecks that limit test capacities [ 11 ]. Eames and Keeling [ 12 ] already showed during the first severe acute respiratory syndrome–related coronavirus pandemic in 2003 that contact tracing, followed by treatment or isolation, is an effective measure in the fight against infectious diseases when testing capacities are limited [ 13 ]. Nevertheless, the effectiveness of contact tracing depends primarily on timely and comprehensive collection and processing of data [ 14 , 15 ]. Manual contact tracing only meets these requirements to a limited extent, as it is time and resource intensive, and prone to errors [ 16 ]. The resulting urgent need for action toward digital solutions has become evident, as health authorities are collapsing under the burden of manual contact tracing [ 16 ].

Contact tracing apps have attracted discussions among politicians, epidemiologists, and the public. These apps aim to systematically identify COVID-19 infection chains and allow a timely and targeted implementation of further measures such as testing and quarantine [ 14 ]. Simulation models indicate that digital contact tracing is more efficient compared to manual solutions and has the potential to prevent up to 80% of all transmissions [ 14 , 17 ]. To realize this potential, a majority of the population has to use the same or compatible COVID-19 tracing apps [ 18 ]. Achieving mass but voluntary acceptance of the technology is a substantial challenge for several governments [ 19 ]. The first positive effects are already expected at a penetration rate of about 20% [ 15 ]. However, the penetration rate in several countries as of October 2020 was far below this value, which illustrates the imperative for action to realize the potential of contact tracing apps [ 20 ]. Although previous studies have focused on the effectiveness [ 14 , 17 , 19 , 21 ] or technical specifications of COVID-19 tracing apps [ 22 ], none have examined the factors that affect a rapid and widespread adoption of a COVID-19 tracing app after its release.

Thereby, the use of COVID-19 tracing apps may be related to various uncertainties. These uncertainties can be classified into general health-related COVID-19 concerns and app-specific risks in the form of performance risks and privacy risks that arise because the apps require the processing of sensitive user data [ 22 - 24 ]. In addition, social risks can occur as people might fear social pressure or social exclusion from using or not using the tracing app [ 25 ]. According to the Uncertainty Reduction Theory (URT), these uncertainties can be reduced by appropriate means such as transparent communication, social influence, and trust [ 26 - 29 ]. Thereby, the uncertainty reduction means can foster the adoption process of technologies in general [ 28 ] and COVID-19 tracing apps in particular. Since COVID-19 tracing apps are mainly released by governments or in cooperation with governmental institutions, trust in the government was examined in addition to the initial trust in a COVID-19 tracing app [ 24 ]. For a deeper understanding of the factors at play, the following research questions (RQs) were examined:

• RQ1: How do transparency, social influence, trust in the government, and initial trust in a COVID-19 tracing app affect the adoption process of the app?
• RQ2: What effect does the actual use of COVID-19 tracing apps have on uncertainties in the form of perceived privacy, performance, and social risks, as well as general COVID-19 concerns?

To address the two RQs, we developed a theoretical model based on URT. For testing the model, a representative sample of potential users of a COVID-19 tracing app were surveyed at two different times (1 week before and 4 weeks after the launch of the app) via structured online surveys. Based on this data, we performed covariance-based structural equation modeling (CB-SEM; n=1003). In the following sections, we provide information about COVID-19 tracing apps, explain the theoretical foundation, and derive the hypotheses.

Contact Tracing Apps as a Countermeasure Against COVID-19

This brief review aims to outline the characteristics of automated contact tracing apps for identifying contacts at risk and controlling disease transmission in humans. So far, several countries and some regions have developed and introduced independent COVID-19 tracing apps, which differ in administrative procedure and technical configuration [ 21 ]. Two major technical approaches exist: (1) GPS data is used to determine whether individuals, respective to their devices, were located within a geographical proximity for a defined period of time and (2) Bluetooth Low Energy is used to track the concrete proximity and exchange encrypted tokens with other devices in the defined proximity [ 30 , 31 ]. In both cases, data is used to notify people that have been in contact with a person who is infected. The recorded data is either stored on central servers (eg, the tracing app of France) or decentralized locations (eg, the tracing app of Germany) on the particular device [ 31 ]. Beside the technical configuration, tracing apps also differ in terms of administrative procedure. Although European tracing apps have been voluntarily used so far, some countries (eg, China) require citizens to install the app [ 24 , 32 ]. Moreover, the source code might be published or withheld by the developers (open source policy). Despite these options, regions need one specific COVID-19 tracing app or at least a suitable interface linking the different apps to achieve a sufficient adoption rate and alert people who are possibly infected [ 22 , 33 ]. A frequently updated overview of the COVID-19 tracing apps used in different regions and their characteristics is provided by MIT Technology Review [ 20 ]. Although Trang et al [ 22 ] showed that app design influences the likelihood of mass acceptance, there is a lack of evidence to what extent administrative aspects affect the (mass) adoption of COVID-19 tracing apps.

Uncertainty Reduction Theory

URT [ 26 ] originally addressed the initial interactions between strangers from a communication science perspective. The core assumption states that individuals face uncertainties in interactions with unknown partners, and individuals attempt to reduce these uncertainties. Berger and Calabrese [ 26 ] described uncertainty as a state in which a person is confronted with several alternatives concerning a stranger’s behavior. More alternatives make the individuals feel more uncomfortable because the other person’s behavior is harder to predict [ 34 ]. Although URT was initially developed to explain initial interactions between individuals, the theory has been applied to other contexts such as recruiting processes [ 35 ], computer-mediated communication [ 36 ], online commerce [ 37 ], or organizational behavior [ 38 ]. Hence, URT is not only limited to the interaction of individuals but is also useful in other settings. For instance, Venkatesh et al [ 28 ] demonstrated that URT is suitable for explaining the technology-supported communication of individuals and institutions in an e-governance setting. Beyond, URT is also suitable in crisis situations in general and in the current COVID-19 pandemic in particular [ 29 ].

The application of URT is appropriate in times of COVID-19 since the situation is marked by various far-reaching uncertainties. Looking at COVID-19 tracing apps, different uncertainties are apparent. First, health care technologies in general often bear uncertainties concerning data privacy [ 39 ]. These uncertainties are also identified in cases of COVID-19 tracing apps, as they require the processing of sensitive personal data [ 22 , 30 ]. Individuals fear that their privacy will be violated and cause undesirable outcomes such as governmental surveillance [ 30 , 40 ]. Moreover, people are concerned that personal data is used to impose quarantine or restrict access to public places for people who do not use a COVID-19 tracing app [ 24 ]. Second, uncertainties about the true performance and functionality of tracing apps are apparent [ 23 ]. Using a mobile app to contain a pandemic is new to individuals in most countries. Hence, they cannot draw on past experiences and might question its utility (eg, false alerts or only few people using the app). Third, social risks are recognizable as people might fear social pressure or social exclusion from using or not using the tracing app [ 25 ]. Beside tracing app–related uncertainties, general health-related COVID-19 concerns arise from the pandemic itself. The main aspects of the four uncertainties considered are summarized in Table 1 . In addition, the four described uncertainties are further reinforced by unverified information and fake news [ 41 - 43 ].

Summary of relevant uncertainties in the context of COVID-19 tracing apps.

Uncertainty Reduction Measures

According to URT, individuals reduce uncertainties by passive (observing), active (target-orientated search), and interactive (interaction with the stranger) information-seeking approaches [ 26 , 27 ]. We discuss transparency, social influence, and (initial) trust, as these factors facilitate individual’s information-seeking strategies [ 28 , 46 - 48 ].

Notably for passive and active strategies, individuals rely on accessible and valuable information [ 46 ]. To enable people to reduce uncertainty through observation or targeted research, information must be available. If no information is obtainable, people cannot reduce uncertainties through observation or targeted research. Therefore, transparency is examined as an enabler for passive and active information-seeking strategies. We defined transparency as “the perceived quality of intentionally shared information from a sender” [ 49 ]. Drawing on recent transparency research, transparency is best understood as a multidimensional construct consisting of disclosure, clarity, and accuracy of information [ 49 - 51 ]. In the context of this study, disclosure is the perception that sufficient relevant information about a COVID-19 tracing app is timely and accessible. Similarly, clarity is the perception that the received information about a COVID-19 tracing app is comprehensible and lucid. For instance, the disclosure of a huge amount of information cannot be considered transparent if the information is not understandable for individuals (eg, because the information is cryptic and only consists of the technical code of the COVID-19 tracing app). This information would hinder an individual’s ability to effectively perform active and passive information seeking. Lastly, accuracy is the perception that the information about a COVID-19 tracing app is correct [ 49 ]. The apparent incorrectness of information would not lower uncertainty but might lead to concerns about hidden governmental intentions. Notably in the context of a pandemic, each transparency dimension contributes to the reduction of uncertainty, as individuals rely on sufficient, relevant, timely, clear, and accurate information to observe the unknown technology and to actively search for information [ 29 ].

Furthermore, interactive information-seeking approaches have been shown to be more efficient than passive or active strategies in reducing uncertainty [ 46 ]. As it is not possible to interact with COVID-19 tracing apps before they are released or to directly communicate with the people responsible for the app, people seek alternatives for interactive information gathering. Therefore, individuals may communicate with their peers who are also affected by the decision whether to use the app or not. This, in turn, has to be regarded as another active information-seeking approach rather than an interactional strategy. Although communication with the social environment is interactive, the social environment is not the publisher of the app, and therefore, referring to URT, social influence is an active information-seeking approach. Social influence is expected to reduce people’s uncertainty about COVID-19 tracing apps, and it is defined “as the degree to which an individual perceives that important others believe he or she should use the new system” [ 52 ]. By knowing the preferences of their social environment, individuals’ attitudes toward using the app might be affected.

Lastly, trust is shown to reduce uncertainties and risks in different settings [ 53 , 54 ], and it is defined as “a psychological state comprising the intention to accept vulnerability based upon positive expectations of the intentions or behaviors of another” [ 55 ]. We distinguish between initial trust in COVID-19 tracing apps and individuals’ trust in their government. Several positive links to uncertainty reduction exist for initial trust in new technologies. For example, in e-commerce, trust lowers customers’ uncertainties about vendor behavior [ 56 , 57 ]. Additionally, initial trust reduces citizens’ uncertainties in the wake of e-governance [ 25 ]. However, initial trust might change in the actual use of the app and become strengthened or weakened according to the specific experiences encountered [ 58 ]. In addition to initial trust, individuals’ trust in their government is another means to reduce uncertainties. As most COVID-19 tracing apps are published by governments, trust in the administration might reduce fears related to app use [ 59 ]. People’s trust in the government is expected to be relatively stable and not fundamentally changeable in the short term [ 60 , 61 ].

Transparency and Initial Trust

Based on the transparency and trust literature, it is widely believed that transparency perceptions are positively related to trust [ 62 ]. This is shown by Schnackenberg et al [ 63 ] who explored the positive role of employees’ transparency perceptions (disclosure, clarity, accuracy) in the context of employees’ trust in their manager in organizational settings. Rawlins [ 51 ] also showed a positive link between transparency and employee trust, and highlighted the mutual relation between transparency and trust. Regarding the consequences of corporate scandals, transparency can be used as a strategic tool to restore stakeholder trust in firms [ 64 ]. In financial markets, transparency is shown to influence citizens’ trust in central banks [ 65 ]. In the case of COVID-19 tracing apps, a certain degree of transparency must be achieved for people to trust the app and use the technology [ 24 ]. The formation of peoples’ initial trust in COVID-19 tracing apps relies on the quality of available information as long as there are no prior interactions between citizens and the app [ 53 , 57 ]. Fulfilling certain information needs (eg, by providing sufficient clear and accurate information) enables people to initially trust a COVID-19 tracing app.

• Hypothesis (H)1: (a) Disclosure, (b) clarity, and (c) accuracy are positively related to individuals’ initial trust in a COVID-19 tracing app.

Trust in the Government and Initial Trust

Trust transfer theory states that individuals’ trust in a specific area can influence initial trust in other domains that are believed to have certain links to the known and trusted domain [ 66 ]. For instance, Lu et al [ 67 ] demonstrated that customers’ trust in internet payment in general influences trust in mobile payment services. As the majority of COVID-19 tracing apps are published by government institutions, trust in the government might affect initial trust in a COVID-19 tracing app. Peoples’ trust in the government is defined as the “perceptions regarding the integrity and ability of the agency providing [a] service” [ 53 ]. When people believe that the government is generally acting in citizens’ best interest and when citizens perceive the government agencies as capable to appropriately offer services, the initial trust in a COVID-19 tracing app is strengthened [ 53 ]. Recent studies on COVID-19 tracing apps noted that trust in the government influences peoples’ attitude toward the specific app [ 59 , 68 ]. In addition, a main reason for general negative attitudes against COVID-19 tracing apps is a lack of trust in the government [ 68 ]. Therefore, based on trust transfer theory, trust in the government fosters peoples’ initial trust in a COVID-19 tracing app [ 28 ].

• H2: Trust in the government is positively related to peoples’ initial trust in a COVID-19 tracing app.

Social Influence, Initial Trust, and Intention to Use

As previously stated, social influence can also aid the understanding of uncertainty reduction, as it might function as a substitute for interaction with the unknown and not yet available technology. Therefore, social interaction serves as an active means to gather information. The presumed reactions of the social environment will influence an individual’s attitude and behavior in a technology adoption context [ 52 ]. In terms of URT, people access their social environment as an active information-seeking means by interacting with their peers to exclude possible consequences of using or not using the specific technology. In this sense, social interaction, just like transparency, is a means of obtaining information and excluding alternatives and, hence, serves to reduce uncertainties. Li et al [ 69 ] showed that social influence is an important factor for the formation of initial trust and is therefore contributing to the exclusion of expectable negative outcomes such as perceived risks. Against this background, we argue that initial trust in a COVID-19 tracing app is not only influenced by transparency and trust in government, but is also affected by social influence.

• H3a: Social influence is positively related to individuals’ initial trust in a COVID-19 tracing app.

In addition, it is well known that social influence is an important antecedent of intention to use new technologies [ 70 - 72 ]. Being part of social groups (eg, family or colleagues) creates pressure on individual behavior, as people try to behave in accordance to established standards [ 73 ]. In health care settings, it has been shown that social influence is, for example, leading to smoking cessation [ 74 ] or supporting to maintain a diet [ 73 , 75 ]. Besides positive effects, the peer group might also foster negative behaviors such as drug abuse [ 76 ]. Therefore, social influence is a major factor to consider in the adoption process of health care technologies. This is particularly reinforced in the case of preventive behaviors like using tracing apps whose positive effect is not directly evident [ 73 ]. Therefore, we expect that social influence is not only influencing one’s initial trust in COVID-19 tracing apps but also impacts one’s intention to use the technology.

• H3b: Social influence is positively related to individuals’ intention to use a COVID-19 tracing app.

Initial Trust and Intention to Use

In the technology acceptance literature, trust has been shown to be positively correlated with the intention to use technology [ 69 ]. For instance, Nicolaou and McKnight [ 77 ] demonstrated that individuals’ trusting beliefs increase the intention to engage in interorganizational information exchange. Furthermore, (initial) trust is identified to be an antecedent of citizens’ intention to use e-governance services [ 28 , 53 ]. Parker et al [ 24 ] argued that the successful launch of mobile apps to fight the COVID-19 pandemic in democratic countries relies on the ability to ensure peoples’ trust in the technology. Based on URT, we argue that initial trust is a means to exclude potential negative behavior of the technology provider. Citizens who trust a COVID-19 tracing app estimate the probability of deceitful intentions as low.

• H4: Initial trust in a COVID-19 tracing app is positively related to individuals’ intention to use it.

Intention to Use and Actual Use

According to the theory of planned behavior [ 78 ], established technology acceptance theories (eg, technology acceptance model [TAM] or unified theory of acceptance and use of technology [UTAUT]) [ 52 , 79 ], and the application of URT in the technology context [ 28 ], explains that individuals’ actual use of new technology is influenced by individuals’ intention to use the technology [ 80 ]. This relationship is also expected to be applicable in the context of COVID-19 tracing apps.

• H5: Intention to use is positively related to individuals’ actual use of a COVID-19 tracing app.

Actual Use and Uncertainty Reduction

Referring to URT, uncertainties concerning data privacy, app performance, social consequences, and general COVID-19 concerns are decreased by the aforementioned means during the adoption process. The actual use of a COVID-19 tracing app is the only available interactive information-seeking possibility for individuals. Therefore, it is effective as it involves a direct interaction with the unknown technology [ 27 ]. This interaction enables individuals to discard uncertainties such as performance uncertainties (eg, functionality and handling) of COVID-19 tracing apps [ 29 ]. However this mean can obviously only be used after the app has been released. The investigation of the relationship between actual use and the reduction of different forms of uncertainty further addresses the applicability of URT for technology acceptance in uncertain environments.

• H6: The actual use of a COVID-19 tracing app is positively related to the reduction of (a) privacy risks, (b) performance risks, (c) social risks, and (d) COVID-19 concerns.

The proposed research model is summarized in Figure 1 .

Proposed research model. H: hypothesis.

Data and Procedure

To investigate the adoption process of COVID-19 tracing apps and test the theoretical model, data on the German “Corona-Warn-App” were collected via the panel platform respondi in two waves using structured online surveys. Members of this panel voluntarily agree to receive invitations to scientific surveys and may unsubscribe or delete their personal information at any time. Participants were assigned a randomly generated identifier, allowing us to match the results of both surveys. Nevertheless, since a third party (respondi) collected the data for both waves, we did not have direct contact with participants or access to identifying participant information. In addition, the survey did not collect any personal identifying information. Consequently, we were able to guarantee the anonymity and privacy of the participants at all times and acted in accordance with the ethical principles of the German Research Foundation.

The data collection of the first wave (t1) lasted 1 week and was completed 1 day before the app was released on June 15, 2020. Prior to the survey, the participants received the official information from the Federal German Government about the app [ 81 ]. For detailed information, see Multimedia Appendix 1 [ 81 ]. The app was developed on Android and iOS platforms by Robert Koch-Institut in conjunction with the private companies SAP, Deutsche Telekom, and other partners on behalf of the German government [ 81 ]. For the administrative conditions, the source code is publicly available on GitHub [ 82 , 83 ]. Furthermore, no registration is required to run the app, and the use is voluntary. The app uses Bluetooth Low Energy technology to record contact between people. Considering data privacy and security concerns, the devices exchange temporary encrypted random codes (Bluetooth ID) with each other [ 84 ]. The use of random codes prevents conclusions from being drawn about individual users or their specific locations. These codes are stored decentralized on the mobile devices, and the tracing data collected are automatically deleted after 14 days [ 85 ].

Subsequently, the participants received a quantitative questionnaire concerning their evaluations and perceptions of the app as well as demographical information. In the second survey wave (t2), the same participants were surveyed again 4 weeks after the app was released. The actual use of the app was queried in addition to their evaluations and perceptions.

To maintain data quality and ensure scale validity, we included three attention checks and screened out participants that failed these tests. Finally, 1373 individuals completed the first survey, and 1050 participated in the second wave, yielding a completion rate of 76.47% (1050/1373). Owing to four “knock-out” criteria regarding low response times, missing data, suspicious response patterns, and outliers, 47 participants were excluded from the analysis to ensure data quality [ 86 ]. A final sample size of 1003 was then obtained. This sample is representative of Germany in terms of gender, age, and education (see Table 2 ).

Representativeness description.

a Calculation of adoption rate in Germany: 16.6 million downloads / approximately 62 million smartphone users (source: Statistisches Bundesamt [ 87 , 88 ], and Robert Koch-Institut [ 83 ]).

To test the proposed research model, we used established scales that have been validated in previous studies. Except for demographics, use behavior (binary), and control variables (gender, age, education), the participants rated all items on 5-point Likert scales. Intention to use was measured by a 3-item scale [ 52 ]. For initial trust in the Corona-Warn-App, we built on a 5-item scale developed by Koufaris and Hampton-Sosa [ 58 ]. Trust in government was examined through a 4-item scale adapted from Bélanger and Carter [ 53 ]. Transparency (reflecting individuals’ perceptions of information quality) was adopted from Schnackenberg et al [ 63 ], and each dimension was based on 4 items. Privacy risks was measured on a 5-item scale developed by Rauschnabel et al [ 44 ], and a 5-item measure was adopted from Featherman and Pavlou [ 89 ] to assess performance and social risks. Finally, general COVID-19 concerns were measured through a 6-item scale by Conway et al [ 45 ].

We calculated differences for the four dimensions of risk perceptions between the two survey waves to measure the change in the perceived risk assessments. The differences were calculated using the following formula: difference variables = risk perception (t1) – risk perception (t2) . The means, SDs, and correlations for all constructs are reported in Table 3 . Age, gender, and education were used as controls.

Mean, SD, and correlations.

a Not applicable.

Before conducting the structural equation modeling (SEM) analysis, we tested the reliability and validity of the measurement model. One item displayed poor factor loadings and was dropped (TR_5). All other factor loadings exceeded the threshold of 0.6. Internal consistency and composite reliability were assumed, as the Cronbach alpha met the quality criteria of >.7, and the average variance extracted exceeded 0.5 [ 90 , 91 ]. Composite reliability of all items exceeded the cut-off value of 0.6 [ 92 ]. The final questionnaire with all constructs, related survey items, their sources, and the aforementioned indexes is presented in Multimedia Appendix 2 [ 44 , 45 , 52 , 53 , 58 , 63 , 89 , 93 ].

Data Analysis

We used the R-based JASP software (University of Amsterdam) environment to evaluate our proposed research model [ 94 ] and the lavaan code to conduct CB-SEM [ 95 ] analysis. Before performing the SEM analyses, we tested the fit, reliability, and validity of the applied model. The comparative fit index (>0.95), Tucker-Lewis index (>0.95), root mean square error of approximation (<0.08), and standardized root mean square residuals (<0.08) complied with the conventional cut-off criteria [ 96 , 97 ]. Based on Kline [ 98 ], the χ ² / df ratios indicated a sufficient model fit across models (<3). Common method bias was not a problem, as the Harman single factor test indicated that only a variance of 27.6% were explained by a single factor consisting of all model items [ 99 ]. In summary, all fit indexes revealed a very good overall model fit (see Table 4 ), with all indicators reaching their respective thresholds.

Covariance-based structural equation modelling results.

a Standardized path coefficients; standard error of the estimators in parentheses.

b N/A: not applicable.

c H: hypothesis.

d PRPP: reduction of privacy risks.

e PR: reduction on performance risks.

f SR: reduction of social risks.

g CC: reduction of COVID-19 concerns.

h RSMEA: root mean square error of approximation.

i SRMR: standardized root mean square residuals.

The standardized path coefficients, significance levels, and fit indexes are summarized in Table 4 . As illustrated in Figure 2 , information disclosure and accuracy are positively related to initial trust, supporting H1a (β=.140; P <.001) and H1c (β=.375; P <.001). In contrast, H1b was rejected (β=–.028; P =.45), as information clarity shows no relation to initial trust. H2 was supported (β=.201; P <.001) as trust in governance and initial trust were positively related. Furthermore, there was support for H3a (β=.377; P <.001) and H3b (β=.207; P <.001), as the results showed a positive relation between social influence toward initial trust and intention to use. The observed relationship between initial trust and intention to use was positive, supporting H4 (β=.670; P <.001). We also found support for H5 (β=.599; P <.001), as intention to use was positively related to the actual use of a COVID-19 tracing app. Finally, we found a positive relationship between actual use and privacy and performance risks, thus supporting H6a (β=.222; P <.001) and H6b (β=.169; P <.001). In contrast, H6c (β=.005; P =.88) and H6d (β=.012; P =.72) were rejected as actual use was not related to social risks or COVID-19 concerns, respectively. The control variables gender and education were not related to the reduction of the four dimensions of uncertainty reduction, while age was negatively related to privacy risk reduction.

Results of structural equation model. H: hypothesis. *** P <.001.

In addition to the hypothesized direct relationships between social influence and the intention to use the COVID-19 tracing app, we conducted a post hoc analysis to investigate the potential indirect effects of social influence on the intention to use the COVID-19 tracing app mediated by initial trust. The mediation effects were examined with the help of the procedure according to Baron and Kenny [ 100 ] and are depicted in Table 5 . We found evidence that the indirect effect was significant (β=.253; P <.001). As the direct effect of social influence toward the intention to use has been shown to be significant before (H5), we postulated that the relationship of social influence and intention to use is partially mediated by initial trust.

Mediating effect.

General Discussion

In this study, we investigated how uncertainty reduction measures can foster the adoption of COVID-19 tracing apps and, consequently, the reduction of uncertainty perception. In this section, we discuss the antecedents of initial trust, intention, and actual use of the app, as well as the reduction of specific uncertainties. Based on URT, transparency and social influence are antecedents of initial trust. In terms of transparency, we found that initial trust in COVID-19 tracing apps is positively influenced by the disclosure and accuracy of information. However, accuracy had a considerably higher effect on initial trust than the disclosure dimension. This shows that, although it is important to receive sufficient information, the perceived validity of the information is crucial. Unexpectedly, we found no effect between information clarity and initial trust in the Corona-Warn-App. This may be due to the peculiarities of the COVID-19 pandemic, as people likely became used to constantly encountering new complex information and thus accepted a lower level of information lucidity. Despite the missing effect between clarity and initial trust, our findings are consistent with the existing transparency-trust literature [ 51 , 62 , 63 ].

As proposed, social influence positively affects individuals’ initial trust. The integration of social influence in the URT context reveals that social influence serves as an active information-seeking strategy, thus meeting the demand of Venkatesh et al [ 28 ] to integrate UTAUT variables into URT. Especially in situations where direct interaction with the unknown technology is not possible, the communication with peers becomes important. In addition, we identified a positive relation between social influence and intention to use. This is in line with technology acceptance and health care literature [ 52 , 70 , 73 ]. In addition, we were able to show that initial trust partially mediates the relationship between social influence and intention to use COVID-19 tracing apps. Furthermore, we examined the effect of trust in the government on initial trust in COVID-19 tracing apps and found a positive relationship between these concepts. This is consistent with the trust transfer theory and current studies on COVID-19 tracing apps [ 59 , 68 ]. It is important to note that trust in government has a smaller effect on individuals’ initial trust compared to transparency and social influence. Therefore, people who are critical of the government can still develop initial trust in the app through other short-term influenceable means such as transparent communication.

Additionally, we observed a positive relation between initial trust and intention to use. This result is consistent with URT [ 27 , 28 ] and confirms the common understanding of trust in the context of technology acceptance (for a meta-analysis, see Wu et al [ 101 ]). As expected, people who have a high intention to use a COVID-19 tracing app are more likely to use it. Nevertheless, our results also revealed, as most studies have, that an intention-behavior gap exists [ 102 ].

Considering uncertainty reduction specifically, we found that the actual app use increases COVID-19 tracing app–related uncertainty reduction. Individuals’ uncertainty reduction of perceived privacy and performance risks was significantly increased by using the app. Thus, we found support for Trang et al [ 22 ], who stated that data privacy and app performance (benefits) need to be considered in the development of tracing apps. In addition, our results did not indicate a reduction of social risks nor a reduction of general COVID-19 concerns. As COVID-19 concerns span broader health-related fears, they cannot be solely linked to the functionality of the app or interaction with it. Tracing apps do not provide direct protection but are mainly intended to identify infection chains to implement further appropriate actions such as intelligent testing and quarantine [ 9 ]. This explains why the use of a COVID-19 tracing app has no impact on the reduction of these general health-related fears. Further, it indicates that people using tracing apps are not getting more reckless but still recognize the virus’s threat. Regarding social risks, the use and nonuse of the app is less visible to nearby people than wearing a face mask or complying with social distance regulations. Therefore, individuals’ actual use behaviors might be unrelated to social consequences as long as the use of such an app is not mandatory, for example, to use public transportation or enter restaurants or other places. For the controls, we found that age was negatively correlated to the reduction of privacy risks. This effect is rather small and in line with research emphasizing that privacy concerns are more pronounced and stable among older people than among younger individuals [ 103 ].

Theoretical Implications

Our study design and findings contribute to the literature in several ways. First, we demonstrated with our study design how mass adoption problems can be investigated over time in the health care management context using the example of a COVID-19 tracing app. By applying URT, we contributed to its empirical validation in general and introduced it to the field of health care management. The application is particularly valuable in the health care context, as this area is characterized by uncertainties that may lead to serious and far-reaching consequences, as is apparent in times of the COVID-19 pandemic [ 104 ]. Second, it was shown that interactive information-seeking strategies, such as app use, are appropriate for reducing related uncertainties (eg, privacy and performance risks). By collecting the data in two measurement periods (before and after the release of the app) and calculating difference variables to quantify the uncertainty reduction, we validated the impact of the use of a technology on uncertainty reduction. The use of specific uncertainty reductions as outcome variables is theoretically stronger for URT than the use of outcome variables such as satisfaction proposed by Venkatesh et al [ 28 ]. Third, further theoretical contributions were made by integrating recent transparency research [ 49 ] into URT. Thereby, our results highlighted the importance of considering transparency as a multidimensional construct [ 49 ]. Transparency perceptions are essential as they form the basis for active and passive information-seeking strategies. By using the recent DCA-transparency scale [ 63 ], we further elaborated on the role of transparency (ie, information quality) in URT as proposed by Venkatesh et al [ 28 ]. Finally, it was shown that trust transfer theory holds true in the investigated setting. Although trust in the government is not a major antecedent for initial trust in COVID-19 tracing apps, individuals’ trust in the government should still be considered in governmental technology publishing.

Implications for Practice

The adoption rates of voluntary COVID-19 tracing apps differ largely among countries and are mostly below the critical thresholds, which hinders their effectiveness [ 14 , 20 ]. To improve acceptance, governments can adopt the following implications in their communication strategies. First, governments that introduce a voluntary COVID-19 tracing app (or other technologies) should engage in a transparent communication process. A supply of sufficient information, which must be perceived as accurate, is thus required. However, transparent communication only works if the service itself exceeds certain standards such as data privacy and security [ 22 ]. Second, interactive information-seeking strategies of individuals must be managed. These strategies (eg, app use) are shown to be efficient in terms of uncertainty reduction. Hence, governments should provide appropriate formats to enable interactive information seeking before release. Such formats can be demo versions, realistic previews, question and answer sessions, or even hackathons. Finally, our findings are extendable to other technologies and settings. For example, if there are other digital trends in the health care system (eg, digital health record or video doctor), our results can be applied to achieve (voluntary) technology (mass) acceptance. Whenever governments or organizations develop and publish new services (eg, disaster alarm app), other uncertainties such as financial risk, time risk, or psychological risk may arise and should be considered. The conscious management of the (transparent) publication process can promote a successful launch of a technology. By understanding the multidimensional nature of perceived information quality, both organizations and governments can reflect and develop their own technology implementation strategy. Hence, many of the implications outlined here may also be relevant to future pandemics and public health crises.

Limitations and Future Research

Although the results of this study provide important insights, the study has some limitations. As the results are based on data related to the German COVID-19 tracing app, the generalizability of our findings for other regions may be restrained due to cultural differences. Thus, future research should expand this study by including other countries. Further, actual app use was self-reported by the participants and might be untrue in some cases. However, the app adoption rate in our sample was comparable with the adoption rate in the German population during the second survey wave (see Table 2 ). To advance URT, researchers can examine the communication channels that are most suitable to ensure transparency and reduce different uncertainties. After some studies have dealt with the design [ 105 ], the technical configuration [ 22 ], and the ethical guidelines [ 106 ], we studied the requirements for adequate app implementation and communication. Therefore, future research should investigate means to ensure mid- and long-term app acceptance and use.

For most of the population, the Corona-Warn-App was a new concept at the time of its release. Since then, the app and its functionality have become relatively well known and widespread. For this reason, follow-up research should investigate the role of descriptive norms (ie, how others actually behave) besides subjective norms, which we have investigated in the form of social influence (ie, how important others think one should behave), for the adoption process [ 107 ].

Moreover, the data underlying this study originated a few days (t1) before and 4 weeks (t2) after the launch of the COVID-19 tracing app in Germany and, thus, between the first and second waves of infection. In the meantime, various measures against the pandemic have been implemented, and more information about the virus, its spread, and mortality are available. These insights should be considered in follow-up studies. For example, the distribution and adoption of new SARS-CoV-2 vaccines represent a milestone in the fight against the pandemic. Therefore, follow-up studies should examine whether these insights influence the use of the COVID-19 tracing app and uncertainty perceptions.

A key strategy in fighting the COVID-19 pandemic is the testing and subsequent isolation of individuals who are potentially infected. The automatic contact tracing via mobile apps offers an important contribution to the decision of which people need to be tested with regard to limited testing capacities. Our study offers original insights on the factors driving the mass acceptance of COVID-19 tracing apps to identify infection chains and control the pandemic. Building on URT and through a longitudinal empirical study on the adoption process, we investigated how uncertainty reduction measures affect the adoption of COVID-19 tracing apps and how their use affects the perception of different risks. We analyzed representative data through CB-SEM. The results revealed that the transparency dimensions of disclosure and accuracy, as well as social influence, trust in government, and initial trust positively affect the adaptation process, whereas no effect was observed for the transparency dimension clarity. Further, we showed that the actual use of COVID-19 tracing apps reduces the perceived uncertainty regarding performance and privacy risks, but no effect on the reduction of social risks and COVID-19 concerns was identified. Finally, we derived theoretical and practical implications concerning the communication strategy of contact tracing apps in particular and for health care technologies in general.

Abbreviations

Multimedia Appendix 1

Multimedia appendix 2.

Conflicts of Interest: None declared.

• < Previous

Home > Theses and Dissertations > Theses > All Theses > 495

Temporary Organizational Change and Uncertainty: Applying Uncertainty Reduction Theory and Style Analyses to Email

Vicki Rhodes , Clemson University Follow

Date of Award

Document type, degree name.

Master of Arts (MA)

Legacy Department

Professional Communication

Committee Chair/Advisor

Katz, Steven B

Committee Member

Williams , Sean

Wiesman , Daryl

This study explores how employees express uncertainty and enact uncertainty reduction techniques through electronic communication, specifically email, during temporary inter-organizational change. The context of the study is within the work environment of a nonprofit entity in the Southern region of the United States that employs just under 20 staff members and coordinates with approximately 135 partner staff affiliates on a daily basis. The Executive Director's medical leave of about three months requires that job responsibilities and organizational roles be temporarily restructured. Because email is the preferred and primary method of communication in this organization, such communications were chosen as the subject for analysis. This pilot case study is unique in that it weds qualitative and quantitative, inductive and deductive, and Uncertainty Reduction Theory and rhetorical style analysis. A mixed methods approach is employed to fully gauge trends within the organization for seeking information. The email data are coded for source origination and, drawing from prior research by Miller and Jablin (1991) and Miller (1996), coded for information-seeking tactics (indirect/disguising conversation, overt/direct, testing, and third party) and information types (appraisal, normative, referent and social). Additionally, the data are classified by parts of speech and grouped by themes that appear which suggest employee values in the diction. An application of Latour and Woolgar's (1986) statement types for modality attributes levels of certainty found within the categories to degrees of ambiguity and anxiety among employees during temporary organizational change. This study incorporates these URT principles and rhetorical approaches to characterize the intricate relationship among uncertainty, information-seeking, diction use, email and temporary change within organizations.

Recommended Citation

Rhodes, Vicki, "Temporary Organizational Change and Uncertainty: Applying Uncertainty Reduction Theory and Style Analyses to Email" (2008). All Theses . 495. https://open.clemson.edu/all_theses/495

Since January 02, 2014

Included in

Work, Economy and Organizations Commons

To view the content in your browser, please download Adobe Reader or, alternately, you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.

Advanced Search

• Notify me via email or RSS
• All Collections
• Expert Gallery
• Theses & Dissertations
• Selected Works Gallery
• Student Works
• Conferences
• Open Access Fund Collection
• Historic Collections

Useful Links

• Author Resources
• Author Rights
• Scholarly Publishing Information
• Graduate School

Home | About | FAQ | My Account | Accessibility Statement

Privacy Copyright

Uncertainty Reduction Theory: 10 Examples and Definition

Viktoriya Sus (MA)

Viktoriya Sus is an academic writer specializing mainly in economics and business from Ukraine. She holds a Master’s degree in International Business from Lviv National University and has more than 6 years of experience writing for different clients. Viktoriya is passionate about researching the latest trends in economics and business. However, she also loves to explore different topics such as psychology, philosophy, and more.

Learn about our Editorial Process

Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

Uncertainty Reduction Theory (URT) is a communication theory that focuses on reducing anxiety in social interactions. It explores how communication is used to gain knowledge, create understanding, and reduce uncertainty.

To communicate well, people must have insight into their conversation partner’s background information.

This knowledge enables them to accurately anticipate the other person’s responses and reactions, forming a strong bond over time.

As an example of uncertainty reduction theory, two new acquaintances may build trust by exchanging names and general facts about themselves. In doing so, they each gain insight into the other person’s beliefs, values, and perspectives.

Such practice allows them to understand each other better and predict their behavior in future interactions. As a result of this chance encounter, two relative strangers can now forge an immediate bond of trust and form lasting relationships.

Definition of Uncertainty Reduction Theory

Uncertainty reduction theory, developed in 1975 by Charles Berger and Richard Calabrese, is one of the few communication theor ies that explicitly considers the initial interaction between people before the actual communication process.

It postulates that when two people first meet, they experience a high level of uncertainty or discomfort due to the unknown.

According to Brunner (2019),

“…uncertainty reduction theory asserts that people need to reduce uncertainty about others by gaining information about them” (p. 208)

This theory suggests that by sharing information about themselves, people can reduce the levels of uncertainty and tension between them to create a more comfortable relationship (Costa, 2015).

So, in simple terms, URT explains how communication is used to reduce the unease experienced between two people when they first meet.

By exchanging information, those involved can build a mutual understanding, which leads to more trust and comfort.

Related Theory: Media Richness Theory

Uncertainty Reduction Theory Examples

• Introducing Yourself : When two people first meet, they usually introduce themselves to each other. This simple act of exchanging names and basic information helps to create a level of familiarity and comfort between them.
• Conversation : Engaging in meaningful conversations is another way to reduce uncertainty. People can get to know each other better and gain a deeper understanding by discussing topics of interest.
• Asking Questions : Asking questions is a great way to learn more about the other person and reduce uncertainty. It could be anything from personal opinions to life experiences. For example, asking someone about their favorite hobby or where they grew up is a great way to reduce tensions.
• Sharing Personal Facts : Talking about personal experiences, such as schooling, family life, or career goals, can help people to get to know each other better. It can also help them to gain insight into the other person’s values and beliefs.
• Nonverbal Cues : The way people carry themselves- their facial expressions, body language, posture, etc.- conveys much information about them. By monitoring nonverbal cues, people can understand the other person’s emotions and reactions (see also: high context communication ).
• Making Eye Contact : Eye contact is one of the most potent nonverbal cues people use to communicate with others. Making eye contact during conversations can create a feeling of trust and understanding between people.
• Body Language : People use body language to display emotions and intentions. Gestures such as smiling, nodding, or putting your hand on someone’s shoulder can help reduce uncertainty and create a feeling of comfort.
• Small Talk : Making small talk, such as discussing the weather or current events, is another way to reduce uncertainty and create a sense of connection between people. Even though it may seem mundane, this type of conversation can be beneficial for getting to know someone better.
• Teasing : Teasing is a great way to reduce uncertainty and create a more comfortable relationship. However, ensuring that the teasing isn’t too personal or offensive is essential.
• Compliments : Complimenting someone on their appearance, work ethic, intelligence, etc., can help to build trust and understanding between two people. People appreciate being recognized and complimented, which helps reduce uncertainty.

Origins of Uncertainty Reduction Theory

The uncertainty reduction theory is based on the information theory developed by Claude Shannon and Warren Weaver in 1948 (Cobley & Schultz, 2013) .

Scientists believe that at the initial stage of communication, uncertainty appears due to the expectation of different behaviors of the interlocutor and/or the high probability of using each of the possible behaviors.

According to information theory, uncertainty decreases with a decrease in alternatives and/or with a repeated selection of the same reaction from all possible in a particular situation (Cobley & Schultz, 2013).

URT was proposed in 1975 to explain the behavior patterns of strangers on first contact. Berger and Calabrese noticed that when communicating with strangers, people experience insecurity because they do not know what to expect (Cushman & Kovačic, 1995).

However, with further communication, people receive more information, contributing to the rapid reduction of uncertainty (and, as a side-effect, reduction in likelihood of the false consensus bias ).

Initially, the theory was a set of axioms that described the relationship between uncertainty and critical factors in developing relationships. Later, out of 7 fundamental axioms, 20 theorems were formulated by deduction.

Main Types of Uncertainty

According to Berger and Calabrese, the level of uncertainty directly depends on the number of options for the expected actions and reactions. So, they distinguished two main types of uncertainty – cognitive and behavioral.

1. Cognitive Uncertainty

Cognitive uncertainty is associated with the lack of knowledge about the other person. It involves questions such as “Who is this person? What are their values, beliefs, and opinions?” (Costa, 2015).

The degree of cognitive uncertainty involved in the beliefs and attitudes that two parties have towards each other is known as cognitive uncertainty.

Early interactions are particularly uncertain due to a lack of understanding regarding the other party’s beliefs or feelings.

2. Behavioral Uncertainty

Behavioral uncertainty is associated with difficulty predicting how the other person will act or react in certain situations.

It involves questions such as “What will they do? How will they respond to my actions?” (Costa, 2015).

The degree of behavioral uncertainty is based on the number of alternative ways an individual can behave in a given situation. Early interactions are particularly uncertain due to a lack of experience with the other person. 

7 Key Axioms of Uncertainty Reduction Theory

U ncertainty reduction theory (URT) has seven fundamental axioms that describe the connection between communication and uncertainty (Floyd et al., 2017) .

• Verbal Communication : As the level of uncertainty between strangers begins to transition into an entry phase, verbal communication will likely increase. This decrease in tension for each individual involved further encourages conversation between them. Thus, with a reduced sense of apprehension comes more talk and dialogue amongst parties.
• Non-Verbal Communication : Nonverbal communication, such as facial expressions and gestures, is also essential in reducing uncertainty. When non-verbal affiliative expressiveness rises, uncertainty levels in a primary interaction situation will plummet.
• Information Seeking : At the onset of any conversation, questions are exchanged to gain clarity and understanding. When there is a high degree of uncertainty present, more queries will be posed as a means to acquire additional information. As levels of ambiguity decline, so does the need for questioning behaviors.
• Intimacy Level: When people experience a higher level of doubt, there is an observable decrease in the intimacy of their discourse. On the other hand, when uncertainty levels are low, one can notice an increase in closeness within the communication.
• Reciprocity : When there is an abundance of doubt, the rate of mutual action increases. Conversely, when uncertainty is low, this leads to a lower level of reciprocity.
• Similarity : When we find similarities between people, it boosts our confidence and decreases doubt. On the other hand, when there is dissimilarity among us, uncertainty increases. Thus, differences in one another lead to a heightened sense of unpredictability, while similarities make us more comfortable (which is explained by the affinity bias ).
• Liking : As the level of uncertainty rises, people’s overall liking for something decreases. On the other hand, as levels of uncertainty fall over time, likability increases in response.

Drawing on their seven original axioms, Berger and Calabrese developed the following set of theorems. These theorems explain how uncertainty is reduced in interpersonal communication.

Benefits of Uncertainty Reduction Theory

URT has several benefits for both individuals and groups in interpersonal communication. They include improved understanding, increased trust, and more explicit expectations.

• Improved Understanding : By utilizing this theory, people can transcend the uncertainty associated with communication and better predict the behavior of others. Furthermore, it provides them with a deeper understanding of how their own actions may be interpreted in any given situation.
• Increased Trust : When two individuals are more familiar with each other, the likelihood of establishing trust is amplified. By quelling any sense of uncertainty and mystery between them, one can safely assume that confidence will grow as a result. Thus, reducing doubt leads to an increase in faith among both parties.
• Clearer Expectations : As people get to know each other better, they are able to establish more precise expectations. It will help them steer clear of potential future disagreements since they understand what actions and attitudes should be anticipated from the other person.

So, URT helps people to better understand and interact with each other. Besides, it also helps them to develop a sense of trust and form clearer expectations. It, in turn, leads to more effective communication, which benefits both parties.

Uncertainty reduction theory proposes that the more conversation exchanges occur between interactants, the lower the uncertainty. 

By engaging in discourse, they gain knowledge and insight, which reduces unease or trepidation. In essence, the key to lowering ambiguity lies in the communication itself!

This theory was initially proposed by Jurgen Habermas and further developed by Charles Berger and Richard Calabrese to explain how individuals can reduce the uncertainty they experience when engaging in interpersonal communication.

URT has numerous benefits for interpersonal communication, including increased understanding, trust, and clear expectations. Ultimately, URT explains why communication is necessary for successful relationships between individuals.

Read Next: Interpersonal Communication Examples

Brunner, B. R. (2019). Public relations theory: Application and understanding. John Wiley & Sons, Inc.

Cobley, P., & Schulz, P. (2013). Theories and models of communication. Walter De Gruyter.

Costa, C. (2015). Uncertainty reduction and game communication: How does uncertainty reduction theory come into play?  https://minds.wisconsin.edu/bitstream/handle/1793/77916/Costa2015.pdf?sequence=1&isAllowed=y

Cushman, D. P., & Kovačic, B. (1995). Watershed research traditions in human communication theory. N.Y. State University of New York Press.

Floyd, K., Schrodt, P., Erbert, L. A., & Trethewey, A. (2017). Exploring communication theory: Making sense of us. Routledge.

• Viktoriya Sus (MA) #molongui-disabled-link Cognitive Dissonance Theory: Examples and Definition
• Viktoriya Sus (MA) #molongui-disabled-link 15 Free Enterprise Examples
• Viktoriya Sus (MA) #molongui-disabled-link 21 Sunk Costs Examples (The Fallacy Explained)
• Viktoriya Sus (MA) #molongui-disabled-link Price Floor: 15 Examples & Definition

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 23 Achieved Status Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Ableism Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Defense Mechanisms Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Theory of Planned Behavior Examples

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

• Memberships

Uncertainty Reduction Theory: Examples and Definition

Uncertainty Reduction Theory: this article provides a practical explanation of the Uncertainty Reduction Theory . After reading, you will understand the basics of this powerful communication theory. Other highlights include: examples of Uncertainty Reduction Theory, the axioms of the theory and its stages of interaction. Enjoy reading!

What is the Uncertainty Reduction Theory?

Charley Berger and Richard Calabrese created the Uncertainty Reduction Theory in 1975 in an attempt to describe the communication process when people meet for the first time.

The theory is concerned with how people communicate and how knowledge is shared and understood. This theory of interpersonal communication is a result of Berger’s and Calabrese’s research ‘Some Explorations in Initial Interaction and Beyond: Towards a Development Theory of Interpersonal Relationship’.

According to the Uncertainty Reduction Theory, it is a typical human trait to doubt future actions of a person with one have just met.

Berger and Calabrese next concluded that if one meets a new person, both have many questions about each other. For this reason, the Uncertainty Reduction Theory focusses on the predictability of the behavior of people.

Based on the Uncertainty Reduction Theory, people should retrieve general demographic information about other people to reduce the level of uncertainty they have about other people behavior.

The theory additionally shows that as soon as the necessary information about interactants has been retrieved, it will be easier for both predict behaviors and actions of each other. Other strategies to reduce uncertainty include the passive strategy, active strategy and interactive strategy.

According to the Uncertainty Reduction Theory, people can have cognitive- and behavioral uncertainty. Both might occur when two people meet for the first time. Cognitive uncertainty is concerned with the beliefs and attitudes , and thus, deals more with discovering who one is.

Behavioral uncertainty is concerned with how an individual would act in a particular situation. For instance, one could have doubts about why another person act in a specific way, and as a consequence, questions may arise that could make the individual uncertain.

This Theory is created based on various axioms. Axioms are statements without proof, but which are generally accepted. It is can additionally also be used for a starting point for discussion. It is expected that because of different axioms, the level of uncertainty reduces. In addition, the theory has classified conversations of interactants in stages. These are later described together with the axioms.

Uncertainty Reduction Theory examples

The Uncertainty Reduction Theory can be applied in any situation because everyday life demonstrates to full of uncertainty, in terms of communication. For instance, people take public transport, and unconsciously they communicate with the transport carriers and passengers and never really know how others would think or react.

Another example can be that an individual is about to make an appointment or when two employees communicate with each other and who have never met in the past. Uncertainty can for this reason be found every day in human behavior.

Axioms of the Uncertainty Reduction Theory

Figure 1 – The Seven Axioms of the Uncertainty Reduction Theory

Verbal communication

Verbal communication is concerned with the number of words that are exchanged during the initial conversation between interactants. It is expected that participants in the conversation have a sense of uncertainty when they first meet, but as more words are exchanged, the uncertainty diminishes and the interactants communicate more easily. As the uncertainty diminishes, communication among two people increases.

Non-verbal expressions

Non-verbal expressions are expressions like hand gestures, eye contact, and the physical distance between two people. As stated in the Uncertainty Reduction Theory, a relation exists between the number of non-verbal expressions and the level of uncertainty.

It is believed that if a person shows more positive non-verbal expressions such as eye contact and smiling, the level of uncertainty of the other person diminishes. When this happens, the interaction between two individuals will increase because of a higher level of trust.

Information seeking

As the level of uncertainty diminishes, the interactants ask more simple questions. These questions could be demographically related because these are simple questions to answer, but also questions about one’s job position or where one grew up or lives.

Personal information about attitudes and opinion are at this stage not shared yet. Both interactants seek information by observing the other person.

According to the Uncertainty Reduction Theory, when the level of uncertainty decreases further, the number of questions asked will also be lower.

Self-disclosure

According to the Uncertainty Reduction Theory, in the self-disclosure axiom, it is believed that individual share more personal information to gain the trust of the other interactant. This happens because the level of uncertainty decreases significantly.

If for some reason one of the interactants still have a high level of uncertainty, it is likely that one of the interactants will share limited personal or sensitive information.

Reciprocity

Reciprocity is concerned with the degree to which interactants expect another to share similar information if one has shared something.

For example, in the self-disclosure example, if one of the interactants share personal information, it is then expected that the other person also shares similar information.

In this way, the level of uncertainty will decrease for both, and interaction increases. Based on the Uncertainty Reduction Theory, as the level of uncertainty diminishes, the need to share personal information decreases because of a higher level of trust.

The Uncertainty Reduction Theory additionally found that the level of uncertainty also decreases when interactants share the same interests. Sharing the same interest eliminate communication barriers and enable the interactants to build a relationship.

Sharing the same interests can be related to many subjects, including personal matters such as opinions but also hobbies.

Liking deals with the emotional aspects of interactants. It is believed that if participants of a conversation have positive feelings about each other, the level of uncertainty will be lower, and the number of conversations increases.

As a result, the interactants quickly build a relationship. According to the Uncertainty Reduction Theory, it is additionally expected that when interactants share positive feelings, it is easier for both to understand another.

The Uncertainty Reduction Theory stages of interaction

The Uncertainty Reduction Theory has classified interactions among interactants into three stages which are underneath described. Each stage presents behaviors that interactants like or dislike.

Based on the interactants’ preferences, it is decided if communication will be continued. The stages are as follow:

The entry stage

Since this is the first stage of interaction between two individuals, communication is mostly based on observing one’s behavioral norms and values. These could include watching how one greets or smile.

Another essential part of the entry stage of the Uncertainty Reduction Theory is to exchange basic information, such as information about one’s job position or place of residence, as previously described in the first axioms.

When there is a mutual interest among the interactants, the relationship will further develop and reach the next stage.

The personal stage

In the personal stage of the Uncertainty Reduction Theory, it is expected that the interactants actively attempt to identify one’s norms, values, and attitudes. The goal is to find mutual indicators that help to develop the relationship further.

Many times, this stage is reached after the interactants have had conversations various times. As the interactants communicate more frequently in this stage, more personal and sensitive information is additionally shared.

The exit stage

The exit stage of the uncertainty reduction can also be seen as an evaluation of the relationship phase.

Interactants will, in this stage, determine if the relationship is going to develop further or if it is going to end. Further development of the relationship can be dependent on various factors.

For instance, if a relationship has to build for economic interests, the relationship will probably further develop, but if the interactants have no mutual interests, further development might not be an option.

Uncertainty Reduction Theory: reasons to reduce uncertainty

Reducing the level of uncertainty among interactants is most likely depended on the importance of future communication with the interactants.

For instance, if an individual is taking pubic transport and meet another person on the way, it might not be necessary to learn from this person.

But if an individual starts a new job position at a new company, the new employee will have an asset if the individual knows how employees and managers think and react to particular situations. In this case, it makes sense to learn more about the people on the work floor and develop relationships.

Another motivation can be that interactants can reward another. Many times, this type of motivation is applied to potential love partners, but it can also be based on friendships with people who can make a difference on both interactants.

In both cases, it makes sense to reduce the level of uncertainty, and for this reason, communication barriers will be eliminated, and relationships are further developed.

Final word on the Uncertainty Reduction Theory

Everyone has a level of uncertainty when people first meet. The motivation to develop a relationship, and thus, lower the level of uncertainty, is depended on the situation and individual’s goal.

According to the Uncertainty Reduction Theory, uncertainty is reduced when interactants have various conversations in which they learn from each other. The more words are exchanged, the lower the level of uncertainty. However, the environmental setting is crucial for determining if a relationship needs to be developed.

If a relationship is going to be developed, the likelihood is high that the relationship will go through the entry stage, personal stage, and exit stage.

It’s Your Turn

What do you think? Are you familiar with Uncertainty Reduction Theory? Do you recognize the explanation or do you have more suggestions? What is your experience with uncertainty reduction?

Share your experience and knowledge in the comments box below.

More informaton about the Uncertainty Reduction Theory

• Bradac, J. J. (2001). Theory comparison: Uncertainty reduction, problematic integration, uncertainty management, and other curious constructs . Journal of Communication, 51(3), 456-476.
• Gudykunst, W. B., & Nishida, T. (1984). Individual and cultural influences on uncertainty reduction . Communications Monographs, 51(1), 23-36.
• Gudykunst, W. B., YANG, S. M., & Nishida, T. (1985). A cross‐cultural test of uncertainty reduction theory: Comparisons of acquaintances, friends, and dating relationships in Japan, Korea, and the United States . Human Communication Research, 11(3), 407-454.
• Kramer, M. W. (1999). Motivation to reduce uncertainty: A reconceptualization of uncertainty reduction theory . Management Communication Quarterly, 13(2), 305-316.
• Parks, M. R., & Adelman, M. B. (1983). Communication networks and the development of romantic relationships: An expansion of uncertainty reduction theory . Human Communication Research, 10(1), 55-79.
• Sunnafrank, M. (1986). Predicted outcome value during initial interactions: A reformulation of uncertainty reduction theory . Human Communication Research , 13(1), 3-33.

How to cite this article: Zeeman, A. (2019). Uncertainty Reduction Theory . Retrieved [insert date] from Toolshero: https://www.toolshero.com/communication-methods/uncertainty-reduction-theory

Original publication date: 12/09/2019 | Last update: 08/28/2024

Add a link to this page on your website: <a href=”https://www.toolshero.com/communication-methods/uncertainty-reduction-theory/”>Toolshero: Uncertainty Reduction Theory</a>

Did you find this article interesting?

Your rating is more than welcome or share this article via Social media!

Average rating 4.1 / 5. Vote count: 10

No votes so far! Be the first to rate this post.

We are sorry that this post was not useful for you!

Let us improve this post!

Tell us how we can improve this post?

Alexander Zeeman

Alexander Zeeman is Content Manager at ToolsHero where he focuses on Content production, Content management and marketing. He is also an International Business student at Rotterdam Business school. Currently, in his study, working on the development of various management competencies and improving operational business processes.

Related ARTICLES

Presentation Skills explained plus examples

Transactional Analysis: the Theory and Examples

Rose of Leary model explained including a Test

Role Playing Game (RPG) explained plus example

Minto Pyramid Principle by Barbara Minto

Listening Comprehension: the Definition and Skills

Also interesting.

Town Hall meeting: the Definition and Basics steps

Keep It Simple Stupid: the KISS principle

Logical Levels of Change (Bateson and Dilts)

Leave a reply cancel reply.

You must be logged in to post a comment.

BOOST YOUR SKILLS

Toolshero supports people worldwide ( 10+ million visitors from 100+ countries ) to empower themselves through an easily accessible and high-quality learning platform for personal and professional development.

By making access to scientific knowledge simple and affordable, self-development becomes attainable for everyone, including you! Join our learning platform and boost your skills with Toolshero.

POPULAR TOPICS

• Change Management
• Marketing Theories
• Problem Solving Theories
• Psychology Theories

ABOUT TOOLSHERO

• Free Toolshero e-book
• Memberships & Pricing

• DOI: 10.4135/9781483376493.n319
• Corpus ID: 15530566

Uncertainty Reduction Theory

• Mark V. Redmond
• Published 2015

54 Citations

Finding joy in uncertainty, 15 years later: considering the ala/cipa dispute through uncertainty reduction theory, communication network in reducing uncertainty, the use of uncertainty reduction theory in communication.

• Highly Influenced

The purposes of interpersonal communication. A survey to find the most likely general reasons why people engage in communication

“balancing field-general and subfield-specific contributions when addressing, utilizing, or assessing a theory’s explanatory power”, uncertainty reduction strategies on share in jar skincare’s purchase intention, managing uncertainty: lone parents' time horizons and agency in the context of the covid-19 pandemic, diffusion in information-seeking networks: testing the interaction of network hierarchy and fluidity with agent-based modeling, the association between uncertainty intolerance, perceived environmental uncertainty, and ego depletion in early adulthood: the mediating role of negative coping styles, 40 references, predicted outcome value during initial interactions a reformulation of uncertainty reduction theory, when ignorance is bliss the role of motivation to reduce uncertainty in uncertainty reduction theory, theory comparison: uncertainty reduction, problematic integration, uncertainty management, and other curious constructs., predicted outcome value and uncertainty reduction theories a test of competing perspectives, from explanation to application, toward a theory of motivated information management, measuring the sources and content of relational uncertainty, expectations about initial interaction an examination of the effects of global uncertainty, some explorations in initial interaction and beyond: toward a developmental theory of interpersonal communication.

• Highly Influential

Communication and Uncertainty Management.

Related papers.

Showing 1 through 3 of 0 Related Papers

An Adaptive Distributionally Robust Optimization Approach for Optimal Sizing of Hybrid Renewable Energy Systems

• Published: 26 August 2024

Cite this article

• Ali Keyvandarian   ORCID: orcid.org/0000-0003-0690-4222 1 &
• Ahmed Saif 1  

41 Accesses

Explore all metrics

Hybrid renewable energy systems (HRESs) that integrate conventional and renewable energy generation and energy storage technologies represent a viable option to serve the energy demand of remote and isolated communities. A common way to capture the stochastic nature of demand and renewable energy supply in such systems is by using a small number of independent discrete scenarios. However, some information is inevitably lost when extracting these scenarios from historical data, thus introducing errors and biases to the design process. This paper proposes two frameworks, namely robust-stochastic optimization and distributionally robust optimization , that aim to hedge against the resulting uncertainty of scenario characterization and probability, respectively, in scenario-based HRES design approaches. Mathematical formulations are provided for the nominal, stochastic, robust-stochastic, distributional robust, and combined problems, and directly-solvable tractable reformulations are derived for the stochastic and the distributional robust cases. Furthermore, an exact column-and-constraint-generation algorithm is developed for the robust-stochastic and combined cases. Numerical results obtained from a realistic case study of a stand-alone solar-wind-battery-diesel HRES serving a small community in Northern Ontario, Canada reveal the performance advantage, in terms of both cost and utilization of renewable sources, of the proposed frameworks compared to classical deterministic and stochastic models, and their ability to mitigate the issue of information loss due to scenario reduction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Robust optimal sizing of a stand-alone hybrid renewable energy system using dynamic uncertainty sets

A robust optimal sizing of renewable-rich multi-source microgrid under uncertainties with multi-storage options

A robust optimization model for microgrid considering hybrid renewable energy sources under uncertainties

Data availability.

The energy output data and the system component data used in this research are available through Homer Pro software [ 16 ], and the demand data are available via Ontario’s Independent Energy System Operator (IESO) website [ 17 ].

The intuition behind this result is that energy loss in the BB charge/discharge cycle is proportional to the amount of energy charged/discharged. Thus, by charging or discharging the “net” amount ( \(|e_t^{in}-e_t^{out}|\) ) only in every period, losses are minimized.

Alismail, F., Xiong, P., Singh, C.: Optimal wind farm allocation in multi-area power systems using distributionally robust optimization approach. IEEE Trans. Power Syst. 33 (1), 536–544 (2017)

Article   Google Scholar  

Allee, A., Williams, N.J., Davis, A., Jaramillo, P.: Predicting initial electricity demand in off-grid Tanzanian communities using customer survey data and machine learning models. Energy Sustain. Dev. 62 , 56–66 (2021)

Bandi, C., Bertsimas, D.: Tractable stochastic analysis in high dimensions via robust optimization. Math. Program. 134 , 23–70 (2012)

Article   MathSciNet   Google Scholar  

Baringo, L., Amaro, R.S.: A stochastic robust optimization approach for the bidding strategy of an electric vehicle aggregator. Electr. Power Syst. Res. 146 , 362–370 (2017)

Ben-Tal, A., Den Hertog, D., De Waegenaere, A., Melenberg, B., Rennen, G.: Robust solutions of optimization problems affected by uncertain probabilities. Manage. Sci. 59 (2), 341–357 (2013)

Ben-Tal, A., Nemirovski, A.: Robust solutions of linear programming problems contaminated with uncertain data. Math. Program. 88 (3), 411–424 (2000)

Ben-Tal, A., Nemirovski, A.: Robust optimization-methodology and applications. Math. Program. 92 , 453–480 (2002)

Bertsimas, D., Shtern, S., Sturt, B.: A data-driven approach to multistage stochastic linear optimization. Manage. Sci. 69 (1), 51–74 (2023)

Bertsimas, D., Zhang, M.: A practically efficient approach for solving adaptive distributionally robust linear optimization problems. In: Optimization Online (2017)

Billionnet, A., Costa, M., Poirion, P.: Robust optimal sizing of a hybrid energy stand-alone system. Eur. J. Oper. Res. 254 (2), 565–575 (2016)

Chen, Y., Li, P., Zhang, B.: Bayesian renewables scenario generation via deep generative networks. In: 2018 52nd Annual Conference on Information Sciences and Systems (CISS), pp. 1–6. IEEE (2018)

Das, B.K., Tushar, M.S.H.K., Hassan, R.: Techno-economic optimisation of stand-alone hybrid renewable energy systems for concurrently meeting electric and heating demand. Sustain. Cities Soc. 68 , 102763 (2021)

Dolatabadi, A., Ebadi, R., Mohammadi-Ivatloo, B.: A two-stage stochastic programming model for the optimal sizing of hybrid PV/diesel/battery in hybrid electric ship system. J. Oper. Autom. Power Eng. 7 (1), 16–26 (2019)

Google Scholar  

Dong, W., Chen, X., Yang, Q.: Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability. Appl. Energy 308 , 118387 (2022)

Fanzeres, B., Street, A., Barroso, L.A.: Contracting strategies for renewable generators: a hybrid stochastic and robust optimization approach. IEEE Trans. Power Syst. 30 (4), 1825–1837 (2014)

HOMER. Energy modeling software for hybrid renewable energy systems. http://homerenergy.com . Accessed 01 March 2023

IESO. Independent electricity system operator website. https://www.ieso.ca . Accessed 01 March 2023

Iverson, Z., Achuthan, A., Marzocca, P., Aidun, D.: Optimal design of hybrid renewable energy systems (HRES) using hydrogen storage technology for data center applications. Renew. Energy 52 , 79–87 (2013)

Jabr, R.A.: Distributionally robust CVaR constraints for power flow optimization. IEEE Trans. Power Syst. 35 (5), 3764–3773 (2020)

Ketchen, D.J., Shook, C.L.: The application of cluster analysis in strategic management research: an analysis and critique. Strateg. Manag. J. 17 (6), 441–458 (1996)

Keyvandarian, A., Saif, A.: Robust optimal sizing of a stand-alone hybrid renewable energy system using dynamic uncertainty sets. Energy Syst., 1–27 (2022)

Keyvandarian, A., Saif, A.: Optimal sizing of a reliability-constrained, stand-alone hybrid renewable energy system using robust satisficing. Renew. Energy (2023)

Li, J., Zhou, J., Chen, B.: Review of wind power scenario generation methods for optimal operation of renewable energy systems. Appl. Energy 280 , 115992 (2020)

Li, R., Guo, S., Yang, Y., Liu, D.: Optimal sizing of wind/concentrated solar plant/electric heater hybrid renewable energy system based on two-stage stochastic programming. Energy 209 , 118472 (2020)

Lin, B., Zhang, C.: A novel hybrid machine learning model for short-term wind speed prediction in inner Mongolia. China. Renew. Energy 179 , 1565–1577 (2021)

Lin, F., Fang, X., Gao, Z.: Distributionally robust optimization: a review on theory and applications. Numer. Algebra Control Optim. 12 (1), 159–212 (2022)

Liu, G., Xu, Y., Tomsovic, K.: Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization. IEEE Trans. Smart Grid 7 (1), 227–237 (2015)

Mohajerin Esfahani, P., Kuhn, D.: Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations. Math. Program. 171 (1), 115–166 (2018)

Mínguez, R., García-Bertrand, R.: Robust transmission network expansion planning in energy systems: improving computational performance. Eur. J. Oper. Res. 248 (1), 21–32 (2016)

Mínguez, R., van Ackooij, W., García-Bertrand, R.: Constraint generation for risk averse two-stage stochastic programs. Eur. J. Oper. Res. 288 (1), 194–206 (2021)

Nazari-Heris, M., Mohammadi-Ivatloo, B.: Application of robust optimization method to power system problems. Classical and recent aspects of power system optimization, pp. 19–32 (2018)

Ning, C., You, F.: Data-driven decision making under uncertainty integrating robust optimization with principal component analysis and kernel smoothing methods. Comput. Chem. Eng. 112 , 190–210 (2018)

Pflug, G.C., Pohl, M.: A review on ambiguity in stochastic portfolio optimization. Set-Valued Var. Anal. 26 , 733–757 (2018)

Poonpun, P., Jewell, W.T.: Analysis of the cost per kilowatt hour to store electricity. IEEE Trans. Energy Convers. 23 (2), 529–534 (2008)

Pouraliakbari-Mamaghani, M., Saif, A., Kamal, N.: Reliable design of a congested disaster relief network: a two-stage stochastic-robust optimization approach. Socioecon. Plann. Sci. 86 , 101498 (2023)

Rahimi, M., Jahanbani Ardakani, F., Ardakani, A.J.: Optimal stochastic scheduling of electrical and thermal renewable and non-renewable resources in virtual power plant. Int. J. Electr. Power Energy Syst. 127 , 106658 (2021)

Saif, A., Zeineldin, H.H., Kennedy, S., Jr. Kirtley, J.L.: Multi-criteria design of a hybrid power system with stochastic supply. In: Proceedings of the 12th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS2012), Istanbul, Turkey (2012)

Shang, C., You, F.: Distributionally robust optimization for planning and scheduling under uncertainty. Comput. Chem. Eng. 110 , 53–68 (2018)

Sharafi, M., ElMekkawy, T.Y., Bibeau, E.L.: Optimal design of hybrid renewable energy systems in buildings with low to high renewable energy ratio. Renew. Energy 83 , 1026–1042 (2015)

Smith, J.E., Winkler, R.L.: The optimizer’s curse: Skepticism and postdecision surprise in decision analysis. Manage. Sci. 52 (3), 311–322 (2006)

Soykan, G., Er, G., Canakoglu, E.: Optimal sizing of an isolated microgrid with electric vehicles using stochastic programming. Sustain. Energy Grids Netw. 32 , 100850 (2022)

Van Ackooij, W., Danti Lopez, I., Frangioni, A., Lacalandra, F., Tahanan, M.: Large-scale unit commitment under uncertainty: an updated literature survey. Ann. Oper. Res. 271 (1), 11–85 (2018)

Van Parys, B.P.G., Mohajerin Esfahani, P., Kuhn, D.: From data to decisions: Distributionally robust optimization is optimal. Manage. Sci. 67 (6), 3387–3402 (2021)

Wang, R., Xiong, J., He, M., Gao, L., Wang, L.: Multi-objective optimal design of hybrid renewable energy system under multiple scenarios. Renew. Energy 151 , 226–237 (2020)

Zakaria, A., Ismail, F.B., Lipu, M.S.H., Hannan, M.A.: Uncertainty models for stochastic optimization in renewable energy applications. Renew. Energy 145 , 1543–1571 (2020)

Zeng, B., Zhao, L.: Solving two-stage robust optimization problems using a column-and-constraint generation method. Oper. Res. Lett. 41 (5), 457–461 (2013)

Zhang, C., Xu, Y., Dong, Z.Y., Ma, J.: Robust operation of microgrids via two-stage coordinated energy storage and direct load control. IEEE Trans. Power Syst. 32 (4), 2858–2868 (2016)

Zhao, C., Guan, Y.: Unified stochastic and robust unit commitment. IEEE Trans. Power Syst. 28 (3), 3353–3361 (2013)

Download references

Acknowledgements

We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), Discovery Grant # RGPIN 04745 held by the second author. We thank the anonymous reviewers for their insightful comments and suggestions that helped us significantly improve the paper.

Author information

Authors and affiliations.

Department of Industrial Engineering, Dalhousie University, 5269 Morris Street, Halifax, NS, B3H 4R2, Canada

Ali Keyvandarian & Ahmed Saif

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Ali Keyvandarian .

Additional information

Communicated by Wim van Ackooij.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: Demand and Supply Scenario Generation Through Clustering

To generate demand and supply scenarios for the stochastic schemes, one potential method involves treating each hour’s data as a potential scenario. However, this can result in a substantial increase in the number of variables and constraints within the model, leading to high computational costs. Additionally, it may lead to overfitting, thereby diminishing the out-of-sample effectiveness of the optimal solution. Among the numerous methods proposed in the literature for scenario generation, Li et al. [ 23 ] showed that distance-matching methods are preferable for wind and solar power outputs. In particular, k-means clustering is suggested as a simple and effective method for distance matching [ 23 ], thus we used it to extract sets of scenarios from the data. To determine the appropriate number of scenarios, denoted as | S |, the elbow method, as described by Ketchen and Shook [ 20 ], is employed. The idea behind this method is to find a balance between capturing sufficient variation through clusters while avoiding overfitting by having too many clusters. The elbow method involves plotting the explained variation against different numbers of clusters. Once a point is reached where the explained variation no longer increases significantly, | S | is set beyond that value. In this case, \(|S|=10\) scenarios are chosen based on the analysis. Figure  5 provides a visual representation of the elbow diagram, which showcases the ratio of “between groups variation” to “within groups variation.” This ratio serves as the explained variation parameter in the plot and is derived from a one-way ANOVA F-test statistic.

Elbow Diagram for various number of clusters

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Keyvandarian, A., Saif, A. An Adaptive Distributionally Robust Optimization Approach for Optimal Sizing of Hybrid Renewable Energy Systems. J Optim Theory Appl (2024). https://doi.org/10.1007/s10957-024-02518-y

Download citation

Received : 01 November 2023

Accepted : 13 August 2024

Published : 26 August 2024

DOI : https://doi.org/10.1007/s10957-024-02518-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Hybrid renewable energy systems
• Distributionally robust optimization
• Stochastic programming
• Robust-stochastic optimization
• Optimization under uncertainty
• Find a journal
• Publish with us
• Track your research