consumer behavior research agenda

• Search Menu
• Browse content in Arts and Humanities
• Browse content in Archaeology
• Anglo-Saxon and Medieval Archaeology
• Archaeological Methodology and Techniques
• Archaeology by Region
• Archaeology of Religion
• Archaeology of Trade and Exchange
• Biblical Archaeology
• Contemporary and Public Archaeology
• Environmental Archaeology
• Historical Archaeology
• History and Theory of Archaeology
• Industrial Archaeology
• Landscape Archaeology
• Mortuary Archaeology
• Prehistoric Archaeology
• Underwater Archaeology
• Urban Archaeology
• Zooarchaeology
• Browse content in Architecture
• Architectural Structure and Design
• History of Architecture
• Residential and Domestic Buildings
• Theory of Architecture
• Browse content in Art
• Art Subjects and Themes
• History of Art
• Industrial and Commercial Art
• Theory of Art
• Biographical Studies
• Byzantine Studies
• Browse content in Classical Studies
• Classical History
• Classical Philosophy
• Classical Mythology
• Classical Literature
• Classical Reception
• Classical Art and Architecture
• Classical Oratory and Rhetoric
• Greek and Roman Papyrology
• Greek and Roman Epigraphy
• Greek and Roman Law
• Greek and Roman Archaeology
• Late Antiquity
• Religion in the Ancient World
• Digital Humanities
• Browse content in History
• Colonialism and Imperialism
• Diplomatic History
• Environmental History
• Genealogy, Heraldry, Names, and Honours
• Genocide and Ethnic Cleansing
• Historical Geography
• History by Period
• History of Emotions
• History of Agriculture
• History of Education
• History of Gender and Sexuality
• Industrial History
• Intellectual History
• International History
• Labour History
• Legal and Constitutional History
• Local and Family History
• Maritime History
• Military History
• National Liberation and Post-Colonialism
• Oral History
• Political History
• Public History
• Regional and National History
• Revolutions and Rebellions
• Slavery and Abolition of Slavery
• Social and Cultural History
• Theory, Methods, and Historiography
• Urban History
• World History
• Browse content in Language Teaching and Learning
• Language Learning (Specific Skills)
• Language Teaching Theory and Methods
• Browse content in Linguistics
• Applied Linguistics
• Cognitive Linguistics
• Computational Linguistics
• Forensic Linguistics
• Grammar, Syntax and Morphology
• Historical and Diachronic Linguistics
• History of English
• Language Evolution
• Language Reference
• Language Acquisition
• Language Variation
• Language Families
• Lexicography
• Linguistic Anthropology
• Linguistic Theories
• Linguistic Typology
• Phonetics and Phonology
• Psycholinguistics
• Sociolinguistics
• Translation and Interpretation
• Writing Systems
• Browse content in Literature
• Bibliography
• Children's Literature Studies
• Literary Studies (Romanticism)
• Literary Studies (American)
• Literary Studies (Asian)
• Literary Studies (European)
• Literary Studies (Eco-criticism)
• Literary Studies (Modernism)
• Literary Studies - World
• Literary Studies (1500 to 1800)
• Literary Studies (19th Century)
• Literary Studies (20th Century onwards)
• Literary Studies (African American Literature)
• Literary Studies (British and Irish)
• Literary Studies (Early and Medieval)
• Literary Studies (Fiction, Novelists, and Prose Writers)
• Literary Studies (Gender Studies)
• Literary Studies (Graphic Novels)
• Literary Studies (History of the Book)
• Literary Studies (Plays and Playwrights)
• Literary Studies (Poetry and Poets)
• Literary Studies (Postcolonial Literature)
• Literary Studies (Queer Studies)
• Literary Studies (Science Fiction)
• Literary Studies (Travel Literature)
• Literary Studies (War Literature)
• Literary Studies (Women's Writing)
• Literary Theory and Cultural Studies
• Mythology and Folklore
• Shakespeare Studies and Criticism
• Browse content in Media Studies
• Browse content in Music
• Applied Music
• Dance and Music
• Ethics in Music
• Ethnomusicology
• Gender and Sexuality in Music
• Medicine and Music
• Music Cultures
• Music and Media
• Music and Religion
• Music and Culture
• Music Education and Pedagogy
• Music Theory and Analysis
• Musical Scores, Lyrics, and Libretti
• Musical Structures, Styles, and Techniques
• Musicology and Music History
• Performance Practice and Studies
• Race and Ethnicity in Music
• Sound Studies
• Browse content in Performing Arts
• Browse content in Philosophy
• Aesthetics and Philosophy of Art
• Epistemology
• Feminist Philosophy
• History of Western Philosophy
• Metaphysics
• Moral Philosophy
• Non-Western Philosophy
• Philosophy of Language
• Philosophy of Mind
• Philosophy of Perception
• Philosophy of Science
• Philosophy of Action
• Philosophy of Law
• Philosophy of Religion
• Philosophy of Mathematics and Logic
• Practical Ethics
• Social and Political Philosophy
• Browse content in Religion
• Biblical Studies
• Christianity
• East Asian Religions
• History of Religion
• Judaism and Jewish Studies
• Qumran Studies
• Religion and Education
• Religion and Health
• Religion and Politics
• Religion and Science
• Religion and Law
• Religion and Art, Literature, and Music
• Religious Studies
• Browse content in Society and Culture
• Cookery, Food, and Drink
• Cultural Studies
• Customs and Traditions
• Ethical Issues and Debates
• Hobbies, Games, Arts and Crafts
• Lifestyle, Home, and Garden
• Natural world, Country Life, and Pets
• Popular Beliefs and Controversial Knowledge
• Sports and Outdoor Recreation
• Technology and Society
• Travel and Holiday
• Visual Culture
• Browse content in Law
• Arbitration
• Browse content in Company and Commercial Law
• Commercial Law
• Company Law
• Browse content in Comparative Law
• Systems of Law
• Competition Law
• Browse content in Constitutional and Administrative Law
• Government Powers
• Judicial Review
• Local Government Law
• Military and Defence Law
• Parliamentary and Legislative Practice
• Construction Law
• Contract Law
• Browse content in Criminal Law
• Criminal Procedure
• Criminal Evidence Law
• Sentencing and Punishment
• Employment and Labour Law
• Environment and Energy Law
• Browse content in Financial Law
• Banking Law
• Insolvency Law
• History of Law
• Human Rights and Immigration
• Intellectual Property Law
• Browse content in International Law
• Private International Law and Conflict of Laws
• Public International Law
• IT and Communications Law
• Jurisprudence and Philosophy of Law
• Law and Politics
• Law and Society
• Browse content in Legal System and Practice
• Courts and Procedure
• Legal Skills and Practice
• Primary Sources of Law
• Regulation of Legal Profession
• Medical and Healthcare Law
• Browse content in Policing
• Criminal Investigation and Detection
• Police and Security Services
• Police Procedure and Law
• Police Regional Planning
• Browse content in Property Law
• Personal Property Law
• Study and Revision
• Terrorism and National Security Law
• Browse content in Trusts Law
• Wills and Probate or Succession
• Browse content in Medicine and Health
• Browse content in Allied Health Professions
• Arts Therapies
• Clinical Science
• Dietetics and Nutrition
• Occupational Therapy
• Operating Department Practice
• Physiotherapy
• Radiography
• Speech and Language Therapy
• Browse content in Anaesthetics
• General Anaesthesia
• Neuroanaesthesia
• Clinical Neuroscience
• Browse content in Clinical Medicine
• Acute Medicine
• Cardiovascular Medicine
• Clinical Genetics
• Clinical Pharmacology and Therapeutics
• Dermatology
• Endocrinology and Diabetes
• Gastroenterology
• Genito-urinary Medicine
• Geriatric Medicine
• Infectious Diseases
• Medical Toxicology
• Medical Oncology
• Pain Medicine
• Palliative Medicine
• Rehabilitation Medicine
• Respiratory Medicine and Pulmonology
• Rheumatology
• Sleep Medicine
• Sports and Exercise Medicine
• Community Medical Services
• Critical Care
• Emergency Medicine
• Forensic Medicine
• Haematology
• History of Medicine
• Browse content in Medical Skills
• Clinical Skills
• Communication Skills
• Nursing Skills
• Surgical Skills
• Browse content in Medical Dentistry
• Oral and Maxillofacial Surgery
• Paediatric Dentistry
• Restorative Dentistry and Orthodontics
• Surgical Dentistry
• Medical Ethics
• Medical Statistics and Methodology
• Browse content in Neurology
• Clinical Neurophysiology
• Neuropathology
• Nursing Studies
• Browse content in Obstetrics and Gynaecology
• Gynaecology
• Occupational Medicine
• Ophthalmology
• Otolaryngology (ENT)
• Browse content in Paediatrics
• Neonatology
• Browse content in Pathology
• Chemical Pathology
• Clinical Cytogenetics and Molecular Genetics
• Histopathology
• Medical Microbiology and Virology
• Patient Education and Information
• Browse content in Pharmacology
• Psychopharmacology
• Browse content in Popular Health
• Caring for Others
• Complementary and Alternative Medicine
• Self-help and Personal Development
• Browse content in Preclinical Medicine
• Cell Biology
• Molecular Biology and Genetics
• Reproduction, Growth and Development
• Primary Care
• Professional Development in Medicine
• Browse content in Psychiatry
• Addiction Medicine
• Child and Adolescent Psychiatry
• Forensic Psychiatry
• Learning Disabilities
• Old Age Psychiatry
• Psychotherapy
• Browse content in Public Health and Epidemiology
• Epidemiology
• Public Health
• Browse content in Radiology
• Clinical Radiology
• Interventional Radiology
• Nuclear Medicine
• Radiation Oncology
• Reproductive Medicine
• Browse content in Surgery
• Cardiothoracic Surgery
• Gastro-intestinal and Colorectal Surgery
• General Surgery
• Neurosurgery
• Paediatric Surgery
• Peri-operative Care
• Plastic and Reconstructive Surgery
• Surgical Oncology
• Transplant Surgery
• Trauma and Orthopaedic Surgery
• Vascular Surgery
• Browse content in Science and Mathematics
• Browse content in Biological Sciences
• Aquatic Biology
• Biochemistry
• Bioinformatics and Computational Biology
• Developmental Biology
• Ecology and Conservation
• Evolutionary Biology
• Genetics and Genomics
• Microbiology
• Molecular and Cell Biology
• Natural History
• Plant Sciences and Forestry
• Research Methods in Life Sciences
• Structural Biology
• Systems Biology
• Zoology and Animal Sciences
• Browse content in Chemistry
• Analytical Chemistry
• Computational Chemistry
• Crystallography
• Environmental Chemistry
• Industrial Chemistry
• Inorganic Chemistry
• Materials Chemistry
• Medicinal Chemistry
• Mineralogy and Gems
• Organic Chemistry
• Physical Chemistry
• Polymer Chemistry
• Study and Communication Skills in Chemistry
• Theoretical Chemistry
• Browse content in Computer Science
• Artificial Intelligence
• Computer Architecture and Logic Design
• Game Studies
• Human-Computer Interaction
• Mathematical Theory of Computation
• Programming Languages
• Software Engineering
• Systems Analysis and Design
• Virtual Reality
• Browse content in Computing
• Business Applications
• Computer Security
• Computer Games
• Computer Networking and Communications
• Digital Lifestyle
• Graphical and Digital Media Applications
• Operating Systems
• Browse content in Earth Sciences and Geography
• Atmospheric Sciences
• Environmental Geography
• Geology and the Lithosphere
• Maps and Map-making
• Meteorology and Climatology
• Oceanography and Hydrology
• Palaeontology
• Physical Geography and Topography
• Regional Geography
• Soil Science
• Urban Geography
• Browse content in Engineering and Technology
• Agriculture and Farming
• Biological Engineering
• Civil Engineering, Surveying, and Building
• Electronics and Communications Engineering
• Energy Technology
• Engineering (General)
• Environmental Science, Engineering, and Technology
• History of Engineering and Technology
• Mechanical Engineering and Materials
• Technology of Industrial Chemistry
• Transport Technology and Trades
• Browse content in Environmental Science
• Applied Ecology (Environmental Science)
• Conservation of the Environment (Environmental Science)
• Environmental Sustainability
• Environmentalist Thought and Ideology (Environmental Science)
• Management of Land and Natural Resources (Environmental Science)
• Natural Disasters (Environmental Science)
• Nuclear Issues (Environmental Science)
• Pollution and Threats to the Environment (Environmental Science)
• Social Impact of Environmental Issues (Environmental Science)
• History of Science and Technology
• Browse content in Materials Science
• Ceramics and Glasses
• Composite Materials
• Metals, Alloying, and Corrosion
• Nanotechnology
• Browse content in Mathematics
• Applied Mathematics
• Biomathematics and Statistics
• History of Mathematics
• Mathematical Education
• Mathematical Finance
• Mathematical Analysis
• Numerical and Computational Mathematics
• Probability and Statistics
• Pure Mathematics
• Browse content in Neuroscience
• Cognition and Behavioural Neuroscience
• Development of the Nervous System
• Disorders of the Nervous System
• History of Neuroscience
• Invertebrate Neurobiology
• Molecular and Cellular Systems
• Neuroendocrinology and Autonomic Nervous System
• Neuroscientific Techniques
• Sensory and Motor Systems
• Browse content in Physics
• Astronomy and Astrophysics
• Atomic, Molecular, and Optical Physics
• Biological and Medical Physics
• Classical Mechanics
• Computational Physics
• Condensed Matter Physics
• Electromagnetism, Optics, and Acoustics
• History of Physics
• Mathematical and Statistical Physics
• Measurement Science
• Nuclear Physics
• Particles and Fields
• Plasma Physics
• Quantum Physics
• Relativity and Gravitation
• Semiconductor and Mesoscopic Physics
• Browse content in Psychology
• Affective Sciences
• Clinical Psychology
• Cognitive Psychology
• Cognitive Neuroscience
• Criminal and Forensic Psychology
• Developmental Psychology
• Educational Psychology
• Evolutionary Psychology
• Health Psychology
• History and Systems in Psychology
• Music Psychology
• Neuropsychology
• Organizational Psychology
• Psychological Assessment and Testing
• Psychology of Human-Technology Interaction
• Psychology Professional Development and Training
• Research Methods in Psychology
• Social Psychology
• Browse content in Social Sciences
• Browse content in Anthropology
• Anthropology of Religion
• Human Evolution
• Medical Anthropology
• Physical Anthropology
• Regional Anthropology
• Social and Cultural Anthropology
• Theory and Practice of Anthropology
• Browse content in Business and Management
• Business Ethics
• Business Strategy
• Business History
• Business and Technology
• Business and Government
• Business and the Environment
• Comparative Management
• Corporate Governance
• Corporate Social Responsibility
• Entrepreneurship
• Health Management
• Human Resource Management
• Industrial and Employment Relations
• Industry Studies
• Information and Communication Technologies
• International Business
• Knowledge Management
• Management and Management Techniques
• Operations Management
• Organizational Theory and Behaviour
• Pensions and Pension Management
• Public and Nonprofit Management
• Strategic Management
• Supply Chain Management
• Browse content in Criminology and Criminal Justice
• Criminal Justice
• Criminology
• Forms of Crime
• International and Comparative Criminology
• Youth Violence and Juvenile Justice
• Development Studies
• Browse content in Economics
• Agricultural, Environmental, and Natural Resource Economics
• Asian Economics
• Behavioural Finance
• Behavioural Economics and Neuroeconomics
• Econometrics and Mathematical Economics
• Economic History
• Economic Systems
• Economic Methodology
• Economic Development and Growth
• Financial Markets
• Financial Institutions and Services
• General Economics and Teaching
• Health, Education, and Welfare
• History of Economic Thought
• International Economics
• Labour and Demographic Economics
• Law and Economics
• Macroeconomics and Monetary Economics
• Microeconomics
• Public Economics
• Urban, Rural, and Regional Economics
• Welfare Economics
• Browse content in Education
• Adult Education and Continuous Learning
• Care and Counselling of Students
• Early Childhood and Elementary Education
• Educational Equipment and Technology
• Educational Strategies and Policy
• Higher and Further Education
• Organization and Management of Education
• Philosophy and Theory of Education
• Schools Studies
• Secondary Education
• Teaching of a Specific Subject
• Teaching of Specific Groups and Special Educational Needs
• Teaching Skills and Techniques
• Browse content in Environment
• Applied Ecology (Social Science)
• Climate Change
• Conservation of the Environment (Social Science)
• Environmentalist Thought and Ideology (Social Science)
• Natural Disasters (Environment)
• Social Impact of Environmental Issues (Social Science)
• Browse content in Human Geography
• Cultural Geography
• Economic Geography
• Political Geography
• Browse content in Interdisciplinary Studies
• Communication Studies
• Museums, Libraries, and Information Sciences
• Browse content in Politics
• African Politics
• Asian Politics
• Chinese Politics
• Comparative Politics
• Conflict Politics
• Elections and Electoral Studies
• Environmental Politics
• European Union
• Foreign Policy
• Gender and Politics
• Human Rights and Politics
• Indian Politics
• International Relations
• International Organization (Politics)
• International Political Economy
• Irish Politics
• Latin American Politics
• Middle Eastern Politics
• Political Behaviour
• Political Economy
• Political Institutions
• Political Methodology
• Political Communication
• Political Philosophy
• Political Sociology
• Political Theory
• Politics and Law
• Public Policy
• Public Administration
• Quantitative Political Methodology
• Regional Political Studies
• Russian Politics
• Security Studies
• State and Local Government
• UK Politics
• US Politics
• Browse content in Regional and Area Studies
• African Studies
• Asian Studies
• East Asian Studies
• Japanese Studies
• Latin American Studies
• Middle Eastern Studies
• Native American Studies
• Scottish Studies
• Browse content in Research and Information
• Research Methods
• Browse content in Social Work
• Addictions and Substance Misuse
• Adoption and Fostering
• Care of the Elderly
• Child and Adolescent Social Work
• Couple and Family Social Work
• Developmental and Physical Disabilities Social Work
• Direct Practice and Clinical Social Work
• Emergency Services
• Human Behaviour and the Social Environment
• International and Global Issues in Social Work
• Mental and Behavioural Health
• Social Justice and Human Rights
• Social Policy and Advocacy
• Social Work and Crime and Justice
• Social Work Macro Practice
• Social Work Practice Settings
• Social Work Research and Evidence-based Practice
• Welfare and Benefit Systems
• Browse content in Sociology
• Childhood Studies
• Community Development
• Comparative and Historical Sociology
• Economic Sociology
• Gender and Sexuality
• Gerontology and Ageing
• Health, Illness, and Medicine
• Marriage and the Family
• Migration Studies
• Occupations, Professions, and Work
• Organizations
• Population and Demography
• Race and Ethnicity
• Social Theory
• Social Movements and Social Change
• Social Research and Statistics
• Social Stratification, Inequality, and Mobility
• Sociology of Religion
• Sociology of Education
• Sport and Leisure
• Urban and Rural Studies
• Browse content in Warfare and Defence
• Defence Strategy, Planning, and Research
• Land Forces and Warfare
• Military Administration
• Military Life and Institutions
• Naval Forces and Warfare
• Other Warfare and Defence Issues
• Peace Studies and Conflict Resolution
• Weapons and Equipment

• < Previous chapter
• Next chapter >

22 Culture and Consumer Behavior: A Review and Agenda for Future Research

Carlos J. Torelli, Anthony J. Petullo Professor of Business Administration, University of Illinois at Urbana-Champaign, USA

Jie (Doreen) Shen, Data Scientist, Meta, USA

Maria A. Rodas, Assistant Professor of Business Administration and Shebik Centennial Fellow, University of Illinois at Urbana-Champaign, USA

• Published: 23 January 2024
• Cite Icon Cite
• Permissions Icon Permissions

Culture is a fundamental driver of consumer behavior. This chapter reviews the three most common approaches for incorporating culture into frameworks of consumer behavior and describes key findings about: (1) the effect of culture on consumers’ attention and information processing, (2) the persuasiveness of culturally matched advertising messages, (3) the contexts in which culture is more likely to impact consumer behavior, and (4) the behavior of bicultural consumers. The chapter concludes with a discussion of a future research agenda that focuses on the study of the tightness–looseness (T-L) distinction for refining cultural predictions, the further integration of the different approaches to cultural research for understanding a variety of consumer phenomena in a globalized world, the adoption of a polycultural approach for explaining the behavior of multicultural consumers with knowledge about two or more cultures, and the potential insights from new methodologies borrowed from neuroscience, machine learning, and big data analyses.

Culture, as a fundamental determinant of people’s wants, drives consumer behavior ( Kotler & Keller, 2009 ). As the world becomes increasingly globalized, both the demand side (i.e., consumer markets) and the supply side (i.e., brand offerings) of the global marketplace are also growing in cultural diversity. Cultural diversity in consumer markets is fueled by the emergence of China, India, and Brazil as engines of growth for the global economy ( Nayyar, 2010 ), as well as by the immigration patterns changing the cultural landscape of developed markets (e.g., growth of Hispanics in the United States or of Muslim populations in Europe). The supply side has also grown in cultural diversity thanks to the emergence of global brands from every corner of the developed and developing world.

Supported by these global trends, a wide range of brands bring a variety of cultures to an increasingly diverse consumer population. In this context, how can we incorporate cultural variables into models of consumer behavior? How can companies develop strategies to successfully connect with consumers from different cultures? What future challenges will marketers face in such a complex environment? This chapter provides answers to these important questions.

Because culture is an elusive concept that has been defined in numerous ways in research across different intellectual traditions ( Kroeber & Kluckhohn, 1952 ), we start by reviewing key approaches for incorporating culture into models of consumer behavior and by identifying the factors that explain the cultural patterning of consumers’ preferences for products and brands. In the consumer behavior literature, three intellectual traditions have dominated the approach to cultural research: the dimensional approach, the dynamic constructivist approach, and the consumer theory of marketplace cultures. Each of these traditions focuses on specific defining aspects of culture and their consequences for consumer behavior. Upon reviewing these approaches, this chapter discusses the effects of culture on attention and information processing, the persuasiveness of culturally matched appeals, the processes by which brands acquire cultural meanings, and the responses of consumers to the cultural meanings in brands. Special attention is devoted to the novel phenomenon of culture mixing or the juxtaposition of multiple cultures in a single-product offering. Finally, the chapter concludes with a discussion of a future research agenda, as well as with a summary of the key findings and practical implications for organizations regarding culture and consumer behavior.

The Dimensional Approach to Culture

According to the dimensional approach to understanding the role of culture in consumer behavior, culture is conceptualized as shared elements “that provide the standards for perceiving, believing, evaluating, communicating, and acting among those who share a language, a historic period, and a geographic location” (p. 408; Triandis, 1996 ). This approach identifies and measures patterns of variations in these shared elements that are organized around a psychological theme, dimension, or syndrome ( Hofstede, 1980 ; Triandis, 1995 , 1996 ). The underlying assumption under the dimensional approach is that, although there may be hundreds of cultures spread throughout the world, they vary according to a handful number of dimensions. Uncovering these dimensions helps categorize them into groups that share the same pattern of beliefs, values, and cognitions, which in turn can be used for predicting attitudes and behaviors of individual group members. In the past decades, the majority of research has focused on the individualism–collectivism classification, followed by the vertical and horizontal subtypes of individualism and collectivism and the related dimension of power distance (see Shavitt et al., 2006 ).

Individualism–Collectivism Classification

This classification relates to the extent to which individuals view themselves as distinct from or connected to others. People who are from individualistic cultures, or who have an independent self-construal, tend to emphasize individual autonomy and separation from others, while people from collectivistic cultures, or who have an interdependent self-construal, tend to prioritize connectedness, social context, and relationships with ingroup members ( Triandis, 1995 ). The individualism–collectivism classification can be used to predict how consumers attend to and process product information, as well as to identify advertisements that will be most persuasive.

Attention and Information Processing.

Because individualism promotes an independent view of the self, people in individualistic cultures (e.g., Americans) tend to have a decontextualized view of the world that focuses on focal objects and their attributes. Thus, individualism fosters a tendency to focus on the attributes of an object, separate from its context, in order to assign it to a category and to use rules about the category to explain and predict the object’s behavior (i.e., analytic thinking style, Nisbett et al, 2001 ). Individualists tend to judge new products introduced by existing brands on the basis of how close the new product is to existing products, or the extent to which the attributes of the parent brand transfer to the new product ( Monga & John, 2007 ). In contrast, because collectivism fosters a view of the self as embedded within a larger social context, people in collectivistic cultures (e.g., East Asians) attend to the relationships between the self, others, and the environment, fostering a tendency to attend to social and environmental contexts as a whole, especially to relationships between focal objects and the environment, and predict events on the basis of such relationships. This way of thinking is often referred to as holistic thinking style ( Nisbett et al., 2001 ). This approach affords an advantage in detecting broader connections between objects, even if subtle. Because they pay more attention to the environment, collectivists more easily identify relationships between an existing brand and a newly introduced product based on complementarity of use or overall reputation (i.e., more subtle connections). For example, Indian (i.e., collectivistic) consumers perceive a higher fit between a distant brand extension, such as a filing cabinet introduced by the Kodak brand, than American (i.e., individualistic) consumers do because collectivists are more able to identify the more subtle usage-based connection between Kodak and filing cabinets (i.e., filing cabinets can be used to store pictures) than individualists are ( Monga & John, 2007 ).

Given individualists’ tendency to process messages analytically, attending to focal images and attributes, relying in categorical knowledge, and making choices based on rules, advertisements in individualistic cultures would benefit from the use of white space as a way of making focal product images/attributes salient. In contrast, given collectivists’ tendency to process ads holistically, attending to relationships between focal images/attributes and the context, advertisements in collectivistic cultures would benefit less from the use of white space; it would be more appropriate for ads to embed focal product images/messages in a background that contains reinforcing contextual information.

Persuasiveness of Culturally Matched Advertising Messages.

One of the most important findings under the dimensional approach, and more specifically the individualism–collectivism classification, is that advertising messages that match cultural value priorities are more persuasive than those that do not. This effect, however, is more evident for shared products (i.e., products for which the purchase decision and usage are likely to include family members and friends, such as laundry detergent and home appliances) than for personal products (i.e., products for which the purchase decision and usage are usually done by the individual, such as chewing gum and running shoes) ( Han & Shavitt, 1994 ). A slogan for a detergent brand like “Solo cleans with a softness that you will love” is more persuasive in the individualistic U.S. culture, while “Solo cleans with a softness that your family will love” appeals to the values of a collectivistic culture like South Korea.

Similarly, individualists are better persuaded by information that addresses their promotion of regulatory concerns (i.e., messages about personal achievement, individuality, uniqueness, and self-improvement), whereas collectivists are better persuaded by information that addresses their prevention regulatory concerns (i.e., messages about harmony, group goals, conformity, and security; see Aaker & Lee, 2001 ). Furthermore, recent research shows that the practice by some firms of adopting asymmetric pricing (i.e., charging different consumers with different prices) is perceived to be less fair by collectivistic (vs. individualistic) consumers because such practice does not match the communal norms of firm benevolence endorsed by collectivists ( Chen et al., 2018 ).

Vertical-Horizontal Distinction

The vertical-horizontal distinction emerges from the observation that American individualism differs from Australian individualism in much the same way that Japanese collectivism differs from the collectivism of the Israeli Kibbutz. Whereas individuals in vertical societies view the self as differing from others along a hierarchy and accept inequality, those in horizontal societies value equality and view the self as having the same status as others in society ( Triandis, 1995 ). Thus, combining the horizontal-vertical distinction with the individualism–collectivism classification produces four cultural orientations: horizontal individualist, vertical individualist, horizontal collectivist, and vertical collectivist.

In vertical individualist societies like the United States, people tend to be concerned with the self-enhancement values of power and achievement, while in horizontal individualist cultures like Australia, people view themselves as equal and avoid status differentiation. In vertical collectivist societies like Japan, conservation values of tradition, conformity, and security are emphasized, while in horizontal collectivist cultural contexts like the Israeli Kibbutz, individuals endorse self-transcendence values that promote the welfare of others ( Shavitt et al., 2006 ; Shavitt et al., 2010 ; Triandis & Gelfand, 1998 ).

Focusing on the vertical and horizontal distinctions, nested within the broader individualism–collectivism classification, affords a more nuanced understanding of culture. Specifically, the tendency by individualists to assign objects to categories and to make predictions about these objects based on category attributes is particularly pronounced among vertical individualists (who are chronically concerned about status and power) when responding to power cues. When presented with information about a focal object, vertical individualists who feel powerful tend to focus on information that is consistent with the stereotype of the category to which the object belongs, while ignoring inconsistent information—often referred to as stereotyping processing ( Torelli & Shavitt, 2011 ). This occurs because attending to stereotypical information helps to confirm prior expectations and to reassert control, thereby protecting a powerful status ( Fiske, 1993 ). For instance, when vertical individualists are presented with an advertisement for a status-enhancing financial advisory service, they recognize better, in a subsequent recognition task, information congruent with the stereotypical image of the status product (e.g., “financial experts graduated from the top-tier universities in the country”) relative to their recognition of incongruent information (e.g., “When you visit Interbank offices, you will feel the warmth of your own home”; Torelli & Shavitt, 2011 ).

In contrast, as stated earlier, collectivists base their perceptions on a holistic view of the target object and its relationship to the social context. This tendency is particularly strong among horizontal collectivists (who are chronically concerned about interdependence and sociability) driven by power motives. Specifically, when presented with information about a focal object, horizontal collectivists motivated to use their power for the benefit of others (i.e., socialized power) tend to engage in individuating processing —or the tendency to individuate and understand others ( Torelli & Shavitt, 2011 ). This involves attending to information that is incongruent with prior expectations, presumably because such information helps to form an accurate impression instrumental for helping others ( Overbeck & Park, 2001 ). For instance, upon priming socialized power, horizontal collectivists presented with an advertisement for a nurturing dog food recognize better, in a subsequent recognition task, information incongruent with the stereotypical image of the nurturing product (e.g., “It has been reported that the company recently influenced distributors to stop carrying competitors’ products”) ( Torelli & Shavitt, 2011 ).

Given vertical individualists’ tendency to stereotype when influenced by power, advertisements that contain power themes would trigger stereotyping processing among vertical individualistic consumers, which in turn would prompt them to attend to information consistent with the categorical knowledge triggered by the focal product (e.g., the horsepower of a pick-up truck), while ignoring inconsistent information (e.g., the softness of the car seat cushion). In contrast, in view of the individuating processing exhibited by horizontal collectivists when motivated to help others, ads that cue responsibility themes would trigger individuating processing among horizontal collectivistic consumers, in turn prompting them to pay special attention to information that is inconsistent with the categorical knowledge triggered by the focal product.

Although appeals to self-enhancement values and openness values seem equally appropriate in individualistic cultures, as both primarily refer to individual interests, a focus on the vertical-horizontal distinction helps us recognize that openness values are more appealing for consumers with a horizontal individualistic orientation, while they are less so for those with a vertical individualistic orientation. In contrast, appeals to self-enhancement values are more appealing for consumers with a vertical individualistic orientation, but less so for those with a horizontal individualistic orientation ( Torelli et al., 2012 ). Similarly, appeals to self-transcendence values are more appealing for consumers with a horizontal collectivistic orientation but are less so for those with a vertical collectivistic orientation. In contrast, appeals to conservation values are more appealing for consumers with a vertical collectivistic orientation but are less so for those with a horizontal collectivistic orientation ( Torelli et al., 2012 ).

Power Distance

Power distance is a cultural dimension that reflects the degree to which differences in power are expected and accepted ( Hofstede, 2001 ). This dimension partially overlaps with the horizontal—vertical distinction, but there are also important conceptual and structural differences between them ( Shavitt et al., 2006 ). From a conceptual standpoint, the horizontal/vertical distinction refers to a hierarchical arrangement of individuals, whereas power distance relates to differences in the acceptance of hierarchies as being valid or important in one’s society. From a structural standpoint, although power distance is conceptualized as a single dimension (from high to low Power Distance Index; Hofstede, 1980 , 2001 ), it is highly correlated with individualism–collectivism. Thus, countries high in power distance tend to also be collectivistic. More specifically, countries topping the list of power distance scores (e.g., China, India, Malaysia, and Mexico) are countries also characterized as vertical collectivists, whereas those lowest in power distance (e.g., Denmark, Sweden, and Norway) are countries also characterized as horizontal individualistic ( Triandis, 1995 ; Triandis & Gelfand, 1998 ). Thus, the high-/low-power distance distinction partially overlaps with the distinction between vertical collectivism and horizontal individualism.

People who believe in power distance (i.e., high on power distance beliefs, or PDB) are comfortable with, and even desire, structure. Therefore, people who are high in PDB tend to categorize products, which affect their judgments. For example, recent research by Lalwani and Forcum (2016) found that people high in PDB are more likely to categorize products by price, resulting in ascribing higher quality to high-priced products. Other research has found that the interaction between PDB and psychological power states, or power cues, can affect people’s attention focus and subsequent behavior. For example, Han et al. (2017) found that in low-PDB contexts, people high in psychological power tended to be self-focused, which led to less charitable giving; whereas in high-PDB contexts, people high (vs. low) in psychological power tended to be more other-focused, which led to more charitable giving.

People high in PDB, given their tendency to expect and accept authority, are likely to be better persuaded by expertise and trustworthiness. Indeed, recent research demonstrates that people high in PDB tend to be better persuaded by celebrity endorsements ( Winterich et al., 2018 ) and by company-designed products over user-designed products ( Paharia & Swaminathan, 2019 ). Consumers high in PDB have a strong desire for status and thus show a stronger preference for status brands ( Kim & Zhang, 2014 ), national (vs. private-label) brands ( Wang et al., 2020 ), and upward (vs. downward) brand extension ( Liu et al., 2015 ). Furthermore, people low in PDB express greater preference for transparency in brand information ( Jain & Jain, 2018 ).

The Dynamic Constructivist Approach to Culture

Although the dimensional approach to culture helps to make predictions about perceptual, attentional, and evaluative tendencies of consumers, it is less useful for identifying specific contexts in which people will exhibit these tendencies. In this tradition, it is often implicitly assumed that culture is constantly operating in the background to trigger these tendencies across contexts. However, empirical evidence does not seem to support this notion, and suggests instead that consumers are strategic in their reliance on cultural cognitions for their judgments and behaviors ( Briley & Aaker, 2006 ; Briley et al., 2000 ; Chiu & Hong, 2006 ). The dynamic constructivist approach to culture addresses this issue by considering that culture is not internalized as a highly general structure, such as a value orientation, but instead as a loose network of domain-specific knowledge structures, such as categories (e.g., individualist values and beliefs), implicit theories (e.g., an individual’s behavior originates in internal dispositions), and cultural icons (e.g., the Statue of Liberty) linked to a central theme (e.g., American culture), which drive judgments and behaviors when made readily accessible by environmental stimuli ( Hong et al., 2000 ). Thus, although people possess cultural knowledge, this knowledge drives behavior only when it is activated , or primed, by cues in the environment that make salient the application of the cultural knowledge network for the task at hand ( Hong et al., 2001 ; Hong et al., 2000 ). The dynamic constructivist approach has been particularly useful for identifying contexts in which culture is an operative construct, as well as to model the behavior of bicultural consumers.

When Does Culture Matter?

Instead of considering culture as a dispositional trait that drives behavior across contexts, research on the dynamic constructivist approach has uncovered situations that render culture salient to guide consumer decisions. The cultural matching effects on advertising persuasiveness described earlier are more likely to emerge when information is processed in a cursory, spontaneous manner, when people are prone to form impressions based on commonly used cultural constructs. For instance, American (vs. Chinese) consumers are better persuaded by promotion than by prevention advertising messages when they relied on automatic processing (e.g., under time constraints or under cognitive load), but less so when they were encouraged to deliberate on the message ( Briley & Aaker, 2006 ). Culture-based product decisions are also more likely to emerge when consumers are prompted to justify their choices through reasoned statements, which renders explanations based on cultural norms particularly salient in memory. For example, Hong Kong Chinese consumers are more likely to choose a compromise option, that aligns with the normative tendency to find “the middle way” fostered in Chinese culture, when prompted to justify their choices ( Briley et al., 2000 ). Similarly, consumers with a salient collective self are more likely to favorably evaluate products that align with the preferences of others when prompted to justify their decisions to these others ( Torelli, 2006 ).

The Behavior of Bicultural Consumers

As stated earlier, globalization has exponentially amplified people’s interactions with lifestyles, customs, and traditions from different cultures. Consequently, the number of individuals who internalize knowledge from two (i.e., bicultural) or more (i.e., multicultural) cultures, as opposed to a single culture, is rapidly on the rise ( Benet-Martínez et al., 2002 ). Based on the notion that culture drives behavior when it is activated, the dynamic constructivist approach helps to explain how bicultural individuals switch cultural frames according to the context. Cultural frame switching refers to the process by which biculturals respond to situationally salient cultural cues or contexts by activating cultural meaning systems. For example, for Hong Kong Chinese students, exposure to Chinese cultural symbols (e.g., a Chinese dragon) increases the degree to which they endorse Chinese values as compared to the exposure to American cultural symbols (e.g., the American flag) ( Hong et al., 2000 ). Although some part of cultural frame switching is automatic, bicultural individuals also consciously choose between cultural frames to meet the cultural demands of the situation ( Birman, 1994 ). For example, an Asian American consumer could alternate their cultural identities and be Asian at home and American at work ( Forehand et al., 2002 ). Prior work has demonstrated such compartmentalization of cultural identities and frame switching across a wide range of cultures and populations ( Lau-Gesk, 2003 ). Recent research has expanded to cultural switching among monocultural individuals exposed to the tenets of a foreign culture (e.g., European Americans living in Chinatown; Alter & Kwan, 2009 ). Cultural frame switching has market consequences, as consumers tend to favor products consistent with the accessible cultural frame. For instance, Mexican American consumers residing in the United States prefer songs from a Hispanic rock band as a way to connect with their Hispanic identity ( Torelli et al., 2017 ).

Consumer Theory of Marketplace Cultures

Consumer culture theory (CCT) refers to a family of phenomenological and ethnographic approaches focused on the dynamic relations between consumers’ actions, the marketplace, and cultural meanings. Rather than considering shared patterns of meanings in a given society, CCT investigates the heterogeneous distribution of cultural meanings, and how consumers use commercial products and images to make collective sense of their environments and guide their actions ( Arnould & Thompson, 2005 ). This more granular approach to studying the interplay between culture and consumption has provided further insights into the ways in which consumers develop a sense of collective identity built around a brand. As we discuss in more detail in the next section, the study of marketplace cultures has helped illuminate the process by which abstract cultural categories are instantiated in products and brands to endow such entities with cultural meanings ( Fournier & Alvarez, 2019 ; McCracken, 1986 ). Brand associations can be embedded within cultural myths or cultural models in a narrative form. For instance, the Mexican beer Corona strongly associated itself with the partying myth of the Mexican spring break, which helped Corona embody such cultural meaning ( Holt, 2004 ). The consumer theory of marketplace cultures also affords a more nuanced understanding of how experiential consumption promotes collective identification grounded in shared cultural meanings. For example, consuming experiences built around the imaginary mountain men who trapped beavers in the Rocky Mountains of the American West not only provides a strong sense of community among consumers, but also strengthens the symbolic meaning of American values of freedom and individualism ( Belk & Costa, 1998 ).

Consumers’ Responses to Cultural Meanings in Products and Brands

Coke and Pepsi are brands with an almost identical core product offering, whose products are similarly priced, are sold through the same distribution channels, and adopt similar promotional strategies. Nonetheless, Coke consistently dominates Pepsi in brand rankings of best global brands. Recent research in consumer behavior argues that Coke’s success can be attributed to its distinctive ability to achieve the status of an icon—both globally and nationally ( Cross, 2002 ; Holt, 2004 ; Torelli, 2013 ).

As stated earlier, culture not only consists of values and beliefs, but also manifests itself in objects such as brands and products. Brands often acquire cultural meanings and come to embody the values and ideals nurtured by a cultural group. Thus, a brand’s cultural meaning is defined in terms of shared agreement that the brand symbolizes an abstract cultural image ( Torelli & Ahluwalia, 2012 ; Torelli et al., 2010 ; Torelli et al., 2020 ). For example, Ford has cultural meaning for Americans because of its strong association with the rugged individualism that characterizes American culture. Brands that are consensually perceived to symbolize an abstract cultural identity are regarded as cultural icons . Thus, iconic brands symbolize the beliefs, ideas, and values of a cultural group. Recent research in consumer behavior has focused on how brands acquire cultural meanings, as well as on the more favorable responses of consumers to the cultural meanings in brands.

How Do Brands Acquire Cultural Meanings?

The brand meaning-making process is a collective effort involving the marketer, consumers, and social influencers (e.g., mass media, celebrities, and social media influencers; Fournier & Alvarez, 2019 ). Brand meanings originate in the culturally constituted world and move into brands through several instruments such as advertising, the fashion system, and reference groups ( McCracken, 1986 ). Consumers appropriate these brand meanings for constructing their individual identities ( Escalas & Bettman, 2005 ), and in a dynamic process of social verification, subjective brand meanings take a concreteness that can be widely recognized by society at large ( Holt, 2004 ; McCracken, 1986 ). In this interactive process, brands that are deemed better cultural carriers are publicly acknowledged as cultural icons. To illustrate this process, let us consider the cultural meaning of Elvis Presley, undoubtedly one of the most widely recognized American icons of the twentieth century.

Elvis, also known as “the King,” was certainly not the first musician to embrace the newly popular sound of rock and roll. Black musicians of his time, such as Arthur Crudup or Big Bill Broonzy, had been performing the new rhythm well before it was Presley’s style ( Farley, 2004 ). However, for multiple reasons, none of these musicians developed the cultural meaning that Elvis did. Because these Black musicians did not embody the image, traits, and values of mainstream, segregated, White American culture in the 1950s, they could not become consensual expressions of cultural ideals of the time. Indeed, Sam Phillips, the head of Sun Records where Elvis recorded his first songs, has often said in interviews that the music industry was looking for a White boy who sang Black, who had the rhyming and soul to do R&B, and that Elvis was this boy ( Guralnick, 1994 ). In addition to embodying traits that characterized American culture at the time, Elvis also embodied in a unique way the key dramas of sex, race, and class that had been suppressed in the conservative and conformist 1950s ( Kellner, 2013 ). By embodying an emerging cultural myth, Elvis became a target of public discourse that facilitated consensus around his cultural meaning ( Fournier & Alvarez, 2019 ; Holt, 2004 ).

Elvis’s story illustrates how a brand, in this case a person brand, acquires cultural meaning by becoming a widely regarded symbol of a particular kind of story found to be valuable by people in a culture ( Holt, 2004 ). Importantly, consumers of his time more easily agreed publicly on Elvis’s cultural meaning due to his embodiment of valued cultural characteristics, allowing him to become a target of cultural discourse. In any culture, there is wide consensus about the importance of valued cultural characteristics, which contributes to the shared reality , or the totality of knowledge that is assumed to be known and shared by others ( Wan et al., 2007 ). As a result, people have an inclination to transmit information that is consistent with the shared reality ( Wan et al., 2010 ). Given the defining role of key cultural characteristics, members of the culture attend to the cultural significance of narratives that support them and evaluate these stories favorably as cultural narratives. Thus, narratives about actors (e.g., product or person brands) that personify cultural aspects are evaluated in cultural terms and hence are more favorably evaluated and more frequently transmitted ( Kashima, 2000 ; Wan et al., 2010 ).

Iconic Brands Resonate with Consumers

The consumer is the central character in the process of creating brand meanings ( Batra, 2019 ). Consumers internalize the cultural meaning of a brand through inferencing and semantic interpretation, as well as through identity processes. Social groups and brand communities “cultivate” a disposition among their members toward certain types of brand meanings (e.g., refinement for high-class individuals; Fournier & Alvarez, 2019 ). Furthermore, consumers appropriate and transmit desirable brand meanings for building their cultural identities. Because culture provides the individual with a strong sense of collective identity ( Chiu & Hong, 2006 ), people are more likely to recruit cultural knowledge for their judgments and behaviors when enacting their cultural identity. Building upon the dynamic constructivist approach to culture, the identity-based motivation theory ( Oyserman, 2009 ) proposes that identities not only include knowledge about group membership and a positive sense of ingroup connections, but also the readiness to act and make sense of the world in identity-congruent ways. In the context of cultural identities, the theory predicts that people will behave in culturally appropriate ways when acting on behalf of their cultural identity. For instance, when making an ethnic identity salient (vs. not), consumers evaluate advertisements targeted to the ethnic group more favorably (e.g., as depicted in copy and ad images; Forehand et al., 2002 ).

Because brands with cultural significance symbolize the values, beliefs, myths, and ideals of a cultural group, consumers with a heightened need to symbolize a cultural identity will judge iconic brands as highly instrumental for fulfilling such needs. By consuming a culturally symbolic brand, one emphasizes the possession of the cultural identity and the alignment with and adherence to the culture ( Torelli et al., 2017 ). Thus, when a cultural identity is temporarily or chronically salient, consumers evaluate more favorably and are even willing to pay more for culturally meaningful brands. For example, recent research found that people born and raised in Minnesota valued Target , a Minnesota icon, more highly (as evidenced by their higher willingness to pay for a set of poker chips with the Target logo stamped on each chip) when their Minnesotan identity was made salient ( Amaral & Torelli, 2018 ). Importantly, because a salient identity brings to mind identity-consistent decisions that do not require further reflection, the higher valuation of iconic brands occurs rather automatically and without conscious deliberation ( Torelli, 2013 ). Indeed, the pleasing processing experience of consuming culturally symbolic brands has been found to underlie the enhanced brand valuation effects. This is illustrated in a study about brand extensions in which both the new product and the brand activate the same cultural identity. Participants in the study evaluated more favorably a new Tequila introduced by Corona (both the brand and the new product activate the Mexican cultural schema) compared to a Tequila introduced by a “beer manufacturer” (neutral condition in which culture is less salient). Furthermore, the more favorable attitudes toward Corona Tequila were mediated by the ease with which participants processed the new brand extension ( Torelli & Ahluwalia, 2012 ). Over time, continued reliance on iconic brands for fulfilling salient cultural identity needs can contribute to developing strong self-brand relationships ( Amaral & Torelli, 2018 ; Escalas & Bettman, 2005 ). In turn, forming a strong bond with a culturally symbolic brand can shield the brand against negative publicity when cultural identity needs are salient ( Swaminathan et al., 2007 ).

The above discussion seems to suggest that identities are stable systems with clear boundaries and structures. However, current conceptualizations rooted in social cognition research assume a not-well-integrated structure and a highly contextualized definition of a social identity depending on what referent group is relevant in a given context ( Oyserman, 2009 ). Indeed, identities not only can be “anchored” in different referent groups, but can also bridge various classifications (e.g., an ethnic and a national identity; Reed et al., 2012 ). Specifically, the boundaries that define a cultural group can be flexible and expanded to fulfill cultural identity goals. This is illustrated in research on cultural distinctiveness ( Torelli et al., 2017 ) or the feeling of separation from the surrounding cultural environment that people often experience when visiting a foreign, unfamiliar culture. This feeling of separation is often accompanied by a desire to connect with familiar things from “home,” which then expands consumers’ ingroup boundaries to include a related cultural group within a broadened definition of home—subsequently triggering a strong pro-ingroup bias. This broadened definition allows them to fulfill the need to connect with home by favoring brands/products associated with the related cultural group. For example, Mexican immigrants living in the United States, who are more likely to be chronically high in cultural distinctiveness, preferred to listen to an Argentine over a Turkish song, compared to Mexicans living in their home country, who are less likely to experience cultural distinctiveness. This is due to the desire of Mexican immigrants to connect with “home,” which prompts the expansion of the cultural boundaries that define their cultural group from Mexican to Latin American, and hence leads to favor an Argentinian product associated with this more broadly defined cultural identity ( Torelli et al., 2017 ).

Multiculturalism in Products and Brands

In globalized markets, symbols of different cultures often occupy the same space at the same time. Unlike the priming of a single cultural frame discussed earlier in the chapter, exposure to symbols of multiple cultures, or culture mixing , activates not only one, but two or more cultural representations at the same time. Recent research has documented the basic cognitive and evaluative processes triggered by culture-mixed stimuli, as well as some of the factors that moderate consumers’ responses to culture mixing in products and brands.

Cognitive and Evaluative Responses to Culture Mixing

When symbols of two cultures are present in the environment, the cognitive representations of both cultures will be activated ( Chiu et al., 2009 ). In turn, the perceiver will attend to the defining characteristics that distinguish the two cultures, which in turn enlarges the perceived differences and incompatibility of the two cultures and renders culture salient as an organizing theme for judgments and decisions. These processes are less likely to occur when only one cultural representation is activated (i.e., monocultural priming), even when that representation is one of a foreign culture ( Torelli et al., 2011 ). Thus, culture mixing draws attention to the conflict between the juxtaposed cultures, resulting in the expectation that cultures are discrete entities with relatively impermeable boundaries, and prompts individuals to judge the world according to the cultural view.

The effect of exposure to culture-mixed stimuli on the cognitive tendency to view the world in cultural terms is illustrated in a study about American consumers’ responses to products associated with either a single culture (monocultural products) or two cultures (culture-mixed products; Torelli et al., 2011 ). In this study, American consumers exposed to culture-mixed products (e.g., iconic Mexican products that carry British brand names) perceived greater cultural differences than those exposed to monocultural products (e.g., culture-neutral products that carry prototypic British brand names). This effect emerges because exposure to the culture-mixed products simultaneously activates representations of Mexican and British cultures, which prompts using culture as an organizing theme for processing information, and hence increases perceived differences between cultures. This effect emerges not only for the target cultures (e.g., Mexican and British), but also for other cultures (e.g., Puerto Rican and Canadian). This tendency toward culture-based judgments is also evident in a study with Beijing Chinese consumers ( Chiu et al., 2009 ). Participants evaluated a McDonald’s hamburger advertisement that was either placed next to another McDonald’s hamburger advertisement (monocultural priming) or next to a traditional Chinese moon cake advertisement (culture mixing). Following the manipulation, the participants were presented with two commercial messages for Timex , one appealing to individualist values and one to collectivist values. The participants then rated how likely it was that a Chinese consumer would choose the individualist and collectivist messages for designing a Chinese website for Timex. Accordingly, compared to those in the monocultural priming condition, those in the culture-mixed condition believed that the Chinese consumer would be more likely to behave according to their cultural prescriptions and choose the collectivist message.

Because experiencing conflict between incongruent concepts is an aversive state ( Meyers-Levy & Tybout, 1989 ), consumers often evaluate culture-mixed products less favorably than monocultural ones. For instance, past research ( Torelli & Ahluwalia, 2012 ) shows that Americans evaluate less favorably a culture-mixed product ( Sony cappuccino machines –the Sony brand is iconic in Japan, whereas Cappuccino machines are iconic in Italy) than a monocultural one ( Sony toaster oven —only the Japanese Sony is culturally symbolic), even when both products have similar levels of moderate fit with the Sony brand. Furthermore, the less favorable evaluations of the culture-mixed product are driven by the subjective experience of disfluency (i.e., feeling that the product is not “right”), triggered by the simultaneous activation of two different cultural schemas.

Personal and Situational Factors Affecting Responses to Culture Mixing

One way to mitigate the spontaneous negative reaction to culture-mixed products is to combine the two contrastive cultural images to reduce the perceived conflict, which might require mustering cognitive resources. Indeed, research suggests that thoughtful elaboration about cultural complexities (either spontaneously or temporarily induced) can attenuate consumers’ negative reactions to culture-mixed products ( Torelli et al., 2011 ). More generally, psychological and situational factors that induce openness to the novel experiences from intercultural encounters promote integrating the contrastive cultures and reducing conflict. For instance, Chen et al. (2016) demonstrate that openness to experience favorably impacts the creative benefits of experiencing culture mixing in the environment. Similarly, Keh et al. (2016) showed that endorsing autonomy values of stimulation and self-direction positively predicts favorable attitudes of Chinese consumers toward culture-mixed phonosemantic brand translations (i.e., from a Western language to Chinese that maintains the foreign-sounding name, while conveying the brand meaning in the Chinese language).

In contrast, factors that promote closemindedness or the protection of one of the cultural identities hinder the reconciliation needed to favorably evaluate culture mixing. Consistent with this notion, De keersmaecker et al. (2016) demonstrate that people who are high in need for cognitive closure, or those who need a firm answer in a psychologically ambiguous situation, are less favorable toward culturally mixed stimuli (e.g., fusion cuisine). Past research also suggests that a motivation to defend a stable cultural worldview free from ambiguity can promote negative responses to culture mixing ( Torelli & Cheng, 2011 ). Indeed, Torelli and colleagues (2011) show that, when under the influence of a culture defense mindset triggered by thoughts of one’s own death (i.e., mortality salience), people are particularly intolerant of contamination of brands symbolizing their culture when cued with culture mixing. In one of their studies, focused on inducing mortality salience, American participants evaluated a marketing plan of Nike (an American icon), which involved some questionable actions to increase its competitiveness in a foreign market (e.g., eliminating the “Swoosh” symbol and replacing the Nike brand name with the Arabic word for “Sportsmanship” to penetrate the Middle East market). Results showed that, only upon making mortality salient, participants evaluated the marketing plan less favorably after evaluating culture-mixed products (e.g., a Chinese brand of breakfast cereal) than after evaluating monocultural products (e.g., an American brand of breakfast cereal).

Accordingly, personal or situational factors that heighten the motivation to defend a valued cultural identity should incite unfavorable responses to culture mixing. Consistent with this notion, Cheon et al. (2016) showed that disgust toward ingroup–outgroup cultural mixing is particularly pronounced among those who endorse higher levels of patriotism. Similarly, Shi et al. (2016) demonstrated that individuals who highly identify with their heritage culture tend to exhibit implicit prejudice toward the foreign culture represented in a culturally mixed stimulus.

Conclusions and Future Directions

As marketing efforts become increasingly globalized, it is key for organizations to understand how the multiculturalism prompted by globalization impacts consumer behavior. Our review sheds light on the cultural factors that shape consumers’ responses in a global marketplace, and it proposes alternatives for organizations to adapt their marketing strategies to address the consumer’s cultural needs. However, many questions remain unanswered. What other cultural dimensions beyond the ones discussed in this chapter have the greatest potential to further refine our predictions of consumer behavior? How can we further integrate the different approaches to cultural research to develop a more comprehensive model of cross-cultural consumer behavior? What happens when more than two cultures influence consumer behavior? What new methodological insights can cultural researchers adopt?

Additional Cultural Dimensions

In trying to uncover the cultural dimensions that would provide a more nuanced understanding of consumer behavior, several dimensions have been proposed, such as uncertainty avoidance ( Hofstede, 2001 ), religious beliefs, and tightness–looseness ( Triandis, 1995 ). Recent research on the tightness–looseness distinction seems to offer the greatest potential ( Li, Gordon, & Gelfand, 2017 ). Tightness (T) refers to cultures in which norms are clearly defined and there is little tolerance of deviant behavior, whereas looseness (L) relates to cultures in which norms are vague, and there is high tolerance of deviant behavior ( Gelfand et al., 2011 ; Pelto, 1968 ; Triandis, 1995 ). According to Li, Gordon, and Gelfand (2017) , it can be expected that ads in tighter cultures (e.g., Singapore) would emphasize adherence to societal standards, whereas ads in looser cultures (e.g., the United States) would focus more on taking risks. Lin, Dahl, and Argo (2017) identified four consumer-relevant research questions under the T-L framework, such as the examination of a potentially bidirectional relationship between tight/loose culture and consumer behavior.

Focusing on tightness–looseness can provide a more nuanced understanding of consumer behavior, especially by integrating it within existing cultural frameworks. For instance, it has been proposed that the T-L distinction seems particularly likely to shed light on the differences between societies that are high in vertical collectivism ( Torelli & Rodas, 2017 ). This cultural syndrome might help to explain the differences between Asian versions of vertical collectivism in which it is normative to suppress the expression of emotions (e.g., Japan, a tight culture) and Latin American versions of the same cultural syndrome in which people freely express their emotions (e.g., Venezuela, a loose culture; Gelfand et al., 2011 ). More research can be conducted by incorporating multiple cultural dimensions and examining their impact on marketing-relevant factors.

Focusing on tightness–looseness could also provide novel insights on some of the phenomena discussed in this chapter, for example, cultural mixing. Recent research has found that people tighten cultural norms in response to ecological threats ( Jackson et al., 2019 ). In one of their studies, the authors found that when people recall a threat such as a foreign attack or a major recession, they are more likely to vote for nationalists politicians, and this is mediated by stronger support for cultural tightness. Future research could explore whether these findings extend to cultural mixing, more specifically, whether ecological threats would result in more negative reactions to cultural mixing. Given the increased number of threats in the consumer’s environment (e.g., COVID-19, global recessions, or political instability), understanding whether consumers are going to be more averse to culture mixing would be of value to managers of global brands.

Integrating Cultural Approaches to Refine Models of Consumer Behavior

Although the three different approaches to cultural research described in this chapter have contributed separately to a more nuanced understanding of how culture impacts consumer behavior, future research integrating these approaches can greatly help to develop a more comprehensive model of consumer behavior in a globalized world. For instance, although the consumer theory of marketplace cultures provides a framework for understanding how consumers develop a collective identity built around a brand, we still lack a thorough understanding of the process by which brands become cultural icons. We argue that this process can be illuminated by integrating this ethnographic approach with an experimental approach aimed at understanding how identity concepts are linked to cultural icons in a cultural knowledge network. Specifically, this integrated approach might help to clearly delineate what is cultural about an iconic brand. If what grants a brand an iconic status is the strength of association with values, beliefs, rituals, lay theories, and other cultural objects, marketers can manage the process of cultural meaning creation by associating the brand with this constellation of cultural elements ( MacInnis et al., 2019 ). This integrated approach might also help to determine whether iconic brands can sustain changes and remake themselves while still retaining their iconic status. Increasingly relevant in this growing age of nationalism concerns, where consumers may stereotype other consumers based on their use of culturally symbolic products and brands, it is not clear how cultural stereotypes enhance, detract from, or alter brand meanings. For example, nationalism could either strengthen the shared cultural meanings of brands, or it could polarize views about those brands and the members of the cultures who use them, depending on their nationality. In this way, nationalism could hurt the equity of brands that had previously received support outside its country of origin.

Exposure to culture-mixed products triggers a variety of cognitive, emotional, and evaluative responses. Multiple situational and dispositional factors have been uncovered as moderators of the extent to which exclusionary or integrative responses are more likely to emerge. However, there is still the need for future research integrating these factors into a comprehensive framework. One fruitful approach might be the categorization of factors into those relating to the culture-mixed stimuli, the individual’s dispositions, environmental factors, and sociocultural and psychological consequences ( Hao et al., 2016 ). For instance, research focusing on features of the stimuli might try to explain how the “fusion” of cultures in a more or less harmonious way, or the strength of the symbolism of the features being mixed, can moderate responses to culture mixing. There is also need for research on how the organization in memory of multicultural knowledge guides people’s responses to culture-mixed stimuli, as well as on the contexts in which exclusionary or integrative responses are more likely to emerge.

From Biculturalism to Polyculturalism

This chapter discussed how cultural influence is not stable and deterministic, but dynamic and dependent on the context. Because it is increasingly common for people to internalize more than one cultural knowledge network (e.g., bicultural individuals), situational cues can determine how people switch between cultural frames for making judgments and decisions. Although recent research suggests that the way in which two cultural frames are organized in memory can impact how people respond to cultural primes (e.g., the frames are integrated vs. kept separately; Mok & Morris, 2013 ), we still know very little about how knowledge of more than two cultures influences consumer behavior.

With globalization, it is increasingly common for people to acquire knowledge about multiple cultures. Polyculturalism has emerged as a novel approach to understand how a multicultural mind works ( Morris et al., 2015 ). This approach proposes that most cultural learning happens implicitly through interaction with different cultures (e.g., via web browsing or when sharing with colleagues from different cultures). Hence, people take influences from multiple cultures and become conduits through which cultures can affect each other. Thus, cultural knowledge is conceived more like a set of “apps” that the individual unwittingly downloads and that can be “launched” to accommodate one’s behavior to a given situation. People flexibly assimilate or contrast their responses to the accessible cultural knowledge in the service of their goals (e.g., adopting a foreign practice as a better solution to a problem) or driven by emotional connections to the accessible culture (e.g., admiration for the culture; Morris et al., 2015 ). The polycultural approach opens avenues for future research about how multicultural consumers navigate a global marketplace.

New Methodological Insights

Like many other areas in psychology, the field of cultural psychology is also being reinvigorated by methodological innovations. In particular, cultural neuroscience is a promising research discipline that has emerged to investigate cultural differences in psychological, neural, and genomic processes ( Chiao et al., 2010 ). Cultural neuroscience specializes in developing culturally appropriate experimental tasks that can be applied in consumer behavior research in order to identify differences in the functional brain activity patterns associated with culture. This can help to further explain the nature of the processes underlying cross-cultural differences in consumer behavior via activation of specific areas of the brain. Another novel methodological approach to cultural research relates to the utilization of machine learning techniques (e.g., natural language processing) and the analyses of big data. An increasing number of empirical research studies have uncovered cross-cultural differences in various aspects of consumer preferences and behaviors, such as emoji usage ( Guntuku et al., 2019 ), recognition of beauty in music ( Claire, 2013 ), and color–emotion association ( Jonauskaite et al., 2019 ). These findings eventually will help the tech industry develop AI systems that can incorporate cultural factors into their algorithms ( Forbus, 2019 ). Future research can benefit from the adoption of these methodologies to further refine our understanding of cross-cultural consumer behavior.

Key Findings

  Cultural patterning of attention and perception: Culture is associated with specific patterns that drive consumers’ processing of advertising messages.

  Cultural matching effects: Advertisements that match cultural value priorities or regulatory orientations are more persuasive.

  Situational effects of culture and frame switching: Cultural influence is dynamic and context dependent.

  Cultural-identity driven consumption: People consume products and brands to symbolize their cultural identities and fulfill salient cultural identity goals.

  Responses to culture mixing: Exposure to culture-mixed products triggers a variety of cognitive, emotional, and evaluative responses. Culture mixing prompts perceptions that differences between cultures are greater than they would otherwise be.

Practical Implications for Organizations about Culture and Consumer Behavior

As marketing efforts become increasingly globalized, it is key for organizations to understand how the multiculturalism prompted by globalization impacts consumer behavior.

Adding themes that match the values of the culture makes ads more persuasive (e.g., ads with cues of power or status in the United States, and tradition and security in Japan and India).

Brands with values that are aligned with specific cultures (e.g., tradition in China) would benefit by imbuing ads with cultural cues (e.g., a Chinese dragon) to activate the brand-compatible cultural knowledge and make their ads more persuasive.

Corporations can build brands with cultural significance (i.e., iconic brands), which are instrumental in fulfilling consumers’ cultural identity goals.

Culture-mixed products can elicit disgust, and they are often evaluated negatively (exclusionary responses). However, people can also respond favorably to culture-mixed products when they can integrate the contrastive cultural frames brought to mind (integrative responses).

Aaker, J. L. , & Lee, A. Y. ( 2001 ). “I” seek pleasures and “we” avoid pains: The role of self-regulatory goals in information processing and persuasion.   Journal of Consumer Research , 28 (1), 33–49.

Google Scholar

Alter, A. L. , & Kwan, V. S. Y. ( 2009 ). Cultural sharing in a global village: Evidence for extracultural cognition in European Americans.   Journal of Personality and Social Psychology , 96 (4), 742–760. doi: 10.1037/a0014036

Amaral, N. B. , & Torelli, C. J. ( 2018 ). Salient cultural identities and consumers’ valuation of identity congruent brands: Consequences for building and leveraging brand equity.   Journal of Management and Training for Industries , 5 (3), 13–30. doi: 10.12792/JMTI.5.3.13

Arnould, E. J. , & Thompson, C. J. ( 2005 ). Consumer culture theory (CCT): Twenty years of research.   Journal of Consumer Research , 31 (4), 868–882. doi: 10.1086/426626

Batra, R. ( 2019 ). Creating brand meaning: A review and research agenda.   Journal of Consumer Psychology , 29 (3), 535–546. doi: 10.1002/jcpy.1122

Belk, R. W. , & Costa, J. A. ( 1998 ). The Mountain Man myth: A contemporary consuming fantasy.   Journal of Consumer Research , 25 (3), 218–240. doi: 10.1086/209536

Benet-Martínez, V. , Leu, J. , Lee, F. , & Morris, M. W. ( 2002 ). Negotiating biculturalism: Cultural frame switching in biculturals with oppositional versus compatible cultural identities.   Journal of Cross-Cultural Psychology , 33 (5), 492–516.

Birman, D. ( 1994 ). Acculturation and human diversity in a multicultural society. In E. Trickett , R. J. Watts , & D. Birman (Eds.), Human diversity: Perspectives on people in context (pp. 261–284). San Francisco: Jossey-Bass.

Google Preview

Briley, D. A. , & Aaker, J. L. ( 2006 ). When does culture matter? Effects of personal knowledge on the correction of culture-based judgments.   Journal of Marketing Research , 43 (3), 395–408.

Briley, D. A. , Morris, M. W. , & Simonson, I. ( 2000 ). Reasons as carriers of culture: Dynamic versus dispositional models of cultural influence on decision making.   Journal of Consumer Research , 27 (2), 157–178.

Chen, H. , Bolton, L. E. , Ng, S. , Lee, D. , & Wang, D. ( 2018 ). Culture, relationship norms, and dual entitlement.   Journal of Consumer Research , 45 (1), 1–20.

Chen, X. , Leung, A. K.-Y. , Yang, D. Y.-J. , Chiu, C.-Y. , Li, Z.-Q. , & Cheng, S. Y.-Y. ( 2016 ). Cultural threats in culturally mixed encounters hamper creative performance for individuals with lower openness to experience.   Journal of Cross-Cultural Psychology , 47 (10), 1321–1334.

Cheon, B. K. , Christopoulos, G. I. , & Hong, Y.-Y. ( 2016 ). Disgust associated with culture mixing: Why and who?   Journal of Cross-Cultural Psychology , 47 (10), 1268–1285.

Chiao, J. Y. , Hariri, A. R. , Harada, T. , Mano, Y. , Sadato, N. , Parrish, T. B. , & Iidaka, T. ( 2010 ). Theory and methods in cultural neuroscience.   Social cognitive and affective neuroscience , 5 (2–3), 356–361.

Chiu, C.-Y. , & Hong, Y.-Y. ( 2006 ). Social psychology of culture . New York: Psychology Press.

Chiu, C.-Y. , Mallorie, L. , Keh, H. T. , & Law, W. ( 2009 ). Perceptions of culture in multicultural space: Joint presentation of images from two cultures increases in-group attribution of culture-typical characteristics.   Journal of Cross-Cultural Psychology , 40 (2), 282–300.

Claire, Q. ( 2013 ). Machine learning analysis of the cultural and cross-cultural aspects of beauty in music. Doctoral dissertation.

Cross, M. ( 2002 ). A century of American icons . Westport, CT: Greenwood Press.

De Keersmaecker, J. , Van Assche, J. , & Roets, A. ( 2016 ). Need for closure effects on affective and cognitive responses to culture fusion.   Journal of Cross-Cultural Psychology , 47 (10), 1294–1306.

Escalas, J. E. , & Bettman, J. R. ( 2005 ). Self-construal, reference groups, and brand meaning.   Journal of Consumer Research , 32 (3), 378–389.

Farley, C. J. (2004). Elvis rocks. But he’s not the first. www.time.com , Downloaded from http://www.time.com/time/arts/article/0,8599,661084,00.html on March 26, 2013.

Fiske, S. T. ( 1993 ). Controlling other people: The impact of power on stereotyping.   American Psychologist , 48 (6), 621–628.

Forbus, K. D. ( 2019 ). Creating AI systems that take culture into account.   The ethical machine . Downloaded from https://ai.shorensteincenter.org/ideas/2019/1/14/creating-ai-systems-that-take-culture-into-account-aps9l .

Forehand, M. R. , Deshpandé, R. , & Reed, A. ( 2002 ). Identity salience and the influence of differential activation of the social self-schema on advertising response.   Journal of Applied Psychology , 87 (6), 1086–1099. doi: 10.1037/0021-9010.87.6.1086

Fournier, S. , & Alvarez, C. ( 2019 ). How brands acquire cultural meaning.   Journal of Consumer Psychology , 29 (3), 519–534.

Gelfand, M. J. , Raver, J. L. , Nishii, L. , Leslie, L. M. , Lun, J. , Lim, B. C. , Duan, L. , Almaliach, A. , Ang, S. , Arnadottir, J. , Aycan, Z. , Boehnke, K. , Boski, P. , Cabecinhas, R. , Chan, D. , Chhokar, J. , D’Amato, A. , Ferrer, M. , Fischlmayr, I. C. , … Yamaguchi, S. ( 2011 ). Differences between tight and loose cultures: A 33-nation study.   Science , 332 (6033), 1100–1104. doi: 10.1126/science.1197754

Guntuku, S. C. , Li, M. , Tay, L. , & Ungar, L. H. (2019). Studying cultural differences in emoji usage across the east and the west. Proceedings of the International AAAI Conference on Web and Social Media , 13 , 226–235.

Guralnick, P. ( 1994 ). Last train to Memphis: The rise of Elvis Presley . Boston: Little, Brown.

Han, D. , Lalwani, A. K. , & Duhachek, A. ( 2017 ). Power distance belief, power, and charitable giving.   Journal of Consumer Research , 44 (1), 182–195.

Han, S.-P. , & Shavitt, S. ( 1994 ). Persuasion and culture: Advertising appeals in individualistic and collectivistic societies.   Journal of Experimental Social Psychology , 30 (4), 326–350.

Hao, J. , Li, D. , Peng, L. , Peng, S. , & Torelli, C. J. ( 2016 ). Advancing our understanding of culture mixing.   Journal of Cross-Cultural Psychology , 47 (10), 1257–1267.

Hofstede, G. H. ( 1980 ). Culture’s consequences: International differences in work-related values . Newbury Park, CA: SAGE.

Hofstede, G. H. ( 2001 ). Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations . Thousand Oaks, CA: SAGE.

Holt, D. B. ( 2004 ). How brands become icons: The principles of cultural branding . Cambridge, MA: Harvard Business School Press.

Hong, Y.-Y. , Ip, G. , Chiu, C.-Y. , Morris, M. W. , & Menon, T. ( 2001 ). Cultural identity and dynamic construction of the self: collective duties and individual rights in Chinese and American cultures.   Social Cognition , 19 (3), 251–268.

Hong, Y.-Y. , Morris, M. W. , Chiu, C.-Y. , & Benet-Martinez, V. ( 2000 ). Multicultural minds: A dynamic constructivist approach to culture and cognition.   American Psychologist , 55 (7), 709–720.

Jackson, J. C. , van Egmond, M. , Choi, V. K. , Ember, C. R. , Halberstadt, J. , Balanovic, J. , Basker, I. N. , Boehnke, K. , Buki, R. , Fulop, M. , Fulmer, A. , Homan, A. C. , Van Kleef, G. A. , Kreemers, L. , Schei, V. , Szabo, E. , Ward, C. , & Gelfand, M. J. ( 2019 ). Ecological and cultural factors underlying the global distribution of prejudice.   PloS one , 14 (9), e0221953. doi: 10.1371/journal.pone.0221953

Jain, S. S. , & Jain, S. P. ( 2018 ). Power distance belief and preference for transparency.   Journal of Business Research, 89, 135–142.

Jonauskaite, D. , Wicker, J. , Mohr, C. , Dael, N. , Havelka, J. , Papadatou-Pastou, M. , Zhang, M. , & Oberfeld, D. ( 2019 ). A machine learning approach to quantify the specificity of colour–emotion associations and their cultural differences.   Royal Society Open Science , 6 (9), 190741.

Kashima, Y. ( 2000 ). Maintaining cultural stereotypes in the serial reproduction of narratives.   Personality and Social Psychology Bulletin , 26 (5), 594–604.

Keh, H. T. , Torelli, C. J. , Chiu, C.-Y. , & Hao, J. ( 2016 ). Integrative responses to culture mixing in brand name translations: The roles of product self-expressiveness and self-relevance of values among bicultural Chinese consumers.   Journal of Cross-Cultural Psychology , 47 (10), 1345–1360.

Kellner, D. ( 2013 ). The Elvis spectacle and the cultural industries.   Essays by Douglas Kellner, Graduate School of Education & Information Studies, UCLA , Downloaded from   http://pages.gseis.ucla.edu/faculty/kellner/2009_essays.html on March 26, 2013.

Kim, Y. , & Zhang, Y. ( 2014 ). The impact of power-distance belief on consumers’ preference for status brands.   Journal of Global Marketing , 27 (1), 13–29.

Kotler, P. , & Keller, K. L. ( 2009 ). A framework for marketing management (4th ed.). Englewood Hills, NJ: Prentice-Hall.

Kroeber, A. L. , & Kluckhohn, C. ( 1952 ). Culture: A critical review of concepts and definitions . Cambridge, MA: Harvard University Press.

Lalwani, A. K. , & Forcum, L. ( 2016 ). Does a dollar get you a dollar’s worth of merchandise? The impact of power distance belief on price-quality judgments.   Journal of Consumer Research , 43 (2), 317–333.

Lau-Gesk, L. G. ( 2003 ). Activating culture through persuasion appeals: An examination of the bicultural consumer.   Journal of Consumer Psychology , 13 (3), 301–315.

Li, R. , Gordon, S. , & Gelfand, M. J. ( 2017 ). Tightness–looseness: A new framework to understand consumer behavior.   Journal of Consumer Psychology , 27 (3), 377–391. doi: https://doi.org/10.1016/j.jcps.2017.04.001

Lin, L. , Dahl, D. W. , & Argo, J. J. ( 2017 ). Refining the tightness and looseness framework with a consumer lens.   Journal of Consumer Psychology , 27 (3), 392–397.

Liu, J. , Ng, S. , & Lim, E. (2015). Moving up or down: power distance belief and the asymmetric effect of vertical brand extension . Paper presented at the AP - Asia-Pacific Advances in Consumer Research, Duluth, MN.

MacInnis, D. J. , Torelli, C. J. , & Park, C. W. ( 2019 ). Creating cultural meaning in products and brands : A psychological perspective. Journal of Consumer Psychology , 29 (3), 555–562.

McCracken, G. ( 1986 ). Culture and consumption: A theoretical account of the structure and movement of the cultural meaning of consumer goods.   Journal of Consumer Research , 13 (1), 71–84.

Meyers-Levy, J. , & Tybout, A. M. ( 1989 ). Schema congruity as a basis for product evaluation.   Journal of Consumer Research , 16 (1), 39–54.

Mok, A. , & Morris, M. W. ( 2013 ). Bicultural self-defense in consumer contexts: Self-protection motives are the basis for contrast versus assimilation to cultural cues.   Journal of Consumer Psychology , 23 (2), 175–188. doi: http://dx.doi.org/10.1016/j.jcps.2012.06.002

Monga, A. B. , & John, D. R. ( 2007 ). Cultural differences in brand extension evaluation: The influence of analytic versus holistic thinking.   Journal of Consumer Research , 33 (4), 529–536.

Morris, M. W. , Chiu, C.-Y. , & Liu, Z. ( 2015 ). Polycultural psychology.   Annual Review of Psychology , 66 (1), 631–659. doi: 10.1146/annurev-psych-010814-015001

Nayyar, D. ( 2010 ). China, India, Brazil and South Africa in the world economy: Engines of growth. In A. U. Santos-Paulino & G. Wan (Eds.), Southern engines of global growth (pp. 9–27). New York: Oxford University Press.

Nisbett, R. E. , Peng, K. , Choi, I. , & Norenzayan, A. ( 2001 ). Culture and systems of thought: Holistic versus analytic cognition.   Psychological Review , 108 (2), 291–310.

Overbeck, J. R. , & Park, B. ( 2001 ). When power does not corrupt: Superior individuation processes among powerful perceivers.   Journal of Personality and Social Psychology , 81 (4), 549–565.

Oyserman, D. ( 2009 ). Identity-based motivation: Implications for action-readiness, procedural-readiness, and consumer behavior.   Journal of Consumer Psychology , 19 (3), 250–260.

Paharia, N. , & Swaminathan, V. ( 2019 ). Who is wary of user design? The role of power-distance beliefs in preference for user-designed products.   Journal of Marketing , 83 (3), 91–107.

Pelto, P. J. ( 1968 ). The differences between “tight” and “loose” societies.   Trans-action , 5 (5), 37–40.

Reed, A. , Forehand, M. R. , Puntoni, S. , & Warlop, L. ( 2012 ). Identity-based consumer behavior.   International Journal of Research in Marketing, 29(4), 310–321. doi: 10.1016/j.ijresmar.2012.08.002

Shavitt, S. , Lalwani, A. K. , Zhang, J. , & Torelli, C. J. ( 2006 ). The horizontal/vertical distinction in cross-cultural consumer research.   Journal of Consumer Psychology , 16 (4), 325–356.

Shavitt, S. , Torelli, C. J. , & Riemer, H. ( 2010 ). Horizontal and vertical individualism and collectivism: implications for understanding psychological processes. In M. J. Gelfand , C.-Y. Chiu , & Y.-y. Hong (Eds.), Advances in culture and psychology (Vol. 1, pp. 309–350). New York: Oxford University Press.

Shi, Y. , Shi, J. , Luo, Y. L. , & Cai, H. ( 2016 ). Understanding exclusionary reactions toward a foreign culture: The influence of intrusive cultural mixing on implicit intergroup bias.   Journal of Cross-Cultural Psychology , 47 (10), 1335–1344.

Swaminathan, V. , Page, K. L. , & Gürhan-Canli, Z. ( 2007 ). “My” brand or “our” brand: The effects of brand relationship dimensions and self-construal on brand evaluations. Journal of Consumer Research , 34 (2), 248–259.

Torelli, C. J. ( 2006 ). Individuality or conformity? the effect of independent and interdependent self-concepts on public judgments. Journal of Consumer Psychology , 16 (3), 240–248.

Torelli, C. J. ( 2013 ). Globalization, culture, and branding: How to leverage cultural equity for building iconic brands in the era of globalization . London: Palgrave Macmillan.

Torelli, C. J. , & Ahluwalia, R. ( 2012 ). Extending culturally symbolic brands: A blessing or a curse?   Journal of Consumer Research , 38 (5), 933–947.

Torelli, C. J. , Ahluwalia, R. , Cheng, S. Y.-y. , Olson, N. J. , & Stoner, J. L. ( 2017 ). Redefining home: How cultural distinctiveness affects the malleability of in-group boundaries and brand preferences.   Journal of Consumer Research , 44 (1), 44–61.

Torelli, C. J. , & Cheng, S. Y.-y. ( 2011 ). Cultural meanings of brands and consumption: A window into the cultural psychology of globalization.   Social and Personality Psychology Compass, 5, 251–262.

Torelli, C. J. , Chiu, C.-Y. , Tam, K.-p. , Au, A. K. C. , & Keh, H. T. ( 2011 ). Exclusionary reactions to foreign cultures: Effects of simultaneous exposure to cultures in globalized space.   Journal of Social Issues , 67 (4), 716–742.

Torelli, C. J. , Keh, H. T. , & Chiu, C.-Y. ( 2010 ). Cultural symbolism of brands. In B. Loken , R. Ahluwalia , & M. J. Houston (Eds.), Brands and brand management: Contemporary research perspectives (pp. 113–132). New York: Routledge.

Torelli, C. J. , Oh, H. , & Stoner, J. L. ( 2020 ). Cultural equity: Knowledge and outcomes aspects.   International Marketing Review , 38 (1), 99–123. doi: 10.1108/imr-12-2018-0366

Torelli, C. J. , Ozsomer, A. , Carvalho, S. , Keh, H. T. , & Maehle, N. (2012). Brand concepts as representations of human values: Do cultural congruity and compatibility between values matter? Journal of Marketing , 76 (July), 92–108.

Torelli, C. J. , & Rodas, M. A. ( 2017 ). Tightness–looseness: Implications for consumer and branding research.   Journal of Consumer Psychology , 27 (3), 398–404. doi: https://doi.org/10.1016/j.jcps.2017.04.003

Torelli, C. J. , Rodas, M. A. , & Stoner, J. L. ( 2017 ). The cultural meaning of brands.   Foundations and Trends® in Marketing, 10(3), 153–214.

Torelli, C. J. , & Shavitt, S. ( 2011 ). The impact of power on information processing depends on cultural orientation.   Journal of Experimental Social Psychology , 47 (5), 959–967. doi: 10.1016/j.jesp.2011.04.003

Triandis, H. C. ( 1995 ). Individualism and collectivism . Boulder, CO: Westview Press.

Triandis, H. C. ( 1996 ). The psychological measurement of cultural syndromes.   American Psychologist , 51 (4), 407–415.

Triandis, H. C. , & Gelfand, M. J. ( 1998 ). Converging measurement of horizontal and vertical individualism and collectivism.   Journal of Personality and Social Psychology , 74 (1), 118–128.

Wan, C. , Chiu, C.-Y. , Tam, K.-P. , Lee, S.-L. , Lau, I. Y.-M. , & Peng, S. ( 2007 ). Perceived cultural importance and actual self-importance of values in cultural identification.   Journal of Personality and Social Psychology, 92, 337–354.

Wan, C. , Torelli, C. J. , & Chiu, C.-Y. ( 2010 ). Intersubjective consensus and the maintenance of normative shared reality.   Social Cognition , 28 (3), 422–446.

Wang, J. J. , Torelli, C. J. , & Lalwani, A. K. ( 2020 ). The interactive effect of power distance belief and consumers’ status on preference for national (vs. private-label) brands.   Journal of Business Research, 107, 1–12.

Winterich, K. P. , Gangwar, M. , & Grewal, R. ( 2018 ). When celebrities count: Power distance beliefs and celebrity endorsements.   Journal of Marketing , 82 (3), 70–86.

• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• 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.

Consumer Collecting Behaviour: A Systematic Review and Future Research Agenda

• Related Documents

Citizen experience in public transport: A systematic review and future research agenda

The smart city journey: a systematic review and future research agenda, conceptualizing cross-country analyses of family firms: a systematic review and future research agenda, the (in)justices of smart local energy systems: a systematic review, integrated framework, and future research agenda, multiple jobholding: an integrative systematic review and future research agenda.

Despite sizable but varying estimates of multiple jobholding (MJH) and decades of research across disciplines (e.g., management, economics, sociology, health and medicine), our understanding of MJH is rather limited. The purpose of this review is to provide a coherent synthesis of the literature on MJH, or working more than one job. Beginning with a discussion of the motivations and demographic predictors that forecast MJH, we note a distinct divide between the research that predicts MJH and the research that examines outcomes, with few studies exploring how motivations might relate to MJH experiences and outcomes. Another significant observation in this review is the inconsistency of findings across and within disciplines regarding whether MJH is depleting or enriching. Using this framework to organize our review, we attempt to reconcile the generally mixed results by presenting research on mechanisms and boundary conditions of MJH to explain how and when multiple jobholders (MJHers) are depleted or enriched. By integrating findings from the literature, we are able to articulate more clearly the paths of depletion and enrichment and discuss how push versus pull-based motivations to hold multiple jobs likely predict these pathways. Finally, we provide a strategic agenda highlighting areas where additional research is urgently needed to equip scholars with practical knowledge on how to help MJHers manage their multiple work roles and how to help organizations manage MJHers.

The integration of circular economy with sustainable consumption and production tools: Systematic review and future research agenda

Remote icu monitoring: using a systematic review to inform a future research agenda in the va, intentions resurrected: a systematic review of entrepreneurial intention research from 2014 to 2018 and future research agenda, foreign location decisions through an institutional lens: a systematic review and future research agenda, copd phenotypes and machine learning cluster analysis: a systematic review and future research agenda, export citation format, share document.

• Reference Manager
• Simple TEXT file

People also looked at

Systematic review article, consumer behavior in augmented shopping reality. a review, synthesis, and research agenda.

• 1 Department of marketing, University of Kiel, Kiel, Germany
• 2 Grenoble École de Management, Grenoble, France

The application of augmented reality (AR) is receiving great interest in e-commerce, m-commerce, and brick-and-mortar-retailing. A growing body of literature has explored several different facets of how consumers react to the upcoming augmented shopping reality. This systematic literature review summarizes the findings of 56 empirical papers that analyzed consumers’ experience with AR, acceptance of AR, and behavioral reactions to AR in various online and offline environments. The review synthesizes current knowledge and critically discusses the empirical studies conceptually and methodologically. Finally, the review outlines the theoretical basis as well as the independent, mediating, moderating, and dependent variables analyzed in previous AR research. Based on this synthesis, the paper develops an integrative framework model, which helps derive directives for future research on augmented shopping reality.

Introduction

The augmented reality (AR) technology supplements the real world with virtual elements. These supplements are often visual like in the mobile game Pokémon Go, where the digital Pokémons extend the physical environment ( Hamari et al., 2019 ), but they could also address other senses like hearing, for example through interactive audio AR in participatory performance ( Nagele et al., 2021 ), smelling in synesthetic visualization of odors with an odor detector (e-nose; Ward et al., 2020 ) or tasting by a pseudo-gustatory display ( Narumi et al., 2011 ). Several reports have recently ranked AR as one of the top 10 technology trends ( Marr, 2020 ; Samsung Business Insights, 2020 ). In a similar vein, the report of Euromonitor International describes “phygital reality” as a top 10 global consumer trend in 2021 ( Westbrook and Angus, 2021 ). Phygital reality is understood as a hybrid bridging the physical and digital world regarding various aspects of human behavior, including living, working, and shopping. According to this report, half of the consumers younger than 45 years have used augmented reality and virtual reality in 2020 ( Westbrook and Angus, 2021 ). Evidently, consumers are increasingly used to integrate this technology into their lives. For example, the Covid-19 pandemic caused lockdowns in 2020 and 2021, calling for social distancing in many countries. Technologies like video conferencing rapidly became widespread, shifting personal and business contacts to virtual rooms. With such developments boosting people’s view on technology ( Hacker et al., 2020 ) and the fast diffusion of devices that principally enable AR-based applications, the relevance of the AR technology will continue to grow. This is particularly true as these mobile devices are often considered “constant companions” (e.g., smartphones or tablet computers). Accordingly, the AR market is expected to reach a US $75 billion in revenue by 2023 ( vXchange, 2020 ) and the global AR and VR market revenue of US $161.1 billion by 2025 ( Vynz Research, 2020 ). In their opinion paper, Dwivedi et al. (2021) , p . 16) recently concluded that augmented reality is still in its infancy, but they forecast that it “will be as prevalent in the marketing of the future as the Internet is today”.

The AR technology has already entered the shopping world. Companies and retailers can feasibly apply AR in e-commerce and m-commerce (e.g., Javornik, 2016b ; Baek et al., 2018 ; Beck and Crié, 2018 ). In these online retailing contexts, AR enables consumers to visualize or even virtually try-on products, such as apparel, eyewear, or cosmetics. AR-enabled virtual try-ons or virtual fitting rooms allow consumers to make better choices. As a positive side effect, this may eventually help decrease the excessive return rates of apparel ordered online ( Narvar, 2017 ). AR can also be helpful in brick-and-mortar retailing ( Hilken et al., 2018 ; Caboni and Hagberg, 2019 ), where the technology can enhance the physical products or shelves with digital information (e.g., van Esch et al., 2019 ; Wedel et al., 2020 ; Joerß et al., 2021 ).

As an umbrella term for AR applications in shopping and retailing environments, we coin the term augmented shopping reality (ASR). However, despite the aforementioned benefits and wide diffusion of AR-enabling devices, the diffusion of ASR is still in an early phase. According to a recent WBR (2020) insights report, only 1 out of 100 retailers is currently using AR. Many companies state that the lack of the ability to currently support these features is the main obstacle. Yet, most of the surveyed managers report that they plan to adopt the technology in the near future. Online sellers and offline retailers require more knowledge about how consumers react to the technology and how to design effective AR applications. For example, based on research insights, ASR could potentially be more effective in addressing different senses when utilizing the crossmodal design paradigm ( Ward et al., 2021 ) which is known to influence decision processes ( Deliza and Macfie, 1996 ) and perceived value ( Teas and Agarwal, 2000 ). As another example, ASR could be more effective making use of the latest research findings on the design of AR information at the point of sale ( Hoffmann et al., 2022 ). Academic ASR research is developing with tremendous speed, but the growing body of literature is very diverse and fragmented in that the extant studies cover different AR applications, shopping settings, and product categories, with each study putting the spotlight on a specific context. Also, the findings are published in different fields, such as business (e.g., Rauschnabel et al., 2018 ; Jessen et al., 2020 ; Smink et al., 2020 ), marketing (e.g., Hilken et al., 2017 ), retailing (e.g., Heller et al., 2019a , b ; Pantano et al., 2017 ; Watson et al., 2020 ), information science (e.g., Huang and Liao, 2015 ; Brito and Stoyanova, 2018 ; McLean and Wilson, 2019 ), and psychology (e.g., Choi and Choi, 2020 ). Consequently, notable voids exist in the literature, among others, regarding whether or not AR taps the same or different ASR functionalities in e-/m-commerce and brick-and-mortar-retailing. Marketers need to know which ASR functionality they can use in which setting, for which product categories, and how they have to design the ASR for different applications. Another void emerges in how the cluttered empirical findings about user experiences, technology acceptance, marketing outcomes, etc. can be integrated into a general customer-centric framework to understand the whole customer journey. To resolve this confusion and provide scholars, managers and ASR designers with a cohesive understanding of the current state-of-art, a systematic integration is needed. To fill these voids, this paper synthesizes the relevant literature’s achievement, develops a new holistic theoretical framework by integrating past empirical findings and enhancing them based on conceptual works, and then outlines future trajectories and research directions.

The paper will answer the following research questions: 1) Are there contingencies between the different ASR functionalities (informing, visualizing, trying-on, and placing) and the context in which they are used, including the retailing channel, product category and AR device? 2) Which theories build the foundation for empirical AR research on consumer behaviors in ASR, and how can these partial explanations be integrated into a sound framework? 3) Which models of consumer behavior have been developed and empirically tested, especially for the different contexts of ASR? 4) Which methodologies have scholars applied, and which research methods are needed in the future given the more mature state of the field? 5) How can the formal functions of the predictor, mediator, and outcome variables of previous AR studies be organized, and which moderators and boundary conditions are relevant for developing one integrative framework model? 6) What are the research gaps in the consumer behavior literature on ASR, and which directions are most relevant for further investigations in this context?

We conduct a systematic literature review to assess the current state-of-the-art of ASR research. The review covers 56 papers, which report empirical studies on consumer behavior in ASR. In particular, we highlight which ASR functions (informing, visualizing, trying-on, and placing) are tested in shopping environments, such as e-commerce, m-commerce, and brick-and-mortar retailing. We systemize and integrate the theoretical basis and conceptual models explored in past research. Footing on this synthesis, the paper develops an integrative framework model that helps derive directions for future research on consumer behavior in ASR. For the first time, we critically review the methodological approaches of past papers and evaluate the research stream from a methodological point of view to provide recommendations for improving the quality of future research. The target audience of this systematic literature review is researchers in marketing, consumer behavior, retailing, media design, and computer science, as well as practitioners in these domains.

Defining augmented shopping reality

Augmented reality.

The AR technology combines real and virtual objects in such a way that they appear to coexist in the same space ( Azuma et al., 2001 ; van Krevelen and Poelman, 2010 ; Skarbez et al., 2021 ). To this end, the technology superimposes digital 3D objects in relation to objects in the analogue world on a screen or any other device display ( Azuma, 1997 ). Furthermore, as a unique property of the AR technology, this augmentation of the real world occurs in real-time ( Azuma, 1997 ; Carmigniani et al., 2011 ), such that users are able to interact with the virtual objects ( Zhou et al., 2008 ). For these reasons, augmentation of the real world with a computer-generated layer and interactivity can be considered the two main features of augmented reality ( Javornik, 2016a ).

The technology, and thus the augmentation of reality, can be achieved on many different types of displays and devices. First, there are fixed interactive screens (e.g., virtual mirrors), computer monitors, and laptops. A second category is portable and handheld devices, such as smartphones, smartwatches, tablet computers, or even optical see-through glasses ( Carmigniani et al., 2011 ; Kim and Hyun, 2016 ; Brito and Stoyanova, 2018 ). Mobile devices are omnipresent nowadays, so they likely boost the diffusion of AR in various settings, opening the technology’s untapped potential. The third category comprises displays of wearable technologies proximal to the user. These include head-mounted displays, such as smart glasses or helmets (e.g., Microsoft HoloLens), which overlay the user’s field of vision with digital objects (e.g., Brito and Stoyanova, 2018 ; Rauschnabel 2018 ; Rauschnabel et al., 2018 ). Finally, in the more distant future, the application of implanted devices, such as lenses, is highly probable ( Flavián et al., 2019 ).

Different fields analyze AR and its practical applications. Research in information technology and computer science explores the technical and functional aspects of the AR technology, such as precise control or exact object positioning ( Zhou et al., 2008 ; Carmigniani et al., 2011 ; Chae et al., 2018 ; Kytö et al., 2018 ). Scholars from different disciplines have also analyzed AR applications through the lenses of their fields, including medicine ( Berryman, 2012 ; Vávra et al., 2017 ), psychology ( Botella et al., 2005 ), education ( Di Serio et al., 2013 ; Bower et al., 2014 ; Harley et al., 2016 ; Chen et al., 2017 ), gaming ( Rauschnabel et al., 2017 ; Hamari et al., 2019 ), or tourism ( Aluri, 2017 ; Chung et al., 2018 ; tom Dieck and Jung, 2018 ). In the business literature, the AR technology has been studied with a focus on production and industry 4.0 ( Masood and Egger, 2019 ; Kaasinen et al., 2020 ) or advertising and branding ( Hopp and Gangadharbatla, 2016 ; Mauroner et al., 2016 ; Yaoyuneyong et al., 2016 ; de Ruyter et al., 2020 ). In this paper, we focus on the applications in retailing environments. Virtual reality (VR) also provides innovative applications for marketing and retailing, and researchers have already analyzed these applications ( Boyd and Koles, 2019 ; Herz and Rauschnabel, 2019 ; Hudson et al., 2019 ; van Berlo et al., 2021 ). However, in contrast to AR, VR creates a complete digital environment where users interact with virtual objects in real-time. AR superimposes computer-generated objects over the real world ( Flavián et al., 2019 ). Therefore, this technology is highly interesting for retailing contexts, such as stationary retailing where AR can provide additional information to physical products or e-tailing where AR can help consumers virtually try-on products. Hence, we focus on AR in this paper.

Augmented shopping reality

AR can be incorporated in retailing settings in several ways, including but not limited to the functionalities of informing, visualizing, trying-on, and placing. We build our typology on prior research that has already suggested classifications of AR functionalities in general (e.g., Azuma, 1997 ) and in retailing contexts. For example, Tan et al. (2021) identified four uses of AR in retailing. However, these categories (entertain, educate customers, evaluate product fit, enhance the postpurchase consumption experience) describe how consumers use the AR technology, while our review will shift the focus on the technological design to disentangle the different functionalities and their uses. Prior research stressed that AR in shopping settings could be used to extend the product, the consumers’ body, and the consumers’ environment ( Javornik 2016a ; Hoffmann et al., 2022 ). Integrating these conceptual foundations, we propose that the AR technology can be used to enhance and support different steps in the customer journey, from searching information over visualizing products to virtually trying on products or virtually placing objects in the consumers’ environment. We accordingly claim that AR provides at least four main groups of functionalities in shopping and retailing settings; we label these ASR functions as informing, visualizing, trying-on, and placing. As a striking advantage of this typology, AR applications can be objectively assigned to the different categories based on their technological design.

The AR technology can be used to enhance physical objects (including products) with virtual information ( Hoffmann et al., 2022 ). This function has been labelled ‘annotation’ by Azuma (1997) and is related to Tan et al.’s (2021) educate category. Tourism agencies use AR to deliver location-based information about sights, or museums provide details about exhibits (CorfuAR; Kourouthanassis et al., 2015 ). Star view apps (Night Sky, Sky View, Star Walk, etc.) are further examples of how AR can deliver context-specific information. In shopping contexts, retailers can apply AR in brick-and-mortar stores to supplement the physical environment with product information ( Hilken et al., 2018 ), such as offering further details about books ( Spreer and Kallweit, 2014 ) or food products ( Joerß et al., 2021 ; Hoffmann et al., 2022 ). Virtual overlays concern the product or specific areas on the packaging and even entire shelves. The conveyed details can be technical details or information about the product origin, ingredients, allergy warnings, etc. As a unique benefit that sets AR systems apart from other traditional means of communication, the AR-enabled information can even provide personalized information ( Hsu et al., 2021 ) without physically altering the product design or its packaging. This aspect is particularly interesting to physical stores because they operate under much stronger space-related constraints in terms of information presentation than do online or mobile shops. In this way, AR opens up virtually unlimited space in the digital world for physical objects at the point of sale. The technology provides shoppers with access to the required information exactly at the place and the time when they need it ( Joerß et al., 2021 ; Hoffmann et al., 2022 ).

Visualizing

The visualization function allows users to see a virtual 3D model of a product or visualize specific aspects of it or certain benefits ( Azuma, 1997 ). Users can interact with the model and turn it to view it from different angles or they might customize the size, colors, and shape. The function has been tested in empirical research, for example, regarding the mobile app of the German car magazine AUTO BILD that can be scanned to experience virtual context ( Rese et al., 2017 ). Other studies tested AR applications to visualize shoes ( Brito and Stoyanova, 2018 ) or mugs ( Huynh et al., 2019 ).

Virtual try-ons allow users to augment themselves with virtual objects. Users of this type of AR application can choose a piece of apparel, shoes, eyewear, cosmetics, or watches and test these products on their own body or their own face in a virtual fitting-room or through a virtual mirror (e.g., Javornik, 2016b ; Hilken et al., 2017 ; Poushneh and Vasquez-Parraga, 2017 ; Yim et al., 2017 ). In particular, sellers of apparel (e.g., Huang and Liao, 2015 ; Baytar et al., 2020 ), eyeglasses and sunglasses (e.g., Ray-Ban Virtual Try-On, Mister Spex), or cosmetics (e.g., Shiseido AR makeup mirror) have developed such virtual try-ons. This AR function is frequently applied in e-commerce and m-commerce to allow consumers try on products in the digital world where testing the product before ordering it is often not feasible or possible. As they help avoid suboptimal decisions, virtual try-ons may be one way to reduce the unreasonably high return rates of non-fitting products. Notably, even brick-and-mortar stores have adapted AR try-on applications, such as virtual mirrors on stationary wide-screen monitors for apparel (e.g., Yuan et al., 2021 ).

The AR function placing (also termed ‘environmental embedding’ by Hilken et al., 2017 or ‘evaluate’ by Tan et al., 2021 ) refers to the augmentation of the physical surrounding of the user with virtual elements. In shopping and retailing contexts, this application is frequently employed for home furniture (e.g., Javornik, 2016b ; Heller et al., 2019a ; Rauschnabel et al., 2019 ). Furniture planners (e.g., IKEA place, Cimagine) invite users to scan or click objects of the catalogue, website or app and place these elements virtually in their physical rooms. Furniture planners support consumers in imagining how these pieces of furniture would look in their rooms. Other applications are paintings ( Mishra et al., 2021 ) or wall paint ( Hilken et al., 2020 ).

Summing up, in shopping contexts, these four AR functionalities differ primarily with regard to the object that is recorded by the device’s camera and which is augmented on the display (the product, a marker, the consumer, or the consumer’s physical surroundings). Secondly, the functionalities differ regarding the attached virtual objects (information, product visualization, and embedded product). Table 1 provides a systematic overview of these differences. Notably, the functions of trying-on and placing both involve a demonstration of the product (like visualizing) but embed the virtual product either in the image of the consumer (trying-on) or the environment (placing). For visualizations of these different functions, we refer readers to some of the empirical articles in our literature review that include pictures of the studied AR technology. For the function ‘informing’, for example, see the shopping apps used by Hoffmann et al. (2022) , Figure 6, 7), Joerß et al. (2021) , p . 520), or Speer and Kallweit. (2014) , p . 22). For the ‘visualizing’ function, consider the CluckAR-app shown by van Esch et al. (2019) , p . 39) or the AUTO BILD ad shown by Rese et al. (2017) , p . 311). Examples for the ‘trying-on’ function are the Ray Ban Website for sunglasses or the Tissot Website for watches shown by Yim et al. (2017) , p . 101). For the function ‘placing’, see the IKEA tool shown by Javornik. (2016b) , p . 97).

TABLE 1 . Applications of AR in retailing.

Arguably, some functionalities may benefit specific product categories (e.g., trying-on for apparel, informing for food). Still, the product categories and functionalities are two distinct aspects that need to be considered separately. For example, AR can add product information to a sweater in a physical store (informing), can visualize the sweater in 3D based on a picture in a catalogue (visualizing), help consumers virtually try on the sweater in e-commerce (trying-on) or the technology can virtually put the sweater in the consumer’s wardrobe (placing).

Search strategy

We conduct a systematic literature review to give an overview of the research in the field of consumer behavior in ASR (see Figure A1 ). We started with a systematic search process following the standard guidelines for systematic reviews ( Moher et al., 2009 ; Palmatier et al., 2018 ; Snyder, 2019 ).

In a first step, we consulted the Web of Science database to search papers using the following terms: (“augmented reality” OR “mixed reality” OR “extended reality”) AND (shopping OR retailing OR e-commerce OR marketing OR consumer OR customer). We allowed only published journal articles. Our initial search resulted in 852 records, which were reduced to 759 when excluding review articles in the search mask (see Figure A1 ).

In a second step, we cleansed the set of papers following pre-defined criteria. We first inspected the title and abstract to include only relevant papers. If necessary, we read the papers to decide whether or not they are appropriate. Selecting only papers with a clear focus on AR, MR, or ER reduced the set of papers to 348. For example, we dropped articles covering the VR technology, but not AR ( Hudson et al., 2019 ). We extracted 84 papers that focus on consumer behavior with regard to AR in retailing to promote products or brands. We excluded studies limited to the technological development, such as comparing different AR technologies. Given our scope on retailing and e-commerce, we also excluded papers on advertising and branding (e.g., Hopp and Gangadharbatla, 2016 ; Yaoyuneyong et al., 2016 ) or active catalogues ( Rese et al., 2014 ). We further excluded research that does not model the consumer process ( Tan et al., 2021 ). We kept only empirical studies with a quantitative methodology, leaving 62 papers. We decided to exclude research with a qualitative approach (e.g., Olsson et al., 2013 ; Scholz and Duffy, 2018 ; Romano et al., 2021 ) from our analysis because these papers cannot be integrated into our systematic reviewing approach. Still, we will use these papers to enrich the evaluation and interpretation of the state-of-the-art in the discussion section. Finally, we excluded papers that did not pass certain predefined quality standards (e.g., no statistical inference tests or very small sample sizes). After this cleansing process, the set was reduced to 52 suitable papers.

In a third step, we inspected the references of the various AR articles and the latest issues of journals that frequently publish AR-related articles in marketing and consumer research. We include four additional papers, ending up with 56 papers for our systematic literature review.

The papers are published in journals with a focus on Marketing, Retailing, Information Science, and Technology. The highest share of papers in the literature review was published in the Journal of Retailing and Consumer Services (17), followed by the Journal of Business Research (8), Journal of the Academy of Marketing Science (3), Journal of Retailing (2), Journal of Interactive Marketing (2), Psychology and Marketing (2), Internet Research (2), International Journal of Advertising (2), International Journal of Retail and Distribution Management (2), and Journal of Fashion Marketing and Management (2). Single papers were identified in several other outlets: Asia Pacific Journal of Marketing and Logistics, Computers in Human Behavior, Cyberpsychology, Behavior, and Social Networking, Informatics, Information Technology and People, Electronic Commerce Research and Applications, International Journal of Human-Computer Interaction, International Journal of Information Management, International Journal of Semantic Computing, Journal of Electronic Commerce Research, Journal of Internet Commerce, Journal of Marketing Management, Technological Forecasting, and Social Change, and Transactions on Marketing Research. The number of papers increases in the last years (2008: 1, 2014: 1, 2015: 1, 2016: 1, 2017: 6, 2018: 6, 2019: 10, 2020: 12, 2021: 16, 2022: 2).

Data analysis

We divide the data analysis process into four thematic sections: applications, theories, consumer processing models, and methods. First, in terms of applications, we explore the contexts in which the AR technology is applied (e-/m-commerce vs. brick-and-mortar). We analyze for which product categories ASR is applied, which ASR functionality is relevant (informing, visualizing, trying-on, placing), and which devices are used (stationary monitors, PC/laptops, mobile device, or head-mounted and wearable devices). Second, we summarize the theoretical basis of the analyzed studies. Third, we identify the main components of the consumer research models that were addressed in the reviewed papers. These models can be formally decomposed into 1) predictors, 2) the mediator variables that capture the underlying process, 3) the outcomes, and 4) the moderator variables that capture relevant boundary conditions or contingencies. Beyond their formal position in the consumer-processing model, we systemize the variables with respect to their conceptual contribution. Specifically, we distinguish the two major categories that capture the defining properties of the AR technology, namely, augmentation and interaction. We also consider three categories that describe consumers’ mental processing (user experience, perceived user benefits, and concerns/barriers). The remaining categories refer to technology acceptance and consumption behavior. Finally, we examine the research methods (survey, experiments) and type of manipulation (if applicable).

Applications

In the first step of the review, we examine how AR is used in retailing settings from the perspective of applications and functionalities. As outlined in Table 2 , we consider various retail settings (e-/m-commerce vs. physical stores; Carboni and Hagbeg, 2019 ) as past research has not yet systematically compared these perspectives, which may enable and require different AR functionalities. We then investigate the product categories that have been researched so far. The categories are extracted through an inductive process while reviewing the literature. Next, we consider the AR functionalities building on the classification developed above (informing, visualizing, trying-on, and placing). Finally, we consider the AR devices, including monitors, PC/laptops, mobile devices, and head-mounted devices ( Flavián et al., 2019 ). While assigning the reviewed articles to these categories is a rather descriptive process, we explore—for the first time in a systematic manner—the contingencies of the retailing setting, the product categories, the AR functions, and the applied devices.

TABLE 2 . Applications of AR in retailing.

In Table 2 , we provide an overview of our coding of the current body of ASR research. About three-quarters of the reviewed studies address AR technologies in e-commerce or m-commerce (e.g., Javornik, 2016b ; Baek et al., 2018 ; Lee et al., 2021 ). Only seven studies focus on the AR technology’s application in brick-and-mortar retailing (e.g., Joerß et al., 2021 ; Yuan et al., 2021 ; Hoffmann et al., 2022 ). Six studies focus neither on e-/m-commerce nor on physical retailing. These studies, for instance, consider more generally the use of AR glasses (e.g., Rauschnabel, 2018 ; Rauschnabel et al., 2018 ) or tangible (vs. gesture-based) interactions with an AR system (e.g., Brito and Stoyanova, 2018 ).

In Table 3 , we detail the examined product categories and related AR functionalities, finding a systematic confound between the analyzed product category and the AR function. In particular, studies exploring the trying-on function often use existing virtual try-on-applications for apparel, accessories, or makeup. Likewise, several studies use existing furniture planners for virtually placing furniture in one’s own rooms at home.

TABLE 3 . Product category and AR functions.

To visualize these results and illustrate how the various applications of AR in retailing are embedded in a broader network, we employed network analysis ( Figure 1 ). Originally used to assess social networks ( Hennig et al., 2012 ), this analytical technique recently became popular to conduct literature reviews (e.g., Hoffmann et al., 2020 ). This analysis visualizes contingencies, which enables us to identify gaps and it builds the foundation for developing our future research agenda. We converted our coding in Table 2 into a 19 × 19 matrix and used this as the starting point for the analysis. The diagonal of this matrix captures the total number of studies that explored this AR factor, while the non-diagonal elements reflect the frequency with which a pair of two factors was investigated in prior research. In the analysis, each AR factor is represented by a node, the size of which indicates the relative frequency with which this factor was studied in the literature. Occurrences of the factor with other AR-related factors in previous studies are illustrated by ties, the size of which indicates the frequency of their co-occurrence. Spatially close relationships in the resulting graphical network consequently indicate which AR factors were studied together in the current literature. More distant relationships concern factors that received less attention or were explored in isolation.

FIGURE 1 . Network analysis of the AR-related factors studied in previous research. Notes. ● Type of retailing, ■ product category, ▲ AR function, ◆ device.

The complete graphical network is represented in Figure 1 , showing that ASR is predominantly investigated in the e-commerce domain in order to try on (or test) products, both for mobile devices or traditional PC and laptops. Several strong ties with the trying-on function accordingly show that this AR function was primarily examined in the fashion industry, cosmetics, and accessories. Furniture is explored in regards to placing. A visual inspection further reveals that conveying information is an understudied functionality of AR. Likewise, the technology appears less central in traditional brick-and-mortar stores, such as enriching grocery purchases. Consequently, devices provided by the company—which may become increasingly relevant in physical shopping contexts like head-mounted displays—received much less attention than devices owned by the consumer. The frequency and diversity of the ties for computers and smartphones imply that research relying on these devices is already rich and heterogeneous but also hints at certain gaps in the literature that will be discussed in our research agenda in greater detail.

In sum, the visualization in Figure 1 depicts the contingencies among the product category, the AR functionality, the retailing channel, and the AR device. These contingencies plague the current body of empirical AR literature and thus reflect the practical challenges when conducting AR research in the past. Still, it is important to mention that different combinations are feasible that may provide so far untapped benefits. When the technological development and diffusion of AR devices (such as head-mounted displays) proceeds, novel applications will emerge and be the subject of research interest (e.g., shopping goods offered in brick-and-mortar stores can be virtually placed in the consumers’ house via head-mounted displays).

The current AR research relies on a rich fundament of well-established theories. Most of our analyzed studies draw on a sound theoretical basis. These papers’ research objectives largely determined the applied theoretical basis. As a general framework, some of them use the S-O-R model ( Mehrabian and Russel, 1974 ). A large number of papers builds on the technology acceptance model ( Davis. 1989 ) or its extensions (e.g., UTUAT) to explain the adoption of the AR technology. Others apply the information systems success model ( DeLone and McLean, 1992 ). Research on the drivers of AR adoption and purchase intentions frequently adopts motivational theories, such as flow ( Csikszentmihalyi, 1997 ), the theory of interactive media effects ( Sundar et al., 2015 ), uses and gratification theory ( Ruggiero, 2000 ), regulatory mode theory ( Higgins et al., 2003 ), or self-determination theory ( Ryan and Deci, 2000 ). Some AR studies specifically focus on the fact that consumers can observe themselves wearing products with the help of the technology. These studies rely on self-referencing ( Rogers et al., 1977 ) and virtual liminoid ( Jung and Pawlowski, 2014 ). Further articles consider whether and how consumers are able to imagine products or their placement better when supported by an AR tool. These papers build on mental imagery ( Schifferstein, 2009 ) and situated cognition ( Robbins and Aydede, 2009 ). For example, the situated cognition perspective posits that consumers more deeply process and remember information when this information is embedded in their environment (e.g., virtually placing furniture in their own living room) and when consumers interact with the information (e.g., actively controlling the angle of the 3D visualization; Hilken et al., 2017 , 2018 ). Finally, some studies take into account the risks and barriers of AR adoption, building on equity theory ( Adams, 1963 ) or the privacy calculus theory ( Culnan and Armstrong, 1999 ).

Consumer models

Past ASR research has been guided by different objectives, such as predicting customer experience, understanding technology adoption, or improving downstream marketing outcomes. Accordingly, the consumer models used so far are cluttered. In this section, we restructure the body of empirical literature systematically to depict the overlaps of the involved variables across the AR studies conducted in the different contexts. On this basis, we will then integrate these partial models.

First, we give an overview of the independent, mediating, moderating, and dependent variables in the consumer behavior models in ASR. The scopes of the different studies vary substantially. For example, while some papers seek to explain purchase intentions as the primary outcome (e.g., Baek et al., 2018 ; Fan et al., 2020 ), others focus on the perceived ease of use as the outcome variable (e.g., Mishra et al., 2021 ). Notably, some studies conceptualize ease of use as a predictor (e.g., Zhang et al., 2019 ), whereas others specify this as a mediator (e.g., Plotkina and Saurel, 2019 ) that translates into purchase intentions as the dependent variable. We will now discuss the function of different variables from the perspective of the individual studies (Are they predictors, mediators, moderators, or outcomes?) before we start reorganizing the variables into an integrative framework.

The predictor variables in the consumer models refer to the factors augmentation, interaction, user experience, user benefit, and concerns/barriers. In terms of augmentation, many studies compare an experimental treatment involving AR to a control group without AR, such as a traditional website of the same brand. Several studies measure the user’s perception of augmented quality as the predictor. In the interaction category, the variables interactivity and stimulated control are frequently analyzed. For user experience as a measured predictor variable, perceived ease of use, aesthetics or visual quality and perceived enjoyment are most often applied. User benefits are analyzed in terms of perceived usefulness, information quality as well as utilitarian and hedonic benefit. Perceived privacy risks are often conceptualized as concerns or barriers.

To explain the process and induced mechanisms when interacting with the AR technology, the extant studies specified mediator variables , which comprise the categories user experience, user benefit, concerns/barriers, and consumption behavior. In the user experience category, many studies capture perceived ease of use, perceived enjoyment and the feeling of spatial presence or telepresence as mediating variables. As user benefit, prior research modelled the perceived usefulness as well as the utilitarian and hedonic benefits. By contrast, perceived intrusiveness is a relevant concern or barrier that explains some users’ reluctance to adopt AR. The literature also suggests mediators that are not specific to the AR technology. This concerns consumption behavior, including brand-related variables like self-brand-connection or brand engagement.

The outcome variables of the consumer models include various aspects of user experience, technology acceptance, and consumption behavior. For user experience, relevant outcomes involve shopping enjoyment or a positive experience. The most widely analyzed variables of technology acceptance are attitude towards the AR and the intention to use it. With regard to consumption behavior, most researchers apply a measurement of purchase intention. Other relevant outcome variables in this category concern brand attitude and word-of-mouth.

Some studies also include moderating variables and boundary conditions that help understand when AR is effective and when not. Moderating variables include aspects of the product, such as product type ( Poushneh, 2018 ; Rauschnabel et al., 2019 ; Fan et al., 2020 ; Mishra et al., 2021 ), product contextuality ( Heller et al., 2019a ), consumer’s brand attachment ( Yuan et al., 2021 ), and price-value trade-off ( Heller et al., 2019a ). Other moderators relate to the national background ( Pantano et al., 2017 ) or sociodemographics ( Zhang et al., 2019 ). Some studies rely on consumer-centred moderators, including technology anxiety ( Kim and Forsythe 2008 ), technology-as-solution-belief ( Joerß et al., 2021 ), involvement ( Kim and Forsythe 2008 ), AR familiarity/experience ( Yim et al., 2017 ; Song et al., 2019 ; Bonnin, 2020 ), processing fluency ( Hilken et al., 2017 ; Heller et al., 2019a ), or assessment orientation ( Heller et al., 2019b ; Jessen et al., 2020 ) as moderators.

We have reorganized the variables in Figure 2 according to their theoretical function in the models (e.g., explaining user experience, technology acceptance, marketing outcomes, etc.). We also indicate whether these variables were initially featured as predictors, mediators, outcomes, or moderators. This re-organization of the variables builds the basis for our theory development towards an integrative consumer-processing model of the AR technology in shopping contexts.

FIGURE 2 . Conceptually-structured overview of the researched variables. Notes. Superscripted numbers indicate the study number (see Table 2 ). Pred = variable used as a predictor variable in the cited study; med = variable used as a mediator in the cited study; out = variable used as an outcome variable in the cited study.

An overview of the methods applied in AR research is presented in Table 4 . The 56 reviewed papers report 85 quantitative studies in total. As shown in Table 4 , 31 papers report surveys and thus correlational data, while 30 papers report experiments. Note that two papers include both survey and experimental research. No clear dominance emerged for the mode of data collection, which happened both online (26 papers) and in the lab (25). Five papers even report evidence from field studies.

TABLE 4 . Methods.

Researchers took different approaches to manipulate AR usage ( Table 4 ). In the most straightforward approach (23 papers), the participants were asked to use an AR system before completing a survey (e.g., Huang and Liao, 2015 , Huang and Liao, 2017 ; McLean and Wilson, 2019 ; van Esch et al., 2019 ; Park and Yoo, 2020 ). Typically, the participants were directed to an existing AR application and asked to download it on their smartphones. This is important to mention because these studies did not include a systematic experimental manipulation with different treatments and/or a control group. Hence, the evidence is of a correlational nature, which needs to be taken into consideration when drawing conclusions.

The second cluster of papers compared the AR system to another system, such as a website with AR and the same website without this technology (e.g., Javornik, 2016b ; Yim et al., 2017 ; Beck and Crié, 2018 ; Bonnin, 2020 ; Watson et al., 2020 ). In their make-up study, Smink et al. (2019) compared the AR with pictures of the participants and pictures of a model. In a within-subject experiment, Baytar et al. (2020) compared physical try-on and then virtual try-on of apparel. Again, most of these studies made use of existing AR tools as the experimental treatment. It is important to distinguish this cluster of papers from the previous one because a systematic and standardized manipulation was, often, possible because several companies host a website where the AR condition can be switched on or off (virtual try-ons; e.g., Poushneh, 2018 ; Javornik, 2016b ; Smink et al., 2019 ). Interestingly, some of these studies find that AR is superior, while others report the opposite ( Plotkina and Saurel, 2019 ), which hints that AR effects are complex and contingent on several factors. For this reason, we will discuss the potential moderator variables later when we develop an integrative framework.

While the papers in the second cluster primarily focus on AR’s overall effectiveness compared to other communication modes, those of the third cluster zoomed in on the AR technology. These papers experimentally manipulated theoretically relevant aspects of the technology, such as the degree of interactivity ( Poushneh and Vasquez-Parraga, 2017 ) and controllability ( Hoffmann et al., 2022 ), markerless vs. marker-based interaction ( Brito & Stoyanova, 2018 ), or the sensory control mode ( Heller et al., 2019a ).

Finally, some researchers ran multi-factorial experiments that manipulated various factors of the AR or one AR factor and a context factor. For example, Hilken et al. (2017) manipulated the stimulated physical control (low/high) and the environmental embedding (low/high). Baek et al. (2018) crossed the AR perspective (self-vs. other-viewing) and two levels of narcissism (high vs. low). Heller et al. (2019b) crossed imagery transformation (low/high) and embedding (low/high) as well as product contextuality (no/yes). Hoffmann et al. (2022) manipulated the AR controllability (low/high) and the AR information detailedness (low/high).

Theory development

Need and rationale of the integrative framework.

Footing on the findings of our review, we now contribute to theory development for AR in retailing settings. Our review pinpoints that previous AR research relies on a solid fundament of well-established theories in the information systems domain, innovation management, and marketing (e.g., technology acceptance model). The field also borrows from related disciplines, such as communication science, social psychology, and cognitive psychology (e.g., uses and gratification theory, flow theory, situated cognition). This breadth and depth of the theoretical grounding attest to the different lenses through which the AR technology is already explored. However, our literature review revealed that the applied theoretical foundations are fragmented and often not AR-specific. This emphasizes the need to synthesize the fragmented theoretical basis and develop an AR-specific theoretical basis. Relatedly, ASR research should extend beyond studying the adaption-based factors and the Technology Acceptance Model (TAM), which is very useful to assess the usability and adoption of the technology but is also not specific to the two core constitutional properties of the AR technology, namely, augmentation and interactivity.

As another consequence of the specific foci, the literature review has revealed that scholars from the various fields adapt different theories with different constructs to explain AR effects. Psychological research is interested in the flow experience during AR usage, while innovation and technology management scholars often study technology acceptance as a relevant evaluation criterion and thus dependent variable. Notably, other disciplines are interested in the more downstream outcomes. Marketing and retailing scholars, for example, would conceptualize these variables as mediators that serve to explain the marketing-relevant variables, such as purchase behavior, loyalty, or word-of-mouth.

Against this background, we suggest an integrative framework. Inspired by similar attempts to integrate partial theories in other fields, such as wearable technologies (e.g., Kalantari 2017 ; Chuah, 2019 ), our theory development rests on a synopsis and refinement of extant work. We integrate conceptual works ( Heller et al., 2019b ; Caboni and Hagberg, 2019 ), qualitative research ( Olsson et al., 2013 ; Scholz and Duffy, 2018 ; Romano et al., 2021 ), literature overviews ( Bonnetti et al., 2018 ; Lavoye et al., 2021 ), and the relevant theoretical foundations. We substantiate this with our summary of empirical findings ( Figure 2 ). We identify the most relevant aspects and integrate them into a new theoretical framework that serves as a guideline for AR research and practice in shopping contexts.

Based on our literature review, we detect several unresolved issues relevant to our theory development. The fragmentation of extant approaches stresses the need to integrate the partial theories and account for contextual aspects. While previous papers have either considered AR in e-commerce (e.g., Javornik, 2016b ) or brick-and-mortar retailing (e.g., Joerß et al., 2021 ), our integrative model integrates findings from both perspectives and includes several moderator variables to account for boundary conditions. Second, as a major theoretical contribution, we distinguish between different features of AR in retailing settings, including informing, visualizing, trying-on, and placing. We detail the contingencies between these AR functionalities and other relevant variables, such as the shopping context, devices, product types, and customer benefit.

Given this holistic inclusion of different variable types, scholars can flexibly apply the framework to different settings. It is noteworthy that not every variable is relevant in every setting. Thus, the framework can be simplified and adapted to the specific context.

Suggestion of an integrative framework

Figure 3 presents the proposed integrative framework. The process-oriented model starts with the technology design. The next steps involve the consumer’s mental processing and adoption of the technology. This paper focuses on ASR, so the outcome variables involve shopping-related consumer reactions and moderators to understand the boundary conditions of the AR technology and its effects.

FIGURE 3 . Framework model of consumer behavior in augmented shopping reality.

For the technology design , we follow previous conceptualizations and distinguish the two key properties of AR: augmentation and interaction (e.g., Azuma, 1997 ; Azuma et al., 2001 ; Javornik, 2016a ). Augmentation concerns the question of which features are augmented and how they are embedded. Hence, we refer to literature that has focussed on the embedding of elements in AR (e.g., Hilken et al., 2017 ). Our model extends these approaches by including all the relevant aspects that can be augmented in shopping settings, including the information, the product, the self, or the environment. This distinction maps onto the ASR functions informing, visualizing, trying-on, and placing. Interaction refers to consumers’ ability to control the virtual elements, such as choosing additional information, transforming visual 2D or 3D elements, etc. This also includes the modalities of the interaction, such as touching, voice-based, or gesture-based.

In terms of consumer’s mental processing , three aspects should be distinguished: user experience, user benefits, and concerns/barriers. While positive user experience and perceived user benefits positively influence downstream variables, the concerns and barriers will hinder technology adoption, negatively impacting marketing outcomes. Core aspects of the user experience involve perceived enjoyment, spatial/telepresence, flow experience, and perceived ease of use. According to the technology acceptance model, user’s experience will also improve perceptions of user benefits (e.g., Huang and Liao, 2015 ; Pantano et al., 2017 ; Rese et al., 2017 ; Zhang et al., 2019 ). The user benefits of AR have initially been described as rather hedonic. However, with a more widespread use of AR as a serious tool that helps consumers make consumption decisions in today’s shopping environments, perceived usefulness and utilitarian aspects will become more relevant. This concerns information delivery, decision support or being a recommendation agent that simplifies consumers’ decisions. Further aspects include sensual and social benefits. Finally, as concerns and barriers to adopting the technology, the perceptions of privacy risks are relevant. Various sensors are active during the AR use (cameras, microphones, GPS information, tracking of the human/device interaction), so consumers’ privacy concerns are a critically relevant topic. Furthermore, there might be a perceived loss of autonomy or a sense of being manipulated, apart from feared side effects (e.g., the impact on the user’s physical or mental health or the implications for other consumers).

As downstream variables , the model includes the most relevant constructs from a marketing perspective, namely 1) whether consumers accept and adopt the technology and 2) whether using the technology alters marketing outcomes (e.g., purchasing). The technology acceptance includes, in particular, consumers’ attitudes towards ASR as well as the (re)use intention. The marketing outcomes include brand attitudes, purchase intentions, word-of-mouth, and loyalty.

Notably, the sequence of the model is not necessarily unidirectional. The user experience evaluation requires consumers first to try the technology (or at least observe others trying it), so user experience can also be conceptualized as a downstream variable in some cases. To avoid disproportionately inflating the framework’s complexity, we focus on the substantive perspective of retailing managers and a long-term perspective. From this perspective, the most relevant direction of the impact operates from the user experience via rational benefits/cost calculation to the technology attitude and ultimately the intention to (re)use the technology. This sequence matches the conceptual models of the previous studies that have already combined user experiences and/or user benefits with (re)use intentions (e.g., Pantano et al., 2017 ; Rese et al., 2017 ; Yim et al., 2017 ; Plotkina and Saurel, 2019 ; Smink et al., 2019 ; Zhang et al., 2019 ; Bonnin, 2020 ; Park and Yoo, 2020 ; Watson et al., 2020 ; Qin et al., 2021b ; Lee et al., 2021 ).

The model also integrates six relevant categories of moderators and boundary conditions . These moderators have not yet been systematically tested, but they appear to be relevant based on our integration of past empirical works enriched with previous conceptual considerations ( Flavián et al., 2019 ) and qualitative studies ( Olsson et al., 2013 ; Scholz and Duffy, 2018 ; Romano et al., 2021 ). First, the retailing channel is an important boundary condition of consumers’ processing of the ASR ( Hilken et al., 2018 ; Caboni and Hagberg, 2019 ). Our review revealed that the ASR functionality is contingent on the retailing channel. In e- and m-commerce, visualizing and virtual try-ons are important functions. In brick-and-mortar retailing, the ability to provide more product information seems more relevant. Second, how consumers process the ASR will further depend on aspects of the technology, such as the AR device that overlays the physical world with virtual information. Consumers may react differently depending on whether the information is displayed on a stationary monitor (e.g., “magic mirror”), handheld-mobile device, wearable and head-mounted devices, or even implanted lenses ( Flavián et al., 2019 ). Third, the user benefit and usefulness of the ASR functionality (informing, visualizing, trying-on, placing) are contingent on the product type. Products with search, credence, and experience qualities require different treatments ( Girard and Dion, 2010 ). Fourth, the shopping situation is important too ( Olsson et al., 2013 ). Relevant aspects are the shopping goal, product involvement, and the social surrounding (private or public shopping in e-commerce or brick-and-mortar retailing). Fifth, complexity is an interesting moderator as past research has shown that, in digital contexts, consumers prefer medium degrees of complexity ( Geissler et al., 2001 ; Mai et al., 2014 ) because fewer complexity evokes boredom, while higher levels evoke the feeling of being overwhelmed. Complexity may hence evoke a curvilinear moderating effect on several variables included in our processing model, such as the perceived ease of use, perceived enjoyment, loss of autonomy, presence, and flow. Scholars and ASR designers need to find the optimal degree of complexity for the AR design, product, and shopping task. Finally, the AR effects depend on consumer traits, such as technology attitudes, innovativeness, AR experience, and processing fluency.

Research agenda

ASR researchers can take the suggested framework ( Figure 3 ) as orientation when developing new study designs. Based on our review and the synthesis of the analyzed literature, we propose directions for future research. Moreover, our literature review points to notable methodological limitations and gaps in the research landscape that need to be addressed.

Ten recommendations for future ASR research

First, most empirical studies in our literature review were conducted in e-commerce and m-commerce settings where websites were enriched by AR (e.g., Beck and Crié, 2018 ; Yim and Park, 2019 ). Only few papers consider ASR in brick-and-mortar stores (e.g., van Esch et al., 2019 ; Joerß et al., 2021 ; Hoffmann et al., 2022 ). This limited focus on digital environments may stem from the wide use of AR in these environments and, thus, the ease of studying them. Still, a rapidly growing number of AR apps exist for physical environments and deserve much greater attention. Our review has shown that the application and effectiveness of AR in shopping environments are highly contextualized and depend on the specific AR functions, devices, product types, and so forth. So far, researchers have adopted theories fitting the particular context in which they conduct their AR studies. Most retailing studies focus on the hedonic benefits of the try-on function, while the benefits of placing and visualizing are less explored. Arguably, the AR function ‘informing’ is more relevant for utilitarian benefits ( Hoffmann et al., 2021 ). However, AR-specific theories for information processing have not yet been applied, so more research is needed to fill this void. Ideally, studies should explicitly model the retailing channel as a moderating variable. In e-commerce, for example, the virtual try-on function is greatly valued for certain products (e.g., apparel, accessories, or cosmetics) and can create hedonic benefits for shoppers. Still, theories that explain consumer reactions in these contexts (e.g., via flow experience) cannot necessarily explain consumer reactions to AR-delivered information functions through mobile applications for groceries in brick-and-mortar settings (e.g., Joerß et al., 2021 ). Here, utilitarian benefits (e.g., transparent and trustworthy information) may be more important in the consumer’s decision-making compared to hedonic benefits and flow experiences.

Second, and related to the previous direction for future research, the AR practice and research in shopping contexts has not yet made full use of AR’s vast range of functionalities. We outlined that the four functionalities informing, visualizing, trying-on, and placing are most relevant for shopping contexts. So far, we see that informing is mainly used for food products in brick-and-mortar contexts, while trying-on and placing are more frequently used in studies on e-commerce. Trying-on is applied for product categories, such as cosmetics, apparel, or glasses, whereas placing is used for furniture or wall paint. Evidently, the AR functions are more flexible, so scholars and practitioners should consider different configurations of AR functionalities in combination with certain product categories and retailing channels.

Third, more research on the role of the AR-enabling device is needed. Mobile devices, like tablets and smartphones, are already widespread and common in use. While the current theoretical approaches apply to these devices, other devices like head-mounted displays, AR glasses, or even AR lenses are still unusual in real-life, everyday applications. Nonetheless, the diffusion of such devices may intensify, and managers need to know how consumers respond to such devices and whether they use them to facilitate their judgments and shopping decisions in online and offline contexts. In addition, nuanced explanations are needed given the differences across devices, including the control function (e.g., haptic or voice), the need to use hands (smartphones vs. glasses), or the ability to move around (PC vs. helmet). For instance, location-based AR-effects (shopping guide in the supermarket, mall) should be embedded in future ASR-specific theories.

Fourth and related to the previous aspect, augmented information can be conveyed through different modalities that address different senses, including visual formats (e.g., images, labels), audio formats (e.g., music, voice), audio-visual formats (videos), and 2D or 3D visualizations (e.g., Azuma, 1997 ; Javornik, 2016a ; Brito and Stoyanova, 2018 ). Marketing research has mainly considered visual augmentations but largely neglected other modalities. Exceptions are Brito and Stoyanova (2018) testing markerless or gesture-based interactions, Huang and Liao (2017) considering haptic imagery, and Heller et al. (2019b) comparing touch control vs. voice control ( Heller et al., 2019b ). More research spanning a more comprehensive array of presentation modes and interaction forms is necessary to prepare the application of advanced technologies in the future.

Fifth, ASR research should also take into account systematic differences among product types, for example, by distinguishing search, experience, and credence goods. For some goods, hedonic experiences through AR usage might entertain consumers (e.g., entertainment products, cosmetics), but information and more utilitarian aspects should be relevant in other consumer decisions or purchases. This distinction is relevant because experiential and materialistic purchases are motivated in distinct ways ( Gilovich et al., 2015 ), so the various AR-functionalities should be differentially relevant too. Further theory development should also include value theories that distinguish, for example, functional, hedonic/experiential or social values that are enjoyed while using AR ( Hilken et al., 2020 ). Deeper theoretical considerations of the consumers’ decision modes (extensive, limited, habitual, impulsive) may further help understand how beneficial consumers experience the AR support to be. Future advances may also need to be able to explain ASR effects at different stages of the customer journey (e.g., Jessen et al., 2020 ). Findings of stage-specific effects in other domains ( Mai et al., 2021 ) imply that the AR-functionalities may be differently relevant across the various consumption stages. For example, while the hedonic experience may motivate users to use the AR technology in the first place, the more utilitarian benefits that materialize with each purchase may become the driving force to encourage continued use of the technology in shopping environments. Future ASR research may therefore require multi-stage theories.

Sixth, more contextual and situational factors should be taken into account too. Consumers’ mood, time pressure, or the presence of other consumers may determine whether (or not) consumers are willing to make use of AR. Plausibly, consumers rely on the AR technology when they have time for their shopping but abandon it when being under pressure or in more stressful shopping environments ( Hoffmann et al., 2022 ). Such knowledge would be important because the technology should unfold its benefit of being a tool or a recommendation agent, especially under such suboptimal conditions. Additionally, when used with mobile devices (smartphones or tablets), the AR technology can also integrate location-based information ( Reitmayr and Drummond, 2006 ). This is already implemented in tourism apps and may likewise be beneficial in other marketing applications. Furthermore, contextual information might be available from other modalities, such as haptic and olfactory information. Future studies should consider such cross-modal effects.

Seventh, our synopsis of past research in the framework model shows that research explaining the (re)use intention of ASR has quite matured, and there are also indications of positive influences on some downstream variables once consumers start using ASR. However, a lack of research persists on how ASR usage translates into marketing outcomes, such as brand image, (re)purchase intention, or word of mouth. We call for more research on the mediating variables and the specific boundary conditions. For example, the ethnographic study by Scholz and Duffy (2018) reveals that ASR can create a more intimate brand-customer relationship. Future research may build on this finding to delve deeper into how ASR can shape brand image, customer-brand relationship, and brand equity. The qualitative research of Romano et al. (2021) has demonstrated that ASR affects users’ consideration and their choice set. Accordingly, more insights into the decision process are needed.

Eighth, since ASR is a relatively young domain, the longitudinal perspective is still missing but very promising. Future research could start, for example, by investigating the adaptation and learning processes of AR users. Although several purported benefits of the technology are supported by empirical evidence, the question remains to be answered as to whether consumers integrate the technology into their daily shopping routines (and if so, how they do this). For example, do consumers consider ASR only as a toy that creates entertaining and hedonic benefits at the moment but which can wear out quickly? Or will ASR—with greater diffusion and familiarity—become a tool that consumers use for information and utilitarian needs on a regular basis? Scholars should therefore study habituation and even potential wear-out effects. Another interesting development, which may become more widespread once the technology has further evolved, is the extension or even substitution of physical products by virtual products as discussed by Rauschnabel (2021) and Dwivedi et al. (2021) .

Ninth, it is principally possible to contextualize, customize, and personalize the AR information for increasing convenience and consumer benefit (e.g., Huynh et al., 2019 ; Hsu et al., 2021 ; Nikhashemi et al., 2021 ). However, data privacy and security are crucial topics that deserve attention ( Hilken et al., 2017 ; Inman and Nikolova, 2017 ; Rauschnabel et al., 2018 ; Smink et al., 2019 ). Future AR research should explore the perceived intrusiveness, loss of autonomy, the willingness to share personal data (i.e., regarding facial recognition or location-based information for personalization), and whether consumers are willing to use ASR apps on their personal smartphones or other devices. Some studies have already relied on equity theory ( Adams 1963 ) to understand how customers balance augmentation quality and the privacy of personal information ( Poushneh 2018 ), but more insights are needed to manage this issue better.

Tenth, research on ASR will be an ongoing process as the technology evolves rapidly. Similarly, organizational and legal conditions will change. Marketing and retailing literature should monitor, for example, which devices are the prime candidates for ASR in future business environments. Also, more experimental technological solutions should be explored, such as lenses or implants, and how consumers will respond to these developments. Likewise, insights are needed into how AI, big data, and machine learning alter the information provided via ASR. Especially concerning consumer trust, knowledge is sparse about who should provide the ASR information (producer, retailer, NGOs, other consumers).

Limitations and methodological aspects

First, a vast majority of the current ASR research explores the effectiveness of the technology (contrasting AR to other technologies) and zooms in on specific properties. However, no study has tackled the factors leading to actual purchase behavior. Therefore, future research should shift the focus more strongly on real purchases (consumer perspective) and actual sales (company perspective, e.g., Tan et al., 2021 ), both in e-commerce and brick-and-mortar stores. Obtaining purchase data in the field will help corroborate the ecological validity of previous findings and provide a more precise estimate of the AR technology’s impact in real shopping environments.

Second, the research designs in the AR literature have certain limitations regarding the sampling. Many studies employ nonsystematic sampling procedures, such as convenience sampling or snowballing, for recruiting participants. Several studies also take advantage of university participation pools. This may be explained by the fact that current designs emphasize internal validity, but external validity should be taken into focus too. As a result of previous sampling procedures, our state of the knowledge is often based on studies with students and younger consumers who are often technology-affine and open to digital solutions. However, older individuals are also very relevant in digital and physical environments. In a similar vein, education levels are typically higher for student samples. A gender bias might also arise and distort our conclusions about ASR. Due to the focus on virtual try-on for apparel and cosmetics, nine of the 56 studies (16%) include female participants only and some other studies include substantially more female than male participants. Considering these specifics and the lack of representativeness, AR research should be complemented by studies in the field that put greater emphasis on external and ecological validity.

Third, this literature review shows that the number of quantitative empirical studies analyzing consumer behavior in ASR is steadily growing. Meta-analyses could provide a valuable approach to integrate findings and estimate general effects and moderations. Yet, quantitative meta-analyses require a small set of pre-defined predictors and outcome variables. Our review reveals that prior studies included about 40 different predictor variables and about 30 different outcome variables (plus about 40 mediating variables). This variety reflects that ASR researchers tried to explain different outcomes (e.g., user experience, technology acceptance, shopping and patronage behavior), and they used very diverse theories to explain these outcomes (e.g., TAM, flow, Uses and Gratifications etc.). Our approach of structuring the literature and integrating different models into a more comprehensive model hopefully sets the stage for future meta-analyses.

Reviewing and synthesizing a comprehensive set of empirical papers on ASR reveals a growing interest in this technology in both research and practice. Prior research efforts have been devoted to consumers’ experience with AR, acceptance of AR, and behavioral reactions to AR in various online and offline retailing settings. However, our literature review also revealed that large differences exist in the studied AR devices, the AR functionalities, and the addressed consumer reactions. It is therefore not surprising that the literature is highly fragmented. The integrated framework developed in this paper can help researchers to approach ASR and its effects holistically as well as to explore the relevant moderators that cause different effects in different situations. However, more research is needed on these moderators, in particular, and the effects of context (e.g., with regard to the devices, addressed senses, retailing channels, shopping goals, product categories, etc.). From a methodological point of view, more laboratory and field experiments are needed to learn more about the causal effects of different ASR designs. The present literature review and the outlined research directions will hopefully guide scholars and provide knowledge to optimize future ASR, being beneficial for both retailers and customers.

Summary statement of contribution

The growing literature on consumer behavior in augmented shopping reality (ASR) is highly fragmented as scholars focus on different retailing settings, products categories, AR functions, and AR devices, using different methods. Our systematic literature review synthesizes the knowledge and critically discusses the empirical studies, both conceptually and methodologically. In addition, the paper develops an integrative framework model, which helps derive directives for future research on consumer behavior in ASR.

Data availability statement

The original contributions presented in the study are cited in the text and reference list, further inquires can be directed to the corresponding author.

Author contributions

SH and RM contributed to conception and design of the study. SH conducted the literature research and review and wrote the first draft of the manuscript. SH and RM contributed to manuscript revision, read, and approved the submitted version.

Acknowledgments

We acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG) within the funding programme Open Access-Publikationskosten.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Adams, J. S. (1963). Towards an understanding of inequity. J. Abnorm. Soc. Psychol. 67 (5), 422–436. doi:10.1037/h0040968

PubMed Abstract | CrossRef Full Text | Google Scholar

Aluri, A. (2017). Mobile augmented reality (MAR) game as a travel guide: Insights from pokémon GO. J. Hosp. Tour. Technol. 8 (1), 55–72. doi:10.1108/jhtt-12-2016-0087

CrossRef Full Text | Google Scholar

Arghashi, V., and Yuksel, C. A. (2022). Interactivity, inspiration, and perceived usefulness! How retailers’ AR-apps improve consumer engagement through flow. J. Retail. Consumer Serv. 64, 102756. doi:10.1016/j.jretconser.2021.102756

Azuma, R. T. (1997). A survey of augmented reality. Presence. (Camb). 6 (4), 355–385. doi:10.1162/pres.1997.6.4.355

Azuma, R. T., Baillot, Y., Behringer, R., Feiner, S., Julier, S., and MacIntyre, B. (2001). Recent advances in augmented reality . Washington, DC: Naval Research Lab .

Google Scholar

Baek, T. H., Yoo, C. Y., and Yoon, S. (2018). Augment yourself through virtual mirror: The impact of self-viewing and narcissism on consumer responses. Int. J. Advert. 37 (3), 421–439. doi:10.1080/02650487.2016.1244887

Barhorst, J. B., McLean, G., Shah, E., and Mack, R. (2021). Blending the real world and the virtual world: Exploring the role of flow in augmented reality experiences. J. Bus. Res. 122, 423–436. doi:10.1016/j.jbusres.2020.08.041

Baytar, F., Chung, T., and Shin, E. (2020). Evaluating garments in augmented reality when shopping online. J. Fash. Mark. Manag. 24 (4), 667–683. doi:10.1108/jfmm-05-2018-0077

Beck, M., and Crié, D. (2018). I virtually try it … I want it ! Virtual fitting room: A tool to increase on-line and off-line exploratory behavior, patronage and purchase intentions I want it! Virtual fitting room: A tool to increase online and offline exploratory behavior, patronage and purchase intentions. J. Retail. Consumer Serv. 40, 279–286. doi:10.1016/j.jretconser.2016.08.006

Berryman, D. R. (2012). Augmented reality: A review. Med. Ref. Serv. Q. 31 (2), 212–218. doi:10.1080/02763869.2012.670604

Bonetti, F., Warnaby, G., and Quinn, L. (2018). “Augmented reality and virtual reality in physical and online retailing: A review, synthesis and research agenda,” in Augmented reality and virtual reality . Editors T. M. Jung, and C. tom Dieck ( Springer ), 119–132.

Bonnin, G. (2020). The roles of perceived risk, attractiveness of the online store and familiarity with AR in the influence of AR on patronage intention. J. Retail. Consumer Serv. 52, 101938. doi:10.1016/j.jretconser.2019.101938

Botella, C. M., Lizandra, M. C. J., Baños, R. M., Raya, M. A., Guillén, V., and Rey, B. (2005). Mixing realities? An application of augmented reality for the treatment of cockroach phobia. Cyberpsychology Behav. 8 (2), 162–171. doi:10.1089/cpb.2005.8.162

Bower, M., Howe, C., McCredie, N., Robinson, A., and Grover, D. (2014). Augmented reality in education–cases, places and potentials. Educ. Media Int. 51 (1), 1–15. doi:10.1080/09523987.2014.889400

Boyd, D. E., and Koles, B. (2019). Virtual reality and its impact on B2B marketing: A value-in-use perspective. J. Bus. Res. 100, 590–598. doi:10.1016/j.jbusres.2018.06.007

Brito, P. Q., and Stoyanova, J. (2018). Marker versus markerless augmented reality. Which has more impact on users? Int. J. Human–Computer. Interact. 34, 819–833. doi:10.1080/10447318.2017.1393974

Caboni, F., and Hagberg, J. (2019). Augmented reality in retailing: A review of features, applications and value. Int. J. Retail Distribution Manag. 47 (11), 1125–1140. doi:10.1108/ijrdm-12-2018-0263

Carmigniani, J., Furht, B., Anisetti, M., Ceravolo, P., Damiani, E., and Ivkovic, M. (2011). Augmented reality technologies, systems and applications. Multimed. Tools Appl. 51 (1), 341–377. doi:10.1007/s11042-010-0660-6

Castillo, S. M. J., and Bigne, E. (2021). A model of adoption of AR-based self-service technologies: A two country comparison. Int. J. Retail Distribution Manag. 49 (7), 875–898. doi:10.1108/ijrdm-09-2020-0380

Chae, H. J., Hwang, J. I., and Seo, J. (2018). “Wall-based space manipulation technique for efficient placement of distant objects in augmented reality,” in Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology , 45–52.

Chen, P., Liu, X., Cheng, W., and Huang, R. (2017). A review of using Augmented Reality in education from 2011 to 2016. Innovations Smart Learn. , 13–18.

Choi, U., and Choi, B. (2020). The effect of augmented reality on consumer learning for search and experience products in mobile commerce. Cyberpsychology, Behav. Soc. Netw. 23 (11), 800–805. doi:10.1089/cyber.2020.0057

Chuah, S. H. W. (2019). Wearable XR-technology: Literature review, conceptual framework and future research directions. Int. J. Technol. Mark. 13 (3-4), 1–259. doi:10.1504/ijtmkt.2019.10021794

Chung, N., Lee, H., Kim, J. Y., and Koo, C. (2018). The role of augmented reality for experience-influenced environments: The case of cultural heritage tourism in Korea. J. Travel Res. 57 (5), 627–643. doi:10.1177/0047287517708255

Csikszentmihalyi, M. (1997). Finding flow: The psychology of engagement with everyday life . New York, NY: Basic Books .

Culnan, M. J., and Armstrong, P. K. (1999). Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organ. Sci. 10 (1), 104–115. doi:10.1287/orsc.10.1.104

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13 (3), 319–340. doi:10.2307/249008

de Ruyter, K., Heller, J., Hilken, T., Chylinski, M., Keeling, D. I., and Mahr, D. (2020). Seeing with the customer’s eye: Exploring the challenges and opportunities of AR advertising. J. Advert. 49 (2), 109–124. doi:10.1080/00913367.2020.1740123

Deliza, R., and MacFie, H. J. (1996). The generation of sensory expectation by external cues and its effect on sensory perception and hedonic ratings: A review. J. Sens. Stud. 11 (2), 103–128. doi:10.1111/j.1745-459x.1996.tb00036.x

DeLone, W. H., and McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Inf. Syst. Res. 3 (1), 60–95. doi:10.1287/isre.3.1.60

Di Serio, A., Ibáñez, M. B., and Kloos, C. D. (2013). Impact of an augmented reality system on students’ motivation for a visual art course. Comput. Educ. 68, 586–596. doi:10.1016/j.compedu.2012.03.002

Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., et al. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. Int. J. Inf. Manag. 59, 102168. doi:10.1016/j.ijinfomgt.2020.102168

Fan, X., Chai, Z., Deng, N., and Dong, X. (2020). Adoption of augmented reality in online retail-ing and consumers’ product attitude: A cognitive perspective. J. Retail. Consumer Serv. 53 (2), 101986. doi:10.1016/j.jretconser.2019.101986

Flavián, C., Ibáñez-Sánchez, S., and Orús, C. (2019). The impact of virtual, augmented and mixed reality technologies on the customer experience. J. Bus. Res. 100, 547–560. doi:10.1016/j.jbusres.2018.10.050

Geissler, G., Zinkhan, G., and Watson, R. T. (2001). Web home page complexity and communi-cation effectiveness. J. Assoc. Inf. Syst. 2 (2), 1–48. doi:10.17705/1jais.00014

Gilovich, T., Kumar, A., and Jampol, L. (2015). A wonderful life: Experiential consumption and the pursuit of happiness. J. Consum. Psychol. 25 (1), 152–165. doi:10.1016/j.jcps.2014.08.004

Girard, T., and Dion, P. (2010). Validating the search, experience, and credence product classification framework. J. Bus. Res. 63 (9-10), 1079–1087. doi:10.1016/j.jbusres.2008.12.011

Hacker, J., vom Brocke, J., Handali, J., Otto, M., and Schneider, J. (2020). Virtually in this together–how web-conferencing systems enabled a new virtual togetherness during the COVID-19 crisis. Eur. J. Inf. Syst. 29 (5), 563–584. doi:10.1080/0960085x.2020.1814680

Hamari, J., Malik, A., Koski, J., and Johri, A. (2019). Uses and gratifications of pokémon go: Why do people play mobile location-based augmented reality games? Int. J. Human–Computer. Interact. 35 (9), 804–819. doi:10.1080/10447318.2018.1497115

Harley, J. M., Poitras, E. G., Jarrell, A., Duffy, M. C., and Lajoie, S. P. (2016). Comparing virtual and location-based augmented reality mobile learning: Emotions and learning outcomes. Educ. Technol. Res. Dev. 64 (3), 359–388. doi:10.1007/s11423-015-9420-7

Heller, J., Chylinski, M., de Ruyter, K., Mahr, D., and Keeling, D. I. (2019a). Let me imagine that for you: Transforming the retail frontline through augmenting customer mental imagery ability. J. Retail. 95 (2), 94–114. doi:10.1016/j.jretai.2019.03.005

Heller, J., Chylinski, M., de Ruyter, K., Mahr, D., and Keeling, D. I. (2019b). Touching the untouchable: Exploring multi-sensory augmented reality in the context of online retailing. J. Retail. 95 (4), 219–234. doi:10.1016/j.jretai.2019.10.008

Hennig, M., Brandes, U., Borgatti, S. P., Pfeffer, J., and Mergel, I. (2012). Studying social networks: A guide to empirical research . Frankfurt, Germany: Campus Verlag .

Herz, M., and Rauschnabel, P. A. (2019). Understanding the diffusion of virtual reality glasses: The role of media, fashion and technology. Technol. Forecast. Soc. Change 138, 228–242. doi:10.1016/j.techfore.2018.09.008

Higgins, E. T., Kruglanski, A. W., and Pierro, A. (2003). “Regulatory mode: Locomotion and assessment as distinct orientations,” in Advances in experimental social psychology . Editor M. P. Zanna (Cambridge, MA: Academic Press ), 293–344.

Hilken, T., de Ruyter, K., Chylinski, M., Mahr, D., and Keeling, D. I. (2017). Augmenting the eye of the beholder: Exploring the strategic potential of augmented reality to enhance online service experiences. J. Acad. Mark. Sci. 45 (6), 884–905. doi:10.1007/s11747-017-0541-x

Hilken, T., Heller, J., Chylinski, M., Keeling, D. I., Mahr, D., and de Ruyter, K. (2018). Making omnichannel an augmented reality: The current and future state of the art. J. Res. Interact. Mark. 12 (4), 509–523. doi:10.1108/jrim-01-2018-0023

Hilken, T., Keeling, D. I., de Ruyter, K., Mahr, D., and Chylinski, M. (2020). Seeing eye to eye: Social augmented reality and shared decision making in the marketplace. J. Acad. Mark. Sci. 48 (2), 143–164. doi:10.1007/s11747-019-00688-0

Hinsch, C., Felix, R., and Rauschnabel, P. A. (2020). Nostalgia beats the wow-effect: Inspiration, awe and meaningful associations in augmented reality marketing. J. Retail. Consumer Serv. 53, 101987. doi:10.1016/j.jretconser.2019.101987

Hoffmann, N. C., Symmank, C., Mai, R., Stok, F. M., Rohm, H., and Hoffmann, S. (2020). The influence of extrinsic product attributes on consumers’ food decisions: Review and network analysis of the marketing literature. J. Mark. Manag. 36 (9-10), 888–915. doi:10.1080/0267257x.2020.1773514

Hoffmann, S., Joerß, T., Mai, R., and Akbar, P. (2022). Augmented reality-delivered product information at the point of sale: When information controllability backfires. J. Acad. Mark. Sci. 50, 743–776. doi:10.1007/s11747-022-00855-w

Hoffmann, S., Mai, R., and Pagel, T. (2021). Toy or tool? Utilitaristischer und hedonischer nutzen mobiler augmented-reality-apps. HMD Praxis der Wirtschaftsinformatik 59 (1), 23–36. doi:10.1365/s40702-021-00822-z

Hopp, T., and Gangadharbatla, H. (2016). Novelty effects in augmented reality advertising environments: The influence of exposure time and self-efficacy. J. Curr. Issues Res. Advert. 37 (2), 113–130. doi:10.1080/10641734.2016.1171179

Hsu, S. H. Y., Tsou, H. T., and Chen, J. S. (2021). Yes, we do. Why not use augmented reality?” customer responses to experiential presentations of AR-based applications. J. Retail. Consumer Serv. 62, 102649. doi:10.1016/j.jretconser.2021.102649

Huang, T.-L., and Liao, S. (2015). A model of acceptance of augmented-reality interactive technology: The moderating role of cognitive innovativeness. Electron. Commer. Res. 15 (2), 269–295. doi:10.1007/s10660-014-9163-2

Huang, T. L., and Liao, S. L. (2017). Creating e-shopping multisensory flow experience through augmented-reality interactive technology. Internet Res. 27 (2), 449–475. doi:10.1108/intr-11-2015-0321

Hudson, S., Matson-Barkat, S., Pallamin, N., and Jegou, G. (2019). With or without you? Interaction and immersion in a virtual reality experience. J. Bus. Res. 100, 459–468. doi:10.1016/j.jbusres.2018.10.062

Huynh, B., Ibrahim, A., Chang, Y. S., Höllerer, T., and O’Donovan, J. (2019). User perception of situated product recommendations in augmented reality. Int. J. Semant. Comput. 13 (3), 289–310. doi:10.1142/s1793351x19400129

Inman, J. J., and Nikolova, H. (2017). Shopper-facing retail technology: A retailer adoption decision framework incorporating shopper attitudes and privacy concerns. J. Retail. 93 (1), 7–28. doi:10.1016/j.jretai.2016.12.006

Javornik, A. (2016a). Augmented reality: Research agenda for studying the impact of its media characteristics on consumer behaviour. J. Retail. Consumer Serv. 30, 252–261. doi:10.1016/j.jretconser.2016.02.004

Javornik, A. (2016b). ‘It’s an illusion, but it looks real!’ Consumer affective, cognitive and behavioural responses to augmented reality applications. J. Mark. Manag. 32 (9-10), 987–1011. doi:10.1080/0267257x.2016.1174726

Jessen, A., Hilken, T., Chylinski, M., Mahr, D., Heller, J., Keeling, D. I., et al. (2020). The playground effect: How augmented reality drives creative customer engagement. J. Bus. Res. 116, 85–98. doi:10.1016/j.jbusres.2020.05.002

Jiang, Y., Wang, X., and Yuen, K. F. (2021). Augmented reality shopping application usage: The influence of attitude, value, and characteristics of innovation. J. Retail. Consumer Serv. 63, 102720. doi:10.1016/j.jretconser.2021.102720

Joerß, T., Hoffmann, S., Mai, R., and Akbar, P. (2021). Digitalization as solution to environmental problems? When users rely on augmented reality-recommendation agents. J. Bus. Res. 128, 510–523. doi:10.1016/j.jbusres.2021.02.019

Jung, Y., and Pawlowski, S. D. (2014). Understanding consumption in social virtual worlds: A sensemaking perspective on the consumption of virtual goods. J. Bus. Res. 67 (10), 2231–2238. doi:10.1016/j.jbusres.2014.01.002

Kaasinen, E., Schmalfuß, F., Özturk, C., Aromaa, S., Boubekeur, M., Heilala, J., et al. (2020). Empowering and engaging industrial workers with Operator 4.0 solutions. Comput. Industrial Eng. 139, 105678. doi:10.1016/j.cie.2019.01.052

Kalantari, M. (2017). Consumers' adoption of wearable technologies: Literature review, synthesis, and future research agenda. Int. J. Technol. Mark. 12 (3), 1–307. doi:10.1504/ijtmkt.2017.10008634

Kim, H. C., and Hyun, M. Y. (2016). Predicting the use of smartphone-based augmented reality (AR): Does telepresence really help? Comput. Hum. Behav. 59, 28–38. doi:10.1016/j.chb.2016.01.001

Kim, J., and Forsythe, S. (2008). Adoption of virtual try-on technology for online apparel shopping. J. Interact. Mark. 22 (2), 45–59. doi:10.1002/dir.20113

Kourouthanassis, P., Boletsis, C., Bardaki, C., and Chasanidou, D. (2015). Tourists responses to mobile augmented reality travel guides: The role of emotions on adoption behavior. Pervasive Mob. Comput. 18, 71–87. doi:10.1016/j.pmcj.2014.08.009

Kowalczuk, P., Siepmann, C., and Adler, J. (2021). Cognitive, affective, and behavioral consumer responses to augmented reality in e-commerce: A comparative study. J. Bus. Res. 124, 357–373. doi:10.1016/j.jbusres.2020.10.050

Kytö, M., Ens, B., Piumsomboon, T., Lee, G. A., and Billinghurst, M. (2018). “Pinpointing: Precise head- and eye-based target selection for augmented reality,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems , 1–14.

Lavoye, V., Mero, J., and Tarkiainen, A. (2021). Consumer behavior with augmented reality in retail: A review and research agenda. The International Review of Retail, Distribution and Consumer Research 31 (3), 299–329. doi:10.1080/09593969.2021.1901765

Lee, H., Xu, Y., and Porterfield, A. (2021). Consumers' adoption of AR-based virtual fitting rooms: From the perspective of theory of interactive media effects. J. Fash. Mark. Manag. 25 (1), 45–62. doi:10.1108/jfmm-05-2019-0092

Mai, R., Hoffmann, S., and Balderjahn, I. (2021)., When drivers become inhibitors of organic consumption: The need for a multistage view J. Acad. Mark. Sci. 49. forthcoming, 1151–1174. doi:10.1007/s11747-021-00787-x

Mai, R., Hoffmann, S., Schwarz, U., Niemand, T., and Seidel, J. (2014). The shifting range of optimal web site complexity. J. Interact. Mark. 28 (2), 101–116. doi:10.1016/j.intmar.2013.10.001

Marr, B. (2020). The top 10 technology trends in retail: How tech will transform shopping in 2020. Forbes . Available at: https://www.forbes.com/sites/bernardmarr/2019/11/25/the-top-10-technology-trends-in-retailhow-tech-will-transform-shopping-in-2020/#5e4e4a0a4e03 .

Masood, T., and Egger, J. (2019). Augmented reality in support of Industry 4.0. Implementation challenges and success factors. Robotics Computer-Integrated Manuf. 58, 181–195. doi:10.1016/j.rcim.2019.02.003

Mauroner, O., Le, L., and Best, S. (2016). Augmented reality in advertising and brand communication: An experimental study. Int. J. Inf. Commun. Eng. 10 (2), 422–425.

McLean, G., and Wilson, A. (2019). Shopping in the digital world: Examining customer engage-ment through augmented reality mobile applications. Comput. Hum. Behav. 101 (8), 210–224. doi:10.1016/j.chb.2019.07.002

Mehrabian, A., and Russell, J. A. (1974). An approach to environmental psychology . MIT Press .

Mishra, A., Shukla, A., Rana, N. P., and Dwivedi, Y. K. (2021). From “touch” to a “multisensory” experience: The impact of technology interface and product type on consumer responses. Psychol. Mark. 38 (3), 385–396. doi:10.1002/mar.21436

Moher, D., Liberati, A., Tetzlaff, J., and Altman, D. G.Prisma Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 6 (7), e1000097. doi:10.1371/journal.pmed.1000097

Nagele, A. N., Bauer, V., Healey, P. G., Reiss, J. D., Cooke, H., Cowlishaw, T., et al. (2021). Interactive audio augmented reality in participatory performance. Front. Virtual Real. 1, 610320. doi:10.3389/frvir.2020.610320

Narumi, T., Nishizaka, S., Kajinami, T., Tanikawa, T., and Hirose, M. (2011). “MetaCookie+,” in In 2011 IEEE Virtual Reality Conference ( IEEE ), 265–266.

Narvar, (2017). Making returns a competitive advantage . Available at: https://see.narvar.com/report-making-returns-a-competitive-advantage-2017-q2.html .

Nikhashemi, S. R., Knight, H. H., Nusair, K., and Liat, C. B. (2021). Augmented reality in smart retailing: A (n)(A) symmetric approach to continuous intention to use retail brands’ mobile AR apps. J. Retail. Consumer Serv. 60, 102464. doi:10.1016/j.jretconser.2021.102464

Olsson, T., Lagerstam, E., Kärkkäinen, T., and Väänänen-Vainio-Mattila, K. (2013). Expected user experience of mobile augmented reality services: A user study in the context of shopping centres. Pers. Ubiquitous Comput. 17 (2), 287–304. doi:10.1007/s00779-011-0494-x

Palmatier, R. W., Houston, M. B., and Hulland, J. (2018). Review articles: Purpose, process, and structure. J. Acad. Mark. Sci. 46, 1–5. doi:10.1007/s11747-017-0563-4

Pantano, E., Rese, A., and Baier, D. (2017). Enhancing the online decision-making process by using augmented reality: A two country comparison of youth markets. J. Retail. Consumer Serv. 38 (5), 81–95. doi:10.1016/j.jretconser.2017.05.011

Park, M., and Yoo, J. (2020). Effects of perceived interactivity of augmented reality on consumer responses: A mental imagery perspective. J. Retail. Consumer Serv. 52, 101912. doi:10.1016/j.jretconser.2019.101912

Plotkina, D., and Saurel, H. (2019). Me or just like me? The role of virtual try-on and physical appearance in apparel M-retailing. J. Retail. Consumer Serv. 51, 362–377. doi:10.1016/j.jretconser.2019.07.002

Poushneh, A. (2018). Augmented reality in retail: A trade-off between user’s control of access to personal information and augmentation quality. J. Retail. Consumer Serv. 41 (2), 169–176. doi:10.1016/j.jretconser.2017.12.010

Poushneh, A., and Vasquez-Parraga, A. Z. (2017). Discernible impact of augmented reality on retail customer’s experience, satisfaction and willingness to buy. J. Retail. Consumer Serv. 34 (2), 229–234. doi:10.1016/j.jretconser.2016.10.005

Qasem, Z. (2021). The effect of positive TRI traits on centennials adoption of try-on technology in the context of E-fashion retailing. Int. J. Inf. Manag. 56, 102254. doi:10.1016/j.ijinfomgt.2020.102254

Qin, H., Osatuyi, B., and Xu, L. (2021a). How mobile augmented reality applications affect continuous use and purchase intentions: A cognition-affect-conation perspective. J. Retail. Consumer Serv. 63, 102680. doi:10.1016/j.jretconser.2021.102680

Qin, H., Peak, D. A., and Prybutok, V. (2021b). A virtual market in your pocket: How does mobile augmented reality (MAR) influence consumer decision making? J. Retail. Consumer Serv. 58 (1), 102337–102656. doi:10.1016/j.jretconser.2020.102337

Rauschnabel, P. A. (2021). Augmented reality is eating the real-world! the substitution of physical products by holograms. Int. J. Inf. Manag. 57, 102279. doi:10.1016/j.ijinfomgt.2020.102279

Rauschnabel, P. A., Felix, R., and Hinsch, C. (2019). Augmented reality marketing: How mobile AR-apps can improve brands through inspiration. J. Retail. Consumer Serv. 49 (7), 43–53. doi:10.1016/j.jretconser.2019.03.004

Rauschnabel, P. A., He, J., and Ro, Y. K. (2018). Antecedents to the adoption of augmented reality smart glasses: A closer look at privacy risks. J. Bus. Res. 92 (11), 374–384. doi:10.1016/j.jbusres.2018.08.008

Rauschnabel, P. A., Rossmann, A., and tom Dieck, M. C. (2017). An adoption framework for mobile augmented reality games: The case of Pokémon Go. Comput. Hum. Behav. 76, 276–286. doi:10.1016/j.chb.2017.07.030

Rauschnabel, P. A. (2018). Virtually enhancing the real world with holograms: An exploration of expected gratifications of using augmented reality smart glasses. Psychol. Mark. 35 (8), 557–572. doi:10.1002/mar.21106

Reitmayr, G., and Drummond, T. W. (2006). “Going out: Robust model-based tracking for outdoor augmented reality,” in 2006 IEEE/ACM international symposium on mixed and augmented reality , 109–118.

Rese, A., Baier, D., Geyer-Schulz, A., and Schreiber, S. (2017). How augmented reality apps are accepted by consumers: A comparative analysis using scales and opinions. Technol. Forecast. Soc. Change 124 (6), 306–319. doi:10.1016/j.techfore.2016.10.010

Rese, A., Schreiber, S., and Baier, D. (2014). Technology acceptance modeling of augmented reality at the point of sale: Can surveys be replaced by an analysis of online reviews? J. Retail. Consumer Serv. 21 (5), 869–876. doi:10.1016/j.jretconser.2014.02.011

Robbins, P., and Aydede, M. (2009). “A short primer on situated cognition,” in The cambridge handbook of situated cognition . Editors P. Robbins, and M. Aydede (New York: Cambridge University Press ), 3–10.

Rogers, T. B., Kuiper, N. A., and Kirker, W. S. (1977). Self-reference and the encoding of personal information. J. Personality Soc. Psychol. 35 (9), 677–688. doi:10.1037/0022-3514.35.9.677

Romano, B., Sands, S., and Pallant, J. I. (2021). Augmented reality and the customer journey: An exploratory study. Australas. Mark. J. 29 (4), 354–363. doi:10.1016/j.ausmj.2020.06.010

Ruggiero, T. E. (2000). Uses and gratifications theory in the 21st century. Mass Commun. Soc. 3 (1), 3–37. doi:10.1207/s15327825mcs0301_02

Ryan, R. M., and Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55 (1), 68–78. doi:10.1037/0003-066x.55.1.68

Saleem, M., Kamarudin, S., Shoaib, H. M., and Nasar, A. (2021). Retail consumers’ behavioral intention to use augmented reality mobile apps in Pakistan. J. Internet Commer. 21, 497–525. doi:10.1080/15332861.2021.1975427

Samsung Business Insights (2020). 5 retail technology trends to watch in 2020 . Available at: https://insights.samsung.com/2020/01/03/5-retail-technology-trends-to-watch-in-2020/ .

Schifferstein, H. N. J. (2009). Comparing mental imagery across the sensory modalities. Imagination, Cognition Personality 28 (4), 371–388. doi:10.2190/ic.28.4.g

Scholz, J., and Duffy, K. (2018). We ARe at home: How augmented reality reshapes mobile marketing and consumer-brand relationships. J. Retail. Consumer Serv. 44, 11–23. doi:10.1016/j.jretconser.2018.05.004

Skarbez, R., Smith, M., and Whitton, M. C. (2021). Revisiting milgram and kishino's reality-virtuality continuum. Front. Virtual Real. 2, 27. doi:10.3389/frvir.2021.647997

Smink, A. R., Frowijn, S., van Reijmersdal, E. A., van Noort, G., and Neijens, P. C. (2019). Try online before you buy: How does shopping with augmented reality affect brand responses and personal data disclosure. Electron. Commer. Res. Appl. 35, 100854. doi:10.1016/j.elerap.2019.100854

Smink, A. R., van Reijmersdal, E. A., van Noort, G., and Neijens, P. C. (2020). Shopping in augmented reality: The effects of spatial presence, personalization and intrusiveness on app and brand responses. J. Bus. Res. 118, 474–485. doi:10.1016/j.jbusres.2020.07.018

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. J. Bus. Res. 104, 333–339. doi:10.1016/j.jbusres.2019.07.039

Song, H. K., Baek, E., and Choo, H. J. (2019). Try-on experience with augmented reality comforts your decision: Focusing on the roles of immersion and psychological ownership. Inf. Technol. People 33 (4), 1214–1234. doi:10.1108/itp-02-2019-0092

Spreer, P., and Kallweit, K. (2014). Augmented reality in retail: Assessing the acceptance and potential for multimedia product presentation at the PoS. Trans. Mark. Res. 1 (1), 23–31. doi:10.15764/mr.2014.01002

Sundar, S. S., Jia, H., Waddell, T. F., and Huang, Y. (2015). “Toward a theory of interactive media effects (TIME),” in The handbook of the psychology of communication technology . Editor S. Sundar (Chichester: John Wiley & Sons ), 47–86.

Tan, Y. C., Chandukala, S. R., and Reddy, S. K. (2021). Augmented reality in retail and its impact on sales. J. Mark. 86, 48–66. doi:10.1177/0022242921995449

Teas, R. K., and Agarwal, S. (2000). The effects of extrinsic product cues on consumers’ perceptions of quality, sacrifice, and value. J. Acad. Mark. Sci. 28 (2), 278–290. doi:10.1177/0092070300282008

tom Dieck, M. C., and Jung, T. (2018). A theoretical model of mobile augmented reality acceptance in urban heritage tourism. Curr. Issues Tour. 21 (2), 154–174. doi:10.1080/13683500.2015.1070801

van Berlo, Z. M., van Reijmersdal, E. A., Smit, E. G., and van der Laan, L. N. (2021). Brands in virtual reality games: Affective processes within computer-mediated consumer experiences. J. Bus. Res. 122, 458–465. doi:10.1016/j.jbusres.2020.09.006

van Esch, P., Arli, D., Gheshlaghi, M. H., Andonopoulos, V., von der Heidt, T., and Northey, G. (2019). Anthropomorphism and augmented reality in the retail environment. J. Retail. Consumer Serv. 49 (7), 35–42. doi:10.1016/j.jretconser.2019.03.002

van Krevelen, D. W. F., and Poelman, R. (2010). A survey of augmented reality technologies, applications and limitations. Int. J. Virtual Real. 9 (2), 1–20. doi:10.20870/ijvr.2010.9.2.2767

Vávra, P., Roman, J., Zonča, P., Ihnát, P., Němec, M., Kumar, J., et al. (2017). Recent development of augmented reality in surgery: A review. J. Healthc. Eng. , 1–9. doi:10.1155/2017/4574172

vXchange, (2020). Top 7 augmented reality statistics for 2020 . Available at: https://www.vxchnge.com/blog/augmented-reality-statistics .

Vynz Research (2020). Augmented reality and virtual reality market . Available at: https://www.vynzresearch.com/ict-media/augmented-reality-and-virtual-realitymarket .

Wang, Y., Ko, E., and Wang, H. (2021). Augmented reality (AR) app use in the beauty product industry and consumer purchase intention. Asia Pac. J. Mark. Logist. 34, 110–131. doi:10.1108/APJML-11-2019-0684

Ward, R. J., Jjunju, F. P. M., Griffith, E. J., Wuerger, S. M., and Marshall, A. (2020). Artificial odour-vision syneasthesia via olfactory sensory argumentation. IEEE Sens. J. 21 (5), 6784–6792. doi:10.1109/jsen.2020.3040114

Ward, R. J., Wuerger, S., and Marshall, A. (2021). Smelling sensations: Olfactory crossmodal correspondences. J. Percept. Imaging 4, 0–12. doi:10.2352/j.percept.imaging.2021.4.2.020402

Watson, A., Alexander, B., and Salavati, L. (2020). The impact of experiential augmented reality applications on fashion purchase intention. Int. J. Retail Distribution Manag. 48 (5), 433–451. doi:10.1108/ijrdm-06-2017-0117

WBR (2020). How new tech is creating seamless mobile shopping experiences! . Available at: https://etailwest.wbresearch.com/downloads/how-new-tech-is-creating-seamless-mobile-shopping-experiences-etail (Accessedon July 14th, 2020).

Wedel, M., Bigné, E., and Zhang, J. (2020). Virtual and augmented reality: Advancing research in consumer marketing. Int. J. Res. Mark. 37 (3), 443–465. doi:10.1016/j.ijresmar.2020.04.004

Westbrook, G., and Angus, A. (2021). Top 10 global consumer trends 2021 . Euromonitor International . Available at: https://go.euromonitor.com/white-paper-EC-2021-Top-10-Global-Consumer-Trends.html .

Whang, J. B., Song, J. H., Choi, B., and Lee, J. H. (2021). The effect of Augmented Reality on purchase intention of beauty products: The roles of consumers’ control. J. Bus. Res. 133, 275–284. doi:10.1016/j.jbusres.2021.04.057

Yaoyuneyong, G., Foster, J., Johnson, E., and Johnson, D. (2016). Augmented reality marketing: Consumer preferences and attitudes toward hypermedia print ads. J. Interact. Advert. 16 (1), 16–30. doi:10.1080/15252019.2015.1125316

Yim, M. Y. C., Chu, S. C., and Sauer, P. L. (2017). Is augmented reality technology an effective tool for e-commerce? An interactivity and vividness perspective. J. Interact. Mark. 39 (3), 89–103. doi:10.1016/j.intmar.2017.04.001

Yim, M. Y. C., and Park, S.-Y. (2019). I am not satisfied with my body, So I like augmented reality (AR)”: Consumer responses to AR-based product presentations. J. Bus. Res. 100 (7), 581–589. doi:10.1016/j.jbusres.2018.10.041

Yoo, J. (2020). The effects of perceived quality of augmented reality in mobile commerce—an application of the information systems success model. Informatics 7 (2), 14–5824. doi:10.3390/informatics7020014

Yuan, C., Wang, S., Yu, X., Kim, K. H., and Moon, H. (2021). The influence of flow experience in the augmented reality context on psychological ownership. Int. J. Advert. 40, 922–944. doi:10.1080/02650487.2020.1869387

Zhang, T., Wang, W. Y. C., Cao, L., and Wang, Y. (2019). The role of virtual try-on technology in online purchase decision from consumers’ aspect. Internet Res. 29 (3), 529–551. doi:10.1108/intr-12-2017-0540

Zhou, F., Duh, H. B. L., and Billinghurst, M. (2008). “Trends in augmented reality tracking, interaction and display: A review of ten years of ismar,” in Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality ( IEEE Computer Society ), 193–202.

FIGURE A1 . Literature search process and papers per year.

Keywords: augmented reality, retailing, e-commerce, m-commerce, consumer behavior

Citation: Hoffmann S and Mai R (2022) Consumer behavior in augmented shopping reality. A review, synthesis, and research agenda. Front. Virtual Real. 3:961236. doi: 10.3389/frvir.2022.961236

Received: 04 June 2022; Accepted: 20 September 2022; Published: 14 October 2022.

Reviewed by:

Copyright © 2022 Hoffmann and Mai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Stefan Hoffmann, [email protected]

To read this content please select one of the options below:

Please note you do not have access to teaching notes, exploring consumer collecting behavior: a conceptual model and research agenda.

Journal of Consumer Marketing

ISSN : 0736-3761

Article publication date: 13 November 2018

Issue publication date: 27 November 2018

The purpose of this paper is to explore the behaviors that revolve around collecting, the motivations behind these behaviors and the psychological benefits collectors receive from engaging in these collecting behaviors.

Design/methodology/approach

A thorough literature review and integration of prominent psychological and social psychology theories are used to propose a conceptual model, several research propositions and potential research questions for future scholarship.

This paper proposes that a collector salient identity and collecting motives drive tension-inducing social and solitary collecting behaviors and that these behaviors in turn reinforce the collector salient identity. Relevant aspects of the collecting phenomenon are explored, and included propositions provide future research direction to validate a proposed conceptual model designed to provide insights into a common consumer behavior.

Originality/value

This paper provides a broad conceptual model and explores several details of consumer collecting behavior as a basis for future research.

• Self-identity
• Identity salience
• Consumer behaviour
• Mortality salience

Ijams Spaid, B. (2018), "Exploring consumer collecting behavior: a conceptual model and research agenda", Journal of Consumer Marketing , Vol. 35 No. 6, pp. 653-662. https://doi.org/10.1108/JCM-05-2017-2224

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

Understanding and shaping consumer behavior in the next normal

Months after the novel coronavirus was first detected in the United States, the COVID-19 crisis continues to upend Americans’ lives and livelihoods. The pandemic has disrupted nearly every routine in day-to-day life. The extent and duration of mandated lockdowns and business closures have forced people to give up even some of their most deeply ingrained habits—whether spending an hour at the gym after dropping the kids off at school, going to a coffee shop for a midday break, or enjoying Saturday night at the movies.

About the authors

This article, a collaboration between McKinsey and the Yale Center for Customer Insights, was written by Tamara Charm, Ravi Dhar, Stacey Haas , Jennie Liu, Nathan Novemsky, and Warren Teichner .

Such disruptions in daily experiences present a rare moment. In ordinary times, consumers tend to stick stubbornly to their habits, resulting in very slow adoption (if any) of beneficial innovations  that require behavior change. Now, the COVID-19 crisis has caused consumers everywhere to change their behaviors —rapidly and in large numbers. In the United States, for example, 75 percent of consumers have tried a new store, brand, or different way of shopping  during the pandemic. Even though the impetus for that behavior change may be specific to the pandemic and transient, consumer companies would do well to find ways to meet consumers where they are today and satisfy their needs in the postcrisis period.

Behavioral science tells us that identifying consumers’ new beliefs, habits, and “peak moments” is central to driving behavioral change. Five actions can help companies influence consumer behavior for the longer term:

• Reinforce positive new beliefs.
• Shape emerging habits with new offerings.
• Sustain new habits, using contextual cues.
• Align messages to consumer mindsets.
• Analyze consumer beliefs and behaviors at a granular level.

Reinforce positive new beliefs

According to behavioral science, the set of beliefs that a consumer holds about the world is a key influencer of consumer behavior. Beliefs are psychological—so deeply rooted that they prevent consumers from logically evaluating alternatives and thus perpetuate existing habits and routines. Companies that attempt to motivate behavioral change by ignoring or challenging consumers’ beliefs are fighting an uphill battle.

The COVID-19 crisis, however, has forced many consumers to change their behaviors, and their new experiences have caused them to change their beliefs about a wide range of everyday activities, from grocery shopping to exercising to socializing. When consumers are surprised and delighted by new experiences, even long-held beliefs can change, making consumers more willing to repeat the behavior, even when the trigger (in this case, the COVID-19 pandemic) is no longer present. In other words, this is a unique moment in time during which companies can reinforce and shape behavioral shifts to position their products and brands better for the next normal.

When consumers are surprised and delighted by new experiences, even long-held beliefs can change, making consumers more willing to repeat the behavior.

For example, approximately 15 percent of US consumers tried grocery delivery for the first time during the COVID-19 crisis. Among those first timers, more than 80 percent say they were satisfied with the ease and safety of the experience; 70 percent even found it enjoyable. And 40 percent intend to continue getting their groceries delivered after the crisis, suggesting that they’ve jettisoned any previously held beliefs about grocery delivery being unreliable or inconvenient; instead, they’ve been surprised and delighted by the benefits of delivery.

Another example of changing beliefs involves at-home exercise. The US online fitness market has seen approximately 50 percent growth in its consumer base since February 2020; the market for digital home-exercise machines has grown by 20 percent. It’s likely that many people who tried those fitness activities for the first time during the pandemic believed that at-home exercise couldn’t meet their exercise needs. That belief has clearly changed for many of these consumers: 55 percent who tried online fitness programs and 65 percent who tried digital exercise machines say they will continue to use them, even after fitness centers and gyms reopen. To reinforce the new belief that online fitness can be motivating and enjoyable, NordicTrack, in a recent TV ad titled “Face Off,” shows that online workouts can foster the same friendly competition and connection that people look for when they go to the gym or attend in-person exercise classes.

An effective way to reinforce a new belief is to focus on peak moments—specific parts of the consumer decision journey that have disproportionate impact and that consumers tend to remember most. Peak moments often include first-time experiences with a product or service, touchpoints at the end of a consumer journey (such as the checkout process in a store), and other moments of intense consumer reaction.

Some companies have focused on enhancing the consumer’s first-time experience. Plant-based-meat  manufacturer Beyond Meat, for instance, was already benefiting from delays in meat production in the early days of the COVID-19 crisis: its sales more than doubled between the first and second quarters of 2020. In collaboration with local restaurants  and catering companies, the company has been delivering free, professionally prepared food to hospitals and other community centers. By giving away Beyond Burgers prepared by professional chefs, Beyond Meat is creating positive first experiences with its product at a time when consumers are more open to trial.

As the consumer journey has changed, so have the peak moments, and it’s crucial for companies to identify and optimize them. For example, a peak moment in a grocery store might be the discovery of an exciting new product on the shelf. In the online-grocery journey, however, a peak moment might instead be on-time delivery or the “unboxing” of the order (the experience of taking the delivered items out of the packaging). Grocers could consider including a handwritten thank-you note or some other surprise, such as a free sample, to reinforce consumers’ positive connections with the experience.

Highly emotional occasions can spark intense consumer reactions and therefore present an opportunity for companies to create peak moments associated with their products or brands. For example, when graduations shifted from formal, large-scale ceremonies to at-home, family celebrations, Krispy Kreme offered each 2020 graduate a dozen specially decorated doughnuts for free. With that promotion, the company connected its brand with an emotional event that may not have been a key occasion for doughnuts prior to the pandemic.

Shape emerging habits with new products

Companies can nudge consumers toward new habits through product innovation. For instance, the COVID-19 crisis has spurred consumers to become more health oriented  and increase their intake of vitamins and minerals. Unilever reported a sales spike in beverages that contain zinc and vitamin C, such as Lipton Immune Support tea. The company is therefore rolling out such products globally. It’s also aligning its innovation priorities with consumers’ emerging health-and-wellness concerns.

Similarly, packaged-food companies can encourage the habit of cooking at home. Spice manufacturer McCormick’s sales in China have sustained double-digit increases compared with 2019, even as the Chinese economy has reopened  and people go back to their workplaces. The same pattern could play out in other countries. Kraft Heinz’s innovation agenda for its international markets now prioritizes products that make home cooking pleasurable, fast, and easy—products such as sauces, dressings, and side dishes. These will be targeted at “light” and “medium” users of Kraft Heinz products.

Sustain new habits, using contextual cues

Habits can form when a consumer begins to associate a certain behavior with a particular context; eventually, that behavior can become automatic. To help turn behaviors into habits, companies should identify the contextual cues that drive the behaviors. A contextual cue can be a particular task, time of day, or object placement. For example, more consumers are keeping hand sanitizer and disinfecting wipes near entryways for easy access and as a reminder to keep hands and surfaces clean. Product packaging and marketing that reinforces the put-it-by-the-door behavior can help consumers sustain the habit.

Some companies may need to identify—and create—new contextual cues. Before the COVID-19 crisis, a contextual cue for chewing-gum consumption was anticipation of a social interaction—for instance, before going to a club, while commuting to work, and after smoking. As social occasions have waned during the pandemic, a chewing-gum manufacturer must look for new contextual cues, focusing largely on solo or small-group activities, such as gaming and crafting. Gum manufacturers could consider designing packaging, flavors, and communications that reinforce those new associations.

Align messages to consumer mindsets

People across the country have felt an intensified mix of anxiety, anger, and fear because of recent events, making marketing a tricky terrain to navigate. The heightened emotions and increased polarization of the past few months could drive lasting changes in consumers’ behavior and shape their long-term preferences. Companies should therefore ensure that all their brand communications are attuned to consumer sentiment. The quality of a company’s communication  and its ability to strike the right tone will increasingly become a competitive advantage.

McKinsey’s consumer-sentiment surveys  show that consumers are paying closer attention to how companies treat their employees  during this crisis—and taking note of companies that demonstrate care and concern for people. That has implications for how brands connect with consumers and what types of messages will resonate. Hair-care brand Olaplex, for example, became one of the most mentioned hair-care brands on social media when it started an affiliate program: the company donated a portion of its proceeds from product sales to customers’ local hairstylists, helping them stay afloat during salon closures.

That said, consumers will see through—and reject—messages and actions that are performative and that seek to commercialize social issues. A brand’s communications must align with its purpose ; otherwise, the messages won’t ring true. Testing marketing messages among a diverse group of consumers, in the context in which those messages will appear, could help prevent costly missteps.

Analyze consumer beliefs and behaviors at a granular level

Consumer beliefs, habits, occasions, and emotional-need states will continue to evolve rapidly over the next year or two as the world awaits a COVID-19 vaccine. For consumer companies to stay abreast of those changes, monitoring product sales alone won’t be sufficient. Companies must also conduct primary consumer-insights work, with a focus on identifying changed behaviors and associated changed beliefs and motivators to get a comprehensive picture of the changing consumer decision journey.

Qualitative, exploratory research will have a particular role to play as a precursor to (and, in some cases, a substitute for) quantitative research. Digital data-gathering and monitoring techniques—such as mobile diaries, social-media “listening,” and artificial-intelligence-driven message boards—will be vital tools to help companies understand emerging behaviors and contextual cues. When structured well, those insights generate new thinking within an organization that can be validated through larger-scale surveys and in-market testing. Companies can then refine their product offerings and marketing messages accordingly.

In addition, granular analyses of footfall data and omnichannel sales will unearth telling details, such as which geographic regions are seeing in-person commerce rebound first and which products consumers are buying (such as smaller pack sizes to avoid sharing, activewear versus office wear, and so on). Whereas in the past, companies might have fielded high-level usage and attitude surveys and brand trackers a few times a year, it’s especially important now for companies to keep a closer eye on the evolution of consumer behavior on a weekly or monthly basis.

The COVID-19 crisis has changed people’s routines at unprecedented speed—and some of those changes will outlast the pandemic. Even in states and cities that have reopened, consumers remain cautious about resuming all of their precrisis activities. We’ve seen differences in consumer behavior across geographic markets and demographic groups, and those differences will only widen during the recovery phase, given that the health, economic, and social impact of COVID-19 isn’t uniform. Companies that develop a nuanced understanding of the changed beliefs, peak moments, and habits of their target consumer bases—and adjust their product offerings, customer experiences, and marketing communications accordingly—will be best positioned to thrive in the next normal.

Tamara Charm is a senior expert in McKinsey’s Boston office; Ravi Dhar is director of the Center for Customer Insights at the Yale School of Management; Stacey Haas is a partner in McKinsey’s Detroit office; Jennie Liu is executive director of the Yale Center for Customer Insights; Nathan Novemsky is a marketing professor at the Yale School of Management; and Warren Teichner is a senior partner in McKinsey’s New Jersey office.

This article was edited by Monica Toriello, an executive editor in the New York office.

Explore a career with us

Related articles.

US consumer sentiment and behaviors during the coronavirus crisis

Consumer sentiment and behavior continue to reflect the uncertainty of the COVID-19 crisis

How consumer-goods companies can prepare for the next normal