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What is Consumer Behavior Research? Definition, Examples, Methods, and Questions

By Nick Jain

Published on: September 8, 2023

What is Consumer Behavior Research

Table of Contents

What is Consumer Behavior Research?

Top 12 consumer behavior research examples, consumer behavior research methods, 12 consumer behavior research questions.

Consumer behavior research is defined as a field of study that focuses on understanding how and why individuals and groups of people make decisions related to the acquisition, use, and disposal of goods, services, ideas, or experiences. This research seeks to uncover the underlying factors and processes that influence consumers’ choices, preferences, and behaviors in the marketplace.

Key aspects of consumer behavior research include:

  • Decision-Making Processes: Researchers investigate the steps consumers take when making purchasing decisions. This involves studying how consumers identify needs, gather information, evaluate options, and ultimately make choices.
  • Psychological Factors: Understanding the psychological aspects of consumer behavior is crucial. This includes exploring concepts such as motivation, perception, learning, memory, and attitudes to determine how they affect consumer choices.
  • Social and Cultural Influences: Consumer behavior is heavily influenced by social and cultural factors. Researchers examine how social groups, family, friends, and cultural norms impact purchasing decisions.
  • Economic Factors: Economic theories and models are used to analyze how factors like income, price sensitivity, and budget constraints affect consumer choices.
  • Marketing and Advertising Effects: Researchers study the impact of marketing strategies, advertising campaigns, branding, and promotions on consumer behavior. This includes assessing the effectiveness of various marketing techniques.
  • Technology and Online Behavior: With the rise of e-commerce and digital technologies, understanding how consumers behave in online environments has become increasingly important. Research in this area focuses on online shopping behavior, website usability, and the influence of online reviews and social media.
  • Consumer Segmentation: Consumer behavior researchers often segment the market to identify different consumer groups based on demographics, psychographics, and behavioral patterns. This assists businesses in customizing their marketing strategies for particular target demographics.
  • Consumer Satisfaction and Loyalty: Researchers study post-purchase behavior, including customer satisfaction and loyalty. They examine what factors lead to repeat purchases and brand loyalty.
  • Ethical and Sustainable Consumption: In recent years, there has been a growing interest in understanding how ethical and sustainable considerations influence consumer choices. Researchers investigate the factors that drive environmentally conscious and socially responsible consumption.
  • Cross-Cultural Analysis: Given the globalization of markets, understanding consumer behavior in different cultural contexts is vital. Researchers analyze how cultural values and norms impact consumer preferences and decision-making.

Consumer behavior research is essential for businesses and marketers to develop effective marketing strategies, product design, pricing strategies, and customer experiences that resonate with their target audience. By gaining insights into consumer behavior, companies can better meet consumer needs and achieve their business objectives.

Consumer Behavior Research Examples

Consumer behavior research encompasses a wide range of topics and methodologies. Here are some examples of consumer behavior research studies and topics:

1. Product Packaging and Perception

Researchers might conduct studies to understand how the design and aesthetics of product packaging influence consumers’ perceptions and purchase decisions. For example, a study could examine how color, shape, and labeling affect consumers’ perceptions of a product’s quality and value.

2. Online Shopping Behavior

With the growth of e-commerce, research often explores various aspects of online shopping behavior. This can include studies on factors influencing shopping cart abandonment, the impact of website design on user experience, or the role of online reviews and ratings in purchase decisions.

3. Brand Loyalty and Customer Retention

Companies often conduct research to understand what factors contribute to brand loyalty and customer retention. This might involve surveys, customer feedback analysis , or loyalty program effectiveness studies.

4. Advertising Effectiveness

Researchers study how different types of advertisements, such as TV commercials, online banner ads, or influencer marketing, influence consumer attitudes and buying behavior. They may use techniques like eye-tracking to assess where consumers focus their attention in advertisements.

5. Price Sensitivity and Promotion Analysis

Research in this area aims to determine how consumers respond to pricing strategies, discounts, and promotions. It might involve experiments to assess the impact of price changes on sales or consumer surveys about their price sensitivity.

6. Consumer Decision-Making Process

Studies on consumer decision-making delve into the steps consumers take when making purchasing decisions. Researchers might use qualitative methods like in-depth interviews to understand the thought processes behind consumer choices.

7. Social Media Influence

Given the prevalence of social media in consumers’ lives, researchers examine how social platforms like Instagram, Facebook, and TikTok influence consumer behavior. They may investigate the role of social media in product discovery, brand engagement, and purchasing decisions.

8. Cross-Cultural Consumer Behavior

Research in this area explores how cultural differences affect consumer preferences and behaviors. For example, a study might investigate how cultural values impact the perception of luxury brands or the acceptance of certain products.

9. Environmental and Sustainability Concerns

Researchers study how consumers’ environmental and sustainability values impact their purchasing decisions. This can include surveys to understand the willingness to pay more for eco-friendly products or the influence of eco-labels.

10. Consumer Satisfaction and Complaint Behavior

Companies often conduct research to assess customer satisfaction and understand how customers express dissatisfaction or complaints. This research can help improve customer service and product quality.

11. Impulse Buying Behavior

Some studies focus on the triggers and factors behind impulse buying, such as point-of-sale displays, limited-time offers, or product placement in stores.

12. Neuromarketing

This emerging field uses neuroscience techniques, such as brain imaging and eye-tracking, to study consumer responses to marketing stimuli, providing insights into subconscious reactions to advertisements and product design.

These are just a few examples of the diverse range of consumer behavior research topics. Researchers employ various research methods, including surveys, experiments, observational studies, and data analysis, to gain insights into consumer behavior and inform marketing strategies and business decisions.

Learn more: What is Consumer Research?

Consumer behavior research employs various methods and techniques to understand and analyze how consumers make decisions, form preferences, and behave in the marketplace. These methods help researchers gather data and insights that can be used to inform marketing strategies, product development, and business decisions. Here are some common consumer behavior research methods:

  • Surveys and Questionnaires: Surveys are a popular method for collecting data on consumer preferences, attitudes, and behaviors. Researchers design structured questionnaires and distribute them to a sample of respondents, either in person, by mail, over the phone, or online. Survey responses are analyzed to identify trends and patterns.
  • Observational Research: Observational research involves the systematic observation of consumer behavior in natural or controlled settings. Researchers may use techniques like video recording, field notes, or mystery shopping to observe how consumers interact with products, make purchase decisions, or navigate retail environments.
  • Experiments: Experimental research allows researchers to manipulate variables and observe their effects on consumer behavior. Controlled experiments often take place in a lab setting, while field experiments occur in real-world contexts. Researchers can study the impact of factors like pricing changes, advertising messages, or product variations.
  • Focus Groups: Focus groups involve gathering a small group of participants to engage in discussions about specific topics or products. These discussions are typically guided by a moderator who asks questions and facilitates conversation. Focus groups provide qualitative insights into consumer perceptions and opinions.
  • In-Depth Interviews: Researchers conduct one-on-one interviews with consumers to gain a deeper understanding of their thoughts, motivations, and decision-making processes. In-depth interviews are flexible and allow researchers to probe into specific areas of interest.
  • Ethnographic Research: Ethnography involves immersing researchers in the lives of consumers and studying their behavior within their natural environments. This method is particularly useful for gaining insights into culture, lifestyle, and the context of consumer decisions.
  • Online Behavior Analysis: With the growth of e-commerce and digital marketing, researchers can collect data on consumer behavior from online sources. This includes analyzing website traffic, click-through rates, online reviews, and social media interactions to understand how consumers engage with brands and products online.
  • Neuroscience and Eye-Tracking: Neuroscience techniques, such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and eye-tracking, can be used to study consumers’ neurological responses and eye movements when exposed to marketing stimuli, providing insights into subconscious reactions.
  • Secondary Data Analysis: Researchers can analyze existing data sources, such as market reports, government statistics, and customer databases, to extract insights about consumer behavior. This method is cost-effective and often used for trend analysis.
  • Big Data Analytics: Companies can leverage big data analytics to analyze vast amounts of data collected from online interactions, transactions, and social media to identify consumer patterns and trends.
  • Psychological Experiments: Researchers may use psychological experiments to study cognitive processes, decision-making heuristics, and biases that influence consumer behavior. This can involve experiments on memory, perception, and motivation.
  • Longitudinal Studies: Longitudinal studies involve tracking the same group of consumers over an extended period to understand how their behavior, preferences, and attitudes change over time.

The choice of research method depends on the research objectives, available resources, and the specific aspects of consumer behavior being studied. Often, a combination of methods is used to obtain a comprehensive understanding of consumer behavior.

Learn more: What is Customer Research?

Consumer Behavior Research Questions

Consumer behavior research often begins with the formulation of research questions that guide the study and help researchers explore specific aspects of consumer behavior. The choice of research questions depends on the goals of the study and the areas of interest. Here are some examples of consumer behavior research questions across various domains:

1. Product Preferences

  • What factors influence consumers’ preferences for eco-friendly products?
  • How do consumers prioritize price versus quality when choosing products in a competitive market?
  • What are the key factors that influence consumers’ decisions to abandon their online shopping carts?
  • How do the design and layout of an e-commerce website affect user engagement and conversion rates?

3. Brand Loyalty

  • What strategies can companies use to build and maintain brand loyalty among their customers?
  • How does consumers’ emotional attachment to a brand impact their purchasing decisions?
  • How do different advertising channels (e.g., TV, social media, email) impact consumer brand recall and purchase intent?
  • What role do emotions play in consumer responses to advertising messages?

5. Price Sensitivity and Promotion

  • How do consumers respond to dynamic pricing strategies in the airline industry?
  • What types of promotions are most effective in influencing consumer behavior during holiday shopping seasons?

6. Cross-Cultural Consumer Behavior

  • How do cultural differences in communication styles impact consumer reactions to advertising campaigns?
  • What cultural factors affect consumers’ perceptions of luxury brands in different regions?

7. Sustainability and Ethical Consumption

  • What motivates consumers to make sustainable and eco-friendly choices in their purchasing behavior?
  • How does transparency in product sourcing and manufacturing impact consumers’ trust and willingness to buy?

8. Social Media Influence

  • To what extent does social media content, such as influencer endorsements and user-generated content, influence consumer purchasing decisions?
  • How do different social media platforms impact the discovery and evaluation of products and services?

9. Customer Satisfaction and Complaint Behavior

  • What factors contribute to customer satisfaction and loyalty in the hospitality industry?
  • How do consumers express their dissatisfaction with products or services, and how can companies effectively address these concerns?

10. Impulse Buying Behavior

  • What situational factors trigger impulse purchases in physical retail stores?
  • How do limited-time offers and flash sales influence online impulse buying?

11. Neuromarketing Insights

  • What brain regions are activated when consumers view emotionally appealing advertisements?
  • How does the placement of key visual elements in advertisements affect consumer attention and recall?

12. Generational Differences

  • How do the shopping behaviors and preferences of Generation Z differ from those of Millennials?
  • What marketing strategies are effective in targeting Baby Boomers for luxury products?

These research questions provide a starting point for investigating various aspects of consumer behavior. Researchers can tailor their inquiries to specific industries, products, or market segments to gain valuable insights into consumer decision-making processes and preferences.

Learn more: What is Customer Feedback Analysis?

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consumer behavior research study

Consumer Behavior Research

Exploring the Depths of Consumer Insights for Strategic Business Growth

In an era where understanding consumer behavior is more than a competitive edge, it’s a survival imperative, NielsenIQ (NIQ) and GfK emerge as pivotal allies. This expertise is essential for businesses in B2C commerce, retail, and beyond, aiming to navigate the complex consumer landscape for informed, strategic decision-making.

Definition and Importance of Consumer Behavior Research

Consumer behavior research is the study of how individuals make decisions to spend their resources on consumption-related items. It involves understanding the what, why, when, and how of consumer purchases. This field is crucial for businesses as it sheds light on consumer preferences, buying patterns, and decision-making processes. By understanding these aspects, companies can tailor their products and marketing strategies effectively, ensuring alignment with consumer needs and market trends, ultimately leading to increased customer satisfaction and loyalty.

Overview of the Impact of Consumer Behavior Research on Marketing Strategies

The insights from consumer behavior research are instrumental in shaping targeted marketing strategies. By understanding consumer motivations and behaviors, businesses can create more relevant and engaging marketing messages, leading to improved customer engagement and retention. This research helps in segmenting the market, identifying potential customers, and understanding the factors that drive consumer decisions. It also aids in predicting future trends, enabling companies to stay ahead of the curve. Effective use of consumer behavior research can lead to the development of products and services that meet the evolving needs of consumers, thereby enhancing brand loyalty and market share.


Consumer and shopper insights

Understand consumer and shopper behavior, demographics, and loyalty with modern, representative consumer panels and customer survey capabilities.

Understanding Consumer Behavior

These diverse influences combine to form unique consumer profiles, which businesses must understand to effectively target their marketing efforts..

Factors Influencing Consumer Behavior

Consumer behavior is influenced by a complex interplay of psychological, social, cultural, and personal factors. Psychological factors include perceptions, attitudes, and motivation, which guide consumers’ emotional and cognitive responses. Social factors encompass family, friends, and societal norms that shape buying habits through peer influence and social trends. Cultural factors involve the broader societal beliefs, values, and customs that dictate consumer behavior in a particular region. Personal factors such as age, occupation, lifestyle, and economic status also significantly impact consumer choices. These diverse influences combine to form unique consumer profiles, which businesses must understand to effectively target their marketing efforts.

The Role of Consumer Behavior in Decision Making

Consumer behavior plays a critical role in the decision-making process. It involves understanding how consumers decide upon their needs and wants, choose among products and brands, and determine their purchase methods. This knowledge is vital for businesses to design and position their offerings in a way that resonates with the target audience. Understanding consumer behavior helps in predicting how consumers will respond to marketing messages and product features, enabling businesses to tailor their strategies to meet consumer needs effectively. It also assists in identifying opportunities for new product development and market expansion.

Consumer Behavior Theories and Models

Consumer behavior theories and models provide frameworks for understanding and predicting consumer actions. The Stimulus-Response Model, for instance, illustrates how marketing stimuli and environmental factors influence consumer responses. Maslow’s Hierarchy of Needs explains consumer motivation in terms of fulfilling basic to complex needs. The Theory of Reasoned Action and the Theory of Planned Behavior focus on the relationship between attitudes, intentions, and behaviors. The Consumer Decision Model outlines the cognitive process involving need recognition, information search, evaluation of alternatives, purchase decision, and post-purchase behavior. These models help businesses in developing strategies that align with consumer psychology and behavioral patterns. They also assist in segmenting the market and targeting consumers with personalized marketing approaches, enhancing the effectiveness of marketing campaigns and product offerings.

Research Methods in Consumer Behavior Research

Customer analytics is vital for businesses across various sectors, including FMCG, sales, and e-commerce. It enables companies to create personalized experiences, improve customer engagement, and boost retention, ultimately leading to increased revenue. By understanding consumer behavior through data analysis, businesses can make informed decisions that resonate with their target audience.

Quantitative Research Methods

Quantitative research methods in consumer behavior research involve structured techniques like surveys and questionnaires to collect numerical data. These methods are useful for gauging consumer attitudes, preferences, and behaviors across larger populations. Statistical analysis of this data helps in identifying trends, testing hypotheses, and making generalizations about consumer behavior. Quantitative research is valuable for businesses as it provides measurable and comparable insights that can guide strategic decision-making. It helps in understanding the magnitude of consumer responses to various marketing stimuli and in assessing the potential market size for new products or services.

Qualitative Research Methods

Qualitative research methods in consumer behavior focus on understanding the deeper motivations, thoughts, and feelings of consumers. Techniques like in-depth interviews, focus groups, and observational studies provide rich, detailed insights that are not typically captured through quantitative methods. This approach is crucial for exploring the underlying reasons behind consumer choices, preferences, and attitudes. Qualitative research helps businesses in gaining a deeper understanding of consumer experiences, emotions, and perceptions, which can be invaluable in developing more effective marketing strategies, product designs, and customer service approaches. It allows companies to explore new ideas and concepts with consumers, gaining insights that can lead to innovation and differentiation in the market.

Experimental Research in Consumer Behavior

Experimental research in consumer behavior involves manipulating one or more variables to observe the effect on another variable, typically consumer behavior or attitudes. This method is used to establish cause-and-effect relationships, providing insights into how changes in product features, pricing, or marketing strategies might influence consumer behavior. Controlled experiments, often conducted in laboratory settings or as field experiments, allow researchers to isolate the effects of specific variables. This type of research is particularly valuable for testing new products, pricing strategies, and marketing messages before full-scale implementation. It helps businesses in making informed decisions based on empirical evidence, reducing the risks associated with new initiatives.

Factors Affecting Consumer Behavior

Psychological factors.

Psychological factors play a significant role in shaping consumer behavior. These include individual motivations, perceptions, attitudes, and beliefs. Motivation drives consumers to fulfill their needs and desires, influencing their buying decisions. Perception, how consumers interpret information, can significantly impact their choices, as it shapes their understanding of products and brands. Attitudes and beliefs, formed through experiences and social influences, guide consumer preferences and loyalty. Understanding these psychological factors is crucial for businesses as they influence how consumers view and interact with products and services. By aligning marketing strategies with consumer psychology, businesses can more effectively influence purchasing decisions and build stronger customer relationships.

Social Factors

Social factors significantly influence consumer behavior, encompassing the impact of society, family, and peer groups. Family members and friends can influence buying decisions through recommendations or shared experiences. Social groups, including social networks and communities, also play a role in shaping consumer preferences and behaviors. The influence of social media has become particularly significant, as it not only connects consumers but also serves as a platform for sharing opinions and experiences about products and services. Understanding these social dynamics is important for businesses as they can leverage social influences through targeted marketing strategies, influencer partnerships, and social media campaigns. Recognizing the power of social factors can help businesses in building brand awareness and loyalty among consumer groups.

Cultural Factors

Cultural factors are deeply ingrained elements that influence consumer behavior, including values, beliefs, customs, and traditions. These factors vary across different regions and societies, affecting how consumers perceive and interact with products and services. Cultural influences can determine consumer preferences, buying habits, and brand perceptions. For instance, color symbolism, dietary preferences, and language can all vary significantly between cultures, impacting marketing strategies and product development. Businesses must understand and respect these cultural nuances to effectively cater to diverse consumer markets. Adapting products and marketing messages to align with cultural values and norms can significantly enhance a brand’s appeal and acceptance in different markets.

Personal Factors

Personal factors, including age, gender, occupation, lifestyle, and economic status, also significantly influence consumer behavior. These factors determine individual needs, preferences, and purchasing power. For example, younger consumers may prioritize trendy and innovative products, while older consumers might value functionality and durability. Lifestyle choices, such as health consciousness or environmental awareness, can also drive consumer preferences and choices. Economic factors, such as income and economic conditions, influence consumers’ ability to purchase and their sensitivity to price changes. Understanding these personal factors is crucial for businesses to segment their market effectively and tailor their products and marketing strategies to meet the specific needs of different consumer groups.

Consumer Purchase Decision Making

Stages of the consumer purchase decision-making process.

The consumer purchase decision-making process typically involves several key stages: problem recognition, information search, evaluation of alternatives, purchase decision, and post-purchase behavior.

In the problem recognition stage, consumers identify a need or desire.

During the information search, they seek out information about products or services that can fulfill their need. In the evaluation stage, consumers compare different options based on attributes such as price, quality, and brand reputation.

The purchase decision involves choosing a product and making the purchase. Finally, in the post-purchase stage, consumers evaluate their satisfaction with the purchase, which can influence future buying decisions and brand loyalty.

Understanding these stages is essential for businesses to effectively influence consumers at each step, from raising awareness to ensuring post-purchase satisfaction.

Influences on Consumer Purchase Decisions

Consumer purchase decisions are influenced by a multitude of factors, including product attributes, brand reputation, marketing messages, social influences, and personal preferences. Product features such as quality, price, and usability are key determinants of consumer choices. Brand reputation, built over time through consistent quality and marketing efforts, also significantly impacts purchase decisions. Marketing messages and advertising play a crucial role in shaping consumer perceptions and driving demand. Social influences, including recommendations from family and friends, as well as online reviews and influencer endorsements, can sway consumer decisions. Personal factors such as individual needs, preferences, and financial constraints also play a critical role. Businesses must consider these diverse influences when developing products and crafting marketing strategies to effectively appeal to their target audience.

Impulse Buying Behavior

Impulse buying behavior refers to unplanned purchases made by consumers, often driven by emotional factors rather than rational decision-making. This type of behavior is typically triggered by external stimuli such as attractive product displays, promotional offers, or persuasive sales tactics. Emotional responses, such as excitement or the desire for instant gratification, also play a significant role in impulse buying. Retailers often leverage this behavior by strategically placing impulse items near checkout areas or using limited-time offers to create a sense of urgency. Understanding the triggers of impulse buying can help businesses in designing marketing strategies and store layouts that encourage such purchases, potentially increasing sales and customer engagement.

Online Shopping and Consumer Behavior

Impact of online shopping on consumer behavior.

The rise of online shopping has significantly impacted consumer behavior, offering convenience, a wider selection of products, and often competitive pricing. Online shopping has changed the way consumers research products, compare prices, and make purchasing decisions. The ease of access to a vast array of products and the ability to shop at any time have increased the frequency and diversity of purchases. Online reviews and ratings have also become important factors in the decision-making process, as consumers increasingly rely on the opinions of others. Additionally, the personalized shopping experiences offered by many online retailers, through targeted recommendations and tailored marketing messages, have further influenced consumer buying habits. Understanding these shifts in consumer behavior is crucial for businesses to adapt their strategies for the digital marketplace, ensuring they meet the evolving needs and expectations of online shoppers.

Factors Influencing Online Buying Behavior

Several factors influence online buying behavior, including website usability, product variety, pricing, customer reviews, and the overall shopping experience. A user-friendly website with easy navigation and a seamless checkout process is crucial for attracting and retaining online shoppers. A diverse product range and competitive pricing are also key factors in attracting consumers. Customer reviews and ratings significantly impact purchase decisions, as they provide social proof and reduce perceived risk. The overall shopping experience, including customer service, delivery options, and return policies, also plays a vital role in influencing online buying behavior. Security and privacy concerns are additional considerations, as consumers are increasingly aware of data protection and online fraud. Businesses must address these factors to create a compelling online shopping experience that meets consumer expectations and drives online sales.

Comparison of Online and Offline Consumer Behavior

Online and offline consumer behaviors exhibit distinct differences, influenced by the unique aspects of each shopping environment. Online shopping offers convenience, a broader selection, and often more competitive pricing, leading to different purchasing patterns compared to offline shopping. Consumers tend to spend more time researching and comparing products online, while offline shopping is often driven by immediate needs and sensory experiences. The tactile experience and instant gratification of offline shopping are not replicable online, but the online environment offers personalized recommendations and a wealth of product information. Offline shopping also provides opportunities for personal interaction and immediate problem resolution, which can enhance customer satisfaction. Understanding these differences is crucial for businesses to tailor their strategies for each channel, ensuring a cohesive and complementary shopping experience that meets the needs and preferences of consumers in both online and offline environments.

Consumer Satisfaction and Loyalty

Importance of customer satisfaction in consumer behavior research.

Customer satisfaction is a critical component of consumer behavior research, as it directly impacts repeat purchases and brand loyalty. Satisfied customers are more likely to become repeat buyers, recommend the brand to others, and provide positive reviews. Customer satisfaction is influenced by various factors, including product quality, customer service, and overall shopping experience. Understanding and measuring customer satisfaction helps businesses identify areas for improvement, enhance customer experiences, and build long-term relationships with consumers. High levels of customer satisfaction lead to increased customer loyalty, which is essential for business growth and sustainability.

Factors Influencing Customer Satisfaction

Customer satisfaction is influenced by a range of factors, including product quality, price, service quality, brand image, and customer expectations. Product quality is a primary determinant of satisfaction, as consumers expect products to perform as advertised. Price also plays a role, as consumers evaluate the value they receive relative to the cost. Service quality, encompassing customer service interactions and the overall shopping experience, significantly impacts satisfaction levels. A positive, helpful, and efficient service experience can enhance satisfaction, while negative experiences can lead to dissatisfaction. Brand image, shaped by marketing communications and past experiences, influences consumer expectations and perceptions. Meeting or exceeding these expectations is key to achieving high levels of customer satisfaction. Additionally, personal factors such as individual needs, preferences, and past experiences also influence satisfaction. Businesses must consider these diverse factors to effectively meet consumer needs and enhance satisfaction levels.

Relationship Between Customer Satisfaction and Loyalty

The relationship between customer satisfaction and loyalty is strong and direct. Satisfied customers are more likely to develop a sense of loyalty to a brand, leading to repeat purchases and positive word-of-mouth recommendations. Loyalty is not just about repeat buying; it also involves an emotional connection and a preference for the brand over competitors. Satisfied customers are also more likely to be forgiving of minor issues and are less sensitive to price changes. Conversely, dissatisfied customers are more likely to switch to competitors and share negative experiences with others. Building customer loyalty requires consistently meeting or exceeding customer expectations, providing high-quality products and services, and maintaining positive customer relationships. Loyal customers are valuable assets to businesses, as they tend to have a higher lifetime value, lower acquisition costs, and can become brand advocates, promoting the brand through their networks.

Consumer Research and Marketing Strategies

Utilizing consumer research to develop effective marketing programs.

Consumer research is a vital tool for developing effective marketing programs. By understanding consumer needs, preferences, and behaviors, businesses can create targeted marketing strategies that resonate with their audience. Consumer research helps in identifying market segments, understanding consumer pain points, and uncovering opportunities for product development or enhancement. It also provides insights into the most effective channels and messages for reaching the target audience. Utilizing consumer research in marketing program development ensures that strategies are data-driven and customer-centric, increasing the likelihood of success. It enables businesses to tailor their marketing efforts to the specific needs and preferences of different consumer segments, improving engagement and response rates. Additionally, ongoing consumer research allows businesses to adapt their marketing strategies in response to changing consumer trends and market conditions, ensuring continued relevance and effectiveness.

Targeting Specific Consumer Segments Based on Research Findings

Targeting specific consumer segments based on research findings is a key strategy for effective marketing. Consumer research provides detailed insights into different consumer groups, including their demographics, psychographics, behaviors, and preferences. By analyzing this data, businesses can identify distinct segments within their target market, each with unique needs and characteristics. Targeting these segments with tailored marketing messages and product offerings increases the relevance and appeal of the brand to each group. For example, a segment characterized by health-conscious consumers would respond more positively to marketing messages emphasizing the health benefits of a product. Segment-specific targeting allows businesses to allocate marketing resources more efficiently, focusing on the most promising segments with the highest potential for conversion and loyalty. It also enhances the customer experience by providing consumers with products and marketing messages that are more closely aligned with their individual needs and preferences.

Adapting Marketing Strategies to Consumer Behavior Trends

Adapting marketing strategies to consumer behavior trends is essential for businesses to stay relevant and competitive. Consumer behavior is constantly evolving, influenced by factors such as technological advancements, cultural shifts, and economic changes. By staying attuned to these trends, businesses can anticipate changes in consumer needs and preferences, and adjust their marketing strategies accordingly. This may involve adopting new marketing channels, such as social media or influencer marketing, to reach consumers where they are most active. It could also mean developing new products or services that align with emerging consumer trends, such as sustainability or personalization. Adapting marketing strategies to consumer behavior trends requires a proactive approach, with ongoing research and analysis to identify emerging patterns. Businesses that successfully adapt to these trends can capture new market opportunities, enhance customer engagement, and maintain a competitive edge.

Case Studies in Consumer Behavior Research

Analysis of real-life examples and their implications.

Real-life case studies in consumer behavior research provide valuable insights into the practical application of theoretical concepts and the effectiveness of different marketing strategies. For example, a case study in the automotive industry might analyze how consumer preferences for eco-friendly vehicles have influenced car manufacturers’ product development and marketing strategies. In the retail sector, a case study could examine the impact of online shopping on brick-and-mortar stores and how these businesses have adapted to the digital era. These case studies offer concrete examples of how businesses have successfully navigated changes in consumer behavior, providing lessons and strategies that can be applied in other contexts. They also highlight the importance of consumer research in identifying market trends, understanding consumer needs, and developing effective marketing strategies. By analyzing real-life examples, businesses can gain a deeper understanding of consumer behavior, learn from the successes and challenges of others, and apply these insights to their own strategies.

Examination of Successful Marketing Campaigns Based on Consumer Behavior Research

Examining successful marketing campaigns that are based on consumer behavior research can provide valuable insights into effective marketing practices. These case studies demonstrate how a deep understanding of consumer needs, preferences, and behaviors can be leveraged to create impactful marketing campaigns. For instance, a campaign that effectively uses consumer data to personalize messages and offers can result in higher engagement and conversion rates. Another example might be a campaign that taps into current consumer trends, such as sustainability or wellness, to resonate with the target audience. Analyzing these successful campaigns can reveal key strategies and tactics that businesses can adopt, such as the use of specific channels, messaging techniques, or promotional offers. These case studies also highlight the importance of data-driven decision-making in marketing, showing how consumer research can inform and guide successful marketing initiatives.

Motivating Consumers and New Product Adoption

Strategies to motivate consumers to adopt new products.

Motivating consumers to adopt new products is a critical challenge for businesses. Effective strategies for encouraging new product adoption include leveraging social proof, offering free trials or samples, and creating educational content. Social proof, such as customer testimonials or influencer endorsements, can reduce perceived risk and increase consumer confidence in trying a new product. Free trials or samples allow consumers to experience the product firsthand, reducing barriers to adoption. Educational content, such as how-to guides or product demonstrations, can help consumers understand the value and benefits of the new product. Additionally, businesses can use targeted marketing campaigns to reach early adopters and innovators who are more likely to try new products and spread the word to others. Creating a sense of urgency or exclusivity around the new product, through limited-time offers or exclusive access, can also motivate consumers to adopt the product more quickly.

Innovations in Consumer Behavior Research for New Product Development

Innovations in consumer behavior research are playing a crucial role in new product development. Advanced analytics and data mining techniques allow businesses to analyze large datasets and uncover deep insights into consumer needs and preferences. Social listening tools enable companies to monitor social media and online conversations, gaining real-time insights into consumer opinions and trends. Virtual reality (VR) and augmented reality (AR) technologies are being used to test consumer reactions to new products in simulated environments, providing valuable feedback before market launch. Behavioral economics principles, such as understanding cognitive biases and decision-making processes, are also being applied to better predict consumer responses to new products. These innovations in consumer behavior research provide businesses with more accurate and comprehensive data, enabling them to develop products that are closely aligned with consumer needs and preferences, increasing the likelihood of market success.

Social Media and Consumer Behavior

Influence of social media on consumer behavior.

Social media has a profound influence on consumer behavior, shaping how consumers discover, research, and share information about products and services. Platforms like Facebook, Instagram, and Twitter serve as important channels for brand communication and engagement. Consumers use social media to seek recommendations, read reviews, and gather opinions from their networks, which significantly influences their purchasing decisions. Brands leverage social media for targeted advertising, influencer partnerships, and content marketing, creating opportunities for direct interaction and engagement with consumers. Social media also facilitates the spread of trends and viral content, quickly influencing consumer preferences and behaviors. The interactive and dynamic nature of social media means that consumer opinions and trends can rapidly change, requiring businesses to be agile and responsive in their social media strategies. Understanding the influence of social media on consumer behavior is essential for businesses to effectively engage with their audience and influence purchasing decisions.

Role of Social Media in Shaping Consumer Perceptions and Purchase Decisions

Recap of the importance of consumer behavior research.

Consumer behavior research is essential for businesses seeking to understand and effectively respond to the evolving needs and preferences of their target audience. It provides valuable insights into why consumers make certain choices, what influences their purchasing decisions, and how they interact with brands. This research is crucial for developing effective marketing strategies, creating products that meet consumer needs, and enhancing the overall customer experience. By staying informed about consumer behavior trends and applying these insights, businesses can improve customer engagement, increase brand loyalty, and drive growth. In today’s competitive marketplace, a deep understanding of consumer behavior is a key differentiator, enabling businesses to create more personalized, relevant, and impactful marketing initiatives.

Future Directions and Emerging Trends in Consumer Behavior Research

The future of consumer behavior research is marked by rapid advancements in technology and data analytics, leading to more sophisticated and nuanced understanding of consumer preferences and behaviors. Emerging trends include the use of artificial intelligence (AI) and machine learning to analyze consumer data, providing deeper and more predictive insights. The integration of biometric data, such as eye tracking and facial recognition, offers new ways to understand consumer responses to marketing stimuli. The growing importance of sustainability and ethical considerations is also influencing consumer behavior, leading to increased demand for eco-friendly and socially responsible products. Additionally, the rise of the experience economy is shifting focus from product features to customer experiences, requiring businesses to create more immersive and engaging customer interactions. Staying abreast of these trends and continuously innovating in consumer behavior research will be crucial for businesses to remain relevant and competitive in the changing market landscape.

How NIQ and GfK Can Help

In the complex world of consumer behavior, NIQ and GfK offer the expertise and tools necessary to navigate this landscape effectively. With comprehensive solutions like:

  • NielsenIQ’s Homescan : Track, diagnose, and analyze consumer behavior from more than 250,000 households across 25 countries.
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  • Consumption moments : Reveal the true motivations behind customer consumption behavior and usage to guide product innovation and marketing strategy.
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  • gfknewron predict : Plan your future using the world’s most comprehensive sales tracking data for Tech & Durables.
  • gfknewron Consumer : Understand your consumers’ behavior to redefine your success

By leveraging these tools, businesses can gain a competitive edge, adapting to market changes and consumer trends with agility and precision.

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Understanding the ever-evolving, always-surprising consumer

For many consumers around the world, a return to normalcy feels so close, yet so far away, in light of the alarming spread of COVID-19 variants. Although it’s unclear what the next 12 to 24 months will bring, what’s almost certain is that consumers won’t simply revert to doing exactly what they did in 2019. In this episode of the McKinsey on Consumer and Retail podcast, three consumer-behavior experts share their insights into how consumers’ spending patterns and purchasing behaviors are changing, and what companies should do given those changes. An edited transcript of the conversation with executive editor Monica Toriello follows. Subscribe to the podcast .

Monica Toriello: Over the past several weeks, people in some parts of the world have resumed their prepandemic habits. Maybe you’ve recently seen a movie at a theater, or flown on an airplane, or even just stopped for a cup of coffee on your way to the office for the first time in over a year. But a return to “normal” won’t look the same for everyone. Today, we’ll hear from three people who intensively study consumer behavior. They’ll share fascinating insights into how consumers are changing and what companies should do about it.

Kari Alldredge is a McKinsey partner based in Minneapolis. Kari has been advising consumer-goods companies for more than 20 years on a variety of topics, and she leads McKinsey’s work in consumer-goods growth transformation. She is an author of several articles, including a recent one on COVID-19’s impact on demand and costs in the consumer-packaged-goods [CPG] industry .

Anne Grimmelt is a senior knowledge expert in McKinsey’s Consumer Packaged Goods Practice. She is based in Stamford, Connecticut. Anne has been one of the driving forces behind McKinsey’s consumer-sentiment survey , which was launched in 2008 and during the pandemic has expanded to 45 countries. It provides a rich fact base for how consumers are feeling about their finances and how their buying behavior is changing.

And our third guest is Anjali Lai, a senior analyst at Forrester. Anjali, who is based in New York, helps chief marketing officers [CMOs] and other business leaders to understand the shifts in consumer behavior and consumer decision making and then to figure out what these changes mean for the future of brands and industries.

[To comply with Forrester’s Citation Policy, this transcript excludes Anjali Lai’s comments. Listen to the full episode on McKinsey.com or on Apple, Google, and other podcast platforms.]

A ‘reversal of fortune’ for big brands

Monica Toriello: Kari, Anne, Anjali, it’s great to have you here today. All three of you have been keeping your fingers on the pulse of consumers, both before and throughout the pandemic. Have there been any surprises? Are consumers doing things that you didn’t expect? Or is there anything that seemed to be going one way in, say, March or April 2020 but is going in a different direction today?

Kari Alldredge: In 2019 or early 2020, the topic on the minds of large branded consumer-packaged-goods manufacturers was portfolio shaping: how to reimagine their portfolios, how to move away from center-of-store food products and big brands and instead engage with consumers in very different, more targeted, niche-oriented ways. The degree to which the pandemic pushed people back toward big brands in the center of the store, and toward cooking at home, has been a complete turnaround, a reversal of fortune, for large CPG companies.

Some of those changes could have been anticipated, but others are quite shocking: the notion that bread baking would become a phenomenon among millennials, or that pet ownership would skyrocket to the extent that it has, and that those same millennials would be willing to spend more than they spend on their daily Starbucks to feed their new pets.

So, many of those companies that were desperately searching for growth 18 months ago now have the opposite problem: their supply chains can’t keep up . The big question for all of them is which of those consumer behaviors are truly going to persist  and be “sticky” coming out of this pandemic? Certainly, the dog that you adopted is likely to stay at your home. But when you go back to ordering your daily Starbucks and spending $7 a day on a coffee, are you going to spend the same amount to feed your pet? Those are the questions that are on many company leaders’ minds.

Anne Grimmelt: As Kari said, we saw a complete shift. Prepandemic, the growth was in smaller, niche brands, but early in the pandemic, it was large CPG players that really gained scale because their products were available on the shelf. They were also brands that were trusted by consumers, so consumers felt good buying them. If you look at point-of-sale data from IRI or Nielsen, you see that large companies—those with more than $2.5 billion in retail sales in the US market—picked up most of the share growth early in the pandemic, whereas smaller and midsize companies, as well as private label, were really not picking up growth.

In the second half of 2020 and in early 2021, small and midsize companies are regaining their sales growth. And we expect that private label is going to be powerful again , because if you dive into the why—why did consumers pick a new brand, and why did they pick the brands they chose?—it was about availability, it was about purpose, but it was also about value . It was about price points. Going forward, value is going to be even more important, and private label will gain strength in the future.

Trust as a strategic imperative

Monica Toriello: All three of you to some extent have written about customer loyalty: how to win it and how to retain it, particularly in an environment where people are willing to try new brands. Anne and Kari, you found that 39 percent of consumers tried new brands during the pandemic. And Anjali, in your research, you found that small brands are particularly good at earning consumers’ trust and consequently their loyalty. In a recent blog post, you wrote, “Now is the time for companies to embrace trust as a strategic imperative.” What does that mean? How should companies do that?

Even relatively mundane CPG companies are thinking about the end-to-end consumer journey, including consumer experience pre- and postpurchase. Kari Alldredge

Kari Alldredge: I’m seeing two interesting things in response to the trends you just talked about, Anjali. One is the degree to which even relatively mundane CPG companies are thinking about the end-to-end consumer journey, including consumer experience pre- and postpurchase, as they try to understand how to serve their existing consumers but also look for new ways to better meet consumer needs. The notion that there is a pre- and postpurchase experience related to a can of soda or a can of soup is a relatively novel idea, right? But, increasingly, the most forward-thinking companies are doing research across that entire journey to be able to understand the needs of consumers as they’re considering the range of options that are available to them—all the way through to satisfaction with usage and even disposal of the packaging of products.

Another interesting thing I’m seeing is a recognition that marketing is a dialogue, and a recognition of the degree to which consumers now “own” or shape the narratives of many brands. This, too, was happening before the pandemic but was vastly accelerated during the pandemic. The notion that a marketer positions the brand and delivers a message and a promise to consumers is really becoming quite an antiquated one, I think, as consumers themselves—through reviews, ratings , blogs, videos, and social-media posts—shape the identity of many of these brands. Recommendations from friends and family become part of the brand’s identity and are critical to shaping both loyalty and consumer trust.

We found in our research that about 33 percent of millennial and Gen Z consumers say they choose to buy a brand from a company that has their values, versus about 12 percent of baby boomers. Anne Grimmelt

Anne Grimmelt: Our research corroborates that. We found in our research that about 33 percent of millennial and Gen Z consumers  say they choose to buy a brand from a company that has their values, versus about 12 percent of baby boomers. But every demographic group is leaning toward that.

Another finding from our research is the reasons why consumers change to a new brand. It is definitely the younger generation that more often indicates that it’s because of purpose. It’s because of what the company stands for, how it treats its employees, et cetera.

Purpose: More than just a buzzword

Monica Toriello: We’ve been hearing a lot about purpose and values, but I also hear some skepticism in certain pockets of the corporate world as to whether an emphasis on corporate purpose  actually pays off. Because there is an attitude–behavior gap, right? What’s your response to a CEO who says, “Consumers like to say they care about purpose and values, but when they’re at the point of deciding to buy something, they truly only care about convenience or price or quality. Purpose is just a buzzword.”

Kari Alldredge: It’s necessary but not sufficient. I think there’s an increasing recognition that alignment with a consumer’s values may put you in the consideration set but won’t drive you over the line to purchase. You still have to have product superiority, whether that’s taste superiority, functional superiority, or a price-to-value equation that works for that particular consumer.

We talk a lot about the pandemic, which definitely shone a light on health in general, but there are other crises—like social justice  and climate change —that have come to light over the past year and a half and that have really shaken the corporate community. These crises have helped companies understand that some of these factors are fundamental in how consumers perceive themselves and the world around them, to the point where we now actually see some change happening.

One of the things that I was struck by was the speed and seriousness with which many of the household-cleaning companies responded to the pandemic and the heroic efforts to convert production capacity to manufacture things like wipes and sanitizer. Yes, some of that was for financial gain, but I think there really was an almost wartime mentality that I saw companies get new energy from.

I think about center-of-store food manufacturers who, prepandemic, maybe viewed themselves as being a bit sleepy and not exciting in terms of attracting the best talent. Now when you hear them talk about what they do, there’s real pride in the fact that they fed America, or they kept America safe. It really changed the way they think about the importance of what they do.

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Sources of insight.

Monica Toriello: All three of you are experts in consumer behavior. But consumers are changing fast and they’re changing constantly. Anjali, in another recent blog post, you wrote, “Rather than expect consumers to settle into a defined postpandemic normal, CMOs should prepare for a constant evolution of consumer needs and expectations over the next 12 to 24 months.” So beyond reading the latest consumer research and analysis, what are the best ways for CMOs and CEOs to understand where consumers are and where they’re headed?

Kari Alldredge: One of the best sources of insights is their online channel partners and their own D2C [direct to consumer] sites . Companies should mine online data to get a quick pulse on the way consumers are thinking or feeling. They should look at ratings and reviews using advanced analytics to understand and see trends and what’s selling on sites like Kroger.com, Walmart.com, or Amazon.com. They could even develop products that they can quickly test in an online environment and then change and adjust, as opposed to thinking about mass development of a product that gets pushed out to thousands and thousands of brick-and-mortar retail stores.

Consumers don’t always know what they want, and they can’t predict how their behavior will change. So traditional consumer research—which asks consumers how likely they are to purchase something—is becoming less relevant or reliable than actual data in market. That’s why data from e-commerce sites can be so valuable.

Anne Grimmelt: Another very powerful way to understand consumers  is by looking at what your peer companies do. You can go to industry conferences like the CAGNY [Consumer Analyst Group of New York] conference and hear a company like L’Oréal talk about how they use their D2C and their online-sales platform to see what type of color lipstick people try—not buy , but try —on their online platform. That information is critical for them to know where to innovate. What are the colors that people want and what are the products that people like to try out on the digital platform?

Similarly, I think it’s very important to keep an open mind beyond your own borders, to realize what’s happening elsewhere in the world. Going back to the topic of purpose, for instance, it is very much alive in the US but it’s also very much alive in Europe. Learning about the power of what consumers demand and how purpose is driving consumer decisions about CPG companies—and what companies in Europe are doing to meet consumer demand—can be valuable, wherever you are in the world.

Kari Alldredge: I think we also shouldn’t underestimate the resilience of consumers and the gravitational pull of life as we knew it before the pandemic. One thing that surprised me even in the past several weeks is the degree to which behaviors have bounced back. If there’s anything I’ve learned over the past 18 months it’s that I don’t have a crystal ball, or if I did, it is certainly broken—because there is no part of this last 18 months that I ever could have in a million years predicted.

At the beginning of the pandemic, one company I work with asked every board member, “When you look back, what’s the one thing that will be blazingly obvious that we either should always have done or never have been doing?” And one of the things that came up was shaking hands: “We’re never going to shake hands again.” But I attended a graduation ceremony in the beginning of June—so, early into the recovery—and what was striking to me is that the dean of that school shook the hand of, and physically embraced, every single one of the thousand students who crossed that stage. And this was at an institution that had been, like most educational institutions, incredibly thoughtful and conservative about their public-health response. Literally days after restrictions were lifted, the urge to connect was so strong that it looked as if the pandemic had never happened.

People are resilient. Hundreds of years of behavior certainly have been meaningfully changed by the past 18 months, but I think a lot of the old behaviors will bounce back pretty quickly.

Monica Toriello: So if you could gather all the CEOs and CMOs of consumer companies in one room and leave them with one message, what would it be? What is the one thing they need to do to position themselves for success in 2021 and 2022?

Anne Grimmelt: My one-liner would be, “Be open to change and be agile .”

Kari Alldredge: I would say, “Listen; don’t tell.”

Kari Alldredge is a partner in McKinsey’s Minneapolis office, and  Anne Grimmelt is a senior knowledge expert in the Stamford office.  Monica Toriello is an executive editor in the New York office.

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The past, present, and future of consumer research

Maayan s. malter.

1 Columbia Business School, Columbia University, New York, NY USA

Morris B. Holbrook

Barbara e. kahn.

2 The Wharton School, University of Pennsylvania, Philadelphia, PA USA

Jeffrey R. Parker

3 Department of Marketing, University of Illinois at Chicago, Chicago, IL USA

Donald R. Lehmann

In this article, we document the evolution of research trends (concepts, methods, and aims) within the field of consumer behavior, from the time of its early development to the present day, as a multidisciplinary area of research within marketing. We describe current changes in retailing and real-world consumption and offer suggestions on how to use observations of consumption phenomena to generate new and interesting consumer behavior research questions. Consumption continues to change with technological advancements and shifts in consumers’ values and goals. We cannot know the exact shape of things to come, but we polled a sample of leading scholars and summarize their predictions on where the field may be headed in the next twenty years.


Beginning in the late 1950s, business schools shifted from descriptive and practitioner-focused studies to more theoretically driven and academically rigorous research (Dahl et al. 1959 ). As the field expanded from an applied form of economics to embrace theories and methodologies from psychology, sociology, anthropology, and statistics, there was an increased emphasis on understanding the thoughts, desires, and experiences of individual consumers. For academic marketing, this meant that research not only focused on the decisions and strategies of marketing managers but also on the decisions and thought processes on the other side of the market—customers.

Since then, the academic study of consumer behavior has evolved and incorporated concepts and methods, not only from marketing at large but also from related social science disciplines, and from the ever-changing landscape of real-world consumption behavior. Its position as an area of study within a larger discipline that comprises researchers from diverse theoretical backgrounds and methodological training has stirred debates over its identity. One article describes consumer behavior as a multidisciplinary subdiscipline of marketing “characterized by the study of people operating in a consumer role involving acquisition, consumption, and disposition of marketplace products, services, and experiences” (MacInnis and Folkes 2009 , p. 900).

This article reviews the evolution of the field of consumer behavior over the past half century, describes its current status, and predicts how it may evolve over the next twenty years. Our review is by no means a comprehensive history of the field (see Schumann et al. 2008 ; Rapp and Hill 2015 ; Wang et al. 2015 ; Wilkie and Moore 2003 , to name a few) but rather focuses on a few key thematic developments. Though we observe many major shifts during this period, certain questions and debates have persisted: Does consumer behavior research need to be relevant to marketing managers or is there intrinsic value from studying the consumer as a project pursued for its own sake? What counts as consumption: only consumption from traditional marketplace transactions or also consumption in a broader sense of non-marketplace interactions? Which are the most appropriate theoretical traditions and methodological tools for addressing questions in consumer behavior research?

A brief history of consumer research over the past sixty years—1960 to 2020

In 1969, the Association for Consumer Research was founded and a yearly conference to share marketing research specifically from the consumer’s perspective was instituted. This event marked the culmination of the growing interest in the topic by formalizing it as an area of research within marketing (consumer psychology had become a formalized branch of psychology within the APA in 1960). So, what was consumer behavior before 1969? Scanning current consumer-behavior doctoral seminar syllabi reveals few works predating 1969, with most of those coming from psychology and economics, namely Herbert Simon’s A Behavioral Model of Rational Choice (1955), Abraham Maslow’s A Theory of Human Motivation (1943), and Ernest Dichter’s Handbook of Consumer Motivations (1964). In short, research that illuminated and informed our understanding of consumer behavior prior to 1969 rarely focused on marketing-specific topics, much less consumers or consumption (Dichter’s handbook being a notable exception). Yet, these works were crucial to the rise of consumer behavior research because, in the decades after 1969, there was a shift within academic marketing to thinking about research from a behavioral or decision science perspective (Wilkie and Moore 2003 ). The following section details some ways in which this shift occurred. We draw on a framework proposed by the philosopher Larry Laudan ( 1986 ), who distinguished among three inter-related aspects of scientific inquiry—namely, concepts (the relevant ideas, theories, hypotheses, and constructs); methods (the techniques employed to test and validate these concepts); and aims (the purposes or goals that motivate the investigation).

Key concepts in the late - 1960s

During the late-1960s, we tended to view the buyer as a computer-like machine for processing information according to various formal rules that embody economic rationality to form a preference for one or another option in order to arrive at a purchase decision. This view tended to manifest itself in a couple of conspicuous ways. The first was a model of buyer behavior introduced by John Howard in 1963 in the second edition of his marketing textbook and quickly adopted by virtually every theorist working in our field—including, Howard and Sheth (of course), Engel-Kollat-&-Blackwell, Franco Nicosia, Alan Andreasen, Jim Bettman, and Joel Cohen. Howard’s great innovation—which he based on a scheme that he had found in the work of Plato (namely, the linkages among Cognition, Affect, and Conation)—took the form of a boxes-and-arrows formulation heavily influenced by the approach to organizational behavior theory that Howard (University of Pittsburgh) had picked up from Herbert Simon (Carnegie Melon University). The model represented a chain of events

where I = inputs of information (from advertising, word-of-mouth, brand features, etc.); C = cognitions (beliefs or perceptions about a brand); A = Affect (liking or preference for the brand); B = behavior (purchase of the brand); and S = satisfaction (post-purchase evaluation of the brand that feeds back onto earlier stages of the sequence, according to a learning model in which reinforced behavior tends to be repeated). This formulation lay at the heart of Howard’s work, which he updated, elaborated on, and streamlined over the remainder of his career. Importantly, it informed virtually every buyer-behavior model that blossomed forth during the last half of the twentieth century.

To represent the link between cognitions and affect, buyer-behavior researchers used various forms of the multi-attribute attitude model (MAAM), originally proposed by psychologists such as Fishbein and Rosenberg as part of what Fishbein and Ajzen ( 1975 ) called the theory of reasoned action. Under MAAM, cognitions (beliefs about brand attributes) are weighted by their importance and summed to create an explanation or prediction of affect (liking for a brand or preference for one brand versus another), which in turn determines behavior (choice of a brand or intention to purchase a brand). This took the work of economist Kelvin Lancaster (with whom Howard interacted), which assumed attitude was based on objective attributes, and extended it to include subjective ones (Lancaster 1966 ; Ratchford 1975 ). Overall, the set of concepts that prevailed in the late-1960s assumed the buyer exhibited economic rationality and acted as a computer-like information-processing machine when making purchase decisions.

Favored methods in the late-1960s

The methods favored during the late-1960s tended to be almost exclusively neo-positivistic in nature. That is, buyer-behavior research adopted the kinds of methodological rigor that we associate with the physical sciences and the hypothetico-deductive approaches advocated by the neo-positivistic philosophers of science.

Thus, the accepted approaches tended to be either experimental or survey based. For example, numerous laboratory studies tested variations of the MAAM and focused on questions about how to measure beliefs, how to weight the beliefs, how to combine the weighted beliefs, and so forth (e.g., Beckwith and Lehmann 1973 ). Here again, these assumed a rational economic decision-maker who processed information something like a computer.

Seeking rigor, buyer-behavior studies tended to be quantitative in their analyses, employing multivariate statistics, structural equation models, multidimensional scaling, conjoint analysis, and other mathematically sophisticated techniques. For example, various attempts to test the ICABS formulation developed simultaneous (now called structural) equation models such as those deployed by Farley and Ring ( 1970 , 1974 ) to test the Howard and Sheth ( 1969 ) model and by Beckwith and Lehmann ( 1973 ) to measure halo effects.

Aims in the late-1960s

During this time period, buyer-behavior research was still considered a subdivision of marketing research, the purpose of which was to provide insights useful to marketing managers in making strategic decisions. Essentially, every paper concluded with a section on “Implications for Marketing Managers.” Authors who failed to conform to this expectation could generally count on having their work rejected by leading journals such as the Journal of Marketing Research ( JMR ) and the Journal of Marketing ( JM ).

Summary—the three R’s in the late-1960s

Starting in the late-1960s to the early-1980s, virtually every buyer-behavior researcher followed the traditional approach to concepts, methods, and aims, now encapsulated under what we might call the three R’s —namely, rationality , rigor , and relevance . However, as we transitioned into the 1980s and beyond, that changed as some (though by no means all) consumer researchers began to expand their approaches and to evolve different perspectives.

Concepts after 1980

In some circles, the traditional emphasis on the buyer’s rationality—that is, a view of the buyer as a rational-economic, decision-oriented, information-processing, computer-like machine for making choices—began to evolve in at least two primary ways.

First, behavioral economics (originally studied in marketing under the label Behavioral Decision Theory)—developed in psychology by Kahneman and Tversky, in economics by Thaler, and applied in marketing by a number of forward-thinking theorists (e.g., Eric Johnson, Jim Bettman, John Payne, Itamar Simonson, Jay Russo, Joel Huber, and more recently, Dan Ariely)—challenged the rationality of consumers as decision-makers. It was shown that numerous commonly used decision heuristics depart from rational choice and are exceptions to the traditional assumptions of economic rationality. This trend shed light on understanding consumer financial decision-making (Prelec and Loewenstein 1998 ; Gourville 1998 ; Lynch Jr 2011 ) and how to develop “nudges” to help consumers make better decisions for their personal finances (summarized in Johnson et al. 2012 ).

Second, the emerging experiential view (anticipated by Alderson, Levy, and others; developed by Holbrook and Hirschman, and embellished by Schmitt, Pine, and Gilmore, and countless followers) regarded consumers as flesh-and-blood human beings (rather than as information-processing computer-like machines), focused on hedonic aspects of consumption, and expanded the concepts embodied by ICABS (Table ​ (Table1 1 ).

Extended ICABS Framework after 1980

Methods after 1980

The two burgeoning areas of research—behavioral economics and experiential theories—differed in their methodological approaches. The former relied on controlled randomized experiments with a focus on decision strategies and behavioral outcomes. For example, experiments tested the process by which consumers evaluate options using information display boards and “Mouselab” matrices of aspects and attributes (Payne et al. 1988 ). This school of thought also focused on behavioral dependent measures, such as choice (Huber et al. 1982 ; Simonson 1989 ; Iyengar and Lepper 2000 ).

The latter was influenced by post-positivistic philosophers of science—such as Thomas Kuhn, Paul Feyerabend, and Richard Rorty—and approaches expanded to include various qualitative techniques (interpretive, ethnographic, humanistic, and even introspective methods) not previously prominent in the field of consumer research. These included:

  • Interpretive approaches —such as those drawing on semiotics and hermeneutics—in an effort to gain a richer understanding of the symbolic meanings involved in consumption experiences;
  • Ethnographic approaches — borrowed from cultural anthropology—such as those illustrated by the influential Consumer Behavior Odyssey (Belk et al. 1989 ) and its discoveries about phenomena related to sacred aspects of consumption or the deep meanings of collections and other possessions;
  • Humanistic approaches —such as those borrowed from cultural studies or from literary criticism and more recently gathered together under the general heading of consumer culture theory ( CCT );
  • Introspective or autoethnographic approaches —such as those associated with a method called subjective personal introspection ( SPI ) that various consumer researchers like Sidney Levy and Steve Gould have pursued to gain insights based on their own private lives.

These qualitative approaches tended not to appear in the more traditional journals such as the Journal of Marketing , Journal of Marketing Research , or Marketing Science . However, newer journals such as Consumption, Markets, & Culture and Marketing Theory began to publish papers that drew on the various interpretive, ethnographic, humanistic, or introspective methods.

Aims after 1980

In 1974, consumer research finally got its own journal with the launch of the Journal of Consumer Research ( JCR ). The early editors of JCR —especially Bob Ferber, Hal Kassarjian, and Jim Bettman—held a rather divergent attitude about the importance or even the desirability of managerial relevance as a key goal of consumer studies. Under their influence, some researchers began to believe that consumer behavior is a phenomenon worthy of study in its own right—purely for the purpose of understanding it better. The journal incorporated articles from an array of methodologies: quantitative (both secondary data analysis and experimental techniques) and qualitative. The “right” balance between theoretical insight and substantive relevance—which are not in inherent conflict—is a matter of debate to this day and will likely continue to be debated well into the future.

Summary—the three I’s after 1980

In sum, beginning in the early-1980s, consumer research branched out. Much of the work in consumer studies remained within the earlier tradition of the three R’s—that is, rationality (an information-processing decision-oriented buyer), rigor (neo-positivistic experimental designs and quantitative techniques), and relevance (usefulness to marketing managers). Nonetheless, many studies embraced enlarged views of the three major aspects that might be called the three I’s —that is, irrationality (broadened perspectives that incorporate illogical, heuristic, experiential, or hedonic aspects of consumption), interpretation (various qualitative or “postmodern” approaches), and intrinsic motivation (the joy of pursuing a managerially irrelevant consumer study purely for the sake of satisfying one’s own curiosity, without concern for whether it does or does not help a marketing practitioner make a bigger profit).

The present—the consumer behavior field today

Present concepts.

In recent years, technological changes have significantly influenced the nature of consumption as the customer journey has transitioned to include more interaction on digital platforms that complements interaction in physical stores. This shift poses a major conceptual challenge in understanding if and how these technological changes affect consumption. Does the medium through which consumption occurs fundamentally alter the psychological and social processes identified in earlier research? In addition, this shift allows us to collect more data at different stages of the customer journey, which further allows us to analyze behavior in ways that were not previously available.

Revisiting the ICABS framework, many of the previous concepts are still present, but we are now addressing them through a lens of technological change (Table ​ (Table2 2 ). In recent years, a number of concepts (e.g., identity, beliefs/lay theories, affect as information, self-control, time, psychological ownership, search for meaning and happiness, social belonging, creativity, and status) have emerged as integral factors that influence and are influenced by consumption. To better understand these concepts, a number of influential theories from social psychology have been adopted into consumer behavior research. Self-construal (Markus and Kitayama 1991 ), regulatory focus (Higgins 1998 ), construal level (Trope and Liberman 2010 ), and goal systems (Kruglanski et al. 2002 ) all provide social-cognition frameworks through which consumer behavior researchers study the psychological processes behind consumer behavior. This “adoption” of social psychological theories into consumer behavior is a symbiotic relationship that further enhances the theories. Tory Higgins happily stated that he learned more about his own theories from the work of marketing academics (he cited Angela Lee and Michel Pham) in further testing and extending them.

ICABS framework in the digital age

Present Methods

Not only have technological advancements changed the nature of consumption but they have also significantly influenced the methods used in consumer research by adding both new sources of data and improved analytical tools (Ding et al. 2020 ). Researchers continue to use traditional methods from psychology in empirical research (scale development, laboratory experiments, quantitative analyses, etc.) and interpretive approaches in qualitative research. Additionally, online experiments using participants from panels such as Amazon Mechanical Turk and Prolific have become commonplace in the last decade. While they raise concerns about the quality of the data and about the external validity of the results, these online experiments have greatly increased the speed and decreased the cost of collecting data, so researchers continue to use them, albeit with some caution. Reminiscent of the discussion in the 1970s and 1980s about the use of student subjects, the projectability of the online responses and of an increasingly conditioned “professional” group of online respondents (MTurkers) is a major concern.

Technology has also changed research methodology. Currently, there is a large increase in the use of secondary data thanks to the availability of Big Data about online and offline behavior. Methods in computer science have advanced our ability to analyze large corpuses of unstructured data (text, voice, visual images) in an efficient and rigorous way and, thus, to tap into a wealth of nuanced thoughts, feelings, and behaviors heretofore only accessible to qualitative researchers through laboriously conducted content analyses. There are also new neuro-marketing techniques like eye-tracking, fMRI’s, body arousal measures (e.g., heart rate, sweat), and emotion detectors that allow us to measure automatic responses. Lastly, there has been an increase in large-scale field experiments that can be run in online B2C marketplaces.

Present Aims

Along with a focus on real-world observations and data, there is a renewed emphasis on managerial relevance. Countless conference addresses and editorials in JCR , JCP , and other journals have emphasized the importance of making consumer research useful outside of academia—that is, to help companies, policy makers, and consumers. For instance, understanding how the “new” consumer interacts over time with other consumers and companies in the current marketplace is a key area for future research. As global and social concerns become more salient in all aspects of life, issues of long-term sustainability, social equality, and ethical business practices have also become more central research topics. Fortunately, despite this emphasis on relevance, theoretical contributions and novel ideas are still highly valued. An appropriate balance of theory and practice has become the holy grail of consumer research.

The effects of the current trends in real-world consumption will increase in magnitude with time as more consumers are digitally native. Therefore, a better understanding of current consumer behavior can give us insights and help predict how it will continue to evolve in the years to come.

The future—the consumer behavior field in 2040 1

Niels Bohr once said, “Prediction is very difficult, especially if it’s about the future.” Indeed, it would be a fool’s errand for a single person to hazard a guess about the state of the consumer behavior field twenty years from now. Therefore, predictions from 34 active consumer researchers were collected to address this task. Here, we briefly summarize those predictions.

Future Concepts

While few respondents proffered guesses regarding specific concepts that would be of interest twenty years from now, many suggested broad topics and trends they expected to see in the field. Expectations for topics could largely be grouped into three main areas. Many suspected that we will be examining essentially the same core topics, perhaps at a finer-grained level, from different perspectives or in ways that we currently cannot utilize due to methodological limitations (more on methods below). A second contingent predicted that much research would center on the impending crises the world faces today, most mentioning environmental and social issues (the COVID-19 pandemic had not yet begun when these predictions were collected and, unsurprisingly, was not anticipated by any of our respondents). The last group, citing the widely expected profound impact of AI on consumers’ lives, argued that AI and other technology-related topics will be dominant subjects in consumer research circa 2040.

While the topic of technology is likely to be focal in the field, our current expectations for the impact of technology on consumers’ lives are narrower than it should be. Rather than merely offering innumerable conveniences and experiences, it seems likely that technology will begin to be integrated into consumers’ thoughts, identities, and personal relationships—probably sooner than we collectively expect. The integration of machines into humans’ bodies and lives will present the field with an expanding list of research questions that do not exist today. For example, how will the concepts of the self, identity, privacy, and goal pursuit change when web-connected technology seamlessly integrates with human consciousness and cognition? Major questions will also need to be answered regarding philosophy of mind, ethics, and social inequality. We suspect that the impact of technology on consumers and consumer research will be far broader than most consumer-behavior researchers anticipate.

As for broader trends within consumer research, there were two camps: (1) those who expect (or hope) that dominant theories (both current and yet to be developed) will become more integrated and comprehensive and (2) those who expect theoretical contributions to become smaller and smaller, to the point of becoming trivial. Both groups felt that current researchers are filling smaller cracks than before, but disagreed on how this would ultimately be resolved.

Future Methods

As was the case with concepts, respondents’ expectations regarding consumer-research methodologies in 2030 can also be divided into three broad baskets. Unsurprisingly, many indicated that we would be using many technologies not currently available or in wide use. Perhaps more surprising was that most cited the use of technology such as AI, machine-learning algorithms, and robots in designing—as opposed to executing or analyzing—experiments. (Some did point to the use of technologies such as virtual reality in the actual execution of experiments.) The second camp indicated that a focus on reliable and replicable results (discussed further below) will encourage a greater tendency for pre-registering studies, more use of “Big Data,” and a demand for more studies per paper (versus more papers per topic, which some believe is a more fruitful direction). Finally, the third lot indicated that “real data” would be in high demand, thereby necessitating the use of incentive-compatible, consequential dependent variables and a greater prevalence of field studies in consumer research.

As a result, young scholars would benefit from developing a “toolkit” of methodologies for collecting and analyzing the abundant new data of interest to the field. This includes (but is not limited to) a deep understanding of designing and implementing field studies (Gerber and Green 2012 ), data analysis software (R, Python, etc.), text mining and analysis (Humphreys and Wang 2018 ), and analytical tools for other unstructured forms of data such as image and sound. The replication crisis in experimental research means that future scholars will also need to take a more critical approach to validity (internal, external, construct), statistical power, and significance in their work.

Future Aims

While there was an air of existential concern about the future of the field, most agreed that the trend will be toward increasing the relevance and reliability of consumer research. Specifically, echoing calls from journals and thought leaders, the respondents felt that papers will need to offer more actionable implications for consumers, managers, or policy makers. However, few thought that this increased focus would come at the expense of theoretical insights, suggesting a more demanding overall standard for consumer research in 2040. Likewise, most felt that methodological transparency, open access to data and materials, and study pre-registration will become the norm as the field seeks to allay concerns about the reliability and meaningfulness of its research findings.

Summary - Future research questions and directions

Despite some well-justified pessimism, the future of consumer research is as bright as ever. As we revised this paper amidst the COVID-19 pandemic, it was clear that many aspects of marketplace behavior, consumption, and life in general will change as a result of this unprecedented global crisis. Given this, and the radical technological, social, and environmental changes that loom on the horizon, consumer researchers will have a treasure trove of topics to tackle in the next ten years, many of which will carry profound substantive importance. While research approaches will evolve, the core goals will remain consistent—namely, to generate theoretically insightful, empirically supported, and substantively impactful research (Table ​ (Table3 3 ).

Future consumer behavior research questions

At any given moment in time, the focal concepts, methods, and aims of consumer-behavior scholarship reflect both the prior development of the field and trends in the larger scientific community. However, despite shifting trends, the core of the field has remained constant—namely, to understand the motivations, thought processes, and experiences of individuals as they consume goods, services, information, and other offerings, and to use these insights to develop interventions to improve both marketing strategy for firms and consumer welfare for individuals and groups. Amidst the excitement of new technologies, social trends, and consumption experiences, it is important to look back and remind ourselves of the insights the field has already generated. Effectively integrating these past findings with new observations and fresh research will help the field advance our understanding of consumer behavior.

1 The other papers use 2030 as a target year but we asked our survey respondents to make predictions for 2040 and thus we have a different future target year.

Publisher’s note

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

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Original research article, consumer behavior toward virtual wine experiences as a technology-based sustainable transformation.

consumer behavior research study

  • 1 Faculty of Economics and Management, Free University of Bozen-Bolzano, Universitaetsplatz, Bozen, Italy
  • 2 Department of Land, Environment, Agriculture and Forestry (TESAF), University of Padova, Viale dell’Università, Padova, Italy
  • 3 Interdepartmental Centre for Research in Viticulture and Enology (CIRVE), Conegliano, Italy
  • 4 Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università, Padova, Italy

Introduction: This study examines the behavior of wine consumers toward virtual wine experiences (VWEs), which are innovative and resilient solutions adopted by actors in the wine and wine tourism sectors during the recent pandemic, with an inherent potential for sustainability. While the phenomenon is still evolving due to the digitalization megatrend and the marketing potential of VWEs for wineries, the literature on this topic is still limited.

Methods: We apply an extended Theory of Planned Behavior (TPB), relying on a large and representative sample of Italian wine consumers to analyze the effect of personal wine involvement, risk attitude, and future wine tourism intention in addition to attitude, subjective norms, and perceived behavioral control.

Results: The results confirm that attitude, subjective norms, perceived behavioral control, wine involvement, and future wine tourism intention positively influence intentions, while risk aversion negatively affects behavior.

Discussion: This first application of the TPB to technology-based wine experiences. It provides key insights for researchers, practitioners (such as wineries and wine tourism stakeholders), and policymakers for the development of VWEs.

1 Introduction

Virtual wine experiences (VWE) can be a useful tool to the wine tourism industry, representing a technology-based sustainable strategy for the resilience of wineries in times of crisis and, potentially, beyond. This technological transformation opens the sector to new potential sustainable scenarios. For example, VWE can reduce people transfers for reaching a destination and the related carbon footprint ( Ozdemir et al., 2023 ).

This sustainability potential can be particularly relevant in the context of wine as it is among the most consumed beverages worldwide. According to recent estimates, in 2021 people consumed over 236 million hectoliters of wine and the trend has been rather stable over the last 10 years ( International Organization of Vine and Wine, 2021 ). Italy, the second largest EU wine market and the third globally, has an estimated consumption of 24.2 million hectoliters. In this country, wine consumption connected to tourism involves about 15 million tourists and generates a revenue of over 2.6 billion euros ( Statista, 2023 ). In 2020, the lockdown measures and the mobility limitations following the Covid-19 pandemic have disrupted many consumption occasions but, at the same time, have also stimulated the diffusion of new ways to drink and experience wine. Internet-based experiences are one of them, which we further define as virtual wine experiences (VWEs). The basic idea behind VWEs is to entertain consumers by offering the possibility to virtually interact with winemakers or wine experts while tasting wine from the comfort and safety of their homes and discovering new brands or wine regions, also delivering educational content. Hence, VWEs were initially developed as an innovative strategy to overcome the imposed limitations (i.e., mobility restrictions and social distancing) and many wineries implemented various forms of VWEs in the aftermath of the pandemic ( Garibaldi and Pozzi, 2020 ). To date, several wine actors like wineries and wine regions are still offering VWEs to interact with consumers all over the world and to attract potential visitors. Recent literature suggests that virtual reality can be used to stimulate onsite visits for wine tourism ( Alebaki et al., 2022 ; Monaco and Sacchi, 2023 ). Studies on virtual tourism also indicate that participation in such experiences can positively influence the intention to visit the virtually browsed destination on-site ( El-Said and Aziz, 2022 ; Lu et al., 2022 ). Therefore, virtual tours may have significant marketing potential. Moreover, VWEs provide several advantages for wine consumers as the possibility to receive wine from faraway wineries at home and taste it under the guidance of a knowledgeable person who provides them with comparable educational content to on-site visits ( Szolnoki et al., 2021 ). This allows the lowering of the costs of both retrieving the product and gathering knowledge about it ( Gastaldello et al., 2022 ). The virtual turn of wine consumption seems to be part of a longer-term strategy for wine operators, several of which are still offering these services. For instance, the governing body of the Conegliano Valdobbiadene Prosecco Superiore DOCG geographical indication is providing virtual tastings to introduce new producers from the region and highlight unique features of the local wines. Several wine producers are also offering pre-recorded or live-streamed guided tastings through different platforms like Wine.com or Divinea.it. Therefore, VWEs may represent a marketing tool for wine regions and their producers including smaller, unknown ones, which represent a conspicuous part of the winery population in Italy (i.e., 44%) ( Nomisma Wine Monitor, 2022 ). Yet, to the best of the authors’ knowledge, the scientific evidence around VWEs is still rather scarce, and little attention has been devoted to investigating the behavioral patterns of their main users, i.e., wine consumers.

To fill this gap, the present work builds on a sound methodology proposing an extended Theory of Planned Behavior (TPB) model to unravel the drivers of wine consumers’ intentions and behavior toward VWEs, intended as virtual wine tastings, virtual winery tours and wine events. Moreover, the study supports the results’ generalizability by making use of a large, nationally representative sample. Findings contribute to the theoretical development of TPB models and provide strategic information to understand consumers’ behavior toward VWEs, highlighting avenues for future research.

The paper is structured as follows. Section 2 reviews the literature on wine tourism digitalization and describes the theoretical framework used and the hypotheses tested, and data and methods are outlined in section 3. Finally, results and discussion are presented, followed by the conclusions.

2 Literature review

2.1 the digitalization of wine consumption.

During the recent pandemic, several wine actors worldwide have implemented VWEs to offer consumers a new way to interactively taste local wines. Recent statistics reveal that six in ten U.S. wineries conducted virtual tastings, and about three in ten Italian wineries declared performing them ( Statista, 2023 ). After the Covid-19 restrictions’ removal, some of them only kept providing VWEs as corporate or group activities upon request e.g., (see Amarendra and Das, 2022 ), while others have maintained them in their offer. Several examples can be found among Italian governing bodies of geographical indications known as Consortia (in Italy called Consorzi di Tutela ), wine organizations (e.g., the German Wine Institute), and single producers. Since the pandemic, the Italian Consorzio of Conegliano Valdobbiadene Prosecco is organizing paid virtual wine tastings during the low season. More precisely, consumers receive Prosecco wine bottles from different producers at home and attend a virtual guided tasting where wine experts of Consortium explain the wine style, terroir, and history behind it. The pandemic has contemporarily played a role in fostering the diffusion of similar tools among consumers, leading them to a behavioral rethinking while acquiring familiarity with streaming platforms ( Alaimo et al., 2020 ).

The phenomenon is gaining increasing attention among academics as well. Pre-Covid literature had already identified virtual reality (VR) as a strategic tool for developing multisensory wine tourism offers ( Martins et al., 2017 ). More recently, researchers explored consumers’ perception of virtual wine tastings via Zoom platform through the 4Es experience economy framework ( Paluch and Wittkop, 2021 ), the virtual embodiment effect occurring in virtual wine tastings and purchase decisions ( Wen and Leung, 2021 ), and the impact of context and tasting environment during in-presence and VR-simulated wine tastings ( Torrico et al., 2020 ). A study done by Amarendra and Das (2022) qualitatively compared virtual and cellar-door wine tourism experiences considering different virtual wine tasting experiences (happy hours, Livestream, and personalized tastings) and tours. The authors highlight the potential of Livestream tasting activities in creating brand loyalty and virtual tours as a long-term destination marketing strategy. Additionally, Szolnoki et al. (2021) conducted a supply analysis for virtual wine tastings involving over 1,000 wineries in 40 countries. The authors identified virtual wine tastings as a valuable and profitable activity to attract new customers and to keep existing ones loyal. Lastly, Gastaldello et al. (2022) explored the drivers of interest in virtual wine tourism experiences on a sample of Italian wine tourists. They found that personal involvement with wine plays a crucial role as a long-term stimulus, jointly with consumers’ willingness to support wineries and acquaintance with other wine digital tools. The authors argue that such experiences should not be seen as a substitute for regular wine tourism but as a separate product or marketing tool for wineries. Moreover, the authors found that the pandemic promoted interest in VWEs, particularly the resulting fear and anxiety, which might have pushed scared tourists to explore virtual options. Similarly, El-Said and Aziz (2022) found that hazard attributes, mostly related to the risk of Covid-19 infection, increased the intention to take virtual tours among individuals from Germany and the Sultanate of Oman.

2.2 The TPB model and the hypotheses development

Ajzen’s Theory of Planned Behavior Ajzen (1991) is one of the most widely applied and validated theory to predict consumer behavior. To date, a plethora of researchers in the field of economics and tourism used this framework or variations of it to explain, for example, consumers’ purchase intentions toward planning or replicating a wine holiday ( Sparks, 2007 ; Quintal et al., 2015 ). TPB postulates that the intention to behave in a particular manner results from the combined effect of subject’s attitude toward that behavior, subjective norms, and perceived behavioral control. Moreover, a subject’s behavior results from the intention and the perceived behavioral control.

Attitude (ATT) can be described as a positive (or negative) feeling toward a given action or, more generally, a behavior. For example, positive feelings toward VWEs can strengthen people’s intention to partake in one, as many tourism studies found that attitude positively predicts travel intentions ( Pratt and Sparks, 2014 ; Quintal et al., 2015 ; Han et al., 2016 ; Meng and Cui, 2020 ). Subjective norms (SN) embody the influence of significant others’ beliefs on one’s intentions to behave in a certain way: when SN is favorable, meaning that the subject’s reference group of people feels the target action is the right thing to do, its effect on the intention is positive. Although the direction of the relationship between SN and intention is supported by empirical evidence, the significance of this effect is controversial. For example, Sparks (2007) applied the TPB to a large sample of Australian wine tourists and found that the effect of SN on the intention to plan a wine holiday was positive but not significant. Diversely, Quintal et al. (2010) proved that SN in the form of social pressure to engage in the target behavior positively affects the intention. Nevertheless, the authors found that the size of this effect differ among the three countries analyzed (Japan, Korea, and China), suggesting that context and culture may play a role in moderating this relationship. Similarly, Sogari et al. (2023) use an extended TPB model on a large international sample to explore consumer’s attitude toward adopting a healthy diet. These authors found significantly heterogeneous positive effects of subjective norms on the intention. Looking at behavioral studies on Italian consumers, which is the context of this paper, the effect of SN tends to be positive and significant ( Vesci and Botti, 2019 ; Caliskan et al., 2021 ; Wolstenholme et al., 2021 ), leading us to expect the same outcome.

The third predictor in TPB is the perceived behavioral control (PBC), which reflects the subject’s belief of having the means to pursue a target behavior. Such means can be tangible, e.g., financial, or intangible, like time or season ( Lam and Hsu, 2006 ; Sparks, 2007 ).

Alike the previous predictors, empirical evidence from past studies proved that the effect of PBC on intention tends to be positive and often of substantial size ( Sparks, 2007 ; Giampietri et al., 2016 ; Tomić Maksan et al., 2019 ; Vesci and Botti, 2019 ; Meng and Cui, 2020 ). Nevertheless, the effect of potential behavioral barriers resulting in PBC, formally referred to as control beliefs ( Ajzen, 2015 ), can be negative whenever the perceived costs of pursuing a behavior are high (e.g., Sogari et al., 2023 ).

Sparks (2007) found PBC to have the greatest effect size among all the predictors (0.40) of future wine tourism intentions, and Giampietri et al. (2018) obtained the same outcome regarding the intention to purchase in short supply chains. Other studies on regular wine consumption ( Tomić Maksan et al., 2019 ), processed red meat consumption reduction ( Wolstenholme et al., 2021 ) or bicycle tourism ( Han et al., 2016 ), found that the path between PBC and intention was always positive and significant but smaller than the one generated by attitude and subjective norms. Hence, while the relative importance of PBC over other antecedents of the intention seems to vary across product categories, we expect PBC to positively predict the intention to partake in a virtual wine tasting experience.

The ultimate result of intention is the behavior, namely the observable response for a target action of interest. According to TPB theory, a subject behavior is the result of his/her intention to perform the behavior and his/her PBC. The relationship between intention and behavior (i.e., the so-called intention-behavior gap) has long been under debate ( Sultan et al., 2020 ). Nevertheless, tourism literature mostly focuses on behavioral and loyalty intentions neglecting behavior, so we rely on the entire TPB as done by the research tackling food and wine consumption. Recent findings confirm the presence of the intention-behavior gap as the variance in behavior explained by the intention tends to be small ( Sultan et al., 2020 ) or moderate ( Tomić Maksan et al., 2019 ). Meanwhile, they also support the existence of a positive relationship between intention and behavior ( Tomić Maksan et al., 2019 ; ElHaffar et al., 2020 ; Sultan et al., 2020 ). Moreover, there is evidence that attitude affects behavior through intention ( Sultan et al., 2020 ; Caliskan et al., 2021 ).

Instead, the effect of PBC on behavior tends to be positive ( Giampietri et al., 2018 ; Sultan et al., 2020 ). Given the increasing diffusion of VWEs prompted by the pandemic and the relatively low time and financial investment required to join one, especially if compared to in-presence alternatives (i.e., winery visits), we believe that PBC would positively predict individuals’ behavior in our research context as well.

Considering these arguments and the current literature on TPB, we postulate the following hypotheses regarding the base TPB model to explain VWEs-related intention (VWEINT) and behavior (VWEBEH):

H1 : Attitude toward virtual wine experiences (ATT) positively affects intention to partake in a virtual wine experience (VWEINT)
H2 : Subjective norm (SN) positively affects the intention to partake in a virtual wine experience (VWEINT)
H3 : Perceived behavioral control (PBC) positively affects the intention to partake in a virtual wine experience (VWEINT)
H4 : Perceived behavioral control (PBC) positively affects the behavior toward virtual wine experience (VWEBEH)
H5 : Intention to partake in a virtual wine experience (VWEINT) positively affects behavior toward virtual wine experiences (VWEBEH)
H6 : Intention (VWEINT) mediates the effect of attitude (ATT) on behavior (VWEBEH).

Nevertheless, past research pointed out that the original TPB cannot predict consumer intention and behavior as it is, and thus needs to be enriched by including other dimensions ( Lam and Hsu, 2004 ). This potentially explains why many studies apply TPB by including predictors to ATT, PBC and SN. Accordingly, we propose an extended version of the TPB model to test the effect of other potential determinants of VWE-related intention and behavior.

The literature shows the critical role of risk in assessing tourism consumer behavior ( Luo and Lam, 2020 ; Villacé-Molinero et al., 2021 ). Indeed, risk has to be accounted when referring to virtual wine tourism experiences as these represent a novel way of experiencing win, especially when customers have a little experience and knowledge of wine. Hence, since consumer decisions are taken in a context of uncertainty, we consider the role of risk attitude. According to Bauer (1960) , risk is connected to outcome unpredictability or undesirability when purchasing a product or a service. Whenever the perceived losses connected to a target action are high, subjects will adjust their risk-taking behavior ( Sarin and Weber, 1993 ). Such behavior is lastly affected by their willingness to take risks, i.e., their risk attitude ( Hillson and Murray-Webster, 2007 ), which is an inherent and stable trait of human beings. Thus, attitude toward risk can lead individuals to either be attracted by riskier options (i.e., risk lovers) or to avoid them (i.e., risk averse individuals) ( Weber et al., 2002 ; Wu and Chang, 2007 ).

At first glance, VWEs may be thought to benefit from a safer perception compared to cellar-door wine experiences. For instance, during the Covid-19 pandemic VWEs were associated with lower perceived losses (e.g., virtual experiences did not expose people to uncontrolled contact with potentially sick individuals). Coherently, recent tourism research has highlighted the negative impact of risk perception ( Villacé-Molinero et al., 2021 ) and risk aversion ( Luo and Lam, 2020 ) on travel intentions. Hence, VWEs may be seen as a safer way to pursue one’s interest in wine. Nevertheless, preliminary evidence suggests that this hypothesis may not be true as these two activities are not considered substitutes ( Gastaldello et al., 2022 ). Contrary, a source of perceived risk may be the novel and virtual nature of VWEs. When tourism experiences are purchased, all people have at hand the product description (e.g., duration, location, etc.), pictures, past experience (if any) and consumer reviews ( Weathers et al., 2007 ). Still, ultimately, they can fully evaluate the quality only after living the real experience. The same happens for VWEs, which are often sold through the same channels as other tourism products and services (e.g., virtual travel agencies). Accordingly, the literature stresses how innovation can bring as much economic rewards as risks when it comes to market acceptance ( Colombo et al., 2017 ), and how such risks can increase for new products due to a combination between limited knowledge and difficulties to evaluate their utility ( Colombo et al., 2017 ; Aboulnasr and Tran, 2020 ).

VWEs are considered new products as they have started to be systematically offered only after the Covid outbreak. Therefore, both own’s and others’ past experiences are likely to be scarce and the perceived risk of unpredictable and undesirable outcomes from the experience can increase dramatically. Since the underlying perceived risk of purchasing a new product as VWE is higher, we expect that risk-averse subjects are likely to show a lower intention toward VWEs as well as a lower likelihood to join one (i.e., the behavior). Based on the above, the following hypotheses are tested:

H7 : Risk attitude (RISKATT) negatively impacts the intention to partake in a virtual wine experience (VWEINT).
H8 : Risk attitude (RISKATT) negatively impacts the behavior toward virtual wine experiences (VWEBEH).

Another critical issue of VWEs is the subjects’ involvement with wine (WI). WI is a form of enduring or personal involvement and, as such, it is connected to the presence of a long-term personal relevance for a given product or service ( Lockshin and Spawton, 2001 ; Ogbeide and Bruwer, 2013 ). The consumption of hedonic products like wine and wine tourism experiences is connected to pleasure and enjoyment, and it is known to generate a greater involvement ( Lesschaeve and Bruwer, 2010 ) which can ultimately affect many aspects of wine consumers’ behavior (e.g., Sparks, 2007 ; Bruwer and Buller, 2013 ). Thus, it is not surprising to find WI as a common trait of wine consumers and visitors of wine regions (e.g., Brown et al., 2007 ). Researchers usually distinguish between low and high-involvement wine consumers. Low-involvement consumers drink wine occasionally and are less interested in the product itself while highly involved consumers are frequent drinkers and wine spenders ( Nella and Christou, 2014 ), and wine is in their lifestyle ( Lockshin and Spawton, 2001 ; Brown et al., 2007 ). Moreover, there is evidence that highly involved wine tourists exhibit stronger wine tourism intentions ( Brown et al., 2007 ; Sparks, 2007 ; Gastaldello et al., 2023 ) and revisit intentions ( Nella and Christou, 2014 ). Since VWEs fall between wine consumption and wine tourism, we suppose that people having a stronger wine involvement exhibit stronger intentions to join a wine-related virtual experience. Therefore, the following hypothesis is postulated:

H9 : Wine involvement (WI) is a positive antecedent of the intention to partake in a virtual wine experience (VWEINT).

Beyond attracting (new) wine consumers, VWEs are an interesting tool to promote wine tourism destinations. Since some traits of regular wine tourism (e.g., the atmospherics of the vineyards and the winery) are missing in VWEs ( Amarendra and Das, 2022 ), virtual and offline experiences (e.g., wine tastings) are not perfect substitutes ( Gastaldello et al., 2022 ). Thus, consumers may conceive the virtual option as a way to discover new wineries that may be visited in the future while lowering time and costs. If so, possessing a strong intention to go on a wine holiday in the next future (e.g., in the next year) should explain the intention toward VWEs, as follows:

H10 : Future wine tourism intentions (FUTWTINT) are a positive antecedent of the intention to partake in a virtual wine experience (VWEINT).

Figure 1 reports all hypothesized paths for the base TPB model (white ovals) and the extended TPB model, with new constructs represented as light-grey ovals.


Figure 1 . Hypothesized base and extended TPB paths. Note: Extended TPB constructs are represented as light-grey ovals; white ovals represent constructs from Ajzen’s original TPB theory. VWEBEH is depicted with a rectangle as it is an observed variable. H6 = mediation effect: RISKATT ➔ VWEINT ➔ VWEBEH.

3 Materials and methods

3.1 data collection.

The study was carried out in Italy in January 2022 through a virtual survey distributed among wine consumers, which constitute the target population. Specifically, respondents had to be wine consumers with past wine tourism experience. People drinking wine less than once a month or purchasing wine less than once per year, and those who had not experienced wine tourism in the last 5 years were screened out through some initial filtering questions. This choice was made to ensure the responses’ reliability as well as to involve consumers with a potentially longer-term interest in wine and wine experiences. Data collection was conducted by a professional online panel provider according to the quota sampling method to obtain a nationally representative sample in terms of age, gender, and geographic area of residence. All participant were Italian residents. A pilot study on a sample of 30 respondents was performed before the data collection to test the clarity and correctness of the questionnaire. The final sample includes 559 complete surveys. The study received ethical approval from the University of Padova in January 2022, and the research fully followed the principles stated by the Declaration of Helsinki.

3.2 Questionnaire description

The structured questionnaire consists of 4 separate sections. The first one includes the above-mentioned filter questions (i.e., past wine tourism experience, wine purchase and consumption frequency). Here, respondents were also provided with an example of a virtual wine experience, described as follows: “A virtual wine tasting involves the home delivery of a number of wine bottles and a tasting experience guided by wine professionals (producers, sommeliers, etc.), which allows you to learn about the wine, the winery, and the wine-growing region without the need to reach it physically.” Other VWE examples mentioned to respondents are virtual winery tours and food and wine events. The second section includes questions to measure the TPB variables measured through several 7-point agree/disagree Likert type scales, namely: intention (1 statement) to participate in a virtual wine experience in the next future (VWEINT), behavior (VWEBEH), attitude toward virtual wine tourism experiences (ATT – 6 items, Cronbach’s alpha = 0.92), subjective norms (SN – 3 items, Cronbach’s alpha = 0.93), and perceived behavioral control (PBC – 3 items, Cronbach’s alpha = 0.79). Scales for measuring ATT, SN and PBC are adapted from Lam and Hsu (2006) and Meng and Cui (2020) .

VWEINT was measured through the following 7-point agree-disagree single-item construct, adapted from Sparks (2007) : “I intend to participate in a virtual wine tourism experience in the next 12 months.” Also, VWEBEH was captured by the following statement (dummy variable): “Have you ever participated in a virtual wine tourism experience (e.g., virtual wine tastings)?.” In this section, we also measured variables to be included in the extended TPB model such as risk attitude (RISKATT), wine involvement (WI), and future wine tourism intention (WTINT). In line with Dohmen et al. (2011) , RISKATT was self-assessed through the following statement: “On a scale from 0 (not at all willing to take risks) to 10 (very willing to take risks), how would you assess your personal preference to take risks?.” For data analysis, this scale was reversed so that higher values indicate greater risk aversion. We opted for this simple measure of risk attitude, as extensively done in the literature ( Meraner and Finger, 2019 ; Höschle et al., 2023 ), to ensure proper survey length (due to the high number of questions in the survey), while producing results that can be compared to other elicitation methods (e.g., lotteries) ( Dohmen et al., 2011 ). As for WI, we opted for Hirche and Bruwer’s (2014) 10-items scale (Cronbach’s alpha = 0.94), ranging from 1 = totally disagree to 7 = totally agree, while WTINT was assessed through a 7-point agree-disagree single-item construct adapted from Sparks (2007) and formulated as follows: “I plan to visit a wine region in the next 12 months.” The single-item constructs were operationalized as scales, following Hair et al. (2019) and Petrescu (2013) . Specifically, factor loadings were set to the square root of the best-guess reliability (0.85), while the error variance term was set to one less than the best-guess reliability. The third section focuses on aspects related to wine consumption and wine tourism habits while the fourth section investigates the socio-demographic characteristics of the sample units.

3.3 Data analysis

For data analysis, the study applied structural equation modelling (SEM) using IBM SPSS AMOS 27 software. First, confirmatory factor analysis (CFA) assessed the validity of the measurement model including all the latent constructs (ATT, SN, PBC, WI, WTINT, RISKATT, VWEINT). Being BEH an observed variable, it was excluded from the CFA analysis. Afterwards, we run the structural model to test both the base version of Ajzen’s Theory of Planned Behavior and the extended TPB framework. Therefore, a Chi-square difference (Δχ 2 ) tested the two models: notably, when a significant difference is shown, the extended version is preferred to the original. The goodness of fit of the models is tested considering the following cut-off values: less than 5 for CMIN/DF, less than 0.9 or more for CFI and TLI, less than 0.07 for RMSEA, less than 0.08 for SRMR ( Hair et al., 2019 ).

4.1 Sample description

The socio-demographic characteristics of the sample are shown in Table 1 . Most respondents are between 35 and 64 years old (69.7%) and come from Northern Italy (47.1%). They are mostly employees (55.3%), with a high school qualification (51.5%), and with a medium economic class level (50.3%). The majority (65.7%) claim that the pandemic did not significantly impact their household income.


Table 1 . Socio-demographic information of the sample (N = 559).

Regarding wine consumption and wine tourism-related habits ( Table 2 ), 59.7% of the sample drinks wine at least 2–3 times a week (27% every day), and 39.9% purchase it at least once a week (8.6% more than once a week). The usual place for buying wine is the supermarket (44.7%) followed by specialized shops (27.4%), and about one-fifth of the respondents purchase wine directly from the producer (19.3%). The average price-per-bottle (0.75 L) paid ranges between 6 to 15 € for more than half of the sample (56%). Most respondents prefer to consume wine at home (69.2%), and about 56% of them normally store up to 5 bottles of wine at home. The 48% travel to a wine region 2–3 times a year, with visiting wineries and purchasing wine as the primary motivation. Finally, 26% of the sample has already taken part in a virtual wine tourism experience prior to the study.


Table 2 . Information on wine consumption and wine tourism habits of the sample ( N  = 559).

4.2 Empirical results

Correlations among variables are reported in Table 3 . Sample respondents show a high PBC (mean value = 5.46), a high positive attitude toward VWEs (5.27) and high subjective norms (4.28). Moreover, they are high involved in wine (4.52) and risk averse (4.64). They declare a great intention toward both future wine tourism (5.34) and virtual wine experiences (4.44).


Table 3 . Correlations and descriptive findings between variables.

The model performance is satisfactory as goodness of fit ( χ 2  = 925.44; DF = 255; p  < 0.001; CMIN/DF = 3.63; CFI = 0.94; TLI = 0.93; RMSEA = 0.069; SRMR = 0.062). For convergent validity, we evaluated the standardized factor loading and construct reliability ( Table 4 ). All standardized factor loadings are above the recommended threshold of 0.5, most of them having an optimal value above 0.7. Similarly, construct reliability for all constructs is above 0.7, and the average variance extracted (AVE) is always above the 0.5 threshold, in line with Hair et al. (2019) guidelines. We confirmed discriminant validity as the squared root of AVE is greater than the correlation between constructs.


Table 4 . Measurement model results from the confirmatory factor analysis.

The base and the extended TPB were estimated ( Table 5 ). The base TPB model shows a good fit: χ 2  = 332.603, df = 71, CMIN/DF = 4.685, CFI = 0.958, TLI = 0.946, SRMR = 0.060, RMSEA = 0.081. The results show that ATT (β = 0.316), SN ( β  = 0.440) and PBC ( β  = 0.125) have a significant and positive effect on the intention to partake in a virtual wine tourism experience. Moreover, intentions have a significant and positive effect on behavior (VWEINT ➔ VWEBEH β  = 0.411) as opposite to PBC, which negatively predicts it (β = −0.114). It follows that H5 is confirmed, while H4 is only partially supported as a significant effect is reported but in a opposite direction than the expected one. R 2 estimates of the two dependent variables suggest the model explains 60.2 and 13.2% of their variance, respectively (see Figure 2 ).


Table 5 . Results for the structural model: comparison between the base TPB model and the extended one.


Figure 2 . Results of the base and extended TPB model estimation. Note: extended TPB constructs are represented as light-grey ovals; white ovals represent constructs from Ajzen’s original TPB. VWEBEH is represented with a rectangle as it is an observed variable. Results of estimations of the base and extended TPB model for each hypothesis tested are reported in small rectangles as follows: βbase = standardized path coefficient from the base TPB model; βext = standardized path coefficient from the extended TPB model. *** p  < 0.001; ** p  < 0.05. Non-significant paths are represented as dotted lines. H6 = mediation effect: RISKATT ➔ VWEINT ➔ VWEBEH.

The extended TPB model shows better goodness of fit than the base model: χ 2  = 995.110; df = 277; CMIN/DF = 3.592; CFI = 0.937; TLI = 0.926; SRMR = 0.064; RMSEA = 0.068. The Chi-square difference between the two models is significant (Δ χ 2  = 662.51; df = 206; p  < 0.0001). Moreover, the Parsimony Normed Fit Index (PNFI) is greater for the extended model (0.780), indicating it performs better than the base TPB (0.739) ( Hair et al., 2019 ). Hence, we can conclude that the extended TPB model represents an improvement to the base TPB framework. Overall, the R 2 of both intention and behavior is greater than in base TPB ( Figure 2 ).

Looking at path estimates, results highlight that ATT (β = 0.304), SN (β = 0.308), and PBC (β = 0.064) significantly and positively affect intentions. Similarly, wine involvement (β = 0.150) and the future wine tourism intention (β = 0.143) are significant antecedents of the intention, as opposed to risk attitude. Furthermore, we find that the behavior is positively determined by the intention (β = 0.371) and negatively affected by risk attitude (β = −0.164) and PBC (β = −0.133). In this case, 63.6% of the variance of VWEINT and 15.6% of VWEBEH are explained. We can conclude that H9, H10, and H8 are supported, while H7 is not.

Finally, we tested whether attitude affects behavior indirectly through intention (H6). The specific indirect effect is positive and significant (β = 0.11; p  = 0.002) with a non-significant direct effect (β = − 0.11; p  = 0.107), showing that intention fully mediates the attitude-behavior relationship. Figure 2 reports the results of the base and extended TPB model for each tested hypothesis.

5 Discussion

5.1 results discussion.

This work implements the full TPB model to analyze virtual wine consumers’ behavior related to dedicated virtual experiences. The research aim is to unravel drivers of intention and behavior toward this novel consumption pattern. In doing this, the study tests 9 causal hypotheses and 1 mediation effect by applying covariance-based SEM.

Results validate the efficacy of the TPB framework to explain the decision-making regarding VWEs’ choice, as all TPB variables significantly predict the intention and behavior under investigation. Going into detail, evidence shows that people’s intention to partake in VWEs is positively driven by subjective norms and their positive evaluation of such experiences (i.e., ATT). This result supports the H1 and H2 hypotheses and in line with the existing literature ( Pratt and Sparks, 2014 ; Quintal et al., 2015 ; Han et al., 2016 ; Meng and Cui, 2020 ). Particularly, peer pressure (SN) emerges as the most powerful predictor of the intention in the base TPB model. We can reasonably explain this result as the novel feature of VWEs and, consequently, with the scarce personal experience of respondents on it. The literature explains this reasoning by stressing the primary role of others’ opinion, i.e., word-of-mouth, in shaping new product purchase decisions, especially when such products are experience goods ( Cui et al., 2012 ; Li et al., 2021 ). Hence, people may strongly rely on their peers’ opinion when building their behavioral decisions on VWEs. Even in the extended model, the effect size of subjective norms slightly decreases but remains comparable to that of attitude.

Contrary to what we expected, the perceived behavioral control exerts a negative impact on the behavior. This result is in contrasts with many past TPB studies on agri-food products’ consumption (see, for example, Sultan et al., 2020 ) and in line with some other ( D'Souza et al., 2022 ). Instead, the perceived easiness of joining an VWE positively influences the intention, although to a minor extent. The contrasting effect of PBC on behavior is not related to the conflicting relationship from the new variables included in the extended model as it is found to be negative already in base TPB estimations. Particularly, the behavior explained by the model reflects whether respondents are VWEs’ consumers. Instead, PBC deals with the respondent’s belief of being in the condition to act according to the intention ( Ajzen, 1991 ). Thus, the negative effect of PBC on behavior indicates that the more respondents feel in control of joining an VWE if they want to, the less likely they are to do it. When variables of the extended model are added to the base TPB (WI, WTINT, RISKATT), the PBC effect on VWEINT is almost halved, while its impact on VWEBEH slightly increases.

Nevertheless, the PBC-VWEBEH relationship does not necessarily hold for future behavior, leaving an open question for the next studies.

The effect of ATT on VWEINT remains consistent in sign as well with a small change in magnitude, and the same is observed for the relationship between intention and behavior. As hypothesized, the intention is a positive predictor of behavior ( ElHaffar et al., 2020 ; Sultan et al., 2020 ): its effect size is greater than that of PBC in the base TPB model, in line with previous findings on food (e.g., Dunn et al., 2011 ; Giampietri et al., 2018 ) and wine consumption behavior ( Tomić Maksan et al., 2019 ). This outcome suggests that the subject’s personal preference for VWEs overcomes the negative effects of tangible and intangible perceived barriers in pursuing the target behavior.

Focusing on the additional variables included in the extended model, both future wine tourism intentions and wine involvement positively affect the intention. This result partially aligns with the findings of the exploratory studies from Gastaldello et al. (2022) and Sparks (2007) , where WI is a positive predictor of interest in VWEs and future wine tourism intentions, respectively. Nevertheless, the former study found the relationship between wine tourism intentions and interest for VWEs to be not significant. This incongruency may be a consequence of different data collection timing or different nature of the outcome variable (i.e., interest instead of intention).

The fact that the effects of WI and WTINT are smaller compared to most TPB predictors except PBC, suggests they are less critical yet positive drivers of the intention to partake in an VWE.

Lastly, risk attitude does not seem to affect the intention, while it negatively impacts the behavior. This evidence highlights the existence of a perceived risk associated with VWEs, perhaps because of their intangible or less realistic nature compared to onsite visits. This effect reasonably stems from the still innovative nature of VWEs that would merit greater awareness among people through information campaigns. In this regard, Monaco and Sacchi (2023) see virtual tourism experiences based on the Metaverse as a strategy that, being more immersive, could reduce the associated perceived risk and prepare visitors for real visits before travelling. At present, risk attitude provides a direction for segmenting wine tourists potentially interested in VWEs, i.e., the less risk-averse individuals.

Alike in Sultan et al. (2020) , both the intention and PBC do explain a small share of the observed behavior analyzed (R 2  = 13%), suggesting that an intention-behavior gap is present and needs further investigations. By including risk attitude to explain behavior in the extended TPB model, the variance explained increases (R 2  = 16%). Still, the model’s explanatory power for VWEBEH is limited compared to VWEINT. It follows that additional potential mediators and moderators should be investigated to detect additional key factors transforming intention into behavior.

Lastly, the presence of full mediation from intention between attitude and behavior, which is in line with recent results obtained by Sultan et al. (2020) , confirms that the effect of attitude transmits to behavior through intentions ( Sultan et al., 2020 ; Caliskan et al., 2021 ). Nevertheless, the small scale of such an effect calls for further investigations into potential interfering factors.

The study is not free from limitations. One limitation of this study is that it only analyzes the effects of certain determinants on the intention and behavior toward VWEs. To gain a better understanding of the phenomenon, it would be beneficial to include additional antecedents from the literature. Furthermore, the study measures behavior using a dichotomous variable without examining the constraints or motivations that hindered participation in VWEs.

5.2 Concluding remarks and future research agenda

Virtual wine experiences (VWEs) represent a novel wine consumption occasion that, following the digitalization megatrend, has the potential to stay. The present study is the first, to the best of the authors’ knowledge, to shed light on the determinants of wine consumers’ intention and behavior toward VWEs and provide valuable insights to academics, sector stakeholders, and policymakers in this regard. Specifically, this research builds on the widely validated framework of the Theory of Planned Behavior while testing an extended model that accounts for relevant constructs related to wine and novel products’ consumption. Academically, the study provides an updated application of the TPB to emerging wine consumers’ behavior, contributing to the related body of literature while providing empirical evidence of the attitude-behavior relationship, as well as evidence supporting the intention-behavior gap. Since VWEs are offered through virtual platforms, the latter gap is reasonably linked to aspects such as subjects’ digitalization and attitude toward technology. Future research could test the mediating role of such constructs in the intention-behavior relationship.

Since personal wine involvement and intention to visit a wine region soon positively predict the VWE intention, virtual wine consumption is more likely to concern highly involved wine consumers (i.e., wine lovers and wine enthusiasts) as well as people having stronger wine tourism intentions. The latter are segments of interest to both rural destinations and single wineries, which might adopt VWEs as a long-term marketing strategy thus favoring the growth of virtual wine consumption.

Nevertheless, the results also indicate that having personal positive feelings about VWEs is even more important than being interested or passionate about wine per se . Behavioral research could further investigate attitude determinants, i.e., behavioral beliefs, while better profiling VWEs consumers from a socio-demographic and psychographic perspective.

Furthermore, subjective norms show an equivalent effect to the attitude in forming VWE-related intentions, suggesting that peer pressure (here, family, and close friends) plays a critical role in shaping them. In this respect, further research may investigate the role of wine experts, connoisseurs, and influencers’ opinions in impacting consumers’ behavior toward VWEs.

The negative effect risk attitude exerts on VWEs’ behavior is reasonably connected to the uncertainty underlying the decision to purchase an experience that has been newly introduced on the market and the subsequent lack of consumer knowledge and experience. If this is the case, increasing market knowledge about VWEs may reduce the potential perceived risks associated with their purchase thus mitigating the detrimental effect of risk attitude on the observed behavior that emerged from this research. While this study does not consider the sources of risk related to VWEs consumption, this is a topic that future research could explore, also applying different techniques to elicit it (e.g., contextualized experiments). Particularly, academics should also test whether an increased product acquaintance would reduce the impact of subjective norms and risk attitude on VWEs behavior.

In this respect, online and offline word-of-mouth might both bridge the abovementioned knowledge gap and the perceived risk connected to VWEs promoting their diffusion and thus an increase in virtual wine consumption.

Some critical reflections arise on VWE with respect to their sustainability potential. As Ozdemir et al. (2023) underline, experiences like VWEs offer consumers an environmentally friendly way to discover new regions, wineries and wines and eventually buy them without traveling, thus lowering the carbon footprint. This aspect does not mean that VWEs should become substitutes of wine holidays or cellar door experiences, but rather a greener complimentary option, among others for shorter trips solely targeted at gathering preliminary information or purchasing wine at the cellar. Therefore, VWE can be both a resilience strategy during crisis and a long-term marketing strategy. Additionally, VWEs can be used to accustom wine drinkers to greener packaging (e.g., bag-in-box). In fact, wineries usually ship the tasting set to participants upon the experience purchase. Thus, they could promote sustainable packaging alternatives by using the in these sets and inform consumers on the related benefits during the experience. Indeed, the literature found evidence that a critical aspect of non-glass wine packaging acceptance is the belief that alternatives would compromise wine quality, and it may be overcome by properly informing consumers, particularly those who are less traditionalist ( Ferrara et al., 2020 ).

Moreover, VWE may embody an economic sustainability dimension for wine stakeholders. For example, they could allow attracting new customers and future visitors, including those living far away from the destination, and offering wine tourism activities in the low season at a relatively low cost in terms of personnel and advertising.

Given the pressing need to strengthen sustainability outlined by the European Sustainable Development Goals (SDGs), sector academics should explore VWEs potential in this respect. Qualitative results of a recent study from Lu et al. (2022) highlight that VTEs could contribute to lower unnecessary greenhouse gasses emissions of the sector associated to transportation, as well as to make destinations virtually accessible to consumers hindered by physical or economic barriers.

Finally, researchers could validate the 4Es framework in virtual wine experiences to explore if and how it differentiates from the one traditionally associated to in-person wine tourism experiences. In this respect, Wei et al. (2023) recently introduced a new dimension, connection, to the four proposed by the original model (entertainment, education, escapism, and aesthetics) to accommodate the unique features of the virtual environment. To conclude, the extent that wine consumption has reached worldwide, and the increasing relevance of digitalization call for further monitoring of the VWEs phenomenon, and eventually infrastructural and learning support to wine operators willing to develop VWEs paired with ever-relevant consumer education.

Data availability statement

The datasets presented in this article are not readily available because the authors cannot make the data freely accessible. Requests to access the datasets should be directed to EG, [email protected] .

Ethics statement

The studies involving humans were approved by Research commission of the Department of Land, Environment, Agriculture and Forestry, University of Padova. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

GG: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Writing – original draft, Writing – review & editing. LR: Funding acquisition, Project administration, Writing – review & editing, Supervision. EG: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Writing – original draft, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.


The authors appreciated the constructive comments from participants at the III AISSA#under40 Conference 2022, which was held in Bolzano (Italy).

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.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

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.

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Keywords: theory of planned behavior, wine, structural equation modeling, consumer behavior, virtual experience

Citation: Gastaldello G, Rossetto L and Giampietri E (2024) Consumer behavior toward virtual wine experiences as a technology-based sustainable transformation. Front. Sustain. Food Syst . 8:1384011. doi: 10.3389/fsufs.2024.1384011

Received: 08 February 2024; Accepted: 11 April 2024; Published: 26 April 2024.

Reviewed by:

Copyright © 2024 Gastaldello, Rossetto and Giampietri. 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: Elisa Giampietri, [email protected]

This article is part of the Research Topic

Strategies Of Digitalization And Sustainability In Agrifood Value Chains

Suboptimal food products in Indonesia: a sustainable consumption behavior choice experiment and unveiling the attributes with a causality approach

  • Original Research
  • Published: 27 April 2024

Cite this article

consumer behavior research study

  • Chih-Cheng Chen 1 ,
  • Faradilah Hanum 2 ,
  • Tat-Dat Bui 3 ,
  • Ming K. Lim 4 , 7 &
  • Ming-Lang Tseng   ORCID: orcid.org/0000-0002-2702-3590 5 , 6 , 7 , 8  

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Suboptimal food products in Indonesia are identified as a set of attributes that indicate the sustainable transformation of consumer behavior. As preferences for suboptimal food products are influenced by attributes, consumers consider attributes in various ways, and the current state is still far behind the desired state in practice. This study aims to explore the attributes that enhance sustainable consumption behavior. An approach combining the fuzzy Delphi method and a fuzzy decision-making trial and evaluation laboratory is employed to assess the interrelationships among the attributes following experts’ judgments, and a choice experiment is used to evaluate the attributes of consumers’ preferences. As a result, socially responsible consumption and information framing represent the critical aspects of achieving sustainable consumer behavior. Socially responsible consumption is also found to be important in influencing consumers’ willingness to adopt SCB. In practice, food waste avoidance and saving behavior are the criteria for enhancing consumers’ acceptance of suboptimal food products.

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This work is also supported by NSTC-111-2221-e-239-034-my3.

This study is partially supported by NSTC 111–2221-E-468–008-MY3, National Science and Technology Council, Taiwan.

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Department of Business Management, National United University, Miaoli City, Taiwan

Chih-Cheng Chen

Department of Business Administration, Asia Management College, Asia University, Taichung City, Taiwan

Faradilah Hanum

Department of Shipping and Transportation Management, National Taiwan Ocean University, Keelung City, Taiwan

Tat-Dat Bui

Adam Smith Business School, University of Glasgow, Glasgow, United Kingdom

Ming K. Lim

Institute of Innovation and Circular Economy, Asia University, Taichung, Taiwan

Ming-Lang Tseng

Department of Medical Research, China Medical University Hospital, China Medical University, 1025, Taichung, Taiwan

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Ming K. Lim & Ming-Lang Tseng

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Sustainable Consumption Behavior Questionnaire–1.

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Chen, CC., Hanum, F., Bui, TD. et al. Suboptimal food products in Indonesia: a sustainable consumption behavior choice experiment and unveiling the attributes with a causality approach. Ann Oper Res (2024). https://doi.org/10.1007/s10479-024-05931-8

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Accepted : 01 March 2024

Published : 27 April 2024

DOI : https://doi.org/10.1007/s10479-024-05931-8

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