Previous Article in Journal
From Green Culture to Innovation: How Internal Marketing Drives Sustainable Performance in Hospitality
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Administrative Sciences
Volume 15
Issue 8
10.3390/admsci15080287
admsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Purchasing Decisions with Reference Points and Prospect Theory in the Metaverse

by
Theodore Tarnanidis
1,*,
Nana Owusu-Frimpong
2,
Bruno Barbosa Sousa
3,4,5,
Vijaya Kittu Manda
6 and
Maro Vlachopoulou
7
1
Department of Organisation Management, Marketing and Tourism, International Hellenic University, Sindos Campus, 57400 Thessaloniki, Greece
2
Office of Doctoral Programs, University of Professional Studies (UPSA), Accra LG 149, Ghana
3
School of Hospitality and Tourism (ESHT), IPCA-Polytechnic University of Cávado and Ave, 4750-810 Barcelos, Portugal
4
UNIAG—Applied Management Research Unit, 4900-347 Viana do Castelo, Portugal
5
CiTUR—Centro de Investigação, Desenvolvimento e Inovação em Turismo, 4750-810 Barcelos, Portugal
6
PBMEIT, Visakhapatnam 530009, India
7
Department of Applied Informatics, University of Macedonia, 54006 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Adm. Sci. 2025, 15(8), 287; https://doi.org/10.3390/admsci15080287 (registering DOI)
Submission received: 21 May 2025 / Revised: 12 July 2025 / Accepted: 18 July 2025 / Published: 23 July 2025
(This article belongs to the Section Strategic Management)
Browse Figures
Versions Notes

Abstract

The aim of this study is to analyze the factors that influence consumer referents or reference points and their interaction during the decision-making process, along with the principles of prospect theory in the metaverse with market and retail examples. We conducted an integrative literature review. Consumers’ preference for reference points is determined and structured during the buying process, which can be affected by potential signals and biased decisions. To guide consumers’ shopping experiences and purchasing behavior in the most effective way, marketers and organizations must investigate the factors that influence consumer reference points beyond physical or tangible attributes. Businesses must be adaptable and adapt their strategies to changing consumer preferences based on reference points. Our findings can advance discussions about how reference points are being used in the market by using consumer decision-making claims in the discursive construction of the metaverse. By comprehending this, developers can create better experiences and assist users in navigating virtual risks. Our research aids us in better comprehending the influence of referents on consumer purchasing decisions in the marketing communications field. Numerous opportunities for academic research into consumer reference points have arisen, in which individuals as digital consumers are influenced by the same biases and heuristics that guide their behavior in reality.

1. Introduction

Using both internal and external information from prior experiences, consumers evaluate their shopping experiences through memory analysis. Also, they use information acquired during the decision process (Kinley et al., 2000; Shah et al., 2016; Bettman & Zins, 1977; Simonson & Tversky, 1992). Consumer reference points influence the decision-making process, which plays a crucial role in individuals’ choices when purchasing goods or services. Decision-making generally involves multiple stages, which involve identifying a problem, searching for information, evaluating alternatives, making a purchase decision, and dealing with post-purchase decisions. Hence, it can be argued that there are various factors that can influence consumer reference points.
According to the literature, it is crucial for consumers to anticipate these factors and shape their buying preferences. With that in mind, this study will attempt to analyze the influencing factors that can function as reference points andassist consumers in forming their purchasing behavior (Payne et al., 1992; Woo & Lam, 1997). The research’s outline begins with an examination of prospect theory through an integrative literature review, which reveals how it is utilized and ultimately affects consumers’ decision-making perspective. Next, this research explored reference points to which customers are attracted in the metaverse. Due to research specificity, the author(s) have focused on specific reference points (like the price and other framing cues) in order to show how they impact the five stages of the consumer decision process in the metaverse. These are problem recognition, information search, alternative evaluation, purchase decision, and post-purchase evaluation. Lastly, it covers concerns related to the transformation of consumer reference points in the new metaverse domain. Marketing decision-makers and academia can benefit from these analyses.
The objective of this paper is to compile the most significant theoretical contributions from the past to the present that utilized prospect theory in metaverse commerce and to further analyze the effects of immersive environments on loss aversion and reference-dependent valuation. Thus, in our current work, we attempt to provide an overview of these issues that have already been successfully applied to diverse application fields in management science by examining them further from the perspective of consumer decision-making in today’s future (like the metaverse). From a researcher’s perspective, it offers insights into the field and highlights promising research opportunities. Furthermore, this paper could be useful for practitioners who desire to comprehend consumer decisions through appropriate methods.

2. Methodology

Through an integrative literature review, we identified key concepts, sources, and gaps in the research associated with the effects of prospect theory by demonstrating how it is utilized and ultimately has an effect on the sequential stages that consumers go through when making a purchase decision in the metaverse. An integrative review has a different purpose than other literature review methods, such as a scoping or systematic review. The aim is to access, analyze, and synthesize the literature on a research topic in a way that helps to generate fresh theoretical frameworks and perspectives (Mak & Thomas, 2022; Munn et al., 2018; Levac et al., 2010; Arksey & O’Malley, 2005).
By employing this methodology, it is feasible to quickly identify any gaps in the current literature that do not fulfill a predefined set of expectations and highlight areas that require further attention. The search strategy was set out by analyzing secondary data with broad inclusion criteria from reliable sources, such as ScienceDirect, Scopus, Google Scholar, Research Gate, EBSCO, SpringerLink, DOA (Directory of Open Access Journals), and other published research. The review process included keywords and themes that addressed the impact of prospect theory on the consumer decision-making process and the metaverse. Thus, this research attempts to answer the main question of why the examination of reference points needs to be studied further from the perspective of consumers, decision-making stages, and the appearance of the metaverse. Further, we demonstrate how prospect theory can be applied to consumer reference points for specific product categories using a case study analysis: the purchase of a carin the metaverse. Afterward, it is recommended to conduct more case-study examinations for grocery and fashion shopping.

3. A History of Prospect Theory in the Future

Consumers identify a need or problems, then they search for the ideal reference points, which may be triggered by a desire for a new product or a solution to a specific issue (Babutsidze, 2007; T. Tarnanidis, 2023). At this point, reference points include personal experiences, recommendations from friends and family, online reviews, and advertising. Afterward, consumers evaluate various alternatives. Product features, brand reputation, pricing, and the opinions of influencers or experts are all reference points. After considering alternatives, consumers make a choice. Referencing points can incorporate discounts, promotions, and the perceived value for money. Consumers assess how satisfied they are with the product or service they have purchased. For example, referring to the actual experience with the product, post-purchase support, and how well it meets expectations are key reference points. Individuals use reference points, whether they are in the context of consumer behavior or not, as benchmarks or standards to evaluate and make judgments. Reference points are comparative standards used by individuals to assess various aspects of their lives or decision-making (Simonson & Tversky, 1992; T. Tarnanidis et al., 2015), i.e., comparing one’s salary to the average income in the industry is a reference point for evaluating personal financial success. In consumer behavior, reference points are frequently employed by people to judge the value, quality, or desirability of items or services (Betts & Taran, 2005; Stommel et al., 2024), i.e., analyzing the features and price of a smartphone/laptop/car to those of other models in the market provides a consumer reference point. These points guide consumer decision-making by providing a basis for comparison among the available options in the market (Werner & Zank, 2019). Based on that, it can be argued that while reference points are broader benchmarks or functions used in a variety of aspects of life, consumer reference points specifically have a crucial role in shaping purchasing decisions and influencing market dynamics. Another branch of research studies reference points that contribute to the development of prospect theory principles (Kahneman & Tversky, 1979; Bouchouicha & Vieider, 2017; Thaler, 1985; Tversky & Kahneman, 1986; Kahneman, 2003). Prospect theory is a fundamental concept in understanding human decision-making, as it examines how people make decisions under uncertainty. The premise is that people tend to underestimate or undervalue outcomes that are more likely than those that can be assured (Burgess et al., 2023). Prospect theory asserts that every decision can be seen as a choice between options (reference points), and to achieve more reliable solutions, one should frame those prospects or violate them by anchoring them differently (Bouchouicha & Vieider, 2017). The implication is that consumers are more likely to avoid risk when making decisions that involve certain gains but are more likely to seek risk when making decisions that involve certain losses (T. Tarnanidis et al., 2024). This is how the S-value mapping is depicted in Figure 1:
The S-value function has the following three characteristics:
(i)
The value part is concave in the domain of gains (<0, x > 0) and convex in the domain of losses (>0, x < 0)
(ii)
The value part is loss-averse, steeper in the domain of losses (Kahneman & Tversky, 1979; Thaler, 1985; Kahneman, 1992; Werner & Zank, 2019).
The value and benefit of a potential gain outweighs the amount of disutility or negativity people absorb from potential loss, according to laboratory experiments (v(x) < |v (−x)|, x > 0), i.e., the “disutility” for losing $40 exceeds the “utility” of obtaining (gaining) the same amount of money (v (40) < v (−40)) (Kahneman & Tversky, 1979; Tversky & Kahneman, 1986). In addition, the S-value function adheres to the rules of decreasing sensitivity. For example, the importance of the pleasure for moving from $40 to $50 is less compared with the movement from $4 to $10 (Heath et al., 1999). The difficulty of predicting how consumers will reframe decision-making problems and act in real-time situations is a disadvantage of prospect theory. The dynamic bias that exists between planned and actual consumer choices is recognized by Barkan et al.’s (2005) investigation into integration and segregation. People tend to have a better understanding of a specific product when they have already acquired it rather than when they are buying it for the first time (Q. Chen et al., 2024; S. Liu et al., 2024). Munro and Sugden (2003) criticized the endowment effect or status-quo bias by pointing out that reference points have only been tested exogenously, which means they have been applied to decision-makers without considering any possible discrepancies in endogenous variables, such as customary or habitual consumption.
It is probable for consumers to search for additional information that can help them transform their initial reference points into more concrete and reliable ones, and their final reference points will be determined by the actual decision frame. The assumption that people are risk-averse for positive frames and risk-seeking for negative frames was proposed by Kahneman and Tversky in 1979. Prospect theory was introduced by Kahneman and Tversky in 1979 and is responsible for explaining anomalies in expected utility theory, but it does not provide a comprehensive theory of decision-making. Also, the decision-making framing is primarily focused on making choices when faced with risk and uncertainty, but it leaves out wider aspects of decision-making. To put it more succinctly, it explains how people actually act rather than how they should act according to rational standards.
Kinley and his co-authors stressed that consumers, during their consumption tasks, have to process a plethora of information arising from personal cues, such as family, friends, co-workers, and culture, and from non-personal (or promotional) cues that originate from marketing mix intensive variables (Kinley et al., 2000). Furthermore, their preferences are determined by their subjective maximization of utility, which is a guide for evaluating particular products. For example, it is envisaged that each option is a collection of attribute claims (Bettman et al., 1998), which consumers subjectively select and anchor in their mindsets (Babutsidze, 2007; Babutsidze, 2012), with the most salient one dominating the others (Beggan, 1994; Koh, 2024; Busemeyer & Johnson, 2003). This strategy is based on individual justifications to constantly reinforce and convince themselves that they have made the best choice. Price and quality are a balancing act (Simonson, 1989; Sheth et al., 1991). As a significant path, they elaborate and make use of diverse reference points.
Social psychologists agree that consumers take into account the possibility of a stimulus from past or current experiences when making a decision. In their research in 1987, Klein and Oglethorpe (1987) posed a question about the meaning of possible reference prices and whether they can be considered as reference points. They discovered that consumers construct reference points, such as the price, for different reasons. They summed up those categories as aspirational price (the price that I would like to pay or a reasonable price); the market price (which is the average retail price); and the historical price (which is the last price I paid or the price I usually pay). Despite their research finding reasonable explanations, their question is not significant (S. Chen et al., 2024; Datt et al., 2024). They asserted that more research is needed to focus on the inner psyche and mindsets in order to determine the meaning and reasoning behind how consumers use and categorize reference points. In order to accomplish this, it is necessary to reveal the factors that influence the conceptualization of reference points.
In the emerging digital frontier of the metaverse, consumers have become active participants. Their consumption experiences involve exploring immersive environments where the lines between identity, status, and consumption become increasingly blurred (Pandey et al., 2024; Tariq, 2025). Users are making complex purchases in environments that are not restricted by physical constraints (C.-W. Chen, 2024; Moustapha et al., 2024). In this case, it is argued that traditional utility-based economic models cannot fully explain decisions. Rather, they require a framework that can capture the perception of gains and losses by people in relation to constructed psychological benchmarks. In the metaverse, reference points can now be derived through social comparisons between avatars, virtual ownership, and digital currency frames (Vrtana & Krizanova, 2023). Predicting user behavior, informing ethical design practices, and guiding consumer protection policies requires understanding how these virtual reference points shape perceived value. In the next table (Table 1), we illustrate how the notion of prospect theory is applied to the metaverse on a daily shopping experience based on usual market data.
Retailers should be aware that even small price increases can cause feelings of loss. Regular pricing history could be embedded into the shopping experience, where users receive notifications like, “Your favorite product is now 20% cheaper than last time”, framing the discount in relation to their past purchases. For example, they can use time-limited offers or expiring virtual coupons in the metaverse to capitalize on loss aversion. When shoppers perceive they will lose a discount, they are more inclined to act quickly, which can lead to an increase in the conversion rate for daily shopping items.
In the metaverse, it is important to emphasize the losses associated with not taking advantage of deals in product descriptions, banners, or virtual ads. Highlighting the potential loss of making a purchase is likely to drive higher engagement. Additionally, businesses in the metaverse can design pricing strategies with small, frequent discounts instead of larger, less frequent savings. For instance, offering micro-discounts on daily items (like groceries or essentials) might create a stronger emotional reaction and encourage repeated shopping. On the other hand, according to mental accounting theory, rewarding shoppers with virtual credits for daily purchases might increase engagement and sales because users do not mentally equate these with “real” losses. An interesting point for further discussion is the endowment effect, which suggests that people assign more value to things they own. In the metaverse, where virtual representations of real-world items are common, this effect could manifest in interesting ways.
Through a scoping literature review based on prospect theory principles, the existing state-of-the-art literature on reference points was examined from the consumers’perspective, with the purpose of trying to comprehensively summarize and synthesize the behavior of the consumers based on the crafting of different referents or reference points and providing direction to future research examinations.

4. Results

Based on the literature review analysis, we present in the next table (Table 2) how the key aspects of prospect theory and consumer reference points are interrelated.
Further, the research findings suggest that reference points are actually points of comparison determined as any stimuli that are related to other stimuli seen from consumers’ and sellers’ perspectives (Pelka et al., 2024; Cheng et al., 2023; Dholakia & Simonson, 2005; Kahneman & Tversky, 1979; Mussweiler, 2003; Tversky & Kahneman, 1991; Lin et al., 2024). According to T. Tarnanidis et al. (2010), consumer reference points can take the form of a single idea, a fact, an event, a person, or just different kinds of information, i.e., explicit and implicit, which in turn might shape overall purchase preferences. Explicit information that acts as a reference point for consumers includes framing effects, product attributes, brand image, optimal prices, store environment, promotion and advertising, salespeople, ethics, organizational culture, contracts, frequent loyalty programs, and assortments, whereas implicit information or reference points include personal goals, hedonic and functional values, perceptions, personal identity, sensations, budget and time constrains, previous experiences, special occasions, and cultural reference points (i.e., reference groups, social values and norms, social class, and social status). In a nutshell, reference points are the criteria or standards that consumers use to evaluate and make decisions about products, services, and brands. Various factors can influence the importance of these reference points in the consumer decision-making process (Wallace & Etkin, 2024). Table 3 illustrates some significant aspects of the most important consumer reference points used in the formulation of the consumer decision-making process. Understanding and strategically responding to consumer reference points are essential for businesses seeking to thrive in competitive markets.
The table above indicates that consumer reference points are the standards or benchmarks consumers use to evaluate products, services, prices, or experiences. We can evaluate more consumer reference points, whether they are internal or external. The basis for internal reference points is past experiences, personal preferences, and individual beliefs, while external reference points are influenced by factors like social norms, marketing messages, and comparisons with competitors. For instance, consumers have various expectations and expectations regarding the performance and quality of a product or service. When speaking about expectations, we can say that they are shaped by various factors, such as past experiences, word-of-mouth recommendations, advertising, and brand reputation. As a result, consumers tend to be satisfied when their actual experiences meet or exceed their expectations. Conversely, when experiences do not meet expectations, it can lead to dissatisfaction and negative perceptions of the brand or product. When speaking about the metaverse, there is a lack of research because the current domain is completely new and incomplete. Reference points, such as past experiences, social influences, and market expectations, will be used by consumers in the metaverse to navigate and make decisions in this new digital environment (Park & Kim, 2023). To give an example, the quality of virtual interactions, product authenticity, and overall experience will be judged against their real-world counterparts. The use of experiences from other digital platforms or virtual reality (VR) environments can help users set their expectations for usability, immersion, and satisfaction in the metaverse. Customers’ expectations for virtual try-ons or demos will be changed by virtual store layouts, based on their experiences with augmented reality apps and personalized avatars (Jin, 2024).
The metaverse allows users to interact with hyper tokens/prices/costs as virtual consumers (T. K. Tarnanidis & Sklavounos, 2024). Just as in reality, they tend to place more importance on potential losses than potential gains (Gao et al., 2024). Also, how choices are framed (e.g., as a potential gain or a loss) affects decisions. For instance, a game might offer rewards framed as a loss (“you will lose 10 points unless you buy this item”), which could be more compelling to users than a gain (“you will earn 10 points if you buy this item”). Despite this, risk-seeking and risk-averse behaviors both follow the same conditions and have potential losses or gains, i.e., gambling on risky virtual investments can be risky, but it can also be risky when dealing with potential gains such as selling too early to guarantee a profit. Social interactions could be affected by loss aversion in a social metaverse where status and reputation are key. Prospect theory’s psychological effects extend beyond economics and social dynamics, as users may hesitate to take actions that risk their virtual identity or social standing (L. Wang et al., 2024). Next, we illustrate how prospect theory can be applied to consumer reference points for a specific product category, i.e., the purchase of a carin the metaverse. We will consider a hypothetical scenario where a virtual dealership in the metaverse sells digital cars with prices and features that influence buyers’ decision-making based on prospect theory’s principles of loss aversion and framing. A scenario involving purchasing virtual cars in the metaverse:
  • The Standard Version of Virtual Car A:
    -
    Virtual coins are the standard price, which is $15,000.
    -
    The features include basic speed, design, and customization options.
  • The Premium Version of Virtual Car B:
    -
    Virtual coins with a standard price of $20,000 are available.
    -
    The features include faster speed, a unique design, and more extensive customization options.
We can assume that consumers have established a reference point based on the price and value of the most recent car they or their friends bought (Virtual Car A). Unless their perceptions are framed differently, they may experience loss aversion when considering upgrading to Virtual Car B. It is probable that we possess consumer behavior data from surveys or tracked sales in the metaverse. For instance, the price of Virtual Car A is considered the reference point for 70% of consumers, and 30% of consumers who have previously purchased premium cars consider it higher. Virtual Car B’s price is 33% more expensive than their reference point ($15,000), which causes the average consumer to experience loss aversion when faced with the option of buying it for $20,000. Instead of focusing on the car’s premium features, they view this additional $5000 as a loss. Despite the added value, 60% of consumers surveyed refused to pay more than $2000 more than their reference point. To change consumers’ perceptions based on prospect theory, the dealership employs various pricing strategies, with their decisions being influenced by their reference points and phobia of perceived losses.
  • Car A’s reference point is $15,000:
    -
    The more expensive Virtual Car B is purchased by 30% of consumers at its full price of $20,000.
    -
    The extra $5000 over the reference price is considered a loss.
  • Discount Framing:
    -
    Virtual Car B’s price being reduced to $18,000 (a 10% discount) convinces 55% of consumers to purchase it with the discount applied.
    -
    The perception is shifted to a gain by this framing because consumers save $2000 compared to the full price, which reduces the perceived loss from their reference point.
  • Scarcity Framing:
    -
    The dealership’s emphasis on Virtual Car B’s limited edition (scarcity framing) triggers loss aversion regarding future availability.
    -
    The premium car is purchased by 75% of consumers at the full price of $20,000 to avoid the perceived loss of missing out.
Analysis and the optimization of strategies in a virtual reality marketplace become easier with the structured view of the scenario (see Figure 2).
According to prospect theory, consumers prioritize avoiding losses over acquiring equivalent gains. So, aloss-averse consumer might reject the upgrade unless convinced that not buying thepremium car now could lead to a bigger loss in the future (e.g., higher prices later, missing out on exclusive features. This example illustrates how prospect theory can be employed to comprehend consumer behavior in the metaverse. By utilizing reference points and leveraging loss aversion and framing principles, businesses can influence how consumers perceive value, leading to more sales of virtual products that are more expensive. Similar research was found in the current literature on consumer reference points (see Bell & Bucklin, 1999; Kaur et al., 2024; Manthiou et al., 2024; Abrokwah-Larbi, 2024; J. Wang et al., 2022). In Appendix A, we offer additional evidence-based examples in retail within the metaverse where the concept of prospect theory can aid in elucidating consumer behavior when shopping in virtual environments (T. Tarnanidis, 2024; Jing et al., 2024; Hajian et al., 2024).
Additionally, consumer perceptions are the interpretation of information about products, services, brands, or experiences by individuals (Dholakia & Simonson, 2005). The perception of consumers is subjective and biased because they can be influenced by a variety of factors, such as personal beliefs, cultural background, advertising, and word of mouth. Furthermore, the same product may be perceived differently by consumers depending on their individual perspectives and experiences (Gupta et al., 2024). In short, consumer reference points give consumers the context they need to evaluate products and services, and expectations set the standards for their experiences. Consumers’ perceptions, on the other hand, are based on their interpretations and evaluations of their experiences, which eventually impact their attitudes and behaviors as consumers (P. Wang et al., 2021). These concepts must be understood by businesses to effectively meet consumer needs and preferences, manage expectations, and build positive brand perceptions.

5. Discussion

The conceptual frameworks developed with the use of the mechanisms of prospect theory and reference points in the metaverse domain are well-described as determinants or factors influencing customers’ service evaluations and purchase decisions. Moreover, we gave examples of pricing, which is one of the most significant visible reference points. Consumer reference points empower individuals to make more informed decisions by considering alternatives and evaluating their preferences against available options. It is clear that past experiences with a brand or product have a significant impact on future decisions. A strong reference point for future purchases can be created by positive experiences. Friends, family, and peers can provide powerful reference points through their opinions and recommendations. The influence of positive reviews and testimonials can be felt during decision-making. Social media, influencers, and societal trends have a significant impact on consumer reference points. The desire to fit in or follow trends can have an effect on decision-making. By highlighting product features, benefits, and unique selling propositions, brands create positive reference points through advertising. A reference point can be found in the reputation of a brand. Consumers have a high level of trust in established brands and may be willing to pay more for perceived quality and reliability. The shaping of reference points is influenced by cultural and social values. Cultural values may influence the preference for products or services. Other people’s experiences are often used as reference points by consumers. Online reviews and ratings have a significant impact on decision-making. Price serves as a crucial reference point. Comparing prices and evaluating the perceived value received for their money is what consumers do.
Reference points can be influenced by psychological factors, such as personality, motivation, and lifestyle, as well as the context in which a decision is made, including time constraints and external circumstances. Sharing experiences with others is often done by consumers, which helps with word-of-mouth marketing. Positive or negative feedback becomes a reference point for potential buyers. This has the potential to affect the decisions of individuals who rely on the opinions and experiences of others. The process is frequently recursive. Brand loyalty can be strengthened by positive experiences with a particular brand or product, which may lead to stronger reference points. If there are negative experiences or changing needs, consumers may revisit the decision-making process and adjust their reference points accordingly. Understanding how consumers use reference points throughout each stage of the decision-making process is crucial for businesses. By influencing and shaping these reference points through effective marketing, customer service, and product quality, companies can ultimately have an impact on consumer choices and brand loyalty. By aligning marketing strategies with consumers’ reference points and framing choices in ways that consider the prospect theory principles, businesses can better anticipate and influence consumer decision-making (T. Tarnanidis et al., 2020). However, future consumer reference points may be influenced by how consumers are likely to prioritize sustainable and ethical practices as environmental concerns become more prominent (eco-friendly operations, fair labor practices, and overall social responsibility). Similarly, personalized experiences will have a significant impact due to the advancements in artificial intelligence (AI) and machine learning. By incorporating immersive and interactive technologies, businesses can make shopping more enjoyable for consumers (visualize items in a real-world setting before making a purchase). Furthermore, future reference points could include the opinions and experiences of influencers and regular users, which may be shared on multiple social media platforms (Balakrishnan et al., 2024). As online transactions become more prevalent, digital identity and cybersecurity will be essential benchmarks (T. K. Tarnanidis & Sklavounos, 2024). The overall experience associated with a brand or product can be a significant reference point beyond just the product itself. The consumer’s decision-making process may be influenced by brands that create memorable and positive experiences (T. Tarnanidis, 2024). The metaverse’s evolution may cause these reference points to shift, resulting in new benchmarks and standards within this digital realm. To meet consumer expectations and provide compelling experiences, companies and developers need to be informed about these reference points (Hadi et al., 2024; Ghali et al., 2024).
As we noted in our paper, there is a large body of related theoretical and empirical work pertaining to several proposed new studies to build off previous research for a novel and incremental contribution to the discipline. Ideally, this may focus on making a contribution that is novel and weighty in terms of moving the state of thoughts further, like new moderators and mediators of the elaboration of reference points that bring out a new and nuanced form of academic tension (see Goldfarb et al., 2022; Sridhar et al., 2022; Van Heerde et al., 2021). Also, it should be noted that an important limitation of our study is that it examines a very narrow frame of reference in marketing. Additionally, the lack of empirical data and a focus on specific product categories (such as convenience goods, shopping goods, specialty goods, and unsought goods) is a significant limitation of the current research. Consumers tend to use a simple decision-making approach when purchasing everyday products, which usually consists of visible reference points that come from marketing mix variables. In specialty and expensive purchases, it is improper to use salient and hidden reference points. During each decision-making stage, consumers are required to utilize multiple reference points.
Prospect theory studies in the future could investigate dynamic models that take into account the evolution of reference points over time, which could be influenced by experience, social influences, or changes in circumstances. This could lead to a better understanding of consumer decision-making dynamics. It is anticipated that the future of prospect theory with consumer reference points will require interdisciplinary research, which will incorporate insights from psychology, economics, marketing, neuroscience, and other fields. This will aid us in further comprehending consumer behavior in a world that is becoming increasingly complex and dynamic. The final point of this paper is to suggest how consumers examine, analyze, and craft reference points through the decision-making process and the applications of prospect theory in the metaverse. The significance of rewarding research opportunities for marketing and decisionscience in the age of consumer products is highlighted by this paper’s main contribution.
Examining the criticisms of prospect theory and its boundary conditions may lead to further recommendations for marketing and decision-making science, as it may be a special case (with a focus on prevention) of another theory (with a focus on regulation). There is a lack of research on how consumer reference points are affected by the metaverse and AI. What are the differences in consumer reference points between AI (1) making recommendations for their short list (as in online retailers) and AI (2) making the actual decision for them? As the metaverse evolves, these reference points may change, leading to new benchmarks and standards within this digital realm.
The findings demonstrate how fundamental concepts from behavioral economics, such as reference points, loss aversion, framing effects, diminishing sensitivity, and mental accounting, have an important impact on shaping consumers’ behavior in the metaverse. Internal reference points are the basis for evaluating prices and deals by consumers, and even small deviations from expected prices can be seen as significant gains or losses. By managing these expectations through dynamic reference pricing, retailers can strategically influence purchasing behavior. Loss aversion adds to this effect, as consumers respond more strongly to potential losses than to equivalent gains.
Quick decision-making can be facilitated by offering time-sensitive offers and highlighting what customers may miss out on. Framing effects reinforce this by showing that negative framing tends to be more persuasive than positive framing, even if the economic value remains the same. When it comes to lower-priced items, decreasing sensitivity reveals that smaller amounts of savings or losses have a greater emotional impact, indicating that frequent micro-discounts on inexpensive products can increase engagement.
Each stage of the consumer decision-making process, from the recognition of problems to post-purchase evaluation, is heavily affected by reference points. In the initialproblem recognition phase, consumers identify a gap between their current and desired states, often triggered by marketing cues or past experiences that activate specific reference points (Schwarz, 2004; Yin et al., 2024). When they are in the information search phase, their search behavior is guided by reference points that they are familiar with, such as brand reputation, reviews, or personal history (Sharma et al., 2023; T. Tarnanidis, 2023). In evaluating alternatives, consumers filter options based on established criteria, such as price, features, and quality, which are shaped by reference anchors (Davis, 2022; T. Tarnanidis et al., 2020). The purchase decision is driven by an alignment between the chosen choice and the consumer’s perceived value framework, based on these prior comparisons (Mead & Williams, 2022; Wu et al., 2024). In the end, the evaluation of the purchase afterward impacts future decisions by either reinforcing positive reference points or adjusting them to deal with dissatisfaction (Obukhovich et al., 2024; Nittala & Moturu, 2023). In practical terms, this process highlights the significance of marketers aligning product messaging and pricing strategies with consumer reference points. Therefore, these stages provide researchers with the opportunity to investigate the evolution and influence of reference points on decision quality in different digital and physical retail environments.

6. Conclusions

By combining insights from prospect theory, decision-making stages, and emerging metaverse environments, this research examines the influence of consumer reference points on purchasing decisions. By studying factors that influence reference points, it improves the understanding of consumer behavior and provides practical insights for marketing professionals. Integrating diverse marketing disciplines, it addresses key aspects of problem recognition, information search, alternative evaluation, and post-purchase behavior, offering both theoretical contributions and practical strategies for optimizing consumer engagement in evolving digital marketplaces.
To sum up, the study demonstrates how businesses must adjust their marketing strategies, product development, and pricing models to meet changing consumer reference points from a global perspective. As technology develops, factors such as AI-driven personalization, immersive shopping experiences, cybersecurity, and sustainability concerns will become key factors in influencing consumer choices globally. The metaverse is anticipated to change these reference points, creating new guidelines for digital interaction and branding. The paper recognizes research gaps and recommends broader empirical studies that go beyond specific product categories and a deeper understanding of how demographic and cultural factors affect consumer decision-making globally. The dynamic evolution of reference points over time should be explored through future studies by integrating interdisciplinary insights from economics, marketing, psychology, and neuroscience. In this era of global market interdependence and rapidly shifting consumer behaviors caused by technological and societal changes, this research is particularly relevant.

6.1. Theoretical Implications

Through our prospect theory research, we have demonstrated how it is incorporated and ultimately affects consumers’ decision-making perspectives. The metaverse evaluates the significance of consumer reference points by examining whatconsumers go through when making a purchasing decision.

6.2. Practical Implications

In the metaverse, retailers have the option to influence shopper behavior by using reference pricing data. Businesses are aided by product development, pricing strategies, and marketing by recognizing what consumers value. The presence of positive reference points can boost brand image, while negative ones can result in dissatisfaction or a loss of trust. Therefore, businesses can adjust their strategies accordingly by tracking consumer reference points, which helps them stay attuned to market trends and changing consumer preferences. Additionally, marketing managers can have an effect on consumer decision-making in the metaverse by portraying offers as potential losses instead of gains in their marketing tactics to promote brands through virtual try-ons, which can simulate ownership and leverage the endowment effect. For instance, they can set prices with dynamic reference points that affect the perception of value and separate costs from benefits by bundling gains and isolating losses.

6.3. Limitations

Although the findings of the study are valuable, their generalizability is limited by their reliance on non-empirical methods. This study is focused on a very limited reference point for consumers in metaverse marketing. The study results can be more generalized by using empirical data that focuses on diverse product categories, such as convenience goods, shopping goods, specialty goods, and unsought goods. Furthermore, the collection of secondary data was limited to English-language articles, which may have excluded relevant studies, and the review was confined to specific databases, such as ScienceDirect, Scopus, Google Scholar, Research Gate, EBSCO, SpringerLink, and other published research.

6.4. Future Research

Future research can be utilized to investigate how consumers make buying/purchasing decisions based on the labeling of varied information. It is challenging to predict future reference points for consumer buying decisions due to the influence of various factors such as technological advancements, cultural shifts, and economic changes (T. Tarnanidis & Manaf, 2024). Additionally, future studies might dive deeper into our empirical context and identify what unique nuances it contains to offer theoretical contributions to the larger literature, i.e., this inductive approach should again connect back to novel and unique findings already well-known in the literature and offer extensions thereof. To meet evolving consumer preferences and reference points, businesses must remain flexible and adjust their strategies. By regularly monitoring trends and engaging with target audiences, one can gain valuable insights into the ever-changing landscape of consumer buying decisions. Therefore, we can argue that the future of consumer reference points’ examination will be characterized by the integration of new technologies (like the stimuli customer touchpoints, personalized content and distinct personas acting as influencers with customized referents) and ethical and health considerations (which vary across global contexts, demographics, psychographics, and behavioral characteristics) towards the increasingly complex landscape of choices and consumer shopping experiences. Furthermore, more research is needed on how demographic factors trigger reference points.

Author Contributions

Conceptualization, T.T. and N.O.-F.; methodology, B.B.S.; formal analysis, M.V.; investigation, V.K.M.; writing—original draft preparation, T.T., M.V. and B.B.S.; writing—review and editing, T.T., N.O.-F. and B.B.S.; visualization, V.K.M.; supervision, N.O.-F.; project administration, T.T. and M.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Appendix A.1. Applying Prospect Theory to Metaverse Grocery Shopping

Visualize someone shopping for groceries in the metaverse with a virtual avatar. Based on their actual reference point, they expect to pay $100 for their weekly groceries. Despite this expectation, the metaverse store provides dynamic pricing.
  • Gain Frame: They notice that their virtual cart totals $90 due to various promotions and deals. While they feel happy about the savings, prospect theory suggests that the happiness they experience from saving $10 is not as intense as the disappointment they would feel from overpaying.
  • Loss Frame: If their total comes to $110 instead of $100, the pain of losing money (spending more than intended) is far more intense than the positive feeling of saving $10, even though the difference is the same.
In this scenario, prospect theory plays a significant role in decision-making, even in a virtual grocery store in the metaverse, as reference points play a key role in how we perceive gains and losses.

Appendix A.2. Applying Prospect Theoryto Metaverse Fashion Shoppingin the Metaverse

Visualize someone browsing a metaverse fashion store, anticipating that they will spend roughly $300 on a complete virtual outfit for their avatar, based on their past real-world fashion experiences.
  • Gain Frame: The store offers a complete collection of dresses, shoes, and accessories for $250, which is less than their reference point of $300. However, prospect theory’s diminished sensitivity to gains causes their joy to be less intense than it could have been, despite experiencing a small gain.
  • Loss Frame: On the other hand, if that same outfit suddenly costs $350 (more than their reference point), they experience a significant sense of loss. Prospect theory explains that their aversion to loss makes the $50 overspend feel bad to a greater degree than a $50 saving would have made them feel good.
In this scenario, prospect theory shapes decisions in metaverse fashion shopping, with reference points like expected prices, digital quality, and ownership influencing how users perceive gains and losses in their virtual purchases. For example, customers may react differently to virtual item prices depending on their reference points and the way offers are framed. Understanding these dynamics enables retailers to strategically design pricing and promotions, which can influence customer decision-making and enhance sales.

References

  1. Abdellaoui, M., Bleichrodt, H., & Paraschiv, C. (2007). Loss aversion under prospect theory: A parameter-free measurement. Management Science, 53(10), 1659–1674. [Google Scholar] [CrossRef]
  2. Abrokwah-Larbi, K. (2024). Transforming metaverse marketing into strategic agility in SMEs through mediating roles of IMT and CI: Theoretical framework and research propositions. Journal of Contemporary Marketing Science, 7(1), 56–83. [Google Scholar] [CrossRef]
  3. Arksey, H., & O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8, 19–32. [Google Scholar] [CrossRef]
  4. Babutsidze, Z. (2007). How do consumers make choices?: A summary of evidence from marketing and psychology (February) [working paper]. United Nations University, Maastricht Economic and Social Research and Training Centre on Innovation and Technology. Available online: https://unu-merit.nl/publications/wppdf/2007/wp2007-005.pdf (accessed on 28 May 2025).
  5. Babutsidze, Z. (2012). How do consumers make choices? A survey of evidence. Journal of Economic Surveys, 26(4), 752–762. [Google Scholar] [CrossRef]
  6. Balakrishnan, J., Das, R., Alalwan, A. A., Raman, R., & Dwivedi, Y. K. (2024). Informative and peripheral metaverse: Which leads to experience? An investigation from the viewpoint of self-concept. Computers in Human Behavior, 156, 108223. [Google Scholar] [CrossRef]
  7. Barberis, N. C. (2013). Thirty years of prospect theory in economics: A review and assessment. Journal of Economic Perspectives, 27(1), 173–196. [Google Scholar] [CrossRef]
  8. Barkan, R., Danziger, S., Ben-Bashat, G., & Busemeyer, J. R. (2005). Framing reference points: The effect of integration and segregation on dynamic inconsistency. Journal of Behavioral Decision Making, 18, 213–226. [Google Scholar] [CrossRef]
  9. Beggan, J. K. (1994). The preference for gains in consumer decision making. Journal of Applied Social Psychology, 24(16), 1407–1427. [Google Scholar] [CrossRef]
  10. Bell, D. R., & Bucklin, R. E. (1999). The role of internal reference points in the category purchase decision. Journal of Consumer Research, 26(2), 128–143. [Google Scholar] [CrossRef]
  11. Bettman, J. R., Luce, M. F., & Payne, J. W. (1998). Constructive consumer choice processes. Journal of Consumer Research, 25(3), 187–217. [Google Scholar] [CrossRef]
  12. Bettman, J. R., & Zins, M. A. (1977). Constructive processes in consumer choice. Journal of Consumer Research, 4, 75–85. [Google Scholar] [CrossRef]
  13. Betts, S. C., & Taran, Z. (2005). Brand as reliability reference point: A test of prospect theory in the used car market. Journal of Academy of Business and Economics, 5(1), 34–38. [Google Scholar]
  14. Bolton, D., & Madhavaram, S. (2025). Impact of customized price promotions on deal response: The roles of functional impulsivity, promotion frame, emotional arousal, and self-enhancement. Journal of Marketing Theory and Practice, 33(3), 412–433. [Google Scholar] [CrossRef]
  15. Bouchouicha, R., & Vieider, F. M. (2017). Accommodating stake effects under prospect theory. Journal of Risk and Uncertainty, 55(1), 1–28. [Google Scholar] [CrossRef]
  16. Burgess, B., Yaoyuneyong, G., Pollitte, W. A., & Sullivan, P. (2023). Adopting retail technology in crises: Integrating TAM and prospect theory perspectives. International Journal of Retail & Distribution Management, 51(7), 939–954. [Google Scholar] [CrossRef]
  17. Busemeyer, J. R., & Johnson, J. G. (2003). Comparing models of preferential choice: Technical report. 19 December 2008. Available online: https://www.researchgate.net/publication/254665050_Comparing_Models_of_Preferential_Choice (accessed on 15 June 2025).
  18. Chen, C.-W. (2024). Utilizing a hybrid approach to identify the importance of factors that influence consumer decision-making behavior in purchasing sustainable products. Sustainability, 16(11), 4432. [Google Scholar] [CrossRef]
  19. Chen, Q., Zhang, X., Liu, S., & Zhu, W. (2024). The inventory bullwhip effect in the online retail supply chain considering the price discount based on different forecasting methods. The Engineering Economist, 69(2), 108–128. [Google Scholar] [CrossRef]
  20. Chen, S., Zhao, J., Xu, H., & Zhu, Z. (2024). When and how does decoy effect work? The roles of salience and risk aversion in the consumer decision-making process. Electronic Commerce Research and Applications, 63, 101354. [Google Scholar] [CrossRef]
  21. Chen, T., Wu, X., Wang, B., & Yang, J. (2025). The role of behavioral decision-making in panic buying events during COVID-19: From the perspective of an evolutionary game based on prospect theory. Journal of Retailing and Consumer Services, 82, 104067. [Google Scholar] [CrossRef]
  22. Cheng, Y.-H., Chuang, S.-C., Lee, C.-F., & Kao, C.-Y. (2023). The influence of price location on reference-price ads. Marketing Intelligence & Planning, 41(1), 83–94. [Google Scholar] [CrossRef]
  23. Chernev, A. (2005). Context effects without a context: Attribute balance as a reason for choice. Journal of Consumer Research, 32(2), 213–223. [Google Scholar] [CrossRef]
  24. Datt, M., Gupta, A., Misra, S. K., & Gupta, M. (2024). Theory of constraints in healthcare: A systematic literature review. International Journal of Quality & Reliability Management, 41(6), 1417–1456. [Google Scholar] [CrossRef]
  25. Davis, D. F. (2022). Distributions distract: How distributions on attribute filters and other tools affect consumer judgments. Journal of Consumer Research, 49(6), 1074–1094. [Google Scholar] [CrossRef]
  26. Dholakia, U. M., & Simonson, I. (2005). The effect of explicit reference points on consumer choice and online bidding behavior. Marketing Science, 24(2), 206–217. [Google Scholar] [CrossRef]
  27. Emami, A., Taheri, Z., & Zuferi, R. (2024). The interplay between framing effects, cognitive biases, and learning styles in online purchasing decision: Lessons for Iranian enterprising communities. Journal of Enterprising Communities: People and Places in the Global Economy, 18(2), 347–371. [Google Scholar] [CrossRef]
  28. Fortin, I., & Hlouskova, J. (2024). Prospect theory and asset allocation. The Quarterly Review of Economics and Finance, 94, 214–240. [Google Scholar] [CrossRef]
  29. Gao, H., Chong, A. Y. L., & Bao, H. (2024). Metaverse: Literature review, synthesis and future research agenda. Journal of Computer Information Systems, 64(4), 533–553. [Google Scholar] [CrossRef]
  30. Ghali, Z., Rather, R. A., & Khan, I. (2024). Investigating metaverse marketing-enabled consumers’ social presence, attachment, engagement and (re) visit intentions. Journal of Retailing and Consumer Services, 77, 103671. [Google Scholar] [CrossRef]
  31. Goldfarb, A., Tucker, C., & Wang, Y. (2022). Conducting research in marketing with quasi-experiments. Journal of Marketing, 86(3), 1–20. [Google Scholar] [CrossRef]
  32. Gupta, R., Rathore, B., Biswas, B., Jaiswal, M., & Singh, R. K. (2024). Are we ready for metaverse adoption in the service industry? Theoretically exploring the barriers to successful adoption. Journal of Retailing and Consumer Services, 79, 103882. [Google Scholar] [CrossRef]
  33. Hadi, R., Melumad, S., & Park, E. S. (2024). The Metaverse: A new digital frontier for consumer behavior. Journal of Consumer Psychology, 34(1), 142–166. [Google Scholar] [CrossRef]
  34. Hajian, A., Daneshgar, S., Sadeghi R, K., Ojha, D., & Katiyar, G. (2024). From theory to practice: Empirical perspectives on the metaverse’s potential. Technological Forecasting and Social Change, 201, 123224. [Google Scholar] [CrossRef]
  35. Heath, C., Larrick, R. P., & Wu, G. (1999). Goals as reference points. Cognitive Psychology, 38, 79–109. [Google Scholar] [CrossRef] [PubMed]
  36. Jin, D. (2024). Humanizing metaverse: Psychological involvement and masstige value in retail versus tourism platforms. International Journal of Consumer Studies, 48(2), e13025. [Google Scholar] [CrossRef]
  37. Jing, Z., Kang, H., Liu, J., & Sun, A. (2024). A new marketing strategy in the digital age: Meta-universe marketing. In Addressing global challenges-exploring socio-cultural dynamics and sustainable solutions in a changing world (pp. 573–580). Routledge. [Google Scholar]
  38. Kahneman, D. (1992). Reference points, anchors norms and mixed feelings. Organizational Behavior and Human Decision Processes, 51, 296–312. [Google Scholar] [CrossRef]
  39. Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American Economic Review, 93(5), 1449–1475. [Google Scholar] [CrossRef]
  40. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291. [Google Scholar] [CrossRef]
  41. Kaur, J., Mogaji, E., Paliwal, M., Jha, S., Agarwal, S., & Mogaji, S. A. (2024). Consumer behavior in the metaverse. Journal of Consumer Behaviour, 23(4), 1720–1738. [Google Scholar] [CrossRef]
  42. Kinley, T. L., Conrad, R., & Brown, G. (2000). Personal vs. non-personal sources of information used in the purchase of men’s apparel. Journal of Consumer Studies & Home Economics, 24(1), 67–73. [Google Scholar] [CrossRef]
  43. Kivetz, R., Netzer, O., & Srinivasan, V. (2004). Alternative models for capturing the compromise effect. Journal of Marketing Research, 41(3), 237–257. [Google Scholar] [CrossRef]
  44. Klein, N. M., & Oglethorpe, J. E. (1987). Cognitive reference points in consumer decision making. Journal of Consumer Research, 14(1), 183–187. [Google Scholar]
  45. Koh, D. (2024). Loss aversion distribution: The science behind loss aversion exhibited by sellers of perishable good. Journal of Behavioral Data Science, 4(1), 63–80. [Google Scholar] [CrossRef]
  46. Lauterbach, B., Mugerman, Y., & Shemesh, J. (2024). Prospect theory in M&A: Do historical purchase prices affect merger offer premiums and announcement returns? Journal of Behavioral and Experimental Finance, 42, 100931. [Google Scholar] [CrossRef]
  47. Levac, D., Colquhoun, H., & O’Brien, K. K. (2010). Scoping studies: Advancing the methodology. Implementation Science, 5(1), 69. [Google Scholar] [CrossRef] [PubMed]
  48. Lin, G., Lin, M. S., & Song, H. (2024). An assessment of prospect theory in tourism decision-making research. Journal of Travel Research, 63(2), 275–297. [Google Scholar] [CrossRef]
  49. Liu, H., Meng-Lewis, Y., Ibrahim, F., & Zhu, X. (2021). Superfoods, super healthy: Myth or reality? Examining consumers’ repurchase and WOM intention regarding superfoods: A theory of consumption values perspective. Journal of Business Research, 137, 69–88. [Google Scholar] [CrossRef]
  50. Liu, S., Gao, L., Chen, J., & Yan, J. (2024). Single-neuron analysis of axon arbors reveals distinct presynaptic organizations between feedforward and feedback projections. Cell Reports, 43(1), 113590. [Google Scholar] [CrossRef] [PubMed]
  51. Mak, S., & Thomas, A. (2022). An introduction to scoping reviews. Journal of Graduate Medical Education, 14(5), 561–564. [Google Scholar] [CrossRef] [PubMed]
  52. Manthiou, A., Luong, V. H., Ayadi, K., & Klaus, P. (2024). The metaverse experience: A big data approach to virtual service consumption. International Journal of Contemporary Hospitality Management. ahead-of-print. [Google Scholar] [CrossRef]
  53. Marshall, R., Huan, T.-C., Xu, Y., & Nam, I. (2011). Extending prospect theory cross-culturally by examining switching behavior in consumer and business-to-business contexts. Journal of Business Research, 64(8), 871–878. [Google Scholar] [CrossRef]
  54. Mead, N. L., & Williams, L. E. (2022). The pursuit of meaning and the preference for less expensive options. Journal of Consumer Research, 49(5), 741–761. [Google Scholar] [CrossRef]
  55. Meunier, L., & Ohadi, S. (2023). When are two portfolios better than one? A prospect theory approach. Theory and Decision, 94(3), 503–538. [Google Scholar] [CrossRef]
  56. Moustapha, F. A., Ertz, M., & Ouerghemmi, C. (2024). Virtual tasting in the metaverse: Technological advances and consumer behavior impacts. Multimodal Technologies and Interaction, 8(10), 92. [Google Scholar] [CrossRef]
  57. Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medical Research Methodology, 18(1), 143. [Google Scholar] [CrossRef] [PubMed]
  58. Munro, A., & Sugden, R. (2003). On the theory of reference-dependent preferences. Journal of Economic Behavior & Organization, 50(4), 407–428. [Google Scholar] [CrossRef]
  59. Mussweiler, T. (2003). Comparison processes in social judgment: Mechanisms and consequences. Psychological Review, 110(3), 472–489. [Google Scholar] [CrossRef] [PubMed]
  60. Nicolau, J. L., Shin, H., Kim, B., & O’Connell, J. F. (2023). The Impact of loss aversion and diminishing sensitivity on airline revenue: Price sensitivity in cabin classes. Journal of Travel Research, 62(3), 685–698. [Google Scholar] [CrossRef]
  61. Nittala, R., & Moturu, V. R. (2023). Role of pro-environmental post-purchase behaviour in green consumer behaviour. Vilakshan—XIMB Journal of Management, 20(1), 82–97. [Google Scholar] [CrossRef]
  62. Obukhovich, S., Sipilä, J., & Tarkiainen, A. (2024). Post-purchase effects of impulse buying: A review and research agenda. Journal of Consumer Behaviour, 23(3), 1512–1527. [Google Scholar] [CrossRef]
  63. Pandey, P. K., Pandey, P. K., Mahajan, S., Behera, J., & Tausif, M. R. (2024). Mitigating negative externalities in the metaverse: Challenges and strategies. In Green metaverse for greener economies (pp. 247–269). CRC Press. [Google Scholar]
  64. Park, J., & Kim, N. (2023). Examining self-congruence between user and avatar in purchasing behavior from the metaverse to the real world. Journal of Global Fashion Marketing, 15(1), 23–38. [Google Scholar] [CrossRef]
  65. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1992). Behavioral decision research: A constructive processing perspective. Annual Review of Psychology, 43, 87–131. [Google Scholar] [CrossRef]
  66. Pelka, S., Bosch, A., Chappin, E. J., Liesenhoff, F., Kühnbach, M., & de Vries, L. (2024). To charge or not to charge? Using prospect theory to model the tradeoffs of electric vehicle users. Sustainability Science, 19, 793–813. [Google Scholar] [CrossRef]
  67. Schwarz, N. (2004). Metacognitive experiences in consumer judgment and decision making. Journal of Consumer Psychology, 14(4), 332–348. [Google Scholar] [CrossRef]
  68. Shah, A. M., Eisenkraft, N., Bettman, J. R., & Chartrand, T. L. (2016). “Paper or plastic?”: How we pay influences post-transaction connection. Journal of Consumer Research, 42(5), 688–708. [Google Scholar] [CrossRef]
  69. Sharma, P., Ueno, A., Dennis, C., & Turan, C. P. (2023). Emerging digital technologies and consumer decision-making in retail sector: Towards an integrative conceptual framework. Computers in Human Behavior, 148, 107913. [Google Scholar] [CrossRef]
  70. Sheth, J. N., Newman, B. I., & Gross, B. L. (1991). Why we buy what we buy: A theory of consumption values. Journal of Business Research, 22(2), 159–170. [Google Scholar] [CrossRef]
  71. Simonson, I. (1989). Choice based on reasons: The case of attraction and compromise effects. Journal of Consumer Research, 16(2), 158–174. [Google Scholar] [CrossRef]
  72. Simonson, I., & Tversky, A. (1992). Choice in context: Tradeoff contrast and extremeness aversion. Journal of Marketing Research, 29(3), 281–295. [Google Scholar] [CrossRef]
  73. Sivakumar, K., & Feng, C. (2019). Patterns of product improvements and customer response. Journal of Business Research, 104, 27–43. [Google Scholar] [CrossRef]
  74. Sridhar, S., Lamberton, C., Marinova, D., & Swaminathan, V. (2022). JM: Promoting catalysis in marketing scholarship. Journal of Marketing, 87(1), 1–9. [Google Scholar] [CrossRef]
  75. Stommel, E., Gottschalck, N., Hack, A., Eddleston, K. A., Kellermanns, F., & Kraiczy, N. (2024). What is your reference point? How price volatility and organizational context affect the reference points of family and nonfamily managers. Small Business Economics, 63(2), 805–829. [Google Scholar] [CrossRef]
  76. Tariq, M. U. (2025). Revolutionizing retail: Enhancing customer experiences with the metaverse. In M. A. Gonçalves Rodrigues, M. A. Carvalho, & J. F. Monteiro Pratas (Eds.), Cases on metaverse and consumer experiences (pp. 175–202). IGI Global Scientific Publishing. [Google Scholar] [CrossRef]
  77. Tarnanidis, T. (2023). Understanding reference points to make purchase decisions: Overview, phases, and time-features. Marketing Science & Inspirations, 18(4), 2–13. [Google Scholar] [CrossRef]
  78. Tarnanidis, T. (Ed.). (2024). Consumer experience and decision-making in the metaverse. IGI Globa. [Google Scholar] [CrossRef]
  79. Tarnanidis, T., & Manaf, A. H. (2024). Research notes on the future of marketing and consumer insights. In T. Tarnanidis, & N. Sklavounos (Eds.), New trends in marketing and consumer science (pp. 324–336). IGI Global. [Google Scholar] [CrossRef]
  80. Tarnanidis, T., Owusu-Frimpong, N., & Marciniak, R. (2010). Consumer choice: Between explicit and implicit reference points. The Marketing Review, 10(3), 269–286. [Google Scholar] [CrossRef]
  81. Tarnanidis, T., Owusu-Frimpong, N., & Nwankwo, S. (2020). How consumers’ make purchase decisions with the use of reference points. Journal of Marketing and Consumer Research, 68, 33–46. [Google Scholar] [CrossRef]
  82. Tarnanidis, T., Owusu-Frimpong, N., Nwankwo, S., & Omar, M. (2015). Why we buy? Modeling consumer selection of referents. Journal of Retailing and Consumer Services, 22(1), 24–36. [Google Scholar] [CrossRef]
  83. Tarnanidis, T., Papathanasiou, J., Osei-Frimpong, K., & Owusu-Frimpong, N. (2024). How young consumers are influenced by the valence of positive and negative frames: A cross-cultural perspective. International Journal of Information and Decision Sciences, 16(2), 109–133. [Google Scholar] [CrossRef]
  84. Tarnanidis, T. K., & Sklavounos, N. (Eds.). (2024). New trends in marketing and consumer science. IGI Global. [Google Scholar] [CrossRef]
  85. Thaler, R. H. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199–214. [Google Scholar] [CrossRef]
  86. Tversky, A. (1972). Elimination by aspects: A theory of choice. Psychological Review, 79(4), 281–299. [Google Scholar] [CrossRef]
  87. Tversky, A., & Kahneman, D. (1986). Rational choice and the framing of decisions. Journal of Business, 59(4), 251–278. [Google Scholar] [CrossRef]
  88. Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: A reference-dependent model. Quarterly Journal of Economics, 106, 1039–1061. [Google Scholar] [CrossRef]
  89. Tversky, A., & Simonson, I. (1993). Context dependent preferences. Management Science, 39, 1179–1189. [Google Scholar] [CrossRef]
  90. Van Heerde, H. J., Moorman, C., Moreau, C. P., & Palmatier, R. W. (2021). Reality check: Infusing ecological value into academic marketing research. Journal of Marketing, 85(2), 1–13. [Google Scholar] [CrossRef]
  91. Vrtana, D., & Krizanova, A. (2023). The power of emotional advertising appeals: Examining their influence on consumer purchasing behavior and brand–customer relationship. Sustainability, 15(18), 13337. [Google Scholar] [CrossRef]
  92. Wallace, S., & Etkin, J. (2024). Range goals as dual reference points. Organizational Behavior and Human Decision Processes, 184, 104340. [Google Scholar] [CrossRef]
  93. Wang, J., Wang, T., Shi, Y., Xu, D., Chen, Y., & Wu, J. (2022). Metaverse, SED model, and new theory of value. Complexity, 2022(1), 4771516. [Google Scholar] [CrossRef]
  94. Wang, L., Feng, X., & Zang, L. (2024). Does risk perception influence individual investors’ crowdfunding investment decision-making behavior in the metaverse tourism? Finance Research Letters, 62, 105168. [Google Scholar] [CrossRef]
  95. Wang, P., Sun, L., Zhang, L., & Niraj, R. (2021). Reference points in consumer choice models: A review and future research agenda. International Journal of Consumer Studies, 45(5), 985–1006. [Google Scholar] [CrossRef]
  96. Werner, K. M., & Zank, H. (2019). A revealed reference point for prospect theory. Economic Theory, 67(4), 731–773. [Google Scholar] [CrossRef]
  97. Woo, K. S., & Lam, S. S. K. (1997). Measuring service quality: A test-retest reliability investigation of servqual. Market Research Society Journal, 39(2), 1–18. [Google Scholar] [CrossRef]
  98. Wu, W., Wang, X., & Xia, Q. (2024). Why leave items in the shopping cart? The impact of consumer filtering behavior. Information Processing & Management, 61(6), 103854. [Google Scholar] [CrossRef]
  99. Yin, X., Li, J., Si, H., & Wu, P. (2024). Attention marketing in fragmented entertainment: How advertising embedding influences purchase decision in short-form video apps. Journal of Retailing and Consumer Services, 76, 103572. [Google Scholar] [CrossRef]
Admsci 15 00287 g001
Figure 1. Hypothetical value function. Source: Adapted from Kahneman and Tversky (1979, p. 279).
Figure 1. Hypothetical value function. Source: Adapted from Kahneman and Tversky (1979, p. 279).
Admsci 15 00287 g001
Admsci 15 00287 g002
Figure 2. Purchase rates by pricing and framing strategy. Source: Created by authors.
Figure 2. Purchase rates by pricing and framing strategy. Source: Created by authors.
Admsci 15 00287 g002
Table 1. Prospect theory’s future vision for consumers’ shopping preferences.
Table 1. Prospect theory’s future vision for consumers’ shopping preferences.
Authors/PublicationsProspect TheoryMetaverse ExamplePrimary ConclusionsPractical Implications
(a) Reference pointsPeople evaluate outcomes relative to a reference point (e.g., the standard price of an item). The perception of a change as a gain or a loss is influenced by this reference point.A shopper in the metaverse buys a carton of milk every day for $2. They may perceive a gain based on their reference point of $2 if they log in and observe that the price has gone down to $1.50. However, if the price increases to $2.50, they will experience a loss, even though the monetary change is only $0.50 either way.In the metaverse, shoppers compare prices to their internal reference points, reacting with greater force when faced with perceived losses than gains.Retailers in the metaverse have the option to influence shopper behavior by utilizing reference pricing data.
(Kahneman & Tversky, 1979), JSTOR;
(Kahneman, 2003), PBE
(T. Tarnanidis et al., 2010), TMR
(Barberis, 2013), JEP
(T. Tarnanidis et al., 2015), JRCS
(T. Tarnanidis, 2023), MSI
(T. Tarnanidis, 2024), IGI
(b) Loss AversionThe idea that people experience losses more acutely than gains is known as loss aversion. In metaverse shopping, this principle can be utilized in multiple ways.Shoppers can buy a discounted digital version of their daily coffee at a virtual store in the metaverse. If the shopper does not take advantage of the offer within an hour, the price will return to normal.It is possible to encourage quicker decision-making by pointing out potential losses (missed deals, time-limited discounts)The pain of losing this discount (perceived loss) will exceed the joy of gaining it, according to prospect theory.
(Tversky & Kahneman, 1991). QJE
(Abdellaoui et al., 2007), MS
(Marshall et al., 2011)JBR
(Sivakumar & Feng, 2019), JBR
(H. Liu et al., 2021), JBR
(Lauterbach et al., 2024), JBEF
(c) Framing EffectsThe way a decision or choice is presented (framed) has an impact on shoppers’ reactions in the metaverse. Different behaviors can be caused by the same outcome being framed as either a gain or a loss.
  • Positive Frame: Today is the day to save $5 on your daily groceries!
  • Negative frame: “Don’t miss out on saving $5!” Tomorrow, prices will return to their normal state.
Promoting losses (Don’t miss) is more effective than promoting gains (Save now). People tend to be more sensitive to the fear of losing a deal than to the satisfaction of obtaining one.Using negative framing (highlighting potential loss) will be more effective in persuading shoppers to take action.
(Tversky, 1972), PR
(Tversky & Kahneman, 1986). JB
(Simonson & Tversky, 1992), JMR
(Bolton & Madhavaram, 2025), JMTP
(Wallace & Etkin, 2024), ORBHDP
(T. Tarnanidis et al., 2024), IJIDS
(Emami et al., 2024), JEC
(d) Diminishing SensitivityThe psychological impact of both gains and losses decreases with the increase in magnitude. When a metaverse shopper saves $2 on a $10 item, they feel a stronger sense of satisfaction than when they save $2 on a $100 item, which feels insignificant. On the other hand, a $2 price increase makes a low-cost item much more unpleasant to deal with.The emotional impact of small price changes on low-cost items is greater, so frequent micro-discounts may lead to more purchases.Small changes are felt more strongly by people when the amounts are small.
(Simonson, 1989), JCR
(Kahneman, 1992), OBHDP
(Kivetz et al., 2004), JMR
(P. Wang et al., 2021), FRL
(Nicolau et al., 2023), JTR
(e) Mental AccountingMoney is classified into various “accounts” by individuals, such as money for groceries or entertainment. Different spending behaviors may result from shoppers treating the virtual and realworld separately in the metaverse.The virtual wallet (which holds cryptocurrency or in-game currency) may be treated differently by shoppers in the metaverse than their real-world bank account. Virtual currency may offer shoppers more comfort when making impulsive purchases because they can mentally separate it from their actual money, even though the value remains the same.The treatment of virtual currency differs from real money, resulting in more impulsive spending on daily shopping items.Retailers can encourage shoppers to spend more freely in the metaverse shopping experience by integrating virtual currencies or reward points.
(Tversky & Simonson, 1993), MS
(Chernev, 2005), JCR
(Van Heerde et al., 2021), JM
(J. Wang et al., 2022), Complexity
(Meunier & Ohadi, 2023), TAD
(L. Wang et al., 2024), JCS
(Fortin & Hlouskova, 2024), QREF
(T. Chen et al., 2025), JRCS
Table 2. Relationship between prospect theory and consumer reference points.
Table 2. Relationship between prospect theory and consumer reference points.
Prospect Theory CharacteristicsConsumer Reference Points’ Implications
Value Function: Prospect theory posits a value function that explains how people perceive gains and losses. Those who are faced with gains tend to be risk-averse, while those who are faced with losses tend to be risk-seeking.Gains: Consumer reference points contribute to defining what individuals consider gains or losses. For example, getting a discount on a product might be perceived as a gain, while paying a higher-than-expected price might be perceived as a loss.
Endowment Effect: The endowment effect, a psychological phenomenon where people tend to ascribe higher value to things merely because they own them, can be explained by prospect theory.Ownership: Consumer reference points are often tied to ownership. Once someone owns a product, that ownership becomes a reference point, and the perceived value of the item increases.
Loss Aversion: Loss aversion is a key concept in prospect theory, stating that losses typically have a greater impact on decision-making than equivalent gains.Losses: Consumer reference points influence what individuals perceive as losses. For instance, if a consumer expected a product to be available at a certain price, paying more than that reference point can be perceived as a loss.
Anchoring: Anchoring is a cognitive bias in which individuals rely too heavily on the first piece of information encountered when making decisions.Anchoring: Consumer reference points can act as anchors. For instance, an initial price tag or an advertised “original” price can serve as a reference point, influencing consumers’ perceptions of value and willingness to pay.
Source: Created by authors.
Table 3. Illustration of the decision-making process and consumer reference points to be applied.
Table 3. Illustration of the decision-making process and consumer reference points to be applied.
Decision Stages (1–5)Practical ImplicationsConsumer ImplicationsImplications for Research
Problem
Recognition		The recognition of a problem or a need is often what kicks off the decision-making process.Consumers may recognize a difference between their present state and their desired state (Schwarz, 2004; Yin et al., 2024).Past experiences, recommendations, or exposure to marketing messages can trigger reference points at this stage.
Information SearchConsumers begin an information search once they recognize the problem.During this stage, consumers are actively seeking information about potential solutions (Sharma et al., 2023; T. Tarnanidis, 2023; T. Tarnanidis & Manaf, 2024).Referencing points are used by consumers when comparing different products or services. They may rely on personal experiences, word of mouth, reviews, and brand reputation as reference points during the information search.
Evaluation of AlternativesConsumers evaluate the available alternatives using their reference points, which align with their preferences and criteria.Consumers can narrow down options by filtering and prioritizing options with reference points (Davis, 2022; T. Tarnanidis et al., 2020).During this stage, consideration is given to factors such as price, quality, brand reputation, features, and reviews.
Purchase DecisionThe final decision made during the decision-making process is the buy decision.The chosen product or service satisfies the customer’s perceived value and fulfills their expectations based on the established reference criteria (Mead & Williams, 2022; Wu et al., 2024; T. Tarnanidis et al., 2015). The pros and cons of the alternatives have been weighed by consumers, taking into account their reference points.
Post-Purchase EvaluationUpon making a purchase, consumers have the option to evaluate their satisfaction with the product or service.Positive reference points for the chosen brand are strengthened when the experience is positive. In the event of a negative outcome, it could alter or create new reference points for future decisions (Obukhovich et al., 2024; Nittala & Moturu, 2023)This evaluation plays a role in creating future reference points.
Source: Created by authors.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Tarnanidis, T.; Owusu-Frimpong, N.; Sousa, B.B.; Manda, V.K.; Vlachopoulou, M. Purchasing Decisions with Reference Points and Prospect Theory in the Metaverse. Adm. Sci. 2025, 15, 287. https://doi.org/10.3390/admsci15080287

AMA Style

Tarnanidis T, Owusu-Frimpong N, Sousa BB, Manda VK, Vlachopoulou M. Purchasing Decisions with Reference Points and Prospect Theory in the Metaverse. Administrative Sciences. 2025; 15(8):287. https://doi.org/10.3390/admsci15080287

Chicago/Turabian Style

Tarnanidis, Theodore, Nana Owusu-Frimpong, Bruno Barbosa Sousa, Vijaya Kittu Manda, and Maro Vlachopoulou. 2025. "Purchasing Decisions with Reference Points and Prospect Theory in the Metaverse" Administrative Sciences 15, no. 8: 287. https://doi.org/10.3390/admsci15080287

APA Style

Tarnanidis, T., Owusu-Frimpong, N., Sousa, B. B., Manda, V. K., & Vlachopoulou, M. (2025). Purchasing Decisions with Reference Points and Prospect Theory in the Metaverse. Administrative Sciences, 15(8), 287. https://doi.org/10.3390/admsci15080287

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop