A Typology of Consumers Based on Their Phygital Behaviors

Abstract

The article aims to identify consumer types based on their attitudes and behaviors toward phygital tools and solutions. The analysis was based on the authors’ empirical research. The research was conducted on a sample of 2160 Polish consumers. The study employed an online survey technique. To determine the types of consumers, a 20-item scale was used, allowing the respondents to express their attitudes toward solutions and tools that improve shopping in the phygital space. The extraction of types was carried out in two steps. The first was cluster analysis, conducted using the hierarchical Ward method with the square of the Euclidean distance, and the second was non-hierarchical cluster analysis using the k-means method. As a result of the analyses, three relatively homogeneous types of consumers were distinguished: phygital integrators, digital frequenters, and physical reality anchors. The behaviours of consumers from each type were examined in the context of their impact on sustainable consumption and the sustainable development of the planet. The proposed typology contributes to developing consumer behavior theory in sustainable consumption environments. It provides practical implications for designing customer experiences that are more inclusive, resource-efficient, and aligned with responsible consumption patterns. Understanding how different consumer groups engage with phygital tools allows businesses and policymakers to tailor strategies that support equitable access to digital services and foster more sustainable, adaptive consumption journeys in an increasingly digitized marketplace.

1. Introduction

The digital economy has created a new consumer who operates at the intersection of two worlds: the physical and the digital. The effects of the recent COVID-19 pandemic have accelerated and dynamized the development of modern technologies, affecting the digital competencies of consumers on an unprecedented scale [1,2]. People from older generations, such as BB and X, have faced the dilemma of adapting to new market conditions or remaining on the edge of the digital reality. In turn, for younger generations, especially Generation Z, the digital world is the only world they know. Also referred to as iGen [3], they are becoming digital natives compared to others who, expanding their previous competencies acquired through physical experiences, migrate to the world of digital opportunities [4].

Consumer behavior in the phygital environment is a relatively new area of research that requires new approaches that consider both cognitive and contextual aspects of physical–digital interactions [5]. Recent studies emphasize the importance of interpretative perspectives and user-centric approaches that better capture the complexity and subjectivity of experiences in this hybrid environment [6,7]. With the accelerating digital transformation, a typology of consumers based on their phygital behaviors becomes essential for understanding new models of segmentation, branding, and the psychology of consumption.

In the context of the dynamic digital transformation, the growing importance of sustainable consumption cannot be overlooked. The development of e-commerce and digital shopping channels has brought about new environmental and social challenges, such as overconsumption, an increase in product returns, higher emissions related to logistics, and additional waste generated from packaging [8]. As noted by Mora et al. [9], digital sales channels can both support and undermine the goals of sustainable development, depending on consumer behavior patterns and the level of environmental awareness. Phygital shopping experiences, which combine physical interactions with digital technologies, offer the potential to design more sustainable consumption pathways, e.g., through better product matching, limiting unnecessary purchases, or using immersive technologies to educate consumers about the environmental impact of their choices.

Consequently, a consumer typology based on behaviors in the phygital environment can represent an important step toward identifying consumer segments more inclined to engage in sustainable consumption, as well as those requiring educational and systemic support. Understanding the preferences and attitudes of different phygital consumer types may enable the design of marketing and communication strategies that promote responsible purchasing decisions [10]. In this context, the present study contributes to the deepening of knowledge about the relationship between digital transformation and consumption practices aligned with the principles of sustainable development.

Therefore, the article aims to distinguish relatively homogeneous types of consumers based on their acceptance of phygital solutions and tools and their willingness/readiness to use them in the purchasing process. With this goal in mind, we put forward three research hypotheses:

H1. 

New information and communication solutions have created an integrated physical and digital space, facilitating the emergence of new types of consumers.

H2. 

Phygital consumers are consumers for whom physical and digital solutions complement each other and create a coherent behavior space. This is the type of consumer whose behavior will significantly influence the transformation of many market areas in the near future.

H3. 

Attitudes and behaviors toward phygital tools and solutions differentiate consumers. Depending on the generational group, consumers treat digital solutions as an extension of physical solutions (so-called digital immigrants) or physical solutions as an extension of digital solutions (so-called digital natives).

The article makes several contributions to closely related streams of consumer behavior research. First, it expands the theories on consumers’ purchasing and consumption behavior operating at the interface of two worlds: physical and digital (phygital consumers). Second, it complements research on social awareness and acceptance of phygital tools and solutions created by Industry 5.0. Third, it contributes to developing cluster analysis methodology and its use in measuring and reporting contemporary socio-economic phenomena. It should be noted that the scientific achievements on consumer attitudes toward phygital tools and solutions are relatively small. Therefore, this article aims to fill this knowledge gap. This work may interest scientists and practitioners involved in implementing modern solutions and tools based on digital technologies, and anyone who wants to expand their knowledge in this area.

This article reviews the literature on consumer research operating at the interface of the physical and digital worlds. Then, we describe the research approach adopted, including survey and analytical methods and the data used. We conclude the article with an overview, a discussion of the results, and a presentation of conclusions. We also describe the limitations of our research and indicate its further directions.

2. Literature Review

2.1. The Digital Economy as a Creator of New Consumption Spaces

The digital economy’s development has significantly transformed how consumers interact with brands of goods and services [11]. Unlike consumers of the late 20th and early 21st centuries—who operated mainly under the influence of mass media, physical sales channels, and limited access to information—today’s consumer operates in a complex phygital environment, combining physical and digital experiences [12]. This transformation has changed the channels of purchase and the psychological, behavioral, and social dimensions of consumption [13]. By creating new consumption spaces, the digital economy has enabled consumers to meet their needs more individually, flexibly, and personally [14].

This transformation is driven by unprecedented access to information. In the digital economy, consumers are permanently connected to the web, with tools to compare offers in real time, browse ratings and reviews, and consume algorithmically personalized content [15]. As Thaler and Sunstein [16] noted in the context of behavioral economics, the architecture of choice—currently dominated by digital interfaces—profoundly impacts individual decisions. Online platforms, mobile applications, and recommendation systems based on artificial intelligence act as digital “nudges,” subtly guiding consumer decisions, often without their full awareness.

At the same time, the digital economy has led to the phenomenon of datafication of consumer behavior, understood as the process of transforming various aspects of social life into digital data that can be stored, analyzed, and used for various purposes [17,18]. Every click, search, or interaction generates data that companies transform into predictive models of individual preferences [19,20]. This level of personalization was unthinkable in the pre-digital era. Ariely [21] emphasized that consumer decisions are rarely rational—they are shaped by context, emotions, and cognitive biases, which are now reinforced by algorithms that can anticipate and use them. Datafication affects many areas of life, from education to professional work. For example, in the context of higher education, Selwyn and Gašević [22] discussed the opportunities and threats associated with datafication, emphasizing the need for an interdisciplinary approach to the analysis of this phenomenon and the consideration of both societal and technical perspectives.

At the same time, the boundaries between physical and digital spaces are becoming increasingly fluid. The rise of phygital experiences—the seamless integration of digital technologies with the physical environment—has become a key factor in consumer engagement [23]. The phygital environment allows consumers to physically touch a product while simultaneously interacting with digital content, e.g., through augmented reality (AR), virtual fitting rooms, or interactive screens [24,25].

This new consumption environment has also reshaped consumer expectations, meeting their hedonism and utilitarianism. Convenience, speed, usefulness, and personalization have become the norm, especially for the digital native generation. However, this convenience often comes at the cost of privacy restriction, which leads to the so-called privacy paradox described by Norberg, Horne, and Horne [26]: On the one hand, consumers declare that they care about their data, and on the other, they share information in exchange for functional or financial benefits.

2.2. Who Is the Modern Consumer?

Although the beginnings of the digital era can be traced back to earlier decades, the 1990s are often cited as a key period of transformation toward a digital economy and the emergence of the digital consumer [27,28].

The modern consumer, operating in the digital economy, displays a number of features that distinguish them from the consumer from before the digital era [29]. These changes have their source in developing information and communication technologies, digitalizing trade and services, and the growing role of data in consumer decision-making. Based on the literature on behavioral economics, economic psychology, and consumer behavior, the following features of the modern consumer can be distinguished:

• Information redundancy and cognitive selectivity: The consumer can access vast data and information, leading to information overload and the so-called choice paradox [30]. This phenomenon causes them to make decisions in a simplified way based on heuristics (e.g., product popularity, ratings of other users, and availability), which is consistent with the findings of Tversky and Kahneman [31] on bounded rationality.
• Omnichannel and seamless shopping experience: Consumers expect seamless transitions between channels—online, mobile, offline—while maintaining a consistent experience. Speed, personalization, and accessibility matter [32].
• Increased susceptibility to behavioral marketing techniques: Companies use behavioral data (e.g., clicks and purchase history) to personalize offers, create nudges, and manipulate choices. Consumers are often unaware that their decisions are shaped by algorithms and predictive mechanisms [16].
• Greater expectations regarding speed, convenience, and personalization: Consumers expect immediate availability of goods and services, intuitive interfaces, and offers tailored to their needs—which forces companies to invest in automation, the development of big data, artificial intelligence (AI), or the logic of “real-time personalization” [20].
• Data sharing ambiguity: Consumers increasingly understand the risks associated with sharing personal data, but they often agree to do so in exchange for convenience or discounts [15], which leads to the previously described privacy paradox [26]. Trust is becoming one of the key resources of brand relationships [33].
• The rise of online communities and reviews: Consumer decision-making processes are now largely shaped by information from social media, rankings, and ratings from other users. The modern consumer operates in an environment of social proof [34]. They trust influencers, YouTubers, local guides, and online reviews more than company information [35].
• Prosumer nature of consumption: Consumers not only buy but also co-create value (e.g., reviews, recommendations, and participation in brand communities). Their influence on the image of brands has become more direct and interactive [36], and with brands, they build their value [23].

Before the digital era, consumer information sources were mainly limited to advertising and retail outlets. However, now we can observe multi-channel and, above all, unlimited and interactive access to many sources of information. Similar changes can be observed in consumer contact with the brand or company itself. Consumers have stopped relying on physical, single-channel contact, increasingly focusing on digital, automated, omnichannel contact. The consumer decision-making style used to be linear, preceded by reflection, while now it is fast, heuristic, and highly contextual. Their purchasing decisions, previously based on loyalty and tradition, have become dynamic and susceptible to personalization.

2.3. The Phygital Consumer: The Consumer on the Edge of Two Worlds

Can we then talk about the existence of a digital consumer? A consumer who bases their decisions, choices, actions, and communication solely on digital solutions? Looking at current consumer behavior, probably not. Despite the many opportunities the digital economy has opened up, modern consumers have not entirely abandoned physical solutions.

Utilizing innovative information technologies to link consumers’ physical locations with the digital space creates entirely new conditions for consumer behavior, especially regarding their decision-making processes. It contributes to the emergence of a new type of consumer—the “phygital consumer,” experiencing a physical–digital space of choices, decisions, and purchasing (and non-purchase) activities. New solutions allow them to experience interactive and unique experiences (consumer experience), and value for the consumer is created through the possibility of a seamless transition (consumer journey) from real space to digital space (and vice versa).

The term “phygital” was first used in 2007 by Chris Weil to describe the inextricable connections between the physical and digital worlds [37]. Since then, the term has been a foundation for theoretical constructs, models, and practices that operate at the intersection of both dimensions of consumer experiences. In the academic literature, the term “phygital” was further developed by Wided Batat, who proposed the phygital customer experience (PH-CX) framework, emphasizing the need for a holistic approach to designing consumer experiences that combine offline and online elements into a coherent whole [7].

Despite its growing importance in market practice [38,39,40,41], the very concept of the “phygital consumer” remains poorly conceptualized in the scientific literature. Few studies systematically attempt to capture this type of consumer as a market entity. The phygital space of consumer behavior is often wrongly identified with the multi-channel availability of the offer for consumers. At the same time, its essence is the qualitative integration of physical and digital experiences [42].

For our work, the phygital consumer is defined as a market participant who, in their behaviors—especially in decision-making and purchasing processes—seamlessly combines solutions available in both the physical (offline) and digital (online) space. Their activity is not limited to using different channels but is based on integrating physical and digital experiences into a coherent and dynamic path (customer journey). The phygital consumer makes real-time decisions, moving between the online and offline worlds, often alternately or in parallel. This allows them to flexibly use the advantages of both spaces and co-create value as part of an interactive shopping experience (consumer experience). In this approach, phygital does not mean only multi-channel but deep integration of channels and consumer environments, redefining modern consumers’ operations.

As businesses and institutions increasingly adopt phygital solutions and consumers show growing interest in integrated experiences, the importance of the phygital consumer as a key market participant is expected to increase. For supply-side entities, this means designing coherent and seamless customer journeys that effectively combine physical and digital elements, responding to changing consumer needs and expectations [7].

The literature increasingly addresses the relationship between the typology of phygital consumers (e-commerce shoppers) and their attitudes toward sustainable consumption. The transition to a digital environment may reinforce certain undesirable phenomena, such as impulsive purchases, overconsumption, or high return rates that generate waste [43]. At the same time, digital consumers do not represent a homogeneous group—they differ in their level of environmental awareness and their willingness to engage with solutions that support sustainable development [44,45]. In this context, distinguishing between types of consumers operating in the phygital environment—from passive, occasional users to advanced digital prosumers—can make a significant contribution to understanding their impact on achieving sustainable development goals. Analyzing consumer profiles not only reveals potential risks associated with the intensification of consumption through digital channels but also helps identify groups for whom effective educational, behavioral (e.g., nudging), or technological strategies can be applied to support responsible purchasing choices.

2.4. The Need for a Consumer Phygital Behavior Typology

As the physical and digital worlds intertwine, classic consumer segmentations—based primarily on demographics, lifestyles, or basic behavioral variables—are proving less and less adequate to describe contemporary consumers’ actual attitudes and actions [46]. In this new reality, phygital behaviors are becoming an important criterion for differentiating consumer attitudes, intentions, and, thus, behaviors [47].

Therefore, there is a need to develop a new typology of consumers, considering their engagement level in hybrid experiences, degree of technological proficiency, openness to new forms of interaction, and awareness of personal data management. Research indicates that consumers differ in the intensity of their use of digital technologies in the purchasing process and their perception of the value of such interactions [24,25]. Some treat digital tools as a primary source of comfort, personalization, and information, while others remain skeptical, preferring direct contact or demonstrating clear privacy concerns [26].

In addition, the contemporary shopping environment requires companies to recognize the existence of these different attitudes, intentions, and behaviors and systematically classify them, adjusting marketing, technological, and communication strategies. Therefore, the typology of consumers based on their phygital behaviors can play descriptive and predictive functions, indicating which groups of consumers are more susceptible to specific forms of engagement, loyalty, or innovation. Such an approach finds justification both in theories of behavioral economics, which emphasize the diversity of decision-making processes [16] and in practical approaches, focusing on designing user-centric consumer experiences [7].

3. Materials and Methods

3.1. Sample and Data Collection

3.1.1. Empirical Basis

The empirical part of the article is based on quantitative primary research. It was conducted employing the survey method, an online survey technique using the Ariadna Nationwide Research Panel [48]. The study adhered to ethical standards, having regard to the International Code of ICC/ESOMAR [49]. The study was anonymous. The collected data did not contain identifying features of the respondents. The research project and tool received a positive opinion from the Human Subject Research Ethics Committee at the University of Economics in Katowice (Opinion No. 008/09/2024).

3.1.2. Research Instrument

The research tool was a questionnaire consisting of 19 substantive questions and eight demographic questions. The substantive questions were formulated in the form of ordinal, bipolar, five- and seven-point scales, as well as in the form of nominal scales and cafeteria closed-ended questions. The reliability of the scales used was assessed by calculating Cronbach’s α coefficient for them, and if we assume, following [50], the permissible level of the coefficient of 0.71 < α < 0.94, then all scales used in the study can be considered reliable. To confirm the reliability of the scales used, the McDonald’s ω coefficient was also calculated [51]. The reliability results for the scales used were almost identical to those obtained using the α coefficient.

3.1.3. Research Procedure

The study was carried out in February 2025 and was preceded by a pilot study. The pilot study was conducted to eliminate possible errors in the research tool. A total of 39,997 people were invited to the survey, of which 4004 responded to the invitation and started filling in the questionnaire (10.01%). Three hundred fifty-six people did not complete the study due to stopping at various stages, and 1342 were rejected due to meeting their corresponding demographic quotas. Ultimately, 2306 people effectively completed the study, of which 146 people were excluded from the database during the collection check, mainly due to completing the questionnaire too quickly (less than 50% of the time allocated for the study). Ultimately, the sample consists of 2160 adult consumers.

3.1.4. Sample Characteristics

The study was conducted on a nationwide random-quota sample of people aged 18 and over, where total quotas were selected on the representation in the general population for the following variables: gender (2 categories) × age (5 categories) × place of residence size (5 categories), i.e., in a total of 50 strata. This means that people were randomly selected while assuming control of the sample structure. The quotas were chosen according to the representation of the population of adult Poles, taking into account the following features: gender, age, and place of residence size. The conducted research sample gives results with a no more than 2% measurement error at a confidence level of α = 0.95. The characteristics of the study sample are presented in

Table 1. Study sample characteristics (N = 2160).

Specification		Number of Observations	Percentage of Observations
Gender	Female	1149	53.2
	Male	1011	46.8
Generation	Z (age 18–24)	228	10.6
	Y (age 25–39)	519	24.0
	X (age 40–59)	792	36.7
	BB (age 60–80)	621	28.7
Education	Lower (primary and basic vocational)	275	12.7
	Secondary (secondary and post-secondary)	918	42.5
	Higher	967	44.8
Economic activity	Secular work	1316	60.9
	No secular work	84.4	39.1
Class of place of residence	Rural	821	38.0
	Small-sized city (up to 20 K inhabitants)	277	12.8
	Medium-sized city (between 20 K and 99 K inhabitants)	445	20.6
	Large-sized city (between 100 K and 500 K inhabitants)	352	16.3
	Major city (over 500 K inhabitants)	265	12.3
Number of persons in the household	1	313	14.5
	2	785	36.3
	3	464	21.5
	4	395	18.3
	5 and more	203	9.4
Subjective assessment of the financial situation of one’s own household	Very difficult	43	2.0
	Difficult	191	8.8
	Satisfying	786	36.4
	Good	845	39.1
	Very good	295	13.7

Data source: Collected by this research.

.

The average age of the surveyed consumers was 47.5. The median age was 48, and the dominant age was 66. The youngest surveyed was 18, and the oldest was 80.

3.2. Measures

3.2.1. Reliability of the Applied Scale

The study used a scale of 20 items, allowing respondents to express their attitude toward tools and solutions that facilitate shopping in the phygital space. The scale was a seven-point ordinal, bipolar scale, where the number −3 meant the answer “definitely negative attitude” and +3 “definitely positive attitude.” The scale’s reliability in the study was confirmed by Cronbach’s alpha and McDonald’s omega tests. The value of α was 0.935, and the value of ω was 0.932. Therefore, if we assume that the permissible level of the coefficients α and ω should be higher than 0.7 [50,51], then the scale used in the study can be considered highly reliable.

3.2.2. Cluster Analysis Procedure

To distinguish relatively homogeneous groups (types) of consumers, cluster analysis was employed, the numerous applications of which in consumer behavior research are mentioned by, among others, Yang, Lee, and Lee [52], Morar, Rusu, and Năstase [53], and Neunhoeffer and Teubner [54]. The typology process was carried out following the procedure proposed by Wegmann et al. [55], which can be presented in three steps: (1) Adoption of typology criteria, i.e., selection of a set of diagnostic variables based on which the typology will be carried out. (2) Delimitation, i.e., grouping consumers according to the adopted diagnostic criterion using cluster analysis algorithms. (3) Evaluation and verification of results and profiling of clusters were conducted, taking into account active variables (purchasing behavior) and descriptive variables (social, economic, and demographic characteristics).

The types of consumers were distinguished using two algorithms. First, the Ward method (hierarchical algorithm) was used with the square of the Euclidean distance, then the k-means method (non-hierarchical algorithm, based on partitioning) was used. The use of both algorithms results from methodological limitations. A non-hierarchical analysis is less sensitive to observations that deviate from the norm and incorrect variables, allowing for better results. However, it requires specifying the target number of groups of individuals to be distinguished, and this is not predetermined. To obtain this information, hierarchical cluster analysis should be used first [46]. The analysis of the agglomeration coefficient and the dendrogram obtained using the Ward method led to the selection of three consumer clusters. The first clear jump in agglomeration distance was revealed when dividing into three clusters, which suggests that further merging of clusters would lead to a significant blurring of their internal coherence [56]. Moreover, according to the approach proposed by Everitt et al. [57], a significant distance on the dendrogram axis at the point of division is considered a strong premise for extracting clusters at this level. After conducting a non-hierarchical analysis, their centroids were finally determined, and each object was assigned to the group whose centroid was closest to it [58].

The iterative process ended after 22 steps, meaning the algorithm has achieved convergence. Convergence was defined as a situation in which the maximum absolute change in the coordinates of any cluster center does not exceed the threshold value of 0.000. In the last iteration, all changes reached zero, meaning that further iterations would not improve the division. The process course indicates a gradual stabilization of cluster centers, which is typical for a well-calibrated k-means algorithm [56,59].

3.2.3. Quality of the Obtained Classification

The classification quality was assessed using the Silhouette score and the Caliński–Harabasz criterion (Pseudo-F). The Silhouette score for three clusters was 0.300632, which means that the value exceeds the threshold of 0.25 and is in the range of 0.26–0.50, indicating an acceptable quality of classification [60]. The Caliński–Harabasz criterion reached the value of 533.3946, which also confirms the obtained classification to be sound—the higher the value of this index, the better the separation of clusters [61]. For three clusters, the Pseudo-F value was higher than for four (456.4607), five (386.0893), or six clusters (342.6836), which additionally justifies the choice of three clusters as the optimal number. The distinguished types were given subjective names that best reflect the behavioral characteristics of consumers assigned to the given types.

All calculations were performed using IBM SPSS Statistics 29 software.

4. Analysis of the Results

As a result of the conducted cluster analysis, three relatively homogeneous types of consumers were distinguished in terms of their attitudes and behaviors toward phygital tools and solutions. The differences between the clusters were statistically significant, as indicated by the Chi-square test results (p < 0.001). These clusters reflect different approaches to integrating physical and digital channels into the purchasing process. The size of the types (the number of observations in each type) and their names are presented in

Table 2. Types of consumers based on their phygital behavior (N = 2160).

Type	Name	Number of Observations	Percentage of Observations
I	Phygital Integrators	968	44.8
II	Digital Frequenters	561	26.0
III	Physical Reality Anchors	631	29.2
Significant		2160	100.0
Lack-of-fit		0	0.0

Data source: Collected by this research.

.

The first type is consumers who combine the physical and digital worlds in a balanced way in the consumption process. They are characterized by a high level of openness to various phygital solutions—basic and advanced (e.g., interactive kiosks, digital labels, and omnichannel loyalty systems). Their behavior indicates a conscious integration of channels based on convenience, functionality, and fluidity of the shopping experience—

Table 3. Characteristics of consumers by their attitude toward phygital tools and solutions (N = 2160, in %).

Specification	Attitude *	Research Sample	Consumer Types
			I	II	III
1. Information touch screens	negative	5.2	1.8	0.4	14.5
	neutral	22.0	13.8	3.0	51.5
	positive	72.8	84.4	96.6	34.0
2. Interactive kiosk for purchasing	negative	9.2	4.0	0.4	24.9
	neutral	24.7	18.5	4.3	52.5
	positive	66.1	77.5	95.3	22.6
3. Virtual fitting rooms/mirrors that allow seeing oneself without physically trying on the product	negative	25.2	27.0	4.2	41.2
	neutral	35.0	38.0	14.8	48.3
	positive	39.8	35.0	80.9	10.5
4. A smart shopping cart that helps organize one’s shopping	negative	14.4	8.8	0.9	34.8
	neutral	30.1	30.3	6.2	51.0
	positive	55.5	60.9	92.9	14.2
5. A smart shelf that works with the client’s application	negative	16.7	12.1	1.2	37.6
	neutral	33.4	30.3	6.2	51.0
	positive	49.9	60.9	92.9	14.2
6. A digital shelf label that works with a customer app	negative	11.1	12.1	1.2	37.6
	neutral	26.8	37.2	6.6	51.5
	positive	62.1	60.9	92.9	14.2
7. QR Codes	negative	10.2	12.1	1.2	37.6
	neutral	24.0	37.2	6.6	51.5
	positive	65.8	50.7	92.2	10.9
8. Chatbots and Virtual Assistants	negative	34.7	39.6	5.1	53.5
	neutral	29.4	32.9	12.7	39.1
	positive	35.9	77.3	96.6	21.0
9. Face Pay	negative	42.0	52.1	6.9	57.7
	neutral	28.3	28.6	18.0	37.1
	positive	29.7	19.3	75.1	5.2
10. Implant payment chip	negative	59.8	74.0	30.5	64.0
	neutral	21.3	16.3	18.2	31.7
	positive	18.9	9.7	51.3	4.3
11. NFC	negative	13.9	8.0	0.8	34.6
	neutral	23.2	19.5	4.6	45.5
	positive	62.9	72.5	97.6	19.9
12. Self-service check-out	negative	8.2	2.9	1.1	37.6
	neutral	15.3	6.8	2.7	39.6
	positive	76.5	90.3	96.2	37.6
13. Self-service scale with detailed product information	negative	5.4	1.9	0.2	15.3
	neutral	16.0	6.6	2.7	42.2
	positive	78.6	91.5	97.1	42.6
14. Unmanned stores (without staff)	negative	23.0	20.4	4.4	43.7
	neutral	30.4	32.7	8.7	46.0
	positive	46.6	46.9	86.9	10.3
15. Beacons for customer location in the outlet	negative	19.5	15.7	2.0	40.9
	neutral	37.5	42.9	10.9	53.1
	positive	43.0	41.4	87.1	6.0
16. Mobile Apps	negative	8.2	1.9	0.7	24.6
	neutral	19.8	11.2	3.2	47.9
	positive	72.0	86.9	96.1	27.5
17. Loyalty programs for online and offline shopping	negative	5.3	1.7	0.2	15.1
	neutral	21.4	6.6	2.3	42.8
	positive	62.5	91.7	97.5	42.1
18. Possibility to ask questions to artificial intelligence	negative	19.7	16.2	0.8	42.1
	neutral	26.4	26.1	7.8	43.3
	positive	53.9	57.7	91.4	14.6
19. Social Commerce	negative	28.0	29.1	5.0	46.8
	neutral	31.2	33.2	12.7	44.7
	positive	40.8	37.7	82.3	8.5
20. Click-and-Collect	negative	8.2	4.1	0.9	21.1
	neutral	22.6	15.9	5.3	48.2
	positive	69.2	80.0	93.8	30.7

* Seven-point Likert scale: −3 = strongly negative, 0 = neutral, +3 = strongly positive. Negative attitudes: −3 to −1; positive attitudes: +1 to +3. Data source: Collected by this research.

. Consumers who form Type I could, therefore, be called phygital integrators. They constitute the sample’s largest group of respondents (44.8%)—Table 2. Importantly, from the perspective of sustainable consumption, phygital integrators tend to adopt a more conscious and selective approach to shopping. By integrating digital and physical channels, they are better able to plan their purchases, avoid impulsive decisions, and reduce excessive buying and returns. This, in turn, can help mitigate negative environmental impacts such as resource waste or increased CO₂ emissions resulting from the transportation of goods.

The second type represents consumers for whom digital channels are the most frequent space of shopping activity. People from this group are characterized by the highest acceptance and use of almost all of the studied phygital tools and solutions, including solutions based on artificial intelligence, chatbots, virtual fitting rooms, location beacons, and biometric payments. In their case, physical channels complement the digital experience, not its foundation—Table 3. Digital frequenters, as this type of consumer could be called, constitute a relatively small group of respondents (26.0%)—Table 2. Importantly, in the context of sustainable development, digital dominants may pose a greater risk of overconsumption and its associated negative environmental consequences. Their frequent online purchases—often made impulsively—can lead to an increased number of returns, which contributes to additional packaging waste and higher emissions resulting from last-mile deliveries. Moreover, the rapid adoption of new technologies does not always go hand in hand with environmental awareness, highlighting the need for continued educational and regulatory efforts.

We can place consumers in the third group at the opposite end of the spectrum. They still base their shopping experiences mainly on traditional channels, although they are not entirely indifferent to selected digital solutions. These consumers favor basic technologies that facilitate shopping (such as mobile applications, self-service checkouts, or click-and-collect). Still, they avoid more advanced tools—especially those based on artificial intelligence, virtual reality, or biometrics—Table 3. In the surveyed sample, consumers from cluster III constitute the second largest group of respondents (29.2%)—Table 2. They could be described as physical reality anchors. In the context of sustainable consumption, consumers anchored in physicality may exhibit limited awareness of the environmental impact of their choices, particularly due to their lower use of digital tools that could support more informed purchasing decisions. However, their reduced activity in digital channels also means lower consumption of resources related to logistics and online packaging, which can have positive environmental effects.

When analyzing the distinguished types of consumers based on their preferences for using physical and digital channels at different stages of the purchasing process, phygital integrators demonstrate a balanced integration of digital and physical channels. Although digital solutions dominate stages such as searching for information (84.9%) or navigating to a store (76.5%), the act of purchase itself remains largely carried out in physical channels (45% still choose to buy offline). Phygital integrators seamlessly combine the possibilities offered by both worlds, digital and physical, choosing them depending on convenience and context (

Table 4. Characteristics of consumers based on their use of physical or digital solutions in the purchasing process (N = 2160, in %).

Specification	Solutions	Research Sample	Consumer Types
			I	II	III
When looking for inspiration/ideas for purchasing new goods/services, I primarily use:	physical	32.8	27.4	20.7	51.8
	digital	67.2	72.6	79.3	48.2
When looking for information about goods/services that interest me, I primarily use:	physical	23.0	15.1	15.7	41.5
	digital	77.0	84.9	84.3	58.5
When looking for a way to a retail/service facility, I first use:	physical	30.0	23.5	20.0	49.0
	digital	70.0	76.5	80.0	51.0
When purchasing goods/services, I primarily use:	physical	47.8	45.0	32.4	65.6
	digital	52.2	55.0	67.6	34.4

Data source: Collected by this research.

). Such a shopping model allows for optimizing the customer experience while simultaneously reducing the negative environmental impact. The integration of channels offers an opportunity to reduce returns and encourage more thoughtful purchases, aligning with the concept of sustainable consumption.

In the case of Cluster II consumers, digital channels dominate the entire purchasing process. The high percentage of digital solutions used at each stage (e.g., 79.3% inspiration, 84.3% information, 80.0% navigation, and as much as 67.6% purchase) indicates their strong roots in the digital environment. Digital frequenters are consumers who are closest to the profile of digital natives, for whom traditional channels provide only marginal support.

Physical reality anchors are consumers who most often use traditional solutions at all stages of the purchasing process (e.g., 65.6% offline purchase and 51.8% information search). Consumers from Cluster III treat digital channels only as an auxiliary tool and adopt phygital solutions to a limited extent (Table 4).

Thanks to their demographic, economic, and social characteristics, the respondents classified into individual consumer types can be described in more detail (

Table 5. Socio-demographic and economic characteristics of selected consumer types (N = 2160, in %).

Specification		Research Sample	Consumer Types
			I	II	III
Gender	Female	53.2	57.1	53.3	47.1
	Male	46.8	42.9	46.7	52.9
Generation	Z (age 18–24)	10.6	11.2	13.7	6.8
	Y (age 25–39)	24.0	24.9	26.0	20.9
	X (age 40–59)	36.7	36.6	35.7	37.7
	BB (age 60–80)	28.7	27.4	24.6	34.5
Education	Primary/lower-secondary	2.7	2.0	3.4	3.2
	Basic vocational	10.0	8.1	9.6	13.5
	Secondary	42.6	40.4	43.8	44.5
	Higher	44.7	49.6	43.1	38.8
Economic activity	Secular work	60.9	63.3	63.8	54.7
	No secular work	39.1	36.7	36.2	45.3
Number of persons in the household	1	14.5	14.2	11.9	17.3
	2	36.3	36.4	31.9	40.3
	3	21.5	22.1	2.6	19.5
	4	18.3	17.7	21.9	16.0
	5 persons and more	9.4	9.7	11.6	7.0
Subjective assessment of the financial situation of one’s own household	Very difficult	2.0	0.6	1.6	4.4
	Difficult	8.8	7.4	6.6	13.0
	Satisfying	36.4	34.9	33.5	41.2
	Good	39.1	42.3	42.8	31.1
	Very good	13.7	14.8	15.5	10.3
Place of residence by the number of inhabitants	Rural	38.0	36.4	38.1	40.4
	Small-sized city (up to 20 K inhabitants)	12.8	12.5	10.7	15.2
	Medium-sized city (between 20 K and 99 K inhabitants)	20.6	21.4	22.1	18.1
	Large-sized city (between 100 K and 500 K inhabitants)	16.3	17.3	15.2	15.8
	Major city (over 500 K inhabitants)	12.3	12.5	13.9	10.5

Data source: Collected by this research.

).

Thus, phygital integrators are professionally active people who assess their financial situation well. Generation X dominates among them, and the predominance of women and people with higher education is the most pronounced of all the distinguished types. Rural residents are the fewest among phygital integrators.

Younger consumers form the type of digital frequenters from Generations Y and Z, who are well-educated and have good and very good financial situations. They create family households with three or more people. They live in medium and large cities more often than consumers of other types.

In turn, physical reality anchors are most often represented by older consumers from Generations BB and X, more often male than female, with a lower level of education and a more difficult financial situation. Compared to other consumers, most live in rural areas, creating one-and two-person households. This cluster also has the most significant proportion of economically inactive people (Table 5).

Data on the respondents’ psychographic characteristics provided interesting information for the conducted typology (

Table 6. Psychographic characteristics of the identified consumer types (N = 2160, in %).

Specification		Research Sample	Consumer Types
			I	II	III
Energy flow direction	I like being the center of attention	20.1	17.8	28.2	16.5
	I avoid being the center of attention	79.9	82.2	71.8	83.5
Ecosystem preferences	I prefer to spend time at home	75.4	75.7	73.1	76.9
	I prefer to go out	24.6	24.3	26.9	23.1
Activity preferences	I prefer to spend my leisure time in an active manner.	61.6	63.1	65.2	55.9
	I prefer to spend my leisure time in a passive manner	38.4	36.9	34.8	44.1
Openness to novelty goods	I prefer what is known and proven	76.3	76.9	65.1	82.1
	I prefer what is new	24.7	23.1	34.9	17.9
Value priorities	Style/appearance is the most important for me	13.6	12.2	16.6	13.0
	Comfort/functionality is the most important for me	84.4	87.8	83.4	87.0
Decision-making style	I care what other people think of me	25.5	24.7	30.7	22.0
	The most important thing for me is what I think about myself	74.5	75.3	69.3	78.0
Attitude toward norms and authority figures	For the common good, I am willing to give up some of my civil liberties and follow the recommendations of the authorities	28.8	27.9	31.7	27.7
	Civil liberties are the most important to me; I do not intend to give them up	71.2	72.1	68.3	72.3
Time horizon	Here and now are the most important for me	50.7	47.8	46.9	58.6
	My thinking is focused on the future	49.3	52.2	53.1	41.4
Approach to social norms	I believe there should be one norm for all	37.4	35.2	36.5	41.4
	I believe there are always exceptions to the rule.	62.6	64.8	63.5	58.6
Attitude toward planning and time	Time is not flexible; one has to take deadlines seriously.	70.8	74.2	69.3	66.7
	Time is a relative term, and deadlines should be treated flexibly.	29.2	25.7	30.5	33.3

Data source: Collected by this research.

). They were collected using an original scale of 10 pairs of opposing statements based on the MBTI personality test (Myers–Briggs Type Indicator). The statements were assigned to specific MBTI dimensions based on classical interpretations of the Myers–Briggs model [62].

Thus, due to their psychographic characteristics, phygital integrators are distinguished by moderate extroversion and high-life activity. Most prefer to spend time actively (63.1%), maintain a balanced approach to novelties (76.9%), and choose known and proven solutions. Values such as functionality and independence in assessment are key to them. They are also characterized by a greater-than-average emphasis on taking deadlines seriously (74.2%). These people are balanced and pragmatic. They are modern consumers but not uncritical.

Digital frequenters demonstrate the highest openness to new experiences (34.9% prefer what is new) and a greater tendency to be the center of attention (28.2%). They are more active in life than others (65.2%) and more often care about social opinion (30.7%). They are dynamic people looking for novelties and are more socially engaged in the context of accepting innovations. Most would give up civil liberties for the common good (31.7%). In their thinking, they focus more on the future than on the present (53.1%).

On the other hand, physical reality anchors present the most conservative psychographic profile. They are characterized by a strong attachment to known patterns (82.1%) and avoiding being the center of attention (83.5%). They prefer explicit social norms (41.4% support one norm for all) and a flexible approach to time (33.3% treat deadlines flexibly). Their thinking is firmly rooted in the present (58.6%). They are traditional, stable, and less inclined to adapt to changes. They are homebodies (76.9%), preferring passive forms of spending free time (44.1%)—Table 6.

5. Discussion

The results are consistent with previous studies on consumer behavior in the phygital environment.

Bartoli et al. [63] pointed out that consumer experiences in phygital environments are shaped by integrating physical and digital elements, which influence the consumer-brand relationship. Our research confirms that phygital integrators actively participate in such environments, combining different channels into a coherent purchase journey.

Furthermore, a systematic review by Mele et al. [42] identified four main aspects of phygital transformation: objects and applications, context (space/place), customer journey, and purchasing experience. Our research fits into this framework by showing how different types of consumers engage with these aspects depending on their preferences and attitudes toward technology.

Additionally, Feijó and Costa’s [64] research highlighted the importance of attitudinal components—cognitive, affective, and behavioral—in shaping consumers’ attitudes toward phygital commerce. Our results indicate that digital frequenters show positive attitudes across all three components, translating into their high acceptance and use of advanced phygital solutions.

Finally, an analysis by Yao et al. [65] suggested that understanding consumer behavior in phygital environments requires an interpretive approach that considers contextual and subjective aspects of experiences. Our research provides insights into these aspects through a psychographic analysis of respondents, showing how personality traits influence attitudes and behaviors toward phygital technologies.

The research results also confirm our hypotheses. First, identifying three types of consumers functioning in the phygital space confirms the existence of different attitudes and adaptation strategies toward solutions integrating digital and physical channels (Hypothesis H3). These differences reflect consumers’ technological advancement level and demographic and psychographic profile, a significant extension of previous segmentation models [66].

Consumers defined as phygital integrators can be considered a practical embodiment of Hypothesis H2. This confirms the growing significance of phygital integrators, who constitute the largest group in the study sample and demonstrate high openness to various phygital solutions. Their attitudes and behaviors can contribute to shaping new consumption models and market strategies. This type consciously and consistently combines physical and digital channels, creating a coherent, complementary consumption space. Similar behaviors were also observed by Sharma et al. [67], who indicated an increase in the number of consumers making choices based on the functional synergy of channels in the omnichannel environment. Importantly, integrators do not abandon traditional forms of purchase but treat technology as a tool to improve the purchasing experience, which also confirms the earlier findings of Ramasundaram et al. [68], speaking about the growing importance of channel fluidity as a consumer value.

In turn, digital dominants represent the most technologically advanced segment of the respondents and, at the same time, most fully reflect the emergence of a new type of consumer in an integrated digital–physical environment (Hypothesis H1). Their dominant presence in digital channels and acceptance of solutions based on artificial intelligence, augmented reality, or biometrics corresponds to the results of the research by Parnwell and Meng [6], in which digital “native users” show significantly greater openness to innovation and greater trust in the automation of consumer experience.

The group of physical reality anchors provides an interesting counterpoint to the other two types. Although they are not entirely detached from digital channels, their behavior indicates moderate and selective technology adoption. They are conservative consumers, consciously limiting their presence in the digital world. These results are consistent with Batat’s [66] observations, which indicate that the gap between digitally inclusive and digitally resistant consumers is growing as the digital economy develops. Pantano and Vannucci [69] also proposed a similar typology, which identified analog consumers as a group that adopts technologies only to a basic extent and is in line with their previous habits.

Demographic and psychographic differentiation between consumer types additionally strengthens the validity of Hypothesis H3, according to which differences in the approach to phygital solutions are correlated with generational affiliation, lifestyle, and personality traits. This confirms the validity of including psychographic factors in segmentation models, which was postulated, among others, by Camoiras-Rodriguez and Varela [70], analyzing the impact of personality traits and lifestyle on the adaptation of digital solutions in a mobile sales environment.

In the context of sustainable consumption, it is important to emphasize that the identified consumer types also differ in terms of the potential environmental impact of their behaviors. Digital dominants, although technologically advanced, may contribute to the phenomenon of overconsumption and high product return rates, which constitutes one of the challenges for sustainable development in online commerce [71]. In contrast, phygital integrators demonstrate greater awareness and intentionality in their purchasing behaviors, striving to combine the convenience of digital channels with the possibility of physical product verification, which can help reduce unnecessary purchases and returns. Meanwhile, a literature review conducted by Vargas-Merino et al. [72] indicates that consumers anchored in physicality, preferring in-store shopping, often act with rationality and frugality, which in the literature is associated with a more sustainable approach to consumption. Thus, research findings suggest that different strategies for adapting to the phygital environment may have varying consequences from the perspective of sustainable consumption, opening new avenues for further studies on the relationship between technological transformation of commerce and its environmental impact.

The study results confirm the validity of the three formulated hypotheses and show that the phygital transformation of consumption does not proceed uniformly but in a differentiated manner, depending on individual preferences, socio-economic context, and personality.

6. Conclusions

In summary, the research results state that new IT and communication solutions have created an integrated physical and digital space conducive to the emergence of a new type of consumer—the phygital consumer. The study allowed us to identify three relatively homogeneous types of consumers in the context of their attitudes and behaviors toward phygital solutions: phygital integrators, digital dominants, and those anchored in physicality. Each of these types represents a different way of perceiving and using the phygital space, reflecting the complexity of consumer behavior in the digital transformation era.

The classification of consumer types presented in the article also helps to better understand the relationship between the digitalization of shopping behaviors and the challenges of sustainable consumption. The authors’ identification of different attitudes and purchasing strategies is crucial for designing educational and regulatory actions that support the development of responsible consumption amid digital transformation.

The article contributes to the theory of consumer behavior—conceptualizing a new typology of consumers—and to managerial practice, offering segmentation tools useful in designing integrated shopping experiences. The results have important implications for marketing strategy developers, especially in personalizing the customer journey and implementing digital technologies, considering their varying acceptance levels.

The article’s authors are aware of the limitations of their research, which cannot be considered representative of the entire population of consumers. The limitations of this work are primarily related to the method of measurement used (online survey), the participation of only active Internet users in the study, and the participation of consumers from only one country (Poland) in the study. In addition, the collected research material was in the form of subjective, declarative opinions of the respondents. Despite this, the conclusions and observations we obtained can serve as an argument in the debate on changes in consumer behavior in the conditions of the digital economy, further consumer education, preventing digital exclusion of older people, people experiencing poverty, and the less educated, as well as sustainable use of resources.

In future studies, it is worth deepening the analysis of the psychological mechanisms behind the choice of phygital tools. It is also worth conducting a typology of consumers from an international perspective, comparing it in different cultural contexts. It would also be reasonable to consider situational variables (e.g., economic situation, pandemic, and legislative changes) influencing the pace of adaptation of phygital tools and to expand the scope of research with longitudinal analysis to track the evolution of consumer types over time.

As consumer behavior transformation continues, further exploration is needed, including emerging technologies such as the Internet of Things (IoT), the metaverse, and generative AI, which have the potential to redefine the boundaries between the physical and digital worlds.