Digital Consumer Behavior in Poland and Its Environmental Impact Within the Framework of Sustainability

Abstract

This study investigates the influence of digital skills, personal innovativeness, and attitudes toward smart home adoption on digital consumer behavior in Poland, as well as the relationship between digital activity and environmental awareness. In the context of growing interest in sustainable development and digital responsibility, the research aims to identify the psychosocial and technological determinants of conscious online behaviors. The study employs a structured survey (n = 1246) using validated scales, which were analyzed through Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). The findings reveal that personal innovativeness is the strongest predictor of digital consumer behavior, followed by digital skills and, to a lesser extent, smart home adoption. Moreover, digitally active consumers demonstrate significantly higher levels of environmental awareness, suggesting that digital engagement fosters pro-environmental attitudes. Gender differences were observed in the strength of these relationships, with digital skills and innovativeness having a greater impact on women’s behavior, while smart home technology attitudes were more relevant among men. These results contribute to the understanding of digital consumer responsibility and its environmental implications, highlighting the importance of digital competence development and technological openness in promoting sustainable consumption patterns.

1. Introduction

The 21st century is a period of intense technological transformation, commonly referred to as the Fourth Industrial Revolution, whose foundation lies in digital transformation. Digitization processes encompass not only the social and economic spheres but also international relations, influencing the way states, organizations, and individuals function. The digital economy is based on the phenomena of datafication—that is, the transformation of reality into data—and networking, which enables real-time information flow and strengthens global interconnections. In this new reality, all market participants must find their place—on both the supply and demand sides [1,2]. Consumers are becoming an integral element of an ecosystem of interconnected digital technologies in a world of pervasive intelligence. Consequently, the development of the digital economy brings about changes in consumer behavior, including what consumers purchase, factors determining purchasing decisions, and attitudes toward online shopping [3]. The development of digital consumer behaviors directly results from the virtualization of commercial and service activities, the level of which is reflected in the Digital Economy and Society Index (DESI) [4]. In 2023, Poland scored 40.3 points (out of a possible 100) on the DESI index, ranking 24th among EU countries (with Finland in first place with 76.2 points, and Romania in 27th with 34.0 points). Poland was selected as the focus of this study due to its unique combination of a low DESI score and a rapid pace of digital transformation. The country represents a digitally emerging market, where consumer behavior is shaped by dynamic technological change and growing environmental awareness.

Consumer behavior has always been a multifaceted area of research, covering the psychological, social, and cultural determinants of the processes of purchasing and using products and services [5,6]. Over time, these behaviors have evolved under the influence of both internal and external factors: technology, culture, individual traits, types of goods and services, motivations, and learning processes [7]. Digital consumer behavior constitutes a specialized sub-discipline that examines these same phenomena in the digital environment [8,9]. Literature reviews indicate growing interest in this research area [9,10,11,12,13,14,15,16,17,18], which is also confirmed by systematic analyses [8,19].

To date, research has primarily focused on the use of new technologies (IoT, big data), digital advertising, behavior in mobile environments, and changes in trade and company strategies [20,21,22,23,24,25]. Although existing studies have yielded important conclusions regarding selected aspects of digital consumer behavior, there is still a lack of approaches that simultaneously consider three key variables: digital skills, personal innovativeness, and attitudes toward smart home technology—especially in the context of their impact on consumers’ ecological awareness and perception of the environmental consequences of their digital activities. In an era of increasing emphasis on sustainable development and digital responsibility, research analyzing digital consumer choices is becoming particularly important—especially in countries such as Poland, which, despite a low DESI index, are dynamically developing in terms of digital behaviors and smart technologies.

The aim of this article is therefore to demonstrate how psychosocial factors (such as personal innovativeness), competency-related factors (digital skills), and technological factors (attitudes toward smart home technologies) influence the behavior of digital consumers in Poland, and to what extent these consumers are aware of the environmental implications of their online actions. This approach fits within the current discourse on digital responsibility and the need to integrate digital transformation with sustainable development strategies.

This article comprises both theoretical and empirical components. The introductory section (Section 1) outlines the background and rationale for examining digital consumer behavior in Poland, along with its implications for environmental sustainability. The theoretical part (Section 2) presents a literature review covering key research concepts such as digital consumer behavior (DCB), digital skills (DS), personal innovativeness (PI), smart home adoption (SHA), and environmental awareness (EA). This section also introduces the conceptual research model and the hypotheses, which are grounded in an analysis of existing national and international studies. The empirical investigation is discussed in the following two sections: Section 3 and Section 4. Section 3 provides a detailed account of the adopted research methodology, including the design of the questionnaire and measurement scales, data collection procedures, and the characteristics of the research sample. Section 4 presents the results of the Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM), including an assessment of model fit, measurement reliability and validity, and the verification of hypotheses. This section also includes a gender moderation analysis using multi-group modeling. In Section 5, the findings are discussed in light of the existing literature, highlighting new theoretical insights and practical implications. Section 6 offers a concise summary of the main results, while Section 7 addresses the study’s limitations and suggests possible directions for future research.

2. Literature Review

2.1. Digital Consumer Behavior

In the digital era, the development of technology and the widespread use of the Internet have contributed to significant changes in consumer behavior. These changes have accelerated particularly over the past two decades. In the digital economy, a new type of conscious and empowered consumer is emerging—one who has access to vast amounts of information, whose decisions are often shaped by social media, and for whom the Internet has become not only a place for shopping but also for working, spending leisure time, and building relationships [26]. In the academic literature, the term netizen has even emerged—referring to a “citizen of the network”, someone who actively uses the Internet and cares about its development [27]. This new consumer is also referred to as the e-consumer [28], online consumer [29], new era consumer [30], trysumer [31], or simply the digital consumer [3,32,33,34].

The concept of digital consumer behavior (DCB) refers to the purchasing patterns and habits of consumers in the digital environment [35,36]. It is an organized, goal-directed sequence of responses to stimuli that appear in the digital space, driven by the desire to satisfy needs [37]. The digital consumer is characterized by the acceptance of innovative technological solutions such as artificial intelligence, VR, IoT, chatbots, or blockchain. Purchase decisions are based on the availability of online information, user reviews, and algorithm-generated recommendations [38,39]. Digital consumer behaviors are primarily manifested in how purchase decisions are made and how services are used in the online environment. Key expressions of such behaviors include online shopping, the selection of digital channels in the decision-making process, the use of mobile shopping apps, and engagement in digital recommendation communities [40,41]. Additionally, e-services—such as e-government, e-health, e-banking, e-culture, or subscription-based services— are gaining popularity, and their attractiveness lies in their accessibility and the ability for consumers to manage the process independently [42,43,44,45]. These behaviors are often supported by mobile technologies, cloud solutions, and artificial intelligence, which further impact customer loyalty and engagement patterns [46].

Digital consumer behavior is also shaped by external conditions such as the development of technological infrastructure, regulatory policy, and social change. However, internal factors—such as the level of digital skills, personal innovativeness, personal values, and individual motivation of consumers—play a critical role in decision-making within the digital environment. Research shows that the readiness to adopt technology strongly correlates with the frequency and quality of its use [47,48]. Personal innovativeness influences openness toward new technological solutions [49], while digital skills are essential for effective participation in the digital world [50]. Moreover, these same variables shape environmental awareness—many pro-ecological attitudes in the online environment stem from a sense of responsibility, not merely social pressure [51].

2.1.1. Digital Skills

Consumers’ digital skills (DS) can be defined as a set of competencies essential for conscious, critical, and responsible use of digital technologies across various domains of consumer activity, including everyday life, professional engagement, and civic participation [52,53]. These skills are understood not only as the technical ability to operate digital tools and devices, but also as cognitive, social, and ethical competencies that enable individuals to select, analyze, and interpret information, as well as to create content within the digital environment [50,54]. The development of digital skills allows consumers to use digital tools effectively, safely, and responsibly—both as users of modern technologies and as active participants in the digital marketplace [55,56]. Furthermore, digital skills may be categorized into functional (technical) and critical competencies. Functional skills relate to the practical use of technologies, whereas critical skills involve the ability to understand how technologies work and to engage in the informed analysis of digital content. It is these critical competencies that underpin a more conscious, creative, and engaged participation of consumers in the digital society [52].

According to the classification adopted within the European DigComp framework, digital competence comprises five areas: information and data literacy, communication and collaboration, digital content creation, safety, and problem-solving [53]. From the perspective of Helsper et al., the digital skills of young consumers are additionally assessed through indicators related to technological, informational, socio-emotional, and critical abilities in the context of online interaction, taking into account both instrumental and reflective dimensions of technology use [52].

The literature identifies the following dimensions of consumers’ digital skills:

• Technological and operational skills, understood as the ability to operate digital devices, software, digital applications, e-commerce platforms, and cloud services. These include practical competencies such as installing shopping apps, filtering product options, and completing mobile payments [54,55,57];
• Cognitive (informational) skills, which relate to the processes of searching for, selecting, evaluating, and processing information. These skills are essential for making informed consumer decisions in online environments, for instance, when comparing the terms of streaming service subscriptions [52,54,58,59];
• Social and communication skills, which involve effective interaction and collaboration with other users or institutions in digital environments. Examples include the use of email and participation in social media platforms [55,57];
• Digital content creation skills, referring to the ability to develop, edit, and publish digital content (such as text, graphics, or video) in accordance with applicable legal and ethical standards. An example includes producing a product video review that complies with copyright regulations and sharing it on digital platforms [54,55];
• Privacy and cybersecurity skills, which encompass awareness of threats related to privacy and online safety, as well as the responsible use of digital technologies [59,60].

Contemporary approaches to digital skills also emphasize the concept of digital maturity, understood as an integrated level of competence, attitudes, and behaviors that enable individuals to use digital technologies in a conscious, ethical, and reflective manner, while demonstrating a readiness for continuous development and adaptation to evolving technological conditions [54,55,59].

This maturity also encompasses an individual’s capacity for self-determination in their use of technology in ways that support psychological development, personal well-being, and a balanced approach to the benefits and risks associated with engagement in digital environments [54,55,56,59].

The development of digital competencies is a prerequisite for the active and conscious participation of consumers in a digital environment dominated by emerging technologies. These skills are essential for individuals to function as fully engaged participants in the digital sphere [50]. A key condition for their development is consumers’ willingness to explore and adopt innovative technological solutions [47].

2.1.2. Personal Innovativeness

The concept of personal innovativeness (PI) refers to an individual’s willingness to experiment with new technological solutions. Agarwal and Prasad [49] defined personal innovativeness in the domain of information technology as the “willingness to try out any new IT”. This construct is rooted in Rogers’ Diffusion of Innovations Theory (IDT), which posits that individuals vary in their degree of innovativeness, influencing the rate and sequence of technology adoption [61]. PI is therefore a specific form of consumer innovativeness, specifically related to information technology.

Personal innovativeness is recognized as a significant individual factor influencing the acceptance and adoption of new technologies [62]. Individuals with high personal innovativeness are more likely to become early adopters—they tend to be more eager to experiment with new solutions and perceive greater potential benefits. Research indicates that PI influences subjective perceptions of technology characteristics, such as perceived usefulness and ease of use, which in turn shape user attitudes and intentions. PI has been applied across numerous studies and technology areas. In the energy sector, it was investigated in smart metering acceptance, where PI determined relationships between perceived risks and usage intention [63]. In e-commerce, personal innovativeness reduces perceived risk and increases users’ openness to services like online banking. Innovative consumers are more willing to explore new digital tools and perceive them as less risky, which enhances their adoption intention [64,65]. In mobile commerce, PI was critical in both the initial adoption and continuance usage phases [66]. In ERP systems, the impact of PI on adoption intentions was stronger among users with less prior experience, suggesting that innovativeness matters most when experience is lacking [67]. In social media, more innovative users were better at adopting new features and functions [68].

Personal innovativeness also plays a key role in developing digital skills and adopting emerging home technologies, especially when these are aligned with environmental goals. Individuals with high PI are more inclined to experiment with new digital tools, which enhances their technological confidence and learning [49,67]. In the context of smart homes, PI has been shown to reduce perceived complexity and increase the likelihood of adopting energy-efficient solutions, such as smart meters [63]. According to recent research, environmentally conscious individuals with high personal innovativeness are also more likely to adopt green technologies due to the perceived personal value of such choices [69].

2.1.3. Smart Home Adoption

Smart homes, also known as automated homes, are residences that use a range of advanced technologies, allowing for remote control, monitoring, and automation through smart devices, such as security systems and everyday appliances [70]. The concept of smart homes has been around for some time, but recently their popularity has increased due to technological advancements and the growing demand for convenience and efficiency [71]. One of the main benefits of a smart home is the ability to remotely control and monitor various aspects of the home using a smartphone or other devices [72]. Homeowners can use their smart devices during daily tasks, such as turning lights on or off, monitoring the environment, adjusting the air conditioning, or locking doors [73,74]. Smart home devices are not only functional, as they ensure consumer convenience, but also provide an added layer of security [75]. In addition to operational efficiency and high convenience, smart home devices have the potential to increase sustainability and energy efficiency [76]. Smart devices can be programmed to turn off when not in use, positively impacting environmental protection and reducing electricity bills [77]. Such actions not only allow us to save money but also reduce our carbon footprint and sustainably protect the environment [78].

Despite the mentioned advantages of smart homes, it is important to note the risks associated with using smart home devices. One of the biggest issues is ensuring privacy and security [79]. As the number of Internet-connected devices grows, the risk of cyberattacks and personal data breaches increases [80,81]. It is important for homeowners to secure their smart home devices and use appropriate security systems, which will significantly help prevent unwanted cyberattacks [82,83].

Currently, smart homes are becoming increasingly advanced and affordable, which translates into growing consumer interest and a positive attitude toward adapting their lives to smart home devices. The wide range of smart home products and their affordability make them accessible to average consumers [84]. Enhancing life with smart home devices and the functional benefits they offer has a positive impact on consumers, encouraging them to expand their environment with additional, new devices [85]. Undoubtedly, smart home adoption is characterized by great diversity in terms of how it is used in everyday life and its multifunctionality. Consumers’ positive attitude toward SHA has a favorable impact on their digital behavior, as it enables them to maintain a sustainable approach across various aspects of daily life.

2.2. Environmental Awareness

Environmental awareness can be defined as people’s awareness of various environmental issues and the possibilities for solving them or contributing to their resolution [86,87]. Another definition describes environmental awareness as the level of an individual’s concern for environmental problems and related aspects [88]. Environmental awareness also entails the responsibility of individuals and communities, as parts of nature, to ensure that present and future generations can live and function in a healthy, clean, and safe environment while adhering to and protecting ecological principles [89]. Moreover, it is worth noting that environmental awareness encompasses not only an individual’s beliefs, convictions, or opinions but also represents a collective societal responsibility that can shape a shared way of thinking and acting regarding environmental protection [90]. Alsmandi [91] states that environmental awareness includes both ecological responsibility and actions aimed at environmental protection, while Kaufmann et al. [92] classify it as people’s awareness that their behaviors and beliefs significantly impact the environment. Based on these definitions, environmental awareness can be understood as being closely linked to the highest level of societal engagement in environmental issues and the willingness to make sacrifices for environmental protection [93].

Environmental awareness is a key element in shaping people’s ecological mindset. An ecological approach means responding to the perception of humans as superior to nature, and the result of aggressively exploiting the natural environment without concern for future generations and treating oneself as a higher or better being compared to other organisms. Shaping ecological awareness in society becomes possible through ecological solutions that ensure good coexistence between humans and nature [94]. Environmental protection on a global scale, in way that is accessible to all living beings, is only possible through the development of ecological awareness among people. In other words, this means expanding their knowledge about the environment and developing pro-ecological behaviors [37]. Ecologically aware individuals not only do not remain indifferent to the environment’s degradation but also take actions that are friendly to the environment. Ecologically conscious individuals can be shaped, especially through education from an early age, which supports prompt action in environmental protection [95]. Ober and Karwot [96] emphasize that ecological awareness can be developed through environmental education and informing consumers, and the increase in ecological awareness leads to the choice of environmentally friendly actions. It is also worth noting that the ever-progressing development of modern technologies has a significant impact on the growth of economic awareness. New technological solutions bring a new quality to actions aimed at developing ecological awareness among consumers [95,97].

2.3. Model Development

The aim of the article is to show how competency-related, psychosocial, and technological factors influence the behavior of digital consumers, as well as to what extent these consumers are aware of the environmental implications of their online activities. We developed a research model based on the literature review findings, as shown in Figure 1. Environmental awareness was included in the model as a key outcome variable, aimed at assessing the impact of digital activity on pro-environmental attitudes. Its inclusion is based on the assumption that consumer behavior in digital environments can foster ecological awareness through access to information and participation in responsible consumption initiatives.

The following research hypotheses were formulated:

H1. 

Digital skills (DS) positively influence digital consumer behavior (DCB).

H2. 

Personal innovativeness (PI) positively influences digital consumer behavior (DCB).

H3. 

Smart home adoption (SHA) positively influences digital consumer behavior (DCB).

H4. 

Digital consumer behavior (DCB) positively influences environmental awareness (EA).

We also attempted to verify the moderating effect of gender on both the factors influencing digital consumer behavior and environmental awareness. The following additional hypotheses were formulated:

H5a. 

Gender moderates the influence of digital skills (DS) on digital consumer behavior (DCB).

H5b. 

Gender moderates the influence of personal innovativeness (PI) on digital consumer behavior (DCB).

H5c. 

Gender moderates the influence of smart home adoption (SHA) on digital consumer behavior (DCB).

H5d. 

Gender moderates the influence of digital consumer behavior (DCB) on environmental awareness (EA).

3. Materials and Methods

3.1. Questionnaire Development

In order to empirically explore the relationship between digital consumer behavior and environmental impact within the framework of sustainability, a structured questionnaire was designed based on validated constructs from the reviewed literature and adopted to the context of Polish consumers.

The questionnaire was divided into five thematic sections, each designed to capture key constructs relevant to the study. Measurement scales in the digital skills (DS) area were taken from Perifanou and Economides [98] and Tzafilkou et al. [99]. The scales for personal innovativeness (PI) and smart home adoption (SHA) are adopted from Baudier et al. [100]. The digital consumer behavior (DCB) measurement was adopted from Vatolkina et al. [101], Fakieh and Happonen [102], Ma et al. [103], and Wolny [104]. The environmental awareness (EA) items were adopted from Barragán-Sánchez et al. [105] and Laaber et al. [56].

Respondents evaluated each statement using a five-point Likert scale, ranging from “strongly disagree” (1) to “strongly agree” (5), allowing for consistent and scalable measurement of attitudes and behaviors (

Table 1. Measuring scales’ items.

Measuring Scales’ Items
Digital Consumer Behavior (DCB)
I use a mobile payment system (e.g., BLIK, Apple Pay). I access my bank account online to check balances and review transaction history. I read newspapers via online sources. I watch films through online streaming services. I listen to music through online services. I purchase tickets for cinema, theatre, or concerts online. I purchase tickets for trains, buses, or flights online. I make accommodation reservations using online booking services. I obtain life, travel, or personal accident insurance online. I schedule medical appointments online. I submit tax declarations via electronic government services. I purchase groceries through online retailers. I sell pre-owned items using online platforms.
Digital Skills (DS)
I can connect a smart device to a WiFi network. I can share my smart device, software, Internet connection, and WiFi. I can communicate with people and/or organizations using various synchronous and asynchronous communication tools and smart devices. I can ask questions and give answers in various social networks and/or e-communities. I can search and find a specific object or similar objects using various search engines and databases, using appropriate keywords and advanced criteria and filters. I can watch (read, listen, view) content in various formats on various smart devices. I can store and synchronize content on the Cloud. I can evaluate whether some information is a hoax, fake, scam, or fraud. I can translate content into another language using translation tools. I can regularly change my passwords and settings of my smart devices and Internet accounts.
Personal innovativeness (PI)
I enjoy experimenting with new digital technologies. When I learn about a new digital technology, I try to use it as soon as possible. Among my peers, I am usually the first to try out new digital technologies.
Smart Home Adoption (SHA)
I find smart home objects useful in a daily life. Using smart home objects increases the chances of achieving things that are important. Using smart home objects helps to accomplish things more quickly. Using smart home objects increase productivity. The use of smart home objects could become a habit for me. I could become addicted to using smart home objects. I could use smart home objects. Using smart home objects could become natural to me.
Environmental Awareness (EA)
I have heard about the environmental impact caused by the use of technologies. I am clear about the environmental impact caused by the use of technologies. I have received courses on the environmental impact caused by the use of technologies. I could clearly define the environmental impact caused by the use of technologies. I consider myself capable of identifying the technological actions that cause the greatest environmental impact. I am able to establish measures that reduce the environmental impact caused by the use of technologies. I am able to help another person to manage situations in which the use of technologies is creating a great environmental impact. I use the Internet to support campaigns for issues like environmental protection or to spread awareness about climate change. I use it (Internet) to improve life in my neighborhood, town, or world.

Source: [56,98,99,100,101,102,103,104,105].

).

3.2. Data Collection

In order to verify the hypotheses, data were collected using the CAWI method. The choice of this method was justified by its wide reach and the ability to reach respondents according to established distributions of gender, age, and place of residence. Additionally, the CAWI method has been frequently used in studies related to consumption and consumer behaviors [106,107,108,109,110,111]. The aim of the study was to analyze the behavior of digital consumers in Poland in terms of psychosociological factors, digital technology-related indicators based on their competencies, and to analyze consumers’ awareness of the impact of their digital actions on the environment.

In the initial phase of the study, a pilot survey was conducted to ensure the quality of the research tool. A total of 20 respondents were selected to assess the preliminary version of the questionnaire, which allowed for evaluating the content and validity of the questions included. After conducting the preliminary research, minor linguistic corrections were made to improve the readability and clarity of the tool, which consequently allowed for the enhancement of the questionnaire and enriched its content.

Before the main study, participants were presented with a declaration of anonymity and confidentiality. The objectives of the study were outlined, as well as how the results would be disseminated. Additionally, respondents were given the opportunity to contact the researchers via email. The study was conducted between 18 November 2024 and 27 January 2025.

The survey was conducted by the Research and Development Centre of the University of Economics in Katowice on the SurveyMonkey research platform, which has a database of approximately 10,000 potential respondents. A total of 1371 people started completing the survey, with 1243 valid (complete) surveys collected. The average time for a respondent to complete the survey was 14 min. The sample was controlled for gender, age, place of residence, household size, professional activity, and subjective assessment of material situation. The collected data are representative of the specific variables and allow for reliable verification of the hypotheses.

3.3. Sample

Field research was conducted on a nationwide sample of 1246 respondents. The socio-demographic structure of the sample was deliberately diversified based on key sociodemographic variables, ensuring the representativeness of the sample in terms of population characteristics relevant to the study. The research sample was designed to be representative with respect to gender, age, and place of residence. The distribution of these characteristics within the sample reflects the structure of the adult population in Poland, as reported by the Central Statistical Office (GUS) [112].

The study comprised 51.1% women and 48.9% men. The largest age group among respondents was individuals aged 65 and older (23.4%), followed by those aged 35–44 (19.2%) and 45–54 (16.8%). The smallest age group comprised respondents aged 16–24 (11.0%). The majority of respondents had higher education (49.2%) or secondary education (38.1%). Participants with vocational and primary education accounted for 10.4% and 2.3% of the sample, respectively. Nearly two-thirds of the respondents were economically active, while 37.6% were not employed at the time of the study. The largest share of respondents were rural residents, accounting for 39.2% of the total sample. One in four participants lived either in medium-sized towns (with populations between 51,000 and 100,000) or in large metropolitan areas with over 501,000 inhabitants. The smallest groups consisted of those residing in towns with up to 50,000 residents and in cities with populations ranging from 101,000 to 500,000. The most common household type among respondents was a two-person household, representing 34.0% of the sample. One-person (24.6%) and three-person (17.6%) households also made up a significant portion. Larger households, with five or more members, accounted for 8.6% of all surveyed units. Half of the respondents described their financial situation as “good”, while nearly one in three assessed it as “neither good nor bad.” A relatively small proportion indicated that their material situation was “poor” (3.8%) or “very poor” (0.8%). Only 9.4% of participants rated their financial situation as “very good” (

Table 2. Characteristics of research sample (n = 1246).

Item		In %
Gender	Women	51.1
	Man	48.9
Age (years)	16–24	11.0
	25–34	15.4
	35–44	19.2
	45–54	16.8
	55–64	14.2
	65 and more	23.4
Education	Primary	2.3
	Vocational	10.4
	Secondary	38.1
	Higher	49.2
Employment status	Employed	62.4
	Not employed	37.6
Place of residence	Rural areas	39.2
	Towns, up to 50,000 residents	4.9
	Towns, 51,000–100,000 residents	26.3
	Towns, 101,000–500,000 residents	3.3
	Towns over 501,000 residents	26.3
Number of household members	1	24.6
	2	34.0
	3	17.6
	4	15.2
	5 and more	8.6
Self-assessment of the material situation	Very bad	0.8
	Bad	3.8
	Neither good nor bad	32.7
	Good	53.3
	Very good	9.4

Source: own study.

).

3.4. Methods of Analysis

This study employed Structural Equation Modeling (SEM) to test the hypotheses concerning the relationships between digital skills (DS), personal innovativeness (PI), smart home adoption (SHA), digital consumer behavior (DCB), and environmental awareness (EA). Analyses were conducted for the entire sample (n = 1243) as well as separately for men and women to explore potential gender-based moderation effects.

Prior to conducting Confirmatory Factor Analysis (CFA) and SEM, normality tests were performed on the data. The results of Mardia’s multivariate normality test (assessing both skewness and kurtosis) indicated significant deviations from a normal distribution. Univariate tests, such as the Anderson–Darling test, confirmed non-normality across all analyzed variables. Given the ordinal nature of the variables (measured on a five-point Likert scale) and the verified lack of normality, all analyses were carried out using the WLSMV estimator (Weighted Least Squares Mean and Variance Adjusted). This estimator is recommended for categorical and ordinal data, as it yields more accurate standard errors and improved model fit statistics [113,114].

The measurement model (CFA) was evaluated in accordance with established standards in the literature [115,116]:

• Model fit was considered acceptable when the values of CFI and TLI exceeded 0.90, RMSEA was below 0.08, and SRMR was below 0.08;
• Convergent validity was assessed using the AVE (Average Variance Extracted) index, which should exceed 0.50;
• Internal consistency was confirmed using Composite Reliability (CR), Cronbach’s alpha (α), and McDonald’s omega (ω), with values above 0.70 being considered acceptable;
• Discriminant validity was examined through inter-construct correlations (expected to be <0.85) and chi-square difference tests between constrained and unconstrained models.

After confirming the adequacy of the measurement model, the structural model was estimated for the full sample. This model included direct paths from digital skills, personal innovativeness, and smart home adoption to digital consumer behavior, as well as a path from DCB to environmental awareness. To explore the moderating role of gender (hypotheses H5a–H5c), separate SEM models were estimated for men and women. Differences in structural paths were analyzed to identify which relationships varied significantly between gender.

To determine whether constructs were measured equivalently across gender groups, a measurement invariance test was conducted using a three-step approach: configural → metric → scalar invariance. The procedure followed recommendations by Milfont and Fischer [117] and Byrne [118]:

• The configural model allows all parameters to vary across groups and serves as the baseline model;
• The metric model (metric invariance) constrains factor loadings to be equal across groups;
• The scalar model (scalar invariance) additionally assumes equality of intercepts across groups.

All analyses were performed in the R environment [119] using the following packages:

• lavaan—for SEM and CFA model estimation [120],
• semTools—for advanced invariance testing and validity assessments [121],
• MVN—for multivariate normality testing [122],
• semPlot—for SEM model visualization [123].

4. Results

Before conducting the structural model analysis, a Confirmatory Factor Analysis (CFA) was performed for the five latent constructs: digital skills (DS), personal innovativeness (PI), smart home adoption (SHA), digital consumer behavior (DCB), and environmental awareness (EA). Due to violations of multivariate normality assumptions (Mardia’s skewness = 32,664.27; kurtosis = 91.54; p < 0.001), the WLSMV estimator was employed, which is suitable for ordinal variables.

The CFA model demonstrated satisfactory fit: CFI = 0.975, TLI = 0.973, RMSEA = 0.095 (90% CI [0.093, 0.096]), SRMR = 0.085. Although the RMSEA slightly exceeded the conventional threshold of 0.08, the high CFI and TLI values, along with an acceptable SRMR, indicate an overall good model fit.

All factor loadings were statistically significant (p < 0.001). Reliability indices for the constructs—Composite Reliability (CR), Cronbach’s alpha (α), McDonald’s omega (ω), and Average Variance Extracted (AVE)—confirmed the internal consistency of the scales: CR ranged from 0.867 to 0.987; both α and ω exceeded 0.90 for most constructs (

Table 3. Construct reliability and validity indicators.

Construct	Items	CR	α	ω	AVE
DCB	13	0.867	0.840	0.851	0.358
DS	10	0.975	0.928	0.940	0.707
PI	3	0.904	0.890	0.897	0.803
EA	9	0.985	0.932	0.943	0.708
SHA	8	0.987	0.920	0.932	0.697

Source: own calculations.

).

Discriminant validity was confirmed through inter-construct correlations below 0.85 and chi-square difference tests (

Table 4. Inter-construct correlations.

Construct	DCB	DS	PI	EA	SHA
DCB	1.000	0.463	0.477	0.357	0.375
DS		1.000	0.321	0.226	0.402
PI			1.000	0.635	0.578
EA				1.000	0.457
SHA					1.000

Source: own calculations.

).

All correlations between the constructs were statistically significant (p < 0.001) and ranged from 0.226 to 0.635. None of the correlation coefficients exceeded 0.85, indicating satisfactory discriminant validity. Chi-square difference tests further confirmed discriminant validity for all construct pairs (p < 0.001).

Structural Equation Modeling (SEM) was used to test the hypothesized relationships among the five latent constructs. The WLSMV estimator was applied. The model demonstrated a moderate fit: χ² (853) = 15,255.31, p < 0.001; CFI = 0.77; TLI = 0.76; RMSEA = 0.100 (90% CI [0.098, 0.102]); SRMR = 0.103. Although the CFI and TLI values fell slightly below conventional thresholds, the model fit was considered acceptable given the complexity of the model and the large sample size (n = 1243). A graphical representation of the estimated model is presented in Figure 2, and detailed path estimates with standard errors, p-values, and 95% confidence intervals are reported in

Table 5. Structural path estimates in the SEM model.

Path	Path Coefficient β	Standard Error	p-Value	95% Confidence Interval
H1: DS → DCB	0.207	0.024	<0.001	[0.160, 0.255]
H2: PI → DCB	0.681	0.028	<0.001	[0.627, 0.735]
H3: SHA → DCB	0.102	0.030	0.001	[0.043, 0.161]
H4: DCB → EA	0.617	0.022	<0.001	[0.574, 0.660]

DCB: R² = 0.705, EA: R² = 0.381. Source: own calculations.

.

Personal innovativeness emerged as the strongest predictor of digital consumer behavior (DCB). Individuals who are more open to new technologies tend to engage more frequently in digital consumer activities, such as online shopping or using digital applications. Digital skills also had a positive impact on DCB, suggesting that digital competence facilitates consumer behavior in online environments. Smart home adoption showed the weakest, yet still statistically significant, effect on DCB. This indicates that while the use of smart home devices may correlate with digital consumer activity, it is not a primary driver of such behavior. Digital consumer behavior had a significant positive influence on environmental awareness. Digitally active individuals were more likely to report heightened ecological awareness. The model accounted for over 70% of the variance in DCB, and for environmental awareness, the explained variance was moderate at 38.1%.

These results confirm hypotheses H1 through H4 and underscore personal innovativeness as the most influential predictor of digital consumer behavior (

Table 6. Verification of research hypotheses 1–4.

Hypothesis	Direction of Influence	Estimate	p-Value	Verification
H1: DS → DCB	+	0.207	<0.001	Supported
H2: PI → DCB	+	0.681	<0.001	Supported
H3: SHA → DCB	+	0.102	0.001	Supported
H4: DCB → EA	+	0.617	<0.001	Supported

Source: own calculations.

).

To examine the moderating role of gender (H5a–c), separate SEM models were estimated for women and men (

Table 7. Structural path estimates in the SEM model by gender.

Path	Male		Female
	Path Coefficient β	p-Value	Path Coefficient β	p-Value
DS → DCB	0.123	0.001	0.281	<0.001
PI → DCB	0.625	<0.001	0.697	<0.001
SHA → DCB	0.175	<0.001	0.059	0.160
DCB → EA	0.638	<0.001	0.576	<0.001

Source: own calculations.

). A graphical representation of the multi-group model is presented in Figure 3.

The obtained effects differed between gender groups. Personal innovativeness had a strong positive effect on digital consumer behavior (DCB) in both groups, though the effect was stronger among women (β = 0.697) compared to men (β = 0.625). Digital skills significantly influenced DCB in both groups as well, with a more pronounced effect in women (β = 0.281) than in men (β = 0.123). Smart home adoption had a statistically significant impact on DCB only in the male group, while the effect was not significant among women (p = 0.160). DCB significantly predicted environmental awareness in both groups, with a slightly stronger effect observed in men.

To examine the stability of the model across gender, a measurement invariance test was conducted, and regression paths in the structural model were evaluated (see

Table 8. Measurement invariance models.

Model	df	χ2	Δχ2	Δdf	p-Value	Description
Configural	1706	16,317	—	—	—	Baseline model
Metric	1744	16,804	120.53	38	<0.001	Equal factor loadings
Scalar	1868	16,652	−150.87	124	1	Intercepts equality not supported

Source: own calculations.

). Model comparison results indicated partial metric invariance, but no full scalar invariance (χ²_diff = 120.53, df = 38, p < 0.001). This may suggest differences in how certain questionnaire items were interpreted or differing baseline levels of the constructs between groups.

The CFA model demonstrated satisfactory fit: CFI = 0.975, TLI = 0.973, RMSEA = 0.095 (90% CI [0.093, 0.096]), and SRMR = 0.085. Although the RMSEA exceeded the conventional threshold of 0.08, the high CFI and TLI values, along with an acceptable SRMR, indicate a reasonably good model fit.

While full measurement invariance between women and men could not be established, the results suggest that the model performs similarly enough across groups to allow for cautious comparisons. Therefore, partial invariance is assumed, meaning that most—though not all—elements of the model are measured in a comparable manner across genders.

The results of the gender moderation analysis confirm that the strength of predictor effects on DCB significantly differs between women and men. Personal innovativeness and digital skills show stronger associations with DCB among women, while smart home adoption has a significant effect only in the male group. In summary, the findings support hypotheses H5a–H5c (

Table 9. Verification of research hypotheses 5a–5c.

Hypothesis	Moderation by Gender	Verification
H5a: DS → DCB	Yes	Supported
H5b: PI → DCB	Yes	Supported
H5c: SHA → DCB	Yes	Supported

Source: own calculations.

).

5. Discussion

The aim of this study was to examine the impact of selected factors: digital skills, personal innovativeness, and attitudes toward smart home technology on digital consumer behavior in Poland, as well as to explore the relationship between online activity and environmental awareness. Based on the proposed theoretical model, four primary hypotheses and four gender moderation hypotheses were tested. The findings from the Structural Equation Modeling (SEM) confirmed most of the assumed relationships.

The results supported hypothesis H1, which posited that digital skills (DS) have a positive and strong effect on digital consumer behavior (DCB). The path coefficient (β = 0.207; p < 0.001) indicates a statistically significant relationship, suggesting that higher levels of digital competence lead to greater consumer activity in digital environments. These findings are consistent with previous research. For instance, Park et al. [124] demonstrated that digital competencies play a crucial role in shaping effective and conscious purchasing decisions in the digital economy. Similarly, Laaber et al. [56] found that consumer digital maturity—understood as a combination of technical and cognitive skills—significantly increases engagement in online activities. Digitally mature consumers navigate online environments more effectively and make more intentional, informed choices. Converging evidence was also presented by Gazzola et al. [125], who emphasized that online consumer skills—comprising the construct of so-called Consumer Empowerment—are among the most influential variables driving sustainable purchasing behavior in digital settings. The authors highlighted that well-developed digital skills enable consumers to compare offers, assess product quality and sustainability, and draw on the experiences of other buyers. A similar view was shared by Malchenko et al. [126], who argued that advanced digital competencies form the foundation for effective consumer participation in the digital marketplace, empowering individuals to act more autonomously and mindfully.

The findings also strongly support hypothesis H2, which stated that personal innovativeness (PI) significantly and positively affects digital consumer behavior (DCB). The high path coefficient (β = 0.681; p < 0.001) indicates a strong association between an individual’s willingness to experiment with new technologies and their engagement in digital consumer activities. Similar conclusions were drawn by Jeong and Choi [127], who found that consumers with high personal innovativeness are more influenced by product attributes such as novelty, aesthetics, and relative advantage. For this group, the intention to purchase wearable devices increased significantly, in contrast to individuals with lower levels of PI, who were less responsive to the novelty factor. These findings suggest that PI not only directly influences purchasing behavior but may also moderate the relationship between perceived product features and purchase intention.

The results also confirm hypothesis H3, indicating that attitudes toward smart home adoption (SHA) significantly affect digital consumer behavior (β = 0.102; p < 0.001), although this effect was the weakest among the examined predictors. This may imply that while the use of smart home devices is associated with digital consumer activity, it is not a primary driver of such behaviors. These results are consistent with previous studies. Adapting everyday life to new digital technologies and learning to use their functionalities requires consumer engagement in a learning process [128]. Although smart home devices are designed to be user-friendly, their configuration and personalization often demand a certain level of digital literacy. The implementation of a smart home involves full adaptation to and acceptance of digital technologies within one’s environment. It is worth referring to the findings of Korean researchers, who point out that smart home adoption is influenced by numerous complex factors, such as consumer income inequality and the housing context. South Korea, as a country with a highly developed technological infrastructure, demonstrates that even basic socio-demographic factors can create various types of gaps and have a significant impact on smart home adoption [129]. Mocrii et al. [82] noted that smart home technologies may offer educational benefits, such as access to new forms of knowledge and the development of new digital behaviors. Regular use of these technologies may naturally enhance digital fluency, confidence, and engagement in digital environments [130]. In contrast, studies by Chang et al. [71] and Paetz et al. [131] revealed that consumers who incorporate smart home devices into their routines also express concerns about privacy breaches, data leakage, loss of control over personal life, and excessive intrusion by producers or service providers. These concerns may lead to the emergence of negative digital behaviors, such as distrust, reduced willingness to adopt, or even outright rejection of digital technologies.

The results of the structural analysis confirm a positive and strong effect of digital consumer behavior (DCB) on environmental awareness (EA), fully supporting hypothesis H4. The path coefficient (β = 0.617; p < 0.001) indicates a significant relationship, suggesting that individuals who are more digitally active, e.g., those using e-services, shopping online, or consuming digital content, tend to report higher levels of environmental awareness. Today, digital consumer behaviors are beginning to surpass traditional behaviors, not only among younger consumers [36,132]. One could argue that digital consumer behavior functions as a meta-variable that mediates the transition from technological engagement to normative attitudes. Naturally, modern technologies, or rather their use, do not inherently generate pro-environmental values. However, they can facilitate their activation. The digitization of consumer behavior can support actions that lead to a reduction in physical consumption (e.g., using e-services instead of physical products) and enhance decision-making aligned with environmental knowledge [133,134,135]. These findings align with the concept presented by Lounis et al. [136], who showed that digital shopping environments—particularly those enhanced with gamification elements—not only foster consumer engagement but also increase readiness to purchase eco-friendly products. Similarly, research by Handayani et al. [137] highlights that growing environmental awareness, fueled by access to digital information and online education, significantly shapes the development of pro-environmental attitudes. Fici et al. [138] further emphasized that modern digital platforms—including immersive environments like the metaverse—engage consumers both emotionally and cognitively, which may increase their susceptibility to environmental messages and foster environmental awareness in digital contexts. Dat et al. [139] rightly point out that strong consumer engagement in digital behaviors contributes to the growth of environmental awareness. Research conducted in Vietnam—a country with a moderately low level of digital maturity—clearly shows that digitalization, through the effective dissemination of information, strengthens consumers’ positive intentions regarding environmental protection. Environmental awareness is shaped by a complex combination of knowledge, social influence, and economic factors operating within a digital context. Jaciow and Wolny [95] found that Gen Z consumers, who are deeply embedded in digital communication channels, exhibit high levels of ecological engagement. Moreover, digital consumer behavior may promote environmentally friendly attitudes through providing the following:

• access to a broader range of information about sustainable products;
• ease of comparing brands in terms of environmental impact;
• participation in online communities promoting zero-waste lifestyles or the circular economy.

It is also important to consider the reverse relationship—the impact of environmental attitudes on digital consumer behaviors. For example, Lin and Dong [140] emphasized that environmental awareness in digital contexts (e.g., purchasing energy-efficient products online) strongly influences purchasing attitudes, especially when consumers perceive both functional and ecological value in products. Their findings suggest that when consumers perceive both ecological and functional value in such products, their purchase intentions—particularly in digital environments—are positively shaped by prior environmental concerns.

A significant aspect of the analysis was the moderating role of gender, although the strength of this effect varied across constructs and between women and men. The influence of digital skills (DS) on digital consumer behavior (DCB) was statistically significant in both groups, but stronger among women (β = 0.281) than men (β = 0.123). This supports hypothesis H5a, which posits that the strength of the DS–DCB relationship significantly differs by gender. The stronger association between DS and DCB among women suggests gender-specific uses of digital competencies. These findings align with Yoleri and Anadolu [141], who reported that women score higher in digital ethics and responsibility, while men tend to excel in general knowledge, functional skills, and digital content creation. This may explain why women, who often use technology more consciously and responsibly, show a stronger link between skills and consumer activity. Moreover, OECD data [142] indicate that women are more likely than men to use digital technologies for social and communicative purposes.

Hypothesis H5b, stating that gender moderates the effect of personal innovativeness (PI) on DCB, was also confirmed. This relationship was slightly stronger among women (β = 0.697; p < 0.001) than men (β = 0.625; p < 0.001). Similar findings were presented by Sohaib et al. [143], who showed that cognitive innovativeness moderates the link between trust in e-commerce and the intention to purchase new products, with a stronger effect observed among female consumers.

The analysis of gender as a moderator between smart home adoption (SHA) and DCB showed a significant effect only among men (β = 0.175). Among women, this effect was not statistically significant (β = 0.059; p = 0.160), thus only partially confirming hypothesis H5c. These findings are in line with Kennedy et al. [144], who noted that men are more proactive in adopting smart home technologies. Similarly, Strengers and Nicholls [145] described men as “digital household managers”, typically responsible for implementing and maintaining home digital systems.

These findings should be interpreted within the specific socio-digital context of Poland. The country’s comparatively low level of digital advancement, as indicated by its DESI ranking [4], alongside distinct patterns of consumer behavior, constitutes a contextual framework that significantly influences the observed relationships. Consequently, the applicability of these results beyond the Polish setting remains limited and should be considered with appropriate caution.

Finally, gender moderation analysis showed that the effect of DCB on environmental awareness (EA) was significant in both groups, but slightly stronger among men (β = 0.638) than women (β = 0.576), indicating a partial moderating effect and supporting hypothesis H5d. These differences may stem from divergent motivations and patterns of technology use. The findings align with prior research on gender roles in ecological and technological attitudes. For example, Brough et al. [146] found that men often perceive environmental actions as “unmanly”, and their engagement in green behaviors depends more heavily on contextual factors (e.g., functionality, innovation). However, when digital consumption is associated with modernity and utility, it becomes a socially “safe space” for men to identify with ecological values. On the other hand, women may express environmental awareness more through social, emotional, and normative drivers rather than digital consumption per se [147,148].

6. Conclusions

The results confirmed that all four examined factors significantly influence consumer activity in the digital environment, with personal innovativeness emerging as the strongest predictor. Additionally, digital consumer behavior (DCB) demonstrated a strong positive relationship with environmental awareness (EA), suggesting that greater engagement in digital services supports a more sustainable approach to environmental issues. The analysis of gender as a moderator revealed that digital skills and personal innovativeness have a stronger effect on women’s consumer behavior, whereas attitudes toward smart home technologies were significant only among men. These differences highlight the importance of incorporating demographic factors when studying technology adoption and pro-environmental attitudes.

This research makes a significant contribution to understanding the mechanisms of responsible digital consumption in the context of sustainable development. It underscores the growing role of digital competencies and openness to innovation in shaping environmental attitudes. From a theoretical perspective, this study enriches the existing body of knowledge by integrating three previously distinct research areas—digital consumer skills, personal innovativeness, and ecological awareness—into a single empirical model. It also demonstrates that DCB serves as a mediating variable between technological and psychological predictors and pro-environmental attitudes, enabling a deeper understanding of the role of digital transformation in advancing sustainability goals, especially responsible consumption. The findings offer a foundation for future cross-national comparative research and provide practical insights for developing initiatives that support pro-environmental consumer behavior in the digital economy.

From a practical standpoint, the results have important application value, especially for companies operating in the e-commerce sector. The findings can support consumer segmentation based on levels of digital competence, personal innovativeness, and environmental awareness. This enables the design and implementation of marketing strategies that promote a long-term balance between economic, social, and environmental goals through the use of modern digital technologies. In particular, these insights can inform consumer education programs by identifying groups with high digital readiness who are also more receptive to messages about sustainability. Such programs may focus on building awareness about the environmental consequences of digital behaviors and promoting eco-responsible technology use. Moreover, the findings can be utilized in user-centered app design by embedding sustainability-oriented features into digital platforms. This includes features such as green nudges, eco-efficiency dashboards, or filters for environmentally certified products, which align with the values of digitally competent users. Designers should consider gender-related differences in motivational triggers, tailoring app functions accordingly. Additionally, the observed correlation between digital consumer behavior and environmental awareness offers valuable guidance for targeted marketing strategies aimed at environmentally conscious audiences. Brands can create segmented campaigns that emphasize innovation and sustainability, especially for consumers with high personal innovativeness. For example, marketing messages highlighting the ecological benefits of smart products, minimalistic consumption, or circular economy services may resonate more effectively with this segment.

E-commerce businesses should take into account the environmental impact of their products and services, promote conscious consumption, and strengthen customer loyalty through brand engagement in ecological initiatives. Recommendations for online retailers aimed at reducing their environmental footprint may include a range of sustainable marketing practices, from promotional strategies to logistics solutions. For example, it is advisable to promote eco-certified products (e.g., EU Ecolabel) and locally sourced items, introduce reusable or returnable packaging, and implement green logistics practices such as order consolidation, optimized delivery routes, and the use of low-emission transport (e.g., cargo bikes, electric vehicles). A crucial component of sustainable e-commerce strategy is also the promotion of eco-friendly products and designing tools that support green decision-making, such as sustainability rankings, “eco” search filters, and carbon footprint labels. Equally important is transparency in product offerings and production processes, ensuring that consumers have access to reliable information about the origin, composition, lifecycle, and certification of products.

Public institutions and non-governmental organizations can also play a vital role in advancing these goals by developing educational and awareness campaigns promoting responsible consumer behavior in the digital realm. Mobile apps and online learning platforms, for instance, can disseminate knowledge about conscious consumption, resource conservation, and the environmental consequences of everyday purchasing decisions. Such efforts are particularly effective in reaching digitally active consumers. These recommendations can be integrated into companies’ ESG (Environmental, Social, Governance) strategies, thereby supporting the achievement of global sustainability goals—in particular, SDG 12: Responsible Consumption and Production.

7. Limitations and Future Research

Despite the broad scope of the study and the application of quantitative methods and validated scales, the present analyses are subject to several important methodological and cognitive limitations. These should be taken into account when interpreting the results, as well as when designing future research in related areas. One key limitation is the cross-sectional nature of the study, which prevents definitive conclusions about causal relationships between the analyzed constructs. Although Structural Equation Modeling (SEM) allows for testing complex relationships between latent variables, it does not eliminate the possibility of reciprocal effects. Establishing the causal order between digital skills, personal innovativeness, attitudes toward smart home technology, and environmental awareness would require a longitudinal research design. Such studies would allow researchers to capture the dynamics of these relationships over time. A second limitation is the lack of consideration for regional and cultural contexts, which may significantly influence levels of digital maturity and environmental awareness. Differences in access to technological infrastructure, lifestyles, and local economic conditions could moderate the observed relationships and should be considered in future research. Another limitation concerns the national specificity of the research context. The cultural norms, infrastructural conditions, and consumer behavior patterns that characterize the Polish environment may not be representative of other countries. To assess the robustness and external validity of the proposed model, future studies should incorporate cross-national comparisons involving societies with varying levels of digital development and environmental awareness. An additional limitation lies in the self-reported nature of the data. All variables were measured using participants’ subjective assessments, which introduces the risk of cognitive biases, such as social desirability bias or the halo effect. The lack of independent verification may affect the reliability and validity of the results. Moreover, a limitation may stem from the use of the WLSMV estimator, which can restrict the comparability of the results with studies conducted on large samples using classical modeling approaches.

Future studies may consider applying data and method triangulation, supplementing declarative measures with alternative sources of information such as behavioral data, digital activity tracking, or participant observation. In light of these limitations, several future research directions are recommended:

• The use of longitudinal and experimental research to enable the analysis of changes in consumer attitudes and behaviors over time, and in response to specific environmental or technological stimuli (e.g., national culture and values, access to emerging technologies);
• The implementation of international comparative studies, which would allow the results obtained in Poland to be contrasted with data from countries with different levels of digital maturity and environmental awareness;
• The examination of potential reverse relationships between environmental awareness and digital consumer behavior, particularly whether pro-environmental attitudes may encourage more sustainable digital practices;
• The application of mixed-methods approaches combining quantitative statistical modeling with qualitative analyses of consumer narratives. Such triangulation would deepen the understanding of motivations, barriers, and values that guide consumers in the digital world.

Pursuing these research directions would not only enable a better understanding of the mechanisms shaping pro-environmental attitudes in the digital economy but would also provide valuable recommendations for the development of sustainable consumption and innovation policies.