From Awareness to Action: A UK-Based Study on Public Perceptions of Digital Pollution

Abstract

Digital pollution, encompassing energy consumption, e-waste, and the environmental impact of digital technologies, poses a significant and increasingly pressing environmental challenge that has received insufficient research attention. This study explores public perceptions, attitudes, and behaviors related to digital pollution, focusing on both individuals’ willingness to pay for environmentally friendly digital solutions and their actions to reduce digital environmental impact. Through a comprehensive survey of 300 UK participants, we examined the associations between demographic factors, knowledge levels, perceptions, and environmental actions. While traditional demographic factors such as age and income showed no significant correlation with willingness to pay, we found strong positive correlations with the frequency of environmental consideration (r = 0.47), willingness to act (r = 0.42), and perceived importance of digital pollution (r = 0.40). Notably, knowledge of digital pollution was not correlated with willingness to pay, while self-assessed tech-savviness and environmental knowledge had positive correlations with both willingness to pay and actions taken. Based on a robust cluster analysis, we identified four distinct participant groups: ’Engaged Eco-Tech Enthusiasts’ (youngest, most tech-savvy, and with the highest willingness to act), ‘Knowledgeable Traditionalists’ (oldest, highest knowledge scores, and moderate action), ‘Unengaged Pragmatists’ (lowest engagement), and ‘Affluent Moderates’ (wealthiest and with moderate engagement). These findings provide valuable insights for developing targeted interventions and communication strategies to address this emerging environmental challenge.

1. Introduction

In recent years, digital technologies have profoundly transformed many aspects of society, including revolutionary advancements in industry, education, and economy. This digital transformation has created a complex environmental dynamic. On the one hand, some digital technologies can help reduce environmental pollution. For example, Shen and Zhang [1] found that the use of advanced digital technologies such as artificial intelligence, the Internet of Things, and cloud computing can help reduce air pollution in cities. They noted that these technologies enable more efficient monitoring and management of pollution and promote green innovation and improved energy efficiency. Similarly, Liu et al. [2] demonstrated that digital technologies can reduce carbon emissions in Chinese cities by improving energy efficiency, promoting green technological innovation, and easing market segmentation. Building on this perspective, Nishant et al. [3] emphasize that AI can facilitate holistic solutions to complex environmental challenges, such as resource conservation and climate change mitigation, while Holzmann and Gregori [4] highlight the transformative potential of digital tools in enabling innovative business models that align with environmental and social objectives. On the other hand, these technologies also generate increasingly significant environmental harm, collectively termed “digital pollution”.

Digital pollution encompasses the environmental impacts of digital technologies, including energy consumption of data centers, water usage for cooling infrastructure, and e-waste from device obsolescence [5]. These impacts pose severe challenges to sustainability efforts.

A key concept in addressing digital pollution is digital sobriety, which refers to the practice of reducing technology use to align with sustainability goals [6]. This approach encourages responsible usage and reduces digital consumption, striving to balance the advantages of technology with its environmental costs.

Recent studies indicate that digital transformation plays a critical role in shaping environmental awareness and behaviors, especially among younger populations [7]. Digital platforms that provide access to green products and information significantly shape consumers’ perceptions and engagement with environmentally friendly products, highlighting the potential of digital tools to promote eco-friendly behaviors. However, despite the growing integration of digital technologies into daily life, public awareness of digital pollution remains low [8].

Digital pollution presents unique challenges for understanding environmental behavior, as its impacts are largely invisible to consumers. Unlike traditional environmental issues, the ‘invisible carbon footprint’ of digital activities—from internet usage to cloud services—is abstract and difficult for users to perceive directly. This invisibility creates challenges for promoting sustainable digital behavior, as individuals struggle to connect their daily digital activities with environmental consequences.

While willingness to pay (WTP) for environmentally friendly products has been extensively studied in domains like food, energy, and fashion, its application in the digital realm remains underexplored. The few existing studies examining WTP in digital contexts suggest modest but real latent demand for greener digital options, indicating that despite the abstract nature of digital environmental impacts, consumers may still be willing to invest in sustainable digital alternatives.

Understanding how individuals perceive the severity of digital pollution and their willingness to take action to mitigate its impacts is crucial for developing effective interventions [9]. In our previous study [10], we quantified and analyzed digital pollution knowledge across various demographic groups and identified significant gaps in digital pollution literacy. Building on this foundation, the current study employs the same UK sample but focuses specifically on participants’ attitudes, perceptions, and behaviors related to digital pollution, with particular emphasis on WTP for environmentally friendly digital solutions and engagement in digital sobriety practices. This study aims to address these gaps through four primary objectives: (1) quantifying WTP and willingness to act to reduce digital pollution in a UK sample; (2) examining how perceptions of digital pollution, perceived personal impact, perceived importance and visibility, self-assessed technological fluency, and environmental knowledge, alongside demographics, relate to WTP and to willingness to act; (3) testing whether objective knowledge predicts WTP and willingness to act, in order to evaluate the knowledge–action gap; and (4) segmenting participants using cluster analysis to identify actionable profiles that can inform targeted communication and policy interventions.

2. Literature Review and Conceptualization

2.1. Theoretical Foundations and Key Concepts

Digital pollution encompasses the environmental impacts associated with the production, use, and disposal of digital technologies and infrastructure, including (a) the high energy consumption and carbon footprint of data centers, (b) water used for cooling servers and maintaining cloud infrastructures, and (c) electronic waste (e-waste) generated by rapid device obsolescence [5]. For example, training large-scale AI models such as GPT-3 has been estimated to emit over 500,000 pounds of CO₂ equivalent [11]. These issues pose severe challenges to sustainability efforts, as growing reliance on digital tools places pressure on natural resources and contributes to global environmental degradation [9].

Digital sobriety, the practice of reducing technology use to align with sustainability goals [6], provides a framework for addressing these impacts through responsible usage. This study is grounded in the Knowledge–Attitude–Behavior (KAB) model, which posits that environmental knowledge influences attitudes and perceptions, which in turn shape behavioral intentions and actions. This model has been widely used in environmental psychology and sustainable consumption research to explain how individuals transition from awareness to action [12]. In the context of digital sustainability, this model includes objective knowledge about environmental impacts, attitudinal perceptions (such as perceived importance and corporate responsibility), and behavioral indicators like WTP for sustainable digital services and the adoption of digital sobriety practices [10,12]. The KAB framework provides a robust structure for mapping the cognitive and motivational drivers behind environmentally significant behaviors in the digital realm.

Guided by this model, we selected variables reflecting each of its core stages: (a) objective digital pollution knowledge (knowledge); (b) environmental attitudes and perceptions (attitudes); (c) WTP for eco-friendly digital services (behavioral intention); and (d) self-reported digital sobriety actions (actual behavior)

This sequence enables theory-driven mapping of the cognitive and motivational precursors to environmentally significant behavior in the digital context. Additionally, we included demographic variables, self-perceived tech-savviness, and perceived environmental knowledge, based on prior research that has shown their relevance in moderating or directly influencing knowledge, attitudes, and behavioral intentions (e.g., [13]).

WTP for environmentally friendly digital services reflects individuals’ readiness to incur personal costs to reduce environmental externalities [14,15]. WTP is influenced by environmental concern, trust in eco certifications, moral norms, and perceived efficacy [16,17].

Recent large-scale studies emphasize that WTP for environmental protection is shaped not only by individual attitudes and perceived efficacy but also by social and institutional factors. For example, Tan et al. [18] found that social trust and past pro-environmental behaviors significantly predicted WTP in China, highlighting the role of interpersonal and collective norms in shaping consumer decisions. Similarly, Ling et al. [19] demonstrated that consumer WTP can directly influence corporate decisions to adopt low-carbon technologies, suggesting that aggregated individual demand can act as a market signal for sustainability-oriented innovation. In the context of digital products, the study by [20] found that consumers in Taiwan were more WTP for e-waste management services when they were perceived as convenient and well-managed, underscoring the role of service design in digital environmental choices. Finally, the PwC (2024) Global Voice of the Consumer Survey [21] reported that consumers worldwide were WTP an average of 9.7% extra for sustainably produced goods, even amid cost-of-living pressures, reinforcing the real-world relevance of WTP in sustainability trends.

In addition to WTP, this study examines actions to reduce digital pollution. Andersen and Mayerl [22] highlight that environmental attitudes may not always translate into immediate behavior changes but can foster support for broader environmental initiatives, underscoring the importance of studying attitudes and their impact on digital behaviors.

2.2. Research Gap and Study Rationale

Cross-country analyses reveal that policy frameworks, education systems, and cultural norms shape public engagement with environmental sustainability [23], indicating caution when generalizing results across contexts and highlighting the need for comparative research in the digital domain.

Despite growing awareness campaigns, empirical research on behavioral dimensions of digital pollution remains limited. Few studies have applied established behavioral models such as KAB to this domain, and WTP for sustainable digital solutions remains largely unexamined. Moreover, cross-cultural analyses are scarce, leaving unanswered questions about the universality of behavioral patterns and the transferability of intervention strategies. While the concept of digital pollution is relatively new and lacks standardized measurement tools, the variables used in this study—objective knowledge, environmental attitudes, WTP, and self-reported digital sobriety actions—have been widely employed in environmental behavior and sustainability research (e.g., [13,24]). Our study therefore adapts these well-established constructs to the digital context for the first time, addressing a gap in the literature by operationalizing digital pollution engagement through a theoretically grounded and replicable framework.

Building on our previous study [10], which quantified and analyzed digital pollution knowledge across various demographic groups and identified significant gaps in digital pollution literacy, the current study examines how knowledge and perceptions influence environmentally friendly behaviors, how demographic factors moderate these relationships, and whether distinct user profiles can be identified.

2.3. Hypotheses

The theoretical framework presented above establishes clear foundations for examining digital pollution behavior. First, the KAB model demonstrates that knowledge shapes attitudes by clarifying consequences and action options, with empirical evidence showing links between environmental knowledge and pro-environmental attitudes [25,26]. Second, the Theory of Planned Behavior establishes that attitudes inform behavioral intentions, including WTP, with meta-analyses confirming that greener attitudes increase WTP for environmental solutions [15,27]. Third, intention–behavior research demonstrates that stronger intentions generally predict greater pro-environmental action, though with some constraints in implementation [22,28]. Finally, both TPB and KAB frameworks recognize that demographic resources and perceived capabilities moderate these relationships [29,30]. Based on these theoretical foundations, we formulate the following hypotheses:

H1: 

Higher objective knowledge about digital pollution will be positively associated with more favorable environmental attitudes and perceptions.

H2: 

Positive environmental attitudes and perceptions will be positively associated with WTP for sustainable digital services.

H3: 

Higher WTP will be positively associated with engagement in digital sobriety actions: WTP is a costed intention.

H4: 

Demographic variables and perceived tech-savviness/environmental knowledge will moderate these relationships.This hypothesis was further divided into H4a, H4b, and H4c, each addressinga moderating effect of a specific variable.

To go beyond average-level associations and explore meaningful patterns within the population, the study employs cluster analysis. This person-cantered approach allows us to identify distinct subgroups based on participants’ knowledge, attitudes, and behaviors related to digital pollution. Segmenting the population in this way enables a more nuanced understanding of digital environmental engagement and supports the development of targeted policy and educational strategies tailored to different user profiles.

This study offers several novel contributions. While WTP has been widely examined in traditional environmental contexts, its application to digital consumption remains limited. We address this gap by exploring WTP and self-reported actions related to digital pollution. Additionally, we employ cluster analysis to identify distinct user profiles, offering new insights into how knowledge, attitudes, and perceptions shape digital environmental behavior. The study combines theory-driven measurements, cluster analysis, and explorations of both WTP and self-reported actions in a nationally diverse UK sample, providing both theoretical insights and practical guidance for policymakers and technology companies seeking to promote more sustainable digital practices.

3. Methods

3.1. Ethical Considerations

This cross-sectional survey study included 300 participants from the United Kingdom, recruited via Prolific (www.prolific.com), an online platform widely used in behavioral and cognitive research for its reliable participant pool and advanced demographic filtering tools. Data collection was conducted between 28 October and 30 October 2024. To ensure sample diversity, pre-screening criteria were applied to include a broad range of age groups (18–80 years), educational backgrounds, and occupational sectors. While true population representativeness cannot be fully achieved through online convenience sampling, Prolific’s built-in filters helped reduce sampling bias. Additionally, we conducted our own demographic analysis to assess the diversity of the sample, as detailed in the Results section.

Participation was voluntary, and only individuals fluent in English and over 18 years of age were eligible. Prior to participation, all respondents provided informed electronic consent through the Prolific platform. The consent form clearly explained the study’s objectives, procedures, data handling practices, and participants’ rights, including the right to withdraw at any time without penalty.

To protect participants’ privacy, all data were fully anonymized at the point of collection, and each participant was assigned a unique identifier code. No personal identifying information was stored alongside survey responses. Electronic consent records are securely stored by the research team in compliance with institutional policies and data protection regulations and are kept separate from survey data to ensure anonymity.

The study protocol, including the informed consent procedure, was reviewed and approved by the Ethics Committee of Ruppin Academic Center (Approval Number: 2024/241), confirming its adherence to ethical standards for human subject research.

3.2. Research Design and Participants

The sample consisted of 300 participants from the UK, recruited via Prolific. Based on a power analysis, a sample size of 81 participants was required to detect a correlation of r = 0.3 with a power of 0.80 and a significance level of α = 0.05. This ensures that the study is adequately powered to identify meaningful associations while minimizing the risk of Type I and Type II errors. Eligibility criteria included being at least 18 years old and fluent in English. The sample was designed to reflect diverse demographics, such as variations in age, sex, education level, and occupation. The participants completed a comprehensive online questionnaire addressing multiple aspects of digital pollution, which was comprised of 47 questions in total.

This study builds on our previous research on public knowledge of digital pollution [10] and focuses on participants’ opinions, perceptions, and behaviors related to digital pollution. We therefore retained the same participant sample from our earlier study, while employing a new questionnaire to enable a more comprehensive assessment of attitudes and behaviors.

3.3. Survey Design and Content

The selection and grouping of variables were based on the KAB model and informed by our conceptual framework (see Figure 1), which posits that knowledge and perceptions influence both WTP and digital sobriety actions, moderated by demographic and self-assessed factors.

The questionnaire consisted of five main sections described in

Table 1. Structure of the study questionnaire.

Section	Subsection/Topic	Number of Questions
(a) Demographics	Age, Gender, Education, Income, and Occupation,	6
(b) Self-Assessment	Tech-Savviness and Environmental Knowledge self-efficacy	3
(c) Environmental Knowledge	General Knowledge on Sustainability and Climate Change	4
(d) Digital Pollution Knowledge	AI and Digital Pollution	2
	Water Usage in Digital Infrastructure	7
	Energy Efficiency in Digital Devices	7
	E-Waste Awareness	2
	Digital Pollution Reduction	7
(e) Confidence in Knowledge Responses	The participant’s confidence in answering the knowledge questions	1
(f) Attitudes, Impact, and Intent	Questions addressing participants’ views on the importance of digital pollution, their perceived impact, and their willingness to change behaviors.	8
Total		47

. While participants completed all sections of the comprehensive questionnaire, this study focused on the sections related to demographics, self-assessment, opinions, and attitudes (sections A, B, and F; see Table 1). The knowledge sections (sections C and D; see Table 1), which were thoroughly analyzed in our earlier study [10], were used to calculate the knowledge scores. Here, we also examined the associations between these knowledge scores and the participants’ perceptions and attitudes, including questions regarding the participants’ digital sobriety (Section F). In Addition, we examined the associations between the demographic characteristics (section A) and the participants’ perceptions and attitudes (Section F). To ensure the content validity and theoretical grounding of the questionnaire, we developed the survey items following a systematic process. This process included an extensive literature review and expert consultations on digital sustainability and environmental literacy. Environmental knowledge items were adapted from validated instruments developed by Pothitou et al. [31] and Boeve-de Pauw et al. [23], covering key topics such as carbon footprint, energy efficiency, and climate change. Digital pollution literacy items were designed based on academic sources addressing AI energy use, data center water consumption, e-waste management, and digital consumption reduction strategies.

A pilot test was conducted with 50 participants. Items with high inter-item correlations (r > 0.80) were removed to avoid redundancy. Construct validity was supported by the theoretical alignment of items and observed correlations between knowledge scores and demographic factors. As most attitudinal and behavioral variables were measured using single items, internal consistency statistics were not applicable for those sections. However, Cronbach’s alpha was calculated for multi-item sections, resulting in acceptable internal reliability (α = 0.7439).

This validation process ensured that the final 47-item questionnaire provides a comprehensive and theoretically grounded measure of knowledge, attitudes, and behaviors related to digital pollution.

3.4. Key Variables and Measurements

The study analyzes multiple variables to understand the associations between participants’ characteristics, knowledge, perceptions, and behaviors regarding digital pollution. The variables, categorized into demographic factors, knowledge assessment, perceptions and attitudes, and behavioral measures, are detailed in

Table 2. Variables and their measurements.

Variable	Measurement
(1) Age	Age in years
(2) Sex	Male/Female
(3) Education Level	“What is your highest level of education?”
(4) Income	“What is the total annual net current income of your household?”
(5) Occupation	“What is your primary occupation?”
(6) Tech-Savviness	“How would you describe your level of tech-savviness?”
(7) Perceived Environmental Knowledge	“How would you rate your knowledge about environmental issues?”
(8) Environmental Knowledge Score	Weighted score based on correct answers to environmental knowledge questions
(9) Digital Pollution Knowledge Score	Weighted score based on correct answers to digital pollution questions
(10) Frequency of Environmental Consideration	“How often do you consider the environmental impact of your digital activities?”
(11) Perceived Importance of Digital Pollution	“How important do you think it is to address digital pollution compared to other environmental issues?”
(12) Perceived Visibility of Digital Pollution	“In your opinion, how visible is the issue of digital pollution compared to other forms of pollution?”
(13) Belief in Digital SELF Impact	“To what extent do you believe your daily digital activities contribute to environmental problems?”
(14) Perceived public awareness	“How would you rate the general public’s awareness of digital pollution?”
(15) Perceived Corporate Role	“How do you perceive the role of technology companies in addressing digital pollution?”
(16) Willingness to Act Score	Composite score based on five routine actions, for which the participants could choose zero to five of the following actions:
	- Turning off devices when not in use
	- Using energy-efficient settings
	- Limiting streaming quality
	- Unsubscribing from unnecessary emails
	- Deleting unused files and apps
(17) Willingness to Pay	“Would you be willing to pay more for digital devices or services that are more environmentally friendly?”

. The selection of these variables was guided by the KAB model, which posits that individuals progress from environmental knowledge (cognitive), through attitudes and perceptions (affective), to behavioral intentions and actual actions (behavioral outcomes). This theoretical framework allowed us to examine how distinct psychological and contextual factors contribute to engagement with digital sustainability. Accordingly, objective digital pollution knowledge was selected as the cognitive component; environmental attitudes and perceptions as the affective component; WTP for sustainable digital services as a behavioral intention; and digital sobriety actions as an observable behavioral outcome.

In addition, demographic variables (e.g., age, income, and education), along with self-assessed tech-savviness and perceived environmental knowledge, were included based on prior empirical work suggesting their relevance as antecedents or moderators in environmental behavior models [13]. These variables were selected to comprehensively examine how individual socio-demographic characteristics and knowledge levels relate to attitudes and actions regarding digital pollution. Detailed response options for each variable can be found in Appendix A. While most variables in this study were assessed using single-item measures, this approach is supported in cases where constructs are concrete, unambiguous, and easily understood by respondents, particularly when the research aims to reduce survey length and fatigue [32,33]. For example, constructs such as WTP and willingness to act have been effectively measured with single items in prior environmental behavior research. Single-item measures have been shown to yield valid and reliable results when measuring clearly defined attributes such as attitudes, self-perceptions, or behavioral intentions. This method was particularly suited for a comprehensive survey aimed at minimizing respondent fatigue while capturing a broad range of constructs related to digital pollution awareness and behavior.

3.5. Analytical Approach

This study employs a dual analytical strategy combining hypothesis testing with exploratory segmentation. Correlation analyses test the KAB framework hypotheses (H1–H3), while cluster analysis identifies distinct user profiles using the same variables. This integrated approach provides both theoretical validation and practical insights for targeted interventions.

3.6. Statistical Analysis

3.6.1. Refinements and Reliability Assessments

To validate the analysis process and the questions in the questionnaire, we administered a pilot questionnaire to a sample of 50 participants. Questions that had high correlation coefficients were reviewed or removed to avoid redundancy. In addition, to evaluate the reliability of the questionnaire, Cronbach’s alpha was calculated, resulting in α = 0.7439. The removal of any individual questions did not significantly improve reliability (change > 0.05), indicating satisfactory internal consistency. As a result, all questions were retained at this stage.

3.6.2. Statistical Tests

Shapiro–Wilk tests revealed that the variables did not have normal distributions. Therefore, non-parametric statistical techniques were used. Spearman’s correlations were employed to evaluate associations between demographic and socioeconomic variables, participants’ digital pollution (DP) knowledge, conceptions, views, and attitudes.

To compare attitudes between different groups, we utilized Wilcoxon non-paired tests. Specifically, these tests were used to examine differences in attitudes between males and females, as well as among various occupational categories.

Additionally, Wilcoxon paired tests were conducted to assess differences between participants’ responses to various aspects of digital pollution. This allowed us to compare, for instance, participants’ attitudes towards different dimensions of digital pollution or their willingness to engage in various environmentally friendly behaviors. Wilcoxon non-paired tests were used to compare continuous characteristics between the different clusters’ ages, digital pollution scores, WTP, and willingness to act. Chi-square tests were used to compare the distributions of males and females across the different clusters (see next section).

All statistical analyses were performed using MATLAB^© version R2024b, with a threshold for statistical significance of p < 0.05 for all analyses.

3.7. Cluster Analysis

A cluster analysis was performed to examine participants’ behaviors and attitudes regarding digital pollution. This analysis aimed to reveal patterns and characteristics that may not be found through classical statistical methods and to identify distinct profiles of environmentally conscious digital users. The cluster analysis was based on key features outlined in the ‘Survey Design and Content’ section (e.g., perceived importance of digital pollution, willingness to act, WTP, and occupational category).

We applied the K-means clustering algorithm [34], which iteratively partitions the data into four clusters, assigns each participant to the nearest cluster, and moves the cluster to the mean (average) of the participants assigned to it. K-means was chosen because it is well suited for large datasets with continuous variables, does not require distributional assumptions, and allows for exploratory identification of meaningful subgroups. Unlike model-based approaches, K-means is computationally efficient and robust for initial segmentation. The selection of variables for clustering was theory-driven, guided by the KAB model, ensuring that clusters reflected meaningful cognitive, affective, and behavioral dimensions of engagement with digital pollution. The optimal number of clusters was determined using the elbow method [35], which evaluates the variance explained as the number of clusters increases. Cluster labeling was informed by both quantitative differences in variable means and alignment with conceptual categories from prior environmental segmentation research (e.g., [36]).

The final solution included four clusters. With a sample of 300 participants, this meets commonly accepted guidelines for cluster analysis that suggest a minimum of 20–30 observations per cluster to ensure interpretability and stability [37]. Furthermore, previous studies of sustainability and green consumer segmentation have employed similar or even smaller sample sizes using K-means clustering, with valid and replicable outcomes (e.g., [30]). The resulting clusters were used to analyze group-level differences in digital pollution perceptions and behaviors, as presented in the next section.

4. Results

The results are presented in two complementary parts: hypothesis testing through correlation analyses (Section 3.2–3.4) and exploratory cluster analysis (Section 3.5). This dual approach tests our theoretical predictions while identifying actionable user segments.

4.1. Socio-Demographic Characteristics of the Survey Participants

The study sample comprised 300 participants from the United Kingdom, with ages ranging from 18 to 80 years (mean = 45.53 ± 13.16). The sample was diverse in terms of sex, education level, income, and occupation, which were distributed as follows: (1) Age Groups: Young Adults (18–34 years), 23.2%; Middle-aged (35–54 years), 46.8%; Older Adults (55+ years), 29.9%. (2) Sex: 58.8% female, 41.2% male. (3) Education Level: High (Bachelor’s degree or higher), 55.5%; Medium (Post-secondary or vocational training), 28.3%; Low (Secondary education or below), 16.2%. (4) Annual Household Income: High (USD 70,001 and above), 30.9%; Medium (USD 15,001–70,000), 54.8%; Low (Up to USD 15,000), 14.3%. (4) Occupational Sectors: Professional/Industry, 30%; Public Sector (Government, Healthcare, and Education), 24%; Other Sectors, 31%; Not Currently Employed (Including Students and Retired), 15%.

Table 3. Demographic and background characteristics of survey participants.

Characteristics	Categories	N	%
Sex	Male	124	41.2
	Female	177	58.8
Age	18–24	13	4.3
	25–34	57	18.9
	35–44	84	27.9
	45–54	57	18.9
	55–64	65	21.6
	65+	25	8.3
Annual household income	No income
	Up to USD 15,000	10	3.3
	USD 15,001–35,000	80	26.6
	USD 35,001–70,000	103	34.2
	USD 70,001–100,000	62	20.6
	USD 101,001–150,000	23	7.6
	More than USD 150,000	8	2.7
Occupations	Unemployed	15	5%
	Students	6	2%
	Government employees	21	7%
	Healthcare professionals	24	8%
	Educator or teachers	28	9%
	Artists or creative professionals	7	2%
	Marketing	20	7%
	Researchers or scientists	6	2%
	Constructions or are skilled tradespersons	9	3%
	Accounting	15	5%
	The technology industry	21	7%
	Non-technology industry	43	14%
	Retired	39	13%
	Other professionals	47	16%

provides a detailed breakdown of the demographic and background characteristics of the survey participants. These categories were used consistently throughout our analyses, including the cluster analysis presented in subsequent sections.

4.2. Willingness to Pay for Environmentally Friendly Digital Devices or Services

One of our key questions in this survey was: “Would you be willing to pay more for digital devices or services that are more environmentally friendly?” which refers to the notion of digital sobriety. A total of 4.3% (13) of the participants replied they would definitely pay for such devices or services, 45% (135) replied they would pay for such services depending on the cost, 24.3% (73) were not sure, and 26.7% (80) replied they would not pay for such devices or services.

We examined associations between WTP and various socio-demographic and other factors. No significant associations were found between WTP and age, income, or digital pollution knowledge score, which leads us to reject hypothesis 1. Several significant positive correlations were identified (see

Table 4. Spearman correlations between WTP for environmentally friendly services/devices and several knowledge and public awareness variables.

Factor	Correlation (r)	95% CI	p-Value	R2 (%)
Frequency of Environmental Consideration	0.47 *	[0.38, 0.56]	<0.0001	22.09
Willingness to Act Score	0.42 *	[0.32, 0.51]	<0.0001	17.64
Perceived Importance of Digital Pollution	0.40 *	[0.30, 0.49]	<0.0001	16.00
Belief in Digital Self-Impact	0.40 *	[0.30, 0.49]	<0.0001	16.00
Perceived Environmental Knowledge	0.26 *	[0.16, 0.37]	<0.0001	6.76
Perceived Visibility of Digital Pollution	0.23 *	[0.13, 0.34]	<0.0001	5.29
Perceived Public Awareness	0.22 *	[0.11, 0.32]	0.0002	4.84
Tech-Savviness	0.13 *	[0.02, 0.24]	0.02	1.69
Perceived Corporate Role	0.12 *	[0.00, 0.23]	0.04	1.44

Note. N = 300. CI = confidence interval. * p < 0.05.

). The strongest correlations with the WTP were observed for the following factors: frequency of considering environmental impact (r = 0.47, p < 0.0001), willingness to act score (r = 0.42, p < 0.0001), perceived importance of digital pollution (r = 0.40, p < 0.0001), and belief in digital contribution to environmental problems (r = 0.40, p < 0.0001), all of which support hypothesis 2. Moderate correlations were found with self-assessed environmental knowledge (r = 0.26, p < 0.0001) and perceived visibility of digital pollution (r = 0.23, p < 0.0001).

Regarding occupational sectors, marketing professionals showed significantly higher WTP compared to other occupations, including the unemployed, healthcare professionals, artists/creative professionals, non-tech industry employees, and retired individuals (p < 0.05 for all comparisons). Although male participants showed a higher WTP compared to females, this difference was not statistically significant.

4.3. Actions Taken by the Participants to Reduce Their Digital Environment Impact

Next, we focused on the participants’ willingness to act to reduce their digital environment impact (which explicitly refers to digital sobriety). The responses revealed that 6.6% of participants took no action, 20.1% took one action, 23.6% took two actions, 24.9% took three actions, 19.6% took four actions, and 4.6% of participants reported taking all five actions (See Figure 2).

Then, we examined the associations between the number of actions taken and various knowledge and awareness factors. Several significant positive correlations were identified (

Table 5. Spearman correlations between the number of actions taken to reduce digital pollution and knowledge and awareness.

Factor	Correlation (r)	95% CI	p-Value	R2 (%)
Frequency of Considering Environmental Impact	0.41	[0.31, 0.50]	<0.0001	16.81
Willingness to Pay	0.42	[0.32, 0.51]	<0.0001	17.64
Perceived Importance of Digital Pollution	0.38	[0.28, 0.48]	<0.0001	14.40
Perceived Environmental Knowledge	0.30	[0.20, 0.40]	<0.0001	9.00
Belief in Digital Self-Impact	0.28	[0.17, 0.38]	<0.0001	7.84
Perceived Corporate Role	0.18	[0.06, 0.28]	0.002	3.24
Income	0.15	[0.04, 0.26]	0.007	16.81

). The strongest correlations were found with the frequency of considering environmental impact (r = 0.41, p < 0.0001) and the WTP for environmentally friendly services/devices (r = 0.42, p < 0.0001) confirming hypothesis 3. However, no statistically significant associations were found between the number of actions and digital pollution knowledge scores or tech-savviness (r = 0.1, 95% CI [0,0.22], p = 0.051, R² = 1%).

Regarding differences between occupations and the number of actions taken, we found that marketing professionals took significantly more actions (mean = 3.2) compared to other occupation groups including unemployed (mean = 2.1), government employees (mean = 2.4), healthcare professionals (mean = 2.0), and others (p < 0.05 for all comparisons). Additionally, tech industry employees and educators (both mean = 2.9) took significantly more actions compared to healthcare professionals and construction/skilled tradespersons (p < 0.05). While male participants reported taking more actions (mean = 2.4) compared to females (mean = 1.4), this difference was not statistically significant.

4.4. Effect Size Analysis

To quantify the magnitude of the relationships identified in our analyses, we examined the R² values (coefficient of determination) from the correlation analyses. For WTP (see Table 4), the strongest effect sizes were observed for the following variables: frequency of environmental consideration (R² = 22.09%), willingness to act score (R² = 17.64%), perceived importance of digital pollution (R² = 16.00%), and belief in digital self-impact (R² = 16.00%). Smaller effect sizes were found for perceived environmental knowledge (R² = 6.76%), perceived visibility of digital pollution (R² = 5.29%), and perceived public awareness (R² = 4.84%). The weakest effects were observed for tech-savviness (R² = 1.69%) and perceived corporate role (R² = 1.44%). For actions taken to reduce digital pollution (see Table 5), the strongest effect sizes were found for WTP (R² = 17.64%) and frequency of environmental consideration (R² = 16.81%). Moderate effects were observed for the perceived importance of digital pollution (R² = 14.40%) and perceived environmental knowledge (R² = 9.00%). Smaller effects were found for belief in digital self-impact (R² = 7.84%), perceived corporate role (R² = 3.24%), and income (R² = 16.81%).

These effect size measurements complement the statistical analysis and provide a clearer understanding of the practical significance of our findings, particularly in identifying which factors have the strongest relationships WTP and the actions taken to reduce digital pollution.

4.5. Cluster Analysis Results

A cluster analysis was conducted to identify distinct patterns in participants’ demographic characteristics, attitudes, and behaviors regarding digital pollution. This approach complements the statistical analyses by providing a comprehensive, multifactorial perspective of how varied factors influence participants’ engagement with digital pollution issues.

These clusters highlight varying combinations of age, knowledge, tech-savviness, and willingness to act across different demographic groups, providing a nuanced perspective of attitudes and behaviors related to digital pollution.

Table 6. Characteristics of clusters identified from key study variables.

Characteristic	Engaged Eco-Tech Enthusiasts	Knowledgeable Traditionalists	Unengaged Pragmatists	Affluent Moderates
(1) Age	38.53	53.60	45.41	40.47
(2) Sex (% Female)	18.33%	75.76%	49.27%	78.08%
(3) Education Level	4.77	4.05	3.42	4.19
(4) Income	4.85	4.86	4.42	5.49
(5) Tech-Savviness	3.83	2.44	2.90	3.05
(6) Perceived Environmental Knowledge	3.63	3.08	2.67	2.85
(7) Environmental Knowledge Score	3.23	3.65	3.33	2.71
(8) Digital Pollution Knowledge Score	19.32	19.75	17.57	16.51
(9) Frequency of Environmental Consideration	3.15	2.18	1.30	2.34
(10) Perceived Importance of Digital Pollution	3.70	3.38	2.00	3.25
(11) Perceived Visibility of Digital Pollution	2.22	1.24	1.13	1.69
(12) Belief in Digital Self-Impact	2.78	2.31	1.61	2.45
(13) Perceived Corporate Role	2.43	2.11	1.91	2.56
(14) Willingness to Act Score	3.70	3.38	2.00	3.25
(15) Willingness to Pay	2.99	2.50	1.31	2.26

describes the unique characteristics of each of the four cluster groups. To better differentiate among the four clusters, we categorized them as follows: ‘Engaged Eco-Tech Enthusiasts‘, ‘Knowledgeable Traditionalists‘, ‘Unengaged Pragmatists‘, and ‘Affluent Moderates‘. Of note, these results partially support hypothesis 4, according to which, demographic variables and perceived tech-savviness/environmental knowledge moderate the relationships between perceptions, intentions (WTP), and behavior (digital sobriety actions).

(1)

‘Engaged Eco-Tech Enthusiasts‘: This cluster is predominantly comprised of male respondents (only 18.33% female) with a mean age of 38.5 years, making it the youngest average age among the clusters, although with considerable age variation (±10.5 years). The participants of this cluster exhibit the highest levels of tech-savviness and education. Their digital pollution knowledge score is among the highest, and they most frequently consider the environmental impact of their digital activities. This cluster also demonstrates the highest willingness to act and pay for reducing digital pollution. They perceive digital pollution as highly important and strongly believe in digital activities contributing to environmental problems. This group combines high awareness with a strong action orientation, positioning them as potential leaders and influencers in addressing digital pollution issues.

(2)

‘Knowledgeable Traditionalists‘: This cluster is characterized by relatively older participants (average age 53.6 ± 11 years) and is predominantly comprised of female respondents (75.76%). They exhibit the highest environmental knowledge score and digital pollution knowledge score among all clusters. However, their tech-savviness level is the lowest among all clusters. Their willingness to act and pay to reduce digital pollution is moderate. They have relatively high incomes and intermediate education levels. This cluster perceives digital pollution as important but reports the lowest perceived visibility of digital pollution. They consider the environmental impact of their digital activities to a moderate degree and have a moderate belief that digital activities contribute to environmental problems.

(3)

‘Unengaged Pragmatists‘: The age of this cluster’s population falls between the other clusters (mean of 45.41 ± 13.6 years). The sex distribution is relatively balanced, with 49.27% female respondents. It has the lowest average education level among all clusters and an intermediate income level. This cluster’s tech-savviness is slightly higher compared to the ‘Knowledgeable Traditionalists‘ group, but still below average. In terms of environmental awareness, this cluster shows lower scores compared to others. They have the second-lowest perceived environmental knowledge and the second-lowest environmental knowledge score. Their digital pollution knowledge score is also below average. Notably, this cluster shows the least engagement with digital pollution issues. They have the lowest scores for considering environmental impact, perceived importance of digital pollution (2.00), and perceived visibility of digital pollution. They reported the lowest level of trust in technology companies’ ability to address digital pollution. Moreover, they expressed the lowest belief that digital activities contribute to environmental problems. Of note, this cluster demonstrates the least willingness to act and pay to reduce digital pollution among all groups.

(4)

‘Affluent Moderates‘: This cluster has the second youngest population, with a mean age of 40.47 ± 11.6 years. It is predominantly comprised of female respondents, with the highest percentage of women (78.08%) among all clusters. They have the second-highest education level and the highest income level across all groups. In terms of tech-savviness, they rank second, showing moderate technological proficiency. However, their environmental knowledge scores are the lowest among all clusters and their digital pollution knowledge score is also the lowest. Their perceived environmental knowledge is moderate. Despite lower knowledge scores, this group shows moderate levels of engagement with digital pollution issues. They consider the environmental impact of their digital activities to a moderate degree and perceive digital pollution as relatively important. Their perceived visibility of digital pollution is the second highest among all clusters. Interestingly, this group has the highest score for the perceived role of technology companies in addressing digital pollution. Their belief in the contribution of digital activities to environmental problems is moderate. In terms of action, they show a moderate willingness to act and pay for reducing digital pollution, ranking second in both categories after the ‘Engaged Eco-Tech Enthusiasts‘ group.

Figure 3 demonstrates the heatmap of the key attributes, by cluster. Each attribute was normalized individually on a scale from zero to one, with zero representing the minimum and one representing the maximum value within each feature. Each value is shown both in its normalization and its original value. In each cell, the top number represents the normalized value, while the original, unnormalized value is displayed below in parentheses. This format highlights variations in age, tech-savviness, knowledge, and willingness to act or pay, providing a clear visual representation of each cluster’s unique characteristics. For example, the ‘Engaged Eco-tech Enthusiasts‘ cluster consists of the youngest participants (with a normalized value of 0), with the highest education level (with a normalized value of 1), while the Affluent Moderates cluster consists of medium level education (normalized value of 0.57) and the lowest digital pollution level (normalized value of 0).

Following the cluster analysis, we conducted Wilcoxon non-paired statistical tests to evaluate the differences between the clusters on key variables, including age, willingness to act to reduce digital pollution, WTP for environmentally friendly digital products, and digital pollution knowledge scores. Chi-square tests were conducted to compare the sex distributions between the clusters. The results revealed significant differences between the clusters for most of the variables. Age differences were significant between some of the clusters, with the most pronounced difference observed between Affluent Moderates and ‘Knowledgeable Traditionalists‘ (p < 0.0001). Sex composition showed highly significant differences across all cluster comparisons (p < 0.0001), indicating substantial variations in sex distributions between the clusters.

The WTP and willingness to act scores also exhibited significant differences in most comparisons, with the most striking contrasts found between ‘Engaged Eco-Tech Enthusiasts‘ and ‘Unengaged Pragmatists‘ in their WTP (p < 0.0001), and between ‘Unengaged Pragmatists‘ and ‘Knowledgeable Traditionalists‘ in their willingness to act (p < 0.0001). Digital pollution scores differed significantly between most clusters, particularly between ‘Affluent Moderates‘ and ‘Knowledgeable Traditionalists‘ (p < 0.0001). These findings underscore the distinct characteristics of the identified clusters while also revealing areas of similarity between certain groups. The complete set of p-values for these comparisons is presented in

Table 7. p-values for inter-cluster comparisons of key variables.

Cluster Comparison		Age	Sex	Willingness to Pay	Willingness to Act	DP Score
Engaged Eco-Tech Enthusiasts	Unengaged Pragmatists	0.006	<0.0001	<0.0001	<0.0001	0.003
Engaged Eco-Tech Enthusiasts	Affluent Moderates	0.424	<0.0001	<0.0001	0.06	<0.0001
Engaged Eco-Tech Enthusiasts	Knowledgeable Traditionalists	p < 0.0001	<0.0001	<0.0001	0.197	0.371
Unengaged Pragmatists	Affluent Moderates	0.053	<0.0001	<0.0001	<0.0001	0.156
Unengaged Pragmatists	Knowledgeable Traditionalists	0.0002	<0.0001	<0.0001	<0.0001	<0.0001
Affluent Moderates	Knowledgeable Traditionalists	<0.0001	<0.0001	0.019	0.453	<0.0001

.

5. Discussion and Implications

5.1. Summary of Key Findings

This study addresses key gaps in environmental behavior research by examining WTP for eco-friendly digital products and engagement in digital sobriety practices. Unlike traditional environmental domains where impacts are visible, digital pollution presents unique challenges due to its largely invisible environmental footprint. By focusing on these behaviors—which emphasize mindful and responsible digital consumption—this research provides an empirically grounded contribution to the emerging discourse on digital pollution. Our analyses revealed complex patterns in the relationships between demographic factors and environmental behavior, with correlation analyses identifying significant associations with WTP for environmentally friendly digital options. Our findings confirmed hypotheses 2 and 3: positive environmental attitudes and perceptions were positively associated with WTP and higher WTP was positively associated with engagement in digital sobriety actions. By contrast, hypothesis 1, which posited that higher objective knowledge about digital pollution would be positively associated with favorable environmental attitudes and perception, was not supported.

These findings were given further nuance by our cluster analysis, that partially supported hypothesis 4 (demographic variables and perceived tech-savviness/environmental knowledge moderate the relationships stated in hypotheses 1, 2, and 3). The ‘Engaged Eco-Tech Enthusiasts’ cluster, comprising younger participants with the highest education levels, demonstrated the strongest WTP and act to reduce digital pollution. This group also reported the highest self-assessed tech-savviness and environmental awareness, suggesting the influence of combined factors. Additionally, our occupational analysis showed that marketing professionals exhibited significantly higher WTP compared to other occupations, including healthcare professionals, artists, and non-tech industry employees.

This complexity in the income–WTP relationship aligns with the findings of Biswas and Roy [15], who demonstrated that perceptual and contextual factors, rather than income alone, determine the WTP for green products in emerging economies. For example, the ‘Affluent Moderates’ cluster illustrated that higher income alone does not necessarily lead to greater environmental engagement, supporting Rafique et al.’s [38] findings that awareness and education about environmental impacts are often more crucial than financial capacity. Their findings showed that despite being in a developing economic region, most citizens showed a WTP for environmental protection once they recognized its importance for health and well-being.

The implications for policy include efforts to promote green consumption prioritizing raising environmental awareness and highlighting perceived value rather than relying solely on financial incentives or targeting high-income groups.

Together, these findings underscore the need to explore multi-factorial influences on environmentally responsible behavior, moving beyond simple bivariate correlations.

5.2. The Knowledge–Action Gap

In the current study, no association was found between the participants’ digital pollution score and their WTP for environmentally friendly digital devices or services. This finding presents an interesting contrast to our findings that there are strong positive correlations between various environmental perceptions and beliefs and the WTP. This knowledge–action gap can be attributed to several factors. First, the complexity of digital pollution knowledge assessment, which involves understanding multiple technical concepts such as the energy consumption of different digital activities, data center operations, and their environmental impact, the environmental costs of device manufacturing and disposal, and the carbon footprint of various online services. This complexity may make it difficult for individuals to translate their knowledge into actionable decisions. Second, the abstract nature of technical knowledge creates additional barriers. Technical aspects of digital pollution (like server energy consumption or e-waste) are less tangible than visible environmental issues (like plastic pollution). The impact of individual digital actions feels remote and hard to quantify, making the connection between daily digital activities and environmental consequences less immediate. Personal perceptions of environmental impact are more influenced by immediate experiences than technical understanding. Additionally, knowledge alone may be insufficient to motivate behavioral change without accompanying emotional or personal engagement with the issue. This suggests that while knowledge regarding digital pollution is important, it does not automatically translate into a WTP for environmentally friendly devices or services. Furthermore, the possibility that knowledge alone may be insufficient to motivate behavioral change without accompanying emotional or personal engagement with the issue. This pattern aligns with similar knowledge–action reported in other sectors. For example, Góralska-Walczak et al. [39] found that employees in the catering sector often exhibited medium-to-low levels of environmental knowledge, with education level and place of residence influencing attitudes but not consistently translating into action.

Further research is needed to fully understand this knowledge–action gap in the context of digital pollution.

5.3. Role of Perceptions and Corporate Responsibility

We found a significant association between participants’ perceptions of digital pollution and their WTP more for environmentally friendly options. Those who viewed digital pollution as more visible and pressing were more likely to express WTP for solutions. This finding aligns with previous studies which have shown that heightened environmental awareness can drive more sustainable consumer behaviors [26]. Horne [17] similarly found that consumers with higher environmental consciousness were more likely to invest in eco-friendly technologies. In conclusion, our findings, along with those from previous studies indicate a broader trend in consumer behavior towards sustainability in the digital realm, where perceptions and attitudes play a crucial role in shaping WTP for eco-friendly technologies. Additionally, these findings, combined with the correlations found between perceptions and the willingness to act, underscore the importance of raising public awareness about digital pollution as a tangible and pressing environmental issue.

Participants who believed in technology companies’ responsibility for mitigating digital pollution, and those who regularly considered their digital activities’ environmental impacts, showed greater WTP for greener options. This finding is consistent with Moisander [40], who noted that perceived corporate environmental responsibility significantly influences green purchasing intentions. Nishant et al. [3] argue that systems-thinking and multilevel approaches can enhance corporate accountability and lead to more effective sustainability interventions. Holzmann and Gregori [4] highlight how digital technologies can foster stakeholder engagement, enabling more inclusive and collaborative efforts in addressing environmental challenges.

These results suggest that raising awareness about both the visibility of digital pollution and corporate responsibility could be key strategies for promoting consumer engagement with sustainable technology. Targeted awareness-raising efforts could benefit from leveraging digital educational tools. A recent systematic review by Hajj-Hassan et al. [41], found that interventions using mobile applications, online games, and virtual reality platforms can enhance environmental awareness and engagement, particularly when culturally adapted and designed to encourage active participation. Such approaches could be especially valuable in the context of digital pollution, where impacts are often less visible and it is harder for individuals to connect to their daily behaviors.

Interestingly, self-assessed tech-savviness and environmental knowledge were positively correlated with both WTP and the number of actions participants took to reduce digital pollution. This implies that individuals who perceive themselves as more informed are not only more aware of the issues but also more proactive in addressing them—even if their actual knowledge regarding digital pollution may be limited.

5.4. Cluster Analysis and Profile-Specific Insights

The cluster analysis enabled a more holistic and deeper understanding of digital pollution-related behaviors. While correlation analyses showed no direct association between individual demographic factors and WTP. Cluster analysis identified meaningful patterns when considering multiple characteristics simultaneously. This analytical approach aligns with Diamantopoulos et al. [30], who observed that socio-demographic characteristics, including education and tech-savviness, were associated with environmental consciousness and behavior. Our analysis identified four distinct participant groups, each defined by a unique combination of characteristics that affect their engagement with digital pollution issues: The ‘Engaged Eco-Tech Enthusiasts’, comprising relatively young and mostly tech-savvy participants, exhibited the strongest willingness to act and pay for environmentally friendly solutions. Their high engagement demonstrates how multiple factors—age, technical literacy, and environmental awareness—work together to promote pro-environmental behavior. In contrast, the ‘Unengaged Pragmatists,’ with average age and tech-savviness but the lowest average education level, exhibited the least engagement.

While tech-savviness might seem like a determining factor, our findings reveal a more nuanced relationship. The ‘Engaged Eco-Tech Enthusiasts’ and ‘Unengaged Pragmatists’ present an interesting contrast: both groups demonstrated high knowledge levels, but their engagement levels differed significantly despite their similar levels of tech-savviness. This suggests that tech-savviness alone does not determine environmental engagement. The ‘Knowledgeable Traditionalists,’ despite being the oldest group and having the highest environmental and digital pollution knowledge, demonstrated only moderate willingness to act and pay. This pattern suggests that the relationship between knowledge, tech-savviness, and environmental engagement is complex and potentially mediated by other factors. However, given their lower tech-savviness and older age, this group may simply not be heavy users of digital technology in the first place. Their moderate willingness to act or pay could reflect their lower overall digital footprint rather than a lack of concern or engagement as Steg and Vlek [42] suggested, environmental behaviors are often context-dependent and influenced by personal circumstances.

The last cluster group, the ‘Affluent Moderates’ further illustrates the complex relationship between income and environmental behavior identified earlier. They show only a moderate willingness to act. This is despite having the highest income and lowest environmental knowledge scores. Together, these traits reinforce our finding that income alone does not determine environmental engagement. Instead, their profile suggests that the interaction between income, knowledge, and other factors shapes environmental behavior. This aligns with Biswas and Roy [15] who emphasized the importance of perceptual and contextual factors.

Our cluster analysis successfully characterized four distinct population groups that differ significantly in their demographic profiles (age and sex composition), attitudes (willingness to pay and act), and knowledge levels. The analysis revealed interesting patterns in how these characteristics combine within each group. While the clusters showed clear demographic distinctions, high levels of digital pollution knowledge were not confined to any particular demographic profile. Both ‘Engaged Eco-Tech Enthusiasts’ and ‘Knowledgeable Traditionalists’ demonstrated similarly high knowledge scores despite their contrasting age groups and tech-savviness levels. This finding suggests that the capacity to understand digital pollution transcends generational and technological boundaries.

These findings have several practical implications for policy and education. First, our findings highlight the need for tailored public awareness campaigns that address the specific characteristics of each group. For instance, efforts to increase digital literacy among ‘Knowledgeable Traditionalists’ could bridge the gap between their high knowledge and moderate action levels.

Second, our analysis of corporate responsibility perceptions reveals an important pathway for policy intervention. The ‘Affluent Moderates’ showed high recognition of corporate responsibility in addressing digital pollution, while also demonstrating willingness to act despite lower knowledge levels. This suggests that emphasizing corporate accountability could be an effective lever for change, particularly among groups who may be less technically knowledgeable but still environmentally conscious. Policy interventions could capitalize on this by developing regulatory frameworks that both respond to and reinforce these public expectations of corporate responsibility. As Dauvergne and Lister [43] argue, regulatory frameworks promoting corporate environmental responsibility can drive innovation in sustainable solutions.

Third, educators and environmental advocates should prioritize integrating discussions of digital pollution into broader environmental education curricula, as suggested by Ardoin et al. [44]. This approach could be particularly effective for groups like the ‘Unengaged Pragmatists’, as it allows them to relate to digital pollution in the context of other environmental issues.

The positive attitudes and behaviors exhibited by tech-savvy ‘Engaged Eco-Tech Enthusiasts’ offer a promising outlook for the future of digital sustainability. This indicates that as digital natives become more aware of the environmental impacts of their online activities, they are also more likely to act. This finding aligns with our correlation analyses showing that tech industry employees and educators took significantly more action compared to other occupations.

This finding underscores the importance of enhancing technological fluency in digital environmental responsibility initiatives. Educational programs and awareness campaigns that leverage technological competency could be particularly effective in promoting sustainable digital practices.

The engagement of this group could have a ripple effect on older generations and less tech-savvy individuals. As digital influencers and early adopters of new technologies, ‘Engaged Eco-Tech Enthusiasts‘ could play a crucial role in normalizing environmentally conscious digital behaviors across broader segments of society. However, the absence of a direct association between objective digital pollution knowledge and WTP contrasts with studies in other environmental contexts—such as energy conservation—where knowledge often shows at least a modest positive correlation with behavioral intentions. This discrepancy may reflect the abstract and less visible nature of digital environmental impacts, underscoring the need for communication strategies that make such impacts more tangible and personally relevant.

5.5. Cross-Cultural Applicability

While the present study focuses on a UK-based sample, the behavioral patterns identified—particularly the segmentation into distinct engagement profiles—may not translate directly to other socio-cultural or policy contexts. Previous research has shown that WTP for environmental initiatives and engagement in pro-environmental behaviors are shaped by national regulatory frameworks, economic incentives, and prevailing social norms (e.g., [18,19,24]). For example, the high WTP and action rates observed among the ‘Engaged Eco-Tech Enthusiasts’ in the UK sample might be even stronger in countries with robust environmental education programs, while the lower engagement of the ‘Unengaged Pragmatists’ could be more pronounced in contexts with weaker environmental policy enforcement. Likewise, the profile of the ‘Affluent Moderates’—characterized by high income but low environmental knowledge—may manifest differently in cultures where environmental awareness is more strongly linked to social status or corporate norms. These considerations suggest that caution is warranted in generalizing these findings beyond the UK, and that comparative, cross-national studies are needed to assess the stability of both the identified clusters and behavioral predictors across different cultural and policy environments.

5.6. Theoretical Implications

This study contributes to the growing body of literature that seeks to apply classical behavioral models, such as the KAB framework [12,13], to the context of digital environmental challenges. By operationalizing constructs like WTP and digital sobriety actions within the KAB model, the findings provide empirical support for its applicability in the domain of digital pollution—a field that remains under-theorized. Notably, the lack of association between objective digital pollution knowledge and behavior highlights the need to refine existing assumptions about linear knowledge-to-action pathways, echoing prior critiques in environmental psychology regarding the ‘value-action gap’. Furthermore, our clustering results extend behavioral segmentation approaches into the digital sustainability domain, suggesting that consumer typologies may be shaped by unique combinations of digital awareness and environmental concern. These insights may inform future theoretical models of eco-behavior in digitally mediated environments.

5.7. Limitations

Several limitations should be acknowledged. First, we did not collect data on participants’ actual usage patterns with digital applications and AI technologies, which could have greatly contributed to our understanding of the different clusters. Second, we relied on self-reported behaviors and attitudes, which may differ from actual behaviors in real-world settings. Future research could benefit from incorporating objective measures of digital usage and environmental actions.

Third, most constructs were measured using single-item indicators, which may reduce the ability to assess internal consistency and latent structure [32,33]. While this approach has been validated for unidimensional, concrete constructs, future research should consider multi-item scales and psychometric testing to enhance measurement robustness.

Fourth, correlation analyses identify associations between variables, not causal relationships. This reflects the complex nature of environmental behavior research, where multiple factors often interact in dynamic ways.

Additionally, while we examined various aspects of digital pollution engagement, there may be other factors not captured in this study that influence individuals’ behaviors and attitudes.

Finally, sampling limitations exist as all participants were UK residents, recruited via the Prolific platform, which may not yield a representative sample. Certain demographic groups may be over- or under-represented, potentially introducing sampling bias.

By analyzing various aspects of the same dataset, we ensured consistency in the sample across both studies while exploring new dimensions of the participants’ engagement with digital pollution. This method provided an opportunity to find associations between knowledge levels, attitudes, perceptions, and behaviors within the same group of respondents. Consequently, it enabled us to gain deeper insights into how awareness and perceptions of digital pollution relate to individuals’ opinions and actions, offering a more comprehensive view of public engagement with this important environmental issue.

6. Summary and Conclusions

This study explored perceptions regarding digital pollution, specifically focusing on WTP for environmentally friendly digital devices and services, as well as the actions taken to reduce digital pollution. Our findings reveal that perceptual and behavioral factors play a critical role in shaping these attitudes, more so than traditional demographic factors like age or income.

Specifically, participants who recognized the visibility and importance of digital pollution were more WTP for eco-friendly options. Furthermore, those who believed in the impact of technology companies and acknowledged their daily digital activities’ contribution to environmental issues showed greater engagement in reducing their digital footprint. These results highlight the importance of awareness and the development of personal responsibility in driving eco-friendly digital behaviors.

Our cluster analysis revealed four distinct groups: (1) ‘Engaged Eco-Tech Enthusiasts‘: The youngest and most tech-savvy participant group, characterized by higher education levels and the strongest willingness to act and pay for reducing digital pollution. (2) ‘Knowledgeable Traditionalists‘: The oldest group with the highest environmental and digital pollution knowledge, but only a moderate willingness to act and pay, possibly due to lower tech-savviness or less frequent use of technology. (3) ‘Unengaged Pragmatists‘: Characterized by moderate age and tech-savviness but the lowest education level, this group showed the least engagement with digital pollution issues. (4) ‘Affluent Moderates‘: Despite having the highest income and lowest environmental knowledge scores, this group demonstrated a moderate willingness to act, challenging assumptions about income and environmental behavior.

This categorization provides valuable insights into tailoring interventions and communication strategies for different audience profiles. Interestingly, we found that knowledge about digital pollution does not necessarily translate into a WTP for eco-friendly solutions, highlighting the complex relationship between knowledge and action in the digital realm.

Our results demonstrate that both self-assessed tech-savviness and environmental knowledge are positively correlated with the WTP and the number of actions taken to mitigate digital pollution. This underscores the importance of education in fostering eco-friendly behavior, suggesting that increasing digital literacy and environmental awareness through targeted educational programs could play a crucial role in promoting sustainable digital practices.

Policy interventions encouraging corporate accountability, combined with public awareness campaigns, could further engage individuals in reducing their digital pollution footprint. As Holzmann and Gregori [4] suggest, digital technologies have the potential to enhance stakeholder inclusion in sustainability initiatives, making these efforts more participatory and impactful. These efforts may be particularly effective if aimed at less tech-savvy and lower-educated populations, as well as groups with lower environmental engagement, such as the ‘Unengaged Pragmatists‘ identified in our cluster analysis. By addressing knowledge gaps and raising awareness about the tangible impacts of digital pollution, these interventions could potentially shift perceptions and behaviors across diverse demographic groups.

Based on the findings of this study, several directions for future research emerge:

(1) Expanding the demographic scope: While this study focused on specific groups, future research could explore how cultural, regional, or generational differences influence perceptions of digital pollution. (2) Investigating additional behavioral factors: Further studies could explore the role of emotional drivers (e.g., environmental guilt) or social influences in motivating actions to reduce digital pollution. (3) Exploring corporate responsibility: Given the strong association between perceptions of corporate responsibility and engagement, future work could analyze how policy frameworks and corporate initiatives affect consumer attitudes toward sustainable digital practices. (4) Longitudinal studies: Future research could monitor changes in perceptions and behaviors over time, particularly in response to public awareness campaigns and policy changes. (5) Technology usage patterns: Investigating the relationship between actual technology usage patterns and willingness to engage in digital pollution reduction efforts could provide valuable insights, particularly for groups such as the ‘Knowledgeable Traditionalists‘.

In conclusion, this study provides important insights into the factors influencing individuals’ engagement with digital pollution issues. By highlighting the importance of perceptions, knowledge, and awareness in shaping eco-friendly digital behaviors, our findings offer a foundation for developing more effective strategies to mitigate digital pollution. The encouraging engagement of younger, tech-savvy individuals presents a promising outlook for the future of digital sustainability. As our reliance on digital technologies continues to grow, understanding and addressing the environmental implications of our digital lives becomes increasingly critical. This research contributes to this vital conversation, paving the way for a more sustainable digital future.